Results for “task work” — Page 13

DOE OSTI2024

Raptor Monitoring and Minimization Technologies

In June 2023, the International Energy Agency Wind Task 34 - Working Together to Resolve the Environmental Effects of Wind Energy (WREN) - organized a forum to discuss monitoring and minimization strategies used to study raptor interactions with wind energy facilities. The forum included experts in raptor movement and behavior, minimization measures, technology validation, and wind energy development from four countries. The experts represented a range of international stakeholder groups including private industry, financial institutions, government agencies, nonprofit organizations, and wildlife consultants. This educational brief summarizes the discussion during the forum and written comments from additional participants who could not attend the live event. Relevant literature was used to provide additional context when needed.

minimization↗

DOE OSTI2024

Transforming the Bootstrap: Using Transformers to Compute Scattering Amplitudes in Planar N = 4 Super Yang-Mills Theory

We pursue the use of deep learning methods to improve state-of-the-art computations in theoretical high-energy physics. Planar N = 4 Super Yang-Mills theory is a close cousin to the theory that describes Higgs boson production at the Large Hadron Collider; its scattering amplitudes are large mathematical expressions containing integer coefficients. In this paper, we apply Transformers to predict these coefficients. The problem can be formulated in a language-like representation amenable to standard cross-entropy training objectives. We design two related experiments and show that the model achieves high accuracy (> 98%) on both tasks. Our work shows that Transformers can be applied successfully to problems in theoretical physics that require exact solutions.

Lance, Dixon↗

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↗

DOE OSTI2022

Legacy surface change analyses of the 1993 Rock Valley earthquake sequence for direct comparison to planned NA-22 underground conventional high-explosive experiments (Source Physics Experiment 3 (SPE3) - RV/DC Task 1.3 FY22 Final Report)

Recent work under two previous phases of the NNSA NA-22 Source Physics Experiment (SPE) have shown that underground chemical high-explosive experiments can produce detectable surface changes that differ in spatial extent and vertical magnitude depending on the geologic media at the site (Schultz-Fellenz et al., 2018; 2020; Crawford et al., 2021). Neither of these two prior phases of SPE identified natural earthquake-related surface effects in the same region that occurred at a similar depth as the explosive experiments for direct comparison. While earthquakes can also produce surface changes that are detectable using remote sensing data analyses, it is expected that the pattern and spatial extent of surface changes would vary between earthquakes and explosions. However, no direct-observed surface-change signature comparison between earthquakes and explosions has ever been performed. The SPE Phase 3 Rock Valley/Direct Comparison (RV/DC) program presents a unique opportunity to investigate and characterize co-occurrence of both earthquakes and explosions. In this project, we worked to address five tasks in a workflow, as follows: 1. Identify and obtain existing high-resolution legacy satellite and aerial imagery as close in time before and after the 1993 Rock Valley earthquake sequence to temporally constrain the analyses. 2. Transform these pre-earthquake and post-earthquake datasets into digital elevation models (DEMs) using geospatial analysis software packages (e.g., ArcGIS, Agisoft Metashape, and Google Earth Engine). 3. Perform DEM differencing analyses to assess and quantify earthquake-related changes from the 1993 sequence, and develop map products that visualize these analyses. 4. Use the analyses from (3) to: (a) assess spatial distribution and magnitude of surface changes due to the 1993 Rock Valley earthquake sequence, and (b) determine parameters of forthcoming, planned explosion-related surface change data collection from sensors mounted on unmanned aerial vehicles (UAVs) (e.g., spatial extent of collection, design and density of survey control, sensors to deploy, forward speed and line spacing of UAV flight lines, flight altitude). 5. Develop a summary report on the analyses, including how the analyses define parameters and identify focus areas for any future surface change analytical field campaigns related to the explosive experiment. Analyzing these legacy data and identifying whether they can detect any surface changes related to the earthquake sequence facilitates opportunities for direct signature comparison of surface change from explosions at one location, which has never previously been performed. Comparing the surface change signatures from a co-located and depth-equivalent earthquake and an explosion could help to advance remote sensing event discrimination techniques. This report summarizes the work completed toward this ambitious goal.

42 ENGINEERING↗

DOE OSTI2017

Agile Task Tracking Tool

The work was created to provide a tool for the purpose of improving the management of tasks associated with Agile projects. Agile projects are typically completed in an iterative manner with many short duration tasks being performed as part of iterations. These iterations are generally referred to as sprints. The objective of this work is to create a single tool that enables sprint teams to manage all of their tasks in multiple sprints and automatically produce all standard sprint performance charts with minimum effort. The format of the printed work is designed to mimic a standard Kanban board. The work is developed as a single Excel file with worksheets capable of managing up to five concurrent sprints and up to one hundred tasks. It also includes a summary worksheet providing performance information from all active sprints. There are many commercial project management systems typically designed with features desired by larger organizations with many resources managing multiple programs and projects. The audience for this work is the small organizations and Agile project teams desiring an inexpensive, simple, user-friendly, task management tool. This work uses standard readily available software, Excel, requiring minimum data entry and automatically creating summary charts and performance data. It is formatted to print out and resemble standard flip charts and provide the visuals associated with this type of work.

Duke, Roger T.↗

DOE OSTI2015

SU-F-207-16: CT Protocols Optimization Using Model Observer

Purpose: To quantitatively evaluate the performance of different CT protocols using task-based measures of image quality. This work studies the task of size and the contrast estimation of different iodine concentration rods inserted in head- and body-sized phantoms using different imaging protocols. These protocols are designed to have the same dose level (CTDIvol) but using different X-ray tube voltage settings (kVp). Methods: Different concentrations of iodine objects inserted in a head size phantom and a body size phantom are imaged on a 64-slice commercial CT scanner. Scanning protocols with various tube voltages (80, 100, and 120 kVp) and current settings are selected, which output the same absorbed dose level (CTDIvol). Because the phantom design (size of the iodine objects, the air gap between the inserted objects and the phantom) is not ideal for a model observer study, the acquired CT images are used to generate simulation images with four different sizes and five different contracts iodine objects. For each type of the objects, 500 images (100 x 100 pixels) are generated for the observer study. The observer selected in this study is the channelized scanning linear observer which could be applied to estimate the size and the contrast. The figure of merit used is the correct estimation ratio. The mean and the variance are estimated by the shuffle method. Results: The results indicate that the protocols with 100 kVp tube voltage setting provides the best performance for iodine insert size and contrast estimation for both head and body phantom cases. Conclusion: This work presents a practical and robust quantitative approach using channelized scanning linear observer to study contrast and size estimation performance from different CT protocols. Different protocols at same CTDIvol setting could Result in different image quality performance. The relationship between the absorbed dose and the diagnostic image quality is not linear.

60 APPLIED LIFE SCIENCES↗

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↗

DOE OSTI2019

Mu2e Tracker's Gas Connection

This report is a resume of the activities in which I was involved during my internship at Fermilab Particle Physics and Accelerator Laboratory in Batavia. The experiment at whom my work is connected is the Mu2e experiment, in particular I worked in the Tracker’s group. The main task of my training program was to design a gas service connection that take place in a small volume located between the Tracker and the Calorimeter (two main elements that constitute the detector). During the initial phase I was lightly trained about the experiment and its general design, then I was supervised on my specific task. All the work aimed to reach a design solution that could meet all the BCs, in this view, a reasonable solution was prototyped and tested. The tests performed at Lab 3 were another fundamental aspect of my experience at Fermilab closing the circle of studying, modelling, machining and testing. I want to thank primarily Aseet Mukherjee and Mete Yucel that were my supervisors, they constantly spent their time attempting to create a favourable workplace, suggesting, listening and meeting all the requests in short time, working for them was absolutely like "working with them", without their help achieve the result would been greatly harder. Giuseppe Gallo deserve a special thanks too for revising mostly of my work and being always available for support. I had also the chance to share my work during the "Weekly Tracker’s Meetings" dealing with other bright engineers and physicists, between them I really appreciate George Ginter, Russel Rucinski, Yujing Sun and Waqar Ahmed. I want also to express my gratitude to Simone Donati, Giorgio Bellettini and Emanuela Barzi that make this unique experience possible organizing this program for decades. Thanks to my Mother, my Father, my Brother, my Nephew, my uncle Damiano and my friend Simona always near, never stopping to dream with me.

43 PARTICLE ACCELERATORS↗

DOE OSTI2020

TRIPWIRE - Radiation and Electromagnetic Laboratory Work Report

This report summarizes the research outcomes for the work performed in fiscal year 2020. The report is divided into two main sections addressing the radiation and electromagnetic sensing components. Both sections present an introduction with theory to lay the foundation of how the systems work. The report is the key deliverable for Task 1: Radiation and Electromagnetic Detector Development - Laboratory Work. This task involves all work related to initial laboratory research associated with the radiation and electromagnetic detector development to reach TRL-3.

73 - NUCLEAR PHYSICS AND RADIATION PHYSICS↗

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↗