Theses and Dissertations from UMD
Permanent URI for this communityhttp://hdl.handle.net/1903/2
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
More information is available at Theses and Dissertations at University of Maryland Libraries.
Browse
33 results
Search Results
Item Automated Management of Network Slices with Service Guarantees(2024) Nikolaidis, Panagiotis; Baras, John; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Future mobile networks are expected to support a diverse set of applications including high-throughput video streaming, delay-sensitive augmented reality applications, and critical control traffic for autonomous driving. Unfortunately, existing networks do not have the required management mechanisms to handle this complex mix of traffic efficiently. At the same time, however, there is a significant effort from both industry and academia to make networks more open and programmable, leading to the emergence of software-defined networking, network function virtualization, and packet-forwarding programming languages. Moreover, several organisations such as the Open Networking Foundation were founded to facilitate innovation and lower the entry barriers in the mobile networking industry. In this setting, the concept of network slicing emerged which involves the partitioning of the mobile network into virtual networks that are tailored for specific applications. Each network slice needs to provide premium service to its users as specified in a service level agreement between the mobile network operator and the customer. The deployment of network slices has been largely realized thanks to network function virtualization. However, little progress has been made on mechanisms to efficiently share the network resources among them. In this dissertation, we develop such mechanisms for the licensed spectrum at the base station, a scarce resource that operators obtain through competitive auctions. We propose a system architecture composed of two new network functions; the bandwidth demand estimator and the network slice multiplexer. The bandwidth demand estimator monitors the traffic of the network slice and outputs the amount of bandwidth currently needed to deliver the desired quality of service. The network slice multiplexer decides which bandwidth demands to accept when the available bandwidth does not suffice for all the network slices. A key feature of this architecture is the separation of the demand estimation task from the contention resolution task. This separation makes the architecture scalable for a large number of network slices. It also allows the mobile network operator to charge fairly each customer based on their bandwidth demands. In contrast, the most common approach in the literature is to learn online how to split the available resources among the slices to maximize a total network utility. However, this approach is neither scalable nor suitable for service level agreements. The dissertation contributes several algorithms to realize the proposed architecture and provisioning methods to guarantee the fulfillment of the service level agreements. To satisfypacket delay requirements, we develop a bandwidth demand estimator based on queueing theory and online learning. To share resources efficiently even in the presence of traffic anomalies, we develop a network slice multiplexer based on the Max-Weight algorithm and hypothesis testing. We implement and test the proposed algorithms on network simulators and 5G testbeds to showcase their efficiency in realistic settings. Overall, we present a scalable architecture that is robust to traffic anomalies and reduces the bandwidth needed to serve multiple network slices.Item Learning Metareasoning Policies for Motion Planning(2023) GOPAL, SIDDHARTH; Herrmann, Jeffrey W; Systems Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Metareasoning is the process of reasoning about reasoning. This thesis applies metareasoning to motion planning and evaluates three different metareasoning policies. Two policies are rule-based policies and are human specified. The third policy is a smart metareasoning policy that learns from the robot's past experiences, particularly the front camera images. The data is obtained by running the robot without a metareasoner in modular test scenarios which can be combined to form multiple real-world situations. The policy is stored in the form of the weights of a neural network. The neural network-based model used for this research is a multi-input classifier that chooses an optimal planner combination from amongst eight different planner combinations. The metareasoners are tested on a Unity simulator with a Clearpath Warthog ground robot. This thesis tests the performance of the robot under eight different test scenarios for eight different planner combinations and shows an improvement in the robot's success rate when using a metareasoner. Lastly, this thesis also provides a comparative study between a rule-based metareasoner and a smart metareasoner by introducing two new test scenarios which are not part of the robot's past experiences.Item Improving the Foundational Knowledge of Dependency in Human Reliability Analysis(2023) Paglioni, Vincent Philip; Groth, Katrina M; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Human reliability analysis (HRA) is the field tasked with understanding, modeling, and quantifying the human contribution to the reliability of complex engineering systems. Human machine teams (HMTs, the groups of operators and human-system interface technologies that together control a system) currently contribute to over 60% of industrial accidents and will continue to servean important operational role in complex engineering systems. As a result, it is critical to develop robust methods for characterizing HMT performance and reliability. One of the factors limiting the technical basis of HRA is the treatment of dependency, how task performances and influencing factors are causally connected. Currently, HRA does not have a sound framework for conceptualizing, modeling, or quantifying dependency. The concept of dependency is poorly defined, the modeling is lacks a causal basis, and the quantification of dependency is unsupported by literature or data. This research closes these gaps in the foundations of HRA dependency by enforcing a rigorous, quantitative causal basis for the conceptualization and modeling of dependency. First, this research addresses the definitional and conceptual foundations of HRA dependency to provide a consistent technical basis for the field. This work proposes a single, complete, and appropriate definition for the general concept of dependency; one that is rooted in causality. This research also provides definitions for dependency-related concepts from multiple fields including probability, statistics, and set theory. The definitional basis laid out by this work standardizes the foundations of the field and promotes the ability to more easily translate between previously disparate HRA methods. Second, this work develops the causal structure of dependency in HRA. Whereas current methods for dependency modeling in HRA focus on correlational attributes, this method recognizes that causality, not correlation, is the driving mechanism of dependency. This work identifies six distinct relationship archetypes (idioms) that describe the general dependency relationships possible between HRA variables. Furthermore, this work creates the graphical structures that describe the idioms using Bayesian Networks (BNs) as the modeling architecture. The task/function-level idiom structures created in this work provide robust, traceable models of dependency relationships that can be used to both build HRA models and decompose full models into more understandable pieces. Third, this work develops the methodology to build and quantify causal, formative dependency BN HRA models using the idiom structures and HRA data. Whereas many HRA methods rely on expert elicitation alone for assigning probabilities, this methodology quantifies the network directly from HRA data. The methodology developed in this work produces a full, causal, formative dependency scenario model without requiring expert elicitation of probabilities. This methodology is implemented to build and quantify a scenario model using real HRA data collected from operator crews working in a full-scope nuclear reactor simulator, which shows both that causal dependency can be modeled and quantified, and that the methodology is traceable and useful. Finally, this work develops a set of recommendations for the collection, storage, and use of HRA data, and for the implementation of this methodology within mature HRA frameworks. This dissertation will improve our knowledge of, and ability to model, dependency in human reliability.Item SENSING AND CONTROL UNDER RESOURCE CONSTRAINTS AND UNCERTAINTY: RISK NEUTRAL AND RISK SENSITIVE APPROACHES(2022) Hartman, David; Baras, John S; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In network estimation and control systems like sensor networks or industrial robotic systems,there are often restrictions or uncertainties that must be taken into account. For example, there are often bandwidth and communication constraints on the estimators or controllers. Additionally, the dynamics model is not always known. Lastly, noise or exogenous disturbances can adversely affect your system. This thesis addresses three problems in sensing and control in both the H2 and risk-sensitivecontrol setting. The first problem stems from restrictions on the communications and battery life of sensors. Because of these restrictions, when estimating a state in a system we must cleverly schedule which sensors can be active. The second problem also stems from communication restrictions. In this setting, the sensors and actuators can only communicate with a small number of neighboring sensors. Therefore, we must solve a distributed control problem. The third problem stems from the dynamics of a system being unknown. In this regard, we must solve a control problem using simulated data instead of a fixed model. The research in this thesis, utilizes tools from optimization, estimation, control, and dynamic programming.Item DEVELOPMENT OF A RELIABILITY DATA COLLECTION FRAMEWORK FOR HYDROGEN FUELING STATION QRA(2021) West, Madison; Groth, Katrina M; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The wider adoption of hydrogen in multiple sectors of the economy requires that safety and risk issues be rigorously investigated. Quantitative Risk Assessment (QRA) is an important tool for enabling safe deployment of hydrogen fueling stations and is increasingly embedded in the permitting process. However, QRA needs reliability data, and currently the available hydrogen safety databases are not in a format conducive for use in QRA. A review of the International Journal of Hydrogen Energy articles on hydrogen fueling station QRA found that lack of hydrogen reliability data is the most common knowledge gap in this field. This study explores what QRA and reliability data currently look like in the context of hydrogen systems. It then presents a new reliability data collection framework for hydrogen systems that overcomes gaps in existing hydrogen safety databases. Current hydrogen safety data collection tools, H2Tools, HIAD, NREL CDPs, and CHS are analyzed and compared for applicability to QRA. Lessons learned from these data collection tools are extracted and combined with best practices from reliability engineering to create an improved database framework for hydrogen reliability data. This framework aims to standardize the hydrogen fueling stations component hierarchy, failure mode taxonomy, and outline high level elements necessary for adequate reliability data collection suitable for use in QRA. This research establishes the groundwork for a collaborative hydrogen reliability database and the future development of data driven hydrogen safety tools.Item Systematic Integration of PHM and PRA (SIPPRA) for Risk and Reliability Analysis of Complex Engineering Systems(2021) Moradi, Ramin; Groth, Katrina; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Complex Engineering Systems (CES) such as power plants, process plants, and manufacturing plants have numerous, interrelated, and heterogeneous subsystems with different characteristics and risk and reliability analysis requirements. With the advancements in sensing and computing technology, abundant monitoring data is being collected. This is a rich source of information for more accurate assessment and management of these systems. The current risk and reliability analysis approaches and practices are inadequate in incorporating various sources of information, providing a system-level perspective, and performing a dynamic assessment of the operation condition and operation risk of CES. In this dissertation, this challenge is addressed by integrating techniques and models from two of the major subfields of reliability engineering: Probabilistic Risk Assessment (PRA) and Prognostics and Health Management (PHM). PRA is very effective at modeling complex hardware systems, and approaches have been designed to incorporate the risks introduced by humans, software, organizational, and other contributors into quantitative risk assessments. However, PRA has largely been used as a static technology mainly used for regulation. On the other hand, PHM has developed powerful new algorithms for understanding and predicting mechanical and electrical device health to support maintenance. Yet, PHM lacks the system-level perspective, relies heavily on operation data, and its outcomes are not risk-informed. I propose a novel framework at the intersection of PHM and PRA which provides a forward-looking, model- and data-driven analysis paradigm for assessing and predicting the operation risk and condition of CES. I operationalize this framework by developing two mathematical architectures and applying them to real-world systems. The first architecture is focused on enabling online system-level condition monitoring. The second architecture improves upon the first and realizes the objectives of using various sources of information and monitoring operation condition together with operational risk.Item Optimization of Signal Routing in Disruption-Tolerant Networks(2021) Singam, Caitlyn; Ephremides, Anthony; Systems Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Communication networks are prone to disruption due to inherent uncertainties such as environmental conditions, system outages, and other factors. However, current state-of-the-art communication protocols are not yet optimized for communication in highly disruption-prone environments, such as deep space, where the risk of such uncertainties is not negligible. This work involves the development of a novel protocol for disruption-tolerant communication across space-based networks that avoids idealized assumptions and is consistent with system limitations. The proposed solution is grounded in an approach to information as a time-based commodity, and on reframing the problem of efficient signal routing as a problem of value optimization. The efficacy of the novel protocol was evaluated via a custom Monte Carlo simulation against other state-of-the-art protocols in terms of maintaining both data integrity and transmission speed, and was found to provide a consistent advantage across both metrics of interest.Item RIKA RESILIENCE: INFORMING SUSTAINABILITY IN THE AGE OF SOCIAL MEDIA(2019) Warrick, Elizabeth Muthoni; Preece, Jennifer; Library & Information Services; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The consensus in systems science is that environmental sustainability ensues from resilience, i.e., underlying capacity for preservation of core functions through adaptation in changed circumstances; and resilience itself is only sustainable when built from internal attributes of the system. Impalpability of internal resilience to external agents contributes to failures of global development in Africa, calling for analysis informed from within. This work proposes that African resilience is founded in Rika. Within Rika, ecological stewardship is integrated with noncompetitive elected representative governance and achieved through scaled modulation of systemic diversities. Eroded at macro level, Rika continues to drive grassroots enterprise. Causal attributes are, therefore, key to understanding sustainability and effecting structural reform of governance at all levels in Africa. Documented international usage of Rika concepts and terminologies has significance for research linking global expansion of Homo sapiens to the development of conceptual thinking in East Africa. Findings are based in research with the Mbeere of Kenya, East Africa, a community of 195,000, whose name Mbeere, means First Peoples. Data extracted regionally from 750,000 social media users informs context. From an indigenist method-as-theory stance (Indigenist Maths), we leverage qualitative and quantitative tools, bolstering capacity of research and practice to serve indigenous goals at the intersection of social media and Place. A dynamic indigenous information world (iWorld) ensues through community interactions interconnecting local knowledge with global information to foster economic enterprise and social ecological stewardship. We term this iWorld, Rikamedia. Examples of resilience attributes emerging from the data include: the Rika ideal of non-competitive governance, potentially impactful of conflicting democratic ideals centered in competitive governance; transcendence of natural hierarchies through unambiguous reciprocated interactions from micro to macro levels of society; design for participatory diversity, equality and inclusion with impacts on systemic divides of gender, age, and access, etc.; and lastly, a learning modality aligns governance with participatory process, emboldens risk tolerance; nurtures diversities and fosters innovation. An entrepreneurial micropilot Bamboo project ensues from community-researcher interactions, with recommendations for agroforestry citizen science, technology, funding, and diaspora capacitation. Findings are scalable in Africa South of the Sahara, and may have significance for resilience when projects incorporate Rika attributes in sustainability planning.Item Semantic Modeling and Control of Urban Water Supply Networks(2020) Peng, Zebo; Austin, Mark A.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Water resources play a central role in the operation of urban systems. Present-day challenges in the use of water resources include over reliance on human-centered system management, and lack of formal approaches to decision-making support. In a step toward mitigating these challenges, the goals of this project are two-fold: first, we explore use of semantic modeling and rule-based reasoning a means to control operations in the water network system operation. The second purpose of this project is to explore opportunities for extending the logic of EPANET software simulation operations to include reasoning that takes into account factors beyond the water network. The case studies integrate time-history simulation and semantic modeling.Item MODELING AND SIMULATION OF NOVEL MEDICAL RESPONSE SYSTEMS FOR OUT-OF-HOSPITAL CARDIAC ARREST(2020) Lancaster, Greg James; Herrmann, Jeffrey W; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Sudden Cardiac Arrest (SCA) is the leading cause of death in the United States, resulting in 350,000 deaths annually. SCA survival requires immediate medical treatment with a defibrillatory shock and cardiopulmonary resuscitation. The fatality rate for out-of-hospital cardiac arrest is 90%, due in part to the reliance on Emergency Medical Services (EMS) to provide treatment. A substantial improvement in survival could be realized by applying early defibrillation to cardiac arrest victims. Automated External Defibrillators (AEDs) allow lay rescuers to provide early defibrillation, before the arrival of EMS. However, very few out-of-hospital cardiac arrests are currently treated with AEDs. Novel response concepts are being explored to reduce the time to defibrillation. These concepts include mobile citizen responders dispatched by a cell phone app to nearby cardiac arrest locations, and the use of drones to deliver AEDs to a cardiac arrest scene. A small number of pilot studies of these systems are currently in progress, however, the effectiveness of these systems remains largely unknown. This research presents a modeling and simulation approach to predict the effectiveness of various response concepts, with comparison to the existing standard of EMS response. The model uses a geospatial Monte Carlo sampling approach to simulate the random locations of a cardiac arrest within a geographical region, as well as both random and fixed origin locations of responding agents. The model predicts response time of EMS, mobile dispatched responders, or drone AED delivery, based on the distance travelled and the mode of transit, while accounting for additional system factors such as dispatch time, availability of equipment, and the reliability of the responders. Response times are translated to a likelihood of survival for each simulated case using a logistic regression model. Sensitivity analysis and response surface designed experiments were performed to characterize the important factors for response time predictions. Simulations of multiple types of systems in an example region are used to compare potential survival improvements. Finally, a cost analysis of the different systems is presented along with a decision analysis approach, which demonstrates how the method can be applied based on the needs and budgets of a municipality.