Results for “relevancy algorithm” — Page 13

NASA NTRS2018

Heasim and Skyback Simulation Tools and Their Application to the Hitomi Mission

We present an introduction to the heasim multimission observation and skyback background, high-energy pseudo Monte Carlo astrophysical simulation tools. Heasim may be used to accurately and efficiently construct flexible image transport system (FITS) event files for simple or composite sources with a wide range of standard and user-defined spatial, spectral, and temporal characteristics. Skyback is designed to enable users to assess the impact of background discrete and diffuse emission on prospective observations, and skyback output may be directly input into heasim. We present a brief overview of heasim and skyback input, algorithms, usage, and output. We also introduce the sxsbranch tool that computes Hitomi soft X-ray spectrometer resolution grade branching ratios, emphasizing its application to simulations. We include several examples of particular relevance to the Hitomi mission.

Loewenstein, Michael↗

NASA NTRSDate not supplied

Verification and Validation of Elastodynamic Simulation Software for Aerospace Research

Physics-based simulation of nondestructive evaluation (NDE) inspection can help to advance the inspectability and reliability of mechanical systems. However, NDE simulations applicable to non-idealized mechanical components often require large compute domains and long run times. This has prompted development of custom NDE simulation software tailored to high performance computing (HPC) hardware. Verification and validation (V&V) is an integral part of developing this software to ensure implementations are robust and applicable to inspection problems, producing tools and simulations suitable for computational NDE research. This presentation addresses factors common to V&V of several elastodynamic simulation codes applicable to ultrasonic NDE. Examples are drawn from in-house simulation software at NASA Langley Research Center, ranging from ensuring reliability in a 1D heterogeneous media wave equation solver to the V&V needs of 3D cluster-parallel elastodynamic software. Factors specific to a research environment are addressed, where individual simulation results can be as relevant as the software product itself. Distinct facets of V&V are discussed including testing to establish software reliability, employing systematic approaches for consistency with fundamental conservation laws, establishing the numerical stability of algorithms, and demonstrating concurrence with empirical data. This talk also addresses V&V practices for small groups of researchers. This includes establishing resources (e.g. time and personnel) for V&V during project planning to mitigate and control the risk of setbacks. Similarly, we identify ways for individual researchers to use V&V during simulation software development itself to both speed up the development process and reduce incurred technical debt.

NDE↗

NASA NTRSDate not supplied

Discovery and Analysis of Rare High-Impact Failure Modes using Adversarial RL-Informed Sampling

Adaptive learning agents have tremendous potential to handle critical tasks currently performed by humans. Unfortunately, due to their complexity, it can be difficult to verify that these learning agents do not have critical failure modes. Standard verification and validation methods often do not apply directly to learning agents and Monte Carlo methods have difficulty covering even a small fraction of the state space, especially in multiagent systems or over long time horizons. To overcome this difficulty, we demonstrate an adaptive stress-testing method based on reinforcement learning of correlations that raise the probability of failure. This approach has three key properties: (1) it is able to find rare failure modes with far greater sample efficiency than Monte Carlo methods, (2) it can estimate the true probability of a failure mode despite the inherent bias in the learning method, and (3) it is capable of learning and resampling compact representations of multimodal failure spaces. These properties are important in practice as we need to find disparate failure modes while accounting for their actual relevance. This is a significant advantage over traditional adaptive stress testing methods that give abstract likelihoods of particular failure instances, but cannot estimate the probability of a broader failure mode. We test our algorithm on a simple problem from the aviation domain where an autonomous aircraft lands in gusty wind conditions. The results suggest that we can find failure modes with far fewer samples than the Monte Carlo approach and simultaneously estimate the probability of failure.

reinforcement learning↗

NASA NTRSDate not supplied

Discovery and Analysis of Rare High-Impact Failure Modes using Adversarial RL-Informed Sampling

Adaptive learning agents have tremendous potential to handle critical tasks currently performed by humans. Unfortunately, due to their complexity, it can be difficult to verify that these learning agents do not have critical failure modes. Standard verification and validation methods often do not apply directly to learning agents and Monte Carlo methods have difficulty covering even a small fraction of the state space, especially in multiagent systems or over long time horizons. To overcome this difficulty, we demonstrate an adaptive stress-testing method based on reinforcement learning of correlations that raise the probability of failure. This approach has three key properties: (1) it is able to find rare failure modes with far greater sample efficiency than Monte Carlo methods, (2) it can estimate the true probability of a failure mode despite the inherent bias in the learning method, and (3) it is capable of learning and resampling compact representations of multimodal failure spaces. These properties are important in practice as we need to find disparate failure modes while accounting for their actual relevance. This is a significant advantage over traditional adaptive stress testing methods that give abstract likelihoods of particular failure instances, but cannot estimate the probability of a broader failure mode. We test our algorithm on a simple problem from the aviation domain where an autonomous aircraft lands in gusty wind conditions. The results suggest that we can find failure modes with far fewer samples than the Monte Carlo approach and simultaneously estimate the probability of failure.

Validation↗

NASA NTRSDate not supplied

Discovery and Analysis of Rare High-Impact Failure Modes using Adversarial RL-Informed Sampling

Adaptive learning agents have tremendous potential to handle critical tasks currently performed by humans. Unfortunately, due to their complexity, it can be difficult to verify that these learning agents do not have critical failure modes. Standard verification and validation methods often do not apply directly to learning agents and Monte Carlo methods have difficulty covering even a small fraction of the state space, especially in multiagent systems or over long time horizons. To overcome this difficulty, we demonstrate an adaptive stress-testing method based on reinforcement learning of correlations that raise the probability of failure. This approach has three key properties: (1) it is able to find rare failure modes with far greater sample efficiency than Monte Carlo methods, (2) it can estimate the true probability of a failure mode despite the inherent bias in the learning method, and (3) it is capable of learning and resampling compact representations of multimodal failure spaces. These properties are important in practice as we need to find disparate failure modes while accounting for their actual relevance. This is a significant advantage over traditional adaptive stress testing methods that give abstract likelihoods of particular failure instances, but cannot estimate the probability of a broader failure mode. We test our algorithm on a simple problem from the aviation domain where an autonomous aircraft lands in gusty wind conditions. The results suggest that we can find failure modes with far fewer samples than the Monte Carlo approach and simultaneously estimate the probability of failure.

Validation↗

NASA NTRS2010

Universal Decoder for PPM of any Order

A recently developed algorithm for demodulation and decoding of a pulse-position- modulation (PPM) signal is suitable as a basis for designing a single hardware decoding apparatus to be capable of handling any PPM order. Hence, this algorithm offers advantages of greater flexibility and lower cost, in comparison with prior such algorithms, which necessitate the use of a distinct hardware implementation for each PPM order. In addition, in comparison with the prior algorithms, the present algorithm entails less complexity in decoding at large orders. An unavoidably lengthy presentation of background information, including definitions of terms, is prerequisite to a meaningful summary of this development. As an aid to understanding, the figure illustrates the relevant processes of coding, modulation, propagation, demodulation, and decoding. An M-ary PPM signal has M time slots per symbol period. A pulse (signifying 1) is transmitted during one of the time slots; no pulse (signifying 0) is transmitted during the other time slots. The information intended to be conveyed from the transmitting end to the receiving end of a radio or optical communication channel is a K-bit vector u. This vector is encoded by an (N,K) binary error-correcting code, producing an N-bit vector a. In turn, the vector a is subdivided into blocks of m = log2(M) bits and each such block is mapped to an M-ary PPM symbol. The resultant coding/modulation scheme can be regarded as equivalent to a nonlinear binary code. The binary vector of PPM symbols, x is transmitted over a Poisson channel, such that there is obtained, at the receiver, a Poisson-distributed photon count characterized by a mean background count nb during no-pulse time slots and a mean signal-plus-background count of ns+nb during a pulse time slot. In the receiver, demodulation of the signal is effected in an iterative soft decoding process that involves consideration of relationships among photon counts and conditional likelihoods of m-bit vectors of coded bits. Inasmuch as the likelihoods of all the m-bit vectors of coded bits mapping to the same PPM symbol are correlated, the best performance is obtained when the joint mbit conditional likelihoods are utilized. Unfortunately, the complexity of decoding, measured in the number of operations per bit, grows exponentially with m, and can thus become prohibitively expensive for large PPM orders. For a system required to handle multiple PPM orders, the cost is even higher because it is necessary to have separate decoding hardware for each order. This concludes the prerequisite background information. In the present algorithm, the decoding process as described above is modified by, among other things, introduction of an lbit marginalizer sub-algorithm. The term "l-bit marginalizer" signifies that instead of m-bit conditional likelihoods, the decoder computes l-bit conditional likelihoods, where l is fixed. Fixing l, regardless of the value of m, makes it possible to use a single hardware implementation for any PPM order. One could minimize the decoding complexity and obtain an especially simple design by fixing l at 1, but this would entail some loss of performance. An intermediate solution is to fix l at some value, greater than 1, that may be less than or greater than m. This solution makes it possible to obtain the desired flexibility to handle any PPM order while compromising between complexity and loss of performance.

Moision, Bruce E.↗

NASA NTRS1989

A new unified architecture of thermal/structural dynamic algorithms - Applications to coupled thermoelasticity

A new unified architecture of robust thermal-structural dynamic algorithms is presented with emphasis on applications to coupled thermoelasticity. The proposed formulations are based on Lax-Wendroff/Taylor-Galerkin explicit time integration methodology. The applicability of the proposed unified architecture to interdisciplinary problems relevant to coupled dynamic thermoelasticity is demonstrated. The basic concepts and characteristic features of the unified formulations are discussed.

Tamma, Kumar K.↗

NASA NTRS2004

Perceptual learning through optimization of attentional weighting: human versus optimal Bayesian learner

Human performance in visual detection, discrimination, identification, and search tasks typically improves with practice. Psychophysical studies suggest that perceptual learning is mediated by an enhancement in the coding of the signal, and physiological studies suggest that it might be related to the plasticity in the weighting or selection of sensory units coding task relevant information (learning through attention optimization). We propose an experimental paradigm (optimal perceptual learning paradigm) to systematically study the dynamics of perceptual learning in humans by allowing comparisons to that of an optimal Bayesian algorithm and a number of suboptimal learning models. We measured improvement in human localization (eight-alternative forced-choice with feedback) performance of a target randomly sampled from four elongated Gaussian targets with different orientations and polarities and kept as a target for a block of four trials. The results suggest that the human perceptual learning can occur within a lapse of four trials (<1 min) but that human learning is slower and incomplete with respect to the optimal algorithm (23.3% reduction in human efficiency from the 1st-to-4th learning trials). The greatest improvement in human performance, occurring from the 1st-to-2nd learning trial, was also present in the optimal observer, and, thus reflects a property inherent to the visual task and not a property particular to the human perceptual learning mechanism. One notable source of human inefficiency is that, unlike the ideal observer, human learning relies more heavily on previous decisions than on the provided feedback, resulting in no human learning on trials following a previous incorrect localization decision. Finally, the proposed theory and paradigm provide a flexible framework for future studies to evaluate the optimality of human learning of other visual cues and/or sensory modalities.

Non-NASA Center↗

NASA NTRSDate not supplied

A Low-Cost and Low-Risk Testbed for Control Design of Launch Vehicles and Landing Systems

This research seeks to bridge the gap between the simulation/theory and the flight-testing phases of control algorithmdesign for launch vehicles (LVs) and landing systems (LSs). Thispaper reviews contemporary testbeds for LV and LS control sys-tem design with an emphasis on reducing costs and risks. Also, anew low-cost and low-risk testbed utilizing a quadcopter, flexibleinverted pendulum, and a hanging pendulum is proposed. It is shown that the planar dynamic response of this proposed testbedcan match the planar dynamic response of an LV. As mission objectives for frequent cislunar and interplanetary travel become more complex, the algorithms that control LVs and LSs need to become more advanced to ensure mission safety and success. These control systems are slow to mature because it is difficult to perform experiments in an environment relevant to the mission in a safe and cost-effective manner. A solution to this problem frequently leveraged in the aviation andsatellite industries involves mimicking the dynamic response ofthe vehicle with a test platform that is low-cost and low-risk.

William J Elke III↗

NASA NTRS2010

Salience Assignment for Multiple-Instance Data and Its Application to Crop Yield Prediction

An algorithm was developed to generate crop yield predictions from orbital remote sensing observations, by analyzing thousands of pixels per county and the associated historical crop yield data for those counties. The algorithm determines which pixels contain which crop. Since each known yield value is associated with thousands of individual pixels, this is a multiple instance learning problem. Because individual crop growth is related to the resulting yield, this relationship has been leveraged to identify pixels that are individually related to corn, wheat, cotton, and soybean yield. Those that have the strongest relationship to a given crop s yield values are most likely to contain fields with that crop. Remote sensing time series data (a new observation every 8 days) was examined for each pixel, which contains information for that pixel s growth curve, peak greenness, and other relevant features. An alternating-projection (AP) technique was used to first estimate the "salience" of each pixel, with respect to the given target (crop yield), and then those estimates were used to build a regression model that relates input data (remote sensing observations) to the target. This is achieved by constructing an exemplar for each crop in each county that is a weighted average of all the pixels within the county; the pixels are weighted according to the salience values. The new regression model estimate then informs the next estimate of the salience values. By iterating between these two steps, the algorithm converges to a stable estimate of both the salience of each pixel and the regression model. The salience values indicate which pixels are most relevant to each crop under consideration.

Wagstaff, Kiri L.↗

NASA NTRS2022

Simultaneous Retrieval of Selected Optical Water Quality Indicators From Landsat-8, Sentinel-2, and Sentinel-3

Constructing multi-source satellite-derived water quality (WQ) products in inland and nearshore coastal waters from the past, present, and future missions is a long-standing challenge. Despite inherent differences in sensors’ spectral capability, spatial sampling, and radiometric performance, research efforts focused on formulating, implementing, and validating universal WQ algorithms continue to evolve. This research extends a recently developed machine-learning (ML) model, i.e., Mixture Density Networks (MDNs) (Pahlevan et al., 2020; Smith et al., 2021), to the inverse problem of simultaneously retrieving WQ indicators, including chlorophyll-a (Chla), Total Suspended Solids (TSS), and the absorption by Colored Dissolved Organic Matter at 440 nm (a cdom (440)), across a wide array of aquatic ecosystems. We use a database of in situ measurements to train and optimize MDN models developed for the relevant spectral measurements (400–800 nm) of the Operational Land Imager (OLI), MultiSpectral Instrument (MSI), and Ocean and Land Color Instrument (OLCI) aboard the Landsat-8, Sentinel-2, and Sentinel-3 missions, respectively. Our two performance assessment approaches, namely hold-out and leave-one-out, suggest significant, albeit varying degrees of improvements with respect to second-best algorithms, depending on the sensor and WQ indicator (e.g., 68%, 75%, 117% improvements based on the hold-out method for Chla, TSS, and a cdom (440), respectively from MSI-like spectra). Using these two assessment methods, we provide theoretical upper and lower bounds on model performance when evaluating similar and/or out-of-sample datasets. To evaluate multi-mission product consistency across broad spatial scales, map products are demonstrated for three near-concurrent OLI, MSI, and OLCI acquisitions. Overall, estimated TSS and a cdom (440) from these three missions are consistent within the uncertainty of the model, but Chla maps from MSI and OLCI achieve greater accuracy than those from OLI. By applying two different atmospheric correction processors to OLI and MSI images, we also conduct matchup analyses to quantify the sensitivity of the MDN model and best-practice algorithms to uncertainties in reflectance products. Our model is less or equally sensitive to these uncertainties compared to other algorithms. Recognizing their uncertainties, MDN models can be applied as a global algorithm to enable harmonized retrievals of Chla, TSS, and a cdom (440) in various aquatic ecosystems from multi-source satellite imagery. Local and/or regional ML models tuned with an apt data distribution (e.g., a subset of our dataset) should nevertheless be expected to outperform our global model.

Machine learning↗

NASA NTRS1975

Finite-element solution for a combined radiative-conductive analysis with mixed diffuse-specular surface characteristics

The finite-element method has been applied to solve a combined radiative-conductive heat transfer problem for a large space telescope similar to those used in orbiting satellites. The derivation of the underlying matrices and associated solution algorithm for a 2-D triangular element is presented in detail. The resulting expressions for this triangular element typify such an analysis, which yields constitutive matrices when the heat equation is cast in the matrix form. The relevant matrices include those pertaining to thermal conductance, internal heat generation, radiative exchanges, and all possible external thermal loadings. Emphasis is placed on the treatment of non-linear radiative interchange between surfaces in an enclosure having mixed diffuse-specular surface characteristics. Essential differences in governing equations describing these distinctive surface characteristics are identified. Concluding remarks are drawn from an example simulating a Cassegrainian space telescope.

Lee, H.-P.↗

NASA NTRSDate not supplied

U.S. GPM Status

The joint U.S.-Japan Global Precipitation Measurement (GPM) mission, now in its tenth year of operations, continues to pursue research on scientific and operational shortcomings in algorithms for retrieving global precipitation from satellite observations. A summary of operations, including the orbit boost, processing, science, and applications is provided. This includes key improvements to the GPM products for which NASA has lead responsibility. This includes refinements to ensure continuity for each product across the boundary in 2014 between the predecessor Tropical Rainfall Measuring Mission (TRMM) and the GPM Core Observatory. Early evaluations of the U.S. Science Team’s Integrated Multi-satellitE Retrievals for GPM (IMERG) in V07 show interesting behavior across the TRMM orbit boost that is highly relevant to the planned orbit boost for the GPM Core observatory. We will present summary statistics that illustrate the tremendous demand that exists in the user community for GPM precipitation products, which on the U.S. side is primarily focused on IMERG, and end by considering major issues that might be addressed in up-coming versions. These include the use of machine learning algorithms, the operational challenge of swarms of “small”, perhaps short-lived satellites, and continuation of precessing calibration satellites after GPM.

GPM↗

NASA NTRS1985

A numerical algorithm for endochronic plasticity and comparison with experiment

A numerical algorithm based on the finite element method of analysis of the boundary value problem in a continuum is presented, in the case where the plastic response of the material is given in the context of endochronic plasticity. The relevant constitutive equation is expressed in incremental form and plastic effects are accounted for by the method of an induced pseudo-force in the matrix equations. The results of the analysis are compared with observed values in the case of a plate with two symmetric notches and loaded longitudinally in its own plane. The agreement between theory and experiment is excellent.

Valanis, K. C.↗

NASA NTRS1987

The sensing and perception subsystem of the NASA research telerobot

A useful space telerobot for on-orbit assembly, maintenance, and repair tasks must have a sensing and perception subsystem which can provide the locations, orientations, and velocities of all relevant objects in the work environment. This function must be accomplished with sufficient speed and accuracy to permit effective grappling and manipulation. Appropriate symbolic names must be attached to each object for use by higher-level planning algorithms. Sensor data and inferences must be presented to the remote human operator in a way that is both comprehensible in ensuring safe autonomous operation and useful for direct teleoperation. Research at JPL toward these objectives is described.

Wilcox, B.↗

NASA NTRS1998

Computational Modeling of Semiconductor Dynamics at Femtosecond Time Scales

The main objective of the Joint-Research Interchange NCC2-5149 was to develop computer codes for accurate simulation of femtosecond pulse propagation in semiconductor lasers and semiconductor amplifiers [I]. The code should take into account all relevant processes such as the interband and intraband carrier relaxation mechanisms and the many-body effects arising from the Coulomb interaction among charge carriers [2]. This objective was fully accomplished. We made use of a previously developed algorithm developed at NASA Ames [3]-[5]. The new algorithm was tested on several problems of practical importance. One such problem was related to the amplification of femtosecond optical pulses in semiconductors. These results were presented in several international conferences over a period of three years. With the help of a postdoctoral fellow, we also investigated the origin of instabilities that can lead to the formation of femtosecond pulses in different kinds of lasers. We analyzed the occurrence of absolute instabilities in lasers that contain a dispersive host material with third-order nonlinearities. Starting from the Maxwell-Bloch equations, we derived general multimode equations to distinguish between convective and absolute instabilities. We find that both self-phase modulation and intensity-dependent absorption can dramatically affect the absolute stability of such lasers. In particular, the self-pulsing threshold (the so-called second laser threshold) can occur at few times the first laser threshold even in good-cavity lasers for which no self-pulsing occurs in the absence of intensity-dependent absorption. These results were presented in an international conference and published in the form of two papers.

Agrawal, Govind P.↗

NASA NTRS2007

Status of the L2 and Lunar Charged Particle Environment Models

The L2 Charged Particle Environment (L2-CPE) model is an engineering tool which provides free field charged particle environments for distant magnetotail, magnetosheath, and solar wind environments. L2-CPE is intended for use in assessing contributions from low energy radiation environments (4.1 keV to few MeV) to radiation dose in thin materials used in construction of spacecraft to be placed in orbit about the Sun-Earth L2 point. This paper describes the status of the current version of the L2-CPE model including structure of the model used to organize plasma environments into solar wind, magnetosheath, and magnetotail environments, the algorithms used to estimate radiation fluence in sparsely sampled environments, the updated graphical user interface, and output options for flux and fluence environments. In addition, we describe the status and plans for updating the model to include environments relevant to lunar programs.

Minow, Joseph I.↗

NASA NTRS1993

Snow and Ice Applications of AVHRR in Polar Regions: Report of a Workshop

The third symposium on Remote Sensing of Snow and Ice, organized by the International Glaciological Society, took place in Boulder, Colorado, 17-22 May 1992. As part of this meeting a total of 21 papers was presented on snow and ice applications of Advanced Very High Resolution Radiometer (AVHRR) satellite data in polar regions. Also during this meeting a NASA sponsored Workshop was held to review the status of polar surface measurements from AVHRR. In the following we have summarized the ideas and recommendations from the workshop, and the conclusions of relevant papers given during the regular symposium sessions. The seven topics discussed include cloud masking, ice surface temperature, narrow-band albedo, ice concentration, lead statistics, sea-ice motion and ice-sheet studies with specifics on applications, algorithms and accuracy, following recommendations for future improvements. In general, we can affirm the strong potential of AVHRR for studying sea ice and snow covered surfaces, and we highly recommend this satellite data set for long-term monitoring of polar process studies. However, progress is needed to reduce the uncertainty of the retrieved parameters for all of the above mentioned topics to make this data set useful for direct climate applications such as heat balance studies and others. Further, the acquisition and processing of polar AVHRR data must become better coordinated between receiving stations, data centers and funding agencies to guarantee a long-term commitment to the collection and distribution of high quality data.

Steffen, K.↗