UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
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 given thesis/dissertation in DRUM.
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item Experimental Design using Bayesian Network Simulation-based Assurance cases(2023) Gattani, Vishal; Herrmann, Jeffrey W; Systems Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Experimental design plays a critical role in ensuring the safety and reliability of systems in various domains. Bayesian belief networks (BBNs) have been widely used as a decision-making tool for probabilistic modeling and analysis of complex systems. This thesis presents an approach for using a BBN to model an assurance case and predict the likelihood of its claims. This can be used to evaluate changes to the experiments that will generate the evidence needed for the assurance case. We present two examples as case studies in the software engineering domain to demonstrate the effectiveness of our approach. The results show that our framework can effectively capture the changes in the degree of belief in a claim under uncertainties and risks associated with the experimental design and provide decision-makers with a more comprehensive understanding of the system under investigation.Item PROGNOSTICS AND SECURE HEALTH MANAGEMENT OF ANALOG CIRCUITS(2022) Khemani, Varun; Pecht, Michael G; Azarian, Michael H; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Analog circuits are a critical part of industrial circuits and systems. Estimates in the literature show that, even though analog circuits comprise less than 20% of all circuits, they are responsible for more than 80% of faults. Hence, analog circuit Prognosis and Health Management (PHM) is critical to the health of industrial circuits. There are a multitude of ways that any analog circuit can fail, which leads to proportional scaling in the number of possible fault classes with number of circuit components. Therefore, this research presents an advanced Design Of Experiments-based (DOE) approach to account for components that degrade in an individual and interacting fashion, to narrow down the number of possible fault classes under consideration. A wavelet-based deep-learning approach is developed that can localize the circuit component that is the source of degradation and predict the exact value of the degraded component. This degraded value is used in conjunction with degradation models to predict when the circuit will fail based on the source of degradation. Increasing outsourcing in the fabrication of electronic circuits has made them susceptible to the insertion of hardware trojans by untrusted foundries. In many cases, hardware trojans are more destructive than software trojans as they cannot be remedied by a software patch and are impossible to repair. Process reliability trojans are a new class of hardware trojans that are inserted through modification of fabrication parameters and accelerate the aging of circuit components. They are challenging to detect through traditional trojan detection methods as they have zero area footprint i.e., require no insertion of additional circuitry. The PHM approach is modified to detect these hardware trojans in order to incorporate circuit security, resulting in the Prognosis and Secure Health Management (PSHM) framework. Deep neural networks achieve state-of-the-art performance on classification and regression applications but are a black-box approach, which is a concern for implementation. Wavelets are approximations of cells found in the human visual cortex and cochlea. They were used to develop wavelet scattering networks (WSNs), which were intended to be an interpretable alternative to deep neural networks. WSNs achieve state-of-the-art performance on low to moderately complex datasets but are inferior to deep neural networks for extremely complex datasets. Improvements are made to WSNs to overcome their shortcomings in terms of performance and learnability. Further applications of the research are highlighted for rotating machinery vibration analytics, functional safety online estimation etc.Item An Agent-Based Modeling Approach to Reducing Pathogenic Transmission in Medical Facilities and Community Populations(2012) Barnes, Sean; Golden, Bruce; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The spread of infectious diseases is a significant and ongoing problem in human populations. In hospitals, the cost of patients acquiring infections causes many downstream effects, including longer lengths of stay for patients, higher costs, and unexpected fatalities. Outbreaks in community populations cause more significant problems because they stress the medical facilities that need to accommodate large numbers of infected patients, and they can lead to the closing of schools and businesses. In addition, epidemics often require logistical considerations such as where to locate clinics or how to optimize the distribution of vaccinations and food supplies. Traditionally, mathematical modeling is used to explore transmission dynamics and evaluate potential infection control measures. This methodology, although simple to implement and computationally efficient, has several shortcomings that prevent it from adequately representing some of the most critical aspects of disease transmission. Specifically, mathematical modeling can only represent groups of individuals in a homogenous manner and cannot model how transmission is affected by the behavior of individuals and the structure of their interactions. Agent-based modeling and social network analysis are two increasingly popular methods that are well-suited to modeling the spread of infectious diseases. Together, they can be used to model individuals with unique characteristics, behavior, and levels of interaction with other individuals. These advantages enable a more realistic representation of transmission dynamics and a much greater ability to provide insight to questions of interest for infection control practitioners. This dissertation presents several agent-based models and network models of the transmission of infectious diseases at scales ranging from hospitals to networks of medical facilities and community populations. By employing these methods, we can explore how the behavior of individual healthcare workers and the structure of a network of patients or healthcare facilities can affect the rate and extent of hospital-acquired infections. After the transmission dynamics are properly characterized, we can then attempt to differentiate between different types of transmission and assess the effectiveness of infection control measures.