SEARCH · Engineering Papers

Results for “Iterative Learning Control”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

44 records · Page 3

All sources NASA NTRS DOE OSTI

NASA NTRS2020

Amino Acid Encoding for Deep Learning Applications

Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-ofthe-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

Hesham ElAbd↗

NASA NTRS2020

Amino Acid Encoding for Deep Learning Applications

Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-of-the-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

Deep-learning↗

NASA NTRS1998

Aerothermodynamic Flight Simulation Capabilities for Aerospace Vehicles

Aerothermodynamics, encompassing aerodynamics, aeroheating, and fluid dynamics and physical processes, is the genesis for the design and development of advanced space transportation vehicles and provides crucial information to other disciplines such as structures, materials, propulsion, avionics, and guidance, navigation and control. Sources of aerothermodynamic information are ground-based facilities, Computational Fluid Dynamic (CFD) and engineering computer codes, and flight experiments. Utilization of this aerothermodynamic triad provides the optimum aerothermodynamic design to safely satisfy mission requirements while reducing design conservatism, risk and cost. The iterative aerothermodynamic process for initial screening/assessment of aerospace vehicle concepts, optimization of aerolines to achieve/exceed mission requirements, and benchmark studies for final design and establishment of the flight data book are reviewed. Aerothermodynamic methodology centered on synergism between ground-based testing and CFD predictions is discussed for various flow regimes encountered by a vehicle entering the Earth s atmosphere from low Earth orbit. An overview of the resources/infrastructure required to provide accurate/creditable aerothermodynamic information in a timely manner is presented. Impacts on Langley s aerothermodynamic capabilities due to recent programmatic changes such as Center reorganization, downsizing, outsourcing, industry (as opposed to NASA) led programs, and so forth are discussed. Sample applications of these capabilities to high Agency priority, fast-paced programs such as Reusable Launch Vehicle (RLV)/X-33 Phases I and 11, X-34, Hyper-X and X-38 are presented and lessons learned discussed. Lastly, enhancements in ground-based testing/CFD capabilities necessary to partially/fully satisfy future requirements are addressed.

Miller, Charles G.↗

NASA NTRS2012

NASA Tech Briefs, August 2012

Topics covered include: Mars Science Laboratory Drill; Ultra-Compact Motor Controller; A Reversible Thermally Driven Pump for Use in a Sub-Kelvin Magnetic Refrigerator; Shape Memory Composite Hybrid Hinge; Binding Causes of Printed Wiring Assemblies with Card-Loks; Coring Sample Acquisition Tool; Joining and Assembly of Bulk Metallic Glass Composites Through Capacitive Discharge; 670-GHz Schottky Diode-Based Subharmonic Mixer with CPW Circuits and 70-GHz IF; Self-Nulling Lock-in Detection Electronics for Capacitance Probe Electrometer; Discontinuous Mode Power Supply; Optimal Dynamic Sub-Threshold Technique for Extreme Low Power Consumption for VLSI; Hardware for Accelerating N-Modular Redundant Systems for High-Reliability Computing; Blocking Filters with Enhanced Throughput for X-Ray Microcalorimetry; High-Thermal-Conductivity Fabrics; Imidazolium-Based Polymeric Materials as Alkaline Anion-Exchange Fuel Cell Membranes; Electrospun Nanofiber Coating of Fiber Materials: A Composite Toughening Approach; Experimental Modeling of Sterilization Effects for Atmospheric Entry Heating on Microorganisms; Saliva Preservative for Diagnostic Purposes; Hands-Free Transcranial Color Doppler Probe; Aerosol and Surface Parameter Retrievals for a Multi-Angle, Multiband Spectrometer LogScope; TraceContract; AIRS Maps from Space Processing Software; POSTMAN: Point of Sail Tacking for Maritime Autonomous Navigation; Space Operations Learning Center; OVERSMART Reporting Tool for Flow Computations Over Large Grid Systems; Large Eddy Simulation (LES) of Particle-Laden Temporal Mixing Layers; Projection of Stabilized Aerial Imagery Onto Digital Elevation Maps for Geo-Rectified and Jitter-Free Viewing; Iterative Transform Phase Diversity: An Image-Based Object and Wavefront Recovery; 3D Drop Size Distribution Extrapolation Algorithm Using a Single Disdrometer; Social Networking Adapted for Distributed Scientific Collaboration; General Methodology for Designing Spacecraft Trajectories; Hemispherical Field-of-View Above-Water Surface Imager for Submarines; and Quantum-Well Infrared Photodetector (QWIP) Focal Plane Assembly.

Source record↗

NASA NTRS2005

Apex Reference Manual 3.0 Beta

Apex is a toolkit for constructing software that behaves intelligently and responsively in demanding task environments. Reflecting its origin at NASA where Apex continues to be developed, current applications include: a) Providing autonomous mission management and tactical control capabilities for unmanned aerial vehicles including an autonomous surveillance helicopter and a simulation prototype of an unmanned fixed-wing aircraft to be used for wildfire mapping; b) Simulating human air traffic controllers, pilots and astronauts to help predict how people might respond to changes in equipment or procedures; and c) Predicting the precise duration and sequence of routine human behaviors based on a human-computer interaction engineering technique called CPM-GOMS. Among Apex s components are a set of implemented reasoning services, such as those for reactive planning and temporal pattern recognition; a software architecture that embeds and integrates these services and allows additional reasoning elements to be added as extensions; a formal language for specifying agent knowledge; a simulation environment to facilitate prototyping and analysis; and Sherpa, a set of tools for visualizing autonomy logic and runtime behavior. In combination, these are meant to provide a flexible and usable framework for creating, testing, and deploying intelligent agent software. Overall, our goal in developing Apex is to lower economic barriers to developing intelligent software agents. New ideas about how to extend or modify the system are evaluated in terms of their impact in reducing the time, expertise, and inventiveness required to build and maintain applications. For example, potential enhancements to the AI reasoning capabilities in the system are reviewed not only for usefulness and distinctiveness, but also for their impact on the readability and general usability of Apex s behavior representation language (PDL) and on the transparency of resulting behavior. A second central part of our approach is to iteratively refine Apex based on lessons learned from as diverse a set of applications as possible. Many applications have been developed by users outside the core development team including engineers, researchers, and students. Usability is thus a central concern for every aspect of Apex visible to a user, including PDL, Sherpa, the Apex installation process, APIs, and user documentation. Apex users vary in their areas of expertise and in their familiarity with autonomy technology. Focusing on usability, a development philosophy summarized by the project motto "Usable Autonomy," has been important part of enabling diverse users to employ Apex successfully and to provide feedback needed to guide iterative, user-centered refinement.

Freed, Michael A.↗

NASA NTRS2004

An Introduction to Flight Software Development: FSW Today, FSW 2010

Experience and knowledge gained from ongoing maintenance of Space Shuttle Flight Software and new development projects including Cockpit Avionics Upgrade are applied to projected needs of the National Space Exploration Vision through Spiral 2. Lessons learned from these current activities are applied to create a sustainable, reliable model for development of critical software to support Project Constellation. This presentation introduces the technologies, methodologies, and infrastructure needed to produce and sustain high quality software. It will propose what is needed to support a Vision for Space Exploration that places demands on the innovation and productivity needed to support future space exploration. The technologies in use today within FSW development include tools that provide requirements tracking, integrated change management, modeling and simulation software. Specific challenges that have been met include the introduction and integration of Commercial Off the Shelf (COTS) Real Time Operating System for critical functions. Though technology prediction has proved to be imprecise, Project Constellation requirements will need continued integration of new technology with evolving methodologies and changing project infrastructure. Targets for continued technology investment are integrated health monitoring and management, self healing software, standard payload interfaces, autonomous operation, and improvements in training. Emulation of the target hardware will also allow significant streamlining of development and testing. The methodologies in use today for FSW development are object oriented UML design, iterative development using independent components, as well as rapid prototyping . In addition, Lean Six Sigma and CMMI play a critical role in the quality and efficiency of the workforce processes. Over the next six years, we expect these methodologies to merge with other improvements into a consolidated office culture with all processes being guided by automated office assistants. The infrastructure in use today includes strict software development and configuration management procedures, including strong control of resource management and critical skills coverage. This will evolve to a fully integrated staff organization with efficient and effective communication throughout all levels guided by a Mission-Systems Architecture framework with focus on risk management and attention toward inevitable product obsolescence. This infrastructure of computing equipment, software and processes will itself be subject to technological change and need for management of change and improvement,

Gouvela, John↗

NASA NTRS2015

Implementing NASA's Capability-Driven Approach: Insight into NASA's Processes for Maturing Exploration Systems

NASA is engaged in transforming human spaceflight. The Agency is shifting from an exploration-based program with human activities focused on low Earth orbit (LEO) and targeted robotic missions in deep space to a more sustainable and integrated pioneering approach. Through pioneering, NASA seeks to address national goals to develop the capacity for people to work, learn, operate, live, and thrive safely beyond the Earth for extended periods of time. However, pioneering space involves more than the daunting technical challenges of transportation, maintaining health, and enabling crew productivity for long durations in remote, hostile, and alien environments. This shift also requires a change in operating processes for NASA. The Agency can no longer afford to engineer systems for specific missions and destinations and instead must focus on common capabilities that enable a range of destinations and missions. NASA has codified a capability driven approach, which provides flexible guidance for the development and maturation of common capabilities necessary for human pioneers beyond LEO. This approach has been included in NASA policy and is captured in the Agency's strategic goals. It is currently being implemented across NASA's centers and programs. Throughout 2014, NASA engaged in an Agency-wide process to define and refine exploration-related capabilities and associated gaps, focusing only on those that are critical for human exploration beyond LEO. NASA identified 12 common capabilities ranging from Environmental Control and Life Support Systems to Robotics, and established Agency-wide teams or working groups comprised of subject matter experts that are responsible for the maturation of these exploration capabilities. These teams, called the System Maturation Teams (SMTs) help formulate, guide and resolve performance gaps associated with the identified exploration capabilities. The SMTs are defining performance parameters and goals for each of the 12 capabilities, developing maturation plans and roadmaps for the identified performance gaps, specifying the interfaces between the various capabilities, and ensuring that the capabilities mature and integrate to enable future pioneering missions. By managing system development through the SMTs instead of traditional NASA programs and projects, the Agency is shifting from mission-driven development to a more flexible, capability-driven development. The process NASA uses to establish, integrate, prioritize, and manage the SMTs and associated capabilities is iterative. NASA relies on the Human Exploration and Operation Mission Directorate's SMT Integration Team within Advanced Exploration Systems to coordinate and facilitate the SMT process. The SMT Integration team conducts regular reviews and coordination meetings among the SMTs and has developed a number of tools to help the Agency implement capability driven processes. The SMT Integration team is uniquely positioned to help the Agency coordinate the SMTs and other processes that are making the capability-driven approach a reality. This paper will introduce the SMTs and the 12 key capabilities they represent. The role of the SMTs will be discussed with respect to Agency-wide processes to shift from mission-focused exploration to a capability-driven pioneering approach. Specific examples will be given to highlight systems development and testing within the SMTs. These examples will also show how NASA is using current investments in the International Space Station and future investments to develop and demonstrate capabilities. The paper will conclude by describing next steps and a process for soliciting feedback from the space exploration community to refine NASA's process for developing common exploration capabilities.

Williams-Byrd, Julie↗

NASA NTRS2017

CisLunar Habitat Internal Architecture Design Criteria

BACKGROUND: In preparation for human exploration to Mars, there is a need to define the development and test program that will validate deep space operations and systems. In that context, a Proving Grounds CisLunar habitat spacecraft is being defined as the next step towards this goal. This spacecraft will operate differently from the ISS or other spacecraft in human history. The performance envelope of this spacecraft (mass, volume, power, specifications, etc.) is being defined by the Future Capabilities Study Team. This team has recognized the need for a human-centered approach for the internal architecture of this spacecraft and has commissioned a CisLunar Phase-1 Habitat Internal Architecture Study Team to develop a NASA reference configuration, providing the Agency with a "smart buyer" approach for future acquisition. THE CISLUNAR HABITAT INTERNAL ARCHITECTURE STUDY: Overall, the CisLunar Habitat Internal Architecture study will address the most significant questions and risks in the current CisLunar architecture, habitation, and operations concept development. This effort is achieved through definition of design criteria, evaluation criteria and process, design of the CisLunar Habitat Phase-1 internal architecture, and the development and fabrication of internal architecture concepts combined with rigorous and methodical Human-in-the-Loop (HITL) evaluations and testing of the conceptual innovations in a controlled test environment. The vision of the CisLunar Habitat Internal Architecture Study is to design, build, and test a CisLunar Phase-1 Habitat Internal Architecture that will be used for habitation (e.g. habitability and human factors) evaluations. The evaluations will mature CisLunar habitat evaluation tools, guidelines, and standards, and will interface with other projects such as the Advanced Exploration Systems (AES) Program integrated Power, Avionics, Software (iPAS), and Logistics for integrated human-in-the-loop testing. The mission of the CisLunar Habitat Internal Architecture Study is to become a forcing function to establish a common understanding of CisLunar Phase-1 Habitation Internal Architecture design criteria, processes, and tools. The scope of the CisLunar Habitat Internal Architecture study is to design, develop, demonstrate, and evaluate a Phase-1 CisLunar Habitat common module internal architecture based on design criteria agreed to by NASA, the International Partners, and Commercial Exploration teams. This task is to define the CisLunar Phase-1 Internal Architecture Government Reference Design, assist NASA in becoming a "smart buyer" for Phase-1 Habitat Concepts, and ultimately to derive standards and requirements from the Internal Architecture Design Process. The first step was to define a Habitat Internal Architecture Design Criteria and create a structured philosophy to be used by design teams as a filter by which critical aspects of consideration would be identified for the purpose of organizing and utilizing interior spaces. With design criteria in place, the team will develop a series of iterative internal architecture concept designs which will be assessed by means of an evaluation criteria and process. These assessments will successively drive and refine the design, leading to the combination and down-selection of design concepts. A single refined reference design configuration will be developed into in a medium-to-high fidelity mockup. A multi-day human-in-the-loop mission test will fully evaluate the reference design and validate its configuration. Lessons learned from the design and evaluation will enable the team to identify appropriate standards for Phase-1 CisLunar Habitat Internal Architecture and will enable NASA to develop derived requirements in support of maturing CisLunar Habitation capabilities. This paper will describe the criteria definition process, workshop event, and resulting CisLunar Phase-1 Habitat Internal Architecture Design Criteria.

Jones, R.↗