Results for “knowledge systems” — Page 77

NASA NTRS2019

A Pulsar-Based Time-Scale from the International Pulsar Timing Array

We have constructed a new time-scale, TT(IPTA16), based on observations of radio pulsars presented in the first data release from the International Pulsar Timing Array (IPTA). We used two analysis techniques with independent estimates of the noise models for the pulsar observations and different algorithms for obtaining the pulsar time-scale. The two analyses agree within the estimated uncertainties and both agree with TT(BIPM17), a post-corrected time-scale produced by the Bureau International des Poids et Mesures (BIPM). We show that both methods could detect significant errors in TT(BIPM17) if they were present. We estimate the stability of the atomic clocks from which TT(BIPM17) is derived using observations of four rubidium fountain clocks at the US Naval Observatory. Comparing the power spectrum of TT(IPTA16) with that of these fountain clocks suggests that pulsar-based time-scales are unlikely to contribute to the stability of the best time-scales over the next decade, but they will remain a valuable independent check on atomic time-scales. We also find that the stability of the pulsar-based time-scale is likely to be limited by our knowledge of solar-system dynamics, and that errors in TT(BIPM17) will not be a limiting factor for the primary goal of the IPTA, which is to search for the signatures of nano-Hertz gravitational waves.

G. Hobbs↗

NASA NTRSDate not supplied

Selection Factors for Space Crops

NASA is actively researching space crop production to determine its potential to contribute to food system security on long duration missions beyond Low Earth Orbit. Our near-term focus is on nutrient and variety supplementation of prepackaged food with fresh produce that requires little or no processing. The longer-term goal is caloric replacement to become less dependent on Earth, and this will require cultivation of staple crops, processing and cooking equipment, integration with spacecraft air, water, and power systems, and automation. There are numerous technology and knowledge gaps remaining for sustainable space crop production systems, but one high-impact area is in the development of crops specifically customized to meet the needs of controlled environment crop production, astronaut health and well-being, and space-unique environments. Modern crop breeding and genome engineering tools are allowing for rapid development of new genotypes with incredible specificity. Targeted aspects to optimize crops for space have been identified and characterized into five categories: plant growth and development, plant physiology, produce nutrition, produce organoleptic acceptability, and postharvest characteristics. Within each category there are several targets that further the development of crop production systems for spaceflight, such as crop size and harvest index, tolerance to specific environmental stresses, optimizing target nutrients that are low or degrade in the packaged diet, maintenance time requirements, and less indigestible structural material. NASA-funded PIs are already beginning to develop candidate crops, and spaceflight testing and validation of novel space crops is on the horizon. Crops developed for space also have the potential to benefit terrestrial controlled environment agriculture crop production systems. This research was supported by NASA’s Space Biology and Human Research Programs.

Space Crop Production↗

NASA NTRSDate not supplied

GL4U: Training the next generation of bioinformaticians, one omics datatype at a time

Spaceflight modifies gene expression in every organism examined to date, including humans. Understanding how these gene expression changes affect physiology is crucial for the development of countermeasures to enable long-duration manned missions. NASA’s GeneLab project provides researchers open access to multi-omics data, including genetic and gene expression data, from spaceflight experiments that can be mined to understand the effects of spaceflight on biological systems. To ensure new knowledge generation through data re-use, it is important to maximize the number of scientists who utilize GeneLab data. Training students on the GeneLab platform is the best way to create long-term adopters of this NASA database and its tools. Turning students into future instructors and advocates will also accelerate the dissemination of these data and tools to the broader scientific community. Therefore, in collaboration with the GeneLab Educational Working Group (EWG), GeneLab has created GeneLab for Colleges and Universities (GL4U). GL4U provides space biology-relevant training in bioinformatics to the next generation of scientists through direct and indirect approaches. The GeneLab team plans to host two annual data processing bootcamps, one for college-level students (direct) and one for college educators (indirect – training of trainers), in which participants learn to analyze GeneLab’s space-relevant omics data. During the bootcamp, educators will receive materials and training to enable them to run the bootcamp at their home institutions or alternatively to adapt the content to implement within existing courses, thereby extending the reach of this initiative. The GL4U direct training pilot program was conducted in June 2021 in collaboration with USRA and San Jose State University (SJSU). During the pilot, SJSU students participated in a week-long bootcamp consisting of space biology-specific lectures and hands-on instruction using Jupyter Notebooks to analyze RNA sequence data. This pilot demonstrates the capacity of GL4U for training young scientists and encouraging data re-use.

Jonathan Matthew Galazka↗

NASA NTRSDate not supplied

Selection Factors for Space Crops

NASA is actively researching space crop production to determine its potential to contribute to food system security on long duration missions beyond Low Earth Orbit. Our near-term focus is on nutrient and variety supplementation of prepackaged food with fresh produce that requires little or no processing. The longer-term goal is caloric replacement to become less dependent on Earth, and this will require cultivation of staple crops, processing and cooking equipment, integration with spacecraft air, water, and power systems, and automation. There are numerous technology and knowledge gaps remaining for sustainable space crop production systems, but one high-impact area is in the development of crops specifically customized to meet the needs of controlled environment crop production, astronaut health and well-being, and space-unique environments. Modern crop breeding and genome engineering tools are allowing for rapid development of new genotypes with incredible specificity. Targeted aspects to optimize crops for space have been identified and characterized into five categories: plant growth and development, plant physiology, produce nutrition, produce organoleptic acceptability, and postharvest characteristics. Within each category there are several targets that further the development of crop production systems for spaceflight, such as crop size and harvest index, tolerance to specific environmental stresses, optimizing target nutrients that are low or degrade in the packaged diet, maintenance time requirements, and less indigestible structural material. NASA-funded PIs are already beginning to develop candidate crops, and spaceflight testing and validation of novel space crops is on the horizon. Crops developed for space also have the potential to benefit terrestrial controlled environment agriculture crop production systems. This research was supported by NASA’s Space Biology and Human Research Programs.

Space Crop Production↗

NASA NTRSDate not supplied

National Campaign Partner Demonstration Team Annual Review April 2023

- NASA developed the Advanced Air Mobility Project (AAM) and the National Campaign (NC) series to identify and address challenges ahead for advanced air mobility concepts. - NC seeks to challenge industry as follows; - Execute progressively more difficult ecosystem-wide system-level safety and integration scenarios - Demonstrate practical and scalable system concepts - Build a knowledge base for development of requirements and standards - There are currently three focus areas within the NASA AAM NC Portfolio - Vehicle Development and Operations: test and inform capabilities that are critical enablers for AAM such as electric aircraft propulsion and increasing levels of automation - Airspace Design and Operations: develop and validate an operational concept to integrate and manage AAM traffic safely and efficiently - Community Integration: understand and address critical barriers to community integration, such as public acceptance (noise, security, privacy, etc.), supporting infrastructure, and local regulation

Eric N Becker↗

NASA NTRSDate not supplied

High-Temperature Mechanical Tensile Testing of Unidirectional Sic/Sic Composites Using A Versatile Lamp Furnace

Silicon carbide (SiC) based ceramic matrix composites (CMCs) are of interest for high-temperature structural applications, including use in turbine engine components. To understand damage mechanisms and predict durability of these systems, in-depth knowledge of their behavior at ambient and high temperatures is necessary. A method for high temperature testing of SiC/SiC minicomposite specimens up to 1500oC in both tensile fast fracture and creep conditions to investigate their thermomechanical behavior will be presented. This method utilizes a novel radiation heat furnace, equipped with infrared lamps with ellipsoidal reflectors, to heat the specimen at the furnace center in user-defined atmospheres. The housing structure of the lamps allows for observation of the specimen during testing using optical methods and for conducting other experimental studies. The goal is to employ nondestructive health monitoring techniques in-situ, including acoustic emission (AE), electrical resistance (ER), and digital image correlation (DIC) to evaluate damage initiation and evolution in the minicomposite specimens during tensile testing. Results from mechanical testing and nondestructive techniques will be presented. Electron microscopy and X-ray micro-computed tomography were used to further characterize specimen damage after testing.

Aeronautics↗

NASA NTRSDate not supplied

Medical Resource Set Bulky Item Trade Space Analysis for Spaceflight Medical Risk

The NASA engineering community utilizes event-driven and fault-tree probabilistic techniques to classify risks in the space environment by taking advantage of the inherent knowledge of complex spaceflight system design and testing to quantify failure risk. In harmonizing the risk of human space flight, answering the question of ‘How do we balance health, performance and resource risks with other engineering risks on long duration space missions?’ remains a deeply challenging and largely qualitative practice. The Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) is one aspect of the efforts by NASA’s Human Research Program (HRP) to quantitatively assess the impact of health and performance risk. One of MEDPRAT’s key features is its high degree of computational efficiency. Coupled with the HRP High Performance Compute cluster located at NASA’s Glenn Research Center, MEDPRAT runs millions of simulated missions in a matter of minutes. This degree of computational efficiency provides the novel opportunity to explore the relationship between medical set mass, volume, and medical resource size. Of particular interest for future human spaceflight missions are ‘bulky’ items, medical resources like devices, which occupy a large portion of the small, allocated mass and volume for the medical set leaving less room for other resources. This talk will present results showing the quantitative impact of forced inclusion of several bulky items across a variety of medical kit constraints, and the effect that a potential research investment into reducing the bulky item mass and volume may have on risk.

Lauren Mcintyre↗

DOE OSTI2020

A Scalable Parallel Hypergraph Generator (HyGen)

Graphs are extensively used to model real-world complex systems. An edge in a graph can model pairwise relationships. However, multiway relationships (connections between three or more vertices) are common in many complex systems such as cellular process, image segmentation, and circuit design. A graph edge cannot model multiway relationships. A hypergraph, which can connect more than two vertices, is thus a better option to model multiway relationships. A large-scale hypergraph analysis has the potential to find useful insights from a complex system and assist in knowledge discovery. Currently a limited number of hypergraphs exists that are representative of real-world datasets. Moreover, real-world hypergraph datasets are small in size and inadequate to incorporate future needs. A graph generator that can produce large-scale synthetic hypergraphs can solve the above mentioned problems. In this paper, we present a scalable parallel hypergraph generator (HyGen) based on the Message Passing Interface (MPI) standard. To generate hypergraphs, HyGen takes the following parameter values as inputs: i) number of vertices, ii) number of hyperedges, iii) number of clusters, iv) vertex distribution, v) hyperedge distribution, vi) local cluster cardinality, and vii) global cluster cardinality. We have demonstrated that HyGen can generate hypergraphs of various sizes in a scalable fashion. HyGen takes approximately four minutes to generate a hypergraph with 4.8 million vertices, 1.6 million hyperedges, and 800 clusters using 1,024 processes on a leadership class computing platform. Our strong and weak scaling experiments on supercomputers demonstrate that HyGen can quickly create large-scale hypergraphs in a parallel manner, thus providing a useful capability for hypergraph analysis.

Hasan, S M Shamimul↗

DOE OSTI2020

Noise-Based Selection of Robust Inherited Model for Accurate Continual Learning

There is a growing demand for an intelligent system to continually learn knowledge from a data stream. Continual learning requires both the preservation of previous knowledge (i.e., avoiding catastrophic forgetting) and the acquisition of new knowledge. Different from previous works that focus only on model adaptation (e.g., regularization, network expansion, memory rehearsal, etc.), we propose a novel training scheme named acquisitive learning (AL), which emphasizes both the knowledge inheritance and knowledge acquisition. AL starts from an elaborately selected model with pre-trained knowledge (the inherited model) and then adapts it to new data using segmented training. The selection is achieved by injecting random noise to various inherited models for better model robustness, which promises higher accuracy in further knowledge acquisition. The approach is validated by the visualization of the loss landscape and quantitative roughness measurement. The combination of the selective inherited model and knowledge acquisition reduces catastrophic forgetting by 10X on the CIFAR-100 dataset.

Du, Xiaocong↗

DOE OSTI2021

A Machine Learning-Based Method to Estimate Transformer Primary-Side Voltages with Limited Customer-Side AMI Measurements

Distribution control applications such as volt/var optimization, network reconfiguration, and distribution automation require accurate knowledge of the distribution system state. The lack of sufficient sensors on the primary side of distribution networks often limits the accuracy of the control decisions by these applications. The deployment of advanced metering infrastructure (AMI) provides utilities an opportunity to translate the AMI data on the secondary onto the primary so that it can be used as pseudo-measurements to augment the limited existing measurements on the primary. This paper develops an approach for estimating service transformer primary-side voltages by using limited secondary-side AMI measurements. The estimated primary-side voltages can be used by utilities as pseudo-measurements for distribution control applications. The detailed secondary model topology, which is an essential input data for many existing algorithms, is not required for the proposed method. The performance of the proposed method is validated by using AMI measurements from the field and an actual distribution feeder model of San Diego Gas & Electric Company.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

A Machine Learning-Based Method to Estimate Transformer Primary-Side Voltages with Limited Customer-Side AMI Measurements: Preprint

Distribution control applications such as volt/var optimization, network reconfiguration, and distribution automation require accurate knowledge of the distribution system state. The lack of sufficient sensors on the primary side of distribution networks often limits the accuracy of the control decisions by these applications. The deployment of advanced metering infrastructure (AMI) provides utilities an opportunity to translate the AMI data on the secondary onto the primary so that it can be used as pseudo-measurements to augment the limited existing measurements on the primary. This paper develops an approach for estimating service transformer primary-side voltages by using limited secondary-side AMI measurements. The estimated primary-side voltages can be used by utilities as pseudo-measurements for distribution control applications. The detailed secondary model topology, which is an essential input data for many existing algorithms, is not required for the proposed method. The performance of the proposed method is validated by using AMI measurements from the field and an actual distribution feeder model of San Diego Gas & Electric Company.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Feasibility Study of Millimeter Wave Radars For Safeguards Applications

Containment and surveillance are fundamental measures in nuclear safeguards. Techniques such as video surveillance and laser curtain for containment provide effective monitoring in areas where maintaining continuity of knowledge is required. These systems, however, can be susceptible to loss of monitoring capabilities under certain environmental conditions such as poor visibility (i.e. low light conditions, smoke, fog, etc.) or extended power loss past the duration that the backup power system is designed for. Brookhaven National Laboratory has been investigating the feasibility of millimeter waves (mmWave) as a new perimeter seal in which radio frequency waves in the range of 60-64 GHz are used to detect and monitor objects of interest. Signals in this frequency range are not susceptible to environmental conditions. For proof-of-concept tests, mmWave sensors from Texas Instruments (TI), specificallyIWR6843, are used in a test bed at BNL's Waste Management facility to simulate the operations at nuclear facilities. The unique design of TI mmWave sensors requires less memory and power consumption compared to counterpart systems. These devices are capable of exporting 3D point-cloud data, which is visualized graphically and compared to videos recorded at the same time to validate the performance of the mmWave sensor. A set of experiments were planned to test the feasibility of the mmWave in this application, including monitoring static containers in a storage area and detecting intrusions at the boundaries of the area. In addition, the experiments also identify potential blind spots relative to sensor position and utilize multiple operating sensors simultaneously to reduce or eliminate such blind spots. The optimal positioning of multiple sensors was determined for the experimental room configuration. In this paper, we will discuss the details of this novel perimeter sealing concept and present the test results.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2023

Path Forward: Materials Data Modernization for ASME Codes and Standards in the Artificial Intelligence Era

Development of the ASME Materials Properties Database was initiated in the early 2010s to support the ASME Codes and Standards. As information technologies advance at an accelerated pace with the artificial intelligence era on the horizon, the ASME Materials Properties Database must be further modernized from a database to a knowledgebase to ride the wave of digital information revolution and effectively support the ASME Codes and Standards in the new era.This paper is intended to provide an overview of the ASME Materials Properties Database and discuss a roadmap for its future development to facilitate understanding of and participation from different sectors of the Codes and Standards community. It first reviews the basic concepts of data, information, knowledge, database, and database system; as well as the pros and cons in different types of data management, and then discusses the path forward for a desired evolution of the database into a self-explanatory and machine-readable knowledgebase that is consistent with human cognitive processes for the Codes and Standards development and furthermore provides resources for data processing and analysis to reach an eventual goal of streamlining the Codes and Standards development from the initial inquiry, throughout data submission, analysis, …, to Codes and Standards rule establishment for final publication.

Ren, Weiju↗

NASA NTRS1990

Execution monitoring for a mobile robot system

Due to sensor errors, uncertainty, incomplete knowledge, and a dynamic world, robot plans will not always be executed exactly as planned. This paper describes an implemented robot planning system that enhances the traditional sense-think-act cycle in ways that allow the robot system monitor its behavior and react in emergencies in real-time. A proposal on how robot systems can completely break away from the traditional three-step cycle is also made.

Miller, David P.↗

DOE OSTI2023

Effects of small marine energy deployments on oceanographic systems

The placement and operation of marine energy deployments in the ocean have the potential to change flow patterns, decrease wave heights, and/or remove energy from the oceanographic system. Changes in oceanographic systems resulting from harvesting marine energy, particularly tidal and wave energy, may be of concern. These changes include alterations in nearfield and farfield physical processes, as well as potential secondary environmental effects such as changes in sediment transport patterns, biological processes, or coastal erosion. Knowledge of changes in oceanographic systems associated with marine energy is primarily available from numerical modeling studies, informed by some laboratory tests and very few field measurements. A literature review was conducted using the Tethys knowledge base and other online sources, building on conclusions from the Ocean Energy Systems-Environmental State of the Science report. Potential changes in oceanographic systems that may be caused by marine energy differ between tidal and wave devices because of different extraction mechanisms and siting locations. Numerical models show that tidal extraction on the order of hundreds of megawatts or with significant channel blockage is required to create changes in oceanographic processes that exceed natural variability. Effects from wave energy extraction in arrays are localized and dependent on array spacing and proximity to the shore. Here the available evidence supports the conclusion that the risk of significant environmental effects from such changes could be retired (i.e., less investigation required for every project) for small deployments—those representative of the state of the industry in 2021. Determining changes in oceanographic systems to be low risk for small deployments can thereby streamline environmental consenting by reducing monitoring needs at this early stage in the industry.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1988

Advanced feature extraction in remote sensing using artificial intelligence and geographic information systems

Traditional computer-assisted image-analysis techniques in remote sensing lag well behind human abilities in terms of both speed and accuracy. A fundamental limitation of computer-assisted techniques is their inability to assimilate a variety of different data types leading to an interpretation in a manner similar to human image interpretation. Expert systems and computer-vision techniques are proposed as a potential solution to these limitations. Some aspects of human expertise in image analysis may be codified into expert systems. Image understanding and symbolic reasoning provide a means of assimilating spatial information and spatial reasoning into the analysis procedure. Knowledge-based image-analysis systems incorporate many of these concepts and have been implemented for some well defined problem domains. Geographic information systems represent an excellent environment for this type of analysis, providing both analytic tools and contextual information to the analysis procedure.

Estes, John E.↗

NASA NTRS1991

Design and analysis tools for concurrent blackboard systems

A set of blackboard system design and analysis tools that consists of a knowledge source organizer, a knowledge source input/output connectivity analyzer, and a validated blackboard system simulation model is discussed. The author presents the structure and functionality of the knowledge source input/output connectivity analyzer. An example outlining the use of the analyzer to aid in the design of a concurrent tactical decision generator for air-to-air combat is presented. The blackboard system design and analysis tools were designed for generic blackboard systems and are application independent.

Mcmanus, John W.↗

NASA NTRS1988

Knowledge representation issues for explaining plans

Explanations are recognized as an important facet of intelligent behavior. Unfortunately, expert systems are currently limited in their ability to provide useful, intelligent justifications of their results. We are currently investigating the issues involved in providing explanation facilities for expert planning systems. This investigation addresses three issues: knowledge content, knowledge representation, and explanation structure.

Prince, Mary Ellen↗