Physical Sciences and Mathematics Commons^™

Genetic Algorighm Representation Selection Impact On Binary Classification Problems, Stephen V. Maldonado

Honors Undergraduate Theses

In this thesis, we explore the impact of problem representation on the ability for the genetic algorithms (GA) to evolve a binary prediction model to predict whether a physical therapist is paid above or below the median amount from Medicare. We explore three different problem representations, the vector GA (VGA), the binary GA (BGA), and the proportional GA (PGA). We find that all three representations can produce models with high accuracy and low loss that are better than Scikit-Learn’s logistic regression model and that all three representations select the same features; however, the PGA representation tends to create lower weights …

Methods For Computing The Global Optimum Of Non-Convex Objectives, Isaac Michael Hawn

Graduate Research Theses & Dissertations

\begin{abstract}In this thesis, we concern ourselves with solving the unconstrained optimization problem % \begin{gather*} \text{Minimize}\; f(x)\\\text{subject to}\; x\in X \end{gather*} % where $f\colon\mathbb{R}^N\to \mathbb{R}$ is a non-convex function, possibly with infinitely many local minima. Solving such a problem, especially in higher dimensions often proves to be an extraordinarily difficult task, either in time complexity or in the methodology itself. Indeed, mathematicians must often resort to algorithms which make use of problem structure and which may not generalize well. In this thesis, we present two algorithms which solve this problem, albeit with their own shortcomings.

First, we present a new, $N$-dimensional …

An Upgraded Photoinjector For The Argonne Wakefield Accelerator, Emily Frame

Graduate Research Theses & Dissertations

The Argonne Wakefield Accelerator (AWA) is planning an upgrade of the drive-beam accelerator’s photoinjector, the driving force of electron generation. The upgrade’s main goal is to improve beam brightness using linear accelerating cavities and a radiofrequency-gun cavity. In the process of this upgrade, one of the beam focusing solenoids is being redesigned. A beam dynamics optimization is performed for two new solenoid designs, with considerations for producing low-charge (∼ 1 nC) electron bunches as well as operations at higher charges (∼ 50 nC). This project focuses on the optimization study for both the low- and high-charge regimes, exploring the impact …

Particle Swarm Optimization For Critical Experiment Design, Cole Michael Kostelac

Masters Theses

“Critical experiments are used by nuclear data evaluators and criticality safety engineers to validate nuclear data and computational methods. Many of these experiments are designed to maximize the sensitivity to a certain nuclide-reaction pair in an energy range of interest. Traditionally, a parameter sweep is conducted over a set of experimental variables to find a configuration that is critical and maximally sensitive. As additional variables are added, the total number of configurations increases exponentially and quickly becomes prohibitively computationally expensive to calculate, especially using Monte Carlo methods.

This work presents the development of a particle swarm optimization algorithm to design …

Pymoocfd - A Multi-Objective Optimization Framework For Cfd, George Martin Cunningham Love

Graduate College Dissertations and Theses

Modern computational resource have solidified the use of computer modeling as an integral part of the engineering design process. This is particularly impressive when it comes to high-dimensional models such as computational fluid dynamics (CFD) models. CFD models are now capable of producing results with a level of confidence that would previously have required physical experimentation. Simultaneously, the development of machine learning techniques and algorithms has increased exponentially in recent years. This acceleration is also due to the widespread availability of modern computational resources. Thus far, the cross-over between these fields has been mostly focused on computer models with low …

Energy Planning Model Design For Forecasting The Final Energy Consumption Using Artificial Neural Networks, Haidy Eissa

Theses and Dissertations

“Energy Trilemma” has recently received an increasing concern among policy makers. The trilemma conceptual framework is based on three main dimensions: environmental sustainability, energy equity, and energy security. Energy security reflects a nation’s capability to meet current and future energy demand. Rational energy planning is thus a fundamental aspect to articulate energy policies. The energy system is huge and complex, accordingly in order to guarantee the availability of energy supply, it is necessary to implement strategies on the consumption side. Energy modeling is a tool that helps policy makers and researchers understand the fluctuations in the energy system. Over the …

Prediction Of Iraqi Stock Exchange Using Optimized Based-Neural Network, Ameer Al-Haq Al-Shamery, Prof. Dr. Eman Salih Al-Shamery

Karbala International Journal of Modern Science

Stock market prediction is an interesting financial topic that has attracted the attention of researchers for the last years. This paper aims at improving the prediction of the Iraq-Stock-Exchange (ISX) using a developed method of feedforward Neural-Networks based on the Quasi-Newton optimization approach. The proposed method reduces the error factor depending on the Jacobian vector and Lagrange multiplier. This improvement has led to accelerating convergence during the learning process. A sample of companies listed on ISX was selected. This includes twenty-six banks for the years from 2010 to 2020. To evaluate the proposed model, the research findings are compared with …

Adadeep: A Usage-Driven, Automated Deep Model Compression Framework For Enabling Ubiquitous Intelligent Mobiles, Sicong Liu, Junzhao Du, Kaiming Nan, Zimu Zhou, Hui Liu, Zhangyang Wang, Yingyan Lin

Research Collection School Of Computing and Information Systems

Recent breakthroughs in deep neural networks (DNNs) have fueled a tremendously growing demand for bringing DNN-powered intelligence into mobile platforms. While the potential of deploying DNNs on resource-constrained platforms has been demonstrated by DNN compression techniques, the current practice suffers from two limitations: 1) merely stand-alone compression schemes are investigated even though each compression technique only suit for certain types of DNN layers; and 2) mostly compression techniques are optimized for DNNs’ inference accuracy, without explicitly considering other application-driven system performance (e.g., latency and energy cost) and the varying resource availability across platforms (e.g., storage and processing capability). To this …

Verification Assisted Gas Reduction For Smart Contracts, Bo Gao, Siyuan Shen, Ling Shi, Jiaying Li, Jun Sun, Lei Bu

Research Collection School Of Computing and Information Systems

Smart contracts are computerized transaction protocols built on top of blockchain networks. Users are charged with fees, a.k.a. gas in Ethereum, when they create, deploy or execute smart contracts. Since smart contracts may contain vulnerabilities which may result in huge financial loss, developers and smart contract compilers often insert codes for security checks. The trouble is that those codes consume gas every time they are executed. Many of the inserted codes are however redundant. In this work, we present sOptimize, a tool that optimizes smart contract gas consumption automatically without compromising functionality or security. sOptimize works on smart contract bytecode, …

Pressure Retarded Osmosis: A Potential Technology For Seawater Desalination Energy Recovery And Concentrate Management, Joshua Benjamin

USF Tampa Graduate Theses and Dissertations

Currently, a significant challenge with reverse osmosis-based desalination is reducing the energy consumption and environmental impacts of the process. This project analyzed the viability of using pressure-retarded osmosis (PRO) for energy recovery in seawater desalination facilities using brine concentrate (the draw solution) and other water sources (the feed solution) such as wastewater effluent. The primary goal of this project is to decrease the cost and overall energy consumption of seawater desalination through PRO-based energy recovery. Process modeling, statistical and sensitivity analysis, energy and cost analysis, geospatial and GIS analysis, laboratory-scale testing, water quality analysis, SEM-EDS microscopy, computational fluid dynamics (CFD), …

Expediting The Accuracy-Improving Process Of Svms For Class Imbalance Learning, Bin Cao, Yuqi Liu, Chenyu Hou, Jing Fan, Baihua Zheng, Jianwei Jin

Research Collection School Of Computing and Information Systems

To improve the classification performance of support vector machines (SVMs) on imbalanced datasets, cost-sensitive learning methods have been proposed, e.g., DEC (Different Error Costs) and FSVM-CIL (Fuzzy SVM for Class Imbalance Learning). They relocate the hyperplane by adjusting the costs associated with misclassifying samples. However, the error costs are determined either empirically or by performing an exhaustive search in the parameter space. Both strategies can not guarantee effectiveness and efficiency simultaneously. In this paper, we propose ATEC, a solution that can efficiently find a preferable hyperplane by automatically tuning the error cost for between-class samples. ATEC distinguishes itself from all …

Constructing Frameworks For Task-Optimized Visualizations, Ghulam Jilani Abdul Rahim Quadri

USF Tampa Graduate Theses and Dissertations

Visualization is crucial in today’s data-driven world to augment and enhance human understanding and decision-making. Effective visualizations must support accuracy in visual task performance and expressive data communication. Effective visualization design depends on the visual channels used, chart types, or visual tasks. However, design choices and visual judgment are co-related, and effectiveness is not one-dimensional, leading to a significant need to understand the intersection of these factors to create optimized visualizations. Hence, constructing frameworks that consider both design decisions and the task being performed enables optimizing visualization design to maximize efficacy. This dissertation describes experiments, techniques, and user studies to …

Orthogonal Inductive Matrix Completion, Antoine Ledent, Rrodrigo Alves, Marius Kloft

Research Collection School Of Computing and Information Systems

We propose orthogonal inductive matrix completion (OMIC), an interpretable approach to matrix completion based on a sum of multiple orthonormal side information terms, together with nuclear-norm regularization. The approach allows us to inject prior knowledge about the singular vectors of the ground-truth matrix. We optimize the approach by a provably converging algorithm, which optimizes all components of the model simultaneously. We study the generalization capabilities of our method in both the distribution-free setting and in the case where the sampling distribution admits uniform marginals, yielding learning guarantees that improve with the quality of the injected knowledge in both cases. As …

Optimizing Hospital Capacity To Deal With Hallway Medicine, Megan Wismer

Undergraduate Student Research Internships Conference

A model adapted from Hillier and Liebermann, 2017 that optimizes hospital capacity to deal with hallway medicine.

Exploiting Block Structures Of Kkt Matrices For Efficient Solution Of Convex Optimization Problems, Zafar Iqbal, Saeid Nooshabadi, Ichitaro Yamazaki, Stanimire Tomov, Jack Dongarra

Michigan Tech Publications

Convex optimization solvers are widely used in the embedded systems that require sophisticated optimization algorithms including model predictive control (MPC). In this paper, we aim to reduce the online solve time of such convex optimization solvers so as to reduce the total runtime of the algorithm and make it suitable for real-time convex optimization.We exploit the property of the Karush–Kuhn–Tucker (KKT) matrix involved in the solution of the problem that only some parts of the matrix change during the solution iterations of the algorithm. Our results show that the proposed method can effectively reduce the runtime of the solvers.

Continuous-Time And Complex Growth Transforms For Analog Computing And Optimization, Oindrila Chatterjee

McKelvey School of Engineering Theses & Dissertations

Analog computing is a promising and practical candidate for solving complex computational problems involving algebraic and differential equations. At the fundamental level, an analog computing framework can be viewed as a dynamical system that evolves following fundamental physical principles, like energy minimization, to solve a computing task. Additionally, conservation laws, such as conservation of charge, energy, or mass, provide a natural way to couple and constrain spatially separated variables. Taking a cue from these observations, in this dissertation, I have explored a novel dynamical system-based computing framework that exploits naturally occurring analog conservation constraints to solve a variety of optimization …

Scheduling Allocation And Inventory Replenishment Problems Under Uncertainty: Applications In Managing Electric Vehicle And Drone Battery Swap Stations, Amin Asadi

Graduate Theses and Dissertations

In this dissertation, motivated by electric vehicle (EV) and drone application growth, we propose novel optimization problems and solution techniques for managing the operations at EV and drone battery swap stations. In Chapter 2, we introduce a novel class of stochastic scheduling allocation and inventory replenishment problems (SAIRP), which determines the recharging, discharging, and replacement decisions at a swap station over time to maximize the expected total profit. We use Markov Decision Process (MDP) to model SAIRPs facing uncertain demands, varying costs, and battery degradation. Considering battery degradation is crucial as it relaxes the assumption that charging/discharging batteries do not …

Designing Targeted Mobile Advertising Campaigns, Kimia Keshanian

USF Tampa Graduate Theses and Dissertations

With the proliferation of smart, handheld devices, there has been a multifold increase in the ability of firms to target and engage with customers through mobile advertising. Therefore, not surprisingly, mobile advertising campaigns have become an integral aspect of firms’ brand building activities, such as improving the awareness and overall visibility of firms' brands. In addition, retailers are increasingly using mobile advertising for targeted promotional activities that increase in-store visits and eventual sales conversions. However, in recent years, mobile or in general online advertising campaigns have been facing one major challenge and one major threat that can negatively impact the …

Yard Layout Optimization For General Cargo Terminal, Zhixiong Liu, Dong Yu, Chunjun Zhang

Journal of System Simulation

Abstract: Yard layout is an important component of the port yard allocation decision which affects the cargo storage capacity and through capacity for the port yard. As to the general cargo yard, combined with the cargo type and the yard storage strategy, the yard layout optimization model for the general cargo terminal is presented based on the statistical analysis for the production data when the optimization aim is minimizing the total horizontal transport distance of the trailer. The yard layout optimization results are employed by the mathematical tool Gurobi for different storage strategies, and the yard layout optimization results are …

A Stochastic Knapsack Game: Revenue Management In Competitions, Yingdong Lu

Applications and Applied Mathematics: An International Journal (AAM)

We study a mathematical model for revenue management under competitions with multiple sellers. The model combines the stochastic knapsack problem, a classic revenue management model, with a non-coorperative game model that characterizes the sellers’ rational behavior. We are able to establish a dynamic recursive procedure that incorporate the value function with the utility function of the games. The formalization of the dynamic recursion allows us to establish some fundamental structural properties.

Multilevel Hierarchical Decomposition Of Finite Element White Noise With Application To Multilevel Markov Chain Monte Carlo, Hillary R. Fairbanks, Umberto E. Villa, Panayot S. Vassilevski

Mathematics and Statistics Faculty Publications and Presentations

In this work we develop a new hierarchical multilevel approach to generate Gaussian random field realizations in an algorithmically scalable manner that is well suited to incorporating into multilevel Markov chain Monte Carlo (MCMC) algorithms. This approach builds off of other partial differential equation (PDE) approaches for generating Gaussian random field realizations; in particular, a single field realization may be formed by solving a reaction-diffusion PDE with a spatial white noise source function as the right-hand side. While these approaches have been explored to accelerate forward uncertainty quantification tasks, e.g., multilevel Monte Carlo, the previous constructions are not directly applicable …

Efficient Attribute-Based Encryption With Repeated Attributes Optimization, Fawad Khan, Hui Li, Yinghui Zhang, Haider Abbas, Tahreem Yaqoob

Research Collection School Of Computing and Information Systems

Internet of Things (IoT) is an integration of various technologies to provide technological enhancements. To enforce access control on low power operated battery constrained devices is a challenging issue in IoT scenarios. Attribute-based encryption (ABE) has emerged as an access control mechanism to allow users to encrypt and decrypt data based on an attributes policy. However, to accommodate the expressiveness of policy for practical application scenarios, attributes may be repeated in a policy. For certain policies, the attributes repetition cannot be avoided even after applying the boolean optimization techniques to attain an equivalent smaller length boolean formula. For such policies, …

Can Parallel Gravitational Search Algorithm Effectively Choose Parameters For Photovoltaic Cell Current Voltage Characteristics?, Alan Kirkpatrick

Honors Projects

This study asks the question “Can parallel Gravitational Search Algorithm (GSA) effectively choose parameters for photovoltaic cell current voltage characteristics?” These parameters will be plugged into the Single Diode Model to create the IV curve. It will also investigate Particle Swarm Optimization (PSO) and a population based random search (PBRS) to see if GSA performs the search better and or more quickly than alternative algorithms

Study On Optimum Cultivation Techniques Of Alfalfa On Songnen Plain, Fengjiu Chai, Zedong Liu

IGC Proceedings (1993-2023)

No abstract provided.

A Survey On Long-Range Wide-Area Network Technology Optimizations, Felipe S. Dantas Silva, Emidio P. Neto, Helder Oliveira, Denis Rosário, Eduardo Cerqueira, Cristiano Both, Sherali Zeadally, Augusto V. Neto

Information Science Faculty Publications

Long-Range Wide-Area Network (LoRaWAN) enables flexible long-range service communications with low power consumption which is suitable for many IoT applications. The densification of LoRaWAN, which is needed to meet a wide range of IoT networking requirements, poses further challenges. For instance, the deployment of gateways and IoT devices are widely deployed in urban areas, which leads to interference caused by concurrent transmissions on the same channel. In this context, it is crucial to understand aspects such as the coexistence of IoT devices and applications, resource allocation, Media Access Control (MAC) layer, network planning, and mobility support, that directly affect LoRaWAN’s …

Structure Determination Of Pd2sin (N = 1 - 9) Clusters, Ryan Carlin

Honors College Theses

The geometry and relative stability of small Pd2Sin (n = 1 - 9) clusters are determined using the B3LYP hybrid density functional theory method. Candidate structures are identified by the utilization of unbiased global optimization procedures. From the lowest energy geometries, binding energy, fragmentation energy, second-order energy difference, and frontier orbital gap energy are calculated to determine cluster stability. Pd2Sin (n = 8 and 9) adapts new lowest energy geometric configurations that have previously not been considered. Pd2Si5 is determined to have a high relative stability of the clusters within this size range.

Optimal Communication Structures For Concurrent Computing, Andrii Berdnikov

Doctoral Dissertations

This research focuses on communicative solvers that run concurrently and exchange information to improve performance. This “team of solvers” enables individual algorithms to communicate information regarding their progress and intermediate solutions, and allows them to synchronize memory structures with more “successful” counterparts. The result is that fewer nodes spend computational resources on “struggling” processes. The research is focused on optimization of communication structures that maximize algorithmic efficiency using the theoretical framework of Markov chains. Existing research addressing communication between the cooperative solvers on parallel systems lacks generality: Most studies consider a limited number of communication topologies and strategies, while the …

Efficient Inversion Of 2.5d Electrical Resistivity Data Using The Discrete Adjoint Method, Diego Domenzain, John Bradford, Jodi Mead

Mathematics Faculty Publications and Presentations

We have developed a memory and operation-count efficient 2.5D inversion algorithm of electrical resistivity (ER) data that can handle fine discretization domains imposed by other geophysical (e.g, ground penetrating radar or seismic) data. Due to numerical stability criteria and available computational memory, joint inversion of different types of geophysical data can impose different grid discretization constraints on the model parameters. Our algorithm enables the ER data sensitivities to be directly joined with other geophysical data without the need of interpolating or coarsening the discretization. We have used the adjoint method directly in the discretized Maxwell’s steady state equation to compute …

Machine Learning Models For Deciphering Regulatory Mechanisms And Morphological Variations In Cancer, Saman Farahmand

Graduate Doctoral Dissertations

The exponential growth of multi-omics biological datasets is resulting in an emerging paradigm shift in fundamental biological research. In recent years, imaging and transcriptomics datasets are increasingly incorporated into biological studies, pushing biology further into the domain of data-intensive-sciences. New approaches and tools from statistics, computer science, and data engineering are profoundly influencing biological research. Harnessing this ever-growing deluge of multi-omics biological data requires the development of novel and creative computational approaches. In parallel, fundamental research in data sciences and Artificial Intelligence (AI) has advanced tremendously, allowing the scientific community to generate a massive amount of knowledge from data. Advances …

Deep Learning And Optimization In Visual Target Tracking, Mohammadreza Javanmardi

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Visual tracking is the process of estimating states of a moving object in a dynamic frame sequence. It has been considered as one of the most paramount and challenging topics in computer vision. Although numerous tracking methods have been introduced, developing a robust algorithm that can handle different challenges still remains unsolved. In this dissertation, we introduce four different trackers and evaluate their performance in terms of tracking accuracy on challenging frame sequences. Each of these trackers aims to address the drawbacks of their peers. The first developed method is called a structured multi-task multi-view tracking (SMTMVT) method, which exploits …