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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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NASA NTRS1990

Neural net diagnostics for VLSI test

This paper discusses the application of neural network pattern analysis algorithms to the IC fault diagnosis problem. A fault diagnostic is a decision rule combining what is known about an ideal circuit test response with information about how it is distorted by fabrication variations and measurement noise. The rule is used to detect fault existence in fabricated circuits using real test equipment. Traditional statistical techniques may be used to achieve this goal, but they can employ unrealistic a priori assumptions about measurement data. Our approach to this problem employs an adaptive pattern analysis technique based on feedforward neural networks. During training, a feedforward network automatically captures unknown sample distributions. This is important because distributions arising from the nonlinear effects of process variation can be more complex than is typically assumed. A feedforward network is also able to extract measurement features which contribute significantly to making a correct decision. Traditional feature extraction techniques employ matrix manipulations which can be particularly costly for large measurement vectors. In this paper we discuss a software system which we are developing that uses this approach. We also provide a simple example illustrating the use of the technique for fault detection in an operational amplifier.

Lin, T.↗

NASA NTRS1998

Information-Adaptive Image Encoding and Restoration

The multiscale retinex with color restoration (MSRCR) has shown itself to be a very versatile automatic image enhancement algorithm that simultaneously provides dynamic range compression, color constancy, and color rendition. A number of algorithms exist that provide one or more of these features, but not all. In this paper we compare the performance of the MSRCR with techniques that are widely used for image enhancement. Specifically, we compare the MSRCR with color adjustment methods such as gamma correction and gain/offset application, histogram modification techniques such as histogram equalization and manual histogram adjustment, and other more powerful techniques such as homomorphic filtering and 'burning and dodging'. The comparison is carried out by testing the suite of image enhancement methods on a set of diverse images. We find that though some of these techniques work well for some of these images, only the MSRCR performs universally well oil the test set.

Park, Stephen K.↗

NASA NTRS2021

Scheduling and Operations of the ECOSTRESS Mission

This paper describes the development and use of an automated scheduling system for the National Aeronautics and Space Administration’s (NASA) ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission. Key to the success of the ECOSTRESS mission has been the use of automated scheduling in mission analysis pre-launch, and in successful operations where automated scheduling was deployed to address several operational challenges. ECOSTRESS uses an adaptation of the Compressed Large-scale Activity Scheduling and Planning (CLASP) system to automatically select science observations respecting area and point target priorities as well as visibility, illumination, onboard storage, and radiation constraints to satisfy high-level prioritized science campaigns. The ECOSTRESS scheduler was used pre-launch to predict the effectiveness of alternative formulations of science campaign definitions accounting for the impact of data volume, keepout, and orbit/illumination/visibility constraints to derive the initial operational science campaign definitions and priorities. The scheduler was then used after instrument checkout for operations. ECOSTRESS has faced multiple operational challenges relating to instrument firmware and hardware, and the scheduler has been updated several times to address these challenges. The instrument Mass Storage Units (MSUs) had operational issues, requiring the scheduler to plan for and schedule commands to handle intricacies of data management. After many months of operations, both MSUs on the instrument became non-functioning and the firmware of the instrument was updated to bypass the MSUs. A further update to the ECOSTRESS scheduler enabled the scheduler to operate in this new operations mode. The ECOSTRESS scheduler has also been updated to improve handling of along-track uncertainty inherent in International Space Station operations. The flexibility and ease of updating of the automated scheduler has been a significant contributor to successful operations of the ECOSTRESS mission.

Padams, Jordan↗

NASA NTRS2011

Multiclass Continuous Correspondence Learning

We extend the Structural Correspondence Learning (SCL) domain adaptation algorithm of Blitzer er al. to the realm of continuous signals. Given a set of labeled examples belonging to a 'source' domain, we select a set of unlabeled examples in a related 'target' domain that play similar roles in both domains. Using these 'pivot samples, we map both domains into a common feature space, allowing us to adapt a classifier trained on source examples to classify target examples. We show that when between-class distances are relatively preserved across domains, we can automatically select target pivots to bring the domains into correspondence.

correspondence learning↗

NASA NTRS1997

Postural perturbations: new insights for treatment of balance disorders

This article reviews the neural control of posture as understood through studies of automatic responses to mechanical perturbations. Recent studies of responses to postural perturbations have provided a new view of how postural stability is controlled, and this view has profound implications for physical therapy practice. We discuss the implications for rehabilitation of balance disorders and demonstrate how an understanding of the specific systems underlying postural control can help to focus and enrich our therapeutic approaches. By understanding the basic systems underlying control of balance, such as strategy selection, rapid latencies, coordinated temporal spatial patterns, force control, and context-specific adaptations, therapists can focus their treatment on each patient's specific impairments. Research on postural responses to surface translations has shown that balance is not based on a fixed set of equilibrium reflexes but on a flexible, functional motor skill that can adapt with training and experience. More research is needed to determine the extent to which quantification of automatic postural responses has practical implications for predicting falls in patients with constraints in their postural control system.

Review↗

NASA NTRS1990

Automatic Calibration Of Manual Machine Tools

Modified scheme uses data from multiple positions and eliminates tedious positioning. Modification of computer program adapts calibration system for convenient use with manually-controlled machine tools. Developed for use on computer-controlled tools. Option added to calibration program allows data on random tool-axis positions to be entered manually into computer for reduction. Instead of setting axis to predetermined positions, operator merely sets it at variety of arbitrary positions.

Gurney, Rex D.↗

DOE OSTI2020

An Update to the National Renewable Energy Laboratory Baseline Wind Turbine Controller

The National Renewable Energy Laboratory's 5-MW wind turbine model is well established as an industry standard and is often used as a comparison model, or a model on which to build upon. Though effective, the legacy controller for the 5-MW wind turbine uses a simple algorithm that is not up to date with many industry standards. Additionally, as the research community has advanced into fast-paced development cycles, as systems engineering tools such as Wind-Plant Integrated System Design & Engineering Model (WISDEM ®) [1] are employed, and as a greater focus on controls co-design practices is encouraged, demand for a generic wind turbine controller has arisen. This work presents updates for the NREL 5-MW controller to a more modern control architecture, and establishes a generic tuning framework that can be easily adapted to various wind turbines. Based on initial results, the updated generic controller eases the automatic tuning process while maintaining or improving the performance of the legacy NREL 5-MW controller.

17 WIND ENERGY↗

DOE OSTI2023

Multisensor Agile Adaptive Sampling (MAAS): A Methodology to Collect Radar Observations of Convective Cell Life Cycle

Abstract Multisensor Agile Adaptive Sampling (MAAS), a smart sensing framework, was adapted to increase the likelihood of observing the vertical structure (with little to no gaps), spatial variability (at subkilometer scale), and temporal evolution (at ∼2-min resolution) of convective cells. This adaptation of MAAS guided two mechanically scanning C-band radars (CSAPR2 and CHIVO) by automatically analyzing the latest NEXRAD data to identify, characterize, track, and nowcast the location of all convective cells forming in the Houston domain. MAAS used either a list of predetermined rules or real-time user input to select a convective cell to be tracked and sampled by the C-band radars. The CSAPR2 tracking radar was first tasked to collect three sector plan position indicator (PPI) scans toward the selected cell. Edge computer processing of the PPI scans was used to identify additional targets within the selected cell. In less than 2 min, both the CSAPR2 and CHIVO radars were able to collect bundles of three to six range–height indicator (RHI) scans toward different targets of interest within the selected cell. Bundles were successively collected along the path of cell advection for as long as the cell met a predetermined set of criteria. Between 1 June and 30 September 2022 over 315 000 vertical cross-section observations were collected by the C-band radars through ∼1300 unique isolated convective cells, most of which were observed for over 15 min of their life cycle. To the best of our knowledge, this dataset, collected primarily through automatic means, constitutes the largest dataset of its kind.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Hindsight logging for model training

In modern Machine Learning, model training is an iterative, experimental process that can consume enormous computation resources and developer time. To aid in that process, experienced model developers log and visualize program variables during training runs. Exhaustive logging of all variables is infeasible, so developers are left to choose between slowing down training via extensive conservative logging, or letting training run fast via minimalist optimistic logging that may omit key information. As a compromise, optimistic logging can be accompanied by program checkpoints; this allows developers to add log statements post-hoc, and "replay" desired log statements from checkpoint---a process we refer to as hindsight logging. Unfortunately, hindsight logging raises tricky problems in data management and software engineering. Done poorly, hindsight logging can waste resources and generate technical debt embodied in multiple variants of training code. In this paper, we present methodologies for efficient and effective logging practices for model training, with a focus on techniques for hindsight logging. Our goal is for experienced model developers to learn and adopt these practices. To make this easier, we provide an open-source suite of tools for Fast Low-Overhead Recovery (flor) that embodies our design across three tasks: (i) efficient background logging in Python, (ii) adaptive periodic checkpointing, and (iii) an instrumentation library that codifies hindsight logging for efficient and automatic record-replay of model-training. Model developers can use each flor tool separately as they see fit, or they can use flor in hands-free mode, entrusting it to instrument their code end-to-end for efficient record-replay. Our solutions leverage techniques from physiological transaction logs and recovery in database systems. Evaluations on modern ML benchmarks demonstrate that flor can produce fast checkpointing with small user-specifiable overheads (e.g. 7%), and still provide hindsight log replay times orders of magnitude faster than restarting training from scratch.

Computer Science↗

DOE OSTI2021

Cleaning Images with Gaussian Process Regression

Many approaches to astronomical data reduction and analysis cannot tolerate missing data: corrupted pixels must first have their values imputed. This paper presents astrofix, a robust and flexible image imputation algorithm based on Gaussian process regression. Through an optimization process, astrofix chooses and applies a different interpolation kernel to each image, using a training set extracted automatically from that image. It naturally handles clusters of bad pixels and image edges and adapts to various instruments and image types. For bright pixels, the mean absolute error of astrofix is several times smaller than that of median replacement and interpolation by a Gaussian kernel. We demonstrate good performance on both imaging and spectroscopic data, including the SBIG 6303 0.4 m telescope and the FLOYDS spectrograph of Las Cumbres Observatory and the CHARIS integral-field spectrograph on the Subaru Telescope.

42 ENGINEERING↗

NASA NTRS1973

A linear accelerator for simulated micrometeors.

Review of the theory, design parameters, and construction details of a linear accelerator designed to impart meteoric velocities to charged microparticles in the 1- to 10-micron diameter range. The described linac is of the Sloan Lawrence type and, in a significant departure from conventional accelerator practice, is adapted to single particle operation by employing a square wave driving voltage with the frequency automatically adjusted from 12.5 to 125 kHz according to the variable velocity of each injected particle. Any output velocity up to about 30 km/sec can easily be selected, with a repetition rate of approximately two particles per minute.

Slattery, J. C.↗

NASA NTRS1985

Physiological considerations for EVA in the Space Station era

The physiological parameters that will affect EMU design and operational scheduling for the Space Station Program are discussed. The system must be designed to safely and effectively handle the increased frequency of extravehicular activity projected for longer flight periods than in the past. In order to minimize the risk of decompression sickness a proper combination of equilibration time to cabin pressure, suit pressure, and prebreathing denitrogenation, is necessary. Effective thermal exchange is projected to be automatically controlled and to have an 8-hr 1000 BTU/hr capability. Physiological changes due to adaptation to weightlessness must be considered in scheduling for their impact on performance/fatigue and in coping with motion sickness. Radiation protection to the limit of 80 rad/quarter must be achieved by scheduling to avoid periodic proton and solar flare exposure, and by additional EMU shielding. Additional EMU considerations are the control of microorganisms and the higher pressure requirement for emergency O2 purge.

Horrigan, D. J., Jr.↗

NASA NTRS1990

Intelligent adaptive structures

'Intelligent Adaptive Structures' (IAS) refers to structural systems whose geometric and intrinsic structural characteristics can be automatically changed to meet mission requirements with changing operational scenarios. An IAS is composed of actuators, sensors, and a control logic; these are integrated in a distributed fashion within the elements of the structure. The IAS concepts thus far developed for space antennas and other precision structures should be applicable to civil, marine, automotive, and aeronautical structural systems.

Wada, Ben K.↗

NASA NTRS1991

Contact dynamics testing of automated three-point docking mechanism

An evaluation of an OMV docking mechanism, based on an adaptation of the Shuttle Flight Support System Pallet Berthing Mechanism was completed. The mechanism uses automatically actuated motorized latches to engage towel bars on the target satellite. LED sensors establish the towel bar position within the capture envelope and the latch capture commands are issued. Then, locking pawls engage the bar, locking and pre-loading the mechanism. Two series of tests were conducted to test nominal and failure mode captures and to evaluate design parameters such as LED sensor locations, automatic closure algorithms, latch closure velocity, position/velocity entry envelopes, and closure method. The first test series involved single latch testing on the Flat Floor Facility, the 6 DOF Facility, and an analytic simulation model. The intent was to compare results in order to validate the various facilities. Reasonably good agreement was achieved. The second test series repeated the single latch testing on the refurbished 6 DOF Facility to validate the facility modifications. The individual latches were tested under free-drift conditions for functionality and performance. Next, the three-latch configuration underwent parametric testing. Test results validated the improved fidelity of the 6 DOF Facility and verified successful docking at the required entry velocity. The tests determined the 'best' design parameter definitions and concluded that the locking pawls should not lock until all three latches completely close.

Rourke, Kenneth H.↗

DOE OSTI2020

An Update to the NREL Baseline Wind Turbine Controller: Preprint

The NREL 5MW wind turbine model is well established as an industry standard and is often used as a comparison model, or a model on which to build upon. Though effective, the legacy controller for the NREL 5MW wind turbine is built on a simple algorithm that is not up to date with many industry standards. Additionally, as the research community has advanced into more fast-paced development cycles, as systems engineering tools such as WISDEM \cite{WISDEM} are employed, and as a greater focus on controls co-design practices is encouraged, a demand for a generic wind turbine controller has arisen. This work presents updates for the NREL 5MW baseline controller to a more modern control architecture, and establishes a generic tuning framework that can be easily adapted to various wind turbines. Based on initial results, the updated generic controller eases the automatic tuning process, while maintaining or improving upon the performance of the legacy NREL 5MW controller.

17 WIND ENERGY↗

NASA NTRS1993

Visual information processing II; Proceedings of the Meeting, Orlando, FL, Apr. 14-16, 1993

Various papers on visual information processing are presented. Individual topics addressed include: aliasing as noise, satellite image processing using a hammering neural network, edge-detetion method using visual perception, adaptive vector median filters, design of a reading test for low-vision image warping, spatial transformation architectures, automatic image-enhancement method, redundancy reduction in image coding, lossless gray-scale image compression by predictive GDF, information efficiency in visual communication, optimizing JPEG quantization matrices for different applications, use of forward error correction to maintain image fidelity, effect of peanoscanning on image compression. Also discussed are: computer vision for autonomous robotics in space, optical processor for zero-crossing edge detection, fractal-based image edge detection, simulation of the neon spreading effect by bandpass filtering, wavelet transform (WT) on parallel SIMD architectures, nonseparable 2D wavelet image representation, adaptive image halftoning based on WT, wavelet analysis of global warming, use of the WT for signal detection, perfect reconstruction two-channel rational filter banks, N-wavelet coding for pattern classification, simulation of image of natural objects, number-theoretic coding for iconic systems.

Huck, Friedrich O.↗

NASA NTRS1994

A User-Centered Approach to Adaptive Hypertext Based on an Information Relevance Model

Rapid and effective to information in large electronic documentation systems can be facilitated if information relevant in an individual user's content can be automatically supplied to this user. However most of this knowledge on contextual relevance is not found within the contents of documents, it is rather established incrementally by users during information access. We propose a new model for interactively learning contextual relevance during information retrieval, and incrementally adapting retrieved information to individual user profiles. The model, called a relevance network, records the relevance of references based on user feedback for specific queries and user profiles. It also generalizes such knowledge to later derive relevant references for similar queries and profiles. The relevance network lets users filter information by context of relevance. Compared to other approaches, it does not require any prior knowledge nor training. More importantly, our approach to adaptivity is user-centered. It facilitates acceptance and understanding by users by giving them shared control over the adaptation without disturbing their primary task. Users easily control when to adapt and when to use the adapted system. Lastly, the model is independent of the particular application used to access information, and supports sharing of adaptations among users.

Mathe, Nathalie↗

DOE OSTI2021

Utilizing the Dynamic Networks Data Processing and Analysis Experiment (DNE18) to Establish Methodologies for the Comparison of Automatic Infrasonic Signal Detectors

The Dynamic Networks Experiment 2018 (DNE18) was a collaborative effort between Los Alamos National Laboratory (LANL), Sandia National Laboratories (SNL), Lawrence Livermore National Laboratory (LLNL) and Pacific Northwest National Laboratory (PNNL) designed to evaluate methodologies for multi-modal data ingestion and processing. One component of this virtual experiment was a quantitative assessment of current capabilities for infrasound data processing, beginning with the establishment of a baseline for infrasound signal detection. To produce such baselines, SNL and LANL exploited a common dataset of infrasound data recorded across a regional network in Utah from December 2010 through February 2011. We utilize two automated signal detectors, the Adaptive F-Detector (AFD) and the Multivariate Adaptive Learning Detector (MALD) to produce automated signal detection catalogs and an analyst-produced catalog. Comparisons indicate that automatic detectors may be able to identify small amplitude, low SNR events that cannot be identified by analyst review. We document detector performance in terms of precision and recall, demonstrating that the AFD is more precise, but the MALD has higher recall. We use a synthetic dataset of signals embedded in pink noise in order to highlight shortcomings in assessing detection algorithms for low signal to noise ratio signals which are commonly of interest to the nuclear monitoring community. For comparisons utilizing the synthetic dataset, the AFD has higher recall while precision is equal for both detectors. These results indicate that both detectors perform well across a variety of background noise environments; however, both detectors fail to identify repetitive, short duration signals arriving from similar backazimuths. These failures represent specific scenarios that could be targeted for further detector development.

97 MATHEMATICS AND COMPUTING↗