Engineering Commons^™

Flexural Strength Of (Hf,Nb,Ta,Ti,Zr)B2–(Hf,Nb,Ta,Ti,Zr)C High-Entropy Dual-Phase Ceramics, Rubia Hassan, William G. Fahrenholtz, Gregory E. Hilmas, Jeremy Watts

Materials Science and Engineering Faculty Research & Creative Works

The room and elevated temperature flexural strengths were measured for (Hf,Nb,Ta,Ti,Zr)B2–(Hf,Nb,Ta,Ti,Zr)C high-entropy dual-phase ceramics. The fracture of ceramics originated from two distinct flaw populations that were either surface defects introduced during specimen preparation or volume defects introduced during processing. The average strength at room temperature was 638 ± 94 MPa with individual bars strengths as high as ∼800 MPa. At 1800°C, average strength increased to 786 ± 265 MPa with individual bar strengths as high as ∼1050 MPa. Above 1800°C, creep dominated resulting in deformation of the bars without fracture. The fracture mode changed from completely trans granular at room …

Roadmap To 100 Gwdc: Scientific And Supply Chain Challenges For Cdte Photovoltaics, B. Edward Sartor, Samantha B. Reese, Lorelle M. Mansfield, Ryan Muzzio, Chun Sheng Jiang, Eric Colegrove, John S. Mangum, Camden L. Kasik, Daniel Z. Shaw, James R. Sites, Ramesh G. Dhere, Alex B. Goldstone, Michael S. Moats

Materials Science and Engineering Faculty Research & Creative Works

This roadmap highlights pathways to expand the CdTe module manufacturing capacity per year to 100 GWDC by 2030 by improving Te extraction from existing supply chains, minimizing Te usage in modules by leveraging thinner absorbers, and focusing research efforts in key areas to improve module efficiencies. Both scientific and supply chain innovations will be necessary to maintain the high compound annual growth rate of the CdTe photovoltaic (PV) industry and cement its role as a key technology for multi-TW-scale PV deployment.

Ms-Yolo: Infrared Object Detection For Edge Deployment Via Mobilenetv4 And Slideloss, Jiali Zhang, Thomas S. White, Haoliang Zhang, Wenqing Hu, Donald C. Wunsch, Jian Liu

Mathematics and Statistics Faculty Research & Creative Works

Infrared imaging has emerged as a robust solution for urban object detection under low-light and adverse weather conditions, offering significant advantages over traditional visible-light cameras. However, challenges such as class imbalance, thermal noise, and computational constraints can significantly hinder model performance in practical settings. To address these issues, we evaluate multiple YOLO variants on the FLIR ADAS V2 dataset, ultimately selecting YOLOv8 as our baseline due to its balanced accuracy and efficiency. Building on this foundation, we present MS-YOLO (MobileNetv4 and SlideLoss based on YOLO), which replaces YOLOv8's CSPDarknet backbone with the more efficient MobileNetV4, reducing computational overhead by 1.5% …

How Do Human And Ai Gender Bias Interact In Hiring Decisions?, Eyuel Getahun, Daniel Burton Shank, Casey I. Canfield, Jessica L. Cundiff, Jenny L. Davis, Celia Freed

Psychological Science Faculty Research & Creative Works

The hiring process is crucial for organizational success but has long been troubled by human biases. Many organizations now include AI in their hiring protocols to mitigate these biases and increase efficiency. However, AI itself can have biases baked-in. Human biases and AI biases are distinct but related; here, we examine how human and AI biases interact to affect hiring outcomes. Through an online experiment, we examine this question in the context of gendered hiring for a male-dominated leadership position in electrical engineering. The study tests how elevated and depressed AI recommendations for male and female job candidates affect participant …

Comparison Of Cnn And Lstm Networks On Human Intention Prediction In Physical Human-Robot Interactions, Khosro Ghorbani Zadeh, Niloofar Zendehdel, George L. Holmes, Keyri Moreno Bonnett, Amy Costa, Devin Michael Burns, Ming-Chuan Leu, Yun Seong Song

Psychological Science Faculty Research & Creative Works

Advancements in robotics and AI have increased the demand for interactive robots in healthcare and assistive applications. However, ensuring safe and effective physical human-robot interactions (pHRIs) remains challenging due to the sophistication of human motor communication and intent recognition. Traditional physics-based models struggle to capture the dynamic nature of human force interactions, limiting robot adaptability. To address these limitations, neural networks (NNs) have been explored for force-movement intention prediction. While multi-layer perceptron (MLP) networks show potential, they struggle with temporal dependencies and generalization. Long Short-Term Memory (LSTM) networks effectively model sequential dependencies, while Convolutional Neural Networks (CNNs) enhance spatial feature …

Security Enhancement In Uav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar

School of Cybersecurity Faculty Publications

As cyber-physical systems (CPSs) increasingly integrate physical and digital realms, securing critical infrastructure, such as the Port of Virginia, becomes paramount. Among CPSs, Unmanned Aerial Vehicles (UAVs) are vital for monitoring, communication, and supporting the command and control through remote reconnaissance and surveillance missions. These UAV applications often require coordination, planning, and runtime reconfiguration, traditionally managed by human decision-makers. However, this approach has limitations, as extensively documented in the literature. Artificial Intelligence (AI) has emerged as a pivotal tool to address these limitations, enhancing risk mitigation and informed decision-making. This research proposes a machine learning (ML) based security mechanism, leveraging …

Security Enhancement In Aav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar

School of Cybersecurity Faculty Publications

As cyber-physical systems (CPSs) increasingly integrate physical and digital realms, securing critical infrastructure, such as the Port of Virginia, becomes paramount. Among CPSs, Autonomous Aerial Vehicles (AAVs) are vital for monitoring, communication, and supporting the command and control through remote reconnaissance and surveillance missions. These AAV applications often require coordination, planning, and runtime reconfiguration, traditionally managed by human decision-makers. However, this approach has limitations, as extensively documented in the literature. Artificial Intelligence (AI) has emerged as a pivotal tool to address these limitations, enhancing risk mitigation and informed decision-making. This research proposes a machine learning (ML) based security mechanism, leveraging …

Long-Term Traffic Prediction Using Deep Learning Long Short-Term Memory, Ange-Lionel Toba, Sameer Kulkarni, Wael Khallouli, Timothy Pennington

School of Cybersecurity Faculty Publications

Traffic conditions are a key factor in our society, contributing to quality of life and the economy, as well as access to professional, educational, and health resources. This emphasizes the need for a reliable road network to facilitate traffic fluidity across the nation and improve mobility. Reaching these characteristics demands good traffic volume prediction methods, not only in the short term but also in the long term, which helps design transportation strategies and road planning. However, most of the research has focused on short-term prediction, applied mostly to short-trip distances, while effective long-term forecasting, which has become a challenging issue …

Tl-Convlstm: A Transfer-Learning-Based Convolutional Lstm To Identify And Forecast Traffic In The Nextg Environments, Bikash Chandra Singh, Peter Foytik, Rafael Diaz, Sachin Shetty

School of Cybersecurity Faculty Publications

Forecasting and categorizing cellular traffic flows and their types are essential functions in intelligent network systems to ensure efficient network optimization. The ever-evolving nature of 5G networks results in fluctuations in traffic patterns over time, leading to a phenomenon known as model drift. Consequently, accurately predicting and identifying cellular traffic patterns becomes a complex task. To tackle this challenge, this article introduces an innovative approach called TL-ConvLSTM, which combines transfer learning with convolutional long short-term memory (ConvLSTM) to effectively combat model drift and provide precise forecasting and recognition of cellular traffic within the network. To accomplish this, we initiate the …

From Spark To Wildfire: Predicting Wildfire Origins And The Efficacy Of “Fire Safe” Cigarettes In California Grasslands, Trevor Blackwell Haltermann

Cal Poly Humboldt theses and projects

As wildfires in North America increase in size and frequency, the societal impacts become greater as well. With greater risk and high fire suppression costs it is pertinent to improve fire investigation methods and limit the potential for human caused ignitions. Two distinct studies were carried out to assess the accuracy of fire pattern indicators and the efficacy of “fire safe” cigarette technology in lowland grass fuel types in California. The first study utilized 24 experimental small-scale fires within a wind tunnel to examine the accuracy of microscale fire pattern indicators and the factors that influence the accuracy of predictions …

Vaim-Cff: A Variational Autoencoder Inverse Mapper Solution To Compton Form Factor Extraction From Deeply Virtual Compton Scattering, Manal Almaeen, Tareq Alghamdi, Brandon Kriesten, Douglas Adams, Yaohang Li, Huey-Wen Lin, Simonetta Liuti

Computer Science Faculty Publications

We develop a new methodology for extracting Compton form factors (CFFs) from deeply virtual exclusive reactions such as the unpolarized DVCS cross section using a specialized inverse problem solver, a variational autoencoder inverse mapper (VAIM). The VAIM-CFF framework not only allows us access to a fitted solution set possibly containing multiple solutions in the extraction of all 8 CFFs from a single cross section measurement, but also accesses the lost information contained in the forward mapping from CFFs to cross section. We investigate various assumptions and their effects on the predicted CFFs such as cross section organization, number of extracted …

Energy-Based Deep Incomplete Multi-View Clustering, Ziyu Wang, Yiming Du, Rui Ning, Lusi Li

Computer Science Faculty Publications

Incomplete multi-view clustering (IMVC) deals with real-world scenarios where certain views are partially missing, posing significant challenges to effective clustering. Most existing IMVC approaches face a trade-off: imputation-free methods suffer from information bias and imbalance, while full-imputation methods risk introducing and propagating noise. To overcome these limitations, we propose Energy-Based Deep Incomplete Multi-View Clustering (Energy-DIMC), a novel selective-imputation framework that leverages energy-based models (EBMs) to guide reliable imputations and robust clustering. EBMs assess data compatibility by assigning lower energy to more coherent structures, effectively modeling complex inter-view and inter-sample dependencies. Inspired by EBMs, Energy-DIMC integrates four key components: 1) a …

Human Perception Of Ai Capabilities At Classifying Perturbed Roadway Signs, Katherine R. Garcia, Jing Chen, Yanru Xiao, Scott Mishler, Cong Wang, Bin Hu

Computer Science Faculty Publications

Artificial Intelligence (AI) is crucial to numerous functions required for driving automation systems, including the computer vision techniques used to detect the roadway environment and make real-time decisions. However, the images used as inputs to the AI system may be maliciously perturbed, or manipulated, causing the AI system to make an incorrect classification. In this study, we examined humans’ perception of the AI’s computer vision capability of classifying various road sign images, including the original images, images with two different types of malicious attacks, and images that are scrambled randomly at the pixel level. Our results showed that participants rated …

Ieee 802.11n And Ieee 802.11ax Networks Under Various Propagation Models, Jun Peng

Electrical and Computer Engineering Faculty Publications

IEEE 802.11n networks are the first generation of Wi-Fi networks to reach data rates of hundreds of megabits per second. They are also the first to use MIMO technologies in Wi-Fi networks. They can operate in both 2.4 GHz and 5 GHz frequency bands and use 20 or 40 MHz channel width. Similarly, IEEE 802.11ax networks can operate in both 2.4 GHz and 5.0 GHz frequency bands and use 20, 40, 80, or 160 MHz channel width. For channel access, IEEE 802.11ax networks use CSMA/CA, multi-user MIMO (MU-MIMO), and OFDMA. They can reach data rates of multiple gigabits per second. …

Integrating Machine Learning And Symbolic Regression For Predicting Damage Initiation In Hybrid Frp Bolted Connections, Sherif Sorour

Mechanical Engineering

No abstract provided.

Artificial Intelligence For Digital Deception: A Study On Detection, Generation, And Evaluation, Tasnim Akter Onisha

College of Graduate Studies: Theses & Dissertations

The rapid advancement of artificial intelligence has significantly influenced digital media, enabling both the detection and generation of synthetic content. This thesis, titled Artificial Intelligence for digital deception: A Study on Detection, Generation, and Evaluation, explores AI’s role in digital deception through three distinct studies focused on facial expression analysis for deepfake detection, machine learning-based spam classification on cloud platforms, and the evaluation of generative AI state-of-the-art text to video models. The first study investigates the effectiveness of facial expression analysis in distinguishing between deepfake and genuine videos. Using Noldus FaceReader 7, participant’s emotional responses were analyzed while viewing deep-fake …

Ease Of Product Disassembly Through A Systematic Structured Time-Based Design For Disassembly Methodology, Emeka S. Igwe

College of Graduate Studies: Theses & Dissertations

This research introduces a systematic time-based design for disassembly (DfD) framework aimed at optimizing product disassembly by addressing important features like liaisons between components in product, component accessibility and the overall modularity of the product. This study specifically covers electromechanical and mechatronic systems in both household and industrial setup, identifying their disassembly challenges and high value pointers for improvement. The methodology involves using a design for disassembly framework called LeanDfD in carrying out a holistic disassembly process and evaluating quantitative metrics like disassembly time and complexity and suggesting further redesign strategies to minimize disassembly time and cost. Adopting this systematic …

Ranking Of Non-Destructive Testing Methods For Ductile Iron Castings For Industry 4.0, Michael W. Jones

College of Graduate Studies: Theses & Dissertations

Non-destructive testing techniques provide a unique solution to provide quality assurance of parts rapidly and efficiently without altering the functional properties of the workpiece. The metal casting industry requires an elevated level of quality assurance, as castings are critical components to several industries. The wrong selection of non-destructive testing methods could increase production costs, quality costs, and potentially permit non-conforming castings to enter the market. The aim of this research is to develop a ranking model for non-destructive testing techniques for ductile iron castings within the context of the fourth industrial revolution. The ranking model analyzes the effectiveness of the …

An Infrared Thermography Assessment For Monitoring Aging Mse Walls, Charles Lawal

College of Graduate Studies: Theses & Dissertations

Mechanically Stabilized Earth (MSE) walls play a vital role in ensuring the stability of transportation infrastructure, yet traditional inspection techniques often struggle to detect subsurface defects and demand significant labor. This research explores the application of Infrared Thermography (IRT) as a non-destructive, cost-effective approach for inspecting MSE walls across Southeast Georgia. By capturing thermal images under diverse environmental and operational conditions, the study aims to identify potential issues, including cracks, voids, delamination, and honeycombing, which can undermine integrity over time. Utilizing advanced image processing tools, the research implements specialized software to enhance the accuracy of defect detection, facilitating the generation …

Pulsed Arc Additive Manufacturing Of A Functionally Graded Er2209 Duplex Stainless Steel And Hsla-100 Structure: Morphology, Characterization, And Mechanical Performance, Stevens G. Hill Jr

College of Graduate Studies: Theses & Dissertations

Wire arc additive manufacturing is a process well suited to the efficient production of large structures. Duplex stainless steel exhibits high corrosion resistance and good strength which can be highly beneficial for industrial use. However, its use is limited due to its cost and complexity in controlling microstructure to achieve desired properties. In many cases, it can be highly beneficial to manufacture a component which uses specialty steel grades such as duplex stainless steel only where necessary, and utilizes more affordable, commonly available steels for reinforcement or bulk structural support. A functionally graded material satisfies these requirements by providing a …

Temporal Analysis Of Construction Safety Incidents In Southeastern U.S. Using Machine Learning Techniques, Mayowa O. Oladele

College of Graduate Studies: Theses & Dissertations

Construction safety incidents remain a significant concern, particularly in the Southeastern U.S. due to the high-risk nature of the industry. Analyzing patterns in these incidents can help improve safety practices and reduce accidents. Machine learning (ML) techniques were employed in this study to identify temporal patterns in construction safety incidents, aiming to enhance proactive safety management. The machine learning methods used in this research included logistic regression, decision trees, random forest, support vector machine (SVM), and K nearest neighbors (KNN).

The objective of the study was to analyze temporal trends in safety incidents and identify the most effective machine learningtechnique …

A Finite Volume-Based Approach For Modeling Hyperbaric Oxygen Therapy For Healing Chronic Wounds, Cade M. Layser

College of Graduate Studies: Theses & Dissertations

The current number of people in America with diabetes is approximately 38.4 million, that population is expected to rise to 54.9 million by 2030. Chronic wounds, which are characterized by impaired or stalled healing, are commonly associated with diabetes. These wounds if not treated properly can cause significant complications such as sepsis, infection, and gangrene which can lead to limb amputation or death of the patient. Limb amputations can be a large financial burden and significantly reduce the patient’s quality of life. The timely and efficient treatment of chronic wounds is essential to prevent adverse patient outcomes. Hyperbaric Oxygen therapy …

Computational Investigation Of Underbody Slant Angle Variation Effect On A Hatchback Vehicle, Nufile Uddin Ahmed

College of Graduate Studies: Theses & Dissertations

Electric Vehicles, despite many advantages over their gasoline-powered counterparts, have the following main challenges towards mass adoption – the charging infrastructure and the range of the vehicle. Among the myriads of factors affecting the range, the coefficient of drag and the required power to overcome it are noteworthy. This study aims to investigate the effect of rear underbody slant angle on a common, simplified hatchback-shaped Electric Vehicle. The rear underbody slant angles (upward) assessed were 0°, 5°, 10°, and 15° and they were subjected to three freestream velocities representative of urban (16 m/s), highway (25 m/s) and interstate (40 m/s) …

Accelerated Test Method (Atm) For The Evaluation Of External Sulfate And Chloride Attack On Concrete Using Artificial Climate Environment (Ace): An Assessment Of Methodology, Eli Walker

College of Graduate Studies: Theses & Dissertations

This study seeks to investigate a testing methodology that utilizes accelerated testing methods (ATM) in conjunction with nondestructive testing methods (NDT) to assess destructive ion attacks on concrete. Through the testing of concrete samples, the impacts of chloride and sulfate ions on concrete will be tested by subjecting concrete to cyclic testing in an artificial climate environment. Electrical resistivity (ER) and ultrasonic pulse velocity (UPV) testing will evaluate the impact of the accelerated testing on the migration of destructive ions within the pore structure of the tested concrete. Based on this investigation, comparisons between accelerated, non-accelerated, and control samples will …

Design Of An Improved Robust Fractional-Order Pid Controller For Buck–Boost Converter Using Snake Optimization Algorithm, Seyyed Morteza Ghamari, Hasan Molaee, Mehrdad Ghahramani, Daryoush Habibi, Asma Aziz

Research outputs 2022 to 2026

With the increasing complexity of modern power systems, effective control of DC–DC converters has become crucial to ensure stability and efficiency. This paper focuses on optimizing the parameters of a known fractional-order proportional–integral–derivative (FOPID) controller for the control of a DC–DC buck–boost converter. The control of a DC–DC buck–boost converter is achieved using aFOPID approach. The gains of this technique have been enhanced utilizing the snake optimization (SO) algorithm. This converter exhibits unfavourable behaviour due to its non-minimum structure, necessitating a well-regulated controller to guarantee stability. The fractional concept is suggested here to enhance the dynamics of the classical PID …

Sensitivity Analysis Of Reservoir Characteristics And Flue Gas Composition For Enhanced Oil Recovery In Heterogeneous Reservoir, Mehdi Nassabeh, Zhenjiang You, Alireza Keshavarz, Stefan Iglauer

Research outputs 2022 to 2026

The success of enhanced oil recovery depends on optimizing various reservoir factors. While CO2 and flue gas injections are common methods, they encounter challenges that require a deep understanding of reservoir characteristics and thorough analysis of pertinent parameters for effective mitigation. This study aimed to explore the synergistic relationship between flue gas compositions, reservoir characteristics, and injection rates for optimal oil recovery. Unlike prior studies that primarily examined the isolated effects of CO2 or flue gas, this research uniquely investigates the combined impact of real-world flue gas compositions from industrial sources under heterogeneous reservoir conditions. Through extensive sensitivity analysis and …

Optimization Of Inverted Perovskite Solar Cells: Simulation Insights Into Nio And Mixed-Cation Integration For Enhanced Efficiency, Bo Yao, Hairul Mardiah Hamzah, Sharifah Fatmadiana Bt Wan Muhammad Hatta, Faiz Arith, Mohammad Nur-E-Alam, Mohammad Aminul Islam

Research outputs 2022 to 2026

Inverted perovskite solar cells are one of the most promising technologies for the next generation of photovoltaics, due to excellent stability and a relatively simple fabrication process. Nevertheless, attaining high efficiency remains a challenge in integrating appropriate materials for HTL and an absorber layer. This work focuses on SCAPS-1D simulations to optimize the design of inverted PSCs using NiO as the HTL and a mixed-cation perovskite, MA₀.₄FA₀.₆PbI₃, as an absorber. By employing a controlled variable method, the thickness of the absorber layer was optimized within the range of 300 nm to 900 nm. The optimal performance was achieved when the …

A Comparative Analysis Of Preprocessing Filters For Deep Learning-Based Equipment Power Efficiency Classification And Prediction Models, Sang-Ha Sung, Chang-Sung Seo, Michael Pokojovy, Sangjin Kim

Mathematics & Statistics Faculty Publications

The quality of input data is critical to the performance of time-series classification models, particularly in the domain for industrial sensor data where noise and anomalies are frequent. This study investigates how various filtering-based preprocessing techniques impact the accuracy and robustness of a Transformer model that predicts power efficiency states (Normal, Caution, Warning) from minute-level IIoT sensor data. We evaluated five techniques: a baseline, Simple Moving Average, Median filter, Hampel filter, and Kalman filter. For each technique, we conducted systematic experiments across time windows (360 and 720 min) that reflect real-world industrial inspection cycles, along with five prediction offsets (up …

Lung Uptake Of Two Spect Markers Identifies Sensitivity To Hyperoxia-Induced Acute Respiratory Distress Syndrome In Rats, Anne V. Clough, Taheri Pardis, Guru P. Sharma, Ming Zhao, Elizabeth R. Jacobs, Said H. Audi

Biomedical Engineering Faculty Research and Publications

Introduction: Exposure of adult rats to hyperoxia is a well-established model of human Acute Respiratory Distress Syndrome (ARDS). Although rats exposed to 100% O₂ display clinical evidence of lung injury after ∼40 h and death by 72 h, rats exposed to 60% O₂ for up to 7 days show little sign of injury. However, when subsequently exposed to hyperoxia, these pre-exposed rats become more susceptible to ARDS. The objective of this study is to evaluate the ability of imaging biomarkers to track this hyperoxia susceptibility and to elucidate underlying mechanisms.

Methods: Sprague-Dawley rats were exposed to either room …

Scoping Review Of Machine Learning Techniques In Marker-Based Clinical Gait Analysis, Kevin N. Dibbern, Maddalena G. Krzak, Alejandro Olivas, Mark V. Albert, Joseph J. Krzak, Karen M. Kruger

Biomedical Engineering Faculty Research and Publications

The recent proliferation of novel machine learning techniques in quantitative marker-based 3D gait analysis (3DGA) has shown promise for improving interpretations of clinical gait analysis. The objective of this study was to characterize the state of the literature on using machine learning in the analysis of marker-based 3D gait analysis to provide clinical insights that may be used to improve clinical analysis and care. Methods: A scoping review of the literature was conducted using the PubMed and Web of Science databases. Search terms from eight relevant articles were identified by the authors and added to by experts in clinical gait …