Peak power demand reduction under moving block signalling using an expert system

The concept of moving block signallings (MBS) has been adopted in a few mass transit railway systems. When a dense queue of trains begins to move from a complete stop, the trains can re-start in very close succession under MBS. The feeding substations nearby are likely to be overloaded and the service will inevitably be disturbed unless substations of higher power rating are used. By introducing starting time delays among the trains or limiting the trains’ acceleration rate to a certain extent, the peak energy demand can be contained. However, delay is introduced and quality of service is degraded. An expert system approach is presented to provide a supervisory tool for the operators. As the knowledge base is vital for the quality of decisions to be made, the study focuses on its formulation with a balance between delay and peak power demand.

Proceedings of the 21st Australian Software Engineering Conference (ASWEC 2010) : 6-9 April 2010, Auckland, New Zealand.

ASWEC is a joint conference of Engineers Australia and the Australian Computer Society reporting through the Engineers Australia/ACS Joint Board on Software Engineering.

Prevalence and determinants of the health promotion and risk reduction practices of younger female survivors of breast cancer

This thesis describes a discrete component of a larger mixed-method (survey and interview) study that explored the health-promotion and risk-reduction practices of younger premenopausal survivors of ovarian, breast and haematological cancers. This thesis outlines my distinct contribution to the larger study, which was to: (1) Produce a literature review that thoroughly explored all longer-term breast cancer treatment outcomes, and which outlined the health risks to survivors associated with these; (2) Describe and analyse the health-promotion and risk-reduction behaviours of nine younger female survivors of breast cancer as articulated in the qualitative interview dataset; and (3) Test the explanatory power of the Precede-Proceed theoretical framework underpinning the study in relation to the qualitative data from the breast cancer cohort. The thesis reveals that breast cancer survivors experienced many adverse outcomes as a result of treatment. While they generally engaged in healthy lifestyle practices, a lack of knowledge about many recommended health behaviours emerged throughout the interviews. The participants also described significant internal and external pressures to behave in certain ways because of the social norms surrounding the disease. This thesis also reports that the Precede-Proceed model is a generally robust approach to data collection, analysis and interpretation in the context of breast cancer survivorship. It provided plausible explanations for much of the data in this study. However, profound sociological and psychological implications arose during the analysis that were not effectively captured or explained by the theories underpinning the model. A sociological filter—such as Turner’s explanation of the meaning of the body and embodiment in the social sphere (Turner, 2008)—and the psychological concerns teased out in Mishel’s (1990) Uncertainty in Illness Theory, provided a useful dimension to the findings generated through the Precede-Proceed model. The thesis concludes with several recommendations for future research, clinical practice and education in this context.

Interaction, privacy and profiling considerations in local mobile social software : a prototype agile ride share system

Agile ridesharing aims to utilise the capability of social networks and mobile phones to facilitate people to share vehicles and travel in real time. However the application of social networking technologies in local communities to address issues of personal transport faces significant design challenges. In this paper we describe an iterative design-based approach to exploring this problem and discuss findings from the use of an early prototype. The findings focus upon interaction, privacy and profiling. Our early results suggest that explicitly entering information such as ride data and personal profile data into formal fields for explicit computation of matches, as is done in many systems, may not be the best strategy. It might be preferable to support informal communication and negotiation with text search techniques.

Expertise in software design : novice and expert models

This paper investigates how software designers use their knowledge during the design process. The research is based on the analysis of the observational and verbal data from three software design teams generated during the conceptual stage of the design process. The knowledge captured from the analysis of the mapped design team data is utilized to generate descriptive models of novice and expert designers. These models contribute to a better understanding of the connections between, and integration of, designer variables, and to a better understanding of software design expertise and its development. The models are transferable to other domains.

Open source software for workflow management : the case of YAWL

"This column is distinguished from previous Impact columns in that it concerns the development tightrope between research and commercial take-up and the role of the LGPL in an open source workflow toolkit produced in a University environment. Many ubiquitous systems have followed this route, (Apache, BSD Unix, ...), and the lessons this Service Oriented Architecture produces cast yet more light on how software diffuses out to impact us all." Michiel van Genuchten and Les Hatton Workflow management systems support the design, execution and analysis of business processes. A workflow management system needs to guarantee that work is conducted at the right time, by the right person or software application, through the execution of a workflow process model. Traditionally, there has been a lack of broad support for a workflow modeling standard. Standardization efforts proposed by the Workflow Management Coalition in the late nineties suffered from limited support for routing constructs. In fact, as later demonstrated by the Workflow Patterns Initiative (www.workflowpatterns.com), a much wider range of constructs is required when modeling realistic workflows in practice. YAWL (Yet Another Workflow Language) is a workflow language that was developed to show that comprehensive support for the workflow patterns is achievable. Soon after its inception in 2002, a prototype system was built to demonstrate that it was possible to have a system support such a complex language. From that initial prototype, YAWL has grown into a fully-fledged, open source workflow management system and support environment

Reduction of nitroaromatic compounds on supported gold nanoparticles by visible and ultraviolet light

Shedding light: Nitroaromatic compounds on gold nanoparticles (3 wt %) supported on ZrO2 can be reduced directly to the corresponding azo compounds when illuminated with visible light or ultraviolet light at 40 °C (see picture). The process occurs with high selectivity and at ambient temperature and pressure, and enables the selection of intermediates that are unstable in thermal reactions.

Development of a software model of a heavy vehicle suspension for research

A Simulink Matlab control system of a heavy vehicle suspension has been developed. The aim of the exercise presented in this paper was to develop a Simulink Matlab control system of a heavy vehicle suspension. The objective facilitated by this outcome was the use of a working model of a heavy vehicle (HV) suspension that could be used for future research. A working computer model is easier and cheaper to re-configure than a HV axle group installed on a truck; it presents less risk should something go wrong and allows more scope for variation and sensitivity analysis before embarking on further "real-world" testing. Empirical data recorded as the input and output signals of a heavy vehicle (HV) suspension were used to develop the parameters for computer simulation of a linear time invariant system described by a second-order differential equation of the form: (i.e. a "2nd-order" system). Using the empirical data as an input to the computer model allowed validation of its output compared with the empirical data. The errors ranged from less than 1% to approximately 3% for any parameter, when comparing like-for-like inputs and outputs. The model is presented along with the results of the validation. This model will be used in future research in the QUT/Main Roads project Heavy vehicle suspensions – testing and analysis, particularly so for a theoretical model of a multi-axle HV suspension with varying values of dynamic load sharing. Allowance will need to be made for the errors noted when using the computer models in this future work.

Building a software architecture for a human-robot team using the ORCA framework

This paper considers the problem of building a software architecture for a human-robot team. The objective of the team is to build a multi-attribute map of the world by performing information fusion. A decentralized approach to information fusion is adopted to achieve the system properties of scalability and survivability. Decentralization imposes constraints on the design of the architecture and its implementation. We show how a Component-Based Software Engineering approach can address these constraints. The architecture is implemented using Orca – a component-based software framework for robotic systems. Experimental results from a deployed system comprised of an unmanned air vehicle, a ground vehicle, and two human operators are presented. A section on the lessons learned is included which may be applicable to other distributed systems with complex algorithms. We also compare Orca to the Player software framework in the context of distributed systems.

Machine learning techniques to improve software quality

A significant proportion of the cost of software development is due to software testing and maintenance. This is in part the result of the inevitable imperfections due to human error, lack of quality during the design and coding of software, and the increasing need to reduce faults to improve customer satisfaction in a competitive marketplace. Given the cost and importance of removing errors improvements in fault detection and removal can be of significant benefit. The earlier in the development process faults can be found, the less it costs to correct them and the less likely other faults are to develop. This research aims to make the testing process more efficient and effective by identifying those software modules most likely to contain faults, allowing testing efforts to be carefully targeted. This is done with the use of machine learning algorithms which use examples of fault prone and not fault prone modules to develop predictive models of quality. In order to learn the numerical mapping between module and classification, a module is represented in terms of software metrics. A difficulty in this sort of problem is sourcing software engineering data of adequate quality. In this work, data is obtained from two sources, the NASA Metrics Data Program, and the open source Eclipse project. Feature selection before learning is applied, and in this area a number of different feature selection methods are applied to find which work best. Two machine learning algorithms are applied to the data - Naive Bayes and the Support Vector Machine - and predictive results are compared to those of previous efforts and found to be superior on selected data sets and comparable on others. In addition, a new classification method is proposed, Rank Sum, in which a ranking abstraction is laid over bin densities for each class, and a classification is determined based on the sum of ranks over features. A novel extension of this method is also described based on an observed polarising of points by class when rank sum is applied to training data to convert it into 2D rank sum space. SVM is applied to this transformed data to produce models the parameters of which can be set according to trade-off curves to obtain a particular performance trade-off.

Fleet safety : risk reduction for work related vehicle use

Research has demonstrated that driving a vehicle for work is potentially one of the most dangerous workplace activities. Although organisations are required to meet legislative obligations under workplace health and safety in relation to work related vehicle use, organisations are often reluctant to acknowledge and address the risks associated with the vehicle as a workplace. Recent research undertaken investigating the challenges associated with driver and organisational aspects of fleet safety are discussed. This paper provides a risk management framework to assist organisations to meet legislative requirements and reduce the risk associated with vehicle use in the workplace. In addition the paper argues that organisations need to develop and maintain a positive fleet safety culture to proactively mitigate risk in an effort to reduce the frequency and severity of vehicle related incidents within the workplace.