SEARCH · Search NASA

Results for “algorithm selection”

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 1,459 records · Page 81

All sources NASA NTRS DOE OSTI

NASA NTRS1991

Autonomous proximity operations using machine vision for trajectory control and pose estimation

A machine vision algorithm was developed which permits guidance control to be maintained during autonomous proximity operations. At present this algorithm exists as a simulation, running upon an 80386 based personal computer, using a ModelMATE CAD package to render the target vehicle. However, the algorithm is sufficiently simple, so that following off-line training on a known target vehicle, it should run in real time with existing vision hardware. The basis of the algorithm is a sequence of single camera images of the target vehicle, upon which radial transforms were performed. Selected points of the resulting radial signatures are fed through a decision tree, to determine whether the signature matches that of the known reference signatures for a particular view of the target. Based upon recognized scenes, the position of the maneuvering vehicle with respect to the target vehicles can be calculated, and adjustments made in the former's trajectory. In addition, the pose and spin rates of the target satellite can be estimated using this method.

Cleghorn, Timothy F.↗

NASA NTRS1992

A method for planning collision free trajectories for two cooperating robots

Robotic systems designed specifically for space applications often exhibit a high level of autonomy to reduce the costs associated with having humans in space. This paper presents a method for autonomously planning collision free paths for two cooperating robots in a static environment. The method performs selective mapping of configuration space while attempting to locate a safe path. The method is based around an algorithm aimed at transversing a space of arbitrary dimension which contains both safe and unsafe regions. While the procedure cannot guarantee finding a solution even if one may exist, it is intended to find a solution to many practical path planning problems within a reasonable amount of time. Results are presented for applying the procedure to two cooperating six degree of freedom manipulators (Puma 560s).

Weaver, Jonathan M.↗

NASA NTRS1992

Enhanced orbital gyrocompassing by the optical flow sensed by an Earth-pointing camera

A new method for improving the orbital gyrocompassing process involving the attitude angle estimation of an earth-pointing satellite in low-Earth orbit uses an electro-optical sensor for direct measurement of the satellite azimuth angle. Simulations have shown that this additional measurement drastically reduces the estimator convergence time, especially when the sun sensor is rendered ineffective, e.g., by high solar elevations. The azimuth-sensing method is based on estimation of the image shift between successive picture frames of an on-board, Earth-pointing, charge-coupled device (CCD) full-matrix camera. The shift-estimation algorithm is based on minimizing a cost function which expresses mean-squared differences in brightness patterns of selected areas of the two frames. An extensive evaluation program with a computer-controlled 2-axis light table and actual satellite images has demonstrated high robustness for a wide range of variation of parameters including image texture content; camera focal length; sampling rate; and number of pixels processed. It was shown to be possible to estimate the azimuth angle within 0.1-0.2 degrees, for a suitably chosen parameter set.

Topaz, Leora↗

NASA NTRS2010

Locating and Quantifying Broadband Fan Sources Using In-Duct Microphones

In-duct beamforming techniques have been developed for locating broadband noise sources on a low-speed fan and quantifying the acoustic power in the inlet and aft fan ducts. The NASA Glenn Research Center's Advanced Noise Control Fan was used as a test bed. Several of the blades were modified to provide a broadband source to evaluate the efficacy of the in-duct beamforming technique. Phased arrays consisting of rings and line arrays of microphones were employed. For the imaging, the data were mathematically resampled in the frame of reference of the rotating fan. For both the imaging and power measurement steps, array steering vectors were computed using annular duct modal expansions, selected subsets of the cross spectral matrix elements were used, and the DAMAS and CLEAN-SC deconvolution algorithms were applied.

Dougherty, Robert P.↗

NASA NTRS2022

A NASA-Air Force Precipitation Analysis for Near-Real-Time Operations

This article describes a new precipitation analysis algorithm developed by NASA for time-sensitive operations at the United States Air Force. Implemented as part of the Land Information System—a land modeling and data assimilation software framework—this NASA–Air Force Precipitation Analysis (NAFPA) combines numerical weather prediction model outputs with rain gauge measurements and satellite estimates to produce global, gridded 3-h accumulated precipitation fields at approximately 10-km resolution. Input observations are subjected to quality control checks before being used by the Bratseth analysis algorithm that converges to optimal interpolation. NAFPA assimilates up to 3.5 million observations without artificial data thinning or selection. To evaluate this new approach, a multiyear reanalysis is generated and intercompared with eight alternative precipitation products across the contiguous United States, Africa, and the monsoon region of eastern Asia. NAFPA yields superior accuracy and correlation over low-latency (up to 14 h) alternatives (numerical weather prediction and satellite retrievals), and often outperforms high-latency (up to 3.5 months) products, although the details for the latter vary by region and product. The development of NAFPA offers a high-quality, near-real-time product for use in meteorological, land surface, and hydrological research and applications.

Africa↗

DOE OSTI2017

Orbital Advection with Magnetohydrodynamics and Vector Potential

Orbital advection is a significant bottleneck in disk simulations, and a particularly tricky one when used in connection with magnetohydrodynamics. We have developed an orbital advection algorithm suitable for the induction equation with magnetic potential. The electromotive force is split into advection and shear terms, and we find that we do not need an advective gauge since solving the orbital advection implicitly precludes the shear term from canceling the advection term. We prove and demonstrate the third order in time accuracy of the scheme. The algorithm is also suited to non-magnetic problems. Benchmarked results of (hydrodynamical) planet–disk interaction and of the magnetorotational instability are reproduced. We include detailed descriptions of the construction and selection of stabilizing dissipations (or high-frequency filters) needed to generate practical results. The scheme is self-consistent, accurate, and elegant in its simplicity, making it particularly efficient for straightforward finite-difference methods. As a result of the work, the algorithm is incorporated in the public version of the Pencil Code, where it can be used by the community.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Reproducibility and Variability of I/O Performance on BG/Q: Lessons Learned from a Data Aggregation Algorithm

Reading and writing data efficiently from different tiers of storage is necessary for most scientific simulations to achieve good performance at scale. Many software solutions have been developed to decrease the I/O bottleneck. One wellknown strategy, in the context of collective I/O operations, is the two-phase I/O scheme. This strategy consists of selecting a subset of processes to aggregate contiguous pieces of data before performing reads/writes. In our previous work, we implemented the two-phase I/O scheme with a MPI-based topology-aware algorithm. Our algorithm showed very good performance at scale compared to the standard I/O libraries such as POSIX I/O and MPI I/O. However, the algorithm had several limitations hindering a satisfying reproducibility of our experiments. In this paper, we extend our work by 1) identifying the obstacles we face to reproduce our experiments and 2) discovering solutions that reduce the unpredictability of our results.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Genetic algorithm optimization of nuclear criticality experiment for reduction of intermediate-energy 239 Pu nuclear data uncertainties

Nuclear criticality experiments are conducted to investigate specific nuclear data important for safe handling and storage of fissile materials, reactor design and operation, and the validation of radiation transport codes. Incorrect or uncertain nuclear data can prohibitively impact operational safety limits, reactor licensing, and predictive simulation capability; therefore, integral measurements from criticality experiments are necessary and should be performed frequently. To maximize the impact of the integral measurements, it is important to consider experiment geometry, material selection, and component dimensions. When taking these considerations into account, the experiment design process becomes iterative and very time intensive. This work utilizes a genetic algorithm to efficiently explore potential nuclear criticality experiment designs for the Laboratory Directed Research & Development project PARADIGM (PARallel Approach of Differential and InteGral Measurements) at Los Alamos National Laboratory. In this paper, the building blocks of the genetic algorithm are discussed in detail, the genetic algorithm methodology is verified, and the genetic algorithm is used to produce three candidate experiment models for the final PARADIGM design. The three candidate models produced by the genetic algorithm consist of copper-reflected assemblies containing 14 repeating units of alumina, graphite, boron, and plutonium plates. Furthermore, in addition to the optimization results, final design considerations are also discussed for designs with a height and/or weight very close to or slightly above assembly machine operational limits.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2019

Estimation of Tropical Cyclone Intensity in the North Atlantic and Northeastern Pacific Basins Using TRMM Satellite Passive Microwave Observations

Here, a statistical passive microwave intensity estimation (PMW-IE) algorithm for estimating the intensity of tropical cyclones (TCs) in the North Atlantic and northeastern and central Pacific basins is developed and tested. The algorithm is derived from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) 85-GHz brightness temperatures and near-surface rain-rate retrievals to provide objective estimates of current maximum sustained surface winds (Vmax) and 6-h future Vmax of TCs. The full record of TRMM data (1998–2013) including 2326 TMI overpasses of 503 TCs is separated into dependent samples (1998–2010) for model development and independent samples (2011–13) for model verification. The best track intensities are used as dependent variables in a stepwise multiple-regression approach. Separately for each basin, three regression models are derived using selected 1) 85-GHz-only variables, 2) rain-rate-only variables, and 3) combined 85-GHz and rain variables. The algorithms are assessed using independent samples and those with contemporaneous aircraft-reconnaissance measurements. Rain-only and combined models perform better than the 85-GHz-only model. Lower errors are found for estimating the 6-h future Vmax than estimating the current Vmax using all three models. This implies that it is optimal to use passive-microwave-retrieved rain variables observed a few hours earlier to estimate TC intensity. The MAE (RMSE) of 6-h future Vmax is 9 (12) kt (1 kt ≈ 0.51 m s -1 ) when testing the combined models with ATL and EPA independent samples. Aircraft-reconnaissance-based independent samples yields a MAE of 9.6 kt and RMSE of 12.6 kt for estimating 6-h future Vmax.

58 GEOSCIENCES↗

NASA NTRS2007

Development and Application of a Portable Health Algorithms Test System

This paper describes the development and initial demonstration of a Portable Health Algorithms Test (PHALT) System that is being developed by researchers at the NASA Glenn Research Center (GRC). The PHALT System was conceived as a means of evolving the maturity and credibility of algorithms developed to assess the health of aerospace systems. Comprising an integrated hardware-software environment, the PHALT System allows systems health management algorithms to be developed in a graphical programming environment; to be tested and refined using system simulation or test data playback; and finally, to be evaluated in a real-time hardware-in-the-loop mode with a live test article. In this paper, PHALT System development is described through the presentation of a functional architecture, followed by the selection and integration of hardware and software. Also described is an initial real-time hardware-in-the-loop demonstration that used sensor data qualification algorithms to diagnose and isolate simulated sensor failures in a prototype Power Distribution Unit test-bed. Success of the initial demonstration is highlighted by the correct detection of all sensor failures and the absence of any real-time constraint violations.

Melcher, Kevin J.↗

NASA NTRS2021

MODIS Reflective Solar Bands Calibration Improvements for Collection 7

Calibration of Terra and Aqua MODIS reflective solar bands (RSB) has evolved significantly since the launch of the first MODIS instrument on the Terra satellite more than 21 years ago. In NASA’s current Collection 6 and 6.1 Level 1B products (C6/C6.1 L1B), the RSB calibration algorithm continues to rely primarily on the onboard solar diffuser to calibrate the instrument gain. Lunar observations are used to track on-orbit changes in the response versus scan angle (RVS), and data from pseudo-invariant desert sites are used to apply adjustments to the gain and RVS calibration for select bands. The resulting reflectance products have in general shown a very stable performance. In recent years, some performance degradation has been noted for a few bands and algorithm changes have been tested to further improve the calibration accuracy for the upcoming Collection 7 (C7) L1B reprocess. In this paper, we present the MODIS RSB calibration improvements that will be included in C7. Major improvements include: applying polarization correction to the desert data before using it to generateRVS for Terra bands 8, 9, 3, and 10; using ocean scene data and an interband calibration approach to correct for long-term drift of Terra bands 11 and 12; applying an updated crosstalk correction to Terra SWIR bands over the entire mission; and using data from deep convective clouds in Terra SWIR band calibration, including the addition of time-dependent RVS for bands 5 and 26. All other minor calibration changes are also covered. Overall, the reflectance differences at nadir between C6.1 and C7 are within a few percent, though the differences increase in some cases at large scan angles. The Terra visible (3, 8-12) and SWIR bands (5-7, 26) have the most significant improvements. For all other Terra bands and all Aqua bands, the C7-C6.1 differences are mostly within 1%

MODIS↗

NASA NTRS2021

MODIS Thermal Emissive Bands Calibration Improvements for Collection 7

The MODIS thermal emissive bands (TEB) radiometric calibration uses a quadratic function for the instrument response, and the calibration coefficients look-up tables (LUTs) are updated using the response of an on-board blackbody (BB). After more than 21 and 19 years on-orbit, the TEB performances for Terra and Aqua MODIS have been generally stable. However, contamination from electronic crosstalk, a known issue since prelaunch, has affected the L1B image quality and measurement accuracy. In addition to the photovoltaic (PV) longwave infrared (LWIR) bands crosstalk correction included in Terra MODIS Collection 6.1 (C6.1), a crosstalk correction for select detectors in the Terra and Aqua mid-wave infrared (MWIR) and Aqua PV LWIR bands are applied in C7. The mission-long crosstalk coefficients for the selected detectors are derived and populated in the form of LUTs. The crosstalk correction is applied to both on-orbit calibration and the algorithm used to generate Earth-view L1B products. Among these detectors, the Aqua MODIS band 24 detector 1 crosstalk has the largest impact on image quality, with striping observed over cold scenes for both Terra and Aqua MODIS. The images of C7 L1B and C6.1 are compared to assess the impact of the correction. Additional assessments using Earthview measurements and inter-comparison also revealed the need for improvement of calibration stability and consistence for select bands. Additional improvements for long-term stability and mirror side consistence were developed using quasideep convective clouds (qDCC), Dome-C, ocean, desert, and inter-comparison with other instruments.

MODIS↗

DOE OSTI2024

Two-Step Hyperparameter Optimization Method: Accelerating Hyperparameter Search by Using a Fraction of a Training Dataset

Abstract Hyperparameter optimization (HPO) is an important step in machine learning (ML) model development, but common practices are archaic—primarily relying on manual or grid searches. This is partly because adopting advanced HPO algorithms introduces added complexity to the workflow, leading to longer computation times. This poses a notable challenge to ML applications, as suboptimal hyperparameter selections curtail the potential of ML model performance, ultimately obstructing the full exploitation of ML techniques. In this article, we present a two-step HPO method as a strategic solution to curbing computational demands and wait times, gleaned from practical experiences in applied ML parameterization work. The initial phase involves a preliminary evaluation of hyperparameters on a small subset of the training dataset, followed by a reevaluation of the top-performing candidate models postretraining with the entire training dataset. This two-step HPO method is universally applicable across HPO search algorithms, and we argue it has attractive efficiency gains. As a case study, we present our recent application of the two-step HPO method to the development of neural network emulators for aerosol activation. Although our primary use case is a data-rich limit with many millions of samples, we also find that using up to 0.0025% of the data—a few thousand samples—in the initial step is sufficient to find optimal hyperparameter configurations from much more extensive sampling, achieving up to 135× speedup. The benefits of this method materialize through an assessment of hyperparameters and model performance, revealing the minimal model complexity required to achieve the best performance. The assortment of top-performing models harvested from the HPO process allows us to choose a high-performing model with a low inference cost for efficient use in global climate models (GCMs).

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2017

Modelling the Passive Microwave Signature from Land Surfaces: A Review of Recent Results and Application to the L-Band SMOS SMAP Soil Moisture Retrieval Algorithms

Two passive microwave missions are currently operating at L-band to monitor surface soil moisture (SM) over continental surfaces. The SMOS sensor, based on an innovative interferometric technology enabling multi-angular signatures of surfaces to be measured, was launched in November 2009. The SMAP sensor, based on a large mesh reflector 6 m in diameter providing a conically scanning antenna beam with a surface incidence angle of 40deg, was launched in January of 2015. Over the last decade, an intense scientific activity has focused on the development of the SM retrieval algorithms for the two missions. This activity has relied on many field (mainly tower-based) and airborne experimental campaigns, and since 2010-2011, on the SMOS and Aquarius space-borne L-band observations. It has relied too on the use of numerical, physical and semi-empirical models to simulate the microwave brightness temperature of natural scenes for a variety of scenarios in terms of system configurations (polarization, incidence angle) and soil, vegetation and climate conditions. Key components of the inversion models have been evaluated and new parameterizations of the effects of the surface temperature, soil roughness, soil permittivity, and vegetation extinction and scattering have been developed. Among others, global maps of select radiative transfer parameters have been estimated very recently. Based on this intense activity, improvements of the SMOS and SMAP SM inversion algorithms have been proposed. Some of them have already been implemented, whereas others are currently being investigated. In this paper, we present a review of the significant progress which has been made over the last decade in this field of research with a focus on L-band, and a discussion on possible applications to the SMOS and SMAP soil moisture retrieval approaches.

SMOS↗

DOE OSTI2020

Considerations for using Privacy Preserving Machine Learning Techniques for Safeguards

In international nuclear safeguards, the International Atomic Energy Agency (IAEA) is tasked with inspecting and verifying nuclear facilities and their activities. Data analytics and machine learning to support inspections require large amounts of data that nuclear facility operators may consider proprietary or sensitive, so the IAEA may not have full access. Allowing computation over private data without compromising its security therefore has value for safeguards inspections and analysis. Privacy-preserving machine learning (PPML) consists of security-focused techniques that allow data analytics and machine learning algorithms to run on sensitive data without revealing it. This includes ideas like homomorphic encryption (HE), secure multiparty computation (SMPC), and secure enclaves. HE allows algorithms and mathematical operations to be conducted directly on the encrypted data instead of first decrypting it. With SMPC, multiple entities collaboratively compute over distributed data such that no party is able to directly view any others’ original data. Secure enclaves allow computation to take place in a separate and heavily blocked-off section of a CPU. Techniques like these allow for several potential use cases in which the security of data is essential. With SMPC, machine learning models can be trained over the input data from multiple entities, resulting in a model that all users can benefit from without leaking the input data from any particular entity. With SMPC or a zero-knowledge proof (ZKP), an algorithm returning some single answer or truth value can be run on someone else’s data without ever needing to see that data, potentially allowing for verification or proof of some underlying question. HE can allow for outsourcing computation on data to a hostile or untrusted environment. Although most of the research in this field resides within the health and financial domains, tools from PPML may have similar applications in nuclear safeguards. Allowing the IAEA to compute over proprietary information, such as process models and raw sensor data using PPML techniques, provides the baseline for running complex analytics without needing direct unencrypted access to the underlying data, maintaining its privacy. Important limitations to consider for these techniques include the efficiency and level of security required. The security of HE and SMPC come at the cost of speed—the significant amount of overhead means that algorithms implemented in these protocols and encryption schemes are slower than when run on plaintext. Additionally, several important parameters determine what techniques or protocols are used based on the security requirements. SMPC protocols may need to be selected for resistance against a party that attempts to deviate from the protocol to distort the result or gain access to additional information, and a protocol secure against these attacks may further increase the overhead of the algorithm.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Identifying Muon Neutrino Charged-Current Interactions in the MicroBooNE Detector

The MicroBooNE experiment is a short-baseline neutrino experiment at the Fermi National Accelerator Laboratory (FNAL) in the US, receiving a highly pure muon neutrino beam from the Booster Neutrino Beam (BNB) and taking data since October 2015. The main physics goal of MicroBooNE is to clarify the nature of the low-energy excess of electron-like events observed by the MiniBooNE Cherenkov detector, which due to its detector technology is unable to resolve whether the observed excess shower-like events are due to electrons or photons. Instead, MicrooBooNE employs cutting-edge liquid argon time projection chamber (LArTPC) technology, which offers excellent spatial and calorimetric resolution, which makes it possible to reconstruct complex neutrino interactions and to efficiently distinguish different final state particle types. This thesis presents a fully-automated event selection of interactions of the types ν μ +Ar → μ - + X and ν μ + Ar → μ - + p + X developed using the Pandora multi-algorithm approach to pattern recognition for LArTPC experiments. The analysis in this work is performed on a subset of the MicroBooNE Run 1 dataset corresponding to 4.52 × 10 19 protons on target acquired by the MicroBooNE detector between February and October 2016. The analysis quantifies the main signal and background interaction channels that produce a reconstructed neutrino interaction with one and two particles in the final state, and quantifies the extent to which different aspects of the Pandora reconstruction affect the selection performance. MicroBooNE is a surface detector, and therefore cosmic-ray tracks are the main observed background. A direction fitting procedure has been implemented in this analysis to formulate directional probabilities, using the Bethe equation and the high-resolution calorimetric information of MicroBooNE, to reduce this cosmic-ray background. This functionality is used to enhance the efficiency of the Pandora cosmic ray tagging logic, and the impact of this procedure on the reconstruction and event selection performance is analysed. The result of this study is two fully-automated selections of ν μ +Ar → μ - + X (57.91% pure) and ν μ + Ar → μ - + p + X (78.30% pure) with efficiencies of 64.35% and 62.65%, respectively.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS1996

A study of GPS measurement errors due to noise and multipath interference for CGADS

This report describes a study performed by the Colorado Center for Astrodynamics Research (CCAR) on GPS measurement errors in the Codeless GPS Attitude Determination System (CGADS) due to noise and multipath interference. Preliminary simulation models fo the CGADS receiver and orbital multipath are described. The standard FFT algorithms for processing the codeless data is described and two alternative algorithms - an auto-regressive/least squares (AR-LS) method, and a combined adaptive notch filter/least squares (ANF-ALS) method, are also presented. Effects of system noise, quantization, baseband frequency selection, and Doppler rates on the accuracy of phase estimates with each of the processing methods are shown. Typical electrical phase errors for the AR-LS method are 0.2 degrees, compared to 0.3 and 0.5 degrees for the FFT and ANF-ALS algorithms, respectively. Doppler rate was found to have the largest effect on the performance.

Axelrad, Penina↗

DOE OSTI2019

A Selective Overview and Comparison of Robust Mixture Regression Estimators

Summary Mixture regression models have been widely used in business, marketing and social sciences to model mixed regression relationships arising from a clustered and thus heterogeneous population. The unknown mixture regression parameters are usually estimated by maximum likelihood estimators using the expectation–maximisation algorithm based on the normality assumption of component error density. However, it is well known that the normality‐based maximum likelihood estimation is very sensitive to outliers or heavy‐tailed error distributions. This paper aims to give a selective overview of the recently proposed robust mixture regression methods and compare their performance using simulation studies.

Yu, Chun↗