Digital Commons Network^™

Predicting Miner Localization In Underground Mine Emergencies Using A Hybrid Cnn-Lstm Model With Data From Delay-Tolerant Network Databases, Patrick Nonguin, Samuel Frimpong, Sanjay Madria

Mining Engineering Faculty Research & Creative Works

Underground mining environments are highly hazardous, often prone to gas explosions, cave-ins, and fires that may trap miners during emergencies. The accurate, real-time localization of miners is vital for effective self-escape and rescue operations. Although the Mine Improvement and New Emergency Response (MINER) Act of 2006 mandates communication and tracking systems, most current solutions rely on low-power devices and line-of-sight methods that are ineffective in GPS-denied, dynamic subsurface conditions. Delay-Tolerant Networking (DTN) has emerged as a promising alternative by supporting message relay through intermittent links. In this work, we propose a deep learning framework that combines Convolutional Neural Networks (CNNs) …

The Advancement Of Automation In Beef Packaging Pack-Off Systems, Matthew Newman

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

Marble Technologies has developed a new system for beef, pork, and lamb producers that improves quality and worker experience in the packing of products. This work explores the augmentation of this system with the introduction of robotic packing to further reduce labor, increasing worker availability for value-added tasks.

Experimentation conducted to date has explored handling delicate meat products in the standard Marble system and the proposed robotic systems. These mechanical subsystems are critical intermediaries in safely and reliably delivering products to robots and from robots to boxes. Two case studies are presented to walk through the process and challenges of …

A Deep Learning Approach For Semantic Segmentation And Its Application On Ctc., Samir Farag Harb

Electronic Theses and Dissertations

This dissertation explores the modeling and analysis of medical images, focusing on the intricate task of colon segmentation and subsequent 3D reconstruction, which are critical steps in Computed Tomography Colonography (CTC) systems. The primary objective of this research is to develop precise segmentation approaches to enhance the accuracy of colon identification and reconstruction from abdominal CT scans. Three distinct segmentation approaches are proposed and evaluated: a Markov Random Field (MRF)-based approach, a convolutional neural network (CNN)-based deep learning (DL) approach, and a sequential episodic training with dual contrastive learning Approach (G-SET-DCL) that has a flavor of few-shot learning (FSL). To …

Experimental Evaluation And Comparison Of Football-Related Head Impacts Among Ovine, Human, And Hybrid Iii Headforms, Madysn D. Cardinal

All Dissertations

Concussions and traumatic brain injury (TBI) remains a significant public health concern, especially in American football, where repeated head impacts pose long-term risks to athlete health. While current helmet testing standards rely heavily on the Hybrid III surrogate headform to evaluate protective performance, these surrogate models lack anatomical accuracy and exclude the presence of a brain component, limiting their ability to capture the true mechanics of concussion. To address this limitation, this research developed and validated a novel inverted impact testing method that allows the impact testing of a Hybrid III headform and a cadaveric specimen. Additionally, this fixture preserves …

Robustness Investigation, Detection, And Defense Of Deep Learning Models Against False Data Injection, Amirhossein Nazeri

All Dissertations

This dissertation addresses the critical challenge of adversarial robustness in deep learning systems, focusing on two fundamental domains: time-series prediction and object detection. As these AI systems become increasingly deployed in safety-critical applications from power grid management to autonomous vehicles their vulnerability to adversarial attacks poses significant risks to infrastructure and human safety.

The first contribution introduces a novel stealthy black-box False Data Injection (FDI) attack specifically designed for quasi-periodic time-series data. Unlike existing attacks that produce easily detectable anomalies, our method generates adversarial perturbations that preserve the underlying periodicity and statistical properties of the data, effectively bypassing traditional anomaly …

Robust Adaptive Rigid Body State And Mass Property Estimation Via Unscented Kalman Filter On Tse(3) With Process Noise Estimation, Herman Gunter

Doctoral Dissertations and Master's Theses

Mass property estimation, including mass, center of mass, and moment of inertia, is a crucial yet challenging problem in spacecraft autonomy and astrodynamics. Knowledge of mass properties of a spacecraft is essential for future astronautical missions, as changes in the mass properties of a spacecraft due to a shift in cargo distribution often require a careful and costly recalculation to ensure applied control inputs produce the desired results. As spacecraft missions grow in both duration and number, meeting the need for precise and accurate measurements becomes increasingly complex. Stochastic effects, such as angle and velocity random walks, along with persistent …

Discovery Of High-Performance Cathode Materials For Protonic Ceramic Fuel Cells, Liang Han

All Dissertations

Environmental pollution and rapid energy consumption have become common problems in global development and will continue to grow with the world population. PCFCs use proton-conducting ceramics as electrolytes, with low activation energy and high ionic conductivity at intermediate temperatures, enabling them to operate at intermediate-temperature conditions, which can effectively solve the problems of poor stability and high cost of exotic materials of traditional solid oxide fuel cells. However, as the operating temperature decreases, the electrocatalytic activity of the cathode decreases significantly, seriously affecting PCFC’s performance. Therefore, developing high-performance cathode material suitable for working under intermediate-temperature conditions has become the key …

Secure Control And Trust Evaluation Framework For Autonomous Transportation Systems, Grace Muriithi

All Dissertations

This dissertation advances the cybersecurity of hybrid tracked vehicles (HTVs) and ship power systems (SPSs) by developing innovative cyber-attack models and corresponding defence frameworks. First, we formulate stealthy false-data-injection attacks (FDIAs) on HTV energy-management systems as a partially observable Markov decision process (POMDP) solved via deep reinforcement learning. A novel sniffing-based reward function guides the attacker to covertly degrade battery capacity and energy efficiency, which we evaluate using custom stealth–impact metrics and a sliding-window anomaly detector (Isolation Forest with Dynamic Time Warping). Additionally, we model sophisticated control-layer attacks in HTVs, including reinforcement-learning-optimised replay attacks and denial-of-service (DoS) attacks targeting generator-speed …

Enhancing Emotional Accuracy And Behavioral Novelty In Robot Behaviors: A Generative-Discriminative Approach, Rista Baral

Boise State University Theses and Dissertations

Recent advancements in language modeling have improved robotic emotion expressiveness, yet several challenges remain. Many existing robotic expression models rely on fixed rules and static frameworks, which limit their ability to capture the dynamic nature of emotional expression. Additionally, these systems often struggle to balance emotional accuracy with generating novel and varied behaviors. These limitations underscore the need for method capable of delivering both emotionally accurate and diverse robot behaviors.

In this thesis, we addresses these challenges by introducing a new framework for expressive behavior generation in robots. We develop a generative model that generates robot behavior sequences that align …

Aging Effect On Effective Connectivity Within The Prefrontal-Motor Network And Postural-Related Muscle Responses During Dual-Task Paradigms, Jiahao Pan

Boise State University Theses and Dissertations

Falls are a significant global public issue for older adults, leading to severe health and social impacts. To understand the underlying mechanism of increased fall risk with aging, loss of postural automaticity has been suggested as increased prefrontal cortex and motor cortices activation, as well as worse balance performance in response to the secondary cognitive task. However, the interaction within these cortical regions and its effects on postural-related muscle responses are poorly understood. This dissertation investigated age-related changes in effective connectivity within the prefrontal-motor network, postural related lower limb muscle response, and biomechanical characteristics in response to the secondary cognitive …

Considerations For Developing Dna-Templated Dye-Aggregate Systems, Simon Kristopher Roy

Boise State University Theses and Dissertations

Molecular excitons have been the subject of significant research interest for nearly 100 years. Molecular excitons arise when conjugated organic molecules (dyes) are excited such that the resulting excitation is delocalized over multiple dyes in a wavelike manner. The shared excited states that allow the excitation energy to hop between molecules is of particular interest for applications that can make use of the wavelike properties of excitons, including applications in quantum information science. A key challenge for developing materials for these advanced applications is to overcome the knowledge gaps associated with precisely templating dyes with spatial control of their aggregation, …

Data-Driven Koopman Theory For Transient Stability And Safety Analysis Of Power Systems With Renewable Penetration, Bhagyashree Umathe

All Dissertations

This dissertation presents a novel approach to analyzing and controlling nonlinear systems using the Koopman operator framework and data-driven methods. Nonlinear power systems, characterized by complex behaviors and sensitivity to initial conditions, pose significant challenges for stability and safety assessment, especially during transient events.

The first part of this work focuses on reachability analysis using the spectral properties of the Koopman operator. By leveraging eigenfunctions extracted from sampled trajectory data, the approach computes forward and backward reachable sets efficiently, even in high-dimensional nonlinear systems, without requiring dense state-space sampling. This method is validated through numerical examples, demonstrating its ability to …

Development Of A High-Performance, Cost-Effective Architecture For Real-Time Fourier Transform Analysis On An Efinix Trion Fpga, Luke Cashwell

Honors Theses

This project presents the development of a high-performance, cost-effective, and powerefficient pipelined architecture designed to execute 10,000 Fast Fourier Transforms (FFTs) per second on an Efinix Trion T120 FPGA. Each FFT processes 4096 signed 13-bit elements, facilitating real-time data analysis for a Time-Domain Impedance Probe (TDIP) operating at a maximum data rate of 490 Mbit/s. The algorithm utilizes the built-in multipliers and dual-port memory cells of the FPGA to optimize data storage, transfer, and processing. It achieves this high performance while only using approximately 4% of the Look-Up Tables (LUTs), 7% of the integrated RAM cells, and 15% of the …

Crevice Corrosion Mechanisms Of Cocrmo Alloys In Orthopedic Implants: Retrieval Analysis, Nano-Tribocorrosion And Cellular Responses, Hwaran Lee

All Dissertations

Mechanically assisted corrosion and non-mechanically (chemically) driven corrosion in modular junctions of orthopedic implants using cobalt-chromium-molybdenum (CoCrMo) alloys remain clinical concerns and may contribute to implant failure. The underlying corrosion mechanisms are not yet fully understood, leaving a critical research gap. We hypothesized that corrosion in modular junctions may be driven by aggressive local environments, including fretting, metal ion release (cobalt ion, Co²⁺), low pH and reactive oxygen species (ROS). This dissertation aims to (1) analyze corrosion modes and potential causes in modular junctions; (2) identify chemically driven corrosion modes on CoCrMo alloys using simulated inflammatory modular taper …

Exploring Methods For Quantifying Uncertainty In Neural Network-Based Turbulence Closures, Cody Grogan

All Graduate Theses and Dissertations, Fall 2023 to Present

Machine Learning (ML) is a very promising field for data-driven modeling of different phenomena. In the field of Computational Fluid Dynamics (CFD), ML is an enticing alternative to traditional methods to improve the accuracy and computational efficiency of simulations. However, many ML models, like Neural Networks, don’t quantify their uncertainty or indicate their confidence in a prediction to those who wish to use them. This is especially important because the use of inaccurate ML predictions in a CFD simulation can greatly impact the validity of a simulation. However, with uncertainty quantification, an ML model can indicate to a modeler the …

Enhanced Point Cloud Generation From A Novel 360° Underwater Lidar, Olagoke E. Daramola

Dissertations

This dissertation presents novel algorithms to improve the mapping capabilities of a 360-degree underwater Pulsed Laser Line Scanner LiDAR (PLLS-360°). Due to its 360° field-of-view (FOV), the PLLS-360° is a compact full-waveform omnidirectional imager suitable for seafloor mapping, underwater asset inspection, object detection, ice-sheet mapping, and construction progress monitoring. The proposed methodology includes an improved waveform fitting technique for saturated waveform recovery, detection array response correction, radiometric corrections, and fusion of LiDAR and sonar bathymetric datasets. The first part of this dissertation assesses the LiDAR’s performance and describes how the data for this unique 360° FOV architecture is processed. The …

Assessing The Robustness Of Test Selection Methods For Deep Neural Networks, Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Wei Ma, Mike Papadakis, Lei Ma, Yves Le Traon

Research Collection School Of Computing and Information Systems

Regularly testing deep learning-powered systems on newly collected data is critical to ensure their reliability, robustness, and efficacy in real-world applications. This process is demanding due to the significant time and human effort required for labeling new data. While test selection methods alleviate manual labor by labeling and evaluating only a subset of data while meeting testing criteria, we observe that such methods with reported promising results are simply evaluated, e.g., testing on original test data. The question arises: are they always reliable? In this article, we explore when and to what extent test selection methods fail. First, we identify …

Engineering Properties And Stress-Strain Behavior Of Bcsa Concrete: Flexural Design Parameters, Strain Measurement Methods, And Models, Gabriel R. Johnson

Graduate Theses and Dissertations

Belitic calcium sulfoaluminate (BCSA) cement concrete has gained popularity due to advantages over portland cement (PC), including, its fast-setting, high early strength gain, high durability, and reduced global warming potential. Though primarily used for pavement repair, it has good potential for structural applications. Building codes like ACI 318:19 permit alternative cement materials like BCSA cement if they can be shown to have adequate and predictable structural, durability, and fire performance comparable to PC concrete. This study evaluates the structural and mechanical behavior of BCSA cement concrete, measuring standard engineering properties and comparing results to PC concrete and code prediction equations. …

Bilateral Leading Edges With Tubercle Modifications: An Experimental Study, Roberto Sanchez

Theses and Dissertations

The increasing demand for better performance and maneuverability from airfoils to sustain superior performance over a wide range of platforms, including vehicles, propellers, and wing designs, continues to grow. Researchers have drawn inspiration from nature, looking at birds of prey, dragonflies, and humpback whales for aerodynamic improvements. Among these, tubercle airfoils, inspired by the humpback whale’s flipper, have gained increasing interest. This experimental study presents the results of bilateral tubercle leading-edge airfoils based on a modified NACA 0018 design with a chord length of 1.82 inches, a span width of 7.5 inches, and an amplitude of 6.3c%. Five configurations were …

Computational Modeling For Automatic Superconducting Cavity Fault Prediction And Classification Using Time Series Signals, Md Monibor Rahman

Electrical & Computer Engineering Theses & Dissertations

Processing multivariate time series signals collected from sensor networks is challenging because of complex temporal dependencies and non-stationarity. With the advent of artificial intelligence (AI) like machine learning and deep learning, it has become possible to process sensor-driven time series data more effectively than traditional statistical methods.

This dissertation aims to develop machine learning and deep learning models to address machine fault diagnosis using multivariate time series signals collected from the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. The first goal of the proposed work is to develop deep learning–based classification models and an unsupervised fault clustering approach …

Towards Improving Computational Modeling Of The Lumbar Spine - Impact Of Material Properties And Laminotomy On Spinal Biomechanics, Isaac K. Kumi

Mechanical & Aerospace Engineering Theses & Dissertations

Spinal ligaments play a crucial role in maintaining the mechanical stability of the lumbar spine. These dense, collagenous tissues not only resist excessive motion but also distribute loads between spinal components to protect neural structures. Traditionally, the mechanical response of ligaments has been modeled using linear elastic assumptions, which treat the tissue as having a constant stiffness regardless of loading history. While this simplifies analysis, it fails to capture the time-dependent behaviors, such as creep, stress relaxation, and hysteresis, that are characteristic of biological tissues.

Viscoelastic modeling may provide a more physiologically accurate representation by incorporating both elastic and viscous …

Systems Statistical Engineering – Hierarchical Fuzzy Constraint Propagation, Hengameh Fakhravar

Engineering Management & Systems Engineering Theses & Dissertations

Driven by the growing need in the 21st century for integrating rigorous statistical analysis into engineering research, there is a movement to develop an integrated statistical engineering science within statistics and quality communities (Hoerl & Snee, 2010; Anderson-Cook et al., 2012). Systems Statistical Engineering research seeks to integrate the Causal Bayesian hierarchical modeling (Pearl, 2009) and cybernetic control theory within Beer’s Viable System Model (1972, 1979, 1985) and the Complex Systems Governance framework (Keating, 2014; Keating & Katina, 2015, 2016) to produce multivariate systemic models for robust dynamic systems mission performance. Cotter & Quigley (2018) set forth the Bayesian systemic …

Analysis On Plasma Propulsion Thruster Material Reconfiguration For Thrust Stabilization, Chinedumu Chidiebere Okafo

Mechanical & Aerospace Engineering Theses & Dissertations

Rocket propulsion has been a primary focus for scientific and technical advancement supporting space exploration. For long-duration, low-thrust space missions, propulsion systems designed with plasma energy as the primary source for generating thrust are of high interest. Plasma energy, like electric propulsion, involves the excited state of ionized atoms, where the ionized particles are separated and then act as a propellant to push a spacecraft in the intended direction for flight. Although more energy efficient compared to traditional rocket systems, plasma propulsion has the deficiency of consistent erosion of the metallic containment substrate, as plasma is being generated and released …

Digital Twins, Ai, And Cybersecurity In Additive Manufacturing: A Comprehensive Review Of Current Trends And Challenges, Md Sazol Ahmmed, Laraib Khan, Muhammad Arif Mahmood, Frank Liou

Mechanical and Aerospace Engineering Faculty Research & Creative Works

The development of Industry 4.0 has accelerated the adoption of sophisticated technologies, including Digital Twins (DTs), Artificial Intelligence (AI), and cybersecurity, within Additive Manufacturing (AM). Enabling real-time monitoring, process optimization, predictive maintenance, and secure data management can redefine conventional manufacturing paradigms. Although their individual importance is increasing, a consistent understanding of how these technologies interact and collectively improve AM procedures is lacking. Focusing on the integration of digital twins (DTs), modular AI, and cybersecurity in AM, this review presents a comprehensive analysis of over 137 research publications from Scopus, Web of Science, Google Scholar, and ResearchGate. The publications are categorized …

Valorizing Cement Kiln Dust And Glass Powder: A Binder Study On Sustainable Partial Cement Replacement, Sai Kiran Simhadri

Civil & Environmental Engineering Theses & Dissertations

The production of cement ranks as a major source of global CO₂ emissions because of the high energy requirements needed to create clinker. The construction industry is actively seeking ways to reduce its carbon footprint. Utilizing industrial waste materials to partially replace cement can lower emissions. This research evaluates a ternary binder system which uses Cement Kiln Dust (CKD) and Glass Powder (GP) to replace a portion of Type IL (Portland Limestone) cement. The mixes were made with a fixed water-to-binder ratio of 0.44 and contained 15% GP by mass while the CKD content ranged from 0% to 10%. The …

Succinctness Meets Transparency A Linking Framework Combining Hyrax And Kzg, And Its Applications, Argha Sardar

Master’s Dissertations

The thesis begins by introducing the concept of zero knowledge and exploring its various paradigms. It then focuses on a interesting problem: the construction of linking proofs. Chap- ter 2 addresses the challenge of binding two distinct polynomial commitment schemes so that they are consistent at a common evaluation point. The chapter further introduces a method for equating two different polynomials with different domains using an affine line construction. By applying certain optimizations to the bulletproofs protocol, the work achieves a reduction in both the prover’s time and the number of rounds required for proving a large committed inner product. …

Life Cycle Assessment Of The Cultivation Of Lettuce In A Hydroponic System Using The Nutrient Film Technique, Raquel Jordan

All Theses

Hydroponic systems are an emerging method of soilless crop cultivation typically done inside a greenhouse where nutrients are delivered dissolved in water rather than being supplied from the soil. Potential benefits of hydroponic systems include reduced irrigation requirements, higher crop yield per area, and the ability to grow crops year-round in nontraditional areas such as urban settings, cold climates, and sites with contaminated soil. However, hydroponic systems tend to have high energy requirements for lighting and climate control, which can yield significant environmental impacts. To explore the environmental impacts of cultivating lettuce in a nutrient film technique (NFT) hydroponic system, …

Effectiveness Of Electrosynthesized Hydrogen Peroxide In Urease Inactivation For Stabilization Of Source-Separated Urine To Recover Urea, Brooke E. Covert

All Theses

The practice of separating urine at a building scale, rather than mixing with domestic wastewater to be treated at water resource recovery facilities, allows for concentrated urine to be directly treated with the aim of resource recovery. However, source separation and treatment present challenges during separation, storage, and transport. This study evaluated the use of hydrogen peroxide-producing electrochemical cells, which represent a technology that uses electricity as an input, to stabilize urine through cathodically produced peroxide to enable downstream water and nutrient recovery. The electrochemical cells have been previously shown to stabilize urine with electrochemically produced peroxide as an effective …

Decision Field Theory For Human-Multi-Robot Collaboration: Human-Centric Decision-Making For Multi-Robot Systems, Ryan Mbagna Nanko

All Theses

At first glance, choosing between an apple and an orange appears to be a straightforward matter of personal taste; however, this seemingly simple preference opens a window into the multifaceted world of decision-making, revealing the complex interplay of cognitive processes, psychological, and behavioral-economic principles that guide our choices \cite{bandyopadhyayRoleAffectDecision2013}. By unpacking these nuanced perspectives, we uncover insights that can drive more effective human-robot interaction and collaboration.

Modeling human cognition requires understanding the evolution of choice utility and the influence of emotions. Decision Field Theory (DFT) stands out by capturing the fluctuating nature in human preferences over time, explaining why choices …

Wires, Roads, And Real-World Challenges: An Interdisciplinary Case Study Bridging Roadway And Electric Utility Design, Juliann A. Lloyd

All Theses

Electric utilities are a foundational component of modern society and depend heavily on civil engineers for planning, design, construction, and maintenance. However, educational content related to this industry is often underrepresented in civil engineering programs. To bridge this gap, a case study activity was implemented in two transportation engineering courses at Clemson University. The case study introduced students to the integration between electric utility infrastructure and roadway design by simulating a realistic and common conflict scenario faced by Departments of Transportation (DOTs) nationwide.

Students were placed in interdisciplinary teams, with each member assigned a specialized role such as Land Agent, …