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Physical Sciences and Mathematics Commons™
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- Forecasting (2)
- Air Force Weather (1)
- Analytical techniques (1)
- Applied Statistics (1)
- Autoassociative Neural Network Mapping (1)
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- Bayes cost (1)
- Bayesian networks (1)
- Chemical analysis (1)
- Conditional probability (1)
- Cost estimating (1)
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- Design of experiments (1)
- Flight Planning (1)
- Heteroassociative Neural Network Mapping (1)
- Laser induced breakdown spectroscopy (1)
- Learning curve models (1)
- Lightning (1)
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- Negative binomial regression (1)
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- Optimization (1)
- Pilot Training NEXT (1)
- Probability distributions (1)
- Production cost (1)
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- Recruiting (1)
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- Signal Data (1)
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Articles 1 - 10 of 10
Full-Text Articles in Physical Sciences and Mathematics
Cost Estimating Using A New Learning Curve Theory For Non-Constant Production Rates, Dakotah Hogan, John J. Elshaw, Clay M. Koschnick, Jonathan D. Ritschel, Adedeji B. Badiru, Shawn M. Valentine
Cost Estimating Using A New Learning Curve Theory For Non-Constant Production Rates, Dakotah Hogan, John J. Elshaw, Clay M. Koschnick, Jonathan D. Ritschel, Adedeji B. Badiru, Shawn M. Valentine
Faculty Publications
Traditional learning curve theory assumes a constant learning rate regardless of the number of units produced. However, a collection of theoretical and empirical evidence indicates that learning rates decrease as more units are produced in some cases. These diminishing learning rates cause traditional learning curves to underestimate required resources, potentially resulting in cost overruns. A diminishing learning rate model, namely Boone’s learning curve, was recently developed to model this phenomenon. This research confirms that Boone’s learning curve systematically reduced error in modeling observed learning curves using production data from 169 Department of Defense end-items. However, high amounts of variability in …
Applications Of Portable Libs For Actinide Analysis, Ashwin P. Rao, John D. Auxier Ii, Dung Vu, Michael B. Shattan
Applications Of Portable Libs For Actinide Analysis, Ashwin P. Rao, John D. Auxier Ii, Dung Vu, Michael B. Shattan
Faculty Publications
A portable LIBS device was used for rapid elemental impurity analysis of plutonium alloys. This device demonstrates the potential for fast, accurate in-situ chemical analysis and could significantly reduce the fabrication time of plutonium alloys.
Autoassociative-Heteroassociative Neural Network, Claudia V. Kropas-Hughes, Steven K. Rogers, Mark E. Oxley, Matthew Kabrisky
Autoassociative-Heteroassociative Neural Network, Claudia V. Kropas-Hughes, Steven K. Rogers, Mark E. Oxley, Matthew Kabrisky
AFIT Patents
An efficient neural network computing technique capable of synthesizing two sets of output signal data from a single input signal data set. The method and device of the invention involves a unique integration of autoassociative and heteroassociative neural network mappings, the autoassociative neural network mapping enabling a quality metric for assessing the generalization or prediction accuracy of the heteroassociative neural network mapping.
Conceptualization And Application Of Deep Learning And Applied Statistics For Flight Plan Recommendation, Nicholas C. Forrest
Conceptualization And Application Of Deep Learning And Applied Statistics For Flight Plan Recommendation, Nicholas C. Forrest
Theses and Dissertations
The Air Forces Pilot Training Next (PTN) program seeks a more efficient pilot training environment emphasizing the use of virtual reality flight simulators alongside periodic real aircraft experience. The PTN program wants to accelerate the training pace and progress in undergraduate pilot training compared to traditional undergraduate pilot training. Currently, instructor pilots spend excessive time planning and scheduling flights. This research focuses on methods to auto-generate the planning of in-flight events using hybrid filtering and deep learning techniques. The resulting approach captures temporal trends of user-specific and program-wide student performance to recommend a feasible set of graded flight events for …
An Analysis Of A Lighting Prediction Threshold For 45th Weather Squadron Electric Field Mill Data, Charles A. Skrovan
An Analysis Of A Lighting Prediction Threshold For 45th Weather Squadron Electric Field Mill Data, Charles A. Skrovan
Theses and Dissertations
The mission of the 45th Weather Squadron (45 WS) is to “exploit the weather to assure safe access to air and space” for Patrick Air Force Base, Cape Canaveral Air Force Station (CCAFS), and Kennedy Space Center (KSC) in support of various operations (United States Air Force, n.d.). To support that mission the 45 WS hosts a suite of weather detection instruments that include a lightning warning system that consists of an array of 31 electric field mills (EFM) and a lightning detection and ranging system (Department of the Air Force, 1976). Electric field mills at Cape Canaveral continuously record …
Ground Weather Radar Signal Characterization Through Application Of Convolutional Neural Networks, Stephen M. Lee
Ground Weather Radar Signal Characterization Through Application Of Convolutional Neural Networks, Stephen M. Lee
Theses and Dissertations
The 45th Weather Squadron supports the space launch efforts out of the Kennedy Space Center and Cape Canaveral Air Force Station for the Department of Defense, NASA, and commercial customers through weather assessments. Their assessment of the Lightning Launch Commit Criteria (LLCC) for avoidance of natural and rocket triggered lightning to launch vehicles is critical in approving space shuttle and rocket launches. The LLCC includes standards for cloud formations, which requires proper cloud identification and characterization methods. Accurate reflectivity measurements for ground weather radar are important to meet the LLCC for rocket triggered lightning. Current linear interpolation methods for ground …
Next-Generation Air Force Weather Metrics Via Bayes Cost Analysis, Brandon M. Bailey
Next-Generation Air Force Weather Metrics Via Bayes Cost Analysis, Brandon M. Bailey
Theses and Dissertations
This research proposes a new methodology for U.S. Air Force weather forecast metrics. Military weather forecasters are essentially statistical classifiers. They categorize future conditions into an operationally relevant category based on current data, much like an Artificial Neural Net or Logistic Regression model. There is extensive literature on statistically-based metrics for these types of classifiers. Additionally, in the U.S. Air Force, forecast errors (errors in classification) have quantifiable operational costs and benefits associated with incorrect or correct classification decisions. There is a methodology in the literature, Bayes Cost, which provides a structure for creating statistically rigorous metrics for classification decisions …
Analysis And Forecasting Of The 360th Air Force Recruiting Group Goal Distribution, Tyler Spangler
Analysis And Forecasting Of The 360th Air Force Recruiting Group Goal Distribution, Tyler Spangler
Theses and Dissertations
This research utilizes monthly data from 2012-2017 to determine economic or demographic factors that significantly contribute to increased goaling and production potential in areas of the 360th Recruiting Groups. Using regression analysis, a model of recruiting goals and production is built to identify squadrons within the 360 RCGs zone that are capable of producing more or fewer recruits and the factors that contribute to this increased or decreased capability. This research identifies that a zones high school graduation rate, the number of recruiters, and the number of JROTC detachments in a zone are positively correlated with recruiting goals and that …
Characterizing Uncertainty In Correlated Response Variables For Pareto Front Optimization, Peter A. Calhoun
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 …
Analysis With Dynamic Bayesian Networks Compared To Simulation, Aaron J. Salazar
Analysis With Dynamic Bayesian Networks Compared To Simulation, Aaron J. Salazar
Theses and Dissertations
This research compares simulations to Dynamic Bayesian Networks in analyzing situations. The research applies models that have known output mean and variance. Queueing systems have theoretical values of the steady-state mean and variance for the number of entities in the system. Monte Carlo simulation development is broken down into two separate approaches: discrete-event simulation and time-oriented simulation. The discrete-event simulation uses pseudo-random numbers to schedule and trigger future events (i.e. customer arrivals and services) and is based on the generated objects.The time-oriented simulation utilizes fixed-width time intervals and updates the system state according to a stochastic process for the set …