A Novel Framework for Optimised Ensemble Classifier. The project aims to develop a novel framework for creating an optimised ensemble classifier that will improve data analysis and the accuracy of many real-world applications such as document analysis, robotics and medical diagnosis. The project plans to develop and investigate novel methods for generating diverse training environment layers, base classifiers and fusion of classifiers. It also plans to design a multi-objective evolutionary algor ....A Novel Framework for Optimised Ensemble Classifier. The project aims to develop a novel framework for creating an optimised ensemble classifier that will improve data analysis and the accuracy of many real-world applications such as document analysis, robotics and medical diagnosis. The project plans to develop and investigate novel methods for generating diverse training environment layers, base classifiers and fusion of classifiers. It also plans to design a multi-objective evolutionary algorithm-based search obtain the optimal number of layers, clusters and base classifiers. The expected outcomes of the proposed framework are advances in classifier learning. The final outcome may be novel methods which will bring in diversity during the learning of the base classifiers and provide an optimal ensemble classifier for real-world applications.Read moreRead less
An automated system for the analysis of road safety and conditions. This project aims to develop an automated system for the analysis of road safety and conditions. Digital video road data is collected over every state road in Queensland annually, and has the potential to provide a range of value-added products for safer roads. This project will develop deep learning based neural network techniques which can learn and classify roadside objects so that video data can be automatically analysed all ....An automated system for the analysis of road safety and conditions. This project aims to develop an automated system for the analysis of road safety and conditions. Digital video road data is collected over every state road in Queensland annually, and has the potential to provide a range of value-added products for safer roads. This project will develop deep learning based neural network techniques which can learn and classify roadside objects so that video data can be automatically analysed allowing the estimation of the proximity of objects for road safety and rating. The expected outcome will be new identification techniques and software which can be incorporated with road data collection systems.Read moreRead less
Deep Learning Architecture with Context Adaptive Features for Image Parsing. This project aims to develop a novel deep learning network architecture with contextual adaptive features for image parsing that can improve the object detection accuracy in real-world applications. A number of innovative methods for deep learning, contextual features and network parameter selection will be developed and investigated. The impact of the proposed architecture and features will be improved object-detection ....Deep Learning Architecture with Context Adaptive Features for Image Parsing. This project aims to develop a novel deep learning network architecture with contextual adaptive features for image parsing that can improve the object detection accuracy in real-world applications. A number of innovative methods for deep learning, contextual features and network parameter selection will be developed and investigated. The impact of the proposed architecture and features will be improved object-detection accuracy and advances in deep learning network architecture for image parsing. The intended outcomes are deep learning network architecture, contextual feature extraction techniques and network parameter optimisation techniques for image parsing.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE140100017
Funder
Australian Research Council
Funding Amount
$394,800.00
Summary
Adaptive Optimisation of Complex Combinatorial Problems. One of the most common problems faced by planners, whether in industry or government, is optimisation, finding the optimal solution to a problem. Even a one per cent improvement in a solution can make a difference of millions of dollars in some cases. Traditionally optimisation problems are solved by analytic means or exact optimisation methods. Today, however, many optimisation problems involve complex combinatorial systems that make such ....Adaptive Optimisation of Complex Combinatorial Problems. One of the most common problems faced by planners, whether in industry or government, is optimisation, finding the optimal solution to a problem. Even a one per cent improvement in a solution can make a difference of millions of dollars in some cases. Traditionally optimisation problems are solved by analytic means or exact optimisation methods. Today, however, many optimisation problems involve complex combinatorial systems that make such traditional approaches unsuitable or intractable. This project aims to assist researchers and practitioners in solving complex combinatorial optimisation problems by adapting the optimisation strategy to the problem being solved, based on problem features such as search space difficulty. Read moreRead less
A Novel Automatic Neural Network Feature Extractor. This project aims to study feature extraction abilities of convolutional as well as traditional neural networks and develop a generic feature extractor which can be applied to wide variety of real-world image and non-image data. New concepts for automatic feature extraction, feature explanation, hybrid evolutionary algorithms and non-iterative ensemble learning will be introduced and evaluated. The expected outcomes are a generic feature extrac ....A Novel Automatic Neural Network Feature Extractor. This project aims to study feature extraction abilities of convolutional as well as traditional neural networks and develop a generic feature extractor which can be applied to wide variety of real-world image and non-image data. New concepts for automatic feature extraction, feature explanation, hybrid evolutionary algorithms and non-iterative ensemble learning will be introduced and evaluated. The expected outcomes are a generic feature extractor for automatically extracting features, an optimiser for finding optimal parameters and non-iterative ensemble learning technique for classification of features into classes. The impact of this project will be automatic feature extractors and classifiers for real-world applications.Read moreRead less
Smart Information Processing for Roadside Fire Risk Assessment Using Computational Intelligence and Pattern Recognition. This project proposes a novel approach for identifying roadside fire risks using pattern recognition and computational intelligence techniques. The video data is collected over every state road in Queensland annually, and has the potential to provide a range of value-added products for safer roads. This project aims to develop new techniques for identification of roadside obje ....Smart Information Processing for Roadside Fire Risk Assessment Using Computational Intelligence and Pattern Recognition. This project proposes a novel approach for identifying roadside fire risks using pattern recognition and computational intelligence techniques. The video data is collected over every state road in Queensland annually, and has the potential to provide a range of value-added products for safer roads. This project aims to develop new techniques for identification of roadside objects so that the data can be automatically analysed allowing the estimation of fire risk factors. The final outcome intends to be techniques for segmentation and classification of roadside objects and estimation of fire risk factors.Read moreRead less
Engineering evolving complex network systems through structure intervention. This project aims to create a theory and technology for engineering complex network systems (CSS) through structural intervention. Complex network systems with evolving components are ubiquitous in nature and society. The science of biological networks, the Internet and large-scale power networks demand tools to understand and influence their evolving dynamics. This project could result in a breakthrough theory in netwo ....Engineering evolving complex network systems through structure intervention. This project aims to create a theory and technology for engineering complex network systems (CSS) through structural intervention. Complex network systems with evolving components are ubiquitous in nature and society. The science of biological networks, the Internet and large-scale power networks demand tools to understand and influence their evolving dynamics. This project could result in a breakthrough theory in network science and technology to augment biological systems and power grids. Expected benefits include cost-effective augmentation of power networks injected with renewable energy sources, and advancing basic biology research.Read moreRead less
Symbolic synthesis of knowledge-based program implementations. Systems with concurrent streams of activity are ubiquitous in computer hardware and software designs, but are conceptually complex, and fraught with faults and inefficiency. The project aims to address these difficulties by automating aspects of system design, to relieve the designer of the need to reason about complex patterns of information flow.
Automatic Energy Tuning of Parallel Applications on a Hybrid Supercomputer. Energy efficiency is a critical challenge in building next-generation supercomputers. This project aims to provide a new energy-tuning tool integrated with Cray’s systems, to simplify the process of tuning hybrid applications and managing efficient energy use. Although hardware components play a dominant role in saving energy, heterogeneous systems offer the opportunity to exploit the extremely high concurrency with mode ....Automatic Energy Tuning of Parallel Applications on a Hybrid Supercomputer. Energy efficiency is a critical challenge in building next-generation supercomputers. This project aims to provide a new energy-tuning tool integrated with Cray’s systems, to simplify the process of tuning hybrid applications and managing efficient energy use. Although hardware components play a dominant role in saving energy, heterogeneous systems offer the opportunity to exploit the extremely high concurrency with modest energy consumption using accelerators. Accordingly, the future of parallel computing must consider the trade-off between obtaining the optimal performance and the allowed power budget. The project plans to design parallel programming environments that support energy analysis and tuning.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE150100720
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Testing Isomorphism of Algebraic Structures. The algorithmic problem of isomorphism testing seeks to decide whether two objects from a mathematical category are essentially the same. This project focuses on the setting when the categories are from algebra, including but not limited to, groups and polynomials. It is a family of fundamental problems in complexity theory, with important applications in cryptography. The project aims to develop efficient algorithms with provable guarantee, or formal ....Testing Isomorphism of Algebraic Structures. The algorithmic problem of isomorphism testing seeks to decide whether two objects from a mathematical category are essentially the same. This project focuses on the setting when the categories are from algebra, including but not limited to, groups and polynomials. It is a family of fundamental problems in complexity theory, with important applications in cryptography. The project aims to develop efficient algorithms with provable guarantee, or formal hardness proofs, for these problems. Algorithms will be implemented to examine the impacts on certain cryptography schemes. The successful completion of this project will enhance the understanding of computational complexities of these problems, and identify the security of certain cryptography schemes.Read moreRead less