Learning Without Labels: A Self-Supervised Learning Approach for Anomaly Detection in Control Systmes

Oil pipelines, water plant systems, and other critical infrastructure are managed and operated by industrial control systems (ICS). These systems safeguard the operations of critical infrastructures, requiring minimal disruption from cyberattacks or malfunctions. The use of anomaly detection methods in control systems (ICS) can reduce system interruptions. However, anomaly detection methods often require annotated data, which may not be available for the control system. Additionally, the datasets used for the control systems do not include sensor outputs and environmental data, resulting in a restricted view of the system. This research investigates how SSL models can be applied to different control system data streams. The study will develop a framework for fusing sensor data, network data, and environmental data. The approach consists of two phases. The first phase includes training SSL models on single data types (e.g., sensor dataset) and an approach for merging multiple data streams. The second phase will combine the SSL model with the aggregated data for anomaly detection. The contributions of this research effort include data fusion framework for control systems and an SSL model trained on the combined dataset without labels for anomaly detection.https://jagworks.southalabama.edu/southalabama-shgrf-posters/1003/thumbnail.jp

Adaptive Core Materials for Wireless Power Transfer: Evaluating MR Fluid in Static and Dynamic Scenarios

This research introduces the behavior of Magnetorheological (MR) material as a core for Wireless Power Transfer (WPT) and Dynamic Wireless Power Transfer (DWPT) systems. MR fluids are flexible and easily fabricable material, although they offer a lower magnetic permeability compared to conventional Ferrite cores. The main objective of this research is to investigate the potential of MR fluid as a core for static and dynamic WPT system if it improves transfer efficiency, specially under misalignment and movement. MR fluids, known for their ability to rapidly change their rheological properties in response to magnetic fields, support the power transmission between transmitter and receiver coils, a critical factor in the efficiency of WPT systems. The research will involve using two prototypes, one for static WPT and one for a dynamic WPT system that can work with different cores like Air, ferrite, flex-ferrite, and MR fluid. These materials are used as a core in this experiment to compare their effectiveness in the system. Misalignment is inevitable in dynamic WPT (DWPT) and MR fluid has potential to perform better in this condition as a core as it has adaptable rheological properties, like viscosity, when exposed to a magnetic field. The study will evaluate the performance of WPT systems integrated with MR fluid under various positions including different misalignment percentages.https://jagworks.southalabama.edu/southalabama-shgrf-posters/1000/thumbnail.jp

Down the Bay Oral History Project Newsletter - Winter 2025

Public newsletter sharing information about progress and discoveries during the Down The Bay Project

Nonlinear Control System Design and Hardware Testing of an Overactuated Multicopter

For the concept of Urban Air Mobility to be implemented into everyday life, the safety of passengers, bystanders, and surrounding infrastructure needs to be held paramount [1]. In previous work done at the University of South Alabama Facility for Aerospace Systems and Technology (FAST) lab, a reconfigurable control law was derived and tested on a scratch built multicopter [2]. The goal of the control law was to stabilize an X8 model multicopter in the event of motor outages [2]. The control law was tested on a simulated model and flight tested on a small-scale X8 multicopter to verify the effectiveness of the control law [2]. In this thesis, the isolated software previously tested was integrated into the FAST Configurable Autopilot and Simulation Software Tool (FASTCASST) software and simulated and flight tested to verify the integration of the control law and aerodynamic model into the FASTCASST ecosystem. Simulations observing the response of the roll and pitch are observed to verify the integration of the aerodynamic model, and simulations observing the altitude, roll angle, pitch angle, and yaw rate are observed to verify the integration and effectiveness of the control law. Finally, a multicopter was flight tested to verify the integration and effectiveness of the control law into the FASTCASST ecosystem

Establishing a Framework for Evaluating Machine Learning Performance and Security across Computational Ecosystems

The rapid evolution of computational ecosystems—ranging from embedded systems and cloud platforms to hybrid and quantum architectures—has introduced new challenges in deploying machine learning (ML) applications. While cloud computing offers scalability, it comes with increased latency and security risks, whereas edge computing, such as FPGA-based systems, provides real-time processing with constrained resources. Hybrid and quantum ecosystems further complicate decision-making, requiring careful trade-offs between performance and security. This research seeks to establish a framework for evaluating ML performance and security risks across these ecosystems, forming the foundation of the Computational Performance And Security System (COMPASS) decision-support tool. The study will systematically investigate key performance indicators—including latency, energy efficiency, and processing power—alongside security concerns such as data privacy, attack vulnerabilities, and system resilience. At this stage, the research focuses on gathering background information, identifying existing gaps, and defining a comparative methodology for analyzing ML deployment trade-offs. The poster will present a literature review, conceptual models, and initial considerations for structuring the COMPASS framework. By addressing these foundational aspects, this work aims to provide a structured approach to optimizing ML performance and security across diverse computing environments.https://jagworks.southalabama.edu/southalabama-shgrf-posters/1015/thumbnail.jp

A Psychometric Evaluation of the Intentionality Bias Task

Intentionality Bias Task (IBT) provides quick and easy assessment of the tendency to overattribute intentionality to the behaviors of others. The IBT has been used repeatedly in research that has attempted to connect intentionality bias to a wide range of disorders and socio-emotional abilities, including schizophrenia spectrum disorder, autism spectrum disorder, and cognitive empathy. To date, however, there has been no systematic examination of the IBT’s psychometric properties. The present study attempts to fill this gap by testing whether the IBT possesses a reliable factor structure. Specifically, the 34 items of the IBT were written to reflect two types of behaviors: prototypically intentional behaviors and prototypically accidental behaviors. This two-factor structure was first tested using confirmatory factor analysis (CFA), which examined factor loadings, modification indices, and item-total correlations. Despite revisions, model fit was found to be less than acceptable. Following this, an exploratory factor analysis was used to identify items that were cross-loading, failing to load on either factor, other otherwise impairing model fit. Again despite revisions, model fit was found to be less than acceptable. The two-factor model was abandoned in favor of a one-factor model containing solely the prototypically accidental (PA) items. While this one-factor model achieved acceptable model fit, the model failed to replicate in a separate sample. Keywords: Intentionality bias, Intentionality Bias Task, Evelyn Rosset, ICED model, NICED mode

Application of Graph Neural Networks with Phase Space Graphs

Non-linear phase-space analysis models data represented as a graph transitioning between states in the time domain. By studying data transitions, we can predict the time a particular behavior occurs and classify the events (states) in a system. For example, we could classify neurological sensor data to determine if a person is asleep (state), or predict the direction in which a stock will move (transitions) based on micro trade patterns. Previous research has demonstrated success in phase-space graphs in classifying malware, detecting network intrusions, and predicting seizures. However, the solutions either require calculating global graph features as inputs to a classifier, which results in information loss, or converting the graph into an image as inputs to convolutional neural networks (CNNs). The CNN solutions, however, require fixed-sized images, which limits the size of a graph. This study proposed graph neural networks (GNNs) to analyze phase-space graphs without the limitations above. GNNs do not limit the graph complexity or size and do not require the upfront calculation of either global or local features, which is time prohibitive. Preliminary results of this research to power measurements from computer operating systems for rootkit detection, indicate that GNNs can obtain a high accuracy (99.6%) with substantially less training time than other methods. Future work includes examining the use of different GNNarchitectures and the effectiveness of the approach for similar problems such as epilepsy prediction and network intrusion detection.https://jagworks.southalabama.edu/southalabama-shgrf-posters/1025/thumbnail.jp

Thermophysical Properties of Aqueous Amine Salts for closed Air Cabin Revitalization

Carbon dioxide (CO2) capture plays a crucial role in closed cabin air revitalization, however current CO2 capture technologies pose several issues. Traditional amine solutions, such as monoethanolamine, are commonly used in CO2 capture on the International Space Station but face drawbacks including high volatility, thermal degradation, and unpleasant odor. To overcome these challenges, amine salt solutions have emerged as promising alternatives. Amine salt solutions have reduced environmental impact, lower corrosion potential, and improved CO2 loading capacity, making them a more efficient and sustainable replacement. Knowing these physical properties are critical for modeling and optimizing a scrubbing process that employs these new solvents. This research investigates the feasibility of using amine salt solutions for CO2 capture by measuring their thermophysical properties—viscosity, surface tension, density, and heat capacity—under different CO2 loading temperatures, with varying salt mixture concentrations. The viscosity, surface tension, density, and heat capacity of amine salt solutions were measured using a rheometer, goniometer, densitometer, and differential scanning calorimeter, respectively. Preliminary findings indicate that CO2 loading slightly increases the amine salts’ viscosity and alters surface tension, which can affect mass transfer reaction CO2 uptake in scrubbing processes. Density and heat capacity measurements provide understanding of the salts’ thermal properties and process efficiency. Current research includes completion of measurements of amine salt mixtures, VFT property modeling, and ApsenPlus+ integration.https://jagworks.southalabama.edu/southalabama-shgrf-posters/1021/thumbnail.jp

Modelled Flooding Impacts on Lower Fish River Watershed

This study investigates the impacts of compound flooding in the Lower Fish River watershed, Baldwin County, Alabama, with a focus on the potential effects of sea level rise due to climate change. Coastal flooding, particularly in smaller watersheds, is a growing concern as it results from the interaction of multiple factors, including rainfall, tidal changes, and extreme weather events. Compound flooding, which involves multiple flood drivers, is expected to worsen with climate change, as increased precipitation and rising sea levels create heightened flood risks. However, existing research on compound flooding predominantly focuses on large-scale watersheds, leaving a knowledge gap in smaller coastal areas. The study utilizes the Sedimentation and River Hydraulics-Two-Dimensional (SRH-2D) model, which simulates two-dimensional flow dynamics and sediment transport, to explore how changes in water levels and precipitation patterns affect flooding in the Lower Fish River basin. By simulating future sea level rise scenarios for 2050 and 2100, this research aims to assess how flood extents and durations will evolve. This research will provide insights into the non-linear impacts of sea level rise on coastal flooding, offering valuable data for local communities in Baldwin County and similar coastal areas. The results will also contribute to the growing body of knowledge on managing flooding risks in smaller, more vulnerable watersheds. The research is still at an early stage and there are no results generated yet, but the expected outcome will show an exponential increase in flooded areas due to sea level rise and a longer flood duration in the watershed.https://jagworks.southalabama.edu/southalabama-shgrf-posters/1032/thumbnail.jp

Investigating the Impact of Instructor Presence in Lecture Videos on Learning Experiences: A Comparative Study

As online learning continues to grow, understanding how instructional design features influence student experience becomes increasingly important. One critical feature in video-based instruction in instructor presence - the degree to which the instructor is visually or vocally present in the lesson. The purpose of this study was to investigate how varying levels of instructor presence in asynchronous lector videos affect students\u27 perceived relatedness, satisfaction, engagement, content retention, and cognitive effort. A mixed methods experimental design was employed, with 93 undergraduate and graduate students from a public university randomly assigned to one of three video lesson conditions: Full Presence (instructor visible and audible), Voice Only (audible instructor but no visible presence), and No Presence (text-based with no instructor cues). Each participant completed a short-term asynchronous mini-course delivered through Canvas, followed by retention quizzes and a post-lesson questionnaire. Qualitive results (ANOVA) showed that instructor presence significantly influenced students\u27 sense of relatedness and satisfaction, particularly when both visual and vocal cues were present. The Full Presence group consistently reported higher levels of relatedness and satisfaction than the No Presence group. Vocal presence alone, as in the Voice Only condition, also showed benefits, particularly when compared to the absence of instructor cues, though not all differences between Full and Voice Only conditions reached statistical significance. For engagement, the Full Presence group reported significantly lower perceived mental effort than the No Presence group, suggesting that instructor presence did not increase cognitive load and may have alleviated some extraneous processing demands associated with text-only formats. However, attention-related results were less conclusive, and comparisons between Full and Voice Only groups did not yield consistent significant differences. Content retention, measured through short quizzes, showed no consistent significant differences across conditions. These results indicate that while instructor presence meaningfully shaped students\u27 affective and perceptual experiences, its direct influence on immediate recall may be limited or dependent on task complexity and duration. Qualitative responses supported the quantitative patterns in that participants in the Full Presence condition more frequently commented about feeling connected to the instructor. These findings reinforce the importance of instructor cues in enhancing the social and emotional tone of online learning environments. Theoretically, the study affirms the relevance of Self-Determination Theory (Ryan & Deci, 2000), the Community of Inquiry (Garrison et al., 2000) framework, Transactional Distance Theory (Moore, 1993), and the Cognitive Theory of Multimedia Learning (Mayer, 2005). Implication for designing videos for online instruction and ideas for future research are discussed. The findings suggest that instructor presence, when deliberately designed and aligned with multimedia learning principles, can play a meaningful role in building engaging and human-centered online learning environments