Electrical and Computer Engineering Commons^™

Design And Control Of Axial Flux Permanent Magnet Coreless Machines With Special Windings, Yaser Chulaee

Theses and Dissertations--Electrical and Computer Engineering

Permanent magnet synchronous machines (PMSMs), particularly those of the axial flux type, are being researched and developed for various applications such as HVAC systems, aviation propulsion, and electric vehicles. The coreless (air-cored) stator axial flux permanent magnet (AFPM) machine topology offers notable advantages over conventional designs by eliminating magnetic cores and their associated losses. These advantages include potentially higher efficiency, zero cogging torque, and reduced audible noise and vibration. Eliminating the magnetic core also allows for more effective cooling systems, as coolants can be in direct contact with the stator windings, potentially improving power density and specific torque.

The absence …

Ultra‐Fast Finite Element Analysis Of Coreless Axial Flux Permanent Magnet Synchronous Machines, Yaser Chulaee, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Large‐scale design optimisation techniques enable the design of high‐performance electric machines. Electromagnetic 3D finite element analysis (FEA) is typically employed in optimisation studies for accurate analysis of axial flux permanent magnet (AFPM) machines, which require extensive computational resources. To reduce the computational burden, a FEA‐based mathematical method relying on the geometric and magnetic symmetry of coreless AFPM machines is proposed to estimate the machine performance indicators using the least number of FEA solutions, thereby significantly lowering the running time. This method is generally applicable to AFPM machines with low saturation effects and cogging torque as exemplified for a printed circuit …

Design And Optimization Of High-Efficiency Coreless Pcb Stator Axial Flux Pm Machines With Minimal Eddy And Circulating Current Losses, Yaser Chulaee, Greg Heins, Ali Mohammadi, Mark Thiele, Dan M. Ionel, Ben Robinson

Electrical and Computer Engineering Graduate Research

This paper proposes a systematic multi-step design procedure for highly efficient printed circuit board (PCB) stator coreless axial flux permanent magnet (AFPM) machines with minimal eddy and circulating current losses. The process begins with initial sizing, providing specific coefficients based on experience with multiple design projects. It continues with the optimization of the machine envelope design using an evolutionary algorithm and computationally efficient 3D finite element analysis (FEA) models. The subsequent step focuses on the detailed design of a PCB stator, aiming to minimize eddy and circulating current losses. Several open circuit loss mitigation techniques are proposed based on analytical …

Design Optimization Of A Direct-Drive Wind Generator With A Reluctance Rotor And A Flux Intensifying Stator Using Different Pm Types, Ali Mohammadi, Oluwaseun A. Badewa, Yaser Chulaee, Donovin D. Lewis, Somasundaram Essakiappan, Madhav Manjrekar

Electrical and Computer Engineering Graduate Research

This paper presents a large-scale multi-objective design optimization for a direct-drive wind turbine generator concept that is based upon an experimentally validated computational model for a small-scale prototype motor of the same type. By integrating an outer reluctance-type rotor and a segmented stator with toroidally wound single-coil modules containing spoke-type PMs, the design optimization aims to minimize losses, active mass, and torque ripple while adhering to a power factor constraint. The AC windings and PMs are positioned in the stator and this concept enhances flux concentration, enabling the use of more affordable high energy non-rare-earth (special type) magnets. The exterior …

Large-Scale Design Optimization Of An Axial-Flux Vernier Machine With Dual Stator And Spoke Pm Rotor For Ev In-Wheel Traction, Ali Mohammadi, Yaser Chulaee, Aaron M. Cramer, Ion G. Boldea, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper presents the optimization study targeting a specific drive cycle for a MAGNUS-type axial-flux permanent magnet vernier machine (AFPMVM). The proposed MAGNUS machine has a novel design with a dual-stator configuration, where only one stator is wound, with a high-polarity spoke permanent magnet (PM) rotor. The machine topology has a 3D flux path, which necessitates the analysis of a large finite element (FE) model. However, due to the computational complexity and time required for such a large FE model, a new approach was developed. This approach involves a computationally efficient finite element analysis (CE-FEA) model combined with a single …

Fake News Detection In Online Platforms, Elena Shushkevich

Doctoral

This thesis presents research conducted during a Ph.D. program at Technological University Dublin from 2020 to 2024. The objective of this research is to develop and evaluate effective methods for detecting and classifying fake news in social media and press, addressing the critical issue of misinformation in the digital age. The relevance of this study is underscored by the increasing prevalence of fake news and its potential societal impact, emphasizing the importance of advanced tools for identifying and mitigating misinformation.

Economical And Environmental Evaluation Of Non-Residential Demand Response In The European Transition To Zero-Carbon Energy, Markus Fleshutz

Theses

To meet climate objectives, major energy consumers with local multi-energy systems (L- MESs) must transition to renewable energy sources soon. The variability of renewable energies requires increased operational flexibility for integration. In this context, demand response (DR), a strategy in which electricity consumers adjust their load profiles in response to incentives, has become crucial, offering cost-effective flexibility. It supports L-MESs in integrating renewable energy, reducing decarbonization costs, and enhancing resilience. However, quantifying the economic DR potentials for L-MESs under carbon emission constraints is complex, especially when considering investment options in distributed energy resources. This complexity hinders the rapid adoption of …

Detection Of Tooth Position By Yolov4 And Various Dental Problems Based On Cnn With Bitewing Radiograph, Kuo Chen Li, Yi-Cheng Mao, Mu-Feng Lin, Yi-Qian Li, Chiung-An Chen, Tsung-Yi Chen, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

Periodontitis is a high prevalence dental disease caused by bacterial infection of the bone that surrounds the tooth. Early detection and precision treatment can prevent more severe symptoms such as tooth loss. Traditionally, periodontal disease is identified and labeled manually by dental professionals. The task requires expertise and extensive experience, and it is highly repetitive and time-consuming. The aim of this study is to explore the application of AI in the field of dental medicine. With the inherent learning capabilities, AI exhibits remarkable proficiency in processing extensive datasets and effectively managing repetitive tasks. This is particularly advantageous in professions demanding …

Enhancing Robotic Exploration Through Semantically-Guided Sampling Strategies, Christopher Alexander Arend Tatsch

Graduate Theses, Dissertations, and Problem Reports (ETD)

From space and deep-sea exploration to disaster response and environmental monitoring, autonomous robots are essential for advancing science, improving safety, and addressing critical challenges. This dissertation introduces a novel open-source strategy for autonomous robotic exploration: the Semantically-Guided Exploration (SGE) framework. Designed for ground vehicles, SGE integrates semantic understanding into the autonomous exploration process, improving decision-making in complex environments. Specifically, the proposed sampling-based approach uses the information from the semantic segmentation of RGB images and depth images to guide the robot's selection of exploration goals. This method enables the robot to steer away from potential dangers such as large rocks and …

A Chinese Power Text Classification Algorithm Based On Deep Active Learning, Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu

Computer Science Faculty Publications

The construction of knowledge graph is beneficial for grid production, electrical safety protection, fault diagnosis and traceability in an observable and controllable way. Highly-precision text classification algorithm is crucial to build a professional knowledge graph in power system. Unfortunately, there are a large number of poorly described and specialized texts in the power business system, and the amount of data containing valid labels in these texts is low. This will bring great challenges to improve the precision of text classification models. To offset the gap, we propose a classification algorithm for Chinese text in the power system based on deep …

Photoluminescence Switching In Quantum Dots Connected With Carboxylic Acid And Thiocarboxylic Acid End-Group Diarylethene Molecules, Ephraiem S. Sarabamoun, Pramod Aryal, Jonathan M. Bietsch, Maurice Curran, Sugandha Verma, Grayson Johnson, Lucy U. Yoon, Amelia G. Reid, Esther H. R. Tsai, Charles W. Machan, Christopher Paolucci, Guijun Wang, Joshua J. Choi

Chemistry & Biochemistry Faculty Publications

We contrast the switching of photoluminescence (PL) of PbS quantum dots (QDs) cross-linked with photochromic diarylethene molecules with different end groups, 4,4′-(1-cyclopentene-1,2-diyl)bis[5-methyl-2-thiophenecarboxylic acid] (1C) and 4,4′-(1-cyclopentene-1,2-diyl)bis[5-methyl-2-thiophenethiocarboxylic acid] (2T). Our results show that the QDs cross-linked with the carboxylic acid end group molecules (1C) exhibit a greater amount of switching in photoluminescence intensity compared to QDs cross-linked with the thiocarboxylic acid end group (2T). We also demonstrate that regardless of the molecule used, greater switching amounts are observed for smaller quantum dots. Varying these parameters allows for the fabrication of photoswitches with tunable PL change. We relate these observations to the …

Automated Flood Depth Estimation On Roadways, Kwame Ampofo, Megan A. Witherow, Alex Glandon, Monibor Rahman, Ahmed Temtam, Mecit Cetin, Khan M. Iftekharuddin

Civil & Environmental Engineering Faculty Publications

Recurrent nuisance flooding is common across many parts of the globe and causes extensive challenges for drivers on the roadways. The prevailing monitoring methods for roadway flooding are costly and not automated or effective. The ubiquity of visual data from cameras and advancements in computing such as deep learning may offer cost-effective methods for automated flood depth estimation on roadways based on reference objects such as cars. However, flood depth estimation faces challenges due to the limited amount of data annotated with water levels and diverse scenes showing reference objects at various scales and perspectives. This study proposes a novel …

Energy Harvesting For Residential Microgrid Distributed Sensor Systems, Devin C. Whalen

Master’s Theses

Microgrids are localized, independent power grids that can operate while connected to the larger electrical grid. These systems make intelligent decisions regarding power management and use an array of components to monitor power generation, consumption, and environmental conditions. While this technology can save end users money, the complexity of installation and maintenance has limited the adoption of microgrids in residential spaces. To simplify this technology for end users, the next evolution of microgrid components includes sensors that are wireless and ambiently powered.

Even with a microgrid installed, significant energy is wasted in residential spaces. To address this loss, energy harvesting …

Energy Efficiency In Additive Manufacturing: Condensed Review, Ismail Fidan, Vivekanand Naikwadi, Suhas Alkunte, Roshan Mishra, Khalid Tantawi

Engineering Technology Faculty Publications

Today, it is significant that the use of additive manufacturing (AM) has growing in almost every aspect of the daily life. A high number of sectors are adapting and implementing this revolutionary production technology in their domain to increase production volumes, reduce the cost of production, fabricate light weight and complex parts in a short period of time, and respond to the manufacturing needs of customers. It is clear that the AM technologies consume energy to complete the production tasks of each part. Therefore, it is imperative to know the impact of energy efficiency in order to economically and properly …

Federated Learning: Overview, Strategies, Applications, Tools And Future Directions, Betul Yurdem, Murat Kuzlu, Mehmet Kemal Gullu, Maliha Tabassum

Engineering Technology Faculty Publications

Federated learning (FL) is a distributed machine learning process, which allows multiple nodes to work together to train a shared model without exchanging raw data. It offers several key advantages, such as data privacy, security, efficiency, and scalability, by keeping data local and only exchanging model updates through the communication network. This review paper provides a comprehensive overview of federated learning, including its principles, strategies, applications, and tools along with opportunities, challenges, and future research directions. The findings of this paper emphasize that federated learning strategies can significantly help overcome privacy and confidentiality concerns, particularly for high-risk applications.

Urban Flood Extent Segmentation And Evaluation From Real-World Surveillance Camera Images Using Deep Convolutional Neural Network, Yidi Wang, Yawen Shen, Behrouz Salahshour, Mecit Cetin, Khan Iftekharuddin, Navid Tahvildari, Guoping Huang, Devin K. Harris, Kwame Ampofo, Jonathan L. Goodall

Civil & Environmental Engineering Faculty Publications

This study explores the use of Deep Convolutional Neural Network (DCNN) for semantic segmentation of flood images. Imagery datasets of urban flooding were used to train two DCNN-based models, and camera images were used to test the application of the models with real-world data. Validation results show that both models extracted flood extent with a mean F1-score over 0.9. The factors that affected the performance included still water surface with specular reflection, wet road surface, and low illumination. In testing, reduced visibility during a storm and raindrops on surveillance cameras were major problems that affected the segmentation of flood extent. …

Advanced Nested Coaxial Thin-Film Zno Nanostructures Synthesized By Atomic Layer Deposition For Improved Sensing Performance, Pengtao Lin, Lari S. Zhang, Kai Zhang, Helmut Baumgart

Electrical & Computer Engineering Faculty Publications

We report a new synthesis method for multiple-walled nested thin-film nanostructures by combining hydrothermal growth methods with atomic layer deposition (ALD) thin-film technology and sacrificial films, thereby increasing the surface-to-volume ratio to improve the sensing performance of novel ZnO gas sensors. Single-crystal ZnO nanorods serve as the core of the nanostructure assembly and were synthesized hydrothermally on fine-grained ALD ZnO seed films. Subsequently, the ZnO core nanotubes were coated with alternating sacrificial coaxial 3D wrap-around ALD Al2O3 films and ALD ZnO films. Basically, the center nanorod was coated with an ALD 3D wrap-around Al₂O₃ sacrificial layer to realize a nested …

Functional Verification Of Additively Manufactured Metallopolymer Structures For Structural Electronics Design, Nathan D. Singhal

Honors Undergraduate Theses

As an attempt to improve the overall cost-effectiveness and ease of structural electronics manufacturing, this study characterizes the mechanical and electrical responses of structures which are fabricated from a novel metallopolymer composite material by fused deposition modeling as they are subjected to quasi-static, uniaxial mechanical tension. Baseline values of tensile properties and electrical resistivity were first obtained via ASTM D638-22 standard testing procedures and linear sweep voltammetry (LSV), respectively. A hybrid procedure to measure in-situ mechanically dependent electrical behavior was subsequently developed and implemented. The mechanical and electromechanical testing was followed by the derivation of stochastic values for several mechanical …

Multilayer Ceramic Capacitor Vibration Source Model Library Development, Yifan Ding, Ming Feng Xue, Jianmin Zhang, Xin Hua, Benjamin Leung, Eric A. Macintosh, Chulsoon Hwang

Electrical and Computer Engineering Faculty Research & Creative Works

When a Multilayer Ceramic Capacitor (MLCC) is Soldered on a Printed Circuit Board (PCB), the Capacitor Deformation Generates a Force on the PCB, Which Serves as a Source to Excite PCB Vibration and Generate Unwanted Acoustic Noise. a MLCC Can Be Used under Any Loading Conditions as Long as the Power Supply Voltage Remains Below its Rated Voltage; Thus, the MLCC Source Mode Can Be Extremely Complicated Because of Different Combinations Including Direct Current Voltage Levels, Alternating Current Noise Amplitudes, and Related Frequencies. When the Power Rail is under Different Loading Conditions, the PCB Vibration Amplitude Can Differ; However, the …

Prompt, Accurate, And Noncontact Material Identification Using A Single Microwave Sensor With Machine Learning Analysis, Chen Zhu, Osamah Alsalman, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

In This Article, a Novel Approach to Microwave Sensors is Proposed and Demonstrated that Allows for Prompt, Accurate, and Noncontact Material Identification Even with Arbitrary Lift-Off Distances between the Sensor and the Material under Test (MUT). a Multilayer Perceptron (MLP) is Trained to Directly Learn the Relation of the Measured Reflection Spectra of a Homemade Open-Ended Coaxial Cable Resonator Probe with Respect to Different MUTs and Then Achieve One-To-One Mapping between a Measured Spectrum and the MUT. as a Proof-Of-Concept Demonstration, the Performance of the MLP Model is Tested using Easily Accessible Materials, Including Wood, Glass, Water, and Metal. Reflection …

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 …

Improved Peec Modeling Of Antennas Through Time-Dependent Partial Elements, Fabrizio Loreto, Giuseppe Pettanice, Martin Stumpf, Albert E. Ruehli, Jonas Ekman, Giulio Antonini

Electrical and Computer Engineering Faculty Research & Creative Works

Over the Last Twenty Years, the Evolution of Communications Has Extended the Systems' Operating Frequency Range to the Tens of GHz. in This Framework, Time Domain Integral Equation-Based (TDIE) Methods for Antenna Modeling Have Gained Increasing Interest. among Them, the Partial Elements Equivalent Circuit (PEEC) Method Turns Out to Be Attractive for its Capability to Provide Compact Circuit Models. Similarly to Other Integral Equation-Based Methods, Like the Method of Moments (MoM) in the Time Domain (TD), the PEEC Method Can Suffer from Late-Time Instabilities. This Work Shows that a Rigorous Computation of the Time-Dependent Partial Elements Leads to an Improved …

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 …

Tilted Fiber Bragg Grating Sensors Based On Time-Domain Measurements With Microwave Photonics, Shiyu Li, Ruimin Jie, Osamah Alsalman, Jie Huang, Chen Zhu

Electrical and Computer Engineering Faculty Research & Creative Works

Tilted fiber Bragg gratings (TFBGs) have garnered substantial research attention and have found widespread applications for sensing a diverse array of physical, chemical, and biological parameters based on optical spectrum measurements. The interrogation of a TFBG sensor typically requires a high-resolution bulky optical spectrum analyzer (OSA) due to the extremely narrow dips caused by the resonance of cladding modes. However, high-resolution OSAs can be costly and have limitations on measuring speed, limiting their practicality. In this paper, a new approach to interrogating TFBG sensors is proposed and experimentally demonstrated based on a microwave photonics technique. Instead of measuring the optical …

Visualization Of Noise Coupling Paths Based On The Reciprocity Theorem, Seungtaek Jeong, Jong Hwa Kwon, Deepak Pai, Jagan Rajagopalan, Chulsoon Hwang

Electrical and Computer Engineering Faculty Research & Creative Works

In this article, visualization of noise coupling paths is proposed and verified. The coupling coefficient (CC) is derived based on the Reciprocity theorem and time-average coupled power, which represents the contribution of each spatial point to the coupled power. The physical meaning of CC is demonstrated by relating the integrations of CC in a volume space occupied by absorbers to the coupled power being suppressed by the absorbers. The proposed method is applied to a practical device and the identified dominant coupling paths are experimentally verified.

Simultaneous Frequency Regulation And Active Power Sharing In Islanded Microgrid Using Deep Reinforcement Learning, Oroghene Oboreh-Snapps, Sophia A. Strathman, Jonathan Saelens, Arnold Fernandes, Lauryn Morris, Praneeth Uddarraju, Jonathan W. Kimball

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a novel approach that integrates deep reinforcement learning (DRL) with the conventional virtual synchronous generator (VSG) to address dual objectives of microgrid (MG) control, frequency regulation and precise active power sharing. MGs typically consist of multiple Inverter-Based-Distributed-Generators (IBDGs) connected in parallel through different line impedances. The conventional active power loop (APL) of the VSG encounters significant steady-state frequency errors as load increases/decreases during islanded operation. To mitigate this issue, secondary-level controllers like proportional-integral (PI) control are added to the APL to regulate the frequency of IBDGs. However, PI control compromises power-sharing capabilities when the impedance values of …

Profitability Analysis Of Time-Restricted Double-Spending Attack On Pow-Based Large Scale Blockchains With The Aid Of Multiple Attacks, Yiming Jiang, Jiangfan Zhang

Electrical and Computer Engineering Faculty Research & Creative Works

We consider the time-restricted double-spending attack (TR-DSA) on the Proof-of-Work-based blockchain, where an adversary conducts a DSA within a finite timeframe and simultaneously launches multiple types of attacks on the blockchain. To be specific, the adversary can conduct attacks to isolate some honest miners and cause block propagation delays among miners to enhance the success probability of the TR-DSA. We first develop the closed-form expression for the success probability of a TR-DSA with the aid of multiple types of attacks, which is leveraged to develop the closed-form expression for the expected profit of a TR-DSA. The numerical analysis reveals that …

Lifelong Learning-Based Optimal Trajectory Tracking Control Of Constrained Nonlinear Affine Systems Using Deep Neural Networks, Irfan Ganie, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This article presents a novel lifelong integral reinforcement learning (LIRL)-based optimal trajectory tracking scheme using the multilayer (MNN) or deep neural network (Deep NN) for the uncertain nonlinear continuous-time (CT) affine systems subject to state constraints. A critic MNN, which approximates the value function, and a second NN identifier are together used to generate the optimal control policies. The weights of the critic MNN are tuned online using a novel singular value decomposition (SVD)-based method, which can be extended to MNN with the N-hidden layers. Moreover, an online lifelong learning (LL) scheme is incorporated with the critic MNN to mitigate …

A Hybrid Model-Based Data-Driven Framework For The Electromagnetic Near-Field Scanning, Yanming Zhang, Lijun Jiang

Electrical and Computer Engineering Faculty Research & Creative Works

This article presents a novel hybrid approach for electromagnetic near-field scanning, combining model-based, i.e., Gaussian processes regression, and data-driven, i.e., dynamic mode decomposition, techniques. We first leverage the Latin hypercube sampling technique to achieve spatially sparse measurements. Subsequently, dynamic mode decomposition is applied to analyze the resulting spatiotemporal data with sparse spatial sampling, enabling the extraction of both frequency information and sparse dynamic modes. Finally, the Gaussian processes regression, also known as the Kriging method, is adopted for the full-state reconstruction. The proposed hybrid approach is benchmarked by an example of the crossed dipole antennas. The obtained results demonstrate that …