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Full-Text Articles in Operational Research
The Use Of Deep Learning And Transfer Learning In Complex Problems, Jacob S. Lang
The Use Of Deep Learning And Transfer Learning In Complex Problems, Jacob S. Lang
Theses and Dissertations
Deep neural networks and transfer learning show potential in addressing complex problems such as the Tower of Hanoi and knapsack problems. The primary aim is to examine how the use of deep neural networks and transfer learning can enhance the ability of artificial learning systems to generalize. Transfer learning plays a crucial role in machine learning, particularly in the domain of artificial neural networks, as it helps overcome the challenges associated with limited data, computational efficiency, and generalization. The methodology used in this research involves the creation of data sets for the Tower of Hanoi and knapsack problems. To predict …
U.S. Army Cadet Command Branch Prediction Model, Daniel M. Krizan
U.S. Army Cadet Command Branch Prediction Model, Daniel M. Krizan
Theses and Dissertations
The current system for providing US Army ROTC cadets their branches leaves significant uncertainty until the final pronouncement of branch assigned. This uncertainty can be alleviated by providing a prediction model for cadets to input personal data and desired branch to identify likelihood of receiving the request. This thesis produces a machine learning model capable of producing branch prediction for cadets.
Bayesian Convolutional Neural Network With Prediction Smoothing And Adversarial Class Thresholds, Noah M. Miller
Bayesian Convolutional Neural Network With Prediction Smoothing And Adversarial Class Thresholds, Noah M. Miller
Theses and Dissertations
Using convolutional neural networks (CNNs) for image classification for each frame in a video is a very common technique. Unfortunately, CNNs are very brittle and have a tendency to be over confident in their predictions. This can lead to what we will refer to as “flickering,” which is when the predictions between frames jump back and forth between classes. In this paper, new methods are proposed to combat these shortcomings. This paper utilizes a Bayesian CNN which allows for a distribution of outputs on each data point instead of just a point estimate. These distributions are then smoothed over multiple …
Meta-Heuristic Optimization Methods For Quaternion-Valued Neural Networks, Jeremiah P. Bill
Meta-Heuristic Optimization Methods For Quaternion-Valued Neural Networks, Jeremiah P. Bill
Theses and Dissertations
In recent years, real-valued neural networks have demonstrated promising, and often striking, results across a broad range of domains. This has driven a surge of applications utilizing high-dimensional datasets. While many techniques exist to alleviate issues of high-dimensionality, they all induce a cost in terms of network size or computational runtime. This work examines the use of quaternions, a form of hypercomplex numbers, in neural networks. The constructed networks demonstrate the ability of quaternions to encode high-dimensional data in an efficient neural network structure, showing that hypercomplex neural networks reduce the number of total trainable parameters compared to their real-valued …
The Autonomous Attack Aviation Problem, John C. Goodwill
The Autonomous Attack Aviation Problem, John C. Goodwill
Theses and Dissertations
An autonomous unmanned combat aerial vehicle (AUCAV) performing an air-to-ground attack mission must make sequential targeting and routing decisions under uncertainty. We formulate a Markov decision process model of this autonomous attack aviation problem (A3P) and solve it using an approximate dynamic programming (ADP) approach. We develop an approximate policy iteration algorithm that implements a least squares temporal difference learning mechanism to solve the A3P. Basis functions are developed and tested for application within the ADP algorithm. The ADP policy is compared to a benchmark policy, the DROP policy, which is determined by repeatedly solving a deterministic orienteering problem as …
Predicting Upper Atmospheric Weather Conditions Utilizing Long-Short Term Memory Neural Networks For Aircraft Fuel Efficiency, Garrett A. Alarcon
Predicting Upper Atmospheric Weather Conditions Utilizing Long-Short Term Memory Neural Networks For Aircraft Fuel Efficiency, Garrett A. Alarcon
Theses and Dissertations
Aviation fuel is a major component of the Air Force (AF) budget, and vital for the core mission of the AF. This study investigated the viability of LSTMs to increase the accuracy of deterministic NWP models, while also investigating the ability to reduce model generation time. Increased forecast accuracy for wind speeds could be implemented into existing flight path models to further increase fuel efficiency, while reduced modeling times would allow flight planners to generate a flight plan in rapid response situations. The most viable model consisted of an ensemble of six LSTMs trained o six coordinates. The model's error …
Modeling Small Unmanned Aerial System Mishaps Using Logistics Regression And Artificial Neural Networks, Sean E. Wolf
Modeling Small Unmanned Aerial System Mishaps Using Logistics Regression And Artificial Neural Networks, Sean E. Wolf
Theses and Dissertations
A dataset of 854 small unmanned aerial system (SUAS) flight experiments from 2005-2009 is analyzed to determine significant factors that contribute to mishaps. The data from 29 airframes of different designs and technology readiness levels were aggregated. 20 measured parameters from each flight experiment are investigated, including wind speed, pilot experience, number of prior flights, pilot currency, etc. Outcomes of failures (loss of flight data) and damage (injury to airframe) are classified by logistic regression modeling and artificial neural network analysis. From the analysis, it can be concluded that SUAS damage is a random event that cannot be predicted with …
Study Of Human Factors Variables In Battle Outcome Prediction Models, David Andrew Glovier
Study Of Human Factors Variables In Battle Outcome Prediction Models, David Andrew Glovier
Engineering Management & Systems Engineering Theses & Dissertations
Over time there have been many improvements in models that are used to predict the outcome of battles. Currently there is much supposition and speculation surrounding the use of human performance related factors as additional inputs to battle simulation models to improve their accuracy. However there is no conclusive scientific evidence which shows that these factors do make a significant difference. This study investigates the use of factors that may impact on the human performance directly or indirectly in battle prediction models. These factors consist of traditional human factors and external factors that may influence the human performance. The research …
Recurrent Neural Networks For Radar Target Identification, Eric T. Kouba
Recurrent Neural Networks For Radar Target Identification, Eric T. Kouba
Theses and Dissertations
A real-time recurrent learning algorithm was applied to a five class radar target identification problem. The wideband radar was assumed to measure both kinematic (tracking information expressed as estimated aspect angles) and high range resolution data from a single, isolated aircraft. The aspect angles (azimuth and elevation) of the aircraft relative to the radar were assumed to be constantly chancing. This created temporal sequences of high range resolution radar signatures that changed as the aspect angles changed. These sequences were used as input features to a recurrent neural network for three radar target identification test cases. The first test case …
Multilayer Perceptrons For Classification, Lisa M. Belue
Multilayer Perceptrons For Classification, Lisa M. Belue
Theses and Dissertations
Techniques for training, testing, and validating multilayer perceptrons are thoroughly examined. Results obtained using perceptrons are compared and contrasted with two multivariate discriminant analysis techniques- logistic regression and k neighbor. Methods for determining significant input features are investigated and a procedure for examining the confidence to place in the significance of these features is developed. Procedures to evaluate the applicability of high-order feature inputs are examined. These methods and procedures are applied to two very different applications. The first application concerns the prediction of Air Force pilot retention/separation rates for input to force projection models. The second application concerns the …