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Full-Text Articles in Computer Sciences

Ai And The Environment: Solutions For Advancing Technology Safely, Kaitlynn Baker Aug 2026

Ai And The Environment: Solutions For Advancing Technology Safely, Kaitlynn Baker

Discovery Day - Daytona Beach

Since 2022, the world of Artificial Intelligence (AI) has boomed. AI went from a special and rare entity to a commonly used resource available to all through web sites, and phone apps. AI has benefitted everyday activities by making office, class, and personal tasks easier through grammar help, informational citations, and as someone to bounce ideas off of. Additionally, many companies have begun utilizing AI to improve customer service and experience, and train workers more efficiently, therefore, saving thousands of dollars. Despite the benefits humans reap from its use, AI has been harming our environment at growing rates. Data centers …

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Assessing Attachment To Ai: Understanding The Theoretical Correlations And Consequences, Brianna Broderick Aug 2026

Assessing Attachment To Ai: Understanding The Theoretical Correlations And Consequences, Brianna Broderick

Discovery Day - Daytona Beach

Current research on Artificial Intelligence (AI) focuses on its capabilities and our understanding of it as an instrumental tool (i.e., utility completing tasks). However, as its ability to replicate natural language improves through both text and voice, an ever-growing number of users have turned to AI for emotional companionship. Concern grows as prior research on technology dependency suggests AI bonding may lead to less interaction with others and, in extreme circumstances, has already led to cases of suicide and divorce. Kasturiaratna & Hartanto (2025) developed the AI Attachment (AIA) scale, consisting of three factors, which include: emotional closeness (i.e., personal …

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Quantifying Grain Size In Scanning Electron Microscopy Images, Katherine Hoffsetz Aug 2026

Quantifying Grain Size In Scanning Electron Microscopy Images, Katherine Hoffsetz

Discovery Day - Daytona Beach

This project explores advanced image analysis techniques to assess the microstructure of highly strained austenitic stainless steel. Utilizing Python imaging libraries such as scikit-image and OpenCV, we aim to extract precise measurements for grain size from scanning electron microscopy (SEM) images. These metrics will be examined against the computed grain sizes of the sample from electron backscatter diffraction measurements. By automating the extraction of grain size measurements from SEM images, this study contributes to steamlining the quality assurance/ quality control of industrially processed materials.

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High-Level Trajectory Learning For Non-Prehensile Object Manipulation With Hierarchical Reinforcement Learning, Gulsum Tuba Cibuk Girgin Aug 2026

High-Level Trajectory Learning For Non-Prehensile Object Manipulation With Hierarchical Reinforcement Learning, Gulsum Tuba Cibuk Girgin

Discovery Day - Daytona Beach

Site exploration requires in-situ resource utilization when the physical properties of resources are unknown. Therefore, a generalizable object manipulation method is crucial for extraterrestrial environments. Existing studies develop reinforcement learning policies that enable interaction with objects, in which quadruped robots learn to reach commanded goals with one foot while balancing with the remaining legs. However, in these studies, goal-oriented task execution relies on high-level trajectories provided by human experts, which limits autonomous robotic operations. In this study, we propose a hierarchical DRL in which a high-level pedipulation policy outputs commands for a low-level reach policy, enabling autonomous, smooth and affordable …

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Ai Race Between The Us And China, Kennedy Lyon-Lindersmith Aug 2026

Ai Race Between The Us And China, Kennedy Lyon-Lindersmith

Discovery Day - Daytona Beach

Technological leadership in AI and semiconductor manufacturing are both directly linked with military power and geopolitical influence. At the same time, the U.S. and China are currently defining the future of conflict in the cyber domain and are in strategic competition as China attempts to displace the U.S. as a global leader in AI. These factors contribute to an important national security threat that the U.S. is facing right now: An AI race between the U.S. and China, specifically regarding military cyber operations. This paper discusses some of the implications of a digital battlefield and analyzes international laws, international institutions, …

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Geometry-Conditioned Adversarial Defense For Sar Automatic Target Recognition Via Regime-Specialist Classification Heads, Skyler Fabre Aug 2026

Geometry-Conditioned Adversarial Defense For Sar Automatic Target Recognition Via Regime-Specialist Classification Heads, Skyler Fabre

Discovery Day - Daytona Beach

This project, titled Geometry-Conditioned Adversarial Defense for SAR Automatic Target Recognition via Regime-Specialist Classification Heads, addresses the critical vulnerability of deep neural networks deployed in Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) systems to adversarial perturbations. This is where imperceptible pixel-level modifications cause confident misclassification, posing serious risks in defense and aerospace applications. The objective is to develop and evaluate RegimeResNet, a geometry-conditioned classification architecture that exploits sensor metadata unique to SAR collection systems. Rather than treating all images uniformly, RegimeResNet partitions the SAR capture space into nine geometric regimes defined by depression angle and target azimuth angle extracted …

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Sequential Causal Architecture For Multimodal Aviation Accident Prediction, Kaitlyn Cavanaugh, Isaac Morrison Aug 2026

Sequential Causal Architecture For Multimodal Aviation Accident Prediction, Kaitlyn Cavanaugh, Isaac Morrison

Discovery Day - Daytona Beach

Aviation accidents are rarely the result of a single failure but rather from a complex causal chain of latent failures. While traditional data mining models often predict incident occurrence, they frequently overlook the sequential mechanics defined by known accident causation theoretical frameworks like the Swiss Cheese Model and the FAA's HFACS. This project addresses the need for interpretable, reliable, multi-stage forecasting by proposing a Sequential Causal Architecture that transforms theoretical causation models into a structured Directed Acyclic Graph (DAG) for multimodal accident causation chain prediction. Data from the NTSB and DOT is used and connected together in a meaningful way …

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Energy-Aware Bimodal Contact Detection For Leg Odometry, Emre Girgin Aug 2026

Energy-Aware Bimodal Contact Detection For Leg Odometry, Emre Girgin

Discovery Day - Daytona Beach

Autonomous exploration of extraterrestrial environments using legged robots requires robust GNSS-free 3D state estimation. Standard leg odometry relies on Zero-Velocity Updates (ZUPT), which assume a grounded foot remains completely stationary. This assumption consistently fails on deformable granular terrain due to unobservable slippage, rapidly degrading state estimation. To mitigate this critical failure mode, we propose a dual contact-detection framework designed to robustly gate an Error-State Extended Kalman Filter (ESEKF) tracking pose, velocity, and IMU biases.   The architecture isolates physical load and kinematics by modeling contact detection as two independent parallel Hidden Markov Models (HMMs). The Load HMM processes Ground Reaction Forces, …

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Motivational Outsourcing: Ai, Self-Determination, And The Changing Nature Of Adult Learning, Zoe Spanos Aug 2026

Motivational Outsourcing: Ai, Self-Determination, And The Changing Nature Of Adult Learning, Zoe Spanos

Discovery Day - Daytona Beach

Artificial intelligence (AI) is increasingly embedded into adult learning and higher education, serving not only as a support tool for cognitive aid but also as a system that can shape how learners regulate their motivation and engagement. This presentation examines how the use of AI in adult learning contexts may support or undermine self-determined motivation, drawing from Self-Determination Theory (SDT). It is a conceptual paper that draws on existing literature and theoretical analysis, examining AI reliance from minimal use to full automation across SDT's three basic psychological needs: autonomy, competence, and relatedness. Motivation is essential for learning, but the quality …

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Lightweight Uav-To-Uav Detection And Tracking For Advanced Air Mobility Applications, Taylor Hostetter Aug 2026

Lightweight Uav-To-Uav Detection And Tracking For Advanced Air Mobility Applications, Taylor Hostetter

Discovery Day - Daytona Beach

Lightweight UAV-to-UAV Detection and Tracking for Advanced Air Mobility Applications addresses the significant challenge of reliable UAV-to-UAV detection on resource-constrained platforms, particularly within Advanced Air Mobility (AAM) environments where dense, low-altitude airspace requires robust detect-and-avoid capabilities. This work presents the development and experimental evaluation of a lightweight detection and tracking framework for autonomous detect-and-avoid applications. The approach is designed to support real-time onboard operation in multi-vehicle environments characteristic of emerging AAM systems. The proposed framework integrates optical and LiDAR sensing with a low-complexity machine learning decision-support layer that reduces false detections without replacing the underlying control-oriented detection pipeline. This design …

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Ai-Driven Predictive Analysis Of Airline Profitability Under Macroeconomic And Sustainable Aviation Fuels, Shivika Singh Aug 2026

Ai-Driven Predictive Analysis Of Airline Profitability Under Macroeconomic And Sustainable Aviation Fuels, Shivika Singh

Discovery Day - Daytona Beach

This study presents an AI-enhanced predictive model to assess airline profitability under the combined influence of macroeconomic trends and the adoption of sustainable aviation fuel (SAF). By including economic indicators such as GDP growth, inflation rates, and fuel price volatility with airline operational data, including ticket prices and fuel costs, the model simulates profitability across multiple carriers. Scenario-based analyses, encompassing optimistic, moderate, and pessimistic projections, illustrate different financial sensitivities between low-cost and legacy airlines.

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Determinants And Invertibility In Finite Modular Systems, Osasu Omobude Aug 2026

Determinants And Invertibility In Finite Modular Systems, Osasu Omobude

Discovery Day - Daytona Beach

This project investigates determinants and matrix invertibility in finite modular systems, focusing on matrices over Zn. Using the Hill cipher as context, it examines the algebraic conditions under which a matrix is invertible in modular arithmetic. In particular, the project studies how the determinant determines invertibility, showing that a matrix over Zn is invertible if and only if its determinant is coprime with n.   The project further compares invertibility over the real numbers with invertibility over modular systems, highlighting the distinction between prime moduli Zp and composite moduli. In the prime case, matrices behave similarly to those over fields, where …

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Designing Process-Focused Feedback For College Writers Using Genai, Beata Blood, Maissane Aik, Zoey Zaldivar Aug 2026

Designing Process-Focused Feedback For College Writers Using Genai, Beata Blood, Maissane Aik, Zoey Zaldivar

Discovery Day - Daytona Beach

As generative AI tools like ChatGPT become more common in higher education, writing instructors face the challenge of guiding students toward effective and ethical use, particularly in asynchronous environments where immediate feedback is limited. This presentation reports on an exploratory study that addresses this challenge by shifting attention from AI’s outputs to students’ moment-by-moment writing processes. Grounded in applied linguistics approaches to writing research and process-tracing methods, the project employed case studies with both expert and novice users of GenAI. Expert participants, including academics and industry professionals, completed writing tasks while integrating AI into their workflows. Their sessions were recorded …

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Small Uas Detection: Threat Intelligence & Risk Management Project, Tyler Johnson Aug 2026

Small Uas Detection: Threat Intelligence & Risk Management Project, Tyler Johnson

Discovery Day - Daytona Beach

The TRANSPORTATION SECURITY ADMINISTRATION / FEDERAL AIR MARSHAL SUAS DETECTION: THREAT INTELLIGENCE & RISK MANAGEMENT PROJECT addresses the emerging safety and security challenges posed by the rapid growth of small Unmanned Aircraft Systems (sUAS) in complex airspace environments. This study analyzed 92 days of sensor-captured Remote Identification (RID) data collected near Fort Lauderdale-Hollywood International Airport (FLL) to assess operational behaviors, aviation risk, and ground risk associated with drone activity. The primary objective of this research is to identify patterns of unauthorized or hazardous sUAS operations to enhance situational awareness and inform actionable risk-mitigation strategies. The analysis identified 335 flights from …

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How Ai Influences The Design Process Of Unmanned Underwater Vehicles’ (Uuvs) 3d Sonar System, Eden Tsouklaris, Abriella Smith, Brianna Broderick, Carissa Aumack, Victoria Cornaro Aug 2026

How Ai Influences The Design Process Of Unmanned Underwater Vehicles’ (Uuvs) 3d Sonar System, Eden Tsouklaris, Abriella Smith, Brianna Broderick, Carissa Aumack, Victoria Cornaro

Discovery Day - Daytona Beach

With the exponential growth of Artificial Intelligence (AI), user interface (UI) designers have explored using AI to shorten design time. This study assessed the effectiveness of UIs designed with AI programs versus manual methods for an Unmanned Underwater Vehicle (UUV) control system. Participants were tasked with designing an interface that would allow submarine operators to monitor and coordinate three UUVs repairing a severed underwater communication cable at a depth of 2,000 meters. The scenario presented several operational challenges (zero visibility, sonar-only perception, data latency, and potential system degradation), requiring participants' designs to maintain spatial awareness and support remote repair tasks. …

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High Tempo Air Operations, Joseph Lipson, Brennan Flanagan, Trevor Sterbens, Brandon Godfrey, Jeremiah Sepich Aug 2026

High Tempo Air Operations, Joseph Lipson, Brennan Flanagan, Trevor Sterbens, Brandon Godfrey, Jeremiah Sepich

Discovery Day - Daytona Beach

Aircraft carrier flight decks are one of the most dangerous work environments in the world, where dozens of aircraft must be moved, fueled, and armed within strict time limits. Currently, Flight Deck Handling Officers track aircraft positions using a physical board with wooden pucks that can be knocked out of place or become outdated during fast-moving operations. This study looks at whether using AI tools helps people design a better digital version of this tracking system. Participants with little design experience were randomly selected and then randomly assigned to one of two groups — one that could use AI tools …

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Humans Vs. Ai: Comparing Approaches To Disaster Response Interface Design, Kelly Nguyen, Olivia Hartmann, Kailey Hrbek, Madeline Nees, Gabrielle Roth, Emily Silliman Aug 2026

Humans Vs. Ai: Comparing Approaches To Disaster Response Interface Design, Kelly Nguyen, Olivia Hartmann, Kailey Hrbek, Madeline Nees, Gabrielle Roth, Emily Silliman

Discovery Day - Daytona Beach

Amphibious emergency support operations involve rapidly changing information, high stress, and significant cognitive demands, which can make decision-making and situation awareness more difficult for operators. When interfaces are poorly designed, they can contribute to issues such as alarm flooding, confusion from incomplete information, and delayed responses, all of which increase operational risk during time-critical disaster situations. This study explores whether using generative AI to assist with interface design will improve performance (output quality and effort) and usability compared to a manual sketch mock-up. Participants were asked to design a dashboard interface to support disaster relief operations following a Category 5 …

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Evaluating The Impact Of Ai-Assisted Tools On Novice Interface Design For Combat Search And Rescue Operations, Louis Pandolfo, Kaylee H. Akerlund, Cassidi Ellison, Sierra Martinez Aug 2026

Evaluating The Impact Of Ai-Assisted Tools On Novice Interface Design For Combat Search And Rescue Operations, Louis Pandolfo, Kaylee H. Akerlund, Cassidi Ellison, Sierra Martinez

Discovery Day - Daytona Beach

Combat Search and Rescue (CSAR) operations are specialized military missions with the goal of rescuing personnel from hostile territory, often involving helicopters and elite teams tasked with locating and stabilizing survivors. It is imperative to a mission's success that any interface used by rescuers is efficient and usable, as they work under high risk, high stress, and time limited conditions. This study examined how access to artificial intelligence (AI) design tools influence novice interface design under a time constraint. Participants completed an interface design exercise based on a simulated U.S. Navy maritime disaster and CSAR mission. The overall aim of …

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Myoelectric Transradial Prosthesis Motor Control With Temporal Convolutional Neural Network Signal Processing, Carolyn Ascha Richardson, Katherine Clark, Francis Genco, Hope Lea, Tobiah Rosser Aug 2026

Myoelectric Transradial Prosthesis Motor Control With Temporal Convolutional Neural Network Signal Processing, Carolyn Ascha Richardson, Katherine Clark, Francis Genco, Hope Lea, Tobiah Rosser

Discovery Day - Daytona Beach

Transradial (below-the-elbow) amputees account for more than half of all upper limb amputations. Myoelectric prosthetic arms, which use electromyography (EMG) sensors on the surface of the forearm to convert electrical signals from residual limb muscles and mimic hand movement, are popular options for these amputees. However, these prostheses are limited in residual muscle detection and movement accuracy. The objective of this project is to affordably manufacture an externally-powered transradial prosthesis prototype through EMG time-versus-voltage readings collected with six Delsys Trigno Avanti and eight Thalmic MYO EMG sensor channels placed along the extensor digitorum, extensor carpi radialis, extensor carpi ulnaris, flexor …

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From Code To Cube: Interactive Fluid Simulation With Real Time Motion Control, Dominic Ziccardi, Carter Groezinger Aug 2026

From Code To Cube: Interactive Fluid Simulation With Real Time Motion Control, Dominic Ziccardi, Carter Groezinger

Discovery Day - Daytona Beach

This research project explores the use of FluidX3D, an open-source lattice Boltzmann method (LBM) solver, to simulate fluid behavior within a three-dimensional cube container. The system supports both standard water models and rheoscopic fluid visualization, allowing detailed observation of complex flow dynamics in real time. The simulation accurately represents fluid motion, gravity-driven behavior, and rotational response within a bounded cubic domain. The longterm objective is to extend this digital simulation into a physical installation consisting of six synchronized square displays arranged to form a cube. This configuration will create a volumetric illusion of fluid occupying a tangible, handheld structure. An …

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Navigating Enhanced Exploration Assistance (Nexa), Azhari Abbas, Caleb Fakunle, Ryan Powell, Donovan Livingston Aug 2026

Navigating Enhanced Exploration Assistance (Nexa), Azhari Abbas, Caleb Fakunle, Ryan Powell, Donovan Livingston

Discovery Day - Daytona Beach

NEXA is an artificial intelligence software platform developed to enhance residential security and property monitoring through seamless integration with autonomous drone systems. This research application of advanced AI in surveillance aims to create a standalone solution capable of real-time threat detection and intelligent alert management. By processing visual and sensory data, NEXA facilitates autonomous drone operation with minimal human intervention. Secure communication channels ensure that instant alerts are delivered to property owners and, potentially, law enforcement, improving response times in security incidents, search-and-rescue operations, and perimeter surveillance. Additionally, NEXA is capable of interfacing with commercially available drone platforms and presents …

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An Evaluation Of Machine Learning Models' Efficacy In Determining Uav Spoofing Attacks, Nicolas Machado, Jaxon Selzer Aug 2026

An Evaluation Of Machine Learning Models' Efficacy In Determining Uav Spoofing Attacks, Nicolas Machado, Jaxon Selzer

Discovery Day - Daytona Beach

An Evaluation of Machine Learning Models' Efficacy in Determining UAV Spoofing Attacks - The rapid integration of Unmanned Aerial Vehicles (UAVs) into urban airspace has introduced significant cybersecurity concerns, particularly due to vulnerabilities in Automatic Dependent Surveillance–Broadcast (ADS-B), which lacks authentication and encryption. This project addresses the problem of detecting spoofing and data manipulation attacks that can compromise UAV safety and mission reliability. The objective of this work is to evaluate the effectiveness of machine learning–based anomaly detection, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, as protocol-agnostic solutions for identifying anomalous UAV behavior. To achieve this, …

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Helio: Heliophysics Enhanced Learning For Intelligent Orbits, Kylie Nager, James Kirk Aug 2026

Helio: Heliophysics Enhanced Learning For Intelligent Orbits, Kylie Nager, James Kirk

Discovery Day - Daytona Beach

HELIO: Heliophysics Enhanced Learning for Intelligent Orbits   Satellite constellations operating in near-Earth space are increasingly vulnerable to space weather disturbances, such as solar flares, coronal mass ejections (CMEs), and high-speed solar wind streams, which degrade communications, destabilize attitude control, and accelerate orbital decay. These disturbances directly threaten mission continuity, constellation availability, and space asset survivability. Current protective approaches rely primarily on ground-based alerts and lack integration with broader space domain awareness, which results in programmed reactive protocols that are often initiated too late to prevent performance degradation and asset loss. The HELIO project addresses this gap by turning space-weather forecasts …

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Interplanetary Trajectory Optimization With Reinforcement Learning, Shiloh Cuffe Aug 2026

Interplanetary Trajectory Optimization With Reinforcement Learning, Shiloh Cuffe

Discovery Day - Daytona Beach

This project investigates the application of reinforcement learning (RL) to optimize low-thrust interplanetary trajectory design, focusing on the Earth-Venus transfer leg of the BepiColombo mission. Traditional trajectory optimization methods, such as patched conics and genetic algorithms, often require simplifying assumptions or complex optimization schemes. This work formulates the trajectory design problem as an optimal control problem (OCP) within a Markov Decision Process (MDP) framework, enabling an RL agent to learn efficient transfer strategies under realistic spacecraft constraints. The objective is to develop an autonomous guidance approach capable of replicating or improving upon established mission designs. The spacecraft is modeled as …

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Generalized Cloud-Based Compressible Aerodynamics Calculator And Simulation Web App, Massimo Mansueto, Liam Griesacker, Andres Torres-Figueroa Aug 2026

Generalized Cloud-Based Compressible Aerodynamics Calculator And Simulation Web App, Massimo Mansueto, Liam Griesacker, Andres Torres-Figueroa

Discovery Day - Daytona Beach

The Generalized Cloud-Based Compressible Aerodynamics Calculator and Simulation Web App focuses on the development of a tool to support the analysis, visualization, and teaching of compressible aerodynamics. In the case of most undergraduate aerospace engineering courses, students rely on static equations, charts, and manual calculations, which can make it difficult to conceptualize complex flow phenomena such as shock waves, expansion fans, and nozzle flow. The purpose of this project is to create an accessible platform that integrates a compressible flow calculator, nozzle sizing tool, and interactive simulations into a single educational resource. The application is implemented using modern web development …

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Feasibility Of High-Throughput Onboard Ai For Mars Rovers Under Solar Constraints, Aashman Gupta Aug 2026

Feasibility Of High-Throughput Onboard Ai For Mars Rovers Under Solar Constraints, Aashman Gupta

Discovery Day - Daytona Beach

This project evaluates the feasibility of sustained onboard AI autonomy for a solar-powered Mars rover by directly linking solar energy availability to achievable compute performance. While Mars solar irradiance and edge computing performance have been studied independently, no unified framework currently couples surface power generation to autonomy throughput in an experimentally validated manner. The project will begin with a simulation of solar power generation for a 1 m² rover-mounted array across a Martian sol, accounting for seasonal variation, dust opacity, and array configuration (fixed versus sun-tracking). The resulting power profile will then be coupled to representative compute platforms running autonomy …

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An Energy-Aware Meta-Learning Framework For Real-Time Lunar Rover Localization Via Adaptive Algorithm Selection, Jose Demedeiros, Garrett Seyler Aug 2026

An Energy-Aware Meta-Learning Framework For Real-Time Lunar Rover Localization Via Adaptive Algorithm Selection, Jose Demedeiros, Garrett Seyler

Discovery Day - Daytona Beach

This work proposes an energy-aware meta-learning framework that selects the single most suitable localization algorithm for a lunar rover, per scene, using only monocular imagery and orbital maps. The goal is to achieve sub-meter accuracy while minimizing onboard compute and energy consumption. We assemble a suite of seven lunar-relevant algorithms spanning relative and absolute localization, including monocular ORB-SLAM3, LuVo homography-based visual odometry, Censible cross-view matching with orbital imagery, crater-based methods (LunarNav and ShadowNav), monocular horizon navigation with a DEM, and DROID-SLAM. Relative methods provide incremental motion updates, while absolute methods deliver global pose fixes; an Extended Kalman Filter fuses these …

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A Qualitative Analysis Of Human-Ai Interaction Through Animated Shapes, Kavya Dipen Shah, Angel Hinojosa, Caroline Deck Aug 2026

A Qualitative Analysis Of Human-Ai Interaction Through Animated Shapes, Kavya Dipen Shah, Angel Hinojosa, Caroline Deck

Discovery Day - Daytona Beach

As technology becomes more advanced, it is important to understand how people perceive the intentions and abilities of machines. This project, conducted in the InTeRACT Lab, explores how we attribute humanlike qualities to different types of agents, ranging from animals to robots. The study analyzes data from an experiment where participants watched animations of moving triangles. Although the videos were identical, participants were told the shapes represented either humans, robots, dogs, or inanimate objects. While previous math-based data showed that these labels changed how people felt, those structured scales didn't allow for a natural, unbiased explanation of what people actually …

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A System Safety Approach To Assuring Artificial Intelligence Enabled Functions In Civil Aviation, Evan Bear, Quinn Galen Aug 2026

A System Safety Approach To Assuring Artificial Intelligence Enabled Functions In Civil Aviation, Evan Bear, Quinn Galen

Discovery Day - Daytona Beach

Artificial intelligence and machine learning techniques are increasingly proposed for use in safety-critical civil aviation functions including perception decision support and pilot assistance. Existing aviation safety and certification standards such as ARP4754A and DO-178C were developed under assumptions of determinism explicit requirements and complete behavioral specification which do not directly apply to learning-enabled systems. This mismatch has created uncertainty regarding how artificial intelligence enabled avionics can be safely assured and certified. This paper presents a system safety approach for assuring artificial intelligence enabled functions within existing aviation certification frameworks. In this approach safety assurance is based on explicitly identifying the …

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A Hybrid Llm-Srgm Framework For Ai-Enabled Reliability Assessment In Safety-Critical Software Systems, Caleb Stone, Shrenik Jadhav Aug 2026

A Hybrid Llm-Srgm Framework For Ai-Enabled Reliability Assessment In Safety-Critical Software Systems, Caleb Stone, Shrenik Jadhav

Discovery Day - Daytona Beach

Ensuring the reliability of software intensive and safety critical systems is a persistent challenge across aerospace, defense, transportation, and other mis- sion focused domains. Traditional software relia- bility growth models (SRGM) provide useful quanti- tative insight into defect discovery trends, but they rely mostly only on numerical failure data and do not use the rich contextual information contained in test logs, anomaly reports, and engineering notes. This paper presents a hybrid framework that com- bines semantic features extracted by a large lan- guage model (LLM) with a non-homogeneous Pois- son process (NHPP) based software reliability growth model. The LLM analyzes …

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