Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “ensemble optimization”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Multi-Parameter Scattering Sensor and Methods

Methods, detectors and systems detect particles and/or measure particle properties. According to one embodiment, a detector for detecting particles comprises: a sensor for receiving radiation scattered by an ensemble of particles; and a processor for determining a physical parameter for the detector, or an optimal detection angle or a bound for an optimal detection angle, for measuring at least one moment or integrated moment of the ensemble of particles, the physical parameter, or detection angle, or detection angle bound being determined based on one or more of properties (a) and/or (b) and/or (c) and/or (d) or ranges for one or more of properties (a) and/or (b) and/or (c) and/or (d), wherein (a)-(d) are the following: (a) is a wavelength of light incident on the particles, (b) is a count median diameter or other characteristic size parameter of the particle size distribution, (c) is a standard deviation or other characteristic width parameter of the particle size distribution, and (d) is a refractive index of particles.

Greenberg, Paul S.↗
NASA NTRS2023

An Ensemble Neural Network Model for Predicting Rare-Earth Oxide and Silicate Heat Capacities at High Temperature

In this work, a neural network model was developed to predict the constant pressure heat capacity for materials in the rare-earth oxide—silica material space. Several model architectures were trained and tested on heat capacity data generated from first-principles density functional theory calculations. Hyperparameter optimization was performed, and the optimal model was selected for heat capacity predictions. The optimal model architecture was found to have a root-mean-squared error of 5.12 ± 3.37 J/mol-K. The optimal model architecture was then used in a bagging ensemble model trained using the leave-one-group-out method to provide error estimates for model predictions. The out-of-bag score for the ensemble model was 0.997. The predicted heat capacities agree well with the DFT and experimental results and were computed orders of magnitude faster than DFT simulations. Machine learning shows the potential to provide a suitable surrogate model for thermochemical property predictions for candidate environmental barrier coating materials but refining of input material features and model architectures could further improve accuracy for these models.

environmental barrier coatings↗
NASA NTRS1987

Statistical mechanics and invariant perception

The problem of discrimination among ensembles of images generated by distortions of a prototype and the addition of noise is considered. As the noise level is increased, the discrimination task becomes qualitatively more difficult in that optimal discrimination requires the computation of increasingly longer-ranged correlations, or the solution of increasingly difficult optimization problems. These results suggest the use of such image ensembles in probing the computational abilities of the human visual system, and possible theoretical implications of such experiments are discussed.

Bialek, William↗
NASA NTRS2005

Single visit completeness optimization

The Terrestrial Planet Finder Coronagraph (TPF-C) is a NASA pre-phase. A orbiting telescope with the goal of detecting and characterizing visible and near-IR light refelected from terrestrial planets. We use observational completeness, defined as the fraction of potentially observable planets that are detected, to determine how well the proposed telescope meets the mission goals. Single visit completeness is optimized by slecting the length of observation per target that maximizes the completeness for the ensemble of stars. In this memo we describe the integration time calculations and two approaches to single visit optimization.

optimization↗
NASA NTRS1978

Modeling the effects of high-G stress on pilots in a tracking task

Air-to-air tracking experiments were conducted at the Aerospace Medical Research Laboratories using both fixed and moving base dynamic environment simulators. The obtained data, which includes longitudinal error of a simulated air-to-air tracking task as well as other auxiliary variables, was analyzed using an ensemble averaging method. In conjunction with these experiments, the optimal control model is applied to model a human operator under high-G stress.

Korn, J.↗
NASA NTRS1991

Timescale algorithms combining cesium clocks and hydrogen masers

The United States Naval Observatory (USNO) atomic timescale, formerly based on an ensemble of cesium clocks, is now produced by an ensemble of cesium clocks and hydrogen masers. In order to optimize stability and reliability, equal clock weighting has been replaced by a procedure reflecting the relative, time-varying noise characteristics of the two different types of clocks. Correlation of frequency drift is required, and residual drift is avoided by the eventual complete deweighting of the masers.

Lee A. Breakiron↗
NASA NTRS1996

Upper Limit of Weights in TAI Computation

The international reference time scale International Atomic Time (TAI) computed by the Bureau International des Poids et Mesures (BIPM) relies on a weighted average of data from a large number of atomic clocks. In it, the weight attributed to a given clock depends on its long-term stability. In this paper the TAI algorithm is used as the basis for a discussion of how to implement an upper limit of weight for clocks contributing to the ensemble time. This problem is approached through the comparison of two different techniques. In one case, a maximum relative weight is fixed: no individual clock can contribute more than a given fraction to the resulting time scale. The weight of each clock is then adjusted according to the qualities of the whole set of contributing elements. In the other case, a parameter characteristic of frequency stability is chosen: no individual clock can appear more stable than the stated limit. This is equivalent to choosing an absolute limit of weight and attributing this to to the most stable clocks independently of the other elements of the ensemble. The first technique is more robust than the second and automatically optimizes the stability of the resulting time scale, but leads to a more complicated computatio. The second technique has been used in the TAI algorithm since the very beginning. Careful analysis of tests on real clock data shows that improvement of the stability of the time scale requires revision from time to time of the fixed value chosen for the upper limit of absolute weight. In particular, we present results which confirm the decision of the CCDS Working Group on TAI to increase the absolute upper limit by a factor of 2.5. We also show that the use of an upper relative contribution further helps to improve the stability and may be a useful step towards better use of the massive ensemble of HP 507IA clocks now contributing to TAI.

Thomas, Claudine↗
NASA NTRS1990

Optimal Allocation Of Tasks In Hypercube Computers

Uniform tasks distributed uniformly or else assigned to one processor. Investigation in theory of scheduling yielded optimal scheme for allocation of tasks among digital data processors in hypercube ensemble. Applies to tasks that require equal time to execute, performed in any order, and between any two of which equal amounts of communication required. Reduces overall processing time for given set of computational tasks.

Salama, Moktar A.↗
NASA NTRS2008

The Limits of Coding with Joint Constraints on Detected and Undetected Error Rates

We develop a remarkably tight upper bound on the performance of a parameterized family of bounded angle maximum-likelihood (BA-ML) incomplete decoders. The new bound for this class of incomplete decoders is calculated from the code's weight enumerator, and is an extension of Poltyrev-type bounds developed for complete ML decoders. This bound can also be applied to bound the average performance of random code ensembles in terms of an ensemble average weight enumerator. We also formulate conditions defining a parameterized family of optimal incomplete decoders, defined to minimize both the total codeword error probability and the undetected error probability for any fixed capability of the decoder to detect errors. We illustrate the gap between optimal and BA-ML incomplete decoding via simulation of a small code.

Undetected Error Rate↗
NASA NTRS2011

Incorporating JULES into NASA's Land Information System (LIS) and Investigations of Land-Atmosphere Coupling

NASA's Land Information System (LIS; lis.gsfc.nasa.gov) is a flexible land surface modeling and data assimilation framework developed over the past decade with the goal of integrating satellite- and ground-based observational data products and advanced land surface modeling techniques to produce optimal fields of land surface states and fluxes. LIS features a high performance and flexible design, and operates on an ensemble of land surface models for extension over user-specified regional or global domains. The extensible interfaces of LIS allow the incorporation of new domains, land surface models (LSMs), land surface parameters, meteorological inputs, data assimilation and optimization algorithms. In addition, LIS has also been demonstrated for parameter estimation and uncertainty estimation, and has been coupled to the Weather Research and Forecasting (WRF) mesoscale model. A visiting fellowship is currently underway to implement JULES into LIS and to undertake some fundamental science on the feedbacks between the land surface and the atmosphere. An overview of the LIS system, features, and sample results will be presented in an effort to engage the community in the potential advantages of LIS-JULES for a range of applications. Ongoing efforts to develop a framework for diagnosing land-atmosphere coupling will also be presented using the suite of LSM and PBL schemes available in LIS and WRF along with observations from the U. S .. Southern Great Plains. This methodology provides a potential pathway to study factors controlling local land-atmosphere coupling (LoCo) using the LIS-WRF system, which will serve as a testbed for future experiments to evaluate coupling diagnostics within the community.

Santanello, Joseph↗
NASA NTRS2014

Assimilation of MODIS Snow Cover Through the Data Assimilation Research Testbed and the Community Land Model Version 4

To improve snowpack estimates in Community Land Model version 4 (CLM4), the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) was assimilated into the Community Land Model version 4 (CLM4) via the Data Assimilation Research Testbed (DART). The interface between CLM4 and DART is a flexible, extensible approach to land surface data assimilation. This data assimilation system has a large ensemble (80-member) atmospheric forcing that facilitates ensemble-based land data assimilation. We use 40 randomly chosen forcing members to drive 40 CLM members as a compromise between computational cost and the data assimilation performance. The localization distance, a parameter in DART, was tuned to optimize the data assimilation performance at the global scale. Snow water equivalent (SWE) and snow depth are adjusted via the ensemble adjustment Kalman filter, particularly in regions with large SCF variability. The root-mean-square error of the forecast SCF against MODIS SCF is largely reduced. In DJF (December-January-February), the discrepancy between MODIS and CLM4 is broadly ameliorated in the lower-middle latitudes (2345N). Only minimal modifications are made in the higher-middle (4566N) and high latitudes, part of which is due to the agreement between model and observation when snow cover is nearly 100. In some regions it also reveals that CLM4-modeled snow cover lacks heterogeneous features compared to MODIS. In MAM (March-April-May), adjustments to snowmove poleward mainly due to the northward movement of the snowline (i.e., where largest SCF uncertainty is and SCF assimilation has the greatest impact). The effectiveness of data assimilation also varies with vegetation types, with mixed performance over forest regions and consistently good performance over grass, which can partly be explained by the linearity of the relationship between SCF and SWE in the model ensembles. The updated snow depth was compared to the Canadian Meteorological Center (CMC) data. Differences between CMC and CLM4 are generally reduced in densely monitored regions.

data assimilation↗
NASA NTRS1995

Repeating sources of classical gamma-ray bursts

From an analysis of the first catalog of the Burst and Transient Source Experiment (BATSE) experiment (Fishman et al. 1993, 1994a) on the Compton Gamma Ray Observatory (CGRO), we find an excessive number of pairs of gamma-ray bursts which are clustered in both a space and time . The angular separation between the two bursts in each pair is less than their positional uncertainties, and the interval between their occurrence times is within several days. Optimizing the signal, we find that the probability of observing such a clustered excess froma Poisson ensemble is approximately 2 x 10(exp -5). We suggest that these bursts arise from repeating sources. A detailed study of the most prolific source, GBS 0855-00, is presented in a separate paper (Wang & Lingenfelter 1993a, b). Unlike most of the 'soft' gamma-ray repeaters, these repeating bursts have relatively hard spectra, complex light curves, and widely varying durations, that are indistinguishable from classical gamma-ray bursts. Although the significance of the clustered excess was optimized a posteriori, because it depends on temporal and spatial bin sized that could not be defined a priori, we can use the optimizations from the first catalog to test subsequent BATSE data sets. Unfortunately, the failure of the on-board tape recorders during the second catalog period seriously reduced the number of accurately positioned bursts (Fishman et al. 1994b), so that we can neither confirm, nor refute, the predicted repitition in that sample, and we must await the results of the third catalog.

Wang, V. C.↗
NASA NTRSDate not supplied

Optimizing a Small RNAseq Analysis Pipeline for NASA GeneLab Using Open-Source Tools and Libraries

Small RNA sequencing (small RNAseq) is a powerful tool for studying the regulation of gene expression in various organisms. Small RNAseq has been leveraged in space biology research to study how expression of small RNAs, e.g. micro RNAs (miRNAs), small interfering RNAs (siRNAs), and piwi-interacting RNAs (piRNAs), change upon exposure to the space environment. NASA GeneLab currently hosts small RNAseq raw data derived from space-relevant experiments on the Open Science Data Repository (OSDR). To maximize the accessibility of these data to the scientific community, in addition to hosting raw data, which is only interpretable by bioinformaticians, GeneLab plans to process all small RNAseq datasets and make those processed data available to the scientific community via the OSDR. In this study, we present the development of the GeneLab standardized pipeline for processing small RNAseq datasets. Using human, plant, and synthetic small RNAseq datasets, we interrogate various open-source software and publicly available databases to evaluate their accuracy and reproducibility in each step of the pipeline. For quality control and adapter detection and trimming, we evaluated TrimGalore!, FASTX, SeqKit, and DNApi methods to optimize alignment to reference genomes. We compared BWA, Bowtie, and Bowtie2 to determine the optimal alignment tool. For each alignment tool we also assessed various reference databases, including Ensembl reference genomes and different types of small RNA reference databases, including genome, hairpin, and miRNA references from the miRbase and MirGeneDB databases. To quantify the aligned data, we compared SAMtools, HTSeq, and RSEM for counting alignment events from each alignment tool used. Finally, we evaluated various tools, including DESeq2 and EdgeR, for data normalization and subsequent differential expression analysis. We will present the results from our comparative analyses for each pipeline step and propose a consensus pipeline for processing small RNAseq data derived from various organisms exposed to the space environment.

SmallRNAseq, NASA GeneLab, quality control, adapte↗
NASA NTRS2011

Reduced Kalman Filters for Clock Ensembles

This paper summarizes the author's work ontimescales based on Kalman filters that act upon the clock comparisons. The natural Kalman timescale algorithm tends to optimize long-term timescale stability at the expense of short-term stability. By subjecting each post-measurement error covariance matrix to a non-transparent reduction operation, one obtains corrected clocks with improved short-term stability and little sacrifice of long-term stability.

Kalman filter↗
NASA NTRS1995

The use of optimal polarizations for studying the microphysics of precipitation: Nonattenuating wavelengths

The objective of this work is to explore relationships between the microphysical properties of precipitation and optimal polarizations. The dependence of three optimal polarization parameters (asymmetry ratio A, optimal tilt tau(sub op), and optimal ellipticity epsilon(sub op) on the reflectivity-weighted mean drop shape, mean canting angle, and standard deviation of a Gaussian canting angle distribution is studied. This is accomplished by using computer simulations that provide the rms scattering matrix for an ensemble of canted drops with a prescribed two-parameter canting angle distribution. Also examined are the effects of propagation on the polarization parameters for nonattenuating wavelengths. The asymmetry ratio A is simply the ratio of the maximal to minimal total backscattered energy (ratio of the largest and smallest eigenvalue of the Graves power matrix G is identically equal to S dagger S). Similar to Z(sub DR), this ratio decreases with increasing mean axial ratio, but unlike Z(sub DR), it is not affected by canting (for a single drop). The dependence of A on the reflectivity-weighted mean drop shape is examined, and a power-law relationship similar to that which exists for Z(sub DR) is established. The asymmetry ratio A can be regarded as a generalization of Z(sub DR) because it requires only a measurement of linear depolarization ratio (in addition to Z(sub DR)), is independent of the propagation phase, and is less sensitive to canting. In a similar manner, the dependence of optimal ellipticity and tilt on the microphysical parameters is studied. In particular, it appears that the rms tilt of the optimal polarization ellipse is proportional to the variance of the canting angle distribution. Several other promising relationships between optimal polarizations and the microphysical variables of an ensemble of hydrometeors are also discussed.

Kwiatkowski, John M.↗
NASA NTRS2021

Automated Data Accountability for Missions in Mars Rover Data

As the Mars Curiosity Rover transmits data to the JPL Ground Data System (GDS), it frequently observes data loss and corruption, requiring re-transmits from the rover and Ground Data System Analysts (GDSA) to monitor the downlink process. As new missions are launched, the GDSA team redistributes analysts to these new missions, causing shortages in previous missions. The GDSA team can significantly benefit from the automation and optimization of the downlink process of telemetry data. In fact, there is a need for a better understanding of why the data is corrupted, so that the GDSA team can best determine the root cause of the issues in the GDS. This paper presents machine learning and deep learning based approaches to automate and optimize the detection of data loss. We first created a pipeline to automatically accumulate data from the telemetry databases (MAROS, Telemetry Data Storage, and GDS Elastic Search Database) in the downlink process. With our newly created datasets, we perform feature selection to supplement the GDSA understanding of the downlink process and provide supplemental analysis on the importance of different features. We implement various machine learning and deep learning based models, including support vector machines, ensemble methods, and deep neural networks and evaluate their accuracies in identifying whether a downlink process is complete or incomplete. We utilize fast hyperparameter optimization methods that allow our models to quickly be re-trained, allowing them to quickly be tuned and optimized on daily incoming data in real time. This hyperparameter optimization also allows our methods to be quickly integrated into other JPL missions. Our results show that our best-performing machine learning and deep learning based models outperform the existing GDSA detection software by 6 accuracy points and can aid analysts by providing insights into the data accountability problem. Since these various machine learning and deep learning approaches vary significantly in interpretability, we provide a discussion on the tradeoffs between their performance and trustworthiness in helping detect issues in data transmission.

Divsalar, Dariush↗
NASA NTRS2003

Quantum Adiabatic Optimization and Combinatorial Landscapes

In this paper we analyze the performance of the Quantum Adiabatic Evolution (QAE) algorithm on a variant of Satisfiability problem for an ensemble of random graphs parametrized by the ratio of clauses to variables, gamma = M / N. We introduce a set of macroscopic parameters (landscapes) and put forward an ansatz of universality for random bit flips. We then formulate the problem of finding the smallest eigenvalue and the excitation gap as a statistical mechanics problem. We use the so-called annealing approximation with a refinement that a finite set of macroscopic variables (verses only energy) is used, and are able to show the existence of a dynamic threshold gamma = gammad, beyond which QAE should take an exponentially long time to find a solution. We compare the results for extended and simplified sets of landscapes and provide numerical evidence in support of our universality ansatz.

Smelyanskiy, V. N.↗
NASA NTRS1983

Structural modelling and control design under incomplete parameter information: The maximum-entropy approach

A stochastic structural control model is described. In contrast to the customary deterministic model, the stochastic minimum data/maximum entropy model directly incorporates the least possible a priori parameter information. The approach is to adopt this model as the basic design model, thus incorporating the effects of parameter uncertainty at a fundamental level, and design mean-square optimal controls (that is, choose the control law to minimize the average of a quadratic performance index over the parameter ensemble).

Hyland, D. C.↗