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.
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Item The Role of 3D Spatiotemporal Telemetry Analysis in Combat Flight Simulation(2024) Mane, Sourabh Vijaykumar; Elmqvist, Niklas Dr; Library & Information Services; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Analyzing 3D telemetry data collected from competitive video games on the internet can support players in improving performance as well as spectators in viewing data-driven narratives of the gameplay. In this thesis, we conduct an in-depth qualitative study on the use of telemetry analysis by embedding over several weeks in a virtual F-14A Tomcat squadron in the multiplayer combat flight simulator DCS World (DCS) (2008). Based on formative interviews with DCS pilots, we design a web-based game analytics framework for rendering 3D telemetry from the flight simulator in a live 3D player, incorporating many of the data displays and visualizations requested by the participants. We then evaluate the framework with real mission data from several air-to-air engagements involving the virtual squadron. Our findings highlight the key role of 3D telemetry playback in competitive multiplayer gaming.Item STATISTICAL DATA FUSION WITH DENSITY RATIO MODEL AND EXTENSION TO RESIDUAL COHERENCE(2024) Zhang, Xuze; Kedem, Benjamin; Mathematical Statistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Nowadays, the statistical analysis of data from diverse sources has become more prevalent. The Density Ratio Model (DRM) is one of the methods for fusing and analyzing such data. The population distributions of different samples can be estimated basedon fused data, which leads to more precise estimates of the probability distributions. These probability distributions are related by assuming the ratios of their probability density functions (PDFs) follow a parametric form. In the previous works, this parametric form is assumed to be uniform for all ratios. In Chapter 1, an extension is made to allow this parametric form to vary for different ratios. Two methods of determining the parametric form for each ratio are developed based on asymptotic test and Akaike Information Criterion (AIC). This extended DRM is applied to Radon concentration and Pertussis rates to demonstrate the use of this extension in univariate case and multivariate case, respectively. The above analysis is made possible when data in each sample are independent and identically distributed (IID). However, in many cases, statistical analysis is entailed for time series in which data appear to be sequentially dependent. In Chapter 2, an extension is made for DRM to account for weakly dependent data, which allows us to investigate the structure of multiple time series on the strength of each other. It is shown that the IID assumption can be replaced by proper stationarity, mixing and moment conditions. This extended DRM is applied to the analysis of air quality data which are recorded in chronological order. As mentioned above, DRM is suitable for the situation where we investigate a single time series based on multiple alternative ones. These time series are assumed to be mutually independent. However, in time series analysis, it is often of interest to detect linear and nonlinear dependence between different time series. In such dependent scenario, coherence is a common tool to measure the linear dependence between two time series, and residual coherence is used to detect a possible quadratic relationship. In Chapter 3, we extend the notion of residual coherence and develop statistical tests for detecting linear and nonlinear associations between time series. These tests are applied to the analysis of brain functional connectivity data.Item Scalable Techniques for Behavioral Analysis and Forecasting(2011) Sliva, Amy; Subrahmanian, V.S.; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The ability to model, forecast, and analyze the behaviors of other agents has applications in many diverse contexts. For example, behavioral models can be used in multi-player games to forecast an opponent's next move, in economics to forecast a merger decision by a CEO, or in international politics to predict the behavior of a rival state or group. Such models can facilitate formulation of effective mitigating responses and provide a foundation for decision-support technologies. Behavioral modeling is a computationally challenging problem--real world data sets can contain on the order of 10^30,000 possible behaviors in any given situation. This work presents several scalable frameworks for modeling and forecasting agent behavior, particularly in the realm of international security dynamics. A probabilistic logic formalism for modeling and forecasting behavior is described, as well as distributed algorithms for efficient reasoning in this framework. To further cope with the scale of this problem, forecasting methods are also introduced that operate directly on time series data, rather than an intermediate behavioral model, to forecast actions and situations at some time in the future. Agent behavior can be adaptive, and in rare circumstances can deviate from the statistically "normal" past behavior. A system is also presented that can forecast when and how such behavioral changes will occur. These forecasting techniques, as well as any arbitrary time series forecasting approach, can be classified by a general axiomatic framework for forecasting in temporal databases. The knowledge gained from behavioral models and forecasts can be employed by decision-makers to develop effective response policies. An efficient framework is provided for identifying the optimal changes to the state of the world to elicit desired behaviors from another agent, balancing cost with likelihood of success. These modeling and analysis tools have also been incorporated into a prototype decision-support system and used in several case studies of real-world international security situations.