Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “AI system”

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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Smart Crop Farming Systems for Artemis Exploration Missions

Space crop production systems that mitigate risks of crew poor performance or illness due to inadequate food and nutrition are needed during manned Artemis exploration missions beyond LEO. Prototype farms must be designed for deployment on ISS and tested in manned platforms: Gateway, lunar habitats, and Mars trans-hab spacecraft in preparation for human missions to Mars. Food production must be optimal and safe for human consumption. Thus, plant growth facilities (i.e. Veggie and APH) can be enhanced with imaging systems (including hyperspectral, multispectral, lidar, and fluorescence imaging systems) for nondestructive monitoring of plant health, stress and assessing food safety. Databases of crop responses to stress obtained during ground studies can be used to develop novel artificial intelligence (AI) algorithms for optimizing crop production (i.e. environmental settings during growth) and for detecting crop indices that ensure food safety. Future farming systems should be sustainable and smart. Novel adaptive AI algorithms requiring limited data sets for calibration are needed for reducing crew intervention during plant cultivation except for maintenance and harvesting events. Eventually, AI driven control systems that include autonomous planting, growing, and harvesting as well as periodic sanitization need evaluation for supplementing crew diets with fresh produce during future Mars exploration missions.

O Monje↗
DOE OSTI2023

Design principles for lifelong learning AI accelerators

Lifelong learning-an agent's ability to learn throughout its lifetime-is a hallmark of biological learning systems and a central challenge for artificial intelligence (AI). The development of lifelong learning algorithms could lead to a range of novel AI applications, but this will also require the development of appropriate hardware accelerators, particularly if the models are to be deployed on edge platforms, which have strict size, weight and power constraints. Here we explore the design of lifelong learning AI accelerators that are intended for deployment in untethered environments. We identify key desirable capabilities for lifelong learning accelerators and highlight metrics to evaluate such accelerators. We then discuss current edge AI accelerators and explore the future design of lifelong learning accelerators, considering the role that different emerging technologies could play. The Perspective explores the future design of lifelong learning artificial intelligence (AI) accelerators that are intended for deployment in untethered environments, identifying key desirable capabilities for such edge AI accelerators and guidance on metrics to evaluate them.

42 ENGINEERING↗
NASA NTRS1990

The Katydid system for compiling KEE applications to Ada

Components of a system known as Katydid are developed in an effort to compile knowledge-based systems developed in a multimechanism integrated environment (KEE) to Ada. The Katydid core is an Ada library supporting KEE object functionality, and the other elements include a rule compiler, a LISP-to-Ada translator, and a knowledge-base dumper. Katydid employs translation mechanisms that convert LISP knowledge structures and rules to Ada and utilizes basic prototypes of a run-time KEE object-structure library module for Ada. Preliminary results include the semiautomatic compilation of portions of a simple expert system to run in an Ada environment with the described algorithms. It is suggested that Ada can be employed for AI programming and implementation, and the Katydid system is being developed to include concurrency and synchronization mechanisms.

Filman, Robert E.↗
NASA NTRS2022

Space Applications of a Trusted AI Framework: Experiences and Lessons Learned

Artificial intelligence (AI), which encompasses machine learning (ML), has become a critical technology due to its well-established success in a wide array of applications. However, the proper application of AI remains a central topic of discussion in many safety-critical fields. This has limited its success in autonomous systems due to the difficulty of ensuring AI algorithms will perform as desired and that users will understand and trust how they operate. In response, there is growing demand for trustability in AI to address both the expectations and concerns regarding its use. The Aerospace Corporation (Aerospace) developed a Framework for Trusted AI (henceforth referred to as the framework) to encourage best practices for the implementation, assessment, and control of AI-based applications. It is generally applicable, being based on terms and definitions that cut across AI domains, and thus is a starting point for practitioners to tailor to their particular application. To help demonstrate how the framework can be tailored into mission assurance guidance for the space domain, Aerospace sought the involvement of the Jet Propulsion Laboratory (JPL) to engage with actual examples of AI-based space autonomy.

Kaufman, James↗
DOE OSTI2026

A Smart Vision-Aided RICH (Robotic Interface Control and Handling) System for VULCAN

High-flux neutron beams and high-efficiency detectors enable rapid neutron diffraction measurements at the Engineering Materials Diffractometer (VULCAN) at the Spallation Neutron Source (SNS), Oak Ridge National Laboratory (ORNL). To optimize beam time utilization, efficient sample exchange, alignment, and automated measurements are essential. Recent advances in artificial intelligence (AI) have expanded the capabilities of robotic systems. Here, we report the development of a Robotic Interactive Control and Handling (RICH) system for sample handling at VULCAN, designed to support high-throughput experiments and reduce overhead time. The RICH system employs a six-axis desktop robot integrated with AI-based computer vision models capable of recognizing and localizing samples in real time from instrument and depth-resolving cameras. Vision algorithms combine these detections to align samples with designated measurement positions or place them within complex sample environments such as furnaces. This integration of machine learning-assisted vision with robotic handling demonstrates the feasibility of autonomous sample detection and preparation, offering a pathway toward fully unmanned neutron scattering experiments.

automation↗
NASA NTRS2024

Soil Salinity Level Assessment and Prediction Integrating UAV-borne Hyperspectral Imaging and Machine Learning Algorithms to Combat Desertification

In response to the ongoing global food crisis, the United Nations has identified “Zero Hunger” as one of its Sustainable Development Goals. A central contributor to the crisis is the process in which agricultural lands go through desertification. Research has shown a direct correlation between soil salinity and desertification - increased salinity levels indicate a higher risk for desertification. Furthermore, researchers have explored various techniques to map soil salinity, but these methods are oftentimes inefficient and don’t address future salinity predictions. To improve desertification monitoring, soil salinity can be observed via hyperspectral imaging on unmanned aerial vehicles (UAVs) to predict the risk of agricultural desertification using artificial intelligence (AI) and machine learning (ML) techniques. A significant gap exists in past research that applies ML and imaging techniques to soil salinity: convolutional neural networks (CNNs) and regression models are rarely leveraged together, despite the efficiency and accuracy of these models. To compensate for this gap, the proposed system leverages the use of these AI and ML models to improve soil assessment and prediction techniques. This approach involves three steps - data collection, image analysis, and future prediction. Using hyperspectral cameras on UAVs to collect the data from the region, a trained CNN model will output estimated soil salinity levels at a specific time. The estimations will then be analyzed by a regression model to assess the accuracy of future soil salinity predictions. The proposed system will identify regions at risk of desertification to help farmers mitigate agricultural loss, in turn helping alleviate the food crisis.

UAV systems↗
NASA NTRS1987

Remote mineralogical and vegetation mapping using imaging spectrometry

Two airborne flight systems were developed for geological studies: the airborne imaging spectrometer (AIS) and the advanced visible and infrared imaging spectrometer. A satellite imaging spectrometer system, the high resolution imaging spectrometer, is currently being developed and is planned for flight on the proposed NASA Earth Orbiting System. Examples of the application of these instruments in mineral and vegetation studies are described. Experiments conducted with AIS over the Cuprite Mining District (Nevada) and the Owens Valley (California) demonstrate the power of this new approach to remote sensing.

King, James, Jr.↗
NASA NTRS2012

GaN-Based Detector Enabling Technology for Next Generation Ultraviolet Planetary Missions

The ternary alloy AlN-GaN-InN system provides several distinct advantages for the development of UV detectors for future planetary missions. First, (InN), (GaN) and (AlN) have direct bandgaps 0.8, 3.4 and 6.2 eV, respectively, with corresponding wavelength cutoffs of 1550 nm, 365 nm and 200 nm. Since they are miscible with each other, these nitrides form complete series of indium gallium nitride (In(sub l-x)Ga(sub x)N) and aluminum gallium nitride (Al(sub l-x)Ga(sub x)N) alloys thus allowing the development of detectors with a wavelength cut-off anywhere in this range. For the 2S0-365 nm spectral wavelength range AlGaN detectors can be designed to give a 1000x solar radiation rejection at cut-off wavelength of 325 nm, than can be achieved with Si based detectors. For tailored wavelength cut-offs in the 365-4S0 nm range, InGaN based detectors can be fabricated, which still give 20-40x better solar radiation rejection than Si based detectors. This reduced need for blocking filters greatly increases the Detective Quantum efficiency (DQE) and simplifies the instrument's optical systems. Second, the wide direct bandgap reduces the thermally generated dark current to levels allowing many observations to be performed at room temperature. Third, compared to narrow bandgap materials, wide bandgap semiconductors are significantly more radiation tolerant. Finally, with the use of an (AI, In)GaN array, the overall system cost is reduced by eliminating stringent Si CCD cooling systems. Compared to silicon, GaN based detectors have superior QE based on a direct bandgap and longer absorption lengths in the UV.

Aslam, S.↗
NASA NTRS1990

ART/Ada and CLIPS/Ada

Although they have reached a point of commercial viability, expert systems were originally developed in artificial intelligence (AI) research environments. Many of the available tools still work best in such environments. These environments typically utilize special hardware such as LISP machines and relatively unfamiliar languages such as LISP or Prolog. Space Station applications will require deep integration of expert system technology with applications developed in conventional languages, specifically Ada. The ability to apply automation to Space Station functions could be greatly enhanced by widespread availability of state-of-the-art expert system tools based on Ada. Although there have been some efforts to examine the use of Ada for AI applications, there are few, if any, existing products which provide state-of-the-art AI capabilities in an Ada tool. The goal of the ART/Ada Design Project is to conduct research into the implementation in Ada of state-of-the-art hybrid expert systems building tools (ESBT's). This project takes the following approach: using the existing design of the ART-IM ESBT as a starting point, analyze the impact of the Ada language and Ada development methodologies on that design; redesign the system in Ada; and analyze its performance. The research project will attempt to achieve a comprehensive understanding of the potential for embedding expert systems in Ada systems for eventual application in future Space Station Freedom projects. During Phase 1 of the project, initial requirements analysis, design, and implementation of the kernel subset of ART-IM functionality was completed. During Phase 2, the effort has been focused on the implementation and performance analysis of several versions with increasing functionality. Since production quality ART/Ada tools will not be available for a considerable time, and additional subtask of this project will be the completion of an Ada version of the CLIPS expert system shell developed by NASA. This tool will provide full syntactic compatibility with any eventual products of the ART/Ada design while allowing SSFP developers early access to this technology.

Culbert, Chris↗
NASA NTRS1987

Theoretical considerations in designing operator interfaces for automated systems

The domains most amenable to techniques based on artificial intelligence (AI) are those that are systematic or for which a systematic domain can be generated. In aerospace systems, many operational tasks are systematic owing to the highly procedural nature of the applications. However, aerospace applications can also be nonprocedural, particularly in the event of a failure or an unexpected event. Several techniques are discussed for designing automated systems for real-time, dynamic environments, particularly when a 'breakdown' occurs. A breakdown is defined as operation of an automated system outside its predetermined, conceptual domain.

Norman, Susan D.↗
DOE OSTI2025

Data Integration and Visualization for Enhanced Resilience and Sustainability in Hydropower (DIVERS-H)

U.S. hydropower plants face potential threats from shrinking water supply, rising demands, and warmer stream temperatures from various causes. Power plant owners, operators, and regulators require new tools to take advantage of and interpret the diverse range of scientific data being produced by both observational methods (for example, satellite, radar, stream gauges) and computer modeling methods that evaluate and predict how earth's dynamic systems (atmosphere, oceans, land surface, and sea ice) are changing and interacting. Combining datasets such as these with AI-based analyses introduces a novel decision support system to help users anticipate and address potential impacts on power generation stations. This new technology has been named DIVERS-H for "Data Integration and Visualization for Enhanced Resilience and Sustainability in Hydropower." In Phase I, technical feasibility was established with the development and demonstration of all the new technologies that are required. Most notably, DIVERS-H will use new artificial intelligence (AI) methods to capture the complex dynamics of water availability, demand, and environmental changes. In addition, new data management software was developed, and a prototype user interface was implemented as the precursor to a full scale decision support system. With technical research complete, the project focus now shifts to development of a commercial software product to provide users with actionable insight into water availability and the risk/resilience of critical systems at their locations of interest. Although DIVER-H was originally conceived as a tool for hydroelectric power applications, the same underlying technology can be readily applied to other water-consuming systems including coal, natural gas, oil, and nuclear power plants.

Chaudhary, Aashish [Kitware, Inc., Clifton Park, N↗
DOE OSTI2021

A science paradigm shift is needed for Earth and Environmental Systems Sciences (EESS) to integrate Knowledge-Guided Artificial Intelligence (KGAI) and lead new EESS-KGAI theories

The focal area of this white paper is learning from complex data through the use of AI techniques and AI-derived model components. Specifically, we advocate for research programs to develop knowledge-guided AI (KGAI) in the Earth and Environmental Systems sciences (EESS) as a basic research paradigm that is separate from (but supports) any specific Earth system model, modeling components, and modeling workflows, and even separates from specific hypothesis-driven questions about individual Earth system processes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1991

SHARP: Spacecraft Health Automated Reasoning Prototype

The planetary spacecraft mission OPS as applied to SHARP is studied. Knowledge systems involved in this study are detailed. SHARP development task and Voyager telecom link analysis were examined. It was concluded that artificial intelligence has a proven capability to deliver useful functions in a real time space flight operations environment. SHARP has precipitated major change in acceptance of automation at JPL. The potential payoff from automation using AI is substantial. SHARP, and other AI technology is being transferred into systems in development including mission operations automation, science data systems, and infrastructure applications.

Atkinson, David J.↗
DOE OSTI2021

Earth System Model Improvement Pipeline via Uncertainty Attribution and Active Learning

Primary focal area: 2 (Predictive Modeling via AI): We develop methods to formally quantify uncertainties in Earth System models for the land-atmosphere coupled system. Science Challenge: Earth system models still have significant biases in historical predictions of the intensity and frequency of water cycling extremes (e.g., droughts and flood events), leading to low confidence in future projections. Uncertainties arise from incomplete understanding of land and atmospheric processes, and insufficient observational constraints on model parameters. Many observations, including those from key DOE investments such as ARM and AmeriFlux, are used to evaluate model performance but have not been used to formally quantify model uncertainty because of the expense of running ESM simulations. An efficient pipeline engaging cutting-edge machine learning (ML) and uncertainty quantification (UQ) methods is needed to improve the predictive understanding of water cycle extremes in the Earth system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

AI-Constrained Bottom-Up Ecohydrology and Improved Prediction of Seasonal, Interannual, and Decadal Flood and Drought Risks

Focal Areas: (2) Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system composed of a hierarchy of models (3)Insight gleaned from complex data (both observed & simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1992

Logic programming and metadata specifications

Artificial intelligence (AI) ideas and techniques are critical to the development of intelligent information systems that will be used to collect, manipulate, and retrieve the vast amounts of space data produced by 'Missions to Planet Earth.' Natural language processing, inference, and expert systems are at the core of this space application of AI. This paper presents logic programming as an AI tool that can support inference (the ability to draw conclusions from a set of complicated and interrelated facts). It reports on the use of logic programming in the study of metadata specifications for a small problem domain of airborne sensors, and the dataset characteristics and pointers that are needed for data access.

Lopez, Antonio M., Jr.↗
DOE OSTI2026

Cooperative Automated Cohort Driving on Connected Infrastructure, Arterial Roadways, and Highways: Final Project Demonstration and System-of-Systems Model Correlation

This project seeks to synergize vehicle automated driving and connectivity data to improve mobility and energy efficiency of groups of mixed vehicles operating in close proximity (vehicle cohort) on various infrastructure. A custom cellular communication network links vehicles operating as a cohort with infrastructure to a centralized system-of-systems digital twin with an AI-based optimal behavior planner. The data contained in this set are from final testing and technology demonstrations to U.S. Department of Energy staff at the American Center for Mobility. The data contain single-lane, single-light scenarios; multi-lane, multi-light arterial scenarios; and limited-access highway scenarios. All test cases were derived from simulations and replicated on the test track. The project employed two and four light-duty vehicles with connectivity and drive automation for the testing. The baseline scenario without connectivity was run under the control of the system-of-systems centralized planner but operating each vehicle with an intelligent driver model controlling the velocity, lane utilization, and vehicle gap. This was to ensure the highest compatibility with the simulation in terms of dynamic behavior. The connected cohort case utilized AI optimization to perform coordinated and cooperative control for energy, as well as safe, comfortable behavior for the cohort. The dataset is appropriately named with unconnected and connected designations, with comparisons sharing the same run index number. The included PowerPoint and PDF files describe the test setup and provide an overview of results from the project. ![image](de-EE0009209_March_2023_Data_Arterial_Scenario_Results.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗