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All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Analyst-centered models for systems design, analysis, and development

Much has been written about the possible use of Expert Systems (ES) technology for strategic defense system applications, particularly for battle management algorithms and mission planning. It is proposed that ES (or more accurately, Knowledge Based System (KBS)) technology can be used in situations for which no human expert exists, namely to create design and analysis environments that allow an analyst to rapidly pose many different possible problem resolutions in game like fashion and to then work through the solution space in search of the optimal solution. Portions of such an environment exist for expensive AI hardware/software combinations such as the Xerox LOOPS and Intellicorp KEE systems. Efforts are discussed to build an analyst centered model (ACM) using an ES programming environment, ExperOPS5 for a simple missile system tradeoff study. By analyst centered, it is meant that the focus of learning is for the benefit of the analyst, not the model. The model's environment allows the analyst to pose a variety of what if questions without resorting to programming changes. Although not an ES per se, the ACM would allow for a design and analysis environment that is much superior to that of current technologies.

Bukley, A. P.↗
NASA NTRS1992

Analyst variability in labeling of unsupervised classifications

Analyst variability in the labeling of unsupervised classifications is tested for Landsat 5 Thematic Mapper image products covering two test sites in southern California. The accuracy of results are tested using samples from a photo interpreted base map of the area. The significance of differences between analysts is indicated by comparing Kappa statistics derived from error matrices. Analyst variability is found to be statistically significant in most cases. Certain analysts provided consistently better results for a given study area or degree of training. This work demonstrates the potential influence of analyst bias on what would otherwise seem to be a fairly objective method and suggests that controls for this subjectivity should be factored into experimental designs.

Mcgwire, Kenneth C.↗
NASA NTRS1993

The Generic Spacecraft Analyst Assistant (gensaa): a Tool for Developing Graphical Expert Systems

During numerous contacts with a satellite each day, spacecraft analysts must closely monitor real-time data. The analysts must watch for combinations of telemetry parameter values, trends, and other indications that may signify a problem or failure. As the satellites become more complex and the number of data items increases, this task is becoming increasingly difficult for humans to perform at acceptable performance levels. At NASA GSFC, fault-isolation expert systems are in operation supporting this data monitoring task. Based on the lessons learned during these initial efforts in expert system automation, a new domain-specific expert system development tool named the Generic Spacecraft Analyst Assistant (GenSAA) is being developed to facilitate the rapid development and reuse of real-time expert systems to serve as fault-isolation assistants for spacecraft analysts. Although initially domain-specific in nature, this powerful tool will readily support the development of highly graphical expert systems for data monitoring purposes throughout the space and commercial industry.

Hughes, Peter M.↗
NASA NTRS1979

USDA analyst review of the LACIE IMAGE-100 hybrid system test

Fifty operational segments from the U.S.S.R., 40 test segments from Canada, and 24 test segments from the United States were used to provide a wide range of geographic conditions for USDA analysts during a test to determine the effectiveness of labeling single pixel training fields (dots) using Procedure 1 on the 1-100 hybrid system, and clustering and classifying on the Earth Resources Interactive Processing System. The analysts had additional on-line capabilities such as interactive dot labeling, class or cluster map overlay flickers, and flashing of all dots of equal spectral value. Results on the 1-100 hybrid system are described and analyst problems and recommendations are discussed.

Ashburn, P.↗
NASA NTRS1991

The Generic Spacecraft Analyst Assistant (GenSAA): A tool for automating spacecraft monitoring with expert systems

Flight Operations Analysts (FOAs) in the Payload Operations Control Center (POCC) are responsible for monitoring a satellite's health and safety. As satellites become more complex and data rates increase, FOAs are quickly approaching a level of information saturation. The FOAs in the spacecraft control center for the COBE (Cosmic Background Explorer) satellite are currently using a fault isolation expert system named the Communications Link Expert Assistance Resource (CLEAR), to assist in isolating and correcting communications link faults. Due to the success of CLEAR and several other systems in the control center domain, many other monitoring and fault isolation expert systems will likely be developed to support control center operations during the early 1990s. To facilitate the development of these systems, a project was initiated to develop a domain specific tool, named the Generic Spacecraft Analyst Assistant (GenSAA). GenSAA will enable spacecraft analysts to easily build simple real-time expert systems that perform spacecraft monitoring and fault isolation functions. Lessons learned during the development of several expert systems at Goddard, thereby establishing the foundation of GenSAA's objectives and offering insights in how problems may be avoided in future project, are described. This is followed by a description of the capabilities, architecture, and usage of GenSAA along with a discussion of its application to future NASA missions.

Hughes, Peter M.↗
NASA NTRS2011

Multi-Instance Learning Models for Automated Support of Analysts in Simulated Surveillance Environments

New generations of surveillance drones are being outfitted with numerous high definition cameras. The rapid proliferation of fielded sensors and supporting capacity for processing and displaying data will translate into ever more capable platforms, but with increased capability comes increased complexity and scale that may diminish the usefulness of such platforms to human operators. We investigate methods for alleviating strain on analysts by automatically retrieving content specific to their current task using a machine learning technique known as Multi-Instance Learning (MIL). We use MIL to create a real time model of the analysts' task and subsequently use the model to dynamically retrieve relevant content. This paper presents results from a pilot experiment in which a computer agent is assigned analyst tasks such as identifying caravanning vehicles in a simulated vehicle traffic environment. We compare agent performance between MIL aided trials and unaided trials.

Birisan, Mihnea↗
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↗
NASA NTRS1987

Launch Processing System operations with a future look to Operations Analyst (OPERA)

The Launch Processing System architecture and the ground support operations required to provide Shuttle System engineers with the capability to safely process and launch an Orbiter are described. The described ground operations are the culmination of eleven years of experience and redesign. Some of the 'lessons learned' are examined, and problem areas which ground support operations have identified over the years as the Shuttle and Launch Processing Systems continue to grow in complexity are discussed. The Operational Analyst for Distributed Systems (OPERA), a proposed set of expert systems for the Launch Processing System Operational assistance, is discussed along with its extensions to prospective future configurations and components for the Launch Processing System.

Heard, Astrid E.↗
NASA NTRS1994

User's guide for the thermal analyst's help desk expert system

A guide for users of the Thermal Analyst's Help Desk is provided. Help Desk is an expert system that runs on a DOS based personal computer and operates within the EXSYS expert system shell. Help Desk is an analysis tool designed to provide users having various degrees of experience with the capability to determine first approximations of thermal capacity for spacecraft and instruments. The five analyses supported in Help Desk are: surface area required for a radiating surface, equilibrium temperature of a surface, enclosure temperature and heat loads for a defined position in orbit, enclosure temperature and heat loads over a complete orbit, and selection of appropriate surface properties. The two geometries supported by Help Desk are a single flat plate and a rectangular box enclosure.

Ormsby, Rachel A.↗
NASA NTRS1994

Reference manual for the Thermal Analyst's Help Desk Expert System

This document provides technical information and programming guidance for the maintenance and future development of the Thermal Analyst's Help Desk. Help Desk is an expert system that operates within the EXSYSTM expert system shell, and is used to determine first approximations of thermal capacity for spacecraft and instruments. The five analyses supported in Help Desk are: (1) surface area required for a radiating surface, (2) equilibrium temperature of a surface, (3) enclosure temperature and heat loads for a defined position in orbit, (4) enclosure temperature and heat loads over a complete orbit and, (5) selection of appropriate surface properties. The two geometries supported by Help Desk are a single flat plate and a rectangular box enclosure. The technical information includes the mathematical approach and analytical derivations used in the analyses such as: radiation heat balance, view factor calculation, and orbit determination with coordinate transformation. The programming guide for developers describes techniques for enhancement of Help Desk. Examples are provided showing the addition of new features, user interface development and enhancement, and external program interfaces.

Ormsby, Rachel A.↗
NASA NTRS2008

Planetary Spatial Analyst

This is a status report for the project entitled Planetary Spatial Analyst (PSA). This report covers activities from the project inception on October 1, 2007 to June 1, 2008. Originally a three year proposal, PSA was awarded funding for one year and required a revised work statement and budget. At the time of this writing the project is well on track both for completion of work as well as budget. The revised project focused on two objectives: build a solid connection with the target community and implement a prototype software application that provides 3D visualization and spatial analysis technologies for that community. Progress has been made for both of these objectives.

Keely, Leslie↗
NASA NTRS1978

Secondary error analysis: The evaluation of analyst dot labeling

The author has identified the following significant results. From this examination of 25 test segments using Al labeling and ground truth labeling, the PCC on type 1 dots was found to be signficantly better for both types of ground truth labeled procedures than the PCC obtained using Al labeling. No significant difference in the PCC was found for type 2 dots. However, in all three treatments, the type 2 dots included pixels which fell on boundaries or were mixed pixels. This accounted for all PCC2 values being equally low. The proportion estimates achieved in these classifications showed no significant differences between procedures.

Havens, K. A.↗