Physical Sciences and Mathematics Commons^™

Benchmarks And Controls For Optimization With Quantum Annealing, Erica Kelley Grant

Doctoral Dissertations

Quantum annealing (QA) is a metaheuristic specialized for solving optimization problems which uses principles of adiabatic quantum computing, namely the adiabatic theorem. Some devices implement QA using quantum mechanical phenomena. These QA devices do not perfectly adhere to the adiabatic theorem because they are subject to thermal and magnetic noise. Thus, QA devices return statistical solutions with some probability of success where this probability is affected by the level of noise of the system. As these devices improve, it is believed that they will become less noisy and more accurate. However, some tuning strategies may further improve that probability of …

Micro Grid Control Optimization With Load And Solar Prediction, Shaju Saha

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Using renewable energy can save money and keep the environment cleaner. Installing a solar PV system is a one-time cost but it can generate energy for a lifetime. Solar PV does not generate carbon emissions while producing power. This thesis evaluates the value of being able to make accurate predictions in the use of solar energy. It uses predicted solar power and load for a system and a battery to store the energy for future use and calculates the operating cost or profit in several designed conditions. Various factors like a different place, tuning the capacity of sources, changing buy/sell …

Structured Data Mining Networks, Time Series, And Time Series Of Networks, Lin Zhang

Legacy Theses & Dissertations (2009 - 2024)

The rate at which data is generated in modern applications has created an unprecedented demand for novel methods to effectively and efficiently extract insightful patterns. Methods aware of known domain-specific structure in the data tend to be advantageous. In particular, a joint temporal and networked view of observations offers a holistic lens to many real-world systems. Example domains abound: activity of social network users, gene interactions over time, a temporal load of infrastructure networks, and others. Existing analysis and mining approaches for such data exhibit limited quality and scalability due to their sensitivity to noise, missing observations, and the need …

Applications Of Mathematical Optimization Methods To Digital Communications And Signal Processing, Spencer Giddens

Theses and Dissertations

Mathematical optimization is applicable to nearly every scientific discipline. This thesis specifically focuses on optimization applications to digital communications and signal processing. Within the digital communications framework, the channel encoder attempts to encode a message from a source (the sender) in such a way that the channel decoder can utilize the encoding to correct errors in the message caused by the transmission over the channel. Low-density parity-check (LDPC) codes are an especially popular code for this purpose. Following the channel encoder in the digital communications framework, the modulator converts the encoded message bits to a physical waveform, which is sent …

Data Assimilation For Conductance-Based Neuronal Models, Matthew Moye

Dissertations

This dissertation illustrates the use of data assimilation algorithms to estimate unobserved variables and unknown parameters of conductance-based neuronal models. Modern data assimilation (DA) techniques are widely used in climate science and weather prediction, but have only recently begun to be applied in neuroscience. The two main classes of DA techniques are sequential methods and variational methods. Throughout this work, twin experiments, where the data is synthetically generated from output of the model, are used to validate use of these techniques for conductance-based models observing only the voltage trace. In Chapter 1, these techniques are described in detail and the …

Target Control Of Networked Systems, Isaac S. Klickstein

Mechanical Engineering ETDs

The control of complex networks is an emerging field yet it has already garnered interest from across the scientific disciplines, from robotics to sociology. It has quickly been noticed that many of the classical techniques from controls engineering, while applicable, are not as illuminating as they were for single systems of relatively small dimension. Instead, properties borrowed from graph theory provide equivalent but more practical conditions to guarantee controllability, reachability, observability, and other typical properties of interest to the controls engineer when dealing with large networked systems. This manuscript covers three topics investigated in detail by the author: (i) the …

Characterizing Uncertainty In Correlated Response Variables For Pareto Front Optimization, Peter A. Calhoun

Theses and Dissertations

Current research provides a method to incorporate uncertainty into Pareto front optimization by simulating additional response surface model parameters according to a Multivariate Normal Distribution (MVN). This research shows that analogous to the univariate case, the MVN understates uncertainty, leading to overconfident conclusions when variance is not known and there are few observations (less than 25-30 per response). This research builds upon current methods using simulated response surface model parameters that are distributed according to an Multivariate t-Distribution (MVT), which can be shown to produce a more accurate inference when variance is not known. The MVT better addresses uncertainty in …

Optimizing The Environmental And Economic Sustainability Of Contingency Base Infrastructure, Jamie E. Filer

Theses and Dissertations

Contingency bases are often located in remote areas with limited access to established infrastructure grids. This isolation leads to standalone systems comprised of inefficient, resource-dependent infrastructure, which yields a significant logistical burden, creates negative environmental impacts, and increases costs. Planners can mitigate these negative impacts by selecting sustainable technologies. However, such alternatives often come at a higher procurement cost and mobilization requirement. Accordingly, this study aims to develop and implement a novel infrastructure sustainability assessment model capable of optimizing the tradeoffs between environmental and economic performance of infrastructure alternatives.

Co-Optimization Of A Robot's Body And Brain Via Evolution And Reinforcement Learning, Jack Felag

Graduate College Dissertations and Theses

Agents are often trained to perform a task via optimization algorithms. One class of algorithms used is evolution, which is ``survival of the fitness'' used to pick the best agents for the objective, and slowly changing the best over time to find a good solution. Evolution, or evolutionary algorithms, have been commonly used to automatically select for a better body of the agent, which can outperform hand-designed models. Another class of algorithms used is reinforcement learning. Through this strategy, agents learn from prior experiences in order to maximize some reward. Generally, this reward is how close the objective is to …

Catgame: A Tool For Problem Solving In Complex Dynamic Systems Using Game Theoretic Knowledge Distribution In Cultural Algorithms, And Its Application (Catneuro) To The Deep Learning Of Game Controller, Faisal Waris

Wayne State University Dissertations

Cultural Algorithms (CA) are knowledge-intensive, population-based stochastic optimization methods that are modeled after human cultures and are suited to solving problems in complex environments. The CA Belief Space stores knowledge harvested from prior generations and re-distributes it to future generations via a knowledge distribution (KD) mechanism. Each of the population individuals is then guided through the search space via the associated knowledge. Previously, CA implementations have used only competitive KD mechanisms that have performed well for problems embedded in static environments. Relatively recently, CA research has evolved to encompass dynamic problem environments. Given increasing environmental complexity, a natural question arises …

Optimizing Cluster Sets For The Scan Statistic Using Local Search, James Shulgan

Graduate Research Theses & Dissertations

In recent years, scattering sensors to produce wireless sensor networks (WSN) has been proposed for detecting localized events in large areas. Because sensor measurements are noisy, the WSN needs to use statistical methods such as the scan statistic. The scan statistic groups measurements into various clusters, computes a cluster statistic for each cluster, and decides that an event has happened if any of the statistics exceeds a threshold. Previous researchers have investigated the performance of the scan statistic to detect events; however, little attention was given to the optimization of which clusters the scan statistic should use. Using the scan …

Testing And Improving An Optimization-Based Digital Colorblindness Corrective Filter, Zachary Kenneth Mcintyre

Senior Projects Fall 2020

Computers often communicate essential information via color which is lost to colorblind users. In order to address this information loss, designers and computer scientists have created a variety of different correction methods to improve computer accessibility. One such method was created by Luke Jefferson and Richard Harvey in their 2006 paper, “Accommodating Color Blind Computer Users” which consists of a difference histogram, differences of key colors, optimization and interpolation to adjust images for specific types of congenital colorblindness. I have recreated their algorithm as well as their original test images. I then conducted extensive tests on challenging images to examine …

Hybrid Electric Vehicle Energy Management Strategy With Consideration Of Battery Aging, Bin Zhou

Dissertations, Master's Theses and Master's Reports

The equivalent consumption minimization strategy (ECMS) is a well-known energy management strategy for Hybrid Electric Vehicles (HEV). ECMS is very computationally efficient since it yields an instantaneous optimal control. ECMS has been shown to minimize fuel consumption under certain conditions. But, minimizing the fuel consumption often leads to excessive battery damage. The objective of this dissertation is to develop a real-time implementable optimal energy management strategy which improves both the fuel economy and battery aging for Hybrid Electric Vehicles by using ECMS. This work introduces a new optimal control problem where the cost function includes terms for both fuel consumption …

Computational Model For Neural Architecture Search, Ram Deepak Gottapu

Doctoral Dissertations

"A long-standing goal in Deep Learning (DL) research is to design efficient architectures for a given dataset that are both accurate and computationally inexpensive. At present, designing deep learning architectures for a real-world application requires both human expertise and considerable effort as they are either handcrafted by careful experimentation or modified from a handful of existing models. This method is inefficient as the process of architecture design is highly time-consuming and computationally expensive.

The research presents an approach to automate the process of deep learning architecture design through a modeling procedure. In particular, it first introduces a framework that treats …

Optimizing Pollution Routing Problem, Shivika Dewan

All Master's Theses

Pollution is a major environmental issue around the world. Despite the growing use and impact of commercial vehicles, recent research has been conducted with minimizing pollution as the primary objective to be reduced. The objective of this project is to implement different optimization algorithms to solve this problem. A basic model is created using the Vehicle Routing Problem (VRP) which is further extended to the Pollution Routing Problem (PRP). The basic model is updated using a Monte Carlo Algorithm (MCA). The data set contains 180 data files with a combination of 10, 15, 20, 25, 50, 75, 100, 150, and …