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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 …

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 …

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 …

Semiconductor Photonic Crystal Surface Emitting Lasers For High Power, High Brightness And High Speed Applications, Chhabindra Gautam

Electrical Engineering Dissertations - Archive

Photonic Crystal Surface Emitting Laser (PCSEL) is the promising third generation semiconductor laser technology as it addresses the challenges of the conventional semiconductor lasers (EEL/VCSEL) by providing single mode high power output with good beam quality (thus high brightness). The large area in plane coupling resulting from the multiple Bragg’s diffraction conditions in the periodic 2D photonic crystal cavities in the lasing cladding region leads to single mode operation over very large area. By model design engineering, such in plane lasing cavity can leak the light out of the plane, which result in surface emission. The ability to scale the …

Eece 7220/8220: Scientific Computing (Syllabus), Madhusudhanan Balasubramanian

Electrical and Computer Engineering Syllabi Archive

Course Description: Review of scientific computing mathematical preliminaries. Topics include numerical linear algebra, orthogonality, eigenvalues, boundary value problems, integral equations and Green's functions, numerical integration, basic iterative methods, preconditioning, parallel programming, and advanced topics.

Artificial Intelligence Enabled Machinery Fault Detection And Diagnosis Using Vibro-Acoustic Signals, Srinivasa Rao Ippili

Theses and Dissertations--Mechanical and Aerospace Engineering

In various industries, the early detection of faults in rotating machinery is crucial to prevent system failures and ensure customer satisfaction. Typically, vibration measurement and diagnosis are employed for fault detection, but this process faces challenges in automation due to the complexity of installing and maintaining accelerometers, particularly in end-of-line quality control or pre-installed machinery health assessments. Acoustic signals, as a form of mechanical wave, offer an alternative for monitoring machinery while in operation. Unlike accelerometers, acoustic transducers are non-contact and easy to set up, enabling real-time data collection without interrupting equipment operation. However, utilizing acoustic signals in manufacturing poses …

Application Of Fusion Based Deep Learning Models To Improve Millimeter Wave Beamforming, Abishek Subramanian

Dissertations, Master's Theses and Master's Reports

This study addresses the challenge of selecting millimeter Wave (mmWave) beamforming pairs for vehicle-to-infrastructure (V2I) communication, to mitigate latency in highly dynamic vehicular environments. We investigate the use of out-of-band sensor data as side information to model mmWave ray tracing paths and predicting a subset of top-K optimal beamforming pairs for efficient and low-latency searches. Unimodal-Fusion Deep Learning (F-DL) networks was applied to enhance mmWave beamforming process. We started by first investigating the centralized architecture, and then explored a novel distributed architecture through federated learning to minimize resource and latency overheads. The distributed architecture incorporates two biased client selection strategies: …

Multiple Imputation For Robust Cluster Analysis To Address Missingness In Medical Data, Arnold Harder, Gayla R. Olbricht, Godwin Ekuma, Daniel B. Hier, Tayo Obafemi-Ajayi

Mathematics and Statistics Faculty Research & Creative Works

Cluster Analysis Has Been Applied To A Wide Range Of Problems As An Exploratory Tool To Enhance Knowledge Discovery. Clustering Aids Disease Subtyping, I.e. Identifying Homogeneous Patient Subgroups, In Medical Data. Missing Data Is A Common Problem In Medical Research And Could Bias Clustering Results If Not Properly Handled. Yet, Multiple Imputation Has Been Under-Utilized To Address Missingness, When Clustering Medical Data. Its Limited Integration In Clustering Of Medical Data, Despite The Known Advantages And Benefits Of Multiple Imputation, Could Be Attributed To Many Factors. This Includes Methodological Complexity, Difficulties In Pooling Results To Obtain A Consensus Clustering, Uncertainty Regarding …

Accelerating Cavity Fault Prediction Using Deep Learning At Jefferson Laboratory, Md M. Rahman, A. Carpenter, K. Iftekharuddin, C. Tennant

Electrical & Computer Engineering Faculty Publications

Accelerating cavities are an integral part of the continuous electron beam accelerator facility (CEBAF) at Jefferson Laboratory. When any of the over 400 cavities in CEBAF experiences a fault, it disrupts beam delivery to experimental user halls. In this study, we propose the use of a deep learning model to predict slowly developing cavity faults. By utilizing pre-fault signals, we train a long short-term memory-convolutional neural network binary classifier to distinguish between radio-frequency (RF) signals during normal operation and RF signals indicative of impending faults. We optimize the model by adjusting the fault confidence threshold and implementing a multiple consecutive …

Comparative Analysis Of Machine Learning Models For Predicting Healthcare Traffic: Insights For Optimized Emergency Response, Shadman Mahmood Khan Pathan, Sakan Binte Imran, M. M. Shabab Iqbal, Muhammad Enayetur Rahman, Md. Nurul Absar Siddiky, Muhammad Rezaur Rahman, Md Rafid Hasan, Nondon Lal Dey, Md Sobuj Hossain

Electrical & Computer Engineering Faculty Publications

Efficient management of healthcare traffic is crucial for ensuring timely access to medical services, particularly in emergency situations where delays can have severe consequences. This study presents a comparative analysis of three widely used machine learning models—Linear Regression, Decision Trees, and Random Forests—aimed at predicting healthcare-related traffic volumes. A large dataset from a metropolitan traffic system was used to train and evaluate the models based on key performance indicators, including Mean Squared Error (MSE), R² Score, and computational efficiency. The results reveal that the Random Forest model offers the best performance, achieving higher predictive accuracy and faster execution times compared …

Distance Estimation Based On Step Frequency Using Accelerometer Data, Rami M. Al-Naimat, Khawlah M. Harahsheh, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

In recent years, smartphone sensors have become one of the most important and easily available sensors to facilitate people's lives, especially in health care and positioning (indoor environments). However, the data coming from smartphone sensors can be distorted during the user’s movement such as irrelevant movements, walk mode, and speed of walking. This distortion (noise) impairs the estimated distance accuracy (accumulative error) which increases with increasing walking distance. In addition, the accuracy of the distance traveled is affected by the user's speed, as the speed affects the step length. This work proposes a novel approach for calculating step length in …

Designing High-Performance Identity-Based Quantum Signature Protocol With Strong Security, Sunil Prajapat, Pankaj Kumar, Sandeep Kumar, Ashok Kumar Das, Sachin Shetty, M. Shamim Hossain

VMASC Publications

Due to the rapid advancement of quantum computers, there has been a furious race for quantum technologies in academia and industry. Quantum cryptography is an important tool for achieving security services during quantum communication. Designated verifier signature, a variant of quantum cryptography, is very useful in applications like the Internet of Things (IoT) and auctions. An identity-based quantum-designated verifier signature (QDVS) scheme is suggested in this work. Our protocol features security attributes like eavesdropping, non-repudiation, designated verification, and hiding sources attacks. Additionally, it is protected from attacks on forgery, inter-resending, and impersonation. The proposed scheme benefits from the traditional designated …

Building Integration Of A Solar Air Heating System, Fernando Guerreiro

Doctoral

In order to achieve the global carbon emission target, the high fraction of locally available renewable energy sources will become necessary to meet energy demand. Solar energy is one of the most important renewable sources locally available for use in space heating, cooling, hot water supply and power production. Building integrated solar thermal systems (BISTS) can be a potential solution towards the enhanced energy efficiency and reduced operational cost in built environment. The current research aimed at developing an active solar air heating collector for building integration. This system consisted of several asymmetric compound parabolic concentrating collectors with inverted transpired …

Aggregation And Oligomerization Characterization Of Ss-Lactoglobulin Protein Using A Solid-State Nanopore Sensor, Mitu C. Acharjee, Brad Ledden, Brian Thomas, Xianglan He, Troy Messina, Jason Giurleo, David Talaga, Jiali Li

Physics Faculty Publications and Presentations

Protein aggregation is linked to many chronic and devastating neurodegenerative human diseases and is strongly associated with aging. This work demonstrates that protein aggregation and oligomerization can be evaluated by a solid-state nanopore method at the single molecule level. A silicon nitride nanopore sensor was used to characterize both the amyloidogenic and native-state oligomerization of a model protein ß-lactoglobulin variant A (βLGa). The findings from the nanopore measurements are validated against atomic force microscopy (AFM) and dynamic light scattering (DLS) data, comparing βLGa aggregation from the same samples at various stages. By calibrating with linear and circular dsDNA, this study …

Advanced Detection Of Sars-Cov-2 And Omicron Variants Via Mxene-Graphene Hybrid Biosensors Utilizing Nucleic Acid Probes, Jiaoli Li, Yuwei Zhang, Congjie Wei, Yanxiao Li, Zhekun Peng, Hsin Yin Chuang, Logan Pearce, Adrianus Boon, Yue-Wern Huang, Dong Hyun Kim, Risheng Wang, Chenglin Wu

Biological Sciences Faculty Research & Creative Works

Low-cost biosensors that can rapidly and widely detect viruses are critical for faster diagnosis and treatment decision-making, especially for infections. The commonly used field-effect transistor is sensitive to the biomarker's detection but struggles with precise detection, particularly of nontargets such as ions and proteins. To overcome this limitation, we developed a field-effect transistor biosensor design based on MXene-graphene materials to increase the accuracy and sensitivity of virus detection. Based on the hybridization process between two complementary DNA strands, single-stranded nucleic acids were immobilized on the sensing surface via 3-aminopropyltriethoxysilane and glutaraldehyde to capture the nucleic acids of the target virus. …

Machine Learning Based Intrusion Detection Framework For Can Bus Vulnerabilities In Modern Vehicles, Obinna C. Agbo

Graduate Theses, Dissertations, and Problem Reports (ETD)

The Controller Area Network (CAN) bus is a crucial communication backbone in modern vehicles, connecting various Electronic Control Units (ECUs). However, inherent design weaknesses such as the lack of encryption and authentication make CAN networks vulnerable to cyber-attacks, including spoofing, Denial of Service (DoS), and fuzzing attacks. This thesis thoroughly evaluates these vulnerabilities and the limitations of existing security frameworks like Message Authentication Codes (MACs) and encryption, advocating for the adoption of Intrusion Detection Systems (IDS) as a more practical solution for CAN bus security. The proposed IDS leverages advanced machine learning techniques to accurately detect intrusions, even under complex …

Analyzing Viability Of Blue Indium Gallium Nitride Leds For Use In Space Missions Using A Low Earth Orbit Cubesa, Bertrand Edward Wieliczko

Graduate Theses, Dissertations, and Problem Reports (ETD)

The payload capacity of spacecraft is constrained by the weight of the craft itself, including fuel and electronic systems. The protective measures used to shield onboard electronics from the harsh space environment, characterized by high-energy particles and significant temperature fluctuations, can further diminish the available payload capacity. This thesis explores the potential of naturally radiation-hard alternatives to commonly used electronic materials, such as Silicon, to reduce the need for shielding and other protective measures, thereby decreasing the weight and cost of space missions.

III-V semiconductor materials, such as Gallium Nitride (GaN), are known for their inherent resilience to temperature swings …

Application Of The Mixed Integer Nonlinear Programming Technique For The Economic Planning Of Transmission Networks In New Manner, Nader Shawky Abdelhakeem, Maged Gamal Lotfy, Mohamed Shibl Albags, Ahmed Saied Elzawawy

Mansoura Engineering Journal

In the recent years, the mathematical planning model of transmission networks based on full AC load flow has been developed. But, due to the model non-convexity and the existing of large number of local solutions, this model can only be used for small size networks as it consumes large unreasonable computation time in addition to its incapability to yield global solution in most cases. To overcome these problems, two new iterative solution procedures have been presented in this paper. In the first one, the objective function is changed to the minimization of the number of new lines required to satisfy …

Midi Recorder And Learning Tool, Caden Dees, Saikishore Gowrishankar, Cameron Johnson, Benjamin Bolyard

Williams Honors College, Honors Research Projects

The project's goal is to create a device that can record MIDI files from any standard 88-key piano as well as teach piano learners how to play songs.

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 …

Distribution System Losses Allocation Based On Circuit Theory With Distributed Generation, Ahmed S. Abdelkader, Ibrahim I. Mansy, Abdelfattah A. Eladl

Mansoura Engineering Journal

Electricity energy is the most convenient and efficient form of energy transmission and conversion. Nonetheless, losses take place while electrical power is traveling from power stations through transmission and distribution networks to the loads. Despite the high efficiency of electrical transmission and distribution networks, the huge amounts of power moving through these networks make the financial loss due to the lost energy very high and its management becomes a critical consideration for both utility providers and regulatory bodies. The efficient management of losses encompasses two major functions: the first is running the system in a way that minimizes the losses; …

Wavelet-Based Harmonization Of Local And Global Model Shifts In Federated Learning For Histopathological Images, W. Farzana, A. Temtam, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Federated Learning (FL) is a promising machine learning approach for development of data-driven global model using collaborative local models across multiple local institutions. However, the heterogeneity of medical imaging data is one of the challenges within FL. This heterogeneity is caused by the variation in imaging scanner protocols across institutions, which may result in weight shift among local models leading to deterioration in predictive accuracy of global model. The prevailing approaches involve applying different FL averaging techniques to enhance the performance of the global model, ignoring the distinct imaging features of the local domain. In this work, we address both …

Autonomous Basketball Court Creation Robot, Bryce Haldeman, Tyler Gray, Dalon Vura

Williams Honors College, Honors Research Projects

The Autonomous Basketball Court Outlining System presents a comprehensive solution for precision court marking. Powered by a 24V lithium-ion battery and driven by a single ST microcontroller, the system autonomously marks the outline of a half basketball court using predefined algorithms. User-friendly features include easy loading of marking material, actuated by gravity or a small servo motor depending on material of choice, ensuring intuitive operation. Safety is prioritized, with the servo motor eliminating high-pressure concerns, and the system maintains a controlled speed accounting for user well-being. Two step and direction servo motors enable accurate linear displacement, facilitating straight lines, and …

Zero-Shot Cross-Lingual Pos Tagging For Filipino, Jimson Paulo Layacan, Isaiah Edri W. Flores, Katrina Bernice M. Tan, Ma. Regina Justina Estuar, Jann Railey E. Montalan, Marlene M. De Leon

Department of Information Systems & Computer Science Faculty Publications

Supervised learning approaches in NLP, exemplified by POS tagging, rely heavily on the presence of large amounts of annotated data. However, acquiring such data often requires significant amount of resources and incurs high costs. In this work, we explore zero-shot cross-lingual transfer learning to address data scarcity issues in Filipino POS tagging, particularly focusing on optimizing source language selection. Our zero-shot approach demonstrates superior performance compared to previous studies, with top-performing fine-tuned PLMs achieving F1 scores as high as 79.10%. The analysis reveals moderate correlations between cross-lingual transfer performance and specific linguistic distances–featural, inventory, and syntactic–suggesting that source languages with …

Efficient Connectivity Management And Path Planning For Iot And Uav Networks, Amirahmad Chapnevis

Theses and Dissertations

This dissertation explores how to better manage resources in mobile networks, especially for enhancing the performance of Unmanned Aerial Vehicles (UAV)-supported IoT networks. We explored ways to set up a flexible communication architecture that can handle large IoT deployments by making good use of mobile core network resources like bearers and data paths. We developed strategies that meet the needs of IoT networks and enhance network performance. We also developed and tested a system that combines traffic from several mobile devices that use the same user identity and network resources within the core mobile network. We used everyday smartphones, SIM …

Photoluminescence Of Beryllium-Related Defects In Gallium Nitride, Mykhailo Vorobiov, Mykhailo Vorobiov

Theses and Dissertations

This study explores the potential of beryllium (Be) as an alternative dopant to magnesium (Mg) for achieving higher hole concentrations in gallium nitride (GaN). Despite Mg prominence as an acceptor in optoelectronic and high-power devices, its deep acceptor level at 0.22 eV above the valence band limits its effectiveness. By examining Be, this research aims to pave the way to overcoming these limitations and extend the findings to aluminum nitride and aluminum gallium nitride (AlGaN) alloy. Key contributions of this work include. i)Identification of three Be-related luminescence bands in GaN through photoluminescence spectroscopy, improving the understanding needed for further material …

Rf Steganography To Send High Security Messages Through Sdrs, Megan K. Patrick

Browse all Theses and Dissertations

This research illustrates a high-security wireless communication method using a joint radar/communication waveform, addressing the vulnerability of traditional low probability of detection (LPD) waveforms to hostile receiver detection via cyclostationary processing (CSP). To mitigate this risk, RF steganography is used, concealing communication signals within linear frequency modulation (LFM) radar signals. The method integrates reduced phase-shift keying (RPSK) modulation and variable symbol duration, ensuring secure transmission while evading detection. Implementation is validated through software-defined radios (SDRs), demonstrating effectiveness in covert communication scenarios. Results include analysis of message reception and cyclostationary features, highlighting the method's ability to conceal messages from hostile receivers. …