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All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Large Terrain Modeling and Visualization for Planets

Physics-based simulations are actively used in the design, testing, and operations phases of surface and near-surface planetary space missions. One of the challenges in realtime simulations is the ability to handle large multi-resolution terrain data sets within models as well as for visualization. In this paper, we describe special techniques that we have developed for visualization, paging, and data storage for dealing with these large data sets. The visualization technique uses a real-time GPU-based continuous level-of-detail technique that delivers multiple frames a second performance even for planetary scale terrain model sizes.

digital elevation map↗
NASA NTRS1990

CLIPS: A tool for corn disease diagnostic system and an aid to neural network for automated knowledge acquisition

This paper describes the building of a corn disease diagnostic expert system using CLIPS, and the development of a neural expert system using the fact representation method of CLIPS for automated knowledge acquisition. The CLIPS corn expert system diagnoses 21 diseases from 52 symptoms and signs with certainty factors. CLIPS has several unique features. It allows the facts in rules to be broken down to object-attribute-value (OAV) triples, allows rule-grouping, and fires rules based on pattern-matching. These features combined with the chained inference engine result to a natural user query system and speedy execution. In order to develop a method for automated knowledge acquisition, an Artificial Neural Expert System (ANES) is developed by a direct mapping from the CLIPS system. The ANES corn expert system uses the same OAV triples in the CLIPS system for its facts. The LHS and RHS facts of the CLIPS rules are mapped into the input and output layers of the ANES, respectively; and the inference engine of the rules is imbedded in the hidden layer. The fact representation by OAC triples gives a natural grouping of the rules. These features allow the ANES system to automate rule-generation, and make it efficient to execute and easy to expand for a large and complex domain.

Wu, Cathy↗
NASA NTRS1991

Expert networks in CLIPS

Rule-based expert systems may be structurally and functionally mapped onto a special class of neural networks called expert networks. This mapping lends itself to adaptation of connectionist learning strategies for the expert networks. A parsing algorithm to translate C Language Integrated Production System (CLIPS) rules into a network of interconnected assertion and operation nodes has been developed. The translation of CLIPS rules to an expert network and back again is illustrated. Measures of uncertainty similar to those rules in MYCIN-like systems are introduced into the CLIPS system and techniques for combining and hiring nodes in the network based on rule-firing with these certainty factors in the expert system are presented. Several learning algorithms are under study which automate the process of attaching certainty factors to rules.

Hruska, S. I.↗
NASA NTRS2009

Comparison of Node-Centered and Cell-Centered Unstructured Finite-Volume Discretizations: Viscous Fluxes - Part 1

Discretization of the viscous terms in current finite-volume unstructured-grid schemes are compared using node-centered and cell-centered approaches in two dimensions. Accuracy and efficiency are studied for six nominally second-order accurate schemes: a node-centered scheme, cell-centered node-averaging schemes with and without clipping, and cell-centered schemes with unweighted, weighted, and approximately mapped least-square face gradient reconstruction. The grids considered range from structured (regular) grids to irregular grids composed of arbitrary mixtures of triangles and quadrilaterals, including random perturbations of the grid points to bring out the worst possible behavior of the solution. Two classes of tests are considered. The first class of tests involves smooth manufactured solutions on both isotropic and highly anisotropic grids with discontinuous metrics, typical of those encountered in grid adaptation. The second class concerns solutions and grids varying strongly anisotropically over a curved body, typical of those encountered in high-Reynolds number turbulent flow simulations. Results from the first class indicate the face least-square methods, the node-averaging method without clipping, and the node-centered method demonstrate second-order convergence of discretization errors with very similar accuracies per degree of freedom. The second class of tests are more discriminating. The node-centered scheme is always second order with an accuracy and complexity in linearization comparable to the best of the cell-centered schemes. In comparison, the cell-centered node-averaging schemes are less accurate, have a higher complexity in linearization, and can fail to converge to the exact solution when clipping of the node-averaged values is used. The cell-centered schemes using least-square face gradient reconstruction have more compact stencils with a complexity similar to the complexity of the node-centered scheme. For simulations on highly anisotropic curved grids, the least-square methods have to be amended either by introducing a local mapping of the surface anisotropy or modifying the scheme stencil to reflect the direction of strong coupling.

Diskin, Boris↗
NASA NTRS2010

Comparison of Node-Centered and Cell-Centered Unstructured Finite-Volume Discretizations: Inviscid Fluxes

Cell-centered and node-centered approaches have been compared for unstructured finite-volume discretization of inviscid fluxes. The grids range from regular grids to irregular grids, including mixed-element grids and grids with random perturbations of nodes. Accuracy, complexity, and convergence rates of defect-correction iterations are studied for eight nominally second-order accurate schemes: two node-centered schemes with weighted and unweighted least-squares (LSQ) methods for gradient reconstruction and six cell-centered schemes two node-averaging with and without clipping and four schemes that employ different stencils for LSQ gradient reconstruction. The cell-centered nearest-neighbor (CC-NN) scheme has the lowest complexity; a version of the scheme that involves smart augmentation of the LSQ stencil (CC-SA) has only marginal complexity increase. All other schemes have larger complexity; complexity of node-centered (NC) schemes are somewhat lower than complexity of cell-centered node-averaging (CC-NA) and full-augmentation (CC-FA) schemes. On highly anisotropic grids typical of those encountered in grid adaptation, discretization errors of five of the six cell-centered schemes converge with second order on all tested grids; the CC-NA scheme with clipping degrades solution accuracy to first order. The NC schemes converge with second order on regular and/or triangular grids and with first order on perturbed quadrilaterals and mixed-element grids. All schemes may produce large relative errors in gradient reconstruction on grids with perturbed nodes. Defect-correction iterations for schemes employing weighted least-square gradient reconstruction diverge on perturbed stretched grids. Overall, the CC-NN and CC-SA schemes offer the best options of the lowest complexity and secondorder discretization errors. On anisotropic grids over a curved body typical of turbulent flow simulations, the discretization errors converge with second order and are small for the CC-NN, CC-SA, and CC-FA schemes on all grids and for NC schemes on triangular grids; the discretization errors of the CC-NA scheme without clipping do not converge on irregular grids. Accurate gradient reconstruction can be achieved by introducing a local approximate mapping; without approximate mapping, only the NC scheme with weighted LSQ method provides accurate gradients. Defect correction iterations for the CC-NA scheme without clipping diverge; for the NC scheme with weighted LSQ method, the iterations either diverge or converge very slowly. The best option in curved geometries is the CC-SA scheme that offers low complexity, second-order discretization errors, and fast convergence.

Diskin, Boris↗
NASA NTRS1991

Integrating CLIPS applications into heterogeneous distributed systems

SOCIAL is an advanced, object-oriented development tool for integrating intelligent and conventional applications across heterogeneous hardware and software platforms. SOCIAL defines a family of 'wrapper' objects called agents, which incorporate predefined capabilities for distributed communication and control. Developers embed applications within agents and establish interactions between distributed agents via non-intrusive message-based interfaces. This paper describes a predefined SOCIAL agent that is specialized for integrating C Language Integrated Production System (CLIPS)-based applications. The agent's high-level Application Programming Interface supports bidirectional flow of data, knowledge, and commands to other agents, enabling CLIPS applications to initiate interactions autonomously, and respond to requests and results from heterogeneous remote systems. The design and operation of CLIPS agents are illustrated with two distributed applications that integrate CLIPS-based expert systems with other intelligent systems for isolating and mapping problems in the Space Shuttle Launch Processing System at the NASA Kennedy Space Center.

Adler, Richard M.↗
NASA NTRS2012

The Invasive Species Forecasting System (ISFS): An iRODS-Based, Cloud-Enabled Decision Support System for Invasive Species Habitat Suitability Modeling

The Invasive Species Forecasting System (ISFS) is an online decision support system that allows users to load point occurrence field sample data for a plant species of interest and quickly generate habitat suitability maps for geographic regions of interest, such as a national park, monument, forest, or refuge. Target customers for ISFS are natural resource managers and decision makers who have a need for scientifically valid, model- based predictions of the habitat suitability of plant species of management concern. In a joint project involving NASA and the Maryland Department of Natural Resources, ISFS has been used to model the potential distribution of Wavyleaf Basketgrass in Maryland's Chesapeake Bay Watershed. Maximum entropy techniques are used to generate predictive maps using predictor datasets derived from remotely sensed data and climate simulation outputs. The workflow to run a model is implemented in an iRODS microservice using a custom ISFS file driver that clips and re-projects data to geographic regions of interest, then shells out to perform MaxEnt processing on the input data. When the model completes, all output files and maps from the model run are registered in iRODS and made accessible to the user. The ISFS user interface is a web browser that uses the iRODS PHP client to interact with the ISFS/iRODS- server. ISFS is designed to reside in a VMware virtual machine running SLES 11 and iRODS 3.0. The ISFS virtual machine is hosted in a VMware vSphere private cloud infrastructure to deliver the online service.

Gill, Roger↗
NASA NTRS1997

Develop Advanced Nonlinear Signal Analysis Topographical Mapping System

During the development of the SSME, a hierarchy of advanced signal analysis techniques for mechanical signature analysis has been developed by NASA and AI Signal Research Inc. (ASRI) to improve the safety and reliability for Space Shuttle operations. These techniques can process and identify intelligent information hidden in a measured signal which is often unidentifiable using conventional signal analysis methods. Currently, due to the highly interactive processing requirements and the volume of dynamic data involved, detailed diagnostic analysis is being performed manually which requires immense man-hours with extensive human interface. To overcome this manual process, NASA implemented this program to develop an Advanced nonlinear signal Analysis Topographical Mapping System (ATMS) to provide automatic/unsupervised engine diagnostic capabilities. The ATMS will utilize a rule-based Clips expert system to supervise a hierarchy of diagnostic signature analysis techniques in the Advanced Signal Analysis Library (ASAL). ASAL will perform automatic signal processing, archiving, and anomaly detection/identification tasks in order to provide an intelligent and fully automated engine diagnostic capability. The ATMS has been successfully developed under this contract. In summary, the program objectives to design, develop, test and conduct performance evaluation for an automated engine diagnostic system have been successfully achieved. Software implementation of the entire ATMS system on MSFC's OISPS computer has been completed. The significance of the ATMS developed under this program is attributed to the fully automated coherence analysis capability for anomaly detection and identification which can greatly enhance the power and reliability of engine diagnostic evaluation. The results have demonstrated that ATMS can significantly save time and man-hours in performing engine test/flight data analysis and performance evaluation of large volumes of dynamic test data.

Jong, Jen-Yi↗
NASA NTRS1999

Clustering of the Diffuse Infrared Light from the COBE DIRBE Maps: Power Spectrum Analysis and Excess Isotropic Component of Fluctuations - 3

The cosmic infrared background (CIB) radiation is the cosmic repository for energy release throughout the history of the universe. The spatial fluctuations of the CIB resulting from galaxy clustering are expected to be at least a few percent on scales of a degree, depending on the luminosity and clustering history of the early universe. Using the all-sky data from the COBE DIRBE instrument at wavelengths 1.25 - 100 microns we attempt to measure the CIB fluctuations. In the near-IR, foreground emission is dominated by small scale structure due to stars in the Galaxy. There we find a strong correlation between the amplitude of the fluctuations and Galactic latitude after removing bright foreground stars. Using data outside the Galactic plane (absolute value of b > 20 deg) and away from the center (90 deg < l < 270 deg) we extrapolate the amplitude of the fluctuations to cosec absolute value of b = 0. We find a positive intercept of delta.F(sub rms) = 15.5(sup +3.7, sub -7.0), 5.9(sup +1.6, sub -3.7), 2.4(sup +0.5, sub -0.9), 2.0(sup +0.25, sub -0.5) nW/sq m.sr at 1.25, 2.2, 3.5 and 4.9 microns respectively, where the errors are the range of 92% confidence limits. For color subtracted maps between band 1 and 2 we find the isotropic part of the fluctuations at 7.6(sup +1.2, sub -2.4) nW/sq m.sr. Based on detailed numerical and analytic models, this residual is not likely to originate from the Galaxy, our clipping algorithm, or instrumental noise. We demonstrate that the residuals from the fit used in the extrapolation are distributed isotropically and suggest that this extra variance may result from structure in the CIB. We also obtain a positive intercept from a linear combination of maps at 1.25 and 2.2 microns. For 2 deg < theta < 15 deg, a power-spectrum analysis yields limits of (theta/5 deg) x delta.F(sub rms)(theta) < 6, 2.5, 0.8, 0.5 nW/sq m.sr at 1.25, 2.2, 3.5 and 4.9 microns respectively. From 10 - 100 microns, the dominant foregrounds are emission by dust in the Solar system and the Galaxy. There the upper limits on the CIB fluctuations are below 1 nW/sq m.sr and are lowest (< equal 0.5 nW/sq m.sr) at 25 microns.

Kashlinsky, A.↗
NASA NTRS1991

ICADS: A cooperative decision making model with CLIPS experts

A cooperative decision making model is described which is comprised of six concurrently executing domain experts coordinated by a blackboard control expert. The focus application field is architectural design, and the domain experts represent consultants in the area of daylighting, noise control, structural support, cost estimating, space planning, and climate responsiveness. Both the domain experts and the blackboard were implemented as production systems, using an enhanced version of the basic CLIPS package. Acting in unison as an Expert Design Advisor, the domain and control experts react to the evolving design solution progressively developed by the user in a 2-D CAD drawing environment. A Geometry Interpreter maps each drawing action taken by the user to real world objects, such as spaces, walls, windows, and doors. These objects, endowed with geometric and nongeometric attributes, are stored as frames in a semantic network. Object descriptions are derived partly from the geometry of the drawing environment and partly from knowledge bases containing prototypical, generalized information about the building type and site conditions under consideration.

Pohl, Jens↗
NASA NTRS2022

Colorado Eastern Plains Agriculture: Rangeland Monitoring to Inform Grazing Management in Eastern Colorado

Adaptive management on cattle ranches requires rangeland managers to decide the location and duration of the cattle grazing activity across different pastures. Biodiversity, forage availability, and cattle health are all affected by rangeland management. Virtual fencing is a tool that rangeland managers can use to potentially increase biodiversity and improve ranching operations. NASA DEVELOP and Colorado State University (CSU) collaborated with the Nature Conservancy (TNC), and Red Top Ranch to demonstrate the efficacy of virtual fencing. We sought to identify annual and monthly biomass patterns on the ranch through the creation of monthly max biomass productivity maps. We utilized a dataset from the Agricultural Research Service (ARS) to calculate biomass on the ranch. To validate our remotely-sensed results, we compared model-predicted biomass values to field-collected biomass clipping data and an additional biomass dataset from the Rangeland Analysis Platform (RAP). We used satellite imagery from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI), Landsat 9 –OLI-2, and Sentinel-2 MultiSpectral Instrument (MSI) for 2021 and 2022. We found that there was heterogeneity in biomass across the ranch, with higher biomass on the western side. The highest peak of biomass was in the summer months, with a smaller peak in mid-September. The ARS biomass dataset had a significant relationship with RAP for 2021. ARS biomass did not have a significant relationship with the biomass field data collected in 2022. The results of our study are aimed to support rotation management, ranch production, biodiversity, and conservation management.

cattle↗
NASA NTRS1993

Rapid prototyping 3D virtual world interfaces within a virtual factory environment

On-going work into user requirements analysis using CLIPS (NASA/JSC) expert systems as an intelligent event simulator has led to research into three-dimensional (3D) interfaces. Previous work involved CLIPS and two-dimensional (2D) models. Integral to this work was the development of the University of Massachusetts Lowell parallel version of CLIPS, called PCLIPS. This allowed us to create both a Software Bus and a group problem-solving environment for expert systems development. By shifting the PCLIPS paradigm to use the VEOS messaging protocol we have merged VEOS (HlTL/Seattle) and CLIPS into a distributed virtual worlds prototyping environment (VCLIPS). VCLIPS uses the VEOS protocol layer to allow multiple experts to cooperate on a single problem. We have begun to look at the control of a virtual factory. In the virtual factory there are actors and objects as found in our Lincoln Logs Factory of the Future project. In this artificial reality architecture there are three VCLIPS entities in action. One entity is responsible for display and user events in the 3D virtual world. Another is responsible for either simulating the virtual factory or communicating with the real factory. The third is a user interface expert. The interface expert maps user input levels, within the current prototype, to control information for the factory. The interface to the virtual factory is based on a camera paradigm. The graphics subsystem generates camera views of the factory on standard X-Window displays. The camera allows for view control and object control. Control or the factory is accomplished by the user reaching into the camera views to perform object interactions. All communication between the separate CLIPS expert systems is done through VEOS.

Kosta, Charles Paul↗
NASA NTRSDate not supplied

Concept-based Analysis of Neural Networks via Vision-Language Models

The analysis of vision-based deep neural networks (DNNs) is highly desirable but it is very challenging due to the difficulty of expressing formal specifications for vision tasks and the lack of efficient verification procedures. In this paper, we propose to leverage emerging multimodal, vision-language, foundation models (VLMs) as a lens through which we can reason about vision models. VLMs have been trained on a large body of images accompanied by their textual description, and are thus implicitly aware of high-level, human-understandable concepts describing the images. We describe a logical specification language Con spec designed to facilitate writing specifications in terms of these concepts. To define and formally check Con spec specifications, we build a map between the internal representations of a given vision model and a VLM, leading to an efficient verification procedure of natural-language properties for vision models. We demonstrate our techniques on a ResNet-based classifier trained on the RIVAL-10 dataset using CLIP as the multimodal model.

Large Vision Language Models↗
NASA NTRS2010

Comparison of Node-Centered and Cell-Centered Unstructured Finite-Volume Discretizations: Viscous Fluxes

Discretization of the viscous terms in current finite-volume unstructured-grid schemes are compared using node-centered and cell-centered approaches in two dimensions. Accuracy and complexity are studied for four nominally second-order accurate schemes: a node-centered scheme and three cell-centered schemes - a node-averaging scheme and two schemes with nearest-neighbor and adaptive compact stencils for least-square face gradient reconstruction. The grids considered range from structured (regular) grids to irregular grids composed of arbitrary mixtures of triangles and quadrilaterals, including random perturbations of the grid points to bring out the worst possible behavior of the solution. Two classes of tests are considered. The first class of tests involves smooth manufactured solutions on both isotropic and highly anisotropic grids with discontinuous metrics, typical of those encountered in grid adaptation. The second class concerns solutions and grids varying strongly anisotropically over a curved body, typical of those encountered in high-Reynolds number turbulent flow simulations. Tests from the first class indicate the face least-square methods, the node-averaging method without clipping, and the node-centered method demonstrate second-order convergence of discretization errors with very similar accuracies per degree of freedom. The tests of the second class are more discriminating. The node-centered scheme is always second order with an accuracy and complexity in linearization comparable to the best of the cell-centered schemes. In comparison, the cell-centered node-averaging schemes may degenerate on mixed grids, have a higher complexity in linearization, and can fail to converge to the exact solution when clipping of the node-averaged values is used. The cell-centered schemes using least-square face gradient reconstruction have more compact stencils with a complexity similar to that of the node-centered scheme. For simulations on highly anisotropic curved grids, the least-square methods have to be amended either by introducing a local mapping based on a distance function commonly available in practical schemes or modifying the scheme stencil to reflect the direction of strong coupling. The major conclusion is that accuracies of the node centered and the best cell-centered schemes are comparable at equivalent number of degrees of freedom.

Diskin, Boris↗
NASA NTRS1991

LinkFinder: An expert system that constructs phylogenic trees

An expert system has been developed using the C Language Integrated Production System (CLIPS) that automates the process of constructing DNA sequence based phylogenies (trees or lineages) that indicate evolutionary relationships. LinkFinder takes as input homologous DNA sequences from distinct individual organisms. It measures variations between the sequences, selects appropriate proportionality constants, and estimates the time that has passed since each pair of organisms diverged from a common ancestor. It then designs and outputs a phylogenic map summarizing these results. LinkFinder can find genetic relationships between different species, and between individuals of the same species, including humans. It was designed to take advantage of the vast amount of sequence data being produced by the Genome Project, and should be of value to evolution theorists who wish to utilize this data, but who have no formal training in molecular genetics. Evolutionary theory holds that distinct organisms carrying a common gene inherited that gene from a common ancestor. Homologous genes vary from individual to individual and species to species, and the amount of variation is now believed to be directly proportional to the time that has passed since divergence from a common ancestor. The proportionality constant must be determined experimentally; it varies considerably with the types of organisms and DNA molecules under study. Given an appropriate constant, and the variation between two DNA sequences, a simple linear equation gives the divergence time.

Inglehart, James↗
DOE OSTI2024

Urban morphology and urban water demand evolution in the Los Angeles region

Detailed description of the dataset sources used in this study, the experimental workflow, and plotting for the paper figures provided at the associated GitHub Meta Repo: https://github.com/IMMM-SFA/Ferencz_et_al_2024_ERL The future water demand projections from this study are hypothetical future water demands that reflect the population and urban land cover changes represented by the scenarios considered. The intent and emphasis of this work is investigating the interactions between population change, evolution of urban morphology, and water demand. These projections are not meant to be likely future demands for specific water providers or the LA region and should not be interpreted as such. The folders contain input and output data for each step of the "Recreate my Experiment" workflow described in the associated GitHub meta-repository as well as data used for plotting Figures for the paper that this dataset supports. Description of each folder's contents and use: Step_1a: Inputs to the associated python script provided on the GitHub repo. Step_1b: Inputs (downscaled population rasters) used by the associated python script provided on the GitHub repo. Original 1-km squared rasters that were downscaled also provided. Step_1c: Urban growth projection rasters corresponding to SSP3 and SSP5 population scenarios are provided in separate subfolders as well as the water provider boundaries used for analysis. Outputs of data processing also provided. Associated python script provided on GitHub. Step_1d: Description of Inputs used by the QGIS Model Builder GUI that automates geospatial processing and clipping the of the high-resolution 60 cm land cover data for each urban land class footprint within a defined polygon boundary. The Model Builder is provided on the GitHub repo and can be used by QGIS. The outputs of this step are in "Clipped Provider Hi Res Landcover". If the user wants to use The Model Builder for different regions of LA or to test our outputs, they will need to download the hi resolution landcover raster listed in the Readme and in Ref [2] of the GitHub Page. Step_1e: All necessary inputs to generate average monthly demand over the 2017-2021 period and the minimum and maximum demands over the 2014-2021 for each water provider. Associated python scripts are on GitHub. Step 2: Output data about land cover metrics (areas and fractions) for each urban land class for each water provider. Associated python script on GitHub. Uses outputs from Step 1d "Clipped Provider Hi Res Landcover" Step 3: Both the Inputs for and Outputs from the urban projection raster analysis Python script on GitHub. The inputs are urban land class rasters for specific SSP and zoning scenarios (low, medium, high) from Step 1c. The outputs are rasters of urban pixels that were converted to a higher land class and the number of land class units that changed (Values of 1, 2, or 3). For example, a value of 2 could be LC 21 -> 23 or LC 22 -> 24. These maps are label "intensification." The other outputs are "urban growth" rasters showing the conversion of non urban to urban land, which are indicated by pixel values of 1. These are used for the urban growth change maps in Figure 3. Step 4: Output projections of indoor and outdoor annual and monthly demands for each water provider for the average, minimum, and maximum monthly demand scenarios for each of the four urban growth scenarios (SSP3 med, SSP5 low, SSP5 med, and SSP5 high). The outputs also include metrics on each water provider used for the demand sensitivity analysis presented in Figure 8. Outputs from Step 4 are used for Figures 4 - 8 of the paper. Figures: This folder has data used for plotting Figures 1 through 5, and 8. Data for Figures 6 and 7 are sourced directly from folders associated with the Processing and Analysis Steps 1 - 4. The GitHub meta repository provides descriptions of how each figure was made and the associated plotting scripts used.

Los Angeles↗
NASA NTRS2015

Rule-Based Flight Software Cost Estimation

This paper discusses the fundamental process for the computation of Flight Software (FSW) cost estimates. This process has been incorporated in a rule-based expert system [1] that can be used for Independent Cost Estimates (ICEs), Proposals, and for the validation of Cost Analysis Data Requirements (CADRe) submissions. A high-level directed graph (referred to here as a decision graph) illustrates the steps taken in the production of these estimated costs and serves as a basis of design for the expert system described in this paper. Detailed discussions are subsequently given elaborating upon the methodology, tools, charts, and caveats related to the various nodes of the graph. We present general principles for the estimation of FSW using SEER-SEM as an illustration of these principles when appropriate. Since Source Lines of Code (SLOC) is a major cost driver, a discussion of various SLOC data sources for the preparation of the estimates is given together with an explanation of how contractor SLOC estimates compare with the SLOC estimates used by JPL. Obtaining consistency in code counting will be presented as well as factors used in reconciling SLOC estimates from different code counters. When sufficient data is obtained, a mapping into the JPL Work Breakdown Structure (WBS) from the SEER-SEM output is illustrated. For across the board FSW estimates, as was done for the NASA Discovery Mission proposal estimates performed at JPL, a comparative high-level summary sheet for all missions with the SLOC, data description, brief mission description and the most relevant SEER-SEM parameter values is given to illustrate an encapsulation of the used and calculated data involved in the estimates. The rule-based expert system described provides the user with inputs useful or sufficient to run generic cost estimation programs. This system's incarnation is achieved via the C Language Integrated Production System (CLIPS) and will be addressed at the end of this paper.

computational intelligence↗
NASA NTRS1975

A multidisciplinary study of earth resources imagery of Australia, Antarctica and Papua, New Guinea

The author has identified the following significant results. A thirteen category recognition map was prepared, showing forest, water, grassland, and exposed rock types. Preliminary assessment of classification accuracies showed that water, forest, meadow, and Niobrara shale were the most accurately mapped classes. Unsatisfactory results, were obtained in an attempt to discrimate sparse forest cover over different substrates. As base elevation varied from 7,000 to 13,000 ft, with an atmospheric visibility of 48 km, no changes in water and forest recognition were observed. Granodiorite recognition accuracy decreased monotonically as base elevation increased, even though the training set location was at 10,000 ft elevation. For snow varying in base elevation from 9400 to 8420 ft, recognition decreases from 99% at the 9400 ft training set elevation to 88% at 8420 ft. Calculations of expected radiance at the ERTS sensor from snow reflectance measured at the site and from Turner model calculations of irradiance, transmission and path radiance, reveal that snow signals should not be clipped, assuming that calculations and ERTS calibration constants were correct.

Fisher, N. H.↗