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Articles 1 - 15 of 15
Full-Text Articles in Physical Sciences and Mathematics
Multi-Commodity Flow Models For Logistic Operations Within A Contested Environment, Isabel Strinsky
Multi-Commodity Flow Models For Logistic Operations Within A Contested Environment, Isabel Strinsky
All Theses
Today's military logistics officers face a difficult challenge, generating route plans for mass deployments within contested environments. The current method of generating route plans is inefficient and does not assess the vulnerability within supply networks and chains. There are few models within the current literature that provide risk-averse solutions for multi-commodity flow models. In this thesis, we discuss two models that have the potential to aid military planners in creating route plans that account for risk and uncertainty. The first model we introduce is a continuous time model with chance constraints. The second model is a two-stage discrete time model …
Optimal Ordering To Maximize Mev Arbitrage, Granton Michael White
Optimal Ordering To Maximize Mev Arbitrage, Granton Michael White
Theses and Dissertations
The rise of cryptocurrencies has brought with it new math problems with new sets of constraints. The MEV problem entails solving for the ordering of pending trades that maximizes a block creator's profit. In decentralized finance, time is a big constraint, so an exhaustive search of all possible orderings is impossible. I propose a solution to the MEV problem that gives a near optimal result that can be solved in a reasonable amount of time. I layout the method and the formulas required for my solution. Additionally, I test my solution on synthesized data to show that it works as …
Novel Approach For Non-Invasive Prediction Of Body Shape And Habitus, Emma Young
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 …
Distributed Control Of Servicing Satellite Fleet Using Horizon Simulation Framework, Scott Plantenga
Distributed Control Of Servicing Satellite Fleet Using Horizon Simulation Framework, Scott Plantenga
Master's Theses
On-orbit satellite servicing is critical to maximizing space utilization and sustainability and is of growing interest for commercial, civil, and defense applications. Reliance on astronauts or anchored robotic arms for the servicing of next-generation large, complex space structures operating beyond Low Earth Orbit is impractical. Substantial literature has investigated the mission design and analysis of robotic servicing missions that utilize a single servicing satellite to approach and service a single target satellite. This motivates the present research to investigate a fleet of servicing satellites performing several operations for a large, central space structure.
This research leverages a distributed control approach, …
Deep Hybrid Modeling Of Neuronal Dynamics Using Generative Adversarial Networks, Soheil Saghafi
Deep Hybrid Modeling Of Neuronal Dynamics Using Generative Adversarial Networks, Soheil Saghafi
Dissertations
Mechanistic modeling and machine learning methods are powerful techniques for approximating biological systems and making accurate predictions from data. However, when used in isolation these approaches suffer from distinct shortcomings: model and parameter uncertainty limit mechanistic modeling, whereas machine learning methods disregard the underlying biophysical mechanisms. This dissertation constructs Deep Hybrid Models that address these shortcomings by combining deep learning with mechanistic modeling. In particular, this dissertation uses Generative Adversarial Networks (GANs) to provide an inverse mapping of data to mechanistic models and identifies the distributions of mechanistic model parameters coherent to the data.
Chapter 1 provides background information on …
Creating The Optimal Wedding Seating Chart, Madison Lane
Creating The Optimal Wedding Seating Chart, Madison Lane
Theses/Capstones/Creative Projects
The purpose of this project is to develop an effective seating arrangement for a wedding reception that enhances the comfort of guests. The ultimate aim is to create a harmonious and enjoyable atmosphere for all attendees. To achieve this, an integer program was designed to optimize the seating arrangement for the author’s upcoming wedding on May 27th, 2023. To ensure accuracy and feasibility, actual feedback was gathered from the guests to evaluate their compatibility and preferences. The proposed seating chart optimization not only addresses the placement of guests but also determines the number of tables required for the reception. The …
Addressing The Challenged Of Dcop Based Decision-Making Algorithms In Modern Power Systems, Luis Daniel Ramirez Burgueno
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 …
A Machine Learning Approach For Predicting Clinical Trial Patient Enrollment In Drug Development Portfolio Demand Planning, Ahmed Shoieb
A Machine Learning Approach For Predicting Clinical Trial Patient Enrollment In Drug Development Portfolio Demand Planning, Ahmed Shoieb
Masters Theses
One of the biggest challenges the clinical research industry currently faces is the accurate forecasting of patient enrollment (namely if and when a clinical trial will achieve full enrollment), as the stochastic behavior of enrollment can significantly contribute to delays in the development of new drugs, increases in duration and costs of clinical trials, and the over- or under- estimation of clinical supply. This study proposes a Machine Learning model using a Fully Convolutional Network (FCN) that is trained on a dataset of 100,000 patient enrollment data points including patient age, patient gender, patient disease, investigational product, study phase, blinded …
Loss Scaling And Step Size In Deep Learning Optimizatio, Nora Alosily
Loss Scaling And Step Size In Deep Learning Optimizatio, Nora Alosily
Dissertations
Deep learning training consumes ever-increasing time and resources, and that is
due to the complexity of the model, the number of updates taken to reach good
results, and both the amount and dimensionality of the data. In this dissertation,
we will focus on making the process of training more efficient by focusing on the
step size to reduce the number of computations for parameters in each update.
We achieved our objective in two new ways: we use loss scaling as a proxy for
the learning rate, and we use learnable layer-wise optimizers. Although our work
is perhaps not the first …
Multilevel Optimization With Dropout For Neural Networks, Gary Joseph Saavedra
Multilevel Optimization With Dropout For Neural Networks, Gary Joseph Saavedra
Mathematics & Statistics ETDs
Large neural networks have become ubiquitous in machine learning. Despite their widespread use, the optimization process for training a neural network remains com-putationally expensive and does not necessarily create networks that generalize well to unseen data. In addition, the difficulty of training increases as the size of the neural network grows. In this thesis, we introduce the novel MGDrop and SMGDrop algorithms which use a multigrid optimization scheme with a dropout coarsening operator to train neural networks. In contrast to other standard neural network training schemes, MGDrop explicitly utilizes information from smaller sub-networks which act as approximations of the full …
Data-Driven Reachability Of Non-Linear Systems Via Optimization Of Chen-Fliess Series, Ivan Perez Avellaneda
Data-Driven Reachability Of Non-Linear Systems Via Optimization Of Chen-Fliess Series, Ivan Perez Avellaneda
Graduate College Dissertations and Theses
A reachable set is the set of all possible states produced by applying a set of inputs, initial states, and parameters. The fundamental problem of reachability is checking if a set of states is reached provided a set of inputs, initial states, and parameters, typically, in a finite time. In the engineering field, reachability analysis is used to test the guarantees of the operation’s safety of a system. In the present work, the reachability analysis of nonlinear control affine systems is studied by means of the Chen-Fliess series. Different perspectives for addressing the reachability problem, such as interval arithmetic, mixed-monotonicity, …
Particle Swarm Optimization For High Rigidity Spectrometer, Yicheng Wang
Particle Swarm Optimization For High Rigidity Spectrometer, Yicheng Wang
Honors Theses
The goal of this project is to find reliable parameter settings for a multi-dimensional global optimizer to optimize the performance of a large acceptance ion optical system for the requirements of nuclear physics experiments. We develop and test the Particle Swarm Optimization (PSO), a global optimization algorithm designed for continuous multi-dimensional problems, on a large acceptance particle beam separator, the High Rigidity Spectrometer (HRS) at the Facility for Rare Isotope Beams (FRIB), which is a laboratory specializing in the production and experimental study of short-lived nuclear matter. We split the HRS into two sections, the High-Transmission Beamline (HTBL) and the …
Peer-To-Peer Energy Trading In Smart Residential Environment With User Behavioral Modeling, Ashutosh Timilsina
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 …
Logistics Planning: Putting Math To Work In A Business Setting, Michael C. Hannan
Logistics Planning: Putting Math To Work In A Business Setting, Michael C. Hannan
Senior Projects Spring 2023
The optimization of business procedures benefits all aspects of the product. Maximizing efficiency can lead to more profits for the business, cheaper products for the consumer, and less fuel consumption for the environment. Tracing the history of optimization, we can see that people have always strived for the most efficient way to allocate scarce resources. However, the field of optimization did not blossom until innovations in mathematics allowed us to solve a majority of real world problems. The discovery of linear and nonlinear programming in the 1940s allowed us to optimize problems that were unsolvable before. This paper introduces how …
Optimal Design And Operation Of Integrated Hydrogen Generation And Utilization Plants, Ijiwole Solomon Ijiyinka
Optimal Design And Operation Of Integrated Hydrogen Generation And Utilization Plants, Ijiwole Solomon Ijiyinka
Graduate Theses, Dissertations, and Problem Reports
There are considerable efforts worldwide for reducing the use of fossil fuel for energy production. While renewable energy sources are being increasingly used, fossil fuel still contribute about 80% of the energy used worldwide. As a result, the level of CO2 is still increasing fast in the atmosphere currently exceeding about 410 parts per million (ppm). For reducing CO2 build up in the atmosphere, various approaches are being investigated. For the electric power generation sector, two key approaches are post-combustion CO2 capture and use of hydrogen as a fuel for power generation. These two solutions can also …