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Scheme : Linkage Projects
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  • Active Funded Activity

    Linkage Projects - Grant ID: LP200301399

    Funder
    Australian Research Council
    Funding Amount
    $480,234.00
    Summary
    Immersive Technologies for Rapid Metallic Tank Inspection and Repairs. Metal tank silos house some of the most dangerous chemicals, which erode the internal structure of the tank over time. It is critical to check the integrity of the tank to prevent disasters from occurring. NDE solutions uses a rapid motion scanner (RMS) to scan the interior surface of the container while it is still full of its storage material. It is the aim of this project to use Augmented Reality, to overlay the scan provi .... Immersive Technologies for Rapid Metallic Tank Inspection and Repairs. Metal tank silos house some of the most dangerous chemicals, which erode the internal structure of the tank over time. It is critical to check the integrity of the tank to prevent disasters from occurring. NDE solutions uses a rapid motion scanner (RMS) to scan the interior surface of the container while it is still full of its storage material. It is the aim of this project to use Augmented Reality, to overlay the scan provided by the RMS, onto the worker's view of the tank, control the robot via. hand gestures, and facilitate remote training/guidance. The result will allow for inspection workers to quickly and accurately the location of critical failures, without performing the hazardous procedures of internal tank inspection.
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    Active Funded Activity

    Linkage Projects - Grant ID: LP190100676

    Funder
    Australian Research Council
    Funding Amount
    $341,853.00
    Summary
    A safety-preserving ecosystem for autonomous driving. In this project, Macquarie University will collaborate with UTS and SilverQuest to develop an innovative safety-preserving ecosystem for autonomous driving. This system will not only be adopted by SilverQuest’s customers (automotive companies) to secure their latest autonomous driving models, but also be commercialised as a toolset that can be plugged into existing autonomous vehicles to detect and prevent malicious attacks on autonomous driv .... A safety-preserving ecosystem for autonomous driving. In this project, Macquarie University will collaborate with UTS and SilverQuest to develop an innovative safety-preserving ecosystem for autonomous driving. This system will not only be adopted by SilverQuest’s customers (automotive companies) to secure their latest autonomous driving models, but also be commercialised as a toolset that can be plugged into existing autonomous vehicles to detect and prevent malicious attacks on autonomous driving models. The project will lead to two innovations: in theory design an attack detection and prevention ecosystem for autonomous driving and in application implement a safety analysis toolset for industry-scale autonomous systems.
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    Active Funded Activity

    Linkage Projects - Grant ID: LP210100414

    Funder
    Australian Research Council
    Funding Amount
    $698,782.00
    Summary
    Transfer Learning for Genome Analysis and Personalised Recommendation. This project aims to improve the accuracy, adaptability, and comprehensiveness of health characteristic predictions and provide personalised recommendations for healthcare service and disease prevention. The deliverables include uncertainty learning and multi-source transfer learning methodologies for predictions based on genome analysis that distils and transfers useful knowledge from multiple sources into an Australian geno .... Transfer Learning for Genome Analysis and Personalised Recommendation. This project aims to improve the accuracy, adaptability, and comprehensiveness of health characteristic predictions and provide personalised recommendations for healthcare service and disease prevention. The deliverables include uncertainty learning and multi-source transfer learning methodologies for predictions based on genome analysis that distils and transfers useful knowledge from multiple sources into an Australian genome analysis model. A federated cross-domain recommender system will be developed to profile individuals and generate personalised recommendations. The outcomes are expected to create a paradigm shift in learning-based prediction and personalised recommendations to support healthcare services in complex environments.
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