Electrical and Computer Engineering Commons^™

Resilience Oriented Distribution System Service Restoration Considering Overhead Power Lines Affected By Hurricanes, Kehkashan Fatima, Hussain Shareef, Flavio Costa

Michigan Tech Publications

In recent years, there has been an increase in the frequency of severe weather events (like hurricanes). These events are responsible for most power outages in power distribution systems (PDSs). Particularly susceptible to storms are overhead PDSs. In this study, the dynamic Bayesian network (DBN)-based failure model was developed for different hurricane scenarios to predict the line failure of overhead lines. Based on the outcomes of the DBN model, a service restoration model was formulated to maximize restored loads and minimize power losses using Particle Swarm Optimization (PSO)-based distributed generation (DG) integration and system reconfiguration. Three different case studies based …

Non-Invasive Way For Detection Of Neonatal Jaundice Using Gbr, Priti V. Bhagat, Mukesh Raghuwanshi, Ashutosh D. Bagde

Chulalongkorn Medical Journal

Background: Neonatal jaundice is a common condition in newborns due to excess levels of bilirubin. The traditional method for bilirubin testing is invasive, i.e., through blood tests only, which is painful to infants. Therefore, this study uses machine learning algorithms to develop a non-invasive way to detect neonatal jaundice.

Objectives: Design a computer-aided support system to detect neonatal jaundice using machine learning algorithm.

Methods: The gradient-boosting regression model is used to predict the bilirubin level. Gradient Boosting is a robust boosting algorithm that combines several weak learners into strong learners, in which each new model is trained to minimize the …

Trustworthy Navigation With Variational Policy In Deep Reinforcement Learning, Karla Bockrath, Liam Ernst, Rohaan Nadeem, Bryan Joseph Pedraza, Dimah Dera

Electrical and Computer Engineering Faculty Publications

Introduction: Developing a reliable and trustworthy navigation policy in deep reinforcement learning (DRL) for mobile robots is extremely challenging, particularly in real-world, highly dynamic environments. Particularly, exploring and navigating unknown environments without prior knowledge, while avoiding obstacles and collisions, is very cumbersome for mobile robots.

Methods: This study introduces a novel trustworthy navigation framework that utilizes variational policy learning to quantify uncertainty in the estimation of the robot’s action, localization, and map representation. Trust-Nav employs the Bayesian variational approximation of the posterior distribution over the policy-based neural network’s parameters. Policy-based and value-based learning are combined to guide the robot’s actions …

Authenticating Electronic Devices Via Multi Tone Analysis, Carl Bohman Jr., Aaron Jennings, Christian Eakins, Mark B. Skouson, Richard Ott, Jamin Mccue

AFIT Patents

Methods and systems for authenticating electronic devices via multi tone analysis. A method for authenticating a device under test (DUT) of a type of DUT includes imparting voltage tones to the DUT. The voltage tones are proximate a frequency of interest that is associated with the type of DUT. Using a measurement response of the DUT to the voltage tones, an electronic signature of the DUT is determined. The DUT is determined to be authentic when the electronic signature of the DUT substantially matches an electronic signature of an authority DUT of the type of DUT.

Maximum Likelihood Symbol Timing Algorithm Based On Cyclic Prefix For Ofdm Systems, Kwame S. Ibwe

Tanzania Journal of Engineering and Technology (TJET)

In this paper, a blind symbol synchronization algorithm is presented for orthogonal frequency-division multiplexing (OFDM) systems, and a timing function based on the redundancy of the cyclic prefix (CP) is introduced. The existing algorithms rely on the prior knowledge of the channel energy distribution i.e. channel power profile. In practical environment the channel power profile is unknown to the receiver and its statistics are expected to be highly changing. Nevertheless, the use of pilot symbols in channel profile estimation reduces efficiency as data subcarriers are used to carry pilots instead of payload. In this paper a timing function that accounts …

Techno-Economic Analysis Of Hybrid Pv-Wind-Diesel Generator Swarm Grid For Rural Electrification In Tanzania, Ibrahim Mwammenywa

Tanzania Journal of Engineering and Technology (TJET)

Remote, rural, and off-grid communities, particularly in Sub-Saharan Africa (SSA), face significant challenges in securing reliable and affordable electricity. While solutions such as solar home systems (SHSs) and diesel generators (DGs) have been adopted by some individuals, their deployment is often not optimized to meet the full load demand. This study addresses this challenge by proposing a hybrid swarm grid (HSG) design that integrates photovoltaic (PV), wind energy, and diesel generators (DGs). Utilizing MATLAB/Simulink and HOMER, the HSG is modeled and optimized for a typical SSA village, incorporating real-world variables such as load demand profiles, solar irradiance, and wind patterns. …

Adoption Of Agrivoltaics In Developing Countries: A Review On Challenges, Opportunities And Future Prospects, Abdi J. Athumani, Pater Makolo

Tanzania Journal of Engineering and Technology (TJET)

This paper provides a review of agrivoltaics technology and how it has been applicable in developing countries, including African countries. Agrivoltaics, the integration of agricultural production with photovoltaic energy generation, offers promising solutions to water- energy-food nexus in developing countries. This technology offers the dual benefit of increasing agricultural productivity while generating clean energy, which is very important for regions facing frequent electricity shortages and declining agricultural yields due to climate change. However, despite its potential, the adoption of agrivoltaics in developing nations remains limited in comparison to developed countries due to financial, technical and policy constraints. This paper explores …

Optimal Location And Sizing Of Facts Devices To Improve Voltage Profiles On A 132 Kv Mwanza-Musoma-Nyamongo Transmission Line, Peter Makolo

Tanzania Journal of Engineering and Technology (TJET)

Recently, utility companies have desired to supply quality, stable and reliable power to customers, and ensure they meet the demand. Flexible AC Transmission Systems (FACTS) dynamic compensator devices such as Static Synchronous Compensator (STATCOM), Static VAr Compensator (SVC) and Unified Power Flow Controller (UPFC) are an impeccable choice, however, cost is one of the limiting factors following these technologies. In addition, using FACTS devices in the system requires a detailed steady state, dynamic and optimisation analysis to effectively meet the purpose and ensure reduced cost. This paper proposes using an optimised FACTS device to improve voltage profile, power transfer, system …

Investigating The Harmonic Content Of A Pwm Inverter With Varying Modulation Index, Peter Makolo

Tanzania Journal of Engineering and Technology (TJET)

Photovoltaic energy is a clean and endless vital renewable energy. In order to generate electricity from solar energy an inverter is required to transform the direct current into alternating current. Also, with the emerging of new ultra-high voltage direct current transmission technology, the rectification and inverters play a great role. Most three-phase two-level inverters draw harmonics that cause heat dissipation, waveform distortions hence affect the electrical loads. Knowing that Total Harmonic Distortion content of voltage source inverter is important and must be within the allowable range. Several schemes are suggested to mitigate the distortion in order to produce as much …

Towards Autonomous Energy Management: Machine Learning For Effective Auditing And Optimization, Sameh O. Abdellatif, Sherif Ashraf, Mira Mohsen

Electrical Engineering

This study presents a fully automated procedure for energy management and auditing, applicable to a diverse range of residential and commercial loads, leveraging machine learning techniques across three key phases: load classification, benchmarking, and smart monitoring. The model effectively categorizes energy loads based on consumption patterns, establishes performance benchmarks through historical data analysis, and employs real-time monitoring to identify inefficiencies and predict future energy usage. Evaluating the model through four distinct case studies demonstrates its capability to optimize energy consumption in a techno-economic manner, achieving significant energy savings of 34.73 MWh/year for essential loads in Egypt, 215.67 MWh/year for HVAC …

Ai-Powered Accessibility Tracker For Inclusive Public Spaces, Yenny Ma, Kevin Beltran

College of Engineering Summer Undergraduate Research Program

This research project will develop and evaluate a smartphone-based, AI-powered system to crowdsource and analyze accessibility features and barriers in public spaces. Using computer vision and geospatial mapping, the system will identify and categorize issues such as uneven sidewalks, missing or inadequate curb ramps, damaged tactile paving, obstructive overhangs, and the absence of visual or auditory wayfinding cues. The overarching goal is to generate a dynamic, real-time accessibility map that empowers individuals with diverse mobility, sensory, and cognitive needs to navigate public spaces more safely and confidently. The project will integrate technologies and methods from applied machine learning, mobile computer …

Impulse, Fall 2025, Jill Fier, Sierra Brown, Jerome J. Lohr College Of Engineering

Impulse (Jerome J. Lohr College of Engineering Publication)

2 | Faculty News
3 | SDSU Vet Hua to Head Civil Engineering
6 | Ad Lunam — To the Moon
10 | Building A Safety Culture
11 | Department News
12 | Surface Mount Technology Kickoff
14 | Construction Management Student, Volleyball Player Building Success Piece by Piece
16 | Student Awards, Honors
18 | Student Competition Results
20 | NASA Win Opens Doors for New Product Development Order
22 | SDSU Engineering Student Tabbed as Noblereach Fellow
26 | Knabach Award Recipient from Apprentice Lineman to Company President
28 | Metzger Create Faculty Endowment for SDSU Engineering
30 …

Inductorless Cascaded Low-Power Dc-Dc Converter: Optimizing Performance Metrics Through Machine Learning Techniques, Ahmed Khaled, Sameh O. Abdellatif

Electrical Engineering

This study presents a groundbreaking methodology for optimizing the operational efficiency of a three-stage boost DC-DC cascaded converter through the application of a Random Forest(RF) machine learning algorithm. A novel figure of merit is meticulously formulated to quantitatively evaluate the converter’s performance, focusing on critical metrics such as power conversion efficiency, output DC ripple levels, and response time. The Random Forest model is trained on a comprehensive dataset encompassing a wide range of resistive and capacitive design parameters, with the figure of merit serving as the output indicator. Rigorous simulations and analyses demonstrate that the integration of LM741 operational amplifiers …

Insect-Foundation: A Foundation Model And Large Multimodal Dataset For Vision-Language Insect Understanding, Thanh-Dat Truong, Hoang-Quan Nguyen, Xuan-Bac Nguyen, Ashley Dowling, Xin Li, Khoa Luu

Electrical Engineering and Computer Science Faculty Publications and Presentations

Multimodal conversational generative AI has shown impressive capabilities in various vision and language understanding through learning massive text-image data. However, current conversational models still lack knowledge about visual insects since they are often trained on the general knowledge of vision-language data. Meanwhile, understanding insects is a fundamental problem in precision agriculture, helping to promote sustainable development in agriculture. Therefore, this paper proposes a novel multimodal conversational model, Insect-LLaVA, to promote visual understanding in insect-domain knowledge. In particular, we first introduce a new large-scale Multimodal Insect Dataset with Visual Insect Instruction Data that enables the capability of learning the multimodal foundation …

Research On The Construction Of Domain Knowledge Graph For Assisted Decision Making And Its Scenario-Oriented Application, Hao Xu, Linlin Ge, Yan Zhang, Sanhong Deng

Journal of Scientific Information Research

[Purpose/significance] This research constructed a domain knowledge graph and its scenario-oriented application framework for decision support at four levels: the data foundation layer, the key technology layer, the domain knowledge graph construction layer, and the scenario-oriented application layer. This framework aims to provide systematic support for knowledge discovery.

[Method/process] Based on the construction of a domain knowledge graph and its scenario-oriented application framework for decision support, this research focuses on the improvement of models and performance evaluation for fine-grained entity and relationship extraction at the discourse level within texts. The optimal model is selected to construct a domain knowledge graph. …

High Torque Density Dual-Stator Vernier Motors With Flux Concentrating Rotors, Esmaeil Mohammadi, Ali Mohammadi, Mohammad Amin Jalali Kondelaji, Pedram Asef, Ion G. Boldea, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Axial Flux Permanent Magnet (AFPM) machines are increasingly being studied for low-speed, direct-drive applications due to their compact structure and high torque output. This study proposes two novel dual-stator AFPM vernier machine topologies: a spoke-type rotor configuration and a back-to-back Halbach array rotor. Both designs employ dual outer stators with 12 double-layer concentrated windings, and high-polarity rotor configurations to enhance flux concentration. A three-dimensional finite element model, which was previously validated by a laboratory prototype motor was utilized to evaluate the electromagnetic characteristics of the proposed topologies. These characteristics include torque density, airgap flux distribution, and harmonic content. Comparative results …

Multi-Phase Wireless Power Transfer With High Power Density Inductive Coils For Electric Drone Charging, Lucas A. Gastineau, Donovin D. Lewis, Omer Onar, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Wireless charging of unmanned ground vehicles and aircraft has been proposed to increase charging reliability and security, allow for autonomous functionality, and either reduce battery size or increase continuous flight time. This paper proposes a three-phase Litz wire primary and a two-phase PCB secondary for high secondary-side power density considering misalignment tolerances, surface and volumetric power density, and coil sizing. Electromagnetic 3D finite element analysis (FEA) simulations are conducted to study variation in mutual inductance and coupling coefficient with different secondary coil sizes and number of turns, horizontal and vertical misalignment between the primary and secondary, and a combination of …

Nonlinear Design Scaling Of Electric Machines Based On Hybrid De And Meta-Modeling Application To Synchronous Motors With Combined Pm Stator And Reluctance Rotor Excitation, Oluwaseun A. Badewa, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper presents an innovative method for nonlinear scaling of electric machines by integrating machine learning (ML)-based meta-modeling with a differential evolution (DE) algorithm. The technique is applied to high-performance combined-excitation synchronous electric motors which exhibit highly nonlinear characteristics, making performance scaling challenging. The proposed approach employs an ML meta-model trained on data obtained from finite element analysis (FEA), utilizing an experimentally validated model for nonlinear scaling and performance prediction at different power ratings. The accuracy of the meta-model in capturing the nonlinear relationships between design parameters and motor performance is first assessed using metrics such as R-squared (R2) and …

Multi Electric Machines With Series And Parallel Electromechanical Combinations For Aircraft, David R. Stewart, Donovin D. Lewis, Matin Vatani, Diego A. Lopez-Guerrero, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

High-performance electric propulsion systems require fault tolerant, power dense, electric machines capable of maintaining high efficiency across a dynamic range of operation. To address these inherently conflicting requirements, multi-motor architectures employing electromechanically coupled modular configurations have been proposed to enhance system efficiency, fault tolerance, and redundancy. This paper investigates four mechanically coupled configurations for a coreless axial flux permanent magnet (CAFPM) motor unit integrating series, parallel, and hybrid architectures with differential and gearbox coupling. Performance and optimal sizing for motors in each configuration are determined through 3D finite element analysis (FEA). To assess fault tolerance and system redundancy, Markov chain …

Design Optimization And Scaling Of Coreless Afpm Machines Using Hybrid Fea-Based Differential Evolution And Machine Learning, Matin Vatani, David R. Stewart, Donovin D. Lewis, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper presents a machine learning (ML) based design framework for the fast and accurate optimization of coreless axial flux permanent magnet (AFPM) machines. Although the absence of magnetic cores eliminates material nonlinearity, the design process remains highly nonlinear due to the complex influence of geometric parameters. To overcome the computational challenges of finite element analysis (FEA)-based optimization, a series of multi-objective differential evolution (MODE) optimizations were conducted across various machine sizes at constant power output. The resulting design data was used to train an artificial neural network (ANN), enabling rapid prediction of machine performance without the need for repeated …