Engineering Commons^™

Ceramic Dielectrics For High Temperature Capacitor Applications, Alan Daniel Devoe

Doctoral Dissertations

Technical progress in microelectronics is allowing operation in environments that were previously considered impossible. Building complete electronic circuits for applications above 450 degrees C, such as the planned NASA missions to Venus, require a variety of active and passive components, including multilayer ceramic capacitors (MLCC).

This study explores calcium zirconate CaZrO3 and magnesium oxide MgO based dielectric compositions for MLCC applications that are capable of operating at extreme temperatures between 450 degrees C and 600 degrees C. Both materials were characterized with respect to impurities, dopants, crystal structure and microstructural development. Dielectric properties were measured to determine permittivity, dissipation factor, …

On The Importance Of Novel Adsorption Processes And Structured Adsorbents For Biogas Upgrading And Storage, Kyle Newport

Doctoral Dissertations

The current adsorption processes investigated for biogas upgrading encounter severe challenges with regards to both adsorbents and processes, which necessitate the design and development of novel approaches to effectively separate carbon dioxide from methane and, hence, produce pipeline-grade renewable natural gas (RNG) as a fuel. These key challenges include (i) moderate CO2/CH4 selectivity, (ii) relatively long cycle times, (iii) high regeneration energy requirements, (iv) high-pressure drop, and (v) short adsorbent lifetime. The current biogas upgrading processes typically rely on pressure swing adsorption (PSA) or temperature swing adsorption (TSA). While PSA cycles are fast, the system requires large amounts of energy …

Characterizations Of Eco-Friendly Class C Fly Ash-Based Alkali-Activated Mixtures For Various 3d-Printing Techniques, Fareh Abudawaba

Doctoral Dissertations

This study explored the potential of using high-calcium class C fly ash (FA) as the main precursor for synthesizing alkali-activated mortar (AAM) suitable for 3D-printed concrete (3DPC) applications. The objective was to broaden the selection of AAMs, designed to suit various printing techniques, including continuous mixing and pumping, large batch mixing, and set-on-demand processes. The study began by assessing the fresh properties of various mixtures, utilizing tests such as open time (OT), initial setting time (IST), immediate axial deformation, and penetration test (PT), to determine the mixtures' extrudability and buildability. The study optimized mixtures with a broad spectrum of cycle …

Power Supply Induced Jitter Analysis In High-Speed Drivers, Yifan Ding

Doctoral Dissertations

The Input/Output Buffer Information Specification (IBIS) model faces challenges in accurately simulating power-supply-induced jitter (PSIJ), particularly under nonlinear and time-varying power noise conditions with pre-driver stages. This work introduces advancements in IBIS model modification algorithms to enhance PSIJ simulation accuracy for high-speed drivers.

First, a correction coefficient-based approach was developed to adjust switching coefficients K_pu and K_pd using pre-driver DC jitter sensitivity, significantly improving model robustness under DC and AC power noise. However, its effectiveness diminished under large noise amplitudes due to coefficient shape dominance.

To address this, a simplified algorithm bypassed correction coefficient, directly correlating switching transitions with jitter …

A Comprehensive Analysis Of Climate Resilience Strategies For Small Island Developing States, Ashley-Ann Davis

Doctoral Dissertations

Small Island Developing States (SIDS) face unique and disproportionate challenges in their efforts to achieve climate resilience due to their geographic vulnerabilities, limited resources, and systemic economic and social constraints. This dissertation provides a comprehensive analysis of climate resilience strategies tailored to SIDS, addressing critical systemic issues and exploring interdisciplinary solutions. It synthesizes findings across four interconnected studies, including an analysis of disaster preparedness and response mechanisms in developing countries to identify gaps and opportunities for improving resilience in extreme events. The research investigates economic factors influencing energy portfolio transitions in the Caribbean, emphasizing the complexities of renewable energy adoption. …

Ecobuoys For Scalable Oceanography, Anuscheh Nawaz, Michael Steele, Ruth Branch, David Burnett, Kuotian Liao, Mallory Parker, Eleftheria Roumeli

Electrical and Computer Engineering Faculty Publications and Presentations

An approach to scalable surface-drifting buoys is needed to enable the high spatial and temporal resolution of oceanographic data that the science and meteorological communities are asking for. With the number of active buoys predicted to increase by a factor of 100 or more, the impact on the environment becomes even more important. Here, we present a pathway to a scalable and sustainable generation of buoys. We identify the main criteria to be used when developing such buoys to be low cost, with reliable data and neutral or even positive environmental impact. For each buoy subsystem—hull, electronics, energy generation and …

Work In Progress: Using Internships As Means For Indirect Assessment Of Abet Criteria 3 “1-7” Student Outcomes, Robert Bass

Electrical and Computer Engineering Faculty Publications and Presentations

The Electrical & Computer Engineering (ECE) Department at Portland State University (PSU) has developed a Power Engineering Internship (PEI) program that provides engineering career development pathways within the electric utility industry. The PEI is supported by several U.S. Department of Energy grants that aim to develop quality career opportunities and develop a future electric utility workforce that represents the nation’s diverse populations. The PSU ECE Department intends to use surveys of internship participants as assessment tools for its ABET accreditation process, in particular, the ABET Criteria 3 Student Outcomes (SOs). SOs relate to the knowledge, skills, and behaviors that students …

An Offline Approach For Frequency Event Detection In Power Systems: Tkeo And Statistical Analysis, Hussain A. Alghamdi, Midrar Adham, Umar Farooq, Robert Bass

Electrical and Computer Engineering Faculty Publications and Presentations

The increasing integration of renewable energy sources in power grids and the transition from conventional thermal-based generation to inverter-based resources for power generation have reduced power system inertia and increased the rate of change of frequency, which constitutes a challenge for frequency stability in modern power systems. These changes necessitate robust frequency event detection algorithms that can rapidly and accurately identify events, providing essential support to maintain stability. This paper proposes a frequency event detection algorithm with six tunable parameters for offline post-processing, leveraging the Teager-Kaiser Energy Operator method and statistical analysis. The algorithm is tested on a dataset comprised …

Grid Services Demonstration Using Service-Oriented Derms, Midrar Adham, Sean Keene, Tylor E. Slay, Jaime T. Kolln, Robert Bass

Electrical and Computer Engineering Faculty Publications and Presentations

This work demonstrates four grid service use cases using a service-oriented DER Management System within an Energy Grid of Things network. Imposed by a set of rules referred to as the Energy Service Interface, the DER Management System uses IEEE 2030.5 as its primary communication protocol. To simulate the efforts of Grid Operators, Grid Service Providers, and consumers to provide grid services, a co-simulation environment that leverages the GridAPPS-D simulation platform was developed. The results show that a service-oriented DER Management System provides a means of achieving reliable and impactful grid services while protecting customers’ privacy and ensuring their agency …

Estimating Electric Power Consumption Of Grid-Enabled Residential Heat Pump Water Heaters, Dana Paresa, Robert Bass

Electrical and Computer Engineering Faculty Publications and Presentations

We present two methods for estimating compressor electric power consumption of residential heat pump water heaters. Power is calculated using data from voltage and current instrumentation, which add costs and potential failure points to the unit. The two methods presented herein use thermal instrumentation or derived data that are typically included within modern residential heat pump water heaters. One method uses data from lower and upper tank temperature sensors. The other method uses an attribute that is commonly available within grid-enabled flexible load heat pump water heaters. For both methods, fit equations were derived, for which coefficients are tuned to …

Characterization And Optimization Of Sand And Tung Oil-Based Resins For Binder-Jet 3d Printing, Daniel I. Ajiola

College of Graduate Studies: Theses & Dissertations

Binder-jet 3D printing as a transformative technology in additive manufacturing, offers the ability to fabricate complex structures with diverse materials. This thesis investigates the use of a sustainable tung oil-based resin to create composites, exploring the potential for an eco-friendly alternative to synthetic binders.

The aim of this research is to develop and characterize a bio-based resin formulation, using tung oil as the primary binder, for application in binder-jet 3D printing with sand as the reinforcement. The resin formulation was prepared by combining tung oil, n-butyl methacrylate, divinylbenzene, and di-tert-butyl peroxide in precise proportions, ensuring a balanced mixture that supports …

Contextual Augmentation In Artificial Intelligence, Emmanuel Joshua Balogun

College of Graduate Studies: Theses & Dissertations

Contextual understanding is a significant challenge of Large Language Models (LLMs), which are typically trained on general-purpose datasets. Due to this, LLMs fail to capture nuanced or domain-specific information and may struggle to interpret user queries accurately. Consequently, prompt engineering can become complex in automating, and LLMs are prone to “hallucinating”—generating random or irrelevant texts—when they lack sufficient context. This undermines their ability to provide focused, accurate responses. Accordingly, this thesis seeks to enhance the contextual understanding capabilities of Artificial Intelligence systems to facilitate more precise and relevant answer generation. Study A looks into a new approach to combating misinformation …

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 …

Insider Threat Agent: A Behavioral Based Zero Trust Access Control Using Machine Learning Agent, Michael Fojude

College of Graduate Studies: Theses & Dissertations

Hybrid work, cloud adoption, and freely available AI‑enabled attack tools have exposed critical weaknesses in perimeter‑centric security. Current breach reports attribute more than one‑third of incidents to insider misuse or credential compromise, yet many organizations still depend on static Role‑ or Attribute‑Based Access Control that neither verifies intent continuously nor adapts to subtle behavioral change. This research addresses that gap by designing and validating a behavioral based Zero Trust Access Control (ZTAC) Agent. A five‑year enterprise log Dataset was extracted and cleansed to establish a high‑fidelity baseline of normal user behavior. Feature engineering captured temporal regularity (login sequence, session duration), …

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 …

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) …

The Strengthening Mechanisms And Incipient Plasticity Of Additively Manufactured Biomedical Refractory High Entropy Alloys, Changxi Liu, Liqiang Wang, Miao Luo, Kuaishe Wang, Marco De Battista, Ling Zhang, Weijie Lu, Lai Chang Zhang, Di Zhang

Research outputs 2022 to 2026

Owing to the cellular structure that limits dislocation motion upon stress loading, additively manufactured (AM) refractory high-entropy alloys (HEAs) exhibit an excellent strength-plasticity synergy. This work integrates micro/nano-mechanical experiments with statistical physics modeling to examine dislocation nucleation and slip in AM-fabricated TiNbTaZrMo HEA. Computational results indicated that the activation volume for initial dislocation nucleation is about one atomic volume, facilitating dislocation initiation. Nanoindentation and in-situ micro-pillar compression reveal no significant pop-in events, indicating that the cellular structure impedes dislocation slip and thus prevents plasticity reduction from dislocation slipping near grain boundaries. This work provides a thorough investigation into the interplay …

Influence Of Time Pressure And Flood Information Type On Flood Alert Effectiveness In Driving, Katherine R. Garcia, Scott Mishler, Jing Chen

Psychology Faculty Publications

Flood alerts are a means of risk communication that alerts the public to potential floods. The purpose of this research was to investigate factors that affected drivers' understanding and actions given a flood presented through a mobile navigation application. Two experiments were conducted to examine the effects of time pressure and type of flood information on drivers' planned actions when faced with potential flooding. Participants were asked about their planned actions given one type of flood information in a driving scenario either with or without time pressure. Our results indicated significant differences in participants' behaviors across the different flood information …

Similarity May Be Safer: The Effect Of Similarity Between Speech-Based Takeover Request Style And Driver Personality On Self-Driving Takeover Performance, Keer Ma, Jianfeng Wu, Yanxi Lin, Zihan Li, Songyang Guo, Dongfang Jiao, Shihan Yu

Psychology Faculty Publications

In Level 3 automated driving, it is critical that drivers can rapidly and effectively shift from non-driving related tasks (NDRT) back to the driving task. While previous research has examined the modality, timing, and vocal characteristics of takeover requests (TORs), little is known about how the style of speech-based TORs interacts with drivers’ personality traits. This study conducted a driving simulator experiment with 49 participants using a 2 × 2 within-subjects design. Drawing on the dominant-submissive dimension of personality, we examined the similarity of personality tendencies between speech-based TORs and drivers under takeover scenarios of varying urgency (low: road construction; …

Trustworthiness And Trust: Identifying Factors That Drive Successful Human-Ai Interaction In Nuclear Power Plant Applications, Yusuke Yamani, Austin Jackson, Casey Kovesdi, Jeffrey Joe, Jeremy Mohon

Psychology Faculty Publications

Emerging technologies such as artificial intelligence (AI) and machine learning are rapidly evolving and promising tools for efficient and continued safe operations of the U.S. nuclear power plants (NPPs). Emerging AI techniques like large language models (LLMs) are one such technology that may help personnel at existing NPPs perform work more efficiently. For example, operators may query the current operational status of a power plant via a chat interface, leveraging LLMs to access plant-related information in an interactive manner rather than manually collecting various sensor data for surveillance or work order tasks. This is a fundamental shift in the way …

Automation To Autonomy: Temporal Dynamics Of Trust And Visual Attention Allocation Did Not Evolve, Tetsuya Sato, Eric Chancey, Yusuke Yamani

Psychology Faculty Publications

Emerging work environments are expected to implement autonomy that performs various functions without human input. Previous works has shown that trust in automation is negatively correlated with visual attention allocation, indicating that trust is a dynamic construct. Moreover, trust in automation and trust in autonomy appears to evolve in similar ways. However, recent work has demonstrated differences between trust in automation and trust in autonomy within Kaber’s (2018) theoretical framework (Sato et al., 2023b). Yet, it is uncertain whether the development of trust and visual attention allocation differs between automation and autonomy. The present study examined the temporal dynamics of …

The Interacting Roles Of Attention Allocation And Trust In Highly Automated Aam Environments, Yusuke Yamani

Psychology Faculty Publications

[First slide]

Mechanisms of attentive visual processing

• Attention control
• Visual search
• Eye movement
• Aging and individual differences

Limits of human performance in applied environment

• Complex displays
• Machine operation
• Surface transportation
• Advanced air mobility
• Nuclear operation

Methods to ameliorate human cognitive performance

• Human-machine interface
• Human autonomy/AI teaming
• Human-systems integration
• Training

Psychosocial Determinants Of Public Transportation Use Among Brazilian And American Users: An Integrated Modeling Approach, Ingrid Luiza Neto, Hartmut Günther, Bryan E. Porter, Taciano L. Milfont, Pastor Willy Gonzales Taco, Caroline Cardoso Machado

Psychology Faculty Publications

Overreliance on cars can promote individual, environmental, economic and social problems, requiring the development of measures to reduce car use and encourage the use of more sustainable transport options. Contributing to this call, here we report a cross-cultural study conducted in Brazil (n = 312) and the United States (n = 518) investigating the applicability of the model of Bamberg and Möser in predicting the use of public transport. Results indicated the model is equivalent across samples, regarding both the measures and the relations between the variables of the model. Intention strongly predicted self-reported public transport behaviour, explaining 70% of …

Smart And Sustainable Regeneration Of Fouled Desalination Membranes Using Artificial Intelligence, Muhammad Mubashir, Mustakeem Mustakeem, Ammar Alnumani, Abdulrahman Abutaleb, Ali Hamoud Naji Sumayli, Tausif Ahmad, Muhammad Rizwan Azhar

Research outputs 2022 to 2026

During the desalination process, scaling, fouling, and degradation are associated issues that lead to a drop in the separation performance of membranes. Membrane regeneration emerges as a critical technology in which upcycling and downcycling can offer a promising avenue for promoting sustainable membrane lifecycle management. Multiple research papers and reviews have critically analyzed the regeneration of membranes, which explains the end-of-cycle assessment and cost analysis of membrane recycling. However, challenges associated with the conventional and innovative regeneration processes are not yet analyzed. The potential impact of artificial intelligence (AI) on membrane regeneration is not explained in the literature. This review …

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 …

Advances, Limitations, And Future Perspectives In 3d Printing Of Porous Glasses: A Technical Note, Hamed Bakhtiari, Mostafa Omidi Bidgoli, Mohammad Hosseini

Research outputs 2022 to 2026

In this study, the latest developments in 3D printing of porous glasses are discussed. Current challenges in 3D printing of porous-based microfluidic devices mostly include the printing resolution which is correlated with the processing time, post treating, and developing tailored materials for porous glasses. Although the latter issue has been resolved to some extent recently, the former has remained a challenge. Currently, the smallest 3D printed feature in a microfluidic structure is 200µm while higher resolutions are required in some applications. On the other hand, previous reports have shown intensive printing time for higher resolutions. To achieve an optimal compromise, …

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 …

Recent Progress In Underground Hydrogen Storage, Muhammad Ali, Abubakar Isah, Nurudeen Yekeen, Aliakbar Hassanpouryouzband, Mohammad Sarmadivaleh, Esuru Rita Okoroafor, Mohammed Al Kobaisi, Mohamed Mahmoud, Volker Vahrenkamp, Hussein Hoteit

Research outputs 2022 to 2026

With the global population anticipated to reach 9.9 billion by 2050 and rapid industrialization and economic growth, global energy demand is projected to increase by nearly 50%. Fossil fuels meet 80% of this demand, resulting in considerable greenhouse gas emissions and environmental challenges. Hydrogen (H2) offers a promising alternative due to its potential for clean combustion and integration into renewable energy systems. Underground H2 storage (UHS) enables long-term, large-scale storage to achieve equilibrium between seasonal supply and demand. This review synthesizes recent advancements in UHS, highlighting progress and persistent challenges. The review explores the complex mechanisms of H2 trapping and …

Ecu-Pmu-Fdi/Tsa, Taylah Griffiths, Mohiuddin Ahmed, Chadni Islam

Research Datasets

Cyberattacks are now targeting the electrical grid due to its inclusion of smart devices, e.g., smart meters, phasor measurement units. The need to understand and review the impacts of attacks is vital. ECU-PMU-FDI/TSA encompasses communications between a phasor measurement unit and a phasor data concentrator, using the IEEE C37.118 protocol. Benign traffic was captured as control, and for attacks false data injection and time synchronization attack traffic were captured.

Fast-Sparse-Spanner: A Practical Algorithm For Constructing Low-Stretch Sparse Geometric Graphs, Fnu Shariful

UNF Graduate Theses and Dissertations

When constructing geometric graphs (vertices are points and edges are line segments connecting point pairs) on pointsets, stretch-factor (worst-case detour between any point pair) is often considered a quality metric. A low stretch-factor (a quantity that is usually > 1) guarantees short paths between all vertex pairs. A geometric graph having a stretch-factor of t is known as a t-spanner. Creating low stretch-factor geometric graphs for large pointsets with a low number of edges is an open problem in computational geometry.

In this work, we have designed and engineered a new simple and practical (fast and memory-efficient) algorithm named Fast-Sparse-Spanner algorithm …