Tyre-force estimation by Kalman inverse filtering: applications to off-highway mining trucks

Kalman inverse filtering is used to develop a methodology for real-time estimation of forces acting at the interface between tyre and road on large off-highway mining trucks. The system model formulated is capable of estimating the three components of tyre-force at each wheel of the truck using a practical set of measurements and inputs. Good tracking is obtained by the estimated tyre-forces when compared with those simulated by an ADAMS virtual-truck model. A sensitivity analysis determines the susceptibility of the tyre-force estimates to uncertainties in the truck's parameters.

Making it work: Identifying the challenges of collaborative international research

In this article, we explore the challenges - and benefits - of conducting collaborative research on an international scale. The authors - from Australia, Canada, and New Zealand - draw upon their experiences in designing and conducting a three-country study. The growing pressures on scholars to work in collaborative research teams are described, and key findings and reflections are presented. It is claimed that such work is a highly complex and demanding extension to the academic's role. The authors conclude that, despite the somewhat negative sense that this reflection may convey, the synergies gained and the valuable comparative learning that took place make overcoming these challenges a worthwhile process. The experiences as outlined in this paper suggest that developing understandings of the challenges inherent in undertaking international collaborative research might well be a required component of the professional development opportunities afforded to new scholars.

Incremental learning for interactive e-mail filtering

In this article, we propose a framework, namely, Prediction-Learning-Distillation (PLD) for interactive document classification and distilling misclassified documents. Whenever a user points out misclassified documents, the PLD learns from the mistakes and identifies the same mistakes from all other classified documents. The PLD then enforces this learning for future classifications. If the classifier fails to accept relevant documents or reject irrelevant documents on certain categories, then PLD will assign those documents as new positive/negative training instances. The classifier can then strengthen its weakness by learning from these new training instances. Our experiments’ results have demonstrated that the proposed algorithm can learn from user-identified misclassified documents, and then distil the rest successfully.

Paediatric research in emergency departments international collaborative (PREDICT): First steps towards the development of an Australian and New Zealand research network

Paediatric emergency research is hampered by a number of barriers that can be overcome by a multicentre approach. In 2004, an Australia and New Zealand-based paediatric emergency research network was formed, the Paediatric Research in Emergency Departments International Collaborative (PREDICT). The founding sites include all major tertiary children’s hospital EDs in Australia and New Zealand and a major mixed ED in Australia. PREDICT aims to provide leadership and infrastructure for multicentre research at the highest standard, facilitate collaboration between institutions, health-care providers and researchers and ultimately improve patient outcome. Initial network-wide projects have been determined. The present article describes the development of the network, its structure and future goals.

Eastern facade to lake, Collaborative Workshop, University of Queensland, St Lucia

View of eastern facade to lake, with Zelman Cowen building behind.

Validation of multicriteria analysis models

Validation procedures play an important role in establishing the credibility of models, improving their relevance and acceptability. This article reviews the testing of models relevant to environmental and natural resource management with particular emphasis on models used in multicriteria analysis (MCA). Validation efforts for a model used in a MCA catchment management study in North Queensland, Australia, are presented. Determination of face validity is found to be a useful approach in evaluating this model, and sensitivity analysis is useful in checking the stability of the model. (C) 2000 Elsevier Science Ltd. All rights reserved.

Interactive email filtering: Learning from misclassified examples

Learning from mistakes has proven to be an effective way of learning in the interactive document classifications. In this paper we propose an approach to effectively learning from mistakes in the email filtering process. Our system has employed both SVM and Winnow machine learning algorithms to learn from misclassified email documents and refine the email filtering process accordingly. Our experiments have shown that the training of an email filter becomes much effective and faster