UNSW Canberra

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  • (2021) Keshk, Marwa
    Thesis
    With the rapid evolution of cyber attack techniques, the security and privacy of Cyber-Physical Systems (CPSs) have become key challenges. CPS environments have several properties that make them unique in efforts to appropriately secure them when compared with the processes, techniques and processes that have evolved for traditional IT networks and platforms. CPS ecosystems are comprised of heterogeneous systems, each with long lifespans. They use multitudes of operating systems and communication protocols and are often designed without security as a consideration. From a privacy perspective, there are also additional challenges. It is hard to capture and filter the heterogeneous data sources of CPSs, especially power systems, as their data should include network traffic and the sensing data of sensors. Protecting such data during the stages of collection, analysis and publication still open the possibility of new cyber threats disrupting the operational loops of power systems. Moreover, while protecting the original data of CPSs, identifying cyberattacks requires intrusion detection that produces high false alarm rates. This thesis significantly contributes to the protection of heterogeneous data sources, along with the high performance of discovering cyber-attacks in CPSs, especially smart power networks (i.e., power systems and their networks). For achieving high data privacy, innovative privacy-preserving techniques based on Artificial Intelligence (AI) are proposed to protect the original and sensitive data generated by CPSs and their networks. For cyber-attack discovery, meanwhile applying privacy-preserving techniques, new anomaly detection algorithms are developed to ensure high performances in terms of data utility and accuracy detection. The first main contribution of this dissertation is the development of a privacy preservation intrusion detection methodology that uses the correlation coefficient, independent component analysis, and Expectation Maximisation (EM) clustering algorithms to select significant data portions and discover cyber attacks against power networks. Before and after applying this technique, machine learning algorithms are used to assess their capabilities to classify normal and suspicious vectors. The second core contribution of this work is the design of a new privacy-preserving anomaly detection technique protecting the confidential information of CPSs and discovering malicious observations. Firstly, a data pre-processing technique filters and transforms data into a new format that accomplishes the aim of preserving privacy. Secondly, an anomaly detection technique using a Gaussian mixture model which fits selected features, and a Kalman filter technique that accurately computes the posterior probabilities of legitimate and anomalous events are employed. The third significant contribution of this thesis is developing a novel privacy-preserving framework for achieving the privacy and security criteria of smart power networks. In the first module, a two-level privacy module is developed, including an enhanced proof of work technique-based blockchain for accomplishing data integrity and a variational autoencoder approach for changing the data to an encoded data format to prevent inference attacks. In the second module, a long short-term memory deep learning algorithm is employed in anomaly detection to train and validate the outputs from the two-level privacy modules.

  • (2020) Ali, Ismail
    Thesis
    Combinatorial optimization problems (COPs) are well-known NP-hard realistic ones. Due to the drawbacks of existing approaches, many researchers proposed different evolutionary algorithms (EAs) for solving them. Differential evolution (DE) is primarily used to solve continuous-based optimization problems and has been considered unsuitable for solving many permutation-based combinatorial ones. However, several efforts to design an efficient discrete DE algorithm have been made in recent years. In this thesis, to efficiently solve COPs, an algorithmic framework involving DE and multiple methodologies is introduced. Firstly, a new best-matched value (BMV) mapping method consisting of a new heuristic for converting solutions with continuous variables to ones with discrete/binary variables and directing the mapped solution towards optimality is developed. Secondly, a complete DE incorporating the BMV and problem-specific components for solving knapsack problems (KPs) is proposed. Also, a new fitness evaluation for calculating and repairing knapsack solutions is introduced. Then, an improved complete DE that uses 1) an improved BMV, 2) a k-means clustering repairing method, 3) an ensemble of mutation and 4) two well-known local searches is developed to solve discrete traveling salesman problems (TSPs). Finally, motivated by the promising performances of the proposed versions of DE, a discrete-binary DE (DBDE) algorithm that uses the good search features of the BMV and most effective introduced components to solve the complex traveling thief problem (TTP) which consists of a TSP and KP is proposed. All the proposed DE versions are tested on three sets of well-known COPs, namely, KPs, TSPs and TTPs. It is found that: 1) the BMV improves the solution quality of the traditional DE by 45.04% and saves 66.56% of the times required to solve them; 2) using extra functions in DE to solve KPs leads to define new better solutions and 98.38% faster performances; 3) adopting a repairing method and ensemble of mutations improves the quality of solutions; 4) the DBDE algorithm exhibits a promising performance for solving generic COPs (TTPs). Finally, as the experimental results discussed in this thesis confirm the suitability of the DBDE algorithm for tackling generic COPs, it is expected that it will significantly advance knowledge of DE by extending the research work previously undertaken on it in the continuous domain to developing DEs that can solve complex COPs.

  • (2020) Pathak, Shivang
    Thesis
    Under the performance based earthquake engineering (PBEE) philosophy, the design of structures to resist collapse requires meeting a target level of collapse prevention. Therefore, the quantification of the collapse capacity of a structure is regarded as an essential step to develop robust designs that can ensure safety under extreme earthquake scenarios. Although, in the recent two decades, tremendous progress has been achieved in the field of computational analysis of deteriorating structures, the accurate prediction of collapse capacity remains a topical issue because current methods used for predicting collapse do not correlate to the exact occurrence of dynamic instability in the structure. In the present study, a new physics-based collapse criterion is proposed. It uses power (energy-rates) to predict seismic collapse capacity of structures. The development of the criterion stems from the principles of Lyapunov stability and dissipative dynamical systems. A series of validated collapse simulation models are developed to illustrate the applicability of this criterion to both vertical gravity load and sidesway collapse mechanisms. Furthermore, a dynamical systems theory algorithm is developed to identify the exact occurrence of dynamic instability. The developed power-based collapse criterion is then optimised by comparing its collapse predictions to those derived from the dynamical systems theory algorithm. Finally, the refined power-criterion is then used to evaluate the collapse risk in realistic RC frame buildings designed to meet existing codes’ requirements. The collapse risk estimates are compared to those derived from the existing techniques. It was found that the proposed criterion can serve as a leading indicator of collapse and can potentially result in economic and safe designs.

  • (2021) Zhang, Junpeng
    Thesis
    Satellite remote sensing videos have become an emerging valuable data source for city-scale surveillance from space. Moving Object Detection (MOD) and Multiple Object Tracking (MOT) methods serve as the stone-steps for the related applications. However, they are challenged by the low spatial resolution and low signal and noise ratio. This thesis aims to meet the challenges and develop effective MOD and MOT techniques for satellite remote sensing videos. For MOD, an Extended Low-rank and Structured Sparse Decomposition (E-LSD) model is proposed to suppress the effect of random noises. E-LSD models moving objects by a sparse foreground matrix, and the structured sparse regularization is imposed on it for exploring the spatial priors on moving objects. This alleviates the false alarm problem caused by noises on satellite remote sensing videos. To promote online processing, an Online Low-rank and Structured Sparse Decomposition (O-LSD) is developed. O-LSD reformulates the E-LSD problem that combines all frames in a video to a sequence of frame-wise separable counterparts by adopting the matrix factorization approximation and stochastic optimization techniques. In this way, extracting moving objects on a frame relies only on the information from current frame and its predecessor frames. In addition to random noises, the local misalignment caused by the motion of satellite platform is another primary source of false alarms in MOD on satellite remote sensing videos. To separate moving objects from it, a Moving-Confidence-assisted Matrix Decomposition (MCMD) model is developed by integrating motion information on moving objects into the foreground regularization. This is an improvement to E-LSD model in suppressing the effect of moving satellite platform in MOD. For MOT, an Incremental Successive Shortest Path (ISSP) tracker is developed. It defines a Maximum A-Posterior (MAP) problem for selecting and linking an optimal set of detection observations for trajectory formation. By fusing the information along the extracted trajectories, the proposed ISSP tracker reduces the fragmentations in the generated trajectories while automatically discarding the false alarms, which makes it more adaptive to the scenarios with inadequate accuracy. The videos captured by SkySat and Jilin-1 satellites were utilized for the testing and evaluation. The experimental results presented in this thesis demonstrate the proposed solutions perform well, which confirms the feasibility of applying satellite remote sensing videos for reliable MOD and MOT.

  • (2021) Huang, Qiuxiang
    Thesis
    Fluid-structure interactions (FSIs) of fluid-conveying collapsible tubes produce rich physiologically significant phenomena in many biological systems. Despite the significant progress made in recent years, the physical mechanisms responsible for the onset of self-excited oscillations of collapsible tubes remain unclear. To study nonlinear dynamic behaviors of collapsible tubes, a numerical framework based on the immersed boundary-lattice Boltzmann method (IB-LBM) for the simulation of FSI in collapsible tubes is developed, and then applied to investigate the physical mechanisms behind self-excited oscillations in a two-dimensional (2D) collapsible channel and a three-dimensional (3D) collapsible tube. In the proposed numerical framework, the lattice Boltzmann method (LBM) is employed to solve the fluid dynamics, while the structural equations for the 2D collapsible channel and 3D collapsible tube are solved by the finite difference method (FDM) and finite element method (FEM), respectively. The immersed boundary method (IBM) is adopted for the fluid and structural coupling. A power-law non-Newtonian fluid model is used to model the non-Newtonian fluid, and large eddy simulation (LES) is used to capture the turbulence in the FSI system. The nonlinear dynamics of a two-sided collapsible channel flow is first investigated. The stability of the hydrodynamic flow and collapsible channel walls are examined for a wide range of Reynolds number (100 < Re < 3000), structure-to-fluid mass ratio (0.3 < M < 20), external pressure (1 < Pe < 10) and wall thickness (0.01D < h < 0.1D). Chaotic behaviors of the collapsible channel flow are characterized and possible routes to chaos as well as physical mechanisms responsible for the onset of self-excited oscillations are identified. Nonlinear and rich dynamic behaviors of the collapsible system are newly observed. Specifically, the system experiences a supercritical Hopf bifurcation leading to period-1 limit cycle oscillations as the Reynolds number increases. The existence of chaotic behavior of the collapsible channel walls is confirmed by a positive dominant Lyapunov exponent and a chaotic attractor in the velocity-displacement phase portrait of the mid-point of the collapsible channel wall. Chaos in the system can be reached via period-doubling and quasi-periodic bifurcations. In addition, it is found that symmetry breaking is not a prerequisite for the onset of self-excited oscillations, but symmetry breaking induced by large mass ratio and external pressure may lead the system into a chaotic state. Unbalanced transmural pressure, wall inertia and shear layer instabilities in the vorticity waves have been found to induce the onset of the self-excited oscillations of the collapsible system. The period-doubling, quasi-periodic and chaotic oscillations are closely associated with vortex pairing and merging of adjacent vortices, and interactions between the upper and lower vorticity rows. The IB-LBM solver is applied to examine the nonlinear dynamics of unsteady flows in 3D collapsible tubes. The stability of the hydrodynamic flow and collapsible tube walls are examined for a range of Reynolds numbers (100 < Re < 1000) from laminar to turbulent. The effects of non-Newtonian rheology on the dynamic behaviors of the collapsible system are examined with a power-law model, and LES is used to model turbulent effects. For the effects of Reynolds number, it is found that the periodic vortex shedding downstream the throat of the elastic tube is responsible for small-amplitude and quasi-periodic self-excited oscillations developed at Re=200. During these oscillations, two regions of wall-thickening are developed near the end of downstream elastic tube (non-dimensional axial distance z/R=7.25) due to compressive stress concentrations, suggesting the potential for fatigue failures at these two regions. A small secondary buckling pattern develops at the downstream end of the elastic tube which has not been observed in the 2D model. The reverse-flow region is open and fills the entire symmetry plane with the major axis in the 3D model while it is closed and cannot fill the entire height of the channel in the 2D model. For the effects of non-Newtonian fluid, it is found that shear-thickening fluids tend to stabilize the collapsible system while shear-thinning fluids will trigger the onset of self-excited oscillations. The deformation of the tube decreases as the power-law index n increases. The tube snaps from a fully constricted state at n=1.6 to an unconstricted state at n=1.7. For turbulent flows at Re=1000, flow bifurcation occurs and the system settles into large-amplitude and quasi-periodic self-excited oscillations which are regular and repetitive. These oscillations are caused by the periodic shedding of vortices downstream of the throat of the elastic tube. The shed vortices feed back periodic perturbations through the elastic tube wall. At two monitor points placed in the downstream rigid tube, small secondary oscillations were found in the time history of pressure and streamwise velocity, which are caused by two jets merging together and interactions at the middle section of the downstream rigid tube.

  • (2020) Hossain, Md. Alamgir
    Thesis
    This dissertation focuses on the energy management of a community microgrid to minimise its operational cost under uncertain power generation, power demand and electricity prices. The study proposes some effective solution approaches to minimise the operational cost of the microgrid. Firstly, a real-time energy management scheme that is free from the effect of uncertainty is developed and compared with existing management schemes to efficiently control the battery energy of a microgrid. In the objective function for the scheme, a dynamic penalty function is added to incentivise battery charging during low electricity prices. The proposed method can reduce operational cost by 12 per cent as compared to a well-established existing one over a time horizon of 96 hours. Secondly, a two-hour ahead energy management approach considering the degradation cost of the battery and a penalty function to reflect the true operational cost is proposed. The optimisation problem formulated is solved using a particle swarm optimisation algorithm which is designed. The proposed energy management approach reduces electricity cost by up to 44.50 per cent compared to a baseline method and 37.16 per cent compared to another existing approach. Finally, day-ahead scheduling of the battery energy is proposed while considering its degradation costs due to charging-discharging cycles. The degradation costs with respect to the depth of charge are modelled and added to the objective function to determine the actual operational costs of the microgrid. A framework to solve the optimisation problem formulated is developed in which particle swarm optimisation, the Rainflow algorithm and scenario techniques are integrated. Uncertainties of variables, such as power generation and electricity prices, are also discussed in the study. Simulation results demonstrate that the proposed method for a day-ahead scheduling program can reduce the operational costs by around 40 per cent compared to the baseline method. Results also reveal that uncertainty in power generation and power demand has no influence on the energy schedule of the battery, but variation in electricity prices has an impact on the outcome. Several pragmatic tests verify the effectiveness of the proposed methods.

  • (2020) Zhu, Yi
    Thesis
    The aim of this thesis is to study the adaption behaviors of self-propelled fish in complex environments. In order to do so, a numerical framework is first developed. In this framework, fish swimming in a viscous incompressible flow is simulated with the immersed boundary--lattice Boltzmann method (IB--LBM). Furthermore, a deep recurrent Q-network (DRQN) is incorporated with the IB--LBM to train the fish model to adapt its motion to optimally achieve a specific task, such as prey capture, rheotaxis and Kármán gaiting. Compared to existing models for fish, this work incorporates the fish position, velocity and acceleration into the state space in the DRQN; and it considers the amplitude and frequency action space as well as the historical effects. This framework makes use of the high computational efficiency of the IB--LBM which is of crucial importance for the effective coupling with learning algorithms. Test cases including point-to-point swimming in quiescent flow and position holding both in a uniform stream and a Kármán vortex street have been conducted to show the effectiveness of the proposed framework. With the proposed method, the effect of vision, superficial neuromast (SN) and canal neuromast (CN) in position holding swimming in a uniform flow are then investigated. It is found that the fish is able to hold position with all those sensory methods. The control with vision is most accurate while the control with CN information is least accurate. In addition, the combination of vision, SN and CN will not improve the control with only vision, but the combination of SN and CN outperforms SN or CN alone. The effect of the undulation frequency on fish's behavior in a Kármán vortex street is finally investigated. Result shows that the swimming is most stable and efficient when the fish is synchronizing its tail-beat frequency with the vortex shedding frequency. Higher undulation frequency will decrease the hydrodynamic efficiency, and lower undulation frequency will decrease swimming stability. The effect of the scale of the vortex street on fish behavior is also investigated. Smaller vortex street makes the swimming more stable and less efficient, while larger vortex street makes the swimming unstable but hydrodynamically efficient.

  • (2021) Wang, Jianshen
    Thesis
    Functionally graded material (FGM) with the volumetric fraction of ceramic and metal changing gradually along the thickness is a prospective armour material with attractive properties. The gradually changing composition of FGM allows a relatively smooth transition from the ceramic rich zone to the metal to be achieved replacing the obvious interface in the bi-layered armour. This smooth transition theoretically leads to the improved ballistic performance of the armour. However, not many studies were reported on revealing the gradually varying microstructure and mechanical response under dynamic loading conditions of FGMs. This limits the practical application of FGM armour. Thus, this thesis aims to inspect the microstructures and mechanical responses of the single composition materials (SCMs) at different locations of an FGM under different loading conditions and understand the reinforcement and failure mechanisms of the SCMs. Ti-Ni-C metal matrix composites (MMCs) including FGMs and SCMs were fabricated with Ti-6Al-4V (Ti64) and Ni coated graphite (NCG) powders using LENS additive manufacturing system. The phase compositions and microstructures of SCMs and FGMs were examined by X-ray diffraction, optical microscopy and scanning electron microscopy. The results showed that Ti2Ni and TiCx phases were in situ synthesized with different quantity correlated to the chemical compositions. The variations of micro-hardness along the direction of composition variation were investigated using hardness tests. It was found that the hardness of the composite was enhanced by the higher content of TiCx and Ti2Ni. The quasi-static and dynamic compression tests were performed on SCMs with different NCG contents using universal testing machine and split Hopkinson pressure bar (SHPB) at various strain rates from 10-3 to 6×102 s-1. The larger amount of NCG in SCM resulted in the increase of strength and the reduction of ductility. The strain rate effect was negligible when the strain rate was lower than 10-1 s-1. However, SCM with higher NCG content exhibited decreased strain rate hardening effect and better resistance to shear fracture at a higher strain rate of approximately 6×102 s-1. The dynamic responses of SCMs with different NCG contents under shock loading were investigated using flyer plate impact experiments conducted with a gas gun. The SCMs containing more NCG exhibited lower spall strength and tended to break into larger number of fragments with smaller size.

  • (2021) Ibrahim, Ahmed
    Thesis
    In this thesis, we aim to generalize the negative imaginary systems theory to a broad class of nonlinear systems. A formal definition will be given for the negative imaginary property in the nonlinear domain by invoking a new dissipativity notion with an appropriate work rate. This formula is considerably more general than the existing classical dissipativity framework. Flexible structures with colocated force actuators and position sensors are dissipative according to this new definition. Having defined the nonlinear negative imaginary property in a time-domain dissipativity framework, we are able to extend some of the main existing results on negative imaginary systems from the linear to nonlinear domain. First, a Lyapunov-based approach will be used to establish the stability robustness of a positive feedback interconnection of negative imaginary systems in the linear case under a set of theoretical assumptions. Then, these assumptions will be adapted in the nonlinear setup to establish the stability robustness analysis of a positive feedback interconnection of nonlinear negative imaginary systems by making use of Lyapunov stability theory and dissipativity techniques. The applicability of this nonlinear stability result will be illustrated through an example of nonlinear mass spring damper system. Furthermore, the nonlinear negative imaginary systems theory will be extended to the case of free motion. It will be shown that, under suitable assumptions, a cascade connection of an affine nonlinear system and single integrator will lead to a nonlinear negative imaginary system (with integrator). Finally, this thesis is concluded by a summary of current progress and a discussion of possible future developments of the nonlinear negative imaginary systems theory.

  • (2021) Neilsen, Rhiannon
    Thesis
    In the contemporary digital age, mass atrocity crimes are increasingly promoted and organised online. Yet, little attention has been afforded to the question of whether proactive cyberspace operations might be used for human protection purposes. Beginning with the framework of the Responsibility to Protect (R2P), this thesis asks: How might cyber-operations be used ethically to protect populations from mass atrocity crimes? To answer this question, I introduce the concept of ‘cyber humanitarian interventions’, and argue that such measures can be used to disrupt potential perpetrators’ means and motivations for atrocities. Specifically, I contend that cyber humanitarian interventions can be used to frustrate potential perpetrators’ communication channels, logistical supply chains, and funding, as well as to stymie potential perpetrators’ desire for violence via online, targeted, tailor-made campaigns based on their big data. These capabilities can be used in an ethically acceptable manner, and thus ought to be pursued prior to the resort to other more forceful measures to protect. Moreover, and perhaps more controversially, I argue that, in some circumstances, there is a qualified responsibility to deceive potential perpetrators – via online disinformation – in order to fulfil responsibilities to protect. This thesis seeks to make three key contributions. First, it contributes to extant literatures on R2P, atrocity prevention, and cyberspace by offering cyber humanitarian interventions as a hitherto neglected tool for human protection. Second, it furthers ethical debates on atrocity prevention by providing an in-depth analysis of how cyber humanitarian interventions can be deployed ethically. Third, it challenges prevailing conceptions of disinformation by arguing that that there is, in fact, a qualified responsibility to deceive potential perpetrators into not committing atrocities via online disinformation. In sum, this thesis aims to bring 21st century capabilities to bear on centuries-old crimes, and highlights cyber humanitarian interventions as a more peaceful, cost-effective, and politically palatable tool to protect vulnerable populations from mass atrocity crimes.