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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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NASA NTRS1996

Learning from Inconsistency

This position paper argues that inconsistencies that occur during the development of a software specification offer an excellent way of learning more about the development process. We base this argument on our work on inconsistency management. Much attention has been devoted recently to the need to allow inconsistencies to occur during software development, to facilitate flexible development strategies, especially for collaborative work. Recent work has concentrated on reasoning in the presence of inconsistency, tracing inconsistencies with 'pollution markers' and supporting resolution. We argue here that one of the most important aspects of inconsistency is the learning opportunity it provides. We are therefore concerned with how to capture this learning outcome so that its significance is not lost. We present a small example of how apprentice software engineers learn from their mistakes, and outline how an inconsistency management tool could support this learning. We then argue that the approach can be used more generally as part of continuous process improvement.

Easterbrook, Steve↗

NASA NTRS1998

NASA/Mir Phase 1: A Lesson Learned in Long Duration Mission Planning and Operations

The NASA/Mir Phase I Program is a precursor to the assembly and operations of the International Space Station (ISS). The Phase I Program covers a four-year period and encompasses over ten shuttle flights to the Mir, culminating with the flight of STS-91 in June 1998. The Mir-Orbiting Station (Mir-OS) provides an excellent environment, both onboard and on the ground, for learning and studying the characteristics associated with crew-tended operations on long duration missions. By the end of the Phase I program, NASA astronauts will have accumulated over two years of continuous operations experience working and living onboard the Mir-OS. Ground controllers, working in Russia and at remote sites in the United States, will have gained valuable experience in the generation, maintenance, and coordination of the information and products required to support the long duration crew members onboard the Mir-OS. The experience gained by the crew and ground support personnel in the area of long duration mission operations has resulted in a number of factors that need to be considered in the definition and implementation of the processes and products for the ISS. While there are many areas in which experience has been gained, this paper focuses on the lessons learned regarding the level of detail and flexibility of crew timelines. Provided in this paper is an overview of the characteristics associated with long duration mission timeline development and execution, a summary of applicable lessons learned identified and documented through participation in the NASA/Mir Phase I Program, a detailed discussion of "Gross Timelines" as implementation which addresses the lessons learned. Also discussed are the challenges that must be overcome in getting the ISS program to recognize, evaluate, and learn from the crew and ground controller experience gained through participation in the NASA/Mir Phase I Program.

Hagopian, Jeff↗

NASA NTRS1994

Operations for Learning with Graphical Models

This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Well-known examples of graphical models include Bayesian net- works, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, and the manipulation of probability models from the exponential family. These operations adapt existing techniques from statistics and automatic differentiation to graphs. Two standard algorithm schemes for learning are reviewed in a graphical framework: Gibbs sampling and the expectation maximization algorithm. Some algorithms are developed in this graphical framework including a generalized version of linear regression, techniques for feed-forward networks, and learning Gaussian and discrete Bayesian networks from data. The paper concludes by sketching some implications for data analysis and summarizing some popular algorithms that fall within the framework presented. The main original contributions here are the decomposition techniques and the demonstration that graphical models provide a framework for understanding and developing complex learning algorithms.

Buntine, Wray L.↗

NASA NTRS1999

Technology Development, Implementation and Assessment: K-16 Pre-Service, In-Service and Distance Learning Initiatives

The technologies associated with distance learning are evolving rapidly, giving to educators a potential tool for enhancing the educational experiences of large numbers of students simultaneously. This enhancement, in order to be effective, must take into account the various agendas of teachers, administrators, state systems, and of course students. It must also make use of the latest research on effective pedagogy. This combination, effective pedagogy and robust information technology, is a powerful vehicle for communicating, to a large audience of school children the excitement of mathematics and science--an excitement that for the most part is now well-hidden. This project,"Technology Development, Implementation and Assessment," proposed to bring to bear on the education of learners in grades 3 - 8 in science and mathematics both advances in information technology and in effective pedagogy. Specifically, the project developed components NASA CONNECT video series--problem-based learning modules that focus on the scientific method and that incorporate problem-based learning scenarios tied to national mathematics and science standards. These videos serve two purposes; they engage students in the excitement of hands-on learning and they model for the teachers of these students the problem-based learning practices that are proving to be excellent ways to teach science and mathematics to school students. Another component of NASA CONNECT is the accompanying web-site.

Williams, William B., Jr.↗

NASA NTRS1997

Learning and Tuning of Fuzzy Rules

In this chapter, we review some of the current techniques for learning and tuning fuzzy rules. For clarity, we refer to the process of generating rules from data as the learning problem and distinguish it from tuning an already existing set of fuzzy rules. For learning, we touch on unsupervised learning techniques such as fuzzy c-means, fuzzy decision tree systems, fuzzy genetic algorithms, and linear fuzzy rules generation methods. For tuning, we discuss Jang's ANFIS architecture, Berenji-Khedkar's GARIC architecture and its extensions in GARIC-Q. We show that the hybrid techniques capable of learning and tuning fuzzy rules, such as CART-ANFIS, RNN-FLCS, and GARIC-RB, are desirable in development of a number of future intelligent systems.

Berenji, Hamid R.↗

NASA NTRS2002

The Relationship Between Fidelity and Learning in Aviation Training and Assessment

Flight simulators can be designed to train pilots or assess their flight performance. Low-Fidelity simulators maximize the initial learning rate of novice pilots and minimize initial costs; whereas, expensive, high-fidelity simulators predict the realworld in-flight performance of expert pilots (Fink & Shriver, 1978 Hays & Singer 1989; Kinkade & Wheaton. 1972). Although intuitively appealing and intellectually convenient to generalize concepts of learning and assessment, what holds true for the role of fidelity in assessment may not always hold true for learning, and vice versa. To bring clarity to this issue, the author distinguishes the role of fidelity in learning from its role in assessment as a function of skill level by applying the hypothesis of Alessi (1988) and reviewing the Laughery, Ditzian, and Houtman (1982) study on simulator validity. Alessi hypothesized that there is it point beyond which one additional unit of flight-simulator fidelity results in a diminished rate of learning. The author of this current paper also suggests the existence of an optimal point beyond which one additional unit of flight-simulator fidelity results in a diminished rate of practical assessment of nonexpert pilot performance.

Noble, Cliff↗

NASA NTRS2002

Lessons Learned and Technical Standards: A Logical Marriage for Future Space Systems Design

A comprehensive database of engineering lessons learned that corresponds with relevant technical standards will be a valuable asset to those engaged in studies on future space vehicle developments, especially for structures, materials, propulsion, control, operations and associated elements. In addition, this will enable the capturing of technology developments applicable to the design, development, and operation of future space vehicles as planned in the Space Launch Initiative. Using the time-honored tradition of passing on lessons learned while utilizing the newest information technology, NASA has launched an intensive effort to link lessons learned acquired through various Internet databases with applicable technical standards. This paper will discuss the importance of lessons learned, the difficulty in finding relevant lessons learned while engaged in a space vehicle development, and the new NASA effort to relate them to technical standards that can help alleviate this difficulty.

Gill, Paul S.↗

NASA NTRS1997

Multiple subclasses of Purkinje cells in the primate floccular complex provide similar signals to guide learning in the vestibulo-ocular reflex

The neural "learning rules" governing the induction of plasticity in the cerebellum were analyzed by recording the patterns of neural activity in awake, behaving animals during stimuli that induce a form of cerebellum-dependent learning. We recorded the simple- and complex-spike responses of a broad sample of Purkinje cells in the floccular complex during a number of stimulus conditions that induce motor learning in the vestibulo-ocular reflex (VOR). Each subclass of Purkinje cells carried essentially the same information about required changes in the gain of the VOR. The correlation of simple-spike activity in Purkinje cells with activity in vestibular pathways could guide learning during low-frequency but not high-frequency stimuli. Climbing fiber activity could guide learning during all stimuli tested but only if compared with the activity present approximately 100 msec earlier in either vestibular pathways or Purkinje cells.

Non-NASA Center↗

NASA NTRS2005

The APPL "Learning Map"

Root Learning, a learning consulting organization with a background in strategic planning, recognizes the knowledge gap that frequently exists between a leadership team and the rest of an organization. Team members supposedly working toward the same goal don't always have the same vision as to where the organization is headed, and they may not understand how the piece they are accountable for fits into the big picture. To address these complex problems, Root Learning utilizes the age-old tools of sarcasm, metaphor and graphics (much in the same way that ASK uses a traditional storytelling format.) The company is best known for creating "Learning Maps" like this one: humorous drawings based on the inner workings of an organization. Their purpose is to put complex topics on the table, to stimulate discussion, and to ultimately give team members a common vision of where the organization is going and what role they personally play in getting there. APPL knows how effective it is to incorporate new and engaging techniques into its knowledge sharing programs. By collaborating with Root Learning, we were able to expand the knowledge of the organization and add one more of these techniques to our repertoire.

Source record↗

NASA NTRS2005

Development of Advanced Verification and Validation Procedures and Tools for the Certification of Learning Systems in Aerospace Applications

Adaptive control technologies that incorporate learning algorithms have been proposed to enable automatic flight control and vehicle recovery, autonomous flight, and to maintain vehicle performance in the face of unknown, changing, or poorly defined operating environments. In order for adaptive control systems to be used in safety-critical aerospace applications, they must be proven to be highly safe and reliable. Rigorous methods for adaptive software verification and validation must be developed to ensure that control system software failures will not occur. Of central importance in this regard is the need to establish reliable methods that guarantee convergent learning, rapid convergence (learning) rate, and algorithm stability. This paper presents the major problems of adaptive control systems that use learning to improve performance. The paper then presents the major procedures and tools presently developed or currently being developed to enable the verification, validation, and ultimate certification of these adaptive control systems. These technologies include the application of automated program analysis methods, techniques to improve the learning process, analytical methods to verify stability, methods to automatically synthesize code, simulation and test methods, and tools to provide on-line software assurance.

Jacklin, Stephen↗

NASA NTRS2006

Near-Miss Evaluation Bias as an Obstacle to Organizational Learning: Lessons from NASA

After the Shuttle Columbia catastrophe, the investigation board (CAIB) stated that NASA needs to develop a "learning culture", meaning a capability to learn from past failures by understanding the technical and organizational causes of these mistakes (CAIB report, 2003). While many organizations learn from obvious failures, we argue that it is harder for organizations to learn from near-miss events (i.e., situations where a failure does not occur but nearly did), because these near-misses are processed as successes. For the shuttle program, prior debris problems could have caused a similar failure as on the Columbia mission except that the large pieces missed the highly sensitive portions of the orbiter. This acceptance of foam debris was adopted as a normal occurrence by the shuttle program managers similar to the problems at the time of the Challenger Disaster (detailed in Vaughan, 1996). We extend that work to show that an outcome bias influences people's evaluation of project managers, such that managers of failed missions were perceived more poorly than managers who made the same decisions but whose mission ended in either success or a near-miss. The similarity of ratings between the near-miss and success condition imply that even when a problem occurs that is clearly linked to prior managerial decisions, if the project is not harmed because of good luck, that manager is not held accountable for faculty decision making and neither the individual manager nor the organization learn from the experience potentially increasing the likelihood of a failure in the future.

Dillon, Robin L.↗

NASA NTRS2006

Learning User Preferences for Sets of Objects

Most work on preference learning has focused on pairwise preferences or rankings over individual items. In this paper, we present a method for learning preferences over sets of items. Our learning method takes as input a collection of positive examples--that is, one or more sets that have been identified by a user as desirable. Kernel density estimation is used to estimate the value function for individual items, and the desired set diversity is estimated from the average set diversity observed in the collection. Since this is a new learning problem, we introduce a new evaluation methodology and evaluate the learning method on two data collections: synthetic blocks-world data and a new real-world music data collection that we have gathered.

preferences↗

NASA NTRS2010

Launch Vehicle Propulsion Life Cycle Cost Lessons Learned

This paper will review lessons learned for space transportation systems from the viewpoint of the NASA, Industry and academia Space Propulsion Synergy Team (SPST). The paper provides the basic idea and history of "lessons learned". Recommendations that are extremely relevant to NASA's future investments in research, program development and operations are"'provided. Lastly, a novel and useful approach to documenting lessons learned is recommended, so as to most effectively guide future NASA investments. Applying lessons learned can significantly improve access to space for cargo or people by focusing limited funds on the right areas and needs for improvement. Many NASA human space flight initiatives have faltered, been re-directed or been outright canceled since the birth of the Space Shuttle program. The reasons given at the time have been seemingly unique. It will be shown that there are common threads as lessons learned in many a past initiative.

Zapata, Edgar↗

NASA NTRS2011

Lessons Learned in Engineering

This Contractor Report (CR) is a compilation of Lessons Learned in approximately 55 years of engineering experience by each James C. Blair, Robert S. Ryan, and Luke A. Schutzenhofer. The lessons are the basis of a course on Lessons Learned that has been taught at Marshall Space Flight Center. The lessons are drawn from NASA space projects and are characterized in terms of generic lessons learned from the project experience, which are further distilled into overarching principles that can be applied to future projects. Included are discussions of the overarching principles followed by a listing of the lessons associated with that principle. The lesson with sub-lessons are stated along with a listing of the project problems the lesson is drawn from, then each problem is illustrated and discussed, with conclusions drawn in terms of Lessons Learned. The purpose of this CR is to provide principles learned from past aerospace experience to help achieve greater success in future programs, and identify application of these principles to space systems design. The problems experienced provide insight into the engineering process and are examples of the subtleties one experiences performing engineering design, manufacturing, and operations.

Blair, J. C.↗

NASA NTRS2011

Lessons Learned in Engineering

Blair, James C.↗

NASA NTRS2010

A National Partnership-Based Summer Learning Initiative to Engage Underrepresented Students with Science, Technology, Engineering and Mathematics

In response to the White House Educate to Innovate campaign, NASA developed a new science, technology, engineering, and mathematics (STEM) education program for non-traditional audiences that also focused on public-private partnerships and nationwide participation. NASA recognized that summer break is an often overlooked but opportune time to engage youth in STEM experiences, and elevated its ongoing commitment to the cultivation of diversity. The Summer of Innovation (SoI) is the resulting initiative that uses NASA's unique missions and resources to boost summer learning, particularly for students who are underrepresented, underserved and underperforming in STEM. The SoI pilot, launched in June 2010, is a multi-faceted effort designed to improve STEM teaching and learning through partnership, multi-week summer learning programs, special events, a national concluding event, and teacher development. The SoI pilot features strategic infusion of NASA content and educational resource materials, sustainability through STEM Learning Communities, and assessments of effectiveness of SoI interventions with other pilot efforts. This paper examines the inception and development of the Summer of Innovation pilot project, including achievements and effectiveness, as well as lessons learned for future efforts.

Melvin, Leland↗

NASA NTRS2012

Space Operations Learning Center

The Space Operations Learning Center (SOLC) is a tool that provides an online learning environment where students can learn science, technology, engineering, and mathematics (STEM) through a series of training modules. SOLC is also an effective media for NASA to showcase its contributions to the general public. SOLC is a Web-based environment with a learning platform for students to understand STEM through interactive modules in various engineering topics. SOLC is unique in its approach to develop learning materials to teach schoolaged students the basic concepts of space operations. SOLC utilizes the latest Web and software technologies to present this educational content in a fun and engaging way for all grade levels. SOLC uses animations, streaming video, cartoon characters, audio narration, interactive games and more to deliver educational concepts. The Web portal organizes all of these training modules in an easily accessible way for visitors worldwide. SOLC provides multiple training modules on various topics. At the time of this reporting, seven modules have been developed: Space Communication, Flight Dynamics, Information Processing, Mission Operations, Kids Zone 1, Kids Zone 2, and Save The Forest. For the first four modules, each contains three components: Flight Training, Flight License, and Fly It! Kids Zone 1 and 2 include a number of educational videos and games designed specifically for grades K-6. Save The Forest is a space operations mission with four simulations and activities to complete, optimized for new touch screen technology. The Kids Zone 1 module has recently been ported to Facebook to attract wider audience.

Lui, Ben↗

NASA NTRS2016

Machine Learning Technologies and Their Applications for Science and Engineering Domains Workshop -- Summary Report

The fields of machine learning and big data analytics have made significant advances in recent years, which has created an environment where cross-fertilization of methods and collaborations can achieve previously unattainable outcomes. The Comprehensive Digital Transformation (CDT) Machine Learning and Big Data Analytics team planned a workshop at NASA Langley in August 2016 to unite leading experts the field of machine learning and NASA scientists and engineers. The primary goal for this workshop was to assess the state-of-the-art in this field, introduce these leading experts to the aerospace and science subject matter experts, and develop opportunities for collaboration. The workshop was held over a three day-period with lectures from 15 leading experts followed by significant interactive discussions. This report provides an overview of the 15 invited lectures and a summary of the key discussion topics that arose during both formal and informal discussion sections. Four key workshop themes were identified after the closure of the workshop and are also highlighted in the report. Furthermore, several workshop attendees provided their feedback on how they are already utilizing machine learning algorithms to advance their research, new methods they learned about during the workshop, and collaboration opportunities they identified during the workshop.

Ambur, Manjula↗