Content-based video indexing for sports applications using integrated multi-modal approach

To sustain an ongoing rapid growth of video information, there is an emerging demand for a sophisticated content-based video indexing system. However, current video indexing solutions are still immature and lack of any standard. This doctoral consists of a research work based on an integrated multi-modal approach for sports video indexing and retrieval. By combining specific features extractable from multiple audio-visual modalities, generic structure and specific events can be detected and classified. During browsing and retrieval, users will benefit from the integration of high-level semantic and some descriptive mid-level features such as whistle and close-up view of player(s).

Application of operations research techniques to improve efficiency in the emergency department

The main aim of this thesis is to analyse and optimise a public hospital Emergency Department. The Emergency Department (ED) is a complex system with limited resources and a high demand for these resources. Adding to the complexity is the stochastic nature of almost every element and characteristic in the ED. The interaction with other functional areas also complicates the system as these areas have a huge impact on the ED and the ED is powerless to change them. Therefore it is imperative that OR be applied to the ED to improve the performance within the constraints of the system. The main characteristics of the system to optimise included tardiness, adherence to waiting time targets, access block and length of stay. A validated and verified simulation model was built to model the real life system. This enabled detailed analysis of resources and flow without disruption to the actual ED. A wide range of different policies for the ED and a variety of resources were able to be investigated. Of particular interest was the number and type of beds in the ED and also the shift times of physicians. One point worth noting was that neither of these resources work in isolation and for optimisation of the system both resources need to be investigated in tandem. The ED was likened to a flow shop scheduling problem with the patients and beds being synonymous with the jobs and machines typically found in manufacturing problems. This enabled an analytic scheduling approach. Constructive heuristics were developed to reactively schedule the system in real time and these were able to improve the performance of the system. Metaheuristics that optimised the system were also developed and analysed. An innovative hybrid Simulated Annealing and Tabu Search algorithm was developed that out-performed both simulated annealing and tabu search algorithms by combining some of their features. The new algorithm achieves a more optimal solution and does so in a shorter time.

Strategic project evaluation for open pit mining ventures using real options and allied econometric techniques

Open pit mine operations are complex businesses that demand a constant assessment of risk. This is because the value of a mine project is typically influenced by many underlying economic and physical uncertainties, such as metal prices, metal grades, costs, schedules, quantities, and environmental issues, among others, which are not known with much certainty at the beginning of the project. Hence, mining projects present a considerable challenge to those involved in associated investment decisions, such as the owners of the mine and other stakeholders. In general terms, when an option exists to acquire a new or operating mining project, , the owners and stock holders of the mine project need to know the value of the mining project, which is the fundamental criterion for making final decisions about going ahead with the venture capital. However, obtaining the mine project’s value is not an easy task. The reason for this is that sophisticated valuation and mine optimisation techniques, which combine advanced theories in geostatistics, statistics, engineering, economics and finance, among others, need to be used by the mine analyst or mine planner in order to assess and quantify the existing uncertainty and, consequently, the risk involved in the project investment. Furthermore, current valuation and mine optimisation techniques do not complement each other. That is valuation techniques based on real options (RO) analysis assume an expected (constant) metal grade and ore tonnage during a specified period, while mine optimisation (MO) techniques assume expected (constant) metal prices and mining costs. These assumptions are not totally correct since both sources of uncertainty—that of the orebody (metal grade and reserves of mineral), and that about the future behaviour of metal prices and mining costs—are the ones that have great impact on the value of any mining project. Consequently, the key objective of this thesis is twofold. The first objective consists of analysing and understanding the main sources of uncertainty in an open pit mining project, such as the orebody (in situ metal grade), mining costs and metal price uncertainties, and their effect on the final project value. The second objective consists of breaking down the wall of isolation between economic valuation and mine optimisation techniques in order to generate a novel open pit mine evaluation framework called the ―Integrated Valuation / Optimisation Framework (IVOF)‖. One important characteristic of this new framework is that it incorporates the RO and MO valuation techniques into a single integrated process that quantifies and describes uncertainty and risk in a mine project evaluation process, giving a more realistic estimate of the project’s value. To achieve this, novel and advanced engineering and econometric methods are used to integrate financial and geological uncertainty into dynamic risk forecasting measures. The proposed mine valuation/optimisation technique is then applied to a real gold disseminated open pit mine deposit to estimate its value in the face of orebody, mining costs and metal price uncertainties.

Smart Measures for Sustainable Outcomes: Integrated Considerations for Sustainable Refurbishment of Office Buildings

The increasing stock of aging office buildings will see a significant growth in retrofitting projects in Australian capital cities. Stakeholders of refitting works will also need to take on the sustainability challenge and realize tangible outcomes through project delivery. Traditionally, decision making for aged buildings, when facing the alternatives, is typically economically driven and on ad hoc basis. This leads to the tendency to either delay refitting for as long as possible thus causing building conditions to deteriorate, or simply demolish and rebuild with unjust financial burden. The technologies involved are often limited to typical strip-clean and repartition with dry walls and office cubicles. Changing business operational patterns, the efficiency of office space, and the demand on improved workplace environment, will need more innovative and intelligent approaches to refurbishing office buildings. For example, such projects may need to respond to political, social, environmental and financial implications. There is a need for the total consideration of buildings structural assessment, modeling of operating and maintenance costs, new architectural and engineering designs that maximise the utility of the existing structure and resulting productivity improvement, specific construction management procedures including procurement methods, work flow and scheduling and occupational health and safety. Recycling potential and conformance to codes may be other major issues. This paper introduces examples of Australian research projects which provided a more holistic approach to the decision making of refurbishing office space, using appropriate building technologies and products, assessment of residual service life, floor space optimisation and project procurement in order to bring about sustainable outcomes. The paper also discusses a specific case study on critical factors that influence key building components for these projects and issues for integrated decision support when dealing with the refurbishment, and indeed the “re-life”, of office buildings.

A framework for analysing system intelligence of the building control system: a study of the integrated building management system

The lack of satisfactory consensus for characterizing the system intelligence and structured analytical decision models has inhibited the developers and practitioners to understand and configure optimum intelligent building systems in a fully informed manner. So far, little research has been conducted in this aspect. This research is designed to identify the key intelligent indicators, and develop analytical models for computing the system intelligence score of smart building system in the intelligent building. The integrated building management system (IBMS) was used as an illustrative example to present a framework. The models presented in this study applied the system intelligence theory, and the conceptual analytical framework. A total of 16 key intelligent indicators were first identified from a general survey. Then, two multi-criteria decision making (MCDM) approaches, the analytic hierarchy process (AHP) and analytic network process (ANP), were employed to develop the system intelligence analytical models. Top intelligence indicators of IBMS include: self-diagnostic of operation deviations; adaptive limiting control algorithm; and, year-round time schedule performance. The developed conceptual framework was then transformed to the practical model. The effectiveness of the practical model was evaluated by means of expert validation. The main contribution of this research is to promote understanding of the intelligent indicators, and to set the foundation for a systemic framework that provide developers and building stakeholders a consolidated inclusive tool for the system intelligence evaluation of the proposed components design configurations.

Utilisation of solar energy for building operation

In face of the increasing concern on global warming and climate change, the interests in the utilization of solar energy for building operation are also rapidly growing. In this paper, the importance of using renewable energy in building operations is first discussed. The potential use of solar energy is then reviewed. Possible applications of solar energy in building operation are also discussed, including the use of solar energy in the forms of daylighting, hot water heating, space heating and cooling and building-integrated photovoltaics. Finally, the research activities in the utilization of solar energy for space cooling at QUT are highlighted.

Operations research for mining : a classification and literature review

In this paper, we describe the main processes and operations in mining industries and present a comprehensive survey of operations research methodologies that have been applied over the last several decades. The literature review is classified into four main categories: mine design; mine production; mine transportation; and mine evaluation. Mining design models are further separated according to two main mining methods: open-pit and underground. Moreover, mine production models are subcategorised into two groups: ore mining and coal mining. Mine transportation models are further partitioned in accordance with fleet management, truck haulage and train scheduling. Mine evaluation models are further subdivided into four clusters in terms of mining method selection, quality control, financial risks and environmental protection. The main characteristics of four Australian commercial mining software are addressed and compared. This paper bridges the gaps in the literature and motivates researchers to develop more applicable, realistic and comprehensive operations research models and solution techniques that are directly linked with mining industries.

Using operations research methodologies to improve operating theatre scheduling

A hospital consists of a number of wards, units and departments that provide a variety of medical services and interact on a day-to-day basis. Nearly every department within a hospital schedules patients for the operating theatre (OT) and most wards receive patients from the OT following post-operative recovery. Because of the interrelationships between units, disruptions and cancellations within the OT can have a flow-on effect to the rest of the hospital. This often results in dissatisfied patients, nurses and doctors, escalating waiting lists, inefficient resource usage and undesirable waiting times. The objective of this study is to use Operational Research methodologies to enhance the performance of the operating theatre by improving elective patient planning using robust scheduling and improving the overall responsiveness to emergency patients by solving the disruption management and rescheduling problem. OT scheduling considers two types of patients: elective and emergency. Elective patients are selected from a waiting list and scheduled in advance based on resource availability and a set of objectives. This type of scheduling is referred to as ‘offline scheduling’. Disruptions to this schedule can occur for various reasons including variations in length of treatment, equipment restrictions or breakdown, unforeseen delays and the arrival of emergency patients, which may compete for resources. Emergency patients consist of acute patients requiring surgical intervention or in-patients whose conditions have deteriorated. These may or may not be urgent and are triaged accordingly. Most hospitals reserve theatres for emergency cases, but when these or other resources are unavailable, disruptions to the elective schedule result, such as delays in surgery start time, elective surgery cancellations or transfers to another institution. Scheduling of emergency patients and the handling of schedule disruptions is an ‘online’ process typically handled by OT staff. This means that decisions are made ‘on the spot’ in a ‘real-time’ environment. There are three key stages to this study: (1) Analyse the performance of the operating theatre department using simulation. Simulation is used as a decision support tool and involves changing system parameters and elective scheduling policies and observing the effect on the system’s performance measures; (2) Improve viability of elective schedules making offline schedules more robust to differences between expected treatment times and actual treatment times, using robust scheduling techniques. This will improve the access to care and the responsiveness to emergency patients; (3) Address the disruption management and rescheduling problem (which incorporates emergency arrivals) using innovative robust reactive scheduling techniques. The robust schedule will form the baseline schedule for the online robust reactive scheduling model.

The case for ICT work-integrated learning from graduates in the workplace

An online survey of recent ICT graduates in the workplace was carried out as part of a recent project funded by the Australian Learning and Teaching Council. The survey was concerned with the ICT curriculum in relation to workplace job requirements and university preparation for these requirements. The survey contained quantitative and qualitative components and findings from the former have been published (Koppi et al., 2009). This paper reports on a quantitative comparison of responses from graduates who had workplace experience and those who did not, and a qualitative analysis of text responses from all ICT graduates to open-ended questions concerning the curriculum and their perceived university preparation for the workplace. The overwhelming response from ICT graduates in the workplace was for more industry related learning. These industry relationships included industry involvement, workplace learning and business experience, up-to-date teaching and technologies, practical applications, and real-world activities. A closer relationship of academia and industry was strongly advocated by ICT graduates in the workplace.

Integrated approach for HP-LNG pump diagnosis and prognostics based on health state probability estimation

This paper presents an innovative prognostics model based on health state probability estimation embedded in the closed loop diagnostic and prognostic system. To employ an appropriate classifier for health state probability estimation in the proposed prognostic model, the comparative intelligent diagnostic tests were conducted using five different classifiers applied to the progressive fault levels of three faults in HP-LNG pump. Two sets of impeller-rubbing data were employed for the prediction of pump remnant life based on estimation of discrete health state probability using an outstanding capability of SVM and a feature selection technique. The results obtained were very encouraging and showed that the proposed prognosis system has the potential to be used as an estimation tool for machine remnant life prediction in real life industrial applications.

Cultural studies and the urgency of interdisciplinarity : sooner, not later, we're going to need a cultural science (Os Estudos Culturais e a urgência por interdisciplinaridade: cedo, e não tarde, vamos precisar de uma Ciência da Cultura)

Most of creativity in the digital world passes unnoticed by the industry practices and policies, and it isn't taken into account in the cultural and economic strategies of the creative industries. We should find ways to catalyze this creative production, showing how the user's contribution may contribute to social learning, cultural and economic advancement. To that effect, we must know what is an open creative system and how it works. Based on this diagnosis, the author that interdisciplinarity is urgent and there is also a need for a science of culture. What is at stake is a strategy of integrated development, as regards the upcoming innovation in its complex, productive and learning aspects.

An integrated approach for precise road reconstruction from aerial imagery and LiDAR data

Accurate and detailed road models play an important role in a number of geospatial applications, such as infrastructure planning, traffic monitoring, and driver assistance systems. In this thesis, an integrated approach for the automatic extraction of precise road features from high resolution aerial images and LiDAR point clouds is presented. A framework of road information modeling has been proposed, for rural and urban scenarios respectively, and an integrated system has been developed to deal with road feature extraction using image and LiDAR analysis. For road extraction in rural regions, a hierarchical image analysis is first performed to maximize the exploitation of road characteristics in different resolutions. The rough locations and directions of roads are provided by the road centerlines detected in low resolution images, both of which can be further employed to facilitate the road information generation in high resolution images. The histogram thresholding method is then chosen to classify road details in high resolution images, where color space transformation is used for data preparation. After the road surface detection, anisotropic Gaussian and Gabor filters are employed to enhance road pavement markings while constraining other ground objects, such as vegetation and houses. Afterwards, pavement markings are obtained from the filtered image using the Otsu's clustering method. The final road model is generated by superimposing the lane markings on the road surfaces, where the digital terrain model (DTM) produced by LiDAR data can also be combined to obtain the 3D road model. As the extraction of roads in urban areas is greatly affected by buildings, shadows, vehicles, and parking lots, we combine high resolution aerial images and dense LiDAR data to fully exploit the precise spectral and horizontal spatial resolution of aerial images and the accurate vertical information provided by airborne LiDAR. Objectoriented image analysis methods are employed to process the feature classiffcation and road detection in aerial images. In this process, we first utilize an adaptive mean shift (MS) segmentation algorithm to segment the original images into meaningful object-oriented clusters. Then the support vector machine (SVM) algorithm is further applied on the MS segmented image to extract road objects. Road surface detected in LiDAR intensity images is taken as a mask to remove the effects of shadows and trees. In addition, normalized DSM (nDSM) obtained from LiDAR is employed to filter out other above-ground objects, such as buildings and vehicles. The proposed road extraction approaches are tested using rural and urban datasets respectively. The rural road extraction method is performed using pan-sharpened aerial images of the Bruce Highway, Gympie, Queensland. The road extraction algorithm for urban regions is tested using the datasets of Bundaberg, which combine aerial imagery and LiDAR data. Quantitative evaluation of the extracted road information for both datasets has been carried out. The experiments and the evaluation results using Gympie datasets show that more than 96% of the road surfaces and over 90% of the lane markings are accurately reconstructed, and the false alarm rates for road surfaces and lane markings are below 3% and 2% respectively. For the urban test sites of Bundaberg, more than 93% of the road surface is correctly reconstructed, and the mis-detection rate is below 10%.

A practical approach for identifying expected solution performance and robustness in operations research applications

A practical approach for identifying solution robustness is proposed for situations where parameters are uncertain. The approach is based upon the interpretation of a probability density function (pdf) and the definition of three parameters that describe how significant changes in the performance of a solution are deemed to be. The pdf is constructed by interpreting the results of simulations. A minimum number of simulations are achieved by updating the mean, variance, skewness and kurtosis of the sample using computationally efficient recursive equations. When these criterions have converged then no further simulations are needed. A case study involving several no-intermediate storage flow shop scheduling problems demonstrates the effectiveness of the approach.

Getting stuck in together : improving work integrated learning from an Australian institution and faculty perspective

According to a recent report Australian higher education is not in crisis. However, we could be doing it better. The report Mapping Australian Higher Education (Norton, 2012) highlights comparative weaknesses such as levels f student engagement; interactions between students and academic staff; and academic staff preferences for research over teaching. The report points out that despite these concerns most graduates continue to get good, well-paid jobs, student satisfaction is improving, and levels of public confidence in Australian higher education are high. It also stresses that ‘the promise of higher education is that it provides adaptable cognitive skills, not that it always provides the job specific skills graduates will need in their future employment’ (Norton, 2012, p.58). This is worth keeping in mind as we contribute to the significant growth in curriculum initiatives aimed at preparing graduates for the world of work. Work Integrated Learning (WIL) is not a new concept but there is increased pressure on higher education globally to address graduate employability skills. The sector is under pressure in an increasingly competitive environment to demonstrate the relevance of courses, accountability and effective use of public funds (Peach & Gamble, 2011). In the Australian context this also means responding to the skills shortage in areas such as engineering, health, construction and business (DEEWR, 2010). This paper provides a brief overview of collaborative efforts over several years to improve the activity of WIL at the Queensland University of Technology (QUT). These efforts have resulted in changes to curriculum, pedagogy, systems and processes, and the initiation of local, regional, national, and international networks. The willingness of students, staff, and industry partners to ‘get stuck in’ and try new approaches in these different contexts can be understood as a form of boundary spanning. That is, the development of the capability to mediate between different forms of expertise and the demands of different contexts in order to nurture student learning and improve the outcomes of higher education through WIL (Peach, Cates, Ilg, Jones, Lechleiter, 2011).

Challenges of Measuring Learning Outcomes for Property Students Engaged in Work Integrated Learning

Work integrated learning (WIL) or professional practice units are recognised as providing learning experiences that help students make successful transitions to professional practice. These units require students to engage in learning in the workplace; to reflect on this learning; and to integrate it with learning at university. However, an analysis of a recent cohort of property economics students at a large urban university provides evidence that there is great variation in work based learning experiences undertaken and that this impacts on students’capacity to respond to assessment tasks which involve critiquing these experiences in the form of reflective reports. This paper highlights the need to recognise the diversity of work based experiences; the impact this has on learning outcomes; and to find more effective and equitable ways of measuring these outcomes. The paper briefly discusses assessing learning outcomes in WIL and then describes the model of WIL in the Faculty of Built Environment and Engineering at the Queensland University of Technology (QUT). The paper elaborates on the diversity of students’ experiences and backgrounds including variations in the length of work experience, placement opportunities and conditions of employment.For example, the analysis shows that students with limited work experience often have difficulty critiquing this work experience and producing high level reflective reports. On the other hand students with extensive, discipline relevant work experience can be frustrated by assessment requirements that do not take their experience into account. Added to this the Global Financial Crisis (GFC) has restricted both part time and full time placement opportunities for some students. These factors affect students’ capacity to a) secure a relevant work experience, b) reflect critically on the work experiences and c) appreciate the impact the overall experience can have on their learning outcomes and future professional opportunities. Our investigation highlights some of the challenges faced in implementing effective and equitable approaches across diverse student cohorts. We suggest that increased flexibility in assessment requirements and increased feedback from industry may help address these challenges.