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
Industrial Transformation Training Centres - Grant ID: IC210100019
Funder
Australian Research Council
Funding Amount
$4,583,816.00
Summary
ARC Training Centre for Optimal Ageing. The ARC Training Centre for Optimal Ageing aims to address issues identified by older adults as essential for quality of life. With our industry partners, we aim to train the next generation of researchers to understand, detect and improve psychosocial factors that support mental activity, physical health and social connectedness, and embrace advances in artificial intelligence, digital-enriched environments and adaptive workplaces to deliver effective dig ....ARC Training Centre for Optimal Ageing. The ARC Training Centre for Optimal Ageing aims to address issues identified by older adults as essential for quality of life. With our industry partners, we aim to train the next generation of researchers to understand, detect and improve psychosocial factors that support mental activity, physical health and social connectedness, and embrace advances in artificial intelligence, digital-enriched environments and adaptive workplaces to deliver effective digital solutions. By developing new capacity and capability to drive the digital transformation of industries supporting our ageing population, our Centre seeks to deliver economic and social benefits that enable Australians to live enriched, healthy and independent lives as they age.Read moreRead less
Next-generation Protein Structural comparison using Information Theory. Progress in protein structural biology relies heavily on key computational technologies, structural alignment being an indispensable one. Despite its importance the structural alignment problem has not been formulated, much less solved, in a consistent and reliable way. This project aims to rectify this by combining novel information-theoretic inference with advances in constraint optimisation and visualisation. State-of-the ....Next-generation Protein Structural comparison using Information Theory. Progress in protein structural biology relies heavily on key computational technologies, structural alignment being an indispensable one. Despite its importance the structural alignment problem has not been formulated, much less solved, in a consistent and reliable way. This project aims to rectify this by combining novel information-theoretic inference with advances in constraint optimisation and visualisation. State-of-the-art alignment methods aim to be produced for biologists to generate statistically-rigorous and biologically-trustworthy alignments, and allow them to visualise structural relationships in unprecedented ways. This project is expected to provide direct payoffs to the fields of protein science, crystallography and bioinformatics.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE170101134
Funder
Australian Research Council
Funding Amount
$360,000.00
Summary
Feasible algorithms for big inference. This project aims to develop algorithms for computationally-intensive statistical tools to analyse Big Data. Big Data is ubiquitous in science, engineering, industry and finance, but needs special machine learning to conduct correct inferential analysis. Computational bottlenecks make many tried-and-true tools of statistical inference inadequate. This project will develop tools including false discovery rate control, heteroscedastic and robust regression an ....Feasible algorithms for big inference. This project aims to develop algorithms for computationally-intensive statistical tools to analyse Big Data. Big Data is ubiquitous in science, engineering, industry and finance, but needs special machine learning to conduct correct inferential analysis. Computational bottlenecks make many tried-and-true tools of statistical inference inadequate. This project will develop tools including false discovery rate control, heteroscedastic and robust regression and mixture models, via Big Data-appropriate optimisation and composite-likelihood estimation. It will make open, well-documented, and accessible software available for the scalable and distributable analysis of Big Data. The expected outcome is a suite of scalable algorithms to analyse Big Data.Read moreRead less
Novel dissimilarity techniques for characterising noisy spatial networks. This project will invent new and widely applicable ways of summarising fundamental characteristics of noisy spatial networks that change slightly in space or time. The techniques developed will be applied to solve important problems in two diverse applications - predicting disease spread in wildlife and protecting human biometric information.
Discovery Early Career Researcher Award - Grant ID: DE190100045
Funder
Australian Research Council
Funding Amount
$377,829.00
Summary
Efficient and effective analytics for real-world time series forecasting. This project aims to create efficient, effective techniques that provide accurate forecasts for heterogeneous sets of time series of varying sizes. Exploiting similarities between time series means using many related series, not larger series when building forecasts. The expected outcomes should be innovative methods that improve accuracy and allow forecasting with shorter time series. The project addresses the need to exp ....Efficient and effective analytics for real-world time series forecasting. This project aims to create efficient, effective techniques that provide accurate forecasts for heterogeneous sets of time series of varying sizes. Exploiting similarities between time series means using many related series, not larger series when building forecasts. The expected outcomes should be innovative methods that improve accuracy and allow forecasting with shorter time series. The project addresses the need to exploit properties of big data accurately in a short time frame, which is transforming many industries. This should enable more accurate and reliable forecasts across industries as diverse as retail, food manufacturing, transport, mining, tourism, energy, and technology.Read moreRead less
Rating and ranking sports players and teams using minimum message length. All sorts of games and sports could use better systems for rating and ranking teams. This is as true in sports-mad Australia as any other country. Improved and more accessible rating systems across a variety of activities should encourage the general public to take a greater interest in the mathematics, statistics, information theory and machine learning behind the systems. With Cadability as our Australia-based interna ....Rating and ranking sports players and teams using minimum message length. All sorts of games and sports could use better systems for rating and ranking teams. This is as true in sports-mad Australia as any other country. Improved and more accessible rating systems across a variety of activities should encourage the general public to take a greater interest in the mathematics, statistics, information theory and machine learning behind the systems. With Cadability as our Australia-based international industry partner, the global use of these systems will be to Australia's economic advantage. Having a more accurate rating system which is wider-reaching both in the number of sports and games and the number of participants per sport and game should also encourage greater participation from the general public.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE170100037
Funder
Australian Research Council
Funding Amount
$329,287.00
Summary
Time series classification for new-generation Earth observation satellites. This project aims to develop time series classification methods for satellite images, to produce accurate temporal land-cover maps. Latest generation satellites have just begun imaging Earth frequently, completely, in high-resolution, and at no charge to end-users – an unprecedented opportunity to monitor the flux of our planet's systems. However, time series classification techniques do not scale to handle such wealth o ....Time series classification for new-generation Earth observation satellites. This project aims to develop time series classification methods for satellite images, to produce accurate temporal land-cover maps. Latest generation satellites have just begun imaging Earth frequently, completely, in high-resolution, and at no charge to end-users – an unprecedented opportunity to monitor the flux of our planet's systems. However, time series classification techniques do not scale to handle such wealth of data. The project anticipates its time series technologies will be applicable in agriculture planning, fire prevention, and disaster mapping, and that substantially greater value can be derived from significant investments into Earth Observation programmes.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
New Statistical Techniques for Galactic Archaeology. This project aims to develop methods that will enable us to exploit new astronomical data. A revolution is under way in stellar astronomy, with new space satellites and Australian survey instruments about to reveal the composition of millions of stars. These data hold the promise of disentangling the origin of our galaxy through identification of star-forming groups that have long ago dispersed into our galaxy. We can also learn about the nucl ....New Statistical Techniques for Galactic Archaeology. This project aims to develop methods that will enable us to exploit new astronomical data. A revolution is under way in stellar astronomy, with new space satellites and Australian survey instruments about to reveal the composition of millions of stars. These data hold the promise of disentangling the origin of our galaxy through identification of star-forming groups that have long ago dispersed into our galaxy. We can also learn about the nuclear and stellar processes that were active in long-dead stars, and hence explain why the Universe is the way it is. This project aims to extend the powerful Minimum Message Length method, never before used in astronomy, to handle this data. This would put us in a unique position to extract new knowledge from the data that will arrive in the next one to five years.Read moreRead less