Search NASA⌕ Search

SEARCH · Search NASA

Results for “Computational Human Performance Modeling”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

Bumper Implementation of LMEEM

The Hypervelocity Impact Technology (HVIT) group at the NASA Johnson Space Center (JSC) maintains the Bumper 3 computer program (referred to as “Bumper”) to perform Micrometeoroid and Orbital Debris (MMOD) risk assessments for spacecraft. To perform its calculations, this program requires detailed mathematical models of the space environments and can include those models representing orbital debris, micrometeoroids, and lunar ejecta. The lunar ejecta environment is a projectile environment specific to the lunar surface. Due to the Moon’s lack of an atmosphere, meteoroids frequently strike the lunar surface. When this happens, the impact ejects material radially away from the impact point, which can pose a potential impact hazard to surface-based hardware. This lunar ejecta environment was previously described in 1969, in NASA Special Publication SP-8013, which was used as the basis for a model that will be referred to in this document as the “SP-8013 environment model,” or simply, “SP-8013.” Since around 2020, this model has been used in MMOD risk assessments for the Human Landing System (HLS) program and other lunar surface programs. A new lunar meteoroid ejecta environment was developed via a computer code named Lunar Meteoroid Ejecta Engineering Model (LMEEM), produced by the Natural Environments Branch (NEB) at Marshall Space Flight Center (MSFC). A global lunar surface environment definition using LMEEM is to be provided in the Cross-Program Design Specification for Natural Environments (DSNE) Revision J in 2024. Contact the NASA MSFC Natural Environments Branch for an in-depth discussion of the LMEEM environment model and its derivation. This environment model will supersede SP-8013 for MMOD risk assessments. To perform such assessments, Bumper must read data from text files output by LMEEM and use that data in risk calculations. The purpose of this document is to: •Demonstrate verification that the Bumper code has properly implemented the LMEEM environment model (Section 3) •Discuss the LMEEM environment as understood through an initial environment file (Section 4) •Compare the calculated risk due to the SP-8013 and LMEEM models (Section 5)

Bumper↗
NASA NTRS1979

Adaptive allocation of decisionmaking responsibility between human and computer in multitask situations

As human and computer come to have overlapping decisionmaking abilities, a dynamic or adaptive allocation of responsibilities may be the best mode of human-computer interaction. It is suggested that the computer serve as a backup decisionmaker, accepting responsibility when human workload becomes excessive and relinquishing responsibility when workload becomes acceptable. A queueing theory formulation of multitask decisionmaking is used and a threshold policy for turning the computer on/off is proposed. This policy minimizes event-waiting cost subject to human workload constraints. An experiment was conducted with a balanced design of several subject runs within a computer-aided multitask flight management situation with different task demand levels. It was found that computer aiding enhanced subsystem performance as well as subjective ratings. The queueing model appears to be an adequate representation of the multitask decisionmaking situation, and to be capable of predicting system performance in terms of average waiting time and server occupancy. Server occupancy was further found to correlate highly with the subjective effort ratings.

Chu, Y.-Y.↗
NASA NTRS2001

Predicting Strength and Fatigue for Suited and Unsuited Conditions from Empirical Data

The need for longer and more labor-intensive extra-vehicular activities (EVA) is required for construction and maintenance of the International Space Station (ISS). Issues pertaining to human performance while wearing a space suit (EMU) for prolonged periods have become more important. This project was conducted to investigate how a pressurized Extra-vehicular Mobility Unit (EMU) affects human upper body joint strength and fatigue and how to predict it from computer models based on the data collected.

Maida, James C.↗
DOE OSTI2019

Performance Assessment Results for the Hanford Integrated Disposal Facility - 19134

In 2014, the US DOE Office of River Protection and its contractors began to develop a performance assessment for the near-surface disposal of low-level and mixed low-level waste at the Hanford Integrated Disposal Facility. The Hanford Integrated Disposal Facility is a doubly-lined landfill that was constructed between 2004 and 2006 to be the disposal facility for the vitrified low-activity waste containers that will be produced at the Hanford Waste Treatment and Immobilization Plant. The disposal facility is also expected to receive solid secondary waste produced at the Hanford Waste Treatment and Immobilization Plant and other solid wastes from site activities. The constructed facility is awaiting authorization to receive waste. Although no waste destined for the facility is currently being generated, operations cannot begin until the facility has received a Disposal Authorization Statement from US DOE and a permit modification from the State of Washington Department of Ecology. Both the Disposal Authorization Statement and permit modification require a demonstration that the system of engineered and natural features of the disposal facility will limit releases from the facility and be protective of human health and the environment for the next 1,000 to 10,000 years. Based on the analyses presented in the Integrated Disposal Facility Performance Assessment, US DOE issued a conditional Disposal Authorization Statement for the facility in June 2018. Demonstrating the long-term performance of the facility to be protective of human health and the environment was evaluated under the requirements of US DOE Order 435.1, Radioactive Waste Management. Computer simulations were performed to evaluate whether or not the disposal facility would comply with the performance objectives specified in the order. The evaluation was performed using numerical models to simulate source term releases from vitrified and cement-based waste forms. Source term releases were subsequently transported through an air pathway and a groundwater pathway to a point of compliance 100 meters from the disposed of waste. In addition, an analysis evaluating the consequences of an inadvertent intrusion into the waste was also performed. In accordance with US DoE's technical standard for performance assessments, deterministic and probabilistic analyses were performed to demonstrate that the Hanford Integrated Disposal Facility, together with the engineered features of the waste forms and natural features of the selected site, provides a reasonable expectation that the low-level waste and mixed low-level waste disposal will meet the radiological performance objectives established by US DOE. Post-closure doses to future members of the public were evaluated after the time of closure and included a 100-year period of institutional controls that limited exposure immediately following closure of the facility. Calculated doses to a future member of the public from the air pathway were below the 100 μSv/yr dose performance objective. Calculated doses to a future member of the public from all exposure pathways, including the air pathway and the groundwater pathway, were below the 250 μSv/yr dose performance objective. Doses following an inadvertent intrusion into the facility after the institutional control period were below the 5 mSv acute dose performance measure and the 1 mSv/yr chronic dose performance measure. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Landscape rules predict optimal superhighways for the first peopling of Sahul

Archaeological data and demographic modelling suggest that the peopling of Sahul required substantial populations, occurred rapidly within a few thousand years and encompassed environments ranging from hyper-arid deserts to temperate uplands and tropical rainforests. How this migration occurred and how humans responded to the physical environments they encountered have, however, remained largely speculative. By constructing a high-resolution digital elevation model for Sahul and coupling it with fine-scale viewshed analysis of landscape prominence, least-cost pedestrian travel modelling and high-performance computing, here we create over 125 billion potential migratory pathways, whereby the most parsimonious routes traversed emerge. Our analysis revealed several major pathways—superhighways—transecting the continent, that we evaluated using archaeological data. These results suggest that the earliest Australian ancestors adopted a set of fundamental rules shaped by physiological capacity, attraction to visually prominent landscape features and freshwater distribution to maximize survival, even without previous experience of the landscapes they encountered.

54 ENVIRONMENTAL SCIENCES↗
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 ↗
NASA NTRS2003

Biological Visualization, Imaging and Simulation(Bio-VIS) at NASA Ames Research Center: Developing New Software and Technology for Astronaut Training and Biology Research in Space

The Bio- Visualization, Imaging and Simulation (BioVIS) Technology Center at NASA's Ames Research Center is dedicated to developing and applying advanced visualization, computation and simulation technologies to support NASA Space Life Sciences research and the objectives of the Fundamental Biology Program. Research ranges from high resolution 3D cell imaging and structure analysis, virtual environment simulation of fine sensory-motor tasks, computational neuroscience and biophysics to biomedical/clinical applications. Computer simulation research focuses on the development of advanced computational tools for astronaut training and education. Virtual Reality (VR) and Virtual Environment (VE) simulation systems have become important training tools in many fields from flight simulation to, more recently, surgical simulation. The type and quality of training provided by these computer-based tools ranges widely, but the value of real-time VE computer simulation as a method of preparing individuals for real-world tasks is well established. Astronauts routinely use VE systems for various training tasks, including Space Shuttle landings, robot arm manipulations and extravehicular activities (space walks). Currently, there are no VE systems to train astronauts for basic and applied research experiments which are an important part of many missions. The Virtual Glovebox (VGX) is a prototype VE system for real-time physically-based simulation of the Life Sciences Glovebox where astronauts will perform many complex tasks supporting research experiments aboard the International Space Station. The VGX consists of a physical display system utilizing duel LCD projectors and circular polarization to produce a desktop-sized 3D virtual workspace. Physically-based modeling tools (Arachi Inc.) provide real-time collision detection, rigid body dynamics, physical properties and force-based controls for objects. The human-computer interface consists of two magnetic tracking devices (Ascention Inc.) attached to instrumented gloves (Immersion Inc.) which co-locate the user's hands with hand/forearm representations in the virtual workspace. Force-feedback is possible in a work volume defined by a Phantom Desktop device (SensAble inc.). Graphics are written in OpenGL. The system runs on a 2.2 GHz Pentium 4 PC. The prototype VGX provides astronauts and support personnel with a real-time physically-based VE system to simulate basic research tasks both on Earth and in the microgravity of Space. The immersive virtual environment of the VGX also makes it a useful tool for virtual engineering applications including CAD development, procedure design and simulation of human-system systems in a desktop-sized work volume.

Smith, Jeffrey↗
DOE OSTI2023

Efficient multi-scale representation of visual objects using a biologically plausible spike-latency code and winner-take-all inhibition

Deep neural networks have surpassed human performance in key visual challenges such as object recognition, but require a large amount of energy, computation, and memory. In contrast, spiking neural networks (SNNs) have the potential to improve both the efficiency and biological plausibility of object recognition systems. Here we present a SNN model that uses spike-latency coding and winner-take-all inhibition (WTA-I) to efficiently represent visual stimuli using multi-scale parallel processing. Mimicking neuronal response properties in early visual cortex, images were preprocessed with three different spatial frequency (SF) channels, before they were fed to a layer of spiking neurons whose synaptic weights were updated using spike-timing-dependent-plasticity. We investigate how the quality of the represented objects changes under different SF bands and WTA-I schemes. We demonstrate that a network of 200 spiking neurons tuned to three SFs can efficiently represent objects with as little as 15 spikes per neuron. Furthermore, studying how core object recognition may be implemented using biologically plausible learning rules in SNNs may not only further our understanding of the brain, but also lead to novel and efficient artificial vision systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

ML-based calibration and control of the GlueX Central Drift Chamber

The GlueX Central Drift Chamber (CDC) in Hall D at Jefferson Lab, used for detecting and tracking charged particles, is calibrated and controlled during data taking using a Gaussian process. The system dynamically adjusts the high voltage applied to the anode wires inside the chamber in response to changing environmental and experimental conditions such that the gain is stabilized. Control policies have been established to manage the CDC's behavior. These policies are activated when the model's uncertainty exceeds a configurable threshold or during human-initiated tests during normal production running. Finally, we demonstrate the system reduces the time detector experts dedicate to calibration of the data offline, leading to a marked decrease in computing resource usage without compromising detector performance.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Creating Continuous Integration Infrastructure for Software Development on U.S. Department of Energy High-Performance Computing Systems

The Exascale Computing Project (ECP) software deployment effort developed and advanced DevOps capabilities. One goal was to enable robust continuous integration (CI) workflows that span the protected high performance computing (HPC) environments found within many of the Department of Energy’s (DOE) national laboratories. This article highlights several challenges encountered with enabling automation, such as charging models for CI jobs, and meeting individualized security requirements that revolve around strongly associating running code with a human identity. Here, it also describes how the Jacamar CI tool evolved to meet latter requirements and became a key aspect of the solutions currently offered. Derived from this experience, we offer a conceptual framework for understanding current and future CI challenges at DOE facilities and offer suggestions for long-term solutions.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

SPACE-Gateway: Modeling the Electrical Performance of the Gateway Power & Propulsion Element (PPE) Solar Arrays

The System Power Analysis for Capability Evaluation (SPACE) computer model was originally developed in 1988 at NASA Glenn Research Center to model the International Space Station (ISS) electrical power system (EPS). Since then, SPACE development has continued with the rise of new NASA human spaceflight missions. Today, SPACE’s uniquely complex and robust solar array model is being leveraged to model the solar arrays on the Gateway Power & Propulsion Element (PPE) for NASA's Artemis Program. This presentation will discuss the model’s integrated features, including its solar cell current-voltage (IV) curve, degradation, temperature, and on-orbit shadowing components. The presentation will also discuss recent SPACE advancements to meet the needs of NASA's latest spaceflight missions.

SPACE↗
DOE OSTI2024

Effects of machine learning errors on human decision-making: manipulations of model accuracy, error types, and error importance

Abstract This study addressed the cognitive impacts of providing correct and incorrect machine learning (ML) outputs in support of an object detection task. The study consisted of five experiments that manipulated the accuracy and importance of mock ML outputs. In each of the experiments, participants were given the T and L task with T-shaped targets and L-shaped distractors. They were tasked with categorizing each image as target present or target absent. In Experiment 1, they performed this task without the aid of ML outputs. In Experiments 2–5, they were shown images with bounding boxes, representing the output of an ML model. The outputs could be correct (hits and correct rejections), or they could be erroneous (false alarms and misses). Experiment 2 manipulated the overall accuracy of these mock ML outputs. Experiment 3 manipulated the proportion of different types of errors. Experiments 4 and 5 manipulated the importance of specific types of stimuli or model errors, as well as the framing of the task in terms of human or model performance. These experiments showed that model misses were consistently harder for participants to detect than model false alarms. In general, as the model’s performance increased, human performance increased as well, but in many cases the participants were more likely to overlook model errors when the model had high accuracy overall. Warning participants to be on the lookout for specific types of model errors had very little impact on their performance. Overall, our results emphasize the importance of considering human cognition when determining what level of model performance and types of model errors are acceptable for a given task.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2003

Predicting and Managing Lighting and Visibility for Human Operations in Space

Lighting is critical to human visual performance. On earth this problem is well understood and solutions are well defined and executed. Because the sun rises and sets on average every 45 minutes during Earth orbit, humans working in space must cope with ~ extremely dynamic lighting conditions varying from very low light conditions to severe glare and contrast conditions. For critical operations, it is essential that lighting conditions be predictable and manageable. Mission planners need to detelmine whether low-light video cameras are required or whether additional luminaires, or lamps, need to be flown . Crew and flight directors need to have up to date daylight orbit time lines showing the best and worst viewing conditions for sunlight and shadowing. Where applicable and possible, lighting conditions need to be part of crew training. In addition, it is desirable to optimize the quantity and quality of light because of the potential impacts on crew safety, delivery costs, electrical power and equipment maintainability for both exterior and interior conditions. Addressing these issues, an illumination modeling system has been developed in the Space Human Factors Laboratory at ASA Johnson Space Center. The system is the integration of a physically based ray-tracing package ("Radiance"), developed at the Lawrence Berkeley Laboratories, a human factors oriented geometric modeling system developed by NASA and an extensive database of humans and their work environments. Measured and published data has been collected for exterior and interior surface reflectivity; luminaire beam spread distribution, color and intensity and video camera light sensitivity and has been associated with their corresponding geometric models. Selecting an eye-point and one or more light sources, including sun and earthshine, a ~ snapshot of the light energy reaching the surfaces or reaching the eye point is computed. This energy map is then used to extract the required information needed for useful predictions. Using a validated, comprehensive illumination model integrated with empirically derived data, predictions of lighting and viewing conditions have been successfully used for Shuttle and Space Station planning and assembly operations. It has successfully balanced the needs for adequate human performance with the utili zation of resources. Keywords: Modeling, ray tracing, luminaires, refl ectivity, luminance, illuminance.

Maida, James C.↗
DOE OSTI2015

SU-E-T-785: Using Systems Engineering to Design HDR Skin Treatment Operation for Small Lesions to Enhance Patient Safety

Purpose: Using systems engineering to design HDR skin treatment operation for small lesions using shielded applicators to enhance patient safety. Methods: Systems engineering is an interdisciplinary field that offers formal methodologies to study, design, implement, and manage complex engineering systems as a whole over their life-cycles. The methodologies deal with human work-processes, coordination of different team, optimization, and risk management. The V-model of systems engineering emphasize two streams, the specification and the testing streams. The specification stream consists of user requirements, functional requirements, and design specifications while the testing on installation, operational, and performance specifications. In implementing system engineering to this project, the user and functional requirements are (a) HDR unit parameters be downloaded from the treatment planning system, (b) dwell times and positions be generated by treatment planning system, (c) source decay be computer calculated, (d) a double-check system of treatment parameters to comply with the NRC regulation. These requirements are intended to reduce human intervention to improve patient safety. Results: A formal investigation indicated that the user requirements can be satisfied. The treatment operation consists of using the treatment planning system to generate a pseudo plan that is adjusted for different shielded applicators to compute the dwell times. The dwell positions, channel numbers, and the dwell times are verified by the medical physicist and downloaded into the HDR unit. The decayed source strength is transferred to a spreadsheet that computes the dwell times based on the type of applicators and prescribed dose used. Prior to treatment, the source strength, dwell times, dwell positions, and channel numbers are double-checked by the radiation oncologist. No dosimetric parameters are manually calculated. Conclusion: Systems engineering provides methodologies to effectively design the HDR treatment operation that minimize human intervention and improve patient safety.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1997

Modeling Visual, Vestibular and Oculomotor Interactions in Self-Motion Estimation

A computational model of human self-motion perception has been developed in collaboration with Dr. Leland S. Stone at NASA Ames Research Center. The research included in the grant proposal sought to extend the utility of this model so that it could be used for explaining and predicting human performance in a greater variety of aerospace applications. This extension has been achieved along with physiological validation of the basic operation of the model.

Perrone, John↗
NASA NTRS2004

Human Motion Tracking at Marshall Space Flight Center's Collaborative Engineering Center ANVIL

The installation and use of electromagnetic human motion trackers requires a specially designed and metal-free environment for optimal performance. Such an area is not readily available at the Marshall Space Flight Center Collaborative Engineering Center ANVIL. Our paper details a systems engineering approach to installing and operating Ascension Technologies Ethernet MotionStar tracking system in a sub-optimal environment, used with the JACK human computer model's motion capture capabilities. We also discuss how this system is integrated into the Marshall Space Flight Center's Human Engineering process.

Henderson, Steven J.↗
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↗