SEARCH · Search NASA

Results for “Continual Learning”

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.

At least 73 records · Page 4

All sources NASA NTRS DOE OSTI

NASA NTRS1990

Neural networks for self-learning control systems

It is shown how a neural network can learn of its own accord to control a nonlinear dynamic system. An emulator, a multilayered neural network, learns to identify the system's dynamic characteristics. The controller, another multilayered neural network, next learns to control the emulator. The self-trained controller is then used to control the actual dynamic system. The learning process continues as the emulator and controller improve and track the physical process. An example is given to illustrate these ideas. The 'truck backer-upper,' a neural network controller that steers a trailer truck while the truck is backing up to a loading dock, is demonstrated. The controller is able to guide the truck to the dock from almost any initial position. The technique explored should be applicable to a wide variety of nonlinear control problems.

Nguyen, Derrick H.↗

NASA NTRS1991

Applications of fuzzy logic to control and decision making

Long range space missions will require high operational efficiency as well as autonomy to enhance the effectivity of performance. Fuzzy logic technology has been shown to be powerful and robust in interpreting imprecise measurements and generating appropriate control decisions for many space operations. Several applications are underway, studying the fuzzy logic approach to solving control and decision making problems. Fuzzy logic algorithms for relative motion and attitude control have been developed and demonstrated for proximity operations. Based on this experience, motion control algorithms that include obstacle avoidance were developed for a Mars Rover prototype for maneuvering during the sample collection process. A concept of an intelligent sensor system that can identify objects and track them continuously and learn from its environment is under development to support traffic management and proximity operations around the Space Station Freedom. For safe and reliable operation of Lunar/Mars based crew quarters, high speed controllers with ability to combine imprecise measurements from several sensors is required. A fuzzy logic approach that uses high speed fuzzy hardware chips is being studied.

Lea, Robert N.↗

NASA NTRS1993

Neuromorphic Learning From Noisy Data

Two reports present numerical study of performance of feedforward neural network trained by back-propagation algorithm in learning continuous-valued mappings from data corrupted by noise. Two types of noise considered: plant noise which affects dynamics of controlled process and data-processing noise, which occurs during analog processing and digital sampling of signals. Study performed with view toward use of neural networks as neurocontrollers to substitute for, or enhance, performances of human experts in controlling mechanical devices in presence of sensor and actuator noise and to enhance performances of more-conventional digital feedback electronic process controllers in noisy environments.

Merrill, Walter C.↗

NASA NTRS2008

The User Community and a Multi-Mission Data Project: Services, Experiences and Directions of the Space Physics Data Facility

From a user's perspective, the multi-mission data and orbit services of NASA's Space Physics Data Facility (SPDF) project offer a unique range of important data and services highly complementary to other services presently available or now evolving in the international heliophysics data environment. The VSP (Virtual Space Physics Observatory) service is an active portal to a wide range of distributed data sources. CDAWeb (Coordinate Data Analysis Web) enables plots, listings and file downloads for current data cross the boundaries of missions and instrument types (and now including data from THEMIS and STEREO). SSCWeb, Helioweb and our 3D Animated Orbit Viewer (TIPSOD) provide position data and query logic for most missions currently important to heliophysics science. OMNIWeb with its new extension to 1- and 5-minute resolution provides interplanetary parameters at the Earth's bow shock as a unique value-added data product. SPDF also maintains NASA's CDF (common Data Format) standard and a range of associated tools including translation services. These capabilities are all now available through webservices-based APIs as well as through our direct user interfaces. In this paper, we will demonstrate the latest data and capabilities now supported in these multi-mission services, review the lessons we continue to learn in what science users need and value in this class of services, and discuss out current thinking to the future role and appropriate focus of the SPDF effort in the evolving and increasingly distributed heliophysics data environment.

Fung, Shing F.↗

NASA NTRS2008

Services, Perspective and Directions of the Space Physics Data Facility

The multi-mission data and orbit services of NASA's Space Physics Data Facility (SPDF) project offer unique capabilities supporting science of the Heliophysics Great Observatory and that are highly complementary to other services now evolving in the international heliophysics data environment. The VSPO (Virtual Space Physics Observatory) service is an active portal to a wide rage of distributed data sources. CDAWeb (Coordinated Data Analysis Web) offers plots, listings and file downloads for current data from many missions across the boundaries of missions and instrument types. CDAWeb now includes extensive new data from STEREO and THEMIS, plus new ROCSAT IPEI data, the latest data from all four TIMED instruments and high-resolution data from all DE-2 experiments. SSCWeb, Helioweb and out 3D Animated Orbit Viewer (TIPSOD) provide position data and identification of spacecraft and ground conjunctions. OMNI Web, with its new extension to 1- and 5-minute resolution, provides interplanetary parameters at the Earth's bow shock. SPDF maintains NASA's CDF (Common Data Format) standard and a range of associated tools including format translation services. These capabilities are all now available through web services based APIs, one element in SPDF's ongoing work to enable heliophysics community development of Virtual discipline Observatories (e.g. VITMO). We will demonstrate out latest data and capabilities, review the lessons we continue to learn in what science users need and value in this class of services, and discuss out current thinking to the future role and appropriate focus of the SPDF effort in the evolving and increasingly distributed heliophysics data environment.

McGuire, Robert E.↗

NASA NTRS2002

A Summary of the NASA ISS Space Debris Collision Avoidance Program

Creating and implementing a process for the mitigation of the impact hazards due to cornets and asteroids will prove to be a complex and involved process. The closest similar program is the collision avoidance process currently used for protection of the International Space Station (ISS). This process, in operation for over three years, has many similarities to the NEG risk problem. By reviewing the ISS program, a broader perspective on the complications of and requirements for a NEO risk mitigation program might be obtained. Specifically, any lessons learned and continuing issues of concern might prove useful in the development of a NEO risk assessment and mitigation program.

Frisbee, Joseph↗

NASA NTRS2011

MODIS On-Orbit Performance and Lessons Learned

MODIS is a key instrument for the NASA's Earth Observing System (EOS) and has successfully operated for more than 11 and 9 years, respectively, on-board the Terra and Aqua spacecraft. MODIS collects data in 36 spectral bands, covering wavelengths from visible (VIS) to long-wave infrared (LWIR). To date, both Terra and Aqua MODIS have produced an unprecedented amount of data products and significantly contributed to the earth remote sensing studies and applications. MODIS was developed with stringent calibration requirements and was, consequently, designed and built with a set of on-board calibrators (OBC), which include a solar diffuser (SD), a solar diffuser stability monitor (SDSM), a blackbody (BB), and a spectroradiometric calibration assembly (SRCA). This presentation briefly reviews MODIS instrument operation and various calibration and characterization activities, It demonstrates both the instrument and the OBC on-orbit performance and discusses lessons learned, particularly focusing on on-orbit changes in sensor responses, optics degradation, and major challenging issues. As expected, Terra and Aqua MODIS on-orbit performance and lessons learned will continue to benefit the operation and calibration of future sensors, such as NPP/JPSS VIIRS and GOES-R ABI.

Xiong, Xiaoxiong↗

NASA NTRS2015

International Space Station (ISS) Environmental Control and Life Support (ECLS) System Overview of Events: 2010-2014

Nov 2, 2014 marked the completion of the 14th year of continuous human presence in space on board the International Space Station (ISS). After 42 expedition crews, over 115 assembly & utilization flights, over 180 combined Shuttle/Station, US & Russian Extravehicular Activities (EVAs), the post-Assembly-Complete ISS continues to fly and the engineering teams continue to learn from operating its systems, particularly the life support equipment. Problems with initial launch, assembly and activation of ISS elements have given way to more long term system operating trends. New issues have emerged, some with gestation periods measured in years. Major events and challenges for each U.S. Environmental Control and Life Support (ECLS) subsystem occurring during calendar years 2010 through 2014 are summarily discussed in this paper, along with look-aheads for what might be coming in the future for each U.S. ECLS subsystem.

Gentry, Gregory J.↗

NASA NTRS2022

Operational maize yield forecasts for Sub-Saharan Africa using Earth observation data and machine learning

Food insecurity continues to grow in Sub-Saharan Africa (SSA). In 2019, chronically malnourished people numbered nearly 240 million, or 20% of the population in SSA. Globally, numerous efforts have been made to anticipate potential droughts, crop conditions, and food shortages to foster improved food insecurity early warning and risk management. To support this goal, we develop an Earth Observation (EO) and machine-learning-based operational, subnational maize yield forecast system and evaluate its out-of-sample forecast skills during the growing seasons for Kenya, Somalia, Malawi, and Burkina Faso. In general, forecast skills improve substantially during the vegetative growth period (VP) and gradually during the reproductive development period (RP). Thus, mid-season assessment can provide effective early warning months before harvest. Skillful forecasts (Nash Sutcliffe Efficiency (NSE) > 0.6 and Mean Absolute Percentage Error (MAPE) < 20%) appear approximately two dekads after the VP; for example, they appear in May in Kenya and Somalia, January in Malawi, and July in Burkina Faso.

Donghoon Lee↗

NASA NTRS1998

The Process of Science Communications at NASA/Marshall Space Flight Center

The communication of new scientific knowledge and understanding is an integral component of science research, essential for its continued survival. Like any learning- based activity, science cannot continue without communication between and among peers so that skeptical inquiry and learning can take place. This communication provides necessary organic support to maintain the development of new knowledge and technology. However, communication beyond the peer-community is becoming equally critical for science to survive as an enterprise into the 21st century. Therefore, scientists not only have a 'noble responsibility' to advance and communicate scientific knowledge and understanding to audiences within and beyond the peer-community, but their fulfillment of this responsibility is necessary to maintain the survival of the science enterprise. Despite the critical importance of communication to the viability of science, the skills required to perform effective science communications historically have not been taught as a part of the training of scientist, and the culture of science is often averse to significant communication beyond the peer community. Thus scientists can find themselves ill equipped and uncomfortable with the requirements of their job in the new millennium. At NASA/Marshall Space Flight Center, we have developed and implemented an integrated science communications process, providing an institutional capability to help scientist accurately convey the content and meaning of new scientific knowledge to a wide variety of audiences, adding intrinsic value to the research itself through communication, while still maintaining the integrity of the peer-review process. The process utilizes initial communication through the world-wide web at the site http://science.nasa.gov to strategically leverage other communications vehicles and to reach a wide-variety of audiences. Here we present and discuss the basic design of the science communications process, now in operation for nearly two years. Serving scientists in Earth Science, Microgravity Science, and Space Science. Critical features of the design are illustrated, and essential skills required to operate the process are defined. Measures of success will also be presented.

Horack, John M.↗

NASA NTRS1998

The Process of Science Communications at NASA/Marshall Space Flight Center

Horack, John M.↗

DOE OSTI2026

Speedup of UEDGE Parameter Scans Using Machine-Learning Optimized OpenMP Parallelization and a Continuation Solver

This article presents the OpenMP parallelization of the preconditioning Jacobian assembly and right‐hand side residual evaluation in UEDGE. A continuation algorithm, utilizing the internal NKSOL implicit Jacobian‐Free Newton‐Krylov solver to efficiently scan physical parameters, is also presented. The implemented parallelization reduces the computational time for a benchmark scan run on 32 threads by compared to the serial version when using trained random forest regression models to identify the optimal decomposition of the system of equations. Random forest regression models applied to the UEDGE time‐dependent and continuation solver algorithms did not yield meaningful improvement in computational performance. A benchmark DIII‐D gas injection rate scan in the 0.35–0.75 kA interval, performed on a test cluster using the parallelized code and continuation solver, produced 1066 steady‐state solutions with a 22 s average wall‐clock computational time per steady‐state solution.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Identifying Vehicle Signals in Continuous Seismic Data Using Unsupervised Machine-Learning Techniques

Seismic sensors deployed near roadways effectively capture ground vibrations generated by passing vehicles. Although both traditional and machine‐learning algorithms have been utilized for analyzing such signals, independent validation of detected vehicle events remains limited. We applied two unsupervised machine‐learning algorithms, uniform manifold approximation and projection for dimension reduction, and hierarchical density‐based spatial clustering of applications with noise, to continuous seismic data collected along a road on the main campus of Oak Ridge National Laboratory. The algorithms identified seven distinct cluster labels across the entire dataset. By comparing these cluster labels with precipitation records from a nearby weather station and image‐derived labels from a local camera system, we identified one cluster associated with rainfall and another with vehicle activity. Our algorithms identified a greater number of vehicle‐related labels compared to the camera‐derived labels because seismic data are unaffected by poor lighting conditions. The arrival times of the newly detected vehicle signals corresponded well with the road’s speed limit, supporting our findings. Our algorithm outperformed the short‐term average/long‐term average method and k‐means clustering. Our results suggest that seismic data, when analyzed with machine‐learning algorithms, can complement existing vehicle monitoring systems, particularly under challenging environmental conditions.

Chai, Chengping [Oak Ridge National Laboratory (OR↗

NASA NTRS1972

Effect of continuous gamma-ray exposure on performance of learned tasks and effect of subsequent fractionated exposures on blood-forming tissue

Sixteen monkeys trained to perform continuous and discrete-avoidance and fixed-ratio tasks with visual and auditory cues were performance-tested before, during, and after 10-day gamma-ray exposures totaling 0, 500, 750, and 1000 rads. Approximately 14 months after the performance-test exposures, surviving animals were exposed to 100-rad gamma-ray fractions at 56-day intervals to observe injury and recovery patterns of blood-forming tissues. The fixed-ratio, food-reward task performance showed a transient decline in all dose groups within 24 hours of the start of gamma-ray exposure, followed by recovery to normal food-consumption levels within 48 to 72 hours. Avoidance tasks were performed successfully by all groups during the 10-day exposure, but reaction times of the two higher dose-rate groups in which animals received 3 and 4 rads per hour or total doses of 750 and 1000 rads, respectively, were somewhat slower.

Spalding, J. F.↗

NASA NTRS2010

LDCM Ground System. Network Lesson Learned

This slide presentation reviews the Landsat Data Continuity Mission (LDCM) and the lessons learned in implementing the network that was assembled to allow for the acquisition, archiving and distribution of the data from the Landsat mission. The objective of the LDCM is to continue the acquisition, archiving, and distribution of moderate-resolution multispectral imagery affording global, synoptic, and repetitive coverage of the earth's land surface at a scale where natural and human-induced changes can be detected, differentiated, characterized, and monitored over time. It includes a review of the ground network, including a block diagram of the ground network elements (GNE) and a review of the RF design and testing. Also included is a listing of the lessons learned.

Gal-Edd, Jonathan↗

DOE OSTI2026

Unbinned extraction of $γ$ from $B\to DK$ with normalizing flows

We introduce an unbinned method for extracting the CKM angle $γ$ from the decay chain $B^\pm \to (D \to K_S π^+ π^-) K^\pm$ using normalizing flows (NFs). The NFs, trained on $D$ decay data, learn a faithful continuous representation of the amplitude and strong phase variation over the $D\to K_Sπ^+π^-$ Dalitz plot whose fidelity improves with increased data sample sizes. With this input, the $B$ decay data can be used to extract the parameters $r_B$, $δ_B$, and $γ$. We test the method on Monte Carlo generated data, where it successfully recovers the injected value of $γ$ within uncertainties. The present implementation propagates statistical uncertainties from finite training data via an ensemble of independently trained flows, and does not attempt to capture the effects of systematic experimental errors. We explore two versions of the method that differ in how the trigonometric constraint on phase variation is encoded, and comment on the possible extension to Bayesian NFs, which would provide direct uncertainty estimates on the learned densities without requiring ensemble training.

Grossman, Yuval [Cornell U., LEPP]↗

DOE OSTI2025

Projections of future climate for U.S. national assessments: past, present, future

Climate assessments consolidate our understanding of possible future climate conditions as represented by climate projections, which are largely based on the output of global climate models. Over the past 30 years, the scientific insights gained from climate projections have been refined through model structural improvements, emerging constraints on climate feedbacks, and increased computational efficiency. Within the same period, the process of assessing and evaluating information from climate projections has become more defined and targeted to inform users. As the size and audience of climate assessments has expanded, the framing, relevancy, and accessibility of projections has become increasingly important. This paper reviews the use of climate projections in national climate assessments (NCA) while highlighting challenges and opportunities that have emerged over time. Reflections and lessons learned address the continuous process to understand the broadening assessment audience and evolving user needs. Insights for future NCA development include (1) identifying benchmarks and standards for evaluating downscaled datasets, (2) expanding efforts to gather research gaps and user needs to inform how climate projections are presented in the assessment (3) providing practitioner guidance on the use, interpretation, and reporting of climate projections and uncertainty to better inform decision-making.

Assessment↗

DOE OSTI2024

Incentivizing and Supporting Early-Stage Solar Innovation in the United States

In 2018, the U.S. Department of Energy's Solar Energy Technologies Office and the National Renewable Energy Laboratory set out to develop a repeatable, predictable prize model (the American-Made Challenges) and an entrepreneurial innovation network (the American-Made Network) to support participants along their innovation journeys. This began with the launch of the Solar Prize. Five years and six rounds later, the Solar Prize is the longest-standing competition of the American-Made Challenges. It has awarded $\$20.1$ million in cash prizes and $\$5.5$ million in technical support vouchers to 140 teams across all aspects of the solar industry. By sharing the outputs of the prize, we hope that our lessons learned will help continue to build the clean tech entrepreneurship support ecosystem.

14 SOLAR ENERGY↗