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All sources NASA NTRS DOE OSTI

DOE OSTI2016

Aerosol Plume Detection Algorithm Based on Image Segmentation of Scanning Atmospheric Lidar Data

An image-processing algorithm has been developed to identify aerosol plumes in scanning lidar backscatter data. The images in this case consist of lidar data in a polar coordinate system. Each full lidar scan is taken as a fixed image in time, and sequences of such scans are considered functions of time. The data are analyzed in both the original backscatter polar coordinate system and a lagged coordinate system. The lagged coordinate system is a scatterplot of two datasets, such as subregions taken from the same lidar scan (spatial delay), or two sequential scans in time (time delay). The lagged coordinate system processing allows for finding and classifying clusters of data. The classification step is important in determining which clusters are valid aerosol plumes and which are from artifacts such as noise, hard targets, or background fields. These cluster classification techniques have skill since both local and global properties are used. Furthermore, more information is available since both the original data and the lag data are used. Performance statistics are presented for a limited set of data processed by the algorithm, where results from the algorithm were compared to subjective truth data identified by a human.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2022

Fatigue in Short-Haul Operations: Regulations and Research

Short-haul multi-segment flight operations are conducted within Part 117 table limits for Flight Time and Flight Duty Period, yet there are few data available regarding the impact on sleep, workload, and performance in these operations. The Civil Aerospace Medical Institute (CAMI) has invested in the planning and execution of a study that will result in data characterizing the effects of short-haul multi-segment flight operations on cumulative sleep loss and pilot workload across trip pairings. Scientific researchers from CAMI, in collaboration with NASA Ames Research Center, are currently running an operational field study to examine the human factors and elements of pilot performance associated with short-haul multi-segment flight operations to better understand the impact of sleep disruption and workload across trip pairings. The first half of this presentation will provide a brief overview of current duty and rest requirements and identify gaps in current operational research data. The second half of the presentation will focus on results from a focus group study which developed the scope of the larger field study, followed by an overview of the current field study.

short-haul↗

DOE OSTI2017

Real-Time Fine Grained Occupancy Estimation Using Depth Sensors on ARM Embedded Platforms

Occupancy estimation is an important primitive for a wide range of applications including building energy efficiency, safety, and security. In this paper, we explore the potential of using depth sensors to detect, estimate, identify, and track occupants in buildings. While depth sensors have been widely used for human detection and gesture recognition, computer vision algorithms are typically run on a powerful computer like XBOX or Intel R CoreTM i7 processor. In this work, we develop a prototype system called FORK using off-the-shelf components that performs the entire depth data processing on a cheaper and low power ARM processor in real-time. As ARM processors are extremely weak in running computer vision algorithms, FORK is designed to detect humans and track them in a very efficient way by leveraging a novel lightweight model based approach instead of traditional approaches based on histogram of oriented gradients (HOG) features. Unlike other camera based approaches, FORK is much less privacy invasive (even if the sensor is compromised). Based on a complete implementation, real-world deployment, and extensive evaluation at realistic scenarios, we observe that FORK achieves over 99% accuracy in real-time (4-9 FPS) in occupancy estimation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

The Zwicky Transient Facility Bright Transient Survey. III. BTSbot: Automated Identification and Follow-up of Bright Transients with Deep Learning

Abstract The Bright Transient Survey (BTS) aims to obtain a classification spectrum for all bright ( m peak ≤ 18.5 mag) extragalactic transients found in the Zwicky Transient Facility (ZTF) public survey. BTS critically relies on visual inspection (“scanning”) to select targets for spectroscopic follow-up, which, while effective, has required a significant time investment over the past ∼5 yr of ZTF operations. We present BTSbot , a multimodal convolutional neural network, which provides a bright transient score to individual ZTF detections using their image data and 25 extracted features. BTSbot is able to eliminate the need for daily human scanning by automatically identifying and requesting spectroscopic follow-up observations of new bright transient candidates. BTSbot recovers all bright transients in our test split and performs on par with scanners in terms of identification speed (on average, ∼1 hr quicker than scanners). We also find that BTSbot is not significantly impacted by any data shift by comparing performance across a concealed test split and a sample of very recent BTS candidates. BTSbot has been integrated into Fritz and Kowalski , ZTF’s first-party marshal and alert broker, and now sends automatic spectroscopic follow-up requests for the new transients it identifies. Between 2023 December and 2024 May, BTSbot selected 609 sources in real time, 96% of which were real extragalactic transients. With BTSbot and other automation tools, the BTS workflow has produced the first fully automatic end-to-end discovery and classification of a transient, representing a significant reduction in the human time needed to scan.

Rehemtulla, Nabeel (ORCID:0000000256832389)↗

DOE OSTI2024

Streaming Large-Scale Microscopy Data to a Supercomputing Facility

Data management is a critical component of modern experimental workflows. As data generation rates increase, transferring data from acquisition servers to processing servers via conventional file-based methods is becoming increasingly impractical. The 4D Camera at the National Center for Electron Microscopy generates data at a nominal rate of 480 Gbit s -1 (87,000 frames s -1 ⁠), producing a 700 GB dataset in 15 s. To address the challenges associated with storing and processing such quantities of data, we developed a streaming workflow that utilizes a high-speed network to connect the 4D Camera’s data acquisition system to supercomputing nodes at the National Energy Research Scientific Computing Center, bypassing intermediate file storage entirely. In this work, we demonstrate the effectiveness of our streaming pipeline in a production setting through an hour-long experiment that generated over 10 TB of raw data, yielding high-quality datasets suitable for advanced analyses. Additionally, we compare the efficacy of this streaming workflow against the conventional file-transfer workflow by conducting a postmortem analysis on historical data from experiments performed by real users. Our findings show that the streaming workflow significantly improves data turnaround time, enables real-time decision-making, and minimizes the potential for human error by eliminating manual user interactions.

4D-STEM↗

NASA NTRS2010

Lessons Learned from Performance Testing of Humans in Spacesuits in Simulated Reduced Gravity

Introduction: The overarching objective of the Integrated Suit Test (IST) series is to evaluate suited human performance using reduced-gravity analogs and learn what aspects of an EVA suit system affect human performance. For this objective to be successfully achieved, the testing methodology should be valid and reproducible, and the partial-gravity simulations must be as accurate and realistic as possible. Objectives: To highlight some of the key lessons learned about partial-gravity analogs and testing methodology, and to suggest considerations for optimizing the effectiveness and quality of results of future tests. Methods: Performance testing of suited and unsuited subjects was undertaken in different reduced-gravity analogs including the Space Vehicle Mockup Facility s Partial Gravity Simulator (POGO), parabolic flight on the C-9 aircraft, underwater environments including NASA s Extreme Environment Mission Operations (NEEMO) and the Neutral Buoyancy Lab (NBL), and in field analogs including Desert Research and Technology Studies (RATS), the Haughton Mars Project (HMP), and the JSC Rock Pile. Subjects performed level walking, incline/decline walking, running, shoveling, picking up and transferring rocks, kneeling/standing, and task boards. Lessons Learned Analogs: No single analog will properly simulate all aspects of the true partial-gravity environment. The POGO is an ideal environment from the standpoint that there are no time limits or significant volumetric constraints, but it does have several limitations. It allows only 2 translational degrees of freedom (DOF) and applies true partial-gravity offload only through the subject s center of gravity (CG). Also, when a subject is doing non-stationary tasks, significant overhead inertia from the lift column seems to have a negative impact on performance. Parabolic flight allows full translational and rotational DOF and applies offload to all parts of the body, but the simulation lasts less than 30 seconds. When this is coupled with the volumetric constraints of the plane, both task selection and data collection options are significantly limited. The underwater environments also allow all 6 DOF and allow off-loading to be applied throughout the body, but the data collection capabilities are limited to little more than subjective ratings. In addition, water drag negatively affects performance of tasks requiring dynamic motion. Field analogs provide the ability to simulate lunar terrain and more realistic mission-like objectives, but all of them operate at 1-g, so suited human performance testing generally must utilize a reduced-mass or "mockup" suit, depending on study objectives. In general, the ground-based overhead-suspension partial-gravity analogs like POGO allow the most diverse data collection methods possible while still simulating partial gravity. However, as currently designed, the POGO has significant limitations. Design of the Active Response Gravity Offload System (ARGOS) has begun and is focusing on adding full x,y,z translational DOF, improved offload accuracy, increased lift capacity, and active control of the x and y axes to minimize offload system inertia. Additionally, a new gimbal is being designed to reduce mass and inertia and to be able to work with different suits, as the current gimbal only supports suited testing with the Mark III Technology Demonstrator Suit (MKIII).

Norcross, Jason R.↗

DOE OSTI2021

The value of human data annotation for machine learning based anomaly detection in environmental systems

Anomaly detection is the process of identifying unexpected data samples in datasets. Automated anomaly detection is either performed using supervised machine learning models, which require a labelled dataset for their calibration, or unsupervised models, which do not require labels. While academic research has produced a vast array of tools and machine learning models for automated anomaly detection, the research community focused on environmental systems still lacks a comparative analysis that is simultaneously comprehensive, objective, and systematic. This knowledge gap is addressed for the first time in this study, where 15 different supervised and unsupervised anomaly detection models are evaluated on 5 different environmental datasets from engineered and natural aquatic systems. To this end, anomaly detection performance, labelling efforts, as well as the impact of model and algorithm tuning are taken into account. As a result, our analysis reveals the relative strengths and weaknesses of the different approaches in an objective manner without bias for any particular paradigm in machine learning. Most importantly, our results show that expert-based data annotation is extremely valuable for anomaly detection based on machine learning.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

A machine-learning-driven data labeling pipeline for scientific analysis in MLExchange

This study introduces a novel labeling pipeline to accelerate the labeling process of scientific data sets by using artificial intelligence (AI)-guided tagging techniques. This pipeline includes a set of interconnected web-based graphical user interfaces (GUIs), where Data Clinic and MLCoach enable the preparation of machine learning (ML) models for data reduction and classification, respectively, while Label Maker is used for label assignment. Throughout this pipeline, data can be accessed through a direct connection to a file system or through Tiled for access through Hypertext Transfer Protocol (HTTP). Our experimental results present three use cases where this labeling pipeline has been instrumental for the study of large X-ray scattering data sets in the area of pattern recognition, the remote analysis of resonant soft X-ray scattering data and the fine-tuning process of foundation models. These use cases highlight the labeling capabilities of this pipeline, including the ability to label large data sets in a short period of time, to perform remote data analysis while minimizing data movement and to enhance the fine-tuning process of complex ML models with human involvement.

Chavez, Tanny (ORCID:0000000193172896)↗

DOE OSTI2015

Case Study for ARRA-funded Ground-Source Heat Pump (GSHP) Demonstration at Oakland University

High initial costs and lack of public awareness of ground-source heat pump (GSHP) technology are the two major barriers preventing rapid deployment of this energy-saving technology in the United States. Under the American Recovery and Reinvestment Act (ARRA), 26 GSHP projects have been competitively selected and carried out to demonstrate the benefits of GSHP systems and innovative technologies for cost reduction and/or performance improvement. This paper highlights the findings of a case study of one of the ARRA-funded GSHP demonstration projects, a ground-source variable refrigerant flow (GS-VRF) system installed at the Human Health Building at Oakland University in Rochester, Michigan. This case study is based on the analysis of measured performance data, maintenance records, construction costs, and simulations of the energy consumption of conventional central heating, ventilation, and air-conditioning (HVAC) systems providing the same level of space conditioning as the demonstrated GS-VRF system. The evaluated performance metrics include the energy efficiency of the heat pump equipment and the overall GS-VRF system, pumping performance, energy savings, carbon emission reductions, and cost-effectiveness of the GS-VRF system compared with conventional HVAC systems. This case study also identified opportunities for reducing uncertainties in the performance evaluation, improving the operational efficiency, and reducing the installed cost of similar GSHP systems in the future.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Biofidelic Digital Head Model Software

Researchers at Los Alamos have extensive capabilities in biofidelic modeling, simulation and software development for predicting traumatic brain injury (TBI). Since injury-causing experiments cannot be performed on live humans, and head dummies are incapable of predicting brain deformations/strain, our approach uses magnetic resonance imaging (MRI) data for the brain combined with advanced computational methods and substructure constitutive properties to identify harmful accelerations that may develop diffused axonal injury in the brain. The goal is to develop this capability to analyze the effectiveness of personal protective equipment (PPE) such as, helmets and the advanced energy-absorbing materials used in PPE to mitigate brain injury. Los Alamos is seeking a collaborative partner (through a cooperative research and development agreement) to develop this capability toward a potential technology that will have commercial viability.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2017

Solar Flare Prediction Model with Three Machine-learning Algorithms using Ultraviolet Brightening and Vector Magnetograms

We developed a flare prediction model using machine learning, which is optimized to predict the maximum class of flares occurring in the following 24 hr. Machine learning is used to devise algorithms that can learn from and make decisions on a huge amount of data. We used solar observation data during the period 2010–2015, such as vector magnetograms, ultraviolet (UV) emission, and soft X-ray emission taken by the Solar Dynamics Observatory and the Geostationary Operational Environmental Satellite . We detected active regions (ARs) from the full-disk magnetogram, from which ∼60 features were extracted with their time differentials, including magnetic neutral lines, the current helicity, the UV brightening, and the flare history. After standardizing the feature database, we fully shuffled and randomly separated it into two for training and testing. To investigate which algorithm is best for flare prediction, we compared three machine-learning algorithms: the support vector machine, k-nearest neighbors (k-NN), and extremely randomized trees. The prediction score, the true skill statistic, was higher than 0.9 with a fully shuffled data set, which is higher than that for human forecasts. It was found that k-NN has the highest performance among the three algorithms. The ranking of the feature importance showed that previous flare activity is most effective, followed by the length of magnetic neutral lines, the unsigned magnetic flux, the area of UV brightening, and the time differentials of features over 24 hr, all of which are strongly correlated with the flux emergence dynamics in an AR.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1979

Visual performance modeling in the human operator simulator

A brief description of the history of the development of the human operator simulator (HOS) model is presented. Features of the HOS micromodels that impact on the obtainment of visual performance data are discussed along with preliminary details on a HOS pilot model designed to predict the results of visual performance workload data obtained through oculometer studies on pilots in real and simulated approaches and landings.

Strieb, M. I.↗

NASA NTRS1986

Highlights of contractor initiatives in quality enhancement and productivity improvement

The NASA/Contractor Team efforts are presented as part of NASA's continuing effort to facilitate the sharing of quality and productivity improvement ideas among its contractors. This complilation is not meant to be a comprehensive review of contractor initiative nor does it necessarily express NASA's views. The submissions represent samples from a general survey, and were not edited by NASA. The efforts are examples of quality and productivity programs in private industry, and as such, highlight company efforts in individual areas. Topics range from modernization of equipment, hardware, and technology to management of human resources. Of particular interest are contractor initiatives which deal with measurement and evaluation data pertaining to quality and productivity performance.

Source record↗

NASA NTRS2010

Smart Ultrasound Remote Guidance Experiment (SURGE)- Concept of Operations Evaluation for Using Remote Guidance Ultrasound for Planetary Space Flight

Introduction Use of remote guidance (RG) techniques aboard the International Space Station (ISS) has enabled astronauts to collect diagnostic-level ultrasound images. Exploration class missions will require this cohort of (typically) non-formally trained sonographers to operate with greater autonomy given the longer communication delays (2 seconds for ISS vs. >6 seconds for missions beyond the Moon) and communication blackouts. To determine the feasibility and training requirements for autonomous ultrasound image collection by non-expert ultrasound operators, ultrasound images were collected from a similar cohort using three different image collection protocols: RG only, RG with a computer-based learning tool (LT), and autonomous image collection with LT. The groups were assessed for both image quality and time to collect the images. Methods Subjects were randomized into three groups: RG only, RG with LT, and autonomous with LT. Each subject received 10 minutes of standardized training before the experiment. The subjects were tasked with making the following ultrasound assessments: 1) bone fracture and 2) focused assessment with sonography in trauma (FAST) to assess a patient s abdomen. Human factors-related questionnaire data were collected immediately after the assessments. Results The autonomous group did not out-perform the two groups that received RG. The mean time for the autonomous group to collect images was less than the RG groups, however the mean image quality for the autonomous group was less compared to both RG groups. Discussion Remote guidance continues to produce higher quality ultrasound images than autonomous ultrasound operation. This is likely due to near-instant feedback on image quality from the remote guider. Expansion in communication time delays, however, diminishes the capability to provide this feedback, thus requiring more autonomous ultrasound operation. The LT has the potential to be an excellent training and coaching component for autonomous ultrasound image collection during exploration missions.

Hurst, Victor, IV↗

NASA NTRS2012

Challenges for Life Support Systems in Space Environments, Including Food Production

Environmental Control and Life Support Systems (ECLSS) refer to the technologies needed to sustain human life in space environments. Histor ically these technologies have focused on providing a breathable atmo sphere, clean water, food, managing wastes, and the associated monitoring capabilities. Depending on the space agency or program, ELCSS has sometimes expanded to include other aspects of managing space enviro nments, such as thermal control, radiation protection, fire detection I suppression, and habitat design. Other times, testing and providing these latter technologies have been associated with the vehicle engi neering. The choice of ECLSS technologies is typically driven by the mission profile and their associated costs and reliabilities. These co sts are largely defined by the mass, volume, power, and crew time req uirements. For missions close to Earth, e.g., low-Earth orbit flights, stowage and resupply of food, some 0 2, and some water are often the most cost effective option. But as missions venture further into spa ce, e.g., transit missions to Mars or asteroids, or surface missions to Moon or Mars, the supply line economics change and the need to clos e the loop on life support consumables increases. These are often ref erred to as closed loop or regenerative life support systems. Regardless of the technologies, the systems must be capable of operating in a space environment, which could include micro to fractional g setting s, high radiation levels, and tightly closed atmospheres, including perhaps reduced cabin pressures. Food production using photosynthetic o rganisms such as plants by nature also provides atmospheric regenerat ion (e.g., CO2 removal and reduction, and 0 2 production), yet to date such "bioregenerative" technologies have not been used due largely t o the high power requirements for lighting. A likely first step in te sting bioregenerative capabilities will involve production of small a mounts of fresh foods to supplement to crew's diet. As humans venture further into space, regenerative life support technologies will becom e more important, and gathering accurate data on their performance an d reliabilities will require long lead times. As we learn more about sustainable living in space, we almost certainly learn more about sust ainable living on Earth.

Wheeler, Raymond M.↗

NASA NTRS2012

I/O Performance Characterization of Lustre and NASA Applications on Pleiades

In this paper we study the performance of the Lustre file system using five scientific and engineering applications representative of NASA workload on large-scale supercomputing systems such as NASA s Pleiades. In order to facilitate the collection of Lustre performance metrics, we have developed a software tool that exports a wide variety of client and server-side metrics using SGI's Performance Co-Pilot (PCP), and generates a human readable report on key metrics at the end of a batch job. These performance metrics are (a) amount of data read and written, (b) number of files opened and closed, and (c) remote procedure call (RPC) size distribution (4 KB to 1024 KB, in powers of 2) for I/O operations. RPC size distribution measures the efficiency of the Lustre client and can pinpoint problems such as small write sizes, disk fragmentation, etc. These extracted statistics are useful in determining the I/O pattern of the application and can assist in identifying possible improvements for users applications. Information on the number of file operations enables a scientist to optimize the I/O performance of their applications. Amount of I/O data helps users choose the optimal stripe size and stripe count to enhance I/O performance. In this paper, we demonstrate the usefulness of this tool on Pleiades for five production quality NASA scientific and engineering applications. We compare the latency of read and write operations under Lustre to that with NFS by tracing system calls and signals. We also investigate the read and write policies and study the effect of page cache size on I/O operations. We examine the performance impact of Lustre stripe size and stripe count along with performance evaluation of file per process and single shared file accessed by all the processes for NASA workload using parameterized IOR benchmark.

Saini, Subhash↗

NASA NTRS2016

Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques

Accurate taxi time prediction is required for enabling efficient runway scheduling that can increase runway throughput and reduce taxi times and fuel consumptions on the airport surface. Currently NASA and American Airlines are jointly developing a decision-support tool called Spot and Runway Departure Advisor (SARDA) that assists airport ramp controllers to make gate pushback decisions and improve the overall efficiency of airport surface traffic. In this presentation, we propose to use Linear Optimized Sequencing (LINOS), a discrete-event fast-time simulation tool, to predict taxi times and provide the estimates to the runway scheduler in real-time airport operations. To assess its prediction accuracy, we also introduce a data-driven analytical method using machine learning techniques. These two taxi time prediction methods are evaluated with actual taxi time data obtained from the SARDA human-in-the-loop (HITL) simulation for Charlotte Douglas International Airport (CLT) using various performance measurement metrics. Based on the taxi time prediction results, we also discuss how the prediction accuracy can be affected by the operational complexity at this airport and how we can improve the fast time simulation model before implementing it with an airport scheduling algorithm in a real-time environment.

airport surface traffic↗

NASA NTRS2009

Management of Service Projects in Support of Space Flight Research

Goal:To provide human health and performance countermeasures, knowledge, technologies, and tools to enable safe, reliable, and productive human space exploration . [HRP-47051] Specific Objectives: 1) Develop capabilities, necessary countermeasures, and technologies in support of human space exploration, focusing on mitigating the highest risks to human health and performance. 2) Define and improve human spaceflight medical, environmental, and human factors standards. 3) Develop technologies that serve to reduce medical and environmental risks, to reduce human systems resource requirements (mass, volume, power, data, etc.) and to ensure effective human-system integration across exploration systems. 4) Ensure maintenance of Agency core competencies necessary to enable risk reduction in the following areas: A. Space medicine B. Physiological and behavioral effects of long duration spaceflight on the human body C. Space environmental effects, including radiation, on human health and performance D. Space "human factors" [HRP-47051]. Service projects can form integral parts of research-based project-focused programs to provide specialized functions. Traditional/classic project management methodologies and agile approaches are not mutually exclusive paradigms. Agile strategies can be combined with traditional methods and applied in the management of service projects functioning in changing environments. Creative collaborations afford a mechanism for mitigation of constrained resource limitations.

Love, J.↗