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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.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Atrial Fibrillation During an Exploration Class Mission

Background: A long-duration exploration class mission is fraught with numerous medical contingency plans. Herein, we explore the challenges of symptomatic atrial fibrillation (AF) occurring during an exploration class mission. The actions and resources required to ameliorate the situation, including the availability of appropriate pharmaceuticals, monitoring devices, treatment modalities, and communication protocols will be investigated. Challenges of Atrial Fibrillation during an Exploration Mission: Numerous etiologies are responsible for the initiation of AF. On Earth, we have the time and medical resources to evaluate and determine the causative situation for most cases of AF and initiate therapy accordingly. During a long-duration exploration class mission resources will be severely restricted. How is one to determine if new onset AF is due to recent myocardial infarction, pulmonary embolism, fluid overload, thyrotoxicosis, cardiac structural abnormalities, or CO poisoning? Which pharmaceutical therapy should be initiated and what potential side effects can be expected? Should anti-coagulation therapy be initiated? How would one monitor the therapeutic treatment of AF in microgravity? What training would medical officers require, and which communication strategies should be developed to enable the best, safest therapeutic options for treatment of AF during a long-duration exploration class mission? Summary: These questions will be investigated with expert opinion on disease elucidation, efficient pharmacology, therapeutic monitoring, telecommunication strategies, and mission cost parameters with emphasis on atrial fibrillation being just one illustration of the tremendous challenges that face a long-duration exploration mission. The limited crew training time, medical hardware, and drugs manifested to deal with such an event predicate that aggressive primary and secondary prevention strategies be developed to protect a multibillion-dollar asset like the International Space Station or a mission to the Moon or Mars. Learning Objectives: The audience will become familiar with the risks and challenges inherent to developing a therapeutic strategy for the treatment of atrial fibrillation during a long-term exploration class mission.

Lipset, Mark A.↗
NASA NTRSDate not supplied

Exploration Atmosphere Demonstration of A Multi-Functional Integrated Medical Device (Tempus Pro)

INTRODUCTION The Exploration Medical Capability (ExMC) element within the Human Research Program and the Exploration Medical Integrated Product Team (XMIPT) within the Mars Campaign Office seek to advance medical system design and risk-informed decision-making for exploration missions. This includes assessment of candidate devices and their compatibility within a variety of increasingly Earth-independent medical scenarios. The Tempus Pro (Remote Diagnostic Technologies, Ltd., Philips Corp., Farnborough, UK) is a commercial-off-the-shelf (COTS), multi-functional integrated medical device capable of vital signs monitoring, with built-in procedure support (iAssist), patient record and telemedicine communication features, and medical imaging (e.g., camera, laryngoscope, ultrasound) that meets multiple exploration medical capability needs. One of several hypobaric atmospheres being considered for exploration vehicles is 8.2 psi with 34% oxygen. To assess the useability of the Tempus Pro in such environments by individuals without formal medical training, the unit was tested in May and June of 2023 at the Johnson Space Center 20-Foot Exploration Atmosphere Chamber in conjunction with the Exploration Atmospheres-2 (EA-2) study. TECHNOLOGY DEMONSTRATION Eight subjects in the EA-2 study were assigned roles as the caregiver or patient – or both in the case of a self-exam – and caregivers were asked to place sensors for 3-Lead ECG, non-invasive blood pressure (NIBP), pulse oximetry (SpO2), and temperature using Tempus Pro’s iAssist. Depending on the procedure, additional tasks included an oropharyngeal exam of the throat with the laryngoscope and capturing ultrasound images of a cardiac subxiphoid view or of the user’s choice from a pre-specified list. Scenarios ranged from remote-guided to fully autonomous operations and surveyed the effectiveness of support, amount of support desired, and importance of support by type (e.g., written crew procedures, iAssist and device guidance, remote console guidance, etc.). Feedback from the subjects and the support personnel was also gathered to inform future designs for training and support and to determine what operations are plausible given different levels of each. An unweighted NASA task load index (TLX) was used to profile the demands of using the device, however the sample size does not support statistics. The research was exploratory in nature and qualitative information was the main goal. Calibration checks on the Tempus Pro were conducted before and after chamber testing to ensure the device was in useable condition. RESULTS All subjects performed their tasks successfully and found the Tempus Pro easy to use with the support provided. The calibration checks outside the chamber showed that the Tempus Pro remained unchanged and measurements inside the chamber were within normal values. Caregivers taking the NASA TLX reported mean scores ≤ 41/100 showing the tasks to be undemanding and less demanding with repeated use (~20/100). Novice users were able to easily connect sensors and take vital signs with the guidance from Tempus Pro’s iAssist feature. For more complex tasks, such as the oropharyngeal exam and ultrasound image acquisition, guidance beyond iAssist was needed, primarily from a supporting physician. Feedback on the importance of support types varied by person and scenario but greater than 50% of the support used by each was non-native to the device. Overall opinions were positive, but the ultrasound users expressed a lack of confidence in their results and 3 out of 4 desired more time, training, or support. Subjects found the sensors to be comfortable and comparable to prior experiences with such devices, except for the blood pressure cuff, which squeezed too tightly for uncomfortably long periods. Many observations provoked discussion, especially regarding ultrasound, that will aide decisions about future demonstrations.

R. S. Miller↗
NASA NTRS1977

Crew efficiency on first exposure to zero-gravity

Activation task performance of Skylab crews showed improved efficiency as experience was gained in weightlessness living. During three activation intervals, less than 12 man-hours were lost due to reduced efficiency (including the effects of motion sensitivity) while almost 200 man-hours of productive work were delivered. Work rate improved for tasks in which simulation and training time were extensive and for tasks which allowed zero-gravity operations to be optimized.

Garriott, O. K.↗
NASA NTRS1979

Learning with imperfectly labeled patterns

The problem of learning in pattern recognition using imperfectly labeled patterns is considered. The performance of the Bayes and nearest neighbor classifiers with imperfect labels is discussed using a probabilistic model for the mislabeling of the training patterns. Schemes for training the classifier using both parametric and non parametric techniques are presented. Methods for the correction of imperfect labels were developed. To gain an understanding of the learning process, expressions are derived for success probability as a function of training time for a one dimensional increment error correction classifier with imperfect labels. Feature selection with imperfectly labeled patterns is described.

Chittineni, C. B.↗
NASA NTRS1982

Survey of multi-function display and control technology

The NASA orbiter spacecraft incorporates a complex array of systems, displays and controls. The incorporation of discrete dedicated controls into a multi-function display and control system (MFDCS) offers the potential for savings in weight, power, panel space and crew training time. The technology applicable to the development of a MFDCS for orbiter application is surveyed. Technology thought to be applicable presently or in the next five years is highlighted. Areas discussed include display media, data handling and processing, controls and operator interactions and the human factors considerations which are involved in a MFDCS design. Several examples of applicable MFDCS technology are described.

Spiger, R. J.↗
NASA NTRS1982

Application of multi-function display and control technology

The NASA orbiter spacecraft incorporates a complex array of systems, displays, and controls. The incorporation of discrete dedicated controls into a multifunction display and control system (MFDCS) offers the potential for savings in weight, power, panel space, and crew training time. Technology identified as applicable to a MFDCS is applied to the orbiter orbital maneuvering system (OMS) and the electrical power distribution and control system (EPDCS) to derive concepts for a MFDCS design. Several concepts of varying degrees of performance and complexity are discussed and a suggested concept for further development is presented in greater detail. Both the hardware and software aspects and the human factors considerations of the designs are included.

Spiger, R. J.↗
NASA NTRS1983

NASA LeRC/Akron University Graduate Cooperative Fellowship Program and Graduate Student Researchers Program

On June 1, 1980, the University of Akron and the NASA Lewis Research Center (LERC) established a Graduate Cooperative Fellowship Program in the specialized areas of Engine Structural Analysis and Dynamics, Computational Mechanics, Mechanics of Composite Materials, and Structural Optimization, in order to promote and develop requisite technologies in these areas of engine technology. The objectives of this program are consistent with those of the NASA Engine Structure Program in which graduate students of the University of Akron participate by conducting research at Lewis. This report is the second on this grant and summarizes the second and third year research effort, which includes the participation of five graduate students where each student selects one of the above areas as his special field of interest. Each student is required to spend 30 percent of his educational training time at the NASA Lewis Research Center and the balance at the University of Akron. His course work is judiciously selected and tailored to prepare him for research work in his field of interest. A research topic is selected for each student while in residence at the NASA Lewis Research Center, which is also approved by the faculty of the University of Akron as his thesis topic for a Master's and/or a Ph.D. degree.

Fertis, D. G.↗
NASA NTRS1988

An expert system for a distributed real-time trainer

The problem addressed by this expert system concerns the expansion of capability of a Real Time Trainer for the Spacelab flight crew. As requirements for more models or fidelity are placed upon the system, expansion is necessary. The simulator can be expanded using a larger processor or by going to a distributed system and expand by adding additional processors. The distributed system is preferable because it is more economical and can be expanded in a more incremental manner. An expert system was developed to evaluate modeling and timing capability within a real time training simulator. The expert system is based upon a distributed configuration. Components of the modeled system are control tasks, network tasks, emulator tasks, processors, displays, and a network. The distributed module expert system (DMES) allows the configuring of processors, tasks, display use, keyboard use, and selection of alternate methods to update the data buffer. Modules can be defined with execution occurring in a specific processor on a network. The system consists of a knowledge front end editor to interactively generate or update the knowledge base, an inference engine, a display module, and a recording module.

Purinton, Steven C.↗
NASA NTRS1990

Adaptive Neurons For Artificial Neural Networks

Training time decreases dramatically. In improved mathematical model of neural-network processor, temperature of neurons (in addition to connection strengths, also called weights, of synapses) varied during supervised-learning phase of operation according to mathematical formalism and not heuristic rule. Evidence that biological neural networks also process information at neuronal level.

Tawel, Raoul↗
NASA NTRS1991

Advanced Software Development Workstation Project, phase 3

ACCESS provides a generic capability to develop software information system applications which are explicitly intended to facilitate software reuse. In addition, it provides the capability to retrofit existing large applications with a user friendly front end for preparation of input streams in a way that will reduce required training time, improve the productivity even of experienced users, and increase accuracy. Current and past work shows that ACCESS will be scalable to much larger object bases.

Source record↗
NASA NTRS1990

FACE computer simulation

A computer simulation of the FACE (Flexible Arm Controls Experiment) was conducted to assess its design for use in the Space Shuttle. The FACE is supposed to be a 14-ft long articulate structure with 4 degrees of freedom, consisting of shoulder pitch and yaw, elbow pitch, and wrist pitch. Kinematics of the FACE was simulated to obtain data on arm operation, function, workspace and interaction. Payload capture ability was modeled. The simulation indicates the capability for detailed kinematic simulation and payload capture ability analysis, and the feasibility of real-time simulation was determined. In addition, the potential for interactive real-time training through integration of the simulation with various interface controllers was revealed. At this stage, the flexibility of the arm was not yet considered.

Sadeh, Willy Z.↗
NASA NTRS1990

Pattern recognition of clouds and ice in polar regions

The study is based on AVHRR imagery and results from Landsat high-spatial-resolution scenes. Among the textual features investigated are the gray level difference vector (GLDV), and sum and difference histogram (SADH) approaches as well as gray level run length, spatial-coherence, and spectral-histogram measures. The traditional stepwise discriminant analysis and neural-network analysis are used for the identification of 20 Arctic surface and cloud classes. A principal-component analysis and hybrid architecture employing a modularized competitive learning layer are utilized. It is pointed out that the cloud-classification accuracy comparable to that of back-propagation could be achieved with a training time two orders of magnitude faster.

Welch, R. M.↗
NASA NTRS1992

Current training: Where are we?

Petroleum Helicopters, Inc. maintains a staff of 750 helicopter pilots. The initial, transition, upgrade, and recurrent training for these pilots requires a significant financial outlay. Since a major portion of that training is done to satisfy the requirements of FAR 61.57, 'Recent Flight Experience, Pilot in Command' and 135.297, 'Pilot in Command: Instrument Proficiency Check Requirements', much could be accomplished using an approved simulator. However, it is imperative that credit be given for training time spent in the simulators and that the device be realistic, practical, and affordable.

Golden, Gerald↗
NASA NTRS1992

Application of neural networks to prediction of advanced composite structures mechanical response and behavior

Two types of neural networks were used to evaluate acousto-ultrasonic (AU) data for material characterization and mechanical reponse prediction. The neural networks included a simple feedforward network (backpropagation) and a radial basis functions network. Comparisons of results in terms of accuracy and training time are given. Acousto-ultrasonic (AU) measurements were performed on a series of tensile specimens composed of eight laminated layers of continuous, SiC fiber reinforced Ti-15-3 matrix. The frequency spectrum was dominated by frequencies of longitudinal wave resonance through the thickness of the specimen at the sending transducer. The magnitude of the frequency spectrum of the AU signal was used for calculating a stress-wave factor based on integrating the spectral distribution function and used for comparison with neural networks results.

Cios, K. J.↗
NASA NTRS1992

Feature selection for neural networks using Parzen density estimator

A feature selection method for neural networks is proposed using the Parzen density estimator. A new feature set is selected using the decision boundary feature selection algorithm. The selected feature set is then used to train a neural network. Using a reduced feature set, an attempt is made to reduce the training time of the neural network and obtain a simpler neural network, which further reduces the classification time for test data.

Lee, Chulhee↗
NASA NTRS1993

A review and analysis of neural networks for classification of remotely sensed multispectral imagery

A literature survey and analysis of the use of neural networks for the classification of remotely sensed multispectral imagery is presented. As part of a brief mathematical review, the backpropagation algorithm, which is the most common method of training multi-layer networks, is discussed with an emphasis on its application to pattern recognition. The analysis is divided into five aspects of neural network classification: (1) input data preprocessing, structure, and encoding; (2) output encoding and extraction of classes; (3) network architecture, (4) training algorithms; and (5) comparisons to conventional classifiers. The advantages of the neural network method over traditional classifiers are its non-parametric nature, arbitrary decision boundary capabilities, easy adaptation to different types of data and input structures, fuzzy output values that can enhance classification, and good generalization for use with multiple images. The disadvantages of the method are slow training time, inconsistent results due to random initial weights, and the requirement of obscure initialization values (e.g., learning rate and hidden layer size). Possible techniques for ameliorating these problems are discussed. It is concluded that, although the neural network method has several unique capabilities, it will become a useful tool in remote sensing only if it is made faster, more predictable, and easier to use.

Paola, Justin D.↗
NASA NTRS1994

Neural network with dynamically adaptable neurons

This invention is an adaptive neuron for use in neural network processors. The adaptive neuron participates in the supervised learning phase of operation on a co-equal basis with the synapse matrix elements by adaptively changing its gain in a similar manner to the change of weights in the synapse IO elements. In this manner, training time is decreased by as much as three orders of magnitude.

Tawel, Raoul↗
NASA NTRS1996

Cloud Classification in Polar and Desert Regions and Smoke Classification from Biomass Burning Using a Hierarchical Neural Network

This research focuses on a new neural network scene classification technique. The task is to identify scene elements in Advanced Very High Resolution Radiometry (AVHRR) data from three scene types: polar, desert and smoke from biomass burning in South America (smoke). The ultimate goal of this research is to design and implement a computer system which will identify the clouds present on a whole-Earth satellite view as a means of tracking global climate changes. Previous research has reported results for rule-based systems (Tovinkere et at 1992, 1993) for standard back propagation (Watters et at. 1993) and for a hierarchical approach (Corwin et al 1994) for polar data. This research uses a hierarchical neural network with don't care conditions and applies this technique to complex scenes. A hierarchical neural network consists of a switching network and a collection of leaf networks. The idea of the hierarchical neural network is that it is a simpler task to classify a certain pattern from a subset of patterns than it is to classify a pattern from the entire set. Therefore, the first task is to cluster the classes into groups. The switching, or decision network, performs an initial classification by selecting a leaf network. The leaf networks contain a reduced set of similar classes, and it is in the various leaf networks that the actual classification takes place. The grouping of classes in the various leaf networks is determined by applying an iterative clustering algorithm. Several clustering algorithms were investigated, but due to the size of the data sets, the exhaustive search algorithms were eliminated. A heuristic approach using a confusion matrix from a lightly trained neural network provided the basis for the clustering algorithm. Once the clusters have been identified, the hierarchical network can be trained. The approach of using don't care nodes results from the difficulty in generating extremely complex surfaces in order to separate one class from all of the others. This approach finds pairwise separating surfaces and forms the more complex separating surface from combinations of simpler surfaces. This technique both reduces training time and improves accuracy over the previously reported results. Accuracies of 97.47%, 95.70%, and 99.05% were achieved for the polar, desert and smoke data sets.

Alexander, June↗