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NASA NTRS2016

Evidence Report: Risk of Performance Errors Due to Training Deficiencies

Substantial evidence supports the claim that inadequate training leads to performance errors. Barshi and Loukopoulos (2012) demonstrate that even a task as carefully developed and refined over many years as operating an aircraft can be significantly improved by a systematic analysis, followed by improved procedures and improved training (see also Loukopoulos, Dismukes, & Barshi, 2009a). Unfortunately, such a systematic analysis of training needs rarely occurs during the preliminary design phase, when modifications are most feasible. Training is often seen as a way to compensate for deficiencies in task and system design, which in turn increases the training load. As a result, task performance often suffers, and with it, the operators suffer and so does the mission. On the other hand, effective training can indeed compensate for such design deficiencies, and can even go beyond to compensate for failures of our imagination to anticipate all that might be needed when we send our crew members to go where no one else has gone before. Much of the research literature on training is motivated by current training practices aimed at current training needs. Although there is some experience with operations in extreme environments on Earth, there is no experience with long-duration space missions where crews must practice semi-autonomous operations, where ground support must accommodate significant communication delays, and where so little is known about the environment. Thus, we must develop robust methodologies and tools to prepare our crews for the unknown. The research necessary to support such an endeavor does not currently exist, but existing research does reveal general challenges that are relevant to long-duration, high-autonomy missions. The evidence presented here describes issues related to the risk of performance errors due to training deficiencies. Contributing factors regarding training deficiencies may pertain to organizational process and training programs for spaceflight, such as when training programs are inadequate or unavailable. Furthermore, failure to match between tasks on the one hand, and learning and memory abilities on the other hand is a contributing factor, especially when individuals' relative efficiency with which new information is acquired, and adjustments made in behavior or thinking, are inconsistent with mission demands. Thus, if training deficiencies are present, the likelihood of errors or of the inability to successfully complete a task increases. What's more, the overall risk to the crew, the vehicle, and the mission increases.

Barshi, Immanuel↗
NASA NTRS1995

Extravehicular activity training and hardware design consideration

Preparing astronauts to perform the many complex extravehicular activity (EVA) tasks required to assemble and maintain Space Station will be accomplished through training simulations in a variety of facilities. The adequacy of this training is dependent on a thorough understanding of the task to be performed, the environment in which the task will be performed, high-fidelity training hardware and an awareness of the limitations of each particular training facility. Designing hardware that can be successfully operated, or assembled, by EVA astronauts in an efficient manner, requires an acute understanding of human factors and the capabilities and limitations of the space-suited astronaut. Additionally, the significant effect the microgravity environment has on the crew members' capabilities has to be carefully considered not only for each particular task, but also for all the overhead related to the task and the general overhead associated with EVA. This paper will describe various training methods and facilities that will be used to train EVA astronauts for Space Station assembly and maintenance. User-friendly EVA hardware design considerations and recent EVA flight experience will also be presented.

Weightlessness↗
NASA NTRSDate not supplied

Enhancing Air Traffic Control Planning with Automatic Speech Recognition

The decisions made during the Federal Aviation Administration Air Traffic Control System Command Center's planning teleconferences hold significant sway over the National Airspace System. Held every two hours, these teleconferences convene air traffic managers and stakeholders from across the nation to discuss airspace conditions, weather, and constraints, leading to the formulation and adjustment of traffic management initiatives. Given the critical nature of these decisions, the need for accurate and efficient record-keeping is paramount. In recent years, the application of automatic speech recognition has gained popularity across diverse industries, including aviation. While traditional applications focus on transcribing air traffic control communication, this paper explores a unique application of automatic speech recognition by converting the audio from planning teleconferences into text transcriptions. This innovative approach addresses key challenges in the field, presenting potential benefits for quality assurance, real-time participation, and downstream natural language processing tasks. A notable breakthrough in the machine learning community, namely the transformer neural network architecture, forms the backbone of the proposed solution in this paper. The transformer architecture's role in this research represents a paradigm shift in the efficiency of automatic speech recognition models. By reducing the amount of in-domain training data required, this architecture allows for the fine-tuning of such models like Whisper, originally pretrained on vast English speech datasets. The adaptability of the transformer architecture proves invaluable in capturing the nuances of aviation terminology and specific language used in planning teleconferences. Leveraging the Whisper model as a baseline, our research details the fine-tuning and validation using a dataset comprising 20 hours of meticulously transcribed planning teleconferences. Notably, the baseline pretrained Whisper model exhibited a word error rate of 18.77%. Through the fine-tuning process, the model achieved a substantial improvement, demonstrating an impressive performance with a reduced word error rate of 6.82%. This substantial decrease in WER not only highlights the effectiveness of the transformer architecture but also emphasizes the practical advancements achieved through the application of automatic speech recognition in this specific domain. The utilization of automatic speech recognition in planning teleconferences in this work introduces several novelties. Firstly, the creation of text transcriptions offers a valuable tool for quality assurance and facilitates the efficient review of teleconferences. This is an important aspect of the proposed solution, given the time-sensitive and high-stakes nature of decisions made during these meetings. Furthermore, text-searchable transcriptions provide a streamlined approach for locating and validating critical information, potentially saving hours of manual effort in searching through audio recordings. Moreover, our research identifies a key use case for external facilities and stakeholders. In situations where attendance at the planning teleconference is not feasible, having access to text transcriptions in real-time or shortly after the teleconference ends, proves to be a time-saving and informative resource. This feature enhances collaboration and ensures that stakeholders can stay abreast of important discussions and decisions even in their absence. Despite the efficiency gains facilitated by the transformer architecture in automatic speech recognition technology, it is essential to acknowledge the human factors in data creation. Subject matter experts play a crucial role in accurately transcribing planning teleconferences due to the specificity and complexity of the information discussed. The research dataset, consisting of 20 hours of transcribed planning teleconferences, forms the foundation for fine-tuning and validating the Whisper model. The achieved word error rate of 6.82% demonstrates promising advancements, particularly in recognizing essential aviation terminology within the teleconferences. In conclusion, this paper presents a comprehensive exploration of the application of automatic speech recognition in Air Traffic Control System Command Center planning teleconferences, leveraging the transformer architecture for enhanced efficiency. The novel contributions lie in the improved accessibility of decision-making records, real-time participation opportunities for external stakeholders, and the potential for downstream natural language processing advancements. As the aviation industry continues to evolve, the integration of automatic speech recognition technologies holds the promise of revolutionizing decision-making processes and contributing to the overall safety and efficiency of air traffic management.

ATM↗
NASA NTRS1996

Daily Supine LBNP Treadmill Exercise Maintains Upright Exercise Capacity During 14 Days of Bed Rest

Exposure to microgravity or bed rest reduces upright exercise capacity. Exercise modes, durations, and intensities which will effectively and efficiently counteract such deconditioning are presently unresolved. We that daily supine treadmill interval training with lower body negative pressure (LBNP) would prevent reduction in upright exercise capacity during 14 days of 6 deg. head-down bed rest (BR). Eight healthy male subjects underwent two 14 day BR protocols separated by 3 months. In a crossover design, subjects either remained at strict BR or performed 40 min of daily exercise consisting of supine walking and running at intensities varying from 40-80% of pre-BR upright peak oxygen uptake (VO2). LBNP during supine exercise was used to provide 1.0 to 1.2 times body weight of footward force. An incremental upright treadmill test to measure submaximal and peak exercise responses was given pre- and post-BR. In the non-exercise condition, peak VO2 and time to exhaustion were reduced 16 +/- 4% and 10 +/- 1% (p less than 0.05), respectively, from pre-BR. With LBNP exercise these variables were not significantly different (NS) from pre-BR. During submaximal treadmill speeds after BR, heart rate was higher (11 +/- 11 bpm, p less than 0.05) and respiratory exchange ratio was elevated (p less than 0.05) in the no exercise condition. Both were maintained at pre-BR levels in the LBNP exercise condition (NS from pre-BR). Since this supine treadmill interval training with addition of LBNP maintained upright exercise responses and capacity during BR, this countermeasure may also be effective during space flight.

Ertl, Andy C.↗
NASA NTRS1998

Accelerated Training for Large Feedforward Neural Networks

In this paper we introduce a new training algorithm, the scaled variable metric (SVM) method. Our approach attempts to increase the convergence rate of the modified variable metric method. It is also combined with the RBackprop algorithm, which computes the product of the matrix of second derivatives (Hessian) with an arbitrary vector. The RBackprop method allows us to avoid computationally expensive, direct line searches. In addition, it can be utilized in the new, 'predictive' updating technique of the inverse Hessian approximation. We have used directional slope testing to adjust the step size and found that this strategy works exceptionally well in conjunction with the Rbackprop algorithm. Some supplementary, but nevertheless important enhancements to the basic training scheme such as improved setting of a scaling factor for the variable metric update and computationally more efficient procedure for updating the inverse Hessian approximation are presented as well. We summarize by comparing the SVM method with four first- and second- order optimization algorithms including a very effective implementation of the Levenberg-Marquardt method. Our tests indicate promising computational speed gains of the new training technique, particularly for large feedforward networks, i.e., for problems where the training process may be the most laborious.

Stepniewski, Slawomir W.↗
NASA NTRS1990

Hubble Space Telescope: Faint object spectrograph instrument handbook. Version 1.1

The Faint Object Spectrograph (FOS) has undergone substantial rework since the 1985 FOS Instrument Handbook was published, and we are now more knowledgeable regarding the spacecraft and instrument operations requirements and constraints. The formal system for observation specification has also evolved considerably, as the GTO programs were defined in detail. This supplement to the FOS Instrument Handbook addresses the important aspects of these changes, to facilitate proper selection and specification of FOS observing programs. Since the Handbook was published, the FOS red detector has been replaced twice, first with the best available spare in 1985 (which proved to have a poor, and steadily degrading red response), and later with a newly developed Digicon, which exhibits a high, stable efficiency and a dark-count rate less than half that of its predecessors. Also, the FOS optical train was realigned in 1987-88 to eliminate considerable beam-vignetting losses, and the collimators were both removed and recoated for greater reflectivity. Following the optics and detector rework, the FOS was carefully recalibrated (although only ambient measurements were possible, so the far-UV characteristics could not be re-evaluated directly). The resulting efficiency curves, including improved estimates of the telescope throughput, are shown. A number of changes in the observing-mode specifications and addition of several optional parameters resulted as the Proposal Instructions were honed during the last year. Target-brightness limitations, which have only recently been formulated carefully, are described. Although these restrictions are very conservative, it is imperative that the detector safety be guarded closely, especially during the initial stages of flight operations. Restrictions on the use of the internal calibration lamps and aperture-illumination sources (TA LEDs), also resulting from detector safety considerations, are outlined. Finally, many changes have been made to the instructions for target acquisition specification.

Ford, Holland C.↗
NASA NTRS1986

Learned pattern recognition using synthetic-discriminant-functions

A method of using synthetic-discriminant-functions to facilitate learning in a pattern recognition system is discussed. Learning is accomplished by continually adding images to the training set used for synthetic discriminant functions (SDF) construction. Object identification is performed by efficiently searching a library of SDF filters for the maximum optical correlation. Two library structures are discussed - binary tree and multilinked graph - along with maximum ascent, back-tracking, perturbation, and simulated annealing searching techniques. By incorporating the distortion invariant properties of SDFs within a library structure, a robust pattern recognition system can be produced.

Jared, David A.↗
NASA NTRS2020

Automated Cardiovascular Pathology Assessment using Semantic Segmentation and Ensemble Learning

Cardiac magnetic resonance imaging provides high spatial resolution, enabling improved extraction of important functional and morphological features for cardiovascular disease staging. Segmentation of ventricular cavities and myocardium in cardiac cine sequencing provides a basis to quantify cardiac measures such as ejection fraction. A method is presented that curtails the expense and observer bias of manual cardiac evaluation by combining semantic segmentation and disease classification into a fully automatic processing pipeline. The initial processing element consists of a robust dilated convolutional neural network architecture for voxel-wise segmentation of the myocardium and ventricular cavities. The resulting comprehensive volumetric feature matrix captures diagnostic clinical procedure data and is utilized by the final processing element to model a cardiac pathology classifier. Our approach evaluated anonymized cardiac images from a training data set of 100 patients (4 pathology groups, 1 healthy group, 20 patients per group) examined at the University Hospital of Dijon. The top average Dice index scores achieved were 0.940, 0.886, 0.849 for structure segmentation of the left ventricle (LV), myocardium and right ventricle (RV) respectively. A 5-ary pathology classification accuracy of 90% was recorded on an independent test set using the trained model. Performance results demonstrate potential for advanced machine learning methods to deliver accurate, efficient and reproducible cardiac pathological assessment.

Semantic Segmentation↗
NASA NTRS2020

Machine Learning-Based Predictive Analytics for Aircraft Engine Conceptual Design

Big data and artificial intelligence/machine learning are transforming the global business environment. Data is now the most valuable asset for enterprises in every industry. Companies are using data-driven insights for competitive advantage. With that, the adoption of machine learning-based data analytics is rapidly taking hold across various industries, producing autonomous systems that support human decision-making. This work explored the application of machine learning to aircraft engine conceptual design. Supervised machine-learning algorithms for regression and classification were employed to study patterns in an existing, open-source database of production and research turbofan engines, and resulting in predictive analytics for use in predicting performance of new turbofan designs. Specifically, the author developed machine learning-based analytics to predict cruise thrust specific fuel consumption (TSFC) and core sizes of high-efficiency turbofan engines, using engine design parameters as the input. The predictive analytics were trained and deployed in Keras, an open-source neural networks application program interface (API) written in Python, with Google’s TensorFlow (an open source library for numerical computation) serving as the backend engine. The promising results of the predictive analytics show that machine-learning techniques merit further exploration for application in aircraft engine conceptual design.

deep-learning↗
NASA NTRS2023

NASA Space Environment Analog for Training, Engineering, Science, and Technology (SEATEST) 6 Detailed Final Report

After more than 50 years since the last crewed lunar landing, plans for more missions to the moon are in development. For these missions, efficient and sustainable logistics will be critical. Additionally, innovative methods of cargo transfer to and from a lunar outpost should be considered for successfully establishing a permanent presence on the moon. SEATEST (Space Environment Analog for Training, Engineering, Science, and Technology) is an immersive mission-analogous operational atmosphere where buoyancy effects and supplemental weights can simulate partial gravity conditions similar to those astronauts will experience on the moon. SEATEST 6 took place at the University of Southern California (USC) Wrigley Marine Science Center on Santa Catalina Island from July 18-30, 2023. The analog was used to collect preliminary logistics data on two different offloading conceptual methods (a davit and a zipline) during a simulated lunar mission. Pre-test analysis indicated for a crew of two on a 14-day mission, approximately three Medium Pressurized Logistics Containers (MPLC) sized logistics containers (or a total of 37.5 single Cargo Transfer Bag Equivalents (CTBE)) would be needed to support a mission. A Computer-Aided Design (CAD) analysis was employed on the SEATEST airlock mockup to determine how many logistic containers would fit with two suited crewmembers, don/doff stands, and hatch operations. It was determined that for SEATEST, a total of 15 1.0 Small Pressurized Logistics Containers (SPLCs) and 8 2.0 SPLCs would adequately fit into the approximate 9.5 cubic meter airlock volume. This does not fully represent a complete 14-day logistic supply; however, it does provide a preliminary estimate to initiate design conversations between logistics teams and crew at this early stage of development. Data were collected in eight logistics transfer scenarios over two days with four scenarios per day. Five test subject crew participated in scenarios as pairs. Scenarios included two sizes of logistics containers – 1.0 SPLC (equivalent to a single Cargo Transfer Bag (CTB) and 2.0 SPLC (equivalent to two CTBs). Planed evaluations included the use of a logistics port compared to transfer through an Airlock hatch, offloading methods based on either a davit or a zipline system, choreography of cargo in the airlock to permit ingress and suit doffing, and dust removal protocols for an understanding of the overall impact to transfer ops. Data collected included objective data (task times for conducting overall tasks and subtasks, full audio/video of test activities, and inadvertent “dings” on hardware) and subjective data (crew consensus of: task acceptability and capability assessment ratings related to best practices, considerations, and constraints for EVA-driven logistics transfer ConOps, sim quality of the test environment, and more general debrief comments). The two logistic offloading transfer concepts (davit, zipline) presented both advantages and limitations. The davit’s flexibility in allowing the crew to pick up the containers without physical interaction was well regarded by the crew. Some limitations of select davit hardware components were noted, but the overall concept was acceptable. The zipline system proved to be the most efficient way of moving logistics from the lander to the airlock and eliminated the need for dust operations. However, extended and repetitive lifting of containers to the line could be fatiguing. In conclusion, logistics transfer could hypothetically be achieved without an offloading method; however, the time requirement for such operations would be prohibitive. Results of crew subjective feedback proposed a combined or hybrid davit/zipline method to increase efficiency.

Logistics↗
NASA NTRS1994

Communication Research in Aviation and Space Operations: Symptoms and Strategies of Crew Coordination

The day-to-day operators of today's aerospace systems work under increasing pressures to accomplish more with less. They work in operational systems which are complex, technology-based, and high-risk; in which incidents and accidents have far-reaching and costly consequences. For these and other reasons, there is concern that the safety net formerly built upon redundant systems and abundant resources may become overburdened. Although we know that human ingenuity can overcome incredible odds, human nature can also fail in unpredictable ways. Over the last 20 years, a large percentage of aviation accidents and incidents have been attributed to human errors rather than hardware or environmental factors alone. A class of errors have been identified which are not due to a lack of individual, technical competencies. Rather, they are due to the failure of teams to utilize readily available resources or information in a timely fashion. These insights began a training revolution in the aviation industry called Cockpit Resource Management, which later became known as Crew Resource Management (CRM) as its concepts and applications extended to teams beyond the flightdeck. Then, as now, communication has been a cornerstone in CRM training since crew coordination and resource management largely resides within information transfer processes--both within flightcrews, and between flightcrews and the ground operations teams that support them. The research I will describe takes its roots in CRM history as we began to study communication processes in order to discover symptoms of crew coordination problems, as well as strategies of effective crew management. On the one hand, communication is often the means or the tool by which team members manage their resources, solve problems, maintain situational awareness and procedural discipline. Conversely, it is the lack of planning and resource management, loss of vigilance and situational awareness, and non-standard communications that are implicated in accidents and incidents. NASA/Ames Crew Factors researchers have been developing a model of effective crew coordination in order to understand the sources of performance breakdowns, and to develop effective solutions and interventions. Because communication is a primary mechanism by which information is received and transmitted, and because it is observable behavior, we focus on these group processes in order to identify patterns of communication that distinguish effective from less effective crew performance. Since a prime objective is to develop training recommendations for enhancing communication skills, we interpret our findings in the context of relevant task and environmental conditions, role and procedural constraints, and the normal real-time parameters of flight operations. Another research objective is to consider how communication and coordination can be enhanced through design. For example, flight deck and hardware design as well as procedural and software design may greatly influence the efficiency with which crews communicate and coordinate their work. In addition, teams and tasks may be designed, organized, and trained so that team interactions with each other are based upon appropriately shared knowledge, procedures and situation awareness. In short, we are interested in enhancing communication practices through (1) the training of specific communication skills, and (2) the design of equipment, tasks, procedures, and teams that optimize smooth, unambiguous communication processes. Two examples of communication research will be described; one in aviation and one in space operations. The first example is a high-fidelity full mission simulation study which investigates the affect of flightdeck automation on crew coordination and communication (contrasting crew performance in the DC-9 vs. MD88). Additional information is contained in the original extended abstract.

Kanki, Barbara G.↗
NASA NTRS2003

Development of Methodologies for IV and V of Neural Networks

Non-deterministic systems often rely upon neural network (NN) technology to "lean" to manage flight systems under controlled conditions using carefully chosen training sets. How can these adaptive systems be certified to ensure that they will become increasingly efficient and behave appropriately in real-time situations? The bulk of Independent Verification and Validation (IV&V) research of non-deterministic software control systems such as Adaptive Flight Controllers (AFC's) addresses NNs in well-behaved and constrained environments such as simulations and strict process control. However, neither substantive research, nor effective IV&V techniques have been found to address AFC's learning in real-time and adapting to live flight conditions. Adaptive flight control systems offer good extensibility into commercial aviation as well as military aviation and transportation. Consequently, this area of IV&V represents an area of growing interest and urgency. ISR proposes to further the current body of knowledge to meet two objectives: Research the current IV&V methods and assess where these methods may be applied toward a methodology for the V&V of Neural Network; and identify effective methods for IV&V of NNs that learn in real-time, including developing a prototype test bed for IV&V of AFC's. Currently. no practical method exists. lSR will meet these objectives through the tasks identified and described below. First, ISR will conduct a literature review of current IV&V technology. TO do this, ISR will collect the existing body of research on IV&V of non-deterministic systems and neural network. ISR will also develop the framework for disseminating this information through specialized training. This effort will focus on developing NASA's capability to conduct IV&V of neural network systems and to provide training to meet the increasing need for IV&V expertise in such systems.

Taylor, Brian↗
NASA NTRS2007

Scaling and Systems Considerations in Pulsed Inductive Thrusters

Performance scaling in pulsed inductive thrusters is discussed in the context of previous experimental studies and modeling results. Two processes, propellant ionization and acceleration, are interconnected where overall thruster performance and operation are concerned, but they are separated here to gain physical insight into each process and arrive at quantitative criteria that should be met to address or mitigate inherent inductive thruster difficulties. The effects of preionization in lowering the discharge energy requirements relative to a case where no preionization is employed, and in influencing the location of the initial current sheet, are described. The relevant performance scaling parameters for the acceleration stage are reviewed, emphasizing their physical importance and the numerical values required for efficient acceleration. The scaling parameters are then related to the design of the pulsed power train providing current to the acceleration stage. The impact of various choices in pulsed power train and circuit topology selection are reviewed, paying special attention to how these choices mitigate or exacerbate switching, lifetime, and power consumption issues.

Polzin, Kurt A.↗
NASA NTRSDate not supplied

Physics and Modeling of Quasi-Steady and Forced Shock Trains

The dual-mode scramjet is a propulsive concept for contemporary hypersonic vehicles promising flight cruise speeds upwards of Mach 5. Despite decades of development and a recent resurgence in interest, such engines are not routinely deployed for commercial or military purposes. Instead, they have been confined to the realm of ground-test research and limited one-off flight test missions. One of the key stumbling blocks on the critical path to deployment of a dual-mode scramjet is the optimal design and stable operation of the isolator, a typically constant area duct that separates the scramjet inlet from the combustor. During ramjet mode at Mach numbers from 3 to 5 and during the transition to scramjet mode at higher Mach numbers, the isolator contains the pseudo-shock. Necessary to meet the back-pressure in the combustor, the pseudo-shock is typically divided into a shock dominated shock train region followed by a turbulence driven mixing region. Fluctuations in combustor pressure due to vehicle maneuvering, throttling, or combustion instabilities drive the shock train to shift streamwise within the isolator. In response to sharp pressure fluctuations, the shock train is unable to stabilize within the isolator, and the scramjet unstarts by ejecting the shock train out the inlet. This work addresses three key gaps in understanding of the isolator pseudo-shock. The phenomenon is studied in three experimental facilities to investigate the quasi-steady and unsteady fluid dynamics of the shock train in fueled and un-fueled test conditions. The strong link between boundary layer morphology and shock train structure is studied in detail and used as a tool to evaluate the state-of-the-art definitions and models of the pseudo-shock. Identifying the limitations of these models, a new process driven flux-conserved pseudo-shock model is proposed. This provides a computationally efficient model grounded in the fluid dynamic mechanisms of the pseudo-shock with better predictive capability than the current state-of-the-art across all facilities tested. The unsteady behavior of the shock train is further investigated to identify and understand two distinct forced dynamic modes: mechanically induced, low-rate forcing and high-rate combustion driven unsteadiness that sheds upstream propagating shocks. The proposed process driven model is found to be effective in the the limiting case of high-rate combustion driven unsteadiness by evaluating the model in a shock stationary reference frame. Further analysis identifies the approximately rigid chain motion of the shock train structure to mechanically induced forcing. The time delay between the onset of forced dynamics is found to saturate to a constant value and maximum shock train speed along the isolator with respect to the perturbation rate of change. The result of this work provides dual-mode scramjet designers with improved tools and physical understanding to develop more efficient, robust, and capable propulsion systems for hypersonic vehicles.

JAXA↗
NASA NTRS1993

Applications of artificial intelligence 1993: Knowledge-based systems in aerospace and industry; Proceedings of the Meeting, Orlando, FL, Apr. 13-15, 1993

The present volume on applications of artificial intelligence with regard to knowledge-based systems in aerospace and industry discusses machine learning and clustering, expert systems and optimization techniques, monitoring and diagnosis, and automated design and expert systems. Attention is given to the integration of AI reasoning systems and hardware description languages, care-based reasoning, knowledge, retrieval, and training systems, and scheduling and planning. Topics addressed include the preprocessing of remotely sensed data for efficient analysis and classification, autonomous agents as air combat simulation adversaries, intelligent data presentation for real-time spacecraft monitoring, and an integrated reasoner for diagnosis in satellite control. Also discussed are a knowledge-based system for the design of heat exchangers, reuse of design information for model-based diagnosis, automatic compilation of expert systems, and a case-based approach to handling aircraft malfunctions.

Fayyad, Usama M.↗
NASA NTRS1997

Autogenic-Feedback Training Exercise (AFTE) Method and System

The Autogenic-Feedback Training Exercise (AFTE) method of the present invention is a combined application of physiologic and perceptual training techniques. such as autogenic therapy and biofeedback. This combined therapy approach produces a methodology that is appreciably more effective than either of the individual techniques used separately. The AFTE method enables sufficient magnitude of control necessary to significantly reduce the behavioral and physiologic reactions to severe environmental stressors. It produces learned effects that are persistent over time and are resistant to extinction and it can be administered in a short period of time. The AFTE method may be used efficiently in several applications, among which are the following: to improve pilot and crew performance during emergency flying conditions; to train people to prevent the occurrence of nausea and vomiting associated with motion and sea sickness, or morning sickness in early pregnancy; as a training method for preventing or counteracting air-sickness symptoms in high-performance military aircraft; for use as a method for cardiovascular training, as well as for multiple other autonomic responses, which may contribute to the alleviation of Space Motion Sickness (SMS) in astronauts and cosmonauts; training people suffering from migraine or tension headaches to control peripheral blood flow and reduce forehead and/or trapezius muscle tension; training elderly people suffering from fecal incontinence to control their sphincter muscles; training cancer patients to reduce the nauseagenic effects of chemotherapy; and training patients with Chronic Intestinal Pseudo-obstruction (CIP).

Cowings, Patricia S.↗
NASA NTRS2010

Use of Dynamic Models and Operational Architecture to Solve Complex Navy Challenges

The United States Navy established 8 Maritime Operations Centers (MOC) to enhance the command and control of forces at the operational level of warfare. Each MOC is a headquarters manned by qualified joint operational-level staffs, and enabled by globally interoperable C41 systems. To assess and refine MOC staffing, equipment, and schedules, a dynamic software model was developed. The model leverages pre-existing operational process architecture, joint military task lists that define activities and their precedence relations, as well as Navy documents that specify manning and roles per activity. The software model serves as a "computational wind-tunnel" in which to test a MOC on a mission, and to refine its structure, staffing, processes, and schedules. More generally, the model supports resource allocation decisions concerning Doctrine, Organization, Training, Material, Leadership, Personnel and Facilities (DOTMLPF) at MOCs around the world. A rapid prototype effort efficiently produced this software in less than five months, using an integrated process team consisting of MOC military and civilian staff, modeling experts, and software developers. The work reported here was conducted for Commander, United States Fleet Forces Command in Norfolk, Virginia, code N5-0LW (Operational Level of War) that facilitates the identification, consolidation, and prioritization of MOC capabilities requirements, and implementation and delivery of MOC solutions.

Grande, Darby↗