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.

Facilitating unlearning during implementation of new technology

Purpose – One of the critical issues for change management, particularly in relation to the implementation of new technologies, is the existence of prior knowledge and established mental models which may hinder change efforts. Understanding unlearning and how it might assist during organizational change is a way to address this resistance. The purpose of this paper is to present research designed to identify specific factors that facilitate unlearning. Design/methodology/approach – Drawing together issues identified as potential influencers of unlearning, a survey questionnaire was developed and administered in an Australian corporation undergoing large-scale change due to the implementation of an enterprise information system. The results were analyzed to identify specific factors that impact on unlearning. Findings – Findings from this paper identify factors that hinder or help the unlearning process during times of change including understanding the need for change, the level of organizational support and training, assessment of the change, positive experience and informal support, the organization's history of change, individual's prior outlooks, and individuals' feelings and expectations. Research limitations/implications – The use of only one organization does not allow for comparisons between organizations of different sizes, cultures or industries and therefore extension of this research is recommended. Practical implications – For practitioners, this paper provides specific elements at both the level of individuals and the organization that need to be considered for optimal unlearning during times of change. Originality/value – Previous literature on unlearning has been predominantly conceptual and theoretical. These empirical findings serve to further an earlier model based on qualitative research into potential influencers of unlearning.

An integrated information management model for ecologically sustainable development

Ecologically sustainable development has become a major feature of legal systems at the international, national and local levels throughout the world. In Australia, governments have responded to environmental crises by enacting legislation imposing obligations and restrictions over privately-owned land. Whilst these obligations and restrictions may well be necessary to achieve sustainability, the approach to management of information concerning these instruments is problematic. For example, management of information concerning obligations and restrictions in Queensland is fragmented, with some instruments registered or recorded on the land title register, some on external registers, and some information only available in the legislation itself. This approach is used in most Australian jurisdictions. This fragmented approach has led to two separate but interconnected problems. First, the Torrens system is no longer meeting its goal of providing a complete and accurate picture of title. Second, this uncoordinated approach to the management of land titles, and obligations and restrictions on land use, has created a barrier to sustainable management of natural resources. This is because compliance with environmental laws is impaired in the absence of easily accessible and accurate information. These problems demonstrate a clear need for reform in this area. To determine how information concerning these obligations and restrictions may be most effectively managed, this thesis will apply a comparative methodology and consider three case studies, which each utilise different models for management of this information. These jurisdictions will be assessed according to a set of guidelines for comparison to identify which features of their systems provide for effective management of information concerning obligations and restrictions on title and use. Based on this comparison, this thesis will devise a series of recommendations for an effective system for the management of information concerning obligations and restrictions on land title and use, taking into account any potential legal issues and barriers to implementation. This series of recommendations for reform will be supplemented by suggested draft legislative provisions.

Detection and prevention of distributed denial of services attacks by collaborative effort of software agents, first prototype implementation

Distributed Denial of Services DDoS, attacks has become one of the biggest threats for resources over Internet. Purpose of these attacks is to make servers deny from providing services to legitimate users. These attacks are also used for occupying media bandwidth. Currently intrusion detection systems can just detect the attacks but cannot prevent / track the location of intruders. Some schemes also prevent the attacks by simply discarding attack packets, which saves victim from attack, but still network bandwidth is wasted. In our opinion, DDoS requires a distributed solution to save wastage of resources. The paper, presents a system that helps us not only in detecting such attacks but also helps in tracing and blocking (to save the bandwidth as well) the multiple intruders using Intelligent Software Agents. The system gives dynamic response and can be integrated with the existing network defense systems without disturbing existing Internet model. We have implemented an agent based networking monitoring system in this regard.

A hidden Markov model and relative entropy rate approach to vision-based dim target detection for UAV sense-and-avoid

Uninhabited aerial vehicles (UAVs) are a cutting-edge technology that is at the forefront of aviation/aerospace research and development worldwide. Many consider their current military and defence applications as just a token of their enormous potential. Unlocking and fully exploiting this potential will see UAVs in a multitude of civilian applications and routinely operating alongside piloted aircraft. The key to realising the full potential of UAVs lies in addressing a host of regulatory, public relation, and technological challenges never encountered be- fore. Aircraft collision avoidance is considered to be one of the most important issues to be addressed, given its safety critical nature. The collision avoidance problem can be roughly organised into three areas: 1) Sense; 2) Detect; and 3) Avoid. Sensing is concerned with obtaining accurate and reliable information about other aircraft in the air; detection involves identifying potential collision threats based on available information; avoidance deals with the formulation and execution of appropriate manoeuvres to maintain safe separation. This thesis tackles the detection aspect of collision avoidance, via the development of a target detection algorithm that is capable of real-time operation onboard a UAV platform. One of the key challenges of the detection problem is the need to provide early warning. This translates to detecting potential threats whilst they are still far away, when their presence is likely to be obscured and hidden by noise. Another important consideration is the choice of sensors to capture target information, which has implications for the design and practical implementation of the detection algorithm. The main contributions of the thesis are: 1) the proposal of a dim target detection algorithm combining image morphology and hidden Markov model (HMM) filtering approaches; 2) the novel use of relative entropy rate (RER) concepts for HMM filter design; 3) the characterisation of algorithm detection performance based on simulated data as well as real in-flight target image data; and 4) the demonstration of the proposed algorithm's capacity for real-time target detection. We also consider the extension of HMM filtering techniques and the application of RER concepts for target heading angle estimation. In this thesis we propose a computer-vision based detection solution, due to the commercial-off-the-shelf (COTS) availability of camera hardware and the hardware's relatively low cost, power, and size requirements. The proposed target detection algorithm adopts a two-stage processing paradigm that begins with an image enhancement pre-processing stage followed by a track-before-detect (TBD) temporal processing stage that has been shown to be effective in dim target detection. We compare the performance of two candidate morphological filters for the image pre-processing stage, and propose a multiple hidden Markov model (MHMM) filter for the TBD temporal processing stage. The role of the morphological pre-processing stage is to exploit the spatial features of potential collision threats, while the MHMM filter serves to exploit the temporal characteristics or dynamics. The problem of optimising our proposed MHMM filter has been examined in detail. Our investigation has produced a novel design process for the MHMM filter that exploits information theory and entropy related concepts. The filter design process is posed as a mini-max optimisation problem based on a joint RER cost criterion. We provide proof that this joint RER cost criterion provides a bound on the conditional mean estimate (CME) performance of our MHMM filter, and this in turn establishes a strong theoretical basis connecting our filter design process to filter performance. Through this connection we can intelligently compare and optimise candidate filter models at the design stage, rather than having to resort to time consuming Monte Carlo simulations to gauge the relative performance of candidate designs. Moreover, the underlying entropy concepts are not constrained to any particular model type. This suggests that the RER concepts established here may be generalised to provide a useful design criterion for multiple model filtering approaches outside the class of HMM filters. In this thesis we also evaluate the performance of our proposed target detection algorithm under realistic operation conditions, and give consideration to the practical deployment of the detection algorithm onboard a UAV platform. Two fixed-wing UAVs were engaged to recreate various collision-course scenarios to capture highly realistic vision (from an onboard camera perspective) of the moments leading up to a collision. Based on this collected data, our proposed detection approach was able to detect targets out to distances ranging from about 400m to 900m. These distances, (with some assumptions about closing speeds and aircraft trajectories) translate to an advanced warning ahead of impact that approaches the 12.5 second response time recommended for human pilots. Furthermore, readily available graphic processing unit (GPU) based hardware is exploited for its parallel computing capabilities to demonstrate the practical feasibility of the proposed target detection algorithm. A prototype hardware-in- the-loop system has been found to be capable of achieving data processing rates sufficient for real-time operation. There is also scope for further improvement in performance through code optimisations. Overall, our proposed image-based target detection algorithm offers UAVs a cost-effective real-time target detection capability that is a step forward in ad- dressing the collision avoidance issue that is currently one of the most significant obstacles preventing widespread civilian applications of uninhabited aircraft. We also highlight that the algorithm development process has led to the discovery of a powerful multiple HMM filtering approach and a novel RER-based multiple filter design process. The utility of our multiple HMM filtering approach and RER concepts, however, extend beyond the target detection problem. This is demonstrated by our application of HMM filters and RER concepts to a heading angle estimation problem.

An integrated rail track degradation model

There has been a worldwide trend to increase axle loads and train speeds. This means that railway track degradation will be accelerated, and track maintenance costs will be increased significantly. There is a need to investigate the consequences of increasing traffic load. The aim of the research is to develop a model for the analysis of physical degradation of railway tracks in response to changes in traffic parameters, especially increased axle loads and train speeds. This research has developed an integrated track degradation model (ITDM) by integrating several models into a comprehensive framework. Mechanistic relationships for track degradation hav~ ?een used wherever possible in each of the models contained in ITDM. This overcc:mes the deficiency of the traditional statistical track models which rely heavily on historical degradation data, which is generally not available in many railway systems. In addition statistical models lack the flexibility of incorporating future changes in traffic patterns or maintenance practices. The research starts with reviewing railway track related studies both in Australia and overseas to develop a comprehensive understanding of track performance under various traffic conditions. Existing railway related models are then examined for their suitability for track degradation analysis for Australian situations. The ITDM model is subsequently developed by modifying suitable existing models, and developing new models where necessary. The ITDM model contains four interrelated submodels for rails, sleepers, ballast and subgrade, and track modulus. The rail submodel is for rail wear analysis and is developed from a theoretical concept. The sleeper submodel is for timber sleepers damage prediction. The submodel is developed by modifying and extending an existing model developed elsewhere. The submodel has also incorporated an analysis for the likelihood of concrete sleeper cracking. The ballast and subgrade submodel is evolved from a concept developed in the USA. Substantial modifications and improvements have been made. The track modulus submodel is developed from a conceptual method. Corrections for more global track conditions have been made. The integration of these submodels into one comprehensive package has enabled the interaction between individual track components to be taken into account. This is done by calculating wheel load distribution with time and updating track conditions periodically in the process of track degradation simulation. A Windows-based computer program ~ssociated with ITDM has also been developed. The program enables the user to carry out analysis of degradation of individual track components and to investigate the inter relationships between these track components and their deterioration. The successful implementation of this research has provided essential information for prediction of increased maintenance as a consequence of railway trackdegradation. The model, having been presented at various conferences and seminars, has attracted wide interest. It is anticipated that the model will be put into practical use among Australian railways, enabling track maintenance planning to be optimized and potentially saving Australian railway systems millions of dollars in operating costs.

Development and implementation of the areawide Dynamic ROad traffic NoisE (DRONE) simulator

This paper discusses the areawide Dynamic ROad traffic NoisE (DRONE) simulator, and its implementation as a tool for noise abatement policy evaluation. DRONE involves integrating a road traffic noise estimation model with a traffic simulator to estimate road traffic noise in urban networks. An integrated traffic simulation-noise estimation model provides an interface for direct input of traffic flow properties from simulation model to noise estimation model that in turn estimates the noise on a spatial and temporal scale. The output from DRONE is linked with a geographical information system for visual representation of noise levels in the form of noise contour maps.

RatSLAM : a hippocampal model for simultaneous localization and mapping

The work presents a new approach to the problem of simultaneous localization and mapping - SLAM - inspired by computational models of the hippocampus of rodents. The rodent hippocampus has been extensively studied with respect to navigation tasks, and displays many of the properties of a desirable SLAM solution. RatSLAM is an implementation of a hippocampal model that can perform SLAM in real time on a real robot. It uses a competitive attractor network to integrate odometric information with landmark sensing to form a consistent representation of the environment. Experimental results show that RatSLAM can operate with ambiguous landmark information and recover from both minor and major path integration errors.

A simultaneous equations model of crash frequency by collision type for rural intersections

Safety at roadway intersections is of significant interest to transportation professionals due to the large number of intersections in transportation networks, the complexity of traffic movements at these locations that leads to large numbers of conflicts, and the wide variety of geometric and operational features that define them. A variety of collision types including head-on, sideswipe, rear-end, and angle crashes occur at intersections. While intersection crash totals may not reveal a site deficiency, over exposure of a specific crash type may reveal otherwise undetected deficiencies. Thus, there is a need to be able to model the expected frequency of crashes by collision type at intersections to enable the detection of problems and the implementation of effective design strategies and countermeasures. Statistically, it is important to consider modeling collision type frequencies simultaneously to account for the possibility of common unobserved factors affecting crash frequencies across crash types. In this paper, a simultaneous equations model of crash frequencies by collision type is developed and presented using crash data for rural intersections in Georgia. The model estimation results support the notion of the presence of significant common unobserved factors across crash types, although the impact of these factors on parameter estimates is found to be rather modest.

Bridging the gap between tertiary education and work : a model of situated learning in accountancy

In professions such as teaching, health sciences (medicine, nursing, allied health), and built environment (engineering), significant work-based learning through practica is an essential element before graduation. However, there is no such requirement in Accountancy. This thesis reports the findings of a qualitative case study of the development and implementation of a Workplace Learning Experience Program in Accountancy at the Queensland University of Technology (QUT) in Australia. The case study of this intervention, based on sociocultural learning theory, provides the grounds for the development of a new model of teaching and learning for accounting education. The survey and interview-based study documents the responses of two cohorts of university students and a group of employers to a work placement program. The study demonstrates that a 100 hour work placement in Accountancy has elements that enhance student learning. It demonstrates the potential value of the application of sociocultural theories of learning, especially the concept of situated learning involving legitimate peripheral participation (Lave & Wenger, 1991). This research establishes the theoretical base for a paradigm shift for the Accountancy profession to acknowledge work placements prior to graduation as a major element of learning. It is argued that the current model of accounting education requires reform to better align university and workplace learning.

A new undergraduate model for broadly based spatial science curriculum at Queensland University of Technology : diversity in surveying curriculum

This paper provides an overview of the current QUT Spatial Science undergraduate program based in Brisbane, Queensland, Australia. It discusses the development and implementation of a broad-based educational model for the faculty of built environment and engineering courses and specifically to the course structure of the new Bachelor of Urban Development (Spatial Science) study major. A brief historical background of surveying courses is discussed prior to the detailing of the three distinct and complementary learning themes of the new course structure with a graphical course matrix. Curriculum mapping of the spatial science major has been undertaken as the course approaches formal review in late 2010. Work-integrated learning opportunities have been embedded into the curriculum and a brief outline is presented. Some issues relevant to the tertiary surveying/ spatial sector are highlighted in the context of changing higher education environments in Australia.

Design and implementation of sensor networks for the observation and research of harmful algal blooms in southern California coastal waters

Harmful Algal Blooms (HABs) have become an important environmental concern along the western coast of the United States. Toxic and noxious blooms adversely impact the economies of coastal communities in the region, pose risks to human health, and cause mortality events that have resulted in the deaths of thousands of fish, marine mammals and seabirds. One goal of field-based research efforts on this topic is the development of predictive models of HABs that would enable rapid response, mitigation and ultimately prevention of these events. In turn, these objectives are predicated on understanding the environmental conditions that stimulate these transient phenomena. An embedded sensor network (Fig. 1), under development in the San Pedro Shelf region off the Southern California coast, is providing tools for acquiring chemical, physical and biological data at high temporal and spatial resolution to help document the emergence and persistence of HAB events, supporting the design and testing of predictive models, and providing contextual information for experimental studies designed to reveal the environmental conditions promoting HABs. The sensor platforms contained within this network include pier-based sensor arrays, ocean moorings, HF radar stations, along with mobile sensor nodes in the form of surface and subsurface autonomous vehicles. FreewaveTM radio modems facilitate network communication and form a minimally-intrusive, wireless communication infrastructure throughout the Southern California coastal region, allowing rapid and cost-effective data transfer. An emerging focus of this project is the incorporation of a predictive ocean model that assimilates near-real time, in situ data from deployed Autonomous Underwater Vehicles (AUVs). The model then assimilates the data to increase the skill of both nowcasts and forecasts, thus providing insight into bloom initiation as well as the movement of blooms or other oceanic features of interest (e.g., thermoclines, fronts, river discharge, etc.). From these predictions, deployed mobile sensors can be tasked to track a designated feature. This focus has led to the creation of a technology chain in which algorithms are being implemented for the innovative trajectory design for AUVs. Such intelligent mission planning is required to maneuver a vehicle to precise depths and locations that are the sites of active blooms, or physical/chemical features that might be sources of bloom initiation or persistence. The embedded network yields high-resolution, temporal and spatial measurements of pertinent environmental parameters and resulting biology (see Fig. 1). Supplementing this with ocean current information and remotely sensed imagery and meteorological data, we obtain a comprehensive foundation for developing a fundamental understanding of HAB events. This then directs labor- intensive and costly sampling efforts and analyses. Additionally, we provide coastal municipalities, managers and state agencies with detailed information to aid their efforts in providing responsible environmental stewardship of their coastal waters.

Implementation of an embedded sensor network for the coordination of Slocum gliders for coastal monitoring and observation

Autonomous Underwater Vehicles (AUVs) are revolutionizing oceanography through their versatility, autonomy and endurance. However, they are still an underutilized technology. For coastal operations, the ability to track a certain feature is of interest to ocean scientists. Adaptive and predictive path planning requires frequent communication with significant data transfer. Currently, most AUVs rely on satellite phones as their primary communication. This communication protocol is expensive and slow. To reduce communication costs and provide adequate data transfer rates, we present a hardware modification along with a software system that provides an alternative robust disruption- tolerant communications framework enabling cost-effective glider operation in coastal regions. The framework is specifically designed to address multi-sensor deployments. We provide a system overview and present testing and coverage data for the network. Additionally, we include an application of ocean-model driven trajectory design, which can benefit from the use of this network and communication system. Simulation and implementation results are presented for single and multiple vehicle deployments. The presented combination of infrastructure, software development and deployment experience brings us closer to the goal of providing a reliable and cost-effective data transfer framework to enable real-time, optimal trajectory design, based on ocean model predictions, to gather in situ measurements of interesting and evolving ocean features and phenomena.

Hydrodynamic and thruster model validation for autonomous underwater vehicles

From Pontryagin’s Maximum Principle to the Duke Kahanamoku Aquatic Complex; we develop the theory and generate implementable time efficient trajectories for a test-bed autonomous underwater vehicle (AUV). This paper is the beginning of the journey from theory to implementation. We begin by considering pure motion trajectories and move into a rectangular trajectory which is a concatenation of pure surge and pure sway. These trajectories are tested using our numerical model and demonstrated by our AUV in the pool. In this paper we demonstrate that the above motions are realizable through our method, and we gain confidence in our numerical model. We conclude that using our current techniques, implementation of time efficient trajectories is likely to succeed.

Towards practical implementation of time optimal trajectories for underwater vehicles

In this paper, we are concerned with the practical implementation of time optimal numerical techniques on underwater vehicles. We briefly introduce the model of underwater vehicle we consider and present the parameters for the test bed ODIN (Omni-Directional Intelligent Navigator). Then we explain the numerical method used to obtain time optimal trajectories with a structure suitable for the implementation. We follow this with a discussion on the modifications to be made considering the characteristics of ODIN. Finally, we illustrate our computations with some experimental results.