Remote sensing to improve structural efficiency of high-speed catamarans. This project aims to develop a monitoring system to remotely measure ship motions, loads and ride control activity under commercial operations. Data will be analysed using advanced statistical methods to inform evidence-based design to improve both structural efficiency and passenger comfort. The research will impact on design rules used worldwide, reducing weight and increasing payload and transport efficiency for this cl ....Remote sensing to improve structural efficiency of high-speed catamarans. This project aims to develop a monitoring system to remotely measure ship motions, loads and ride control activity under commercial operations. Data will be analysed using advanced statistical methods to inform evidence-based design to improve both structural efficiency and passenger comfort. The research will impact on design rules used worldwide, reducing weight and increasing payload and transport efficiency for this class of vessel. A "Smart” semi-autonomous interface will be developed to provide on-board seakeeping guidance to the ship captain. This technology will have significant benefits such as increased ship safety, vessel longevity and improving passenger comfort for all types of vessels worldwide including high-speed catamarans.Read moreRead less
Industrial Transformation Training Centres - Grant ID: IC220100003
Funder
Australian Research Council
Funding Amount
$4,930,205.00
Summary
ARC Training Centre for Biofilm Research and Innovation . The ARC Training Centre for Biofilm Research and Innovation aims to transform biofouling management strategies for maritime platforms by building on local and international expertise to mentor and train the next generation of interdisciplinary scientists and engineers. Anticipating evolving regulatory stringency, this project expects to establish a dynamic environment for industry partners, students and scientists to collaborate and devel ....ARC Training Centre for Biofilm Research and Innovation . The ARC Training Centre for Biofilm Research and Innovation aims to transform biofouling management strategies for maritime platforms by building on local and international expertise to mentor and train the next generation of interdisciplinary scientists and engineers. Anticipating evolving regulatory stringency, this project expects to establish a dynamic environment for industry partners, students and scientists to collaborate and develop biofilm management strategies. Expected outcomes include new and enhanced collaborations that advance and translate knowledge to better manage biofouling. The significant benefits will include a generation of industry-focused researchers critical for growing Australia’s Defence industry.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE220100265
Funder
Australian Research Council
Funding Amount
$417,000.00
Summary
A closed-loop human–agent learning framework to enhance decision making. This project aims to design a foundational human–agent learning framework to augment the decision making process, using reinforcement and closed-loop mechanisms to enable symbiosis between a human and an artificial-intelligence agent. It envisages significant new technologies to promote controllability and efficient and safe exploration of an environment for decision actions – drastically boosting learning effectiveness and ....A closed-loop human–agent learning framework to enhance decision making. This project aims to design a foundational human–agent learning framework to augment the decision making process, using reinforcement and closed-loop mechanisms to enable symbiosis between a human and an artificial-intelligence agent. It envisages significant new technologies to promote controllability and efficient and safe exploration of an environment for decision actions – drastically boosting learning effectiveness and interpretability in decision making. Expected outcomes will benefit national cybersecurity by improving our understanding of vulnerabilities and threats involving decision actions, and by ensuring that human feedback and evaluations can help prevent catastrophic events in explorations of dynamic and complex environments.Read moreRead less
Industrial Transformation Research Hubs - Grant ID: IH180100002
Funder
Australian Research Council
Funding Amount
$5,000,000.00
Summary
ARC Research Hub for Driving Farming Productivity and Disease Prevention. The ARC Research Hub for Driving Farming Productivity and Disease Prevention aims to increase farm production and disease prevention through advancing and transferring new artificial intelligence technologies into industrial deployment. The Hub will combine machine vision, machine learning, software quality control, engineering, biology, and farming industries to develop technologies to build more intelligent systems. Thes ....ARC Research Hub for Driving Farming Productivity and Disease Prevention. The ARC Research Hub for Driving Farming Productivity and Disease Prevention aims to increase farm production and disease prevention through advancing and transferring new artificial intelligence technologies into industrial deployment. The Hub will combine machine vision, machine learning, software quality control, engineering, biology, and farming industries to develop technologies to build more intelligent systems. These dynamic systems will help determine what goal to achieve and the most efficient plan to achieve it. This Hub is expected to contribute to higher farming efficiency, lower production costs and fewer disease risks, giving the Australian industry new business opportunities and an international competitive advantage.Read moreRead less
Physics-aware machine learning for data-driven fire risk prediction. The 2019/20 Australian fire season was unprecedented in its extent, impact, and the response of fire agencies. In this project, we aim to answer the question: was the scale of these fires driven by known drivers of fire (drought, weather, fuels and ignitions), or were fundamentally new undescribed processes and phenomena involved? We will accomplish this by developing an innovative, physics-aware machine learning model of fire ....Physics-aware machine learning for data-driven fire risk prediction. The 2019/20 Australian fire season was unprecedented in its extent, impact, and the response of fire agencies. In this project, we aim to answer the question: was the scale of these fires driven by known drivers of fire (drought, weather, fuels and ignitions), or were fundamentally new undescribed processes and phenomena involved? We will accomplish this by developing an innovative, physics-aware machine learning model of fire risk and spread, trained and validated on a two-decade satellite fire record. The predictive ability of the model will be tested on the 2019/20 fire season to determine if novel drivers of fire can be identified, and the model itself will be operationalised into a novel short-to-mid term fire risk prediction tool. Read moreRead less