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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.

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At least 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

Host-Based Data Exfiltration Detection via System Call Sequences

The host-based detection of malicious data exfiltration activities is currently a sparse area of research and mostly limited to methods that analyze network traffic or signature based detection methods that target specific processes. In this paper we explore an alternative method to host-based detection that exploits sequences of system calls and new collection methods that allow us to catch these activities in real time. We show that system calls sequences can be found to reach a steady state across processes and users, and explore the viability of new methods as heuristics for profiling user behaviors.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2011

A Components Database Design and Implementation for Accelerators and Detectors

Many accelerator and detector systems being fabricated for the PEP-II Accelerator and BABAR Detector needed configuration control and calibration measurements tracked for their components. Instead of building a database for each distinct system, a Components Database was designed and implemented that can encompass any type of component and any type of measurement. In this paper we describe this database design that is especially suited for the engineering and fabrication processes of the accelerator and detector environments where there are thousands of unique component types. We give examples of information stored in the Components Database, which includes accelerator configuration, calibration measurements, fabrication history, design specifications, inventory, etc. The World Wide Web interface is used to access the data, and templates are available for international collaborations to collect data off-line.

43 PARTICLE ACCELERATORS↗
DOE OSTI2012

Interactive Visual Analysis of High Throughput Text Streams

The scale, velocity, and dynamic nature of large scale social media systems like Twitter demand a new set of visual analytics techniques that support near real-time situational awareness. Social media systems are credited with escalating social protest during recent large scale riots. Virtual communities form rapidly in these online systems, and they occasionally foster violence and unrest which is conveyed in the users language. Techniques for analyzing broad trends over these networks or reconstructing conversations within small groups have been demonstrated in recent years, but state-of- the-art tools are inadequate at supporting near real-time analysis of these high throughput streams of unstructured information. In this paper, we present an adaptive system to discover and interactively explore these virtual networks, as well as detect sentiment, highlight change, and discover spatio- temporal patterns.

sentiment analysis↗
DOE OSTI2017

Methane trace-gas sensing enabled by silicon photonic integration

Due to the high radiative forcing of methane, fugitive natural gas leaks pose a significant challenge to the near-term environmental viability of oil and gas extraction. Reducing the subsequent environmental impact requires cost-effective sensor nodes for reliable, rapid, and continuous identification of extraneous methane emissions. The efficacy of laser spectroscopy has been widely demonstrated in both environmental and medical applications due to its sensitivity and specificity to the target analyte. However, the present cost and the lack of manufacturing scalability of free-space optical systems can limit their viability for economical wide-area sensor networks in localized leak detection applications. In this paper, we will review the development and performance of a cost-effective silicon photonic sensing platform. This platform uses silicon photonic waveguide and packaging integration technologies to enable on-chip evanescent field spectroscopy of methane.

03 NATURAL GAS↗
DOE OSTI2018

Target of Opportunity Observations of Gravitational Wave Events with LSST

The discovery of the electromagnetic counterparts to the binary neutron star merger GW170817 has opened the era of GW+EM multi-messenger astronomy. Exploiting this breakthrough requires increasing samples to explore the diversity of kilonova behaviour and provide more stringent constraints on the Hubble constant, and tests of fundamental physics. LSST can play a key role in this field in the 2020s, when the gravitational wave detector network is expected to detect higher rates of merger events involving neutron stars ($\sim$10s per year) out to distances of several hundred Mpc. Here we propose comprehensive target-of-opportunity (ToOs) strategies for follow-up of gravitational-wave sources that will make LSST the premiere machine for discovery and early characterization for neutron star mergers and other gravitational-wave sources.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Target of Opportunity Observations of Gravitational Wave Events with LSST

The discovery of the electromagnetic counterparts to the binary neutron star merger GW170817 has opened the era of GW+EM multi-messenger astronomy. Exploiting this breakthrough requires increasing samples to explore the diversity of kilonova behaviour and provide more stringent constraints on the Hubble constant, and tests of fundamental physics. LSST can play a key role in this field in the 2020s, when the gravitational wave detector network is expected to detect higher rates of merger events involving neutron stars ($\sim$10s per year) out to distances of several hundred Mpc. Here we propose comprehensive target-of-opportunity (ToOs) strategies for follow-up of gravitational-wave sources that will make LSST the premiere machine for discovery and early characterization for neutron star mergers and other gravitational-wave sources.

Margutti, R↗
DOE OSTI2021

Design and Performance of a Smartphone-based Cosmic Ray Observatory

This dissertation examines the feasibility of appropriating the global network of smartphones as an observatory for ultra-high-energy cosmic rays. An application and cloud-based data-acquisition system are first proposed for such an observatory, in which heterogenous devices self-calibrate and server feedback optimizes individual device triggers. Methods of monitoring and manually controlling this network are also examined. Detection efficiencies for cosmic muons, MeV-scale gamma rays, and 120 GeV protons are then measured for this trigger, with which a Monte Carlo pixel model is fine-tuned. Extending this model to cosmic ray showers, the effective area of the observatory for super-GZK primaries is shown to equal that of the Pierre Auger Observatory with a global participation rate under 0.1%, far below initial estimates presented in Ref. [83]. A vastly improved sensitivity to photons and more precise modeling of electrons are primarily responsible for this discrepancy, though more refined models of the combinatorial background may yet lead to more stringent requirements.

Swaney, Jeff↗
DOE OSTI2015

Energy reconstruction in a highly granularity semi-digital hadronic calorimeter

A semi-digital hadronic calorimeter using Glass Resistive Plate Chambers (GRPCs) is one of the calorimeters candidates proposed for particle physics experiments at the future electrons collider. It is a high granular calorimeter which is required for application of the particle flow algorithm in order to improve the jet energy resolution as one of the goals of this experiments. We discussed the energy reconstruction, based on digital and semi-Digital methods, to study the effect on the improvement of the single particle energy resolution and the linearity of the detector response. This study was performed with the GEANT4 simulation. Results on the energy resolution and linearity, for negative pions over an energy range from 1 to 100 GeV are presented and compared with different energy reconstruction methods including Artificial Neural Networks. (authors)

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Unveiling Hidden Lyman Alpha Emitters in the DESI DR1 Data

We present an automatic method based on machine-learning convolutional neural network (CNN) architecture to detect Lyman alpha emitters (LAE) hidden in the Data Release 1 spectroscopic dataset of the Dark Energy Spectroscopic Instrument (DESI). Those LAEs mostly have incorrect redshift estimations because the current DESI pipeline is not designed to detect and measure the redshifts of galaxies at $z>2$. To uncover those sources, we first visually inspect thousands of DESI spectra and construct a sample, consisting of both LAEs and non-LAEs, for training and testing the CNN-based model to (1) detect LAEs in DESI spectra and (2) determine their Ly$α$ redshifts. The final model yields $95.2\%$ purity and $95.9\%$ completeness for detecting LAEs. We apply this model to approximately $2\times10^{6}$ spectra of sources targeted as emission-line galaxies and detect 19,685 LAEs from $z\sim2$ to $3.5$ within 12 minutes with a single GPU, illustrating the high efficiency of this model for identifying LAEs. The detected LAEs are mostly at the bright end of the luminosity function with Ly$α$ luminosity $L_{\rm Lyα} \gtrsim 10^{43}$ erg/s. The high signal-to-noise composite spectrum of the detected LAEs further shows various spectral features, including P-Cygni profiles of metal lines and MgII emission lines, possible indicators of Lyman continuum escape fraction, revealing the rich astrophysical information in this LAE sample. Finally, this sample can be used to train and validate the pipelines for redshift determination of LAEs for the preparation of the DESI-II survey.

Chan, Jui-Kuan [Taiwan, Natl. Taiwan U.] (ORCID:00↗
NASA NTRS1985

A second catalog of gamma ray bursts: 1978 - 1980 localizations from the interplanetary network

Eighty-two gamma ray bursts were detected between 1978 September 14 and 1980 February 13 by the experiments of the interplanetary network (Prognoz 7, Venera 11 and 12 SIGNE experiments, Pioneer Venus Orbiter, International Sun-Earth Explorer 3, Helios 2, and Vela). Sixty-five of these events have been localized to annuli or error boxes by the method of arrival time analysis. The distribution of sources is consistent with isotropy, and there is no statistically convincing evidence for the detection of more than one burst from any source position. The localizations are compared with those of two previous catalogs.

Atteia, J. L.↗
DOE OSTI2021

Detecting False Data Injection Attacks in Smart Grids: A Semi-Supervised Deep Learning Approach

The dependence on advanced information and communication technology increases the vulnerability in smart grids under cyber-attacks. Recent research on unobservable false data injection attacks (FDIAs) reveals the high risk of secure system operation, since these attacks can bypass current bad data detection mechanisms. To mitigate this risk, this paper proposes a data-driven learning-based algorithm for detecting unobservable FDIAs in distribution systems. We use autoencoders for efficient dimension reduction and feature extraction of measurement datasets. Further, we integrate the autoencoders into an advanced generative adversarial network (GAN) framework, which successfully detects anomalies under FDIAs by capturing the unconformity between abnormal and secure measurements. Also, considering that the datasets collected from practical power systems are partially labeled due to expensive labeling costs and missing labels, the proposed method only requires a few labeled measurement data in addition to unlabeled data for training. Numerical simulations in three-phase unbalanced IEEE 13-bus and 123-bus distribution systems validate the detection accuracy and efficiency of this method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2010

Real-time nonlinear correction of back-focal-plane detection in optical tweezers

Photodiode based detection of laser trapped beads using forward scattered light is a frequently employed technique for position measurement. There is a nonlinear relationship between photodiode outputs and bead position but for small displacements linear approximation holds well. Traditionally, the nonlinearity is compensated by normalizing the photodiode's position signal with the intensity signal and then using a polynomial fit in the range where voltage to position mapping is one to one. In this article, this range is extended by using the intensity signal as an independent input along with the two position signals. A map from the input signals to the actual position values is obtained. This mapping is one-to-one for a larger range that results in an increased detection range. An artificial neural network that facilitates implementation is employed for this purpose. This scheme is implemented on a Field Programmable Gate Array based data acquisition and control hardware with closed loop bandwidth of 50 kHz. Detection of the order of 350 nm from the center of detection laser is demonstrated for a 500 nm diameter bead compared to 180 nm achieved by a polynomial fit.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1994

Neural network classification of clinical neurophysiological data for acute care monitoring

The purpose of neurophysiological monitoring of the 'acute care' patient is to allow the accurate recognition of changing or deteriorating neurological function as close to the moment of occurrence as possible, thus permitting immediate intervention. Results confirm that: (1) neural networks are able to accurately identify electroencephalogram (EEG) patterns and evoked potential (EP) wave components, and measuring EP waveform latencies and amplitudes; (2) neural networks are able to accurately detect EP and EEG recordings that have been contaminated by noise; (3) the best performance was obtained consistently with the back propagation network for EP and the HONN for EEG's; (4) neural network performed consistently better than other methods evaluated; and (5) neural network EEG and EP analyses are readily performed on multichannel data.

Sgro, Joseph↗
DOE OSTI2018

Situ/FASGuard: Combining Anomaly Detection with Signature Generation

Despite the best efforts of cyber security analysts, networked computing assets are regularly compromised, resulting in the loss of intellectual property, the disclosure of state secrets, and financial damages in the billions. A 2014 report from the Center for Strategic and International Studies estimated the global cost of cyber crime at $\$$400 billion annually. There has also been a rise of sophisticated attack groups that continually develop novel methods of penetrating networks that current technologies are typically unable to detect.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Artificial Neural Network as the FPGA Trigger in the Cyclone V based Front-End for a Detection of Neutrino-Origin Showers

Neutrinos play a fundamental role in the understanding of the origin of ultra-high-energy cosmic rays. They interact through charged and neutral currents in the atmosphere generating extensive air showers. However, their a very low rate of events potentially generated by neutrinos is a significant challenge for a detection technique and requires both sophisticated algorithms and high-resolution hardware. A trigger based on a artificial neural network was implemented into the Cyclone{sup R} V E FPGA 5CEFA9F31I7 - the heart of the prototype Front-End boards developed for tests of new algorithms in the Pierre Auger surface detectors. Showers for muon and tau neutrino initiating particles on various altitudes, angles and energies were simulated in CORSICA and Offline platforms giving pattern of ADC traces in Auger water Cherenkov detectors. The 3-layer 12-8-1 neural network was taught in MATLAB by simulated ADC traces according the Levenberg-Marquardt algorithm. Results show that a probability of a ADC traces generation is very low due to a small neutrino cross-section. Nevertheless, ADC traces, if occur, for 1-10 EeV showers are relatively short and can be analyzed by 16-point input algorithm. We optimized the coefficients from MATLAB to get a maximal range of potentially registered events and for fixed-point FPGA processing to minimize calculation errors. New sophisticated triggers implemented in Cyclone{sup R} V E FPGAs with large amount of DSP blocks, embedded memory running with 120 - 160 MHz sampling may support a discovery of neutrino events in the Pierre Auger Observatory. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Intrusion Detection for Cybersecurity of Power Electronics Dominated Grids: Inverters PQ Set-Points Manipulation

This work provides cybersecurity analytics for a high photovoltaic (PV) penetrated distribution network, representing future power electronics dominated grid (PEDG). Firstly, the impact of active and reactive power (PQ) set-points manipulation on the network is studied. Then, an intrusion detection system (IDS) is developed for identifying the potentially compromised PV inverters in the network. The proposed IDS is based on defining the normal, safe, and abnormal operation regions of the PV inverters from point of view of the steady state voltage stability of the network. These three-operation regions are identified by utilizing active power, reactive power, and voltage (PQV) limits of each specific grid-following inverter in the network. Each grid-following inverter's PQV contour includes the information of network topology, inverter ratings, and inverter controller. The developed PQ limits are integrated into the centralized secondary control layer for realization of the proposed IDS. Furthermore, the proposed secondary control layer is capable of providing remedial actions during an anomaly event to enhance the grid resiliency. The theoretical analyses are verified by several attack scenarios for a network of grid-following inverters.

41 EE - Solar Energy Technologies Office (EE-4S)↗