Digital Commons Network^™

Efficient Operation Of Direct Coupled Solar Home Pv System: A Case Of Solar Home Pv System Installed In Dodoma, Tanzania, Sarah P. Ayeng'o

Tanzania Journal of Engineering and Technology (TJET)

Direct coupled Photovoltaic (PV) system is a common topology among most of the off-grid communities in the world. This topology has less components hence resulting in low investment costs. However, it suffers much losses when battery operating voltage is far from maximum power point voltage of PV modules. This scenario can be attributed by different factors such as; variation in solar radiation, load profile and temperature. Efficient operation of these systems is required in order to reduce losses. Usually in direct coupled system, PV modules are connected in parallel with batteries through a charge controller hence making PV output to …

State-Of-Charge Estimation Using Deep Learning For Electric Vehicles, Samer Yahya Ribhe Tahboub

LSU Master's Theses

This thesis investigates the application of deep learning models for State of Charge (SOC) estimation in Battery Management Systems (BMS) for electric vehicles (EVs), focusing on optimizing EV range, lifespan, and performance while addressing challenges like range anxiety. The study explores three deep learning architectures—Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU)—each designed to capture complex temporal dependencies in battery data. The LSTM model is trained on EV battery data, including voltage, current, temperature, and SOC, providing a strong baseline for SOC estimation. The BiLSTM model enhances accuracy by processing data in both forward and backward …

Lagrange Multipliers As A Metric For Early-Stage Shipboard Power System Design, Magdalen A. Barnes

Theses and Dissertations--Electrical and Computer Engineering

Integrated power systems are essential in the operation of electric ships and must function under a diverse range of conditions. In addition to routine operation, ships should be prepared to respond to challenging events that require significant amounts of power. Demanding scenarios, coupled with complex and interconnected power systems, present challenging issues. The power and energy ratings of equipment are crucial factors in the operational success of the ship; however, to meet economic constraints, balance between performance and cost must be upheld. Details regarding equipment models and specifications are typically unavailable during early-stage design. Power system modeling and simulation can …

Smart Homes, Grids, And Electric Vehicles Large-Scale Integration Studies Employing Machine Learning And Optimization Techniques, Rosemary E. Alden

Theses and Dissertations--Electrical and Computer Engineering

Residential digital twins are fundamental to the smart grid transition, and thus, must be both accurate and representative of existing homes and scalable for large distribution systems. Within this dissertation, new machine learning (ML) and physics-based methodologies are applied to the major individual residential loads, energy storage devices, and resources in the US to develop computationally efficient digital twins and new optimal control strategies for the virtual power plant (VPP) concept. Big data from experimental field demonstrations with dedicated metering, thousands of residential smart meter profiles, and large national human behavior surveys are employed to develop ultra-fast scalable residential load …

Fusion-Based Utilization And Synthesis Of Efficient Detections, Ethan C. Rogers, Parker H. Liberatore, Thomas G. James

Endeavors: Mississippi State Undergraduate Research Journal

This study aimed to develop hardware and software for an object detection fusion system, using three different sensors. The system was built and studied with the motivating application of autonomous drones searching for and detecting people in a search-and-rescue scenario. The system’s performance was compared to that of individual sensors deployed for the same task. The focus of the research was to prove the competence and benefits of a decision-level fusion method as it was applied to a lightweight object detection architecture, and the driving motivators behind the study were simplicity in implementation and good computational performance. In short, the …

Data-Driven Forecasting Of Power Demand Via Convex Optimization, Mersedeh Ashraphijuo

Electrical Engineering Dissertations - Archive

This dissertation develops interpretable, data-driven frameworks for short-term power demand forecasting using convex optimization and advanced feature engineering. The models combine historical load data, calendar structures, and meteorological variables to deliver accurate point, quantile, and probabilistic forecasts. By leveraging multi-periodic Fourier features, temperature-based regressors, and autoregressive memory, the proposed approach balances predictive performance with interpretability and scalability. Evaluations on multi-year datasets across US regions show consistent accuracy gains over benchmarks, while preserving transparency critical for real-world deployment. Beyond power systems, the framework generalizes to other time series applications in data science and AI, offering a robust, explainable alternative to black-box …

Characterization And Modeling Of Polyphase Machines And Power Conditioning Components, Nathan Lockhart

Electrical Engineering Dissertations - Archive

Microgrid power configurations have become increasingly prevalent in recent power systems due to the rise of power electronic energy generation, energy storage, and the many diverse electrical demands. Microgrids offer numerous advantages over traditional power electronic networks, which rely on large rotating generators to supply power over extensive distances to multiple users. Remote power grids are particularly beneficial for smaller networks that may be isolated or have unique power requirements, often incorporating energy storage to enhance operational flexibility. Advances in power electronics, such as medium voltage DC distribution, are enhancing the reliability, redundancy, and integration capabilities of isolated microgrids.

To …

Novel Electrophysiological Biomarkers In Pediatric Drug Resistant Epilepsy And Genetic Epilepsy Syndromes, Sakar Rijal

Bioengineering Dissertations - Archive

Pediatric epilepsies, particularly those that are drug-resistant or genetically driven, represent some of the most complex neurological disorders encountered in childhood. Central to their pathophysiology is a disruption in the delicate balance between cortical excitation and inhibition (E/I), often resulting from impaired GABAergic interneuron function. This imbalance manifests as aberrant network dynamics and altered neural oscillations, giving rise to seizures and long-term cognitive impairments. In this thesis, we developed a translational framework to identify electrophysiological biomarkers that (i) assess cortical E/I imbalance and (ii) map epileptogenic zones, with the aim of enhancing diagnosis, guiding surgical planning, and informing therapeutic monitoring …

Vision Of Nuclear Power Deployment In Latin America And The Caribbean: A Focus On Small Modular Reactors And The Regional Experience Of Central Argentina De Elementos Modulares, Alejandra Loreto Alfaro, Florencia Rentería, Camila Araujo

International Journal of Nuclear Security

Increasing the number of nuclear power reactors in the Latin American and Caribbean region presents technical, financial, regulatory, and environmental challenges. Focused on fostering economic stability, growth, and human capacity development, the deployment of small modular reactors (SMRs) emerges as a key aspect in the region’s energy landscape. The emergence of SMRs represents an opportunity for multidisciplinary cooperation among different sectors. To comprehensively address the challenges related to the protection of nuclear facilities in the region, the Tlatelolco Treaty and the Non-Proliferation Treaty should be strengthened as legally binding instruments to enforce the safety and safeguarding principles integral to the …

Autonomous Vehicle Platooning, Tony Abelson

McNair Summer Research Program

This research investigates the construction and performance optimization of two autonomous vehicles with using "Platooning," a strategy aimed at reducing fuel consumption and enhancing transportation efficiency. Platooning allows one vehicle to follow another closely, minimizing aerodynamic drag and improving fuel economy. This study addresses the growing need for sustainable transportation solutions in the context of increasing urbanization and environmental concerns, emphasizing the importance of efficient autonomous vehicle operation. The primary objectives of this research are to assemble autonomous vehicles from scratch and to optimize their performance in both individual and platoon operations. The methodology involves using Traxxas Slash chassis and …

Electro-Vascular Dynamics During Auditory Processing: Variation Along The Schizotypy Continuum, John P. Mclinden

Open Access Dissertations

Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) are complementary noninvasive neuroimaging modalities that have contributed greatly to our understanding of the mechanisms of auditory processing. As an emerging technology, fNIRS has produced promising insights into the mechanisms of auditory processing, although considerable work remains to characterize hemodynamic responses in auditory tasks. In addition, the complementary nature of EEG and fNIRS and the relationships between the signals they record have been underexplored. In particular, nonlinear interactions between these signals remain under-characterized, despite the potential benefit to future multimodal neuroimaging studies. A challenge in understanding these interactions is the influence of systemic …

Dc To Ac Voltage Source Converter Control System, Mohummad Tark Elgassier, Jordan M. Reichhardt, Brayden Richard Young

Electrical Engineering

The Voltage Source Converter Control System integrates a control system for a 2-phase half-bridge voltage source converter (VSC) utilizing a TMS320F2837xD Dual-Core Real-Time Microcontroller. VSCs are critical for the integration of power production into the power grid. They allow for accurate control over DC-AC energy conversion. Additionally with the improvement of renewable energies VSCs play a critical role in controlling the flow of these environmentally controlled and more unpredictable sources of energy. The control system regulates the output current and voltage, regulates switching patterns, and maintains power stability under varying loads. Within the control system Digital control systems, PWM generation, …

Smart Irrigation System Using Iot And Lstm For Optimal Water Management, Farley Y. Ruiz

Electrical Engineering Theses - Archive

This thesis presents the design and implementation of a smart irrigation system that combines Internet of Things hardware with a Long Short-Term Memory (LSTM) neural network for predictive soil moisture management. The goal is an affordable and reliable solution that uses real-time sensor data and environmental data to schedule irrigation before the substrate moisture drops below its target range. The system integrates soil moisture, temperature, humidity, and sensors on an Arduino Nano that communicates wirelessly with a Raspberry Pi. The Raspberry Pi runs a Python/Flask backend that collects and processes data, executes the LSTM model, and serves a secure web …

Power Quality Event Diagnosis Using Multi-Rate Neural Networks, Jordan D. Lloyd

Electrical Engineering Theses - Archive

Power quality disturbances (PQDs) are among the primary challenges facing modern electrical systems, as they degrade the performance and lifespan of connected equipment. This thesis investigates the relationship between the rate at which voltage waveform data are sampled, the reliability of these measurements, and the ability of deep neural networks to classify PQDs accurately. A one-dimensional convolutional neural network (CNN) was trained and evaluated across multiple sampling rates and signal-to-noise ratios to quantify how information loss in the temporal and spectral domains affects classification reliability. The results demonstrate that model accuracy degrades nonlinearly as sampling rate and signal-to-noise ratio (SNR) …

Enhanced Load Detection With Data-Driven Appliance Signatures Using Mixed Integer Linear Programming In Non-Intrusive Load Monitoring, Marina Materikina

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

Despite the numerous research studies and interest in the non-intrusive load monitoring (NILM) area to improve energy efficiency, the problem of accurate and precise disaggregation of electrical devices has not been solved yet. The goal of our research is to build a method with a focus on higher accuracy on complex state-based appliances, which most approaches struggle to detect due to their power signal complexity and low consumption. Our approach is NILM with data-driven signatures (DS), with the ability to potentially predict power usage over time that would work great for suitable applications such as demand response, anomaly detection, and …

Enhancing Irs Localization Via Ml-Driven Aoa And Distance Estimation, Nikhila Kotlapara Surendran

Theses and Dissertations

Intelligent Reflecting Surfaces (IRSs) are transforming next-generation wireless networks by enabling dynamic control of the propagation environment for improved localization and security. This thesis advances Angle-of-Arrival (AoA) and distance estimation in IRS-assisted systems using a hybrid architecture with active sensing and passive reflecting elements. A modified MUSIC algorithm, optimized for near-field localization, decouples 2‑D AoA and 1‑D range estimation using a grid-and-peak pseudo-spectrum scan, achieving sub-degree AoA (0.003 rad) and sub-meter range (0.05 m) accuracy with over 100× lower complexity than classical 3D MUSIC.

The study further applies the algorithm to the DeepMIMO dataset (O1_28 scenario, 3.5 GHz) using …

Fast Demodulation Of Ofdr-Based Distributed Sensing Based On Enhanced Buneman Frequency Estimation, Zhaopeng Zhang, Bo Liu, Xiao Liu, Caiyun Li, Osamah Alsalman, Chen Zhu

Electrical and Computer Engineering Faculty Research & Creative Works

Aiming at realizing high-efficiency distributed strain sensing through optical frequency domain reflectometry (OFDR), this paper introduces a fast demodulation algorithm to determine strain-induced spectral shifts from coarse Rayleigh backscattering (RBS) cross-correlation spectra. The proposed approach employs an enhanced Buneman frequency estimation (BFE) algorithm, enabling direct spectral shift analysis across coarse signals. By applying this algorithm, the need for dense interpolation in the conventional cross-correlation demodulation process - typically required for a finer spectral sampling interval but at the cost of demodulation efficiency - can be eliminated. Both theoretical analysis and experimental investigation reveal the equivalence of the BFE and conventional …

Ms-Yolo: Infrared Object Detection For Edge Deployment Via Mobilenetv4 And Slideloss, Jiali Zhang, Thomas S. White, Haoliang Zhang, Wenqing Hu, Donald C. Wunsch, Jian Liu

Mathematics and Statistics Faculty Research & Creative Works

Infrared imaging has emerged as a robust solution for urban object detection under low-light and adverse weather conditions, offering significant advantages over traditional visible-light cameras. However, challenges such as class imbalance, thermal noise, and computational constraints can significantly hinder model performance in practical settings. To address these issues, we evaluate multiple YOLO variants on the FLIR ADAS V2 dataset, ultimately selecting YOLOv8 as our baseline due to its balanced accuracy and efficiency. Building on this foundation, we present MS-YOLO (MobileNetv4 and SlideLoss based on YOLO), which replaces YOLOv8's CSPDarknet backbone with the more efficient MobileNetV4, reducing computational overhead by 1.5% …

A Large Electroencephalogram Database Of Freewill Reaching And Grasping Tasks For Brain Machine Interfaces, Bhoj Raj Thapa, John Boggess, Jihye Bae

Electrical and Computer Engineering Faculty Publications

Brain machine interfaces (BMIs) offer great potential to improve the quality of life for individuals with neurological disorders or severe motor impairments. Among various neural recording modalities, electroencephalogram (EEG) is particularly favorable for BMIs due to its noninvasive nature, portability, and high temporal resolution. Existing EEG datasets for BMIs are often limited to experimental settings that fail to address subjects’ freewill in decision making. We present a large EEG dataset, containing a total of 6808 trials, recorded from 23 healthy young adults (eight females and 15 males with an age range from 18 to 24 years) while performing reaching and …

Speed Variation Based Power Regulation Concept For Dynamic Wireless Charging, Donovin D. Lewis, Sudarshan T. Harave, Omer Onar, Mostak Mohammad, Veda Prakash Galigekere, Gui-Jia Su, Dan Ionel

Electrical and Computer Engineering Graduate Research

On-road wireless charging of electric vehicles (EVs) in-motion could potentially reduce range anxiety or battery size with wide-spread deployment. The planning and implementation of such systems are greatly complicated due to their susceptibility to load variation inherent to traffic flow. This paper proposes a method for derisking the potential for traffic slowdowns by compensating for reduced vehicle speed and investigates how implementation may affect system performance. A load modeling case study is presented at 200kW for a mile of high-speed roadway employing speed-based power regulation with results indicating average power usage and maximum car hosting capability can be reduced by …

Fault-Tolerant High-Power Two-Phase Coreless Afpm Motor-Drive System Concept, Yaser Chulaee, Ali Mohammadi, Matin Vatani, Aaron M. Cramer, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper proposes a fault-tolerant, high-power two-phase coreless axial flux permanent magnet (AFPM) motor drive system concept. Initially, the performance of a two-phase variant of the coreless AFPM machine is compared with that of its three-phase counterpart using both 3D finite element analysis (FEA) and experiments. Thanks to the intrinsic unique features of coreless AFPM machines, the analysis of the results demonstrates that the two-phase configuration has comparable power density (kW/kg) and efficiency. In terms of fault tolerance, the two-phase configuration exhibits superior performance, as the mutual inductance between phases is zero, resulting in an electric machine with magnetically decoupled …

A Framework For Fair And Trustworthy Multimodal Video Anomaly Detection: Fusion, Dataset Quality Auditing, Explainability, And Fairness, Omeshamisu Judith Anigala

Electronic Theses and Dissertations

No abstract provided.

St-Hybrid: Dynamic Graph Learning With Multi-Scale Spatio-Temporal Attention For Traffic Forecasting, Dhe Yeong Ewaza Tchalla

Electronic Theses and Dissertations

Accurate short-term traffic forecasting is central to modern Intelligent Transportation Systems, supporting route guidance, adaptive signal control, and incident response. Yet producing reliable predictions remains difficult because traffic is highly non-stationary. The relationships among roadway sensors shift during congestion, incidents, weather changes, or fluctuations in demand, and the temporal structure of traffic spans several scales from abrupt minute-level variations to broader daily and weekly rhythms. Models that rely on fixed spatial graphs or a single temporal scale tend to miss these evolving and layered dependencies. This thesis addresses these challenges by developing a graph-learning framework that adapts to changing traffic …

Performance Evaluation Of Managed Switch Configurations For Secure And Efficient Plc-Based Industrial Automation Networks, Ahmed Salama

All Graduate Theses, Dissertations, and Other Capstone Projects

This thesis explores how managed switches can improve network performance and security in PLC-based industrial systems. Using simulations in GNS3 and Cisco Packet Tracer, and packet analysis via Wireshark, the study compares unmanaged and managed switch configurations. Redundancy protocols, particularly STP and RSTP, are evaluated under failure scenarios. Results show that RSTP offers faster recovery times, making it more suitable for time-sensitive environments. Additionally, managed switch features like VLANs, port security, and MAC filtering significantly reduce vulnerabilities and improve network segmentation. The findings highlight the importance of incorporating both cybersecurity and redundancy in industrial network design as systems move toward …

C 3 An: Custom, Compact And Composite Ai Systems - A Neurosymbolic Approach: 4Th-Generation Evolution Of Intelligent Systems, Amit P. Sheth, Kaushik Roy, Revathy Venkataramanan, Venkatesan Nadimuthu

Publications

Artificial Intelligence (AI) systems continue to evolve rapidly. From the architecture perspective, it is evolving from large, monolithic models trained on massive internet data to complex, multi-component “compound” systems and “agentic” frameworks capable of semi-autonomous decision-making. These systems show immense promise yet face numerous challenges in reliability, consistency, transparency, and alignment with user goals. In this article, we propose Custom, Compact and Composite AI with Neurosymbolic (C3AN) approach, a framework that paves way to 4th-generation of AI that integrates data, knowledge, and human expertise to build robust, intelligent and trustworthy AI systems defined by 14 foundation elements.

Custom emphasizes …

Optimizing Material Selection And Operational Conditions For Xhv Systems: Lessons From Aisi 1020 And 316l Comparative Studies, Aiman H. Al-Allaq, Md Abdullah Mamun, Matt Poelker, Abdelmageed Elmustafa

Mechanical & Aerospace Engineering Faculty Publications

In this study, AISI 1020 low-carbon steel was investigated as a cost-effective alternative to SS316L stainless steel for reaching extreme high vacuum (XHV) conditions. After being baked at 400°C, a vacuum chamber made of low-carbon steel material exhibited an outgassing rate approximately 2000 times smaller than a similar chamber made of stainless steel. Its activation energy for hydrogen diffusion (27 kJ/mol) is less than half that of stainless steel (60.3 kJ/mol), indicating more efficient hydrogen removal during bakeout. MolFlow+ simulations supported the experimental data and demonstrated the importance of system geometry optimization and minimizing stainless steel content for achieving optimal …

Performance Evaluation Of Strategic Bandwidth Block Assignment For Interference Reduction In High-Density 5g Nr Cell Environments, Pasapong Wongprasert

Chulalongkorn University Theses and Dissertations (Chula ETD)

Due to the increasing demand for mobile data, mobile network operators (MNOs) typically rely on cell densification to increase capacity. However, doing so also introduces more inter-cell interference (ICI), leading to lower spectral efficiency and making further capacity enhancements challenging. This thesis explores a bandwidth block assignment method for ICI mitigation. The core idea is to divide the bandwidth of an NR carrier into two partially overlapping bandwidth parts (BWPs) to reduce the impact of ICI. By allowing the two BWPs to share the same synchronization signal block (SSB), the same attach and handover configurations can be applied. A modified …

Real-Time Monitoring System For Sedentary Behavior Using Pose-Estimation Approach, Pheng Ou Chea

Chulalongkorn University Theses and Dissertations (Chula ETD)

Long-term sedentary behavior, defined as extended periods of sitting or inactivity, increases the risk of obesity, cardiovascular disease, and other health problems. These risks are often underestimated due to low levels of physical activity. This study proposes the use of You Only Look Once (YOLO)–based pose estimation for dwell-time analysis to monitor sedentary behavior. We examined posture over an entire day to assess prolonged inactivity and encourage individuals to balance energy expenditure with available time—such as engaging in exercise in the early morning or evening according to recommended guidelines. For employees across generations, as well as for vulnerable groups such …

Bismuth Surfactant-Mediated Epitaxy Of Gaas(Bi) On On-Axis Ge(100) Substrates, Valgeir Sigmarsson

Chulalongkorn University Theses and Dissertations (Chula ETD)

In this work, Bi-surfactant-mediated epitaxy is applied in GaAs(Bi) film deposition on nom- inally on-axis Ge(100) substrates to investigate its effect on mitigating the formation of anti-phase domains (APDs) inherent to the polar-on-non-polar GaAs/Ge(100) heterostructure. GaAs(Bi) layers were deposited by solid-source molecular beam epitaxy (MBE) with a V/III ratio of ~90 and stepwise variations in Bi flux between samples. The Bi-free GaAs sample exhibited an unstable RHEED pattern indicating ill-ordered growth mode, confirmed by AFM showing an amor- phous surface with an RMS value of ~20 nm. In contrast, Bi-assisted growth produced stable RHEED (1×3) surface reconstructions. AFM revealed single-crystal …

การจัดสรรกำลังการผลิตอย่างเหมาะที่สุดสำหรับระบบผลิตไฟฟ้าพลังงานแสงอาทิตย์ที่มีระบบกักเก็บพลังงานแบบแบตเตอรี่เพื่อการซื้อขายพลังงานแบบหนึ่งวันล่วงหน้า, กรวิชญ์ ธนบำรุงกูล

Chulalongkorn University Theses and Dissertations (Chula ETD)

ไฟฟ้าพลังงานแสงอาทิตย์มีความสำคัญมากขึ้นในตลาดการซื้อขายพลังงาน วิทยานิพนธ์นี้เสนอการจัดสรรกำลังการผลิตอย่างเหมาะที่สุดสำหรับระบบผลิตไฟฟ้าพลังงานแสงอาทิตย์ที่มีระบบกักเก็บพลังงานแบบแบตเตอรี่เพื่อให้ได้รายได้สุทธิต่อวันสูงสุดในการซื้อขายพลังงานแบบหนึ่งวันล่วงหน้า ใช้วิธีโปรแกรมเชิงเส้นแบบผสมจำนวนเต็มไบนารีในการหาค่าเหมาะที่สุด และพิจารณาปรับการทำงานของระบบ ณ เวลาจริง กับสัญญาแบบ Partial-firm ตามระเบียบคณะกรรมการกำกับกิจการพลังงาน (กกพ.) สำหรับโรงไฟฟ้าพลังงานหมุนเวียนเอกชน ใช้ข้อมูลการพยากรณ์พลังงานแสงอาทิตย์แบบหนึ่งวันล่วงหน้าในการวางแผนและใช้ข้อมูลพลังงานแสงอาทิตย์จริงในการควบคุม ณ เวลาจริง เพื่อกำหนดการอัดและคายประจุของแบตเตอรี่และการจัดสรรกำลังผลิตสำหรับระบบผลิตไฟฟ้าพลังงานแสงอาทิตย์ในการซื้อขายพลังงาน ปัจจัยสำคัญที่ส่งผลต่อขนาดของระบบกักเก็บพลังงาน เพื่อให้ผลิตไฟฟ้าได้ตามเงื่อนไขของสัญญา คือ การเรียกใช้งานความพร้อมจ่ายไฟฟ้า ส่วนการควบคุม ณ เวลาจริง วิธีที่ทำรายได้สุทธิสูงสุดคือการควบคุมเชิงทำนายแบบจำลอง (MPC) โดยการหาค่าเหมาะที่สุดในทุกขั้นเวลาอย่างต่อเนื่อง ผลการศึกษากรณีสัญญา Partial-firm ขายไฟฟ้าที่ขนาด 10 เมกะวัตต์ พบว่าขนาดที่เหมาะสมของระบบไฟฟ้าพลังงานแสงอาทิตย์คือ 18 เมกะวัตต์ และระบบกักเก็บพลังงานแบบแบตเตอรี่ที่ขนาด 35 เมกะวัตต์-ชั่วโมง