Digital Commons Network^™

Adaptive Resilient Control For A Class Of Nonlinear Distributed Parameter Systems With Actuator Faults, Hasan Ferdowsi, Jia Cai, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a new model-based fault resilient control scheme for a class of nonlinear distributed parameter systems (DPS) represented by parabolic partial differential equations (PDE) in the presence of actuator faults. A Luenberger-like observer on the basis of nonlinear PDE representation of DPS is developed with boundary measurements. A detection residual is generated by taking the difference between the measured output of the DPS and the estimated one given by the observer. Once a fault is detected, an unknown actuator fault parameter vector together with a known basis function is utilized to adaptively estimate the fault dynamics. A novel …

Meta-Icvi: Ensemble Validity Metrics For Concise Labeling Of Correct, Under- Or Over-Partitioning In Streaming Clustering, Niklas M. Melton, Sasha Petrenko, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

Understanding the performance and validity of clustering algorithms is both challenging and crucial, particularly when clustering must be done online. Until recently, most validation methods have relied on batch calculation and have required considerable human expertise in their interpretation. Improving real-time performance and interpretability of cluster validation, therefore, continues to be an important theme in unsupervised learning. Building upon previous work on incremental cluster validity indices (iCVIs), this paper introduces the Meta- iCVI as a tool for explainable and concise labeling of partition quality in online clustering. Leveraging a time-series classifier and data-fusion techniques, the Meta- iCVI combines the outputs …

Modeling Wideband Radiated Emissions From Pcbs In Shielding Enclosures Based On Single-Plane Phaseless Near-Field Scanning, Zhifei Xiao, Zi An Wang, Li Jun Jiang, Ping Li

Electrical and Computer Engineering Faculty Research & Creative Works

This article presents a wideband phase less source reconstruction method (SRM) for the evaluation of the radiated emissions from printed circuit boards (PCBs) in shielding enclosures. The PCBs are modeled with equivalent dipoles, and the numerical Green's function (NGF) is deployed to establish the relationship between the equivalent source and the input near-field (NF) data, thus the electromagnetic influences of the surrounding environments comprehensively accounted. This method only requires magnitude-only NF scanning over a single plane, thus significantly decreasing the NF measurement difficulty. To remedy the lack of phase information of the NF data, the input NF data are equally …

Fabrication And Analysis Of A Gas Sensor Utilizing A Fabry-Perot Interferometer With Varied Thicknesses Of Zif-8 Coating On End-Faced Single-Mode Optical Fiber, Nahideh Salehifar, Rex E. Gerald, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

This research investigates the potential of integrating optical fiber and sensitive thin film coating materials to develop diverse chemical sensing platforms. We utilized a layer-by-layer synthesis technique, along with specific pre- post treatment methods, to create highly effective nonporous metal-organic frameworks (MOFs), specifically ZIF-8, ranging from the nanometer scale to the micrometer scale (800 nm to 110 μm). Additionally, we successfully cultivated ZIF-8 on both sides of a single-mode fiber (SMF) with thicknesses of 25 μm and 50 μm. In a separate experiment, we successfully grew ZIF-8 on one side of an optical fiber, achieving a thickness of 10 μm. …

Multimode Fiber-Based Interferometric Sensors With Microwave Photonics, Chen Zhu, Shuaifei Tian, Lingmei Ma, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Interferometry is one of the most widely used investigative techniques in various fields. With the implementation of interferometry on optical fibers, fiber optic interferometers (FOIs) have gained tremendous growth and advancement in the past four decades and have been explored for measurements of a diverse array of physical, chemical, and biological parameters. FOIs are typically constructed using single-mode fibers (SMFs) and are interrogated in the optical domain using probing light with a tightly controlled state of polarization (SOP), to ensure high-quality interference signals that facilitate sensing applications. The stringent requirement on the single-mode operation, as well as SOP, has hindered …

A Robust Demand Regulation Strategy For Ders In A Single-Controllable Active Distribution Network, Shah Fahad, Arman Goudarzi, Rui Bo, Muhammad Waseem, Rashid Al-Ammari, Atif Iqbal

Electrical and Computer Engineering Faculty Research & Creative Works

Over the past decade, pq regulation schemes for a single-controllable active distribution network (adn) using coordination among a network of virtual synchronous generators (vsgs) have been proposed. However, considering the variable nature of intermittent renewable energy sources (iress), coupling a cluster of iress with the point of common coupling (pcc) of adn could inflict transient issues for the power management of the whole adn. To counter these challenges, the proposed study has three main objectives: 1) to propose a modified mathematical model that represents the apparent resistance-reactance at the pcc of adn in relation to the pq coordination among the …

Instantaneous Current And Average Power Flow Characterization Of A Dc-Dc-Dc Triple Active Bridge Converter, Jonathan Saelens, Lauryn Morris, Oroghene Oboreh-Snapps, Arnold Fernandes, Praneeth Uddarraju, Sophia A. Strathman, Jonathan W. Kimball

Electrical and Computer Engineering Faculty Research & Creative Works

The Triple Active Bridge (TAB) is a Three-Port Power Converter that Facilitates Bi-Directional Power Flow and Provides Galvanic Isolation, Making It a Subject of Significant Research Attention. This is Attributed to its Diverse Applications in High-Frequency DC-DC Conversion, Electric Vehicles, Renewable Energy Integration, and Micro-Grids. Controlling the System at Run-Time Involves Modification of the Two Phase-Shift Parameters between Respective Bridges. by Analyzing the Fundamental Converter Operating Equations, Future Control Designers Can Use This Framework to Optimize Control Schemes to Mitigate the Under-Determined Nature of the TAB Converter. in This Paper, We Elucidate the Foundational Operational Principles of the TAB and …

Addressing Reactive Power Sharing In Parallel Inverter Islanded Microgrid Through Deep Reinforcement Learning, Oroghene Oboreh-Snapps, Sophia A. Strathman, Jonathan Saelens, Arnold Fernandes, Jonathan W. Kimball

Electrical and Computer Engineering Faculty Research & Creative Works

Parallel Inverter Microgrids (MGs) Present a Significant Challenge in the Form of Inverter-Based Distributed Generators (IBDGs) Connected with Varying Line Impedances, Potentially Leading to Substantial Reactive Power-Sharing Errors (RPSE). This Paper Proposes the Fusion of Data-Driven Control into the Conventional Virtual Synchronous Generator in a Bid to Minimize the Sharing Error. First, All State Variables Associated with Each IBDG in the Microgrid Are Sensed and Used as Input Data for a Deep Reinforcement Learning (DRL) Agent. Next, the DRL Agent, motivated by a Unique Reward Function, is Trained to Satisfy Two Objectives: (1) Ensure the Output Voltage of All IBDGs …

Optimal Trajectory Tracking For Uncertain Linear Discrete-Time Systems Using Time-Varying Q-Learning, Maxwell Geiger, Vignesh Narayanan, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This Article Introduces a Novel Optimal Trajectory Tracking Control Scheme Designed for Uncertain Linear Discrete-Time (DT) Systems. in Contrast to Traditional Tracking Control Methods, Our Approach Removes the Requirement for the Reference Trajectory to Align with the Generator Dynamics of an Autonomous Dynamical System. Moreover, It Does Not Demand the Complete Desired Trajectory to Be Known in Advance, Whether through the Generator Model or Any Other Means. Instead, Our Approach Can Dynamically Incorporate Segments (Finite Horizons) of Reference Trajectories and Autonomously Learn an Optimal Control Policy to Track Them in Real Time. to Achieve This, We Address the Tracking Problem …

Prediction Of Carbonation Capacity Of Scms Using Ensemble Learning Method, Kangyi Cai, Jian Liu, Edward Mwanza, Mahelet G. Fikru, Hongyan Ma, Donald C. Wunsch

Economics Faculty Research & Creative Works

The utilization of supplementary cementitious materials (SCMs) subjected to carbonation processing represents a viable strategy to mitigate anthropogenic CO₂ emissions associated with concrete production, potentially contributing to the achievement of carbon neutrality. However, existing studies have limitations in effectively predicting the varying carbonation capacities of different SCMs, a gap that this research aims to address. Recent research efforts focused on the carbonation of waste-material-sourced SCMs are reviewed, along with a comparative discussion on diverse carbonation methods. A detailed data set encapsulating the properties of SCMs, and carbonation configurations was compiled. At the same time, six ensemble learning models were …

A Structure From Motion Photogrammetric Method For Continuously Measuring And Tracking The Deformations Of Soils During Triaxial Testing, Xiaolong Xia

Doctoral Dissertations

"Measuring soil deformation characteristics during triaxial testing is an important part of characterizing the stress-strain behavior of both saturated and unsaturated soils. The total and local deformations and volume changes of a soil specimen are indispensable parameters that need to be measured and are of great significance for understanding volume change and shear strength characteristics of the soil. Over the past several decades, numerous efforts have been made to measure the soil deformations during triaxial testing. However, it remains a great challenge for researchers to measure the volume-changes of unsaturated soils due to the existence of the air phase.

This …

Synthesis And Characterization Of Quantum Materials, Yunsheng Qiu

Doctoral Dissertations

"In this study, attempts were made to grow quantum materials that have recently undergone a profound change of perspective. These materials are involved in intricate macroscopic properties rooted in the subtle nature of quantum physics. To explore our understanding of quantum materials, this study includes three projects: Magnetic Topological Insulators, Topological Superconductors, and high-temperature superconductors.

A Cr-doped Sb₂Te₃ is added to the category for the magnetic topological insulators project. Their transport properties are studied, and the origin of ferromagnetism is studied. Anomalous Hall effect is observed in the Hall measurements, and serval factors (cooling rate, dopant deficiency) …

Re-Evaluating Missouri’S Strategic Element Potential: A Geochemical Study Of The Mesoproterozoic Fe-Cu-Co-Ree Deposits In Southeast Missouri, Usa, Brandon James Sullivan

Doctoral Dissertations

"Iron-oxide-copper-gold (IOCG) deposits are poorly understood mineral systems. For example, we do not know why Cu- and Co-rich IOCG deposits typically occur proximal to Fe ore deposits that are notably Cu and Co-poor, such as Iron Oxide Apatite (IOA) deposits. To better understand the formation of IOA and IOCG deposits in Missouri, USA, this PhD thesis examines the genesis of the Kratz Spring IOA and the Boss Central Dome IOCG deposits. This study presents the first constraints on formation conditions and fluid sources in the studied deposits using integrated petrographic, mineral composition, and Fe isotope analyses of oxide minerals. Observations …

Investigation Of Asphaltene Instability Under Gas Injection In Unconventional Reservoirs And Its Impact On Oil Recovery Performance, Mukhtar Elturki

Doctoral Dissertations

"Unconventional reservoirs, such as shale gas and tight oil formations, have become an important source of energy in recent years. However, these reservoirs often contain high levels of asphaltenes, which can lead to deposition and blockages in production wells and reduce the flow of hydrocarbons during gas enhanced oil recovery (GEOR) techniques. This study aims to experimentally evaluate the impact of miscible and immiscible carbon dioxide (CO₂) and nitrogen (N₂) on asphaltene deposition and its impact on oil recovery performance in unconventional shale reservoirs.

This research began with a comprehensive literature review and data analysis of GEOR methods in unconventional …

Control And Optimization Of Energy Storage System In Power Distribution System, Waqas Ur Rehman

Doctoral Dissertations

"The widespread adoption of electric vehicles (EVs) and transportation electrification is encumbered by two chief barriers: i) the limited driving range of EVs in the market today and ii) inadequate fast-charging infrastructure for long-distance trips. Extreme fast charging (XFC) technology can recharge EVs in less than 10 minutes for 200 miles range. Firstly, a novel robust optimization-based mixed integer linear programming model is proposed to size a battery energy storage system (BESS) and PV system in an XFCS. In this part, it is assumed that the sizing and location of the XFCS are known. Secondly, the aforesaid assumption is relaxed, …

Chirality Determination Using Three-Wave Mixing Microwave Spectroscopy, Nicole Taylor Moon

Doctoral Dissertations

"Rotational spectroscopy has established itself as a reliable gas-phase spectroscopic technique for the structural determination of molecules. This reliability has stemmed from both advancements in microwave technology and a willingness from the community to push the boundaries of the field. In this dissertation, the boundaries are tested in both how well the technique can determine the structure of molecules exhibiting large amplitude motion and through chirality determination. The first half of this dissertation explores the use of deep averaging to determine the structure of silicon containing molecules in collaboration with Dr. Guirgis from the College of Charleston. For each of …

The Science Of Gravitational-Wave Sources And Beyond Compact Binary Coalescences, Yanyan Zheng

Doctoral Dissertations

"This work focuses on the field of gravitational-wave astronomy by extending the scope of detectable sources beyond compact binary coalescences, All the gravitational-wave detections so far come from compact binary coalescences. Focusing on core-collapse supernovae as promising sources for short gravitational-wave transients, this work reports optically targeted searches for gravitational-wave emitted by core-collapse supernovae during the third observing run of the LIGO and Virgo detectors. It also predicts the search sensitivity for the ongoing fourth and forthcoming fifth observing runs. Moreover, the work introduces a novel computational framework for testing the spatial distribution of binary black hole sources, allowing for …

Material Models For Simulation Of Steel Hot Rolling Process Using Experimentally Determined Properties At Elevated Temperatures And Strain Rates, Siva Sai Krishna Dasari

Doctoral Dissertations

"Hot rolling is one of the most important and complex process that includes large material deformation, different contact conditions, complex geometries and different temperature gradients along with micro structural changes such as viscoplasticity, dynamic recrystallization, and static softening. In the present work, the viscoplasticity, damage evolution, static softening and void closure of alloyed metal during hot rolling process was numerically investigated. Gleeble^TM hot compression and double hit tests were performed to provide experimental stress-strain curves at recrystallization temperatures and at different strain rates. Johnson-Cook viscoplastic model was calibrated using experimental stress-strain curves. Static softening model was developed at varying …

Design, Synthesis, And Characterization Of Complex Chalcogenides For Energy Storage And Energy Conversion Applications, Srikanth Balijapelly

Doctoral Dissertations

"Through this investigation, complex chalcogenides with the combination of main group metals, transition metals, and rare earth metals have been synthesized using the building block approach and their structure-property relationships are evaluated. The main emphasis of research is on rationally designing new materials for applications in sodium and lithium ion conducting solid electrolytes, cathodes, thermoelectrics, and nonlinear optics. Along with the experimental studies, theoretical calculations are also employed to better understand the physicochemical properties of the synthesized compounds.

The first part of the research will discuss designing alkali ion containing complex chalcogenides using the building block approach. This investigation resulted …

Advanced Mass Spectrometry Method Development And Applications For Assessment Of Traumatic Brain Injury And Legionella Pneumophila Dissinfection With Copper, Austin Chase Sigler

Doctoral Dissertations

"Pathological processes often involve complex biochemical changes which can be assessed using advanced mass spectrometry. In this present dissertation, two fields were studied: traumatic brain injury (TBI), and water contamination by L. pneumophilia. TBI is a pressing public health concern for which current clinical tools remain inadequate. We present newly developed mass spectrometric methods to access metabolites associated with blast induced TBI. We applied these methods to the biofluids of soldiers conducting explosives training. Significant changes in several metabolites were observed between pre- and post-blast specimens, including changes that increased with repeated exposure. These changes point to the possibility of …

Deep Learning Techniques For Image Segmentation In Dermoscopic Skin Cancer Images, Norsang Lama

Doctoral Dissertations

"Melanoma is recognized as the most lethal type of skin cancer, responsible for a significant proportion of skin cancer-related deaths. However, early detection of melanoma is essential for successful treatment outcomes. Computer-aided skin cancer diagnosis tools can save lives by enabling earlier detection of skin cancer. Image segmentation is a crucial step in computer-aided diagnosis as it allows the detection of critical features or regions in an image. Thus, an accurate image segmentation method is necessary to create a more precise computer-aided diagnostic tool for skin cancer diagnosis. This dissertation includes investigating and developing deep learning techniques to improve image …

Improvements In Biomedical Image Analysis With Computational Intelligence And Data Fusion Techniques, Akanksha Maurya

Doctoral Dissertations

"An estimated 2 million new cases of basal cell carcinoma (BCC) are diagnosed each year in the United States, making it one of the most common skin cancers. Earlier detection of these cancers enables less invasive biopsies. Clinical detection consists of a preliminary visual observation of these skin lesions by an experienced dermatologist making it a specialized task highly dependent on their time, availability, and resources. Hence, there is a need for automating this process that can assist healthcare staff. In recent years, deep learning (DL) has been used extensively and successfully to diagnose different cancers in dermoscopic images. Telangiectasia …

Catalytic Control Of The Nanomorphology And Mechanical Properties Of Aliphatic Shape-Memory Aerogels, A B M Shaheen Ud Doulah

Doctoral Dissertations

"Shape-memory poly(isocyanurate-urethane) (PIR-PUR) aerogels are nanoporous solids that can return to their original shape after being compressed, through a heating actuation step. This thesis compares the effectiveness of various metal ions as catalysts in the formation of PIR-PUR aerogels, and explores the correlation between catalytic activity, nanomorphology, and mechanical properties of the resulting aerogels. The gelation rate was found to increase from Fe to Cu and then decline from Cu to Ga in the periodic table. CuCl₂ was found to be the fastest catalyst, and FeCl₃ the slowest. The morphology of the aerogels changed from bicontinuous to spheroidal …

Understanding Catalyst Design Principles In Transition Metal Mixed Anionic Chalcogenides For Electrocatalytic Energy Conversion, Ibrahim Abdullahi

Doctoral Dissertations

"This research focused on the synthetic design of transition metal mixed anionic chalcogenide catalysts containing various ligand types around the central metal atom (chalcogen anion and chalcogen-based organic ligand) generating diverse crystal structure types applied for water splitting and carbon dioxide reduction reactions (CO₂RR).

A series of catalysts were synthesized starting with isolated metal complexes (ME_n) with a central metal core (M = Co, Ni, Cu, and Cr) through molecular clusters, to bulk nanostructured solids of similar M-E coordination. Bis(dichalcogenoimidodiphosphinato) were employed as ligands in the metal complexes, and anionic chalcogen (E = S, Se, and …

Improving Decision-Making Processes In Construction And Infrastructure Bidding: Qualitative And Quantitative Approaches Including Graph Theory, Game Theory, And Machine Learning, Muaz Osman Elzubeir Ahmed

Doctoral Dissertations

"Construction and infrastructure bidding is a highly competitive process that entails various uncertainties faced by contractors. Contractors weigh various factors to determine the expected benefits of a construction project and decide their bid value. However, the situation is more complex in multi-stage bidding (MSG), where general contractors must account for the bids of their subcontractors and face a greater threat of the winner’s curse (i.e., situation where the winning contractor underestimates the actual cost of the project). As such, bidding-related complexities, risks, and uncertainties, if uncontrolled, can lead to the rise of claims and disputes between stakeholders. Existing research falls …

Methodology For Parameter Determination For Simulation Software, Aaron Flood

Doctoral Dissertations

"This research aims to present a methodology for optimizing input datasets to increase the accuracy of mathematical models and accelerate their application to engineering problems. To accomplish this goal, this work focused on the application of mathematical models to metal additive manufacturing (AM), specifically the thermal history of laser directed energy deposition (DED) of aluminum alloys. The initial steps of this body of work were to develop a mathematical model that is capable of simulating the metal AM process and applying it to the laser DED of aluminum. It was validated using the well characterized material Ti-64 and shown to …

Secure And Privacy-Preserving Federated Learning With Rapid Convergence In Leo Satellite Networks, Mohamed Elmahallawy

Doctoral Dissertations

"The advancement of satellite technology has enabled the launch of small satellites equipped with high-resolution cameras into low Earth orbit (LEO), enabling the collection of extensive Earth data for training AI models. However, the conventional approach of downloading satellite-related data to a ground station (GS) for training a centralized machine learning (ML) model faces significant challenges. Firstly, the transmission of raw data raises security and privacy concerns, especially in military applications. Secondly, the download bandwidth is limited, which puts a stringent limit on image transmissions to the GS. Lastly, LEO satellites have sporadic visibility with the GS, and orbit the …

Modeling And Control For Precision Robotic Machining, Patrick Bazzoli

Doctoral Dissertations

"Robots are used in a wide variety of manufacturing applications, but machining applications in which robots can excel are limited by their lower accuracy and stiffness relative to traditional CNC machines. This work is composed of two parts: one to evaluate a robot’s accuracy and one to compensate for the vibrations of the robot due to its lower stiffness.

In order to evaluate whether a robot has the necessary accuracy to perform a given machining task, Paper 1 discusses a novel Model Invalidation method. This methodology provides a statistical framework as well as a measurement strategy for determining if a …

Mission-Driven Inverse-Design Of Conventional And Unconventional Airplane Configurations Using Machine Learning, Rohan Srinivas Sharma

Doctoral Dissertations

"A key challenge in airplane design has been the long-desired ability to quickly create an airplane design with specific performance properties. To tackle this problem, researchers have traditionally leveraged design space exploration methodologies to iteratively explore a design space via surrogate models. On the other hand, inverse design is a process wherein performance objectives are fixed while a suitable design for satisfying these requirements is calculated. In recent years, artificial neural networks (ANNs) have been explored as viable surrogate models because traditional surrogate models struggle with the large number of output to input mapping needed for inverse design problems. In …

Accurate And Time Efficient Signal Integrity And Power Integrity Modeling Of High-Speed Digital Systems, Chaofeng Li

Doctoral Dissertations

"Signal integrity (SI) and power integrity (PI) play an important role in the modern high-speed digital system design, which are closely related to the printed circuit board (PCB) dielectric material property, the PCB interconnect performance, and the power delivery network (PDN) on PCB. Generally, the full-wave simulation is used to accurately analyze and evaluate the designed PCB. But full-wave simulation is not a good option for the complex PCB structure with high aspect ratio, for example, PCB vias, and PDN, which will require significant computing time and storage resources. Equivalent circuit models have been developed to efficiently predict the electrical …