Digital Commons Network^™

Development Of A Configurable Real-Time Event Detection Framework For Power Systems Using Swarm Intelligence Optimization, Umar Farooq

Dissertations and Theses

Modern power systems characterized by complex topologies require accurate situational awareness to maintain an adequate level of reliability. Since they are large and spread over wide geographical areas, occurrence of failures is inevitable in power systems. Various generation and transmission disturbances give rise to a mismatch between generation and demand, which manifest as frequency events. These events can take the form of negligible frequency deviations or more severe emergencies that can precipitate cascading outages, depending on the severity of the disturbance and efficacy of remedial action schema. The impacts of such events have become more critical with recent decline in …

Making Curry With Rice: An Optimizing Curry Compiler, Steven Libby

Dissertations and Theses

In this dissertation we present the RICE optimizing compiler for the functional logic language Curry. This is the first general optimizing compiler for a functional logic language. Our work is based on the idea of compiling through program transformations, which we have adapted from the functional language compiler community. We also present the GAS system for generating new program transformations, which uses the power of functional logic programming to provide a flexible framework for describing transformations. This allows us to describe and implement a wide range of optimizations including inlining, shortcut deforestation, unboxing, and case shortcutting, a new optimization we …

Using Intrinsically-Typed Definitional Interpreters To Verify Compiler Optimizations In A Monadic Intermediate Language, Dani Barrack

Dissertations and Theses

Compiler optimizations are critical to the efficiency of modern functional programs. At the same time, optimizations that unintentionally change the semantics of programs can systematically introduce errors into programs that pass through them. The question of how to best verify that optimizations and other program transformations preserve semantics is an important one, given the potential for error introduction. Dependent types allow us to prove that properties about our programs are correct, as well as to design data types and interpreters in such a way that they are correct-by-construction. In this thesis, we explore the use of dependent types and intrinsically-typed …

Quantum Grover's Oracles With Symmetry Boolean Functions, Peng Gao

Dissertations and Theses

Quantum computing has become an important research field of computer science and engineering. Among many quantum algorithms, Grover's algorithm is one of the most famous ones. Designing an effective quantum oracle poses a challenging conundrum in circuit and system-level design for practical application realization of Grover's algorithm.

In this dissertation, we present a new method to build quantum oracles for Grover's algorithm to solve graph theory problems. We explore generalized Boolean symmetric functions with lattice diagrams to develop a low quantum cost and area efficient quantum oracle. We study two graph theory problems: cycle detection of undirected graphs and generalized …

Proximal Policy Optimization For Radiation Source Search, Philippe Erol Proctor

Dissertations and Theses

Rapid localization and search for lost nuclear sources in a given area of interest is an important task for the safety of society and the reduction of human harm. Detection, localization and identification are based upon the measured gamma radiation spectrum from a radiation detector. The nonlinear relationship of electromagnetic wave propagation paired with the probabilistic nature of gamma ray emission and background radiation from the environment leads to ambiguity in the estimation of a source's location. In the case of a single mobile detector, there are numerous challenges to overcome such as weak source activity, multiple sources, or the …

Forecasting Optimal Parameters Of The Broken Wing Butterfly Option Strategy Using Differential Evolution, David Munoz Constantine

Dissertations and Theses

Obtaining an edge in financial markets has been the objective of many hedge funds, investors, and market participants. Even with today's abundance of data and computing power, few individuals achieve a consistent edge over an extended time. To obtain this edge, investors usually use options strategies. The Broken Wing Butterfly (BWB) is an options strategy that has increased in popularity among traders. Profit is generated primarily by exploiting option value time decay. In this thesis, the selection of entry and exit BWB parameters, such as profit and loss targets, are optimized for an in-sample period. Afterward, they are used to …

Convex And Nonconvex Optimization Techniques For Multifacility Location And Clustering, Tuyen Dang Thanh Tran

Dissertations and Theses

This thesis contains contributions in two main areas: calculus rules for generalized differentiation and optimization methods for solving nonsmooth nonconvex problems with applications to multifacility location and clustering. A variational geometric approach is used for developing calculus rules for subgradients and Fenchel conjugates of convex functions that are not necessarily differentiable in locally convex topological and Banach spaces. These calculus rules are useful for further applications to nonsmooth optimization from both theoretical and numerical aspects. Next, we consider optimization methods for solving nonsmooth optimization problems in which the objective functions are not necessarily convex. We particularly focus on the class …

On Dc And Local Dc Functions, Liam Jemison

University Honors Theses

In this project we investigate the class of functions which can be represented by a difference of convex functions, hereafter referred to simply as 'DC' functions. DC functions are of interest in optimization because they allow the use of convex optimization techniques in certain non-convex problems. We present known results about DC and locally DC functions, including detailed proofs of important theorems by Hartman and Vesely.

We also investigate the DCA algorithm for optimizing DC functions and implement it to solve the support vector machine problem.

Aggregated Water Heater System (Awhs) Optimization For Ancillary Services, Manasseh Obi

Dissertations and Theses

In this dissertation, I present a two-stage optimization routine that schedules an Aggregated Water Heater System (AWHS) to concurrently provide three utility ancillary services, namely, frequency regulation, frequency response, and peak demand mitigation.

Water heaters can be controlled to manage their energy take, the amount of energy a water heater can absorb upon command. The AWHS is a model aggregation of thousands of water heaters, the energy take and power characteristics of which are based on U.S Census household data and usage behavior patterns. The aggregate energy take available in the AWHS may be dispatched en masse for participation in …

Dictionary Learning For Image Reconstruction Via Numerical Non-Convex Optimization Methods, Lewis M. Hicks

University Honors Theses

This thesis explores image dictionary learning via non-convex (difference of convex, DC) programming and its applications to image reconstruction. First, the image reconstruction problem is detailed and solutions are presented. Each such solution requires an image dictionary to be specified directly or to be learned via non-convex programming. The solutions explored are the DCA (DC algorithm) and the boosted DCA. These various forms of dictionary learning are then compared on the basis of both image reconstruction accuracy and number of iterations required to converge.

The Optimization Of Machining Parameters For Milling Operations By Using The Nelder Mead Simplex Method, Yubin Lee

Dissertations and Theses

Machining operations need to be optimized to maximize profit for computer numerical control (CNC) machines. Although minimum production time could mean high productivity, it can not guarantee maximum profit rate in CNC milling operations. The possible range of machining parameters is limited by several constraints, such as maximum machine power, surface finish requirements, and maximum cutting force for the stability of milling operations. Among CNC machining parameters, cutting speed and feed have the greatest effect on machining operations. Therefore, cutting speed and feed are considered as main process variables to maximize the profit rate of CNC milling operations.

A variety …

Utilizing Parallelism In The Conjugate Gradient Algorithm, Adam James Craig

University Honors Theses

This paper develops the original conjugate gradient method and the idea of preconditioning a system. I also propose a unique type of additive-Schwarz preconditioner that can be solved in parallel, which creates a speed increase for large systems. To show this, I developed a C++11 linear algebra library used in conjunction with the OpenMP parallel computing library to empirically show a speed increase.

Efficient Methods For Robust Circuit Design And Performance Optimization For Carbon Nanotube Field Effect Transistors, Muhammad Ali

Dissertations and Theses

Carbon nanotube field-effect transistors (CNFETs) are considered to be promising candidate beyond the conventional CMOSFET due to their higher current drive capability, ballistic transport, lesser power delay product and higher thermal stability. CNFETs show great potential to build digital systems on advanced technology nodes with big benefits in terms of power, performance and area (PPA). Hence, there is a great need to develop proven models and CAD tools for performance evaluation of CNFET-based circuits. CNFETs specific parameters, such as number of tubes, pitch (spacing between the tubes) and diameter of CNTs determine current driving capability, speed, power consumption and area …

Design Optimization For A Cnc Machine, Alin Resiga

Dissertations and Theses

Minimizing cost and optimization of nonlinear problems are important for industries in order to be competitive. The need of optimization strategies provides significant benefits for companies when providing quotes for products. Accurate and easily attained estimates allow for less waste, tighter tolerances, and better productivity. The Nelder-Mead Simplex method with exterior penalty functions was employed to solve optimum machining parameters. Two case studies were presented for optimizing cost and time for a multiple tools scenario. In this study, the optimum machining parameters for milling operations were investigated. Cutting speed and feed rate are considered as the most impactful design variables …

Generalized Differential Calculus And Applications To Optimization, R. Blake Rector

Dissertations and Theses

This thesis contains contributions in three areas: the theory of generalized calculus, numerical algorithms for operations research, and applications of optimization to problems in modern electric power systems. A geometric approach is used to advance the theory and tools used for studying generalized notions of derivatives for nonsmooth functions. These advances specifically pertain to methods for calculating subdifferentials and to expanding our understanding of a certain notion of derivative of set-valued maps, called the coderivative, in infinite dimensions. A strong understanding of the subdifferential is essential for numerical optimization algorithms, which are developed and applied to nonsmooth problems in operations …

Convex And Nonconvex Optimization Techniques For The Constrained Fermat-Torricelli Problem, Nathan Lawrence

University Honors Theses

The Fermat-Torricelli problem asks for a point that minimizes the sum of the distances to three given points in the plane. This problem was introduced by the French mathematician Fermat in the 17th century and was solved by the Italian mathematician and physicist Torricelli. In this thesis we introduce a constrained version of the Fermat-Torricelli problem in high dimensions that involves distances to a finite number of points with both positive and negative weights. Based on the distance penalty method, Nesterov’s smoothing technique, and optimization techniques for minimizing differences of convex functions, we provide effective algorithms to solve the problem. …

The Majorization Minimization Principle And Some Applications In Convex Optimization, Daniel Giles

University Honors Theses

The majorization-minimization (MM) principle is an important tool for developing algorithms to solve optimization problems. This thesis is devoted to the study of the MM principle and applications to convex optimization. Based on some recent research articles, we present a survey on the principle that includes the geometric ideas behind the principle as well as its convergence results. Then we demonstrate some applications of the MM principle in solving the feasible point, closest point, support vector machine, and smallest intersecting ball problems, along with sample MATLAB code to implement each solution. The thesis also contains new results on effective algorithms …

Helly's Theorem And Its Equivalences Via Convex Analysis, Adam Robinson

University Honors Theses

Helly's theorem is an important result from Convex Geometry. It gives sufficient conditions for a family of convex sets to have a nonempty intersection. A large variety of proofs as well as applications are known. Helly's theorem also has close connections to two other well-known theorems from Convex Geometry: Radon's theorem and Carathéodory's theorem. In this project we study Helly's theorem and its relations to Radon's theorem and Carathéodory's theorem by using tools of Convex Analysis and Optimization. More precisely, we will give a novel proof of Helly's theorem, and in addition we show in a complete way that these …

Finite Sample Properties Of Minimum Kolmogorov-Smirnov Estimator And Maximum Likelihood Estimator For Right-Censored Data, Jerzy Wieczorek

Dissertations and Theses

MKSFitter computes minimum Kolmogorov-Smirnov estimators (MKSEs) for several different continuous univariate distributions, using an evolutionary optimization algorithm, and recommends the distribution and parameter estimates that best minimize the Kolmogorov-Smirnov (K-S) test statistic. We modify this tool by extending it to use the Kaplan-Meier estimate of the cumulative distribution function (CDF) for right-censored data. Using simulated data from the most commonly-used survival distributions, we demonstrate the tool's inability to consistently select the correct distribution type with right-censored data, even for large sample sizes and low censoring rates. We also compare this tool's estimates with the right-censored maximum likelihood estimator (MLE). While …

Hypoid Gear Optimization, Selvaraj Ramachandran

Dissertations and Theses

A hypoid gear optimization procedure using the method of feasible directions has been developed. The objective is to reduce the gear set weight with bending strength, contact strength and facewidth-diametral pitch ratio as constraints. The objective function weight, is calculated from the geometric approximation of the volume of the gear and pinion. The design variables selected are number of gear teeth, diametral pitch, and facewidth. The input parameters for starting the initial design phase are power to be transmitted, speed, gear ratio, type of application, mounting condition, type of loading, and the material to be used. In the initial design …

An Improved Approach For "Optimization" Of Multiple Policy Objectives, Perihan Soliman Tawfik

Dissertations and Theses

This thesis involved three categories of activity; development and testing of an expanded version of ELECTRE II, also the development of a computer software program for ELECTRE II. The expanded version of ELECTRE II took the form of an input aiding questionnaire along with a tailored structure to suit a particular problem. The contents of the questionnaire were based on geueral problem solving concepts (techniques, strategies) gleaned from the systems science literature. This questionnaire assumed a programmed instruction format in contrast to that of an interactive computer software package, so that it would not be prohibitive in terms of expenses …

Complex Systems And The Price-Resource Directive Coordination Procedure, Chamberlain Lambros Foes

Dissertations and Theses

In this thesis, the problem considered is that of linear static optimization of a large system which is composed of a finite number of subsystems, each characterized by its own constraint matrix and objective function.

The total system is itself constrained by resource availabilities and other factors, and its objective function is the mathematical linear sum of the objective function of the subsystems. The total system constraints couple together all the subsystems. The total system is first reformulated as a two-level problem by decoupling the total system constraints utilizing an arbitrary partition of the total system resources and other factors …