Digital Commons Network^™

Q-Learning For Sum-Throughput Optimization In Wireless Visible-Light Uav Networks, Yuwei Long, Nan Cen

Computer Science Faculty Research & Creative Works

Unmanned Aerial Vehicles (UAVs) Have Been Adopted as Aerial Base Stations (ABSs) to Provide Wireless Connectivity to Ground Users in Events of Increased Network Demand, and Points-Of-Failure Infrastructure (Such as in Disasters). However, with the Existing Crowded Radio Frequency (RF) Spectrum, UAV ABSs Cannot Provide High-Data-Rate Communication Required in 5G and beyond. to Address This Challenge, Visible Light Communication (VLC) is Proposed to Be Equipped on UAVs to Take Advantage of the Flexible and On-Demand Deployment Feature of the UAV, and the High-Data-Rate Communication of the VLC. However, VLC Has Strong Alignment Requirements between Transceivers, Therefore, How to Determine the …

Lightesd: Fully-Automated And Lightweight Anomaly Detection Framework For Edge Computing, Ronit Das, Tie (Tony) T. Luo

Computer Science Faculty Research & Creative Works

Anomaly Detection is Widely Used in a Broad Range of Domains from Cybersecurity to Manufacturing, Finance, and So On. Deep Learning based Anomaly Detection Has Recently Drawn Much Attention Because of its Superior Capability of Recognizing Complex Data Patterns and Identifying Outliers Accurately. However, Deep Learning Models Are Typically Iteratively Optimized in a Central Server with Input Data Gathered from Edge Devices, and Such Data Transfer between Edge Devices and the Central Server Impose Substantial overhead on the Network and Incur Additional Latency and Energy Consumption. to overcome This Problem, We Propose a Fully Automated, Lightweight, Statistical Learning based Anomaly …

A Distributed Algorithm For Identifying Strongly Connected Components On Incremental Graphs, S. Srinivasan, A. Khanda, S. Srinivasan, A. Pandey, S. (Sajal) K. Das, S. Bhowmick, B. Norris

Computer Science Faculty Research & Creative Works

Incremental graphs that change over time capture the changing relationships of different entities. Given that many real-world networks are extremely large, it is often necessary to partition the network over many distributed systems and solve a complex graph problem over the partitioned network. This paper presents a distributed algorithm for identifying strongly connected components (SCC) on incremental graphs. We propose a two-phase asynchronous algorithm that involves storing the intermediate results between each iteration of dynamic updates in a novel meta-graph storage format for efficient recomputation of the SCC for successive iterations. To the best of our knowledge, this is the …

Optimizing Federated Learning In Leo Satellite Constellations Via Intra-Plane Model Propagation And Sink Satellite Scheduling, Mohamed Elmahallawy, Tie (Tony) T. Luo

Computer Science Faculty Research & Creative Works

The advances in satellite technology developments have recently seen a large number of small satellites being launched into space on Low Earth orbit (LEO) to collect massive data such as Earth observational imagery. The traditional way which downloads such data to a ground station (GS) to train a machine learning (ML) model is not desirable due to the bandwidth limitation and intermittent connectivity between LEO satellites and the GS. Satellite edge computing (SEC), on the other hand, allows each satellite to train an ML model onboard and uploads only the model to the GS which appears to be a promising …

Promoting Enhanced Decision-Making And Proactive Management For Modularization In The Construction Industry Using Risk-Based Approach, Analytical Hierarchy Process, And Graph Theory, Mohamad Abdul Nabi

Doctoral Dissertations

"Modularization is an approach that can potentially mitigate many of the current construction industry’s challenges. Despite the documented advantages of modularization, the industry is still not able to capitalize on its full potential and benefits. The latter is due to the outdated decision-making models that may not reflect the multiple factors involved in modular construction, the unique source of risks associated with modular construction methods, need for continuous improvement, and complex contractual requirements and disputes. As such, the goal is to promote the use of modularization in the construction industry by enhancing decision-making and proactive risk management. The associated objectives …

An Effective Transfer Learning Based Landmark Detection Framework For Uav-Based Aerial Imagery Of Urban Landscapes, Bishwas Praveen, Vineetha Menon, Tathagata Mukherjee, Bryan Mesmer, Sampson Gholston, Steven Corns

Engineering Management and Systems Engineering Faculty Research & Creative Works

Aerial imagery captured through airborne sensors mounted on Unmanned Aerial Vehicles (UAVs), aircrafts, satellites, etc. in the form of RGB, LiDAR, multispectral or hyperspectral images provide a unique perspective for a variety of applications. These sensors capture high-resolution images that can be used for applications related to mapping, surveying, and monitoring of crops, infrastructure, and natural resources. Deep learning based algorithms are often the forerunners in facilitating practical solutions for such data-centric applications. Deep learning-based landmark detection is one such application which involves the use of deep learning algorithms to accurately identify and locate landmarks of interest in images captured …

Show-Me Resilience: Assessing And Reconciling Rural Leaders’ Perceptions Of Climate Resilience In Missouri, Zachary J. Miller, Caleb O'Brien, Casey I. Canfield, Lauren Sullivan

Engineering Management and Systems Engineering Faculty Research & Creative Works

Rural areas of the United States play a vital role in coping with, adapting to and mitigating climate change, yet they often lag urban areas in climate planning and action. Rural leaders—e.g., policymakers, state/federal agency professionals, non-profit organization leadership, and scholars – are pivotal for driving the programs and policies that support resilient practices, but our understanding of their perspectives on climate resilience writ large is limited. We conducted semi-structured interviews with 23 rural leaders in Missouri to elucidate their conceptualizations of climate resilience and identify catalysts and constraints for climate adaptation planning and action across rural landscapes. We investigated …

Deep Reinforcement Learning For Approximate Policy Iteration: Convergence Analysis And A Post-Earthquake Disaster Response Case Study, Abhijit Gosavi, L. (Lesley) H. Sneed, L. A. Spearing

Engineering Management and Systems Engineering Faculty Research & Creative Works

Approximate Policy Iteration (API) is a Class of Reinforcement Learning (RL) Algorithms that Seek to Solve the Long-Run Discounted Reward Markov Decision Process (MDP), Via the Policy Iteration Paradigm, Without Learning the Transition Model in the Underlying Bellman Equation. Unfortunately, These Algorithms Suffer from a Defect Known as Chattering in Which the Solution (Policy) Delivered in Each Iteration of the Algorithm Oscillates between Improved and Worsened Policies, Leading to Sub-Optimal Behavior. Two Causes for This that Have Been Traced to the Crucial Policy Improvement Step Are: (I) the Inaccuracies in the Policy Improvement Function and (Ii) the Exploration/exploitation Tradeoff Integral …

Modeling Of A Microstrip Line Referenced To A Meshed Return Plane, Zeyi Sun, Jian Liu, Xiaoyan Xiong, Yuan Liu, Victor Khilkevich, Donghyun Kim, Daryl G. Beetner

Engineering Management and Systems Engineering Faculty Research & Creative Works

Transmission Lines Referenced to Meshed Return Planes Are Widely Used Because of the Physical Flexibility Imparted by the Meshed Plane. Poor Accounting for the Meshed Ground, However, Can Lead to Severe Signal Integrity and Radio Frequency Interference Issues. Full-Wave Simulation Can Characterize the Electrical Performance at an Early Design Stage, But It is Both Time and Computational Resource Consuming. to Make the Simulation More Efficient, a Method is Proposed in This Study to Model Transmission Lines with a Meshed Reference Ground using 2D Analysis. the 2D Analysis is Performed at Several Locations Along the Length of the Trace above the …

A Genome-Wide Association Study Coupled With Machine Learning Approaches To Identify Influential Demographic And Genomic Factors Underlying Parkinson’S Disease, Md Asad Rahman, Jinling Liu

Engineering Management and Systems Engineering Faculty Research & Creative Works

Background: Despite the recent success of genome-wide association studies (GWAS) in identifying 90 independent risk loci for Parkinson's disease (PD), the genomic underpinning of PD is still largely unknown. At the same time, accurate and reliable predictive models utilizing genomic or demographic features are desired in the clinic for predicting the risk of Parkinson's disease. Methods: To identify influential demographic and genomic factors associated with PD and to further develop predictive models, we utilized demographic data, incorporating 200 variables across 33,473 participants, along with genomic data involving 447,089 SNPs across 8,840 samples, both derived from the Fox Insight online study. …

Improving Social Bot Detection Through Aid And Training, Ryan Kenny, Baruch Fischhoff, Alex Davis, Casey I. Canfield

Engineering Management and Systems Engineering Faculty Research & Creative Works

Objective: We test the effects of three aids on individuals' ability to detect social bots among Twitter personas: a bot indicator score, a training video, and a warning. Background: Detecting social bots can prevent online deception. We use a simulated social media task to evaluate three aids. Method: Lay participants judged whether each of 60 Twitter personas was a human or social bot in a simulated online environment, using agreement between three machine learning algorithms to estimate the probability of each persona being a bot. Experiment 1 compared a control group and two intervention groups, one provided a bot indicator …

Approximation Of Optimal Control Surfaces For The Bass Model With Stochastic Dynamics, Gabriel Nicolosi, Christopher Griffin

Engineering Management and Systems Engineering Faculty Research & Creative Works

The Bass diffusion equation is a well-known and established modeling approach for describing new product adoption in a competitive market. This model also describes diffusion phenomena in various contexts: infectious disease spread modeling and estimation, rumor spread on social networks, prediction of renewable energy technology markets, among others. Most of these models, however, consider a deterministic trajectory of the associated state variable (e.g., market-share). In reality, the diffusion process is subject to noise, and a stochastic component must be added to the state dynamics. The stochastic Bass model has also been studied in many areas, such as energy markets and …

Usdot Tier-1 University Transportation Center For Advancing Cybersecurity Research And Education, Yunpeng Jack Zhang, Kailai Wang, Lingguang Song, Amaury Lendasse, Houbing Herbert Song, Zhu Han, Rakesh Verma, Arlei Silva, Carlos Rubio-Medrano, Zhixia Richard Li, Guohui Zhang

Engineering Management and Systems Engineering Faculty Research & Creative Works

The Transportation Cybersecurity Center for Advanced Research and Education (Cyber-Care) is a Us Department of Transportation (Usdot) Tier-1 University Transportation Center (Utc) Funded in 2023. Cyber-Care Primarily Focuses on the Usdot Statutory Research Priority Area of 'reducing Transportation Cybersecurity Risks.' Cyber-Care Aims to Establish a Fundamental Knowledge Basis and Explore Advanced Theory to Mitigate the Impacts of Large-Scale Cyberattacks on Transportation Infrastructure and Connected and Automated Vehicle (Cav) Systems. the Research Projects at Cyber-Care Will Develop Conceptual Frameworks, Construct Comprehensive Datasets, Explore Novel Analytical Approaches, Support the Implementation of Public Policies and Infrastructure Investments, and Build a High-Quality Industry Workforce …

Improved Quantification Of Defect Cross-Section For Active Microwave Thermography, Logan M. Wilcox, Kristen M. Donnell

Electrical and Computer Engineering Faculty Research & Creative Works

Active microwave thermography (AMT) is an integrated nondestructive testing and evaluation (NDT&E) technique that features a microwave-based excitation and subsequent thermographic inspection via an infrared camera. AMT has been successfully employed in several industries including aerospace and civil for NDT&E inspections. Since the excitation is microwave-based, an antenna is used to irradiate the sample under test and hence the heating pattern will vary spatially (following the antenna pattern). This nonuniform thermal excitation may limit the ability of AMT to quantify defect cross-sections. Therefore, this work seeks to expand the capabilities of AMT by incorporating a post-processing technique to improve defect …

Optimal Adaptive Tracking Control Of Partially Uncertain Nonlinear Discrete-Time Systems Using Lifelong Hybrid Learning, Behzad Farzanegan, Rohollah Moghadam, Sarangapani Jagannathan, Pappa Natarajan

Electrical and Computer Engineering Faculty Research & Creative Works

This article addresses a multilayer neural network (MNN)-based optimal adaptive tracking of partially uncertain nonlinear discrete-time (DT) systems in affine form. By employing an actor–critic neural network (NN) to approximate the value function and optimal control policy, the critic NN is updated via a novel hybrid learning scheme, where its weights are adjusted once at a sampling instant and also in a finite iterative manner within the instants to enhance the convergence rate. Moreover, to deal with the persistency of excitation (PE) condition, a replay buffer is incorporated into the critic update law through concurrent learning. To address the vanishing …

A Real-Time Microwave Camera Prototype With Zero-Bias Diode Detectors For Emi Source Imaging, Xin Yan, Liang Liu, Victor Khilkevich

Electrical and Computer Engineering Faculty Research & Creative Works

Emission Source Microscopy (ESM) Could Be Utilized to Localize the Electromagnetic Interference (EMI) Sources that Contribute to the Far-Field Radiation. in Those Cases, the Electrical Field over a Two-Dimensional Plane is Collected by Mechanical Scanning, Resulting in a Long Measurement Time and the Presence of Mechanical Errors. in This Work, a Microwave Camera based on a Two-Dimensional Array of Elliptical Slot Antennas and Diode Detectors is Presented. Multiplexers Are Utilized to Access the Output of Each Detector and the Scanning of the Whole Array Could Be Done Multiple Times Per Second.

Lifelong Learning-Based Multilayer Neural Network Control Of Nonlinear Continuous-Time Strict-Feedback Systems, Irfan Ahmad Ganie, S. (Sarangapani) Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In This Paper, We Investigate Lifelong Learning (LL)-Based Tracking Control for Partially Uncertain Strict Feedback Nonlinear Systems with State Constraints, employing a Singular Value Decomposition (SVD) of the Multilayer Neural Networks (MNNs) Activation Function based Weight Tuning Scheme. the Novel SVD-Based Approach Extends the MNN Weight Tuning to (Formula Presented.) Layers. a Unique Online LL Method, based on Tracking Error, is Integrated into the MNN Weight Update Laws to Counteract Catastrophic Forgetting. to Adeptly Address Constraints for Safety Assurances, Taking into Account the Effects Caused by Disturbances, We Utilize a Time-Varying Barrier Lyapunov Function (TBLF) that Ensures a Uniformly Ultimately …

A Combined Model For Transient And Self-Heating Of Snapback Type Esd Protection Devices, Xin Yan, Seyed Mostafa Mousavi, Li Shen, Yang Xu, Wei Zhang, Sergej Bub, Steffen Holland, David Pommerenke, Daryl G. Beetner

Electrical and Computer Engineering Faculty Research & Creative Works

A simplified physics-based model for predicting the transient response and self-heating behavior of silicon-controlled rectifier (SCR) snapback-type transient voltage suppressors (TVS) is presented. Comparisons of the predicted quasi-static behavior and transient waveforms with measurements suggest that the proposed model accurately captures the most important characteristics of the device. Its simplified nature means it can be easily tuned using only data obtained from package-level measurements.

Efficient High Order Ensemble For Fluid Flow, John Carter

Doctoral Dissertations

"This thesis proposes efficient ensemble-based algorithms for solving the full and reduced Magnetohydrodynamics (MHD) equations. The proposed ensemble methods require solving only one linear system with multiple right-hand sides for different realizations, reducing computational cost and simulation time. Four algorithms utilize a Generalized Positive Auxiliary Variable (GPAV) approach and are demonstrated to be second-order accurate and unconditionally stable with respect to the system energy through comprehensive stability analyses and error tests. Two algorithms make use of Artificial Compressibility (AC) to update pressure and a solenoidal constraint for the magnetic field. Numerical simulations are provided to illustrate theoretical results and demonstrate …

Interval Analysis Method For The Uncertainty And Sensitivity Characterization In Transmission Line Systems, Ping Yuan, Lijun Jiang

Electrical and Computer Engineering Faculty Research & Creative Works

Uncertainty in electronic fabrication process could cause serious yield issues and stability concerns. Hence, identifying the resultant range caused by the uncertainty in the system is a critical topic in modern EDA design process. Monte Carlo method is considered as a golden standard but with many drawbacks. In the paper, we propose to use the interval analysis (IA) to analyze the signal integrity and power integrity problems in transmission line (TL) systems. Using the interval representing the uncertainty range of parameters in the system, the uncertainty range of the system can be derived by the derived analytical expression. It is …

Effects Of Downwash When Measuring Gas Concentration With Uav-Mounted Gas Sensors, Jacob Lane Brinkman

Doctoral Dissertations

"This research concludes that the downwash region of a rotary-wing unmanned aerial vehicle (UAV) does not produce pressure variations substantial enough to cause significant changes in density-based gas concentration measurements. US regulations evaluate gas concentration as a function of gas density, and accurate concentration measurements are critical for protecting health and safety, which means evaluation of actual downwash effects on measured gas concentration is essential.

During experimentation, the UAV was fixed to a stand in a sealed chamber filled with NOx (NO and NO₂) of constant concentration. The UAV rotors ran at hovering speeds, and gas concentrations were …

Fiber Optic Sensors For Liquid Identifications, Wassana Naku

Doctoral Dissertations

"The fiber optic Fabry-Perot interferometer (FPI) is a widely utilized sensing configuration, offering numerous advantages over conventional electronic sensors, including high accuracy, distributed sensing capabilities, immunity to electromagnetic interference, and compact size. In this study, we propose a remarkably simple fiber optic-tip sensor system combined with machine learning techniques for the identification of pure and volatile organic liquids (VOLs).

A liquid droplet forms an extrinsic FPI (EFPI), with its effective reflectance being a function of the droplet's length. As the droplet evaporates, its length decreases. We conducted immersion tests using optical fiber tip sensors and monitored the time-transient responses of …

Cfd Analysis For Beyond Bubbly Gas-Liquid Two-Phase Flows In A Large Diameter Pipe, Sungje Hong

Doctoral Dissertations

"Due to the complexity of multiphase flow phenomena, numerical analysis for multiphase turbulent flow is not as reliable as single-phase computational fluid dynamics (CFD). A literature review has revealed that the current efforts on multiphase flow simulation have focused on small diameter channels under very restricted flow conditions and have been conducted without identifying some important procedures. To expand CFD applications to a wide range of two-phase flow conditions in large diameter channels, this study aims to validate the current CFD models for vertical concurrent air-water two-phase flow simulations beyond bubbly flows. First, a numerical model developed to describe dynamical …

Characterization And Improvement Of Powder Properties Through Plasma Spheroidization For Use In The Laser Powder Bed Fusion Process, M. Hossein Sehhat

Doctoral Dissertations

"This research was conducted to characterize the powder properties using different characterization techniques to enhance the powder behavior in the Laser Powder Bed Fusion (LPBF) process. One of the shortcomings of LPBF process is the high cost of gas-atomized powder feedstock. To overcome this downside, the cheaper water-atomized powder was characterized and used in LPBF process, and its performance was compared with those of gas-atomized powder. Also, the plasma spheroidization process was used to improve the powder characteristics; if the spheroidized powder could function as well as the virgin powder, plasma spheroidization process could be applied to the used powders …

Identification, Mapping, Analysis, And Dating Of Composite Mega Landslides In The Colorado Plateau, Conor Maris Watkins

Doctoral Dissertations

"This research consists of three articles focusing on landslide complexes within the Grand Canyon region of the Colorado Plateau. This is a vast and remote region of rugged topography. The landslide complexes in this study are greater than 10 million cubic meters in volume. The slides are located within different areas of the region and consist of differing geology but share multiple commonalities. Stratigraphic and structural controls tend to dictate where the slides occur. All of the landslides occur where carbonates and/or sandstones overlie over consolidated shales. The shales are susceptible to creep and strain softening where lateral confinement has …

Sustainable Development: A Data Analysis Investigation For A New Conceptual Model, Case Study Comparison And Validation Techniques, Tiffanie Marie Toles

Doctoral Dissertations

"The three pillars model for sustainability, often represented with three intersecting circles described as economic, environmental, and social factors with sustainability being at the center is a complex and philosophically open model [1]. As society promotes efforts to reduce carbon impacts, there becomes a need to critically review the models employed for understanding. This research presents a validated methodology, an updated conceptual configuration of sustainability for overall use, as well as a sustainable development performance measurement system. Using the 2020 Sustainable Development Goals Index Data, 232 indicators from 193 countries were used to evaluate the efficacy of using more than …

Development Of A Mission Design Tool For Computing Libration Point Formations Using Differential Corrections Initialized With A Genetic Algorithm, Donna Jennings

Doctoral Dissertations

"The growing interest in deep space spacecraft missions and distributed spacecraft formations has established the need for state-of-the-art advancements in the areas of relative dynamics and mission design. The focus of this research is to develop methods for identifying natural (i.e. ballistic) relative formation trajectories in the vicinity of the collinear libration points. A natural formation decreases the station keeping costs, lowering the consumption of spacecraft resources and extending mission lifetime. This research adapts both numerical and machine learning approaches to the problem of mission design. A genetic algorithm was first used to identify a multi-agent spacecraft formation in the …

Recurrent Event Data Analysis With Mismeasured Covariates, Ravinath Alahakoon Mudiyanselage

Doctoral Dissertations

"Consider a study with n units wherein every unit is monitored for the occurrence of an event that can recur with random end of monitoring. At each recurrence, p concomitant variables associated to the event recurrence are recorded with q (q ≤ p) collected with errors. Of interest in this dissertation is the estimation of the regression parameters of event time regression models accounting for the covariates. To circumvent the problem of bias and consistency associated with model's parameter estimation in the presence of measurement errors, we propose inference for corrected estimating functions with well-behaved roots under additive measurement errors …

The Application Of Statistical Modeling To Identify Genetic Associations With Mild Traumatic Brain Injury Outcomes, Caroline Schott

Masters Theses

"Traumatic brain injury (TBI) is a growing health concern, with millions of TBI diagnoses in the United States each year. The vast majority of TBI diagnoses are mild traumatic brain injuries (mTBI), which can be challenging to manage due to variation in symptoms and outcomes. Most individuals with mTBI successfully recover quickly, but a small subset has a delayed recovery. Although the factors that contribute to this variation in recovery are not clearly understood, it is possible that genetic differences may play a role. Very few studies have investigated the association between single nucleotide polymorphisms (SNPs) with mTBI outcomes and …

Suppressing Aluminum/Silica Exchange Reaction Between High Aluminum Steel And Mold Flux During Continuous Casting Process, Kuanysh Nurbekuly Yermukhanbetov

Masters Theses

"Mold flux plays one of the critical roles in continuous casting of steel. It performs five primary functions: thermal and chemical insulation, lubrication between the steel strand and mold, absorption of inclusions, and promotion of even heat flux. The aluminum/silica exchange reaction occurring between steel and mold flux during the continuous casting process poses significant challenges in the steel industry. This reaction can lead to various defects in the cast product and adversely affect its surface quality, as well as downstream processing.

In this work, effectiveness of two approaches, namely slag dopant additions and electrochemical techniques, in suppressing the exchange …