Physical Sciences and Mathematics Commons^™

Credible Optimum Selection Of Guidance System Simulation Based On Entropy Weight Vikor Method, Wenguang Yang, Yunjie Wu

Journal of System Simulation

Abstract: As the core component of the missile system, the guidance system plays an increasingly important role in the design of missile system. In order to improve the accuracy of the guidance system, this paper obtains the experimental data under a number of parameter design schemes by means of simulation experiments. The problem of simulation credibility verification of guidance system is transformed into multi-attribute decision-making optimization problem, and a parameter optimization method of guidance system based on improved VIKOR method is designed. The improved VIKOR method overcomes the phenomenon of rank reversal and ensures that the optimal final compromise solution …

Deep Representation Learning For Clustering And Domain Adaptation, Mohsen Kheirandishfard

Computer Science and Engineering Dissertations

Representation learning is a fundamental task in the area of machine learning which can significantly influence the performance of the algorithms used in various applications. The main goal of this task is to capture the relationships between the input data and learn feature representations that contain the most useful information of the original data. Such representations can be further leveraged in many machine learning applications such as clustering, natural language analysis, recommender systems, etc. In this dissertation, we first present a theoretical framework for solving a broad class of non-convex optimization problems. The proposed method is applicable to various tasks …

High Multiplicity Strip Packing Problem With Three Rectangle Types, Andy Yu

Electronic Thesis and Dissertation Repository

The two-dimensional strip packing problem (2D-SPP) involves packing a set R = {r₁, ..., r_n} of n rectangular items into a strip of width 1 and unbounded height, where each rectangular item r_i has width 0 < w_i ≤ 1 and height 0 < h_i ≤ 1. The objective is to find a packing for all these items, without overlaps or rotations, that minimizes the total height of the strip used. 2D-SPP is strongly NP-hard and has practical applications including stock cutting, scheduling, and reducing peak power demand in smart-grids.

This thesis considers …

Electromagnetic Analysis Of Bidirectional Reflectance From Roughened Surfaces And Applications To Surface Shape Recovery, Julian Antolin Camarena

Physics & Astronomy ETDs

Scattering from randomly rough surfaces is a well-established sub area of electrodynamics. There remains much to be done since each surface and optical processes that may occur in within the scattering medium, and countless other scenarios, is different. There are also illumination models that describe lighting in a scene on the macroscopic scale where geometrical optics can be considered adequate. Of particular interest for us is the intersection of the physical scattering theories and the illumination models. We present two contributions: 1) A minimum of two independent images are needed since any opaque surface can be uniquely specified in terms …

Interactive Fitness Domains In Competitive Coevolutionary Algorithm, Atm Golam Bari

USF Tampa Graduate Theses and Dissertations

Evolutionary Algorithms (EA) have been successfully applied to a wide range of optimization and search problems where no mathematical model of the quality of a candidate solution is available. Interactive Evolutionary Algorithms (IEA) and Competitive Coevolutionary Algorithms (CCoEA) go one step further by being able to tackle problems where the only means to evaluate the quality of a candidate solution is via interactions. In a typical IEA, interactions take place between the solution being evolved and human evaluators. In a CCoEA, interactions take place between solutions themselves, without need for human interaction. This dissertation identifies computer-aided learning as an application …

Optimal Sampling Paths For Autonomous Vehicles In Uncertain Ocean Flows, Andrew J. De Stefan

Dissertations

Despite an extensive history of oceanic observation, researchers have only begun to build a complete picture of oceanic currents. Sparsity of instrumentation has created the need to maximize the information extracted from every source of data in building this picture. Within the last few decades, autonomous vehicles, or AVs, have been employed as tools to aid in this research initiative. Unmanned and self-propelled, AVs are capable of spending weeks, if not months, exploring and monitoring the oceans. However, the quality of data acquired by these vehicles is highly dependent on the paths along which they collect their observational data. The …

An Information Theory Model For Optimizing Quantitative Magnetic Resonance Imaging Acquisitions, Drew Mitchell

Dissertations & Theses (Open Access)

Quantitative magnetic resonance imaging (qMRI) is a powerful group of imaging techniques with a growing number of clinical applications, including synthetic image generation in post-processing, automatic segmentation, and diagnosis of disease from quantitative parameter values. Currently, acquisition parameter selection is performed empirically for quantitative MRI. Tuning parameters for different scan times, tissues, and resolutions requires some measure of trial and error. There is an opportunity to quantitatively optimize these acquisition parameters in order to maximize image quality and the reliability of the previously mentioned methods which follow image acquisition.

The objective of this work is to introduce and evaluate a …

Improving Optimization Of Convolutional Neural Networks Through Parameter Fine-Tuning, Nicholas C. Becherer, John M. Pecarina, Scott L. Nykl, Kenneth M. Hopkinson

Faculty Publications

In recent years, convolutional neural networks have achieved state-of-the-art performance in a number of computer vision problems such as image classification. Prior research has shown that a transfer learning technique known as parameter fine-tuning wherein a network is pre-trained on a different dataset can boost the performance of these networks. However, the topic of identifying the best source dataset and learning strategy for a given target domain is largely unexplored. Thus, this research presents and evaluates various transfer learning methods for fine-grained image classification as well as the effect on ensemble networks. The results clearly demonstrate the effectiveness of parameter …

Some Theoretical Links Between Shortest Path Filters And Minimum Spanning Tree Filters, Sravan Danda, Aditya Challa, B. S.Daya Sagar, Laurent Najman

Journal Articles

Edge-aware filtering is an important pre-processing step in many computer vision applications. In the literature, there exist several versions of collaborative edge-aware filters based on spanning trees and shortest path heuristics which work well in practice. For instance, tree filter (TF) which is recently proposed based on a minimum spanning tree (MST) heuristic yields promising results in many filtering applications. However, links between the tree-based filters and shortest path-based filters are faintly explored. In this article, we introduce an edge-aware generalization of the TF termed as UMST filter based on a subgraph generated by edges of all MSTs. The major …

Structured Sparsity Promoting Functions: Theory And Applications, Erin Tripp

Dissertations - ALL

Motivated by the minimax concave penalty based variable selection in high-dimensional linear regression, we introduce a simple scheme to construct structured semiconvex sparsity promoting functions from convex sparsity promoting functions and their Moreau envelopes. Properties of these functions are developed by leveraging their structure. In particular, we show that the behavior of the constructed function can be easily controlled by assumptions on the original convex function. We provide sparsity guarantees for the general family of functions via the proximity operator. Results related to the Fenchel Conjugate and Łojasiewicz exponent of these functions are also provided. We further study the behavior …

A Decision-Making Framework For Hybrid Resource Recovery Oriented Wastewater Systems, Nader Rezaei

USF Tampa Graduate Theses and Dissertations

Water shortage, water contamination, and the emerging challenges in sustainable water resources management (e.g., the likely impacts of climate change and population growth) necessitate adopting a reverse logistics approach, which is the process of moving wastewater from its typical final destination back to the water supply chain for reuse purposes. This practice not only reduces the negative impacts of wastewater on the environment, but also provides an alternative to withdrawal from natural water resources, forming a closed-loop water supply chain. However, the design of such a supply chain requires an appropriate sustainability assessment, which simultaneously accounts for economic, environmental, and …

Smart Home Simulation In The Virtual World, Thomas Jones-Moore, David Son

Scholars Week

The goal of this project is to produce a 'smart home' by using IoT and RFID like things in the virtual world to help solve problems. Some of these problems can be CPR training, etc. Used as an evaluation platform of suggested hardware to get a desired (or best fit) set of smart objects, or combinations with computer vision. Cost model to determine best fit based on: accuracy, lowest cost, easiest deployment, etc.

Paper Structure Formation Simulation, Tyler R. Seekins

Electronic Theses and Dissertations

On the surface, paper appears simple, but closer inspection yields a rich collection of chaotic dynamics and random variables. Predictive simulation of paper product properties is desirable for screening candidate experiments and optimizing recipes but existing models are inadequate for practical use. We present a novel structure simulation and generation system designed to narrow the gap between mathematical model and practical prediction. Realistic inputs to the system are preserved as randomly distributed variables. Rapid fiber placement (~1 second/fiber) is achieved with probabilistic approximation of chaotic fluid dynamics and minimization of potential energy to determine flexible fiber conformations. Resulting digital packed …

Framework For Algorithmically Optimizing Longitudinal Health Outcomes: Examples In Cancer Radiotherapy And Occupational Radiation Protection, Lydia Joyce Wilson

LSU Doctoral Dissertations

Background: Advancements in the treatment of non-infectious disease have enabled survival rates to steadily increase in recent decades (e.g., diabetes, heart disease, and cancer). Epidemiological studies have revealed that the treatments for these diseases can have life-threatening and/or life–altering effects. Thus, realizing the full beneficial potential of advanced treatments necessitates new tools to algorithmically consider all major components of the health outcome, including benefit and detriment. The goal of this dissertation was to develop a framework for improving projected health outcomes following planned radiation exposures in consideration of all beneficial and detrimental, early and late, and fatal and non-fatal …

Combinatorial Optimization: Introductory Problems And Methods, Erin Brownell

Honors Scholar Theses

This paper will cover some topics of combinatorial optimization, the study of finding the best possible arrangement of a set of discrete objects. These topics include the shortest path problem and network flows, which can be extended to solve more complex problems. We will also briefly cover some basics of graph theory and solving linear programming problems to give context to the reader.

Gem-Pso: Particle Swarm Optimization Guided By Enhanced Memory, Kevin Fakai Chen

Honors Projects

Particle Swarm Optimization (PSO) is a widely-used nature-inspired optimization technique in which a swarm of virtual particles work together with limited communication to find a global minimum or optimum. PSO has has been successfully applied to a wide variety of practical problems, such as optimization in engineering fields, hybridization with other nature-inspired algorithms, or even general optimization problems. However, PSO suffers from a phenomenon known as premature convergence, in which the algorithm's particles all converge on a local optimum instead of the global optimum, and cannot improve their solution any further. We seek to improve upon the standard Particle Swarm …

Re-Org: An Online Repositioning Guidance Agent, Muralidhar Konda, Pradeep Varakantham, Aayush Saxena, Meghna Lowalekar

Research Collection School Of Computing and Information Systems

No abstract provided.

Learning Two-Layer Neural Networks With Symmetric Inputs, Rong Ge, Rohith Kuditipudi, Zhize Li, Xiang Wang

Research Collection School Of Computing and Information Systems

We give a new algorithm for learning a two-layer neural network under a very general class of input distributions. Assuming there is a ground-truth two-layer network $y = A \sigma(Wx) + \xi$, where A, W are weight matrices, $\xi$ represents noise, and the number of neurons in the hidden layer is no larger than the input or output, our algorithm is guaranteed to recover the parameters A, W of the ground-truth network. The only requirement on the input x is that it is symmetric, which still allows highly complicated and structured input. Our algorithm is based on the method-of-moments framework …

Enhancing Portability In High Performance Computing: Designing Fast Scientific Code With Longevity, Jason Orender

Computer Science Theses & Dissertations

Portability, an oftentimes sought-after goal in scientific applications, confers a number of possible advantages onto computer code. Portable code will often have greater longevity, enjoy a broader ecosystem, appeal to a wider variety of application developers, and by definition will run on more systems than its pigeonholed counterpart. These advantages come at a cost, however, and a rational approach to balancing costs and benefits requires a systemic evaluation. While the benefits for each application are likely situation-dependent, the costs in terms of resources, including but not limited to time, money, computational power, and memory requirements, are quantifiable. This document will …

Two-On-One Pursuit With A Non-Zero Capture Radius, Patrick J. Wasz

Theses and Dissertations

In this paper, we revisit the "Two Cutters and Fugitive Ship" differential game that was addressed by Isaacs, but move away from point capture. We consider a two-on-one pursuit-evasion differential game with simple motion and pursuers endowed with circular capture sets of radius l > 0. The regions in the state space where only one pursuer effects the capture and the region in the state space where both pursuers cooperatively and isochronously capture the evader are characterized, thus solving the Game of Kind. Concerning the Game of Degree, the algorithm for the synthesis of the optimal state feedback strategies of the …

Centroidal Voronoi Tessellation With Local Optimization, Tianyu Ye, Yiqun Wang, Dongming Yan, Junhai Yong

Journal of System Simulation

Abstract: Centroidal Voronoi tessellation is a special geometric structure, which has many applications in various fields such as geographical information system, signal processing, mesh generation/optimization, visualization and so on. Due to the highly non-convex nature of the CVT energy function, the existing methods for computing CVT have several drawbacks, which always trap into local minima. We propose generation optimization and stochastic optimization schemes for further reducing the CVT energy. Experimental results show that the proposed method improves both quality and efficiency compared to the recent approaches.

An Evaluation Of Training Size Impact On Validation Accuracy For Optimized Convolutional Neural Networks, Jostein Barry-Straume, Adam Tschannen, Daniel W. Engels, Edward Fine

SMU Data Science Review

In this paper, we present an evaluation of training size impact on validation accuracy for an optimized Convolutional Neural Network (CNN). CNNs are currently the state-of-the-art architecture for object classification tasks. We used Amazon’s machine learning ecosystem to train and test 648 models to find the optimal hyperparameters with which to apply a CNN towards the Fashion-MNIST (Mixed National Institute of Standards and Technology) dataset. We were able to realize a validation accuracy of 90% by using only 40% of the original data. We found that hidden layers appear to have had zero impact on validation accuracy, whereas the neural …

Prioritizing Stream Barrier Removal To Maximize Connected Aquatic Habitat And Minimize Water Scarcity, Maggi Kraft, David E. Rosenberg, Sarah E. Null

Watershed Sciences Student Research

Instream barriers, such as dams, culverts, and diversions, alter hydrologic processes and aquatic habitat. Removing uneconomical and aging instream barriers is increasingly used for river restoration. Historically, selection of barrier removal projects used score‐and‐rank techniques, ignoring cumulative change and the spatial structure of stream networks. Likewise, most water supply models prioritize either human water uses or aquatic habitat, failing to incorporate both human and environmental water use benefits. Here, a dual‐objective optimization model identifies barriers to remove that maximize connected aquatic habitat and minimize water scarcity. Aquatic habitat is measured using monthly average streamflow, temperature, channel gradient, and geomorphic condition …

Artificial Fish Swarm And Feedback Linearization Of Flue Gas Denitration Control Based On Neural Network, Yuguang Niu, Pan Yan, Wenyuan Huang

Journal of System Simulation

Abstract: According to the present situation of SCR flue gas dentration control system in thermal power plant, an optimum proposal that control valve and concentration transmitter are added in the inlet of the SCR reactor is presented, and the corresponding control strategy is given. At the entrance of the SCR reactor, the receding horizon algorithm combined with the single neuron adaptive algorithm and the artificial fish swarm algorithm (RSNAAFS) is used to control branch valves to pretreat NO_X in the exhaust flue gas. At the outlet of the SCR reactor, the neural network based on feedback linearization algorithm (NNFL) …

Real-Time Pricing Strategy Considering The Risk Of Smart Grid, Hongbo Zhu, Gao Yan, Yeming Dai

Journal of System Simulation

Abstract: The real-time electricity price mechanism is an ideal method to adjust the power balance between supply and demand in smart grid. Its implementation has profound impacts on the users' behavior and the operation and management of electricity power grid’s safety. The users’ demand behavior plays a regulatory role in designing real-time electricity pricing strategy. Aiming at maximizing social welfare, the dynamic change of users’ aggregate demand is analyzed, which corrects the electricity risk items in online real-time risk model in the way of changing the individual user’s power fluctuations to all the users’ demand power fluctuations, and the optimization …

Optimization Of Q-Btgsid Based On Sensitivity Analysis, Shuwei Jia

Journal of System Simulation

Abstract: In order to make up for the defect that relative degree of incidence, absolute degree of incidence and synthetic degree of incidence are limited in the range of (0.5, 1], this paper attempts to improve the degree of grey incidence. A control factor of “λ” and the metric space are set up to adjust. A new model is established and its specific properties are studied. It is proved that the new model satisfies the grey incidence axioms and the range of degree of grey incidence can be extended to (0, 1]. We put forward four principles of …

Optimization Of Scheduling Rule Of Unidirectional Material Handling System With Short-Cut, Juntao Li, Kun Xia, Kise Hiroshi

Journal of System Simulation

Abstract: To decrease the interference and improve the performance of a unidirectional circulation-type material handling system on a single loop with a shortcut, the interference and scheduling problem between AGVs are studied. According to the actual situation of material handling system, the interferences of two scheduling rules (random rule and order rule) are analyzed. An optimal scheduling rule under the interference case—exchange order rule is proposed. Different scheduling rules have an influence on the interference between AGVs and then have an important effect on the efficiency of the whole system. Experiment results show that the exchange order (E-Order) rule …

Optimization Of Mathematical Functions Using Gradient Descent Based Algorithms, Hala Elashmawi

Mathematics Theses

Optimization problem involves minimizing or maximizing some given quantity for certain constraints. Various real-life problems require the use of optimization techniques to find a suitable solution. These include both, minimizing or maximizing a function. The various approaches used in mathematics include methods like Linear Programming Problems (LPP), Genetic Programming, Particle Swarm Optimization, Differential Evolution Algorithms, and Gradient Descent. All these methods have some drawbacks and/or are not suitable for every scenario. Gradient Descent optimization can only be used for optimization when the goal is to find the minimum and the function at hand is differentiable and convex. The Gradient Descent …

Second-Order Generalized Differentiation Of Piecewise Linear-Quadratic Functions And Its Applications, Hong Do

Wayne State University Dissertations

The area of second-order variational analysis has been rapidly developing during the recent years with many important applications in optimization. This dissertation is devoted to the study and applications of the second-order generalized differentiation of a remarkable

class of convex extended-real-valued functions that is highly important in many aspects of nonlinear and variational analysis, specifically those related to optimization and stability.

The first goal of this dissertation is to compute the second-order subdifferential of the functions described above, which will be applied in the study of the stability of composite optimization problems associated with piecewise linear-quadratic functions, known as extended …

Optimizing Glide-Flight Paths, Rory Cveta O'Daly Maglich

Senior Projects Spring 2019

Flight is no rare event in today's society, and aviation is a global industry that significantly contributes to carbon emissions and global warming. Thus, my project theorizes how aviation might be better optimized at a fundamental level to improve aerodynamic efficiency and reduce carbon emissions. This is done by analyzing two systems of flight: gliding and powered flight. In pursuit of an understanding of a hybrid of these flight systems, I first look to qualitatively analyze the benefit of gliding over powered aviation. Powering an aircraft involves an engine that generates thrust, while gliding only involves three forces: lift, drag, …