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At least 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Comparison of source-location algorithms for atmospheric samplers

Numerous algorithms have been developed to determine the source characteristics for an atmospheric release, e.g., (Bieringer et al., 2017). This study compares three models that have been applied to the data collected by the International Monitoring System operated by the Comprehensive Nuclear-Test-Ban Treaty Preparatory Commission to estimate source event parameters. Each model uses a different approach to estimate the parameters. A deterministic model uses a possible source region (PSR) approach (Ringbom et al., 2014) that is based on the correlation between predicted and measured sample values. An unnamed model developed at Pacific Northwest National Laboratory (herein called BAYEST) uses a Bayesian formulation (Eslinger et al., 2019; Eslinger et al., 2020; Eslinger and Schrom, 2016). The FREAR model uses a different Bayesian formulation (De Meutter and Hoffman, 2020; De Meutter et al., 2021; Pieter De Meutter, 2021). The performance of the three source-location models is evaluated with 100 synthetic release cases for the single xenon isotope, 133 Xe. The release cases resulted in detections in a fictitious network with 120 noble gas samplers. All three source-location models use the same sampling data. The two Bayesian models yield more accurate location estimates than the deterministic PSR model, with FREAR having slightly better location performance than BAYEST. Samplers with collection periods of 3, 6, 8, 12, and 24-h were used. Results from BAYEST show that location accuracy improves with each reduction in sample collection length. The BAYEST model is slightly better for estimating the start time of the release. The PSR model has about the same spread in start times as the FREAR model, but the PSR results have a better average start time. The Bayesian source-location algorithms give more accurate results than the PSR approach, and provide release magnitude estimates, while the base PSR model does not estimate the release magnitude. This investigation demonstrates that a reasonably dense sampling grid will sometimes yield poor location and time estimates regardless of the model. The poor estimates generally coincide with cases where there is a much larger distance between the release point and the first detecting sampler than the average sampler spacing.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2016

Automatic Distribution Network Reconfiguration: An Event-Driven Approach

This paper proposes an event-driven approach for reconfiguring distribution systems automatically. Specifically, an optimal synchrophasor sensor placement (OSSP) is used to reduce the number of synchrophasor sensors while keeping the whole system observable. Then, a wavelet-based event detection and location approach is used to detect and locate the event, which performs as a trigger for network reconfiguration. With the detected information, the system is then reconfigured using the hierarchical decentralized approach to seek for the new optimal topology. In this manner, whenever an event happens the distribution network can be reconfigured automatically based on the real-time information that is observable and detectable.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2010

Developing Empirical Lightning Cessation Forecast Guidance for the Cape Canaveral Air Force Station and Kennedy Space Center

This research addresses the 45th Weather Squadron's (45WS) need for improved guidance regarding lightning cessation at Cape Canaveral Air Force Station and Kennedy Space Center (KSC). KSC's Lightning Detection and Ranging (LDAR) network was the primary observational tool to investigate both cloud-to-ground and intracloud lightning. Five statistical and empirical schemes were created from LDAR, sounding, and radar parameters derived from 116 storms. Four of the five schemes were unsuitable for operational use since lightning advisories would be canceled prematurely, leading to safety risks to personnel. These include a correlation and regression tree analysis, three variants of multiple linear regression, event time trending, and the time delay between the greatest height of the maximum dBZ value to the last flash. These schemes failed to adequately forecast the maximum interval, the greatest time between any two flashes in the storm. The majority of storms had a maximum interval less than 10 min, which biased the schemes toward small values. Success was achieved with the percentile method (PM) by separating the maximum interval into percentiles for the 100 dependent storms.

LDAR (LIGHTNING DETECTION AND RANGING)↗
DOE OSTI2013

An Evaluation of Machine Learning Methods to Detect Malicious SCADA Communications

Critical infrastructure Supervisory Control and Data Acquisition (SCADA) systems were designed to operate on closed, proprietary networks where a malicious insider posed the greatest threat potential. The centralization of control and the movement towards open systems and standards has improved the efficiency of industrial control, but has also exposed legacy SCADA systems to security threats that they were not designed to mitigate. This work explores the viability of machine learning methods in detecting the new threat scenarios of command and data injection. Similar to network intrusion detection systems in the cyber security domain, the command and control communications in a critical infrastructure setting are monitored, and vetted against examples of benign and malicious command traffic, in order to identify potential attack events. Multiple learning methods are evaluated using a dataset of Remote Terminal Unit communications, which included both normal operations and instances of command and data injection attack scenarios.

critical infrastructure protection↗
DOE OSTI2026

Ensemble Federated Machine Learning‐Based Cybersecurity Situational Awareness in Microgrid Network

Cyber-physical microgrids are vulnerable to stealthy cybersecurity threats that disguise their actions through the exploitation of system knowledge. Such actions can severely impacts microgrids deployed in defense bases, slowing the response time of military forces during national emergencies. Several machine-learning algorithms have been proposed to detect intrusions in the grid networks; however, these traditional machine-learning algorithms lack data privacy and are subject to several adversarial machine-learning threats. This paper proposes a novel federated machine learning (FML)-based three-model framework to detect and identify stealthy data-integrity attacks while ensuring data privacy in microgrid networks. The proposed architecture uses a variational mode decomposition technique to extract derived features from incoming measurement and control datasets. The extraction of these derived features allows FML models to learn minute variations in data patterns that allow them to perform significantly better than the models trained with generic datasets consisting of raw features. Our experimental results show the efficient performance of the proposed methodology against different types of data integrity attacks while considering primary and secondary controllers in microgrids. Further, the applied FML-integrated random forest ensemble algorithm outperforms the existing generic FML algorithms during noisy and noise-free datasets with prediction latencies of only 91–134 µs per sample within the 0.1 s sampling interval and requires communication bandwidth of around ∼8.25 KB/s at the control center and ∼2.7 KB/s per edge client for communication.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2011

Mapping Diffuse Seismicity for Geothermal Reservoir Management with Matched Field Processing: Year 2 Report

Detecting and locating seismic events in managed underground geothermal reservoirs can become increasingly difficult during certain reservoir management operations. For example, during fluid injection procedures, large numbers of microearthquakes frequently occur during a relatively short amount of time. On seismic records, these events can overlap or have poor signal-to-noise ratios (SNR). This can obscure the onset of smaller signals and complicate the picking of seismic phases needed for earthquake location algorithms. To accurately illuminate the active areas of the underground reservoir through time however, a complete earthquake catalog down to very small magnitudes is necessary. To aid in the seismic characterization of reservoir fracture networks, we propose to complement traditional earthquake detection and location techniques with the empirical matched field processing (MFP) method. The principle of the empirical MFP method is to calculate propagation from source to receivers for pre-existing events, also called master templates. Then MFP matches the spatial structure of incoming seismicity observed by a network of sensors to master templates keyed to potential event locations. We demonstrate that empirical MFP can complement existing catalogs and techniques by using archived seismic information to increase earthquake catalog completeness. We identify and construct representative master templates using the Southern California Earthquake Data Center (SCEDC) earthquake catalog and archived waveform data of events originating within the Salton Sea Geothermal Field (SSGF). We identify 231 master events that have good quality seismic records at four or more seismic stations. We apply the empirical MFP method to continuous seismic data collected at the SSGF between November 2009 and December 2010. The MFP method successfully identified 5357 local events, while the original catalog only reported 1536 events. This increase in the number of events allows us to better determine the active fractures of the geothermal reservoir. We model the Coulomb stress change due to events between M2 – M3.5 that occurred during the January 2010 seismic swarm. Our results show a good correlation between regions of increased Coulomb stress change due to double-couple shear events on faults and the locations of subsequent microseismicity. In addition, we investigate the lower event detection magnitude threshold of our surface network using the MFP method. It clearly shows the event detection threshold is extended to include many smaller earthquakes with magnitudes down to M0.0. Therefore, we believe that the empirical MFP method, when combined with conventional methods, significantly improves network detection capabilities.

15 GEOTHERMAL ENERGY↗
DOE OSTI2012

A Dynamically Reconfigurable System for Closed-Loop Measurements of Network Traffic

Streaming network traffic measurement and analysis is critical for detecting and preventing any real-time anomalies in the network. The high speeds and complexity of today's networks, coupled with ever evolving threats, necessitate closing of the loop between measurements and their analysis in real time. The ensuing system demands high levels of programmability and processing where streaming measurements adapt to the changing network behavior in a goal-oriented manner. In this work, we exploit the features and requirements of the problem and develop an application-specific FPGA-based closed-loop measurement (CLM) system. We make novel use of fine-grained partial dynamic reconfiguration (PDR) as underlying reprogramming paradigm, performing low-latency just-in-time compiled logic changes in FPGA fabric corresponding to the dynamic measurement requirements. Our innovative dynamically reconfigurable socket offers 3× logic savings over conventional static solutions, while offering much reduced reconfiguration latencies over conventional PDR mechanisms. Here, we integrate multiple sockets in a highly parallel CLM framework and demonstrate its effectiveness in identifying heavy flows in streaming network traffic. The results using an FPGA prototype offer 100 percent detection accuracy while sustaining increasing link speeds.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Deep Learning-Based Data Forgery Detection in Automatic Generation Control

Automatic Generation Control (AGC) is a key control system in the power grid. It is used to calculate the Area Control Error (ACE) based on frequency and tie-line power flow between balancing areas, and then adjust power generation to maintain the power system frequency in an acceptable range. However, attackers might inject malicious frequency or tie-line power flow measurements to mislead AGC to do false generation correction which will harm the power grid operation. Such attacks are hard to be detected since they do not violate physical power system models. In this work, we propose algorithms based on Neural Network and Fourier Transform to detect data forgery attacks in AGC. Different from the few previous work that rely on accurate load prediction to detect data forgery, our solution only uses the ACE data already available in existing AGC systems. In particular, our solution learns the normal patterns of ACE time series and detects abnormal patterns caused by artificial attacks. Evaluations on the real ACE dataset show that our methods have high detection accuracy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Detection of Isotopes in Urban Source Search Low-Count Gamma Spectra Using Hopfield Neural Networks

Source search campaigns involve measurements of background gamma-ray spectra with a mobile detector-spectrometer traveling along arbitrarily chosen trajectories over a wide screening area. Radiation counts are typically measured with a tellurium-doped sodium iodide [NaI(Tl)] scintillator detector-spectrometer in short acquisition intervals, usually 1 s. The objective is to detect orphan isotopes with half-lives shorter than those of the isotopes in the natural background. In principle, radioisotopes can be identified by their unique gamma emission spectrum. However, detecting orphan isotopes in search data is challenging because low counts measured in short acquisition intervals result in incomplete spectral lines. In this study, we investigate the performance of a Hopfield neural network (HNN) that implements an auto-associative memory for the detection of isotopes of interest in an urban search campaign. The HNN is trained on one example of gamma spectra with well-resolved spectral lines of each isotope of interest. During testing, the auto-associative memory implementation of the HNN processes low-count gamma spectra with partially complete isotopic lines by matching incoming measurements to the closest one of its memory-stored patterns. The testing database consisted of almost 10 000 1-s gamma spectra, including measurements of orphan isotopes 137 Cs, 241 Am, and 131 I, obtained during two urban search surveys with a NaI(Tl) detector. The performance of the HNN detection algorithm was evaluated using precision, recall, and F1 scores, and benchmarked with a multiple linear regression (MLR) identification algorithm. In conclusion, the test results demonstrate that HNN outperforms MLR in the detection of all the isotopes of interest.

Auto associative memory↗
DOE OSTI2020

Modelling Framework For Fire And Smoke Detection In Imagery

This framework was developed to ingest images/video images to train and test artificial neural network structures for image-based detection of fire and smoke. The code includes data preprocessing, model development, and testing. The framework is designed to work with RGB video imagery.

Griffel, LloydM.↗
DOE OSTI2024

Continuous automatic polarization channel stabilization from heterodyne detection of coexisting dim reference signals

Quantum networking continues to encode information in polarization states due to ease and precision. The variable environmental polarization transformations induced by deployed fiber need correction for deployed quantum networking. Here, we present a method for automatic polarization compensation (APC) and demonstrate its performance on a metropolitan quantum network. Designing an APC involves many design decisions as indicated by the diversity of previous solutions in the literature. Our design leverages heterodyne detection of wavelength-multiplexed dim classical references for continuous high-bandwidth polarization measurements used by newly developed multi-axis (non-)linear control algorithm(s) for complete polarization channel stabilization with no downtime. This enables continuous relatively high-bandwidth correction without significant added noise from classical reference signals. We demonstrate the performance of our APC using a variety of classical and quantum characterizations. Finally, we use C-band and L-band APC versions to demonstrate continuous high-fidelity entanglement distribution on a metropolitan quantum network with an average relative fidelity of 0.94 ± 0.03 for over 30 hrs.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Utilization of the Critic Subnetwork of a Generative Adversarial Network as Detector of Morphological Material Change in Image Data

The resolution of computed tomography (CT) has become high enough to monitor morphological changes due to aging in materials in long-term applications. For this work, we explored the utility of the critic of a generative adversarial network (GAN) to automatically detect such changes. The GAN was trained with images of pristine Pharmatose, which is used as a surrogate energetic material. It is important to note that images of the material with altered morphology were only used during the test phase. The GAN-generated images reproduced the microstructure of Pharmatose well, although some unrealistic particle fusion was seen. Calculated morphological metrics (volume fraction, interfacial line length, and local thickness) for the synthetic images also showed good agreement with the training data, albeit with signs of mode collapse in the interfacial line length. While the critic exposed changes in particle size, it showed limited ability to distinguish images by particle shape. The detection of shape differences was also a more challenging task for the selected morphological metrics that related to energetic material performance. We further tested the critic with images of aged Pharmatose. Subtle changes due to aging are difficult for the human analyst to detect; but both critic and morphological metrics analysis showed image differentiation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A framework to enhance disaster debris estimation with AI and aerial photogrammetry

This study addresses the critical need to enhance disaster preparedness and response, focusing on hurricane impact assessment and debris estimation. Accurate assessments in this context are critical for post-event search-and-rescue (SAR) operations and resource distribution. Recent computing advancements are revealing the potential of unmanned aerial vehicles (UAVs) and artificial intelligence (AI) technologies in collecting data and assisting with post-hurricane reconnaissance. However, the use of AI and UAV photogrammetry for accurate disaster impact analysis remains underexplored. To this end, this study proposes a damage and debris analysis framework harnessing reality capture through aerial imagery and photogrammetry. Within this framework, a region-based neural network is leveraged to detect debris locations in aerial imagery with favorable performance. In a testbed within the Beaumont-Port Arthur region, in Southeast Texas, this study performs 3D reality captures of the built environment. Since the accuracy of the 3D reality capture is of importance in research areas associated with time-sensitive disaster response, we further investigate the optimal 2D aerial imagery overlap ratio required to generate a sufficiently accurate 3D model for disaster impact analysis and debris volume estimation. Results indicate that, in the case of aerial imagery for infrastructure systems, a minimum of 60 % overlap is recommended for damage assessment and debris analysis. In contrast, for flat green areas, a minimum of 50 % overlap is adequate. Overall, for disaster response applications, our study reveals that an overlap ratio between 60 % and 70 % is optimal for achieving a balance between time efficiency and data quality in aerial data collection. Furthermore, these quantitative recommendations are crucial for enabling efficient disaster response efforts. Additionally, our study outcomes will improve disaster impact analysis and facilitating timely and effective response strategies.

Artificial intelligence↗
DOE OSTI2022

Automated bubble analysis of high-speed subcooled flow boiling images using U-net transfer learning and global optical flow

Capturing and analyzing the bubble dynamics is crucial to improving the understanding of boiling heat transfer mechanisms and predicting boiling heat transfer coefficient and boiling crisis. High speed video (HSV) imaging has been used for decades towards this end. Still, there is no universal approach to quantitatively analyze bubble dynamics from HSV images. In this study, we propose a data-driven post-processing approach to segment, track, and identify wall-attached vapor bubbles from HSV images of the boiling process in subcooled flow conditions. Firstly, we employ a transfer learning framework with a U-Net-based convolution neural network (CNN) architecture to detect and segment bubbles in HSV images of diverse contrast and surface texture using very little data (e.g., 10 images) for training. Then, we evaluate the trained CNN model with 100 ground-truth images, and the validation results show that the model accuracy and precision in detecting the optical footprint of bubbles are higher than 90%. Finally, we suggest a criterion to identify a condensing bubble based on the divergence of the bubble displacement, which is calculated from sequential segmented bubble images using a global optical flow code. Using this combination of machine learning and optical flow, we can identify nucleation sites and track the growth of bubbles nucleating at each site to quantify nucleation site density, nucleation frequency, and other fundamental boiling parameters. The proposed system is validated using results obtained on a special heater, which enables both infrared (IR) thermometry and HSV imaging on a metallic surface. We compare the fundamental boiling parameters obtained by the two different diagnostics. The results show good agreement. In conclusion, the difference between the measurements of nucleation site density, averaged nucleation frequency, and averaged growth time performed with the two techniques is always within ± 20% and mostly ± 10% of the values measured with IR thermometry.

42 ENGINEERING↗
DOE OSTI2021

Evaluating the state-of-the-art in remote volcanic eruption characterization Part II: Ulawun volcano, Papua New Guinea

Retrospective eruption characterization is valuable for advancing our understanding of volcanic systems and evaluating our observational capabilities, especially with remote technologies (defined here as a space-borne system or non-local, ground-based instrumentation which include regional and remote infrasound sensors). In June 2019, the open-system Ulawun volcano, Papua New Guinea, produced a VEI 4 eruption. We combined data from satellites (including Sentinel-2, TROPOMI, MODIS, Himawari-8), the International Monitoring System infrasound network, and GLD360 globally detected lightning with information from the local authorities and social media to characterize the pre-, syn- and post-eruptive behaviour. The Rabaul Volcano Observatory recorded ~24 h of seismicity and detected SO 2 emissions ~16 h before the visually-documented start of the Plinian phase on 26 June at 04:20 UTC. Infrasound and SO2 detections suggest the eruption started during the night on 24 June 2019 at 10:39 UTC ~38 h before ash detections with a gas-dominated jetting phase. Local reports and infrasound detections show that the second phase of the eruption started on 25 June 19:28 UTC with ~6 h of jetting. The first detected lightning occurred on 26 June 00:14 UTC, and ash emissions were first detected by Himawari-8 at 01:00 UTC. Post-eruptive satellite imagery indicates new flow deposits to the south and north of the edifice and ash fall to the west and southwest. In particular, regional infrasound data provided novel insight into eruption onset and syn-eruptive changes in intensity. We conclude that, while remote observations are sufficient for detection and tracking of syn-eruptive changes, key challenges in data latency, acquisition, and synthesis must be addressed to improve future near-real-time characterization of eruptions at minimally-monitored or unmonitored volcanoes.

58 GEOSCIENCES↗
DOE OSTI2017

Diffraction imaging for seal evaluation using ultra high resolution 3D seismic data

Cap rock is any impermeable or low permeability formation that may trap oil, gas or water, preventing it from migrating to the surface. Evaluation of sealing properties of the cap rock is a critical task for seismic exploration. It is important for oil and gas prospecting as well as for locating reservoirs appropriate for carbon dioxide storage. The latter has been proposed as one solution to global climate change caused by heat-trapping anthropogenic gasses in the atmosphere. A fluid escaped from the reservoir fills voids in the overlying strata thereby causing local changes of acoustic properties. We aim to explore such fluid-saturated areas and thus evaluate sealing properties of underlying cap rocks. We assume that local acoustic impedance changes caused by fluid migration generate seismic diffractions. We isolate diffracted waves from P-Cable seismic data acquired over the area of interest and perform diffraction analysis of the near-surface interval (above 200 m). In the diffraction image obtained, we interpret an extensive system of broad sinuous channels, narrow linear faults, and diffuse fracture networks. In addition, we detect high diffractivity anomalies, which extend vertically and laterally. We note no evidence of these anomalies in a conventional seismic image and associated seismic attribute (similarity and fracture density) volumes. We associate this high diffractivity to a variable pore fluid composition and saturation, which we infer to be due to hydrocarbon migration from the underlying strata. Consequently, we conclude poor seal quality for the underlying cap rocks. During data processing, we test diffraction focusing analysis for velocity model building. In conclusion, this option appears quite valuable for P-Cable data processing because of short offset and limitations of the standard velocity analysis.

58 GEOSCIENCES↗
DOE OSTI2017

Measuring global monopole velocities, one by one

We present an estimation of the average velocity of a network of global monopoles in a cosmological setting using large numerical simulations. In order to obtain the value of the velocity, we improve some already known methods, and present a new one. This new method estimates individual global monopole velocities in a network, by means of detecting each monopole position in the lattice and following the path described by each one of them. Using our new estimate we can settle an open question previously posed in the literature: velocity-dependent one-scale (VOS) models for global monopoles predict two branches of scaling solutions, one with monopoles moving at subluminal speeds and one with monopoles moving at luminal speeds. Previous attempts to estimate monopole velocities had large uncertainties and were not able to settle that question. Our simulations find no evidence of a luminal branch. We also estimate the values of the parameters of the VOS model. With our new method we can also study the microphysics of the complicated dynamics of individual monopoles. Finally we use our large simulation volume to compare the results from the different estimator methods, as well as to asses the validity of the numerical approximations made.

79 ASTRONOMY AND ASTROPHYSICS↗