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Detecting And Analyzing Frequency Events In Power Systems Using Tunable Parameters-Based Algorithms: Development, Optimization, And Analysis, Hussain A. Alghamdi

Dissertations and Theses

This dissertation addresses the challenge of detecting frequency events in diverse power systems by enhancing existing frequency event detection methods through detection process modifications and developing unique tunable parameters. Since system characteristics differ across regions, frequency event detection algorithms must be customized by domain experts for each balancing area using tunable parameters. By optimizing these parameters for specific power system, the algorithms can accurately detect frequency events and can also be used for further analysis to determine trends in frequency events over time, ensuring system stability.

This dissertation focuses on the enhancement and optimization of frequency event detection algorithms. These …

Load Forecasting And Modeling For Power System, Han Guo

Electrical Engineering Theses and Dissertations

Accurate load forecasting and modeling play a pivotal role in ensuring the stability, reliability, and economic efficiency of modern power systems. With the increasing integration of renewable energy sources, distributed energy resources, and demand-side management strategies, power systems are becoming more dynamic and complex, making traditional load forecasting methods inadequate. This dissertation introduces two novel approaches to address the challenges associated with day-ahead load forecasting and load modeling.

First, a Diffusion Model-Based Probabilistic Day-Ahead Load Forecasting (PDALF) Framework is proposed to enhance the accuracy and robustness of load forecasting. By employing a conditional denoising diffusion probabilistic model (DDPM), the framework, …

Who Owns The Wind: The Absence Of Community Wind Farms In California, Sky Berry-Weiss

Master's Projects and Capstones

Community ownership structures for wind farms have been around for decades, particularly in European countries, due to high socioeconomic benefits. Given these significant benefits, one might expect community wind to thrive in the United States—especially in a state like California, which prides itself on progressive climate policy and renewable energy leadership. Yet utility-scale community wind remains largely absent from research on California’s energy system, raising questions about its existence in the state. To pinpoint how many utility-scale community owned wind farms are in California, this study surveys every operational wind turbine in the state. After classifying each wind farm by …

Machine Learning-Driven Optimization Of Piezoelectric Energy Harvesters For Low-Frequency Applications, Kyrillos Selim, Login Moustafa, Sameh O. Abdellatif

Electrical Engineering

To enhance energy harvesting efficiency, this paper explores the optimization of a cantilever-based piezoelectric energy harvester by integrating advanced machine learning (ML) methodologies. Leveraging a meticulously trained model on data sourced from a sophisticated two-dimension (2D) COMSOL Multiphysics numerical simulation, the study focuses on the critical input parameters, particularly the dimensions of the piezoelectric thin film. Through extensive simulations, the analysis delves into power density extraction and resonance frequency for various configurations. The culmination of rigorous simulations and analysis has led to the identification of an optimal design configuration for the cantilever piezoelectric energy harvester, characterized by a length of …

Aerial Robotic Studies Of Volcanic Co2 Emissions, John Ericksen

Computer Science ETDs

Volcanic systems are inherently complex, involving dynamic interactions among magma flow, gas emissions, and atmospheric dispersion. This dissertation focuses on developing and analyzing autonomous UAS algorithms for efficiently surveying volcanic CO2 plumes, introducing several novel methods: the LoCUS algorithm, a swarm coordination and self-healing algorithm that supports gradient-based plume tracking, a transect-based technique that employs a 2D Gaussian fit to calculate CO2 plume flux, and the Sketch algorithm for rapid plume boundary tracing. By treating multiple UAS as a single scientific instrument, these methods leverage swarm algorithms to use in-situ data in ways impossible with individual drones. Validated through simulations …

Improved Electric Load Modeling Of Residential Air Conditioning, Julius Johnson

UNLV Theses, Dissertations, Professional Papers, and Capstones

The recent increase in energy production from renewable resource introduces a new challenge in managing and maintaining balance between electricity supply and demand, due to uncertainty and variability of wind speed and solar irradiance. To address this growing problem, demand-side management, such as Demand Response (DR) programs, is employed to adjust power consumption. Residential air conditioners (ACs) are the most suitable candidates for DR, due to their intensive power consumption and inherent thermal inertia that allows flexibility in their operations (by adjusting their set-point temperatures) without sacrificing customer comfort. Most prior research on AC load models assumes that such a …

El, A Navigational Assistant Based Upon Echolocation, Arthur Mazer

UNLV Theses, Dissertations, Professional Papers, and Capstones

This thesis investigates the integration of a parametric speaker with a microphone array to enhance the echolocation of objects. A parametric speaker focuses ultrasonic and audible waves in a specified direction. This endows the parametric speaker with the capacity to focus waves across a wide frequency spectrum enabling adaptation of the frequency to environmental considerations.Beam forming allows one to focus a microphone array in a specified direction. The thesis investigates different microphone array configurations for the purpose of enhancing the echolocation ability of an echolocation device. The primary goal of the thesis is the construction and testing of an echolocation …

An Edge Computing Device Optimized And Transfer Learning Enhanced Deep Learning Model For Detecting Wildfire Flame And Smoke, Giovanny Vazquez

UNLV Theses, Dissertations, Professional Papers, and Capstones

The integration of autonomous unmanned aerial vehicles (UAVs) with edge computing technology and deep learning (DL)-based object detection offers a groundbreaking solution for real-time wildfire detection, enabling rapid data processing directly on devices and minimizing response delays in critical scenarios. However, although showing early promise, performance is often constrained by limited training data and edge computing devices that lack graphics processing unit (GPU) acceleration. This thesis seeks to address these limitations in two stages.First, this work explores the transformative potential of Transfer Learning (TL) to enhance wildfire object detection model accuracy while also investigating TL’s impact, for DL-based object detection …

Explorations Of Amplified Feedback In Quantum Circuits, Maxwell B. Weiner

Dartmouth College Master’s Theses

The Josephson Traveling Wave Parametric Amplifier (TWPA) has emerged as a key technology for high-fidelity qubit readout in superconducting quantum computing. By leveraging the nonlinear inductance of an array of Josephson Junctions, the TWPA enables broadband, near-quantum-limited amplification with minimal added noise, significantly improving the signal-to-noise ratio in qubit measurements. Unlike traditional resonant parametric amplifiers, which suffer from bandwidth constraints, the traveling wave design of the TWPA allows for wideband operation, making it particularly suited for multiplexed readout of both simple qubits and large-scale quantum processors.

In this thesis, we explore how the TWPA can be integrated into a feedback …

High Dynamic Range Actively Quenched Silicon-Germanium Single-Photon Avalanche Diodes, Abraham Castaneda

UNLV Theses, Dissertations, Professional Papers, and Capstones

Single-photon avalanche diodes (SPADs) are solid-state devices capable of providing large current pulses in the milliampere range in response to incident photons. The large gain inherent to SPADs makes them a popular technology for photon-counting applications, but their operation can be hindered by long recharge times, which necessitates the use of active quenching to reduce dead times and increase detection rates. For the prompt gamma/neutron radiation experiments conducted at the Nevada National Security Site, photomultiplier tubes (PMTs) have been the primary photodetector of choice, but their continued use is expected to dwindle as it becomes increasingly difficult to source quality …

Real-World Implementation Of A Noninvasive, Ai-Augmented, Anemia-Screening Smartphone App And Personalization For Hemoglobin Level Self-Monitoring, Robert G. Mannino, Julie Sullivan, Jennifer K. Frediani, Paul George, Jeremy Whitson, James Tumlin, L. Andrew Lyon, Erika A. Tyburski, Wilbur A. Lam

Engineering Faculty Articles and Research

Anemia, characterized by low blood hemoglobin (Hgb) levels, afflicts >2 billion individuals worldwide. Here, we report real-world data generated by a smartphone app that noninvasively screens for anemia using only “fingernail selfies.” App data for anemia screening were obtained from >1.4 million uses across the United States enabling geographic mapping of Hgb levels. Of those, 9,061 users also self-reported complete blood count Hgb levels for comparison, resulting in accuracy and performance that match gold standard laboratory testing and a sensitivity and specificity of 89% and 93%, respectively, when using an anemia cutoff of 12.5 g/dL. Geotagged data enabled construction of …

Transfer Learning For Temporal Logic Objectives, Lucas M. Santana Rovira

McKelvey School of Engineering Graduate Student Theses & Dissertations

Reinforcement learning algorithms can enable autonomous systems to learn the control skills needed to accomplish a task specified by a linear temporal logic formula. However, they cannot be transferred to a new task, even when the two are very similar. For each new task, the policy must be redesigned from scratch, which is a common limitation of existing reinforcement learning methods for temporal logic tasks. A proposed solution to this problem leverages the similarity between past and new tasks to reuse already learned control skills to accomplish the new task, with minimal or no retraining.

Rather than learning a single …

Designing An Economically Viable Off-Grid Photovoltaic System Considering The Battery Discharge Rate, Ahmed S. Abdelrazek, Eslam Mohamed Ahmed, Mahmoud Elsisi, Mokhtar Said

Mansoura Engineering Journal

The primary significance of harnessing power from clean and renewable sources lies in the fact that numerous rural areas are far from the utility system. Among the renewable energy technologies catering to power needs in residential areas is the solar photovoltaic (PV) system. Despite the potential of PV technology and the abundant sun radiation exposure in Hurghada, Egypt, there is a lack of empirical studies evaluating the feasibility of off-grid power production using this system. A notable aspect of this study is the consideration of the discharge rate of the battery for sizing the off-grid system, involving the distribution of …

Development Of Interactive Games On An Affordable Braille Display, Daniel Tsivkovski, Dylan Ravel, Maryam Etezad

Student Scholar Symposium Abstracts and Posters

Developing an affordable and STEM learning-focused Braille display addresses a significant disparity in the market for Braille displays, where most fail to provide a cost-effective, accessible, and education-oriented solution. This research aims to bridge this gap through innovative hardware and software development, offering a comprehensive learning experience to elementary school children (K-6) who are blind/visually impaired. The hardware features a piezo-electric tactile display that displays up to six Braille characters at once or a shape in an 8x8 pin array configuration. The educational software includes a user-friendly website packed with engaging STEM activities specifically designed for blind/visually impaired children. The …

Adaptive Noise Estimation And Denoising With Deep Learning For Nmr Spectroscopy, Naveen Asokan

McKelvey School of Engineering Graduate Student Theses & Dissertations

Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful analytical technique widely used for molecular structure elucidation in chemistry, biology, and medicine. However, spectral accuracy is often degraded by noise—particularly in low acquisition time settings—resulting in reduced resolution and obscured chemical features. While traditional noise reduction techniques such as signal averaging can improve spectral quality, they require longer acquisition times, limiting their utility in real-time and high-throughput applications.

This thesis presents a deep learning-based denoising framework designed to enhance the quality of complex-valued NMR spectra. The proposed model, built upon a U-Net architecture, incorporates both real and imaginary components of the …

Praxly: An Online Ide For The Praxis Cs Test Pseudocode, Benjamin Saupp

James Madison Undergraduate Research Journal (JMURJ)

No abstract provided.

Characterization Of A Magnetically Contained Hot Filament Plasma Source With A Wide-Sweeping Langmuir Probe, Jonas Rowan

Doctoral Dissertations and Master's Theses

Ionospheric plasma research in the Space and Atmospheric Instrumentation Laboratory’s Space Plasma Chamber has been hindered by the lack of a suitable plasma diagnostic instrument and understanding of its hot-filament plasma source. This thesis describes efforts made to remedy both problems. A wide-range Sweeping Langmuir Probe was developed with a ±35 V sweeping range to fully analyze ion and electron saturation regions in the entire IV curve. A method was derived to estimate the chamber source’s filament temperatures. The new Langmuir probe was integrated into a refurbished automated system designed in Python to measure plasma parameters for various chamber conditions, …

A Wireless Flex Sensors Based Man-Machine Interface, Maram F. Badkook, Razan A. Alshehri, Saeed Qaiser

Effat Undergraduate Research Journal

Abstract. The recent technological advancements are focusing on developing the smart systems to facilitate the subscribers and to improve their lifestyle. The machine learning algorithms and artificial intelligence are becoming the elementary tools, which are used in the establishment of modern smart systems across the globe. In this study, a flex sensors-based Man to Machine Interface (MMI) is proposed. It is beneficial for a category of people with reduced mobility or special needs like deaf, dumb, parallelized, etc. The system prototype is realized by using an array of flex sensors and a front-end processor, wirelessly connected to the central processing …

Pressure Switch Tester, Michael Starasinich, Andrew Dobson, Jordan Huschen, Mitchell Smith, Melvin Brown Iii

Honors Capstones

Abstract—- The Pressure Switch Test Stand is a comprehensive and automated testing solution designed to streamline the actuation and validation of simultaneously. At the core of the system is a PLC-controlled architecture that enables precise control and simulation of pressure conditions, replicating real-world scenarios to ensure accurate and reliable switch performance.

This test stand integrates robust mechanical fixtures, a reliable pneumatic control system, custom-designed electrical circuits, and a modular software interface, working in unison to deliver consistent and high-fidelity testing outcomes. Each pressure switch undergoes a sequence of pressure ramps and holds, with the system continuously monitoring switch states, activation …

Validating An Electronics Cooler Experiment And Optimizing Its Performance Using Ansys Icepak, Daniel V. Curl

Mechanical Engineering Undergraduate Honors Theses

Researchers at the University of Arkansas' Mechanical and Electrical Engineering Research Departments have designed and built a cold plate and substrate design for a 10 kV SiC MOSFET power module. Many variables define the design of the cold plate and substrate system and have quantifiable effects on the device’s performance. These variables include but are not limited to geometry and material selection of the fins and substrate. Manufacturing many test designs, running experiments, and comparing their performance is a time-consuming and expensive design optimization method that may be effective for some low-cost applications. Still, simulation is often a more cost-effective …

Development Of A Cost-Effective Daq For Measuring Brake Performance In Race Cars, Adrin Alias

2025 Spring Honors Capstone Projects - Archive

This project explores the feasibility of creating a cost-effective data acquisition (DAQ) system for high-speed, real-time brake performance testing of Formula SAE racecars. The research addresses the limitations of the current MoTeC DAQ system currently employed by the team, which is costly and time-consuming to set up for on-car testing. The team will use a brake dynamometer for steady-state comparisons of different brake pad compounds (senior design project), but evaluating real-world performance requires on-car testing. By systematically comparing various hardware platforms, sensors, communication protocols, and storage solutions, this project aims to balance cost-efficiency with reliability and performance. The research evaluates …

An Automatic Colorectal Polyps Detection Approach For Ct Colonography., Mohamed Yousuf

Electronic Theses and Dissertations

Colon cancer, also known as colorectal cancer, is a significant health concern, with increasing incidence rates, particularly among individuals under 50. This rise has led experts to recommend the introduction of regular screenings at 45 years of age for adults at average risk. Early detection through such screenings can identify precancerous polyps, allowing their removal before they develop into cancer. This proactive approach has the potential to reduce colorectal cancer deaths by up to 60%. In addition, research indicates that people diagnosed before age 50 have better survival rates, which emphasizes the importance of early diagnosis. Therefore, adhering to recommended …

Hybrid Fea And Meta-Modeling For De Optimization Of A Highly Saturated Spoke Ipm, Oluwaseun A. Badewa, Marcelo Silva, Rosemary E. Alden, Pedram Asef, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper introduces a novel approach for high performance electric motor design that combines machine learning (ML)-based meta-modeling with a differential evolution (DE) optimization algorithm. The method leverages finite element analysis (FEA) results to train the ML meta-model, enabling efficient design optimization for high-power density cored machines, such as spoke interior permanent magnet motors (IPM), which exhibit complex nonlinearities and saturation effects. This hybrid ML-DE framework seeks to provide an alternative for physics-based electric motor design and optimization, offering significant reductions in computational effort while maintaining accuracy. The meta-model’s accuracy in capturing the nonlinear relationships between design parameters, core losses, …

Implementation Of Residual Tandem Neural Networks For Photonic Inverse Design, Ponthea A. Zahraii

Electrical Engineering and Computer Science (MS) Theses

Deep-learning approaches can greatly benefit the modeling and design of nanophotonic and optical structures. Traditional full-wave simulations are time and resource-intensive, which can act as a bottleneck in photonic design. On the other hand, deep-learning approaches for designing the response of nanophotonic geometries can be computationally inexpensive and produce accurate and efficient results. In this project, we specifically investigate the case of optical forces near meta-structures. We propose using an inverse design approach with residual blocks to account for the deep nature of this architecture and inherently address the non-uniqueness problem. A tandem approach, which consists of two interconnected models, …

Modality Distillation Using A Sam-Guided Multimodal Teacher For Unimodal Wildfire Segmentation And Temperature Prediction, Michael N. Marinaccio

All Theses

Wildfires are one of the world’s most devastating natural disasters that affect the environment, communities, and more critically, humans that live in and around those communities. Due to the threat of large-scale destruction in landscapes and human inhabited areas, it has become increasingly more important to develop wildfire detection, management, and suppression strategies to mitigate and prevent these negative outcomes. Wildfire research encompasses many different areas. Most notably, the development of communication, navigation, remote sensing, and monitoring systems. In wildfire monitoring, limitations discovered in-ground and satellite observation have shifted the focus toward Unmanned Aerial Vehicle (UAV) based wildfire research, which …

Deep Learning In Lung Cancer Pre- And Post-Radiation Therapy: Diagnosis Of Malignancy And Radiation-Induced Lung Injury From 3d X-Ray Ct., Benjamin Peter Veasey

Electronic Theses and Dissertations

Lung cancer remains the leading cause of cancer-related mortality worldwide, with early detection and accurate diagnosis being critical for improving patient outcomes. Additionally, the progression of Radiation-Induced Lung Injury (RILI) following Stereotactic Body Radiation Therapy (SBRT) for lung cancer presents a significant diagnostic challenge. This dissertation addresses these challenges by developing deep learning-based diagnostic tools for both pre-treatment lung nodule malignancy classification and post-treatment RILI identification using 3D X-ray CT imaging. The research is divided into two primary objectives. First, for lung nodule malignancy classification, we developed a biopsy-confirmed dataset, called NLSTx, to train and evaluate deep learning models while …

Physics Embedded Neural Network: A Novel Data-Free Numerical Method For Solving Computational Physics Problems, Pawan Gaire

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

A novel approach for solving partial differential equations (PDEs) using neural networks for scientific computing is introduced. The proposed approach, referred to as physics-embedded neural network (PENN), features a unique architecture that incorporates the PDE and boundary conditions information directly within the final fully-connected layer of the feed-forward neural network (NN). The key aspect of PENN is the parallel numerical embedding of a differential equation associated with physical problems within the activation function of the network’s final layer. This integration leads to a new class of computational solvers competitive with classical methods like the Finite Element Method (FEM) and capable …

Modeling Degradation In Li-Ion Batteries: Analyzing Single-Cell Degradation And Applying It To Customized Pack-Level, Mohannad Y. Alkhalil

Durham School of Architectural Engineering and Construction: Dissertations, Theses, and Student Research

The rapid expansion of lithium-ion (Li-ion) battery applications in areas such as electric vehicles (EVs), renewable energy storage, and portable electronics has drawn attention to the need for improving their performance, safety, and longevity. As Li-ion batteries become essential across technologies, understanding degradation mechanisms is critical for optimizing design and ensuring reliable operation. This work provides a detailed overview of modeling degradation in Li-ion batteries, focusing on single-cell behavior and its implications for pack-level performance.

The PyBaMM (Python Battery Mathematical Modeling) package, an open-source battery simulation tool written in Python, is used for single-cell simulations. Liionpack, another Python-based library, is …

Characterizing Human Mobility Patterns In Saudi Arabia Using Cellular Data, Meshal Alnefaie

Theses and Dissertations

The study analyzes human mobility in Saudi Arabia. Using crowd-source data, Riyadh mobility is analyzed to find trends and highlight mobility patterns of individuals in Riyadh. Then, the mobility of Riyadh is compared with that of Jeddah and Dammam in a similar data collection and analysis. Four mobility metrics are utilized: Number of Visited Locations (NLOC), Number of Unique Locations (NULOC), Radius of Gyration (RGYR), and Distance Traveled (DTRV). The results show interesting outcomes about individuals in the three cities. Although these cities are far from each other, they observe the same mobility patterns. These findings have the potential to …

Blockchain-Integrated Version Control For Secure And Transparent Software Supply Chains, Iwinosa W. Aideyan

All Theses

The software supply chain encompasses all stages of software development and delivery from initial coding and version control to integration and deployment. As development environments become increasingly distributed and reliant on external dependencies, ensuring the integrity, auditability, and consistency of code changes has become a pressing challenge. Traditional version control systems like Git, while effective for collaboration and tracking revisions, do not inherently provide tamper-evident commit histories. Features such as history rewriting (e.g., git rebase, git push --force) can be exploited to manipulate commit logs without detection, posing risks in security-sensitive domains. This thesis proposes a blockchain-integrated version control framework …