SONET++: A Knowledge Graph of Geographic Categories based on OSM Tag Representation
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Fan, Junchuan↗
SEARCH · Engineering Papers
Results for “knowledge base”
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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Investigation into Reductive Separation of Tc-99 from Low Activity Waste Off-Gas Condensate by Zero Valent Iron Materials - 20311
This study is investigating the potential for reductive separation of technetium-99 (Tc) from off-gas condensate generated from vitrification of legacy tank waste at the Hanford Tank Waste Treatment and Immobilization Plant. Management of Tc is a high-priority activity for the U.S. Department of Energy (DOE) Office of Environmental Management (EM) complex, which must be addressed based on knowledge gaps and technology development needs outlined in the DOE-EM Technetium Management Program Plan [1]. Technetium is one of the main risk drivers in the permanent disposal of low activity waste (LAW) by vitrification at the Hanford Site due to its high volatility and only fractional incorporation into borosilicate glass. Volatilized Tc will be captured by an off-gas treatment system, and the current plan is to continuously recycle generated condensate back to the LAW glass melter feed. Off-gas recycling is effective at increasing Tc loading in the LAW glass, but it also disproportionately increases concentrations of sulfate, halides, and other volatile compounds impeding overall LAW processing leading to significantly greater waste glass volume requirements. Removal of Tc from secondary liquid waste offers an alternative disposal route to continuous off-gas recycling which may result in significant reduction of the volumes of LAW waste glass and substantial cost savings. The objective of this research funded by the DOE-EM Minority Serving Institutions Partnership Program (MSIPP) is to determine the suitability of zero valent iron (ZVI) as a separation technology to remove Tc from the off-gas condensate stream to minimize its recycle. While ZVI has been proposed for various remediation applications with Tc, it has not been studied for its suitability for removing Tc from the LAW off-gas condensate. The goal is to identify the optimal conditions for which Tc{sup (VII)} will be reduced to Tc{sup (IV)} species and incorporated into an iron matrix. This paper presents a programmatic overview of our evaluation of commercial ZVI materials for their efficiency towards Tc{sup (VII)} reductive removal from simplified off-gas simulant solutions under aerobic conditions. Tc concentrations in solutions were monitored for up to 30 days to determine efficiency and kinetics of reductive removal of Tc. The best performing candidate materials were selected for further experiments evaluating the effects of (1) variable ionic strength, (2) the presence of chromium and corrosive anions, and (3) ZVI aging on Tc{sup (VII)} reductive removal in batch experiments. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Workshop on Using a Git-based Repository for ASTM Committee Technical Knowledge Capture [Slides]
Consensus technical standards maintained by ASTM International rely on individual contributors to act as technical contacts, effectively stewards, for a particular standard. As a particular standard’s technical content evolves over time, the contact must update relevant tables, graphics, and supporting discussion. However, the details of the data sources and/or processes used to perform such updates may be separate enough from the standard such that expert knowledge is required to perform these updates. As a result of this need for expert knowledge, and the desire to capture that knowledge, subcommittee E10.05 (Nuclear Radiation Metrology) has experimented with using a git-based repository to capture data and processes to reproduce figures needed by E10.05 to maintain the ASTM standards under their jurisdiction. This workshop will provide an overview of the git-based repository site’s organization, operation, and outputs. It will then lead interested workshop participants through adding content, not yet already included in the repository to the site.
42 ENGINEERING↗
Expert systems and simulation models; Proceedings of the Seminar, Tucson, AZ, November 18, 19, 1985
The seminar presents papers on modeling and simulation methodology, artificial intelligence and expert systems, environments for simulation/expert system development, and methodology for simulation/expert system development. Particular attention is given to simulation modeling concepts and their representation, modular hierarchical model specification, knowledge representation, and rule-based diagnostic expert system development. Other topics include the combination of symbolic and discrete event simulation, real time inferencing, and the management of large knowledge-based simulation projects.
Source record↗
Reasoning about software specifications - A case study
The launch intercept condition (LIC) problem was analyzed to better understand the complexity of the automated reasoning process and the nature and role of background knowledge. An extension to resolution-based logic processing systems, called constraint logic programming, is described. Consideration is also given to a knowledge-driven incremental reasoning architecture which integrates a generic constraint logic programming system with a knowledge base of the domain.
Wild, Chris↗
Strategies for adding adaptive learning mechanisms to rule-based diagnostic expert systems
Rule-based diagnostic expert systems can be used to perform many of the diagnostic chores necessary in today's complex space systems. These expert systems typically take a set of symptoms as input and produce diagnostic advice as output. The primary objective of such expert systems is to provide accurate and comprehensive advice which can be used to help return the space system in question to nominal operation. The development and maintenance of diagnostic expert systems is time and labor intensive since the services of both knowledge engineer(s) and domain expert(s) are required. The use of adaptive learning mechanisms to increment evaluate and refine rules promises to reduce both time and labor costs associated with such systems. This paper describes the basic adaptive learning mechanisms of strengthening, weakening, generalization, discrimination, and discovery. Next basic strategies are discussed for adding these learning mechanisms to rule-based diagnostic expert systems. These strategies support the incremental evaluation and refinement of rules in the knowledge base by comparing the set of advice given by the expert system (A) with the correct diagnosis (C). Techniques are described for selecting those rules in the in the knowledge base which should participate in adaptive learning. The strategies presented may be used with a wide variety of learning algorithms. Further, these strategies are applicable to a large number of rule-based diagnostic expert systems. They may be used to provide either immediate or deferred updating of the knowledge base.
Stclair, D. C.↗
Nondeterministic data base for computerized visual perception
A description is given of the knowledge representation data base in the perception subsystem of the Mars robot vehicle prototype. Two types of information are stored. The first is generic information that represents general rules that are conformed to by structures in the expected environments. The second kind of information is a specific description of a structure, i.e., the properties and relations of objects in the specific case being analyzed. The generic knowledge is represented so that it can be applied to extract and infer the description of specific structures. The generic model of the rules is substantially a Bayesian representation of the statistics of the environment, which means it is geared to representation of nondeterministic rules relating properties of, and relations between, objects. The description of a specific structure is also nondeterministic in the sense that all properties and relations may take a range of values with an associated probability distribution.
Yakimovsky, Y.↗
Region-adaptive, Error-controlled Scientific Data Compression using Multilevel Decomposition
The increase of computer processing speed is significantly outpacing improvements in network and storage bandwidth, leading to the big data challenge in modern science, where scientific applications can quickly generate much more data than that can be transferred and stored. As a result, big scientific data must be reduced by a few orders of magnitude while the accuracy of the reduced data needs to be guaranteed for further scientific explorations. Moreover, scientists are often interested in some specific spatial/temporal regions in their data, where higher accuracy is required. The locations of the regions requiring high accuracy can sometimes be prescribed based on application knowledge, while other times they must be estimated based on general spatial/temporal variation. In this paper, we develop a novel multilevel approach which allows users to impose region-wise compression error bounds. Our method utilizes the byproduct of a multilevel compressor to detect regions where details are rich and we provide the theoretical underpinning for region-wise error control. With spatially varying precision preservation, our approach can achieve significantly higher compression ratios than single-error bounded compression approaches and control errors in the regions of interest.We conduct the evaluations on two climate use cases – one targeting small-scale, node features and the other focusing on long, areal features. For both use cases, the locations of the features were unknown ahead of the compression. By selecting approximately 16% of the data based on multi-scale spatial variations and compressing those regions with smaller error tolerances than the rest, our approach improves the accuracy of post-analysis by approximately 2 × compared to single-error-bounded compression at the same compression ratio. Using the same error bound for the region of interest, our approach can achieve an increase of more than 50% in overall compression ratio.
Gong, Qian↗
Comprehension and retrieval of failure cases in airborne observatories
This paper describes research dealing with the computational problem of analyzing and repairing failures of electronic and mechanical systems of telescopes in NASA's airborne observatories, such as KAO (Kuiper Airborne Observatory) and SOFIA (Stratospheric Observatory for Infrared Astronomy). The research has resulted in the development of an experimental system that acquires knowledge of failure analysis from input text, and answers questions regarding failure detection and correction. The system's design builds upon previous work on text comprehension and question answering, including: knowledge representation for conceptual analysis of failure descriptions, strategies for mapping natural language into conceptual representations, case-based reasoning strategies for memory organization and indexing, and strategies for memory search and retrieval. These techniques have been combined into a model that accounts for: (a) how to build a knowledge base of system failures and repair procedures from descriptions that appear in telescope-operators' logbooks and FMEA (failure modes and effects analysis) manuals; and (b) how to use that knowledge base to search and retrieve answers to questions about causes and effects of failures, as well as diagnosis and repair procedures. This model has been implemented in FANSYS (Failure ANalysis SYStem), a prototype text comprehension and question answering program for failure analysis.
Alvarado, Sergio J.↗
System Diagnostic Builder - A rule generation tool for expert systems that do intelligent data evaluation
Consideration is given to the System Diagnostic Builder (SDB), an automated knowledge acquisition tool using state-of-the-art AI technologies. The SDB employs an inductive machine learning technique to generate rules from data sets that are classified by a subject matter expert. Thus, data are captured from the subject system, classified, and used to drive the rule generation process. These rule bases are used to represent the observable behavior of the subject system, and to represent knowledge about this system. The knowledge bases captured from the Shuttle Mission Simulator can be used as black box simulations by the Intelligent Computer Aided Training devices. The SDB can also be used to construct knowledge bases for the process control industry, such as chemical production or oil and gas production.
Nieten, Joseph↗
Fault Tree Based Diagnosis with Optimal Test Sequencing for Field Service Engineers
When field service engineers go to customer sites to service equipment, they want to diagnose and repair failures quickly and cost effectively. Symptoms exhibited by failed equipment frequently suggest several possible causes which require different approaches to diagnosis. This can lead the engineer to follow several fruitless paths in the diagnostic process before they find the actual failure. To assist in this situation, we have developed the Fault Tree Diagnosis and Optimal Test Sequence (FTDOTS) software system that performs automated diagnosis and ranks diagnostic hypotheses based on failure probability and the time or cost required to isolate and repair each failure. FTDOTS first finds a set of possible failures that explain exhibited symptoms by using a fault tree reliability model as a diagnostic knowledge to rank the hypothesized failures based on how likely they are and how long it would take or how much it would cost to isolate and repair them. This ordering suggests an optimal sequence for the field service engineer to investigate the hypothesized failures in order to minimize the time or cost required to accomplish the repair task. Previously, field service personnel would arrive at the customer site and choose which components to investigate based on past experience and service manuals. Using FTDOTS running on a portable computer, they can now enter a set of symptoms and get a list of possible failures ordered in an optimal test sequence to help them in their decisions. If facilities are available, the field engineer can connect the portable computer to the malfunctioning device for automated data gathering. FTDOTS is currently being applied to field service of medical test equipment. The techniques are flexible enough to use for many different types of devices. If a fault tree model of the equipment and information about component failure probabilities and isolation times or costs are available, a diagnostic knowledge base for that device can be developed easily.
Iverson, David L.↗
Onorbit IMU alignment error budget
The Star Tracker, Crew Optical Alignment Sight (COAS), and Inertial Measurement Unit (IMU) from a complex navigation system with a multitude of error sources were combined. A complete list of the system errors is presented. The errors were combined in a rational way to yield an estimate of the IMU alignment accuracy for STS-1. The expected standard deviation in the IMU alignment error for STS-1 type alignments was determined to be 72 arc seconds per axis for star tracker alignments and 188 arc seconds per axis for COAS alignments. These estimates are based on current knowledge of the star tracker, COAS, IMU, and navigation base error specifications, and were partially verified by preliminary Monte Carlo analysis.
Corson, R. W.↗
Solar System Exploration, 1995-2000
Goals for planetary exploration during the next decade include: (1) determine how our solar system formed, and understand whether planetary systems are a common phenomenon through out the cosmos; (2) explore the diverse changes that planets have undergone throughout their history and that take place at present, including those that distinguish Earth as a planet; (3) understand how life might have formed on Earth, whether life began anywhere else in the solar system, and whether life (including intelligent beings) might be a common cosmic phenomenon; (4) discover and investigate natural phenomena that occur under conditions not realizable in laboratories; (5) discover and inventory resources in the solar system that could be used by human civilizations in the future; and (6) make the solar system a part of the human experience in the same way that Earth is, and hence lay the groundwork for human expansion into the solar system in the coming century. The plan for solar system exploration is motivated by these goals as well as the following principle: The solar system exploration program will conduct flight programs and supporting data analysis and scientific research commensurate with United States leadership in space exploration. These programs and research must be of the highest scientific merit, they must be responsive to public excitement regarding planetary exploration, and they must contribute to larger national goals in technology and education. The result will be new information, which is accessible to the public, creates new knowledge, and stimulates programs of education to increase the base of scientific knowledge in the general public.
Squyres, S.↗
Pointing Knowledge for SPARCLE and Space-Based Doppler Wind Lidars in General
The SPAce Readiness Coherent Lidar Experiment (SPARCLE) will fly on a space shuttle to demonstrate the use of a coherent Doppler wind lidar to accurately measure global tropospheric winds. To achieve the LOS (Line of Sight) accuracy goal of approx. m/s, the lidar system must be able to account for the orbiter's velocity (approx. 7750 m/s) and the rotational component of the earth's surface motion (approx. 450 m/s). For SPARCLE this requires knowledge of the attitude (roll, pitch and yaw) of the laser beam axis within an accuracy of 80 microradians. (approx. 15 arcsec). Since SPARCLE can not use a dedicated star tracker from its earth-viewing orbiter bay location, a dedicated GPS/INS (Global Positioning System/Inertial Navigation System) will be attached to the lidar instrument rack. Since even the GPS/INS has unacceptable drifts in attitude information, the SPARCLE team has developed a way to periodically scan the instrument itself to obtain less than 10 microradian (2 arcsec) attitude knowledge accuracy that can then be used to correct the GPS/INS output on a 30 minute basis.
Emmitt, G. D.↗
A Double-Edged Sword: The Role of Prior Knowledge in Memory Aging
People accumulate knowledge throughout their lifespan and the accumulated knowledge influences how we encode and retrieve information in memory processing. This study aims to investigate the role of knowledge in associative memory across the adult lifespan, and specifically examines the effects of two material properties that interact with prior knowledge: congruency – whether the material is congruent with people’s prior knowledge, and ambiguity – whether the material is ambiguous to interpret based on prior knowledge. 273 participants (aged 22–70 years old) completed an incidental memory task online. Participants were shown pictures depicting an object in a scene and judged if the object was likely or unlikely to be in the particular scene. Later, in the recognition test, participants were asked to identify if the exact picture was presented earlier. The pictures were manipulated to have varying levels of congruency, meaning that some depicted likely object–scene pairs and some unlikely. We also measured how different the likely/unlikely judgment for each object–scene pair was across all participants to determine the ambiguity level of the object–scene pair: some were more likely to receive diverse responses across people, whereas others are unambiguously consistent (or inconsistent) with common knowledge shared by most people. We used mixed-effects logistic regressions to predict memory outcome for each trial as a function of age, age 2 , congruency/ambiguity, and their interactions. The object–scene pairs perceived as congruent had higher hit rates than incongruent ones, as well as higher false alarm rates, especially in middle-aged and older people. Higher ambiguity was also related to both greater true and false memory, independent of age. Finally, the effect of ambiguity only emerged when the object–scene pair was perceived incongruent. The results suggest that people rely on prior knowledge to process new information and that this reliance improves hit responses, but also induces false memories particularly for middle-aged and older people, suggesting a double-edged role of knowledge in associative memory and its disproportionate influence on memory aging. Over-reliance on knowledge in older adults, which has been suspected in other cognitive processes, may be one of the mechanisms underlying associative memory decrease in aging.
60 APPLIED LIFE SCIENCES↗
Plant diversity to support humans in a CELSS ground based demonstrator
A controlled ecological life support system (CELSS) for human habitation in preparation for future long duration space flights is considered. The success of such a system depends upon the feasibility of revitalization of food resources and the human nutritional needs which are to be met by these food resources. Edible higher plants are prime candidates for the photoautotrophic components of this system if nutritionally adequate diets can be derived from these plant sources to support humans. Human nutritional requirements information based on current knowledge are developed for inhabitants envisioned in the CELSS ground based demonstrator. Groups of plant products that can provide the nutrients are identified.
Howe, J. M.↗
Ground-Based Gas-Liquid Flow Research in Microgravity Conditions: State of Knowledge
During the last decade, ground-based microgravity facilities have been utilized in order to obtain predictions for spacecraft system designers and further the fundamental understanding of two-phase flow. Although flow regime, pressure drop and heat transfer coefficient data has been obtained for straight tubes and a limited number of fittings, measurements of the void fraction, film thickness, wall shear stress, local velocity and void information are also required in order to develop general mechanistic models that can be utilized to ascertain the effects of fluid properties, tube geometry and acceleration levels. A review of this research is presented and includes both empirical data and mechanistic models of the flow behavior.
McQuillen, J.↗
Sensor Selection and Data Validation for Reliable Integrated System Health Management
For new access to space systems with challenging mission requirements, effective implementation of integrated system health management (ISHM) must be available early in the program to support the design of systems that are safe, reliable, highly autonomous. Early ISHM availability is also needed to promote design for affordable operations; increased knowledge of functional health provided by ISHM supports construction of more efficient operations infrastructure. Lack of early ISHM inclusion in the system design process could result in retrofitting health management systems to augment and expand operational and safety requirements; thereby increasing program cost and risk due to increased instrumentation and computational complexity. Having the right sensors generating the required data to perform condition assessment, such as fault detection and isolation, with a high degree of confidence is critical to reliable operation of ISHM. Also, the data being generated by the sensors needs to be qualified to ensure that the assessments made by the ISHM is not based on faulty data. NASA Glenn Research Center has been developing technologies for sensor selection and data validation as part of the FDDR (Fault Detection, Diagnosis, and Response) element of the Upper Stage project of the Ares 1 launch vehicle development. This presentation will provide an overview of the GRC approach to sensor selection and data quality validation and will present recent results from applications that are representative of the complexity of propulsion systems for access to space vehicles. A brief overview of the sensor selection and data quality validation approaches is provided below. The NASA GRC developed Systematic Sensor Selection Strategy (S4) is a model-based procedure for systematically and quantitatively selecting an optimal sensor suite to provide overall health assessment of a host system. S4 can be logically partitioned into three major subdivisions: the knowledge base, the down-select iteration, and the final selection analysis. The knowledge base required for productive use of S4 consists of system design information and heritage experience together with a focus on components with health implications. The sensor suite down-selection is an iterative process for identifying a group of sensors that provide good fault detection and isolation for targeted fault scenarios. In the final selection analysis, a statistical evaluation algorithm provides the final robustness test for each down-selected sensor suite. NASA GRC has developed an approach to sensor data qualification that applies empirical relationships, threshold detection techniques, and Bayesian belief theory to a network of sensors related by physics (i.e., analytical redundancy) in order to identify the failure of a given sensor within the network. This data quality validation approach extends the state-of-the-art, from red-lines and reasonableness checks that flag a sensor after it fails, to include analytical redundancy-based methods that can identify a sensor in the process of failing. The focus of this effort is on understanding the proper application of analytical redundancy-based data qualification methods for onboard use in monitoring Upper Stage sensors.
Garg, Sanjay↗