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ISS users generate scientific data files on orbit that require console operators to retrieve and deliver them for analysis. Automating the downlink process using TReK CFDP and DTN provides ground flight controllers and PD teams increased efficiency, reducing workload and resulting in cost savings without reduced services provided.
Pose and orientation of an object is one of the central issues in 3-D recognition problems. Most of today's available techniques require considerable pre-processing such as detecting edges or joints, fitting curves or surfaces to segment images, and trying to extract higher order features from the input images. We present a method based on analytical geometry, whereby all the rotation parameters of any quadric surface are determined and subsequently eliminated. This procedure is iterative in nature and was found to converge to the desired results in as few as three iterations. The approach enables us to position the quadric surface in a desired coordinate system, and then to utilize the presented shape information to explicitly represent and recognize the 3-D surface. Experiments were conducted with simulated data for objects such as hyperboloid of one and two sheets, elliptic and hyperbolic paraboloid, elliptic and hyperbolic cylinders, ellipsoids, and quadric cones. Real data of quadric cones and cylinders were also utilized. Both of these sets yielded excellent results.
Qualification and certification of safety critical parts is a hurdle to the adoption of metallic additively manufactured components for aerospace vehicle applications. Challenges include variability in part properties due to inconsistent defect distribution and microstructure. Understanding of the process through finite element modeling (FEM), and process control through in-situ monitoring, may result in significant improvements; however, solutions useful to manufacturers will require large volumes of data and automated data utilization. Toward this end, a generalizable automated FEM calibration paradigm is developed. This paradigm leverages existing and novel tools from machine learning and uncertainty quantification to enable the automatic calibration of FEMs without requiring prior knowledge of the model performance across input parameter space, including meshing and solver settings, which can require time consuming manual model probing or cause noisy and inconsistent predictions. The result is a probabilistic distribution of calibrated and validated FEM input parameters targeting measured data.
This program was developed to fill a need within the Wallops Flight Facility workflow for automation of the development of vertical plan limit lines used by flight safety officers during the conduct of expendable launch vehicle missions. Vertical plane present-position-based destruct lines have been used by range safety organizations at numerous launch ranges to mitigate launch vehicle risks during the early phase of flight. Various ranges have implemented data submittal and processing workflows to develop these destruct lines. As such, there is significant prior art in this field. The ElLimits program was developed at NASA's Wallops Flight Facility to automate the process for developing vertical plane limit lines using current computing technologies. The ElLimits program is used to configure launch-phase range safety flight control lines for guided missiles. The name of the program derives itself from the fundamental quantity that is computed - flight elevation limits. The user specifies the extent and resolution of a grid in the vertical plane oriented along the launch azimuth. At each grid point, the program computes the maximum velocity vector flight elevation that can be permitted without endangering a specified back-range location. Vertical plane x-y limit lines that can be utilized on a present position display are derived from the flight elevation limit data by numerically propagating 'streamlines' through the grid. The failure turn and debris propagation simulation technique used by the application is common to all of its analysis options. A simulation is initialized at a vertical plane grid point chosen by the program. A powered flight failure turn is then propagated in the plane for the duration of the so-called RSO reaction time. At the end of the turn, a delta-velocity is imparted, and a ballistic trajectory is propagated to impact. While the program possesses capability for powered flight failure turn modeling, it does not require extensive user inputs of vehicle characteristics (e.g., thrust and aerodynamic data), nor does it require reams of turn data after the traditional fashion of the Air Force ranges. The program requires a nominal trajectory table (time, altitude, range, velocity, and flight elevation) and makes heavy use of it to initialize and model a failure turn.
Deep Space Telecommunications Requirements: 1) Automated file transfer across inter-planetary distances; 2) Limited communication periods; 3) Reliable transport; 4) Delay and Disruption Tolerant; and 5) Asymmetric Data Channels.
Collection of programs supports robotic research. Report describes computer-vision software library NASA's Jet Propulsion Laboratory. Programs evolved during past 10 years of research into robotics. Collection includes low- and high-level image-processing software proved in applications ranging from factory automation to spacecraft tracking and grappling. Programs fall into several overlapping categories. Image utilities category are low-level routines that provide computer access to image data and some simple graphical capabilities for displaying results of image processing.
Investigation of the feasibility of preparing geologic maps automatically through computer processing of calibrated narrow-band visible and near IR reflectivity data collected with a 12-channel scanner. Five procedures were followed in the computer analysis of the radiances recorded as voltages on analog magnetic tape. Three recognition maps have been generated iteratively using a progressively more complex classification scheme. The overall accuracy of the first recognition map was 80%, but the discrimination of the limestone and dolomite was only 50-60%. All three maps are very accurate outcrop maps. The results demonstrate the feasibility of automated geologic mapping in this relatively simple setting.
Architectural requirements of a minicomputer-based specialized complex for automated digital analysis of multizonal video data are examined. The logic structure of multizonal video data and the complex mathematical provision required for the analysis of such data are described. The composition of the specialized complex, its operating system, and the required set of peripheral devices are discussed. It is noted that although much of the analysis can be automated, the operator-computer dialog mode is essential for certain stages of the analysis.
In cooperation with the NASA Crustal Dynamics Project initiative placing workstation computers at each of its laser ranging stations to handle data filtering and normalpointing, MLRS personnel have developed a new generation of software to provide the same services for the lunar laser ranging data type. The Unix operating system and X windows/Motif provides an environment for both batch and interactive filtering and normalpointing as well as prediction calculations. The goal is to provide a transportable and maintainable data reduction environment. This software and some sample displays are presented. that the lunar (or satellite) datacould be processed on one computer while data was taken on the other. The reduction of the data was totally interactive and in no way automated. In addition, lunar predictions were produced on-site, another first in the effort to down-size historically mainframe-based applications. Extraction of earth rotation parameters was at one time attempted on site in near-realtime. In 1988, the Crustal Dynamics Project SLR Computer Panel mandated the installation of Hewlett-Packard 9000/360 Unix workstations at each NASA-operated laser ranging station to relieve the aging controller computers of much of their data and communications handling responsibility and to provide on-site data filtering and normal pointing for a growing list of artificial satellite targets. This was seen by MLRS staff as an opportunity to provide a better lunar data processing environment as well.
Existing and planned space missions to a variety of planetary and satellite surfaces produce an ever increasing volume of spectral data. Understanding the scientific informational content in this large data volume is a daunting task. Fortunately various statistical approaches are available to assess such data sets. Here we discuss an automated classification scheme based on Kohonen Self-organizing maps (SOM) we have developed. The SUM process produces an output layer were spectra having similar properties lie in close proximity to each other. One major effort is partitioning this output layer into appropriate regions. This is prefonned by defining dosed regions based upon the strength of the boundaries between adjacent cells in the SOM output layer. We use the Davies-Bouldin index as a measure of the inter-class similarities and intra-class dissimilarities that determines the optimum partition of the output layer, and hence number of SOM clusters. This allows us to identify the natural number of clusters formed from the spectral data. Mineral spectral libraries prepared at Arizona State University (ASU) and John Hopkins University (JHU) are used to test and evaluate the classification scheme. We label the library sample spectra in a hierarchical scheme with class, subclass, and mineral group names. We use a portion of the spectra to train the SOM, i.e. produce the output layer, while the remaining spectra are used to test the SOM. The test spectra are presented to the SOM output layer and assigned membership to the appropriate cluster. We then evaluate these assignments to assess the scientific meaning and accuracy of the derived SOM classes as they relate to the labels. We demonstrate that unsupervised classification by SOMs can be a useful component in autonomous systems designed to identify mineral species from reflectance and emissivity spectra in the therrnal IR.
We developed a package to process and analyze the data from the digital version of the Second Palomar Sky Survey. This system, called SKICAT, incorporates the latest in machine learning and expert systems software technology, in order to classify the detected objects objectively and uniformly, and facilitate handling of the enormous data sets from digital sky surveys and other sources. The system provides a powerful, integrated environment for the manipulation and scientific investigation of catalogs from virtually any source. It serves three principal functions: image catalog construction, catalog management, and catalog analysis. Through use of the GID3* Decision Tree artificial induction software, SKICAT automates the process of classifying objects within CCD and digitized plate images. To exploit these catalogs, the system also provides tools to merge them into a large, complete database which may be easily queried and modified when new data or better methods of calibrating or classifying become available. The most innovative feature of SKICAT is the facility it provides to experiment with and apply the latest in machine learning technology to the tasks of catalog construction and analysis. SKICAT provides a unique environment for implementing these tools for any number of future scientific purposes. Initial scientific verification and performance tests have been made using galaxy counts and measurements of galaxy clustering from small subsets of the survey data, and a search for very high redshift quasars. All of the tests were successful, and produced new and interesting scientific results. Attachments to this report give detailed accounts of the technical aspects for multivariate statistical analysis of small and moderate-size data sets, called STATPROG. The package was tested extensively on a number of real scientific applications, and has produced real, published results.
We developed a package to process and analyze the data from the digital version of the Second Palomar Sky Survey. This system, called SKICAT, incorporates the latest in machine learning and expert systems software technology, in order to classify the detected objects objectively and uniformly, and facilitate handling of the enormous data sets from digital sky surveys and other sources. The system provides a powerful, integrated environment for the manipulation and scientific investigation of catalogs from virtually any source. It serves three principal functions: image catalog construction, catalog management, and catalog analysis. Through use of the GID3* Decision Tree artificial induction software, SKICAT automates the process of classifying objects within CCD and digitized plate images. To exploit these catalogs, the system also provides tools to merge them into a large, complex database which may be easily queried and modified when new data or better methods of calibrating or classifying become available. The most innovative feature of SKICAT is the facility it provides to experiment with and apply the latest in machine learning technology to the tasks of catalog construction and analysis. SKICAT provides a unique environment for implementing these tools for any number of future scientific purposes. Initial scientific verification and performance tests have been made using galaxy counts and measurements of galaxy clustering from small subsets of the survey data, and a search for very high redshift quasars. All of the tests were successful and produced new and interesting scientific results. Attachments to this report give detailed accounts of the technical aspects of the SKICAT system, and of some of the scientific results achieved to date. We also developed a user-friendly package for multivariate statistical analysis of small and moderate-size data sets, called STATPROG. The package was tested extensively on a number of real scientific applications and has produced real, published results.
NASA's Earth Science Data and Information System (ESDIS) Project has been processing information for the registration of Digital Object Identifiers (DOI) for the last five years of which an automated system has been in operation for the last two years. The ESDIS DOI registration system has registered over 2000 DOIs with over 1000 DOIs held in reserve until all required information has been collected. By working towards the goal of assigning DOIs to the 8000+ data collections under its management, ESDIS has taken the first step towards facilitating the use of data citations with those products. Jeanne Behnke, ESDIS Deputy Project Manager has reviewed and approved the poster.
The design methodology employed in testing the applicability of Ada in large-scale combat simulations is described. Ada was considered as a substitute for FORTRAN to lower life cycle costs and ease the program development efforts. An object-oriented approach was taken, which featured definitions of military targets, the capability of manipulating their condition in real-time, and one-to-one correlation between the object states and real world states. The simulation design process was automated by the problem statement language (PSL)/problem statement analyzer (PSA). The PSL/PSA system accessed the problem data base directly to enhance the code efficiency by, e.g., eliminating non-used subroutines, and provided for automated report generation, besides allowing for functional and interface descriptions. The ways in which the methodology satisfied the responsiveness, reliability, transportability, modifiability, timeliness and efficiency goals are discussed.
Constructing Artificial Intelligence application systems which rely on both symbolic and non-symbolic processing places heavy demands on the communication of data between dissimilar languages. This paper describes STAR (Simple Tool for Automated Reasoning), a computer language for the development of AI application systems which supports the transfer of data structures between a symbolic level and a non-symbolic level defined in languages such as FORTRAN, C and PASCAL. The organization of STAR is presented, followed by the description of an application involving STAR in the interpretation of airborne imaging spectrometer data.
This paper addresses the application of spray-automated balancing of rotors, and the influence that various operating parameters will have on balancing performance. Spray-automated balancing uses the fuel-air repetitive explosion process to imbed short, discrete bursts of high velocity, high temperature powder into a rotating part at an angle selected to reduce unbalance of the part. The shortness of the burst, the delay in firing of the gun, the speed of the disk and the variability in speed all influence the accuracy and effectiveness of the automated balancing process. The paper evaluates this influence by developing an analytical framework and supplementing the analysis with empirical data obtained while firing the gun at a rotating disk. Encouraging results are obtained, and it is shown that the process should perform satisfactorily over a wide range of operating parameters. Further experimental results demonstrate the ability of the method to reduce vibration levels induced by mass unbalance in a rotating disk.
The Second International Symposium featured 135 oral presentations in these 12 categories: Future Missions and Operations; System-Level Architectures; Mission-Specific Systems; Mission and Science Planning and Sequencing; Mission Control; Operations Automation and Emerging Technologies; Data Acquisition; Navigation; Operations Support Services; Engineering Data Analysis of Space Vehicle and Ground Systems; Telemetry Processing, Mission Data Management, and Data Archiving; and Operations Management. Topics focused on improvements in the productivity, effectiveness, efficiency, and quality of mission operations, ground systems, and data acquisition. Also emphasized were accomplishments in management of human factors; use of information systems to improve data retrieval, reporting, and archiving; design and implementation of logistics support for mission operations; and the use of telescience and teleoperations.
Frequency of false alarms reduced. Faults in complicated dynamic system (e.g., antenna-aiming system, telecommunication network, or human heart) detected automatically by method of automated, continuous monitoring. Obtains time-series data by sampling multiple sensor outputs at discrete intervals of t and processes data via algorithm determining whether system in normal or faulty state. Algorithm implements, among other things, hidden first-order temporal Markov model of states of system. Mathematical model of dynamics of system not needed. Present method is "prior" method mentioned in "Improved Hidden-Markov-Model Method of Detecting Faults" (NPO-18982).