Engineering Commons^™

Interaction Effects And Selecting Regression Models Of Taylor Swift Song Popularity, Halle Schneidewind

Industrial Engineering Undergraduate Honors Theses

Understanding music popularity and what drives it is important not only for artists but for other individuals who are financially tied to music sales including producers, writers, and record labels. Studies have been done to define how a song’s popularity can be measured, what attributes or features are drivers for popularity, and to what extent can a song’s popularity even be predicted. This paper takes two linear regression approaches to predicting the popularity of a Taylor Swift song on Spotify based on auditory features the Spotify API estimates and historic popularity of songs on Spotify. One model takes into consideration …

Nonconvex Optimization For Statistical Learning With Structured Sparsity, Chengyu Ke

Operations Research and Engineering Management Theses and Dissertations

Sparse learning problems, known as feature selection problems or variable selection problems, are a popular branch in the field of statistical learning. When faced with a dataset with only a few observations but a large number of features, we are interested in extracting the most useful features automatically by solving an optimization problem. In this dissertation, we start by introducing a novel penalty function as well as an iterative reweighted algorithm to solve the group sparsity problem, a special type of feature selection problems. The penalty function, named group LOG, shows a better ability to recover the ground-truth compared to …

A Machine Learning Framework For Hypersonic Vehicle Design Exploration, Atticus Beachy

Browse all Theses and Dissertations

The design of Hypersonic Vehicles (HVs) requires meeting multiple unconventional and often conflicting design requirements in a hostile, high-energy environment. The most fundamental difference between ordinary aerospace design and hypersonic flight is that the extreme conditions of hypersonic flight require parts to perform multiple functions and be tightly integrated, resulting in significant coupled effects. Critical couplings among the disciplines of aerodynamics, structures, propulsion, and thermodynamics must be investigated in the early stages of design exploration to reduce the risk of requiring major design changes and cost overruns later. In addition, due to a lack of validated test data within the …

Study Of Stochastic Market Clearing Problems In Power Systems With High Renewable Integration, Saumya Sakitha Sashrika Ariyarathne

Operations Research and Engineering Management Theses and Dissertations

Integrating large-scale renewable energy resources into the power grid poses several operational and economic problems due to their inherently stochastic nature. The lack of predictability of renewable outputs deteriorates the power grid’s reliability. The power system operators have recognized this need to account for uncertainty in making operational decisions and forming electricity pricing. In this regard, this dissertation studies three aspects that aid large-scale renewable integration into power systems. 1. We develop a nonparametric change point-based statistical model to generate scenarios that accurately capture the renewable generation stochastic processes; 2. We design new pricing mechanisms derived from alternative stochastic programming …

Efficient Approaches To Steady State Detection In Multivariate Systems, Honglun Xu

Open Access Theses & Dissertations

Steady state detection is critically important in many engineering fields such as fault detection and diagnosis, process monitoring and control. However, most of the existing methods are designed for univariate signals. In this dissertation, we proposed an efficient online steady state detection method for multivariate systems through a sequential Bayesian partitioning approach. The signal is modeled by a Bayesian piecewise constant mean and covariance model, and a recursive updating method is developed to calculate the posterior distributions analytically. The duration of the current segment is utilized to test the steady state. Insightful guidance is provided for hyperparameter selection. The effectiveness …

Equity Of Urban Neighborhood Infrastructure: A Data-Driven Assessment, Zheng Li

Civil and Environmental Engineering Theses and Dissertations

Neighborhood infrastructure, such as sidewalks, medical facilities, public transit, community gathering places, and tree canopy, provides essential support for safe, healthy, and
resilient communities. This thesis proposes, develops, and implements an innovative approach to thoroughly examine the presence and condition of neighborhood infrastructure.
It demonstrates the necessity of considering multiple infrastructure types when studying
neighborhood infrastructure and its equity. This thesis provides an automated assessment
framework as well as case studies among four major metropolitan cities across the United
States, which expands the research opportunities for future infrastructure-related research.

Uncertainties In Internal Pressure Of Oil Pipelines And Implications For The Reliability Analysis, Yue Liu

Electronic Thesis and Dissertation Repository

The internal pressure is the most important operational load for oil and gas pipelines. The maximum operating pressure (MOP) is the maximum pressure the pipeline is qualified to be operated according to a given standard. In deterministic fitness-for-service (FFS) assessment of in-service pipelines containing flaws such as corrosion defects and cracks, the remaining pressure containment capacity of the pipeline is evaluated and compared with MOP multiplied by a factor of safety to determine if immediate rehabilitation actions for the pipeline are necessary. However, the actual internal pressure of an in-service pipeline is however uncertain and fluctuates with time. Due to …

Machine Learning With Topological Data Analysis, Ephraim Robert Love

Doctoral Dissertations

Topological Data Analysis (TDA) is a relatively new focus in the fields of statistics and machine learning. Methods of exploiting the geometry of data, such as clustering, have proven theoretically and empirically invaluable. TDA provides a general framework within which to study topological invariants (shapes) of data, which are more robust to noise and can recover information on higher dimensional features than immediately apparent in the data. A common tool for conducting TDA is persistence homology, which measures the significance of these invariants. Persistence homology has prominent realizations in methods of data visualization, statistics and machine learning. Extending ML with …

The Wargaming Commodity Course Of Action Automated Analysis Method, William T. Deberry

Theses and Dissertations

This research presents the Wargaming Commodity Course of Action Automated Analysis Method (WCCAAM), a novel approach to assist wargame commanders in developing and analyzing courses of action (COAs) through semi-automation of the Military Decision Making Process (MDMP). MDMP is a seven-step iterative method that commanders and mission partners follow to build an operational course of action to achieve strategic objectives. MDMP requires time, resources, and coordination – all competing items the commander weighs to make the optimal decision. WCCAAM receives the MDMP's Mission Analysis phase as input, converts the wargame into a directed graph, processes a multi-commodity flow algorithm on …

Machine Learning Morphisms: A Framework For Designing And Analyzing Machine Learning Work Ows, Applied To Separability, Error Bounds, And 30-Day Hospital Readmissions, Eric Zenon Cawi

McKelvey School of Engineering Theses & Dissertations

A machine learning workflow is the sequence of tasks necessary to implement a machine learning application, including data collection, preprocessing, feature engineering, exploratory analysis, and model training/selection. In this dissertation we propose the Machine Learning Morphism (MLM) as a mathematical framework to describe the tasks in a workflow. The MLM is a tuple consisting of: Input Space, Output Space, Learning Morphism, Parameter Prior, Empirical Risk Function. This contains the information necessary to learn the parameters of the learning morphism, which represents a workflow task. In chapter 1, we give a short review of typical tasks present in a workflow, as …

Computational Simulation And Analysis Of Neuroplasticity, Madison E. Yancey

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Homeostatic synaptic plasticity is the process by which neurons alter their activity in response to changes in network activity. Neuroscientists attempting to understand homeostatic synaptic plasticity have developed three different mathematical methods to analyze collections of event recordings from neurons acting as a proxy for neuronal activity. These collections of events are from control data and treatment data, referring to the treatment of neuron cultures with pharmacological agents that augment or inhibit network activity. If the distribution of control events can be functionally mapped to the distribution of treatment events, a better understanding of the biological processes underlying homeostatic synaptic …

Bayesian Inspired Multi-Fidelity Optimization With Aerodynamic Design, Christopher Corey Fischer

Browse all Theses and Dissertations

In most engineering design problems, there exist multiple models of varying fidelities for use in predicting a single system response such as Computational Fluid Dynamics (CFD) models constructed using Potential Flow, Euler equations, or full physics Navier Stokes implementation. Engineering design is constantly pushing the forefront of the field through imposing stricter and more complex constraints on system performance, thus elevating the need for use of high-fidelity models in the design process. Increasing fidelity level often correlates to an increase in cost (financial, computational time, and computational resources). Traditional design processes rely upon low-fidelity models for expedience and resource savings. …

Statistical Methods To Unravel Cortical Mechanism Of Perception And Response To Auditory Stimuli, Ladan Moheimanian

Legacy Theses & Dissertations (2009 - 2024)

Behavioral responses to auditory stimuli have a critical role in our daily activities. The perception of these stimuli and the generation of appropriate behavioral responses requires the interaction of thousands of neurons in the auditory-motor pathways in the brain. Despite their importance, still many neuroscientific questions about these interactions are remained to be answered. This may result from the limitations of brain recordings as well as statistical methods to analyze brain recordings. In this dissertation, I investigated underlying mechanisms that govern these neural interactions in the auditory-motor pathways using novel statistical techniques applied to the brain recordings from the surface …

An Approach To Cluster And Benchmark Regional Emergency Medical Service Agencies, Swetha Kondapalli

Browse all Theses and Dissertations

Emergency Medical Service (EMS) providers are the first responders for an injured patient on the field. Their assessment of patient injuries and determination of an appropriate hospital play a critical role in patient outcomes. A majority of states in the US have established a state-level governing body (e.g., EMS Division) that is responsible for developing and maintaining a robust EMS system throughout the state. Such divisions develop standards, accredit EMS agencies, oversee the trauma system, and support new initiatives through grants and training. But to do so, these divisions require data to enable them to first understand the similarities between …

Online Clustering With Bayesian Nonparametrics, Matthew D. Scherreik

Browse all Theses and Dissertations

Clustering algorithms, such as Gaussian mixture models and K-means, often require the number of clusters to be specified a priori. Bayesian nonparametric (BNP) methods avoid this problem by specifying a prior distribution over the cluster assignments that allows the number of clusters to be inferred from the data. This can be especially useful for online clustering tasks, where data arrives in a continuous stream and the number of clusters may dynamically change over time. Classical BNP priors often overestimate the number of clusters, however, leading researchers to develop new priors with more control over this tendency. To date, BNP algorithms …

Daily And Seasonal Variability Of Offshore Wind Power On The Central California Coast And Statewide Demand, Matthew Douglas Kehrli

Physics

No abstract provided.

Optical Vortex And Poincaré Analysis For Biophysical Dynamics, Anindya Majumdar

Dissertations, Master's Theses and Master's Reports

Coherent light - such as that from a laser - on interaction with biological tissues, undergoes scattering. This scattered light undergoes interference and the resultant field has randomly added phases and amplitudes. This random interference pattern is known as speckles, and has been the subject of multiple applications, including imaging techniques. These speckle fields inherently contain optical vortices, or phase singularities. These are locations where the intensity (or amplitude) of the interference pattern is zero, and the phase is undefined.

In the research presented in this dissertation, dynamic speckle patterns were obtained through computer simulations as well as laboratory setups …

Hierarchical Bayesian Data Fusion Using Autoencoders, Yevgeniy Vladimirovich Reznichenko

Master's Theses (2009 -)

In this thesis, a novel method for tracker fusion is proposed and evaluated for vision-based tracking. This work combines three distinct popular techniques into a recursive Bayesian estimation algorithm. First, semi supervised learning approaches are used to partition data and to train a deep neural network that is capable of capturing normal visual tracking operation and is able to detect anomalous data. We compare various methods by examining their respective receiver operating conditions (ROC) curves, which represent the trade off between specificity and sensitivity for various detection threshold levels. Next, we incorporate the trained neural networks into an existing data …

Analyzing Sensor Based Human Activity Data Using Time Series Segmentation To Determine Sleep Duration, Yogesh Deepak Lad

Masters Theses

"Sleep is the most important thing to rest our brain and body. A lack of sleep has adverse effects on overall personal health and may lead to a variety of health disorders. According to Data from the Center for disease control and prevention in the United States of America, there is a formidable increase in the number of people suffering from sleep disorders like insomnia, sleep apnea, hypersomnia and many more. Sleep disorders can be avoided by assessing an individual's activity over a period of time to determine the sleep pattern and duration. The sleep pattern and duration can be …

Computational Ultrasound Elastography: A Feasibility Study, Yu Wang

Dissertations, Master's Theses and Master's Reports

Ultrasound Elastography (UE) is an emerging set of imaging modalities used to assess the biomechanical properties of soft tissues. UE has been applied to numerous clinical applications. Particularly, results from clinical trials of UE in breast lesion differentiation and staging liver fibrosis indicated that there was a lack of confidence in UE measurements or image interpretation. Confidence on UE measurements interpretation is critically important for improving the clinical utility of UE. The primary objective of my thesis is to develop a computational simulation platform based on open-source software packages including Field II, VTK, FEBio and Tetgen. The proposed virtual simulation …

Landfill Elevated Internal Temperature Detection And Landfill Fire Index Assessment For Fire Monitoring, Aurora Musilli

Theses and Dissertations

Landfill fires are becoming a real threat to both people and environment due to lack of predictions and control methods. Processing of the infrared band from level-1 satellite images was employed and decades worth of archived data from USGS Earth Explorer databases were analyzed to obtain surface temperature values of Atlantic Waste Landfill, Virginia and Bridgeton Landfill, Missouri. Multitemporal thermal maps and frequency of maxima analysis maps of these two landfills showed the hotspots spreading through the waste site. A Landfill Fire Index (LFI) was created by investigating eight factors that give information about the hazardousness of the landfill conditions …

A Traders Guide To The Predictive Universe- A Model For Predicting Oil Price Targets And Trading On Them, Jimmie Harold Lenz

Doctor of Business Administration Dissertations

At heart every trader loves volatility; this is where return on investment comes from, this is what drives the proverbial “positive alpha.” As a trader, understanding the probabilities related to the volatility of prices is key, however if you could also predict future prices with reliability the world would be your oyster. To this end, I have achieved three goals with this dissertation, to develop a model to predict future short term prices (direction and magnitude), to effectively test this by generating consistent profits utilizing a trading model developed for this purpose, and to write a paper that anyone with …

Design & Analysis Of A Computer Experiment For An Aerospace Conformance Simulation Study, Ryan W. Gryder

Theses and Dissertations

Within NASA's Air Traffic Management Technology Demonstration # 1 (ATD-1), Interval Management (IM) is a flight deck tool that enables pilots to achieve or maintain a precise in-trail spacing behind a target aircraft. Previous research has shown that violations of aircraft spacing requirements can occur between an IM aircraft and its surrounding non-IM aircraft when it is following a target on a separate route. This research focused on the experimental design and analysis of a deterministic computer simulation which models our airspace configuration of interest. Using an original space-filling design and Gaussian process modeling, we found that aircraft delay assignments …

Modeling User Transportation Patterns Using Mobile Devices, Erfan Davami

Electronic Theses and Dissertations

Participatory sensing frameworks use humans and their computing devices as a large mobile sensing network. Dramatic accessibility and affordability have turned mobile devices (smartphone and tablet computers) into the most popular computational machines in the world, exceeding laptops. By the end of 2013, more than 1.5 billion people on earth will have a smartphone. Increased coverage and higher speeds of cellular networks have given these devices the power to constantly stream large amounts of data. Most mobile devices are equipped with advanced sensors such as GPS, cameras, and microphones. This expansion of smartphone numbers and power has created a sensing …

Carbon 1d/2d Nanoelectronics : Integration And Device Applications, Zhaoying Hu

Legacy Theses & Dissertations (2009 - 2024)

Graphene is a one-atom thick planar monolayer of sp2-bonded carbon atoms organized in a hexagonal crystal lattice. A single walled carbon nanotube (CNT) can be thought of as a graphene sheet rolled up into a seamless hollow cylinder with extremely high length-to-diameter ratio. Their ultra-thin body, large surface area, and exceptional electronic, optical and mechanical properties make these low-dimensional carbon materials ideal candidates for electronic applications. However, adopting low-dimensional carbon materials into semiconductor industry faces significant material and integration challenges. There is an urgent need for research at fundamental and applicative levels to find a roadmap for carbon nanomaterial to …

Analysis Of Long-Term Utah Temperature Trends Using Hilbert-Haung Transforms, Brent H. Hargis

Theses and Dissertations

We analyzed long-term temperature trends in Utah using a relatively new signal processing method called Empirical Mode Decomposition (EMD). We evaluated the available weather records in Utah and selected 52 stations, which had records longer than 60 years, for analysis. We analyzed daily temperature data, both minimum and maximums, using the EMD method that decomposes non-stationary data (data with a trend) into periodic components and the underlying trend. Most decomposition algorithms require stationary data (no trend) with constant periods and temperature data do not meet these constraints. In addition to identifying the long-term trend, we also identified other periodic processes …

Three-Dimensional Modeling Of The Human Jaw/Teeth Using Optics And Statistics., Aly Saber Abdelrahim

Electronic Theses and Dissertations

Object modeling is a fundamental problem in engineering, involving talents from computer-aided design, computational geometry, computer vision and advanced manufacturing. The process of object modeling takes three stages: sensing, representation, and analysis. Various sensors may be used to capture information about objects; optical cameras and laser scanners are common with rigid objects, while X-ray, CT and MRI are common with biological organs. These sensors may provide a direct or an indirect inference about the object, requiring a geometric representation in the computer that is suitable for subsequent usage. Geometric representations that are compact, i.e., capture the main features of the …

Applications Of Transit Signal Priority Technology For Transit Service, Frank Anthony Consoli

Electronic Theses and Dissertations

This research demonstrated the effectiveness of Transit Signal Priority (TSP) in improving bus corridor travel time in a simulated environment using real world data. TSP is a technology that provides preferential treatment to buses at signalized intersections. By considering different scenarios of activating bus signal priority when a bus is 3 or 5 minutes behind schedule, it was demonstrated that bus travel times improved significantly while there is little effect on delays for crossing street traffic. The case of providing signal priority for buses unconditionally resulted in significant crossing street delays for some signalized intersections with only minor improvement to …

Optimized Correlation Of Geophysical And Geotechnical Methods In Sinkhole Investigations: Emphasizing On Spatial Variations In West-Central Florida, Henok Gidey Kiflu

USF Tampa Graduate Theses and Dissertations

Abstract

Sinkholes and sinkhole-related features in West-Central Florida (WCF) are commonly identified using geotechnical investigations such as standard penetration test (SPT) borings and geophysical methods such as ground penetrating radar (GPR) and electrical resistivity tomography (ERT). Geophysical investigation results can be used to locate drilling and field testing sites while geotechnical investigation can be used to ground truth geophysical results. Both methods can yield complementary information. Geotechnical investigations give important information about the type of soil, groundwater level and presence of low-density soils or voids at the test location, while geophysical investigations like GPR surveys have better spatial coverage and …

Thermal And Electrical Behaviors Of Selected Geomaterials, Joon Kyu Lee

Electronic Thesis and Dissertation Repository

Geomaterials can be often classified into two groups: virgin geomaterials such as soil and rock, and by-product materials such as mine tailings, coal fly/bottom ash, foundry sand, kiln dust, blast furnace/steel slag, reclaimed concrete and asphalt. Studies on these materials and their mixtures have been carried out extensively for geoengineering applications, including the characterization of mechanical properties such as the strength, compressibility, compactivity and permeability, as well as mineralogical and geochemical properties. The goal of this study is to investigate the thermal and electrical properties of selected geomaterials and their mixtures for enhancement of knowledge and engineering applications. The thesis …