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NASA NTRS2006

Conflict Detection Using Variable Four-Dimensional Uncertainty Bounds to Control Missed Alerts

Decision-support tools for maintaining pairwise aircraft separation rely on conflict detection to alert the operator when the predicted trajectories of aircraft will result in a loss of separation. But aircraft frequently do not follow their predicted trajectories exactly. This can cause missed alerts and the failure of strategic separation procedures. We present a technique for modeling a bounded region of uncertainty around a four-dimensional predicted trajectory and an algorithm for detecting conflicts between trajectories modeled in this way that avoids missed alerts as long as the aircraft remain within the specified regions of uncertainty. In addition, we present an algorithm for detecting the intrusion of a trajectory modeled in this way into an area hazard modeled as a polygonal region. The size of the region of uncertainty can vary along the trajectory continually and independently in the along-path, cross-track, and vertical dimensions, providing an opportunity to reduce the likelihood of false alerts while protecting against typical prediction errors. The algorithm has been implemented in the Autonomous Operations Planner, a NASA Langley prototype decision support tool for airborne self-separation.

Karr, David A.↗

DOE OSTI2024

Machine Learning-Driven Reliability Estimation of PV Inverters Considering Alert-Ambient Variability

Weather-induced spatio-temporal degradation limits outdoor PV inverter lifetime and reliability, necessitating advanced data analysis. This study employs a top-down, data-driven approach utilizing multiple machine learning (ML) algorithms to estimate inverter reliability in a 1.4 MW PV power plant, considering factors such as irradiance, humidity, temperature, time of day, and weather conditions. An extensive alert dataset from 17 identical inverters, including alert types, propagation, and frequency, reveals significant correlations with environmental factors and inverter output power, enabling the construction of a performance reliability model. Dual-stage supervised-ML models are evaluated for accuracy, with the ‘classification-regression’ model by an artificial neural network (ANN) tested on the averaged “Alert-Ambient” dataset, which is outperformed by ‘clustering-regression’ models using random forest (RF) and K-Nearest Neighbors (KNN) on individual inverter datasets. K-means clustering applies principal component analysis to reduce dimensions, achieving improved accuracy beyond the 80% achieved by ANN on the averaged dataset. Second-stage regression estimates inverter reliability with a mean square error of 0.0195 on the averaged dataset and as low as 0.002 on individual inverter datasets using RF. Furthermore, these findings highlight the method's suitability for estimating PV inverter output reliability under ambient conditions, essential for digital twin development and related applications.

14 SOLAR ENERGY↗

NASA NTRSDate not supplied

Cabin Crew Alertness and Performance During Long-Haul Flights

INTRODUCTION: -Sleep loss and circadian disruption pose a significant risk in aviation. -Previous literature has shown that inflight rest facilities influence alertness and performance among pilots, but few studies have evaluated cabin crew. -The aim of this research was to assess alertness and performance among cabin crew members sleeping in different rest locations during a long-haul out-and-back trip. METHODS: -Twenty-nine cabin crewmembers flew the same long-haul route (outbound and inbound). -Participants were randomly assigned to fly on an aircraft with a bunk in both directions or to fly an aircraft with a bunk in one direction and with a high comfort jump seat (HCJS) in the other direction. -Throughout the study they completed a Karolinska Sleepiness Scale (KSS) and a 5-minute Psychomotor Vigilance Task (PVT) at the beginning and at the end of each flight. RESULTS: -A series of mixed-effects models were performed to assess the changes in KSS and PVT when crewmembers slept in the bunk during both directions of flight (bunk-only) compared to when sleep was obtained in the HCJS during one direction and bunk in the other (bunk + HCJS). -There was no significant difference in KSS alertness between the two conditions. - There were no significant differences in PVT response speed or lapses between the two conditions. CONCLUSIONS: -Limitations: small number of participants. Our study stopped abruptly because of the COVID-19 pandemic which limited our sample size. -Further research is needed to understand how other factors such as duty start time and workload might influence the sleep of cabin crewmembers during long-haul flights.

long-haul↗

DOE OSTI2021

Swift /UVOT follow-up of gravitational wave alerts in the O3 era

ABSTRACT In this paper, we report on the observational performance of the Swift Ultra-violet/Optical Telescope (UVOT) in response to the gravitational wave (GW) alerts announced by the Advanced Laser Interferometer Gravitational Wave Observatory and the Advanced Virgo detector during the O3 period. We provide the observational strategy for follow-up of GW alerts and provide an overview of the processing and analysis of candidate optical/UV sources. For the O3 period, we also provide a statistical overview and report on serendipitous sources discovered by Swift/UVOT. Swift followed 18 GW candidate alerts, with UVOT observing a total of 424 deg2. We found 27 sources that changed in magnitude at the 3σ level compared with archival u- or g-band catalogued values. Swift/UVOT also followed up a further 13 sources reported by other facilities during the O3 period. Using catalogue information, we divided these 40 sources into five initial classifications: 11 candidate active galactic nuclei (AGNs)/quasars, three cataclysmic variables (CVs), nine supernovae, 11 unidentified sources that had archival photometry, and six uncatalogued sources for which no archival photometry was available. We have no strong evidence to identify any of these transients as counterparts to the GW events. The 17 unclassified sources are likely a mix of AGN and a class of fast-evolving transient, and one source may be a CV.

Oates, S. R.↗

DOE OSTI2023

Introduction to ALERT: A User-Interface Tool for Resilient Optimization

As the cyber-physical systems grow in complexity, there is a need for proactive resilience strategies that involve online, adaptive control actions to best prepare for any impending adversarial events. In this technical effort, supported by RD2C LDRD initiative, the project team designed and demonstrated online strategies – referred to as ALERT controls – for proactive and adaptive tuning of existing optimal controls in a microgrid, with quantifiably assured margins of resilience to various cyber-physical adversarial events. This ALERT functionality is made available to the end-users, e.g., the system operators, via an interactive user-interface. The end-users will not only be able to use the interface to visualize the system’s operation under various cyber-physical adversarial scenarios, but also evaluate the amount of tolerance the system has against selected adversarial perturbations of interest (e.g., malfunctioning sensors, suspected attacked measurements) via the adversarial plots. In this technical report, we briefly outline the algorithmic modules of the developed ALERT control technology, and introduce the user-interface tool that allows end-users (e.g., microgrid operators) to enter their system description, specify various operational and resilience requirements, and evaluate the impact of the control decisions via illustrative plots.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Improving Detection of Disease Re-emergence Using a Web-Based Tool (RED Alert): Design and Case Analysis Study

Background: Currently, the identification of infectious disease re-emergence is performed without describing specific quantitative criteria that can be used to identify re-emergence events consistently. This practice may lead to ineffective mitigation. In addition, identification of factors contributing to local disease re-emergence and assessment of global disease re-emergence require access to data about disease incidence and a large number of factors at the local level for the entire world. This paper presents Re-emerging Disease Alert (RED Alert), a web-based tool designed to help public health officials detect and understand infectious disease re-emergence. Objective: Our objective is to bring together a variety of disease-related data and analytics needed to help public health analysts answer the following 3 primary questions for detecting and understanding disease re-emergence: Is there a potential disease re-emergence at the local (country) level? What are the potential contributing factors for this re-emergence? Is there a potential for global re-emergence? Methods: We collected and cleaned disease-related data (eg, case counts, vaccination rates, and indicators related to disease transmission) from several data sources including the World Health Organization (WHO), Pan American Health Organization (PAHO), World Bank, and Gideon. We combined these data with machine learning and visual analytics into a tool called RED Alert to detect re-emergence for the following 4 diseases: measles, cholera, dengue, and yellow fever. We evaluated the performance of the machine learning models for re-emergence detection and reviewed the output of the tool through a number of case studies. Results: Our supervised learning models were able to identify 82%-90% of the local re-emergence events, although with 18%-31% (except 46% for dengue) false positives. This is consistent with our goal of identifying all possible re-emergences while allowing some false positives. The review of the web-based tool through case studies showed that local re-emergence detection was possible and that the tool provided actionable information about potential factors contributing to the local disease re-emergence and trends in global disease re-emergence. Conclusions: To the best of our knowledge, this is the first tool that focuses specifically on disease re-emergence and addresses the important challenges mentioned above.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Alert Classification for the ALeRCE Broker System: The Light Curve Classifier

We present the first version of the Automatic Learning for the Rapid Classification of Events (ALeRCE) broker light curve classifier. ALeRCE is currently processing the Zwicky Transient Facility (ZTF) alert stream, in preparation for the Vera C. Rubin Observatory. The ALeRCE light curve classifier uses variability features computed from the ZTF alert stream and colors obtained from AllWISE and ZTF photometry. We apply a balanced random forest algorithm with a two-level scheme where the top level classifies each source as periodic, stochastic, or transient, and the bottom level further resolves each of these hierarchical classes among 15 total classes. This classifier corresponds to the first attempt to classify multiple classes of stochastic variables (including core- and host-dominated active galactic nuclei, blazars, young stellar objects, and cataclysmic variables) in addition to different classes of periodic and transient sources, using real data. We created a labeled set using various public catalogs (such as the Catalina Surveys and Gaia DR2 variable stars catalogs, and the Million Quasars catalog), and we classify all objects with ≥6 g-band or ≥6 r-band detections in ZTF (868,371 sources as of 2020 June 9), providing updated classifications for sources with new alerts every day. For the top level we obtain macro-averaged precision and recall scores of 0.96 and 0.99, respectively, and for the bottom level we obtain macro-averaged precision and recall scores of 0.57 and 0.76, respectively. Updated classifications from the light curve classifier can be found at the ALeRCE Explorer website (http://alerce.online).

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Combined Pre-supernova Alert System with KamLAND and Super-Kamiokande

Preceding a core-collapse supernova (CCSN), various processes produce an increasing amount of neutrinos of all flavors characterized by mounting energies from the interior of massive stars. Among them, the electron antineutrinos are potentially detectable by terrestrial neutrino experiments such as KamLAND and Super-Kamiokande (SK) via inverse beta decay interactions. Once these pre-supernova (pre-SN) neutrinos are observed, an early warning of the upcoming CCSN can be provided. In light of this, KamLAND and SK, both located in the Kamioka mine in Japan, have been monitoring pre-SN neutrinos since 2015 and 2021, respectively. Recently, we performed a joint study between KamLAND and SK on pre-SN neutrino detection. A pre-SN alert system combining the KamLAND detector and the SK detector was developed and put into operation, which can provide a supernova alert to the astrophysics community. Fully leveraging the complementary properties of these two detectors, the combined alert is expected to resolve a pre-SN neutrino signal from a 15 M ⊙ star within 510 pc of the Earth at a significance level corresponding to a false alarm rate of no more than 1 per century. For a Betelgeuse-like model with optimistic parameters, it can provide early warnings up to 12 hr in advance.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

DMTN-093: Design of the LSST Alert Distribution System

We describe the proposed design and implementation of the LSST Alert Distribution System, which provides rapid dissemination of alerts to community alert brokers. At time of writing, this service is still under development; this “living document” describes current thinking, but is expected to evolve over the course of LSST construction.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

DMTN-221: Periodicity Analysis in Alert Production

The baselined timeseries features to be computed in Alert Production include a Lomb-Scargle periodogram ran on two classes of variable systems: RR Lyrae and Eclipsing Binaries. Based on a simulated LSST-like cadence light curves taken from the Extended LSST Astronomical Time-series Classification Challenge (ELAsTiCC) we perform an end-to-end test to characterize the periodicity recovery on the Alert Production multi-band light curves. In both variable classes, we found that a single-band Lomb-Scargle implementation yields to a low fraction of recovered periods, with a significant preference on the simple periodic phenomena such as RR Lyrae. We also investigated the results from a multi-band Lomb-Scargle and found an increased fraction of recovered periodicities above 15% for the eclipsing binaries, and over 80$\%$ for the RR Lyrae stars. Our findings suggest that a multi-band Lomb-Scargle should be implemented for searching periodic phenomena through AP. We also asses the computational and scientific performance of several configurations on simulated alert data and find that our current configuration scales linearly with the number of detections while assuming an heuristic frequency grid.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Vera C. Rubin Observatory Prompt Products: alert packets data

Data products produced by prompt and daily processing of images obtained in the Legacy Survey of Space and Time. These include realtime alerts sent to community alert brokers, newly-discovered Solar System Objects reported to the Minor Planet Center, processed visit and difference images, and source catalogs. Prompt Products are not a static single data release but continually grow throughout the ten-year LSST survey. This dataset is a subset of the full data release consisting of a dataset named alert packets. This dataset contains measurements for 5-sigma sources detected in difference images that were issued to the community brokers.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1974

Processing electrophysiological signals for the monitoring of alertness

Mathematical techniques are described for processing EEG signals associated with varying states of alertness. Fast algorithms for implementing real-time computations of alertness estimates were developed. A realization of the phase-distortionless digital filter is presented which approaches real-time filtering and a transform for EEG signals. This transform provides information for the alertness estimates and can be performed in real time. A statistical test for stationarity in EEG signals is being developed that will provide a method for determining the duration of the EEG signals necessary for estimating the short-time power or energy spectra for nonstationary analysis of EEG signals.

Lai, D. C.↗

NASA NTRS1992

Experimental evaluation of candidate graphical microburst alert displays

The topics addressed are: (1) experimental evaluation of candidate graphical microburst displays; (2) microburst detection and alerting; (3) previous part-task simulator experiment-comparison of presentation modes; (4) presentation mode comparison-results; (5) advantages of graphical mode of presentation; (6) graphical microburst alert experiment-objectives; and graphical microburst alert experiment-overview; and (7) candidate display design.

Wanke, Craig↗

NASA NTRS1994

Effects of historical and predictive information on ability of transport pilot to predict an alert

In the aviation community, the early detection of the development of a possible subsystem problem during a flight is potentially useful for increasing the safety of the flight. Commercial airlines are currently using twin-engine aircraft for extended transport operations over water, and the early detection of a possible problem might increase the flight crew's options for safely landing the aircraft. One method for decreasing the severity of a developing problem is to predict the behavior of the problem so that appropriate corrective actions can be taken. To investigate the pilots' ability to predict long-term events, a computer workstation experiment was conducted in which 18 airline pilots predicted the alert time (the time to an alert) using 3 different dial displays and 3 different parameter behavior complexity levels. The three dial displays were as follows: standard (resembling current aircraft round dial presentations); history (indicating the current value plus the value of the parameter 5 sec in the past); and predictive (indicating the current value plus the value of the parameter 5 sec into the future). The time profiles describing the behavior of the parameter consisted of constant rate-of-change profiles, decelerating profiles, and accelerating-then-decelerating profiles. Although the pilots indicated that they preferred the near term predictive dial, the objective data did not support its use. The objective data did show that the time profiles had the most significant effect on performance in estimating the time to an alert.

Trujillo, Anna C.↗

NASA NTRS1994

Flight controller alertness and performance during MOD shiftwork operations

Decreased alertness and performance associated with fatigue, sleep loss, and circadian disruption are issues faced by a diverse range of shiftwork operations. During STS operations, MOD personnel provide 24 hr. coverage of critical tasks. A joint JSC and ARC project was undertaken to examine these issues in flight controllers during MOD shiftwork operations. An initial operational test of procedures and measures was conducted during STS-53 in Dec. 1992. The study measures included a background questionnaire, a subjective daily logbook completed on a 24 hr. basis (to report sleep patterns, work periods, etc.), and an 8 minute performance and mood test battery administered at the beginning, middle, and end of each shift period. Seventeen Flight controllers representing the 3 Orbit shifts participated. The initial results clearly support further data collection during other STS missions to document baseline levels of alertness and performance during MOD shiftwork operations. These issues are especially pertinent for the night shift operations and the acute phase advance required for the transition of day shift personnel into the night for shuttle launch. Implementation and evaluation of the countermeasure strategies to maximize alertness and performance is planned. As STS missions extend to further extended duration orbiters, timelines and planning for 24 circadian disruption will remain highly relevant in the MOD environment.

Kelly, Sean M.↗

NASA NTRS1994

Crew factors in flight operations 9: Effects of planned cockpit rest on crew performance and alertness in long-haul operations

This study examined the effectiveness of a planned cockpit rest period to improve alertness and performance in long-haul flight operations. The Rest Group (12 crew members) was allowed a planned 40 minute rest period during the low workload, cruise portion of the flight, while the No-Rest Group (9 crew members) had a 40 minute planned control period when they maintained usual flight activities. Measures used in the study included continuous ambulatory recordings of brain wave and eye movement activity, a reaction time/vigilance task, a wrist activity monitor, in-flight fatigue and alertness ratings, a daily log for noting sleep periods, meals, exercise, flight and duty periods, and the NASA Background Questionnaire. The Rest Group pilots slept on 93 percent of the opportunities, falling asleep in 5.6 minutes and sleeping for 25.8 minutes. This nap was associated with improved physiological alertness and performance compared to the No-Rest Group. The benefits of the nap were observed through the critical descent and landing phases of flight. The nap did not affect layover sleep or the cumulative sleep debt. The nap procedures were implemented with minimal disruption to usual flight operations and there were no reported or identified concerns regarding safety.

Rosekind, Mark R.↗

NASA NTRS1994

Alertness Management In Flight Operations: A NASA Education and Training Module

Since 1980, the NASA Ames Fatigue Countermeasures Program has been conducting research on sleep, circadian rhythms, and fatigue in a variety of flight operations 1. An original goal of the program was to return the scientific and operational knowledge to the aviation industry. To meet this goal, the NASA Ames Fatigue Countermeasures Program has created an Education and Training Module entitled, "Strategies for Alertness Management in Flight Operations." The Module was designed to meet three objectives: 1) explain the current state of knowledge about the physiological mechanisms underlying fatigue, 2) demonstrate how this knowledge can be applied to improve flight crew sleep, performance, and alertness, and 3) offer countermeasure recommendations. The Module is composed of two components: 1) a 60-minute live presentation provided by a knowledgeable individual and 2) a NASA/FAA Technical Memorandum (TM) that contains the presentation materials and appendices with complementary information. The TM is provided to all individuals attending the live presentation. The Module content is divided into three parts: 1) basic information on sleep, sleepiness, circadian rhythms, fatigue, and how flight operations affect these physiological factors, 2) common misconceptions about sleep, sleepiness, and fatigue, and 3) alertness management strategies. The Module is intended for pilots, management personnel, schedulers, flight attendants, and the many other individuals involved in the aviation system.

Rosekind, Mark R.↗

NASA NTRS2002

Crew Factors in Flight Operations XIV: Alertness Management in Regional Flight Operations Education Module

Regional operations encompass a broad range of pilots and equipment. This module is intended to help all those involved in regional aviation, including pilots, schedulers, dispatchers, maintenance technicians, policy makers, and others, to understand the physiological factors underlying fatigue, how flight operations affect fatigue, and what can be done to counteract fatigue and maximize alertness and performance in their operations. The overall purpose of this module is to promote aviation safety, performance, and productivity. It is intended to meet three specific objectives: (1) to explain the current state of knowledge about the physiological mechanisms underlying fatigue; (2) to demonstrate how this knowledge can be applied to improving flight crew sleep, performance, and alertness; and (3) to offer strategies for alertness management. Aviation Safety Reporting System (ASRS) and National Transportation Safety Board (NISH) reports are used throughout this module to demonstrate that fatigue is a safety issue in the regional operations community. The appendices at the end of this module include the ASRS reports used for the examples contained in this publication, brief introductions to sleep disorders and relaxation techniques, summaries of relevant NASA publications, and a list of general readings on sleep, sleep disorders, and circadian rhythms.

Rosekind, Mark R.↗