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Articles 181 - 210 of 3047
Full-Text Articles in Physical Sciences and Mathematics
Nowcasting Solar Euv Irradiance With Photospheric Magnetic Fields And The Mg Ii Index, Kara L. Kniezewski, Samuel J. Schonfeld, Carl J. Henney
Nowcasting Solar Euv Irradiance With Photospheric Magnetic Fields And The Mg Ii Index, Kara L. Kniezewski, Samuel J. Schonfeld, Carl J. Henney
Student Publications
A new method to nowcast spectral irradiance in extreme ultraviolet (EUV) and far ultraviolet (FUV) bands is presented here, utilizing only solar photospheric magnetograms and the Mg II index (i.e., the core-to-wing ratio). The EUV and FUV modeling outlined here is a direct extension of the SIFT (Solar Indices Forecasting Tool) model, based on Henney et al. (2015, https://doi.org/10.1002/2014sw001118). SIFT estimates solar activity indices using the earth-side solar photospheric magnetic field sums from global magnetic maps generated by the ADAPT (Air Force Data Assimilative Photospheric Flux Transport) model. Utilizing strong and weak magnetic field sums from ADAPT maps, Henney …
Exploring Quaternion Neural Network Loss Surfaces, Jeremiah Bill, Bruce A. Cox
Exploring Quaternion Neural Network Loss Surfaces, Jeremiah Bill, Bruce A. Cox
Faculty Publications
This paper explores the superior performance of quaternion multi-layer perceptron (QMLP) neural networks over real-valued multi-layer perceptron (MLP) neural networks, a phenomenon that has been empirically observed but not thoroughly investigated. The study utilizes loss surface visualization and projection techniques to examine quaternion-based optimization loss surfaces for the first time. The primary contribution of this research is the statistical evidence that QMLP models yield smoother loss surfaces than real-valued neural networks, which are measured and compared using a robust quantitative measure of loss surface “goodness” based on estimates of surface curvature. Extensive computational testing validates the effectiveness of these surface …
Pulsed-Power Neutron Production With Deuterated Polymer Accelerator Targets, Anthony O. Hagey, Juan J. Manfredi, Whitman T. Dailey, S. L. Jackson, E. R. Kaiser, J, W. Schumer
Pulsed-Power Neutron Production With Deuterated Polymer Accelerator Targets, Anthony O. Hagey, Juan J. Manfredi, Whitman T. Dailey, S. L. Jackson, E. R. Kaiser, J, W. Schumer
Faculty Publications
This document presents an investigation of the effect of deuterated accelerator targets on the neutron fluence from a local mass injection dense plasma focus (LMIDPF) driven by the United States Naval Research Laboratory's (NRL) Hawk pulsed-power generator. Deuterated targets were made using two methods: a well-established thin-film casting technique for flat targets and a more novel additive manufacturing technique that allowed targets to be three-dimensional (3D) printed in both flat and conical geometries. These targets were then tested during neutron-producing experiments conducted using Hawk, and theneutron fluences measured for the various target types were compared. Additive manufacturing was used as …
Effect Of Fabrication Parameters On The Ferroelectricity Of Hafnium Zirconium Oxide Films: A Statistical Study, Guillermo A. Salcedo, Ahmad E. Islam, Elizabeth Reichley, Michael Dietz, Christine M. Schubert Kabban, Kevin D. Leedy, Tyson C. Back, Weison Wang, Andrew Green, Timothy S. Wolfe, James M. Sattler
Effect Of Fabrication Parameters On The Ferroelectricity Of Hafnium Zirconium Oxide Films: A Statistical Study, Guillermo A. Salcedo, Ahmad E. Islam, Elizabeth Reichley, Michael Dietz, Christine M. Schubert Kabban, Kevin D. Leedy, Tyson C. Back, Weison Wang, Andrew Green, Timothy S. Wolfe, James M. Sattler
Faculty Publications
Ferroelectricity in hafnium zirconium oxide (Hf1−xZrxO2) and the factors that impact it have been a popular research topic since its discovery in 2011. Although the general trends are known, the interactions between fabrication parameters and their effect on the ferroelectricity of Hf1−xZrxO2 require further investigation. In this paper, we present a statistical study and a model that relates Zr concentration (x), film thickness (tf), and annealing temperature (Ta) with the remanent polarization (Pr) in tungsten (W)-capped Hf1−xZrxO2. …
Scriptblock Smuggling: Uncovering Stealthy Evasion Techniques In Powershell And .Net Environments, Anthony J. Rose, Scott R. Graham, Christine M. Schubert, Jacob Krasnov, Wayne C. Henry
Scriptblock Smuggling: Uncovering Stealthy Evasion Techniques In Powershell And .Net Environments, Anthony J. Rose, Scott R. Graham, Christine M. Schubert, Jacob Krasnov, Wayne C. Henry
Faculty Publications
The Antimalware Scan Interface (AMSI) plays a crucial role in detecting malware within Windows operating systems. This paper presents ScriptBlock Smuggling, a novel evasion and log spoofing technique exploiting PowerShell and .NET environments to circumvent the AMSI. By focusing on the manipulation of ScriptBlocks within the Abstract Syntax Tree (AST), this method creates dual AST representations, one for compiler execution and another for antivirus and log analysis, enabling the evasion of AMSI detection and challenging traditional memory patching bypass methods. This research provides a detailed analysis of PowerShell’s ScriptBlock creation and its inherent security features and pinpoints critical limitations in …
Data Supporting Research On Personalized Learning Paths, Sean Mochocki, Mark Reith
Data Supporting Research On Personalized Learning Paths, Sean Mochocki, Mark Reith
Faculty Publications
Personalized Learning Paths (PLPs) are a key application of Artificial Intelligence in E-Learning. In contrast to regular Learning Paths, they return a unique sequence of learning materials identified as meeting the individual needs of the students. In the literature, PLPs are often created from knowledge graphs, which assist with ordering topics and their associated learning materials. Knowledge graphs are typically directed and acyclic, to capture prerequisite relationships between topics, though they can also have bidirectional edges when these prerequisite relationships are not necessary. This data package provides a primarily un-directed knowledge graph, with associated repository of open-source learning materials that …
The Impact Of Data Preparation And Model Complexity On The Natural Language Classification Of Chinese News Headlines, Torrey J. Wagner, Dennis Guhl, Brent T. Langhals
The Impact Of Data Preparation And Model Complexity On The Natural Language Classification Of Chinese News Headlines, Torrey J. Wagner, Dennis Guhl, Brent T. Langhals
Faculty Publications
Given the emergence of China as a political and economic power in the 21st century, there is increased interest in analyzing Chinese news articles to better understand developing trends in China. Because of the volume of the material, automating the categorization of Chinese-language news articles by headline text or titles can be an effective way to sort the articles into categories for efficient review. A 383,000-headline dataset labeled with 15 categories from the Toutiao website was evaluated via natural language processing to predict topic categories. The influence of six data preparation variations on the predictive accuracy of four algorithms was …
Laboratory Exercise For The Radiometry Student, Michael A. Marciniak
Laboratory Exercise For The Radiometry Student, Michael A. Marciniak
Faculty Publications
The U.S. Air and Space Forces require optical expertise among their personnel. The Air Force Institute of Technology offers a graduate optics curriculum, which includes a three-course sequence to educate students in the optical concepts of radiometry and radiometric instrumentation. We find radiometry is often a deceptively difficult concept for students to master. To address this, we have developed an experiment in our optics-laboratory coursework to help them gain this mastery. A Fourier-transform infrared spectrometer (FTS) is used to collect spectral data from an unknown sample. FTS calibration and data collection are discussed here, as are the two specific samples …
Relative Vectoring Using Dual Object Detection For Autonomous Aerial Refueling, Derek B. Worth, Jeffrey L. Choate, James Lynch, Scott L. Nykl, Clark N. Taylor
Relative Vectoring Using Dual Object Detection For Autonomous Aerial Refueling, Derek B. Worth, Jeffrey L. Choate, James Lynch, Scott L. Nykl, Clark N. Taylor
Faculty Publications
Once realized, autonomous aerial refueling will revolutionize unmanned aviation by removing current range and endurance limitations. Previous attempts at establishing vision-based solutions have come close but rely heavily on near perfect extrinsic camera calibrations that often change midflight. In this paper, we propose dual object detection, a technique that overcomes such requirement by transforming aerial refueling imagery directly into receiver aircraft reference frame probe-to-drogue vectors regardless of camera position and orientation. These vectors are precisely what autonomous agents need to successfully maneuver the tanker and receiver aircraft in synchronous flight during refueling operations. Our method follows a common 4-stage process …
Reinforcement Learning For Team Based Air Combat Maneuvering Decisions With Directed Energy Weaponry, Joshua D. Combs
Reinforcement Learning For Team Based Air Combat Maneuvering Decisions With Directed Energy Weaponry, Joshua D. Combs
Theses and Dissertations
Leveraging the Advanced Framework for Simulation, Integration, and Modeling (AFSIM) we investigate the use of reinforcement learning (RL) techniques for imbuing AUCAV agents with high-quality behaviors for the within-visual-range air combat maneuvering problem (ACMP). We formulate the 2v2 WVR ACMP as a Markov decision process wherein friendly AUCAVs are equipped with DEW capabilities and operate with 6 degrees of freedom. We utilize the Double Deep Q-Network RL algorithm, which centrally trains two friendly AUCAVs and employ a phased learning approach, initially exposing the AUCAVs to a dense reward environment for early training, followed by a sparse reward environment to encourage …
Random Forests For Detecting Weak Signals And Extracting Physical Information: A Case Study Of Magnetic Navigation, Mohammadamin Moradi, Zheng-Meng Zhai, Aaron Nielsen, Ying-Cheng Lai, Aaron P. Nielsen
Random Forests For Detecting Weak Signals And Extracting Physical Information: A Case Study Of Magnetic Navigation, Mohammadamin Moradi, Zheng-Meng Zhai, Aaron Nielsen, Ying-Cheng Lai, Aaron P. Nielsen
Faculty Publications
It has been recently demonstrated that two machine-learning architectures, reservoir computing and time-delayed feed-forward neural networks, can be exploited for detecting the Earth’s anomaly magnetic field immersed in overwhelming complex signals for magnetic navigation in a GPS-denied environment. The accuracy of the detected anomaly field corresponds to a positioning accuracy in the range of 10–40 m. To increase the accuracy and reduce the uncertainty of weak signal detection as well as to directly obtain the position information, we exploit the machine-learning model of random forests that combines the output of multiple decision trees to give optimal values of the physical …
Seasonal Variability And Predictability Of Monsoon Precipitation In Southern Africa, Matthew F. Horan, Fred Kucharski, Moetasim Ashfaq
Seasonal Variability And Predictability Of Monsoon Precipitation In Southern Africa, Matthew F. Horan, Fred Kucharski, Moetasim Ashfaq
Faculty Publications
Rainfed agriculture is the mainstay of economies across Southern Africa (SA), where most precipitation is received during the austral summer monsoon. This study aims to further our understanding of monsoon precipitation predictability over SA. We use three natural climate forcings, El Niño–Southern Oscillation, Indian Ocean Dipole (IOD), and the Indian Ocean Precipitation Dipole (IOPD)—the dominant precipitation variability mode—to construct an empirical model that exhibits significant skill over SA during monsoon in explaining precipitation variability and in forecasting it with a five-month lead. While most explained precipitation variance (50%–75%) comes from contemporaneous IOD and IOPD, preconditioning all three forcings is key …
Design Considerations For The Use Of The Julia Programming Language In Future Quantum Networking Simulation Software, Takashi Joubert
Design Considerations For The Use Of The Julia Programming Language In Future Quantum Networking Simulation Software, Takashi Joubert
Theses and Dissertations
Given the prevalence of Python-based packages in the existing quantum network simulation ecosystem, we attempt to assess what might be realistically gained by switching to Julia. We focus our experimental activities on three areas: 1) surveying the characteristics of Julia as they tie into robust framework development, 2) presenting benchmarks that compare Julia and Python with respect to elements of possible simulation workloads, and 3) producing a tangible lightweight Julia architecture for modeling components in a manner similar to SeQUeNCe. Our analysis suggests that while Julia does o.er performance advantages over Python over certain workloads, knowing the reasons for why …
Opportunities And Limitations: Integrating Narrative Ai Into Game-Based Assessment Creation And Evaluating Student Impacts, Kevin B. Patterson
Opportunities And Limitations: Integrating Narrative Ai Into Game-Based Assessment Creation And Evaluating Student Impacts, Kevin B. Patterson
Theses and Dissertations
The Department of Defense (DoD) has identified the need for a technically proficient workforce in the areas of science, technology, engineering, and mathematics. To meet this need, the DoD is actively seeking innovative technology capable of creating workforce development opportunities that are both accessible and effective. Educational research indicates serious games provide a potential avenue to achieve this goal. Unfortunately, a limited number of tools that simplify the game development process and leverage artificial intelligence are available. Content-generating artificial intelligence might help reduce instructor and game-based assessment designers' workloads while promoting individualized learning in students. This research presents a novel …
Simulating Human-Autonomous Aircraft Teams In An Anti-Access Area Denial (A2ad) Environment, Michael Kaminski
Simulating Human-Autonomous Aircraft Teams In An Anti-Access Area Denial (A2ad) Environment, Michael Kaminski
Theses and Dissertations
The role of autonomy has evolved recently, demanding tighter integration between human and autonomous systems, particularly in highly contested A2AD environments. Near-peer adversaries have modernized their integrated air defense systems (IADS), diminishing the current advantages of the United States Air Force. To regain air dominance, efforts like the Collaborative Combat Aircraft (CCA) program are underway, aiming to deploy unmanned autonomous alongside manned next-generation fighter aircraft. This research assesses various operational concepts, focusing on autonomous tactics post-manned fighter loss, strike timing of independent teams, and weapon configuration observability. Using the Advanced Framework for Simulation, Integration and Modeling (AFSIM), an agent-based model …
Federated Analysis Of Wearables Data For United States Air Force Mental And Physical Readiness, Sung Yong O
Federated Analysis Of Wearables Data For United States Air Force Mental And Physical Readiness, Sung Yong O
Theses and Dissertations
This thesis investigates the use of machine learning and deep learning models within a federated learning framework to predict physical and mental readiness in military personnel, using wearable technology data. The collaboration with the 711th Human Performance Wing’s STRONG Lab highlights the importance of readiness as emphasized by the National Defense and Security Strategies. The study evaluates various predictive models, incorporating federated learning to ensure data privacy and security in healthcare systems. By analyzing a comprehensive dataset, the research aims to contribute to military readiness enhancement through technological advancements, supporting health and wellness initiatives to bolster the effectiveness of military …
Advanced Oxidation Of Tert-Butanol With Ultraviolet Light Emitting Diodes And Hydrogen Peroxide, Lauren R. Mainolfi
Advanced Oxidation Of Tert-Butanol With Ultraviolet Light Emitting Diodes And Hydrogen Peroxide, Lauren R. Mainolfi
Theses and Dissertations
Tert-butanol (TBA), a versatile chemical widely used in industrial processes, poses exposure risks through inhalation, ingestion, and skin contact. Environmental contamination, often from industrial activities, emphasizes the importance of robust waste management and monitoring to protect water supplies from potential TBA migration and ensure drinking water safety. This study employed hydrogen peroxide (H2O2): TBA molar ratios of 100, 200, 400, and 500:1 in a Continuous Flow Stirred-Tank Reactor (CSTR) with UV-LED as the TBA degradation mechanism in an Advanced Oxidation Process (AOP). The UV-LEDs and H2O2 were synergistically employed to generate hydroxyl radicals in an advanced oxidation process. Gas chromatography–mass …
Signal-To-Image Method For Counterfeit Detection In Layered Security Paradigm, Jordan Williamson
Signal-To-Image Method For Counterfeit Detection In Layered Security Paradigm, Jordan Williamson
Theses and Dissertations
National level attention, resources, and priority regarding critical infrastructure have increased in recent years. This has led to adversaries and defenders exchanging positions between fortification and exploitation. One area that continues to be vulnerable is supply chain attacks like counterfeit insertion. This work investigates the application of converting collected signals into images from devices that may be considered for these critical networks. There are several aspects regarding the conversion of signals into images - specifically Red, Green, Blue (RGB) images. The methodology proposed here is potentially ideal fit for an initial security layer by achieving comparable classification results as more …
Advanced Oxidation Of Methyl Tert Butyl Ether With Ultraviolet Light Emitting Diodes, Aaron R. Neal
Advanced Oxidation Of Methyl Tert Butyl Ether With Ultraviolet Light Emitting Diodes, Aaron R. Neal
Theses and Dissertations
Methyl Tert-Butyl Ether (MTBE) is a volatile, soluble, organic compound introduced to gasoline within the United States as a fuel oxygenator in 1979. MTBE is a common groundwater pollutant due to its high water solubility and resistance to bio-degradation; it commonly infiltrates groundwater sources via leaking underground storage tanks or fuel spills. This research employed ultraviolet light emitting diodes (UV-LEDs) and hydrogen peroxide (H2O2) induced advanced oxidation process (AOP) to treat MTBE contaminated water at the bench scale. Using four H2O2:MTBE molar peroxide ratios (100:1, 200:1, 400:1, 500:1), MTBE degradation was induced …
Proving The Existence Of Equichordal Tight Fusion Frames Using The Newton–Kantorovich Theorem, Staci R. Davis
Proving The Existence Of Equichordal Tight Fusion Frames Using The Newton–Kantorovich Theorem, Staci R. Davis
Theses and Dissertations
An equichordal tight fusion frame (ECTFF) is an example of an optimal packing of subspaces. In particular, an ECTFF is an optimal packing of points in the Grassmannian with respect to chordal distance. Equivalently, every ECTFF is an arrangement of subspaces that meet certain criteria; tightness and equichordality. The existence of an ECTFF can be rephrased as, a certain polynomial mapping as a root. Hence, one can prove the existence of an ECTFF by applying Newton–Kantorovich theorem to this polynomial mapping, given a close enough approximation of one. Newton–Kantorovich requires checking an inequality within a neighborhood of the approximate root, …
Sensor-Based Vehicle Classification Using Machine Learning, Luke Mcfadden
Sensor-Based Vehicle Classification Using Machine Learning, Luke Mcfadden
Theses and Dissertations
This research investigates the classification of vehicles into heavy and light categories using acoustic, seismic, and magnetic sensor data. The effectiveness of using frequency domain data and classical machine learning techniques, is compared with the effectiveness of using time-series data and neural networks. The primary aim in doing so was to understand if modern neural network architectures could effectively remove the need for more traditional frequency based signals processing. A significant deliverable of this thesis was the feature importance determined for each of the three phenomenological types found within the data (acoustic, seismic, and magnetic). By analyzing the importance of …
Global Empirical Model Of Sporadic-E Occurrence Rates, Eli V. Parsch
Global Empirical Model Of Sporadic-E Occurrence Rates, Eli V. Parsch
Theses and Dissertations
Encompassing the Earth, the ionosphere presents unique challenges to modern communication, navigation, and surveillance technologies. Intense ionization enhancements known as sporadic-E (Es), can degrade and disrupt signals in unpredictable ways. Much work has been done to understand this phenomenon and more recent efforts have attempted to model its behavior and impacts. However, there have been limited efforts at modeling global Es occurrence rates (OR) at high time resolutions. This study develops a global empirical model of blanketing sporadic-E (fbEs) occurrence rates using a Karhunen-Lo´eve Expansion (KLE) of global fbEs OR climatologies built with Global Positioning System radio occultations (GPS-RO) and …
Optimization Of Bluetooth Auracast Broadcast Audio Transmissions Via Signal Modeling, Lauren J. Puseman
Optimization Of Bluetooth Auracast Broadcast Audio Transmissions Via Signal Modeling, Lauren J. Puseman
Theses and Dissertations
Since 1994, Bluetooth has been used as a Personal Area Network (PAN) to transfer data between devices within a short range. After thirty years of progressive improvement in functionality, security, range, and power efficiency, the Bluetooth Special Interest Group has released a new feature called Auracast, which allows users to tune into nearby public audio streams and receive the feed directly to their wireless headphones or hearing aids. Soon, venues such as convention centers, museums, public forums, and sporting arenas can implement Auracast to better suit their needs. In addition to these public benefits, the Department of Defense can take …
Measuring The Presentation Of Supporting Content For A Set Of Learning Objectives Throughout The States Of An Educational Game Tree, Michael L. Hastriter Jr.
Measuring The Presentation Of Supporting Content For A Set Of Learning Objectives Throughout The States Of An Educational Game Tree, Michael L. Hastriter Jr.
Theses and Dissertations
In an era of evolving warfare, the Department of Defense (DoD) recognizes the value of serious games as immersive tools for teaching critical concepts. This thesis introduces a pioneering framework tailored to enhance learning objectives through the presentation of educational game content. This addresses the unique needs of the DoD and other educators who use games by providing measurements to assess educational games. This researches investigates how instructors might assess games as potential teaching tools. It establishes a five-phase process, providing a framework to assess educational games against predefined learning objectives and informing future game development. This thesis demonstrates games …
Neutron Spectrum Unfolding From Activation Foils Irradiated In Gamble Ii Using Deuterated Polyethylene Anodes, Christopher J. Smith
Neutron Spectrum Unfolding From Activation Foils Irradiated In Gamble Ii Using Deuterated Polyethylene Anodes, Christopher J. Smith
Theses and Dissertations
Neutrons can cause irreparable harm to electronics, and experiments are needed to understand their effects fully. This work explores using pulsed power, which is low-cost compared to other sources, to create a useful neutron-rich environment. Deuterated polyethylene was used as anode and catcher material to generate the neutrons from the Deuterium-Deuterium (DD) fusion reaction. A simple, effective, and repeatable method was employed to directly cast deuterated polyethylene onto polyethylene sheets to fabricate the anode and catcher. The DD reactions were made by an ion beam-driven pulsed power generator, Gamble II, with the catcher located in the cathode. Zinc, copper, aluminum, …
Spectroscopic Characterization Of Trivalent Holmium In Liyf4 And Bay2F8 Crystals, Vivian R. Hedberg
Spectroscopic Characterization Of Trivalent Holmium In Liyf4 And Bay2F8 Crystals, Vivian R. Hedberg
Theses and Dissertations
Yttrium-fluoride crystals doped with trivalent holmium are promising laser gain media for mid-infrared laser systems. To accurately model laser systems, a complete optical characterization of the laser gain medium is required. The key modeling parameters were obtained by a spectroscopic investigation of Ho3+ in LiYF4 and BaY2F8 crystal hosts. Several important mid-infrared laser transitions were characterized at nominal Ho3+ ion concentrations of 10-30 at.%. Polarized emission cross-sections were determined from the fluorescence spectra using the Füchtbauer-Ladenburg method. Polarized absorption cross-sections were obtained using the Integral Reciprocity method. The upper-state lifetimes were measured at room …
Single Particle Analysis Of Operation Roller Coaster Environmental Samples, Caleb J. Sapp
Single Particle Analysis Of Operation Roller Coaster Environmental Samples, Caleb J. Sapp
Theses and Dissertations
The release of plutonium and other actinides into the environment following a nuclear detonation or accident, poses a health risk to biological systems. The spread, or transportation of that plutonium and its daughter products through environmental and meteorological means increases the potential affect and inherent danger within the surrounding area. In the 1960’s, a study was conducted in a joint effort by the US and Great Britain to collect information regarding the spread of plutonium released into the environment through a sub-critical detonation. Soil samples from these tests were collected by the USAF in 2015, approximately 50 years after these …
Modeling & Engineering Of Usmepcom Business Intelligence Data, Merrick A. Bedford
Modeling & Engineering Of Usmepcom Business Intelligence Data, Merrick A. Bedford
Theses and Dissertations
This thesis investigates the USMEPCOM’s issue of modeling and engineering Business Intelligence data centralized around the MEPS of Excellence (MOE) program. MEPS around the US conduct military personnel in-processing and in doing so have a vested interest in the standardization and application of the data associated with such processes. There are currently 65 MEPS stations and one RPS that handle military personnel onboarding paperwork and make determinations for military eligibility. This topic is important due to the MEPS cloud data processing system modernization efforts requiring data processing adaptation to ensure relevant and meaningful usage of current data.
A Random Forest-Based Q-Learning Algorithm: Toward Interpretable Artificial Intelligence, Victor R. Rae
A Random Forest-Based Q-Learning Algorithm: Toward Interpretable Artificial Intelligence, Victor R. Rae
Theses and Dissertations
A growing demand exists for interpretable artificial intelligence models, leading to extensive research efforts to enhance the explainability and transparency of policies generated by reinforcement learning (RL) methods. This research develops random forest-based RL algorithms as a logical progression in this academic pursuit. The algorithms are evaluated using three standard benchmark environments from OpenAI gym — CartPole, MountainCar, and LunarLander — and compared to implementations of the Deep Q-learning Network (DQN) and Double DQN (DDQN) algorithms for various metrics, including performance, robustness, efficiency, and interpretability. The random forest-based algorithms exhibit superior performance to both neural network-based algorithms in two out …
Standardization Of Risk Classifications For Unmanned Space Vehicle Missions, Collin A. Gwaltney
Standardization Of Risk Classifications For Unmanned Space Vehicle Missions, Collin A. Gwaltney
Theses and Dissertations
This paper seeks to model risk classification levels (A-D) for 122 Space Vehicle programs. Models include multinomial logistic regression as well as random forest, a machine learning technique based on decision trees. We use independent variables (IVs) which are theoretically correlated to risk class for the regression and one random forest model. We then include all IVs and allow the random forest technique to use those which provide the most information on risk class before paring down the number of IVs to only 7. We show that the accuracy of predictions increases from 62% to 87% by using random forest …