Physical Sciences and Mathematics Commons^™

Constrained Collective Movement In Human-Robot Teams, Joshua Fagan

Doctoral Dissertations

This research focuses on improving human-robot co-navigation for teams of robots and humans navigating together as a unit while accomplishing a desired task. Frequently, the team’s co-navigation is strongly influenced by a predefined Standard Operating Procedure (SOP), which acts as a high-level guide for where agents should go and what they should do. In this work, I introduce the concept of Constrained Collective Movement (CCM) of a team to describe how members of the team perform inter-team and intra-team navigation to execute a joint task while balancing environmental and application-specific constraints. This work advances robots’ abilities to participate along side …

Evaluation Of Distributed Programming Models And Extensions To Task-Based Runtime Systems, Yu Pei

Doctoral Dissertations

High Performance Computing (HPC) has always been a key foundation for scientific simulation and discovery. And more recently, deep learning models' training have further accelerated the demand of computational power and lower precision arithmetic. In this era following the end of Dennard's Scaling and when Moore's Law seemingly still holds true to a lesser extent, it is not a coincidence that HPC systems are equipped with multi-cores CPUs and a variety of hardware accelerators that are all massively parallel. Coupling this with interconnect networks' speed improvements lagging behind those of computational power increases, the current state of HPC systems is …

Better Understanding Genomic Architecture With The Use Of Applied Statistics And Explainable Artificial Intelligence, Jonathon C. Romero

Doctoral Dissertations

With the continuous improvements in biological data collection, new techniques are needed to better understand the complex relationships in genomic and other biological data sets. Explainable Artificial Intelligence (X-AI) techniques like Iterative Random Forest (iRF) excel at finding interactions within data, such as genomic epistasis. Here, the introduction of new methods to mine for these complex interactions is shown in a variety of scenarios. The application of iRF as a method for Genomic Wide Epistasis Studies shows that the method is robust in finding interacting sets of features in synthetic data, without requiring the exponentially increasing computation time of many …

Secured Information Dissemination And Misbehavior Detection In Vanets, Ayan Roy

Doctoral Dissertations

"In a connected vehicle environment, the vehicles in a region can form a distributed network (Vehicular Ad-hoc Network or VANETs) where they can share traffic-related information such as congestion or no-congestion with other vehicles within its proximity, or with a centralized entity via. the roadside units (RSUs). However, false or fabricated information injected by an attacker (or a malicious vehicle) within the network can disrupt the decision-making process of surrounding vehicles or any traffic-monitoring system. Since in VANETs the size of the distributed network constituting the vehicles can be small, it is not difficult for an attacker to propagate an …

Social Media Analytics With Applications In Disaster Management And Covid-19 Events, Md Yasin Kabir

Doctoral Dissertations

"Social media such as Twitter offers a tremendous amount of data throughout an event or a disastrous situation. Leveraging social media data during a disaster is beneficial for effective and efficient disaster management. Information extraction, trend identification, and determining public reactions might help in the future disaster or even avert such an event. However, during a disaster situation, a robust system is required that can be deployed faster and process relevant information with satisfactory performance in real-time. This work outlines the research contributions toward developing such an effective system for disaster management, where it is paramount to develop automated machine-enabled …

Task-Based Runtime Optimizations Towards High Performance Computing Applications, Qinglei Cao

Doctoral Dissertations

The last decades have witnessed a rapid improvement of computational capabilities in high-performance computing (HPC) platforms thanks to hardware technology scaling. HPC architectures benefit from mainstream advances on the hardware with many-core systems, deep hierarchical memory subsystem, non-uniform memory access, and an ever-increasing gap between computational power and memory bandwidth. This has necessitated continuous adaptations across the software stack to maintain high hardware utilization. In this HPC landscape of potentially million-way parallelism, task-based programming models associated with dynamic runtime systems are becoming more popular, which fosters developers’ productivity at extreme scale by abstracting the underlying hardware complexity.

In this context, …

Direct Calculation Of Configurational Entropy: Pair Correlation Functions And Disorder, Clifton C. Sluss

Doctoral Dissertations

Techniques such as classical molecular dynamics [MD] simulation provide ready access to the thermodynamic data of model material systems. However, the calculation of the Helmholtz and Gibbs free energies remains a difficult task due to the tedious nature of extracting accurate values of the excess entropy from MD simulation data. Thermodynamic integration, a common technique for the calculation of entropy requires numerous simulations across a range of temperatures. Alternative approaches to the direct calculation of entropy based on functionals of pair correlation functions [PCF] have been developed over the years. This work builds upon the functional approach tradition by extending …

Tokamak 3d Heat Load Investigations Using An Integrated Simulation Framework, Thomas Looby

Doctoral Dissertations

Reactor class nuclear fusion tokamaks will be inherently complex. Thousands of interconnected systems that span orders of magnitude in physical scale must operate cohesively for the machine to function. Because these reactor class tokamaks are all in an early design stage, it is difficult to quantify exactly how each subsystem will act within the context of the greater systems. Therefore, to predict the engineering parameters necessary to design the machine, simulation frameworks that can model individual systems as well as the interfaced systems are necessary. This dissertation outlines a novel framework developed to couple otherwise disparate computational domains together into …

Iterative Random Forest Based High Performance Computing Methods Applied To Biological Systems And Human Health, Angelica M. Walker

Doctoral Dissertations

As technology improves, the field of biology has increasingly utilized high performance computing techniques to analyze big data and provide insights into biological systems. A reproducible, efficient, and effective method is required to analyze these large datasets of varying types into interpretable results. Iterative Random Forest (iRF) is an explainable supervised learner that makes few assumptions about the relationships between variables and is able to capture complex interactions that are common in biological systems. This forest based learner is the basis of iRF-Leave One Out Prediction (iRF-LOOP), an algorithm that uses a matrix of data to produce all-to-all predictive networks. …

Toward Scalable Morphogenetic Engineering: Natural Computing In Sph Swarm Control, Allen C. Mcbride

Doctoral Dissertations

Artificial morphogenesis (or morphogenetic engineering) seeks inspiration from developmental biology to engineer self-organizing systems. The Morphgen language uses partial differential equations (PDEs) to express artificial morphogenetic processes as spatial fields describing large numbers of agents in the continuum limit. I present an approach to compile such systems of PDEs by discretizing their behavior to derive controllers for finite numbers of agents of finite size. This approach builds on a generalization of methods to control swarms of robots based on the computational fluid dynamics technique of smoothed particle hydrodynamics (SPH). I address potential scalability and efficiency challenges in SPH robotics by …

Control And Calibration Strategies For Quantum Simulation, Paul M. Kairys

Doctoral Dissertations

The modeling and prediction of quantum mechanical phenomena is key to the continued development of chemical, material, and information sciences. However, classical computers are fundamentally limited in their ability to model most quantum effects. An alternative route is through quantum simulation, where a programmable quantum device is used to emulate the phenomena of an otherwise distinct physical system. Unfortunately, there are a number of challenges preventing the widespread application of quantum simulation arising from the imperfect construction and operation of quantum simulators. Mitigating or eliminating deleterious effects is critical for using quantum simulation for scientific discovery. This dissertation develops strategies …

Theoretical And Experimental Application Of Neural Networks In Spaceflight Control Systems, Pavel Galchenko

Doctoral Dissertations

“Spaceflight systems can enable advanced mission concepts that can help expand our understanding of the universe. To achieve the objectives of these missions, spaceflight systems typically leverage guidance and control systems to maintain some desired path and/or orientation of their scientific instrumentation. A deep understanding of the natural dynamics of the environment in which these spaceflight systems operate is required to design control systems capable of achieving the desired scientific objectives. However, mitigating strategies are critically important when these dynamics are unknown or poorly understood and/or modelled. This research introduces two neural network methodologies to control the translation and rotation …

Secure And Efficient Information Management In Delay(Disruption) Tolerant Network, Shudip Datta

Doctoral Dissertations

"In environments like international military coalitions on the battlefield or multi-party relief work in a disaster zone, multiple teams are deployed to serve different mission goals by the command-and-control center (CC). They may need to survey damages and send information to the CC for situational awareness and also transfer messages to each other for mission purposes. However, due to the damaged network infrastructure in the emergency, nodes need to relay messages using the store and forward paradigm, also called Delay-tolerant Networks (DTNs). In DTN, the limited bandwidth, energy, and contacts among the nodes, and their interdependency impose several challenges such …

Deep Learning-Based Surrogate Models For Post-Earthquake Damage Assessment, Xinzhe Yuan

Doctoral Dissertations

"Seismic damage assessment is a critical step to enhance community resilience in the wake of an earthquake. This study aims to develop deep learning-based surrogate models for widely used fragility curves to achieve more accurate and rapid assessment in practice. These surrogate models are based on artificial neural networks trained from the labelled ground motions whose resulting damage classes on targeted structures are determined by nonlinear time history analyses. The development of various surrogate models is progressed in four phases. In Phase I, the multilayer perceptron (MLP) is used to develop multivariate seismic classifiers with up to 50 hand-crafted intensity …

Representation Learning On Heterogeneous Spatiotemporal Networks, Dakshak Keerthi Chandra

Doctoral Dissertations

“The problem of learning latent representations of heterogeneous networks with spatial and temporal attributes has been gaining traction in recent years, given its myriad of real-world applications. Most systems with applications in the field of transportation, urban economics, medical information, online e-commerce, etc., handle big data that can be structured into Spatiotemporal Heterogeneous Networks (SHNs), thereby making efficient analysis of these networks extremely vital. In recent years, representation learning models have proven to be quite efficient in capturing effective lower-dimensional representations of data. But, capturing efficient representations of SHNs continues to pose a challenge for the following reasons: (i) Spatiotemporal …

Advances And Applications In High-Dimensional Heuristic Optimization, Samuel Alexander Vanfossan

Doctoral Dissertations

“Applicable to most real-world decision scenarios, multiobjective optimization is an area of multicriteria decision-making that seeks to simultaneously optimize two or more conflicting objectives. In contrast to single-objective scenarios, nontrivial multiobjective optimization problems are characterized by a set of Pareto optimal solutions wherein no solution unanimously optimizes all objectives. Evolutionary algorithms have emerged as a standard approach to determine a set of these Pareto optimal solutions, from which a decision-maker can select a vetted alternative. While easy to implement and having demonstrated great efficacy, these evolutionary approaches have been criticized for their runtime complexity when dealing with many alternatives or …

Persistent Stealthy Attacks And Their Detection In Large Distributed Cyber-Physical Systems, Simon Bech Thougaard

Doctoral Dissertations

"Cyber-Physical Systems (CPS) are increasingly targeted by attackers using a wide and evolving array of methods. When these systems are distributed, every node represents a potential vulnerability, and secure system design must take this into account. Distributed CPSs also have the potential to better detect and handle attacks, by leveraging redundancies of physical measurements between adjacent nodes. The main purpose of this research is to determine the conditions that render a distributed CPS more resistant to attacks, and the conditions that render it more vulnerable. The work is centered around two separate applications: The Smart Grid and Autonomous Drone Swarms. …