Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Event Detection”

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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A semi-supervised machine learning detector for physics events in tokamak discharges

Databases of physics events have been used in various fusion research applications, including the development of scaling laws and disruption avoidance algorithms, yet they can be time-consuming and tedious to construct. This paper presents a novel application of the label spreading semi-supervised learning algorithm to accelerate this process by detecting distinct events in a large dataset of discharges, given few manually labeled examples. A high detection accuracy (>85%) for H-L back transitions and initially rotating locked modes is demonstrated on a dataset of hundreds of discharges from DIII-D with manually identified events for which only 3 discharges are initially labeled by the user. Lower yet reasonable performance (~75%) is also demonstrated for the core radiative collapse, an event with a much lower prevalence in the dataset. Additionally, analysis of the performance sensitivity indicates that the same set of algorithmic parameters is optimal for each event. This suggests that the method can be applied to detect a variety of other events not included in this paper, given that the event is well described by a set of 0D signals robustly available on many discharges. Procedures for analysis of new events are demonstrated, showing automatic event detection with increasing fidelity as the user strategically adds manually labeled examples. Detections on Alcator C-Mod and EAST are also shown, demonstrating the potential for this to be used on a multi-tokamak dataset.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Applying Waveform Correlation and Waveform Template Metadata to Aftershocks in the Middle East to Reduce Analyst Workload

Organizations that monitor for underground nuclear explosive tests are interested in techniques that automatically characterize recurring events such as aftershocks to reduce the human analyst effort required to produce high-quality event bulletins. Waveform correlation is a technique that is effective in finding similar waveforms from repeating seismic events. In this study, we apply waveform correlation in combination with template event metadata to two aftershock sequences in the Middle East to seek corroborating detections from multiple stations in the International Monitoring System of the Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization. We use waveform templates from stations that are within regional distance of aftershock sequences to detect subsequent events, then use template event metadata to discover what stations are likely to record corroborating arrival waveforms for recurring aftershock events at the same location, and develop additional waveform templates to seek corroborating detections. We evaluate the results with the goal of determining whether applying the method to aftershock events will improve the choice of waveform correlation detections that lead to bulletin-worthy events and reduction of analyst effort.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
NASA NTRS2020

Potential of GPM IMERG Precipitation Estimates to Monitor Natural Disaster Triggers in Urban Areas: the Case of Rio deJaneiro, Brazil

Extreme rainfall can be a catastrophic trigger for natural disaster events at urban scales. However, there remains large uncertainties as to how satellite precipitation can identify these triggers at a city scale. The objective of this study is to evaluate the potential of satellite-based rainfall estimates to monitor natural disaster triggers in urban areas. Rainfall estimates from the Global Precipitation Measurement (GPM) mission are evaluated over the city of Rio de Janeiro, Brazil, where urban floods and landslides occur periodically as a result of extreme rainfall events. Two rainfall products derived from the Integrated Multi-satellite Retrievals for GPM (IMERG), the IMERG Early and IMERG Final products, are integrated into the Noah Multi-Parameterization (Noah-MP) land surface model in order to simulate the spatial and temporal dynamics of two key hydrometeorological disaster triggers across the city over the wet seasons during 2001-2019. Here, total runoff (TR) and rootzone soil moisture (RZSM) are considered as flood and landslide triggers, respectively. Ground-based observations at 33 pluviometric stations are interpolated, and the resulting rainfall fields are used in an in-situ precipitation-based simulation, considered as the reference for evaluating the IMERG-driven simulations. The evaluation is performed during the wet seasons (November-April), when average rainfall over the city is 4.4mm/day. Results show that IMERG products show low spatial variability at the city scale, generally overestimate rainfall rates by 12-35%, and impacts on TR and RZSM vary spatially mostly as a function of land cover and soil types. Results based on statistical and categorical metrics show that IMERG skill in detecting extreme events is moderate, with IMERG Final performing slightly better for most metrics. By analyzing two recent storms, we observe that IMERG detects mostly hourly extreme events, but underestimates rainfall rates, resulting in underestimated TR and RZSM. An evaluation of normalized time series using percentiles shows that both satellite products have significantly improved skill in detecting extreme events when compared to the evaluation using absolute values, indicating that IMERG precipitation could be potentially used as a predictor for natural disasters in urban areas.

IMERG↗
NASA NTRS2007

Solutions Network Formulation Report. NASA's Potential Contributions in Remote Quorum Sensing and the Management of Harmful Algal Blooms

This candidate solution proposes to use the night-imaging capabilities of the HSTC from SAC-C and of the HSC from SAC-D/Aquarius to detect bioluminescent events associated with HABs (harmful algal blooms). Once detected, this information could be fed to the NOAA CSCOR (Center for Sponsored Coastal Ocean Research) Harmful Algal Bloom Event Response Program, which acts quickly to fund the mobilization of research teams and to engage local agencies in a response. The HSC/HSTC data can serve as input to the HABSOS decision support system to provide information on location, extent, and duration of HAB events. Society will benefit from improved protection of the health of humans beings, aquatic ecosystems, and coastal economies. This work supports coastal management, public health, and homeland security applications.

Fletcher, Rose↗
NASA NTRS2015

Application of Data Cubes for Improving Detection of Water Cycle Extreme Events

As part of an ongoing NASA-funded project to remove a longstanding barrier to accessing NASA data (i.e., accessing archived time-step array data as point-time series), for the hydrology and other point-time series-oriented communities, "data cubes" are created from which time series files (aka "data rods") are generated on-the-fly and made available as Web services from the Goddard Earth Sciences Data and Information Services Center (GES DISC). Data cubes are data as archived rearranged into spatio-temporal matrices, which allow for easy access to the data, both spatially and temporally. A data cube is a specific case of the general optimal strategy of reorganizing data to match the desired means of access. The gain from such reorganization is greater the larger the data set. As a use case of our project, we are leveraging existing software to explore the application of the data cubes concept to machine learning, for the purpose of detecting water cycle extreme events, a specific case of anomaly detection, requiring time series data. We investigate the use of support vector machines (SVM) for anomaly classification. We show an example of detection of water cycle extreme events, using data from the Tropical Rainfall Measuring Mission (TRMM).

water cycle extreme events↗
DOE OSTI2020

Systems, methods and computer program products for self-tuning sensor data processing

Systems and methods are disclosed that include tools that utilize Dynamic Detector Tuning (DDT) software that identifies near-optimal parameter settings for each sensor using a neuro-dynamic programming (reinforcement learning) paradigm. DDT adapts parameter values to the current state of the environment by leveraging cooperation within a neighborhood of sensors. The key metric that guides the dynamic tuning is consistency of each sensor with its nearest neighbors: parameters are automatically adjusted on a per station basis to be more or less sensitive to produce consistent agreement of detections in its neighborhood. The DDT algorithm adapts in near real-time to changing conditions in an attempt to automatically self-tune a signal detector to identify (detect) only signals from events of interest. The disclosed systems and methods reduce the number of missed legitimate detections and the number of false detections, resulting in improved event detection.

Draelos, Timothy J.↗
NASA NTRSDate not supplied

Capturing Complex Multivariate Time Series Interactions to Detect High-Risk Adverse Events During Flight

The reduction of aviation safety metrics below target thresholds continue to drive down the number of aviation fatalities and accidents. To meet future safety demands, sustained efforts by aviation agencies promoting safety assurance processes and systems have prompted ongoing research on identifying and mitigating in-flight risks. With the projected increase in passenger load factor and rollout of more autonomous systems into the national airspace, the need to detect high-risk events in-time or ahead-of-time is becoming increasingly crucial. New anomaly detection and precursor identification algorithms will need to scale to different airframes, levels of autonomy, and system complexity. While the pervasiveness of deep learning has resulted in the development of performant anomaly detection methods, these sophisticated models currently suffer from low end-user interpretability. Building off our previous work on identifying adverse events in multivariate flight data during descent, we propose a data-driven approach for detecting in-flight adverse events caused by the complex interplay of flight variables. Our approach utilizes ordinal patterns of important aircraft stability variables (e.g., airspeed and descent rate) to capture multivariate flight dynamics that can be used to predict the onset of unstable approaches, a high-risk adverse event that can occur during approach. Through the use of ordinal patterns, we aim to create more interpretable detection models of in-flight adverse events that can be translated to future autonomous systems without difficulty. Our analysis shows the presence of distinct ordinal pattern distributions that can be used to predict unstable approaches 1 minute ahead of time with an accuracy of 0.69 and a recall of 0.73 and 30 seconds ahead with an accuracy of 0.70 and a recall of 0.86.

Risk detection↗
DOE OSTI2020

Microseismic Event Denoising: Removal of Borehole Waves

The vertical borehole array at Farnsworth Unit, TX is used to monitor microseismic activity in the subsurface around the Carbon Capture and Sequestration (CCS) reservoir. The array consists of 16 3-component seismometers spaced vertically in a single borehole. Tube or borehole waves traveling up or down the borehole can corrupt signals of interest, such as microseismic events. A denoising convolutional neural network (DCNN) was trained to remove borehole waves from seismic waveforms of microseismic events for the purpose of reducing unwanted signal detections and better characterizing events of interest. This R&D leverages the work of Sandia colleague Rigo Tibi, who used a DCNN developed by Greg Beroza at Stanford University to improve the signal-to-noise ratio (SNR) of teleseismic events detected by the International Monitoring System.

58 GEOSCIENCES↗
DOE OSTI2022

International Radiological/Nuclear Training for Emergency Response - Major Public Events Virtual Workshop: Radiation Detection and Emergency Response Equipment (Day 2) [Slides]

The objective of this presentation is to familiarize participants with the different types of radiation detection systems and their practical applications for radiological emergency response. The specific goals are for participants to: (1) Understand the Three Step Process for Radiological Response of (i) Search and/or Survey, (ii) Radioisotope Identification, and (iii) Source Recovery, (2) Recognize the types of radiation detection equipment and their applications, and (3) View examples of common radiation detection instrumentation with operational videos.

61 RADIATION PROTECTION AND DOSIMETRY↗
NASA NTRS1972

Electronic circuit detects left ventricular ejection events in cardiovascular system

Electronic circuit processes arterial blood pressure waveform to produce discrete signals that coincide with beginning and end of left ventricular ejection. Output signals provide timing signals for computers that monitor cardiovascular systems. Circuit operates reliably for heart rates between 50 and 200 beats per minute.

Gebben, V. D.↗
NASA NTRS1983

Detection of flare like events and their relationship to presumed spot regions on V471 Tau - A solar-stellar connection

Since its discovery as an eclipsing binary with a white dwarf companion by Nelson and Young (1970), V471 Tau (BD + 16 deg 516) has been an object of considerable importance. The K dwarf companion has been the subject of intensive studies because it demonstrates many of the properties normally attributed to classical RS CVn stars. In the present investigation, it is demonstrated that the K dwarf companion exhibits also flaring properties which characterize the BY Dra stars. In a sense, V471 Tau may be the critical link by providing information that the distinctions between RS CVn phenomena and BY Dra phenomena are artificial and misleading, arising entirely from historical processes. Observations in the optical and the X-ray wavelength regions are reported. A study of the occurrence times of flare events in terms of the ephemeris of the photometric wave which is known to migrate through the light curve of V471 Tau reveals a strong correlation, suggesting that the flares occur near regions of the K dwarf where spot groups are inferred.

Klimke, A.↗
NASA NTRS1987

Optical flash background rates

Flash background rates are measured from August 18-September 5, 1985 using the Lowell 0.61-m telescope at CTIO and the 28-cm f/10 Schmidt-Cassegrain telescope at ESO. During 230.1 hours of observing 40 flash events were detected, and it was observed that the background events were dominated by meteors and satellites passing through the field-of-view. The measured flash rate is compared with that of Pedersen et al. (1984), and it is determined that the background flash rate that is applicable to the data of Pedersen et al. for February 8, 1984 is 0.023 events/hr. The morphology of that flash, which appears to be controlled by a gamma-ray burster, is examined.

Schaefer, B. E.↗
DOE OSTI2023

System and method associated with expedient detection and reconstruction of cyber events in a compact scenario representation using provenance tags and customizable policy

A system associated with detecting a cyber-attack and reconstructing events associated with a cyber-attack campaign, is disclosed. The system performs various operations that include receiving an audit data stream associated with cyber events. The system identifies trustworthiness values in a portion of data associated with the cyber events and assigns provenance tags to the portion of the data based on the identified trustworthiness values. An initial visual representation is generated based on the assigned provenance tags to the portion of the data. The initial visual representation is condensed based on a backward traversal of the initial visual representation in identifying a shortest path from a suspect node to an entry point node. A scenario visual representation is generated that specifies nodes most relevant to the cyber events associated with the cyber-attack based on the identified shortest path.A corresponding method and computer-readable medium are also disclosed.

Source record↗