Digital Commons Network^™

The Method Of Power Networks Mode Optimization In Basis Of Genetic Algorithm, Tulkin Shernazarovich Gayibov, Gulnaz Makhmutovna Turmanova

Technical science and innovation

One of the main tasks solved in planning short-term and managing operational modes of electric power systems (EPS) is the optimization of their network modes on the adjustable parameters. For modern complex EPS, this task is often characterized by the multi-extremality of the objective function, the appearance of discontinuous functions, the presence of initial information of a probabilistic and partially uncertain nature. In such conditions, solving the problem by traditional algorithms using mainly linear and nonlinear programming methods, Lagrange, gradient, etc., is associated with a number of difficulties in simplifying them and bringing them to a convenient form for calculations. …

Methodology For Creating An Intelligent Mechatron Module Based On A Synergistic Approach, Nurbek Rustamovich Matyokubov, Temurbek Omonboyevich Rakhimov

Technical science and innovation

This article is devoted to the methodology of creating an intelligent mechatron module based on a synergistic approach. Creating a multi-coordinate intelligent mechatron module and expanding the functional capabilities of the controlled object based on them, allows to optimize the dynamic characteristics. Also, the methodology for creating an intelligent mechatronic module based on a synergistic approach allows for the integration of electrical, magnetic, and mechanical components into a single circuit, the integration of technical parameter modification, and the integration of processing functions into a single information-measuring module. In addition, the article proposes a Eurorhythm scheme for expressing how …

Comparative Assessment Of Battery And Hydrogen Energy Storage In A Grid-Connected Heat-Electricity Integrated Microgrid, Naglaa Elsherbiny, Mohamed Elgamal, Islam Ismael, Akram Elmitwally

Mansoura Engineering Journal

Given the significant challenges of conventional power systems, including high emissions and operational costs, transitioning to microgrids (MGs) is increasingly critical. This paper presents a comprehensive evaluation of three energy management strategies for a grid-connected, heat-electricity integrated MG: (i) a base case without storage, (ii) a battery-based energy storage system (B-BESS), and (iii) a hydrogenbased energy storage system (H-BESS) comprising an electrolyzer (ELC), hydrogen tank (HT), and fuel cell (FC). This study optimizes operational profit and emissions reduction by integrating renewable energy and heat recovery mechanisms. Simulation results show that, compared to the base case, both B-BESS and H-BESS significantly …

Climate Change Projections Through Optimized Machine Learning And Deep Learning Models: A Comprehensive Analysis, Hala H. Youssef, Salma S. Mohammed, Hanan M. Amer, Abeer T. Khalil

Mansoura Engineering Journal

One major worldwide issue that has far-reaching effects on environmental stability and the avoidance of natural disasters is climate change. Developing efficient mitigating strategies depends on precise climate change forecasts. To predict important climate change indicators, including temperature fluctuations, greenhouse gas (CO2, N2O, CH4, and SF6) emissions, population dynamics, sea level changes, Arctic Sea ice extent, and Antarctica mass, this study evaluated the predictive capabilities of several Machine Learning (ML) and Deep Learning (DL) techniques. The set of machine learning algorithms includes Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN), Support Vector Machines (SVMs), Artificial Neural Networks (ANN), Random Forest (RF), …

An Integrated Pcb-Based Heating And Auto-Ranging Platform For Volatile Organic Compound (Voc) Detection Using Carbon Nanotube Based Sensors, Thomas Kalach

USF Tampa Graduate Theses and Dissertations

This thesis presents the development, characterization, and integration of a novel low-cost, high-dynamic-range sensor platform for the detection of volatile organic compounds (VOCs), leveraging the unique electrical properties of carbon nanotube (CNT) thin films. The platform introduces a fully integrated auto-ranging analog front-end circuit capable of real-time resistance measurement spanning over eight orders of magnitude ranging from tens of ohms to hundreds of megaohms, without compromising signal resolution or precision. This was achieved through a digitally controlled, multi-path feedback architecture and controllable current source.

To further enhance sensor performance, the system incorporates a copper trace heater beneath the sensor array, …

Performance Analysis Of Power System Dynamic Stability Of The Nigerian 330kv Transmission Network, Charles Evuarherhe Ikeyin, Benjamin Olabisi Akinloye

Al-Bahir

Nigeria's power sector faces persistent challenges, including dynamic instability, poor voltage regulation, and frequent grid collapses, primarily driven by reactive power deficits and transmission distances exceeding 200 km in northern regions. To bridge critical research gaps, this study employs a rigorous, multi-method approach through comprehensive EMTP-RV (Electromagnetic Transients Program) analysis of Nigeria’s 330kV 52-bus grid, PV curve-based vulnerability indexing to identify cascade-critical buses, and a novel Cost-benefit Pareto framework for optimal capacitor placement. Methodology was validated against IEEE (Institute of Electrical and Electronics Engineers) 14-bus benchmarks. Validated against IEEE 14-bus benchmarks, simulations revealed severe voltage instability in northern …

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 …

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. …

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 …

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 …

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 …

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 …

Torque-Speed Characteristic Estimation Based On Gaussian Processes And Adaptive Sampling Strategy For Permanent Magnet Synchronous Machines, Marcelo D. Silva, Pedram Asef, Oluwaseun A. Badewa, Rosemary E. Alden, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Internal Permanent Magnet Synchronous Machines (IPMs) are widely used and typically optimized to meet specific performance requirements. Parameters such as base speed, maximum torque, and maximum speed commonly define the torque- speed characteristic of a given design. This study introduces a novel machine learning approach for statistically estimating the torque-speed characteristics of IPMs using Gaussian Process Regression (GPR), which models predictions as random variables. By leveraging uncertainty quantification, the study explores sampling strategies that enable the construction of a high-precision meta-model with minimal error and uncertainty. The proposed adaptive sampling strategy, combined with GPR, accurately estimates torque-speed characteristics and associated …

Csc36000 - Modern Distributed Computing Assignment, Saptarashmi Bandyopadhyay

Open Educational Resources

This assignment covers standard performance metrics for Distributed Systems and the basics of Multiprocessing for CSC36000 - Modern Distributed Computing at the City College of New York CUNY. It is an interactive coding assignment intended to be executed in a Python notebook.

Building A Smart Transportation Network To Prevent Multi-Vehicle Collisions During Sudden Slowdowns, Leo Huang, Patrick Zhao

College of Engineering Summer Undergraduate Research Program

This proposed SURP project aims to design and evaluate a smart transportation network capable of preventing multiple-vehicle collisions due to sudden slowdowns in traffic. This project will simulate abrupt braking scenarios and implement adaptive vehicle-to-vehicle (V2V) communication protocols. By enhancing real-time awareness and responsiveness among vehicles, the system will reduce pileup risks and improve road safety.

Spectral Modeling Of Electromagnetic Radiation From Power Converters, Regan A. Varner

Theses and Dissertations

Switching power converters are increasingly utilized in contemporary electronics. They are becoming more compact and operate at higher frequencies, which contributes to radiated electromagnetic interference (EMI). This EMI is important to quantify for electromagnetic compatibility of the converters with other nearby systems. In this thesis, analytical models have been developed for both ideal and non-ideal switching waveforms, employing piecewise functions to describe time-domain waveforms, with their corresponding frequency spectra derived and compared to empirical spectra. The results demonstrate that the comparisons with measured frequency data exhibit agreement with the analytical models up to approximately 25 MHz. Additionally, our analysis indicates …

Towards Robust Bmocz Schemes For Non-Coherent Wireless Communication, Anthony Joseph Perre

Theses and Dissertations

Non-coherent communication has emerged as a promising approach to address several challenges at the physical layer. A particular scheme called binary modulation on conjugate-reciprocal zeros (BMOCZ) uses the zeros (i.e, roots) of a polynomial to convey information bits. In this thesis, we strive to improve the reliability of BMOCZ in various scenarios. In particular, we utilize machine learning (ML) methods to determine the parameters for BMOCZ and improve upon the decoding performance. Moreover, we introduce a smooshed BMOCZ variant to combat typical impairments encountered within wireless communications, including timing offsets (TOs) for noncoherent orthogonal frequency division multiplexing (OFDM). Through numerical …

Resilience Engineering Via Bifurcation And Ecological Network Analysis: Demonstrated In An Electric Power Case Study, Rogelio Gracia Otalvaro

Doctoral Dissertations and Master's Theses

Modern systems are increasingly complex, interconnected cyber-physical systems that combine digital controls with physical infrastructure. This integration, along with the constant introduction of new technologies and actors into the network, enables reliable operation but introduces vulnerabilities to unexpected and varied disruptions and cascading failures, making resilience a critical concern. Traditional risk management and resilience assessment methods often struggle with the nonlinearity and dynamic behavior of these systems. This dissertation proposes a novel approach combining Bifurcation Analysis (BA) and Ecological Network Analysis (ENA) to enhance the understanding and improvement of system resilience. BA, a mathematical method from dynamical systems theory, is …

Design Of A Robust Adaptive Cascade Fractional-Order Proportional–Integral–Derivative Controller Enhanced By Reinforcement Learning Algorithm For Speed Regulation Of Brushless Dc Motor In Electric Vehicles, Seyyed Morteza Ghamari, Mehrdad Ghahramani, Daryoush Habibi, Asma Aziz

Research outputs 2022 to 2026

Brushless DC (BLDC) motors are commonly used in electric vehicles (EVs) because of their efficiency, small size and great torque-speed performance. These motors have a few benefits such as low maintenance, increased reliability and power density. Nevertheless, BLDC motors are highly nonlinear and their dynamics are very complicated, in particular, under changing load and supply conditions. The above features require the design of strong and adaptable control methods that can ensure performance over a broad spectrum of disturbances and uncertainties. In order to overcome these issues, this paper uses a Fractional-Order Proportional-Integral-Derivative (FOPID) controller that offers better control precision, better …

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 …

A Risk-Averse Data-Driven Distributionally Robust Optimization Method For Transmission Power Systems Under Uncertainty, Mehrdad Ghahramani, Daryoush Habibi, Asma Aziz

Research outputs 2022 to 2026

The increasing penetration of renewable energy sources and the consequent rise in forecast uncertainty have underscored the need for robust operational strategies in transmission power systems. This paper introduces a risk-averse, data-driven distributionally robust optimization framework that integrates unit commitment and power flow constraints to enhance both reliability and operational security. Leveraging advanced forecasting techniques implemented via gradient boosting and enriched with cyclical and lag-based time features, the proposed methodology forecasts renewable generation and demand profiles. Uncertainty is quantified through a quantile-based analysis of forecasting residuals, which forms the basis for constructing data-driven ambiguity sets using Wasserstein balls. The framework …

Construction, Optimization, And Characterization Of An Undergraduate Cold Cathode Table-Top Electron Accelerator For Radiation Physics Education, Caitlin Balmer, Mason Skeath, Mehran M. Zaini, Peter Zencak, Erin M. Craig

Journal of the Symposium of University Research and Creative Expression

Project Mentor(s): Mehran Zaini, PhD; Peter Zencak

Rising cancer cases spurred advancements in radiation therapy modalities, including electron accelerators. In this report, descriptive and diagnostic analysis was utilized to develop and characterize a functional, low energy cold cathode table-top electron accelerator for radiation physics experimentation in Central Washington University’s (CWU) undergraduate radiation lab. The device features a tungsten cathode (TC), brass anode, and copper Faraday Cup (FC) in a vacuum, enclosed by blue-tinted polyvinyl chloride (PVC). TC electron emission was facilitated by applied electric fields from input voltages of 1000 V to 5000 V. FC collected electron current in the …

Series Reports From Professor Wei’S Group Of Chongqing University: Advancements In Electrochemical Energy Conversions (2/4): Report 2: High-Performance Water Splitting Electrocatalysts, Ling Zhang, Wang-Yang Wu, Qiu-Yue Hu, Shi-Dan Yang, Li Li, Rui-Jin Liao, Zi-Dong Wei

Journal of Electrochemistry

The unavailability of high-performance and cost-effective electrocatalysts has impeded the large-scale deployment of alkaline water electrolyzers. Professor Zidong Wei’s group has focused on resolving critical challenges in industrial alkaline electrolysis, particularly elucidating hydrogen and oxygen evolution reaction (HER/OER) mechanisms while addressing the persistent activity-stability trade-off. This review summarizes their decade-long progress in developing advanced electrodes, analyzing the origins of sluggish alkaline HER kinetics and OER stability limitations. Professor Wei proposes a unifying “12345 Principle” as an optimization framework. For HER electrocatalysts, they have identified that metal/metal oxide interfaces create synergistic “chimney effect” and “local electric field enhancement effect”, enhancing selective …

Local Electric Fields Coupled With Cl− Fixation Strategy For Improving Seawater Oxygen Reduction Reaction Performance, Yu-Rong Liu, Miao Zhang, Yan-Hui Yu, Ya-Lin Liu, Jing Li, Xiao-Dong Shi, Zhen-Ye Kang, Dao-Xiong Wu, Peng Rao, Ying Liang, Xin-Long Tian

Journal of Electrochemistry

Development of robust electrocatalyst for oxygen reduction reaction (ORR) in a seawater electrolyte is the key to realize seawater electrolyte-based zinc-air batteries (SZABs). Herein, constructing a local electric field coupled with chloride ions (Cl⁻) fixation strategy in dual single-atom catalysts (DSACs) was proposed, and the resultant catalyst delivered considerable ORR performance in a seawater electrolyte, with a high half-wave potential (E_1/2) of 0.868 V and a good maximum power density (Pmax) of 182 mW·cm⁻² in the assembled SZABs, much higher than those of the Pt/C catalyst (E_1/2: 0.846 V; Pmax: 150 mW·cm^{−2 …}

Bridging Materials And Energy Storage Mechanisms In Zn-I2 Batteries, Rong-Qi Liu, Wen-Shuo Shang, Jin-Tao Zhang

Journal of Electrochemistry

Zinc-iodine (Zn-I₂) batteries have emerged as a compelling candidate for large-scale energy storage, driven by the growing demand for safe, cost-effective, and sustainable alternatives to conventional systems. Benefiting from the inherent advantages of aqueous electrolytes and zinc metal anodes, including high ionic conductivity, low flammability, natural abundance, and high volumetric capacity, Zn-I₂ batteries offer significant potential for grid-level deployment. This review provides a comprehensive overview of recent progress in three critical domains: positive-electrode engineering, zinc anode stabilization, and in situ characterization methods. On the cathode side, anchoring iodine to conductive matrices effectively mitigates polyiodide shuttling and enhances …

Significantly Enhanced Oxygen Reduction Reaction Activity In Co-N-C Catalysts Through Synergistic Boron Doping, Chang Lan, Jing-Sen Bai, Xin Guan, Shuo Wang, Nan-Shu Zhang, Yu-Qing Cheng, Jin-Jing Tao, Yu-Yi Chu, Mei-Ling Xiao, Chang-Peng Liu, Wei Xing

Journal of Electrochemistry

The weak adsorption energy of oxygen-containing intermediates on Co center leads to a considerable performance disparity between Co-N-C and costly Pt benchmark in catalyzing oxygen reduction reaction (ORR). In this work, we strategically engineer the active site structure of Co-N-C via B substitution, which is accomplished by the pyrolysis of ammonium borate. During this process, the in-situ generated NH₃ gas plays a critical role in creating surface defects and boron atoms substituting nitrogen atoms in the carbon structure. The well-designed CoB₁N₃ active site endows Co with higher charge density and stronger adsorption energy toward oxygen species, …

Enhancement Of The Received Signal Strength In Smart Grid Communication Systems, Doaa Talaat Elsherbiny, Mona Mohamed Shokair Prof., Mohamed Shalaby, Salah Elden Khamis, Sameh A. Napoleon

Journal of Engineering Research

Smart grids are networks that contain intelligence from the generation stage to the distribution stage. These grids can actually have excellent power efficiency and automated control thanks to intelligence. Within smart grids, the communication mechanism is a crucial area of study. There should be a dependable communication system in the smart grid. A comprehensive mathematical model for a communication system within smart grids is derived in this work. Additionally, the simulation findings validate the mathematical model that was derived. Additionally, experiments are being conducted to apply polar convolutional parallel code PCPC to enhance the performance of the suggested communication system. …