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.
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Item Design and Characterization of Additively Manufactured Lightweight Metal Structures with Equivalent Compliance and Fatigue Resistance(2021) Santos, Luis S; Bruck, Hugh A; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Additive Manufacturing (AM) has been a disruptive manufacturing technology allowing for control of geometric features and material distributions, potentially starting at the atomistic level, to realize structures with lighter weights. However, it is still begin used primarily as a rapid prototyping tool due to challenges arising from various issues that need to be addressed before commercial parts can be deployed. Three of those issues are: (1) characterization of mechanical properties that may vary spatially, (2) identification of novel defects in the parts, and (3) new design approaches that account for the unique capabilities of AM processes and their impact on fatigue resistance.This dissertation addresses these three issues by developing a cyclical indentation technique to characterize the fatigue properties of geometric features only capable with AM. The method produces the degradation of the material stiffness as the number of cyclic loads increases and is capable of generating an entire S-N curve with a single test at sub-millimeter scales. Geometric features are then analyzed by running a thermal and mechanical simulation of a Direct Metal Laser Sintering (DMLS) printing process. The new simulation can account for buckling of features with high aspect ratios, such as low percentage infills or high levels of unit cell porosity, and predicts distortions with less than 5% error. This computational approach is useful for analyzing parts before printing and informs designers about regions in the part that may need modification to prevent buckling. Finally, the experimental and computational techniques are combined to design structures with macroscale topological features and microscale unit cell features that are fatigue resistant.Item THE COMPUTATION OF VERB-ARGUMENT RELATIONS IN ONLINE SENTENCE COMPREHENSION(2020) Liao, Chia-Hsuan; Lau, Ellen; Linguistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Understanding how verbs are related to their arguments in real time is critical to building a theory of online language comprehension. This dissertation investigates the incremental processing of verb-argument relations with three interrelated approaches that use the event-related potential (ERP) methodology. First, although previous studies on verb-argument computations have mainly focused on relating nouns to simple events denoted by a simple verb, here I show by investigating compound verbs I can dissociate the timing of the subcomputations involved in argument role assignment. A set of ERP experiments in Mandarin comparing the processing of resultative compounds (Kid bit-broke lip: the kid bit his lip such that it broke) and coordinate compounds (Store owner hit-scolded employee: the store owner hit and scolded an employee) provides evidence for processing delays associated with verbs instantiating the causality relation (breaking-BY-biting) relative to the coordinate relation (hitting-AND-scolding). Second, I develop an extension of classic ERP work on the detection of argument role-reversals (the millionaire that the servant fired) that allows me to determine the temporal stages by which argument relations are computed, from argument identification to thematic roles. Our evidence supports a three-stage model where an initial word association stage is followed by a second stage where arguments of a verb are identified, and only at a later stage does the parser start to consider argument roles. Lastly, I investigate the extent to which native language (L1) subcategorization knowledge can interfere with second language (L2) processing of verb-argument relations, by examining the ERP responses to sentences with verbs that have mismatched subcategorization constraints in L1 Mandarin and L2 English (“My sister listened the music”). The results support my hypothesis that L1 subcategorization knowledge is difficult for L2 speakers to override online, as they show some sensitivity to subcategorization violations in offline responses but not in ERPs. These data indicate that computing verb-argument relations requires accessing lexical syntax, which is vulnerable to L1 interference in L2. Together, these three ERP studies allow us to begin to put together a full model of the sub-processes by which verb-argument relations are constructed in real time in L1 and L2.Item AVIATION CONGESTION MANAGEMENT IMPROVEMENTS IN MODELING THE PREDICTION, MITIGATION, AND EVALUATION OF CONGESTION IN THE NATIONAL AIRSPACE SYSTEM(2014) Vlachou, Kleoniki; Lovell, David J.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The air transportation system in the United States is one of the most complex systems in the world. Projections of increasing air traffic demand in conjunction with limited capacity, that is volatile and affected by exogenous random events, represent a major problem in aviation system management. From a management perspective, it is essential to make efficient use of the available resources and to create mechanisms that will help alleviate the problems of the imbalance between demand and capacity. Air traffic delays are always present and the more air traffic increases the more the delays will increase with very unwanted economic impacts. It is of great interest to study them further in order to be able to more effectively mitigate them. A first step would be to try to predict them under various circumstances. A second step would be to develop various mechanisms that will help in reducing delays in different settings. The scope of this dissertation is to look closer at a threefold approach to the problem of congestion in aviation. The first effort is the prediction of delays and the development of a model that will make these predictions under a wide variety of distributional assumptions. The work presented here is specifically on a continuum approximation using diffusion methods that enables efficient solutions under a wide variety of distributional assumptions. The second part of the work effort presents the design of a parsimonious language of exchange, with accompanying allocation mechanisms that allow carriers and the FAA to work together quickly, in a Collaborative Decision Making environment, to allocate scarce capacity resources and mitigate delays. Finally, because airlines proactively use longer scheduled block times to deal with unexpected delays, the third portion of this dissertation presents the assessment of the monetary benefits due to improvements in predictability as manifested through carriers' scheduled block times.Item Authority Flow-based Ranking in Heterogeneous Networks: Prediction, Personalization, and Learning to Rank(2014) Sayyadi Harikandehei, Hassan; Raschid, Louiqa; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Many real-world datasets, including biological networks, the Web, and social media, can be effectively modeled as networks or graphs, in which nodes represent entities of interest and links mimic the interactions or relationships among them. Such networks often contain multiple entity or relationship types, which are referred to as heterogeneous networks. Networks also evolve due to the existence of temporal features that characterize the entities or to the temporal relationships among them. Finding important/authoritative entities in real-world networks is a long-standing and well-defined challenge. In this dissertation, I focus on two variants of the problem. The first is the prediction of the ranking of scientific publications in a future state of a citation network. I introduce a new measure labeled the future PageRank score. I develop FutureRank, a prediction algorithm for predicting the future PageRank scores from the historical network structure, and evaluate the FutureRank algorithm on multiple bibliographic dataset. Next, I focus on personalized ranking in social media. I extend a social media dataset to include relationships (edge types) between authors, blog posts, categories (topics) of the posts, and events (collections of posts). I then apply personalized ranking algorithms over the historical posts and events that have been visited by a user and use the ranking to recommend additional posts. I evaluate the personalized recommendations through an experiment with real users, as well as an extensive study of synthetic users whose preferences are defined based on intuitive criteria. Finally, I present an approach for learning to rank (algorithms) applied to heterogeneous networks. Existing methods for learning to rank are typically limited to content-based features, while many real world problems correspond to relational features. I develop a framework for learning to rank, which targets authority flow-based ranking models on heterogeneous networks. I propose algorithms for both pointwise and pairwise learning. However, this framework can easily utilize any loss function from a non-relational learning domain. Experiments show that even with a small amount of training data, both pointwise and pairwise algorithms perform successfully and converge very fast. In addition, these solutions are shown to be robust against noise.Item The Temporal Dimension of Linguistic Prediction(2013) Chow, Wing Yee; Phillips, Colin; Linguistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This thesis explores how predictions about upcoming language inputs are computed during real-time language comprehension. Previous research has demonstrated humans' ability to use rich contextual information to compute linguistic prediction during real-time language comprehension, and it has been widely assumed that contextual information can impact linguistic prediction as soon as it arises in the input. This thesis questions this key assumption and explores how linguistic predictions develop in real-time. I provide event-related potential (ERP) and reading eye-movement (EM) evidence from studies in Mandarin Chinese and English that even prominent and unambiguous information about preverbal arguments' structural roles cannot immediately impact comprehenders' verb prediction. I demonstrate that the N400, an ERP response that is modulated by a word's predictability, becomes sensitive to argument role-reversals only when the time interval for prediction is widened. Further, I provide initial evidence that different sources of contextual information, namely, information about preverbal arguments' lexical identity vs. their structural roles, may impact linguistic prediction on different time scales. I put forth a research framework that aims to characterize the mental computations underlying linguistic prediction along a temporal dimension.Item Predictive Analytics Lead to Smarter Self-Organizing Directional Wireless Backbone Networks(2013) Coleman, David M.; Davis, Christopher C; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Directional wireless systems are becoming a cost-effective approach towards providing a high-speed, reliable, broadband connection for the ubiquitous mobile wireless devices in use today. The most common of these systems consists of narrow-beam radio frequency (RF) and free-space-optical (FSO) links, which offer speeds between 100Mbps and 100Gbps while offering bit-error-rates comparable to fixed fiber optic installations. In addition, spatial and spectral efficiencies are accessible with directional wireless systems that cannot be matched with broadcast systems. The added benefits of compact designs permit the installation of directional antennas on-board unmanned autonomous systems (UAS) to provide network availability to regions prone to natural disasters, in maritime situations, and in war-torn countries that lack infrastructure security. In addition, through the use of intelligent network-centric algorithms, a flexible airborne backbone network can be established to dodge the scalability limitations of traditional omnidirectional wireless networks. Assuring end-to-end connectivity and coverage is the main challenge in the design of directional wireless backbone (DWB) networks. Conflating the duality of these objectives with the dynamical nature of the environment in which DWB networks are deployed, in addition to the standardized network metrics such as latency-minimization and throughput maximization, demands a rigorous control process that encompasses all aspects of the system. This includes the mechanical steering of the directional point-to-point link and the monitoring of aggregate network performance (e.g. dropped packets). The inclusion of processes for topology control, mobility management, pointing, acquisition, and tracking of the directional antennas, alongside traditional protocols (e.g. IPv6) provides a rigorous framework for next-generation mobile directional communication networks. This dissertation provides a novel approach to increase reliability in reconfigurable beam-steered directional wireless backbone networks by predicating optimal network reconfigurations wherein the network is modeled as a giant molecule in which the point-to-point links between two UASs are able to grow and retract analogously to the bonds between atoms in a molecule. This cross-disciplinary methodology explores the application of potential energy surfaces and normal mode analysis as an extension to the topology control optimization. Each of these methodologies provides a new and unique ability for predicting unstable configurations of DWB networks through an understanding of second-order principle dynamics inherent within the aggregate configuration of the system. This insight is not available through monitoring individual link performance. Together, the techniques used to model the DWB network through molecular dynamics are referred to as predictive analytics and provide reliable results that lead to smarter self-organizing reconfigurable beam-steered DWB networks. Furthermore, a comprehensive control architecture is proposed that complements traditional network science (e.g. Internet protocol) and the unique design aspects of DWB networks. The distinct ability of a beam-steered DWB network to adjust the direction of its antennas (i.e. reconfigure) in response to degraded effects within the atmosphere or due to an increased separation of nodes, is not incorporated in traditional network processes such re-routing mechanism, and therefore, processes for reconfiguration can be abstracted which both optimize the physical interconnections while maintaining interoperability with existing protocols. This control framework is validated using network metrics for latency and throughput and compared to existing architectures which use only standard re-routing mechanisms. Results are shown that validate both the analogous molecular modeling of a reconfigurable beam-steered directional wireless backbone network and a comprehensive control architecture which coalesces the unique capabilities of reconfiguration and mobility of mobile wireless backbone networks with existing protocols for networks such as IPv6.Item Quantitative Prediction of Tip-Sample Repulsive Forces and Sample Deformation in Tapping-Mode Frequency and Force Modulation Atomic Force Microscopy(2008-08-27) Crone, Joshua C; Solares, Santiago D; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The ability to predict sample deformation and the resultant interaction forces is a vital component to preventing sample damage and acquiring accurate height traces in atomic force microscopy (AFM). By using the recently developed frequency and force modulation (FFM) control scheme, a prediction method is developed by coupling previously developed analytical work with numerical integration of the equation of motion for the AFM tip. By selecting a zero resonance frequency shift, the sample deformation is found to depend only on those parameters defining the tip-sample interaction forces. The results are represented graphically and through a multiple regression model so that the user can predict the tip penetration and maximum repulsive force with knowledge of the maximum attractive force and steepness of the repulsive regime in the tip-sample interaction force curve. The prediction model is shown to be accurate for a wide range of imaging conditions.Item A New Physics-of-Failure Based VLSI Circuits Reliability Simulation and Prediction Methodology(2007-08-27) QIN, JIN; Bernstein, Joseph B; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)It has long been a challenge for reliability engineers to provide accurate VLSI circuits reliability simulation and prediction. The decreasing feature sizes, coupled with non-ideal voltage scaling, raises new reliability concerns such as negative bias temperature instability (NBTI) and adversely affects those long-existed failure mechanisms: electromigration (EM), hot carrier degradation (HCD) and time dependent dielectric breakdown (TDDB). The multiple failure mechanisms effect, together with the increasing circuit complexity make the prediction more difficult to tackle with. A new physics-of-failure based VLSI circuit reliability prediction methodology is proposed to handle the simulation and prediction challenges. The new methodology takes an unique top-down, bottom-up approach to reduce the modeling and simulation complexity. Detailed application breakdown reveals the cell's operation profile. Cell-level reliability characterization provides accurate operation-based dynamic stress modeling by utilizing the physics-of-failure models. For each failure mechanism, the best-fit lifetime distribution is selected to provide reliability prediction. The application-specific circuit reliability is further predicted by considering the system structure. A 90nm 64Kb SRAM module is designed and used as an example to demonstrate the prediction methodology. With the given application profile, simulation results showed that TDDB is the most serious reliability concern for the SRAM bit cell, NBTI is in the second place, and HCD has a negligible degradation effect. The memory core's reliability prediction shows the core has a low constant failure rate (2.90E-4 FIT) before 5.8E+4 hours, and an increasing failure rate after that because NBTI wearout starts to kick in.Item A Reliable Travel Time Prediction System With Sparsely Distributed Detectors(2007-05-22) Zou, Nan; Chang, Gang-Len; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This study aims to develop a travel time prediction system that needs only a small number of reliable traffic detectors to perform accurate real-time travel time predictions under recurrent traffic conditions. To ensure its effectiveness, the proposed system consists of three principle modules: travel time estimation module, travel time prediction module, and the missing data estimation module. The travel time estimation module with its specially designed hybrid structure is responsible for estimating travel times for traffic scenarios with or without sufficient field observations, and for supplying the estimated results to support the prediction module. The travel time prediction module is developed to take full advantage of various available information, including historical travel times, geometric features, and daily/weekly traffic patterns. It can effectively deal with various traffic patterns with its multiple embedded models, including the primary module of a multi-topology Neural Network model with a rule-based clustering function and the supplemental module of an enhanced k-Nearest Neighbor model. To contend with the missing data issue, which occurs frequently in any real-world system, this study incorporates a missing data estimation module in the travel time prediction system, which is based on the multiple imputation technique to estimate both the short- and long-term missing traffic data so as to avoid interrupting the operations. The system developed in this study has been implemented with data from 10 roadside detectors on a 25-mile stretch of I-70 eastbound, and its performance has been tested against actual travel time data collected by an independent evaluation team. Results of extensive evaluation have indicated that the developed system is capable of generating reliable prediction of travel times under various types of traffic conditions and outperforms both state-of-the-practice and state-of-the-art models in the literature. Its embedded missing data estimation models also top existing methods and are able to maintain the prediction system under a reliable state when one of its detectors at a key location experience the data missing rate from 20% to 100% during uncongested, congested and transition periods.Item Making Forecasts for Chaotic Processes in the Presence of Model Error(2006-02-20) Danforth, Christopher M; Yorke, James A; Kalnay, Eugenia; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Numerical weather forecast errors are generated by model deficiencies and by errors in the initial conditions which interact and grow nonlinearly. With recent progress in data assimilation, the accuracy in the initial conditions has been substantially improved so that accounting for systematic errors associated with model deficiencies has become even more important to ensemble prediction and data assimilation applications. This dissertation describes two new methods for reducing the effect of model error in forecasts. The first method is inspired by Leith (1978) who proposed a statistical method to account for model bias and systematic errors linearly dependent on the flow anomalies. DelSole and Hou (1999) showed this method to be successful when applied to a very low order quasi-geostrophic model simulation with artificial "model errors." However, Leith's method is computationally prohibitive for high-resolution operational models. The purpose of the present study is to explore the feasibility of estimating and correcting systematic model errors using a simple and efficient procedure that could be applied operationally, and to compare the impact of correcting the model integration with statistical corrections performed a posteriori. The second method is inspired by the dynamical systems theory of shadowing. Making a prediction for a chaotic physical process involves specifying the probability associated with each possible outcome. Ensembles of solutions are frequently used to estimate this probability distribution. However, for a typical chaotic physical system H and model L of that system, no solution of L remains close to H for all time. We propose an alternative and show how to "inflate" or systematically perturb the ensemble of solutions of L so that some ensemble member remains close to H for orders of magnitude longer than unperturbed solutions of L. This is true even when the perturbations are significantly smaller than the model error.