Engineering Commons^™

Unmanned Aircraft Systems For Precision Meteorology: An Analysis Of Gnss Position Measurement Error And Embedded Sensor Development, Karla S. Ladino

Theses and Dissertations--Biosystems and Agricultural Engineering

The overarching objective of this research was to enhance our comprehension of the three-dimensional precision of meteorological measurements obtained using small unmanned aircraft systems (UAS). Two complimentary experiments were conducted to achieve this objective.

The first experiment entailed the development and implementation of a system to determine the global navigation satellite system (GNSS) position accuracy on a UAS platform. This system was utilized to assess the static and dynamic accuracy of L1 and L1/L2 GNSS receivers in real-time kinematic (RTK) and non-RTK fix modes. Adjusted two-sample t-tests revealed significant differences in horizontal and vertical error between RTK and non-RTK receivers …

Life Cycle Assessment Of Air Classification As A Sulfur Mitigation Technology In Pine Residue Feedstocks, Ashlee Edmonson

Theses and Dissertations--Biosystems and Agricultural Engineering

Sulfur accumulation during biofuel production is pollutive, toxic to conversion catalysts, and causes the premature breakdown of processing equipment. Air classification is an effective preprocessing technology for ash and sulfur removal from biomass feedstocks. A life cycle assessment (LCA) sought to understand the environmental impacts of implementing air classification as a sulfur-mitigation technique for pine residues. Energy demand and material balance for preprocessing were simulated using SimaPro and the Argonne National Laboratory’s GREET model, specifically focusing on comparing the global warming potential (GWP) of grid electricity versus bioelectricity scenarios. Overall, the grid electricity scenario had a GWP impact over 7 …

Multi-Agent Learning For Game-Theoretical Problems, Kshitija Taywade

Theses and Dissertations--Computer Science

Multi-agent systems are prevalent in the real world in various domains. In many multi-agent systems, interaction among agents is inevitable, and cooperation in some form is needed among agents to deal with the task at hand. We model the type of multi-agent systems where autonomous agents inhabit an environment with no global control or global knowledge, decentralized in the true sense. In particular, we consider game-theoretical problems such as the hedonic coalition formation games, matching problems, and Cournot games. We propose novel decentralized learning and multi-agent reinforcement learning approaches to train agents in learning behaviors and adapting to the environments. …

A Secure And Distributed Architecture For Vehicular Cloud And Protocols For Privacy-Preserving Message Dissemination In Vehicular Ad Hoc Networks, Hassan Mistareehi

Theses and Dissertations--Computer Science

Given the enormous interest in self-driving cars, Vehicular Ad hoc NETworks (VANETs) are likely to be widely deployed in the near future. Cloud computing is also gaining widespread deployment. Marriage between cloud computing and VANETs would help solve many of the needs of drivers, law enforcement agencies, traffic management, etc. The contributions of this dissertation are summarized as follows: A Secure and Distributed Architecture for Vehicular Cloud: Ensuring security and privacy is an important issue in the vehicular cloud; if information exchanged between entities is modified by a malicious vehicle, serious consequences such as traffic congestion and accidents can …

Machine-Learning-Powered Cyber-Physical Systems, Enrico Casella

Theses and Dissertations--Computer Science

In the last few years, we witnessed the revolution of the Internet of Things (IoT) paradigm and the consequent growth of Cyber-Physical Systems (CPSs). IoT devices, which include a plethora of smart interconnected sensors, actuators, and microcontrollers, have the ability to sense physical phenomena occurring in an environment and provide copious amounts of heterogeneous data about the functioning of a system. As a consequence, the large amounts of generated data represent an opportunity to adopt artificial intelligence and machine learning techniques that can be used to make informed decisions aimed at the optimization of such systems, thus enabling a variety …

Hard-Hearted Scrolls: A Noninvasive Method For Reading The Herculaneum Papyri, Stephen Parsons

Theses and Dissertations--Computer Science

The Herculaneum scrolls were buried and carbonized by the eruption of Mount Vesuvius in A.D. 79 and represent the only classical library discovered in situ. Charred by the heat of the eruption, the scrolls are extremely fragile. Since their discovery two centuries ago, some scrolls have been physically opened, leading to some textual recovery but also widespread damage. Many other scrolls remain in rolled form, with unknown contents. More recently, various noninvasive methods have been attempted to reveal the hidden contents of these scrolls using advanced imaging. Unfortunately, their complex internal structure and lack of clear ink contrast has prevented …

Deep Learning-Based Intrusion Detection Methods For Computer Networks And Privacy-Preserving Authentication Method For Vehicular Ad Hoc Networks, Ayesha Dina

Theses and Dissertations--Computer Science

The incidence of computer network intrusions has significantly increased over the last decade, partially attributed to a thriving underground cyber-crime economy and the widespread availability of advanced tools for launching such attacks. To counter these attacks, researchers in both academia and industry have turned to machine learning (ML) techniques to develop Intrusion Detection Systems (IDSes) for computer networks. However, many of the datasets use to train ML classifiers for detecting intrusions are not balanced, with some classes having fewer samples than others. This can result in ML classifiers producing suboptimal results. In this dissertation, we address this issue and present …

Peer-To-Peer Energy Trading In Smart Residential Environment With User Behavioral Modeling, Ashutosh Timilsina

Theses and Dissertations--Computer Science

Electric power systems are transforming from a centralized unidirectional market to a decentralized open market. With this shift, the end-users have the possibility to actively participate in local energy exchanges, with or without the involvement of the main grid. Rapidly reducing prices for Renewable Energy Technologies (RETs), supported by their ease of installation and operation, with the facilitation of Electric Vehicles (EV) and Smart Grid (SG) technologies to make bidirectional flow of energy possible, has contributed to this changing landscape in the distribution side of the traditional power grid.

Trading energy among users in a decentralized fashion has been referred …

Building Energy Modeling And Studies Of Electric Power Distribution Systems With Distributed Energy Resources, Evan S. Jones

Theses and Dissertations--Electrical and Computer Engineering

There is significant opportunity for savings in energy and investment from improved performance of electric Power Distribution Systems (PDSs) through optimal planning and operation of conventional voltage-controlling devices. Novel multi-step model conversion and optimal capacitor planning (OCP) procedures are proposed for large-scale utility PDSs and are exemplified with an existing utility circuit of approximately 4,000 buses. Simulated optimal control and operation is achieved with a cluster-based approach that utilizes load-forecasting to minimize equipment degradation by intelligently dispersing device setting adjustments over time such that they remain most applicable. Improved performance may also be achieved through smart building technologies and Virtual …

Optimal Design Of Special High Torque Density Electric Machines Based On Electromagnetic Fea, Murat G. Kesgin

Theses and Dissertations--Electrical and Computer Engineering

Electric machines with high torque density are essential for many low-speed direct-drive systems, such as wind turbines, electric vehicles, and industrial automation. Permanent magnet (PM) machines that incorporate a magnetic gearing effect are particularly useful for these applications due to their potential for achieving extremely high torque density. However, when the number of rotor polarities is increased, there is a corresponding need to increase the number of stator slots and coils proportionally. This can result in manufacturing challenges. A new topology of an axial-flux vernier-type machine of MAGNUS type has been presented to address the mentioned limitation. These machines can …

Computational Investigation Of A Series Of Small Molecules As Potential Compounds For Lysyl Hydroxylase-2 (Lh2) Inhibition, Yazdan Maghsoud, Erik Antonio Vázquez-Montelongo, Xudong Yang, Chengwen Liu, Zhifeng Jing, Juhoon Lee, Matthew Harger, Ally K. Smith, Miguel Espinoza, Hou-Fu Guo, Jonathan M. Kurie, Kevin N. Dalby, Pengyu Ren, G. Andrés Cisneros

Markey Cancer Center Faculty Publications

The catalytic function of lysyl hydroxylase-2 (LH2), a member of the Fe(II)/αKG-dependent oxygenase superfamily, is to catalyze the hydroxylation of lysine to hydroxylysine in collagen, resulting in stable hydroxylysine aldehyde-derived collagen cross- links (HLCCs). Reports show that high amounts of LH2 lead to the accumulation of HLCCs, causing fibrosis and specific types of cancer metastasis. Some members of the Fe(II)/αKG-dependent family have also been reported to have intramolecular O2 tunnels, which aid in transporting one of the required cosubstrates into the active site. While LH2 can be a promising target to combat these diseases, efficacious inhibitors are still lacking. We …

A Phase Change Memory And Dram Based Framework For Energy-Efficient And High-Speed In-Memory Stochastic Computing, Supreeth Mysore

Theses and Dissertations--Electrical and Computer Engineering

Convolutional Neural Networks (CNNs) have proven to be highly effective in various fields related to Artificial Intelligence (AI) and Machine Learning (ML). However, the significant computational and memory requirements of CNNs make their processing highly compute and memory-intensive. In particular, the multiply-accumulate (MAC) operation, which is a fundamental building block of CNNs, requires enormous arithmetic operations. As the input dataset size increases, the traditional processor-centric von-Neumann computing architecture becomes ill-suited for CNN-based applications. This results in exponentially higher latency and energy costs, making the processing of CNNs highly challenging.

To overcome these challenges, researchers have explored the Processing-In Memory (PIM) …

Establishing The Foundation To Robotize Complex Welding Processes Through Learning From Human Welders Based On Deep Learning Techniques, Rui Yu

Theses and Dissertations--Electrical and Computer Engineering

As the demand for customized, efficient, and high-quality production increases, traditional manufacturing processes are transforming into smart manufacturing with the aid of advancements in information technology, such as cyber-physical systems (CPS), the Internet of Things (IoT), big data, and artificial intelligence (AI). The key requirement for integration with these advanced information technologies is to digitize manufacturing processes to enable analysis, control, and interaction with other digitized components. The integration of deep learning algorithm and massive industrial data will be critical components in realizing this process, leading to enhanced manufacturing in the Future of Work at the Human-Technology Frontier (FW-HTF).

This …

A Flexible Photonic Reduction Network Architecture For Spatial Gemm Accelerators For Deep Learning, Bobby Bose

Theses and Dissertations--Electrical and Computer Engineering

As deep neural network (DNN) models increase significantly in complexity and size, it has become important to increase the computing capability of specialized hardware architectures typically used for DNN processing. The major linear operations of DNNs, which comprise the fully connected and convolution layers, are commonly converted into general matrix-matrix multiplication (GEMM) operations for acceleration. Specialized GEMM accelerators are typically employed to implement these GEMM operations, where a GEMM operation is decomposed into multiple vector-dot-product operations that run in parallel. A common challenge that arises in modern DNNs is the mismatch between the matrices used for GEMM operations and the …

Application Of Conventional Feedforward And Deep Neural Networks To Power Distribution System State Estimation And State Forecasting, James Paul Carmichael

Theses and Dissertations--Electrical and Computer Engineering

Classical neural networks such as feedforward multilayer perceptron models (MLPs) are well established as universal approximators and as such, show promise in applications such as static state estimation in power transmission systems. This research investigates the application of conventional neural networks (MLPs) and deep learning based models such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to mitigate challenges in power distribution system state estimation and forecasting based upon conventional analytic methods. The ability of MLPs to perform regression to perform power system state estimation will be investigated. MLPs are considered based upon their promise to learn …

Modeling The Early Visual System, Nicholas Lanning

Theses and Dissertations--Electrical and Computer Engineering

There are two encoding schema present in simple cells in the early visual system of vertebrates: the retinal simple cells activate highly when the receptive field contains a center surround stimulus, while the primary visual cortex’s (V1) simple cells activate highly when the receptive field contains visual edges. Work has been done in the past to enforce constraints on visual machine learning such that the retinal or V1 encoding is learned, but this work is often done to emulate retinal and V1 encoding in a vacuum. Recent work using convolutional neural networks focuses on anatomical constraints along with a supervised …

Development Of Polymeric Sorbents As Reusable Filtration Systems For Remediation Of Pfas Contaminated Water, E. Molly Frazar

Theses and Dissertations--Chemical and Materials Engineering

Decades of use of per- and polyfluoroalkyl substances (PFAS) in a multitude of consumer and industry-based products have led to a devastating amount of soil and water contamination. Although these chemicals and compounds possess advantageous qualities – such as that of PFAS in the role of fire-fighting foams that have no doubt saved countless lives and homes, we must take responsibility for the anthropogenic hazards that threaten our global health. This entails being able to cost-effectively remediate problems created in the past from overuse of toxic substances that could negatively impact our future, and in this case, the future of …

Mechanical And Adhesive Properties Of Supramolecular, Mussel-Inspired Hydrogels, Daniel Darby

Theses and Dissertations--Chemical and Materials Engineering

The exceptional mechanical and adhesive properties of mussel byssal threads come from supramolecular interactions of ligands found in the proteins comprising them. Ligands can form hydrophobic interactions, π-π stacking interactions, and hydrogen bonds, but the strongest supramolecular interaction they demonstrate is metal-ligand coordination. Specifically, Histidine (His-) and nitrodopamine (nDOPA) ligands coordinate to metals in a bidentate fashion and take on tris-, bis-, and mono- modalities (3, 2, or 1 ligand per ion, respectively). The distribution of these modalities is controlled by equilibrium thermodynamics. These same ligands can be used as crosslinks in supramolecular hydrogel networks. Supramolecular hydrogel networks are dissipative, …

Application Of Multi-Scale Computational Techniques To Complex Materials Systems, Mujan N. Seif

Theses and Dissertations--Chemical and Materials Engineering

The applications of computational materials science are ever-increasing, connecting fields far beyond traditional subfields in materials science. This dissertation demonstrates the broad scope of multi-scale computational techniques by investigating multiple unrelated complex material systems, namely scandate thermionic cathodes and the metallic foam component of micrometeoroid and orbital debris (MMOD) shielding. Sc-containing "scandate" cathodes have been widely reported to exhibit superior properties compared to previous thermionic cathodes; however, knowledge of their precise operating mechanism remains elusive. Here, quantum mechanical calculations were utilized to map the phase space of stable, highly-faceted and chemically-complex W nanoparticles, accounting for both finite temperature and chemical …

Surface Properties, Work Function, And Thermionic Electron Emission Characterization Of Materials For Next-Generation Dispenser Cathodes, Antonio Mantica

Theses and Dissertations--Chemical and Materials Engineering

A dispenser cathode’s ability to thermionically emit electrons is highly dependent on its material properties, especially those of the surface. Understanding the relationship between surface properties and electron emission, therefore, is vital to reach the next generation of the many vacuum electron devices (VEDs) that rely on the physics of electron emission. In the past century, many techniques have been developed to characterize material surfaces and quantify thermionic emission. These techniques are based on a wide range of different physical phenomena, including measuring photoemission via the photoelectric effect, measuring the electrostatic potential between metals in electrical contact, and current collection …

Molecular Understanding Of Zwitterions And Quantum Computing For Sustainability, Manh Tien Nguyen

Theses and Dissertations--Chemical and Materials Engineering

The sustainable development of society needs sustainable energy solutions and the mitigation of greenhouse gas emissions. One key subject in this area is the development of safe and efficient ion-based batteries. Moreover, CO₂ capture is a crucial pathway in mitigating emissions from the combustion of fossil fuels. Ongoing efforts are to improve both technologies' safety and efficiency. This thesis presents our efforts to conduct computational research on understanding advanced zwitterionic electrolytes and CO₂ capture. Chapters 2-4 illustrate the computational research to understand ionic solvation in zwitterionic electrolytes. Solid-state electrolytes are essential for safer batteries. While solid polymer electrolytes …

Developing And Modeling Tunable Nanofiltration And Functionalized Membranes For The Separation Of Organics And Inorganics From Water: Per- And Polyfluoroalkyl Substances, Lanthanides, And More, Francisco Cecil Leniz-Pizarro

Theses and Dissertations--Chemical and Materials Engineering

In a world where clean water predictability is challenged by both global warming and the contamination of our natural resources, it is our responsibility to advance water separation technologies into more efficient processes and to lessen their environmental footprint. Pore and surface functionalization of membranes can enhance the separation of ions from water and even help overcome intrinsic challenges that some current separation technologies possess. If we begin with a complete fundamental understanding of the nanoscale interactions that occur in the process of separating ions from water, then we can engineer new functionalized composite membranes to provide alternative processes keeping …

Solid-State Electrolytes For Long Duration Molten Sodium Batteries, Ryan C. Hill

Theses and Dissertations--Chemical and Materials Engineering

Rechargeable batteries have become a staple of modern society, driving the development and expansion of portable electronics and electric vehicles. The long lifetime and high energy density provided by batteries have made them excellent candidates for use in the emerging field of long duration, grid-scale energy storage. However, traditional battery chemistries, such as Li-ion, are not an ideal solution for grid-scale storage, due to high-cost raw materials that are often sourced from volatile markets. Sodium-based batteries are a promising alternative for long duration storage, owing to the domestic abundance of raw materials and energy densities that nearly rival that of …

Leveraging Cell-Substrate Adhesion And Cell Migratory Properties In Skeletal Muscle Constructs And Cancer Metastasis Assays, Lauren E. Mehanna

Theses and Dissertations--Chemical and Materials Engineering

Volumetric muscle loss (VML) remains one of the few skeletal muscle injuries without a reliable and repeatable treatment. In large volume muscle injuries, muscle fibers as well as the surrounding connective tissue are damaged, preventing therapeutic muscle stem cells, called myogenic progenitor cells (MPCs), from reaching the injury site and initiating repair. There is a clinical need to rapidly fabricate in vitro muscle tissue constructs that mimic the native tissue organization, with aligned myotubes, for insertion and integration at the patient’s injury site. In this dissertation, we utilize the MPC’s natural propensity to close gaps across an injury site to …

The Application Of Photoelectron Spectroscopies In Analyzing The Impact Of Interfacial Energetics On Perovskite Solar Cells, Tuo Liu

Theses and Dissertations--Chemistry

In recent years, organic-inorganic metal halide perovskites (HP) have garnered tremendous attention in photovoltaic research. This attention is attributed to their low cost and excellent optoelectronic properties, including large absorption coefficients, tunable bandgaps, long charge-carrier diffusion lengths, and low densities of deep trap states. Inverted p-i-n architecture perovskite solar cells (PSCs) are of intense interest and are generally regarded as more amenable to low-temperature solution processing. Nevertheless, the development of inverted PSCs is lagging the conventional architecture devices. Imperfect energy level alignments and charge carrier recombination, especially at the interface between perovskite and electron transport layers (ETLs), are two main …

Mechanical And Morphological Characterization Of Energy Materials By Nanoindentation And Scanning Probe Microscopy, Jacob Hempel

Theses and Dissertations--Physics and Astronomy

The demand for robust materials in energy-based applications such as solid electrolytes for batteries, piezoelectric composites for mechanical energy harvesting, or new polymer materials for polymer binders in composite electrodes is on the rise. Mechanical degradation is the most common way for devices to fail in operation, thus a thorough understanding of a material’s mechanical properties is essential for application purposes.

In this dissertation, the mechanical characterization of novel boron cluster solid electrolytes is presented. Therein, we determine the elastic modulus and hardness of these compounds using instrumented indentation. A comparison is made against other solid electrolytes. It is found …

Determining Kinetic Parameters Of Cellulose And Lignin Pyrolysis By Gaussian Process Regression (Gpr) Method, Pichayaporn Viriya-Amornkij, Kazunori Kuwana

Progress in Scale Modeling, an International Journal

The ignition and flame-spread processes in the forest and urban fires involve the pyrolysis reactions of biomass materials. One of the most common methods for estimating the fire performance of a material is the evaluation of kinetic parameters, i.e., activation energy (𝐸), pre-exponential factor (𝐴), and reaction model (𝑓(𝛼)), from thermogravimetric analysis (TG) data. Typically, 𝐸 is estimated based on an Arrhenius-type equation such as Kissinger, Kissinger-Akahira-Sunose (KAS), and Friedman equations. Then, its value is adjusted along with other parameters by assuming a reaction model, e.g., the 𝑛-order model. This study proposes a Gaussian process regression (GPR) method to determine …

Effect Of Hydrogen Sulfide Content On The Combustion Characteristics Of Biogas Fuel In Homogenous Charge Compression Ignition Engines, Mohammad Alrbai, Adnan Darwish Ahmad, Sameer Al-Dahidi, Ahmad M. Abubaker, Loiy Al-Ghussain, Hassan S. Hayajneh, Nelson Akafuah

Institute of Research for Technology Development Faculty Publications

The use of biogas fuel in homogeneous charge compression ignition (HCCI) engines has been promoted recently due to the environmental advantages. However, hydrogen sulfide (H2 S) forms a non-environment friendly content and biogas impurity that its removal is associated with high costs. In this study, the effect of using biogas fuel in the HCCI engines under different operating conditions is investigated to shed light on the best scenarios of biogas combustion, even with the presence of H2S contents. A modified reaction mechanism is introduced by considering the re- actions of H2 S with other species in the air-fuel mixture. The …

Spf-R Online User Guide, Eric Green, Paul Ross, Christopher Blackden, William Staats, Reginald Souleyrette

Kentucky Transportation Center Research Report

SPF-R Online greatly improves and simplifies the SPF development process. Building on the original SPF-R script, the online tool eliminates many of the technical barriers associated with the original process, allowing for a much larger audience to take advantage of SPF-R’s capabilities. Like the original SPF-R script, SPF-R Online lets users quickly explore the effect of roadway network heterogeneity on SPF development, compare CURE plots , and compare goodness-of-fit measures to decide which SPF is most appropriate for a given dataset.

Scale Modeling Of An Appearance Of Downwash Pattern Of Hot Smoke Ejected From Chimney In The Turbulent Cross Flow, Xangpheuak Inthavideth, Nobumasa Sekishita, Sounthisack Phommachanh, Yuji Nakamura

Progress in Scale Modeling, an International Journal

This study aims to elucidate the scaling law to provide the critical condition on appearance of the downwash pattern of the hot smoke ejected from a chimney in a turbulent cross flow. A specially designed wind tunnel with an active turbulence generator developed by Makita was adopted to offer a quasi-isotropic turbulence field in a lab-scale test facility. A heated jet with smoke is issued into the cross flow from the vertically oriented chimney placed in the test section of the wind tunnel. In this study, the experimental parameters considered are temperature of the heated jet (smoke), jet ejected velocity, …