Engineering Commons^™

Learning–Assisted Constraint Filtering To Enhance Power System Optimization Performance, Fouad Hasan

LSU Doctoral Dissertations

Machine learning (ML) is a powerful tool that provides meaningful insights for operators to make fast and efficient decisions by analyzing data from power systems. ML techniques have great potential to assist in solving optimization problems within a shorter time frame and with less computational burden. AC optimal power flow (ACOPF), dynamic economic dispatch (D-ED), and security-constrained unit commitment (SCUC) are the three energy management optimization functions studied in this dissertation. ACOPF is solved every 5~15 minutes. Because of the nonconvex and complex nature of ACOPF, solving this problem for large systems is computationally expensive and time-consuming. Classification and regression …

Renewable Energy Educational Delivery System, Ryan Integlia

Florida Conference on Recent Advances in Robotics

Technology advancing rapidly and affecting our daily lives in more ways than ever before, it is clear that technology is now a driving force in our world. STEM disciplines are one of the key driving forces behind human progress, as their efforts have provided us with new levels of comfort and security. Every facet of engineering contributes to enhancing almost every aspect of our lives. As technology continues to evolve, the demand for skilled engineers will only increase. Therefore, it is essential that STEM education system adapts to the needs of today's digitized, diversified, and rapidly changing society through an …

Towards A Mobile Ad-Hoc Mesh Network Establishing Emergency Drone System, Ryan Integlia

Florida Conference on Recent Advances in Robotics

A drone based mobile ad-hoc mesh network for emergency communications is discussed. The in-progress project seeks to improve emergency and disaster area communication systems by creating a mobile, ad-hoc wireless network with an array of microcomputers, a GPS receiver, IMU, network adapter and drone. The Linux based platform includes network management, data collection, and integration with visualization. The expected outcome of this project is the establishment of a wireless mesh network capable of self-healing to support emergency response.

Towards Optimal Operation And Control Of Emerging Electric Distribution Networks, Jimiao Zhang

Theses and Dissertations

The growing integration of power-electronics converters enabled components causes low inertia in the evolving electric distribution networks, which also suffer from uncertainties due to renewable energy sources, electric demands, and anomalies caused by physical or cyber attacks, etc. These issues are addressed in this dissertation. First, a virtual synchronous generator (VSG) solution is provided for solar photovoltaics (PVs) to address the issues of low inertia and system uncertainties. Furthermore, for a campus AC microgrid, coordinated control of the PV-VSG and a combined heat and power (CHP) unit is proposed and validated. Second, for islanded AC microgrids composed of SGs and …

High-Speed Photonic Signal Processing For Free Space Optical Stealth Communication And Interference Management, Yang Qi

Theses and Dissertations

This dissertation focuses on free space optical stealth communication and wideband interference management techniques. The coexistence of multiple high-speed wireless communication systems generates wideband interference, which poses a challenge to the contemporary framework of signal processing. To protect the security and privacy of users' communications, stealth communication techniques that hide and recover private information against eavesdropping attacks are necessary. The main challenge in stealth information recovery and interference management is to separate the signal of interest from noise and wideband interference [1]. This dissertation proposes and experimentally demonstrates various techniques for stealth communication and wideband interference management using photonic signal …

A Novel Graph Neural Network-Based Framework For Automatic Modulation Classification In Mobile Environments, Pejman Ghasemzadeh

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Automatic modulation classification (AMC) refers to a signal processing procedure through which the modulation type and order of an observed signal are identified without any prior information about the communications setup. AMC has been recognized as one of the essential measures in various communications research fields such as intelligent modem design, spectrum sensing and management, and threat detection. The research literature in AMC is limited to accounting only for the noise that affects the received signal, which makes their models applicable for stationary environments. However, a more practical and real-world application of AMC can be found in mobile environments where …

Clean Energy At The Crossroads Of America: An Integrated Resource Plan For Northern Indiana Public Service Company, Llc (Nipsco), Saad Z. Khan, Shend Boshnjaku

Master's Projects and Capstones

This paper outlines an Integrated Resource Plan (IRP) for the Indiana electric utility company, NIPSCO, looking forward to the year 2050 and evaluating different pathways to net zero emissions from the power generation sector. It is a plan for the future, identifying the most cost-effective and reliable mix of resources to meet the energy needs of NIPSCO's customers and reaching decarbonization goals by mid-century.

Extracting Edges In Space And Time During Visual Fixations, Lynn Schmittwilken, Marianne Maertens

MODVIS Workshop

No abstract provided.

A Graph-Based Approach For Adaptive Serious Games, Nidhi G. Patel

Theses and Dissertations

Traditional education systems are based on the one-size-fits-all approach, which lacks personalization, engagement, and flexibility necessary to meet the diverse needs and learning styles of students. This encouraged researchers to focus on exploring automated, personalized instructional systems to enhance students’ learning experiences. Motivated by this remark, this thesis proposes a personalized instructional system using a graph method to enhance a player’s learning process by preventing frustration and avoiding a monotonous experience. Our system uses a directional graph, called an action graph, for representing solutions to in-game problems based on possible player actions. Through our proposed algorithm, a serious game integrated …

Method Of Evanescently Coupling Whispering Gallery Mode Optical Resonators Using Liquids, Hengky Chandrahalim, Kyle T. Bodily

AFIT Patents

The present invention relates to evanescently coupling whispering gallery mode optical resonators having a liquid coupling as well as methods of making and using same. The aforementioned evanescently coupling whispering gallery mode optical resonators having a liquid couplings provide increased tunability and sensing selectivity over current same. The aforementioned. Applicants’ method of making evanescent-wave coupled optical resonators can be achieved while having coupling gap dimensions that can be fabricated using standard photolithography. Thus economic, rapid, and mass production of coupled WGM resonators-based lasers, sensors, and signal processors for a broad range of applications can be realized.

Real-Time Suitable Predictive Control Using Spat Information From Automated Traffic Lights, Pradeep Bhat, Bo Chen

Michigan Tech Publications

Traffic intersections throughout the United States combine fixed, semi-actuated, and fully actuated intersections. In the case of the semi-actuated and actuated intersections, uncertainties are considered in phase duration. These uncertainties are due to car waiting queues and pedestrian crossing. Intelligent transportation systems deployed in traffic infrastructure can communicate Signal and Phase Timing messages (SPaT) to vehicles approaching intersections. In the connected and automated vehicle ecosystem, the fuel savings potential has been explored. Prior studies have predominantly focused on fixed time control for the driver. However, in the case of actuated signals, there is a different and significant challenge due to …

Stability Impacts Of Sandia Frequency Shift Anti-Islanding On A Grid-Connected Inverter, Daniel Mendoza

Electrical and Computer Engineering Master's Theses

Most renewable energy generation connected to the distribution level of the electric grid is connected through an inverter. To prevent an unintentional islanding condition, these inverters employ a variety of anti-islanding protection schemes. The Sandia Frequency Shift (SFS) method of anti-islanding protection is considered to be the most effective scheme built into grid-connected inverters. Much of what makes it so effective is the postive feedback loop that it creates when the inverter’s connection to the bulk power system is lost. However, this same positive feedback can have negative impacts on the stability of the inverter even when it is connected …

Piezoelectric And Conductive Polymer Based Flexible Devices Enabling Cardiovascular Health Sensing And Energy Harvesting, Andrew Closson

Dartmouth College Ph.D Dissertations

Piezoelectric materials show great promise for low-power wearable and implantable sensing, but their rigidity makes it challenging to integrate them with biological tissue. To address this, researchers have started exploring polymer-based functional materials that offer flexibility and are suitable for interfacing with the human body. However, these materials are still in their early stages, and a framework is necessary to illustrate how these materials, in conjunction with novel fabrication techniques and device designs, can enable the development of multi-functional sensing and energy harvesting devices.

This thesis utilizes highly scalable fabrication methods for functional polymers to build and test a flexible …

Robust Analog Circuit Parameter Optimization With Sampling-Efficient Reinforcement Learning, Jian Gao

McKelvey School of Engineering Graduate Student Theses & Dissertations

Design automation of analog circuits has been a longstanding challenge in the integrated circuit field. Recently, multiple methods based on learning or optimization have demonstrated great promise in automating device sizing for analog circuits. However, they often ignore the strong susceptibility of analog circuits to process, voltage, and temperature (PVT) variations or suffer from low sampling efficiency to train algorithms. To address these critical limitations, this thesis proposes RoSE, the first Robust analog circuit parameter optimization framework with high Sampling Efficience by synergistically combining Bayesian Optimization (BO) and reinforcement learning (RL). Its core is to use the fast convergence of …

Reinforcement Learning-Based Resilience And Decision Making In Cyber-Physical Systems, Fisayo Sangoleye

Electrical and Computer Engineering ETDs

Cyber-physical systems (CPS) transform how humans interact with technology by integrating sensing, computation, networking, and control with physical processes to facilitate smart services and innovative applications in our environments. Recent advances in CPS have led to rapid growth in the amount of information constantly generated by people, systems, and processes. Most of this information, however, is underutilized due to the lack of efficient information utilization and decision-making techniques. Also, the increasing interconnectivity of CPSs presents security risks that, if left unaddressed, could be highly disruptive to systems, processes, and economies. In this dissertation, we present a study and proposal of …

Modeling And Characterization Of Cmos Logic Gates Under Large Signal Rf Injection, Zahra Abedi

Electrical and Computer Engineering ETDs

This research addresses the issue of electromagnetic interference (EMI) in digital electronics, which can cause undesired behavior in active electronic components and systems. As technology advances and transistor size decreases, devices become more susceptible to EMI. In addition, as voltage is scaled down to save energy, silicon chips become more susceptible to soft errors. While there are several ways to mitigate the impact of severe EMI on digital systems, minimal electromagnetic coupling can still cause significant problems. We develop analytical models to predict device upset due to EMI. The study focuses on identifying vulnerable parameters of operation related to device …

Incentives To Learn: A Location-Based Federated Learning Model, Ryan Kilpatrick-Morrison Brown

Electrical and Computer Engineering ETDs

Federated Learning (FL) effectiveness depends, among others, on the quality and quantity of the training data and process realized at the end computing nodes. In this paper, we introduce a novel location-based federated learning model, enabled by a low-cost and fast deployable Reconfigurable Intelligent Surfaces (RIS) - based approach that allows to accurately determine the distributed computing nodes’ positions. Furthermore, in order to train a global model to support different types of smart city applications, while considering two types of servers, offering a prime and common service, respectively, under different costs, the proposed location-based FL model is complemented by an …

Machine Learning Based Prediction Models For Silicon Heterojunction Solar Cell Optimization, Rahul Jaiswal

Electrical and Computer Engineering ETDs

Silicon heterojunction solar cell of Heterojunction with Thin Intrinsic Layer (HIT) structure is a commercially available technology, and its market share will significantly increase by the next decade. With such a significant market share, any minor improvement in the device’s overall efficiency can be beneficial three folds - customer return on investment, industry revenue, and the overall carbon footprint (from manufacturing to recycling/ disposing of the device). Conventionally, device optimization for solar cells has been achieved using a hit & trial approach where multiple experiments are done to evaluate the best process conditions and device parameters. This approach has some …

Vi Energy-Efficient Memristor-Based Neuromorphic Computing Circuits And Systems For Radiation Detection Applications, Jorge Iván Canales Verdial

Electrical and Computer Engineering ETDs

Radionuclide spectroscopic sensor data is analyzed with minimal power consumption through the use of neuromorphic computing architectures. Memristor crossbars are harnessed as the computational substrate in this non-conventional computing platform and integrated with CMOS-based neurons to mimic the computational dynamics observed in the mammalian brain’s visual cortex. Functional prototypes using spiking sparse locally competitive approximations are presented. The architectures are evaluated for classification accuracy and energy efficiency. The proposed systems achieve a 90% true positive accuracy with a high-resolution detector and 86% with a low-resolution detector.

Energy-Efficient Operation And Performance Optimization In Computing Systems, Nafis Irtija

Electrical and Computer Engineering ETDs

In the modern world, an expansive range of computing systems are being used, and in all of these systems, energy efficiency and performance optimization are of utmost significance. But performance optimization means different things for different computing systems. Because of their difference in nature, computing systems have different requirements for efficiency and optimization. Distributed systems such as smart-grid systems, edge-computing environments, and blockchain systems focus on the behavior of the agents. Thus, the application of network economics principles, such as Game Theory and Contract Theory, can improve the operation of these systems in a multitude of ways. On the other …

Long-Term Human Video Activity Quantification In Collaborative Learning Environments, Venkatesh Jatla

Electrical and Computer Engineering ETDs

Research on video activity detection has mainly focused on identifying well-defined human activities in short video segments, often requiring large-parameter systems and extensive training datasets. This dissertation introduces a low-parameter, modular system with rapid inference capabilities, capable of being trained on limited datasets without transfer learning from large-parameter systems. The system accurately detects specific activities and associates them with students in real-life classroom videos. Additionally, an interactive web-based application is developed to visualize human activity maps over long classroom videos.

Long-term video activity detection in classrooms presents challenges, such as multiple simultaneous activities, rapid transitions, long-term occlusions, duration exceeding 15 …

Designing The Power System Of A Robot, Nolan Hays

Honors College Theses

This thesis outlines the acquired skills and knowledge acquired through the course of four years of Murray State University’s School of Engineering. The capstone for students in the department was to team up and complete an interdisciplinary senior design project using skills from various tracks of engineering. For this thesis, there is a greater emphasis on the electrical engineering track. The objective of the project was to build an autonomous robot to complete the various tasks within the scope of the IEEE SoutheastCon 2023 Hardware Competition. The robot was controlled via a Raspberry Pi 4 Model B. There were three …

Sensor Updates For Bigheaded Carp-Tracking Autonomous Boat, Jordan Kaufmann

Honors College Theses

Bigheaded carp are an invasive species that overpopulate and compete with the native species of Kentucky Lake as well as many other North American aquatic ecosystems. The movement patterns of Bigheaded carp are being studied nationwide by the United States Geological Survey and multiple universities. These studies ultimately seek to control their spread and reduce or reverse the ecosystem destabilization caused by this invasive species. Such studies are currently conducted manually on Kentucky Lake by graduate students affiliated with the Murray State University (MSU) Biology Department and Hancock Biological Station, and these manual studies are an arduous and time-consuming effort. …

Rebuilding Grid Governance, Joel B. Eisen, Heather E. Payne

BYU Law Review

As climate change sharpens the focus on our electricity systems, there is widespread agreement that the institutions that govern our electric grid must change to realize a clean energy future in the timescale necessary. Scholars are actively debating how grid governance needs to change, but in this Article we demonstrate that current proposals are insufficient because they do not contemplate “rebuilding.” This Article defines “rebuilding” as ending entities tasked with grid governance and creating new ones to take their place. We propose what no one else has: an overarching framework for rebuilding any grid governance institutions.

This Article discusses when …

The Use Of Scattering Cancellation To Cloak And Decouple Slot Antennas And Antenna Arrays, Daniel Ferro

Honors Theses

The concept of cloaking has been prevalent in emerging research, being able to hide oneself completely from outside observers would be a huge benefit for covert surveillance and other fields. This idea has expanded past optical invisibility into the field of electromagnetic invisibility. To accomplish this form of cloaking, a non-natural material, called metamaterials, must be used. Furthermore, metamaterials can be used in a variety of different techniques including transformation-based cloaking, transmission line cloaking, and scattering cancellation cloaking. One application of cloaking is to use scattering cancellation to decouple two antennas that are placed too closely together such as in …

Mitigating Adverse Impacts Of Increased Electric Vehicle Charging On Distribution Transformers, Akansha Jain

Theses and Dissertations

There is a growing interest in electric transportation, and the number of electric vehicles (EVs) is increasing. The resulting increase in EV charging power demand has an adverse impact on the existing power grids, especially the distribution transformers. The repeated and continued overloading caused by EV charging can significantly reduce their operational life. This dissertation aims to comprehensively study the adverse impacts of EV charging on distribution transformers and provide robust and practical solutions to mitigate it. A typical North American secondary distribution system with different EV penetration levels and four realistic residential EV charging scenarios are used for the …

Design Of High-Power Ultra-High-Speed Permanent Magnet Machine, Md Khurshedul Islam

Theses and Dissertations

The demand for ultra-high-speed machines (UHSM) is rapidly growing in high-tech industries due to their attractive features. A-mechanically-based-antenna (AMEBA) system is another emerging application of UHSM. It enables portable wireless communication in the radio frequency (RF)-denied environment, which was not possible until recently. The AMEBA system requires a high-power (HP) UHSM for its effective communication performance. However, at the expected rotational speed range of 0.5 to 1 million rpm, the power level of UHSM is limited, and no research effort has succeeded to improve the power level of UHSM.

The design of HP-UHSM is highly iterative, and …

Secure And Efficient Federated Learning, Xingyu Li

Theses and Dissertations

In the past 10 years, the growth of machine learning technology has been significant, largely due to the availability of large datasets for training. However, gathering a sufficient amount of data on a central server can be challenging. Additionally, with the rise of mobile networking and the large amounts of data generated by IoT devices, privacy and security issues have become a concern, resulting in government regulations such as GDPR, HIPAA, CCPA, and ADPPA. Under these circumstances, traditional centralized machine learning methods face a problem in that sensitive data must be kept locally for privacy reasons, making it difficult to …

Eddy Current Defect Response Analysis Using Sum Of Gaussian Methods, James William Earnest

Theses and Dissertations

This dissertation is a study of methods to automatedly detect and produce approximations of eddy current differential coil defect signatures in terms of a summed collection of Gaussian functions (SoG). Datasets consisting of varying material, defect size, inspection frequency, and coil diameter were investigated. Dimensionally reduced representations of the defect responses were obtained utilizing common existing reduction methods and novel enhancements to them utilizing SoG Representations. Efficacy of the SoG enhanced representations were studied utilizing common Machine Learning (ML) interpretable classifier designs with the SoG representations indicating significant improvement of common analysis metrics.

Relaxing Dc Capacitor Voltage Of Power Electronic Converters To Enhance Their Stability Margins, Ali Zakerian

Theses and Dissertations

Recently, due to the increasing adoption of distributed energy resource (DER) technologies including battery energy storage (BES) and electric vehicle (EV) systems, bidirectional power converters are becoming more popular. These converters are broadly utilized as interface devices and provide a bidirectional power flow in applications where the primary power supply can both supply and receive energy. A dc capacitor, called the dc-link, is an important component of such bidirectional converters. For a wide range of applications, the converter is required to control the dc-link voltage. Commonly, a proportional-integrating (PI) controller is used by the dc capacitor voltage controller to generate …