Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “human systems 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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS1971

New techniques for the analysis of manual control systems

Studies are summarized on the application of advanced analytical and computational methods to the development of mathematical models of human controllers in multiaxis manual control systems. Specific accomplishments include the following: (1) The development of analytical and computer methods for the measurement of random parameters in linear models of human operators. (2) Discrete models of human operator behavior in a multiple display situation were developed. (3) Sensitivity techniques were developed which make possible the identification of unknown sampling intervals in linear systems. (4) The adaptive behavior of human operators following particular classes of vehicle failures was studied and a model structure proposed.

Bekey, G. A.↗
NASA NTRS1983

Conceptual models of information processing

The conceptual information processing issues are examined. Human information processing is defined as an active cognitive process that is analogous to a system. It is the flow and transformation of information within a human. The human is viewed as an active information seeker who is constantly receiving, processing, and acting upon the surrounding environmental stimuli. Human information processing models are conceptual representations of cognitive behaviors. Models of information processing are useful in representing the different theoretical positions and in attempting to define the limits and capabilities of human memory. It is concluded that an understanding of conceptual human information processing models and their applications to systems design leads to a better human factors approach.

Stewart, L. J.↗
NASA NTRS1991

Requirements for psychological models to support design: Towards ecological task analysis

Cognitive engineering is largely concerned with creating environmental designs to support skillful and effective human activity. A set of necessary conditions are proposed for psychological models capable of supporting this enterprise. An analysis of the psychological nature of the design product is used to identify a set of constraints that models must meet if they can usefully guide design. It is concluded that cognitive engineering requires models with resources for describing the integrated human-environment system, and that these models must be capable of describing the activities underlying fluent and effective interaction. These features are required in order to be able to predict the cognitive activity that will be required given various design concepts, and to design systems that promote the acquisition of fluent, skilled behavior. These necessary conditions suggest that an ecological approach can provide valuable resources for psychological modeling to support design. Relying heavily on concepts from Brunswik's and Gibson's ecological theories, ecological task analysis is proposed as a framework in which to predict the types of cognitive activity required to achieve productive behavior, and to suggest how interfaces can be manipulated to alleviate certain types of cognitive demands. The framework is described in terms, and illustrated with an example from the previous research on modeling skilled human-environment interaction.

Kirlik, Alex↗
DOE OSTI2024

Envisioning U.S. Climate Predictions and Projections to Meet New Challenges

In the face of a changing climate, the understanding, predictions, and projections of natural and human systems are increasingly crucial to prepare and cope with extremes and cascading hazards, determine unexpected feedbacks and potential tipping points, inform long-term adaptation strategies, and guide mitigation approaches. Increasingly complex socio-economic systems require enhanced predictive information to support advanced practices. Such new predictive challenges drive the need to fully capitalize on ambitious scientific and technological opportunities. These include the unrealized potential for very high-resolution modeling of global-to-local Earth system processes across timescales, reduction of model biases, enhanced integration of human systems and the Earth Systems, better quantification of predictability and uncertainties; expedited science-to-service pathways, and co-production of actionable information with stakeholders. Enabling technological opportunities include exascale computing, advanced data storage, novel observations and powerful data analytics, including artificial intelligence and machine learning. Looking to generate community discussions on how to accelerate progress on U.S. climate predictions and projections, representatives of Federally-funded U.S. modeling groups outline here perspectives on a six-pillar national approach grounded in climate science that builds on the strengths of the U.S. modeling community and agency goals. This calls for an unprecedented level of coordination to capitalize on transformative opportunities, augmenting and complementing current modeling center capabilities and plans to support agency missions. Tangible outcomes include projections with horizontal spatial resolutions finer than 10 km, representing extremes and associated risks in greater detail, reduced model errors, better predictability estimates, and more customized projections to support next generation climate services.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2021

Marshall Space Flight Center Faculty Fellowship Program

The 2020 Marshall Aerospace Fellowship Program involved 11 faculty in the laboratories and departments at Marshall Space Flight Center and one faculty researcher working from Colorado. These faculty engineers and scientists worked with NASA collaborators on NASA projects, bringing new perspectives and solutions to bear. This Technical Memorandum is a compilation of nine research reports of the 2020 Marshall Aerospace Fellowship program, along with the Program Announcement (Appendix A) and the Program Description (Appendix B). The research affected the following five areas: (1) Materials (2) Spacecraft systems (3) Human-System Interactions (4) Vehicle systems (5) Space science Additive manufacturing was the single materials investigation during the summer of 2020. Spacecraft systems tasks dominated the fellows’ explorations: covering camera sensors for lunar surface mobility, a real-time system for navigation and mapping, flexible sensor modeling, and transmitter design for high data rate telemetry. One project concentrated on Human-System Interactions. Another fellow designed a model to predict heat leak into a cryogen tank, as in most vehicle systems. One of the scientists worked on a proposed space-based gravitational wave interferometer that will detect Galactic white dwarf binaries. Another space science fellow used a mature astrophysical code to synthesize galactic stellar populations. Our goal is to continue the Marshall Aerospace Program funded by Center project offices.

N. F. Six↗
NASA NTRSDate not supplied

Computational Models of Trustworthiness and Trust in Autonomous Cyber-Physical-Human Systems

In this paper, we propose an approach to developing a concept of actionable trust in multi-agent,cyber-physical-human systems in safety-critical and time-critical environment of air transportation. Actionable trust requires computational models of trustworthiness and trust, for use during system design and in real time, during operations. We describe the models, examine their computability and scalability, as well as what remains to be done.

Autonomous Systems↗
NASA NTRS1983

Models of human problem solving - Detection, diagnosis, and compensation for system failures

The role of the human operator as a problem solver in man-machine systems such as vehicles, process plants, transportation networks, etc. is considered. Problem solving is discussed in terms of detection, diagnosis, and compensation. A wide variety of models of these phases of problem solving are reviewed and specifications for an overall model outlined.

Rouse, W. B.↗
NASA NTRS2012

A Human View Model for Socio-Technical Interactions

The Human View was developed as an additional architectural viewpoint to focus on the human part of a system. The Human View can be used to collect and organize data in order to understand how human operators interact and impact the other elements of a system. This framework can also be used to develop a model to describe how humans interact with each other in network enabled systems. These socio-technical interactions form the foundation of the emerging area of Human Interoperability. Human Interoperability strives to understand the relationships required between human operators that impact collaboration across networked environments, including the effect of belonging to different organizations. By applying organizational relationship concepts from network theory to the Human View elements, and aligning these relationships with a model developed to identify layers of coalition interoperability, the conditions for different levels for Human Interoperability for network enabled systems can be identified. These requirements can then be captured in the Human View products to improve the overall network enabled system.

Handley, Holly A.↗
NASA NTRS2014

Evolutionary Agent-Based Simulation of the Introduction of New Technologies in Air Traffic Management

Accurate simulation of the effects of integrating new technologies into a complex system is critical to the modernization of our antiquated air traffic system, where there exist many layers of interacting procedures, controls, and automation all designed to cooperate with human operators. Additions of even simple new technologies may result in unexpected emergent behavior due to complex human/ machine interactions. One approach is to create high-fidelity human models coming from the field of human factors that can simulate a rich set of behaviors. However, such models are difficult to produce, especially to show unexpected emergent behavior coming from many human operators interacting simultaneously within a complex system. Instead of engineering complex human models, we directly model the emergent behavior by evolving goal directed agents, representing human users. Using evolution we can predict how the agent representing the human user reacts given his/her goals. In this paradigm, each autonomous agent in a system pursues individual goals, and the behavior of the system emerges from the interactions, foreseen or unforeseen, between the agents/actors. We show that this method reflects the integration of new technologies in a historical case, and apply the same methodology for a possible future technology.

Simulation↗
NASA NTRS1991

Recent technology products from Space Human Factors research

The goals of the NASA Space Human Factors program and the research carried out concerning human factors are discussed with emphasis given to the development of human performance models, data, and tools. The major products from this program are described, which include the Laser Anthropometric Mapping System; a model of the human body for evaluating the kinematics and dynamics of human motion and strength in microgravity environment; an operational experience data base for verifying and validating the data repository of manned space flights; the Operational Experience Database Taxonomy; and a human-computer interaction laboratory whose products are the display softaware and requirements and the guideline documents and standards for applications on human-computer interaction. Special attention is given to the 'Convoltron', a prototype version of a signal processor for synthesizing the head-related transfer functions.

Jenkins, James P.↗
DOE OSTI2021

Mathematical model of a personalized neoantigen cancer vaccine and the human immune system

Cancer vaccines are an important component of the cancer immunotherapy toolkit enhancing immune response to malignant cells by activating CD4 + and CD8 + T cells. Multiple successful clinical applications of cancer vaccines have shown good safety and efficacy. Despite the notable progress, significant challenges remain in obtaining consistent immune responses across heterogeneous patient populations, as well as various cancers. We present a mechanistic mathematical model describing key interactions of a personalized neoantigen cancer vaccine with an individual patient’s immune system. Specifically, the model considers the vaccine concentration of tumor-specific antigen peptides and adjuvant, the patient’s major histocompatibility complexes I and II copy numbers, tumor size, T cells, and antigen presenting cells. We parametrized the model using patient-specific data from a clinical study in which individualized cancer vaccines were used to treat six melanoma patients. Model simulations predicted both immune responses, represented by T cell counts, to the vaccine as well as clinical outcome (determined as change of tumor size). This model, although complex, can be used to describe, simulate, and predict the behavior of the human immune system to a personalized cancer vaccine.

60 APPLIED LIFE SCIENCES↗
NASA NTRSDate not supplied

Space Radiation and Central Nervous System Impacts: NASA Standards and Evidence

It is well understood that large radiation localized doses to the brain cause clinically significant impacts to the central nervous system in human populations. However, the effects in adults exposed to lower doses remain unclear due to lack of data in relevant human cohorts. The impact of exposure to high-energy particles is even less understood. NASA’s Human Research Program relies heavily on model systems to characterize the impacts of the space radiation environment on the human brain and how potential changes may effect mission success and long term health and well-being. Animal, cellular, and molecular experiments implicate multiple – and possibly related – mechanisms that mediate impacts to the central nervous system in model systems including, but not limited to inflammation, immune responses, oxidative stress, metabolism, myelination, molecule transport, electrophysiology, and a variety of “omic” changes. While animal studies demonstrate potential changes across a number of cognitive and behavioral domains the direct applicability to the astronaut population remains unclear. Furthermore data access experiments and model systems can be inconsistent and dependent on multiple experimental variables indicating a clear need for robust validation. To minimize potential impacts to astronauts NASA limits dose to the CNS based on a combination of terrestrial epidemiology informed by experimental evidence in model systems. To date no recommendations have been provided by the National Committee on Radiation Protection and Measurements. This presentation will provide an overview of NASA’s current dose limits for CNS exposure to space radiation as well as highlights of the current state of evidence and ongoing research.

S Robin Elgart↗
DOE OSTI2023

cTULIP: application of a human-based RNA-seq primary tumor classification tool for cross-species primary tumor classification in canine

The domestic dog, Canis familiaris, is quickly gaining traction as an advantageous model for use in the study of cancer, one of the leading causes of death worldwide. Naturally occurring canine cancers share clinical, histological, and molecular characteristics with the corresponding human diseases. In this study, we take a deep-learning approach to test how similar the gene expression profile of canine glioma and bladder cancer (BLCA) tumors are to the corresponding human tumors. We likewise develop a tool for identifying misclassified or outlier samples in large canine oncological datasets, analogous to that which was developed for human datasets. We test a number of machine learning algorithms and found that a convolutional neural network outperformed logistic regression and random forest approaches. We use a recently developed RNA-seq-based convolutional neural network, TULIP, to test the robustness of a human-data-trained primary tumor classification tool on cross-species primary tumor prediction. Our study ultimately highlights the molecular similarities between canine and human BLCA and glioma tumors, showing that protein-coding one-to-one homologs shared between humans and canines, are sufficient to distinguish between BLCA and gliomas. The results of this study indicate that using protein-coding one-to-one homologs as the features in the input layer of TULIP performs good primary tumor prediction in both humans and canines. Furthermore, our analysis shows that our selected features also contain the majority of features with known clinical relevance in BLCA and gliomas. Our success in using a human-data-trained model for cross-species primary tumor prediction also sheds light on the conservation of oncological pathways in humans and canines, further underscoring the importance of the canine model system in the study of human disease.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support

Sensitivity analysis (SA) is en route to becoming an integral part of mathematical modeling. The tremendous potential benefits of SA are, however, yet to be fully realized, both for advancing mechanistic and data-driven modeling of human and natural systems, and in support of decision making. In this perspective paper, a multidisciplinary group of researchers and practitioners revisit the current status of SA, and outline research challenges in regard to both theoretical frameworks and their applications to solve real-world problems. Six areas are discussed that warrant further attention, including (1) structuring and standardizing SA as a discipline, (2) realizing the untapped potential of SA for systems modeling, (3) addressing the computational burden of SA, (4) progressing SA in the context of machine learning, (5) clarifying the relationship and role of SA to uncertainty quantification, and (6) evolving the use of SA in support of decision making. An outlook for the future of SA is provided that underlines how SA must underpin a wide variety of activities to better serve science and society.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

LLMs and GenAI Tools to Depict Contributions of Human Systems to Spaceflight Tasks Execution

Recent advancements in Artificial Intelligence and Machine Learning (AI/ML) technologies, particularly Large Language Models (LLMs) capable of sophisticated syntax analysis, offer substantial potential in automating complex processes, thereby saving time and human resources. This study explores the development of an LLM-driven model designed to analyze and categorize a diverse set of Mars mission tasks into 18 predefined Human System Task Categories (HSTCs) based on their textual descriptions. As part of developing the Crew Health and Performance – Probabilistic Risk Assessment (CHP-PRA projects Performance Risk Model (PRisM) proof-of-concept, we established a framework to project performance scores from small-scale tests onto a preliminary list of Mars tasks. The foundation of our model was a comprehensive spreadsheet populated by NASA experts and clinicians, which detailed each Mars task alongside binary indicators of HSTC involvement. This dataset enabled the initial application of supervised ML, training and testing on existing HSTC labels. The HSTCs were originally defined from a medical system perspective, focusing on task impairments due to deteriorated human health. To expand our model's scope to include categories impacting performance, we face the challenge of generating binary labels (0 or 1) for new categories without pre-existing data. We address this by employing Generative AI (GenAI) software to determine whether a given task involved a new category by asking, "Does task A involve using category B?" We validate our approach by comparing the GenAI's binary classifications with the expert-provided labels for existing HSTCs. Notably, we utilize Ollama [4], a locally hosted GenAI tool that does not require cloud access, thus safeguarding NASA's proprietary data from unauthorized exposure. This study demonstrates the feasibility of leveraging cutting-edge AI tools to advance research, paving the way for automation and rapid decision-making in space exploration.

Mona Matar↗
NASA NTRS2000

The NASA Aviation Safety Program: Overview

In 1997, the United States set a national goal to reduce the fatal accident rate for aviation by 80% within ten years based on the recommendations by the Presidential Commission on Aviation Safety and Security. Achieving this goal will require the combined efforts of government, industry, and academia in the areas of technology research and development, implementation, and operations. To respond to the national goal, the National Aeronautics and Space Administration (NASA) has developed a program that will focus resources over a five year period on performing research and developing technologies that will enable improvements in many areas of aviation safety. The NASA Aviation Safety Program (AvSP) is organized into six research areas: Aviation System Modeling and Monitoring, System Wide Accident Prevention, Single Aircraft Accident Prevention, Weather Accident Prevention, Accident Mitigation, and Synthetic Vision. Specific project areas include Turbulence Detection and Mitigation, Aviation Weather Information, Weather Information Communications, Propulsion Systems Health Management, Control Upset Management, Human Error Modeling, Maintenance Human Factors, Fire Prevention, and Synthetic Vision Systems for Commercial, Business, and General Aviation aircraft. Research will be performed at all four NASA aeronautics centers and will be closely coordinated with Federal Aviation Administration (FAA) and other government agencies, industry, academia, as well as the aviation user community. This paper provides an overview of the NASA Aviation Safety Program goals, structure, and integration with the rest of the aviation community.

Shin, Jaiwon↗