Search NASA⌕ Search

SEARCH · Search NASA

Results for “Risk 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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Sequential Probability Ratio Test for Collision Avoidance Maneuver Decisions Based on a Bank of Norm-Inequality-Constrained Epoch-State Filters

Sequential probability ratio tests explicitly allow decision makers to incorporate false alarm and missed detection risks, and are potentially less sensitive to modeling errors than a procedure that relies solely on a probability of collision threshold. Recent work on constrained Kalman filtering has suggested an approach to formulating such a test for collision avoidance maneuver decisions: a filter bank with two norm-inequality-constrained epoch-state extended Kalman filters. One filter models 1he null hypothesis 1ha1 the miss distance is inside the combined hard body radius at the predicted time of closest approach, and one filter models the alternative hypothesis. The epoch-state filter developed for this method explicitly accounts for any process noise present in the system. The method appears to work well using a realistic example based on an upcoming highly-elliptical orbit formation flying mission.

Carpenter, J. R.↗
NASA NTRSDate not supplied

NASA Omics Archive Project

The space environment consists of a complex set of hazards including altered gravity, radiation, psychological/physiological stress, isolation, and confinement leading to complex biological responses. Advances in biotechnology capabilities offer considerable potential to provide novel insight into those responses as well as innovative diagnostic, treatment, and countermeasure solutions for astronauts as NASA begins to travel beyond low Earth orbit. Omics data (genomics, transcriptomics, proteomics, etc.) is one example that can provide NASA with critical knowledge of how a crewmember’s genetics, environment, and lifestyle can be used to develop individualized approaches for disease prevention, advance diagnostics, and improve treatment strategies. NASA ventured into the field of omics on human subjects with the successful completion of the NASA Twins Study which was the first step in mapping the multi-omic profile of astronauts to understand and mitigate the health consequences of spaceflight. The Human Research Program aims to build upon the success of the Twins Study with the NASA Omics Archive flight study, establishing a longitudinal biospecimen archive and efficiently generating a comprehensive high-quality multi-omic dataset from astronauts for the purpose of studying molecular, metabolic, and microbial changes associated with longduration spaceflight missions. The goal is to facilitate scientific and medical research community efforts to characterize and mitigate spaceflight health and performance risks. In this presentation, we will review details regarding the biospecimen and data archive to be generated by the NASA Omics Archive flight study. Data generated as part of this project will be archived in the NASA Life Sciences Portal (NLSP) and be made available for future hypothesis-driven research efforts or occupational surveillance through Institutional Review Board-approved data sharing and retrospective data requests submitted to the Life Sciences Data Archive (LSDA) team. We will also present results of a ground study performed to evaluate in-house procedures, new sample collection hardware, and vendor capabilities. The data repository generated and the samples to be archived by this study will enable future research efforts to assess an astronauts’ unique molecular and genetic profile with respect to individual spaceflight responses. Results of which will be instrumental in enabling precision health capabilities to better assess and mitigate spaceflight risks, detect disease states earlier, and actively monitor countermeasure treatments, ultimately improving clinical outcomes during future exploration class missions.

C. A. Theriot↗
NASA NTRS2012

Remote Sensing and Modeling of Landslides: Detection, Monitoring and Risk Evaluation

Landslides are one of the most pervasive hazards in the world, resulting in more fatalities and economic damage than is generally recognized_ Occurring over an extensive range of lithologies, morphologies, hydrologies, and climates, mass movements can be triggered by intense or prolonged rainfall, seismicity, freeze/thaw processes, and antbropogertic activities, among other factors. The location, size, and timing of these processes are characteristically difficult to predict and assess because of their localized spatial scales, distribution, and complex interactions between rainfall infiltration, hydromechanical properties of the soil, and the underlying surface composition. However, the increased availability, accessibility, and resolution of remote sensing data offer a new opportunity to explore issues of landslide susceptibility, hazard, and risk over a variety of spatial scales. This special issue presents a series of papers that investigate the sources, behavior, and impacts of different mass movement types using a diverse set of data sources and evaluation methodologies.

Kirschbaum, Dalia↗
NASA NTRS2001

Risk reduction using DDP (Defect Detection and Prevention): Software support and software applications

Risk assessment and mitigation is the focus of the Defect Detection and Prevention (DDP) process, which has been applied to spacecraft technology assessments and planning, both hardware and software. DDP's major elements and their relevance to core requirement engineering concerns are summarized. The accompanying research demonstration illustrates DDP's tool support, and further customizations for application to software.

Software-specific customizations↗
NASA NTRS1992

The effect of differences in time to detection of circulating microbubbles on the risk of decompression sickness

Circulating microbubbles (CMB) are frequently detected prior to the appearance of symptoms of Decompression Sickness (DCS). It is difficult to analyze the effect of CMB on symptoms due to differences in the time to detection of CMB. This paper uses survival analysis models to evaluate the risk of symptoms in the presence of CMB. Methods: Information on 81 exposures to an altitude of 6,400 m (6.5 psi) for a period of three hours, with simulated extravehicular activities, was examined. The presence or absence of CMB was included as a time dependent covariate of the Cox proportional hazards regression model. Using this technique, the subgroup of exposures with CMB was analyzed further. Mean (S.D.) time in minutes to onset of CMB and symptoms were 125 (63) and 165 (33) respectively, following the three hours exposure. The risk of symptoms (17/81) increased 14 times in the presence of CMB, after controlling for variations in time to detection of CMB. Further, the risk was lower when time to detection of CMB was greater than 60 minutes (risk ratio = 0.96; 95 percent confidence intervals = 0.94 - 0.99 0.99 P less than 0.01) compared to CMB before 60 minutes at altitude. Conclusions: Survival analysis showed that individual risk of DCS changes significantly due to variations in time to detection of CMB. This information is important in evaluating the risk of DCS in the presence of CMB.

Kumar, K. V.↗
NASA NTRSDate not supplied

Understanding the Impact of Unobservable Variables on the Performance of Predictive Models: The Need for Feature Space Partitioning and Fusion

When developing predictive models over a dataset, the model is globally optimized across the entire feature space to learn a decision boundary. However, when unobservable variables—which cannot be measured or estimated—interact with the observable variables, this can negatively impact the optimization applied to the decision boundary since the data samples introduced by unobservable variables may have little to no association with the applied global optimization. This, consequently, penalizes the entire decision boundary and model performance. This paper examines some of the detrimental effects of unobservable variables, particularly their role in creating new modes in the distribution of observable variables and reducing the separability of class distributions. Such challenges result in skewed or warped decision boundaries and decreased accuracy of model predictions, particularly for interpretable models like logistic regression and decision trees. Through two illustrative case examples, we highlight the need to address the challenges imposed by unobservable variables. We propose a strategy to mitigate these challenges by creating local regions within the feature space through partitioning. This enables the optimization of local models within the regions to overcome the impact of unobservability in different feature space localities. Research into a more sophisticated partitioning strategy and where the partition should be relative to the sample of interest is left as future work. Through the analysis of the impact of unobservability and the development of a partitioning method, we demonstrate the clear need for a partitioning strategy that integrates knowledge from multiple local models to estimate risk factors using information fusion. Thus, we establish the foundation and motivation for using partitioning and information fusion to overcome the effects of unobservability in predictive models. Formal fusion methods, such as Dempster-Shafer theory, can better leverage the information from local regions to improve the performance of interpretable predictive models in the presence of unobservable variables.

Time Series Data↗
NASA NTRS2014

Interrelationships Between Receiver/Relative Operating Characteristics Display, Binomial, Logit, and Bayes' Rule Probability of Detection Methodologies

Unknown risks are introduced into failure critical systems when probability of detection (POD) capabilities are accepted without a complete understanding of the statistical method applied and the interpretation of the statistical results. The presence of this risk in the nondestructive evaluation (NDE) community is revealed in common statements about POD. These statements are often interpreted in a variety of ways and therefore, the very existence of the statements identifies the need for a more comprehensive understanding of POD methodologies. Statistical methodologies have data requirements to be met, procedures to be followed, and requirements for validation or demonstration of adequacy of the POD estimates. Risks are further enhanced due to the wide range of statistical methodologies used for determining the POD capability. Receiver/Relative Operating Characteristics (ROC) Display, simple binomial, logistic regression, and Bayes' rule POD methodologies are widely used in determining POD capability. This work focuses on Hit-Miss data to reveal the framework of the interrelationships between Receiver/Relative Operating Characteristics Display, simple binomial, logistic regression, and Bayes' Rule methodologies as they are applied to POD. Knowledge of these interrelationships leads to an intuitive and global understanding of the statistical data, procedural and validation requirements for establishing credible POD estimates.

Generazio, Edward R.↗
NASA NTRS1992

Time to detection of circulating microbubbles as a risk factor for symptoms of altitude decompression sickness

This study investigated the association between time at onset of circulating microbubbles (CMB) and symptoms of altitude decompression sickness (DCS), using Cox proportional hazard regression models. The study population consisted of 125 individuals who participated in direct ascent, simulated extravehicular activities profiles. Using individual CMB status as a time-dependent variable, we found that the hazard for symptoms increased significantly (at the end of 180 min at altitude) in the presence of CMB (Hazard Ratio = 29.59; 95 percent confidence interval (95 percent CI) = 7.66-114.27), compared to no CMB. Further examination was conducted on the subgroup of individuals who developed microbubbles during the test (n = 49), by using Cox regression. Individuals with late onset of CMB (greater than 60 min at altitude) showed a significantly reduced risk of symptoms (hazard ratio = 0.92; 95 percent CI = 0.89-0.95), compared to those with early onset (equal to or less than 60 min), while controlling for other risk factors. We conclude that time to detection of circulating microbubbles is an independent determinant of symptoms of DCS.

Kumar, K. V.↗
NASA NTRS2003

Development of a safe ground to space laser propagation system for the optical communications telescope laboratory

Furthering pursuits in high bandwidth communications to future NASA deep space and neat-Earth probes, the Jet Propulsion Laboratory (JPL) is building the Optical communications Telescope Laboratory (OCTL) atop Table Mountain in Southern California. This R&D optical antenna will be used to develop optical communication strategies for future optical ground stations. Initial experiments to be conducted include propagating high-powered, Q-switched laser beams to retro-reflecting satellites. Yet laser beam propagation from the ground to space is under the cognizance of various government agencies, namely: the Occupational Safety and Health Administration (ISHA) that is responsible for protecting workforce personnel; the Federal Aviation Administration (FAA) responsible for protecting pilots and aircraft; and the Laser Clearinghouse of Space Command responsible for protecting space assets. To ensure that laser beam propagation from the OCTL and future autonomously operated ground stations comply with the guidelines of these organizations, JPL is developing a multi-tiered safety system that will meet the coordination, monitoring, and reporting functions required by the agencies. At Tier 0, laser operators will meet OSHA safety standards for protection and access to the high power lasers area will be restricted and interlocked. Tier 1, the area defined from the telescope dome out to a range of 3.4-km, will utilize long wave infrared camera sensors to alert operators of at risk aircraft in the FAA controlled airspace. Tier 2, defined to extend from 3.4-km out to the aircraft service ceiling in FAA airspace, will detect at risk aircraft by radar. Lastly, beam propagation into space, defined as Tier 3, will require coordination with the Laser Clearinghouse. A detailed description of the four tiers is presented along with the design of the integrated monitoring and beam transmission control system.

laser safety↗
NASA NTRS2018

Using ADOPT Algorithm and Operational Data to Discover Precursors to Aviation Adverse Events

The US National Airspace System (NAS) is making its transition to the NextGen system and assuring safety is one of the top priorities in NextGen. At present, safety is managed reactively (correct after occurrence of an unsafe event). While this strategy works for current operations, it may soon become ineffective for future airspace designs and high density operations. There is a need for proactive management of safety risks by identifying hidden and "unknown" risks and evaluating the impacts on future operations. To this end, NASA Ames has developed data mining algorithms that finds anomalies and precursors (high-risk states) to safety issues in the NAS. In this paper, we describe a recently developed algorithm called ADOPT that analyzes large volumes of data and automatically identifies precursors from real world data. Precursors help in detecting safety risks early so that the operator can mitigate the risk in time. In addition, precursors also help identify causal factors and help predict the safety incident. The ADOPT algorithm scales well to large data sets and to multidimensional time series, reduce analyst time significantly, quantify multiple safety risks giving a holistic view of safety among other benefits. This paper details the algorithm and includes several case studies to demonstrate its application to discover the "known" and "unknown" safety precursors in aviation operation.

aviation safet↗
NASA NTRSDate not supplied

Multiclass Flight Anomaly Detection Using Sensor Fusion Based on Dempster-Shafer Theory

As aviation systems in commercial operations continue to grow in complexity, the anomalies exhibited by these systems become more elaborate and difficult to detect. To address the challenge of detecting these complex anomalies, deep learning models have been used extensively in aviation anomaly detection studies, at the expense of end-user interpretability. Aiming to maintain the same level of interpretability as traditional threshold-exceedance methods, we continue our development of prediction models using ordinal patterns and their distributions throughout the flight. Specifically, this study extends our work into multiclass anomaly detection using sensor fusion based on Dempster-Shafer theory (DST), a second-order probability theory used to combine information from different sources of evidence. Our approach uses DST toreduce the uncertainty in the class predictions of an ensemble of classifiers. These classifiers rely on the similarity between flight data and class templates to make a prediction of the state of the aircraft. Our approach aims to take advantage of simple models trained on interpretable features (ordinal patterns) to correctly predict an anomaly and identify the flight dynamics linked to the anomaly. Our results show an improvement when using DST-based sensor fusion over simple majority voting. Additionally, our results provide insight into aircraft states linked to rare high-risk anomalies.

Risk detection↗
NASA NTRSDate not supplied

Multiclass Flight Anomaly Detection Using Sensor Fusion Based on Dempster-Shafer Theory

As aviation systems in commercial operations continue to grow in complexity, the anomalies exhibited by these systems become more elaborate and difficult to detect. To address the challenge of detecting these complex anomalies, deep learning models have been used extensively in aviation anomaly detection studies, at the expense of end-user interpretability. Aiming to maintain the same level of interpretability as traditional threshold-exceedance methods, we continue our development of prediction models using ordinal patterns and their distributions throughout the flight. Specifically, this study extends our work into multiclass anomaly detection using sensor fusion based on Dempster-Shafer theory (DST), a second-order probability theory used to combine information from different sources of evidence. Our approach uses DST to reduce the uncertainty in the class predictions of an ensemble of classifiers. These classifiers rely on the similarity between flight data and class templates to make a prediction of the state of the aircraft. Our approach aims to take advantage of simple models trained on interpretable features (ordinal patterns) to correctly predict an anomaly and identify the flight dynamics linked to the anomaly. Our results show an improvement when using DST-based sensor fusion over simple majority voting. Additionally, our results provide insight into aircraft states linked to rare high-risk anomalies.

Risk detection↗
NASA NTRS2002

Intrusion detection: systems and models

This paper puts forward a review of state of the art and state of the applicability of intrusion detection systems, and models. The paper also presents a classfication of literature pertaining to intrusion detection.

intrusion detection risk security↗
NASA NTRS2003

Optimizing spacecraft design - optimization engine development : progress and plans

At JPL and NASA, a process has been developed to perform life cycle risk management. This process requires users to identify: goals and objectives to be achieved (and their relative priorities), the various risks to achieving those goals and objectives, and options for risk mitigation (prevention, detection ahead of time, and alleviation). Risks are broadly defined to include the risk of failing to design a system with adequate performance, compatibility and robustness in addition to more traditional implementation and operational risks. The options for mitigating these different kinds of risks can include architectural and design choices, technology plans and technology back-up options, test-bed and simulation options, engineering models and hardware/software development techniques and other more traditional risk reduction techniques.

risk↗
NASA NTRS2007

Detection of Orbital Debris Collision Risks for the Automated Transfer Vehicle

In this paper, we present a general collision risk assessment method, which has been applied through numerical simulations to the Automated Transfer Vehicle (ATV) case. During ATV ascent towards the International Space Station, close approaches between the ATV and objects of the USSTRACOM catalog will be monitored through collision rosk assessment. Usually, collision risk assessment relies on an exclusion volume or a probability threshold method. Probability methods are more effective than exclusion volumes but require accurate covariance data. In this work, we propose to use a criterion defined by an adaptive exclusion area. This criterion does not require any probability calculation but is more effective than exclusion volume methods as demonstrated by our numerical experiments. The results of these studies, when confirmed and finalized, will be used for the ATV operations.

Peret, L.↗