Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Physical Sciences and Mathematics (12)
- Computer Sciences (9)
- Operations Research, Systems Engineering and Industrial Engineering (9)
- Computer Engineering (5)
- Operational Research (5)
-
- Artificial Intelligence and Robotics (4)
- Digital Communications and Networking (3)
- Nuclear Engineering (3)
- Data Science (2)
- Other Operations Research, Systems Engineering and Industrial Engineering (2)
- Aerospace Engineering (1)
- Atomic, Molecular and Optical Physics (1)
- Chemistry (1)
- Civil Engineering (1)
- Civil and Environmental Engineering (1)
- Data Storage Systems (1)
- Econometrics (1)
- Economics (1)
- Electrical and Computer Engineering (1)
- Information Security (1)
- Physics (1)
- Radiochemistry (1)
- Signal Processing (1)
- Social and Behavioral Sciences (1)
Articles 1 - 20 of 20
Full-Text Articles in Engineering
Enabling Rapid Chemical Analysis Of Plutonium Alloys Via Machine Learning-Enhanced Atomic Spectroscopy Techniques, Ashwin P. Rao
Enabling Rapid Chemical Analysis Of Plutonium Alloys Via Machine Learning-Enhanced Atomic Spectroscopy Techniques, Ashwin P. Rao
Theses and Dissertations
Analytical atomic spectroscopy methods have the potential to provide solutions for rapid, high fidelity chemical analysis of plutonium alloys. Implementing these methods with advanced analytical techniques can help reduce the chemical analysis time needed for plutonium pit production, directly enabling the 80 pit-per-year by 2030 manufacturing goal outlined in the 2018 Nuclear Posture Review. Two commercial, handheld elemental analyzers were validated for potential in situ analysis of Pu. A handheld XRF device was able to detect gallium in a Pu surrogate matrix with a detection limit of 0.002 wt% and a mean error of 8%. A handheld LIBS device was …
An Exploratory Analysis Of Time Series Econometric Data For Retention Forecasting Using Deep Learning, John C. O'Donnell
An Exploratory Analysis Of Time Series Econometric Data For Retention Forecasting Using Deep Learning, John C. O'Donnell
Theses and Dissertations
Officer retention in the Air Force has been researched many times in an attempt to better predict the personnel needs of the Air Force for the future. There has been previous work done in regards to specific AFSCs and how their retention compares to specific yet similar private sector jobs. This study considers different econometric time series statistics as a feature space and an average Air Force officer separation rate as the response variable for the multivariate time series analysis deep learning techniques. The econometric indicators used in this study are New Business Formations, New Durable Good Orders, and the …
Analysis Of Generalized Artificial Intelligence Potential Through Reinforcement And Deep Reinforcement Learning Approaches, Jonathan Turner
Analysis Of Generalized Artificial Intelligence Potential Through Reinforcement And Deep Reinforcement Learning Approaches, Jonathan Turner
Theses and Dissertations
Artificial Intelligence is the next competitive domain; the first nation to develop human level artificial intelligence will have an impact similar to the development of the atomic bomb. To maintain the security of the United States and her people, the Department of Defense has funded research into the development of artificial intelligence and its applications. This research uses reinforcement learning and deep reinforcement learning methods as proxies for current and future artificial intelligence agents and to assess potential issues in development. Agent performance were compared across two games and one excursion: Cargo Loading, Tower of Hanoi, and Knapsack Problem, respectively. …
Deep Learning Approach To Multi-Phenomenological Nuclear Fuel Cycle Signals For Nonproliferation Applications, Preston J. Dicks
Deep Learning Approach To Multi-Phenomenological Nuclear Fuel Cycle Signals For Nonproliferation Applications, Preston J. Dicks
Theses and Dissertations
In order to reduce the time required for data analysis and decision-making relevant to nuclear proliferation detection, Artificial Intelligence (AI) techniques are applied to multi-phenomenological signals emitted from nuclear fuel cycle facilities to identify non-human readable characteristic signatures of operations for use in detecting proliferation activities. Seismic and magnetic emanations were collected in the vicinity of the High Flux Isotope Reactor (HFIR) and the McClellan Nuclear Research Center (MNRC). A novel bi-phenomenology DL network is designed to test the viability of transfer learning between nuclear reactor facilities. It is found that the network produces an 84.1% accuracy (99.4% without transient …
Training Logic And Random Forest Models To Predict It Spending, Jacob P. Batt
Training Logic And Random Forest Models To Predict It Spending, Jacob P. Batt
Theses and Dissertations
The Air Force must modernize, but the distribution of funds for technology remains as tight as ever. To this end, the Air Force Audit Agency is looking to utilize machine learning techniques to enhance their capabilities. This research explores Logistic Regression and Random Forest modeling to streamline data collection and cost classification. The final Logistic Regression model identified 4 significant attributes out of the 36 given and was 85 accurate in predicting whether a purchase amount was over or under $10,000. To expand beyond binary classification, a six-category classification Random Forest model was developed. It identified 6 significant attributes and …
Application Of Machine Learning Models With Numerical Simulations Of An Experimental Microwave Induced Plasma Gasification Reactor, Owen D. Sedej
Application Of Machine Learning Models With Numerical Simulations Of An Experimental Microwave Induced Plasma Gasification Reactor, Owen D. Sedej
Theses and Dissertations
This thesis aims to contribute to the future development of this technology by providing an in-depth literature review of how this technology physically operates and can be numerically modeled. Additionally, this thesis reviews literature of machine learning models that have been applied to gasification to make accurate predictions regarding the system. Finally, this thesis provides a framework of how to numerically model an experimental plasma gasification reactor in order to inform a variety of machine learning models.
Double Cone Flow Field Reconstruction Between Mach 4 And 12 Using Machine Learning Techniques, Trevor A. Toros
Double Cone Flow Field Reconstruction Between Mach 4 And 12 Using Machine Learning Techniques, Trevor A. Toros
Theses and Dissertations
No abstract provided.
Using Custom Ner Models To Extract Dod Specific Entities From Contracts, Kayla P. Haberstich
Using Custom Ner Models To Extract Dod Specific Entities From Contracts, Kayla P. Haberstich
Theses and Dissertations
The Air Force Sustainment Center collected 3.7 million contracts onto the Air Force Research Laboratory’s high power computers. They are in the format of a .pdf or scanned document, making them unstructured data. The Data Analytics Resource Team extracted the documents into a textual format for use in further analysis. This thesis looks to extract four DOD specific entities (NSN, Part Number, CAGE Code, and Supplier Name) from the contracts using custom NER models. This newly extracted information will allow the Air Force to identify what parts are supplied by which vendors. This information along with historical CLIN pricing for …
Contract Information Extraction Using Machine Learning, Zachary E. Butcher
Contract Information Extraction Using Machine Learning, Zachary E. Butcher
Theses and Dissertations
The Air Force Sustainment Center assisted by the Data Analytics Resource Team and the Defense Logistics Agency collected four million contracts onto one of the Air Force Research Laboratory’s high power computers. This thesis focuses on the effort to determine if parts are available through those contracts. Some information is extracted using machine learning in combination with natural language processing. Where machine learning methods are unsuccessful or inappropriate, text mining techniques, such as pattern recognition and rules, are used. Upon completion, the information is combined into a Gantt chart for quick evaluation. Only 21% of the contracts have their information …
Indoor Navigation Using Convolutional Neural Networks And Floor Plans, Ricky D. Anderson
Indoor Navigation Using Convolutional Neural Networks And Floor Plans, Ricky D. Anderson
Theses and Dissertations
The goal of this thesis is to evaluate a new indoor navigation technique by incorporating floor plans along with monocular camera images into a CNN as a potential means for identifying camera position. Building floor plans are widely available and provide potential information for localizing within the building. This work sets out to determine if a CNN can learn the architectural features of a floor plan and use that information to determine a location. In this work, a simulated indoor data set is created and used to train two CNNs. A classification CNN, which breaks up the floor plan into …
Joint 1d And 2d Neural Networks For Automatic Modulation Recognition, Luis M. Rosario Morel
Joint 1d And 2d Neural Networks For Automatic Modulation Recognition, Luis M. Rosario Morel
Theses and Dissertations
The digital communication and radar community has recently manifested more interest in using data-driven approaches for tasks such as modulation recognition, channel estimation and distortion correction. In this research we seek to apply an object detector for parameter estimation to perform waveform separation in the time and frequency domain prior to classification. This enables the full automation of detecting and classifying simultaneously occurring waveforms. We leverage a lD ResNet implemented by O'Shea et al. in [1] and the YOLO v3 object detector designed by Redmon et al. in [2]. We conducted an in depth study of the performance of these …
Lightning Prediction For Space Launch Using Machine Learning Based Off Of Electric Field Mills And Lightning Detection And Ranging Data, Anson Cheng
Theses and Dissertations
Kennedy Space Center and Cape Canaveral Air Station, FL, where the Air Force conducts space launches, are in an area of frequent lightning strikes, which is main obstacle in meeting launch goals. The 45th Weather Squadron (45th WS) ensures that any weather safety requirements are met during pre-launch and actual space launch. Using only summer months from three years’ worth of lightning detection and ranging (LDAR) and electric field mill (EFM) data from KSC, several feedforward neural networks are constructed. Separate models are built for each EFM and trained by adjusting parameters to forecast lightning 30 minutes out in the …
Algorithm Selection Framework: A Holistic Approach To The Algorithm Selection Problem, Marc W. Chalé
Algorithm Selection Framework: A Holistic Approach To The Algorithm Selection Problem, Marc W. Chalé
Theses and Dissertations
A holistic approach to the algorithm selection problem is presented. The “algorithm selection framework" uses a combination of user input and meta-data to streamline the algorithm selection for any data analysis task. The framework removes the conjecture of the common trial and error strategy and generates a preference ranked list of recommended analysis techniques. The framework is performed on nine analysis problems. Each of the recommended analysis techniques are implemented on the corresponding data sets. Algorithm performance is assessed using the primary metric of recall and the secondary metric of run time. In six of the problems, the recall of …
A Machine Learning Approach To Characterizing Particle Morphology In Nuclear Forensics, Daniel A. Gum
A Machine Learning Approach To Characterizing Particle Morphology In Nuclear Forensics, Daniel A. Gum
Theses and Dissertations
A machine learning approach is taken to characterizing a group of synthetic uranium bearing particles. SEM images of these lab-created particles were converted into a binary representation that captured morphological features in accordance with a guide established by Los Alamos National Laboratory. Each particle in the dataset contains an association with chemical creation conditions: processing method, precipitation temperature and pH, calcination temperature are most closely tied to particle morphology. Additionally, trained classifiers are able to relate final products between particles, implying that morphological features are shared between particles with similar composition.
Machine Learning Models Of C-17 Specific Range Using Flight Recorder Data, Marcus Catchpole
Machine Learning Models Of C-17 Specific Range Using Flight Recorder Data, Marcus Catchpole
Theses and Dissertations
Fuel is a significant expense for the Air Force. The C-17 Globemaster eet accounts for a significant portion. Estimating the range of an aircraft based on its fuel consumption is nearly as old as flight itself. Consideration of operational energy and the related consideration of fuel efficiency is increasing. Meanwhile machine learning and data-mining techniques are on the rise. The old question, "How far can my aircraft y with a given load cargo and fuel?" has given way to "How little fuel can I load into an aircraft and safely arrive at the destination?" Specific range is a measure of …
Confidence Inference In Defensive Cyber Operator Decision Making, Graig S. Ganitano
Confidence Inference In Defensive Cyber Operator Decision Making, Graig S. Ganitano
Theses and Dissertations
Cyber defense analysts face the challenge of validating machine generated alerts regarding network-based security threats. Operations tempo and systematic manpower issues have increased the importance of these individual analyst decisions, since they typically are not reviewed or changed. Analysts may not always be confident in their decisions. If confidence can be accurately assessed, then analyst decisions made under low confidence can be independently reviewed and analysts can be offered decision assistance or additional training. This work investigates the utility of using neurophysiological and behavioral correlates of decision confidence to train machine learning models to infer confidence in analyst decisions. Electroencephalography …
Methods To Address Extreme Class Imbalance In Machine Learning Based Network Intrusion Detection Systems, Russell W. Walter
Methods To Address Extreme Class Imbalance In Machine Learning Based Network Intrusion Detection Systems, Russell W. Walter
Theses and Dissertations
Despite the considerable academic interest in using machine learning methods to detect cyber attacks and malicious network traffic, there is little evidence that modern organizations employ such systems. Due to the targeted nature of attacks and cybercriminals’ constantly changing behavior, valid observations of attack traffic suitable for training a classifier are extremely rare. Rare positive cases combined with the fact that the overwhelming majority of network traffic is benign create an extreme class imbalance problem. Using publically available datasets, this research examines the class imbalance problem by using small samples of the attack observations to create multiple training sets that …
Data-Driven Process Discovery: A Discrete Time Algebra For Relational Signal Analysis, David M. Conrad
Data-Driven Process Discovery: A Discrete Time Algebra For Relational Signal Analysis, David M. Conrad
Theses and Dissertations
This research presents an autonomous and computationally tractable method for scientific process analysis, combining an iterative algorithmic search and a recognition technique to discover multivariate linear and non-linear relations within experimental data series. These resultant data-driven relations provide researchers with a potentially real-time insight into experimental process phenomena and behavior. This method enables the efficient search of a potentially infinite space of relations within large data series to identify relations that accurately represent process phenomena. Proposed is a time series transformation that encodes and compresses real-valued data into a well-defined, discrete-space of 13 primitive elements where comparative evaluation between variables …
A Fortran Based Learning System Using Multilayer Back-Propagation Neural Network Techniques, Gregory L. Reinhart
A Fortran Based Learning System Using Multilayer Back-Propagation Neural Network Techniques, Gregory L. Reinhart
Theses and Dissertations
An interactive computer system which allows the researcher to build an optimal neural network structure quickly, is developed and validated. This system assumes a single hidden layer perceptron structure and uses the back- propagation training technique. The software enables the researcher to quickly define a neural network structure, train the neural network, interrupt training at any point to analyze the status of the current network, re-start training at the interrupted point if desired, and analyze the final network using two- dimensional graphs, three-dimensional graphs, confusion matrices and saliency metrics. A technique for training, testing, and validating various network structures and …
Using Discovery-Based Learning To Prove The Behavior Of An Autonomous Agent, David N. Mezera
Using Discovery-Based Learning To Prove The Behavior Of An Autonomous Agent, David N. Mezera
Theses and Dissertations
Computer-generated autonomous agents in simulation often behave predictably and unrealistically. These characteristics make them easy to spot and exploit by human participants in the simulation, when we would prefer the behavior of the agent to be indistinguishable from human behavior. An improvement in behavior might be possible by enlarging the library of responses, giving the agent a richer assortment of tactics to employ during a combat scenario. Machine learning offers an exciting alternative to constructing additional responses by hand by instead allowing the system to improve its own performance with experience. This thesis presents NOSTRUM, a discovery-based learning DBL system …