Exploring Emerging Collective Behaviours in Large-Scale Data-Driven Networked Systems. Understanding emerging collective behaviours in large-scale data-driven networked systems and developing methodology and approach for pattern identification and intervention are very important for high impact applications such as smart energy supply using smart meters. This project will propose a new theory for the developments, which will enhance Australia's leading position in this research and provide a cut ....Exploring Emerging Collective Behaviours in Large-Scale Data-Driven Networked Systems. Understanding emerging collective behaviours in large-scale data-driven networked systems and developing methodology and approach for pattern identification and intervention are very important for high impact applications such as smart energy supply using smart meters. This project will propose a new theory for the developments, which will enhance Australia's leading position in this research and provide a cutting-edge technology for industrial applications and training of the next generation of leading researchers.Read moreRead less
Dynamic Deep Learning for Electricity Demand Forecasting. This project aims at developing a deep learning technology for high resolution electricity demand forecasting and residential demand response modelling. Electricity consumption data are dynamic and highly uncertain. The deep learning technology expects to provide accurate demand forecasting, and thus enabling optimal use of existing
grid assets and guiding future investments. The expected outcome can support data-driven decision-making in ....Dynamic Deep Learning for Electricity Demand Forecasting. This project aims at developing a deep learning technology for high resolution electricity demand forecasting and residential demand response modelling. Electricity consumption data are dynamic and highly uncertain. The deep learning technology expects to provide accurate demand forecasting, and thus enabling optimal use of existing
grid assets and guiding future investments. The expected outcome can support data-driven decision-making in Australia's electricity distribution network planning and operation by considering future challenges such as integrating battery storage and electric vehicles into the grid, and thus providing reliable energy. The project expects to train next generation expert workforce for Australia's future power grid.Read moreRead less
Visual Tracking: Geometric Fitting and Filtering. One of the most elementary things that people and sighted animals do is to follow moving objects with their eyes. Movement is a cue to the importance and relevance of objects in a scene. Visually tracking objects allows the determination of important characteristics - distance to the object, shape of the object, likely behaviour of the object etc. Though systems have been built that can perform visual tracking: accuracy and reliability must be i ....Visual Tracking: Geometric Fitting and Filtering. One of the most elementary things that people and sighted animals do is to follow moving objects with their eyes. Movement is a cue to the importance and relevance of objects in a scene. Visually tracking objects allows the determination of important characteristics - distance to the object, shape of the object, likely behaviour of the object etc. Though systems have been built that can perform visual tracking: accuracy and reliability must be improved though a better understanding of the underlying processes. Applications include visual inspection (industrial automation), surveillance (civil and military), robot vision for scene understanding and navigation, multimedia production (automatic human motion capture for example), and human computer interfaces.
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A probabilistic framework for nonlinear dimensionality reduction algorithms. The Twin Measures Framework is a novel platform for analysing existing dimensionality reduction methods and the invention of new ones. This research will radically improve image analysis, with beneficial applications from pharmaceutical drug design through to border protection.
Pattern-Based Video Coding Techniques for Real-Time Low Bit-Rate and Low Complexity Encoding Applications. This project will benefit the National Research Priority on Frontier Technology with applications in video surveillance, smart home design, and patient monitoring. It will enable Australia to lead the world in setting up coding standards and thus impact directly on the manufacturing initiatives of the multimedia communication and entertainment industries. Telecommunication industries will b ....Pattern-Based Video Coding Techniques for Real-Time Low Bit-Rate and Low Complexity Encoding Applications. This project will benefit the National Research Priority on Frontier Technology with applications in video surveillance, smart home design, and patient monitoring. It will enable Australia to lead the world in setting up coding standards and thus impact directly on the manufacturing initiatives of the multimedia communication and entertainment industries. Telecommunication industries will be the immediate beneficiary by enabling quality live video transmissions at low bit rates in a cost-effective manner. This project will improve the ability of large organisations to operate virtually across huge distances in Australia with the aid of reliable multimedia communications using distributed devices of limited power and processing capacity.Read moreRead less
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
Mining multi-typed and dynamic graphs. Large volumes of data collected nowadays from real-world applications are often represented as graphs. The nodes and the edges of such graphs represent different types of entities and interactions, and they have time information. This project will develop algorithms that mine efficiently such multi-typed and dynamic graphs.
Interpretable Behaviour Analysis with External Structured Knowledge. This project aims to develop novel interpretable neural models for predictive analytics tasks on human behaviour, operating on sequence behaviour data associated with external supportive structured knowledge. It is expected to present theoretical foundations for robust representation learning on heterogeneous behaviour data and interpretable machine reasoning models, which can support a broad scope of intelligent systems. Expec ....Interpretable Behaviour Analysis with External Structured Knowledge. This project aims to develop novel interpretable neural models for predictive analytics tasks on human behaviour, operating on sequence behaviour data associated with external supportive structured knowledge. It is expected to present theoretical foundations for robust representation learning on heterogeneous behaviour data and interpretable machine reasoning models, which can support a broad scope of intelligent systems. Expected outcomes will be a next-generation interpretable behaviour analysis system with versatile abilities to reason over various data structures and provide a high-level interpretability about its reasoning procedure. The benefits will span the research and industry sectors, e.g., retail, healthcare, service provider.Read moreRead less
Learning the Focus of Attention to Detect Distributed Coordinated Attacks. Cyber security analysts need to detect and respond to attacks as soon as possible, to minimise the damage attackers can inflict. However, the growth in highly distributed attacks that span multiple networks has meant that massive volumes of data need to be analysed. While machine learning techniques can help filter the data, we need techniques that can automatically provide a focus of attention for analysts on the most re ....Learning the Focus of Attention to Detect Distributed Coordinated Attacks. Cyber security analysts need to detect and respond to attacks as soon as possible, to minimise the damage attackers can inflict. However, the growth in highly distributed attacks that span multiple networks has meant that massive volumes of data need to be analysed. While machine learning techniques can help filter the data, we need techniques that can automatically provide a focus of attention for analysts on the most relevant observations. Our aim is to devise a novel suite of attention mechanisms that can focus the search of machine learning techniques for cyber security. The results of this project will improve the accuracy and efficiency of detecting distributed attacks across multiple networks.Read moreRead less
Low-cost Sensing Methods and Hybrid Learning Models. This project aims to revolutionise the theory and practice of sensing and monitoring by developing novel Artificial Intelligence and Internet of Things technologies. This project expects to generate new knowledge in the area of Artificial Intelligence of Things by combining sensing, machine learning, and big data analytics. Expected outcomes of this project include novel low-cost sensing methods and new hybrid machine learning models for predi ....Low-cost Sensing Methods and Hybrid Learning Models. This project aims to revolutionise the theory and practice of sensing and monitoring by developing novel Artificial Intelligence and Internet of Things technologies. This project expects to generate new knowledge in the area of Artificial Intelligence of Things by combining sensing, machine learning, and big data analytics. Expected outcomes of this project include novel low-cost sensing methods and new hybrid machine learning models for predictive sensory data analytics. This should provide significant benefits, such as substantially reduced operating and service costs and improved accuracy for real-time monitoring in the fields where cheap-to-implement and easy-to-service monitoring systems over large geographical areas are imperative.Read moreRead less