Artificial Intelligence and Robotics Commons^™

High-Fidelity Soh Prediction In Lithium-Ion Batteries Using Hybrid Ml Networks, Shafiyee Islam, Gon Namkoong

Electrical & Computer Engineering Faculty Publications

Accurate and efficient prediction of lithium-ion battery state of health (SOH) is critical for ensuring reliability in electric vehicles, grid storage, and aerospace systems. Traditional SOH estimation methods often struggle with nonlinear degradation behaviors and lack sensitivity to subtle electrochemical signals, limiting their real-world deployment. To address these challenges, this study examines hybrid deep learning models that integrate differential capacity (dQ/dV) analysis to enhance predictive accuracy. Four hybrid architectures - hybrid CNN-LSTM multihead, CNN extractor for LSTM, DNN-LSTM, and DNN Bi-LSTM - were developed and evaluated using the NASA randomized battery usage dataset, offering a realistic benchmark under diverse operational …

Leveraging Large Language Models To Create Learner Personas For Training Design, N. Lovett, M. Yang, K. Herman, B. Li, M. Sosonkina, W. Purwanto, P. Jiang, M. H. Wu

Electrical & Computer Engineering Faculty Publications

This case examines the innovative use of Large Language Models (LLMs) to generate learner personas for developing learner-centered cybersecurity training materials when direct access to initial learner data is not available. The team developed a nine-stage iterative process for creating and refining AI-generated personas to address this constraint, integrating ethical review, stakeholder feedback, and action research principles. The process expanded upon Kouprie and Visser’s (2009) empathic design framework to ensure cultural responsiveness and mitigate potential biases in LLM outputs. Through multiple refinement cycles, initial generic personas evolved into detailed, context-rich archetypes which informed the development of effective and context-responsive training …

A Novel Intelligent Thermal Feedback Framework For Electric Motor Protection In Embedded Robotic Systems, Mohamed Shili, Salah Hammedi, Hicham Chaoui, Khaled Nouri

Electrical & Computer Engineering Faculty Publications

As robotic systems advance in autonomy and sophistication while being used in uncertain environments, the challenge of building reliable and robust electric motors that are embedded into robotic systems has never been a more important engineering problem. Thermal distress caused by extended operation or excessive loading can negatively affect a motor’s performance and efficiency and lead to catastrophic hardware failure. This paper proposes a novel intelligent control framework that includes real-time thermal feedback for hybrid electric motors that are embedded into robotic systems. The framework relies on adaptive control techniques and lightweight machine learning techniques to estimate internal motor temperatures …

Feel Bad To Discard A Fashion Product: How Ai Designers Influence Individuals' Sustainable Consumption, Ha Kyung Lee, Dooyoung Choi

Educational Leadership & Workforce Development Faculty Publications

This study explores how AI technology in fashion design influences consumers' sustainable consumption behaviors, focusing on emotional attachment to products. By comparing AI-generated and human-designed fashion items, the study examines how designer type impacts negative emotions about discarding products, mediated by emotional attachment. Results from two experimental studies reveal that designer type significantly affects negative emotions toward discarding human-designed items, but emotional attachment was not influenced by designer type in the first study. This lack of difference may be due to personal characteristics that moderate the effect. The second study found that individuals who perceive AI as human-like form stronger …

Towards Dynamic Learner State: Orchestrating Ai Agents And Workplace Performance Via The Model Context Protocol, Mohan Yang, Nolan Lovett, Belle Li, Zhen Hou

Educational Leadership & Workforce Development Faculty Publications

Current learning and development approaches often struggle to capture dynamic individual capabilities, particularly the skills they acquire informally every day on the job. This dynamic creates a significant gap between what traditional models think people know and their actual performance, leading to an incomplete and often outdated understanding of how ready the workforce truly is, which can hinder organizational adaptability in rapidly evolving environments. This paper proposes a novel dynamic learner-state ecosystem—an AI-driven solution designed to bridge this gap. Our approach leverages specialized AI agents, orchestrated via the Model Context Protocol (MCP), to continuously track and evolve an individual’s multi-dimensional …

Exploring The Impact Of Value Co-Creation Through Ai-Driven Chatbbots On Customer Repeat Purchases, Dooyoung Choi, Jaeha Lee

Educational Leadership & Workforce Development Faculty Publications

Drawing on the Stimulus-Organism-Response (S-O-R) framework, this study explores how perceived value co-creation during chatbot interactions influences customer repeat purchase intentions through cognitive, emotional, and social responses to chatbots. A survey of 220 participants revealed that perceived value co-creation significantly affected repeat purchase intentions, with cognitive evaluations, emotional reactions, and social value serving as key mediators. However, the direct effect of value co-creation on purchase intentions was not significant. The findings suggest that while value co-creation enhances consumer engagement, repeat purchases occur only when consumers experience positive cognitive, emotional, and social outcomes. Therefore, it is crucial for retailers to incorporate …

Analysing Nontraditional Students' Chatgpt Interaction, Engagement, Self-Efficacy And Performance: A Mixed-Methods Approach, Mohan Yang, Shiyan Jiang, Belle Li, Kristin Herman, Tian Luo, Shanan Chappell Moots, Nolan Lovett

STEMPS Faculty Publications

Generative artificial intelligence brings opportunities and unique challenges to nontraditional higher education students, stemming, in part, from the experience of the digital divide. Providing access and practice is critical to bridge this divide and equip students with needed digital competencies. This mixed-methods study investigated how nontraditional higher education students interact with ChatGPT in multiple courses and examined relationships between ChatGPT interactions, engagement, self-efficacy and performance. Data were collected from 73 undergraduate and graduate students through chat logs, course reflections and artefacts, surveys and interviews. ChatGPT interactions were analysed using four metrics: prompt number, depth of knowledge (DoK), prompt relevance and …

First Amendment Roadblock? Regulating The Misuse Of Generative Ai Technologies: Impersonation And Appropriation Of Likeness Without Permission, Muhammad Rabiu

Cybersecurity Undergraduate Research Showcase

This paper initially explores the misuse of generative AI technologies, particularly their role in impersonating or appropriating individuals' likeness without consent. It will then analyze technical and legal mitigation strategies and propose recommendations to address this issue in light of the First Amendment’s constitutional Freedom of Speech provision.

An Introduction To The Time-Independent Schrödinger Equation And Methods To Solve It, Vu Giang, Alex Gnech

OUR Journal: ODU Undergraduate Research Journal

The Time-Independent Schrödinger Equation is a linear elliptic PDE that describes quantum-mechanical systems. Its significance in the science of submicroscopic phenomena, particularly quantum mechanics, is as central as Newton’s laws of motion are to classical mechanics. This study uses various methods, including novel neural networks and finite difference schemes, to solve the one-dimensional two-body equation.

Instructional Systems Design: The Diffusion And Adoption Of Technology: (Volume 2), Cassandra Celaya (Author), Pamela J. Downing (Author), Jessica Shifflett (Author), Debbie Gdula (Author), Tracie Barr (Author), Miguel Ramlatchan (Author & Editor)

University Administration Bookshelf

Instructional designers, instructional systems designers, and other educational technologists are, by their nature, innovators. These professionals apply and extend the applied science of learning, systems, communication, and instructional design theory to help students learn. Technology in some capacity is used to make the connections between subject matter experts, teachers, instructors, and their learners. It is common for instructional designers to seek new tools, techniques, and innovations for the improvement of learning, access, quality, and student satisfaction. However, the adoption and diffusion of new educational technology and innovation is a complex process that depends on many variables. Understanding these processes and …

Etdsuite: A Toolkit To Mine Electronic Theses And Dissertations To Enrich Scholarly Big Data Using Natural Language Processing And Computer Vision, Muntabir Hasan Choudhury

Computer Science Theses & Dissertations

In the past decades, there has been a growing interest in mining scientific documents to obtain domain knowledge automatically. One of the understudied types of scientific documents is Electronic Theses and Dissertations (ETDs), as ETDs have distinct features compared with conference proceedings and journal articles. ETDs usually serve as partial requirements of academic degrees for students pursuing higher education. They are book-length documents (i.e., 100 – 400 pages long), and the topics may shift across chapters, exhibit the significant contribution of a student’s research over the entire degree pursuing period, and have unique metadata schema and page layouts. However, the …

An Integrated Theoretical Socio-Technical Framework For Implementing Service Robots’ Integration In Healthcare, Sujatha Alla

Engineering Management & Systems Engineering Theses & Dissertations

Healthcare workers, either clinical or non-clinical, are obligated to serve patients. However, lack of a sufficient number of professionals leads to burnout, severe stress, and, consequently, decreased quality of services. In this context, very few countries have been successful in employing service robots to perform dull, dirty, and/or dangerous tasks related to patient wellbeing/healthcare, while most countries are still skeptical about it. As robotics advances, there is an opportunity for healthcare to take advantage of this technology to reduce personnel workload and to reduce the possibility of exposure to contagious pathogens. However, healthcare is a vulnerable environment and requires critical …

Machine Learning And Simulation Techniques For Detecting Buoys From Lidar Data, Christopher Adolphi

Electrical & Computer Engineering Theses & Dissertations

Maritime autonomy, specifically the use of autonomous and semi-autonomous maritime vessels, is a key enabling technology supporting a set of diverse and critical research areas, including coastal and environmental resilience, assessment of waterway health, ecosystem/asset monitoring and maritime port security. Critical to the safe, efficient and reliable operation of an autonomous maritime vessel is its ability to perceive the external environment through onboard sensors. The main sensor utilized in this research is a LiDAR sensor. This sensor is able to generate point clouds of the surrounding environment, of which a machine learning model is used to label each point in …

Explainable Artificial Intelligence: Methods And Evaluation, Gayane Grigoryan

Engineering Management & Systems Engineering Theses & Dissertations

A wide array of techniques within explainable artificial intelligence (XAI) have been developed to measure the importance of features in machine learning models. A notable portion of these methods draws upon principles of cooperative game theory (CGT), with the Shapley value emerging as a widely used solution concept. Despite the rising prominence of the Shapley value, other promising solutions from cooperative game theory—such as the Nucleolus, Banzhaf power index, Shapley-Shubik power index, and solutions to conflicting claims problems—have been comparatively overlooked, even though they hold significant potential. In this dissertation, multiple XAI methods based on these other CGT solutions are …

Who Wrote The Scientific News? Improving The Discernibility Of Llms To Human-Written Scientific News, Dominik Soós

Computer Science Theses & Dissertations

Large Language Models (LLMs) have rapidly advanced the field of Natural Language Processing and become powerful tools for generating and evaluating scientific text. Although LLMs have demonstrated promising as evaluators for certain text generation tasks, there is still a gap until they are used as reliable text evaluators for general purposes. In this thesis project, I attempted to fill this gap by examining the discernibility of LLMs from human-written and LLM-generated scientific news. This research demonstrated that although it was relatively straightforward for humans to discern scientific news written by humans from scientific news generated by GPT-3.5 using basic prompts, …

Deep Learning In Reproducing Kernel Banach Spaces, Mingsong Yan

Mathematics & Statistics Theses & Dissertations

Deep learning has achieved immense success in the past decade. The goal of this dissertation is to understand deep learning through the framework of reproducing kernel Banach spaces (RKBSs), which were originally proposed for promoting sparse solutions. We begin by considering learning problems in a general functional setting, and establishing explicit and data-dependent representer theorems for both minimal norm interpolation (MNI) problems and regularization problems. These theorems provide a crucial foundation for the subsequent results derived for both sparse learning and deep learning. Next, we investigate the essential properties of RKBSs capable of encouraging sparsity in learning solutions. With the …

Privacy-Preserving Deep Learning Framework For Iot Malware Detection, Sabbir Ahmed Khan

Computer Science Theses & Dissertations

Cyberattacks on IoT devices are accelerating at an unprecedented rate, largely driven by IoT malware activities. The IoT malware attacks typically comprise three stages: intrusion, infection, and monetization. Existing IoT malware detection methods fail to identify malicious activities at the intrusion and infection stages and thus cannot stop potential attacks timely. In our research, we have leveraged power side-channel information as input to our deep learning model to identify malware at early stages of intrusion on IoT devices. But, deploying a resource-intensive deep learning model on highly resource-constrained IoT devices is a significant challenge. Consequently, utilizing a Machine Learning as …

Mesostructure Reconstruction Of Prepreg Platelet Molded Composite With Artificial Intelligence, Richard Larson

Mechanical & Aerospace Engineering Theses & Dissertations

Prepreg platelet molded composites (PPMC) are long, discontinuous fiber reinforced polymer materials. PPMC are an important subcategory of composite materials as they are processible into geometrically complex structures and can be produced via high-throughput manufacturing processes, however they have higher stiffness and strength as compared to traditional discontinuous fiber reinforced polymers. However, there is inherent randomness in the structure of PPMCs and as such, PPMC parts frequently require per part testing that is cost prohibitive.

Herein, a method using artificial intelligence is proposed as a more cost-effective method of inspecting PPMC parts. Different artificial intelligence (AI) architectures are explored to …

Harnessing Social Media For Disaster Response: Intelligent Identification Of Reliable Rescue Requests During Hurricanes, Wael Khallouli

Engineering Management & Systems Engineering Theses & Dissertations

Hurricanes pose a significant threat to both human lives and infrastructure. Decision-makers face substantial challenges during such events, as they must act quickly to address victims’ needs. Social media platforms provide a valuable source for quick and real-time information. Recent hurricane events have shown that people turn to social media to call for help when official communication channels, such as 911, are overwhelmed. However, extracting actionable information from the massive number of messages posted on social media is challenging. Furthermore, verifying social media messages posted by the public is a critical concern for disaster response practitioners, making them hesitant to …

Boring But Demanding: Using Secondary Tasks To Counter The Driver Vigilance Decrement For Partially Automated Driving, Scott Mishler, Jing Chen

Psychology Faculty Publications

Objective

We investigated secondary–task–based countermeasures to the vigilance decrement during a simulated partially automated driving (PAD) task, with the goal of understanding the underlying mechanism of the vigilance decrement and maintaining driver vigilance in PAD.

Background

Partial driving automation requires a human driver to monitor the roadway, but humans are notoriously bad at monitoring tasks over long periods of time, demonstrating the vigilance decrement in such tasks. The overload explanations of the vigilance decrement predict the decrement to be worse with added secondary tasks due to increased task demands and depleted attentional resources, whereas the underload explanations predict the vigilance …

The Vulnerabilities Of Artificial Intelligence Models And Potential Defenses, Felix Iov

Cybersecurity Undergraduate Research Showcase

The rapid integration of artificial intelligence (AI) into various commercial products has raised concerns about the security risks posed by adversarial attacks. These attacks manipulate input data to disrupt the functioning of AI models, potentially leading to severe consequences such as self-driving car crashes, financial losses, or data breaches. We will explore neural networks, their weaknesses, and potential defenses. We will discuss adversarial attacks including data poisoning, backdoor attacks, evasion attacks, and prompt injection. Then, we will explore defense strategies such as data protection, input sanitization, and adversarial training. By understanding how adversarial attacks work and the defenses against them, …

The Security Of Deep Neural Networks, Jalaya Allen

Cybersecurity Undergraduate Research Showcase

Our society has transitioned from our primitive lifestyle to soon, an increasingly automatic one. That idea is further exemplified as we shift into an AI era, better known as Artificial intelligence. Artificial Intelligence is classified as computer systems that can perform tasks that typically require human intelligence. However, a common thought or question that most might have is, how is this done? How does AI process information the way we want it to and have access to so much information? AI is trained by systems called AI models. These modeling programs are trained on data to recognize patterns or make …

High-Resolution And Quality Settings With Latent Consistency Models, Steven Chen, Junrui Zhang, Rui Ning

Cybersecurity Undergraduate Research Showcase

Diffusion Models have become powerful generative models which is capable of synthesizing high-quality images across various domains. This paper explores Stable Diffusion and mostly focuses on Latent Diffusion Models. Latent Consistency Models can enhance the inference with minimal iterations. It demonstrates the performance in image in-painting and class-conditional synthesis tasks. Throughout the experiment different datasets and parameter configurations, the paper highlights the image quality, processing time, and parameter. It also discussed the future directions including adding trigger-based implementation and emotional-based themes to replace the prompt.

Performing Information Extraction For Mission Engineering Applications, Samuel R. Koski

Engineering Management & Systems Engineering Theses & Dissertations

The process of extracting structured data from unstructured and semi-structured text is manual, time consuming and error prone. Current natural language processing approaches for automating this process are difficult to verify for non-trivial and context-sensitive corpora. Large Language Models (LLMs) like ChatGPT have become a subject of considerable interest, opening a promising avenue of exploration. However, there is limited evidence on the performance of LLMs for information extraction.

In this dissertation, an approach is proposed to evaluate the accuracy of Stanford OpenIE and OpenAI's ChatGPT for this purpose. This includes comparing Resource Description Framework (RDF) triples extracted by each of …

Scaled And Graduated Learning In Deep Relu Networks And Reconstructing Depp Inelastic Scattering Kinematics, Abdullah Ayar Farhat

Mathematics & Statistics Theses & Dissertations

To address computational challenges in learning deep neural networks, properties of deep RELU networks were studied to develop a multi-scale learning model. The multi-scale model was compared to the multi-grade learning models. Unlike the deep neural network learned from the standard single-scale, single-grade model, the multi-scale neural networks use low scale information from all hidden layers, and thusly provide a robust approximation method that requires fewer parameters, lower computational time, and is resistant to noise. It is shown that the multiscale method is not subject to issues arising from the vanishing gradient problem. This allows very deep multi-scale networks to …

Computational Modeling And Analysis Of Facial Expressions And Gaze For Discovery Of Candidate Behavioral Biomarkers For Children And Young Adults With Autism Spectrum Disorder, Megan Anita Witherow

Electrical & Computer Engineering Theses & Dissertations

Facial expression production and perception in autism spectrum disorder (ASD) suggest the potential presence of behavioral biomarkers that may stratify individuals on the spectrum into prognostic or treatment subgroups. High-speed internet and the ease of technology have enabled remote, scalable, affordable, and timely access to medical care, such as measurements of ASDrelated behaviors in familiar environments to complement clinical observation. Machine and deep learning (DL)-based analysis of video tracking (VT) of expression production and eye tracking (ET) of expression perception may aid stratification biomarker discovery for children and young adults with ASD. However, there are open challenges in 1) facial …

Time Series Models For Predicting Application Gpu Utilization And Power Draw Based On Trace Data, Dorothy Xiaoshuang Parry

Electrical & Computer Engineering Theses & Dissertations

This work explores collecting performance metrics and leveraging various statistical and machine learning time series predictive models on a memory-intensive application, Inception v3. Trace data collected using nvidia-smi measured GPU utilization and power draw for two runs of Inception3. Experimental results from the statistical and machine learning-based time series predictive algorithms showed that the predictions from statistical-based models were unable to capture the complex changes in the trace data. The Probabilistic TNN model provided the best results for the power draw trace, according to the test evaluation metrics. For the GPU utilization trace, the RNN models produced the most accurate …

A Comparison Of Machine Learning Surrogate Models Of Street-Scale Flooding In Norfolk, Virginia, Diana Mcspadden, Steven Goldenberg, Binata Roy, Malachi Schram, Jonathan L. Goodall, Heather Richter

Community & Environmental Health Faculty Publications

Low-lying coastal cities, exemplified by Norfolk, Virginia, face the challenge of street flooding caused by rainfall and tides, which strain transportation and sewer systems and can lead to personal and property damage. While high-fidelity, physics-based simulations provide accurate predictions of urban pluvial flooding, their computational complexity renders them unsuitable for real-time applications. Using data from Norfolk rainfall events between 2016 and 2018, this study compares the performance of a previous surrogate model based on a random forest algorithm with two deep learning models: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The comparison of deep learning to the random …

Bert-Based Detection Of Ai-Generated Text For Content Verification, Soham Biren Katlariwala

2024 REYES Proceedings

With advancements in AI-driven natural language generation, distinguishing between AI-generated and human-written text has become imperative for ensuring content authenticity across industries. This study explores the effectiveness of Bidirectional Encoder Representations from Transformers (BERT) in addressing this classification challenge. Utilizing a diverse dataset and robust preprocessing techniques, BERT achieved a peak F1-score of 0.94364, outperforming traditional models such as Logistic Regression and Support Vector Machines. The results underscore the potential of transformer-based models in addressing real-world con- tent verification problems. Future enhancements include fine-tuning and expanding datasets for greater generalizability.

Predicting Compressive Strength Of Concrete Incorporating Fly Ash, Blast Furnace Slag, And Superplasticizer Using Machine Learning Techniques, Muhammad Faisal Yaqub

2024 REYES Proceedings

Concrete is the second most essential element in the construction industry, and its strength requirements vary based on the specific conditions of each project. However, determining the compressive strength of concrete involves laboratory tests, which wastes a lot of time and money. Researchers have developed machine learning models that predict the compressive strength of cement-based concrete having various mixes. In this research, the compressive strength of concrete incorporating fly ash, blast furnace slag, and superplasticizer is predicted using different machine learning models, namely, Linear Regression, Random Forest Regression, Decision Tree Regression, Extreme Gradient Boosting, Light Gradient Boosting, AdaBoost, and CatBoost …