Engineering Commons^™

Energy Planning Model Design For Forecasting The Final Energy Consumption Using Artificial Neural Networks, Haidy Eissa

Theses and Dissertations

“Energy Trilemma” has recently received an increasing concern among policy makers. The trilemma conceptual framework is based on three main dimensions: environmental sustainability, energy equity, and energy security. Energy security reflects a nation’s capability to meet current and future energy demand. Rational energy planning is thus a fundamental aspect to articulate energy policies. The energy system is huge and complex, accordingly in order to guarantee the availability of energy supply, it is necessary to implement strategies on the consumption side. Energy modeling is a tool that helps policy makers and researchers understand the fluctuations in the energy system. Over the …

A Longitudinal Analysis Of Pathways To Computing Careers: Defining Broadening Participation In Computing (Bpc) Success With A Rearview Lens, Mercy Jaiyeola

Theses and Dissertations

Efforts to increase the participation of groups historically underrepresented in computing studies, and in the computing workforce, are well documented. It is a national effort with funding from a variety of sources being allocated to research in broadening participation in computing (BPC). Many of the BPC efforts are funded by the National Science Foundation (NSF) but as existing literature shows, the growth in representation of traditionally underrepresented minorities and women is not commensurate to the efforts and resources that have been directed toward this aim.

Instead of attempting to tackle the barriers to increasing representation, this dissertation research tackles the …

Uncertainty-Aware Deep Learning For Prediction Of Remaining Useful Life Of Mechanical Systems, Samuel J. Cornelius

Theses and Dissertations

Remaining useful life (RUL) prediction is a problem that researchers in the prognostics and health management (PHM) community have been studying for decades. Both physics-based and data-driven methods have been investigated, and in recent years, deep learning has gained significant attention. When sufficiently large and diverse datasets are available, deep neural networks can achieve state-of-the-art performance in RUL prediction for a variety of systems. However, for end users to trust the results of these models, especially as they are integrated into safety-critical systems, RUL prediction uncertainty must be captured. This work explores an approach for estimating both epistemic and heteroscedastic …

The Development Of Authentic Virtual Reality Scenarios To Measure Individuals’ Level Of Systems Thinking Skills And Learning Abilities, Vidanelage L. Dayarathna

Theses and Dissertations

This dissertation develops virtual reality modules to capture individuals’ learning abilities and systems thinking skills in dynamic environments. In the first chapter, an immersive queuing theory teaching module is developed using virtual reality technology. The objective of the study is to present systems engineering concepts in a more sophisticated environment and measure students learning abilities. Furthermore, the study explores the performance gaps between male and female students in manufacturing systems concepts. To investigate the gender biases toward the performance of developed VR module, three efficacy measures (simulation sickness questionnaire, systems usability scale, and presence questionnaire) and two effectiveness measures (NASA …

Advanced Analytics In Smart Manufacturing: Anomaly Detection Using Machine Learning Algorithms And Parallel Machine Scheduling Using A Genetic Algorithm, Meiling He

Theses and Dissertations

Industry 4.0 offers great opportunities to utilize advanced data processing tools by generating Big Data from a more connected and efficient data collection system. Making good use of data processing technologies, such as machine learning and optimization algorithms, will significantly contribute to better quality control, automation, and job scheduling in Smart Manufacturing. This research aims to develop a new machine learning algorithm for solving highly imbalanced data processing problems, implement both supervised and unsupervised machine learning auto-selection frameworks for detecting anomalies in smart manufacturing, and develop a genetic algorithm for optimizing job schedules on unrelated parallel machines. This research also …

A Deep Recurrent Neural Network With Iterative Optimization For Inverse Image Processing Applications, Masaki Ikuta

Theses and Dissertations

Many algorithms and methods have been proposed for inverse image processing applications, such as super-resolution, image de-noising, and image reconstruction, particularly with the recent surge of interest in machine learning and deep learning methods.

As for Computed Tomography (CT) image reconstruction, the most recently proposed methods are limited to image domain processing, where deep learning is used to learn the mapping between a true image data set and a noisy image data set in the image domain. While deep learning-based methods can produce higher quality images than conventional model-based algorithms, these methods have a limitation. Deep learning-based methods used in …

Functional Material Systems For Stimuli-Responsive Interference Coloration, Milad Momtaz

Theses and Dissertations

Part I: Responsive Interference Coloration (RIC) Systems for High-Performance Humidity Sensing

High-humidity conditions (85−100% relative humidity) have a variety of effects on many aspects of our daily lives. In spite of significant progress in the development of structural coloration-based humidity sensors, enhancing the sensitivity and visual humidity resolution of these sensors at high-humidity environment remains a big challenge. In this work, high-performance colorimetric humidity sensors based on environment-friendly konjac glucomannan (KGM) are introduced. These sensors are fabricated via thin-film interference and prepared using a simple, affordable, and scalable method. An effective approach is shown for markedly improving the sensitivity and …

An Innovative Triboelectric Stent Sensor: Prospective Cardiovascular Health Monitor Device, Ulises Vidaurri Romero

Theses and Dissertations

Previous research that has focused on TENGs have lacked the proper application of this energy generator. Currently, heart disease remains the leading cause of death in the United States. With increasing demand for self-sustainable medical devices, this nitinol health monitor sensor device (NHMS) integrates the TENG applications with medical applications. The NHMS features memory shape nitinol electrodes that preserves the device structure while using PDMS and PVDF triboelectric effect to measure heart rate, blood pressure, and breathing patterns. Three constant pressures were measured in this study. At a constant pressure of stage 1, the NHMS produces an average AC of …

Ionospheric F-Layer Dipole Flute Instability Effects On Electromagnetic Scattering In A Magnetohydrodynamic Plasma, Andrew J. Knisely

Theses and Dissertations

The ionosphere has significant impact on radio frequency (RF) applications such as satellites, over-the-horizon radar, and commercial communication systems. The dynamic processes effecting the behavior of the ionic content leads to a variety of instabilities that adversely affect the quality of RF signals. In the F-layer ionosphere, flute instability persists, appearing as two radial regions of high and low density perturbations elongated along the earth's geomagnetic field lines. The sizes of flute structures are comparable to the wavelengths in the high frequency spectrum. The objective is to characterize the high frequency scattering of an incident field by developing a 3D …

Robot Area Coverage Path Planning In Aquatic Environments, Nare Karapetyan

Theses and Dissertations

This thesis is motivated by real world problems faced in aquatic environments. It addresses the problem of area coverage path planning with robots - the problem of moving an end-effector of a robot over all available space while avoiding existing obstacles. The problem is considered first in a 2D space with a single robot for specific environmental monitoring operations, and then with multi-robot systems — a known NP-complete problem. Next we tackle the coverage problem in 3D space - a step towards underwater mapping of shipwrecks or monitoring of coral reefs.

The first part of this thesis leverages human expertise …

Wavelet Methods For Very-Short Term Forecasting Of Functional Time Series, Jared K. Nystrom

Theses and Dissertations

Space launch operations at Kennedy Space Center and Cape Canaveral Space Force Station (KSC/CCSFS) are complicated by unique requirements for near-real time determination of risk from lightning. Lightning forecast weather sensor networks produce data that are noisy, high volume, and high frequency time series for which traditional forecasting methods are often ill-suited. Current approaches result in significant residual uncertainties and consequentially may result in forecasting operational policies that are excessively conservative or inefficient. This work proposes a new methodology of wavelet-enabled semiparametric modeling to develop accurate and timely forecasts robust against chaotic functional data. Wavelets methods are first used to …

Enterprise Resource Allocation For Intruder Detection And Interception, Adam B. Haywood

Theses and Dissertations

This research considers the problem of an intruder attempting to traverse a defender's territory in which the defender locates and employs disparate sets of resources to lower the probability of a successful intrusion. The research is conducted in the form of three related research components. The first component examines the problem in which the defender subdivides their territory into spatial stages and knows the plan of intrusion. Alternative resource-probability modeling techniques as well as variable bounding techniques are examined to improve the convergence of global solvers for this nonlinear, nonconvex optimization problem. The second component studies a similar problem but …

Deep Learning For Weather Clustering And Forecasting, Nathaniel R. Beveridge

Theses and Dissertations

Clustering weather data is a valuable endeavor in multiple respects. The results can be used in various ways within a larger weather prediction framework or could simply serve as an analytical tool for characterizing climatic differences of a particular region of interest. This research proposes a methodology for clustering geographic locations based on the similarity in shape of their temperature time series over a long time horizon of approximately 11 months. To this end an emerging and powerful class of clustering techniques that leverages deep learning, called deep representation clustering (DRC), are utilized. Moreover, a time series specific DRC algorithm …

Improvements To Emissive Plume And Shock Wave Diagnostics And Interpretation During Pulsed Laser Ablation Of Graphite, Timothy I. Calver

Theses and Dissertations

This dissertation covers nanosecond pulsed laser ablation of graphite for 4-5.7 J/cm² fluences with 248 nm and 532 nm lasers in 1-180 Torr helium, argon, nitrogen, air, and mixed gas. Three experiments were performed to improve the interpretation of common diagnostics used to characterize pulsed laser ablation, find simple but universal scaling relationships for comparing dynamics across different materials and ablation conditions, and provide a systematic analysis of graphite emissive plume and shock wave dynamics. A scaling of the Sedov-Taylor energy ratio was developed and validated for a range of studies despite differences in wavelength, pulse duration, fluence, and …

Neutron Energy Tuning Assemblies For Nuclear Weapon Environment Applications At The National Ignition Facility, Nicholas J. Quartemont

Theses and Dissertations

An energy tuning assembly was developed to spectrally shape the National Ignition Facility deuterium-tritium fusion neutron source to a notional thermonuclear and prompt fission neutron spectrum to fulfill neutron source capability gaps. The experimental neutron environment was characterized with activation dosimetry, neutronics and covariance models, and unfolded to determine the as-fielded neutron spectrum. The first energy tuning assembly was demonstrated to create synthetic spectrally accurate post-detonation fission products, enhancing U.S. technical nuclear forensics capabilities. ATHENA, a second-generation energy tuning assembly, was also optimized to meet similar objectives, but the new platform neutron fluence efficiency was increased by a factor of …

Transport, Photoluminescence & Photoconduction Characteristics Of Free Standing Two-Dimensional Γ-Alumina & Titanium Superlattice Doped Two-Dimensional Γ-Alumina Grown By Graphene-Assisted Atomic Layer Deposition, Elaheh Kheirandish

Theses and Dissertations

This study presents a facile high-yield bottom-up fabrication, morphology, crystallographic and optoelectronic characterization of free-standing quasi-2D γ-alumina, a non van der Waals 2D material. The synthesis comprises a multi-cycle atomic layer deposition (ALD) of amorphous alumina on a porous interconnected graphene foam as a growth scaffold and removed next by annealing and sintering the alumina/graphene/alumina sandwich at ~ 800 °C in air . The crystallographic and structural characteristics of the formed non-van der Waals quasi 2D γ-alumina were studied by X-ray diffraction (XRD), selected area electron diffraction (SAED), and high-resolution transmission electron microscopy (HRTEM). This analysis revealed the synthesized 2D …

Development Of A 7 Dof Exoskeleton Robot For Rehabilitation Of Lower Extremity, Sk Hasan

Theses and Dissertations

The World Health Organization reports that worldwide about 1 billion people have some form ofdisability. Of these, 110-190 million people have significant difficulties in functioning (mainly upper and lower extremity disability) independently. The major causes of human lower extremity disability include stroke, trauma, spinal cord injuries, and muscular dystrophy. Every 40 seconds, someone in the United States has a stroke. A statistic shows that approximately 65% of post-stroke patients suffer lower extremity impairment. Rehabilitation programs are the main method to promote functional recovery in disabled individuals. The conventional therapeutic approach requires a long commitment from a therapist or a clinician. …

Theoretical And Computational Modeling Of Contaminant Removal In Porous Water Filters, Aman Raizada

Theses and Dissertations

Contaminant transport in porous media is a well-researched problem across many scientific and engineering disciplines, including soil sciences, groundwater hydrology, chemical engineering, and environmental engineering. In this thesis, we attempt to tackle this multiscale transport problem using the upscaling approach, which leads to the development of macroscale models while considering a porous medium as an averaged continuum system.

First, we describe a volume averaging-based method for estimating flow permeability in porous media. This numerical method overcomes several challenges faced during the application of traditional permeability estimation techniques, and is able to accurately provide the complete permeability tensor of a porous …

Medical Image Segmentation Using Machine Learning, Masoud Khani

Theses and Dissertations

Image segmentation is the most crucial step in image processing and analysis. It can divide an image into meaningfully descriptive components or pathological structures. The result of the image division helps analyze images and classify objects. Therefore, getting the most accurate segmented image is essential, especially in medical images. Segmentation methods can be divided into three categories: manual, semiautomatic, and automatic. Manual is the most general and straightforward approach. Manual segmentation is not only time-consuming but also is imprecise. However, automatic image segmentation techniques, such as thresholding and edge detection, are not accurate in the presence of artifacts like noise …

On-Chip Nanoscale Plasmonic Optical Modulators, Abdalrahman Mohamed Nader Abdelhamid

Theses and Dissertations

In this thesis work, techniques for downsizing Optical modulators to nanoscale for the purpose of utilization in on chip communication and sensing applications are explored. Nanoscale optical interconnects can solve the electronics speed limiting transmission lines, in addition to decrease the electronic chips heat dissipation. A major obstacle in the path of achieving this goal is to build optical modulators, which transforms data from the electrical form to the optical form, in a size comparable to the size of the electronics components, while also having low insertion loss, high extinction ratio and bandwidth. Also, lap-on-chip applications used for fast diagnostics, …

Rebalancing Shared Mobility Systems By User Incentive Scheme Via Reinforcement Learning, Matthew Brian Schofield

Theses and Dissertations

Shared mobility systems regularly suffer from an imbalance of vehicle supply within the system, leading to users being unable to receive service. If such imbalance problems are not mitigated some users will not be serviced. There is an increasing interest in the use of reinforcement learning (RL) techniques for improving the resource supply balance and service level of systems. The goal of these techniques is to produce an effective user incentivization policy scheme to encourage users of a shared mobility system to slightly alter their travel behavior in exchange for a small monetary incentive. These slight changes in user behavior …

One Dimensional Study Of Magnetoplasmadynamic Thrusters For A Potential New Class Of Heavy Ion Drivers For Plasma Jet Driven Magnetoinertial Fusion, Patrick M. Brown

Theses and Dissertations

Plasma Jet Driven Magnetoinertial Fusion (PJMIF) requires high velocity heavy ion drivers in order to compress a magnetized target to fusion conditions. Previous work with heavy ion drivers has revealed sub-par accelerations due to plasma instabilities; thus, it is necessary to investigate new methods of heavy ion plasma acceleration. One such method is Magnetoplasmadynamic (MPD) thrusters. Past studies of these thrusters have been conducted at an initial temperature at or below the energy of full ionization. Here MPD thrusters are investigated using a Godunov type MHD solver with a Harten-Lax van Leer-D (HLLD) flux solving scheme assuming the plasma is …

Statistically Defensible Wind Tunnel Models, Timothy A. Roche

Theses and Dissertations

Wind tunnels are used to test scale-model air frames in order to collect aerodynamic data. The Subsonic Aerodynamic Research Laboratory (SARL) Wind Tunnel is a low speed wind tunnel located at Wright-Patterson Air Force Base. The SARL Wind Tunnel team approached AFIT for assistance in creating statistically defensible models for the conditions inside the wind tunnel. During a wind tunnel test, pressure sensors cannot be placed at the test model. Instead, pressure is measured by a pitot probe permanently mounted in the corner of the test chamber. The pressure at the model location is predicted from the measurements taken by …

Stock Markets Performance During A Pandemic: How Contagious Is Covid-19?, Yara Abushahba

Theses and Dissertations

Background and Motivation: The coronavirus (“COVID-19”) pandemic, the subsequent policies and lockdowns have unarguably led to an unprecedented fluid circumstance worldwide. The panic and fluctuations in the stock markets were unparalleled. It is inarguable that real-time availability of news and social media platforms like Twitter played a vital role in driving the investors’ sentiment during such global shock.

Purpose:The purpose of this thesis is to study how the investor sentiment in relation to COVID-19 pandemic influenced stock markets globally and how stock markets globally are integrated and contagious. We analyze COVID-19 sentiment through the Twitter posts and investigate its …

Design Of A Novel Manual And Automated Penetration Testing Framework For Connected Industrial Control Systems (Ics), Rafat Elsharef

Theses and Dissertations

This research presents the design of new framework—a manually executed and an automated penetration testing process for Connected Industrial Control Systems (ICS). Both frameworks were built using open-source security software and ICS equipment currently used in critical infrastructure, manufacturing companies, and other institutions in the United States and around the world. Existing penetration testing frameworks have largely been focused on manual testing and are specific to Information Technology (IT). In addition, a new severity scoring system framework, called Common Vulnerability Scoring System for Industrial Control Systems (CVSS-ICS), was recommended for calculating the severity score in Industrial Control Systems (ICS).The broader …

Development Of Novel Compound Controllers To Reduce Chattering Of Sliding Mode Control, Mehran Rahmani

Theses and Dissertations

The robotics and dynamic systems constantly encountered with disturbances such as micro electro mechanical systems (MEMS) gyroscope under disturbances result in mechanical coupling terms between two axes, friction forces in exoskeleton robot joints, and unmodelled dynamics of robot manipulator. Sliding mode control (SMC) is a robust controller. The main drawback of the sliding mode controller is that it produces high-frequency control signals, which leads to chattering. The research objective is to reduce chattering, improve robustness, and increase trajectory tracking of SMC. In this research, we developed controllers for three different dynamic systems: (i) MEMS, (ii) an Exoskeleton type robot, and …

A Simulation Framework For Traffic Safety With Connected Vehicles And V2x Technologies, Md Abu Sayed

Theses and Dissertations

With the advancement in automobile technologies, existing research shows that connected vehicle (CV) technologies can provide better traffic safety through Surrogate Safety Measure (SSM). CV technologies involves two network systems: traffic network and wireless communication network. We found that the research in the wireless communication network for CV did not interact properly with the research in SSM in transportation network, and vice versa. Though various SSM has been proposed in previous studies, a few of them have been tested in simulation software in limited extent. On the other hand, A large body of researchers proposed various communication architecture for CV …

Application Of Deep Learning For Imaging-Based Stream Gaging, Ryan Lee Vanden Boomen

Theses and Dissertations

In the field of water resources management, one vital instrument utilized is the stream gage. Stream gages monitor and record flow and water height within some water body. The United States Geological Survey maintains a network of stream gages at many locations across the country. Many of these sites are also equipped with webcams monitoring the state of the water body at the moment of measurement. Previous studies have outlined methods to approximate stream gage data remotely with limitations such as the requirement of detailed depth information for each site. This study seeks to create a process for training a …

Wound Image Classification Using Deep Convolutional Neural Networks, Behrouz Rostami

Theses and Dissertations

Artificial Intelligence (AI) includes subfields like Machine Learning (ML) and DeepLearning (DL) and discusses intelligent systems that mimic human behaviors. ML has been used in a wide range of fields. Particularly in the healthcare domain, medical images often need to be carefully processed via such operations as classification and segmentation. Unlike traditional ML methods, DL algorithms are based on deep neural networks that are trained on a large amount of labeled data to extract features without human intervention. DL algorithms have become popular and powerful in classifying and segmenting medical images in recent years. In this thesis, we shall study …

Development Of A Cyberinfrastructure For Assessment Of The Lower Rio Grande Valley North And Central Watersheds Characteristics, Linda Isabel Navarro Navarro

Theses and Dissertations

Due to an increase in urbanization in the Lower Rio Grande Valley (LRGV), there have been substantial modifications to hydrology causing a decline in water quality to the Laguna Madre watershed. The major concern is the inflow of freshwater from the North and Central waterways released to the Lower Laguna Madre which is designated as an impaired watershed for high concentrations of bacteria and low dissolved oxygen. The objective of this study is to perform a watershed characterization to determine potential pollution sources of each watershed by developing a cyberinfrastructure and collect a wide inventory of data. The objective will …