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Air Force Institute of Technology

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Articles 661 - 690 of 2678

Full-Text Articles in Physical Sciences and Mathematics

Estimation Of Atmospheric Turbulence Using Differential Motion Of Extended Features In Time-Lapse Imagery, Santasri Bose-Pillai, Jack E. Mccrae, Christopher A. Rice, Ryan A. Wood, Conner E. Murphy, Steven T. Fiorino Oct 2018

Estimation Of Atmospheric Turbulence Using Differential Motion Of Extended Features In Time-Lapse Imagery, Santasri Bose-Pillai, Jack E. Mccrae, Christopher A. Rice, Ryan A. Wood, Conner E. Murphy, Steven T. Fiorino

Faculty Publications

We address the design, development, and testing of a pointer/tracker as a probe beam for the purpose of making high-speed, aero-optical measurements of the flow over a scaled beam director turret. The tracker uses retro-reflection of the probe beam off of a Reflexite annulus surrounding the turret. The constraints of the design required a near-total-commercial off the shelf system that could be quickly installed and removed in a rented aircraft. Baseline measurements of environmental vibrations are used to predict pointing performance; mitigation of line-of-sight jitter on the probe beam is achieved through passive isolation and the design of relay optics. …

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Equiangular Tight Frames That Contain Regular Simplices, Matthew C. Fickus, John Jasper, Emily J. King, Dustin G. Mixon Oct 2018

Equiangular Tight Frames That Contain Regular Simplices, Matthew C. Fickus, John Jasper, Emily J. King, Dustin G. Mixon

Faculty Publications

An equiangular tight frame (ETF) is a type of optimal packing of lines in Euclidean space. A regular simplex is a special type of ETF in which the number of vectors is one more than the dimension of the space they span. In this paper, we consider ETFs that contain a regular simplex, that is, have the property that a subset of its vectors forms a regular simplex. As we explain, such ETFs are characterized as those that achieve equality in a certain well-known bound from the theory of compressed sensing. We then consider the so-called binder of such an …

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Equiangular Tight Frames From Group Divisible Designs, Matthew C. Fickus, John Jasper Oct 2018

Equiangular Tight Frames From Group Divisible Designs, Matthew C. Fickus, John Jasper

Faculty Publications

An equiangular tight frame (ETF) is a type of optimal packing of lines in a real or complex Hilbert space. In the complex case, the existence of an ETF of a given size remains an open problem in many cases. In this paper, we observe that many of the known constructions of ETFs are of one of two types. We further provide a new method for combining a given ETF of one of these two types with an appropriate group divisible design (GDD) in order to produce a larger ETF of the same type. By applying this method to known …

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Design And Optimization Of A 3-D Plasmonic Huygens Metasurface For Highly-Efficient Flat Optics, Bryan M. Adomanis Sep 2018

Design And Optimization Of A 3-D Plasmonic Huygens Metasurface For Highly-Efficient Flat Optics, Bryan M. Adomanis

Theses and Dissertations

For miniaturization of future USAF unmanned aerial and space systems to become feasible, accompanying sensor components of these systems must also be reduced in size, weight and power (SWaP). Metasurfaces can act as planar equivalents to bulk optics, and thus possess a high potential to meet these low-SWaP requirements. However, functional efficiencies of plasmonic metasurface architectures have been too low for practical application in the infrared (IR) regime. Huygens-like forward-scattering inclusions may provide a solution to this deficiency, but there is no academic consensus on an optimal plasmonic architecture for obtaining efficient phase control at high frequencies. This dissertation asks …

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Reconstruction Of The 3d Temperature And Species Concentration Spatial Distribution Of A Jet Engine Exhaust Plume Using An Infrared Fourier Transform Spectrometer Hyperspectral Imager, Mason D. Paulec Sep 2018

Reconstruction Of The 3d Temperature And Species Concentration Spatial Distribution Of A Jet Engine Exhaust Plume Using An Infrared Fourier Transform Spectrometer Hyperspectral Imager, Mason D. Paulec

Theses and Dissertations

The measurement of combustion byproducts is useful for determining pollution of any fuel burning application, efficiency of combustion, and determining detectability of aircraft exhausts. Both intrusive and non-intrusive techniques have been utilized to measure these quantities. For the majority of the non-intrusive techniques, the absorption and emission spectra of the gases are utilized for measurements. For this research, the use of the Telops Infrared Fourier Transform Spectrometer (IFTS) Hyperspectral Imager (HSI) was explored within the scope of combustion diagnostic methods, as an option for remote measurements of a jet turbine to determine concentration of species and temperature of the combustion …

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A Macro-Level Order Metric For Self-Organizing Adaptive Systems, David W. King, Gilbert L. Peterson Sep 2018

A Macro-Level Order Metric For Self-Organizing Adaptive Systems, David W. King, Gilbert L. Peterson

Faculty Publications

Analyzing how agent interactions affect macro-level self-organized behaviors can yield a deeper understanding of how complex adaptive systems work. The dynamic nature of complex systems makes it difficult to determine if, or when, a system has reached a state of equilibrium or is about to undergo a major transition reflecting the appearance of self-organized states. Using the notion of local neighborhood entropy, this paper presents a metric for evaluating the macro-level order of a system. The metric is tested in two dissimilar complex adaptive systems with self-organizing properties: An autonomous swarm searching for multiple dynamic targets and Conway's Game of …

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The Effectiveness Of Using Diversity To Select Multiple Classifier Systems With Varying Classification Thresholds, Harris K. Butler Iv, Mark A. Friend, Kenneth W. Bauer, Trevor J. Bihl Sep 2018

The Effectiveness Of Using Diversity To Select Multiple Classifier Systems With Varying Classification Thresholds, Harris K. Butler Iv, Mark A. Friend, Kenneth W. Bauer, Trevor J. Bihl

Faculty Publications

In classification applications, the goal of fusion techniques is to exploit complementary approaches and merge the information provided by these methods to provide a solution superior than any single method. Associated with choosing a methodology to fuse pattern recognition algorithms is the choice of algorithm or algorithms to fuse. Historically, classifier ensemble accuracy has been used to select which pattern recognition algorithms are included in a multiple classifier system. More recently, research has focused on creating and evaluating diversity metrics to more effectively select ensemble members. Using a wide range of classification data sets, methodologies, and fusion techniques, current diversity …

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A Methodology For Evaluating Relational And Nosql Databases For Small-Scale Storage And Retrieval, Ryan D. Engle Sep 2018

A Methodology For Evaluating Relational And Nosql Databases For Small-Scale Storage And Retrieval, Ryan D. Engle

Theses and Dissertations

Modern systems record large quantities of electronic data capturing time-ordered events, system state information, and behavior. Subsequent analysis enables historic and current system status reporting, supports fault investigations, and may provide insight for emerging system trends. Unfortunately, the management of log data requires ever more efficient and complex storage tools to access, manipulate, and retrieve these records. Truly effective solutions also require a well-planned architecture supporting the needs of multiple stakeholders. Historically, database requirements were well-served by relational data models, however modern, non-relational databases, i.e. NoSQL, solutions, initially intended for “big data” distributed system may also provide value for smaller-scale …

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Breaking Down The Barriers To Operator Workload Estimation: Advancing Algorithmic Handling Of Temporal Non-Stationarity And Cross-Participant Differences For Eeg Analysis Using Deep Learning, Ryan G. Hefron Sep 2018

Breaking Down The Barriers To Operator Workload Estimation: Advancing Algorithmic Handling Of Temporal Non-Stationarity And Cross-Participant Differences For Eeg Analysis Using Deep Learning, Ryan G. Hefron

Theses and Dissertations

This research focuses on two barriers to using EEG data for workload assessment: day-to-day variability, and cross- participant applicability. Several signal processing techniques and deep learning approaches are evaluated in multi-task environments. These methods account for temporal, spatial, and frequential data dependencies. Variance of frequency- domain power distributions for cross-day workload classification is statistically significant. Skewness and kurtosis are not significant in an environment absent workload transitions, but are salient with transitions present. LSTMs improve day- to-day feature stationarity, decreasing error by 59% compared to previous best results. A multi-path convolutional recurrent model using bi-directional, residual recurrent layers significantly increases …

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Application Of Spectral Solution And Neural Network Techniques In Plasma Modeling For Electric Propulsion, Joseph R. Whitman Sep 2018

Application Of Spectral Solution And Neural Network Techniques In Plasma Modeling For Electric Propulsion, Joseph R. Whitman

Theses and Dissertations

A solver for Poisson's equation was developed using the Radix-2 FFT method first invented by Carl Friedrich Gauss. Its performance was characterized using simulated data and identical boundary conditions to those found in a Hall Effect Thruster. The characterization showed errors below machine-zero with noise-free data, and above 20% noise-to-signal strength, the error increased linearly with the noise. This solver can be implemented into AFRL's plasma simulator, the Thermophysics Universal Research Framework (TURF) and used to quickly and accurately compute the electric field based on charge distributions. The validity of a machine learning approach and data-based complex system modeling approach …

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Evaluation And Quantification Of Diffractive Plenoptic Camera Algorithm Performance, Jack A. Shepherd Iii Sep 2018

Evaluation And Quantification Of Diffractive Plenoptic Camera Algorithm Performance, Jack A. Shepherd Iii

Theses and Dissertations

A diffractive plenoptic camera is a novel approach to the traditional plenoptic camera which replaces the main optic with a Fresnel zone plate making the camera sensitive to wavelength instead of range. However, algorithms are necessary to reconstruct the image produced by plenoptic cameras. While many algorithms exist for traditional plenoptic cameras, their ability to create spectral images in a diffractive plenoptic camera is unknown. This paper evaluates digital refocusing, super resolution, and 3D deconvolution through a Richardson-Lucy algorithm as well as a new Gaussian smoothing algorithm. All of the algorithms worked well near the Fresnel zone plate design wavelength, …

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Techniques For Improved Space Object Detection Performance From Ground-Based Telescope Systems Using Long And Short Exposure Images, David J. Becker Aug 2018

Techniques For Improved Space Object Detection Performance From Ground-Based Telescope Systems Using Long And Short Exposure Images, David J. Becker

Theses and Dissertations

Space object detection is of great importance in the highly dependent yet competitive and congested space domain. Detection algorithms employed play a crucial role in fulfilling the detection component in the space situational awareness mission to detect, track, characterize and catalog unknown space objects. Many current space detection algorithms use a matched filter or a spatial correlator on long exposure data to make a detection decision at a single pixel point of a spatial image based on the assumption that the data follows a Gaussian distribution. This research focuses on improving current space object detection algorithms and developing new algorithms …

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Compressive Sampling For Phenotype Classification, Eric L. Brooks Aug 2018

Compressive Sampling For Phenotype Classification, Eric L. Brooks

Theses and Dissertations

Phenotype classification has become an increasingly important genomic research method for disease identification and treatment. Phenotype classification is the investigation into the genetic information concerned with locating biomarkers (features) in order to identify an observed effect. The primary challenge associated with phenotype classification is with analyzing the data due to the inherent high-dimensionality of DNA data. As a result, phenotype classification faces challenges with feature selection, and consequently, classification accuracy. This research developed a methodology to alleviate these challenges while improving classification performance. The methodology leverages concepts of compressive sampling, to arrive at a process that identifies features most relevant …

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Numerical Simulation Of High Energy Laser Propagation, Dana F. Morrill Aug 2018

Numerical Simulation Of High Energy Laser Propagation, Dana F. Morrill

Theses and Dissertations

High energy lasers have many applications, such as in aerospace, weapons, wireless power transfer, and manufacturing. Fluid-laser interaction is important to predicting power at receiver, and other measures of laser beam quality. Typically the carrying medium of the laser is modeled statistically. This dissertation describes a novel method of coupling fluid dynamics to beam propagation in free space. The coupled laser-fluid solver captures dynamic interaction of fluid temperature and beam intensity. Ultimately, the model captures the effects of fluid convection in the laser intensity-field. Boundary conditions play an important role for fluid dynamics, more so than for beam dynamics. Simulation …

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Statistical Inference To Evaluate And Compare The Performance Of Correlated Multi-State Classification Systems, Beau A. Nunnally Aug 2018

Statistical Inference To Evaluate And Compare The Performance Of Correlated Multi-State Classification Systems, Beau A. Nunnally

Theses and Dissertations

The current emphasis on including correlation when comparing diagnostic test performance is quite important, however, there are cases in which correlation effects may be negligible with respect to inference. This proposed work examines the impact of including correlation between classification systems with continuous features by comparing the optimal performance of two diagnostic tests with multiple outcomes as well as providing inference for a sequence of tests. We define the optimal point using Bayes Cost, a metric that sums the weighted misclassifications within a diagnostic test using a cost/benefit structure. Through simulation, we quantify the impact of correlation on standard errors …

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Automating Mobile Device File Format Analysis, Richard A. Dill Aug 2018

Automating Mobile Device File Format Analysis, Richard A. Dill

Theses and Dissertations

Forensic tools assist examiners in extracting evidence from application files from mobile devices. If the file format for the file of interest is known, this process is straightforward, otherwise it requires the examiner to manually reverse engineer the data structures resident in the file. This research presents the Automated Data Structure Slayer (ADSS), which automates the process to reverse engineer unknown file for- mats of Android applications. After statically parsing and preparing an application, ADSS dynamically runs it, injecting hooks at selected methods to uncover the data structures used to store and process data before writing to media. The resultant …

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Cyber Anomaly Detection: Using Tabulated Vectors And Embedded Analytics For Efficient Data Mining, Robert J. Gutierrez, Kenneth W. Bauer, Bradley C. Boehmke, Cade M. Saie, Trevor J. Bihl Aug 2018

Cyber Anomaly Detection: Using Tabulated Vectors And Embedded Analytics For Efficient Data Mining, Robert J. Gutierrez, Kenneth W. Bauer, Bradley C. Boehmke, Cade M. Saie, Trevor J. Bihl

Faculty Publications

Firewalls, especially at large organizations, process high velocity internet traffic and flag suspicious events and activities. Flagged events can be benign, such as misconfigured routers, or malignant, such as a hacker trying to gain access to a specific computer. Confounding this is that flagged events are not always obvious in their danger and the high velocity nature of the problem. Current work in firewall log analysis is manual intensive and involves manpower hours to find events to investigate. This is predominantly achieved by manually sorting firewall and intrusion detection/prevention system log data. This work aims to improve the ability of …

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Evaluation Criteria For Selecting Nosql Databases In A Single Box Environment, Ryan D. Engle, Brent T. Langhals, Michael R. Grimaila, Douglas D. Hodson Aug 2018

Evaluation Criteria For Selecting Nosql Databases In A Single Box Environment, Ryan D. Engle, Brent T. Langhals, Michael R. Grimaila, Douglas D. Hodson

Faculty Publications

In recent years, NoSQL database systems have become increasingly popular, especially for big data, commercial applications. These systems were designed to overcome the scaling and flexibility limitations plaguing traditional relational database management systems (RDBMSs). Given NoSQL database systems have been typically implemented in large-scale distributed environments serving large numbers of simultaneous users across potentially thousands of geographically separated devices, little consideration has been given to evaluating their value within single-box environments. It is postulated some of the inherent traits of each NoSQL database type may be useful, perhaps even preferable, regardless of scale. Thus, this paper proposes criteria conceived to …

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Securing Zigbee Commercial Communications Using Constellation Based Distinct Native Attribute Fingerprinting, Christopher M. Rondeau, J. Addison Betances, Michael A. Temple Jul 2018

Securing Zigbee Commercial Communications Using Constellation Based Distinct Native Attribute Fingerprinting, Christopher M. Rondeau, J. Addison Betances, Michael A. Temple

Faculty Publications

This work provides development of Constellation Based DNA (CB-DNA) Fingerprinting for use in systems employing quadrature modulations and includes network protection demonstrations for ZigBee offset quadrature phase shift keying modulation. Results are based on 120 unique networks comprised of seven authorized ZigBee RZSUBSTICK devices, with three additional like-model devices serving as unauthorized rogue devices. Authorized network device fingerprints are used to train a Multiple Discriminant Analysis (MDA) classifier and Rogue Rejection Rate (RRR) estimated for 2520 attacks involving rogue devices presenting themselves as authorized devices. With MDA training thresholds set to achieve a True Verification Rate (TVR) of TVR = …

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Arrhenius Rate Chemistry-Informed Inter-Phase Source Terms (Arciist), Matthew J. Schwaab, Robert B. Greendyke, Bryan J. Steward Jul 2018

Arrhenius Rate Chemistry-Informed Inter-Phase Source Terms (Arciist), Matthew J. Schwaab, Robert B. Greendyke, Bryan J. Steward

Faculty Publications

Currently, in macro-scale hydrocodes designed to simulate explosive material undergoing shock-induced ignition, the state of the art is to use one of numerous reaction burn rate models. These burn models are designed to estimate the bulk chemical reaction rate. Unfortunately, these burn rate models are largely based on empirical data and must be recalibrated for every new material being simulated. We propose that the use of Arrhenius Rate Chemistry-Informed Interphase Source Terms (ARCIIST) in place of empirically derived burn models will improve the accuracy for these computational codes. A reacting chemistry model of this form was developed for the cyclic …

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Cybersecurity Architectural Analysis For Complex Cyber-Physical Systems, Martin Trae Span Iii, Logan O. Mailloux, Michael R. Grimaila Jul 2018

Cybersecurity Architectural Analysis For Complex Cyber-Physical Systems, Martin Trae Span Iii, Logan O. Mailloux, Michael R. Grimaila

Faculty Publications

In the modern military’s highly interconnected and technology-reliant operational environment, cybersecurity is rapidly growing in importance. Moreover, as a number of highly publicized attacks have occurred against complex cyber-physical systems such as automobiles and airplanes, cybersecurity is no longer limited to traditional computer systems and IT networks. While architectural analysis approaches are critical to improving cybersecurity, these approaches are often poorly understood and applied in ad hoc fashion. This work addresses these gaps by answering the questions: 1. “What is cybersecurity architectural analysis?” and 2. “How can architectural analysis be used to more effectively support cybersecurity decision making for complex …

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Effects Of Dynamic Goals On Agent Performance, Nathan R. Ball Jun 2018

Effects Of Dynamic Goals On Agent Performance, Nathan R. Ball

Theses and Dissertations

Autonomous systems are increasingly being used for complex tasks in dynamic environments. Robust automation needs to be able to establish its current goal and determine when the goal has changed. In human-machine teams autonomous goal detection is an important component of maintaining shared situational awareness between both parties. This research investigates how different categories of goals affect autonomous change detection in a dynamic environment. In order to accomplish this goal, a set of autonomous agents were developed to perform within an environment with multiple possible goals. The agents perform the environmental task while monitoring for goal changes. The experiment tests …

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Investigation Of Scramjet Flowfield Temperatures At The Boundary Layer With Hyperspectral Imaging, Amy M. Kerst Jun 2018

Investigation Of Scramjet Flowfield Temperatures At The Boundary Layer With Hyperspectral Imaging, Amy M. Kerst

Theses and Dissertations

Within the domain of chemical propulsion, the fields of combustion diagnostics and computational fluid dynamics each have a long history, and both have led to a better understanding of complex phenomena yielding practical improvements in propulsion systems. As more exotic forms of propulsion are developed, the importance of -- and often the challenges with -- both diagnostic and simulation capabilities also increase. In the case of scramjet combustion, these challenges primarily arise from the highly turbulent environment in the combustion cavity, and the high-speed, compressible nature of the flowfield. Efforts are underway to develop computer models of scramjet combustion environments …

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Efficient Phase Retrieval For Off-Axis Point Spread Functions, Salome Esteban Carrasco Jun 2018

Efficient Phase Retrieval For Off-Axis Point Spread Functions, Salome Esteban Carrasco

Theses and Dissertations

A novel pairing of phase retrieval tools allows for efficient estimation of pupil phase in optical systems from images of point spread functions (PSFs). The phase retrieval algorithm uses correlation of modeled phase in the focal plane to decouple aberrations that are difficult to identify in complex PSFs. The use of a phase kernel that departs from the Fresnel approximation for off-axis PSFs is a more accurate representation of wavefront phase in finite conjugate imaging. The combination of the approximation and phase correlation algorithm can be more efficient and accurate than generic algorithms.

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Photovoltaic System Optimization For An Austere Location Using Time Series Data, Torrey J. Wagner, Eric Lang, Warren Assink, Douglas S. Dudis Jun 2018

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.

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Narrowing The Scope Of Failure Prediction Using Targeted Fault Load Injection, Paul L. Jordan, Gilbert L. Peterson, Alan C. Lin, Michael J. Mendenhall, Andrew J. Sellers May 2018

Narrowing The Scope Of Failure Prediction Using Targeted Fault Load Injection, Paul L. Jordan, Gilbert L. Peterson, Alan C. Lin, Michael J. Mendenhall, Andrew J. Sellers

Faculty Publications

As society becomes more dependent upon computer systems to perform increasingly critical tasks, ensuring that those systems do not fail becomes increasingly important. Many organizations depend heavily on desktop computers for day-to-day operations. Unfortunately, the software that runs on these computers is written by humans and, as such, is still subject to human error and consequent failure. A natural solution is to use statistical machine learning to predict failure. However, since failure is still a relatively rare event, obtaining labelled training data to train these models is not a trivial task. This work presents new simulated fault-inducing loads that extend …

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Improved Space Object Detection Using Short-Exposure Image Data With Daylight Background, David J. Becker, Stephen C. Cain May 2018

Improved Space Object Detection Using Short-Exposure Image Data With Daylight Background, David J. Becker, Stephen C. Cain

Faculty Publications

No abstract provided.

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Cross-Participant Eeg-Based Assessment Of Cognitive Workload Using Multi-Path Convolutional Recurrent Neural Networks, Ryan G. Hefron, Brett J. Borghetti, Christine M. Schubert Kabban, James Christensen, Justin Estep Apr 2018

Cross-Participant Eeg-Based Assessment Of Cognitive Workload Using Multi-Path Convolutional Recurrent Neural Networks, Ryan G. Hefron, Brett J. Borghetti, Christine M. Schubert Kabban, James Christensen, Justin Estep

Faculty Publications

Applying deep learning methods to electroencephalograph (EEG) data for cognitive state assessment has yielded improvements over previous modeling methods. However, research focused on cross-participant cognitive workload modeling using these techniques is underrepresented. We study the problem of cross-participant state estimation in a non-stimulus-locked task environment, where a trained model is used to make workload estimates on a new participant who is not represented in the training set. Using experimental data from the Multi-Attribute Task Battery (MATB) environment, a variety of deep neural network models are evaluated in the trade-space of computational efficiency, model accuracy, variance and temporal specificity yielding three …

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Uncertainty Evaluation In The Design Of Structural Health Monitoring Systems For Damage Detection, Christine M. Schubert Kabban, Richard P. Uber, Kevin J. Lin, Bin Lin, M. Bhuiyan, Victor Giurgiutiu Apr 2018

Uncertainty Evaluation In The Design Of Structural Health Monitoring Systems For Damage Detection, Christine M. Schubert Kabban, Richard P. Uber, Kevin J. Lin, Bin Lin, M. Bhuiyan, Victor Giurgiutiu

Faculty Publications

The validation of structural health monitoring (SHM) systems for aircraft is complicated by the extent and number of factors that the SHM system must demonstrate for robust performance. Therefore, a time- and cost-efficient method for examining all of the sensitive factors must be conducted. In this paper, we demonstrate the utility of using the simulation modeling environment to determine the SHM sensitive factors that must be considered for subsequent experiments, in order to enable the SHM validation. We demonstrate this concept by examining the effect of SHM system configuration and flaw characteristics on the response of a signal from a …

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Reference Dependence Of The Two-Determinant Coupled-Cluster Method For Triplet And Open-Shell Singlet States Of Biradical Molecules, Jesse J. Lutz, Marcel Nooijen, Ajith Perera, Rodney J. Bartlett Apr 2018

Reference Dependence Of The Two-Determinant Coupled-Cluster Method For Triplet And Open-Shell Singlet States Of Biradical Molecules, Jesse J. Lutz, Marcel Nooijen, Ajith Perera, Rodney J. Bartlett

Faculty Publications

We study the performance of the two-determinant (TD) coupled-cluster (CC) method which, unlike conventional ground-state single-reference (SR) CC methods, can, in principle, provide a naturally spin-adapted treatment of the lowest-lying open-shell singlet (OSS) and triplet electronic states. Various choices for the TD-CC reference orbitals are considered, including those generated by the multi-configurational self-consistent field method. Comparisons are made with the results of high-level SR-CC, equation-of-motion (EOM) CC, and multi-reference EOM calculations performed on a large test set of over 100 molecules with low-lying OSS states. It is shown that in cases where the EOMCC reference function is poorly described, TD-CC …

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