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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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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 NTRS1991

Communications and control for electric power systems

The first section of the report describes the AbNET system, a hardware and software communications system designed for distribution automation (it can also find application in substation monitoring and control). The topology of the power system fixes the topology of the communications network, which can therefore be expected to include a larger number of branch points, tap points, and interconnections. These features make this communications network unlike any other. The network operating software has to solve the problem of communicating to all the nodes of a very complex network in as reliable a way as possible even if the network is damaged, and it has to do so with minimum transmission delays and at minimum cost. The design of the operating protocols is described within the framework of the seven-layer Open System Interconnection hierarchy of the International Standards Organization. Section 2 of the report describes the development and testing of a high voltage sensor based on an electro-optic polymer. The theory of operation is reviewed. Bulk fabrication of the polymer is discussed, as well as results of testing of the electro-optic coefficient of the material. Fabrication of a complete prototype sensor suitable for use in the range 1-20 kV is described. The electro-optic polymer is shown to be an important material for fiber optic sensing applications. Appendix A is theoretical support for this work. The third section of the report presents the application of an artificial neural network, Kohonen's self-organizing feature map, for the classification of power system states. This classifier maps vectors of an N-dimensional space to a 2-dimensional neural net in a nonlinear way preserving the topological order of the input vectors. These mappings are studied using a nonlinear power system model.

Kirkham, H.↗
NASA NTRS1999

Neural Network and Regression Approximations Used in Aircraft Design

NASA Lewis Research Center's CometBoards Test Bed was used to create regression and neural network models for a High-Speed Civil Transport (HSCT) aircraft. Both approximation models that replaced the actual analysis tool predicted the aircraft response in a trivial computational effort. The models allow engineers to quickly study the effects of design variables on constraint and objective values for a given aircraft configuration. For example, an engineer can change the engine size by 1000 pounds of thrust and quickly see how this change affects all the output values without rerunning the entire simulation. In addition, an engineer can change a constraint and use the approximation models to quickly reoptimize the configuration. Generating the neural network and the regression models is a time-consuming process, but this exercise has to be carried out only once. Furthermore, an automated process can reduce calculations substantially.

Patnaik, Surya N.↗
NASA NTRSDate not supplied

Guiding Integration of Formal Verification in Assurance Cases

Assurance cases are being increasingly acknowledged as away to build trust in complex systems with autonomous capabilities. An assurance case is a comprehensive, defensible, and valid justification that a system will function as intended for a specific mission and operating environment. Formal verification is often reserved for the most critical components of such systems. However, formal verification tools are often complex, and their usage is subject to many constraints and contextual dependencies. This can raise challenges both for performing the verification as well as reflecting the verification results appropriately in the assurance case, especially for non-expert users of the verification tool. To address these challenges, we present a tool-supported methodology for integrating formal verification results in an assurance case by capturing key verification method information in a rigorously constructed assurance case. In particular, we capture the tool specification in terms of its inputs, outputs, and assurance constraints as assumptions over inputs and guarantees provided over its outputs. The tool specification is parametrized over the inputs and outputs to both guide the intended application of the tool, as well as to check that the tool has been applied following the stated assumptions and that the guarantees hold. We define a generic tool assurance argument pattern that enables integration of the verification results in the assurance case by allowing custom refinement and automated instantiation for each tool use. We demonstrate our methodology on two formal verification tools and their applications to the verification of neural network properties for the aircraft domain.

Assurance Cases↗
NASA NTRS1990

Establishing a communications-intensive network to resolve artificial intelligence issues within NASA's Space Station Freedom research centers community

MITRE Corporation's, A Review of Space Station Freedom Program Capabilities for the Development and Application of Advanced Automation, cites as a critical issue the following situation, extant at the NASA facilities visited in the course of preparing the review: The major issues noted with regard to design and research facilities deal with cooperative problem solving, technology transfer, and communication between these facilities. While the authors were visiting lab and test beds to collect information, personnel at many of these facilities were interested in any information they could collect on activities at other facilities. A formal means of gathering this information could not be identified by these personnel. While communication between some facilities was taking place or was planned, for technology transfer or coordination of schedules (e.g., for SADP demonstrations), poor communication between these facilities could lead to a lack of technical standards, duplication of effort, poorly defined interfaces, scheduling problems, and increased cost. Formal mechanisms by which effective communication and cooperative problem solving can take place, and information can be disseminated, must be defined. A solution is proposed for the communications aspects of the issues addressed above; and offered at the same time a solution which can prove effective in dealing with some of the problems being encountered with expertise being lost via retirement or defection to the private sector. The proffered recommendations are recognizably cost-effective and tap the rising sector of expert knowledge being produced by the American academic community.

Howard, E. Davis, III↗
NASA NTRS1990

Expert systems and advanced automation for space missions operations

Increased complexity of space missions during the 1980s led to the introduction of expert systems and advanced automation techniques in mission operations. This paper describes several technologies in operational use or under development at the National Aeronautics and Space Administration's Goddard Space Flight Center. Several expert systems are described that diagnose faults, analyze spacecraft operations and onboard subsystem performance (in conjunction with neural networks), and perform data quality and data accounting functions. The design of customized user interfaces is discussed, with examples of their application to space missions. Displays, which allow mission operators to see the spacecraft position, orientation, and configuration under a variety of operating conditions, are described. Automated systems for scheduling are discussed, and a testbed that allows tests and demonstrations of the associated architectures, interface protocols, and operations concepts is described. Lessons learned are summarized.

Durrani, Sajjad H.↗
NASA NTRS1990

Adaptive pattern recognition by mini-max neural networks as a part of an intelligent processor

In this decade and progressing into 21st Century, NASA will have missions including Space Station and the Earth related Planet Sciences. To support these missions, a high degree of sophistication in machine automation and an increasing amount of data processing throughput rate are necessary. Meeting these challenges requires intelligent machines, designed to support the necessary automations in a remote space and hazardous environment. There are two approaches to designing these intelligent machines. One of these is the knowledge-based expert system approach, namely AI. The other is a non-rule approach based on parallel and distributed computing for adaptive fault-tolerances, namely Neural or Natural Intelligence (NI). The union of AI and NI is the solution to the problem stated above. The NI segment of this unit extracts features automatically by applying Cauchy simulated annealing to a mini-max cost energy function. The feature discovered by NI can then be passed to the AI system for future processing, and vice versa. This passing increases reliability, for AI can follow the NI formulated algorithm exactly, and can provide the context knowledge base as the constraints of neurocomputing. The mini-max cost function that solves the unknown feature can furthermore give us a top-down architectural design of neural networks by means of Taylor series expansion of the cost function. A typical mini-max cost function consists of the sample variance of each class in the numerator, and separation of the center of each class in the denominator. Thus, when the total cost energy is minimized, the conflicting goals of intraclass clustering and interclass segregation are achieved simultaneously.

Szu, Harold H.↗
NASA NTRS1985

Information flow and work productivity through integrated information technology

The work environment surrounding integrated office systems is reviewed. The known effects of automated office technologies is synthesized and their known impact on work efficiency is reviewed. These effects are explored with regard to their impact on networks, work flow/processes, as well as organizational structure and power. Particular emphasis is given to structural changes due to the introduction of newer information technologies in organizations. The new information technologies have restructed the average organization's middle banks and, as a consequence, they have shrunk drastically. Organizational pyramids have flattened with fewer levels since executives have realized that they can get ahold of the needed information via the new technologies quicker and directly and do not have to rely on middle-level managers. Power shifts are typically accompanied with the introduction of these technologies resulting in the generation of a new form of organizational power.

Wigand, R. T.↗
NASA NTRS1991

Polymer Infiltration Studies

Progress was made on the preparation of carbon fiber composites using advanced polymer resins. Processes reported include powder towpreg process, weaving towpreg made from dry powder prepreg, composite from powder coated towpreg, and toughening of polyimide resin (PMR) composites by semi-interpenetrating networks. Several important areas of polymer infiltration into fiber bundles will be researched. Preparation to towpreg for textile preform weaving and braiding and for automated tow placement is a major goal, as are the continued development of prepregging technology and the various aspects of composite part fabrication.

Marchello, Joseph M.↗
NASA NTRS2006

Estimation of Stability and Control Derivatives of an F-15

A technique for real-time estimation of stability and control derivatives (derivatives of moment coefficients with respect to control-surface deflection angles) was used to support a flight demonstration of a concept of an indirect-adaptive intelligent flight control system (IFCS). Traditionally, parameter identification, including estimation of stability and control derivatives, is done post-flight. However, for the indirect-adaptive IFCS concept, parameter identification is required during flight so that the system can modify control laws for a damaged aircraft. The flight demonstration was carried out on a highly modified F-15 airplane (see Figure 1). The main objective was to estimate the stability and control derivatives of the airplane in nearly real time. A secondary goal was to develop a system to automatically assess the quality of the results, so as to be able to tell a learning neural network which data to use. Parameter estimation was performed by use of Fourier-transform regression (FTR) a technique developed at NASA Langley Research Center. FTR is an equation- error technique that operates in the frequency domain. Data are put into the frequency domain by use of a recursive Fourier transform for a discrete frequency set. This calculation simplifies many subsequent calculations, removes biases, and automatically filters out data beyond the chosen frequency range. FTR as applied here was tailored to work with pilot inputs, which produce correlated surface positions that prevent accurate parameter estimates, by replacing half the derivatives with predicted values. FTR was also set up to work only on a recent window of data, to accommodate changes in flight condition. A system of confidence measures was developed to identify quality-parameter estimates that a learning neural network could use. This system judged the estimates primarily on the basis of their estimated variances and of the level of aircraft response. The resulting FTR system was implemented in the Simulink software system and auto-coded in the C programming language for use on the Airborne Research Test System (ARTS II) computer installed in the F-15 airplane. The Simulink model was also used in a control room that utilizes the Ring Buffered Network Bus hardware and software, making it possible to evaluate test points during flights. In-flight parameter estimation was done for piloted and automated maneuvers, primarily at three test conditions. Figure 2 shows results for pitching moment due to symmetric stabilator actuations for a series of three pitch doublet maneuvers (in a doublet maneuver, a command to change attitude in a given direction by a given amount is followed immediately by a command to change attitude in the opposite direction by the same amount). A time window of 5 seconds was used. The portions of the curves shown in red are those that passed the confidence tests. The technique showed good convergence for most derivatives for both kinds of maneuvers - typically within a few seconds. The confidence tests were marginally successful, and it would be necessary to refine them for use in an IFCS.

Smith, Mark↗
NASA NTRS2003

Accelerating the Design of Space Vehicles

One of NASA's key goals is to increase the safety and reduce the cost of space transportation. Thus, a key element of NASA's new Integrated Space Transportation Plan is to develop new propulsion, structures, and operations for future generations of reusable launch vehicles (RLVs). As part of this effort to develop the next RLV, the ClCT Program's Computing, Networking, and Information Systems (CNIS) Project is developing and demonstrating collaborative software technologies that use the collective power of the NASA Grid to accelerate spacecraft design. One of these technologies, called AeroDB, automates the execution and monitoring of computational fluid dynamics (CFD) parameter studies on the NASA Grid. About the NASA Grid The NASA Grid, or Information Power Grid,. is being developed to leverage the distributed resources of NASA's many computers. instruments, simulators, and data storage systems. The goal is to use these combined resources to sdve difficult NASA challenges, such as iimulating the entire flight of a space vehicle from ascent to descent.To realize the vision of the NASA Grid, the CNIS Project is developing the software framework and protocols for building domain-specific environments and interfaces, new Grid services based on emerging industry standards, and advanced networking and computing testbeds to support new Grid-based applications such as AeroDB.

Laufenberg, Larry↗
NASA NTRS1981

The NASA Tracking and Data Relay Satellite System and its impact on spacecraft support in the space transportation system era

The space tracking, data acquisition and communication network systems and capabilities available to NASA are discussed, with emphasis on the Tracking and Data Relay Satellite System (TDRSS). Scheduled to come into operation in 1983, TDRSS will be the prime support system for communication with the Space Shuttle, Spacelab, and the automated spacecraft to be launched into earth orbit by the Space Transportation System. TDRSS will consist of two specialized data relay satellites in geosynchronous orbit, about 130 deg apart; each TDRSS spacecraft has a three-axis stabilized configuration, with sun-oriented solar panels, and will weigh about 2200 kg at launch. The NASA Spaceflight Tracking and Data Network, currently used to support 30-40 spacecraft per day, is described, and plans for changes in the network are discussed. The 26 antennas of the network will be consolidated into an expanded Deep Space Network, after TDRSS is in operation.

Smylie, R. E.↗
NASA NTRS1986

Unattended deep space station tracking station development: Monitor and control technology

The major developments leading to successful demonstration of fully unattended operation of a Deep Space Network (DSN) station (DSS 13) are reviewed. Unattended operation was demonstrated by reliably tracking, commanding, and delivering telemetry from the Pioneer 8 spacecraft. Transfer of automated monitor and control technology to DSN implementation is summarized, along with related accomplishments.

Foster, C.↗
NASA NTRS1988

KAM (Knowledge Acquisition Module): A tool to simplify the knowledge acquisition process

Analysts, knowledge engineers and information specialists are faced with increasing volumes of time-sensitive data in text form, either as free text or highly structured text records. Rapid access to the relevant data in these sources is essential. However, due to the volume and organization of the contents, and limitations of human memory and association, frequently: (1) important information is not located in time; (2) reams of irrelevant data are searched; and (3) interesting or critical associations are missed due to physical or temporal gaps involved in working with large files. The Knowledge Acquisition Module (KAM) is a microcomputer-based expert system designed to assist knowledge engineers, analysts, and other specialists in extracting useful knowledge from large volumes of digitized text and text-based files. KAM formulates non-explicit, ambiguous, or vague relations, rules, and facts into a manageable and consistent formal code. A library of system rules or heuristics is maintained to control the extraction of rules, relations, assertions, and other patterns from the text. These heuristics can be added, deleted or customized by the user. The user can further control the extraction process with optional topic specifications. This allows the user to cluster extracts based on specific topics. Because KAM formalizes diverse knowledge, it can be used by a variety of expert systems and automated reasoning applications. KAM can also perform important roles in computer-assisted training and skill development. Current research efforts include the applicability of neural networks to aid in the extraction process and the conversion of these extracts into standard formats.

Gettig, Gary A.↗
NASA NTRS1995

Gamma Ray Burst Optical Counterpart Search Experiment (GROCSE)

GROCSE (Gamma-Ray Optical Counterpart Search Experiments) is a system of automated telescopes that search for simultaneous optical activity associated with gamma ray bursts in response to real-time burst notifications provided by the BATSE/BACODINE network. The first generation system, GROCSE 1, is sensitive down to Mv (approximately) 8.5 and requires an average of 12 seconds to obtain the first images of the gamma ray burst error box defined by the BACODINE trigger. The collaboration is now constructing a second generation system which has a 4 second slewing time and can reach Mv (approximately) 14 with a 5 second exposure. GROCSE 2 consists of 4 cameras on a single mount. Each camera views the night sky through a commercial Canon lens (f/1.8, focal length 200 mm) and utilizes a 2K x 2K Loral CCD. Light weight and low noise custom readout electronics were designed and fabricated for these CCDs. The total field of view of the 4 cameras is 17.6 x 17.6 (degree). GROCSE 2 will be operated by the end of 1995. In this paper, the authors present an overview of the GROCSE system and the results of measurements with a GROCSE 2 prototype unit.

Park, H. S.↗
NASA NTRS1999

The Ionosphere and Ocean Altimetry

The accuracy of satellite-based single-frequency radar ocean altimeters benefits from calibration of the total electron content (TEC) of the ionosphere below the satellite. Data from the global network of Global Positioning System (GPS) receivers provides timely, continuous, and globally well-distributed measurements of ionospheric electron content. We have created a daily automated process called Daily Global Ionospheric Map (Daily-GIM) whose primary purpose is to use global GPS data to provide ionospheric calibration data for the Geosat Follow-On (GFO) ocean altimeter. This process also produces an hourly time-series of global maps of the electron content of the ionosphere. This system is designed to deliver "quick-look" ionospheric calibrations within 24 hours with 90+% reliability and with a root-mean-square accuracy of 2 cm at 13.6 GHz. In addition we produce a second product within 72 hours which takes advantage of additional GPS data which were not available in time for the first process. The diagram shows an example of a comparison between TEC data from the Topographic Experiment (TOPEX) ocean altimeter and Daily-GIM. TEC are displayed in TEC units, TECU, where 5 TECU is 1 cm at 13.6 GHz. Data from a single TOPEX track is shown. Also shown is the Bent climatological model TEC for the track. Although the GFO satellite is not yet in its operational mode, we have been running Daily-GIM reliably (much better than 90%) with better than 2-cm accuracy (based on comparisons against TOPEX) for several months. When timely ephemeris files for the European Remote Sensing Satellite 2 (ERS-2) are available, daily ERS-2 altimeter ionospheric calibration files are produced. When GFO ephemeris files are made available to us, we produce GFO ionosphere calibration files. Users of these GFO ionosphere calibration files find they are a great improvement over the alternative International Reference Ionosphere 1995 (IRI-95) climatological model. In addition, the TOPEX orbit determination team at JPL has been using the global ionospheric maps to calibrate the single frequency GPS data from the TOPEX receiver, and report highly significant improvements in the ephemeris. The global ionospheric maps are delivered daily to the International GPS Service (IGS), making them available to the scientific community. Additional information is contained in the original.

Lindqwister, Ulf J.↗
NASA NTRSDate not supplied

Investigating the Use of Machine Learning (ML) to Assess Tropospheric Doppler Radar Wind Profiler (TDRWP) Data Quality

Manual Quality Control (MQC) of Tropospheric Doppler Radar Wind Profiler (TDRWP) data is essential for defining an accurate climatology for downstream aerospace vehicle assessments. MQC traditionally takes around 30.5 hours per year of radar data. The Marshall Space Flight Center Natural Environments Branch (MSFC NE) used machine learning (ML) to test the feasibility of automating the MQC process, showing a potential to reduce labor by 300%. However, analysis of the model showed some false positives. We compared a neural network to the model to validate it and develop a process for assessing comparable solutions in the future.

Corey Walker↗