Discovery Early Career Researcher Award - Grant ID: DE220101277
Funder
Australian Research Council
Funding Amount
$427,600.00
Summary
Temporal-Spatial Data Analytics for Stochastic Power System Stability. The modern power system is evolving towards a renewable-energy dominated, digitalized "data-intensive" system, where enormous data are measured in multiple timescales, different locations, and in diverse structures. This project will develop a novel data-driven framework for power system stability analysis. This project will deliver new knowledge about instability phenomena and mechanism of power systems with high-level renew ....Temporal-Spatial Data Analytics for Stochastic Power System Stability. The modern power system is evolving towards a renewable-energy dominated, digitalized "data-intensive" system, where enormous data are measured in multiple timescales, different locations, and in diverse structures. This project will develop a novel data-driven framework for power system stability analysis. This project will deliver new knowledge about instability phenomena and mechanism of power systems with high-level renewable energies, faster-than-real-time system instability risk detection, and rule-based stability control. These research outcomes will form the basis of an innovative theoretical foundation to guide new technologies for power utilities for stability assessment and enhancement in the digitalized era.Read moreRead less
Efficient Management of Things for the Future World Wide Web. The future World Wide Web will connect billions of physical objects, which will offer exciting capabilities to change the world and improve the quality of human lives, just as what the Web has done in the past 20 years. Effectively and efficiently managing things is one inevitable challenge in this new era and is much more complicated than managing traditional Web documents. This project aims to focus on this key problem and develop n ....Efficient Management of Things for the Future World Wide Web. The future World Wide Web will connect billions of physical objects, which will offer exciting capabilities to change the world and improve the quality of human lives, just as what the Web has done in the past 20 years. Effectively and efficiently managing things is one inevitable challenge in this new era and is much more complicated than managing traditional Web documents. This project aims to focus on this key problem and develop novel techniques for linking resource-constrained things to the Web, searching them using a new search engine, as well as discovering latent relationships among things for advanced management tasks such as things recommendation and composition.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE180100950
Funder
Australian Research Council
Funding Amount
$368,446.00
Summary
Building intelligence into online video services by learning user interests. This project aims to build an intelligent video streaming service by characterising users’ view interest patterns and predict user interest changes through learning data from Internet to address the challenge caused by astronomic video population. The outcomes of the project will be of great values for users and our society by intelligently filtering out valueless, harmful, illegal and unwanted videos in advance.
Robust learning of dynamic systems. Robots and other autonomous machines use models of the real world to predict the result of their actions and make decisions, but existing methods used for machine-learning are unreliable in many cases and can be easily fooled. This project aims to make machine-learning of dynamic system models reliable, accurate, and secure. The outcomes of this project will be new models and algorithms that ensure safety and increase accuracy of models learned from data. This ....Robust learning of dynamic systems. Robots and other autonomous machines use models of the real world to predict the result of their actions and make decisions, but existing methods used for machine-learning are unreliable in many cases and can be easily fooled. This project aims to make machine-learning of dynamic system models reliable, accurate, and secure. The outcomes of this project will be new models and algorithms that ensure safety and increase accuracy of models learned from data. This project will benefit robotics, control engineering, infrastructure automation, and other fields that demand the capability to model physical systems from limited data. It will also improve cybersecurity by making learning algorithms resilient to deliberate attacks with false data.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE180100158
Funder
Australian Research Council
Funding Amount
$348,026.00
Summary
A large-scale distributed experimental facility for the internet of things. This project aims to establish a large-scale, real-world experimental facility for the Internet of Things (IoT), which is currently missing in Australia, as well as in the rest of the world. The project is expected to be an essential instrument to achieve Australia’s leadership on key enabling technologies of the IoT, and to provide Australian research community with a unique platform for large-scale experimentation and ....A large-scale distributed experimental facility for the internet of things. This project aims to establish a large-scale, real-world experimental facility for the Internet of Things (IoT), which is currently missing in Australia, as well as in the rest of the world. The project is expected to be an essential instrument to achieve Australia’s leadership on key enabling technologies of the IoT, and to provide Australian research community with a unique platform for large-scale experimentation and evaluation of IoT technologies and services. The project will also serve as a vehicle for the education and training of Australia’s next generation of scholars and engineers, and contribute to Australia’s scientific visibility.Read moreRead less
Smart micro learning with open education resources. This project aims to enhance personalised learning systems for mobile device users . Open online education is gaining in popularity with its ease of use. The project tackles the problems in relation to more and more popular mobile and ‘micro learning’, where people learn on the move and within small units of time. Ontology and machine learning technologies used in this project will help to optimise the offering of open education resources, by p ....Smart micro learning with open education resources. This project aims to enhance personalised learning systems for mobile device users . Open online education is gaining in popularity with its ease of use. The project tackles the problems in relation to more and more popular mobile and ‘micro learning’, where people learn on the move and within small units of time. Ontology and machine learning technologies used in this project will help to optimise the offering of open education resources, by providing solutions meeting each individual learner’s needs. The main outcome will consolidate a cloud based micro learning framework through integrating a group of novel algorithms.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200100245
Funder
Australian Research Council
Funding Amount
$410,518.00
Summary
Bayesian nonparametric learning for practical sequential decision making. This project aims to develop new methods to support practical sequential decision making under uncertainty. It expects to pave the way for the next generation of sequential decision making uniquely characterised by uncertainty modelling, high sample-efficiency, efficient environment change adaptation, and automatical reward function learning. The expected outcomes will advance machine learning knowledge with a new deep lea ....Bayesian nonparametric learning for practical sequential decision making. This project aims to develop new methods to support practical sequential decision making under uncertainty. It expects to pave the way for the next generation of sequential decision making uniquely characterised by uncertainty modelling, high sample-efficiency, efficient environment change adaptation, and automatical reward function learning. The expected outcomes will advance machine learning knowledge with a new deep learning schema for data modelling and sequential decision-making knowledge with a novel deep reinforcement learning methodology. These developments have immediate applications in autonomous vehicles, advanced manufacturing, and dynamic pricing, with scientific, economic, and social benefits for Australia and the world.Read moreRead less
Combating Fake News on Social Media: From Early Detection to Intervention. The project aims to detect fake news early to minimise the negative impact of false information. This project expects to devise novel solutions to address technical challenges for detection of fake news with scarce signals. Expected outcomes of this project include a suite of data mining and machine learning models for identification of fake news from the social media stream, prediction of user propagation of false infor ....Combating Fake News on Social Media: From Early Detection to Intervention. The project aims to detect fake news early to minimise the negative impact of false information. This project expects to devise novel solutions to address technical challenges for detection of fake news with scarce signals. Expected outcomes of this project include a suite of data mining and machine learning models for identification of fake news from the social media stream, prediction of user propagation of false information as well as recommendation of truthful news to counteract adversarial fake news. This project should generate technologies that enhance the integrity of the online echo system and benefit media providers and online population within Australia and across the world. Read moreRead less
A cloud computing environment for managing foreign exchange risk. This project involves using parallel computing and machine learning to improve the real-time handling of a bank's foreign exchange risk. It will lead to improved techniques to manage exchange rate variations, enabling banks to better assess their risk and will ultimately lead to improved services for Australian companies.