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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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At least 289 records · Page 16

All sources NASA NTRS DOE OSTI

NASA NTRS1975

Remote sensing of water quality in reservoirs and lakes in semi-arid climates

Overlake measurements using aerial cameras (remote sensing) combined with water truth collected from boats most economically provided wide-band photographs rather than precise spectra. With use of false color infrared film (400-950 nm), the reflected spectral signatures seen from hundreds to thousands of meters above the lake merged to produce various color tones. Such colors were easily and inexpensively obtained and could be recognized by lake management personnel without any prior training. The characteristic spectral signatures of various algal types were also recognizable in part by the color tone produced by remote sensing.

Anderson, H. M.↗

NASA NTRS2010

Design and Testing of a Prototype Lunar or Planetary Surface Landing Research Vehicle (LPSLRV)

This handbook describes a two-semester senior design course sponsored by the NASA Office of Education, the Exploration Systems Mission Directorate (ESMD), and the NASA Space Grant Consortium. The course was developed and implemented by the Mechanical and Aerospace Engineering Department (MAE) at Utah State University. The course final outcome is a packaged senior design course that can be readily incorporated into the instructional curriculum at universities across the country. The course materials adhere to the standards of the Accreditation Board for Engineering and Technology (ABET), and is constructed to be relevant to key research areas identified by ESMD. The design project challenged students to apply systems engineering concepts to define research and training requirements for a terrestrial-based lunar landing simulator. This project developed a flying prototype for a Lunar or Planetary Surface Landing Research Vehicle (LPSRV). Per NASA specifications the concept accounts for reduced lunar gravity, and allows the terminal stage of lunar descent to be flown either by remote pilot or autonomously. This free-flying platform was designed to be sufficiently-flexible to allow both sensor evaluation and pilot training. This handbook outlines the course materials, describes the systems engineering processes developed to facilitate design fabrication, integration, and testing. This handbook presents sufficient details of the final design configuration to allow an independent group to reproduce the design. The design evolution and details regarding the verification testing used to characterize the system are presented in a separate project final design report. Details of the experimental apparatus used for system characterization may be found in Appendix F, G, and I of that report. A brief summary of the ground testing and systems verification is also included in Appendix A of this report. Details of the flight tests will be documented in a separate flight test report. This flight test report serves as a complement to the course handbook presented here. This project was extremely ambitious, and achieving all of the design and test objectives was a daunting task. The schedule ran slightly longer than a single academic year with the complete design closure not occurring until early April. Integration and verification testing spilled over into late May and the first flight did not occur until mid to late June. The academic year at Utah State University ended on May 8, 2010. Following the end of the academic year, testing and integration was performed by the faculty advisor, paid research assistants, and volunteer student help

Murphy, Gloria A.↗

DOE OSTI2021

Few-shot Learning for Post-disaster Structure Damage Assessment

Automating post-disaster damage assessment with remote sensing data is critical for faster surveys of structures impacted by natural disasters. One significant obstacle to training state-of-the-art deep neural networks to support this automation is that large quantities of labelled data are often required. However, obtaining those labels is particularly unrealistic to support post-disaster damage assessment in a timely manner. Few-shot learning methods could help to mitigate this by reducing the amount of labelled data required to successfully train a model while achieving satisfactory results. To this end, we explore a feature reweighting method to the YOLOv3 object detection architecture to achieve few-shot learning of damage assessment models on the xBD dataset. Our results show that the feature reweighting approach yield improved mAP over the baseline with significantly fewer labelled samples. In addition, we use t-SNE to analyze the class-specific reweighting vectors generated by the reweighting module in order to evaluate their inter-class and intra-class similarity. We find that the vectors form clusters based on class, and that these clusters overlap with visually similar classes. Those results show the potential to employ this few-shot learning strategy for rapid damage assessment with post-event remote sensing images.

Bowman, Jordan↗

NASA NTRS2003

A Fast Implementation of the ISOCLUS Algorithm

Unsupervised clustering is a fundamental tool in numerous image processing and remote sensing applications. For example, unsupervised clustering is often used to obtain vegetation maps of an area of interest. This approach is useful when reliable training data are either scarce or expensive, and when relatively little a priori information about the data is available. Unsupervised clustering methods play a significant role in the pursuit of unsupervised classification. One of the most popular and widely used clustering schemes for remote sensing applications is the ISOCLUS algorithm, which is based on the ISODATA method. The algorithm is given a set of n data points (or samples) in d-dimensional space, an integer k indicating the initial number of clusters, and a number of additional parameters. The general goal is to compute a set of cluster centers in d-space. Although there is no specific optimization criterion, the algorithm is similar in spirit to the well known k-means clustering method in which the objective is to minimize the average squared distance of each point to its nearest center, called the average distortion. One significant feature of ISOCLUS over k-means is that clusters may be merged or split, and so the final number of clusters may be different from the number k supplied as part of the input. This algorithm will be described in later in this paper. The ISOCLUS algorithm can run very slowly, particularly on large data sets. Given its wide use in remote sensing, its efficient computation is an important goal. We have developed a fast implementation of the ISOCLUS algorithm. Our improvement is based on a recent acceleration to the k-means algorithm, the filtering algorithm, by Kanungo et al.. They showed that, by storing the data in a kd-tree, it was possible to significantly reduce the running time of k-means. We have adapted this method for the ISOCLUS algorithm. For technical reasons, which are explained later, it is necessary to make a minor modification to the ISOCLUS specification. We provide empirical evidence, on both synthetic and Landsat image data sets, that our algorithm's performance is essentially the same as that of ISOCLUS, but with significantly lower running times. We show that our algorithm runs from 3 to 30 times faster than a straightforward implementation of ISOCLUS. Our adaptation of the filtering algorithm involves the efficient computation of a number of cluster statistics that are needed for ISOCLUS, but not for k-means.

Memarsadeghi, Nargess↗

NASA NTRS1993

A review and analysis of neural networks for classification of remotely sensed multispectral imagery

A literature survey and analysis of the use of neural networks for the classification of remotely sensed multispectral imagery is presented. As part of a brief mathematical review, the backpropagation algorithm, which is the most common method of training multi-layer networks, is discussed with an emphasis on its application to pattern recognition. The analysis is divided into five aspects of neural network classification: (1) input data preprocessing, structure, and encoding; (2) output encoding and extraction of classes; (3) network architecture, (4) training algorithms; and (5) comparisons to conventional classifiers. The advantages of the neural network method over traditional classifiers are its non-parametric nature, arbitrary decision boundary capabilities, easy adaptation to different types of data and input structures, fuzzy output values that can enhance classification, and good generalization for use with multiple images. The disadvantages of the method are slow training time, inconsistent results due to random initial weights, and the requirement of obscure initialization values (e.g., learning rate and hidden layer size). Possible techniques for ameliorating these problems are discussed. It is concluded that, although the neural network method has several unique capabilities, it will become a useful tool in remote sensing only if it is made faster, more predictable, and easier to use.

Paola, Justin D.↗

DOE OSTI2025

Thermodynamic Cloud Phase Classifications Using Machine Learning at NSA and ANX

Vertically resolved thermodynamic cloud phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) THERMOCLDPHASE Value-Added Product (VAP) uses a multi-sensor approach to classify thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave radiometer-derived liquid water path, and radiosonde temperature measurements. The measured voxels are classified as ice, snow, mixed-phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multilayer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with one year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1-score, and mean Intersection over Union (IOU). Analysis of ML confidence scores shows ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential datastreams for ML thermodynamic cloud phase predictions. The ML models’ generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. Finally, we evaluate the ML models’ response to simulated instrument outages and signal degradation.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1983

Application of the Hardman methodology to the Army Remotely Piloted Vehicle (RPV)

The application of the HARDMAN Methodology to the Remotely Piloted Vehicle (RPV) is described. The methodology was used to analyze the manpower, personnel, and training (MPT) requirements of the proposed RPV system design for a number of operating scenarios. The RPV system is defined as consisting of the equipment, personnel, and operational procedures needed to perform five basic artillery missions: reconnaissance, target acquisition, artillery adjustment, target designation and damage assessment. The RPV design evaluated includes an air vehicle (AV), a modular integrated communications and navigation system (MICNS), a ground control station (GCS), a launch subsystem (LS), a recovery subsystem (RS), and a number of ground support requirements. The HARDMAN Methodology is an integrated set of data base management techniques and analytic tools, designed to provide timely and fully documented assessments of the human resource requirements associated with an emerging system's design.

Source record↗

NASA NTRS2007

Window Observational Research Facility (WORF)

This viewgraph document concerns the Window Observational Research Facility (WORF) Rack, a unique facility designed for use with the US Lab Destiny Module window. WORF will provide valuable resources for Earth Science payloads along with serving the purpose of protecting the lab window. The facility can be used for remote sensing instrumentation test and validation in a shirt sleeve environment. WORF will also provide a training platform for crewmembers to do orbital observations of other planetary bodies. WORF payloads will be able to conduct terrestrial studies utilizing the data collected from utilizing WORF and the lab window.

Pelfrey, Joseph↗

DOE OSTI2020

Development of Remote Cutting Tools for use at ATR - 20438

As a result of years of operations of the Advanced Test Reactor (ATR) at the Idaho National Lab (INL), the ATR canal has become congested due to the storage of materials including waste consisting of highly irradiated remote handled (RH) metals. In order to support its continuing nuclear mission the ATR requires canal space to support fuel storage and future core internal change out (CIC) components. INL staff determined that the canal must be cleaned out and waste removed to efficiently utilize the valuable canal floor space. It was determined that a methodology for processing, packaging, characterizing, and removing current and future irradiated hardware from the ATR canal is needed. Orano Federal Services (OFS) and its subcontractor Babcock Services Inc. (BSI) were selected to provide remote tooling in order to safely and efficiently size reduce the material currently stored in the ATR canal. INL developed detailed requirements that established the design and performance requirements for the tooling. Orano TN and BSI had recently successfully completed similar tasks in clearing highly activated materials from spent fuel pools and canals at commercial nuclear plants. That experience directly translated into a proven approach that could be used at the ATR thereby saving time and money and lowering risks. Based on BSI's demonstrated proficiency in the design and deployment of remote size reduction specialty tooling at comparable commercial facilities, Orano Federal Services was confident that a solution to INL's requirements could be delivered. The tooling had to be designed to be compatible with the operational requirements of the ATR canal as well as the functionality needed by the facility operators that will use the equipment. Some of the major design requirements and functions of the equipment included remote operations, capability to shear highly irradiated sections of aluminum and steel tubing, pipe, and components ranging in size from 0.6 cm to 16.5 cm diameter into 1.5 m -1.8 m lengths, utilizing buoyancy compensation to assist with underwater operations, and use commercially available existing technology and components as much as practical. The design team evaluated and selected commercially available equipment that could be modified to meet the design and performance specifications. In addition, a waste sizing table (WST) was designed and fabricated to further ensure the size reduction equipment would perform as expected and give the facility operators a stable and usable remote, underwater work platform. After successful development of the tooling, a mock up test and operator training was conducted to ensure the tooling performed as expected and met the requirements of INL. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Scaling HPC Education

Throughout the cyberinfrastructure community there are a large range of resources available to train faculty and young scholars about successful utilization of computational resources for research. The challenge that the community faces is that training materials abound, but they can be difficult to find, and often have little information about the quality or relevance of offerings. Building on existing software technology, we propose to build a way for the community to better share and find training and education materials through a federated training repository. In this scenario, organizations and authors retain physical and legal ownership of their materials by sharing only catalog information, organizations can refine local portals to use the best and most appropriate materials from both local and remote sources, and learners can take advantage of materials that are reviewed and described more clearly. In this paper, we introduce the HPC ED pilot project, a federated training repository that is designed to allow resource providers, campus portals, schools, and other institutions to both incorporate training from multiple sources into their own familiar interfaces and to publish their local training materials.

community engagement↗

DOE OSTI2024

LBNF Target Complex Remote Handling

The Long Baseline Neutrino Facility (LBNF) near site at the Fermilab campus in Illinois, USA includes a target complex which houses a 1.5 – 1.8m long graphite neutrino production target, a pre-target baffle, three neutrino focusing horns (A,B,C), and a decay pipe 200m in length leading to an energy absorber / beam dump housed in an adjacent absorber building. The prompt dose rates are such that the Target Hall must be unoccupied while beam is on target. During maintenance periods, the Target Hall can be occupied by trained personnel. However, residual dose rates of activated components necessitate certain operations, such as the annual replacement of the target, be executed remotely by personnel located in a shielded room within the Target Hall. This presentation covers details designed into the infrastructure, equipment, and procedures to allow for successful execution of remote handling operations.

43 PARTICLE ACCELERATORS↗

NASA NTRS1998

Development of a Portable, Ground-Based Ozone Lidar Instrument for Tropospheric Ozone Research and Educational Training

The objective of this project is to develop a portable, eye-safe, ground-based ozone lidar instrument specialized for ozone differential absorption lidar (DIAL) measurements in the troposphere. This prototype instrument is intended to operate at remote field sites and to serve as the basic unit for monitoring projects requiring multi-instrument networks, such as that discussed in the science plan for the Global Tropospheric Ozone Project (GTOP). This instrument will be based at HU for student training in lidar technology as well as atmospheric ozone data analysis and interpretation. It will be also available for off-site measurement campaigns and will serve as a test bed for further instrument development. Later development beyond this grant to extend the scientific usefulness of the instrument may include incorporation of an aerosol channel and upgrading the laser to make stratospheric ozone measurements. Undergraduate and graduate students have been and will be active participants in this research effort.

Chyba, Thomas↗

NASA NTRS1993

Application of neural network to remote sensing of soil moisture using theoretical polarimetric backscattering coefficients

This paper investigates the potential application of neural network to inversion of soil moisture using polarimetric remote sensing data. The neural network used for the inversion of soil parameters is multi-layer perceptron trained with the back-propagation algorithm. The training data include the polarimetric backscattering coefficients obtained from theoretical surface scattering models together with an assumed nominal range of soil parameters which are comprised of the soil permittivity and surface roughness parameters. Soil permittivity is calculated from the soil moisture and the assumed soil texture based on an empirical formula at C-, L-, and P-bands. The rough surface parameters for the soil surface, which is described by the Gaussian random process, are the root-mean-square (rms) height and correlation length. For the rough surface scattering, small perturbation method is used for the L-band frequency, and Kirchhoff approximation is used for the C-band frequency to obtain the corresponding backscattering coefficients. During the training, the backscattering coefficients are the inputs to the neural net and the output from the net are compared with the desired soil parameters to adjust the interconnecting weights. The process is repeated for each input-output data entry and then for the entire training data until convergence is reached. After training, the backscattering coefficients are applied to the trained neural net to retrieve the soil parameters which are compared with the desired soil parameters to verify the effectiveness of this technique. Several cases are examined. First, for simplicity, the correlation length and rms height of the soil surface are fixed while soil moisture is varied. Soil moisture obtained using the neural networks with either L-band or C-band backscattering coefficients for the HH and VV polarizations as inputs is in good agreement with the desired soil moisture. The neural net output matches the desired output for the soil moisture range of 16 to 60 percent for the C-band case. The next case investigated is to vary both soil moisture and rms height while keeping the correlation length fixed. For this case, C-band backscattering coefficients are not sufficient for retrieving two parameters because the Kirchhoff approximation gives the same HH and VV backscattering coefficients. Therefore, the backscattering coefficients at two different frequency bands are necessary to find both the soil moisture and rms height. Finally, the neural nets are also applied to simultaneously invert soil moisture, rms height, and correlation length. Overall, the soil moisture retrieved from the neural network agrees very well with the desired soil moisture. This suggests that the neural network shows potential for retrieval of soil parameters from remote sensing data.

Wang, L.↗

NASA NTRS1975

ERIPS: Earth Resource Interactive Processing System

The ERIPS is an interactive computer system used in the analysis of remotely sensed data. It consists of a set of software programs which are executed on an IBM System/360 Model 75J computer under the direction of a trained analyst. The software was a derivative of the Purdue LARSYS program and has evolved to include an extensive pattern recognition system and a number of manipulative, preprocessing routines which prepare the imagery for the pattern recognition application. The original purpose of the system was to analyze remotely sensed data, to develop and perfect techniques to process the data, and to determine the feasibility of applying the data to significant earth resources problems. The System developed into a production system. Error recovery and multi-jobbing capabilities were added to the system.

Quinn, M. J.↗

NASA NTRS2017

Medical Support for ISS Crewmember Training in Star City, Russia

Medical support of spaceflight training operations across international lines is a unique circumstance with potential applications to other aerospace medicine support scenarios. KBRwyle's Star City Medical Support Group (SCMSG) has fulfilled this role since the Mir-Shuttle era, with extensive experience and updates to share with the greater AsMA community. OVERVIEW: The current Soyuz training flow for assigned ISS crewmembers takes place in Star City, Russia. Soyuz training flow involves numerous activities that pose potential physical and occupational risks to crewmembers, including centrifuge runs and pressurized suit simulations at ambient and hypobaric pressures. In addition, Star City is a relatively remote location in a host nation with variable access to reliable, Western-standard medical care. For these reasons, NASA's Human Health & Performance contract allocates full-time physician support to assigned ISS crewmembers training in Star City. The Star City physician also treats minor injuries and illnesses as needed for both long- and short-term NASA support personnel traveling in the area, while working to develop and maintain relationships with local health care resources in the event of more serious medical issues that cannot be treated on-site. The specifics of this unique scope of practice will be discussed. SIGNIFICANCE: ISS crewmembers training in Star City are at potential physical and occupational risk of trauma or dysbarism during nominal Soyuz training flow, requiring medical support from an on-duty aerospace medicine specialist. This support maintains human health and performance by preserving crewmember safety and well-being for mission success; sharing information regarding this operational model may contribute to advances in other areas of international, military, and civilian operational aerospace medicine.

Chough, Natacha↗

NASA NTRS2017

Medical Support for ISS Crewmember Training in Star City, Russia

Chough, Natacha↗

DOE OSTI2021

Learning Management System User Requirements for the National Nuclear Security Administration's International Nuclear Safeguards Engagement Program

The National Nuclear Security Administration's (NNSA) International Nuclear Safeguards Engagement Program (INSEP) is considering investing in new tools that would allow the program to support its partner states from a distance. At the same time, the program is considering approaches that would allow several organizations, including NNSA, IAEA, national laboratories and contractor staff, to collaborate in the development and maintenance of instructional content. Software systems known as Learning Management Systems (LMSs) might represent a mechanism through which INSEP could accomplish these goals (collaborative development and remote support). To assess the usefulness of an LMS, INSEP has specified its needs for delivering online training and compared those needs to the capability of a range of LMSs. This comparison will allow INSEP to determine whether an LMS would be a useful tool and may set the stage for a "make-buy" decision in the future. The study team concluded that INSEP's content development and delivery needs align well with the capabilities of the leading LMSs on the market today and that that INSEP performance requirements allow for a customized approach using existing training portals that are already available to NNSA. Additional work would be required to specify the desired processes for developing online training and outreach materials, structuring the databases, specifying the data that should be collected, and detailing the desired system reports and documentation.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2021

Hands-On Experimental Training [Slides]

Training provided includes: Nuclear Criticality Safety Fundamentals, Sub-Critical "Hands On" Demonstration, Hand-Stacking and Remote Approach to Critical Using the Planet Assembly, Flattop Free-Run Demonstration, and Godiva-IV Critical Assembly Demonstration.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗