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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Sensory Bias Predicts Postural Stability, Anxiety, and Cognitive Performance in Healthy Adults Walking in Novel Discordant Conditions

We designed a gait training study that presented combinations of visual flow and support surface manipulations to investigate the response of healthy adults to novel discordant sensorimotor conditions. We aimed to determine whether a relationship existed between subjects visual dependence and their scores on a collective measure of anxiety, cognition, and postural stability in a new discordant environment presented at the conclusion of training (Transfer Test). A treadmill was mounted to a motion base platform positioned 2 m behind a large visual screen. Training consisted of three walking sessions, each within a week of the previous visit, that presented four 5-minute exposures to various combinations of support surface and visual scene manipulations, all lateral sinusoids. The conditions were scene translation only, support surface translation only, simultaneous scene and support surface translations in-phase, and simultaneous scene and support surface translations 180 out-of-phase. During the Transfer Test, the trained participants received a 2-minute novel exposure. A visual sinusoidal roll perturbation, with twice the original flow rate, was superimposed on a sinusoidal support surface roll perturbation that was 90 out of phase with the scene. A high correlation existed between normalized torso translation, measured in the scene-only condition at the first visit, and a combined measure of normalized heart rate, stride frequency, and reaction time at the transfer test. Results suggest that visually dependent participants experience decreased postural stability, increased anxiety, and increased reaction times compared to their less visually dependent counterparts when negotiating novel discordant conditions.

Brady, Rachel A.↗
NASA NTRS2011

Social-Cognitive Biases in Simulated Airline Luggage Screening

This study illustrated how social cognitive biases affect the decision making process of air1ine luggage screeners. Participants (n = 96) performed a computer simulated task to detect hidden weapons in 200 x-ray images of passenger luggage. Participants saw each image for two (high time pressure) or six seconds (low time pressure). Participants observed pictures of the "passenger" who owns the luggage . The "pre-anchor group" answered questions about the passenger before the luggage image appeared, the "post-snchor" group answered questions after the luggage appeared, and the "no-anchor group" answered no questions. Participants either stopped or did not stop the bag. and rated their confidence in their decision. Participants under high time pressure had lower hit rates and higher false alarms, Significant differences between the pre-, no-, and post-anchor groups were based on the gender and race of the passengers. Participants had higher false alarm rates in response to male than female passengers.

Brown, Jeremy R.↗
NASA NTRS2012

Real-Time Cognitive Computing Architecture for Data Fusion in a Dynamic Environment

A novel cognitive computing architecture is conceptualized for processing multiple channels of multi-modal sensory data streams simultaneously, and fusing the information in real time to generate intelligent reaction sequences. This unique architecture is capable of assimilating parallel data streams that could be analog, digital, synchronous/asynchronous, and could be programmed to act as a knowledge synthesizer and/or an "intelligent perception" processor. In this architecture, the bio-inspired models of visual pathway and olfactory receptor processing are combined as processing components, to achieve the composite function of "searching for a source of food while avoiding the predator." The architecture is particularly suited for scene analysis from visual data and odorant.

Duong, Tuan A.↗
NASA NTRS2012

System and method for cognitive processing for data fusion

A system and method for cognitive processing of sensor data. A processor array receiving analog sensor data and having programmable interconnects, multiplication weights, and filters provides for adaptive learning in real-time. A static random access memory contains the programmable data for the processor array and the stored data is modified to provide for adaptive learning.

Duong, Tuan A.↗
NASA NTRS2015

Reconfigurable, Cognitive Software-Defined Radio

Software-defined radio (SDR) technology allows radios to be reconfigured to perform different communication functions without using multiple radios to accomplish each task. Intelligent Automation, Inc., has developed SDR platforms that switch adaptively between different operation modes. The innovation works by modifying both transmit waveforms and receiver signal processing tasks. In Phase I of the project, the company developed SDR cognitive capabilities, including adaptive modulation and coding (AMC), automatic modulation recognition (AMR), and spectrum sensing. In Phase II, these capabilities were integrated into SDR platforms. The reconfigurable transceiver design employs high-speed field-programmable gate arrays, enabling multimode operation and scalable architecture. Designs are based on commercial off-the-shelf (COTS) components and are modular in nature, making it easier to upgrade individual components rather than redesigning the entire SDR platform as technology advances.

Bhat, Arvind↗
NASA NTRS2017

Prediction of Cognitive States During Flight Simulation Using Multimodal Psychophysiological Sensing

The Commercial Aviation Safety Team found the majority of recent international commercial aviation accidents attributable to loss of control inflight involved flight crew loss of airplane state awareness (ASA), and distraction was involved in all of them. Research on attention-related human performance limiting states (AHPLS) such as channelized attention, diverted attention, startle/surprise, and confirmation bias, has been recommended in a Safety Enhancement (SE) entitled "Training for Attention Management." To accomplish the detection of such cognitive and psychophysiological states, a broad suite of sensors was implemented to simultaneously measure their physiological markers during a high fidelity flight simulation human subject study. Twenty-four pilot participants were asked to wear the sensors while they performed benchmark tasks and motion-based flight scenarios designed to induce AHPLS. Pattern classification was employed to predict the occurrence of AHPLS during flight simulation also designed to induce those states. Classifier training data were collected during performance of the benchmark tasks. Multimodal classification was performed, using pre-processed electroencephalography, galvanic skin response, electrocardiogram, and respiration signals as input features. A combination of one, some or all modalities were used. Extreme gradient boosting, random forest and two support vector machine classifiers were implemented. The best accuracy for each modality-classifier combination is reported. Results using a select set of features and using the full set of available features are presented. Further, results are presented for training one classifier with the combined features and for training multiple classifiers with features from each modality separately. Using the select set of features and combined training, multistate prediction accuracy averaged 0.64 +/- 0.14 across thirteen participants and was significantly higher than that for the separate training case. These results support the goal of demonstrating simultaneous real-time classification of multiple states using multiple sensing modalities in high fidelity flight simulators. This detection is intended to support and inform training methods under development to mitigate the loss of ASA and thus reduce accidents and incidents.

Harrivel, Angela R.↗
NASA NTRS2017

Formal Assurance for Cognitive Architecture Based Autonomous Agent

Autonomous systems are designed and deployed in different modeling paradigms. These environments focus on specific concepts in designing the system. We focus our effort in the use of cognitive architectures to design autonomous agents to collaborate with humans to accomplish tasks in a mission. Our research focuses on introducing formal assurance methods to verify the behavior of agents designed in Soar, by translating the agent to the formal verification environment Uppaal.

Bhattacharyya, Siddhartha↗
NASA NTRS2018

Managing Variability: A Cognitive Ethnography of the Work of Airline Dispatchers

Airline dispatchers' workflow is often described in broad terms like 'flight planning' and 'flight following'. Such high-level descriptions fail to recognize the number and complexity of tasks involved in these activities. An ethnographic study was conducted at three US airlines to understand the cognitive workload involved in flight planning. Fuel planning was identified as one of five key flight planning tasks. Fuel planning was conducted concurrently with other planning and monitoring tasks which often led to interruptions. Planning fuel was dynamic, with recalculations required whenever other factors varied (e.g., payload, route, alternates). This rework increased workload and opportunities for error while reducing efficiency. Four main factors contributed variability to fuel planning: contingency planning, load planning, pilots, and station operations. Strategies for managing variability included pattern identification, use of buffers, rounding up, and leveraging software tools. Software design often added to workload by forcing dispatchers to attend to low level tasks.

dispatcher↗
NASA NTRS2018

Metabrain for Embedded Cognition (MBEC)

This study presents the application of Hidden Markov Models (HMM) to determine specialized features without expert input. Specifically, the application of such a method for classification of high multi-path fading is targeted, for demonstrating the feasibility of such an approach. This is the first step in the development of a meta-brain for embedded cognition (M-BEC) suite that can be used to apply machine learning to various communication systems at NASA GRC. The project explores the concept of fading and how it affects communication systems in a negative way. Currently, supervised learning methods are used to study the effects of fading on space links. However, such models rely on expert features to make predictions as to the state of a link and whether fading is present. This project offers the possibility of having the HMM learn what characteristics are important and make predictions based on those characteristics. This project explores Hidden Markov Models, their theory and applications to various problems, as well as the underlying equations and assumptions. A preliminary result is presented and recommendations are made as to the use of such an approach for communications systems.

Propagation↗
NASA NTRS2019

Multi-Band and Multi-Function Reconfigurable Gallium Nitride Based Fully Solid-State Microwave Power Module for Cognitive Radio/Radar Platforms

This paper presents as a proof-of-concept (POC) the design, integration, and performance of a novel reconfigurable S-/X-band Gallium Nitride (GaN) based fully solid-state microwave power module (SSMPM) for the role as the transmit module in a cognitive radio (CR). The SSMPM synergistically integrates multiple amplifiers through diplexing and high power switches to enable a single SSMPM capable of functioning as both S-/X-band amplifiers for telemetry, tracking, and command (TT&C), telecommunications, and science data downlink or as X-band radar for proximity sensing onboard a planetary exploration spacecraft. Integration of an electric field shaping field plate (FP) onto the GaN high electron mobility transistors (HEMTs) in this SSMPM provides increased performance and reliability for operation in the harsh conditions of space. This SSMPM is capable of delivering saturated power (Psat) of 39 dBm (8 W continuous wave (CW)) at S-band, P(sub sat) of 43 dBm (20 W CW) at X-band, and P(sub sat) of >50 dBm (>100 W Pulsed) at X-band.

Simons, Rainee N.↗
NASA NTRS2019

Cognitive Communications for NASA Space Systems

The Cognitive Communications Project at the NASA Glenn Research Center develops applications of data-driven, non-deterministic methods to improve the autonomy of space communication. The project emphasizes development of decentralized space networks with artificial intelligence agents optimizing communication link throughput, data routing, and system-wide asset scheduling. This paper discusses the objectives, approaches, and opportunities of the research to address growing needs of the space communications community.

Chelmins, David↗
NASA NTRSDate not supplied

Aerospace Cognitive Engineering Laboratory (ACELAB) Simulator for Electric Vertical Takeoff and Landing (eVOTL) Research and Development

A new generation of aerospace innovators are looking for ways to quickly and efficiently transport people in a safe and environmentally friendly manner. In the not-too-distant future, passengers and goods are expected to routinely fly aboard a new breed of cleaner, smarter air vehicles. This represents a new and significant challenge to the Federal Aviation Agency (FAA) which is responsible for aircraft certification, pilot licensing, operating approval and airspace integration. To help streamline this process, NASA has formulated its Advanced Air Mobility (AAM) project to provide research capabilities for development and evaluation of these new concepts and an environment where industry and regulators can work together to understand the requirements and work toward consensus standards for the new market. This paper will describe the development of the Aerospace Cognitive Engineering Lab Rapid Automation Test (ACELeRATE) simulator. ACELeRATE is an adaptable fixed-base aircraft simulator focused on the investigation of the performance and interaction of pilots and increasingly automated aircraft systems. ACELeRATE can be re-configured to support various simulation environments. The simulator includes a simple reconfigurable cockpit placed within a 10-foot spherical dome with a cluster of real-time image generators, high-resolution displays and highly realistic scenery with the surrounding digital terrain and required cultural area details (e.g., hangars, runways, ramp areas, taxiways, test range apparatus, buildings with designated rooftop landing areas, and other man-made 3D structures). This paper will also describe the various hardware and software tools employed in the ACELeRATE simulator, including engineering tools used by NASA for electric Vertical Takeoff and Landing (eVTOL) vehicle equations of motion, wind-model simulation in an urban environment, as well as the various modeling techniques and tools used to quickly generate highly realistic 3D terrain models for low level flight including urban terrain and obstacle depictions.

AAM Simulation Cockpit↗
NASA NTRS2021

Dynamic Ensemble Prediction of Cognitive Performance in Space

Astronauts are exposed to a unique set of stressors in spaceflight. Microgravity, isolation, confinement, and environmental and operational hazards: all of these can impact sleep, vigilant attention, and alertness, which are critical to mission success. In this paper, we seek to understand the most important predictors of alertness over the course of a space mission, using self-reported, cognitive, and environmental data collected from 24 astronauts on 6-month missions to the International Space Station (ISS). Alertness was repeatedly and objectively assessed on the ISS with a brief 3-minute Psychomotor Vigilance Test (PVT) that is highly sensitive to sleep deprivation. To relate PVT performance to time-varying and sparsely-measured environmental, operational, and psychological covariates, we propose a n ensemble prediction model comprising of linear mixed effects regression, random forest, and functional concurrent regression models. An extensive cross-validation procedure reveals that this ensemble outperforms any one of its components alone. We also discover that a participant’s past performance, reported fatigue and stress, and temperature and radiation exposure were among the most important variables associated with alertness. This method is broadly applicable to environmental studies where the main goal is accurate, individualized prediction involving a mixture of person-level traits and irregularly measured time series.

Danni Tu↗
NASA NTRS2001

Switching between simple cognitive tasks: the interaction of top-down and bottom-up factors

How do top-down factors (e.g., task expectancy) and bottom-up factors (e.g., task recency) interact to produce an overall level of task readiness? This question was addressed by factorially manipulating task expectancy and task repetition in a task-switching paradigm. The effects of expectancy and repetition on response time tended to interact underadditively, but only because the traditional binary task-repetition variable lumps together all switch trials, ignoring variation in task lag. When the task-recency variable was scaled continuously, all 4 experiments instead showed additivity between expectancy and recency. The results indicated that expectancy and recency influence different stages of mental processing. One specific possibility (the configuration-execution model) is that task expectancy affects the time required to configure upcoming central operations, whereas task recency affects the time required to actually execute those central operations.

Cognition↗
NASA NTRS2016

A Cognitive Systems Engineering Approach to Developing HMI Requirements for New Technologies

This document examines the challenges inherent in designing and regulating to support human-automation interaction for new technologies that will deployed into complex systems. A key question for new technologies, is how work will be accomplished by the human and machine agents. This question has traditionally been framed as how functions should be allocated between humans and machines. Such framing misses the coordination and synchronization that is needed for the different human and machine roles in the system to accomplish their goals. Coordination and synchronization demands are driven by the underlying human-automation architecture of the new technology, which are typically not specified explicitly by the designers. The human machine interface (HMI) which is intended to facilitate human-machine interaction and cooperation, however, typically is defined explicitly and therefore serves as a proxy for human-automation cooperation requirements with respect to technical standards for technologies. Unfortunately, mismatches between the HMI and the coordination and synchronization demands of the underlying human-automation architecture, can lead to system breakdowns. A methodology is needed that both designers and regulators can utilize to evaluate the expected performance of a new technology given potential human-automation architectures. Three experiments were conducted to inform the minimum HMI requirements a detect and avoid system for unmanned aircraft systems (UAS). The results of the experiments provided empirical input to specific minimum operational performance standards that UAS manufacturers will have to meet in order to operate UAS in the National Airspace System (NAS). These studies represent a success story for how to objectively and systematically evaluate prototype technologies as part of the process for developing regulatory requirements. They also provide an opportunity to reflect on the lessons learned from a recent research effort in order to improve the methodology for defining technology requirements for regulators in the future. The biggest shortcoming of the presented research program was the absence of the explicit definition, generation and analysis of potential human-automation architectures. Failure to execute this step in the research process resulted in less efficient evaluation of the candidate prototypes technologies in addition to the complete absence of different approaches to human-automation cooperation. For example, all of the prototype technologies that were evaluated in the research program assumed a human-automation architecture that relied on serial processing from the automation to the human. While this type of human-automation architecture is typical across many different technologies and in many different domains, it ignores different architectures where humans and automation work in parallel. Defining potential human-automation architectures a priori also allows regulators to develop scenarios that will stress the performance boundaries of the technology during the evaluation phase. The importance of adding this step of generating and evaluating candidate human-automation architectures prior to formal empirical evaluation is discussed.

human systems integration↗