Zero Defect Manufacturing of Complex Assemblies. The aim of this research project is to develop the tools required to design and implement zero defect manufacturing systems. It is intended that generic guidelines will be developed for achieving zero defect manufacturing of complex assemblies in a cost effective manner. Methodologies and techniques derived from these guidelines will be tested and validated on an existing door trim assembly production line. This project with its emphasis on utilis ....Zero Defect Manufacturing of Complex Assemblies. The aim of this research project is to develop the tools required to design and implement zero defect manufacturing systems. It is intended that generic guidelines will be developed for achieving zero defect manufacturing of complex assemblies in a cost effective manner. Methodologies and techniques derived from these guidelines will be tested and validated on an existing door trim assembly production line. This project with its emphasis on utilising manufacturing systems involving a mix of human and robot based operations and in process inspection techniques to achieve defect free manufacturing is particularly relevant to medium size component suppliers.Read moreRead less
Discovering justified knowledge from data. Knowledge discovery from data has assumed a critical role in numerous areas of science, commerce and public administration. However, its effectiveness is limited by the undesirable propensity of current techniques to make many false, as well as real, discoveries. This research will rectify that problem, a critical outcome given the potential cost of making decisions or setting policy using flawed information. For example, it may prevent the adoption of ....Discovering justified knowledge from data. Knowledge discovery from data has assumed a critical role in numerous areas of science, commerce and public administration. However, its effectiveness is limited by the undesirable propensity of current techniques to make many false, as well as real, discoveries. This research will rectify that problem, a critical outcome given the potential cost of making decisions or setting policy using flawed information. For example, it may prevent the adoption of ineffective strategies for addressing land degradation; inappropriately targeted public health expenditure; expensive development and clinical trialing of drugs which prove ineffective; and wasted police and security investigations into unfounded suspicions of criminal or terrorist activity.Read moreRead less
Learning Semi-Naive Bayesian Classifiers from Numeric Data. This project addresses research priority 3, offering frontier technologies. It will deliver better and faster classification technologies that greatly help accomplish many real-world tasks including medical diagnosis, fraud detection, spam filtering and webpage search, where accurate and fast classification is critical to save life, increase efficiency, reduce crime and conserve resources. Hence this project addresses priority 4 as well ....Learning Semi-Naive Bayesian Classifiers from Numeric Data. This project addresses research priority 3, offering frontier technologies. It will deliver better and faster classification technologies that greatly help accomplish many real-world tasks including medical diagnosis, fraud detection, spam filtering and webpage search, where accurate and fast classification is critical to save life, increase efficiency, reduce crime and conserve resources. Hence this project addresses priority 4 as well, better safeguarding Australia from disease and crime. This project will also support a young research group of international standing. It will train the involved researchers to attain a high level of proficiency and excellence in machine learning research and development.Read moreRead less
Advanced Bayesian Networks for Epidemiology. We will demonstrate the potential of advanced Artificial Intelligence for medical informatics by extending the capabilities of Bayesian Networks. Bayesian Networks excel when researchers need to combine causal and diagnostic reasoning in areas characterised by uncertainty. But they have one flaw which hinders their use: they do not yet easily mix continuous and discrete variables. We will extend them to handle such mixes, then demonstrate how much the ....Advanced Bayesian Networks for Epidemiology. We will demonstrate the potential of advanced Artificial Intelligence for medical informatics by extending the capabilities of Bayesian Networks. Bayesian Networks excel when researchers need to combine causal and diagnostic reasoning in areas characterised by uncertainty. But they have one flaw which hinders their use: they do not yet easily mix continuous and discrete variables. We will extend them to handle such mixes, then demonstrate how much they can improve on current methods for predicting, among other things, coronary heart disease (CHD).Read moreRead less
Multi-Ontologies meet UML: Improving the Software Engineering of Multi-Agent Systems. Multi-agent systems are a new style of software well suited for open, dynamic, distributed, global, heterogeneous environments such as the Internet. Systematic methods are needed to allow multi-agent systems to reason effectively with high level knowledge. This research draws on software engineering practice to develop a theory and methodology for multi-ontologies for expressing knowledge within multi-agent sys ....Multi-Ontologies meet UML: Improving the Software Engineering of Multi-Agent Systems. Multi-agent systems are a new style of software well suited for open, dynamic, distributed, global, heterogeneous environments such as the Internet. Systematic methods are needed to allow multi-agent systems to reason effectively with high level knowledge. This research draws on software engineering practice to develop a theory and methodology for multi-ontologies for expressing knowledge within multi-agent systems that facilitate adaptation and change.
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Explaining the outcomes of complex computational models. This project aims to develop new algorithms that automatically generate explanations for the results produced by complex computational models. In recent times, these models have become increasingly accurate, and hence pervasive. However, the reasoning of Deep Neural Networks and Bayesian Networks, and of complex Regression models and Decision Trees is often unclear, impairing effective decision making by practitioners who use the results o ....Explaining the outcomes of complex computational models. This project aims to develop new algorithms that automatically generate explanations for the results produced by complex computational models. In recent times, these models have become increasingly accurate, and hence pervasive. However, the reasoning of Deep Neural Networks and Bayesian Networks, and of complex Regression models and Decision Trees is often unclear, impairing effective decision making by practitioners who use the results of these models or investigate the decisions made by the systems. Practical benefits of clear decision making reasoning by complex computational models include reduced risk, increased productivity and revenue, appropriate adoption of technologies including improved education for practitioners, and improved outcomes for end users. Significant benefits will be demonstrated through the evaluations with practitioners in the areas of healthcare and energy.Read moreRead less
Extending association rule discovery to numeric data. This project tackles a key limitation of association-rule discovery, which is one of the main techniques used in data mining. Much valuable data is numeric. However, association-rule discovery cannot satisfactorily model numeric data, a limitation that has greatly restricted its application. This project investigates a novel new technique that overcomes this limitation. Impact-rule discovery finds associations with numeric distributions. ....Extending association rule discovery to numeric data. This project tackles a key limitation of association-rule discovery, which is one of the main techniques used in data mining. Much valuable data is numeric. However, association-rule discovery cannot satisfactorily model numeric data, a limitation that has greatly restricted its application. This project investigates a novel new technique that overcomes this limitation. Impact-rule discovery finds associations with numeric distributions. This allows data analysts to discover precisely the type of information that they usually seek from numeric data, for example, how to maximize either average or aggregate measures of outcomes such as health, compliance, profit, or accuracy.Read moreRead less
Intelligent Structured Knowledge Source Integration via Software Agents. This project aims to use flexible information agents to integrate the World Wide Web with a machine readable ontology, namely a large, consistent collection of common sense knowledge. The best developed ontology in the world is Cyc. Cyc's repository of general purpose knowledge is rich and stable, but has a major limitation in requiring its knowledge to be hand-entered by experts. The outcomes of the project will be increas ....Intelligent Structured Knowledge Source Integration via Software Agents. This project aims to use flexible information agents to integrate the World Wide Web with a machine readable ontology, namely a large, consistent collection of common sense knowledge. The best developed ontology in the world is Cyc. Cyc's repository of general purpose knowledge is rich and stable, but has a major limitation in requiring its knowledge to be hand-entered by experts. The outcomes of the project will be increased functionality for ontologies, to enable expert reasoning programs wishing to use a formal ontology such as Cyc to have access to the wealth of knowledge on the World Wide Web.Read moreRead less
Thermo-mechanical interactive atlas of basin evolution. We propose to create a thermo-mechanical interactive atlas of basin evolution as a desktop geodynamic modelling resource for exploration geologists. The atlas will allow the user to iteratively run our 2D/3D basin modelling software ELLIPSIS to alter model parameters until they match observed basin geometries or thermal history. Model iteration will be based on interactive user evaluation of model outputs and genetic algorithms, which pr ....Thermo-mechanical interactive atlas of basin evolution. We propose to create a thermo-mechanical interactive atlas of basin evolution as a desktop geodynamic modelling resource for exploration geologists. The atlas will allow the user to iteratively run our 2D/3D basin modelling software ELLIPSIS to alter model parameters until they match observed basin geometries or thermal history. Model iteration will be based on interactive user evaluation of model outputs and genetic algorithms, which progressively modify the solution set by mimicking the evolutionary behavior of biological systems (selection, cross-over and mutation), until an acceptable result is achieved. The interactive atlas will be applied to Australian and international case studies.Read moreRead less
Temporal and spatial Bayesian network modelling for improved fog forecasting. This project aims to improve the accuracy of fog forecasting by explicitly modelling the spatial and temporal uncertainties surrounding fog formation. It is expected weather forecast services will adopt our approach to improve their predictions of fog, which will in turn help transport companies save costs, cut emissions and improve safety.