Variable Structure Complex Network Systems with Smart Grid Applications. This project aims to establish a breakthrough theory and technology to help deliver reliability and security of complex network systems, which are subject to structure changes, against faults and cyberattacks. Expected outcomes include a new theory that lays the foundation for understanding such systems, innovative algorithms and tools for their design, and a practical software platform used for ensuring reliability and sec ....Variable Structure Complex Network Systems with Smart Grid Applications. This project aims to establish a breakthrough theory and technology to help deliver reliability and security of complex network systems, which are subject to structure changes, against faults and cyberattacks. Expected outcomes include a new theory that lays the foundation for understanding such systems, innovative algorithms and tools for their design, and a practical software platform used for ensuring reliability and security of such systems. It will be applied directly to critical infrastructure such as the national power grid to help maintain lifeline resilience and achieve economic benefits. It will also provide an opportunity to train the next generation engineers in this cutting-edge technology for Australia.Read moreRead less
Advanced Machine Learning with Bilevel Optimization. There is an urgent need to develop a new machine learning (ML) paradigm that can overcome data-privacy and model-size constraints in real-world applications. This project aims to develop an advanced paradigm of ML with bilevel optimisation, called bilevel ML. A theoretically-guaranteed fast approximate solver and a new fuzzy bilevel learning framework will be developed to achieve the aim in complex situations; a methodology to transfer knowled ....Advanced Machine Learning with Bilevel Optimization. There is an urgent need to develop a new machine learning (ML) paradigm that can overcome data-privacy and model-size constraints in real-world applications. This project aims to develop an advanced paradigm of ML with bilevel optimisation, called bilevel ML. A theoretically-guaranteed fast approximate solver and a new fuzzy bilevel learning framework will be developed to achieve the aim in complex situations; a methodology to transfer knowledge and an approach to fast-adapt bilevel optimization solutions when required computing resources change. The anticipated outcomes should significantly improve the reliability of ML with benefits for safety learning and computing resource optimisation in ML-based data analytics.Read moreRead less
Accelerated Finite-time Learning and Control in Cyber-Physical Systems. Efficient learning and control in cyber-physical systems such as smart grids and robotic systems are very important for achieving economic and social benefits. This project aims to establish a breakthrough accelerated finite-time dynamics theory and technology to assist in delivering efficient learning and control. Expected outcomes include new distributed accelerated finite-time dynamics based learning and control algorithm ....Accelerated Finite-time Learning and Control in Cyber-Physical Systems. Efficient learning and control in cyber-physical systems such as smart grids and robotic systems are very important for achieving economic and social benefits. This project aims to establish a breakthrough accelerated finite-time dynamics theory and technology to assist in delivering efficient learning and control. Expected outcomes include new distributed accelerated finite-time dynamics based learning and control algorithms and tools for optimal operations in cyber-physical systems. This should provide significant benefits including a practical technology for industry applications in smart grids and robotic systems, and training of the next generation engineers in this technology for Australia.Read moreRead less