A direct drive linear tube generator for ocean wave energy conversion. This project aims to investigate a direct drive linear electromagnetic generator system for the maximum wave energy conversion and frequency bandwidth. This system has a translator of a multiple degree of freedom non-linear oscillator system built with the Halbach ring array pattern and ferro-fluid bearings. To establish wave energy conversion science, this project will investigate the device, its integration with a buoy stru ....A direct drive linear tube generator for ocean wave energy conversion. This project aims to investigate a direct drive linear electromagnetic generator system for the maximum wave energy conversion and frequency bandwidth. This system has a translator of a multiple degree of freedom non-linear oscillator system built with the Halbach ring array pattern and ferro-fluid bearings. To establish wave energy conversion science, this project will investigate the device, its integration with a buoy structure under wave loadings and automatic control of power conversion and conditioning. The outcome could meet demands for wave energy conversion technologies that reduce power generation cost and emissions, benefiting the Australian economy and environment.Read moreRead less
Geological sequestration of carbon dioxide in deep saline aquifers: coupled flow-mechanical considerations. Deep saline aquifers have been routinely proposed as sites for long-term, large-scale storage of carbon dioxide (CO2) emissions, as an option to assist the abatement of global warming. This project investigates expected engineering behaviour of deep saline aquifer reservoirs and their stability following CO2 sequestration.
Surviving the data deluge: Scalable feature extraction, discrimination and analysis for computer vision tasks using compressed sensed data. Strategically, our pioneering solutions besides being technically and socially significant, open fresh options for sensor-agnostic data analysis. The technical significance lies through the creation of new technologies for the critical national and global security markets, currently overwhelmed by data. The social significance arises from our solutions being ....Surviving the data deluge: Scalable feature extraction, discrimination and analysis for computer vision tasks using compressed sensed data. Strategically, our pioneering solutions besides being technically and socially significant, open fresh options for sensor-agnostic data analysis. The technical significance lies through the creation of new technologies for the critical national and global security markets, currently overwhelmed by data. The social significance arises from our solutions being privacy preserving, providing new avenues for the production of novel, socially acceptable products for aged care monitoring. Our methods spearhead future advancement in diverse disciplines due to the wide applicability of the methods to other sensor networks (Square Kilometre Array) and data types, providing new frameworks for addressing crucial problems of data management. Read moreRead less
Advanced membranes for energy-efficient electrochemical conversion of carbon dioxide to fuel. This project proposes to develop a technology to convert carbon dioxide to liquid fuels using renewable energy as the required energy source. The project will therefore help in the mitigation of carbon dioxide emissions and offset the depletion of fossil fuel reserves.
Novel membranes and membrane structures using electrospinning. This project aims to develop novel membrane support materials and novel membrane structures to enhance chemical separation processes. These materials can be used in desalination and water treatment, reducing the resistance to water flows. In turn, this will reduce the energy required to produce fresh drinking water for Australians, as well as the cost. The approach will also be applied to carbon dioxide capture from flue gas streams, ....Novel membranes and membrane structures using electrospinning. This project aims to develop novel membrane support materials and novel membrane structures to enhance chemical separation processes. These materials can be used in desalination and water treatment, reducing the resistance to water flows. In turn, this will reduce the energy required to produce fresh drinking water for Australians, as well as the cost. The approach will also be applied to carbon dioxide capture from flue gas streams, increasing the energy efficiency of these processes, so that they can become economically viable. The project has the potential to develop localised manufacturing operations to produce these materials, adding value to Australian manufactured products.Read moreRead less
Flow-induced vibration of slender structures and its control. This project aims to expand substantially the state of knowledge on the flow-induced vibrations of bluff, slender structures such as cylinders, beams, and cables. A framework is expected to be developed that describes the flow-induced vibration of these structures systematically, adding new data and drawing links between previously disparate areas of research. The significance of such a framework is great, as not only is flow-induced ....Flow-induced vibration of slender structures and its control. This project aims to expand substantially the state of knowledge on the flow-induced vibrations of bluff, slender structures such as cylinders, beams, and cables. A framework is expected to be developed that describes the flow-induced vibration of these structures systematically, adding new data and drawing links between previously disparate areas of research. The significance of such a framework is great, as not only is flow-induced vibration a problem in many engineering applications (such as marine oil risers, chimneys, and bridges) it can also be exploited for renewable energy generation. Control mechanisms are also expected to be developed to maximise the energy generation potential.Read moreRead less
Using data mining methods to remove uncertainties in sensor data streams. This project will develop key techniques for removing uncertainties in sensor data streams and thus improve the monitoring quality of sensor networks. The expected outcomes will benefit Australia by enabling improved, lower-cost monitoring of natural resources and management of stock raising.
Deep Data Mining for Anomaly Prediction from Sensor Data Streams. Sensor data streams are crucial for anomaly predictions in real-life monitoring. However, balancing efficiency and accuracy in predicting anomalies with sensor streams is a great challenge; it requires new techniques that go beyond detecting anomalies and predicting trends. This project will develop a deep mining method for anomaly prediction from sensor streams; it will comprise mining algorithms at various levels - from compress ....Deep Data Mining for Anomaly Prediction from Sensor Data Streams. Sensor data streams are crucial for anomaly predictions in real-life monitoring. However, balancing efficiency and accuracy in predicting anomalies with sensor streams is a great challenge; it requires new techniques that go beyond detecting anomalies and predicting trends. This project will develop a deep mining method for anomaly prediction from sensor streams; it will comprise mining algorithms at various levels - from compressing massive raw data, to recognition of abnormal waveforms preceding anomalies, and to retrieving and summarising similar past anomalies for creating descriptions of future anomalies. The project will demonstrate our method in health/environment monitoring applications, and its adoption will save resources, money and lives.Read moreRead less
Target-agnostic analytics: building agile predictive models for big data. This project aims to develop target-agnostic analytics, creating models of data that can be queried about any variable or feature without having to be relearned. Government and business collect vast quantities of data, but these are wasted if we cannot use them to predict the future from the past. Presently, big-data analytics is effective at predicting a single pre-defined target variable, yet in many applications, what w ....Target-agnostic analytics: building agile predictive models for big data. This project aims to develop target-agnostic analytics, creating models of data that can be queried about any variable or feature without having to be relearned. Government and business collect vast quantities of data, but these are wasted if we cannot use them to predict the future from the past. Presently, big-data analytics is effective at predicting a single pre-defined target variable, yet in many applications, what we know about a system and what we want to find out are far more complex. This project expects to yield novel target-agnostic technologies with associated publications and open-source software. The project will expand the capabilities of machine learning, providing better use of the massive data assets collected across most public, commercial and industry sectors.Read moreRead less
Stay well: Analysing lifestyle data from smart monitoring devices. Pervasive health monitoring devices provide a rich data source with opportunity to continuously extract patterns and guide individuals towards their goals of wellbeing. To exploit this nexus between machine learning and pervasive computing, this project aims to solve the computational problems to analyse data from such wearable devices, applying rigorous statistical models to discover latent patterns and groupings. The significan ....Stay well: Analysing lifestyle data from smart monitoring devices. Pervasive health monitoring devices provide a rich data source with opportunity to continuously extract patterns and guide individuals towards their goals of wellbeing. To exploit this nexus between machine learning and pervasive computing, this project aims to solve the computational problems to analyse data from such wearable devices, applying rigorous statistical models to discover latent patterns and groupings. The significance lies in solving fundamental problems related to heterogeneous, multi-level, mixed-type time series data. The proposed outcomes are expected to enable monitoring of people 'in the wild', away from doctors and hospitals, thus significantly reducing the burgeoning cost of hospital visits and stays.Read moreRead less