Physical Sciences and Mathematics Commons^™

Low-Reynolds-Number Locomotion Via Reinforcement Learning, Yuexin Liu

Dissertations

This dissertation summarizes computational results from applying reinforcement learning and deep neural network to the designs of artificial microswimmers in the inertialess regime, where the viscous dissipation in the surrounding fluid environment dominates and the swimmer’s inertia is completely negligible. In particular, works in this dissertation consist of four interrelated studies of the design of microswimmers for different tasks: (1) a one-dimensional microswimmer in free-space that moves towards the target via translation, (2) a one-dimensional microswimmer in a periodic domain that rotates to reach the target, (3) a two-dimensional microswimmer that switches gaits to navigate to the designated targets in …

Design And Analysis Of Strategic Behavior In Networks, Sixie Yu

McKelvey School of Engineering Theses & Dissertations

Networks permeate every aspect of our social and professional life.A networked system with strategic individuals can represent a variety of real-world scenarios with socioeconomic origins. In such a system, the individuals' utilities are interdependent---one individual's decision influences the decisions of others and vice versa. In order to gain insights into the system, the highly complicated interactions necessitate some level of abstraction. To capture the otherwise complex interactions, I use a game theoretic model called Networked Public Goods (NPG) game. I develop a computational framework based on NPGs to understand strategic individuals' behavior in networked systems. The framework consists of three …

Model-Based Deep Learning For Computational Imaging, Xiaojian Xu

McKelvey School of Engineering Theses & Dissertations

This dissertation addresses model-based deep learning for computational imaging. The motivation of our work is driven by the increasing interests in the combination of imaging model, which provides data-consistency guarantees to the observed measurements, and deep learning, which provides advanced prior modeling driven by data. Following this idea, we develop multiple algorithms by integrating the classical model-based optimization and modern deep learning to enable efficient and reliable imaging. We demonstrate the performance of our algorithms by validating their performance on various imaging applications and providing rigorous theoretical analysis.

The dissertation evaluates and extends three general frameworks, plug-and-play priors (PnP), regularized …

Geometric Algorithms For Modeling Plant Roots From Images, Dan Zeng

McKelvey School of Engineering Theses & Dissertations

Roots, considered as the ”hidden half of the plant”, are essential to a plant’s health and pro- ductivity. Understanding root architecture has the potential to enhance efforts towards im- proving crop yield. In this dissertation we develop geometric approaches to non-destructively characterize the full architecture of the root system from 3D imaging while making com- putational advances in topological optimization. First, we develop a global optimization algorithm to remove topological noise, with applications in both root imaging and com- puter graphics. Second, we use our topology simplification algorithm, other methods from computer graphics, and customized algorithms to develop a high-throughput …

Abm Simulation Model Of A Pandemic For Optimizing Vaccination Strategy, Gibeom Park

Theses and Dissertations

This study presents a process-oriented hybrid model for individuals' immune responses and interactions involving vaccination to describe the trend of contagious disease and estimate the future societal cost. The model considers "recovery" as a non-absorbing state and incorporates various infection stage states including two symptomatic states. To model contagiousness to be consistent with the current pandemic and include that the spread of a disease depends on the mobility of people, we developed an Agent-Based Simulator that fitted to the particular model used in this study and can test various what-if scenarios. We improved the simulator considerably by appying data structures …

Debiasing Cyber Incidents – Correcting For Reporting Delays And Under-Reporting, Seema Sangari

Doctor of Data Science and Analytics Dissertations

This research addresses two key problems in the cyber insurance industry – reporting delays and under-reporting of cyber incidents. Both problems are important to understand the true picture of cyber incident rates. While reporting delays addresses the problem of delays in reporting due to delays in timely detection, under-reporting addresses the problem of cyber incidents frequently under-reported due to brand damage, reputation risk and eventual financial impacts.

The problem of reporting delays in cyber incidents is resolved by generating the distribution of reporting delays and fitting modeled parametric distributions on the given domain. The reporting delay distribution was found to …

Optimization Of Quantum Circuits Using Spin Bus Multiqubit Gates For Quantum Dots, Miguel Gonzalo Rodriguez

Open Access Theses & Dissertations

The current conventional method for designing quantum circuits is to employ a number of single- and two-qubit gates, which often necessitate a lengthy sequence, imposing severe constraints on quantum coherence and quantum circuit complexity. Coupling multiple spin qubits to a common spin chain can result in a generically multiqubit gate. It is demonstrated that the multiqubit gate can substantially reduce the depth of quantum circuits and establish multiqubit entanglement considerably more quickly.

Development Of A Reverse Engineered, Parameterized, And Structurally Validated Computational Model To Identify Design Parameters That Influence American Football Faceguard Performance, William Ferriell

All Dissertations

Traumatic brain injury (TBI) continues to have the greatest incidence among athletes participating in American football. The headgear design research community has focused on developing accurate computational and experimental analysis techniques to better assess the ability of headgear technology to attenuate impacts and protect athletes from TBI. Despite efforts to innovate the headgear system, minimal progress has been made to innovate the faceguard. Although the faceguard is not the primary component of the headgear system that contributes to impact attenuation, faceguard performance metrics, such as weight, structural stiffness, and visual field occlusions, have been linked to athlete safety. To improve …

Tempering The Adversary: An Exploration Into The Applications Of Game Theoretic Feature Selection And Regression, Stephen Mcgee

All Dissertations

Most modern machine learning algorithms tend to focus on an "average-case" approach, where every data point contributes the same amount of influence towards calculating the fit of a model. This "per-data point" error (or loss) is averaged together into an overall loss and typically minimized with an objective function. However, this can be insensitive to valuable outliers. Inspired by game theory, the goal of this work is to explore the utility of incorporating an optimally-playing adversary into feature selection and regression frameworks. The adversary assigns weights to the data elements so as to degrade the modeler's performance in an optimal …

Development Of Software Tools For Efficient And Sustainable Process Development And Improvement, Jake P. Stengel

Theses and Dissertations

Infrastructure is a key component in the well-being of our society that leads to its growth, development, and productive operations. A well-built infrastructure allows the community to be more competitive and promotes economic advancement. In 2021, the ASCE (American Society of Civil Engineers) ranked the American infrastructure as substandard, with an overall grade of C-. The overall ranking suffers when key infrastructure categories are not maintained according to the needs of the population. Therefore, there is a need to consider alternative methods to improve our infrastructure and make it more sustainable to enhance the overall grade. One of the challenges …

Comparing Learned Representations Between Unpruned And Pruned Deep Convolutional Neural Networks, Parker Mitchell

Master's Theses

While deep neural networks have shown impressive performance in computer vision tasks, natural language processing, and other domains, the sizes and inference times of these models can often prevent them from being used on resource-constrained systems. Furthermore, as these networks grow larger in size and complexity, it can become even harder to understand the learned representations of the input data that these networks form through training. These issues of growing network size, increasing complexity and runtime, and ambiguity in the understanding of internal representations serve as guiding points for this work.

In this thesis, we create a neural network that …

Multi-Device Data Analysis For Fault Localization In Electrical Distribution Grids, Jacob D L Hunte

Electronic Thesis and Dissertation Repository

The work presented in this dissertation represents work which addresses some of the main challenges of fault localization methods in electrical distribution grids. The methods developed largely assume access to sophisticated data sources that may not be available and that any data sets recorded by devices are synchronized. These issues have created a barrier to the adoption of many solutions by industry. The goal of the research presented in this dissertation is to address these challenges through the development of three elements. These elements are a synchronization protocol, a fault localization technique, and a sensor placement algorithm.

The synchronization protocol …

Autonomous Eco-Driving With Traffic Light And Lead Vehicle Constraints: An Application Of Best Constrained Interpolation, Yara Hazem Mohamed Mahmoud

Masters Theses

Eco-Driving is a critical technology for improving automotive transportation efficiency. It is achieved by modifying the driving trajectory over a particular route to minimize required propulsion energy. Eco-Driving can be approached as an optimal control problem subject to driving constraints such as traffic lights and positions of other vehicles. Best interpolation in a strip is a problem in approximation theory and optimal control. The solution to this problem is a cubic spline. In this research we demonstrate the connection between Eco-Driving and best interpolation in the strip. By exploiting this connection, we are able to generate optimal Eco-Driving trajectories that …

An Optimization Model For Minimization Of Systemic Risk In Financial Portfolios, Zachary Alexander Gelber

Master's Theses

In this thesis, we study how sovereign credit default swaps are able to measure systemic risk as well as how they can be used to construct optimal portfolios to minimize risk. We define the clustering coefficient as a proxy for systemic risk and design an optimization problem with the goal of minimizing the mean absolute deviation of the clustering coefficient on a group of nine European countries. Additionally, we define a metric we call the diversity score that measures the diversification of any given portfolio. We solve this problem for a baseline set of parameters, then spend the remainder of …

On Class Imbalanced Learning:Design Of Non-Parametricclassifiers, Performance Indices, And Deep Oversampling Strategies., Sankha Mullick Dr.

Doctoral Theses

The relevance of classification is almost endless in the everyday application of machine learning. However, the performance of a classifier is only limited to the fulfillment of the inherent assumptions it makes about the training examples. For example, to facilitate unbiased learning a classifier is expected to be trained with an equal number of labeled data instances from all of the classes. However, in a large number of practical applications such as anomaly detection, semantic segmentation, disease prediction, etc. it may not be possible to gather an equal number of diverse training points for all the classes. This results in …

Pymoocfd - A Multi-Objective Optimization Framework For Cfd, George Martin Cunningham Love

Graduate College Dissertations and Theses

Modern computational resource have solidified the use of computer modeling as an integral part of the engineering design process. This is particularly impressive when it comes to high-dimensional models such as computational fluid dynamics (CFD) models. CFD models are now capable of producing results with a level of confidence that would previously have required physical experimentation. Simultaneously, the development of machine learning techniques and algorithms has increased exponentially in recent years. This acceleration is also due to the widespread availability of modern computational resources. Thus far, the cross-over between these fields has been mostly focused on computer models with low …

Sensitivity Analysis Of Basins Of Attraction For Gradient-Based Optimization Methods, Gillian King

Honors Projects

This project is an analysis of the effectiveness of five distinct optimization methods in their ability in producing clear images of the basins of attraction, which is the set of initial points that approach the same minimum for a given function. Basin images are similar to contour plots, except that they depict the distinct regions of points--in unique colors--that approach the same minimum. Though distinct in goal, contour plots are useful to basin research in that idealized basin images can be inferred from the steepness levels and location of extrema they depict. Effectiveness of the method changes slightly depending on …

Sensitivity Analysis Of Basins Of Attraction For Nelder-Mead, Sonia K. Shah

Honors Projects

The Nelder-Mead optimization method is a numerical method used to find the minimum of an objective function in a multidimensional space. In this paper, we use this method to study functions - specifically functions with three-dimensional graphs - and create images of the basin of attraction of the function. Three different methods are used to create these images named the systematic point method, randomized centroid method, and systemized centroid method. This paper applies these methods to different functions. The first function has two minima with an equivalent function value. The second function has one global minimum and one local minimum. …

Finding Optimal Cayley Map Embeddings Using Genetic Algorithms, Jacob Buckelew

Honors Program Theses

Genetic algorithms are a commonly used metaheuristic search method aimed at solving complex optimization problems in a variety of fields. These types of algorithms lend themselves to problems that can incorporate stochastic elements, which allows for a wider search across a search space. However, the nature of the genetic algorithm can often cause challenges regarding time-consumption. Although the genetic algorithm may be widely applicable to various domains, it is not guaranteed that the algorithm will outperform other traditional search methods in solving problems specific to particular domains. In this paper, we test the feasibility of genetic algorithms in solving a …

Dynamic Nonlinear Gaussian Model For Inferring A Graph Structure On Time Series, Abhinuv Uppal

CMC Senior Theses

In many applications of graph analytics, the optimal graph construction is not always straightforward. I propose a novel algorithm to dynamically infer a graph structure on multiple time series by first imposing a state evolution equation on the graph and deriving the necessary equations to convert it into a maximum likelihood optimization problem. The state evolution equation guarantees that edge weights contain predictive power by construction. After running experiments on simulated data, it appears the required optimization is likely non-convex and does not generally produce results significantly better than randomly tweaking parameters, so it is not feasible to use in …

Genetic Algorighm Representation Selection Impact On Binary Classification Problems, Stephen V. Maldonado

Honors Undergraduate Theses

In this thesis, we explore the impact of problem representation on the ability for the genetic algorithms (GA) to evolve a binary prediction model to predict whether a physical therapist is paid above or below the median amount from Medicare. We explore three different problem representations, the vector GA (VGA), the binary GA (BGA), and the proportional GA (PGA). We find that all three representations can produce models with high accuracy and low loss that are better than Scikit-Learn’s logistic regression model and that all three representations select the same features; however, the PGA representation tends to create lower weights …

Particle Swarm Optimization For Critical Experiment Design, Cole Michael Kostelac

Masters Theses

“Critical experiments are used by nuclear data evaluators and criticality safety engineers to validate nuclear data and computational methods. Many of these experiments are designed to maximize the sensitivity to a certain nuclide-reaction pair in an energy range of interest. Traditionally, a parameter sweep is conducted over a set of experimental variables to find a configuration that is critical and maximally sensitive. As additional variables are added, the total number of configurations increases exponentially and quickly becomes prohibitively computationally expensive to calculate, especially using Monte Carlo methods.

This work presents the development of a particle swarm optimization algorithm to design …

An Upgraded Photoinjector For The Argonne Wakefield Accelerator, Emily Frame

Graduate Research Theses & Dissertations

The Argonne Wakefield Accelerator (AWA) is planning an upgrade of the drive-beam accelerator’s photoinjector, the driving force of electron generation. The upgrade’s main goal is to improve beam brightness using linear accelerating cavities and a radiofrequency-gun cavity. In the process of this upgrade, one of the beam focusing solenoids is being redesigned. A beam dynamics optimization is performed for two new solenoid designs, with considerations for producing low-charge (∼ 1 nC) electron bunches as well as operations at higher charges (∼ 50 nC). This project focuses on the optimization study for both the low- and high-charge regimes, exploring the impact …

Methods For Computing The Global Optimum Of Non-Convex Objectives, Isaac Michael Hawn

Graduate Research Theses & Dissertations

\begin{abstract}In this thesis, we concern ourselves with solving the unconstrained optimization problem % \begin{gather*} \text{Minimize}\; f(x)\\\text{subject to}\; x\in X \end{gather*} % where $f\colon\mathbb{R}^N\to \mathbb{R}$ is a non-convex function, possibly with infinitely many local minima. Solving such a problem, especially in higher dimensions often proves to be an extraordinarily difficult task, either in time complexity or in the methodology itself. Indeed, mathematicians must often resort to algorithms which make use of problem structure and which may not generalize well. In this thesis, we present two algorithms which solve this problem, albeit with their own shortcomings.

First, we present a new, $N$-dimensional …

Multi-Agent Pathfinding In Mixed Discrete-Continuous Time And Space, Thayne T. Walker

Electronic Theses and Dissertations

In the multi-agent pathfinding (MAPF) problem, agents must move from their current locations to their individual destinations while avoiding collisions. Ideally, agents move to their destinations as quickly and efficiently as possible. MAPF has many real-world applications such as navigation, warehouse automation, package delivery and games. Coordination of agents is necessary in order to avoid conflicts, however, it can be very computationally expensive to find mutually conflict-free paths for multiple agents – especially as the number of agents is increased. Existing state-ofthe- art algorithms have been focused on simplified problems on grids where agents have no shape or volume, and …

Variational Data Assimilation For Two Interface Problems, Xuejian Li

Doctoral Dissertations

“Variational data assimilation (VDA) is a process that uses optimization techniques to determine an initial condition of a dynamical system such that its evolution best fits the observed data. In this dissertation, we develop and analyze the variational data assimilation method with finite element discretization for two interface problems, including the Parabolic Interface equation and the Stokes-Darcy equation with the Beavers-Joseph interface condition. By using Tikhonov regularization and formulating the VDA into an optimization problem, we establish the existence, uniqueness and stability of the optimal solution for each concerned case. Based on weak formulations of the Parabolic Interface equation and …

Design, Analysis, And Optimization Of Traffic Engineering For Software Defined Networks, Mohammed Ibrahim Salman

Browse all Theses and Dissertations

Network traffic has been growing exponentially due to the rapid development of applications and communications technologies. Conventional routing protocols, such as Open-Shortest Path First (OSPF), do not provide optimal routing and result in weak network resources. Optimal traffic engineering (TE) is not applicable in practice due to operational constraints such as limited memory on the forwarding devices and routes oscillation. Recently, a new way of centralized management of networks enabled by Software-Defined Networking (SDN) made it easy to apply most traffic engineering ideas in practice. \par Toward creating an applicable traffic engineering system, we created a TE simulator for experimenting …