Digital Commons Network^™

A Fast Awe-Augmented Wideband Discontinuous Galerkin Frequency-Domain Method In Solving Electromagnetic Wave Equations, Shi Min Liu, Li (Lijun) Jun Jiang, Ping Li

Electrical and Computer Engineering Faculty Research & Creative Works

In this research, a discontinuous Galerkin frequency-domain (DGFD) method is proposed to solve the electromagnetic wave equations. Different to the explicit time-domain analysis, the electric-field and magnetic-field in frequency-domain wave equations are coupled together implicitly, thus the established matrix equation is globally coupled, which is computational expensive if solving it directly. To address this issue, a Block-diagonal preconditioner and a restriction operator are applied to the matrix equation, which splits the global matrix into a number of small local matrix systems pertinent to each subdomain. Then, a direct solver can be employed to solve them with a FETI-like procedure. Besides, …

Jointly-Learnt Networks For Future Action Anticipation Via Self-Knowledge Distillation And Cycle Consistency, Md Moniruzzaman, Zhaozheng Yin, Zhihai He, Ming-Chuan Leu, Ruwen Qin

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Future action anticipation aims to infer future actions from the observation of a small set of past video frames. In this paper, we propose a novel Jointly learnt Action Anticipation Network (J-AAN) via Self-Knowledge Distillation (Self-KD) and cycle consistency for future action anticipation. In contrast to the current state-of-the-art methods which anticipate the future actions either directly or recursively, our proposed J-AAN anticipates the future actions jointly in both direct and recursive ways. However, when dealing with future action anticipation, one important challenge to address is the future's uncertainty since multiple action sequences may come from or be followed by …

Particle Swarm Optimization For Critical Experiment Design, Cole Michael Kostelac

Masters Theses

“Critical experiments are used by nuclear data evaluators and criticality safety engineers to validate nuclear data and computational methods. Many of these experiments are designed to maximize the sensitivity to a certain nuclide-reaction pair in an energy range of interest. Traditionally, a parameter sweep is conducted over a set of experimental variables to find a configuration that is critical and maximally sensitive. As additional variables are added, the total number of configurations increases exponentially and quickly becomes prohibitively computationally expensive to calculate, especially using Monte Carlo methods.

This work presents the development of a particle swarm optimization algorithm to design …

Maintenance Optimization In A Digital Twin For Industry 4.0, Abhijit Gosavi, Vy Khoi Le

Engineering Management and Systems Engineering Faculty Research & Creative Works

The advent of Internet of Things and artificial intelligence in the era of Industry 4.0 has transformed decision-making within production systems. In particular, many decisions that previously required significant human activity are now made automatically with minimal human intervention via so-called digital twins (DTs). In the context of maintenance and reliability modeling, this naturally calls for new paradigms that can be seamlessly integrated within DTs for decision-making. The input data for time to failure needed in reliability computations are directly collected from the work center in a digital setting and often do not satisfy a known distribution. A neural network …

Complex System Methodology For Meta Architecture Optimization Of The Kidney Transplant System Of Systems, Richard Threlkeld, Lirim Ashiku, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

The demand for kidney transplants is growing and far outweighs the supply of kidneys. With approximately 20 percent of kidneys discarded each year, there is an opportunity to develop a methodology to analyze the key performance attributes of the complex kidney transplant system. This paper designs and analyzes a meta-architecture for the organ procurement system with a use case in the kidney transplant system. The complex systems and interfaces create emergent behavior that will affect the key performance attributes of Performance, Discard Rate, Observed over Expected Kidney Transplants, Affordability, and Acceptability. These key performance attributes, interfaces, and rules are developed …

Passive Intermodulation Under Different Spring Contact Conditions, Shengxuan Xia, Emmanuel Olugbade, Yuchu He, Yansheng Wang, Hanfeng Wang, Krishna Rao, Marco Poort, Haicheng Zhou, Warren Lee, Nicholas Mcdonnell, Jonghyun Park, Chulsoon Hwang

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Modularized designs have been widely used in today's consumer electronic devices and flexible RF springs are used for electrical connections between the modules. In the meantime, aluminum alloy material becomes a common chassis option. It is well known that the oxidized chassis surface introduces a certain level of nonlinearity when contacted by the springs, as known as passive intermodulation (PIM). PIM is one of the well-known root causes of the RF desensitization (desense). This paper is focused on investigating the relationship between PIM and contact conditions of the springs, especially contact area. The PIM level behavior is explained mathematically by …

Liquid Core Detection And Strand Condition Monitoring In A Continuous Caster Using Optical Fiber, Deva Prasaad Neelakandan, Dinesh Reddy Alla, Jie Huang, Ronald J. O'Malley

PSMRC Faculty Research

Real-time monitoring of the liquid core position during the continuous casting of steel has been demonstrated using low-cost distributed optical-fiber-based strain sensors. These sensors were installed on the containment roll support structures in the segments of a production continuous caster to detect the position of the solid–liquid interface and monitor the strand condition during the continuous casting. Distributed Fiber Bragg Grating sensors (FBGs) were used in this work to monitor strain at six roll positions in the caster. The sensor performance was first validated by comparing optical strain measurements with conventional strain gauge measurements in the lab. Next, optical strain …

Persistent Stealthy Attacks And Their Detection In Large Distributed Cyber-Physical Systems, Simon Bech Thougaard

Doctoral Dissertations

"Cyber-Physical Systems (CPS) are increasingly targeted by attackers using a wide and evolving array of methods. When these systems are distributed, every node represents a potential vulnerability, and secure system design must take this into account. Distributed CPSs also have the potential to better detect and handle attacks, by leveraging redundancies of physical measurements between adjacent nodes. The main purpose of this research is to determine the conditions that render a distributed CPS more resistant to attacks, and the conditions that render it more vulnerable. The work is centered around two separate applications: The Smart Grid and Autonomous Drone Swarms. …

Strain And Temperature Measurement From Distributed Fiber Optic Sensors In Performance-Based Fire Engineering, Yanping Zhu

Doctoral Dissertations

"Strain and temperature measurement of steel and concrete structures under fires is critical to the understanding of structural performance yet historically challenging due to a significant gap in measurement science. This Ph.D. study characterizes in six parts low-cost distributed fiber optic sensors and applies them to strain, and temperature measurements based on Rayleigh and Brillouin light scattering. The first part aims to quantify the Rayleigh scattering based measurement accuracy of distributed fiber optic sensors under a heating-holding load protocol and characterize the effect of polymer coating on the sensors as the polymer softens and melts at elevated temperatures. The second …

Development And Application Of Ensemble Machine Learning Algorithms To Enable A Priori, High-Fidelity, And Prompt Predictions And Optimizations Of Complex Materials And Systems, Taihao Han

Doctoral Dissertations

"Complex materials consisting of multiple chemical components/phases have been widely used in different engineering applications that have strict criteria. To optimize the mixture design of complex materials, researchers invest enormous resources in cumbersome experiments. However, due to substantial variations in precursors and processing techniques, it has been challenging to develop theoretical frameworks (i.e., empirical models) that could produce reliable predictions of properties of complex materials. In recent years, scientists have harnessed the power of machine learning (ML) and “Big” data to uncover the underlying mixture design-property correlations and produce high-fidelity predictions of properties of complex materials. The ML models autonomously …

Forecasting Nodal Price Difference Between Day-Ahead And Real-Time Electricity Markets Using Long-Short Term Memory And Sequence-To-Sequence Networks, Ronit Das, Rui Bo, Haotian Chen, Waqas Ur Rehman, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

Price forecasting is at the center of decision making in electricity markets. Much research has been done in forecasting energy prices for a single market while little research has been reported on forecasting price difference between markets, which presents higher volatility and yet plays a critical role in applications such as virtual trading. To this end, this paper takes the first attempt at it and employs novel deep learning architecture with Bidirectional Long-Short Term Memory (LSTM) units and Sequence-to-Sequence (Seq2Seq) architecture to forecast nodal price difference between day-ahead and real-time markets. In addition to value prediction, these deep learning architectures …

A Drone-Based Application For Scouting Halyomorpha Halys Bugs In Orchards With Multifunctional Nets, Francesco Betti Sorbelli, Federico Coro, Sajal K. Das, Emanuele Di Bella, Lara Maistrello, Lorenzo Palazzetti, Cristina M. Pinotti

Computer Science Faculty Research & Creative Works

In this work, we consider the problem of using a drone to collect information within orchards in order to scout insect pests, i.e., the stink bug Halyomorpha halys. An orchard can be modeled as an aisle-graph, which is a regular and constrained data structure formed by consecutive aisles where trees are arranged in a straight line. For monitoring the presence of bugs, a drone flies close to the trees and takes videos and/or pictures that will be analyzed offline. As the drone's energy is limited, only a subset of locations in the orchard can be visited with a fully charged …

Optimizing Exterior Lighting Illuminance And Spectrum For Human, Environmental, And Economic Factors., S. M. Simmons, Stuart Werner Baur, William L. Gillis, Devin Michael Burns, Heath Pickerill

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

With the recent widespread adoption of LED lighting in outdoor areas, numerous concerns have been raised about the potential for harmful effects on humans, animals, plants, and the night sky. These stem from the high blue light content of some LED bulbs and an incentive to increase lighting levels caused by higher efficiency and lower costs. While new lighting installations are often described as environmentally friendly due to their energy efficiency, factors such as light pollution are often neglected or not given enough weight. This research focuses on optimizing the design of exterior lighting for human, environmental, and economic factors …

Fan-Shaped Model For Generating The Anisotropic Catchment Area Of Subway Stations Based On Feeder Taxi Trips, Shiwei Liu, Yajuan Deng, Xianbiao Hu, Jiaxing Zhou, Jianming Chen

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

The catchment areas of subway stations have always been considered as a circular shape in previous research. Although some studies show the catchment area may be affected by road conditions, public transportation, land use, and other factors, few studies have discussed the shape of the catchment area. This study focuses on analyzing the anisotropy of catchment areas and developing a sound methodology to generate them. Based on taxi global positioning system (GPS) data, this paper first proposes a data mining method to identify feeder taxi trips around subway stations. Then, a fan-shaped model is proposed and applied to Xi'an Metro …

An Awe-Enhanced Wideband Subdomain Dg-Rtc Algorithm In Solving Electromagnetic Problems, Shimin Liu, Li (Lijun) Jun Jiang, Ping Li

Electrical and Computer Engineering Faculty Research & Creative Works

In this research, a Robin transmission condition enriched frequency-domain discontinuous Galerkin (DG-RTC) method is proposed to solve the electromagnetic problems. The proposed DG-RTC method directly discretizes the vector wave equation in each subdomain, and the numerical flux is used for the information communication of solutions between neighbor subdomains. Compared with the traditional Maxwell's equations-based DG (ME-DG) method, the proposed DG-RTC method merely solves the E-field in the whole computational region, while the H-field is only present at the interfaces of adjacent subdomains, which thus significantly reduces the number of unknowns. Moreover, the finalized globally coupled matrix equation is split into …

Novel Data-Driven Spatial-Spectral Correlated Scheme For Dimensionality Reduction Of Hyperspectral Images, Yanming Zhang, Ping Yuan, Lijun Jiang, Hong Tat Ewe

Electrical and Computer Engineering Faculty Research & Creative Works

Hyperspectral imaging technology has been popularly applied in remote sensing because it collects echoed signals from across the electromagnetic (EM) spectrum and thereby contributes fruitfully spatial-spectral information. However, the processing or storage of high-data-volume hyperspectral images (HSIs), also viewed as snapshots varying with the EM spectrum, burdens the hardware resources, especially for the high spectral resolution and spatial resolution cases. To address this challenge, a novel unsupervised dimensionality reduction method based on the dynamic mode decomposition (DMD) algorithm is proposed to analyze hyperspectral data. This method decomposes the spatial-spectral HSIs in terms of spatial dynamic modes and corresponding spectral patterns. …

Dc Ir-Drop Analysis Of Power Distribution Networks By A Robin Transmission Condition-Enhanced Discontinuous Galerkin Method, An Fa Yang, Min Tang, Jun Fa Mao, Li (Lijun) Jun Jiang, Hakan Bagcii, Ping Li

Electrical and Computer Engineering Faculty Research & Creative Works

In this work, a novel Robin transmission condition (RTC)-enhanced discontinuous Galerkin (DG) method is proposed for the dc IR-drop analysis of power distribution networks with Joule heating effects included. Unlike the conventional DG method, the proposed DG method straightforwardly applied to discretize the second-order spatial partial differential governing equations for the electrostatic potential $\Phi $ and the steady-state temperature $T$. The numerical flux in DG used to facilitate the information exchange among neighboring subdomains introduces two additional variables: the current density $J$ for the electrical potential equation and the thermal flux $q$ for the thermal equation. To solve them, at …

Early Wildfire Detection Using Uavs Integrated With Air Quality And Lidar Sensors, Doaa Rjoub, Ahmad Alsharoa, Ala'eddin Masadeh

Electrical and Computer Engineering Faculty Research & Creative Works

Every year, wildfires burn out countless hectares of lands, resulting in ecological, environmental, and economic damage. This paper presents an energy management system that consists of an unmanned aerial vehicle (UAV) equipped with air quality and light detection and ranging (LiDAR) sensors for monitoring forests and recognizing flames early. We develop a novel approach for autonomous patrolling system. This approach has the advantage of effectively detecting wildfire incidents, while optimizing the energy consumption of the UAV's battery to cover large areas. When a wildfire is detected, the UAV is able to transmit real-time data, such as sensor readings and LiDAR …

Robust Modifications To Model Reference Adaptive Control For Reference Voltage Tracking In A Dual Active Bridge Dc-Dc Converter, Kartikeya J.P. Veeramraju, Alvaro Cardoza, Jagannathan Sarangapani, Jonathan W. Kimball

Electrical and Computer Engineering Faculty Research & Creative Works

Model Reference Adaptive Control (MRAC) is useful for achieving a desired dynamic response with no prior knowledge of system parameters. Traditional MRAC is sensitive to noise in the state variables, leading to adaptation parametric drift. The drift in parameters, if left unchecked, leads to loss of closed loop stability. In power electronic systems, the ripple in the output voltage serves as a bounded noise, which leads to MRAC instability. This drift phenomenon is observed through the simulation and hardware experimentation of a Dual Active Bridge (DAB) converter and mitigated using deadzone modification and σ modification approaches. These two methods are …

New Hybrid Model For Evaluating The Frequency-Dependent Leakage Inductance Of A Variable Inductance Transformer (Vit), Angshuman Sharma, Jonathan W. Kimball

Electrical and Computer Engineering Faculty Research & Creative Works

Skin and proximity effects can cause a significant drop in the effective leakage inductance of a transformer when the operating frequency is increased. Although the magnetic image method-based double-2-D model can calculate the low-frequency leakage inductance with sufficient accuracy, it is inherently a frequency-independent model. While Dowell's 1-D model uses frequency-dependent relations to account for both skin and proximity effects, its accuracy is severely affected by the assumed winding geometry. In this paper, a hybrid model is proposed that uses superposition to combine a modified Dowell's model with the double-2-D model. The proposed model is investigated on a variable inductance …

A Spline-Based Partial Element Equivalent Circuit Method For Electrostatics, Riccardo Torchio, Maximilian Nolte, Sebastian Schops, Albert E. Ruehli

Electrical and Computer Engineering Faculty Research & Creative Works

This contribution investigates the connection between Isogeometric Analysis (IgA) and the Partial Element Equivalent Circuit (PEEC) method for electrostatic problems. We demonstrate that using the spline-based geometry concepts from IgA allows for extracting circuit elements without an explicit meshing step. Moreover, the proposed IgA-PEEC method converges for complex geometries up to three times faster than the conventional PEEC approach and, in turn, it requires a significantly lower number of degrees of freedom to solve a problem with comparable accuracy. The resulting method is closely related to the isogeometric boundary element method. However, it uses lowest-order basis functions to allow for …

Chimeranet: U-Net For Hair Detection In Dermoscopic Skin Lesion Images, Norsang Lama, Reda Kasmi, Jason R. Hagerty, R. Joe Stanley, Reagan Harris Young, Jessica Miinch, Januka Nepal, Anand Nambisan, William V. Stoecker

Electrical and Computer Engineering Faculty Research & Creative Works

Hair and ruler mark structures in dermoscopic images are an obstacle preventing accurate image segmentation and detection of critical network features. Recognition and removal of hairs from images can be challenging, especially for hairs that are thin, overlapping, faded, or of similar color as skin or overlaid on a textured lesion. This paper proposes a novel deep learning (DL) technique to detect hair and ruler marks in skin lesion images. Our proposed ChimeraNet is an encoder-decoder architecture that employs pretrained EfficientNet in the encoder and squeeze-and-excitation residual (SERes) structures in the decoder. We applied this approach at multiple image sizes …

Hamiltonian-Driven Adaptive Dynamic Programming With Efficient Experience Replay, Yongliang Yang, Yongping Pan, Cheng Zhong Xu, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This article presents a novel efficient experience-replay-based adaptive dynamic programming (ADP) for the optimal control problem of a class of nonlinear dynamical systems within the Hamiltonian-driven framework. The quasi-Hamiltonian is presented for the policy evaluation problem with an admissible policy. With the quasi-Hamiltonian, a novel composite critic learning mechanism is developed to combine the instantaneous data with the historical data. In addition, the pseudo-Hamiltonian is defined to deal with the performance optimization problem. Based on the pseudo-Hamiltonian, the conventional Hamilton–Jacobi–Bellman (HJB) equation can be represented in a filtered form, which can be implemented online. Theoretical analysis is investigated in terms …

De-Embedding For Coupled Three-Port Devices, Yuandong Guo, Bo Pu, Donghyun Kim, Jun Fan

Electrical and Computer Engineering Faculty Research & Creative Works

In many applications, the device under test (DUT) is embedded into a test setup. Various de-embedding techniques have been proposed to expose the real electrical behaviors of a DUT, e.g., the traditional thru-reflect-line and short-open-load-thru algorithms, where the T-matrix and its inverse form are adopted in the mathematical process. In the fields of radiofrequency and electromagnetic compatibility, a DUT may have three coupled ports, and the symmetry in the associated S-matrix breaks down, because the numbers of entry and exist ports are not equal, which results in a non-square T-matrix based upon the definitions. Given that the inverse expression of …

Concurrent Learning-Based Neuro-Adaptive Robust Tracking Control Of Wheeled Mobile Robot: An Event-Triggered Design, Krishanu Nath, Manas Kumar Bera, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, an event-based neuro-adaptive robust tracking controller for a perturbed and networked differential drive mobile robot (DMR) is designed with concurrent learning. A radial basis function neural network, which approximates an unknown perturbation, is used to design an adaptive sliding mode controller (SMC). The RBFNN weights and SMC parameters are estimated online using an adaptive tuning law to ensure performance with reduced chattering. To improve the convergence of RBFNN weight estimation error, a concurrent learning-based adaptive law is derived, which uses measured online and recorded data. Further, a suitable triggering condition is designed to achieve a reduced number …

An Energy-Efficient Internet Of Things Relaying System For Delay-Constrained Applications, Abdullah M. Almasoud, Ahmad Alsharoa, Daji Qiao, Ahmed E. Kamal

Electrical and Computer Engineering Faculty Research & Creative Works

The emerging Internet-of-things (IoT) systems contain a large number of small wireless devices with limited energy, communication, and computational capabilities. In such systems, a helping station located between the IoT devices and backhaul servers can be deployed to broadcast the IoT devices to the backhaul networks. This paper investigates a hybrid energy-efficient framework using multiple energy harvested relays with data buffering capabilities. The relays are powered by a hybrid energy supply consisting of a traditional electric grid and renewable energy grid. We propose an energy efficient novel approach aiming to support the wireless uplink transmission from IoT devices to backhaul …

A New Discontinuous Conduction Mode In A Transformer Coupled High Gain Dc-Dc Converter, Kartikeya J.P. Veeramraju, Jacob Eisen, Joshua L. Rovey, Jonathan W. Kimball

Electrical and Computer Engineering Faculty Research & Creative Works

Electrospray space propulsion thrusters often employ transformer coupled high gain dc-dc converters. These thrusters have a unique load resistance profile that can vary dramatically during operation. Transformer coupled high gain dc-dc converters working in very low power modes can enter a unique discontinuous conduction mode (DCM) caused by a coupling effect between the boost and magnetization inductors in the converter. The transformer magnetization current prevents the boost inductors from discharging properly, which causes premature loss of boost action and higher than expected boost inductor voltages. This mechanism generates abnormally high voltages on the converter's output side, leading to converter and …

Polybenzodiazine Aerogels: All-Nitrogen Analogues Of Polybenzoxazines Synthesis, Characterization, And High-Yield Conversion To Nanoporous Carbons, Vaibhav A. Edlabadkar, Saidulu Gorla, Rushi U. Soni, A. B.M.Shaheen Ud Doulah, Joseph Gloriod, Samuel Hackett, Nicholas Leventis, Chariklia Sotiriou-Leventis

Chemistry Faculty Research & Creative Works

Tetrahydroquinazoline (THQ) was designed as an all-nitrogen analogue of main-stream benzoxazine monomers. THQ solutions in DMF gelled at 100 °C via HCl-catalyzed ring-opening polymerization to polybenzodiazine (PBDAZ) wet gels, which were dried in an autoclave with supercritical fluid CO₂ to aerogels. These as-prepared PBDAZ-100 aerogels undergo ring-fusion aromatization at 240 °C under O₂. This oxidized form is referred to as PBDAZ-240. Chemical identification of PBDAZ-100 and PBDAZ-240 relied on consideration of all nine possible polymerization pathways, in combination with elemental analysis, infrared and solid-state 13C NMR spectroscopy, and 15N NMR spectroscopy of aerogels from the selectively 15N-enriched …

Effects Of Vacancies And Electron Temperature On The Electron Phonon Coupling In Cubic Silicon Carbide And Their Connection To The Inelastic Thermal Spike, Salah Al-Smairat

Doctoral Dissertations

“The electron-phonon interaction is an important interaction in many solids as it influences transport phenomena and related quantities such as the electrical and thermal conductivities, especially in nuclear and space applications. The importance of the electron-phonon interaction in primary damage production in 3C-SiC is the subject of this research.

The electron-phonon coupling factor was calculated using a hybrid Density Functional Perturbation Theory - Classical Electron Gas model. The coupling factor was calculated as a function of electron temperature in pristine and defective 3C-SiC, and relaxed defective cells. The electron-phonon coupling is found to depend strongly on the electronic temperature and …

The Utilization And Loss Of Available Energy In Aerospace Systems, Mohammad Abbas

Doctoral Dissertations

“Theoretical principles and analytical methodology for the control volume-based energy availability methodology for aerospace vehicles are developed; applications are made to jet-propelled and rocket-propelled vehicles as well as to stand-alone engine systems. Energy availability utilization characteristics of a modeled turbojet engine are studied across a wide range of operating conditions of throttle setting, flight altitude, and flight Mach number. The method is also extended to consider jet-powered vehicles. Fundamental principles regarding entropy generation and energy availability are developed, including directly linking entropy generation and maximum range and endurance of a powered aircraft. Theory and application of the energy utilization methodology …