Engineering Commons^™

When Does Global Search Pay For Itself? A Measured Exploration Cost And Energy Break-Even Analysis Of A Metaheuristic Search Controller In Solar Energy Systems, Mohamed Ali Muammar Ezgour

Communications of the IIMA

Autonomous energy systems increasingly delegate the choice of operating point to embedded search algorithms, trading a fast local optimizer that can settle on a wrong point against a slower global search that guarantees the right one at a measurable cost. This paper reframes maximum power point tracking under partial shading as that decision and measures its economics on a fixed photovoltaic plant in MATLAB/Simulink. A Hippopotamus Optimization global search handed over to incremental conductance is compared with incremental conductance alone across seventeen initial duty cycles and thirty random seeds. The hybrid reached the global peak in all thirty seeds, whereas …

Individualized Bayesian Inference Identifies Novel Genetic Variants For Parkinson's Disease, Jin Ren, Yasaman J. Soofi, Md Asad Rahman, Qing Lu, Jinling Liu

Engineering Management and Systems Engineering Faculty Research & Creative Works

Parkinson's disease (PD) is a complex neurodegenerative disorder with a significant genetic component. While genome-wide association studies (GWAS) have been instrumental in identifying genetic variants associated with PD, the reliance on large sample sizes and population-level analyses may overlook variants with lower minor allele frequencies or individual-specific relevance. Individualized Bayesian Inference (IBI) offers a promising method to complement GWAS by identifying and prioritizing candidate genetic markers at both the individual and patients-like-me subgroup levels. This study evaluates the application of IBI to PD genetics, using GWAS as a baseline for comparison. We analyzed genetic data from the Fox Insight online …

Critical Core Technology Breakthroughs In Large-Scale Models: Industrialization Strategies And Policy Implications, Zhongqi Wu, Yinshan Liu, Tao Dai, Xiaolong Zheng

Bulletin of Chinese Academy of Sciences (Chinese Version)

As a pivotal direction for breakthroughs in key core technologies within the artificial intelligence domain, large-scale models hold strategic significance in securing national scientific and technological sovereignty. This study employs a multidimensional framework encompassing “technological breakthroughs, industrial transformation, and governance policies” to systematically investigate the developmental trajectories and industrialization bottlenecks of large-scale models. At the technological level, while large-scale models exhibit exponential growth in parameter scale and computing power demands, they face critical challenges including the scarcity of high-quality data, insufficient transfer learning capabilities, and reliability-explainability trade-offs. Industrially, these models are reshaping the global industrial chain landscape through a dual-track …

Evolutionary Trajectory Of Ai And Robotics Integration, Jiannan Zhu, Jianfeng Guo, Siyao Liu, Qi Cao

Bulletin of Chinese Academy of Sciences (Chinese Version)

The convergence of artificial intelligence (AI) and robotics is a cornerstone for the intelligent transformation of the physical world. This study conducts a systematic analysis of over 230,000 publications from the Web of Science and WIPO databases between 1982 and 2025 to dissect the evolutionary trajectory of AI and robotics integration since the 20th century. The findings identify four distinct developmental stages—independent exploration, functional coupling, primary intelligence, and intelligent symbiosis—while delineating the key technological breakthroughs and paradigm characteristics of each phase. Furthermore, seven core research thrusts are distilled, including motion planning and autonomous decision-making and perception & environmental understanding. An …

The Self-Aware Room: A Framework For Operational Self-Awareness In Evolvable Blended Environments, David B. Smith

Publications and Research

The Self-Aware Room (SAR) is a room-scale research environment developed within the larger Balanced Blended Space and Blended Reality Performance System research trajectory. Rather than treating the room as a conventional “smart” environment composed of fixed automation technologies, SAR approaches it as an evolvable blended environment made from physical, virtual, conceptual, sensory, computational, and performative relationships. Its defining feature is not any particular sensor, model, or output device, but the set of transformations through which physical activity becomes structured observation, bounded representation, interpreted state, governed decision, and mediated response.

This paper develops the conceptual and methodological foundations of SAR as …

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 …

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 …

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 …

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 …

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 …

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, …

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 …

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 …

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 …

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 …

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 …

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 …

Cars Imass - Comparing Llm Vs Human Operator Effectiveness In Multi-Agent Swarm Coordination, Gatlin Nelson

Discovery Day - Daytona Beach

Title: Dual-Perspective Risk Analysis for Human-LLM Decision Comparison in UAV Swarm Navigation   Unmanned aerial vehicle (UAV) swarms operating in low-altitude wireless network environments encounter localized disruptions that degrade positioning and navigation metrics. These disruptions are modeled as geographic failure zones with defined boundaries. A UAV discovers a zone by entering it and observing degraded performance on its onboard systems. This work assumes that affected UAVs can autonomously retreat to safety using onboard sensors and focuses on the subsequent rerouting decision. Once recovered, the system generates candidate repositioning points surrounding the vehicle, each scored using Conditional Value-at-Risk (CVaR). A human operator …

Phaëthon System, Brady Roudabush, Lauren Gallo, Emelia Thompson, Jacob Woods

Discovery Day - Daytona Beach

Phaëthon System is the project name for the Search and Rescue Drone Initiative. This initiative will improve the current search and rescue drone industry by introducing new techniques to get through dense forest canopies and other places where an overhead view is not useful. The Phaëthon System uses a swarm of drones that can penetrate under the tree canopy to map and search with the utmost efficiency and safety for rescuers. A command drone is launched to survey the overall search area, and set up a communications and data link. The next component is then released, which is a swarm …

Accelerating Search And Rescue Response: A Simulation Study On The Dynamic Efficiency Of Flocking-Enabled Drone Swarms, Sophia Beckwith, Carys Del Prete

Discovery Day - Daytona Beach

This project explores how imitations observed in animal group behavior, specifically flocking in birds, can be applied to the functionality of autonomous drone systems to aid in search and rescue efforts. The goal is to demonstrate how incorporating code based on the Boids, Vicsck and predictive control linear algebraic mathematical models for drone flight controls and the collective behaviors of flocks will increase the efficiency of drone maneuvers, allowing them to reorganize and fill gaps when one is removed. A MATLAB-based simulation was developed to model the behaviors using research conducted on the symmetric and synchronized behaviors observed from flocks …

Emergency Risk Dispatch For Integrated Electricity-Heat Systems Under Typhoon Disasters, Tongchui Liu, Lian Tan, Dongxuan Bao, Lanting Zeng, Pengfei Hou, Ronghua Ling

Journal of System Simulation

Abstract: The spatiotemporal randomness of typhoon movement paths leads to uncertain operational risks for integrated electricity-heat systems (IEHS), making it difficult to balance system risk controllability and dispatch economy. To tackle this problem, an emergency risk dispatch (ERD) method for IEHS under typhoon disasters is proposed. An ERD model for IEHS under typhoon disasters is established within the model predictive control framework. Based on the uncertainty of typhoon wind speed prediction, a moment-based ambiguity set for uncertain equipment component failures is constructed, and a distributionally robust chance-constrained ERD model is formulated. An approximation method based on worst-case conditional value-at-risk (WC-CVaR) …

Bi-Level Coordinated Scheduling And Optimization Of Power Systems Based On Stackelberg-Gmo, Yuanxing Zhang, Jianfeng Li, Taoyong Li, Linjuan Zhang, Jincheng Liu, Bin Li

Journal of System Simulation

Abstract: , To balance the interests of the power grid and the demand side, and achieve coordinated improvements in system economic efficiency, environmental friendliness, and renewable energy accommodation capacity, this paper proposes a bi-level coordinated scheduling model based on the Stackelberg game and the GMO. A leader-follower game model incorporating carbon emission constraints and multi-scenario stochastic constraints for photovoltaic generation is constructed, with the grid operator as the leader and EVs/V2G and energy storage as the followers, resolving the core contradiction between global optimization and individual rationality. The spatio-temporal stochastic characteristics of EV travel, the cycle life of energy storage …

Energy Management Method For Integrated Energy Driven By Users’ Social Attributes, Yankai Zhu, Yujing Huang, Qinghua Wang, Xiaoning Zhang, Fang Fang, Yuguang Niu

Journal of System Simulation

Abstract: To explore a new interaction mechanism between an energy service provider (ESP) and multiple users, this paper proposes a complex modeling and energy management method for integrated energy systems driven by users' social attributes. A multi-agent interaction framework comprising an ESP and user clusters is established. To maximize the ESP's operational benefit and minimize users' energy costs, a leader-follower game-based energy management model is established within a reinforcement learning framework, and a distributed collaborative solution algorithm combining Q-learning and quadratic programming is proposed. Simulation results show that, compared with the traditional integrated demand response method, consideration of users' social …

Missing-Data-Tolerant Diffusion-Based Wind Power Scenario Forecasting Method, Yingying Shi, Xiaochong Dong, Guobin Fu, Miaomiao Ma, Yanhe Li, Xuebin Wang

Journal of System Simulation

Abstract: To address the issue of error accumulation in traditional "imputation-then-forecasting" approaches, a missing data tolerant diffusion framework (MDTDF) is proposed. An XGBoost regression model is employed to map numerical weather prediction data into deterministic power forecasts. The encoder in the denoising network extracts temporal features, which are fused with the deterministic forecasts and fed into the decoder through a cross-attention mechanism to guide the denoising process. A historical constraint mechanism is introduced to directly utilize incomplete historical data and dynamically correct the denoising result at each step through sample gradient updates and noise injection guided by historical information. The …

Optimal Scheduling Of Virtual Power Plants Considering Photothermal Power Stations And Hydrogen Energy Utilization, Yousong Chen, Ruofa Cheng, Yi Liu, Yi Zhang, Zhihao Zuo

Journal of System Simulation

Abstract: To enhance the operational stability and low-carbon performance of virtual power plants (VPPs) with high shares of renewable energy, a coordinated dispatch model integrating concentrated solar power (CSP) plants with power-to-gas (P2G) and carbon capture is developed. An optimal VPP scheduling strategy is proposed, combining a stepped carbon trading mechanism with a compensation coefficient and dynamic hydrogen blending. To address multi-source uncertainties in wind power, CSP power, and loads, an envelope boundary model is used for simulation. Information gap decision theory (IGDT) is applied to provide customized solutions for decision-makers with different risk preferences. A bi-objective optimization model is …

Time-Step Relaxation Method For Simulation With Time-Sensitive Interactions, Zhaopeng Liu, Kaidi Jin, Xunyun Liu, Dongao Zhou, Xinhai Xu

Journal of System Simulation

Abstract: In time-driven military simulation, time-step setting is a key technology for balancing operational efficiency and simulation accuracy. Based on the current research on time-step setting in military simulation, a trajectory spatiotemporal intersection calculation model is designed to address the performance bottleneck caused by the minimum time step, thus removing the constraints imposed on the minimum time step by interactions such as high-speed target detection and jamming. Experiments involving detection interaction scenarios are designed to verify the effectiveness of the model in preventing missed interactions and improving simulation efficiency.

A Review Of Spatial Indexing Technologies For Large-Scale Combat Simulation, Kaidi Jin, Xunyun Liu, Dongao Zhou, Yang Wang, Zhaopeng Liu

Journal of System Simulation

Abstract: The real-time performance and scalability of large-scale combat simulations are constrained by performance bottlenecks in spatial queries caused by massive numbers of dynamic entities. Spatial indexing technology becomes the key to solving this problem by establishing an efficient mapping between locations and entities. This paper reviews spatial indexing technologies in large-scale combat simulations. Based on an analysis of the core requirements for index structures in combat simulations, various indexing technologies along three main lines are examined: static indexing, dynamic optimization, and distributed parallelism. The principles, evolution, and applicability boundaries of these technologies are also examined, and their query and …

A Method For Assessing The Contribution Degree Of An Aviation Delivery System And Identifying Key Equipment, Xiaofeng Liu, Chengze Jiang, Xingyu Chen, Deyin Jiang, Bolin Shang, Bifeng Song

Journal of System Simulation

Abstract: Based on the delivery efficiency and delivery quality, a general aviation delivery system effectiveness evaluation model was constructed, and a calculation method of system contribution degree based on efficiency was given. By combining the system calculation experiment and simulation experiment based on agent-based modeling and simulation (ABMS), the design idea of the Monte Carlo simulation experiment for key equipment identification and equipment technology development trend analysis was sorted out, and the key equipment identification method based on ABMS and contribution evaluation was proposed. By taking the intercontinental long-range aviation delivery mission as an example, a variety of simulation experiments …

Fault-Tolerant Control Method For All-Type Actuator Faults Of Evtol Aircraft, Juan Wang, Guorui Li, Zhiyong Fan, Huijie Chen

Journal of System Simulation

Abstract: To address all-type actuator faults, especially nonlinear distortion issues, in multi-rotor eVTOL aircraft, a novel fault-tolerant control method was proposed. A fault function was established at the rotor speed level, and a fault-tolerant control algorithm combining dynamic robust nonsingular fast integral terminal sliding mode with a high-order finite-time disturbance observer was designed to achieve fault-tolerant control through rotor redundancy allocation. The nonsingular fast integral terminal sliding mode algorithm was improved, and a dynamic system containing actuator faults and their derivatives was constructed via a dynamic surface to achieve system convergence within finite time, avoiding the singularity and chattering problems …

Learning Evolution Modeling Of Multi-Cycle Nested Cloud Manufacturing Service Ecosystem, Fang Li, Deyu Zhou, Gang Wang, Guangjun Liu, Qi Hu

Journal of System Simulation

Abstract: In view of the lack of comprehensive consideration of the individual adaptability changes of enterprises caused by the collaborative governance mechanism among multiple manufacturing units and the overall evolution trend of the system in existing learning evolution models, this proposed a learning evolution model of multi-cycle nested cloud manufacturing service ecosystem. At the micro level, the adaptive linkage decision-making among multiple manufacturing units within the enterprise was achieved in the individual layer through the nesting of planning-readiness-execution-assessment (PREA) loops and OODA loops; at the macro level, the closed-loop simulation of the individual layer, organizational layer, and social layer was …