Search NASA⌕ Search

SEARCH · Search NASA

Results for “Computational Intelligence”

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.

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

At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Online model-based diagnosis to support autonomous operation of an advanced life support system

This article describes methods for online model-based diagnosis of subsystems of the advanced life support system (ALS). The diagnosis methodology is tailored to detect, isolate, and identify faults in components of the system quickly so that fault-adaptive control techniques can be applied to maintain system operation without interruption. We describe the components of our hybrid modeling scheme and the diagnosis methodology, and then demonstrate the effectiveness of this methodology by building a detailed model of the reverse osmosis (RO) system of the water recovery system (WRS) of the ALS. This model is validated with real data collected from an experimental testbed at NASA JSC. A number of diagnosis experiments run on simulated faulty data are presented and the results are discussed.

Non-NASA Center↗
NASA NTRS2005

Down-to-Earth Benefits of Space Exploration: Past, Present, Future

A ventricular device that helps a weakened heart keep pumping while awaiting a transplant. A rescue tool for extracting victims from dangerous situations such as car wrecks. A video analysis tool used to investigate the bombing at the 1996 Olympics in Atlanta. A sound-differentiation tool for safer air traffic control. A refrigerator that run without electricity or batteries. These are just a few of the spin-offs of NASA technology that have benefited society in recent years. Now, as NASA sets its vision on space exploration, particularly of the moon and Mars, even more benefits to society are possible. This expansion of societal benefits is tied to a new emphasis on technology infusion or spin-in. NASA is seeking partners with industry, universities, and other government laboratories to help the Agency address its specific space exploration needs in five areas: (1) advanced studies, concepts, and tools; (2) advanced materials; (3) communications, computing, electronics, and imaging; (4) software, intelligent systems, and modeling; and (5) power, propulsion, and chemical systems. These spin-in partnerships will offer benefits to U.S. economic development as well as new products for the global market. As a complement to these spin-in benefits, NASA also is examining the possible future spin-outs of the innovations related to its new space exploration mission. A matrix that charts NASA's needs against various business sectors is being developed to fully understand the implications for society and industry of spin-in and spin-out. This matrix already has been used to help guide NASA s efforts to secure spin-in partnerships. This paper presents examples of NASA spin-offs, discusses NASA s present spin-in/spin-out projects for pursuing partnerships, and considers some of the future societal benefits to be reaped from these partnerships. This paper will complement the proposed paper by Frank Schowengerdt on the Innovative Partnerships Program structure and how to work with the PP.

Neumann, Benjamin↗
NASA NTRS2013

Cognitive Networking With Regards to NASA's Space Communication and Navigation Program

This report describes cognitive networking (CN) and its application to NASA's Space Communication and Networking (SCaN) Program. This report clarifies the terminology and framework of CN and provides some examples of cognitive systems. It then provides a methodology for developing and deploying CN techniques and technologies. Finally, the report attempts to answer specific questions regarding how CN could benefit SCaN. It also describes SCaN's current and target networks and proposes places where cognition could be deployed.

machine learning↗
NASA NTRS2014

Swarmie User Manual: A Rover Used for Multi-agent Swarm Research

The ability to create multiple functional yet cost effective robots is crucial for conducting swarming robotics research. The Center Innovation Fund (CIF) swarming robotics project is a collaboration among the KSC Granular Mechanics and Regolith Operations (GMRO) group, the University of New Mexico Biological Computation Lab, and the NASA Ames Intelligent Robotics Group (IRG) that uses rovers, dubbed "Swarmies", as test platforms for genetic search algorithms. This fall, I assisted in the development of the software modules used on the Swarmies and created this guide to provide thorough instructions on how to configure your workspace to operate a Swarmie both in simulation and out in the field.

Robotics↗
DOE OSTI2025

Privacy-Aware RAG-Enabled LLMs for Collaborative AI in Organizations

Recent advancements in Large Language Models (LLMs) based on Transformer architectures have significantly improved capabilities in natural language processing and generation. However, deploying LLMs for inter-organizational communication poses challenges, in ensuring privacy and facilitating effective collaboration. This paper introduces a novel decentralized inference meta-agent chatbot that leverages privacy-aware Retrieval-Augmented Generation (RAG)-enabled LLMs for collaborative AI communication across organizations. Built on Microsoft’s Autogen, the platform enables LLMs to autonomously refine responses, enhancing accuracy and relevance. It incorporates advanced hallucination mitigation techniques using Uptrain and a privacy-focused RAG framework that employs synthetic document generation to protect sensitive information. Comprehensive evaluations demonstrate the platform’s effectiveness in maintaining contextual relevance and stringent privacy standards, effectively addressing critical challenges in LLM-enhanced collaborative AI communication. This work represents a significant step toward secure and efficient inter-organizational collaboration using advanced generative AI technologies.

97 - MATHEMATICS AND COMPUTING↗
NASA NTRS2005

Evolutionary Computing for Low-thrust Navigation

The development of new mission concepts requires efficient methodologies to analyze, design and simulate the concepts before implementation. New mission concepts are increasingly considering the use of ion thrusters for fuel-efficient navigation in deep space. This paper presents parallel, evolutionary computing methods to design trajectories of spacecraft propelled by ion thrusters and to assess the trade-off between delivered payload mass and required flight time. The developed methods utilize a distributed computing environment in order to speed up computation, and use evolutionary algorithms to find globally Pareto-optimal solutions. The methods are coupled with two main traditional trajectory design approaches, which are called direct and indirect. In the direct approach, thrust control is discretized in either arc time or arc length, and the resulting discrete thrust vectors are optimized. In the indirect approach, a thrust control problem is transformed into a costate control problem, and the initial values of the costate vector are optimized. The developed methods are applied to two problems: 1) an orbit transfer around the Earth and 2) a transfer between two distance retrograde orbits around Europa, the closest to Jupiter of the icy Galilean moons. The optimal solutions found with the present methods are comparable to other state-of-the-art trajectory optimizers and to analytical approximations for optimal transfers, while the required computational time is several orders of magnitude shorter than other optimizers thanks to an intelligent design of control vector discretization, advanced algorithmic parameterization, and parallel computing.

optimization↗
NASA NTRS1988

An intelligent tutoring system for space shuttle diagnosis

An Intelligent Tutoring System (ITS) transcends conventional computer-based instruction. An ITS is capable of monitoring and understanding student performance thereby providing feedback, explanation, and remediation. This is accomplished by including models of the student, the instructor, and the expert technician or operator in the domain of interest. The space shuttle fuel cell is the technical domain for the project described below. One system, Microcomputer Intelligence for Technical Training (MITT), demonstrates that ITS's can be developed and delivered, with a reasonable amount of effort and in a short period of time, on a microcomputer. The MITT system capitalizes on the diagnostic training approach called Framework for Aiding the Understanding of Logical Troubleshooting (FAULT) (Johnson, 1987). The system's embedded procedural expert was developed with NASA's C-Language Integrated Production (CLIP) expert system shell (Cubert, 1987).

Johnson, William B.↗
NASA NTRS1984

An intelligent flight-management aid for procedure execution

A computer program is described that contains a model of the procedures used in the operation of a twin engine aircraft. This program, by comparing the model to the aircraft state, can determine when a procedure (or checklist) should be or is invoked and when each step (detectable by a change in the aircraft state) is completed. Thus, the program tracks the flight crew's procedure execution through changes in the aircraft state. Data were used for evaluation from an earlier experiment on a Link GAT-II simulator. The program had been able to identify practically all of the errors identified by human judges as well as locate some missed by them. It is felt that this model could significantly aid flight crews.

Hammer, J. M.↗
NASA NTRS1990

Beam rider for an Articulated Robot Manipulator (ARM) accurate positioning of long flexible manipulators

Laser beam positioning and beam rider modules were incorporated into the long hollow flexible segment of an articulated robot manipulator (ARM). Using a single laser beam, the system determined the position of the distal ARM endtip, with millimetric precision, in six degrees of freedom, at distances of up to 10 meters. Preliminary designs, using space rated technology for the critical systems, of a two segmented physical ARM, with a single and a dual degree of freedom articulation, were developed, prototyped, and tested. To control the positioning of the physical ARM, an indirect adaptive controller, which used the mismatch between the position of the laser beam under static and dynamic conditions, was devised. To predict the behavior of the system and test the concept, a computer simulation model was constructed. A hierarchical artificially intelligent real time ADA operating system program structure was created. The software was designed for implementation on a dedicated VME bus based Intel 80386 administered parallel processing multi-tasking computer system.

Malachowski, M. J.↗
DOE OSTI2026

Quantum Computing for AI-based Design and Optimization of Electric Motors

Knowledge-based artificial intelligence and hierarchical fuzzy logic offer an interpretable framework for electricvehicle motor preliminary design, but their computational burden grows with linguistic granularity and coupled design-space size. This paper presents a reduced quantum reformulation of the hierarchical fuzzy inference of air-gap flux density, a representative level-one motor-design parameter. Starting from the published electric-vehicle motor-design framework, a three-term fuzzy prototype is constructed from the original inference structure. The reduced model is then reformulated as a modular quantum register-oracle system, in which each hierarchical subrelation is encoded as a block oracle and evaluated through superpositionbased candidate-label testing. The proposed modular quantum formulation reproduces the reduced classical prototype after block fusion. A resource analysis shows that the reduced modular system requires seven qubits per block and twenty-two qubits in a straightforward four-block implementation. Finally, a crossovercomplexity model is derived to identify the regime in which quantum candidate search may become favorable relative to hierarchical fuzzy inference. The results show that no quantum advantage should be claimed for the present one-output reduced benchmark, but that a plausible crossover emerges for larger joint candidate spaces and higher linguistic granularity. The work therefore establishes a technically consistent starting point for future quantum-assisted electric-vehicle motor-design optimization.

Kumar, Praveen [ORNL] (ORCID:0000000291877857)↗
NASA NTRS1995

Automated Management Of Documents

Report presents main technical issues involved in computer-integrated documentation. Problems associated with automation of management and maintenance of documents analyzed from perspectives of artificial intelligence and human factors. Technologies that may prove useful in computer-integrated documentation reviewed: these include conventional approaches to indexing and retrieval of information, use of hypertext, and knowledge-based artificial-intelligence systems.

Boy, Guy↗
DOE OSTI2026

Ecosystems for Scientific Computing in the Age of AI

Scientific computing is at an inflection point. Artificial intelligence (AI) is reshaping how scientific software is developed, how teams collaborate, how projects are governed, and how the next generation is trained. Drawing on insights from a 2025 workshop report, this article argues that the future of discovery will depend on agile, robust ecosystems built through socio-technical co-design—the intentional integration of technical and human systems. This perspective is essential for ensuring that future scientific computing remains trustworthy, sustainable, and scalable. It combines advances in AI, high-performance computing, and software with new models for cross-disciplinary collaboration, education, and workforce development. Key recommendations include building modular, trustworthy AI-enabled software ecosystems; enabling teams to integrate AI into scientific workflows while preserving human creativity, integrity, and rigor; and developing adaptive training pathways that keep pace with rapid technological change. By sharing these perspectives, we hope to stimulate broader community dialogue and encourage coordinated action.

AI↗
NASA NTRS1986

Intelligent user interface concept for space station

The space station computing system must interface with a wide variety of users, from highly skilled operations personnel to payload specialists from all over the world. The interface must accommodate a wide variety of operations from the space platform, ground control centers and from remote sites. As a result, there is a need for a robust, highly configurable and portable user interface that can accommodate the various space station missions. The concept of an intelligent user interface executive, written in Ada, that would support a number of advanced human interaction techniques, such as windowing, icons, color graphics, animation, and natural language processing is presented. The user interface would provide intelligent interaction by understanding the various user roles, the operations and mission, the current state of the environment and the current working context of the users. In addition, the intelligent user interface executive must be supported by a set of tools that would allow the executive to be easily configured and to allow rapid prototyping of proposed user dialogs. This capability would allow human engineering specialists acting in the role of dialog authors to define and validate various user scenarios. The set of tools required to support development of this intelligent human interface capability is discussed and the prototyping and validation efforts required for development of the Space Station's user interface are outlined.

Comer, Edward↗
DOE OSTI2025

Videos, photos, and AI-derived grain size data associated with “High-throughput AI Video Surveys Enable Reproducible Multiscale Sediment Size Mapping, with Implications for Hydrobiogeochemical Parameterization”

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “High-throughput AI Video Surveys Enable Reproducible Multiscale Sediment Size Mapping, with Implications for Hydrobiogeochemical Parameterization” under review. This data package includes five data types: 1) raw photos and videos from drone survey and walking smartphone surveys; 2) images derived from raw videos; 3) manual labeling of reference scales; 4) metadata for all images and photo resolution derived from artificial intelligence (AI) models or manual labels, 5) grain size data obtained from AI models for all photos, 6) metadata and grain size data after quality control, 7) summaries of sample efficiency for all data, and 8) computational fluid dynamics (CFD) data used to support hydro-biogeochemical (HBGC) parameter estimation. Such data is used to 1) demonstrate significant improvements in accuracy, efficiency, and quality control for grain size data collection with the help of AI models, 2) study the spatial heterogeneity of grain size and observation reproducibility based on tens of thousands of data points generated by the AI models, and 3) evaluate the impacts of grain size heterogeneity on key HBGC parameters across sediment-to-reach and hourly-to-yearly scales. In particular, the data package contains 116 folders and 179696 files. The files include 41 videos in .mov format, 64047 photos in .jpg format, 13541 video-derived photos in .png format, 12747 segmentation mask data in .tif format, 12747 segmentation data in .json format, 24771 .csv files that with metadata and grain size for each individual photo as well as water depth and velocity data from CFD and observation, 51791 .txt files of raw AI predicted labels, and 11 flight record data in .srt format. The summary for all metadata and grain size statistics information is included in “Scales_V3_NG.csv” and “Statistics_V3_NG.csv”. The summary for data that pass data quality control (QC) level 0-2 is included in “QCStatistics_V3_NG.csv”. The QC level 0 represents photos whose photo resolution is positive, excluding photos that miss reference scale. The QC level 1 means reference scale circularity uncertainty is less than 5% for smartphone images while representing photo resolution is larger than 0.44 mm/pixel for drone images. The QC level 2 means excluding photos whose grain number is less than 100, a minimum number of grains recommended by classic literature. The summary for each video’s name, length, frame rates, survey area, grain number, survey efficiency, etc. can be found in “QCSummary_V3_NG.csv”. The summary for site name, GPS coordinates, and number of images at each site can be found in “SitesSummary_V3_*.csv” files. Overall computational efficiency summary is reported in Table 4 of accompanying manuscript. Additionally, the nitrate concentration data used in this work was downloaded from an existing dataset published on ESS-DIVE (Boat-Dragged Sensor Hanford Reach.csv; Conner A. et al., 2020). We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Port of Benton, and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the data were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate data collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗