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.

Uncertainty based MDO of UAS using HAPMOEA

CFD has been successfully used in the optimisation of aerodynamic surfaces using a given set of parameters such as Mach numbers and angle of attack. While carrying out a multidisciplinary design optimisation one deals with situations where the parameters have some uncertain attached. Any optimisation carried out for fixed values of input parameters gives a design which may be totally unacceptable under off-design conditions. The challenge is to develop a robust design procedure which takes into account the fluctuations in the input parameters. In this work, we attempt this using a modified Taguchi approach. This is incorporated into an evolutionary algorithm with many features developed in house. The method is tested for an UCAV design which simultaneously handles aerodynamics, electromagnetics and maneuverability. Results demonstrate that the method has considerable potential.

Improved correlation-based modal strain energy method for global damage detection of truss bridge structures

The previous investigations have shown that the modal strain energy correlation method, MSEC, could successfully identify the damage of truss bridge structures. However, it has to incorporate the sensitivity matrix to estimate damage and is not reliable in certain damage detection cases. This paper presents an improved MSEC method where the prediction of modal strain energy change vector is differently obtained by running the eigensolutions on-line in optimisation iterations. The particular trail damage treatment group maximising the fitness function close to unity is identified as the detected damage location. This improvement is then compared with the original MSEC method along with other typical correlation-based methods on the finite element model of a simple truss bridge. The contributions to damage detection accuracy of each considered mode is also weighed and discussed. The iterative searching process is operated by using genetic algorithm. The results demonstrate that the improved MSEC method suffices the demand in detecting the damage of truss bridge structures, even when noised measurement is considered.

UAS Mission Path Planning System, (MPPS) using hybrid-game coupled to multi-objective optimizer

This paper presents the application of advanced optimization techniques to unmanned aerial system mission path planning system (MPPS) using multi-objective evolutionary algorithms (MOEAs). Two types of multi-objective optimizers are compared; the MOEA nondominated sorting genetic algorithm II and a hybrid-game strategy are implemented to produce a set of optimal collision-free trajectories in a three-dimensional environment. The resulting trajectories on a three-dimensional terrain are collision-free and are represented by using Bézier spline curves from start position to target and then target to start position or different positions 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 adding a hybrid-game strategy to a MOEA and for a MPPS.

Active flow control bump design using hybrid Nash-game coupled to evolutionary algorithms

In this paper, the optimal design of an active flow control device; Shock Control Bump (SCB) on suction and pressure sides of transonic aerofoil to reduce transonic total drag is investigated. Two optimisation test cases are conducted using different advanced Evolutionary Algorithms (EAs); the first optimiser is the Hierarchical Asynchronous Parallel Evolutionary Algorithm (HAPMOEA) based on canonical Evolutionary Strategies (ES). The second optimiser is the HAPMOEA is hybridised with one of well-known Game Strategies; Nash-Game. Numerical results show that SCB significantly reduces the drag by 30% when compared to the baseline design. In addition, the use of a Nash-Game strategy as a pre-conditioner of global control saves computational cost up to 90% when compared to the first optimiser HAPMOEA.

On the optimal capacity expansion of electric power systems

The analysis of investment in the electric power has been the subject of intensive research for many years. The efficient generation and distribution of electrical energy is a difficult task involving the operation of a complex network of facilities, often located over very large geographical regions. Electric power utilities have made use of an enormous range of mathematical models. Some models address time spans which last for a fraction of a second, such as those that deal with lightning strikes on transmission lines while at the other end of the scale there are models which address time horizons consisting of ten or twenty years; these usually involve long range planning issues. This thesis addresses the optimal long term capacity expansion of an interconnected power system. The aim of this study has been to derive a new, long term planning model which recognises the regional differences which exist for energy demand and which are present in the construction and operation of power plant and transmission line equipment. Perhaps the most innovative feature of the new model is the direct inclusion of regional energy demand curves in the nonlinear form. This results in a nonlinear capacity expansion model. After review of the relevant literature, the thesis first develops a model for the optimal operation of a power grid. This model directly incorporates regional demand curves. The model is a nonlinear programming problem containing both integer and continuous variables. A solution algorithm is developed which is based upon a resource decomposition scheme that separates the integer variables from the continuous ones. The decompostion of the operating problem leads to an interactive scheme which employs a mixed integer programming problem, known as the master, to generate trial operating configurations. The optimum operating conditions of each trial configuration is found using a smooth nonlinear programming model. The dual vector recovered from this model is subsequently used by the master to generate the next trial configuration. The solution algorithm progresses until lower and upper bounds converge. A range of numerical experiments are conducted and these experiments are included in the discussion. Using the operating model as a basis, a regional capacity expansion model is then developed. It determines the type, location and capacity of additional power plants and transmission lines, which are required to meet predicted electicity demands. A generalised resource decompostion scheme, similar to that used to solve the operating problem, is employed. The solution algorithm is used to solve a range of test problems and the results of these numerical experiments are reported. Finally, the expansion problem is applied to the Queensland electricity grid in Australia.

On the optimal capacity expansion of electric power systems

The analysis of investment in the electric power has been the subject of intensive research for many years. The efficient generation and distribution of electrical energy is a difficult task involving the operation of a complex network of facilities, often located over very large geographical regions. Electric power utilities have made use of an enormous range of mathematical models. Some models address time spans which last for a fraction of a second, such as those that deal with lightning strikes on transmission lines while at the other end of the scale there are models which address time horizons consisting of ten or twenty years; these usually involve long range planning issues. This thesis addresses the optimal long term capacity expansion of an interconnected power system. The aim of this study has been to derive a new, long term planning model which recognises the regional differences which exist for energy demand and which are present in the construction and operation of power plant and transmission line equipment. Perhaps the most innovative feature of the new model is the direct inclusion of regional energy demand curves in the nonlinear form. This results in a nonlinear capacity expansion model. After review of the relevant literature, the thesis first develops a model for the optimal operation of a power grid. This model directly incorporates regional demand curves. The model is a nonlinear programming problem containing both integer and continuous variables. A solution algorithm is developed which is based upon a resource decomposition scheme that separates the integer variables from the continuous ones. The decompostion of the operating problem leads to an interactive scheme which employs a mixed integer programming problem, known as the master, to generate trial operating configurations. The optimum operating conditions of each trial configuration is found using a smooth nonlinear programming model. The dual vector recovered from this model is subsequently used by the master to generate the next trial configuration. The solution algorithm progresses until lower and upper bounds converge. A range of numerical experiments are conducted and these experiments are included in the discussion. Using the operating model as a basis, a regional capacity expansion model is then developed. It determines the type, location and capacity of additional power plants and transmission lines, which are required to meet predicted electicity demands. A generalised resource decompostion scheme, similar to that used to solve the operating problem, is employed. The solution algorithm is used to solve a range of test problems and the results of these numerical experiments are reported. Finally, the expansion problem is applied to the Queensland electricity grid in Australia

Large margin vector quantization

In this paper we describe the Large Margin Vector Quantization algorithm (LMVQ), which uses gradient ascent to maximise the margin of a radial basis function classifier. We present a derivation of the algorithm, which proceeds from an estimate of the class-conditional probability densities. We show that the key behaviour of Kohonen's well-known LVQ2 and LVQ3 algorithms emerge as natural consequences of our formulation. We compare the performance of LMVQ with that of Kohonen's LVQ algorithms on an artificial classification problem and several well known benchmark classification tasks. We find that the classifiers produced by LMVQ attain a level of accuracy that compares well with those obtained via LVQ1, LVQ2 and LVQ3, with reduced storage complexity. We indicate future directions of enquiry based on the large margin approach to Learning Vector Quantization.

Optimising figure of merit for phonetic spoken term detection

This paper introduces a novel technique to directly optimise the Figure of Merit (FOM) for phonetic spoken term detection. The FOM is a popular measure of sTD accuracy, making it an ideal candiate for use as an objective function. A simple linear model is introduced to transform the phone log-posterior probabilities output by a phe classifier to produce enhanced log-posterior features that are more suitable for the STD task. Direct optimisation of the FOM is then performed by training the parameters of this model using a non-linear gradient descent algorithm. Substantial FOM improvements of 11% relative are achieved on held-out evaluation data, demonstrating the generalisability of the approach.

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.

The application of phonetic distribution normalisation to likelihood-maximising speech enhancement for robust ASR

Traditional speech enhancement methods optimise signal-level criteria such as signal-to-noise ratio, but such approaches are sub-optimal for noise-robust speech recognition. Likelihood-maximising (LIMA) frameworks on the other hand, optimise the parameters of speech enhancement algorithms based on state sequences generated by a speech recogniser for utterances of known transcriptions. Previous applications of LIMA frameworks have generated a set of global enhancement parameters for all model states without taking in account the distribution of model occurrence, making optimisation susceptible to favouring frequently occurring models, in particular silence. In this paper, we demonstrate the existence of highly disproportionate phonetic distributions on two corpora with distinct speech tasks, and propose to normalise the influence of each phone based on a priori occurrence probabilities. Likelihood analysis and speech recognition experiments verify this approach for improving ASR performance in noisy environments.

Wellness in older adults

Understanding perception of wellness in older adults is a question to be understood against the backdrop of concerns about whether global ageing and the ‘bulge’ of ageing baby boomers will increase health care cost beyond what modern economies can deal with. Older adults who age in a healthy way and who take responsibility for their own health offer a positive alternative and change the perception that older adults are a burden on their society’s health system. The concept of successful ageing introduced by Rowe and Kahn (1987; 1997) suggested that older adults age successfully if they avoid disease and disability, maintain high cognitive and physical functioning and remain actively engaged with life. This concept, however, did not reflect older adults’ own perceptions of what constitutes successful ageing or how perceptions of wellness or health-related quality of life influenced the older adult’s understanding of his or her own health and ageing. A research project was designed to examine older adults’ perceptions of wellness in order to gain an understanding of the factors that influence perception of their own wellness. Specifically, the research wanted to explore two aspects: whether belonging to a unique organisation, in this instance a Returned Services Club, influenced perceptions of wellness; and whether there are significant gender differences for the perception of wellness. A mixed method project with two consecutive studies was designed to answer these questions: a quantitative survey of members of a Returned Services Club and of the surrounding community in Queensland, Australia, and a qualitative study conducting focus groups to explore findings of the survey. The results of the survey were used to determine the composition of the focus groups. The participants for the first study, (N=257), community living adults 65 years and older, were chosen from the membership role of a Returned Services Club or recruited by personal approach from the community surrounding the Services Club. Participants completed a survey that consisted of a perception of wellness instrument, a health-related quality of life instrument, and questions on morbidities, modifiable life style factors and demographics. Data analysis found that a number of individual factors influenced perception of wellness and health-related quality of life. Positive influences were independent mobility, exercise and gambling at non-hazardous levels, and negative influences were hearing loss, memory problems, chronic disease and being single. Membership of the Services Club did not contribute to perception of wellness beyond being a member of a social group. While there may have been an expectation that members of an organisation that is traditionally associated with high alcohol use and problematic gambling may have lower perceptions of wellness, this study suggested that the negative influences may have been counteracted by the positive effects of social interaction, thus having neither negative nor positive influences on perception of wellness. There were significant differences in perception of wellness and in health-related quality of life for women and men. The most significant difference was for women aged 85-90 who had significantly lower scores for perception of wellness than men or than any other age group. This result was the impetus for conducting focus groups with adults aged 85-90 years of age. Focus groups were conducted with 24 women and four men aged 85-90 to explore the survey findings for this age group. Results from the focus groups indicated that for older adults perception of wellness was a multidimensional construct of more complexity than indicated by the survey instrument. Elite older women (women over 85 years of age) related their perception of wellness to their ability to do what they wanted to do, and what they wanted to do significantly more than anything else, was to stay connected to family, friends and the community to which they belonged. From the focus group results it appeared that elite older women identified with the three elements of successful ageing – low incidence of disability and disease, high physical and cognitive functioning, and active engagement with life – but not in a flat structure. It appears that for elite older women good physical and mental health function to enable social connectedness. It is the elements of health that impact on the ability to do what they wanted to do that were identified as key factors: independent mobility, hearing and memory - factors that impact on the ability to interact socially. These elements were only identified when they impacted on the person’s ability to do what they wanted to do, for example mobility problems that were managed were not considered a problem. The study also revealed that older women use selection, optimisation and compensation to meet their goal of staying socially connected. The shopping centre was a key factor in this goal and older women used shopping centres to stay connected to the community and for exercise as well as shopping. Personal and public safety and other environmental concerns were viewed in the same context of enabling or disabling social connectedness. This suggested that for elite older women the model of successful ageing was hierarchical rather than flat, with social connectedness at the top, supported by cognitive functioning and good physical and mental health. In conclusion, this research revealed that perception of wellness in older adults is a complex, multidimensional construct. For older adults good health is related to social connectedness and is not a goal in itself. Health professionals and the community at large have a responsibility to take into account the ability of the older adult to stay socially connected to their community and to enable this, if the goal is to keep older adults healthy for as long as possible. Maintaining or improving perception of wellness in older adults will require a broad biopsychosocial approach that utilises findings such as older adults’ use of shopping centres for non-shopping purposes, concerns about personal and environmental safety and supporting older adults to maintain or improve their social connectedness to their communities.

Multilevel converter structure and control

In recent years, multilevel converters are becoming more popular and attractive than traditional converters in high voltage and high power applications. Multilevel converters are particularly suitable for harmonic reduction in high power applications where semiconductor devices are not able to operate at high switching frequencies or in high voltage applications where multilevel converters reduce the need to connect devices in series to achieve high switch voltage ratings. This thesis investigated two aspects of multilevel converters: structure and control. The first part of this thesis focuses on inductance between a DC supply and inverter components in order to minimise loop inductance, which causes overvoltages and stored energy losses during switching. Three dimensional finite element simulations and experimental tests have been carried out for all sections to verify theoretical developments. The major contributions of this section of the thesis are as follows: The use of a large area thin conductor sheet with a rectangular cross section separated by dielectric sheets (planar busbar) instead of circular cross section wires, contributes to a reduction of the stray inductance. A number of approximate equations exist for calculating the inductance of a rectangular conductor but an assumption was made that the current density was uniform throughout the conductors. This assumption is not valid for an inverter with a point injection of current. A mathematical analysis of a planar bus bar has been performed at low and high frequencies and the inductance and the resistance values between the two points of the planar busbar have been determined. A new physical structure for a voltage source inverter with symmetrical planar bus bar structure called Reduced Layer Planar Bus bar, is proposed in this thesis based on the current point injection theory. This new type of planar busbar minimises the variation in stray inductance for different switching states. The reduced layer planar busbar is a new innovation in planar busbars for high power inverters with minimum separation between busbars, optimum stray inductance and improved thermal performances. This type of the planar busbar is suitable for high power inverters, where the voltage source is supported by several capacitors in parallel in order to provide a low ripple DC voltage during operation. A two layer planar busbar with different materials has been analysed theoretically in order to determine the resistance of bus bars during switching. Increasing the resistance of the planar busbar can gain a damping ratio between stray inductance and capacitance and affects the performance of current loop during switching. The aim of this section is to increase the resistance of the planar bus bar at high frequencies (during switching) and without significantly increasing the planar busbar resistance at low frequency (50 Hz) using the skin effect. This contribution shows a novel structure of busbar suitable for high power applications where high resistance is required at switching times. In multilevel converters there are different loop inductances between busbars and power switches associated with different switching states. The aim of this research is to consider all combinations of the switching states for each multilevel converter topology and identify the loop inductance for each switching state. Results show that the physical layout of the busbars is very important for minimisation of the loop inductance at each switch state. Novel symmetrical busbar structures are proposed for multilevel converters with diode-clamp and flying-capacitor topologies which minimise the worst case in stray inductance for different switching states. Overshoot voltages and thermal problems are considered for each topology to optimise the planar busbar structure. In the second part of the thesis, closed loop current techniques have been investigated for single and three phase multilevel converters. The aims of this section are to investigate and propose suitable current controllers such as hysteresis and predictive techniques for multilevel converters with low harmonic distortion and switching losses. This section of the thesis can be classified into three parts as follows: An optimum space vector modulation technique for a three-phase voltage source inverter based on a minimum-loss strategy is proposed. One of the degrees of freedom for optimisation of the space vector modulation is the selection of the zero vectors in the switching sequence. This new method improves switching transitions per cycle for a given level of distortion as the zero vector does not alternate between each sector. The harmonic spectrum and weighted total harmonic distortion for these strategies are compared and results show up to 7% weighted total harmonic distortion improvement over the previous minimum-loss strategy. The concept of SVM technique is a very convenient representation of a set of three-phase voltages or currents used for current control techniques. A new hysteresis current control technique for a single-phase multilevel converter with flying-capacitor topology is developed. This technique is based on magnitude and time errors to optimise the level change of converter output voltage. This method also considers how to improve unbalanced voltages of capacitors using voltage vectors in order to minimise switching losses. Logic controls require handling a large number of switches and a Programmable Logic Device (PLD) is a natural implementation for state transition description. The simulation and experimental results describe and verify the current control technique for the converter. A novel predictive current control technique is proposed for a three-phase multilevel converter, which controls the capacitors' voltage and load current with minimum current ripple and switching losses. The advantage of this contribution is that the technique can be applied to more voltage levels without significantly changing the control circuit. The three-phase five-level inverter with a pure inductive load has been implemented to track three-phase reference currents using analogue circuits and a programmable logic device.