Innovative Data Driven Techniques for Structural Condition Monitoring . Safe and sustainable infrastructure involves the development and application of structural monitoring and assessment techniques for condition evaluation. This project develops an innovative structure condition monitoring approach based on the emerging digital technologies on image processing, data analytics and machine learning techniques, for better infrastructure asset management under operational environment. Expected out ....Innovative Data Driven Techniques for Structural Condition Monitoring . Safe and sustainable infrastructure involves the development and application of structural monitoring and assessment techniques for condition evaluation. This project develops an innovative structure condition monitoring approach based on the emerging digital technologies on image processing, data analytics and machine learning techniques, for better infrastructure asset management under operational environment. Expected outcomes of this project enhance the capacity to conduct the operational monitoring and data interpretation to deliver the best life cycle performance of infrastructure. This project should provide significant benefits to Australia in infrastructure asset management by reducing the interruption of infrastructure operations.Read moreRead less
Robust and Explainable 3D Computer Vision. Computer vision is increasingly relying on deep learning which is fragile, opaque and fails catastrophically without warning. This project aims to address these problems by developing new theory in graph representation of 3D geometric and image data, hierarchical graph simplification and novel modules designed specifically for deep learning over geometric graphs. Using these modules, it aims to design graph convolutional network architectures for self-s ....Robust and Explainable 3D Computer Vision. Computer vision is increasingly relying on deep learning which is fragile, opaque and fails catastrophically without warning. This project aims to address these problems by developing new theory in graph representation of 3D geometric and image data, hierarchical graph simplification and novel modules designed specifically for deep learning over geometric graphs. Using these modules, it aims to design graph convolutional network architectures for self-supervised learning that are robust to failures and provide explainable decisions for object detection and scene segmentation. The outcomes are expected to advance theory in robust deep learning and benefit 3D mapping, surveying, infrastructure monitoring, transport and robotics industries.Read moreRead less
Adapting Automation Transparency to Allow Accurate Use by Humans . The project will conduct the human factors research urgently required to discover how best to make automation in high-risk work settings more transparent and usable by humans. In safety-critical work contexts such as defence and aviation, automated decision aids improve human decision-making. Unfortunately however, catastrophic accidents have occurred because human operators have either not followed correct automated advice, or f ....Adapting Automation Transparency to Allow Accurate Use by Humans . The project will conduct the human factors research urgently required to discover how best to make automation in high-risk work settings more transparent and usable by humans. In safety-critical work contexts such as defence and aviation, automated decision aids improve human decision-making. Unfortunately however, catastrophic accidents have occurred because human operators have either not followed correct automated advice, or followed incorrect automated advice. A series of human factors studies using unmanned vehicle control, air traffic control, and submarine track management tasks (including testing experts in field settings) will discover how best to design transparent automation that can be safely and efficiently used by humans.Read moreRead less
Excessive sitting and population health: strengthening the science and the relevance to policy and practice. The majority of Australian adults spend most of their waking hours sitting; this increases the likelihood of developing diseases of inactivity, including diabetes, heart disease and some cancers. New research will investigate what factors encourage excessive sitting and what the health benefits are for people who deliberately do less sitting.