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Articles 1 - 6 of 6
Full-Text Articles in Multivariate Analysis
Cost Estimation Trends For Major Defense Acquisition Programs, Sammantha Jones, Edward D. White, Jonathan D. Ritschel, Shawn M. Valentine
Cost Estimation Trends For Major Defense Acquisition Programs, Sammantha Jones, Edward D. White, Jonathan D. Ritschel, Shawn M. Valentine
Faculty Publications
The authors use both descriptive and inferential techniques to investigate average and standard deviation trends in cost estimates for major defense acquisition programs (MDAPs) grouped into decades from the 1970s to 2010s. For total program-cost-growth factors (CGFs), the 2010s exhibited lower CGFs compared to the 1990s. For the program-acquisition-unit cost (PAUC) CGFs, the 2010s appear lower than the 1990s and borderline lower than the 1970s. A statistically significant decreasing trend in the standard deviations of total program CGFs throughout the decades was identified. This lowering variability trend also appeared for PAUC CGFs from the 1980s onward. This finding appears to …
Analytic Case Study Using Unsupervised Event Detection In Multivariate Time Series Data, Jeremy M. Wightman
Analytic Case Study Using Unsupervised Event Detection In Multivariate Time Series Data, Jeremy M. Wightman
Theses and Dissertations
Analysis of cyber-physical systems (CPS) has emerged as a critical domain for providing US Air Force and Space Force leadership decision advantage in air, space, and cyberspace. Legacy methods have been outpaced by evolving battlespaces and global peer-level challengers. Automation provides one way to decrease the time that analysis currently takes. This thesis presents an event detection automation system (EDAS) which utilizes deep learning models, distance metrics, and static thresholding to detect events. The EDAS automation is evaluated with case study of CPS domain experts in two parts. Part 1 uses the current methods for CPS analysis with a qualitative …
Statistical Inference On Desirability Function Optimal Points To Evaluate Multi-Objective Response Surfaces, Peter A. Calhoun
Statistical Inference On Desirability Function Optimal Points To Evaluate Multi-Objective Response Surfaces, Peter A. Calhoun
Theses and Dissertations
A shortfall of the Derringer and Suich (1980) desirability function is lack of inferential methods to quantify uncertainty. Most articles for addressing uncertainty usually involve robust methods, providing a point estimate that is less affected by variation. Few articles address confidence intervals or bands but not specifically for the Derringer and Suich method. This research provides two valuable contributions to the field of response surface methodology. The first contribution is evaluating the effect of correlation and plane angles on Derringer and Suich optimal solutions. The second contribution proposes and compares 8 inferential methods--both univariate and multivariate--for creating confidence intervals on …
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.
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 …
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 …