Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “SAMPLED DATA”

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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

System and Method for Outlier Detection via Estimating Clusters

An efficient method and system for real-time or offline analysis of multivariate sensor data for use in anomaly detection, fault detection, and system health monitoring is provided. Models automatically derived from training data, typically nominal system data acquired from sensors in normally operating conditions or from detailed simulations, are used to identify unusual, out of family data samples (outliers) that indicate possible system failure or degradation. Outliers are determined through analyzing a degree of deviation of current system behavior from the models formed from the nominal system data. The deviation of current system behavior is presented as an easy to interpret numerical score along with a measure of the relative contribution of each system parameter to any off-nominal deviation. The techniques described herein may also be used to "clean" the training data.

Iverson, David J.↗
NASA NTRS2011

Shuttle Hypervelocity Impact Database

With three missions outstanding, the Shuttle Hypervelocity Impact Database has nearly 3000 entries. The data is divided into tables for crew module windows, payload bay door radiators and thermal protection system regions, with window impacts compromising just over half the records. In general, the database provides dimensions of hypervelocity impact damage, a component level location (i.e., window number or radiator panel number) and the orbiter mission when the impact occurred. Additional detail on the type of particle that produced the damage site is provided when sampling data and definitive analysis results are available. Details and insights on the contents of the database including examples of descriptive statistics will be provided. Post flight impact damage inspection and sampling techniques that were employed during the different observation campaigns will also be discussed. Potential enhancements to the database structure and availability of the data for other researchers will be addressed in the Future Work section. A related database of returned surfaces from the International Space Station will also be introduced.

Hyde, James L.↗
DOE OSTI2018

Characteristics of Optically Thin Coastal Florida Cumuli Derived From Surface-Based Lidar Measurements

The optical depths and sizes of optically thin passive shallow cumuli are characterized with a micropulse lidar. The measurements are from a coastal south Florida location spanning a 10-week synoptically suppressed winter time period with almost no upper-level clouds present. Clouds are distinguished from aerosol through visual comparison to zenith-viewing time-lapse camera images, establishing an instrument-specific particle backscatter coefficient threshold. Sun photometers provide a continuous independent aerosol optical depth estimate applied within the lidar retrieval of cloud optical depth. The focus is on clouds that do not attenuate the lidar signal. Clouds with optical depths less than one constitute more than 13% of the sampled data set, with decreasing optical depths occurring more frequently. Most of the clouds are less than 60 m thick and are located below 1 km. The optically thin regions occur most frequently in individual 15-s samples, corresponding to a horizontal width of less than 100 m, with occasional stratiform segments extending up to 2 km. Many segments border optically thicker regions, but some are self-contained, reflecting small-scale humidity variations near-saturation. To our knowledge, this study is the first to apply micropulse lidar data toward characterizing these low, shallow, broken clouds, too small for robust characterization from space but whose ubiquitiousness renders them radiatively important.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

sin 2 θ eff lept and M W (indirect) extracted from 9 fb -1 μ + μ - event sample at CDF

Here, we report on the extraction of $\sin^2\theta^{\rm lept}_{\rm eff}$ and indirect measurement of the mass of the W boson from the forward-backward asymmetry of $\mu^+\mu^-$ events in the $Z$ boson mass region. The data sample collected by the CDF detector corresponds to the full 9 fb$^{-1}$ run II sample. We measure $\sin^2 \theta^{\rm lept}_{\rm eff} = 0.2315 \pm 0.0010$, $ \sin^2 \theta_W = 0.2233 \pm 0.0009$ and $M_W ({\rm indirect}) = 80.365 \pm 0.047 \;{\rm GeV}/c^2$, where each uncertainty includes both statistical and systematic contributions. Comparison with the results of the D0 collaboration are presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Observation of Higgs Boson Decay to Bottom Quarks

Here, the observation of the standard model (SM) Higgs boson decay to a pair of bottom quarks is presented. The main contribution to this result is from processes in which Higgs bosons are produced in association with a W or Z boson (VH), and are searched for in final states including 0, 1, or 2 charged leptons and two identified bottom quark jets. The results from the measurement of these processes in a data sample recorded by the CMS experiment in 2017, comprising 41.3 fb -1 of proton-proton collisions at $\sqrt{s}$ = 13 TeV, are described. When combined with previous VH measurements using data collected at $\sqrt{s}$ = 7, 8, and 13 TeV, an excess of events is observed at m H =125 GeV with a significance of 4.8 standard deviations, where the expectation for the SM Higgs boson is 4.9. The corresponding measured signal strength is 1.01 ± 0.22. The combination of this result with searches by the CMS experiment for H→$b\bar{b}$ in other production processes yields an observed (expected) significance of 5.6 (5.5) standard deviations and a signal strength of 1.04±0.20.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Efficient Interdependent Systems Recovery Modeling with DeepONets

Modeling the recovery of interdependent critical infrastructure is a key component of quantifying and optimizing societal resilience to disruptive events. However, simulating the recovery of large-scale interdependent systems under random disruptive events is computationally expensive. Therefore, we propose the application of Deep Operator Networks (DeepONets) in this paper to accelerate the recovery modeling of interdependent systems. DeepONets are ML architectures which identify mathematical operators from data. The form of governing equations DeepONets identify and the governing equation of interdependent systems recovery model are similar. Therefore, we hypothesize that DeepONets can efficiently model the interdependent systems recovery with little training data. We applied DeepONets to a simple case of four interdependent systems with sixteen states. DeepONets, overall, performed satisfactorily in predicting the recovery of these interdependent systems for out of training sample data when compared to reference results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Quantitative Exposure Assessment Professional Judgment Validation Study

Professional judgement is a key element of an exposure assessment program. Industrial hygienists frequently rely on it to make judgements about the acceptability of occupational exposures to chemical, biological, and physical agents. Often this must be done with little or no quantitative sampling data. This is especially true in the case of research and development, where activities may be short duration and non-routine. In this situation there are very limited opportunities for sampling, and therefore, professional judgement becomes a major tool industrial hygienists rely upon. One of the limitations of professional judgement is that exposures may be misclassified. That is, an exposure deemed to be unacceptable may in fact be acceptable and unnecessary effort and expense may be incurred as a result. On the other hand, an exposure may be judged acceptable when it is in fact unacceptable, resulting in an overexposure and potential injury or illness. It is therefore crucial that professional judgement be validated. In this study we evaluated 106 acceptable exposure determinations with retrospective quantitative exposure monitoring to validate the performance of Sandia National Laboratory industrial hygienists at performing exposure determinations with professional judgement.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1992

Unsupervised segmentation of polarimetric SAR data using the covariance matrix

A method for unsupervised segmentation of polarimetric synthetic aperture radar (SAR) data into classes of homogeneous microwave polarimetric backscatter characteristics is presented. Classes of polarimetric backscatter are selected on the basis of a multidimensional fuzzy clustering of the logarithm of the parameters composing the polarimetric covariance matrix. The clustering procedure uses both polarimetric amplitude and phase information, is adapted to the presence of image speckle, and does not require an arbitrary weighting of the different polarimetric channels; it also provides a partitioning of each data sample used for clustering into multiple clusters. Given the classes of polarimetric backscatter, the entire image is classified using a maximum a posteriori polarimetric classifier. Four-look polarimetric SAR complex data of lava flows and of sea ice acquired by the NASA/JPL airborne polarimetric radar (AIRSAR) are segmented using this technique. The results are discussed and compared with those obtained using supervised techniques.

Rignot, Eric J. M.↗
NASA NTRS2006

NASA EVM Overview and Case Study

The presentation gives an overview of the National Aeronautics and Space Administration (NASA) Earned Value Management (EVM) structure. We briefly talk about the current EVM high-level policies within NASA and the EVM governing structure. It touches on the roles and responsibilities of EVM Focal Points within the Agency. We will also discuss the approach that MSFC followed in implementing EVM and better data analysis within the Habitat Holding Racks (HHR) Project. We will address the approach used at the Marshall Space Flight Center (MSFC) to effectively equip and support MSFC projects in applying a sound EVM and data analysis process. In addition, we will show metrics associated with the HHR project before and after the implementation of EVM on the project. We will discuss the monthly report, using sample data, that the project manager used each month to assess the performance of the project. The data received from EVM helped create a solid method for assessing the project s performance. The use of EVM data analysis can be an effective and efficient tool in today s environment with increasing workloads and downsizing workforces. EVM provides project managers with information that can be used in the decision making process.

Kerby, Jerald G.↗
NASA NTRS2006

Estimating Random Errors Due to Shot Noise in Backscatter Lidar Observations

In this paper, we discuss the estimation of random errors due to shot noise in backscatter lidar observations that use either photomultiplier tube (PMT) or avalanche photodiode (APD) detectors. The statistical characteristics of photodetection are reviewed, and photon count distributions of solar background signals and laser backscatter signals are examined using airborne lidar observations at 532 nm using a photon-counting mode APD. Both distributions appear to be Poisson, indicating that the arrival at the photodetector of photons for these signals is a Poisson stochastic process. For Poisson-distributed signals, a proportional, one-to-one relationship is known to exist between the mean of a distribution and its variance. Although the multiplied photocurrent no longer follows a strict Poisson distribution in analog-mode APD and PMT detectors, the proportionality still exists between the mean and the variance of the multiplied photocurrent. We make use of this relationship by introducing the noise scale factor (NSF), which quantifies the constant of proportionality that exists between the root-mean-square of the random noise in a measurement and the square root of the mean signal. Using the NSF to estimate random errors in lidar measurements due to shot noise provides a significant advantage over the conventional error estimation techniques, in that with the NSF uncertainties can be reliably calculated from/for a single data sample. Methods for evaluating the NSF are presented. Algorithms to compute the NSF are developed for the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar and tested using data from the Lidar In-space Technology Experiment (LITE). OCIS Codes:

Liu, Zhaoyan↗
DOE OSTI2022

Privacy policy robustness to reverse engineering

Differential privacy policies allow one to preserve data privacy while sharing and analyzing data. However, these policies are susceptible to an array of attacks. In particular, often a portion of the data desired to be privacy protected is exposed online. Access to these pre-privacy protected data samples can then be used to reverse engineer the privacy policy. With knowledge of the generating privacy policy, an attacker can use machine learning to approximate the full set of originating data. Bayesian inference is one method for reverse engineering both model and model parameters. We present a methodology for evaluating and ranking privacy policy robustness to Bayesian inference-based reverse engineering, and demonstrated this method across data with a variety of temporal trends.

Kusne, Aaron Gilad↗
DOE OSTI2023

A search for heavy Higgs bosons decaying into vector bosons in same-sign two-lepton final states in $pp$ collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector

A search for heavy Higgs bosons produced in association with a vector boson and decaying into a pair of vector bosons is performed in final states with two leptons (electrons or muons) of the same electric charge, missing transverse momentum and jets. A data sample of proton–proton collisions at a centre-of-mass energy of 13 TeV recorded with the ATLAS detector at the Large Hadron Collider between 2015 and 2018 is used. The data correspond to a total integrated luminosity of 139 fb –1 . The observed data are in agreement with Standard Model background expectations. The results are interpreted using higher-dimensional operators in an effective field theory. Upper limits on the production cross-section are calculated at 95% confidence level as a function of the heavy Higgs boson’s mass and coupling strengths to vector bosons. Limits are set in the Higgs boson mass range from 300 to 1500 GeV, and depend on the assumed couplings. The highest excluded mass for a heavy Higgs boson with the coupling combinations explored is 900 GeV. Limits on coupling strengths are also provided.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Search for new physics in final states with an energetic jet or a hadronically decaying $W$ or $Z$ boson and transverse momentum imbalance at $\sqrt{s}=13\text{ }\text{ }\mathrm{TeV}$

A search for new physics using events containing an imbalance in transverse momentum and one or more energetic jets arising from initial-state radiation or the hadronic decay of W or Z bosons is presented. A data sample of proton-proton collisions at s=13 TeV, collected with the CMS detector at the LHC and corresponding to an integrated luminosity of 35.9 fb-1, is used. The observed data are found to be in agreement with the expectation from standard model processes. The results are interpreted as limits on the dark matter production cross section in simplified models with vector, axial-vector, scalar, and pseudoscalar mediators. Interpretations in the context of fermion portal and nonthermal dark matter models are also provided. In addition, the results are interpreted in terms of invisible decays of the Higgs boson and set stringent limits on the fundamental Planck scale in the Arkani-Hamed, Dimopoulos, and Dvali model with large extra spatial dimensions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Unsupervised Dictionary Learning for Signal-to-Noise Ratio Enhancement of Array Data

Waveform enhancement methods generally explore lateral coherency in arrivals, often assuming a linear moveout across an array, as exhibited by plane waves. Here, we illustrate how unsupervised dictionary learning combined with orthogonal matching pursuit for feature extraction can be used for signal-to-noise ratio (SNR) enhancement. In this strategy, waveform characteristics are directly learned from provided data samples; the created dictionary is then used for signal extraction. This combination prevents the need to set a predefined dictionary, and it becomes computationally efficient because learning is only done on smaller data portions. Because the dictionary is learned from data, there is no assumption regarding wavefront shape or form. Tests on synthetic and field data demonstrate the better denoising performance in terms of SNR enhancement compared to other methods.

58 GEOSCIENCES↗
DOE OSTI2019

Sandia-UT Academic Alliance Project Summary

This project seeks to leverage various hyperspectral tensor products for the purposes of target classification/detection/prediction. In addition to hyperspectral, these products may be images, time series, geometries, or other modalities. The scenarios in which the targets of interest must be identified are typically from remote sensing platforms such as satellites. As such, there are numerous real-world constraints that drive algorithmic formulation. Cost, complexity, and feasibility of the algorithm should all be considered. Targets of interest are exceedingly rare, and collecting many data samples is prohibitively expensive. Furthermore, model interpretability is paramount due to the application space. The goal of this project is to develop a constrained supervised tensor factorization framework for use on hyperspectral data products. Supervised tensor factorizations already exist in the literature, although they have not seen widespread adoption in the remote sensing domain. The novelty of this project will be the formulation and inclusion of constraints that take into account mission considerations and physics based limits to learn a factorization that is both physically interpretable and mission deployable. This will represent a new contribution to the field of remote sensing for performing supervised learning tasks with hyperspectral data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

SNM Radiation Signature Classification Using Different Semi-Supervised Machine Learning Models

The timely detection of special nuclear material (SNM) transfers between nuclear facilities is an important monitoring objective in nuclear nonproliferation. Persistent monitoring enabled by successful detection and characterization of radiological material movements could greatly enhance the nuclear nonproliferation mission in a range of applications. Supervised machine learning can be used to signal detections when material is present if a model is trained on sufficient volumes of labeled measurements. However, the nuclear monitoring data needed to train robust machine learning models can be costly to label since radiation spectra may require strict scrutiny for characterization. Therefore, this work investigates the application of semi-supervised learning to utilize both labeled and unlabeled data. As a demonstration experiment, radiation measurements from sodium iodide (NaI) detectors are provided by the Multi-Informatics for Nuclear Operating Scenarios (MINOS) venture at Oak Ridge National Laboratory (ORNL) as sample data. Anomalous measurements are identified using a method of statistical hypothesis testing. After background estimation, an energy-dependent spectroscopic analysis is used to characterize an anomaly based on its radiation signatures. In the absence of ground-truth information, a labeling heuristic provides data necessary for training and testing machine learning models. Supervised logistic regression serves as a baseline to compare three semi-supervised machine learning models: co-training, label propagation, and a convolutional neural network (CNN). In each case, the semi-supervised models outperform logistic regression, suggesting that unlabeled data can be valuable when training and demonstrating value in semi-supervised nonproliferation implementations.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
NASA NTRS1972

Flight-test experience in digital control of a remotely piloted vehicle.

The development of a remotely piloted vehicle system consisting of a remote pilot cockpit and a ground-based digital computer coupled to the aircraft through telemetry data links is described. The feedback control laws are implemented in a FORTRAN program. Flight-test experience involving high feedback gain limits for attitude and attitude rate feedback variables, filtering of sampled data, and system operation during intermittent telemetry data link loss is discussed. Comparisons of closed-loop flight tests with analytical calculations, and pilot comments on system operation are included.

Edwards, J. W.↗
NASA NTRS2009

A Local Scalable Distributed Expectation Maximization Algorithm for Large Peer-to-Peer Networks

This paper offers a local distributed algorithm for expectation maximization in large peer-to-peer environments. The algorithm can be used for a variety of well-known data mining tasks in a distributed environment such as clustering, anomaly detection, target tracking to name a few. This technology is crucial for many emerging peer-to-peer applications for bioinformatics, astronomy, social networking, sensor networks and web mining. Centralizing all or some of the data for building global models is impractical in such peer-to-peer environments because of the large number of data sources, the asynchronous nature of the peer-to-peer networks, and dynamic nature of the data/network. The distributed algorithm we have developed in this paper is provably-correct i.e. it converges to the same result compared to a similar centralized algorithm and can automatically adapt to changes to the data and the network. We show that the communication overhead of the algorithm is very low due to its local nature. This monitoring algorithm is then used as a feedback loop to sample data from the network and rebuild the model when it is outdated. We present thorough experimental results to verify our theoretical claims.

Bhaduri, Kanishka↗