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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

HydraGNN_Predictive_GFM_2024 - Ensemble of predictive graph foundation models for ground state atomistic materials modeling

We provide the ensemble of fifteen pre-trained graph foundation models (GFMs) for atomistic materials modeling applications. Each one of the fifteen GFMs has been trained on five open-source datasets that (once aggregated) amount to over 154 million atomistic structures, which cover over two-thirds of the natural elements of the periodic table and that comprises a broad set of organic and inorganic compounds. This vast set of atomistic structures comprises ground state configurations that are dynamically stable (i.e., equilibrated structures with atomic forces approximately close to zero values) as well as dynamically unstable structures (i.e., non-equilibrium structures with non-negligible non-zero values of atomic forces). The ensemble of datasets aggregated does NOT include excited states. The datasets have been curated to remove atomistic structures with spectral norm of the force tensor above 100 eV/angstrom. Moreover, a linear term of the energy was computed for each dataset using a linear regression model that uses the chemical concentration of each natural element as regressor. The linear term predicted by the linear regression model has been subtracted from each original energy value to perform a re-alignment of the energy values across different electronic structures approximation theories performed to generate the diverse multi-source, multi-fidelity datasets. The folder "ADIOS_files" contains the set of pre-processed datasets in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used for the development and training of GFMs in this work. The "ADIOS_files" directory contains 6 sub-directories named as follows: - ANI1x-v3.bp - MPTrj-v3.bp - OC2020-20M-v3.bp - OC2020-v3.bp - OC2022-v3.bp - qm7x-v3.bp Each sub-directory contains the pre-processed datasets converted in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used to the development, training, and performance testing of the ensemble go predictive graph foundation models. Each GFM was developed using HydraGNN (https://github.com/ORNL/HydraGNN) as underlying graph neural network (GNN) architecture. The multi-task learning (MTL) capability of HydraGNN was used to simultaneously train the GFMs on labeled values for direct predictions of energy (a total system property of an atomistic structure that measures the chemical stability) and atomic forces (an atomic level property of an atomistic structure that measures the dynamical stability). The hyper parameters of the GFM have been tuned using scalable hyperparameter optimization (HPO) algorithms implemented in the software DeepHyper (https://github.com/deephyper/deephyper). The pre-training of each HPO trial was performed using distributed data parallelism (DDP) to scale the training across 128 compute nodes of the exascale OLCF supercomputer Frontier. Each HPO trial was trained only for 10 epochs and an early stopping was performed to avoid wasting significant computational resources on GNN architectures that were clearly underperforming. For each HPO trial, the 'omnistat' tool developed by (AMD Research - Advanced Micro Device) was used to measure the total energy consumption in kWh. The ensemble of GFMs was obtained by selecting the fifteen best performing HPO trials. Four models have been selected for their clear advantage in accuracy, and these are the GFMs with IDs 229, 156, 147, 260. Additional eleven models have been selected based on judicious balance between accuracy and energy consumption needed for training, and these are the GFMs with IDs 165, 78, 137, 1, 175, 171, 181, 67, 179, 167, 351. Each selected GFM of the ensemble was continued to cumulate a total of at most 30 epochs. In some cases, the total number of epochs actually performed was les than 30 due to two combined factors: (1) the size of the GFM (i.e., the number of model parameters to train) and (2) the total wall-clock time for which the computational resources could be allocated on OLCF-Frontier. The "Ensemble_of_models" directory contains 15 sub-directories named as follows: - gfm_0.229 - gfm_0.156 - gfm_0.147 - gfm_0.260 - gfm_0.165 - gfm_0.78 - gfm_0.137 - gfm_0.1 - gfm_0.175 - gfm_0.171 - gfm_0.181 - gfm_0.67 - gfm_0.179 - gfm_0.167 - gfm_0.351 Each one of these sub-directories refers to one of the fifteen HPO trials that have been selected to continue the pre-training with at most 30 epochs. With each sub-directory associated with a specific HPO trial, the following files can be found: - config.json: file for argument parsing to develop and train an HydraGNN architecture - gfm_0.ID_epoch_N.pk: file with model parameters for HPO ID trial after N epochs of training The ensemble of fifteen GFM architectures was used for (1) ensemble averaging to stabilize the predictions of energy and atomic forces after pre-training for post-processing analysis and (2) ensemble uncertainty quantification (UQ). The code used to develop, pre-train, and load the pre-trained models for post-processing analysis is available on the ORNL-GitHub at the following link: https://github.com/ORNL/HydraGNN/tree/Predictive_GFM_2024

36 MATERIALS SCIENCE↗
DOE OSTI2024

Application of Modified Meshgraphnets for Subsurface Prediction during CO2 Sequestration

In the face of the increasingly dire consequences of anthropogenic climate change, capturing and storing carbon dioxide is paramount. However, several impediments exist to the safe and effective subsurface storage of CO2, such as cost of transport, identification of suitable sites for subsurface storage, and assessment of long-term risk from storage in subsurface aquifers. Accurate subsurface modeling is necessary to ensure that CO2 storage is both safe and effective. Still, such modeling has traditionally required either substantial time and computational power (numerical simulation) or a substantial amount of pre-existing data for training (machine learning models). Additionally, these models lack flexibility in dealing with both changes in discretization of the input data and generalizability beyond the data on which they are trained. In order to address these issues, this research applies graph neural networks (GNNs) to predict subsurface saturation and pressure during CO₂ injection in a model of the Illinois Basin-Decatur Project (IBDP). GNNs provide a flexible, intuitive method for representing and manipulating complex unstructured data, which is often found in many practical domain problems such as fluid flow and subsurface characterization. These unstructured grids are easily represented in GNNs by representing spatially-localized features such as permeability, porosity, saturation, and pressure as nodes in a graph and relationships between these properties as edges connecting these nodes. This research applies a specific GNN model called MeshGraphNets (MGN) to model the change in CO2 saturation and pressure over a 50-month time period (36 months of injection, 14 months post-injection). The MGN model leverages a message passing process that allows the network to learn both the spatial and temporal dynamics of this system simultaneously. Additionally, training on a limited dataset (64 realizations, 20 time points each) resulted in a high degree of accuracy in saturation prediction both within the same timeframe as the training (20 months, 0.039 average RMSE) and when projecting out to the end of injection (36 months, 0.053 average RMSE). Temporal predictions such as those generated by MGNs and other similar models are prone to accumulated error over time; in order to address this, a multi-step rollout (MSR) training process was applied to calculate training loss. This method mimics the forward prediction during inference by “rolling out” multiple time points in a single training step using the previous prediction as input to the MGN model. By calculating the loss several time steps forward from the current prediction, the model is forced to find a more stable state over time. Application of MSR to the MGN model resulted in an average 15% reduction in inference error over time during forward prediction. This study showcases the immense potential of GNNs as a game-changing methodology for predicting pressure and saturation evolution in CCS projects, ultimately paving the way for more sustainable and effective carbon storage solutions. Presentation prepared for the 2024 AiChE Annual Meeting, October 27 to November 1 2024, San Diego, CA.

Holcomb, Paul↗
NASA NTRS2023

The Value of a Spaceflight Clinical Decision Support System for Earth-Independent Medical Operations

As NASA prepares for crewed lunar missions over the next several years, plans are also underway to journey farther into deep space. Deep space exploration will require a paradigm shift in astronaut medical support toward progressively earth-independent medical operations (EIMO). The Exploration Medical Capability (ExMC) element of NASA’s Human Research Program (HRP) is investigating the feasibility and value of advanced capabilities to promote and enhance EIMO. Currently, astronauts rely on real-time communication with ground-based medical providers. However, as the distance from Earth increases, so do communication delays and disruptions. Moreover, resupply and evacuation will become increasingly complex, if not impossible, on deep space missions. In contrast to today’s missions in low earth orbit (LEO), where most medical expertise and decision-making are ground-based, an exploration crew will need to autonomously detect, diagnose, treat, and prevent medical events. Due to the sheer amount of pre-mission training required to execute a human spaceflight mission, there is often little time to devote exclusively to medical training. One potential solution is to augment the long duration exploration crew’s knowledge, skills, and abilities with a clinical decision support system (CDSS). An analysis of preliminary data indicates the potential benefits of a CDSS to mission outcomes when augmenting cognitive and procedural performance of an autonomous crew performing medical operations, and we provide an illustrative scenario of how such a CDSS might function.

Brian K. Russell↗
NASA NTRS1987

Training for long duration space missions

The successful completion of an extended duration manned mission to Mars will require renewed research effort in the areas of crew training and skill retention techniques. The current estimate of inflight transit time is about nine months each way, with a six month surface visit, an order of magnitude beyond previous U.S. space missions. Concerns arise when considering the level of skill retention required for highly critical, one time operations such as an emergency procedure or a Mars orbit injection. The factors responsible for the level of complex skill retention are reviewed, optimal ways of refreshing degraded skills are suggested, and a conceptual crew training design for a Mars mission is outlined. Currently proposed crew activities during a Mars mission were reviewed to identify the spectrum of skills which must be retained over a long time period. Skill retention literature was reviewed to identify those factors which must be considered in deciding when and which tasks need retraining. Task, training, and retention interval factors were identified. These factors were then interpreted in light of the current state of spaceflight and adaptive training systems.

Goldberg, Joseph H.↗
DOE OSTI2020

Root identification in minirhizotron imagery with multiple instance learning

In this study, multiple instance learning (MIL) algorithms to automatically perform root detection and segmentation in minirhizotron imagery using only image-level labels are proposed. Root and soil characteristics vary from location to location, and thus, supervised machine learning approaches that are trained with local data provide the best ability to identify and segment roots in minirhizotron imagery. However, labeling roots for training data (or otherwise) is an extremely tedious and time-consuming task. This paper aims to address this problem by labeling data at the image level (rather than the individual root or root pixel level) and train algorithms to perform individual root pixel level segmentation using MIL strategies. Three MIL methods (multiple instance adaptive cosine coherence estimator, multiple instance support vector machine, multiple instance learning with randomized trees) were applied to root detection and compared to non-MIL approaches. The results show that MIL methods improve root segmentation in challenging minirhizotron imagery and reduce the labeling burden. In our results, multiple instance support vector machine outperformed other methods. The multiple instance adaptive cosine coherence estimator algorithm was a close second with an added advantage that it learned an interpretable root signature which identified the traits used to distinguish roots from soil and did not require parameter selection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Generalization of Deep-Learning Models for Classification of Local Distance Earthquakes and Explosions across Various Geologic Settings

Although accurately classifying signals from earthquakes and explosions at local distance (<250 km) remains an important task for seismic network operations, the growing volume of available seismic data presents a challenge for analysts using traditional source discrimination techniques. In recent years, deep-learning models have proven effective at discriminating between low-magnitude earthquakes and explosions measured at local distances, but it is not clear how well these models are capable of generalizing across different geological settings. To address the issue of generalization between regions, we train deep-learning models (convolutional neural networks [CNNs]) on time–frequency representations (scalograms) of three-component earthquake and explosion signals from eight different regions in the continental United States. We explore scenarios where models are trained on data from all regions, individual regions, or all but one region. We find that although CNN models trained on individual regions do not necessarily generalize well across different settings, models trained on multiple regions that include diverse path coverage generalize to new regions, with station-level accuracy of up to 90% or more for data sets from unseen regions. In general, CNN-based discrimination models significantly outperform models based on uncorrected P/S ratio (measured in the 10–18 Hz frequency band), even when CNN models are tested on data from entirely unseen regions.

58 GEOSCIENCES↗
NASA NTRS1982

Lessons learned - Americans in space

Milestones in the learning process during development of spaceflight capability for different NASA missions are traced from the initial launch of the Mercury escape rocket to the advent of the Shuttle. A detached heat shield on the first manned Mercury orbital flight led to a decision for reentry with retrorockets attached, a situation which demonstrated a need for on-line communications with the spacecraft construction engineers. The Gemini program served to develop EVA techniques and docking procedures essential for the Apollo missions. Apollo 8 circled the moon after only one manned test flight of the equipment due to the success of the first flight. Lunar landing simulation training exposed the astronauts to situations beyond the human ability to respond. The training was justified when computer overload required the pilot to retake manual control of some spacecraft functions during the successful first lunar landing. All lessons previously learned were used to safely return the damaged Apollo 13 to earth. The Skylab missions provided an opportunity to maintain rotating teams of flight controllers on a 24-hr/day basis for periods of months. Finally, the six-year hiatus of U.S. manned space activities between the end of the Skylab missions and the first Shuttle flight provided time for incorporating previous lessons into new procedures, training strategies, and data resources.

Kranz, E.↗
NASA NTRS2004

Algorithm for Training a Recurrent Multilayer Perceptron

An improved algorithm has been devised for training a recurrent multilayer perceptron (RMLP) for optimal performance in predicting the behavior of a complex, dynamic, and noisy system multiple time steps into the future. [An RMLP is a computational neural network with self-feedback and cross-talk (both delayed by one time step) among neurons in hidden layers]. Like other neural-network-training algorithms, this algorithm adjusts network biases and synaptic-connection weights according to a gradient-descent rule. The distinguishing feature of this algorithm is a combination of global feedback (the use of predictions as well as the current output value in computing the gradient at each time step) and recursiveness. The recursive aspect of the algorithm lies in the inclusion of the gradient of predictions at each time step with respect to the predictions at the preceding time step; this recursion enables the RMLP to learn the dynamics. It has been conjectured that carrying the recursion to even earlier time steps would enable the RMLP to represent a noisier, more complex system.

Parlos, Alexander G.↗
DOE OSTI2020

A Fully Automatic Method for Rapidly Mapping Impacted Area by Natural Disaster

Deep learning based change detection methods have achieved the state-of-the-art performance in several recent studies. However, such methods usually are supervised, and therefore a large number of training samples is often a requisite. Manually preparing those training samples is not only expensive but also time-consuming, which does not fit the need of rapidly mapping the impacted area caused by nature disaster for further rescue mission and damage assessment. In this study, a fully automatic method was proposed to address the issue by automating training sample generation for mapping the impacted area caused by nature disaster. We used the 2011 tornado event in Joplin, Missouri, US, as an example of its application. The generated impacted area map was both visually and quantitatively evaluated against the ground truth data collected by US Federal Emergency Management Agency (FEMA). The results show that the map matches well with the FEMA ground truth data with 86% of major-damaged and destroyed buildings identified by FEMA on the ground also detected by this fully automatic framework using very high resolution (VHR) satellite images.

Liu, Tao↗
NASA NTRS1982

Vertical axis wind turbine drive train transient dynamics

Start up of a vertical axis wind turbine causes transient torque oscillations in the drive train with peak torques which may be over two and one half times the rated torque of the turbine. A computer code, based on a lumped parameter model of the drive train, was developed and tested for the low cost 17 meter turbine; the results show excellent agreement with field data. The code was used to predict the effect of a slip clutch on transient torque oscillations. It was demonstrated that a slip clutch located between the motor and brake can reduce peak torques by thirty eight percent.

Clauss, D. B.↗
NASA NTRS2011

Space Flight Resource Management Training for International Space Station Flight Controllers

Training includes both SFRM-dedicated lessons and SFRM training embedded into technical lessons. Goal is to reduce certification times by 50% and integrated simulations by 75-90%. SFRM is practiced, evaluated and debriefed in part task trainers and full-task simulation lessons. SFRM model and training are constantly being evaluated against student/management feedback, best practices from industry/ military, and latest research.

O'Keefe, William S.↗
NASA NTRSDate not supplied

Evaluation of Performance, Fatigue and Workload During Real-time, Reactive Telerobotic Mission Control Operations

Real-time, reactive telerobotic mission control operations require personnel to actively operate and manage remotely controlled vehicles or robots in real time. Due to the physical separation of the vehicle from the operator, such operations present many additional factors that can influence fatigue (i.e., degraded mental performance) and workload (i.e., mental and physical cost of task requirements), making it difficult to assess how long an individual can safely and accurately conduct operations. The upcoming Volatiles Investigating Polar Exploration Rover (VIPER) mission involves remotely controlling a lunar vehicle from an Earth-based mission control station. METHODS: In order to determine how long personnel could successfully maintain alertness and performance while operating a rover, we studied seven trained operators (i.e., 5 drivers, 2 real-time scientists) in a simulated mission control environment. We further assessed workload in order to determine whether perceived workload levels would change relative to time on task and by time of day. Operators completed two five-hour simulations in a randomized order, beginning at noon and at midnight. Performance was evaluated every 30 minutes using the psychomotor vigilance task (PVT), Karolinska Sleepiness Scale (KSS), and NASA Task Load Index (NASA-TLX). RESULTS: On average, participants rated themselves as sleepier (5.06 ± 2.28) on the night drives compared to the day drives (3.12 ± 1.44; P < .0001). Workload was rated higher during the day drives (37.93 ± 20.09) compared to night (32.09 ± 21.74; P < .01). Lastly, participants had no change in average reaction time between the two drives. CONCLUSIONS: From the analysis, we observed performance began to degrade after approximately three hours of driving. Our findings suggest that rotating drivers at least every three hours would be prudent to allow for breaks, and to minimize performance degradation, particularly during night shifts.

fatigue↗
DOE OSTI2021

Conditioned Simulation of Ground-Motion Time Series at Uninstrumented Sites Using Gaussian Process Regression

Ground-motion time series are essential input data in seismic analysis and performance assessment of the built environment. Because instruments to record free-field ground motions are generally sparse, methods are needed to estimate motions at locations with no available ground-motion recording instrumentation. In this study, given a set of observed motions, ground-motion time series at target sites are constructed using a Gaussian process regression (GPR) approach, which treats the real and imaginary parts of the Fourier spectrum as random Gaussian variables. Model training, verification, and applicability studies are carried out using the physics-based simulated ground motions of the 1906 Mw 7.9 San Francisco earthquake and Mw 7.0 Hayward fault scenario earthquake in northern California. Additionally, the method’s performance is further evaluated using the 2019 Mw 7.1 Ridgecrest earthquake ground motions recorded by the Community Seismic Network stations located in southern California. These evaluations indicate that the trained GPR model is able to adequately estimate the ground-motion time series for frequency ranges that are pertinent for most earthquake engineering applications. The trained GPR model exhibits proper performance in predicting the long-period content of the ground motions as well as directivity pulses.

58 GEOSCIENCES↗
NASA NTRS1994

Shared virtual environments for aerospace training

Virtual environments have the potential to significantly enhance the training of NASA astronauts and ground-based personnel for a variety of activities. A critical requirement is the need to share virtual environments, in real or near real time, between remote sites. It has been hypothesized that the training of international astronaut crews could be done more cheaply and effectively by utilizing such shared virtual environments in the early stages of mission preparation. The Software Technology Branch at NASA's Johnson Space Center has developed the capability for multiple users to simultaneously share the same virtual environment. Each user generates the graphics needed to create the virtual environment. All changes of object position and state are communicated to all users so that each virtual environment maintains its 'currency.' Examples of these shared environments will be discussed and plans for the utilization of the Department of Defense's Distributed Interactive Simulation (DIS) protocols for shared virtual environments will be presented. Finally, the impact of this technology on training and education in general will be explored.

Loftin, R. Bowen↗
NASA NTRS1988

An intelligent tutoring system for space shuttle diagnosis

An Intelligent Tutoring System (ITS) transcends conventional computer-based instruction. An ITS is capable of monitoring and understanding student performance thereby providing feedback, explanation, and remediation. This is accomplished by including models of the student, the instructor, and the expert technician or operator in the domain of interest. The space shuttle fuel cell is the technical domain for the project described below. One system, Microcomputer Intelligence for Technical Training (MITT), demonstrates that ITS's can be developed and delivered, with a reasonable amount of effort and in a short period of time, on a microcomputer. The MITT system capitalizes on the diagnostic training approach called Framework for Aiding the Understanding of Logical Troubleshooting (FAULT) (Johnson, 1987). The system's embedded procedural expert was developed with NASA's C-Language Integrated Production (CLIP) expert system shell (Cubert, 1987).

Johnson, William B.↗
NASA NTRS2002

Exercise Versus +Gz Acceleration Training

Decreased working capacity and "orthostatic" intolerance are two major problems for astronauts during and after landing from spaceflight in a return vehicle. The purpose was to test the hypotheses that (1) supine-passive-acceleration training, supine-interval-exercise plus acceleration training, and supine exercise plus acceleration training will improve orthostatic tolerance (OT) in ambulatory men; and that (2) addition of aerobic exercise conditioning will not influence this enhanced OT from that of passive-acceleration training. Seven untrained men (24-38 yr) underwent 3 training regimens (30 min/d x 5d/wk x 3wk on the human-powered centrifuge - HPC): (a) Passive acceleration (alternating +1.0 Gz to 50% Gzmax); (b) Exercise acceleration (alternating 40% - 90% V02max leg cycle exercise plus 50% of HPCmax acceleration); and (c) Combined intermittent exercise-acceleration at 40% to 90% HPCmax. Maximal supine exercise workloads increased (P < 0.05) by 8.3% with Passive, by 12.6% with Exercise, and by 15.4% with Combined; but maximal V02 and HR were unchanged in all groups. Maximal endurance (time to cessation) was unchanged with Passive, but increased (P < 0.05) with Exercise and Combined. Resting pre-tilt HR was elevated by 12.9% (P < 0.05) only after Passive training, suggesting that exercise training attenuated this HR response. All resting pre-tilt blood pressures (SBP, DBP, MAP) were not different pre- vs. post-training. Post-training tilt-tolerance time and HR were increased (P < 0.05) only with Passive training by 37.8% and by 29.1%, respectively. Thus, addition of exercise training attenuated the increased Passive tilt tolerance. Resting (pre-tilt) and post-tilt cardiac R-R interval, stroke volume, end-diastolic volume, and cardiac output were all uniformly reduced (P < 0.05) while peripheral resistance was uniformly increased (P < 0.05) pre-and post-training for the three regimens indicating no effect of any training regimen on those cardiovascular variables. Plasma volume (% delta) was uniformly decreased by 8% to 14% (P < 0.05) at tilt-tolerance pre- vs. post-training for all regimens indicating no effect of these training regimens on the level of vascular fluid shifts.

Greenleaf, John E.↗
NASA NTRS2002

Second Generation RLV Space Vehicle Concept

NASA has a long history of conducting development programs and projects in a consistent fashion. Systems Engineering within those programs and projects has also followed a given method outlined by such documents as the NASA Systems Engineering Handbook. The relatively new NASA Space Launch Initiative (SLI) is taking a new approach to developing a space vehicle, with innovative management methods as well as new Systems Engineering processes. With the program less than a year into its life cycle, the efficacy of these new processes has yet to be proven or disproven. At $776M for phase 1, SLI represents a major portion of the NASA focus; however, the new processes being incorporated are not reflected in the training provided by NASA to its engineers. The NASA Academy of Program and Project Leadership (APPL) offers core classes in program and project management and systems engineering to NASA employees with the purpose of creating a "knowledge community where ideas, skills, and experiences are exchanged to increase each other's capacity for strong leadership". The SLI program is, in one sense, a combination of a conceptual design program and a technology program. The program as a whole doesn't map into the generic systems engineering project cycle as currently, and for some time, taught. For example, the NASA APPL Systems Engineering training course teaches that the "first step in developing an architecture is to define the external boundaries of the system", which will require definition of the interfaces with other systems and the next step will be to "define all the components that make up the next lower level of the system hierarchy" where fundamental requirements are allocated to each component. Whereas, the SLI technology risk reduction approach develops architecture subsystem technologies prior to developing architectures. The higher level architecture requirements are not allowed to fully develop and undergo decomposition and allocation down to the subsystems before the subsystems must develop allocated requirements based on the highest level of requirements. In the vernacular of the project cycles prior to the mid 1990's, the architecture definition portion of the program appears to be at a generic Phase A stage, while the subsystems are operating at Phase B. Even the management structure of the SLI program is innovative in its approach to Systems Engineering and is not reflected in the APPL training modules. The SLI program has established a Systems Engineering office as an office separate from the architecture development or the subsystem technology development, while that office does have representatives within these other offices. The distributed resources of the Systems Engineering Office are co-located with the respective Project Offices. This template is intended to provide systems engineering as an integrated function at the Program Level. the program management of SLI and the MAT agree that "program/project managers and the systems engineering team must work closely together towards the single objective of delivering quality products that meet the customer needs". This paper will explore the differences between the methods being taught by NASA, which represent decades of ideas, and those currently in practice in SLI. Time will tell if the innovation employed by SLI will prove to be the model of the future. For now, it is suggested that the training of the present exercise the flexibility of recognizing the new processes employed by a major new NASA program.

Bailey, Michelle↗
NASA NTRS1972

Method for analyses of cyclic physiological data that are nonstationary in time.

The proposed method of studying biologic rhythms permits the biologist to view physiological data dynamically without assuming that the data are stationary in time. Vector representation of data points is employed, and the summation of the vectors (train of vectors) produces a summation dial that is able to detect dynamic changes in the time of the peak as well as random walks (arrhythmia).

Winget, C. M.↗