SEARCH · Search NASA

Results for “event classification”

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 595 records · Page 33

All sources NASA NTRS DOE OSTI

DOE OSTI2016

IMPULSIVITY PARAMETER FOR SOLAR FLARES

Three phases are typically observed during solar flares: the preflare, impulsive, and decay phases. During the impulsive phase, it is believed that the electrons and other particles are accelerated after the stored energy in the magnetic field is released by reconnection. The impulsivity of a solar flare is a quantifiable property that shows how quickly this initial energy release occurs. It is measured via the impulsivity parameter, which we define as the inverse of the overall duration of the impulsive phase. We take the latter as the raw width of the most prominent nonthermal emission of the flare. We computed this observable over a work sample of 48 M-class events that occurred during the current Solar Cycle 24 by using three different methods. The first method takes into account all of the nonthermal flare emission and gives very accurate results, while the other two just cover fixed energy intervals (30–40 keV and 25–50 keV) and are useful for fast calculations. We propose an alternative way to classify solar flares according to their impulsivity parameter values, defining three different types of impulsivity, namely, high, medium, and low. This system of classification is independent of the manner used to calculated the impulsivity parameter. Lastly, we show the relevance of this tool as a discriminator of different HXR generation processes.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Smart sensor for online situational awareness in power grids

Waveforms in power grids typically reveal a certain pattern with specific features and peculiarities driven by the system operating conditions, internal and external uncertainties, etc. This prompts an observation of different types of waveforms at the measurement points (substations). An innovative next-generation smart sensor technology includes a measurement unit embedded with sophisticated analytics for power grid online surveillance and situational awareness. The smart sensor brings additional levels of smartness into the existing phasor measurement units (PMUs) and intelligent electronic devices (IEDs). It unlocks the full potential of advanced signal processing and machine learning for online power grid monitoring in a distributed paradigm. Within the smart sensor are several interconnected units for signal acquisition, feature extraction, machine learning-based event detection, and a suite of multiple measurement algorithms where the best-fit algorithm is selected in real-time based on the detected operating condition. Embedding such analytics within the sensors and closer to where the data is generated, the distributed intelligence mechanism mitigates the potential risks to communication failures and latencies, as well as malicious cyber threats, which would otherwise compromise the trustworthiness of the end-use applications in distant control centers. The smart sensor achieves a promising classification accuracy on multiple classes of prevailing conditions in the power grid and accordingly improves the measurement quality across the power grid.

Dehghanian, Payman↗

DOE OSTI2012

Validity of Three Recently Proposed Prognostic Grading Indexes for Breast Cancer Patients With Radiosurgically Treated Brain Metastases

Purpose: We tested the validity of 3 recently proposed prognostic indexes for breast cancer patients with brain metastases (METs) treated radiosurgically. The 3 indexes are Diagnosis-Specific Graded Prognostic Assessment (DS-GPA), New Breast Cancer (NBC)-Recursive Partitioning Analysis (RPA), and our index, sub-classification of RPA class II patients into 3 sub-classes (RPA class II-a, II-b and II-c) based on Karnofsky performance status, tumor number, original tumor status, and non-brain METs. Methods and Materials: This was an institutional review board-approved, retrospective cohort study using our database of 269 consecutive female breast cancer patients (mean age, 55 years; range, 26-86 years) who underwent Gamma Knife radiosurgery (GKRS) alone, without whole-brain radiation therapy, for brain METs during the 15-year period between 1996 and 2011. The Kaplan-Meier method was used to estimate the absolute risk of each event. Results: Kaplan-Meier plots of our patient series showed statistically significant survival differences among patients stratified into 3, 4, or 5 groups based on the 3 systems (P<.001). However, the mean survival time (MST) differences between some pairs of groups failed to reach statistical significance with all 3 systems. Thus, we attempted to regrade our 269 breast cancer patients into 3 groups by modifying our aforementioned index along with the original RPA class I and III, (ie, RPA I+II-a, II-b, and II-c+III). There were statistically significant MST differences among these 3 groups without overlap of 95% confidence intervals (CIs) between any 2 pairs of groups: 18.4 (95% CI = 14.0-29.5) months in I+II-a, 9.2 in II-b (95% CI = 6.8-12.9, P<.001 vs I+II-a) and 5.0 in II-c+III (95% CI = 4.2-6.8, P<.001 vs II-b). Conclusions: As none of the new grading systems, DS-GPS, BC-RPA and our system, was applicable to our set of radiosurgically treated patients for comparing survivals after GKRS, we slightly modified our system for breast cancer patients.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2020

Initial Implementation Of Machine Learning System for SRF Cavity Fault Classification at CEBAF

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory is high power CW electron accelerator. It uses a mixture of two types of SRF modules, older, lower energy C20/C50 modules and newer higher energy C100 modules, arrayed in two anti-parallel linear accelerators. Accurately classifying the type of cavity faults is essential to maintaining and improving accelerator performance. Each C100 cryomodule contains eight 7-cell cavities. When a cavity fault occurs within a cryomodule, all eight cavities generate 17 waveforms each containing 8192 points. This data is exported from EPICS and saved for review. Analysis of these waveforms is time intensive and requires subject matter expertise (SME). SMEs examine the data from each event and label it according to one of several known cavity fault types. Multiple machine learning models have been developed on this labeled dataset with sufficient performance to warrant the creation of a limited machine learning software system for use by accelerator operations staff. This paper discusses the transition from model development to implementation of a prototype system.

Carpenter, A.↗

NASA NTRS1992

Aerosols and polar stratospheric clouds measurements during the EASOE campaign

Preliminary results of observations performed using two different lidar systems during the EASOE (European Arctic Stratospheric Ozone Experiment), which has taken place in the winter of 1991-1992 in the northern hemisphere lattitude regions, are presented. The first system is a ground based multiwavelength lidar intended to perform measurements of the ozone vertical distribution in the 5 km to 40 km altitude range. It was located in Sodankyla (67 degrees N, 27 degrees E) as part of the ELSA experiment. The objectives of the ELSA cooperative project is to study the relation between polar stratospheric cloud events and ozone depletion with high vertical resolution and temporal continuity, and the evolution of the ozone distribution in relation to the position of the polar vortex. The second system is an airborne backscatter lidar (Leandre) which allows for the study of the 3-D structure and the optical properties of polar stratospheric clouds. The Leandre instrument is a dual-polarization lidar system, emitting at 532 nm, which allows for the determination of the type of clouds observed, according to the usual classification of polar stratospheric clouds. More than 60 hours of flight were performed in Dec. 1991, and Jan. and Feb. 1992 in Kiruna, Sweden. The operation of the Leandre instrument has led to the observation of the short scale variability of the Pinatubo volcanic cloud in the high latitude regions and to several episodes of polar stratospheric clouds. Preliminary analysis of the data is presented.

Haner, D.↗

DOE OSTI2020

A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics

A general agreement in psycholinguistics claims that syntax and meaning are unified precisely and very quickly during online sentence processing. Although several theories have advanced arguments regarding the neurocomputational bases of this phenomenon, we argue that these theories could potentially benefit by including neurophysiological data concerning cortical dynamics constraints in brain tissue. In addition, some theories promote the integration of complex optimization methods in neural tissue. In this paper we attempt to fill these gaps introducing a computational model inspired in the dynamics of cortical tissue. In our modeling approach, proximal afferent dendrites produce stochastic cellular activations, while distal dendritic branches–on the other hand–contribute independently to somatic depolarization by means of dendritic spikes, and finally, prediction failures produce massive firing events preventing formation of sparse distributed representations. The model presented in this paper combines semantic and coarse-grained syntactic constraints for each word in a sentence context until grammatically related word function discrimination emerges spontaneously by the sole correlation of lexical information from different sources without applying complex optimization methods. By means of support vector machine techniques, we show that the sparse activation features returned by our approach are well suited—bootstrapping from the features returned by Word Embedding mechanisms—to accomplish grammatical function classification of individual words in a sentence. In this way we develop a biologically guided computational explanation for linguistically relevant unification processes in cortex which connects psycholinguistics to neurobiological accounts of language. We also claim that the computational hypotheses established in this research could foster future work on biologically-inspired learning algorithms for natural language processing applications.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

An islanding detection methodology combining decision trees and Sandia frequency shift for inverter-based distributed generations

Distributed generations (DGs) for grid-connected applications require an accurate and reliable islanding detection methodology (IDM) for secure system operation. This paper presents an IDM for grid-connected inverter-based DGs. The proposed method is a combination of passive and active islanding detection techniques for aggregation of their advantages and elimination/minimisation of the drawbacks. In the proposed IDM, the passive method utilises critical system attributes extracted from local voltage measurements at target DG locations as well as employs decision tree-based classifiers for characterisation and detection of islanding events. The active method is based on Sandia frequency shift technique and is initiated only when the passive method is unable to differentiate islanding events from other system events. Thus, the power quality degradation introduced into the system by active islanding detection techniques can be minimised. Furthermore, a combination of active and passive techniques allows detection of islanding events under low power mismatch scenarios eliminating the disadvantage associated with the use of passive techniques alone. Finally, detailed case study results demonstrate the effectiveness of the proposed method in detection of islanding events under various power mismatch scenarios, load quality factors and in the presence of single or multiple grid-connected inverter-based DG units.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2012

MULTIMOMENT RADIO TRANSIENT DETECTION

We present a multimoment technique for signal classification and apply it to the detection of fast radio transients in incoherently dedispersed data. Specifically, we define a spectral modulation index in terms of the fractional variation in intensity across a spectrum. A signal whose intensity is distributed evenly across the entire band has a lower modulation index than a spectrum whose intensity is localized in a single channel. We are interested in broadband pulses and use the modulation index to excise narrowband radio frequency interference by applying a modulation index threshold above which candidate events are removed. The technique is tested both with simulations and using data from known sources of radio pulses (RRAT J1928+15 and giant pulses from the Crab pulsar). The method is generalized to coherent dedispersion, image cubes, and astrophysical narrowband signals that are steady in time. We suggest that the modulation index, along with other statistics using higher order moments, should be incorporated into signal detection pipelines to characterize and classify signals.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2013

SPECTROSCOPIC OBSERVATIONS OF SN 2012fr: A LUMINOUS, NORMAL TYPE Ia SUPERNOVA WITH EARLY HIGH-VELOCITY FEATURES AND A LATE VELOCITY PLATEAU

We present 65 optical spectra of the Type Ia SN 2012fr, 33 of which were obtained before maximum light. At early times, SN 2012fr shows clear evidence of a high-velocity feature (HVF) in the Si II {lambda}6355 line that can be cleanly decoupled from the lower velocity ''photospheric'' component. This Si II {lambda}6355 HVF fades by phase -5; subsequently, the photospheric component exhibits a very narrow velocity width and remains at a nearly constant velocity of {approx}12,000 km s{sup -1} until at least five weeks after maximum brightness. The Ca II infrared triplet exhibits similar evidence for both a photospheric component at v Almost-Equal-To 12,000 km s{sup -1} with narrow line width and long velocity plateau, as well as an HVF beginning at v Almost-Equal-To 31,000 km s{sup -1} two weeks before maximum. SN 2012fr resides on the border between the ''shallow silicon'' and ''core-normal'' subclasses in the Branch et al. classification scheme, and on the border between normal and high-velocity Type Ia supernovae (SNe Ia) in the Wang et al. system. Though it is a clear member of the ''low velocity gradient'' group of SNe Ia and exhibits a very slow light-curve decline, it shows key dissimilarities with the overluminous SN 1991T or SN 1999aa subclasses of SNe Ia. SN 2012fr represents a well-observed SN Ia at the luminous end of the normal SN Ia distribution and a key transitional event between nominal spectroscopic subclasses of SNe Ia.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1973

Apollo 16 rocks - Classification and petrogenetic model

The Apollo 16 rocks include cataclastic anorthosites, two varieties of unequilibrated breccia, two varieties of partly to fully equilibrated breccia, and a sequence of partially melted breccias. The latter, which dominate the Apollo 16 collection, include glass, divitrified glass, mesostasis-olivine-plagioclase rock, mesostasis-rich basalt, basalt, and poikilitic rocks. All sequence members contain vesicles and relics of plagioclase, olivine, pink spinel, and lithic fragments. Their equilibrated matrices define a series from glass, to a plagioclase-olivine-mesostasis assemblage displaying spherulitic and skeletal shaped crystals, through a plagioclase-pyroxene-olivine assemblage displaying euhedral shaped crystals. Such data suggest that the sequence lithologies were derived from breccias or soils that were partially melted in an impact event.

Warner, J. L.↗

DOE OSTI2026

Protection System Validation with Machine Learning Anomaly Classification

A poster for the Early Career Poster Session. Power system protection devices have transitioned over the past few decades from mechanical to analog devices, then to solid state and finally digital. Relays and their associated critical network of equipment have significantly increased in complexity. Even internally, relays have gained significant intricacy, with relatively simple overcurrent or differential functions now being assisted by a myriad of other functions. This is necessary as the grid becomes more complex, but it brings increased difficulty in monitoring and upkeep. Misoperation caused by improper relay settings or malicious actions is a constant challenge faced by all utilities. These improper settings can be difficult to identify and may require exhaustive post-mortem analysis, typically after a major outage event has already occurred. A mechanism is needed for monitoring the behavior of protection systems to validate that they act and perform as expected. This work presents a concept for a machine learning (ML) system capable of validating the performance of protection systems by classifying anomalous events and characterizing protection system responses based solely on available current and voltage measurements. As a first step in its development, an experimental dataset is generated, and a random forest model is implemented with high accuracy in distinguishing four power system scenarios.

24 - POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Power quality disturbances diagnosis: A 2D densely connected convolutional network framework

The fast and accurate diagnosis of power quality disturbances (PQD) aids in avoiding shutdowns and unnecessary procedures, concerning electric energy distribution systems. As such, a number of techniques have been tested and applied in order to reach this objective. Majority of the techniques applied are two-step based. On the first step, power quality disturbances features are extracted. Second step, considering features extracted, disturbance classification is implemented. Recently, relevant literature has presented data-driven signal processing-based approaches, as deep convolutional neural networks (DCNN), which can implement both processing steps while providing automated recognition of patterns and outliers in data. However, not considered by state-of-art, power quality disturbances are evolving in nature, while all possible regularities might not be represented in the dataset. In this work a 2 Dimension Densely Connected Convolutional Network (2D-DenseNet) framework is presented. Further, a case study with synthetic disturbance events are analyzed. Easy-to-implement formulation, built on the 2D-DenseNet, without hard-to-design parameters, highlight potential aspects for real-life implementation.

42 ENGINEERING↗

DOE OSTI2021

Anomaly Detection, Localization and Classification using Drifting Synchrophasor Data Streams

With ongoing automation and digitization of the electric power system, several Phasor Measurement Units(PMUs) have been deployed for monitoring and control. PMU data can have multiple anomalies, and many of the researchers in the past have concentrated on training machine/deep learning algorithms offline for anomaly detection over PMU data (i.e., not in real time). These machine/deep learning algorithms, when trained offline on a sample rather than a population of the dataset, fail to consider the dynamic behavior of the power grid in real-time, resulting in low accuracy. Considering the dynamic behavior of the power grid (e.g., change in load, generation, distributed energy resources (DERs) switching, network, controls), the definition of data anomalies varies in time and requires online training. A fundamental challenge is to enable online (i.e., real-time) training of machine/deep learning algorithms for anomaly detection over streaming PMU data. While machine/deep learning is often desirable to manage data streams, training a deep learning algorithm over streaming PMU data is nontrivial due to changes in data statistics caused by dynamic streaming data. This paper proposes PMUNET: a novel device-level deep learning-based data-driven approach for anomaly detection, localization, and classification over streaming PMU data, using online learning and multivariate data-drift detection algorithm .Two variants of PMUNET, Dynamic data Change Driven Learning (DCDL) and Continuity Driven Learning (CDL), are proposed and compared. DCDL aims to train the deep learning algorithm whenever the definition of anomaly changes due to the power grid dynamics. On the other hand, CDL continuously trains the deep learning algorithm over the PMU data-stream. The experimental results verify that DCDL outperforms CDL and other efficient anomaly detection methods over multiple events such as faults and load/ generator/capacitor/DERs variations/switching for IEEE 14 and 39 Bus test system as well as real PMU industrial data. The result verifies that DCDL variant of PMUNET improves over existing approach with a gain of 2% - 10% in terms of accuracy, false-positive rate, and false-negative rate.

adversarial deep learning↗

NASA NTRS2010

Recent Results on SNRs and PWNe from the Fermi Large Area Telescope

Topics include: Fermi LAT Collaboration groups; galactic results from LAT; a GeV, wide-field instrument; the 1FGL catalog, the Fermi LAT 1FGL source catalog, unidentified gamma-ray sources; variability in 1FGL sources; curvature in 1FGL sources; spectral-variability classification; pulsars and their wind nebulae; gamma-ray pulsars and MSPs; GeV PWN search; Crab pulsar and nebula; Vela X nebular of Vela pulsar; MSH 15-52; supernova remnants, resolved GeV sources, galactic transients, LAT unassociated transient detections; gamma rays from a nova; V407 Cyngi - a symbiotic nova; V407 Cygni: a variable star; and March 11 - a nova. Summary slides include pulsars everywhere, blazars, LAT as an electron detector, cosmic ray spectrum, the Large Area Telescope, the Fermi Observatory, LAT sensitivity with time, candidate gamma-ray events, on-orbit energy calibration and rate, a 1 year sky map, LAT automated science processing, reported GeV flares, early activity and spectacular flare, gamma-ray transients near the galactic plane , two early unassociated transients, counter part search - Fermi J0910-5404; counterpart search 3EG J0903-3531, and a new LAT transient - J1057-6027.

Hays, Elizabeth↗

NASA NTRS2017

A Model-Based, Bayesian Solution for Characterization of Complex Damage Scenarios in Aerospace Composite Structures

Ultrasonic damage detection and characterization is commonly used in nondestructive evaluation (NDE) of aerospace composite components. In recent years there has been an increased development of guided wave based methods. In real materials and structures, these dispersive waves result in complicated behavior in the presence of complex damage scenarios. Model-based characterization methods utilize accurate three dimensional finite element models (FEMs) of guided wave interaction with realistic damage scenarios to aid in defect identification and classification. This work describes an inverse solution for realistic composite damage characterization by comparing the wavenumber-frequency spectra of experimental and simulated ultrasonic inspections. The composite laminate material properties are first verified through a Bayesian solution (Markov chain Monte Carlo), enabling uncertainty quantification surrounding the characterization. A study is undertaken to assess the efficacy of the proposed damage model and comparative metrics between the experimental and simulated output. The FEM is then parameterized with a damage model capable of describing the typical complex damage created by impact events in composites. The damage is characterized through a transdimensional Markov chain Monte Carlo solution, enabling a flexible damage model capable of adapting to the complex damage geometry investigated here. The posterior probability distributions of the individual delamination petals as well as the overall envelope of the damage site are determined.

H. Reed↗

DOE OSTI2017

Proton Track Identication in MicroBooNE Simulation for Neutral Current Elastic Events

The net contribution of the strange quark spins to the proton spin, Δs, can be determined from neutral current elastic neutrino-proton interactions at low momentum transfer combined with data from electron-proton scattering. The probability of neutrino-proton interactions depends in part on the axial form factor, which represents the spin structure of the proton and can be separated into its quark flavor contributions. Low momentum transfer neutrino neutral current interactions can be measured in MicroBooNE, a high-resolution liquid argon time projection chamber (LArTPC) in its first year of running in the Booster Neutrino Beamline at Fermilab. The signal for these interactions in MicroBooNE is a single short proton track. We present our work on the automated reconstruction and classification of proton tracks in LArTPCs, an important step in the determination of neutrinonucleon cross sections and the measurement of Δs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS2000

Three Conservation Applications of Astronaut Photographs of Earth: Tidal Flat Loss (Japan), Elephant Impacts on Vegetation (Botswana), and Seagrass and Mangrove Monitoring (Australia)

NASA photographs taken from low Earth orbit can provide information relevant to conservation biology. This data source is now more accessible due to improvements in digitizing technology, Internet file transfer, and availability of image processing software. We present three examples of conservation-related projects that benefited from using orbital photographs. (1) A time series of photographs from the Space Shuttle showing wetland conversion in Japan was used as a tool for communicating about the impacts of tidal flat loss. Real-time communication with astronauts about a newsworthy event resulted in acquiring current imagery. These images and the availability of other high resolution digital images from NASA provided timely public information on the observed changes. (2) A Space Shuttle photograph of Chobe National Park in Botswana was digitally classified and analyzed to identify the locations of elephant-impacted woodland. Field validation later confirmed that areas identified on the image showed evidence of elephant impacts. (3) A summary map from intensive field surveys of seagrasses in Shoalwater Bay, Australia was used as reference data for a supervised classification of a digitized photograph taken from orbit. The classification was able to distinguish seagrasses, sediments and mangroves with accuracy approximating that in studies using other satellite remote sensing data. Orbital photographs are in the public domain and the database of nearly 400,000 photographs from the late 1960s to the present is available at a single searchable location on the Internet. These photographs can be used by conservation biologists for general information about the landscape and in quantitative applications.

Lulla, Kamlesh P.↗

DOE OSTI2025

A Novel Machine Learning Workflow to Classify Mobile Home Parks at Scale

Understanding the built environment is essential to the overall study of population dynamics, grid infrastructure, emergency response, among others. In the United States there are multiple classifications for buildings within the built environment such as residential, signifying family homes while commercial buildings consist of apartments or larger structures which are multi-purpose. While there is a high level of understanding of where these aforementioned structures are located, there is a third class of structures, mobile home parks (MHP) which have been under-represented in the literature despite there being an estimated 2.7 million of them within the United States. Research has shown that individuals who reside in MHP are at higher risk to extreme events due to their location and structural integrity of residence. Attention must now turn to identifying MHP at scale to help first responders and policy makers understand where these at risk populations reside. To address for this gap, we develop a novel methodology to infer MHP at scale based off morphologies derived at a building level. Here, we show that across 3 million buildings in 6 states within the United States it is possible to identify MHP with 83% accuracy. This novel approach to identify MHP from other structures within the built environment using a machine learning approach provides a new tool to leverage in relation to helping at-risk populations.

Stipek, Clinton [Oak Ridge National Laboratory (OR↗