Results for “task work” — Page 9

NASA NTRS2015

Fall 2015 NASA Internship, and Space Radiation Health Project

This fall, I was fortunate enough to have been able to participate in an internship at NASA's Lyndon B. Johnson Space Center. I was placed into the Human Health & Performance Directorate, where I was specifically tasked to work with Dr. Zarana Patel, researching the impacts of cosmic level radiation on human cells. Using different laboratory techniques, we were able to examine the cells to see if any damage had been done due to radiation exposure, and if so, how much damage was done. Cell culture samples were exposed at different doses, and fixed at different time points so that we could accumulate a large pool of quantifiable data. After examining quantifiable results relative to the impacts of space radiation on the human body at the cellular and chromosomal level, researchers can defer to different areas of the space program that have to do with astronaut safety, and research and development (extravehicular mobility unit construction, vehicle design and construction, etc.). This experience has been very eye-opening, and I was able to learn quite a bit. I learned some new laboratory techniques, and I did my best to try and learn new ways to balance such a hectic work and school schedule. I also learned some very intimate thing about working at NASA; I learned that far more people want to watch you succeed, rather than watch you fail, and I also learned that this is a place that is alive with innovators and explorers - people who have a sole purpose of exploring space for the betterment of humanity, and not for any other reason. It's truly inspiring. All of these experiences during my internship have impacted me in a really profound way, so much that my educational and career goals are completely different than when I started. I started out as a biotechnology major, and I discovered recently toward the end of the internship, that I don't want to work in a lab, nor was I as enthralled by biological life sciences as a believed myself to be. Taking that all into consideration, I've actually changed my major to mechanical engineering. I discovered that I enjoy building things, and I enjoy learning about materials and interactions between different things. And I quickly became obsessed with rocket and aerospace engineering, so I've decided that after a mechanical engineering degree, I will be pursuing an advanced degree in aerospace engineering. One final way that I was effected by this internship, is that I discovered that I don't want to have a career at NASA. I love this agency with all of my heart, but I refuse to allow my innovation to be bound by a scientifically illiterate congress. As such, I have decided to pursue commercial aerospace companies, such as Space, XCOR, Masten Space Systems, Orbital ATK, and many, many, more. Maybe one day I'll end up back here. I believe in what this agency is doing with my whole heart, and it's unfortunate to see them curtailed in some capacities as a result of budgetary constraints, brought on by people who don't fully understand the effort behind putting people in to space. All in all, this experience has been the best experience of my life - literally a childhood dream came true during this experience - and I cannot adequately explain how grateful I am to have been here for the past sixteen weeks.

Patience, Luke↗

NASA NTRS2017

Ground-Based Observing Campaign of Briz-M Debris

In 2015, NASA's Orbital Debris Program Office (ODPO) completed the installation of the Meter Class Autonomous Telescope (MCAT) on Ascension Island. MCAT is a 1.3m optical telescope designed with a fast tracking capability for observing orbital debris at all orbital regimes (Low-Erath orbits to Geosyncronous (GEO) orbits) from a low latitude site. This new asset is dedicated year-round for debris observations, and its location fills a geographical gap in the Ground-based Electro Optical Space Surveillance (GEODSS) network. A commercial off the shelf (COTS) research grade 0.4m telescope (named the Benbrook telescope) will also be installed on Ascension at the end of 2016. This smaller version is controlled by the same master software, designed by Euclid Research, and can be tasked to work independently or in concert with MCAT. Like MCAT, it has a the same suite of filters, a similar field of view, and a fast-tracking Astelco mount, and is also capable of tracking debris at all orbital regimes. These assets are well suited for targeted campagins or surveys of debris. Since 2013, NASA's ODPO has also had extensive access to the 3.8m infrared UKIRT telescope, located on Mauna Kea. At nearly 14,000-ft, this site affords excellent conditions for collecting both photometery and spectroscopy at near-IR (0.9 - 2.5 micrometers SWIR) and thermal-IR (8 - 25 micrometers; LWIR) regimes, ideal for investigating material properties as well as thermal characteristics and sizes of debris. For the purposes of understanding orbital debris, taking data in both survey mode as well as targeting individual objects for more in-depth characterizations are desired. With the recent break-ups of Briz-M rocket bodies, we have collected a suite of data in the optical, near-infrared, and mid-infrared of in-tact objects as well as those classified as debris. A break-up at GEO of a Briz-M rocket occurred in January, 2016, well timed for the first remote observing survey-campaign with MCAT. Access to the 3.8m UKIRT telescope has also allowed for investigating this break-up in the near-infrared at wavelengths where debris is often much more reflective, allowing for the potential detection of a smaller population of these debris. In addition, a suite of near-IR reflectance spectroscopy (0.8-2.5 micrometers) and thermal-IR (8-15 micrometers) of individual in-tact and debris Briz-M objects has been collected. Analysis of the survey data will be discussed.

Lederer, S. M.↗

NASA NTRSDate not supplied

Deformable Mirror Technology Roadmap: Architecting A Path to TRL5 for Future Exoplanet Direct Imaging Space Missions

The Deformable Mirror Technology Roadmap (DMTR) is a working group tasked by NASA’s Exoplanet Program Office to study the path to bring deformable mirror (DM) systems to a Technology Readiness Level 5. DMs, and their drive electronics and harnessing, are the critical component of any exoplanet direct imaging coronagraph, and there is no device that exists today which can meet the ambitious performance goals expected for NASA’s Habitable Worlds Observatory (HWO). Here we present progress on surveying the field of DM technologies, defining a first cut set of device requirements, and recommending a development and verification maturation program.

Tyler D. Groff↗

NASA NTRS1994

A virtual work space for both hands manipulation with coherency between kinesthetic and visual sensation

This paper describes the construction of a virtual work space for tasks performed by two handed manipulation. We intend to provide a virtual environment that encourages users to accomplish tasks as they usually act in a real environment. Our approach uses a three dimensional spatial interface device that allows the user to handle virtual objects by hand and be able to feel some physical properties such as contact, weight, etc. We investigated suitable conditions for constructing our virtual work space by simulating some basic assembly work, a face and fit task. We then selected the conditions under which the subjects felt most comfortable in performing this task and set up our virtual work space. Finally, we verified the possibility of performing more complex tasks in this virtual work space by providing simple virtual models and then let the subjects create new models by assembling these components. The subjects can naturally perform assembly operations and accomplish the task. Our evaluation shows that this virtual work space has the potential to be used for performing tasks that require two-handed manipulation or cooperation between both hands in a natural manner.

Ishii, Masahiro↗

NASA NTRS1989

OTVE turbopump condition monitoring, task E.5

Recent work has been carried out on development of isotope wear analysis and optical and eddy current technologies to provide bearing wear measurements and real time monitoring of shaft speed, shaft axial displacement and shaft orbit of the Orbit Transfer Vehicle hydrostatic bearing tester. Results show shaft axial displacement can be optically measured (at the same time as shaft orbital motion and speed) to within 0.3 mils by two fiberoptic deflectometers. Evaluation of eddy current probes showed that, in addition to measuring shaft orbital motion, they can be used to measure shaft speed without having to machine grooves on the shaft surface as is the usual practice for turbomachinery. The interim results of this condition monitoring effort are presented.

Coleman, Paul T.↗

NASA NTRS1999

Phase 1 Program Joint Report

This report consists of inputs from each of the Phase I Program Joint Working Groups. The Working Groups were tasked to describe the organizational structure and work processes that they used during the program, joint accomplishments, lessons learned, and applications to the International Space Station Program. This report is a top-level joint reference document that contains information of interest to both countries.

Nield, George C.↗

DOE OSTI2024

Tandem Predictions for HPC Jobs

At the core of the predictive analytics applied to High Performance Computing (HPC), the most prominent tasks are the prediction of job runtimes and the prediction of job queue times, both of which have the potential for informing HPC users during their every-day decision making. Accurate runtime predictions can help users better choose so-called wallclock times at job submission, decreasing the odds of their jobs waiting in queues longer than necessary. The accurate and timely queue time predictions offered for the available partitions can inform the favorable selection of partitions for running jobs. This potential is well understood as we see in the abundance of research studies that propose solutions for these tasks, including the work published in the last several years. These tasks are seemingly receptive to the Machine Learning (ML) solutions, considering that there is no shortage of training data where HPC centers over time run millions and millions of jobs. However, we study the existing research literature, as well as look for examples in the toolchains supported on the exemplar HPC facilities, and, surprisingly, do not find any practical solutions that are ready to be adopted. We interpret this as a manifestation of the shortage of UX/UI efforts that support HPC analytics and also as a sign that the research has not come to the consensus on solving these tasks. In this study, we aim to shed new light on the long-running task of job queue time prediction by exploring the utility of runtime predictions in improving prediction accuracy and, actually, predicting these two metrics together, in tandem. In other words, we show how runtime predictions become valuable input in the queue time modeling. We challenge the existing approaches to feature engineering for the queue time prediction and describe promising results we obtained for a large dataset of HPC jobs from a supercomputer at the National Renewable Energy Laboratory.

HPC↗

NASA NTRS2018

San Francisco Bar Pilot Fatigue Study

The purpose of this study was to evaluate the extent of fatigue among San Francisco Bar Pilots (Maritime Pilots) and its potential impact on safety, and to make recommendations concerning how the risk of fatigue could be managed. Information was gathered via a literature review, observations of Bar Pilots at work, surveys, a task analysis, and an analysis of dispatch records.The work of San Francisco Bar Pilots involves an unusual mix of activities and job demands. Their work calls for situational awareness, reasoning, communication, and perceptual abilities comparable to those required by airline pilots and air traffic controllers. Errors can have severe consequences for public safety and the environment, as well as significant financial costs. Fatigue is increasingly recognized as a hazard that must be managed by the transportation industry. The reduced sleep quality and quantity experienced by personnel who work at night, in conjunction with human circadian rhythms can lead to an operationally significant level of cognitive impairment. The cognitive impact of fatigue includes functions that are critical to safe maritime piloting, such as vigilance, judgment, reaction time and communication. The surveys distributed to Bar Pilots did not uncover evidence of widespread fatigue. The survey results also suggested that fatigue was not a major concern of Bar Pilots. Bar Pilots had overall low scores on the subjective fatigue measures used in the survey, and generally assessed the safety risk due to fatigue as low. Compared to air traffic controllers, Bar Pilots gave significantly lower ratings on questions concerning the prevalence and impact of fatigue. The application of fatigue modeling software to Bar Pilot dispatch records identified that in most cases, pilot's cognitive effectiveness was predicted to be acceptable during their duty periods. However, these results could not be verified with objective data.The study identified a number of fatigue issues that deserve attention. These include Bar Pilot work periods that frequently infringe on the circadian low, consecutive work periods without a significant break, consecutive periods of night work, unpredictable work schedules, start time variability, the potential for sleep inertia, and the number of pilots on the board at any given time.

fatigue↗

DOE OSTI2025

Tandem Predictions for HPC Jobs: Preprint

At the core of the predictive analytics applied to High Performance Computing (HPC), the most prominent tasks are the prediction of job runtimes and the prediction of job queue times, both of which have the potential for informing HPC users during their every-day decision making. Accurate runtime predictions can help users better choose so-called wallclock times at job submission, decreasing the odds of their jobs waiting in queues longer than necessary. The accurate and timely queue time predictions offered for the available partitions can inform the favorable selection of partitions for running jobs. This potential is well understood as we see in the abundance of research studies that propose solutions for these tasks, including the work published in the last several years. These tasks are seemingly receptive to the Machine Learning (ML) solutions, considering that there is no shortage of training data where HPC centers over time run millions and millions of jobs. However, we study the existing research literature, as well as look for examples in the toolchains supported on the exemplar HPC facilities, and, surprisingly, do not find any practical solutions that are ready to be adopted. We interpret this as a manifestation of the shortage of UX/UI efforts that support HPC analytics and also as a sign that the research has not come to the consensus on solving these tasks. In this study, we aim to shed new light on the long-running task of job queue time prediction by exploring the utility of runtime predictions in improving prediction accuracy and, actually, predicting these two metrics together, in tandem. In other words, we show how runtime predictions become valuable input in the queue time modeling. We challenge the existing approaches to feature engineering for the queue time prediction and describe promising results we obtained for a large dataset of HPC jobs from a supercomputer at the National Renewable Energy Laboratory.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1989

Planning time-optimal robotic manipulator motions and work places for point-to-point tasks

A method is presented which combines simple time-optimal motions in an optimal manner to yield the minimum-time motions for an important class of complex manipulator tasks composed of point-to-point moves such as assembly, electronic component insertion, and spot welding. This method can also be used to design manipulator actions and work places so that tasks can be completed in minimum time. The method has been implemented in a computer-aided design software system. Several examples are presented. Experimental results show the method's validity and utility.

Dubowsky, S.↗

NASA NTRS1996

International Space Station ECLSS Technical Task Agreement Summary Report

A summary of work accomplished under Technical Task Agreement by the Marshall Space Flight Center (MSFC) documents activities regarding the Environmental Control and Life Support Systems (ECLSS) of the International Space Station (ISS) program. These MSFC activities were in-line to the designing, the development, the testing, and the flight of ECLSS equipment. MSFC's unique capabilities for performing integrated system testing and analyses, and its ability to perform some tasks cheaper and faster to support ISS program needs are the basis for the Technical Task Agreement activities. Tasks were completed in the Water Recovery Systems, Air Revitalization Systems, and microbiology areas. The results of each task is described in this summary report.

Minton-Summers, S.↗

NASA NTRS2000

Small Engine Technology (SET) Task 23 ANOPP Noise Prediction for Small Engines, Wing Reflection Code

The work performed under Task 23 consisted of the development and demonstration of improvements for the NASA Aircraft Noise Prediction Program (ANOPP), specifically targeted to the modeling of engine noise enhancement due to wing reflection. This report focuses on development of the model and procedure to predict the effects of wing reflection, and the demonstration of the procedure, using a representative wing/engine configuration.

Lieber, Lysbeth↗

NASA NTRSDate not supplied

Natural Language Processing Analysis of Notices to Airmen for Air Traffic Management Optimization

With new emerging technologies in the field of NLP, we explore their applications to digitize and analyze heritage Air Traffic Management (ATM) documents for planning and optimizing airspace operations. Specifically, this research focuses on harvesting semi-structured or un-structured information contained in Notices to Airmen (NOTAMs). Using NLP and other advanced data analytics, we will construct a data-driven framework which facilitates finding language patterns and the use of pretrained language models for classification and extraction of useful airspace constraints and restrictions. These may lead to tools that assist airspace users in understanding the constraints more efficiently, contributing to better route planning and safer execution. This paper explores three workflows entailing different NLP tasks. First, unsupervised techniques like word embedding and topic modeling are used for pattern finding and document classification. Second, a dataset is created by extracting information from the semi-structured NOTAM format as metadata for categorizing, visualizing, and extracting key entities driving NOTAM content. Third, modern pre-built deep learning based transformer models such as BERT, RoBERTa, and XLNet are evaluated on the question answering task, an even more robust approach to information extraction, as well as their respective fine-tuning tasks. In this work we include various performance metrics for the trained models to evaluate both accuracy and precision and we show that the models can be generalized for their respective tasks. The research work developed shows promise in uncovering trends in digital NOTAMs in the NAS and also offers a new framework for digitizing and inferring insights from free-form legacy NOTAMs, that are yet to be digitized.

Natural Language Processing↗

NASA NTRSDate not supplied

Natural Language Processing (NLP) Analysis of NOTAMs for Air Traffic Management Optimization

With new emerging technologies in the field of NLP, we explore their applications to digitize and analyze heritage Air Traffic Management (ATM) documents for planning and optimizing airspace operations. Specifically, this research focuses on harvesting semi-structured or un-structured information contained in Notices to Airmen (NOTAMs). Using NLP and other advanced data analytics, we will construct a data-driven framework which facilitates finding language patterns and the use of pretrained language models for classification and extraction of useful airspace constraints and restrictions. These may lead to tools that assist airspace users in understanding the constraints more efficiently, contributing to better route planning and safer execution. This paper explores three workflows entailing different NLP tasks. First, unsupervised techniques like word embedding and topic modeling are used for pattern finding and document classification. Second, a dataset is created by extracting information from the semi-structured NOTAM format as metadata for categorizing, visualizing, and extracting key entities driving NOTAM content. Third, modern pre-built deep learning based transformer models such as BERT, RoBERTa, and XLNet are evaluated on the question answering task, an even more robust approach to information extraction, as well as their respective fine-tuning tasks. In this work we include various performance metrics for the trained models to evaluate both accuracy and precision and we show that the models can be generalized for their respective tasks. The research work developed shows promise in uncovering trends in digital NOTAMs in the NAS and also offers a new framework for digitizing and inferring insights from free-form legacy NOTAMs, that are yet to be digitized. Video is an mp4 download, with a play time of 9 min 35 secs.

Natural Language Processing↗

NASA NTRS1987

User engineering: A new look at system engineering

User Engineering is a new System Engineering perspective responsible for defining and maintaining the user view of the system. Its elements are a process to guide the project and customer, a multidisciplinary team including hard and soft sciences, rapid prototyping tools to build user interfaces quickly and modify them frequently at low cost, and a prototyping center for involving users and designers in an iterative way. The main consideration is reducing the risk that the end user will not or cannot effectively use the system. The process begins with user analysis to produce cognitive and work style models, and task analysis to produce user work functions and scenarios. These become major drivers of the human computer interface design which is presented and reviewed as an interactive prototype by users. Feedback is rapid and productive, and user effectiveness can be measured and observed before the system is built and fielded. Requirements are derived via the prototype and baselined early to serve as an input to the architecture and software design.

Mclaughlin, Larry L.↗

NASA NTRS2005

Asynchronous Messaging and Data Transfer in a Spacecraft: An Implementation

Data transfer and messaging is an important part of a spacecraft. Creating a standard protocol for messaging that can be used for a variety of applications is an extremely beneficial project at the Jet Propulsion Laboratory (JPL). The Asynchronous Messaging Service (AMS) is a protocol outlining how subsystems initialize and conduct communication between each other. There are currently two implementations of AMS in the works. At JPL, my task is to get a working implementation of AMS onto vxWorks as a proof of concept. An Autocoder, a program used to convert visually created state chart diagrams to C++, has also been created to accomplish a part of the implementation. I was assigned to make the program portable on any Unix type environment. Lastly, I was to develop a program to demonstrate messaging between two FireWire cards running vxworks.

Moholt, Joseph M.↗

NASA NTRS1990

Task decomposition for multilimbed robots to work in the reachable-but-unorientable space

Multilimbed industrial robots that have at least one arm and two or more legs are suggested for enlarging robot workspace in industrial automation. To plan the motion of a multilimbed robot, the arm-leg motion-coordination problem is raised and task decomposition is proposed to solve the problem; that is, a given task described by the destination position and orientation of the end-effector is decomposed into subtasks for arm manipulation and for leg locomotion, respectively. The former is defined as the end-effector position and orientation with respect to the legged main body, and the latter as the main-body position and orientation in the world coordinates. Three approaches are proposed for the task decomposition. The approaches are further evaluated in terms of energy consumption, from which an optimal approach can be selected.

Su, Chao↗

NASA NTRS2001

Kinetic Alfven Wave Electron Acceleration on Auroral Field Lines

Major results of the S3-3 Langmuir sweep study are published. Studies show statistics and average density and temperature variation on auroral field lines up to 8000 km altitude. Alfven wave papers were published. Our model of Alfven wave propagation on auroral field lines was successfully extended to handle varying density and magnetic field for the inertial mode. The study showed that Alfven wave can create time-dispersed electron signatures. A study was undertaken to extend Langmuir sweep I-V curves to handle the case of an kappa electron distribution as well as Maxwellian. The manuscript is in preparation. Participated in International Space Science Institute study of Alfvenic structures which resulted in a group review paper. The proposed work was to develop an extended model of Alfven wave propagation along auroral field lines to study electron acceleration. As part of this work, a major task was to characterize density and temperature along auroral field lines by using spacecraft Langmuir sweep data. The work that was completed under this funding was successful at both tasks. Three papers have been published as part of this work and a fourth manuscript is in preparation.

Kletzing, Craig A.↗