Financial incentives and advanced construction procurement systems

Construction clients often use financial incentives to encourage stakeholder motivation and commitment to voluntary higher-order project goals. Despite the increased use of financial incentives, there is little literature addressing means of optimizing outcomes. Using a case study methodology, the examination of a successful Australian construction project demonstrates the features of a positively geared procurement approach that promotes the effectiveness of financial incentives. The research results show that if the incentive system is perceived to be fair and is applied to reward exceptional performance, and not to manipulate, then contractors are more likely to be positively motivated.

Research training and national innovation systems in Australia, Finland and the United States : a policy and systems study supported by 30 case studies of research students in the fields of geospatial science, wireless communication, biosciences, and materials science and engineering

Reforms to the national research and research training system by the Commonwealth Government of Australia sought to effectively connect research conducted in universities to Australia's national innovation system. Research training has a key role in ensuring an adequate supply of highly skilled people for the national innovation system. During their studies, research students produce and disseminate a massive amount of new knowledge. Prior to this study, there was no research that examined the contribution of research training to Australia's national innovation system despite the existence of policy initiatives aiming to enhance this contribution. Given Australia's below average (but improving) innovation performance compared to other OECD countries, the inclusion of Finland and the United States provided further insights into the key research question. This study examined three obvious ways that research training contributes to the national innovation systems in the three countries: the international mobility and migration of research students and graduates, knowledge production and distribution by research students, and the impact of research training as advanced human capital formation on economic growth. Findings have informed the concept of a research training culture of innovation that aims to enhance the contribution of research training to Australia's national innovation system. Key features include internationally competitive research and research training environments; research training programs that equip students with economically-relevant knowledge and the capabilities required by employers operating in knowledge-based economies; attractive research careers in different sectors; a national commitment to R&D as indicated by high levels of gross and business R&D expenditure; high private and social rates of return from research training; and the horizontal coordination of key organisations that create policy for, and/or invest in research training.

Creating deep and broad change through research and systems approaches in early childhood education for sustainability

The co-authors raise two matters they consider essential for the future development of ECEfS. The first is the need to create deep foundations based in research. At a time of increasing practitioner interest, research in ECEfS is meagre. A robust research community is crucial to support quality in curriculum and pedagogy, and to promote learning and innovation in thinking and practice. The second 'essential' for the expansion and uptake of ECEfS is broad systemic change. All level within the early childhood education system - individual teachers and classrooms, whole centres and schools, professional associations and networks, accreditation and employing authorities, and teacher educators - must work together to create and reinforce the cultural and educational changes required for sustainability. This chapter provides explanations of processes to engender systemic change. It illustrates a systems approach, with reference to a recent study focused on embedding EfS into teacher education. This study emphasises the apparent contradiction that the answer to large-scale reform lies with small-scale reforms that build capacity and make connections.

Early childhood education for sustainability : why it matters, what it is, and how whole centre action research and systems thinking can help

The global financial crisis, global pandemics, global warming and peak oil are indicative of a world facing major environmental, social and economic problems. At the same time, world population continues to rise and global inequalities deepen. Children are the most vulnerable to the impacts of unsustainable living with specific harms arising because of their physical and cognitive vulnerabilities. Nevertheless, children do not have to be victims in the face of these challenges. Education, including early childhood education, has an important role to in building resilience and capabilities in children that equip them as active and informed citizens now and in the future and who are capable of contributing to healthy and sustainable ways of living. Drawing on educational change literature, action research, education for sustainability, health promotion and systems theory, this paper outlines three strategies that can help reorient early childhood education towards sustainability. One strategy is the adoption of whole centre approaches to sustainability and education for sustainability. This means working across the whole of a centre’s operations – curriculum and pedagogy, physical and social environments, its partnerships and community connections. The second strategy – applied in conjunction with the first – is the use of action research to investigate the early childhood setting and to create the desired changes. The third strategy is the adoption of systems thinking as a way of leveraging support and momentum for change so that education for sustainability goes beyond the initiatives of individual teachers and centres, and becomes a systems-wide imperative.

Transaction mining for fraud detection in ERP Systems

Despite all attempts to prevent fraud, it continues to be a major threat to industry and government. Traditionally, organizations have focused on fraud prevention rather than detection, to combat fraud. In this paper we present a role mining inspired approach to represent user behaviour in Enterprise Resource Planning (ERP) systems, primarily aimed at detecting opportunities to commit fraud or potentially suspicious activities. We have adapted an approach which uses set theory to create transaction profiles based on analysis of user activity records. Based on these transaction profiles, we propose a set of (1) anomaly types to detect potentially suspicious user behaviour, and (2) scenarios to identify inadequate segregation of duties in an ERP environment. In addition, we present two algorithms to construct a directed acyclic graph to represent relationships between transaction profiles. Experiments were conducted using a real dataset obtained from a teaching environment and a demonstration dataset, both using SAP R/3, presently the predominant ERP system. The results of this empirical research demonstrate the effectiveness of the proposed approach.

Fraud detection in ERP systems using scenario matching

ERP systems generally implement controls to prevent certain common kinds of fraud. In addition however, there is an imperative need for detection of more sophisticated patterns of fraudulent activity as evidenced by the legal requirement for company audits and the common incidence of fraud. This paper describes the design and implementation of a framework for detecting patterns of fraudulent activity in ERP systems. We include the description of six fraud scenarios and the process of specifying and detecting the occurrence of those scenarios in ERP user log data using the prototype software which we have developed. The test results for detecting these scenarios in log data have been verified and confirm the success of our approach which can be generalized to ERP systems in general.

Improvements to the fire performance of light gauge steel floor systems

Light gauge steel frame (LSF) structures are increasingly used in commercial and residential buildings because of their non-combustibility, dimensional stability and ease of installation. A common application is in floor-ceiling systems. The LSF floor-ceiling systems must be designed to serve as fire compartment boundaries and provide adequate fire resistance. Fire-rated floor-ceiling assemblies have been increasingly used in buildings. However, limited research has been undertaken in the past and hence a thorough understanding of their fire resistance behaviour is not available. Recently a new composite floor-ceiling system has been developed to provide higher fire rating. But its increased fire rating could not be determined using the currently available design methods. Therefore a research project was conducted to investigate its structural and fire resistance behaviour under standard fire conditions. This paper presents the results of full scale experimental investigations into the structural and fire behaviour of the new LSF floor system protected by the composite ceiling unit. Both the conventional and the new floor systems were tested under structural and fire loads. It demonstrates the improvements provided by the new composite panel system in comparison to conventional floor systems. Numerical studies were also undertaken using the finite element program ABAQUS. Measured temperature profiles of floors were used in the numerical analyses and their results were compared with fire test results. Tests and numerical studies provided a good understanding of the fire behaviour of the LSF floor-ceiling systems and confirmed the superior performance of the new composite system.

Automated symbolic and numeric procedure for manipulator rigid-body dynamic significance analysis and simplification

This paper describes an automated procedure for analysing the significance of each of the many terms in the equations of motion for a serial-link robot manipulator. Significance analysis provides insight into the rigid-body dynamic effects that are significant locally or globally in the manipulator's state space. Deleting those terms that do not contribute significantly to the total joint torque can greatly reduce the computational burden for online control, and a Monte-Carlo style simulation is used to investigate the errors thus introduced. The procedures described are a hybrid of symbolic and numeric techniques, and can be readily implemented using standard computer algebra packages.

Combining Cartesian and polar coordinates in IBVS

Image-based visual servo (IBVS) is a simple, efficient and robust technique for vision-based control. Although technically a local method in practice it demonstrates almost global convergence. However IBVS performs very poorly for cases that involve large rotations about the optical axis. It is well known that re-parameterizing the problem by using polar, instead of Cartesian coordinates, of feature points overcomes this limitation. First, simulation and experimental results are presented to show the complementarity of these two parameter-izations. We then describe a new hybrid visual servo strategy based on combining polar and Cartesian image Jacobians. © 2009 IEEE.

Adaptive depth estimation in image based visual servo control of dynamic systems

This paper considers the question of designing a fully image based visual servo control for a dynamic system. The work is motivated by the ongoing development of image based visual servo control of small aerial robotic vehicles. The observed targets considered are coloured blobs on a flat surface to which the normal direction is known. The theoretical framework is directly applicable to the case of markings on a horizontal floor or landing field. The image features used are a first order spherical moment for position and an image flow measurement for velocity. A fully non-linear adaptive control design is provided that ensures global stability of the closed-loop system. © 2005 IEEE.

Definition of airworthiness categories for civil Unmanned Aircraft Systems (UAS)

This paper introduces a novel strategy for the specification of airworthiness certification categories for civil unmanned aircraft systems (UAS). The risk-based approach acknowledges the fundamental differences between the risk paradigms of manned and unmanned aviation. The proposed airworthiness certification matrix provides a systematic and objective structure for regulating the airworthiness of a diverse range of UAS types and operations. An approach for specifying UAS type categories is then discussed. An example of the approach, which includes the novel application of data-clustering algorithms, is presented to illustrate the discussion.

Augmenting RatSLAM using FAB-MAP-based visual data association

This paper investigates the use of the FAB-MAP appearance-only SLAM algorithm as a method for performing visual data association for RatSLAM, a semi-metric full SLAM system. While both systems have shown the ability to map large (60-70km) outdoor locations of approximately the same scale, for either larger areas or across longer time periods both algorithms encounter difficulties with false positive matches. By combining these algorithms using a mapping between appearance and pose space, both false positives and false negatives generated by FAB-MAP are significantly reduced during outdoor mapping using a forward-facing camera. The hybrid FAB-MAP-RatSLAM system developed demonstrates the potential for successful SLAM over large periods of time.

Airborne systems laboratory for automation research

This paper presents an Airborne Systems Laboratory for Automation Research. The Airborne Systems Laboratory (ASL) is a Cessna 172 aircraft that has been specially modified and equipped by ARCAA specifically for research in future aircraft automation technologies, including Unmanned Airborne Systems (UAS). This capability has been developed over a long period of time, initially through the hire of aircraft, and finally through the purchase and modification of a dedicated flight-testing capability. The ASL has been equipped with a payload system that includes the provision of secure mounting, power, aircraft state data, flight management system and real-time subsystem. Finally, this system has been deployed in a cost effective platform allowing real-world flight-testing on a range of projects.

Optimal mission path planning (MPP) for an air sampling unmanned aerial system

This paper presents advanced optimization techniques for Mission Path Planning (MPP) of a UAS fitted with a spore trap to detect and monitor spores and plant pathogens. The UAV MPP aims to optimise the mission path planning search and monitoring of spores and plant pathogens that may allow the agricultural sector to be more competitive and more reliable. The UAV will be fitted with an air sampling or spore trap to detect and monitor spores and plant pathogens in remote areas not accessible to current stationary monitor methods. The optimal paths are computed using a Multi-Objective Evolutionary Algorithms (MOEAs). Two types of multi-objective optimisers are compared; the MOEA Non-dominated Sorting Genetic Algorithms II (NSGA-II) and Hybrid Game are implemented to produce a set of optimal collision-free trajectories in three-dimensional environment. The trajectories on a three-dimension terrain, which are generated off-line, are collision-free and are represented by using Bézier spline curves from start position to target and then target to start position or different position with altitude constraints. The efficiency of the two optimization methods is compared in terms of computational cost and design quality. Numerical results show the benefits of coupling a Hybrid-Game strategy to a MOEA for MPP tasks. The reduction of numerical cost is an important point as the faster the algorithm converges the better the algorithms is for an off-line design and for future on-line decisions of the UAV.

Bayesian hybrid algorithms and models : implementation and associated issues

This thesis addresses computational challenges arising from Bayesian analysis of complex real-world problems. Many of the models and algorithms designed for such analysis are ‘hybrid’ in nature, in that they are a composition of components for which their individual properties may be easily described but the performance of the model or algorithm as a whole is less well understood. The aim of this research project is to after a better understanding of the performance of hybrid models and algorithms. The goal of this thesis is to analyse the computational aspects of hybrid models and hybrid algorithms in the Bayesian context. The first objective of the research focuses on computational aspects of hybrid models, notably a continuous finite mixture of t-distributions. In the mixture model, an inference of interest is the number of components, as this may relate to both the quality of model fit to data and the computational workload. The analysis of t-mixtures using Markov chain Monte Carlo (MCMC) is described and the model is compared to the Normal case based on the goodness of fit. Through simulation studies, it is demonstrated that the t-mixture model can be more flexible and more parsimonious in terms of number of components, particularly for skewed and heavytailed data. The study also reveals important computational issues associated with the use of t-mixtures, which have not been adequately considered in the literature. The second objective of the research focuses on computational aspects of hybrid algorithms for Bayesian analysis. Two approaches will be considered: a formal comparison of the performance of a range of hybrid algorithms and a theoretical investigation of the performance of one of these algorithms in high dimensions. For the first approach, the delayed rejection algorithm, the pinball sampler, the Metropolis adjusted Langevin algorithm, and the hybrid version of the population Monte Carlo (PMC) algorithm are selected as a set of examples of hybrid algorithms. Statistical literature shows how statistical efficiency is often the only criteria for an efficient algorithm. In this thesis the algorithms are also considered and compared from a more practical perspective. This extends to the study of how individual algorithms contribute to the overall efficiency of hybrid algorithms, and highlights weaknesses that may be introduced by the combination process of these components in a single algorithm. The second approach to considering computational aspects of hybrid algorithms involves an investigation of the performance of the PMC in high dimensions. It is well known that as a model becomes more complex, computation may become increasingly difficult in real time. In particular the importance sampling based algorithms, including the PMC, are known to be unstable in high dimensions. This thesis examines the PMC algorithm in a simplified setting, a single step of the general sampling, and explores a fundamental problem that occurs in applying importance sampling to a high-dimensional problem. The precision of the computed estimate from the simplified setting is measured by the asymptotic variance of the estimate under conditions on the importance function. Additionally, the exponential growth of the asymptotic variance with the dimension is demonstrated and we illustrates that the optimal covariance matrix for the importance function can be estimated in a special case.