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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Cooperative intelligent robotics in space III; Proceedings of the Meeting, Boston, MA, Nov. 16-18, 1992

The present volume on cooperative intelligent robotics in space discusses sensing and perception, Space Station Freedom robotics, cooperative human/intelligent robot teams, and intelligent space robotics. Attention is given to space robotics reasoning and control, ground-based space applications, intelligent space robotics architectures, free-flying orbital space robotics, and cooperative intelligent robotics in space exploration. Topics addressed include proportional proximity sensing for telerobots using coherent lasar radar, ground operation of the mobile servicing system on Space Station Freedom, teleprogramming a cooperative space robotic workcell for space stations, and knowledge-based task planning for the special-purpose dextrous manipulator. Also discussed are dimensions of complexity in learning from interactive instruction, an overview of the dynamic predictive architecture for robotic assistants, recent developments at the Goddard engineering testbed, and parallel fault-tolerant robot control.

Erickson, Jon D.↗
NASA NTRS1996

Integrated Aeropropulsion Control System Design

%T Integrated Aeropropulsion Control System Design%A C-F. Lin%A Francis X. Hurley%A Jie Huang%A F. Y. Hadaegh%J International Conference on Control and Information(psi)995%C Hong Kong%D June 1995%K aeropropulsion, control, system%U http://jpltrs.jpl.nasa.gov/1995/95-0658.pdfAn integrated intelligent control approach is proposed to design a high performance control system for aeropropulsion systems based on advanced sensor processing, nonlinear control and neural fuzzy control integration. Our approach features the following innovations:??e complexity and uncertainty issues are addressed via the distributed parallel processing, learning, and online reoptimization properties of neural networks.??e nonlinear dynamics and the severe coupling can be naturally incorporated into the design framework.??e knowledge base and decision making logic furnished by fuzzy systems leads to a human intelligence enhanced control scheme.In addition, fault tolerance, health monitoring and reconfigurable control strategies will be accommodated by this approach to ensure stability, graceful degradation and reoptimization in the case of failures, malfunctions and damage.!.

aeropropulsion control systems↗
DOE OSTI2021

Development of phenomena identification and ranking table for Westinghouse lead fast reactor’s safety

The Westinghouse Lead-cooled Fast Reactor (LFR) is a medium-size, passively safe, economic, Gen-IV nuclear reactor. An important effort within the Westinghouse LFR program is the development of the safety analysis methodology, which comprises computer code development, model development, and experimental testing. A key initial task associated with the development of the safety analysis methodology is the identification of processes and phenomena that affect the plant's capability to meet selected safety performance indicators. This is accomplished through the development of a Phenomena Identification and Ranking Table (PIRT) for selected accident scenarios which, for this specific PIRT effort, included selected postulated design basis accidents and hypothetical beyond design basis accidents in LFRs. This paper describes the role of PIRT in the development of the Westinghouse LFR safety analysis methodology and the process used in the PIRT development. Specifically, the Westinghouse LFR PIRT assessed importance of pertinent phenomena and identified gaps in their knowledge-base by evaluating current modeling capabilities and data available for validation. The ultimate goal was to provide guidance on computer code development and validation efforts and to prioritize testing to support LFR design and licensing. The key phenomena and processes that are deemed highly important for the safety performance indicators, but for which the state of knowledge is low, are presented. The testing program and analyses development are planned to address significant phenomena in the PIRT.

Lead fast reactor↗
NASA NTRS1991

Search-based model identification of smart-structure damage

This paper describes the use of a combined model and parameter identification approach, based on modal analysis and artificial intelligence (AI) techniques, for identifying damage or flaws in a rotating truss structure incorporating embedded piezoceramic sensors. This smart structure example is representative of a class of structures commonly found in aerospace systems and next generation space structures. Artificial intelligence techniques of classification, heuristic search, and an object-oriented knowledge base are used in an AI-based model identification approach. A finite model space is classified into a search tree, over which a variant of best-first search is used to identify the model whose stored response most closely matches that of the input. Newly-encountered models can be incorporated into the model space. This adaptativeness demonstrates the potential for learning control. Following this output-error model identification, numerical parameter identification is used to further refine the identified model. Given the rotating truss example in this paper, noisy data corresponding to various damage configurations are input to both this approach and a conventional parameter identification method. The combination of the AI-based model identification with parameter identification is shown to lead to smaller parameter corrections than required by the use of parameter identification alone.

Glass, B. J.↗
NASA NTRS1990

Autonomous power expert system

The Autonomous Power Expert (APEX) system was designed to monitor and diagnose fault conditions that occur within the Space Station Freedom Electrical Power System (SSF/EPS) Testbed. APEX is designed to interface with SSF/EPS testbed power management controllers to provide enhanced autonomous operation and control capability. The APEX architecture consists of three components: (1) a rule-based expert system, (2) a testbed data acquisition interface, and (3) a power scheduler interface. Fault detection, fault isolation, justification of probable causes, recommended actions, and incipient fault analysis are the main functions of the expert system component. The data acquisition component requests and receives pertinent parametric values from the EPS testbed and asserts the values into a knowledge base. Power load profile information is obtained from a remote scheduler through the power scheduler interface component. The current APEX design and development work is discussed. Operation and use of APEX by way of the user interface screens is also covered.

Walters, Jerry L.↗
NASA NTRS1990

AiGERM: A logic programming front end for GERM

AiGerm (Artificially Intelligent Graphical Entity Relation Modeler) is a relational data base query and programming language front end for MCC (Mission Control Center)/STP's (Space Test Program) Germ (Graphical Entity Relational Modeling) system. It is intended as an add-on component of the Germ system to be used for navigating very large networks of information. It can also function as an expert system shell for prototyping knowledge-based systems. AiGerm provides an interface between the programming language and Germ.

Hashim, Safaa H.↗
NASA NTRS1992

Meta-tools for software development and knowledge acquisition

The effectiveness of tools that provide support for software development is highly dependent on the match between the tools and their task. Knowledge-acquisition (KA) tools constitute a class of development tools targeted at knowledge-based systems. Generally, KA tools that are custom-tailored for particular application domains are more effective than are general KA tools that cover a large class of domains. The high cost of custom-tailoring KA tools manually has encouraged researchers to develop meta-tools for KA tools. Current research issues in meta-tools for knowledge acquisition are the specification styles, or meta-views, for target KA tools used, and the relationships between the specification entered in the meta-tool and other specifications for the target program under development. We examine different types of meta-views and meta-tools. Our current project is to provide meta-tools that produce KA tools from multiple specification sources--for instance, from a task analysis of the target application.

Eriksson, Henrik↗
DOE OSTI2022

A cosine-based correlation information entropy approach for building automatic fault detection baseline construction

Building automatic fault detection and diagnosis (AFDD) technologies have shown great potential for energy savings. To enable AFDD, a baseline depicting the normal operation mode is needed to detect whether the building operation deviates from normality. Existing research using physics-based knowledge and models for AFDD has mainly taken a trial-and-error approach to determine if a given baseline is sufficient via empirical experiments. A mechanism to support decisions such as how many samples and what samples should be included in the baseline is currently lacking. In this study, a data-driven method for AFDD baseline construction based on information entropy is developed. The entropy is derived based on cosine similarity among typical building automation system measurements in conjunction with outdoor weather information. The performance of the proposed method is evaluated using real building data. Evaluation results indicate that the fault detection strategy adopting the proposed method has similar or better accuracy in detecting faults compared to the same fault detection strategy using the baseline construction method from the literature. Additionally, the use of entropy enables the proposed method to automatically construct and assess the baseline consisting of information-rich samples.

42 ENGINEERING↗
NASA NTRS2003

Recent Advances in Hydrogen Peroxide Propulsion Test Capability at NASA's Stennis Space Center E-Complex

In recent years, the rocket propulsion test capability at NASA's John C. Stennis Space Center's (SSC) E-Complex has been enhanced to include facilitization for hydrogen peroxide (H2O2) based ground testing. In particular, the E-3 test stand has conducted numerous test projects that have been reported in the open literature. These include combustion devices as simple as small-scale catalyst beds, and larger devices such as ablative thrust chambers and a flight-type engine (AR2-3). Consequently, the NASA SSC test engineering and operations knowledge base and infrastructure have grown considerably in order to conduct safe H2O2 test operations with a variety of test articles at the component and engine level. Currently, the E-Complex has a test requirement for a hydrogen peroxide based stage test. This new development, with its unique set of requirements, has motivated the facilitization for hydrogen peroxide propellant use at the E-2 Cell 2 test position in addition to E-3. Since the E-2 Cell 2 test position was not originally designed as a hydrogen peroxide test stand, a facility modernization-improvement project was planned and implemented in FY 2002-03 to enable this vertical engine test stand to accomodate H2O2. This paper discusses the ongoing enhancement of E-Complex ground test capability, specifically at the E-3 stand (Cell 1 and Cell 2) and E-2 Cell 2 stand, that enable current and future customers considerable test flexibility and operability in conducting their peroxide based rocket R&D efforts.

Jacks, Thomas E.↗
NASA NTRS2003

Expanding Hydrogen Peroxide Propulsion Test Capability at NASA's Stennis Space Center E-Complex

In recent years, the rocket propulsion test capability at NASA s John C. Stennis Space Center's (SSC) E-Complex has been enhanced to include facilitization for hydrogen peroxide (H2O2) based ground testing. In particular, the E-3 test stand has conducted numerous test projects that have been reported in the open literature. These include combustion devices as simple at small-scale catalyst beds, and larger devices such as ablative thrust chambers and a flight-type engine (AR2-3). Consequently, the NASA SSC test engineering and operations knowledge base and infrastructure have grown considerably in order to conduct safe H2O2 test operations with a variety of test articles at the component and engine level. Currently, the E-Complex has a test requirement for a hydrogen peroxide based stage test. This new development, with its unique set of requirements, has motivated the facilitization for hydrogen peroxide propellant use at the E-2 Cell 2 test position in addition to E-3. Since the E-2 Cell 2 test position was not originally designed as a hydrogen peroxide test stand, a facility modernization- improvement project was planned and implemented in FY 2002-03 to enable this vertical engine test stand to accommodate H2O2. This paper discusses the ongoing enhancement of E-Complex ground test capability, specifically at the E-3 stand (Cell 1 and Cell 2) and E-2 Cell 2 stand, that enable current and future customers considerable test flexibility and operability in conducting their peroxide based rocket R&D efforts.

Jacks, Thomas E.↗
NASA NTRS1990

IDEF5 Ontology Description Capture Method: Concept Paper

The results of research towards an ontology capture method referred to as IDEF5 are presented. Viewed simply as the study of what exists in a domain, ontology is an activity that can be understood to be at work across the full range of human inquiry prompted by the persistent effort to understand the world in which it has found itself - and which it has helped to shape. In the contest of information management, ontology is the task of extracting the structure of a given engineering, manufacturing, business, or logistical domain and storing it in an usable representational medium. A key to effective integration is a system ontology that can be accessed and modified across domains and which captures common features of the overall system relevant to the goals of the disparate domains. If the focus is on information integration, then the strongest motivation for ontology comes from the need to support data sharing and function interoperability. In the correct architecture, an enterprise ontology base would allow th e construction of an integrated environment in which legacy systems appear to be open architecture integrated resources. If the focus is on system/software development, then support for the rapid acquisition of reliable systems is perhaps the strongest motivation for ontology. Finally, ontological analysis was demonstrated to be an effective first step in the construction of robust knowledge based systems.

Menzel, Christopher P.↗
DOE OSTI2022

Distilling Knowledge from Ensembles of Cluster-Constrained-Attention Multiple-Instance Learners for Whole Slide Image Classification

The peculiar nature of whole slide imaging (WSI), digitizing conventional glass slides to obtain multiple high resolution images which capture microscopic details of a patient’s histopathological features, has garnered increased interest from the computer vision research community over the last two decades. Given the unique computational space and time complexity inherent to gigapixel-size whole slide image data, researchers have proposed novel machine learning algorithms to aid in the performance of diagnostic tasks in clinical pathology. One effective algorithm represents a Whole slide image as a bag of smaller image patches, which can be represented as low-dimension image patch embeddings. Weakly supervised deep-learning methods, such as cluster-constrained-attention multiple instance learning (CLAM), have shown promising results when combined with image patch embeddings. While traditional ensemble classifiers yield improved task performance, such methods come with a steep cost in model complexity. Through knowledge distillation, it is possible to retain some performance improvements from an ensemble, while minimizing costs to model complexity. In this work, we implement a weakly supervised ensemble using clustering-constrained-attention multiple-instance learners (CLAM), which uses attention and instance-level clustering to identify task salient regions and feature extraction in whole slides. By applying logit-based and attention-based knowledge distillation, we show it is possible to retain some performance improvements resulting from the ensemble at zero cost to model complexity.

Alamudun, Folami↗
NASA NTRS1982

An overview of expert systems

An expert system is defined and its basic structure is discussed. The knowledge base, the inference engine, and uses of expert systems are discussed. Architecture is considered, including choice of solution direction, reasoning in the presence of uncertainty, searching small and large search spaces, handling large search spaces by transforming them and by developing alternative or additional spaces, and dealing with time. Existing expert systems are reviewed. Tools for building such systems, construction, and knowledge acquisition and learning are discussed. Centers of research and funding sources are listed. The state-of-the-art, current problems, required research, and future trends are summarized.

Gevarter, W. B.↗
NASA NTRS1990

SURE (Science User Resource Expert): A science planning and scheduling assistant for a resource based environment

SURE (Science User Resource Expert) is one of three components that compose the SURPASS (Science User Resource Planning and Scheduling System). This system is a planning and scheduling tool which supports distributed planning and scheduling, based on resource allocation and optimization. Currently SURE is being used within the SURPASS by the UARS (Upper Atmospheric Research Satellite) SOLSTICE instrument to build a daily science plan and activity schedule and in a prototyping effort with NASA GSFC to demonstrate distributed planning and scheduling for the SOLSTICE II instrument on the EOS platform. For the SOLSTICE application the SURE utilizes a rule-based system. Development of a rule-based program using Ada CLIPS as opposed to using conventional programming, allows for capture of the science planning and scheduling heuristics in rules and provides flexibility in inserting or removing rules as the scientific objectives and mission constraints change. The SURE system's role as a component in the SURPASS, the purpose of the SURE planning and scheduling tool, the SURE knowledge base, and the software architecture of the SURE component are described.

Thalman, Nancy E.↗
DOE OSTI2024

Hierarchical Conditioning of Diffusion Models Using Tree-of-Life for Studying Species Evolution

A central problem in biology is to understand how organisms evolve and adapt to their environment by acquiring variations in the observable characteristics or traits of species across the tree of life. With the growing availability of large-scale image repositories in biology and recent advances in generative modeling, there is an opportunity to accelerate the discovery of evolutionary traits automatically from images. Toward this goal, we introduce Phylo-Diffusion, a novel framework for conditioning diffusion models with phylogenetic knowledge represented in the form of HIERarchical Embeddings (HIER-Embeds). We also propose two new experiments for perturbing the embedding space of Phylo-Diffusion: trait masking and trait swapping, inspired by counterpart experiments of gene knockout and gene editing/swapping. Our work represents a novel methodological advance in generative modeling to structure the embedding space of diffusion models using tree-based knowledge. Our work also opens a new chapter of research in evolutionary biology by using generative models to visualize evolutionary changes directly from images. We empirically demonstrate the usefulness of Phylo-Diffusion in capturing meaningful trait variations for fishes and birds, revealing novel insights about the biological mechanisms of their evolution. (Model and code can be found at imageomics.github.io/phylo-diffusion)

Khurana, Mridul↗
NASA NTRS2015

Human Systems Integration (HSI) Practitioner's Guide

The NASA/SP-2015-3709, Human Systems Integration (HSI) Practitioner's Guide, also known as the "HSIPG," provides a tool for implementing HSI activities within the NASA systems engineering framework. The HSIPG is written to aid the HSI practitioner engaged in a program or project (P/P), and serves as a knowledge base to allow the practitioner to step into an HSI lead or team member role for NASA missions. Additionally, this HSIPG is written to address the role of HSI in the P/P management and systems engineering communities and aid their understanding of the value added by incorporating good HSI practices into their programs and projects. Through helping to build a community of knowledgeable HSI practitioners, this document also hopes to build advocacy across the Agency for establishing strong, consistent HSI policies and practices. Human Systems Integration (HSI) has been successfully adopted (and adapted) by several federal agencies-most notably the U.S. Department of Defense (DoD) and the Nuclear Regulatory Commission (NRC)-as a methodology for reducing system life cycle costs (LCCs). These cost savings manifest themselves due to reductions in required numbers of personnel, the practice of human-centered design, decreased reliance on specialized skills for operations, shortened training time, efficient logistics and maintenance, and fewer safety-related risks and mishaps due to unintended human/system interactions. The HSI process for NASA establishes how cost savings and mission success can be realized through systems engineering. Every program or project has unique attributes. This HSIPG is not intended to provide one-size-fits-all recommendations for HSI implementation. Rather, HSI processes should be tailored to the size, scope, and goals of individual situations. The instructions and processes identified here are best used as a starting point for implementing human-centered system concepts and designs across programs and projects of varying types, including manned and unmanned, human spaceflight, aviation, robotics, and environmental science missions. The practitioner using this guide should have expertise in Systems Engineering or other disciplines involved in producing systems with anticipated human interactions. (See section 1.6 of this guide for further discussion on HSI discipline domains.) The HSIPG provides an "HSI layer" to the NASA Systems Engineering Engine (SEE), detailed in NASA Procedural Requirement (NPR) 7123.1B, NASA Systems Engineering Processes and Requirements, and further explained in NASA/SP-2007-6105, Systems Engineering Handbook (see HSIPG Table 2.2-1, NASA Documents with HSI Content, for specific references and document versions).

Zumbado, Jennifer Rochlis↗
NASA NTRS1993

Neural network applications

A design procedure is suggested for neural networks which accommodates the inclusion of such knowledge-based systems techniques as fuzzy logic and pairwise comparisons. The use of these procedures in the design of applications combines qualitative and quantitative factors with empirical data to yield a model with justifiable design and parameter selection procedures. The procedure is especially relevant to areas of back-propagation neural network design which are highly responsive to the use of precisely recorded expert knowledge.

Padgett, Mary L.↗
NASA NTRS1992

The Role of Reformulation in the Automatic Design of Satisfiability Procedures

Recently there has been increasing interest in the problem of knowledge compilation (Selman & Kautz91). This is the problem of identifying tractable techniques for determining the consequences of a knowledge base. We have developed and implemented a technique, called DRAT, that given a theory, i.e., a collection of firstorder clauses, can often produce a type of decision procedure for that theory that can be used in the place of a general-purpose first-order theorem prover for determining many of the consequences of that theory. Hence, DRAT does a type of knowledge compilation. Central to the DRAT technique is a type of reformulation in which a problem's clauses are restated in terms of different nonlogical symbols. The reformulation is isomorphic in the sense that it does not change the semantics of a problem.

VanBaalen, Jeffrey↗