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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS1979

LACIE analyst interpretation keys

Two interpretation aids, 'The Image Analysis Guide for Wheat/Small Grains Inventories' and 'The United States and Canadian Great Plains Regional Keys', were developed during LACIE phase 2 and implemented during phase 3 in order to provide analysts with a better understanding of the expected ranges in color variation of signatures for individual biostages and of the temporal sequences of LANDSAT signatures. The keys were tested using operational LACIE data, and the results demonstrate that their use provides improved labeling accuracy in all analyst experience groupings, in all geographic areas within the U.S. Great Plains, and during all periods of crop development.

Baron, J. G.↗
NASA NTRS1980

Evaluation of Bayesian Sequential Proportion Estimation Using Analyst Labels

The author has identified the following significant results. A total of ten Large Area Crop Inventory Experiment Phase 3 blind sites and analyst-interpreter labels were used in a study to compare proportional estimates obtained by the Bayes sequential procedure with estimates obtained from simple random sampling and from Procedure 1. The analyst error rate using the Bayes technique was shown to be no greater than that for the simple random sampling. Also, the segment proportion estimates produced using this technique had smaller bias and mean squared errors than the estimates produced using either simple random sampling or Procedure 1.

Lennington, R. K.↗
NASA NTRS1989

Training for spacecraft technical analysts

Deep space missions such as Voyager rely upon a large team of expert analysts who monitor activity in the various engineering subsystems of the spacecraft and plan operations. Senior teammembers generally come from the spacecraft designers, and new analysts receive on-the-job training. Neither of these methods will suffice for the creation of a new team in the middle of a mission, which may be the situation during the Magellan mission. New approaches are recommended, including electronic documentation, explicit cognitive modeling, and coached practice with archived data.

Ayres, Thomas J.↗
NASA NTRS2015

Thermal Hardware for the Thermal Analyst

The presentation will be given at the 26th Annual Thermal Fluids Analysis Workshop (TFAWS 2015) hosted by the Goddard Space Flight Center (GSFC) Thermal Engineering Branch (Code 545). NCTS 21070-1. Most Thermal analysts do not have a good background into the hardware which thermally controls the spacecraft they design. SINDA and Thermal Desktop models are nice, but knowing how this applies to the actual thermal hardware (heaters, thermostats, thermistors, MLI blanketing, optical coatings, etc...) is just as important. The course will delve into the thermal hardware and their application techniques on actual spacecraft. Knowledge of how thermal hardware is used and applied will make a thermal analyst a better engineer.

0000↗
DOE OSTI2012

Storing and managing information artifacts collected by information analysts using a computing device

Systems and computer-implemented processes for storage and management of information artifacts collected by information analysts using a computing device. The processes and systems can capture a sequence of interactive operation elements that are performed by the information analyst, who is collecting an information artifact from at least one of the plurality of software applications. The information artifact can then be stored together with the interactive operation elements as a snippet on a memory device, which is operably connected to the processor. The snippet comprises a view from an analysis application, data contained in the view, and the sequence of interactive operation elements stored as a provenance representation comprising operation element class, timestamp, and data object attributes for each interactive operation element in the sequence.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2015

A Comparative Study of Automated Infrasound Detectors - PMCC and AFD with Analyst Review.

Automated detections calculated by the progressive multi-channel correlation (PMCC) method (Cansi, 1995) and the adaptive F detector (AFD) (Arrowsmith et al., 2009) are compared to the signals identified by five independent analysts. Each detector was applied to a four-hour time sequence recorded by the Korean infrasound array CHNAR. This array was used because it is composed of both small (<100 m) and large (~1000 m) aperture element spacing. The four hour time sequence contained a number of easily identified signals under noise conditions that have average RMS amplitudes varied from 1.2 to 4.5 mPa (1 to 5 Hz), estimated with running five-minute window. The effectiveness of the detectors was estimated for the small aperture, large aperture, small aperture combined with the large aperture, and full array. The full and combined arrays performed the best for AFD under all noise conditions while the large aperture array had the poorest performance for both detectors. PMCC produced similar results as AFD under the lower noise conditions, but did not produce as dramatic an increase in detections using the full and combined arrays. Both automated detectors and the analysts produced a decrease in detections under the higher noise conditions. Comparing the detection probabilities with Estimated Receiver Operating Characteristic (EROC) curves we found that the smaller value of consistency for PMCC and the larger p-value for AFD had the highest detection probability. These parameters produced greater changes in detection probability than estimates of the false alarm rate. The detection probability was impacted the most by noise level, with low noise (average RMS amplitude of 1.7 mPa) having an average detection probability of ~40% and high noise (average RMS amplitude of 2.9 mPa) average detection probability of ~23%.

58 GEOSCIENCES↗
DOE OSTI2017

Developing a Streamlined Approach to Criticality Safety Analyst Training/Qualification

The Los Alamos National Laboratory (LANL) Criticality Safety Analyst (CSA) Qualification Standard was a multidivisional effort forged in late 2012. The result of the effort is a well-documented and efficient approach to training, encompassing local and national Nuclear Criticality Safety (NCS) training courses for incoming criticality safety staff, as well as a model for professional development of qualified CSAs at LANL. Five years later, the approach to training and qualification for CSAs has evolved into a streamlined safety management function, integrated in all phases of employee recruitment, development, deployment, and management.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

Tutorial: Lessons Learned for Behavior Analysts from Data Scientists

Big data is a computing term used to refer to large and complex data sets, typically consisting of terabytes or more of diverse data that is produced rapidly. The analysis of such complex data sets requires advanced analysis techniques with the capacity to identify patterns and abstract meanings from the vast data. The field of data science combines computer science with mathematics/statistics and leverages artificial intelligence, in particular machine learning, to analyze big data. This field holds great promise for behavior analysis, where both clinical and research studies produce large volumes of diverse data at a rapid pace (i.e., big data). This article presents basic lessons for the behavior analytic researchers and clinicians regarding integration of data science into the field of behavior analysis. We provide guidance on how to collect, protect, and process the data, while highlighting the importance of collaborating with data scientists to select a proper machine learning model that aligns with the project goals and develop models with input from human experts. Here, we hope this serves as a guide to support the behavior analysts interested in the field of data science to advance their practice or research, and helps them avoid some common pitfalls.

42 ENGINEERING↗
DOE OSTI2019

Deep Learning Models Augment Analyst Decisions for Event Discrimination

Long-term seismic monitoring networks are well positioned to leverage advances in machine learning because of the abundance of labeled training data that curated event catalogs provide. In this study, we explore the use of Convolutional and Recurrent Neural Networks to accomplish discrimination of explosive and tectonic sources for local distances. Using a 5-year event catalog generated by the University of Utah Seismograph Stations we train models to produce automated event labels using 90 s event spectrograms from 3-component and single-channel sensors. Both network architectures are able to replicate analyst labels above 98%. Most commonly, model error is the result of label error (70% of cases). Accounting for mislabeled events (~1% of the catalog) model accuracy for both models increases to above 99%. Finally, classification accuracy remains above 98% for shallow tectonic events, indicating that spectral characteristics controlled by event depth do not play a dominant role in event discrimination.

58 GEOSCIENCES↗
DOE OSTI2016

Contingency Contractor Optimization Tool Prototype (Phase 3 Extension- Analyst Tutorial)

This tutorial walks the user through analysis examples using the Contingency Contractor Optimization Tool Prototype. The examples are designed to showcase key capabilities of the tool. The main goal of this tutorial is to provide examples of how to use the tool to perform analyses to those users acting in the analyst role. All examples and locations used in the prototype are fictional, but are intended to be realistic. Users reading this manual are expected to have a basic understanding and familiarity with the Contingency Contractor Optimization Tool Prototype. This tutorial includes scenarios for the occurrence of two wars, Prussia and New Granada.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2018

Patterns of Analyst Attention

This report describes a seedling project in which we developed experimental paradigms for studying patterns of analyst attention to streaming data. The project identified key structure features that can be used to generate appropriate stimuli for nearly any mission domain.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Relationship Extraction: Automatic Information Extraction and Organization for Supporting Analysts in Threat Assessment

In order for analysts to be able to do their work, they sift through hundreds, thousands, or even millions of documents to make connections between entities of interest. This process is time consuming, tedious, and prone to potential error from missed connections or connections made that should not have been. There exist many tools in natural language processing, or NLP, to extract information from documents. However, when it comes to relationship extraction, there has been varied success. This project began with a goal to solve the relationship extraction problem which developed into a deeper understanding of the problem and the associated challenges for solving this problem on a general scale. In this report, we explain our research and approach to relationship extraction, identify other auxiliary problems in NLP that provide additional challenges to solving relationship extraction generally, explain our analysis of the current state of relationship extraction, and postulate future work to address these problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

An introduction to developing GitLab/Jacamar runner analyst centric workflows at Sandia

This document provides very basic background information and initial enabling guidance for computational analysts to develop and utilize GitOps practices within the Common Engineering Environment (CEE) and High Performance Computing (HPC) computational environment at Sandia National Laboratories through GitLab/Jacamar runner based workflows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Complete Demonstration of a Prototype Version of FORCE User Interface and Conduct Analyst Survey Collecting Feedback on Interface Features and Usability

In 2024 the US Department of Energy (DOE) Office of Nuclear Energy (NE) Integrated Energy System (IES) program continued to develop the Framework for Optimization of Resources and Economics (FORCE) analysis ecosystem into a more traditional toolset with simplified software installation, automated workflows, and interactive results visualization. The DOE-NE Nuclear Energy Advanced Modeling and Simulation (NEAMS) Workbench continued to be leveraged for user input, application workflow and runtime environment, and interactive results visualization capabilities. This report documents the demonstration of a FORCE User Interface (UI) prototype and the results of a survey of analysts’ using the Holistic Energy Resource Optimization Network (HERON) tool in FORCE with the prototype UI.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

As-Run Neutronic Calculations of HFIR Mini-Fuel Experiments: Guidelines for Analysts

This report provides guidelines for analysts performing radiation transport calculations for mini-fuel experiments irradiated in the High Flux Isotope Reactor (HFIR). Procedures, practical recommendations, and modeling conventions which are essential to ensuring consistency and high fidelity in as-run simulations have been compiled. The content is intended to be a portion of the official training material for new staff in this area, offering both technical references and practical guidance derived from previous experience. This report is a living document and will be periodically updated to incorporate new developments, methodological improvements, and lessons learned from ongoing and future irradiation campaigns.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Jobs, jobs, jobs: what’s an analyst to do?

Analysts and economists often face the task of using employment metrics to characterize industries of interest. Some key challenges can be understanding where to find employment metrics, the differences in various employment metrics, and when each metric should be used. This article analyzes a variety of publicly available employment data for the United States and compares these data. A detailed description of the intricacies of each data source is provided, which covers factors such as regionality, industry breakout, periodicity, and the types of jobs included. This article provides several case study examples, using the oil and gas extraction, coal mining, and chemical manufacturing sectors to portray challenges data users may face when developing employment estimates that suit their needs. Data users should be aware of a variety of data sources to understand alternative analysis options when data limitations are present and to determine which data source best meets their needs. Instances may occur in which information from one dataset may be used to help impute missing values.

99 GENERAL AND MISCELLANEOUS↗