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(2014)ThesisA huge amount of personal data is shared in real time by online users, increasingly using mobile devices and (unreliable) wireless channels. There is a large industry effort in aggregation and analysis of this data to provide personalised services, and a corresponding research effort to enable processing of such data in a secure and privacy preserving way. Secret sharing is a mechanism that allows private data sharing, revealing the information only to a select group. A parallel research effort has been invested in addressing the performance of real time mobile communication on lossy wireless channel, commonly improved by using erasure codes. In this thesis, we bring together the theoretically related fields of secret sharing and erasure coding, to provide a rich source of solutions to the two problem areas. Our aim is to enable solutions that deliver the required performance level while being efficient and implementable. The thesis has the following contributions. We evaluate the applicability of a new class of Maximum Distance Separable (MDS) erasure codes to transmission of real time content to mobile devices and demonstrate that the systematic code outperforms the non-systematic variant in regards to computation complexity and buffer size requirements, making it practical for mobile devices. We propose a new Layered secret sharing scheme for real time data sharing in Online Social Networks (OSNs). The proposed scheme enables automated profile sharing in OSN groups with fine-grained privacy control, via a multi-secret sharing scheme comprising of layered shares. The scheme does not require reliance on a trusted third party. Compared to independent sharing of specific profile attributes (e.g. text, images or video), the scheme does not leak any information about what is shared, including the number of attributes and it introduces a relatively small computation and communications overhead. Finally, we investigate the links between MDS codes and secret sharing schemes, motivated by the inefficiency of the commonly used Shamir scheme. We derive the theoretical links between MDS codes and secret sharing schemes and propose a novel MDS code based construction method for strong ramp schemes. This allows the use of existing efficient implementations of MDS codes for secret sharing and secure computing applications. We demonstrate that strong ramp schemes deliver a significant reduction of processing time and communication overhead, compared to Shamir scheme.
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(2014)ThesisInternational outsourcing has become a competitive strategy in manufacturing industries due to the issue of limited resources and associated economic benefits. Although international outsourcing seems to be a cost efficient way of production, the concerns about its impacts on the sustainability of societies are rising significantly. In order to have sustainable societies, the industrial managers need to control and quantify the static and dynamic social, economic and environmental impacts of their international outsourcing decisions. Nevertheless, there is a gap in the literature for quantifying these social, economic, and environmental impacts. Therefore, the objective of this thesis is to develop a decision support system to quantify the explained impacts. In this research, the number of employees in the industries, gross value added (GVA) and CO2 emissions were selected as the social, economic and environmental indicators at the macro level respectively. Input output analysis and linear programming were implemented by programming in Matlab software as the research methodology for studying the static impacts of international outsourcing. Then, different econometric methods were applied with system dynamic method for investigating the dynamic impacts. Five different case studies have been investigated in this research. The static results depicted that the international outsourcing reduces not only the aforementioned sustainability indicators of the outsourcer industry but also for the domestic suppliers of the outsourcer industry. In these case studies, the sustainability indicators in the outsourcee countries were also quantified. In the last three case studies, static evaluation demonstrated that the international outsourcing will increase the total CO2 emissions and number of employees while decrease the total GVA. The dynamic results in the last two case studies presented that international outsourcing will increase the combined CO2 emissions and number of employees of the outsourcer and outsourcee countries while reducing the combined GVA of countries from the global perspective. In contrast, the results of third case study demonstrated that the international outsourcing will increase all aforementioned sustainability indicators of the outsourcer and outsourcee countries together. The reason for the different results in terms of the total GVA is the various production technologies of countries used in the future.
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(2015)ThesisAdvanced application of Combined Heat and Power (CHP) systems as an on-site energy generation is subject not only to optimal system configuration, but also continuous operations management. This is vital in manufacturing plants where several types of energy forms are required dynamically. However, existing shortcomings and challenges have hindered the maximum utilisation of CHP technology as a potential on-site energy generation option. Consequently, this study on the one hand addresses optimal CHP configuration through integrated sizing and operational strategy selection. This is achieved through simulation of on-site energy system operation where comprehensive models of individual components and their potential operational strategies are formulated. Moreover, real-time operations management of CHP systems is developed and evaluated to enable optimal integration of controllable energy supply options into on-site energy systems. Thus, the concept of real-time optimisation is implemented to track a changing optimum. The proposed solution consists of two modules, i.e. the simulation module and optimisation module that can interact with the dynamic operating environments. The simulation module represents comprehensive modelling of individual components and the required interactivity with an operating environment. This also keeps the system operation history and provides look-ahead capability to impact past decisions and future system fluctuations. Moreover, the optimisation module guarantees economic dispatch of the energy system components in addition to unit commitment at short time intervals. The developed modules’ interaction decomposes the problem formulation to cope with concurrent computation and communication requirements. This will overcome the limitations of traditional optimisation techniques to simultaneously encompass system complexity and operational aspects in real-time. On the whole, the developed Real-time Integrated Simulation and Optimisation methodology (RISO) assures practical and intelligent control of potential components on a continuous basis. As a result, the proposed solution provides successful CHP system integration into the manufacturing plants to assure optimal configuration and operations management. Moreover, the generic and comprehensive control strategy facilitates the required foundation to integrate multiple and different types of on-site energy system technologies, such as intermittent renewable energy suppliers.
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(2013)ThesisDue to the general failure of Global Positioning System (GPS) for indoor positioning, non-satellite-based technologies are important for indoor localization. Using wireless networks based on the Received Signal Strength (RSS) location fingerprinting technique is the most popular positioning method used for indoor environments. This research proposes a new positioning technique based on fingerprinting that utilises one of the most available signals of opportunity (SoOP), which is frequency modulated (FM) broadcast radio signals. Then the fusion of FM and Wi-Fi is investigated. The result outperformed the previous methods in terms of accuracy and a more robust and reliable positioning system is presented. Moreover, an analytical framework for estimating the accuracy performance of fingerprinting indoor positioning systems is suggested. Using this model, the most common signal distances such as Euclidean, Manhattan and Chebychev are fully analyses and compared together both mathematically and by simulation so that we can identify which provides least positioning error. Crame-Rao lower bound (CRB) is widely used for assessing localization performance limits but the recent measurement revealed that CRB does not always represent an actual lower bound for indoor positioning. We utilise and modify two more advanced lower bounds and propose an optimization trend in system configuration such that the attained root mean square error in the position estimator gets closer to the minimal attainable variance in the fingerprinting position estimator. Finally, a new method for error estimation in indoor localization systems is designed and novel precision measurements factors for fingerprinting method is developed. Thus the quality of service of the positioning system is improved and the integrity of the system is guaranteed. In this research, the problem of evaluation and enhancement the accuracy of fingerprinting positioning systems utilizing terrestrial FM signals is addressed and analytical frameworks and appropriate solid tools for designing more precise indoor positioning systems are developed. In summary, the FM-based positioning analysis, analytical position error estimation tools, statistical analysis on the accuracy of the indoor positioning systems, and the design criteria tools in this thesis are novel and provide interesting insights into the positioning system performance. These tools are used to optimise the system performance under given performance objectives and constraints.
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(2015)ThesisGrid-connected photovoltaic power plants (GCPPPs) are affected by grid requirements and affect the system by different control strategies. The GCPPP should support the system based on the grid code requirements including FRT capability, reactive power injection during voltage sags and power quality. From the GCPPP side, the main reasons for inverter disconnection are: loss of synchronization, excessive ac currents, excessive dc voltage, and voltage rise in non-faulty phases under unbalanced voltage sags due to the reactive current injection. The thesis deals with large-scale GCPPPs studies covering both static and dynamic aspects. The focus on the static studies is to support the grid voltages by the capability of the inverter to inject reactive currents. Four different methods based on the grid codes are designed and applied to a 10-MVA GCPPP. Considering dynamic studies, different control strategies are proposed to address the mentioned problems for inverter disconnection. The solutions are offered for three main controller strategies: controlling only the positive-sequence, controlling positive- and negative sequences and individual control of the phase currents. Dealing with the positive-sequence control of the currents, inverter disconnection issues are addressed for single-stage dc-ac and two-stage dc-dc-ac conversions by applying a current limiter and using an advanced phase-locked-loop (PLL). The difference in these two conversions is the protection of the dc voltage from overvoltage. While the dc voltage in the single-stage conversion is self-protected, three different methods are proposed to control the dc voltage in the two-stage conversion under voltage sag conditions. Limitations on using PI controllers in the current loops are demonstrated when controlling both positive- and negative-sequences of the grid currents. An alternative method based on resonant controllers is proposed with the capability to operate without a PLL for grid synchronization. The individual control of the phases is proposed, allowing the injection of a different reactive current to each phase. This prevents from overvoltage in the non-faulty phases during unbalanced voltage sags. For this control strategy a frequency-adaptive PLL is also designed that enables the extraction of the individual phase angles. Finally, the results confirm the viability of the proposed solutions using both simulation and experiments.
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(2012)ThesisCell immunoisolation systems are fast becoming a favourable approach to cure various challenging diseases and disorders such as type I diabetes. Although the addition of biological molecules to cell immunoisolation devices can significantly enhance their performance by supporting cell viability and function, little is known about their effects on the immunoisolating membrane properties especially its permselectivity. Therefore, this research focused on examining the effect of combining biological molecules with a synthetic polymer on the permeability of hydrogels, with a specific emphasis on encapsulation of insulin producing cells for treatment of diabetes. The research aimed at achieving an optimum balance between a controlled permselectivity and cell survival support. It was hypothesised that covalent incorporation of small amounts of model extracellular matrix (ECM) molecules, heparin and gelatin, would support cell viability without compromising the controlled permselectivity and physico-mechanical properties of the base PVA network. Varying the number of functional groups per PVA backbone successfully controlled the PVA permeability and physico-mechanical properties. A suitable degree of permselectivity was achieved by the highly crosslinked hydrogels. Covalent incorporation of heparin and gelatin at low percentage was successfully achieved without interfering with either their biofunctionalities or the base PVA properties, including its permselectivity. Moreover, the incorporated ECM analogues supported the viability and metabolic activity of pancreatic β-cell lines encapsulated for two weeks. Consequently, biosynthetic hydrogels composed of permselective PVA base material and a small amount of biological molecules show promise as immunoisolating materials for cell-based therapy.
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(2018)ThesisThe novel PosiDAF process that uses cationic-polymer modified bubbles has been suggested as an alternative to conventional dissolved air flotation for the separation of algae. However, a cationic PosiDAF effluent compared to an anionic influent as detected by charge measurements indicated that effluent contained high polymer residuals and was undesirable. To prevent this, prior research investigated stronger polymer-bubble adhesion by developing hydrophobically modified polymers (HMPs) of poly(dimethylaminoethyl methacrylate) (PDMAEMA). However, while bench scale tests using the HMPs were successful, commercially available poly(diallyldimethylammonium chloride) (PDADMAC) outperformed the HMPs in pilot scale, suggesting that PDADMAC has a more suitable polymer backbone. Moreover, algal organic matter (AOM) released by cells, particularly biopolymers, was observed to influence cell separation. Further research is required to investigate alternate polymers and to determine more precisely the underlying mechanisms governing polymer-bubble-AOM interactions in PosiDAF. In this study, PDADMAC was modified with various aromatic and aliphatic pendant groups to generate several HMPs. Select HMPs of PDADMAC and previously investigated PDMAEMA were compared to evaluate polymer-bubble attachment and PosiDAF performance to separate algae and cyanobacteria. The composition of AOM, particularly biopolymers from each strain tested was characterised and their influence examined by conducting experiments with various AOM, protein and carbohydrate concentrations. The results showed that HMP coated bubbles had lower surface tensions and consequently, anionic effluents and strong polymer-bubble adhesion. Concurrently, cell separation was either comparable, or slightly better between HMPs. However, separation effectiveness varied for several algae, indicating that AOM impacted separation. Moreover, cell separation of the strains increased to > 95% when exudates from the best separated strain were added. On bulk and molecular characterisation of the cultures, the best separated strain was found to be biopolymer rich in comparison to the other strains. Hence, proteins and carbohydrates were dosed to study their influence and were observed to either depress or enhance the flotation depending on their character. It was concluded that the interplay of biopolymers with polymer-bubble-cell was responsible for the variations in cell removal observed across several strains. Overall, with a well-defined AOM character and low polymer residual in effluent, PosiDAF has been demonstrated as a robust and sustainable process.
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(2016)ThesisSeasonal rainfall forecasts are in high demand for users such as irrigators and water managers in decision making and risk management. Both statistical and dynamical models are widely used to generate probabilistic rainfall forecasts in advance for a season. Statistical prediction systems establish a stationary relationship between the predictor and the predictand variables, for example, sea surface temperature anomaly (SSTA) patterns over the Indian and Pacific Oceans are found to provide probabilistic seasonal rainfall forecast throughout Australia. On the other hand, dynamical models are based on the laws of physics and thus they can capture non-linear interactions of the atmosphere, land and ocean. In the case of seasonal forecasts, these models use a two-tiered process by predicting global Sea Surface Temperatures (SSTs) first; an atmospheric general circulation model (GCM) is subsequently forced by the pre-forecast SST to make a seasonal prediction. Improvement in predicted SST is therefore significant for issuing better concurrent seasonal rainfall forecasts. Consequently the motivation of this research is to improve seasonal SST forecasting on a global scale and accordingly provide concurrent seasonal rainfall prediction by applying statistical techniques and similarly forcing a climate model. For the purpose of improving seasonal SST forecasting, this thesis presents a multimodel combination approach which considers intermodel dependency between the multiple participating GCMs. The algorithm provides globally gridded SSTA for a season ahead based on the degree of correlation between the forecast errors and the relative size of each model s error variance. This methodology demonstrates an attractive way of improving seasonal SSTA forecasts over the majority of grid cells in the globe compared to the recent multimodel approach wherein the correlations are ignored. The standard practice of multimodel approach by pooling the models over a common time period can, however, cause loss of information if some models have a longer period of data. Hence this thesis also presents another simple approach of combining models which have variable data lengths. In the second part of this thesis, concurrent seasonal rainfall forecasts are issued from the improved SST, and single model SST predictions separately. The statistical techniques Bayesian Joint Probability (BJP) and Bayesian Model Averaging (BMA) are applied to translate seasonal rainfall forecasts using six predicted SSTA indices over the Indian and Pacific Oceans. The BJP-BMA approach shows encouraging results derived from improved SSTA indices, although no upper atmosphere predictor variable is considered. In addition, the global climate model ACCESS is used to issue concurrent seasonal rainfall prediction on a global scale from the improved forecast SST. The results indicate that there is merit in formulating global seasonal rainfall forecasts from the predictive uncertainty reduced SST, rather than relying on a single model predicted SST.
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(2017)ThesisConcentrated leak erosion is a form of internal erosion and piping which occurs in embankment dams, levees, and their foundations. Internal erosion and piping is the cause of about half of large embankment dam failures. Most internal erosion and piping failures occur due to concentrated leak erosion which is initiated by a crack in an embankment or its foundation. This research is focused on the development of suitable numerical simulation procedures for predicting the potential location of cracks, crack width and depth in embankment dams. The sub-modelling technique is employed in the methodology in order to reduce the otherwise prohibitive computational time required to model crack growth. For this technique, two procedures, global modelling and sub-modelling are required. The global modelling is to analyse the development of potential cracking zones during and after construction. The global model is based on the use of commercial finite element software Abaqus. The parametric studies of the global model investigated the relative importance of various factors on the potential cracking zones. The sub-modelling is to analyse crack propagation in the potential cracking zones predicted by the global model. The sub-model is based on the use of the scaled boundary finite element method combined with the quadtree mesh. The framework of the linear elastic fracture mechanics is used to investigate the fracture behaviour of core soils. The maximum circumferential stress theory including the T-stress is suggested for the crack propagation criterion. Three case studies of Dartmouth Dam, Lake Buffalo Dam and Red Willow Dam have been performed to test the applicability of the developed simulation procedures in practice. The predicted location of cracks, crack depth and width show reasonable agreements with the field observations. A novel, simple and efficient one-point return mapping SBFEM formulation for the elastoplastic analysis has been developed. The accuracy and efficiency of the formulation are demonstrated using numerical benchmark examples. The developed formulation has been also applied to the image-based elastoplastic analysis. It demonstrates that the developed SBFEM formulation combined with the quadtree image decomposition provides an attractive way to perform virtual testing of elastoplastic responses of materials and structures.
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(2018)ThesisWildfires affect ecological processes, threaten human lives and cause economic losses. Understanding of fire patterns is required to better support the planning of sustainable fire management and risk reduction activities. Fire occurrence and fire size are two essential fire pattern components that describe the distribution of fires and the impacts of fires on landscapes and ecosystems. They vary substantially within and between regions due to variation in weather, fuel, topography and ignition sources. In this thesis, remotely sensed and administrative records as well as Generalised Linear Models and Generalised Additive Models have been used to understand fire occurrence patterns in the south-eastern part of Australia, as well as to obtain knowledge on the patterns of fire occurrence and fire size in the inland semi-arid riverine area. The results suggest that in the south-eastern Australia, wildfires are more likely to occur in mountainous areas, forests, savannas, and in areas with high vegetation coverage and near human infrastructures, while they are less likely to occur on grasslands and shrublands. Environmental variables are strong individual predictors while anthropogenic variables contribute more to the final model. Fire-ignition drivers and their effects vary across ecoregions. There are non-linear relationships between the probability of fire ignition and some of its drivers e.g. the Normalized Difference Vegetation Index. This study also reveals that on the NSW side of the Riverina bioregion, human-caused fires mostly occur in spring and summer while natural fires are clustered in summer. Forested wetlands and dry lands experience summer and spring-summer fire regime, respectively. Fire probabilities are higher in forested wetlands than in dry lands and in areas with intermediate inundation frequencies. Weather, fuel and ignition sources are comparably important in regulating human-caused ignitions, while weather contributes more than fuel in driving natural ignitions. Larger-size Fires that burned Entirely in forested Wetlands (FEW) are associated with higher ambient rainfall conditions of the 6th, 13-14th and 17-18th months before fires. Fire danger index is more powerful than other ambient weather factors in explaining the FEW size. The contributing and the most effective factors become different when fires burned in dry lands are incorporated.