Engineering Commons^™

Optimization Of Human Interactions In The College Campus Model Via Simio Integration, Benjamin E. Chaback

Doctoral Dissertations and Master's Theses

College campuses are a significant part of life in some cities. Many students each year attend university, pursuing additional knowledge from faculty members. Both staff and faculty members rely on these students to have successful jobs and to ensure the university functions. Yet recently, more and more students are attending, leading to overcrowding, lower admission rates, and difficulty getting into good programs. Previous work exists on qualitative student affairs and quantitative retention data, yet little on using simulations to model this problem. This work aimed to (a) Determine the ability to successfully model human interactions/people flow on a college campus, …

Strategy For Predictive Control Of The Rectification Process Based On A Model Controller With A Given Forecast, Ildar Rafkatovich Sultanov

Chemical Technology, Control and Management

A method is being developed to optimize the generated controls for the multicomponent distillation process with prediction, based on predictive data with a moving horizon. The difference between this method and the classical modeling approach, in which the percentage of the degree of opening of valves installed on the output streams of the column is used as control actions, is that control occurs on the feedback principle. The proposed method is based on the use of a dynamic process model to optimize control actions in real time in order to achieve certain production targets. The essence of the MPC approach …

Synthesize A Neural Network Parameter Optimizer For An Adaptive Pid Controller, Nashvandova Gulruxsor Murot Qizi

Chemical Technology, Control and Management

Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters superstructuring. In the paper, the questions of optimization of PID-regulator parameters with application of methods of neural network technology are considered. A methodology for selecting the architecture of neural network optimizer designed to determine the tuned parameters of PID regulator is proposed. The algorithm of training of the neural network, with the set on the basis of the method of inverse gradient propagation is offered. The proposed improved PID-neural regulator allowed to provide stabilization of neural network operation and its trainability in the control loop …

Milp Modeling Of Matrix Multiplication: Cryptanalysis Of Klein And Prince, Murat Burhan İlter, Ali Aydın Selçuk

Turkish Journal of Electrical Engineering and Computer Sciences

Mixed-integer linear programming (MILP) techniques are widely used in cryptanalysis, aiding in the discovery of optimal linear and differential characteristics. This paper delves into the analysis of block ciphers KLEIN and PRINCE using MILP, specifically calculating the best linear and differential characteristics for reduced-round versions. Both ciphers employ matrix multiplication in their diffusion layers, which we model using multiple XOR operations. To this end, we propose two novel MILP models for multiple XOR operations, which use fewer variables and constraints, proving to be more efficient than standard methods for XOR modeling. For differential cryptanalysis, we identify characteristics with a probability …

A Novel Physics-Assisted Genetic Algorithm For Decoupling Capacitor Optimization, Li Jiang, Ling Zhang, Shurun Tan, Da Li, Chulsoon Hwang, Jun Fan, Er Ping Li

Electrical and Computer Engineering Faculty Research & Creative Works

This article proposes a new physics-assisted genetic algorithm (PAGA) for decoupling capacitor (decap) optimization in power distribution networks (PDNs), which is a highly efficient approach to minimizing the number of decaps within an enormous search space. In the proposed PAGA method, the priority of the decap ports is first determined based on their physical loop inductances. Then, an initial solution is quickly obtained by placing decaps sequentially on the port with the highest priority. Subsequently, a GA with prior physical knowledge is developed to find better decap solutions progressively. A port removal scheme that eliminates the low-priority ports and a …

Complete Solution Of The Lady In The Lake Scenario, Alexander Von Moll, Meir Pachter

Faculty Publications

In the Lady in the Lake scenario, a mobile agent, L, is pitted against an agent, M, who is constrained to move along the perimeter of a circle. L is assumed to begin inside the circle and wishes to escape to the perimeter with some finite angular separation from M at the perimeter. This scenario has, in the past, been formulated as a zero-sum differential game wherein L seeks to maximize terminal separation and M seeks to minimize it. Its solution is well-known. However, there is a large portion of the state space for which the canonical solution does not …

Adaptation Algorithm For Self-Tuning Of Parameters Of Models Of Multi-Stage Flotation Processes, Nilufar Sharifzhanova, Maksadhan Yakubov, Francesco Gregoretti

Technical science and innovation

Modern methods for solving problems of planning the execution of batches of tasks in multi-stage systems are characterized by the presence of restrictions on their dimensionality, the impossibility of guaranteed obtaining better results in comparison with fixed packages for different values of the input parameters of the problem. In the article, the author solved the problem of optimizing the composition of job packages running in multi-stage systems using the branch and bound method. Research has been carried out on various ways to form package execution orders tasks in multi-stage systems (heuristic rules for ordering packages tasks in the sequence of …

Cost Minimizing Energy Management Control Scheme For Microgrids Considering Dynamic Electricity Prices, Levi T. Miller

All Graduate Theses and Dissertations, Fall 2023 to Present

As countries develop and technology improves, the world is using more energy than ever before. This fact along with several other political, social, and economic factors has resulted in simultaneous energy and climate crises. A partial solution to both problems is bringing clean energy sources of electricity closer to the customers who use that energy. A microgrid is a smaller version of the national electric grid where smaller electricity generators are networked with local consumers and controlled independently of the main grid. Because control of electricity sources and loads are transferred to local controllers, the flexibility with which they can …

Application Of Evolutionary Algorithms For Optimization Of Operation Modes Of Regional Electric Power Systems, Isamiddin Khakimovich Siddikov, Oksana Vitalevna Porubay

Chemical Technology, Control and Management

The paper presents the possibilities of using evolutionary algorithms to solve the problem of optimizing the operation modes of electric power facilities in the presence of constraints in the form of inequalities and equalities. The limits of constraints have a variable character, depending on the generated and consumed energy. Existing methods used for the optimization of modes are based on general principles and approaches to optimization, which usually adapt to the specifics of the problem. In electric power facilities, optimization problems have some peculiarities, among which is the presence of multiple constraints applied to both independent and dependent variables. Many …

Optimizing High-Performance Computing Design: The Impacts Of Bandwidth And Topology Across Workloads For Distributed Shared Memory Systems, Jonathan A. Milton

Electrical and Computer Engineering ETDs

With the complexity of high-performance computing designs continuously increasing, the importance of evaluating with simulation also grows. One of the key design aspects is the network architecture; topology and bandwidth greatly influence the overall performance and should be optimized. This work uses simulations written to run in the Structural Simulation Toolkit software framework to evaluate a variety of architecture configurations, identify the optimal design point based on expected workload, and evaluate the changes with increased scale. The results show that advanced topologies outperform legacy architectures justifying the additional design complexity; and that after a certain point increasing the bandwidth provides …

Performance Modeling And Optimization For A Fog-Based Iot Platform, Shensheng Tang

Electrical and Computer Engineering Faculty Publications

A fog-based IoT platform model involving three layers, i.e., IoT devices, fog nodes, and the cloud, was proposed using an open Jackson network with feedback. The system performance was analyzed for individual subsystems, and the overall system was based on different input parameters. Interesting performance metrics were derived from analytical results. A resource optimization problem was developed and solved to determine the optimal service rates at individual fog nodes under some constraint conditions. Numerical evaluations for the performance and the optimization problem are provided for further understanding of the analysis. The modeling and analysis, as well as the optimization design …

Novel Approach For Non-Invasive Prediction Of Body Shape And Habitus, Emma Young

Electronic Theses and Dissertations

While marker-based motion capture remains the gold standard in measuring human movement, accuracy is influenced by soft-tissue artifacts, particularly for subjects with high body mass index (BMI) where markers are not placed close to the underlying bone. Obesity influences joint loads and motion patterns, and BMI may not be sufficient to capture the distribution of a subject’s weight or to differentiate differences between subjects. Subjects in need of a joint replacement are more likely to have mobility issues or pain, which prevents exercise. Obesity also increases the likelihood of needing a total joint replacement. Accurate movement data for subjects with …

Addressing The Challenged Of Dcop Based Decision-Making Algorithms In Modern Power Systems, Luis Daniel Ramirez Burgueno

Open Access Theses & Dissertations

Natural disasters have been determined as the leading cause of power outages, causing not only huge economic losses, but also the interruption of crucial welfare activities and the arise of security concerns. Because of the later, decision-making considering grid modernization, power system economics, and system resiliency has been a crucial theme in power systemsâ?? research. The need to better withstand catastrophic events and reducing the dependency of bulky generating units has propelled the development and better management of behind-the-meter generation or distributed energy resources (DERs). DERs can assist in the grid in different manners, not only by meeting energy demand …

Chance Constrained Stochastic Optimal Control Of Discrete Time Linear Stochastic Systems With Applications In Multi-Satellite Operations, Shawn Priore

Electrical and Computer Engineering ETDs

Stochastic disturbances arise in a variety of engineering applications. For tractability, Gaussian disturbances are often assumed. However, this may not always be valid, such as when a disturbance exhibits heavy-tailed or skewed phenomena. As autonomous systems become more ubiquitous, non-Gaussian disturbances will become more common due to the compounding effects of sensing, actuation, and external forces. Despite this, little has been done to develop formal methods that are both computationally efficient and allow for analytical assurances with non-Gaussian disturbances. Addressing convex polytopic set acquisition and non-convex collision avoidance chance constraints with quantile and moment-based reformulations, this dissertation proposes novel stochastic …

Network Economics-Based Crowdsourcing In Online Social Networks, Natasha S. Kubiak

Electrical and Computer Engineering ETDs

This thesis addresses the challenge of user recruitment by various competing marketing agencies (MAs) in Online Social Networks. A labor economics approach, following the principles of contract theory, is devised to enable MAs to reveal the potential of each participating user to contribute a personalized level of quality and quantity of information to the crowdsourcing process. The MAs objective is to maximize their personal benefit, i.e., total utility obtained, given its budget. The latter optimization problem is formulated as a Generalized Colonel Blotto (GCB) game among the MAs, where each MA aims at incentivizing each user to report its information. …

Unmanned-Aircraft-System-Assisted Early Wildfire Detection With Air Quality Sensors †, Doaa Rjoub, Ahmad Alsharoa, Ala'eddin Masadeh

Electrical and Computer Engineering Faculty Research & Creative Works

Numerous Hectares of Land Are Destroyed by Wildfires Every Year, Causing Harm to the Environment, the Economy, and the Ecology. More Than Fifty Million Acres Have Burned in Several States as a Result of Recent Forest Fires in the Western United States and Australia. According to Scientific Predictions, as the Climate Warms and Dries, Wildfires Will Become More Intense and Frequent, as Well as More Dangerous. These Unavoidable Catastrophes Emphasize How Important Early Wildfire Detection and Prevention Are. the Energy Management System Described in This Paper Uses an Unmanned Aircraft System (UAS) with Air Quality Sensors (AQSs) to Monitor Spot …

A Novel Covid-19 Herd Immunity-Based Optimizer For Optimal Accommodation Of Solar Pv With Battery Energy Storage Systems Including Variation In Load And Generation, Sumanth Pemmada, Nita Patne, Divyesh Kumar, Ashwini Manchalwar

Turkish Journal of Electrical Engineering and Computer Sciences

The world has now looked towards installing more renewable energy sources type distributed generation (DG), such as solar photovoltaic DG (SPVDG), because of its advantages to the environment and the quality of power supply it produces. However, these sources' optimal placement and size are determined before their accommodation in the power distribution system (PDS). This is to avoid an increase in power loss and deviations in the voltage profile. Furthermore, in this article, solar PV is integrated with battery energy storage systems (BESS) to compensate for the shortcomings of SPVDG as well as the reduction in peak demand. This paper …

Scheduling Electric Vehicle Charging For Grid Load Balancing, Zhixin Han, Katarina Grolinger, Miriam Capretz, Syed Mir

Electrical and Computer Engineering Publications

In recent years, electric vehicles (EVs) have been widely adopted because of their environmental benefits. However, the increasing volume of EVs poses capacity issues for grid operators as simultaneously charging many EVs may result in grid instabilities. Scheduling EV charging for grid load balancing has a potential to prevent load peaks caused by simultaneous EV charging and contribute to balance of supply and demand. This paper proposes a user-preference-based scheduling approach to minimize costs for the user while balancing grid loads. The EV owners benefit by charging when the electricity cost is lower, but still within the user-defined preferred charging …

Decoupling Optimization For Complex Pdn Structures Using Deep Reinforcement Learning, Ling Zhang, Li Jiang, Jack Juang, Zhiping Yang, Er Ping Li, Chulsoon Hwang

Electrical and Computer Engineering Faculty Research & Creative Works

This Article Presents a New Optimization Method for Complex Power Distribution Networks (PDNs) with Irregular Shapes and Multilayer Structures using Deep Reinforcement Learning (DRL), Which Has Not Been Considered Before. a Fast Boundary Integration Method is Applied to Compute the Impedance Matrix of a PDN Structure. Subsequently, a New DRL Algorithm based on Proximal Policy Optimization (PPO) is Proposed to Optimize the Decoupling Capacitor (Decap) Placement by Minimizing the Number of Decaps While Satisfying the Desired Target Impedance. in the Proposed Approach, the PDN Structure Information is Encoded into Matrices and Serves as the Input of the DRL Algorithm, Which …

Personalizing Student Graduation Paths Using Expressed Student Interests, Nicolas Dobbins, Ali R. Hurson, Sahra Sedigh

Electrical and Computer Engineering Faculty Research & Creative Works

This paper proposes an intelligent recommendation approach to facilitate personalized education and help students in planning their path to graduation. The goal is to identify a path that aligns with a student's interests and career goals and approaches optimality with respect to one or more criteria, such as time-to-graduation or credit hours taken. The approach is illustrated and verified through application to undergraduate curricula at the Missouri University of Science and Technology.

Peer-To-Peer Energy Trading In Smart Residential Environment With User Behavioral Modeling, Ashutosh Timilsina

Theses and Dissertations--Computer Science

Electric power systems are transforming from a centralized unidirectional market to a decentralized open market. With this shift, the end-users have the possibility to actively participate in local energy exchanges, with or without the involvement of the main grid. Rapidly reducing prices for Renewable Energy Technologies (RETs), supported by their ease of installation and operation, with the facilitation of Electric Vehicles (EV) and Smart Grid (SG) technologies to make bidirectional flow of energy possible, has contributed to this changing landscape in the distribution side of the traditional power grid.

Trading energy among users in a decentralized fashion has been referred …

Tutorial - Shodhguru Labs: Optimization And Hyperparameter Tuning For Neural Networks, Kaushik Roy

Publications

Neural networks have emerged as a powerful and versatile class of machine learning models, revolutionizing various fields with their ability to learn complex patterns and make accurate predictions. The performance of neural networks depends significantly on the appropriate choice of hyperparameters, which are critical factors governing their architecture, regularization, and optimization techniques. As the demand for high-performance neural networks grows across diverse applications, the need for efficient optimization and hyperparameter tuning methods becomes paramount. This paper presents a comprehensive exploration of optimization strategies and hyperparameter tuning techniques for neural networks. Neural networks have emerged as a powerful and versatile class …

2d Hybrid Analytical Model Based Performance Optimization For Linear Induction Motors, Michael Thamm

Electronic Theses and Dissertations

In this thesis the domain of double-layer, single-sided, 3-phase, integral slot winding, linear induction motor (LIM)s is analyzed. Motor meta parameters such as slots and poles are difficult to optimize since they drastically effect the configuration of the motor and require heuristic optimization implementations.

A non-dominated sorting genetic algorithm II (NSGAII) was implemented with the Platypus-Opt Python library. It serves as a robust, yet flexible integration while maximizing thrust and minimizing the mass of each motor iteration. Each iteration was accurately modelled using the hybrid analytical model (HAM), producing the necessary performance parameters for the NSGAII’s objective function. Field plotting …

Ai-Driven Security Constrained Unit Commitment Using Predictive Modeling And Eigen Decomposition, Talha Iqbal

Graduate Theses, Dissertations, and Problem Reports

Security Constrained Unit Commitment (SC-UC) is a complex large scale mix integer constrained optimization problem solved by Independent System Operators (ISOs) in the daily planning of the electricity markets. After receiving offers and bids, ISOs have only few hours to clear the day-ahead electricity market. It requires a lot of computational effort and a reasonable time to solve a large-scale SC-UC problem. However, exploiting the fact that a UC problem is solved several times a day with only minor changes in the system data, the computational effort can be reduced by learning from the historical data and identifying the patterns …

Multiple Uav-Lidar Placement Optimization Under Road Priority And Resolution Requirements, Zachary Osterwisch, Omar Rinchi, Ahmad Alsharoa, Hakim Ghazzai, Yehia Massoud

Electrical and Computer Engineering Faculty Research & Creative Works

An unmanned aerial vehicle (UAV) integrated with the remote sensing technology of light detection and ranging (LiDAR) can provide accurate and real-time road traffic information. In this paper, we propose to equip UAVs with LiDAR sensors for Intelligent Transportation Systems (ITS) applications. The goal is to find the optimal 3D placement of multiple UAV-LiDAR (ULiDs) for a given road segmentation. We formulate an optimization problem to find the optimal placement such that the road coverage efficiency is maximized. The optimization problem is constrained by notable ULiD specifications such as field-of-view (FoV), point-cloud density, geographic information system (GIS) location, and road …

Optimizing Technical And Economic Aspects Of Off-Grid Hybrid Renewable Systems: A Case Study Of Manoka Island, Cameroon, Reagan J. J. Molu, Serge R. D. Naoussi, Patrice Wira, Wulfran F. Mbasso, Saatong T. Kenfack, Barun K. Das, Enas Ali, Muhannad J. Alshareef, Sherif S. M. Ghoneim

Research outputs 2022 to 2026

The lack of accessible and reliable electrical energy in Cameroon has become a pervasive obstacle to the nation's progress, with energy availability, quality, and cost identified as key hindrances to development over the past 15 years. Conventional solutions that rely on combustion engines and electrochemical storage systems have proven to be cost-prohibitive, limited in power output, and constrained in capacity. The dependence on traditional diesel generators has perpetuated maintenance challenges and a continuous demand for fuel supply, while the accompanying noise and pollution have restricted their use in residential areas. Recognizing the imperative of reducing dependence on fossil fuels and …

Optimal Design Of Special High Torque Density Electric Machines Based On Electromagnetic Fea, Murat G. Kesgin

Theses and Dissertations--Electrical and Computer Engineering

Electric machines with high torque density are essential for many low-speed direct-drive systems, such as wind turbines, electric vehicles, and industrial automation. Permanent magnet (PM) machines that incorporate a magnetic gearing effect are particularly useful for these applications due to their potential for achieving extremely high torque density. However, when the number of rotor polarities is increased, there is a corresponding need to increase the number of stator slots and coils proportionally. This can result in manufacturing challenges. A new topology of an axial-flux vernier-type machine of MAGNUS type has been presented to address the mentioned limitation. These machines can …

Model-Based Deep Learning For Computational Imaging, Xiaojian Xu

McKelvey School of Engineering Theses & Dissertations

This dissertation addresses model-based deep learning for computational imaging. The motivation of our work is driven by the increasing interests in the combination of imaging model, which provides data-consistency guarantees to the observed measurements, and deep learning, which provides advanced prior modeling driven by data. Following this idea, we develop multiple algorithms by integrating the classical model-based optimization and modern deep learning to enable efficient and reliable imaging. We demonstrate the performance of our algorithms by validating their performance on various imaging applications and providing rigorous theoretical analysis.

The dissertation evaluates and extends three general frameworks, plug-and-play priors (PnP), regularized …

Power Market Cybersecurity And Profit-Targeting Cyberattacks, Qiwei Zhang

Doctoral Dissertations

The COVID-19 pandemic has forced many companies and business to operate through remote platforms, which has made everyday life and everyone more digitally connected than ever before. The cybersecurity has become a bigger priority in all aspects of life. A few real-world cases have demonstrated the current capability of cyberattacks as in [1], [2], and [3]. These cases invalidate the traditional belief that cyberattacks are unable to penetrate real-world industrial systems. Beyond the physical damage, some attackers target financial arbitrage advantages brought by false data injection attacks (FDIAs) [4]. Malicious breaches into power market operations could induce catastrophic consequences on …

Warp-Aware Adaptive Energy Efficiency Calibration For Multi-Gpu Systems, Zhuowei Wang, Xiaoyu Song, Lianglun Cheng, Hai Wan, Wuqing Zhao, Tao Wang

Electrical and Computer Engineering Faculty Publications and Presentations

Massive GPU acceleration processors have been used in high-performance computing systems. The Dennard-scaling has led to power and thermal constraints limiting the performance of such systems. The demand for both increased performance and energy-efficiency is highly desired. This paper presents a multi-layer low-power optimisation method for warps and tasks parallelisms. We present a dynamic frequency regulation scheme for performance parameters in terms of load balance and load imbalance. The method monitors the energy parameters in runtime and adjusts adaptively the voltage level to ensure the performance efficiency with energy reduction. The experimental results show that the multi-layer low-power optimisation with …