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(2022)ThesisEmerging modes of air transport such as autonomous airport shuttle and air taxi are potentially efficient alternatives to current transport practices such as bus and train. This thesis examines bus shuttle service within an airport and air metro as two examples of network design. Within an airport, the bus shuttle serves passengers between the terminals, train stations, parking lots, hotels, and shopping areas. Air metro is a type of pre-planned service in urban air mobility that accommodates passengers for intra- or inter-city trips. The problems are to optimise the service, and the outputs including the optimal fleet size, dispatch pattern and schedule. Based on the proposed time-space networks, the service network design problems are formulated as mixed integer linear programs. The heterogeneous multi-type bus fleet case and stochastic demand case are extended for the airport shuttle case, while a rolling horizon optimisation is adopted for the air metro case. In the autonomous airport inter-terminal bus shuttle case, a Monte Carlo simulation-based approach is proposed to solve the case with demand stochasticity, which is then further embedded into an "effective" passenger demand framework. The "effective" demand is the summation of mean demand value and a safety margin. By comparing the proposed airport shuttle service to the current one, it is found that the proposed service can save approximately 27% of the total system cost. The results for stochastic problem suggest estimating the safety margin to be 0.3675 times of the standard deviation brings the best performance. For the second case, the service network design is extended with a pilot scheduling layer and simulation is undertaken to compare the autonomous (pilot-less) and piloted service design. The results suggest that an autonomous air metro service would be preferable if the price of an autonomous aircraft is less than 1.6 times the price of a human-driven one. The results for rolling horizon optimisation suggest to confirm the actual demand at least 45 minutes prior to departure. Based on data from the Sydney (Australia) region, the thesis provides information directly relevant for the service network design of emerging modes of air transport in the city.
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(2022)ThesisThe rapid development of robotics has benefited by more and more people putting their attention to it. In the 1920s, ‘Robota’, a similar concept, was first known to the world. It is proposed in Karel Capek’ s drama, Rossum’ s Universal Robots (RUR). From then on, numbers of automatic machines were created all over the world, which are known as the robots of the early periods. Gradually, the demand for robots is growing for the purpose of fulfilling tasks instead of humans. From industrial uses, to the military, to education and entertainment, di↵erent kinds of robots began to serve humans in various scenarios. Based on this, how to control the robot better is becoming a hot topic. For the topic of navigating and controlling mobile robots, number of related problems have been carried out. Obstacle avoidance, path planning, cooperative work of multi-robots. In this thesis, we focus on the first two problems, and mention the last one as a future direction in the last part. For obstacle avoidance, we proposed algorithms for both 2D planar environ- ments and 3D space environments. The example cases we raise are those that need to be addressed but have always been ignored. To be specific, the motion of the obstacles are not fixed, the shape of the obstacles are changeable, and the sensors that could be deployed for underwater environments are limited. We even put those problems together to solve them. The methods we proposed are based on the biologically inspired algorithm and Back Propagation Neural network (BPNN). In addition, we put e↵orts into trajectory planning for robots. The two scenarios we set are self-driving cars on the road and reconnaissance and surveillance of drones. The methods we deployed are the Convolutional Neural Network (CNN) method and the two-phase strategy, respectively. When we proposed the strategies, we gave a detailed description of the robot systems, the proposed algorithms. We showed the performance with simulation results to demonstrate the solutions proposed are feasible. For future expectations, there are some possible directions. When applying traditional navigation algorithms, for example, biologically inspired algorithms, we have to pay attention to the limitations of the environment. However, high-tech algorithms sometimes are not computationally friendly. How to combine them together so as to fulfill the tasks perfectly while the computational e ciency is not too high is a worthy topic. In addition, extending the obstacle avoidance al- gorithms to more competitive situations, such as applying to autonomous UAVs, is also being considered. Moreover, for cooperation among multi robots, which could be regarded as Network Control System (NCS), the issues, such as how to complete their respective tasks, how to choose the optimal routes for them are worth attention by researchers. All in all, there is still a long way to go for the development of navigation and control of mobile robots. Despite this, we believe we do not need to wait for too long time to see the revolution of robots.
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(2022)ThesisThis thesis investigates the effect of fuel aromatic content on soot distribution, particle morphology and internal structure inside the cylinder of an optically accessible engine. A set of custom-made jet fuels with 4%, 14% and 24% aromatic content are studied first with the 24% aromatics fuel (AR24) used for more detailed follow-up study of soot particle evolution along the flame development path. Time-resolved imaging of cool flame, OH* chemiluminescence signals and soot luminosity are performed to visualise the overall reaction development. Planar laser-induced fluorescence imaging of HCHO and incandescence imaging of soot are also performed to obtain detailed understanding of reactions and soot distributions. Soot is analysed at a particle level. Using the thermophoresis-based particle sampling method, soot aggregates are collected from multiple in-bowl locations. Up to four soot sampling probes are installed on the piston-bowl wall with 60° spacing angles for simultaneous sampling from the same firing cycle. These sampling locations represent a jet-wall impingement point (JW), an up-swirl point (US), a down-swirl point 1 (DS1) and a down-swirl point 2 (DS2) along the sooting flame path. The subsequent transmission electron microscope (TEM) imaging of the collected soot particles enables structural analysis of soot particles as well as sub-nano-scale carbon layers. The results showed that the aromatic content has little impact on reactions and flame development among the tested fuels. However, the soot formation starts to occur earlier, and its growth rate is much higher for a higher aromatic fuel. The carbon-layer fringe analysis shows more mature, graphitised structures with higher aromatics at both formation-dominant and oxidation-dominant stages. For a selected AR24 fuel, the carbon-layer fringe analysis indicates continued oxidation during the flame penetration along the piston-bowl wall. Regarding the particle structure evolution, it is characterised by high formation of small aggregates at JW point, simultaneous aggregation and oxidation at US and DS1 point with the latter more prone to aggregation, and significant oxidation at DS2 point.
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Modeling ride-sourcing and public transport services in a multi-modal network with multi-class users(2022)ThesisRide-sourcing services are rapidly spreading around the world. The ride-sourcing service refers to a point-to-point on-demand ride service operated by various companies, which organize and coordinate drivers using their vehicles to provide passengers with ride services. How ride-sourcing services and public transport are interacting with each other and thus yielding system-wide impacts have not received sufficient attention. This thesis extends the literature by proposing multi-class, multi-modal traffic assignment models to optimize the transport system with the presence of ride-sourcing and public transport services. The first part of the thesis develops a stylized model with a simple network with single origin-destination pair in order to analytically examine the mode choice behavior of travelers and the operation strategies of a public transport operator and a ride-sourcing operator. In such a multi-modal system, users may travel by bus, train, or ride-sourcing service. In particular, we develop a tractable bi-level model that quantifies the user equilibrium travel choices in the lower-level, where the travel choice equilibrium can be formulated as a variational inequality problem, and optimizes the operation strategies of the public transport operator that aims to minimize total system cost and the ride-sourcing operator that aims to maximize its profit in the upper-level. The existence and uniqueness of the multi-modal travel choice equilibrium are also analyzed. How the operation decision variables might affect users' mode choices and system performance is investigated both analytically and numerically. The second part of the thesis extends the stylized model to a general network model, which includes also solo-driving, and multiple OD pairs to depict a more realistic problem setting. The general network model is applied on a case study in the context of Sydney. The existence and uniqueness are also investigated for the general network model. The method of Frank-Wolfe combined with diagonalization is applied to generate numerical solutions, and illustrate the analytical observations and generate further understanding. The results show that the total system cost can be reduced while the profit of the ride-sourcing company can be increased under appropriate operating strategies of the public transport operator and the ride-sourcing operator.
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(2022)ThesisIn many real-world applications, bipartite graphs are naturally used to model relationships between two types of entities. Community discovery over bipartite graphs is a fundamental problem and has attracted much attention recently. However, all existing studies overlook the weight (e.g., influence or importance) of vertices in forming the community, thus missing useful properties of the community. In this thesis, we propose a novel cohesive subgraph model named Pareto-optimal (α, β)-community, which is the first to consider both structure cohesiveness and weight of vertices on bipartite graphs. The proposed Pareto-optimal (α, β)-community model follows the concept of (α, β)-core by im- posing degree constraints for each type of vertices, and integrates the Pareto-optimality in mod- eling the weight information from two different types of vertices. An online query algorithm is developed to retrieve Pareto-optimal (α, β)-communities with the time complexity of O(p · m) where p is the number of resulting communities, and m is the number of edges in the bipartite graph G. To support efficient query processing over large graphs, we also develop index-based approaches. A complete index is proposed, and the query algorithm based on I achieves linear query processing time regarding the result size (i.e., the algorithm is optimal). Nevertheless, the index incurs prohibitively expensive space complexity. To strike a balance between query effi- ciency and space complexity, a space-efficient compact index is proposed. Computation-sharing strategies are devised to improve the efficiency of the index construction process for the index. Extensive experiments on 9 real-world graphs validate both the effectiveness and the efficiency of our query processing algorithms and indexing techniques.
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(2022)ThesisWith the increasing integration of variational renewable energy and the more active demand side responses, there are more challenges in maintaining secure and reliable power system operation due to the escalated stochasticity and variations in the system. This can be experienced from recent rolling blackouts over the world. Event-driven load shedding (ELS) serves as a fast and effective stability control scheme for power system after a risky disturbance occurs, which can suppress grid oscillation, recover system stability, and prevent cascading failure. Unlike the response-driven control schemes, ELS executes the load shedding action immediately following the disturbance, which aims to control power system stability at an earlier stage with the minimum amount of control cost. The digitalized power systems deploy advanced measurement devices such as phasor measurement units and smart meters, which provides the adequate sensing infrastructure to implement real-time stability assessment and control. However, the conventional approaches for ELS rely on numerical simulations and iterative optimizations which are computationally burdensome and thus slow reactive to the real-time system variations. More recently, artificial intelligence (AI) techniques provide a new way to realize real-time ELS owing to their fast decision making capability. This research identifies the key issues in existing AI based ELS approaches and proposes a series of novel methodologies based on deep learning techniques to enhance overall ELS performance in practical situations. A deep neural network (DNN) model is first presented to improve the decision-making accuracy on ELS strategy. Moreover, considering the unbalanced control cost induced by an over- and under-estimated ELS amount, a risk-averse learning method for DNN is proposed to increase the likelihood of control success with negligible impairment on control cost. On top of those, a GraphSAGE-based ELS model is proposed to capture and embed the topological structure of power system into deep learning, which further improves the overall control performance of ELS. The proposed methodologies have been tested on New England 39 bus system and Nordic power system. The proposed deep learning methods have shown more exceptional control performance of ELS as compared to the existing methods.
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(2022)ThesisDeep neural networks have been predominant in AI applications during the past decade. Inspired by the success of deep learning in image and text domains, graph neural networks (GNNs) have been extensively developed for graphs in various applications. There are various topics in the current study for GNNs that have raised a great interest in the research community. In this thesis, we mainly focus on two of them, explainability and semi-supervised learning for GNNs. Semi-supervised learning is a major task for GNNs and exploring the explainability of GNNs helps us to understand these models better, which also benifits GNN based semi-supervised learning. The first problem is the explainability of GNNs. Similar to all other neural network based models, GNNs suffer from the black-box problem as people cannot understand the mechanism underlying them. To solve this problem, several GNN explainability methods have been proposed to explain the decisions made by GNNs. We conducted comprehensive experimental studies of the state-of-the-art GNN explainability methods based on the existing evaluation metrics. Furthermore, we proposed a new evaluation metric and benchmark the existing GNN explainability with our proposed novel metric on real-world datasets. The second problem is the semi-supervised learning for GNNs. A majority of GNNs studies focus on semi-supervised learning due to the challenge of labeled data shortage in graph-based tasks. To address this challenge, Graph Neural Networks (GNNs) use message passing frameworks to combine information from unlabeled data with labeled data. However, the use of unlabeled data under the message passing framework is indirect in the training process where unlabeled data does not supervise the training process. To tackle this problem, we propose a novel dual-view cooperative training framework, which allows the unlabeled data to directly supervise the training process. We further use a GNN explainability method to justify our framework and provide theoretical analysis.
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(2022)ThesisChitosan is a promising material for making edible, active and biodegradable packaging films for foods; however, pure chitosan films have poor mechanical and barrier properties. This Master of Philosophy study was conducted with the aim to improve the physicochemical and biological properties of chitosan films by incorporating epoxy activated agarose (EAA) and three flavonoids, namely catechin, quercetin and luteolin into the film. Chitosan films were prepared with chitosan of three molecular weights (low, medium and high) and by drying at 21 °C, 40 °C and 50 °C. EAA and the flavonoids were incorporated into chitosan, both at 1-10%. With increased MW of chitosan, the film thickness, tensile strength (TS), elongation at break (EAB), and swelling ability increased while the moisture content, solubility, water vapor permeability (WVP) and the melting temperature declined. Higher drying temperatures led to greater TS and higher melting temperature for the films. Incorporation of the EAA significantly improved the moisture related properties and flexibility of the chitosan films. Moreover, with higher amounts of EAA, the film thickness and opacity increased while the TS and thermal stability declined. Incorporation of flavonoids had significant (type and concentration dependent) impact on the physicochemical and biological properties of chitosan films. Addition of flavonoids up to 5% resulted in films with greater TS, EAB and thermal stability, whereas at concentrations of up to 3%, the films produced had improved WVP. All the chitosan-flavonoid composite films exhibited antimicrobial activity against Listeria monocytogenes, Salmonella typhimurium, Escherichia coli and Staphylococcus aureus. Beef samples wrapped with pure chitosan or chitosan-flavonoid composite films had significantly lower microbial counts and a more reddish color after two weeks of storage at 4 °C than those packaged with cling wrap. Storage of the chitosan films at 21 °C and 4 °C for six weeks resulted in significant reductions in the TPC, TFC, antioxidant activity and the flexibility of the films, which occurred at a faster rate at 21 °C. Overall, this study demonstrated that incorporation of EAA and flavonoids at appropriate levels can significantly improve some of the physicochemical and biological properties of chitosan films.
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(2022)ThesisEye movement detection, separating the eye positions into distinct oculomotor events such as saccade and fixation, has been associated with cognitive load classification, referring to the process of estimating the mental effort involved with a certain task. However, there exist three questions remaining to be answered for wearable applications: (i) will algorithms originally developed for fixation and saccade detection from gaze positions give similar accuracy from pupil center positions, particularly when the head is not fixed?; (ii) how much improvement to the performance of cognitive load classification can be achieved by separating fixation and saccade?; and (iii) will the fixation- and saccade-related measure be affected by differing cognitive load processes from diverse task designs? Regarding the first research question, three representative saccade detection algorithms are applied to both pupil center positions and gaze positions collected with and without head movement, and their performance is evaluated against a stimulus-based ground truth under different measures. Results from a novel dataset recorded using wearable infrared cameras indicate that saccade/fixation detection using pupil center positions generally pro- vides better performance than using gaze positions with an 8.6% improvement in Cohen’s Kappa. Regarding the second and third research questions, statistical tests of several pupil-related measures extracted from all samples, fixation-only samples and saccade-only samples are evaluated for varied cognitive load levels, which indicate that pupil-related measures from fixation-only samples can be used as a substitute for those from all samples in distinguish- ing different levels of cognitive loads. From the statistical test results of several fixation- and saccade-related measures across two task types, the possibility for such measures to distinguish varied cognitive load levels, together with their trends among varied cognitive load levels are different under varied cognitive load processes. Furthermore, for the cognitive load classification systems trained with and without fixation- and saccade-related features, accuracy can be improved by 14.0%-23.4% for a random forest classifier across two different task types by including fixation and saccade-related features. In general, this thesis contributes to fixation and saccade based cognitive load classification research by demonstrating that pupil center positions can be used as an alternative to gaze positions for fixation and saccade detection in a wearable context, and moreover, fixation and saccade separation can improve the cognitive load classification performance.
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(2022)ThesisCentral nervous system diseases, including neurodegenerative diseases and brain malignancies, remain a critical medical challenge. Nanoparticle-enabled therapy and diagnosis have demonstrated promises to address brain diseases. However, one of the physical barriers is the blood-brain barrier (BBB), which largely restricts nanoparticle transport to the brain. To address this issue, many strategies have been developed. One of the important strategies is binding nanoparticles to a targeting ligand (e.g., transporter proteins) that deliver nanoparticles to brain tissues via transcytosis. However, this approach is limited by the poor specificity of transporter proteins, and none of these proteins are unique to the brain, so this can lead to undesirable efficiency in crossing the blood-brain barrier. In this thesis, I use brain microvascular endothelial cell membranes that are homologous to those in the BBB to coat iron oxide nanoparticles, preserving the complex antigenic information on the cell membranes and achieving biofunctionalization of nanomaterials. I demonstrated that the cell membrane-coated iron oxide nanoparticles have a core-shell structure, good biocompatibility, and stability. By using in vitro models, the cell membrane-coated iron oxide nanoparticle exhibited cell-specific targeting and BBB crossing efficiency.