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Quantative metrics were determined for system control performance, workload for control, monitoring performance, and workload for monitoring. Pilot tasks were allocated for navigation and guidance of automated commercial V/STOL aircraft in all weather conditions using an optimal control model of the human operator to determine display elements and design.
Rendezvous during the unmanned space exploration missions, such as a Mars Rover/Sample Return will require a completely automatic system from liftoff to docking. A conceptual design of an automated rendezvous, proximity operations, and docking system is being implemented and validated at the Johnson Space Center (JSC). The emphasis is on the progress of the development and testing of a prototype system for control of the rendezvous vehicle during proximity operations that is currently being developed at JSC. Fuzzy sets are used to model the human capability of common sense reasoning in decision making tasks and such models are integrated with the expert systems and engineering control system technology to create a system that performs comparably to a manned system.
The Horizon Mission (HM) methodology was developed to provide a means of identifying and evaluating highly innovative, breakthrough technology concepts (BTCs) and for assessing their potential impact on advanced space missions. The methodology is based on identifying new capabilities needed by hypothetical 'horizon' space missions having performance requirements that cannot be met even by extrapolating known space technologies. Normal human evaluation of new ideas such as BTCs appears to be governed (and limited) by 'inner models of reality' defined as paradigms. Thus, new ideas are evaluated by old models. This paper describes the use of the HM Methodology to define possible future paradigms that would provide alternatives to evaluation by current paradigms. The approach is to represent a future paradigm by a set of new BTC-based capabilities - called a paradigm abstract. The paper describes methods of constructing and using the abstracts for evaluating BTCs for space applications and for exploring the concept of paradigms and paradigm shifts as a representation of technology innovation.
A simple analytical criterion is provided for deciding whether a human or automation is best for a failure detection task. The method is based on expected-value decision theory in much the same way as is signal detection. It requires specification of the probabilities of misses (false negatives) and false alarms (false positives) for both human and automation being considered, as well as factors independent of the choice--namely, costs and benefits of incorrect and correct decisions as well as the prior probability of failure. The method can also serve as a basis for comparing different modes of automation. Some limiting cases of application are discussed, as are some decision criteria other than expected value. Actual or potential applications include the design and evaluation of any system in which either humans or automation are being considered.
Two experiments were conducted to evaluate the deadline model for speed-accuracy tradeoffs. According to the deadline model, participants in speeded-response tasks terminate stimulus discrimination as soon as it has run to completion or as soon as a predetermined time deadline has arrived, whichever comes first. Speed is traded for accuracy by varying the time deadlines; short deadlines yield fast but sometimes inaccurate responses, whereas long deadlines allow for slow, accurate responses. A new prediction of this model, based on a comparison of reaction time distributions, was derived and tested in experiments involving the joint manipulation of speed stress and stimulus discriminability. Clear violations of this prediction were observed when participants made relative brightness judgments (Experiment 1) and when they made lexical decisions (Experiment 2), rejecting both the deadline model and the fast-guess model. Several alternative models for speed-accuracy tradeoffs, including random-walk and accumulator models, are compatible with the results.
Results are reported from a new paradigm that uses movement-related brain potentials to detect response preparation based on partial information. The paradigm uses a hybrid choice-reaction go/nogo procedure in which decisions about response hand and whether to respond are based on separate stimulus attributes. A lateral asymmetry in the movement-related brain potential was found on nogo trials without overt movement. The direction of this asymmetry depended primarily on the signaled response hand rather than on properties of the stimulus. When the asymmetry first appeared was influenced by the time required to select the signaled hand, and when it began to differ on go and nogo trials was influenced by the time to decide whether to respond. These findings indicate that both stimulus attributes were processed in parallel and that the asymmetry reflected preparation of the response hand that began before the go/nogo decision was completed.
NASA spacecraft design requirements for occupant protection are a combination of the Brinkley Dynamic Response Criteria and injury assessment reference values (IARV) extracted from anthropomorphic test devices (ATD). For the ATD IARVs, the requirements specify the use of the 5th percentile female Hybrid III and the 95th percentile male Hybrid III. Each of these ATDs is required to be fitted with an articulating pelvis (also known as the aerospace pelvis) and a straight spine. The articulating pelvis is necessary for the ATD to fit into spacecraft seats, while the straight spine is required as injury metrics for vertical accelerations are better defined for this configuration. Sled testing of the Hybrid III 5th Percentile Female Anthropomorphic Test Device (ATD) was performed at Wright-Patterson Air Force Base (WAPFB). Two 5th Percentile ATDs were tested, the Air Force Research Lab (AFRL) and NASA owned Hybrid III ATDs with aerospace pelvises. Testing was also conducted with a NASA-owned 95th Percentile Male Hybrid III with aerospace pelvis at WPAFB. Testing was performed using an Orion seat prototype provided by Johnson Space Center (JSC). A 5-point harness comprised of 2 inch webbing was also provided by JSC. For suited runs, a small and extra-large Advanced Crew Escape System (ACES) suit and helmet were also provided by JSC. Impact vectors were combined frontal/spinal and rear/lateral. Some pure spinal and rear axis testing was also performed for model validation. Peak accelerations ranged between 15 and 20-g. This range was targeted because the ATD responses fell close to the IARV defined in the Human-Systems Integration Requirements (HSIR) document. Rise times varied between 70 and 110 ms to assess differences in ATD responses and model correlation for different impact energies. The purpose of the test series was to evaluate the Hybrid III ATD models in Orion-specific landing orientations both with and without a spacesuit. The results of these tests were used by the NASA Engineering and Safety Center (NESC) to validate the finite element model (FEM) of the Hybrid III 5th Percentile Female ATD. Physical test data was compared to analytical predictions from simulations, and modelling uncertainty factors have been determined for each injury metric. Additionally, the test data has been used to further improve the FEM, particularly in the areas of the ATD preload, harness, and suit and helmet effects.
This work presents the design recipe, fabrication process and characterization of tissue-simulating materials, configured as a physical model to mimic the electrical and some physical properties of an abdominal cavity. The complete three layer design is called the human core model (HCM). To our knowledge, presented is the first hybrid skin-muscle phantom developed to mimic the electrical properties of the intervening tissue layers of an abdominal cavity within the frequency band of 1 GHz 2 GHz, a band of interest for human body sensing due to its deep detection depth. To complete the model, a liquid blood phantom was also developed and characterized in this frequency band. The phantoms were characterized with respect to complex dielectric constant using the Agilent 85070E dielectric probe kit, and the results compared to the Gabriel model, which is used as the standard for human tissue characterization. Since the tissue impedance is an important parameter for characterizing on-body sensors and near field antenna performance, the impedance of the phantoms were also calculated and presented in the analysis.
The NASA Glenn Research Center’s Power Architecture and Analysis Branch performs spacecraft electrical power system (EPS) performance analysis using the System Power Analysis for Capability Evaluation (SPACE) model. Accurately predicting EPS performance requires a wide range of inputs, including the spacecraft’s position, attitude, solar array temperature, load demand, and data on the EPS itself. By operating as a combined analysis tool with integrated orbit mechanics, attitude, shadowing, thermal, solar array, battery, and electrical component models, SPACE provides flexibility for the analyst to examine various scenarios without a time-consuming iterative process involving separate analysis tools and groups. SPACE also achieves short run times by tailoring the complexity of the included models, providing enough fidelity to ensure accurate EPS performance predictions without being bogged down by unnecessary detail. These advantages that SPACE has over similar power system models has contributed to its successful use on a variety of human spaceflight programs for over three decades, and makes it applicable across the development cycle from early conceptual design up through on-orbit operations.
Compositional verification is a promising approach to addressing the state explosion problem associated with model checking. One compositional technique advocates proving properties of a system by checking properties of its components in an assume-guarantee style. However, the application of this technique is difficult because it involves non-trivial human input. This paper presents a novel framework for performing assume-guarantee reasoning in an incremental and fully automated fashion. To check a component against a property, our approach generates assumptions that the environment needs to satisfy for the property to hold. These assumptions are then discharged on the rest of the system. Assumptions are computed by a learning algorithm. They are initially approximate, but become gradually more precise by means of counterexamples obtained by model checking the component and its environment, alternately. This iterative process may at any stage conclude that the property is either true or false in the system. We have implemented our approach in the LTSA tool and applied it to the analysis of a NASA system.
For many years, the robotic community sought to develop robots that can eventually operate autonomously and eliminate the need for human operators. However, there is an increasing realization that there are some tasks that human can perform significantly better but, due to associated hazards, distance, physical limitations and other causes, only robot can be employed to perform these tasks. Remotely performing these types of tasks requires operating robots as human surrogates. While current "hand master" haptic systems are able to reproduce the feeling of rigid objects, they present great difficulties in emulating the feeling of remote/virtual stiffness. In addition, they tend to be heavy, cumbersome and usually they only allow limited operator workspace. In this paper a novel haptic interface is presented to enable human-operators to "feel" and intuitively mirror the stiffness/forces at remote/virtual sites enabling control of robots as human-surrogates. This haptic interface is intended to provide human operators intuitive feeling of the stiffness and forces at remote or virtual sites in support of space robots performing dexterous manipulation tasks (such as operating a wrench or a drill). Remote applications are referred to the control of actual robots whereas virtual applications are referred to simulated operations. The developed haptic interface will be applicable to IVA operated robotic EVA tasks to enhance human performance, extend crew capability and assure crew safety. The electrically controlled stiffness is obtained using constrained ElectroRheological Fluids (ERF), which changes its viscosity under electrical stimulation. Forces applied at the robot end-effector due to a compliant environment will be reflected to the user using this ERF device where a change in the system viscosity will occur proportionally to the force to be transmitted. In this paper, we will present the results of our modeling, simulation, and initial testing of such an electrorheological fluid (ERF) based haptic device.
Constellation Energy and the United States Department of Energy (DOE) have established a public/private partnership to implement a pilot digital upgrade to replace legacy analog, safety-related reactor protection and emergency safety feature actuation systems (RPS/ESFAS) with modern digital systems. This effort is occurring at Constellation’s Limerick Generating Station. This project is being performed in accordance with industry processes that have been adapted to better support digital upgrades. These processes include IP-ENG-001, Standard Design Process, NISP-EN-04, Standard Digital Engineering Process and Electric Power Research Institute Report 3002011816, Digital Engineering Guide. The Light Water Reactor Sustainability (LWRS) Program at the Idaho National Laboratory (INL) has been supporting this effort. Latest INL HFE efforts in support of this project have focused on definition and implementation of the Human Factors Engineering (HFE) Program in support of Constellation design and related licensing efforts. This paper will present the development of the HFE 3D modeling execution of the Main Control Room safety upgrades. New equipment and modifications are planned for upgrading the new safety systems. 3D modeling allows for visualization of the new equipment and modifications to be shared with engineering and design teams for evaluation and review. Anthropometric considerations such as sight lines and functional reach can be evaluated directly using the 3D model. This effort has helped to provide visualizations for additional review with operations personnel and project stakeholders.
Utility owners and operators of commercial nuclear power plants in the United States (U.S.) are and will be modernizing their nuclear power plants by performing a digital transformation involving design of an integrated set of systems that together enable a technology centric operating plant. The Plant Modernization Pathway of the U.S. Department of Energy Light Water Reactor Sustainability Program has a strategic action plan that lays the groundwork for a digital transformation of the nuclear industry. The model for this transformation is an advanced concept of operations, with an end point vision, “To achieve the maximum aggregate benefit enabled by this digital transformation.” To achieve this, the digital infrastructure for a nuclear plant must be designed as an integrated set of systems that together enable a technology centric operating model. The digital transformation process obviously needs to involve technology considerations and systems engineering, but it also needs to include human and organizational expertise. Thus, human and organizational factors, including sociotechnical systems methods and techniques (e.g., Cognitive Systems Engineering, Systems Theoretic Accident Modeling and Processes, human systems integration, and Macroergonomics) need to be considered for digital transformation projects in order to effectively integrate human and organizational expertise efforts into the new work system that results from nuclear power plant digital modernization. That is, the work system is the basic unit of sociotechnical systems analysis and contains three components: personnel, technical, and organization and management. These components should be jointly optimized with respect to the interdependence of systems performance criteria of effectiveness, efficiency and safety. Joint optimization can be achieved through the application of three human and organization functions: knowledge representation, knowledge elicitation, and cross-functional integration. This report provides a strategic framework for effective integration of human and organizational expertise within nuclear power plant digital modernization efforts.
The Near Earth Asteroid (NEA) Scout is a solar sail mission whose objective is to scout at least one Near Earth Asteroid to help prepare for human missions to Near Earth Asteroids. NEA Scout will launch as a secondary payload on the first SLS-Orion mission. NEA Scout will perform a small trim maneuver shortly after deploy from the spent SLS upper stage using a cold gas propulsion system, but from that point on will depend entirely on the solar sail for thrust. As such, it is important to accurately characterize the thrust of the sail in order to achieve mission success. Additionally, the solar sail creates a relatively large solar disturbance torque that must be mitigated. For early mission design studies a flat plate model of the solar sail with a fixed center of pressure was adequate, but as mission concepts and the sail design matured, greater fidelity was required. Here we discuss the progress to a three-dimensional sail model that includes the effects of tension and thermal deformation that has been derived from a large structural Finite Element Model (FEM) developed by the Langley Research Center. We have found that the deformed sail membrane affects torque relatively much more than thrust; a flat plate model could potentially model thrust well enough to close mission design studies, but a three-dimensional solar sail is essential to control system design. The three-dimensional solar sail model revealed that thermal deformations of unshielded booms would create unacceptably large solar disturbance torques. The original large FEM model was used in control and mission simulations, but was resulted in simulations with prohibitive run times. This led us to adapt the Generalized Sail Model (GSM) of Rios-Reyes. A design reference sail model has been baselined for NEA Scout and has been used to design the mission and control system for the sailcraft. Additionally, since NEA Scout uses reaction wheels for attitude pointing and control, the solar torque model is essentially to successfully design the NEA Scout momentum management control system. We have also updated the estimate of diffusivity used for the aluminized sail material based on optical testing of wrinkled sail material. The model presented here represents the current state of the art of NASA's ability to model solar sail thrust and torque.
Gateway was intended to be humanity’s first space station around the Moon, but its development has been paused as the National Aeronautics and Space Administration (NASA) shifts focus to achieving the United States’ National Space Policy goals. Instead of an orbiting lunar outpost, NASA will now pursue the development of a lunar surface base to support a sustained human presence on the Moon. Before the program’s pause, Gateway’s Induced Environments team worked to ensure payloads and elements (i.e., modules) complied with induced environment requirements. Methods developed and insights gained from this work will have applicability to NASA’s Moon Base and the potential repurposing of Gateway elements and payloads, as well as to induced environments modeling for future space stations. The Gateway program’s induced environment included molecular contamination, electric thruster plume sputter and redeposition, and lunar dust transfer from the Human Landing System (HLS). Primary sources of external molecular contamination included materials outgassing, chemical thruster plume contamination, and vacuum venting. The focus of this paper will be on element- and payload-level materials outgassing analyses performed for Gateway Configuration 1, extending the previously-developed framework for Gateway system-level external molecular contamination modeling. Gateway Configuration 1 consisted of the Power and Propulsion Element (PPE) and the Habitation and Logistics Outpost (HALO). It also included payloads like the European Radiation Sensor Array (ERSA) attached to PPE and the Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES) attached to HALO. The element- and payload-level analyses to be introduced in this paper for HALO, HERMES, and ERSA enabled high-fidelity descriptions of Gateway’s external molecular contamination environment. Approaches to geometric modeling, meshing, outgassing rate assignment, molecular transport modeling, and analysis methodology will be presented. Element and payload contaminant deposition onto sensitive Gateway receiver surfaces will be summarized and results compared to induced environment requirements. While these results incorporate refinements made over the course of the program, they were not intended to be final. Therefore, modeling assumptions and inputs, potential improvements, and lessons-learned will be documented to inform future work on Moon Base, repurposed elements and payloads, and other space stations.
Large language models (LLMs) show promise in supporting differential diagnosis, but their performance is challenging to evaluate due to the unstructured nature of their responses, and their accuracy compared to existing diagnostic tools is not well characterized. To assess the current capabilities of LLMs to diagnose genetic diseases, we benchmarked these models on 5213 previously published case reports using the Phenopacket Schema, the Human Phenotype Ontology and Mondo disease ontology. Prompts generated from each phenopacket were sent to seven LLMs, including four generalist models and three LLMs specialized for medical applications. The same phenopackets were used as input to a widely used diagnostic tool, Exomiser, in phenotype-only mode. The best LLM ranked the correct diagnosis first in 23.6% of cases, whereas Exomiser did so in 35.5% of cases. While the performance of LLMs for supporting differential diagnosis has been improving, it has not reached the level of commonly used traditional bioinformatics tools. Future research is needed to determine the best approach to incorporate LLMs into diagnostic pipelines.
Understanding how microbes impact an ecosystem has improved through advances of molecular and genetic tools, but creating complex systems that emulate natural biology goes beyond current technology. In fact, many chemical, biological, and metabolic pathways of even model organisms are still poorly characterized. Even then, standard laboratory techniques for testing microbial impact on environmental change can have many drawbacks; they are time-consuming, labor intensive, and are at risk of contamination. By having an automated process, many of these problems can be reduced or even eliminated. We are developing a benchtop system that can run for long periods of time without the need for human intervention, involve multiple environmental stressors at once, perform real-time adjustments of stressor exposure based on current state of the population, and minimize contamination risks. Our prototype device allows operators to generate an analogue of real world micro-scale ecosystems that can be used to model the effects of disruptive environmental change on microbial ecosystems. It comprises of electronics, mechatronics, and fluidics based systems to control, measure, and evaluate the before and after state of microbial cultures from exposure to environmental stressors. Currently, it uses four parallel growth chambers to perform tests on liquid cultures. To measure the population state, optical sensors (LED/photodiode) are used. Its primary selection pressure is UV-C radiation, a well-studied stressor known for its cell- and DNA-damaging effects and as a mutagen. Future work will involve improving the current growth chambers, as well as implementing additional sensors and environmental stressors into the system. Full integration of multiple culture testing will allow inter-culture comparisons. Besides the temperature and OD sensors, other types of sensors can be integrated such as conductivity, biomass, pH, and dissolved gasses such as CO and O. Additional environmental stressor systems like temperature (extreme heat or cold), metal toxicity, and other forms of radiation will increase the scale and testing range.
Residential electricity demand at granular scales is driven by what people do and for how long. Accurately forecasting this demand for applications like microgrid management and demand response therefore requires generative models for activities that can produce realistic daily activity sequences, capturing both the timing and duration of human behavior. This paper develops a generative model of human activity sequences using nationally representative time-use diaries at a 10-min resolution. We use this model to quantify which demographic factors are most critical for improving predictive performance. We propose a hierarchical semi-Markov framework that addresses two key modeling challenges. First, a time-inhomogeneous Markov router learns the patterns of “which activity comes next.” Second, a semi-Markov hazard component explicitly models activity durations, capturing “how long” activities realistically last. To ensure statistical stability when data are sparse, the model pools information across related demographic groups and time blocks. The entire framework is trained and evaluated using survey design weights to ensure our findings are representative of the U.S. population. On a held-out test set, we demonstrate that explicitly modeling durations with the hazard component provides a substantial and statistically significant improvement over purely Markovian models. Furthermore, our analysis reveals a clear hierarchy of demographic factors: Sex, Day-Type, and Household Size provide the largest predictive gains, while Region and Season, though important for energy calculations, contribute little to predicting the activity sequence itself. The result is an interpretable and robust generator of synthetic activity traces, providing a high-fidelity foundation for downstream energy systems modeling.