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Articles 1 - 13 of 13
Full-Text Articles in Physical Sciences and Mathematics
An Integrated Space Test Lexicon: A Taxonomy For The Integrated Test And Evaluation Of Space Systems, Stephen Tullino, Andrew Keys, Robert A. Bettinger, Amy M. Cox, David R. Jacques
An Integrated Space Test Lexicon: A Taxonomy For The Integrated Test And Evaluation Of Space Systems, Stephen Tullino, Andrew Keys, Robert A. Bettinger, Amy M. Cox, David R. Jacques
Faculty Publications
The proposed Integrated Space Test Lexicon is intended to amalgamate the numerous definitions of integrated (IT or IT&E), development test (DT or DT&E), and operational test (OT or OT&E) into unified, service-wide definitions, aligned with the Space Test Enterprise Vision. Refining such definitions will help distill the core characteristics of these fundamental test types to first identify space system activities composing what is traditionally known as DT and OT, then to provide a means of how these activities fit into the IT paradigm and support space system development. In forging a common understanding of how DT and OT support space …
Hyperspectral Point Cloud Projection For The Semantic Segmentation Of Multimodal Hyperspectral And Lidar Data With Point Convolution-Based Deep Fusion Neural Networks, Kevin T. Decker, Brett J. Borghetti
Hyperspectral Point Cloud Projection For The Semantic Segmentation Of Multimodal Hyperspectral And Lidar Data With Point Convolution-Based Deep Fusion Neural Networks, Kevin T. Decker, Brett J. Borghetti
Faculty Publications
The fusion of dissimilar data modalities in neural networks presents a significant challenge, particularly in the case of multimodal hyperspectral and lidar data. Hyperspectral data, typically represented as images with potentially hundreds of bands, provide a wealth of spectral information, while lidar data, commonly represented as point clouds with millions of unordered points in 3D space, offer structural information. The complementary nature of these data types presents a unique challenge due to their fundamentally different representations requiring distinct processing methods. In this work, we introduce an alternative hyperspectral data representation in the form of a hyperspectral point cloud (HSPC), which …
Multicollinearity Applied Stepwise Stochastic Imputation: A Large Dataset Imputation Through Correlation‑Based Regression, Benjamin D. Leiby, Darryl K. Ahner
Multicollinearity Applied Stepwise Stochastic Imputation: A Large Dataset Imputation Through Correlation‑Based Regression, Benjamin D. Leiby, Darryl K. Ahner
Faculty Publications
This paper presents a stochastic imputation approach for large datasets using a correlation selection methodology when preferred commercial packages struggle to iterate due to numerical problems. A variable range-based guard rail modification is proposed that benefits the convergence rate of data elements while simultaneously providing increased confidence in the plausibility of the imputations. A large country conflict dataset motivates the search to impute missing values well over a common threshold of 20% missingness. The Multicollinearity Applied Stepwise Stochastic imputation methodology (MASS-impute) capitalizes on correlation between variables within the dataset and uses model residuals to estimate unknown values. Examination of the …
Emotion Classification Of Indonesian Tweets Using Bidirectional Lstm, Aaron K. Glenn, Phillip M. Lacasse, Bruce A. Cox
Emotion Classification Of Indonesian Tweets Using Bidirectional Lstm, Aaron K. Glenn, Phillip M. Lacasse, Bruce A. Cox
Faculty Publications
Emotion classification can be a powerful tool to derive narratives from social media data. Traditional machine learning models that perform emotion classification on Indonesian Twitter data exist but rely on closed-source features. Recurrent neural networks can meet or exceed the performance of state-of-the-art traditional machine learning techniques using exclusively open-source data and models. Specifically, these results show that recurrent neural network variants can produce more than an 8% gain in accuracy in comparison with logistic regression and SVM techniques and a 15% gain over random forest when using FastText embeddings. This research found a statistical significance in the performance of …
Machine Learning Prediction Of Dod Personal Property Shipment Costs, Tiffany Tucker [*], Torrey J. Wagner, Paul Auclair, Brent T. Langhals
Machine Learning Prediction Of Dod Personal Property Shipment Costs, Tiffany Tucker [*], Torrey J. Wagner, Paul Auclair, Brent T. Langhals
Faculty Publications
U.S. Department of Defense (DoD) personal property moves account for 15% of all domestic and international moves - accurate prediction of their cost could draw attention to outlier shipments and improve budget planning. In this work 136,140 shipments between 13 personal property shipment hubs from April 2022 through March 2023 with a total cost of $1.6B were analyzed. Shipment cost was predicted using recursive feature elimination on linear regression and XGBoost algorithms, as well as through neural network hyperparameter sweeps. Modeling was repeated after removing 28 features related to shipment hub location and branch of service to examine their influence …
Development Of Advanced Machine Learning Models For Analysis Of Plutonium Surrogate Optical Emission Spectra, Ashwin P. Rao, Phillip R. Jenkins, John D. Auxier Ii, Michael B. Shattan, Anil Patnaik
Development Of Advanced Machine Learning Models For Analysis Of Plutonium Surrogate Optical Emission Spectra, Ashwin P. Rao, Phillip R. Jenkins, John D. Auxier Ii, Michael B. Shattan, Anil Patnaik
Faculty Publications
This work investigates and applies machine learning paradigms seldom seen in analytical spectroscopy for quantification of gallium in cerium matrices via processing of laser-plasma spectra. Ensemble regressions, support vector machine regressions, Gaussian kernel regressions, and artificial neural network techniques are trained and tested on cerium-gallium pellet spectra. A thorough hyperparameter optimization experiment is conducted initially to determine the best design features for each model. The optimized models are evaluated for sensitivity and precision using the limit of detection (LoD) and root mean-squared error of prediction (RMSEP) metrics, respectively. Gaussian kernel regression yields the superlative predictive model with an RMSEP of …
Monitoring Fine-Scale Forest Health Using Unmanned Aerial Systems (Uas) Multispectral Models, Benjamin T. Fraser, Russell G. Congalton
Monitoring Fine-Scale Forest Health Using Unmanned Aerial Systems (Uas) Multispectral Models, Benjamin T. Fraser, Russell G. Congalton
Faculty Publications
Forest disturbances—driven by pests, pathogens, and discrete events—have led to billions of dollars in lost ecosystem services and management costs. To understand the patterns and severity of these stressors across complex landscapes, there must be an increase in reliable data at scales compatible with management actions. Unmanned aerial systems (UAS or UAV) offer a capable platform for collecting local scale (e.g., individual tree) forestry data. In this study, we evaluate the capability of UAS multispectral imagery and freely available National Agricultural Imagery Program (NAIP) imagery for differentiating coniferous healthy, coniferous stressed, deciduous healthy, deciduous stressed, and degraded individual trees throughout …
Per-Pixel Cloud Cover Classification Of Multispectral Landsat-8 Data, Salome E. Carrasco [*], Torrey J. Wagner, Brent T. Langhals
Per-Pixel Cloud Cover Classification Of Multispectral Landsat-8 Data, Salome E. Carrasco [*], Torrey J. Wagner, Brent T. Langhals
Faculty Publications
Random forest and neural network algorithms are applied to identify cloud cover using 10 of the wavelength bands available in Landsat 8 imagery. The methods classify each pixel into 4 different classes: clear, cloud shadow, light cloud, or cloud. The first method is based on a fully connected neural network with ten input neurons, two hidden layers of 8 and 10 neurons respectively, and a single-neuron output for each class. This type of model is considered with and without L2 regularization applied to the kernel weighting. The final model type is a random forest classifier created from an ensemble of …
The Effects Of Individual Differences, Non‐Stationarity, And The Importance Of Data Partitioning Decisions For Training And Testing Of Eeg Cross‐Participant Models, Alexander J. Kamrud [*], Brett J. Borghetti, Christine M. Schubert Kabban
The Effects Of Individual Differences, Non‐Stationarity, And The Importance Of Data Partitioning Decisions For Training And Testing Of Eeg Cross‐Participant Models, Alexander J. Kamrud [*], Brett J. Borghetti, Christine M. Schubert Kabban
Faculty Publications
EEG-based deep learning models have trended toward models that are designed to perform classification on any individual (cross-participant models). However, because EEG varies across participants due to non-stationarity and individual differences, certain guidelines must be followed for partitioning data into training, validation, and testing sets, in order for cross-participant models to avoid overestimation of model accuracy. Despite this necessity, the majority of EEG-based cross-participant models have not adopted such guidelines. Furthermore, some data repositories may unwittingly contribute to the problem by providing partitioned test and non-test datasets for reasons such as competition support. In this study, we demonstrate how improper …
Multi-Objective Database Queries In Combined Knapsack And Set Covering Problem Domains, Sean A. Mochocki, Gary B. Lamont, Robert C. Leishman, Kyle J. Kauffman
Multi-Objective Database Queries In Combined Knapsack And Set Covering Problem Domains, Sean A. Mochocki, Gary B. Lamont, Robert C. Leishman, Kyle J. Kauffman
Faculty Publications
Database queries are one of the most important functions of a relational database. Users are interested in viewing a variety of data representations, and this may vary based on database purpose and the nature of the stored data. The Air Force Institute of Technology has approximately 100 data logs which will be converted to the standardized Scorpion Data Model format. A relational database is designed to house this data and its associated sensor and non-sensor metadata. Deterministic polynomial-time queries were used to test the performance of this schema against two other schemas, with databases of 100 and 1000 logs of …
Securing Photovoltaic (Pv) System Deployments With Data Diodes, Robert D. Larkin, Torrey J. Wagner, Barry E. Mullins
Securing Photovoltaic (Pv) System Deployments With Data Diodes, Robert D. Larkin, Torrey J. Wagner, Barry E. Mullins
Faculty Publications
A survey of a typical photovoltaic (PV) system with and without the cybersecurity protections of a data diode is explored. This survey includes a brief overview of Industrial Control Systems (ICS) and their relationship to the Internet of Things (IoT), Industrial Internet of Things (IIoT), and Industry 4.0 terminology. The cybersecurity features of eight data diodes are compared, and the cyber attack surface, attack scenarios, and mitigations of a typical PV system are discussed. After assessing cybersecurity, the economic considerations to purchase a data diode are considered. At 13.19 cents/kWh, the sale of 227,445 kWh is needed to fund one …
The Analytics Managers Ultimate Guide For Working With Universities, Robert J. Mcgrath
The Analytics Managers Ultimate Guide For Working With Universities, Robert J. Mcgrath
Faculty Publications
The challenges organizations are having related to finding (and retaining) deep analytical talent did not materialize out of thin air…or overnight. Analytics and Data science – and the role of the analytics professional – has evolved over the last several decades and has been fueled by our ability to capture and process increasingly larger and more complex variations of data and our desire to gain increasingly granular insights to fuel innovation and creativity. While many organizations recognize that a partnership with a university can be a resource to many of these challenges, the best way to start a conversation with …
Photovoltaic System Optimization For An Austere Location Using Time Series Data, Torrey J. Wagner, Eric Lang, Warren Assink, Douglas S. Dudis
Photovoltaic System Optimization For An Austere Location Using Time Series Data, Torrey J. Wagner, Eric Lang, Warren Assink, Douglas S. Dudis
Faculty Publications
In this work we test experimental photovoltaic, storage and generator technologies and investigate their potential to meet austere location energy needs. After defining the energy requirements and insolation of a 1,100-person base, we develop a microgrid model and simulation. Cost optimizations were then performed using hourly time-series data to explore the cost and performance trade-space of a PV-battery-generator system. The work highlights the cost of resiliency and the dependencies of optimum system component sizes on duration and the fully burdened cost of fuel.