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109 results for “aviation”
Magnetic Resonance Imaging (MRI) and Decline of Aging Aviator Performance
ClinicalTrials.gov study NCT01120860. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Age-related Longitudinal Changes in Aviator Performance
ClinicalTrials.gov study NCT01364753. IPD Sharing: Not stated. Countries: 1. Publications: 0.
LMOS Scientific Aviation In-Situ Data
LMOS_AircraftInSitu_ScientificAviation_Data_1 is the Lake Michigan Ozone Study (LMOS) in-situ data collected onboard the Scientific Aviation aircraft during the LMOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA, Electric Power Research Institute (EPRI), National Science Foundation (NSF), Lake Michigan Air Directors Consortium (LADCO) and its member states, and several research groups at universities. Data collection is complete.Elevated spring and summertime ozone levels remain a challenge along the coast of Lake Michigan, with a number of monitors recording levels/amounts exceeding the 2015 National Ambient Air Quality Standards (NAAQS) for ozone. The production of ozone over Lake Michigan, combined with onshore daytime “lake breeze” airflow is believed to increase ozone concentrations at locations within a few kilometers off shore. This observed lake-shore gradient motivated the Lake Michigan Ozone Study (LMOS). Conducted from May through June 2017, the goal of LMOS was to better understand ozone formation and transport around Lake Michigan; in particular, why ozone concentrations are generally highest along the lakeshore and drop off sharply inland and why ozone concentrations peak in rural areas far from major emission sources. LMOS was a collaborative, multi-agency field study that provided extensive observational air quality and meteorology datasets through a combination of airborne, ship, mobile laboratories, and fixed ground-based observational platforms. Chemical transport models (CTMs) and meteorological forecast tools assisted in planning for day-to-day measurement strategies. The long term goals of the LMOS field study were to improve modeled ozone forecasts for this region, better understand ozone formation and transport around Lake Michigan, provide a better understanding of the lakeshore gradient in ozone concentrations (which could influence how the Environmental Protection Agency (EPA) addresses future regional ozone issues), and provide improved knowledge of how emissions influence ozone formation in the region.
Environmentally Responsible Aviation Project
<p>Created in 2009 as part of NASA&#39;s Aeronautics Research Mission Directorate&#39;s Integrated Systems Research Program, the Environmentally Responsible Aviation (ERA) Project explores and documents the feasibility, benefits and technical risk of vehicle concepts and enabling technologies to reduce aviation&rsquo;s impact on the environment.<br /><br />Current-generation aircraft already benefit from the NASA investments in aeronautical research that have improved fuel efficiencies, lowered noise levels and reduced harmful emissions. Although substantial progress has been made, much more needs to be done.<br /><br />Forecasts call for the nation&#39;s air transportation system to expand significantly within the next two decades. Such an expansion could bring adverse environmental impacts. To neutralize or reduce these impacts is the goal of the ERA Project and its focused research.<br /><br />The project is organized to:</p><ul><li>Mature promising technology and advanced aircraft configurations that meet mid-term goals &mdash; in the next five to 10 years &mdash; for community noise, fuel burn and nitrogen oxides (NOx) emissions as described in the <a target="_blank" href="http://www.whitehouse.gov/sites/default/files/microsites/ostp/aero-rdplan-2010.pdf">National Aeronautics Research and Development Plan</a> and;</li><li>Determine the potential impact of these advanced aircraft designs and technologies if successfully implemented into the air transportation system.</li></ul><p>&nbsp;</p><p><strong>Research Challenges</strong><br />To enable advanced aircraft configurations that might enter service by 2025, the ERA Project is working on technologies that will simultaneously:</p><p>&nbsp;</p><ul><li>Reduce aircraft drag by 8%</li><li>Reduce aircraft weight by 10%</li><li>Reduce engine specific fuel consumption by 15%</li><li>Reduce oxides of nitrogen emissions of the engine by 75%</li><li>Reduce aircraft noise by 1/8 compared with current standards.</li></ul><p><br /><strong>Organization</strong><br />The ERA Project is comprised of three subprojects: Airframe Technology, Propulsion Technology and Vehicle Systems Integration. Work within the project is coordinated with system-level research performed by other programs within NASA&#39;s Aeronautics Research Mission Directorate as well as other federal government agencies.<br /><br />NASA has also put mechanisms in place to engage academia and industry, including working groups and technical interchange meetings; Space Act Agreements for cooperative partnerships; and the NASA Research Announcement process that provides for full and open competition for the best and most promising research ideas. The ERA Project disseminates all of its research results to the widest practical extent.</p>
EMAC/MESSy v2.54.0 model (EMAC) aviation emission inventories as calculated by AirTraf 2.0
<p>The data provided is a ASCII file which contains an aviation emission inventories for one year of a traffic sample over Europe (85 flights) as calculated by AirTraf submodel within the global modelling system EMAC. The submodel AirTraf enabling aircraft trajectory planning is implemented in the chemistry-climate model EMAC with the coupling to the submodel AirTraf. EMAC is a numerical chemistry and climate simulation system that includes submodels describing tropospheric and middle atmosphere processes and their interaction with oceans, land, and influences coming from anthropogenic emissions. It comprises the second version of the Modular Earth Submodel System (MESSy2) to link multi-institutional computer codes, in which the core atmospheric model is the fifth generation European Center Hamburg general circulation model (ECHAM5). We use a horizontal resolution of T42, with 31 hybrid vertical pressure levels up to 10 hPa (~30 km, T42L31ECMWF) and a time step of 20 minutes, with meteorology nudged to ERA5 reanalysis data as boundary conditions. AirTraf allows to calculate aircraft trajectories for given city-pairs with respect to dedicated routing strategies, considering meteorological conditions calculated by ECHAM5 online. Associated with these identified aircraft trajectories, the climate effect from aviation emissions along these trajectories is calculated from the submodel ACCF version 1.0 of EMAC. ACCF employs the aCCFs, that provide spatially and temporally resolved information on the climate effects of aviation emissions to quantify CO<sub>2</sub> and non-CO<sub>2</sub> effects. Specifically, they allow identifying regions of the atmosphere where aviation emissions induce a strong climate effect, e.g. via the formation of warming contrails or the production of radiatively active species like ozone. Thus, using these aCCFs, the estimated climate effect of aviation emissions and their spatial and temporal variability is available for the model domain: Subsequently, this is provided to AirTraf in order to enable not only estimating climate effects for calculated aircraft trajectories, but also planning of climate-optimized flight trajectories. The combination of EMAC/AirTraf/ACCF is applied here to simulate flight trajectories as great circle routes between city pairs (equal to AirClim), considering the variability of synoptic weather patterns in a continuous representation of the global atmosphere, and to quantitatively assess total climate effect and overall performance of flights.</p>
Aviation Portable Oxygen Delivery System
ClinicalTrials.gov study NCT03425409. IPD Sharing: NO. Countries: 0. Publications: 0.
Changes in Aviators' Body Core Temperature Measurements at F-35
ClinicalTrials.gov study NCT03234270. IPD Sharing: NO. Countries: 0. Publications: 0.
Greener Aviation with Virtual Sensors: A Case Study
The environmental impact of aviation is enormous given the fact that in the US alone there are nearly 6 million flights per year of commercial aircraft. This situation has driven numerous policy and procedural measures to help develop environmentally friendly technologies which are safe and affordable and reduce the environmental impact of aviation. However, many of these technologies require significant initial investment in newer aircraft fleets and modifications to existing regulations which are both long and costly enterprises. We propose to use an anomaly detection method based on Virtual Sensors to help detect overconsumption of fuel in aircraft which relies only on the data recorded during flight of most existing commercial aircraft, thus significantly reducing the cost and complexity of implementing this method. The Virtual Sensors developed here are ensemble-learning regression models for detecting the overconsumption of fuel based on instantaneous measurements of the aircraft state. This approach requires no additional information about standard operating procedures or other encoded domain knowledge. We present experimental results on three data sets and compare five different Virtual Sensors algorithms. The first two data sets are publicly available and consist of a simulated data set from a flight simulator and a real-world turbine disk.We show the ability to detect anomalies with high accuracy on these data sets. These sets contain seeded faults, meaning that they have been deliberately injected into the system. The second data set is from realworld fleet of 84 jet aircraft where we show the ability to detect fuel overconsumption which can have a significant environmental and economic impact. To the best of our knowledge, this is the first study of its kind in the aviation domain.
Aviation System Monitoring and Modeling Project
Air transportation, one of the most important modes of transportation, is also one of the safest. Nevertheless, the public demands that safety levels continuously improve and that the absolute number of aviation accidents continue to decline, even as air-traffic levels increase. On February 12, 1997, after the tragedy of TWA 800, President William J. Clinton declared, “We will achieve a national goal of reducing the fatal aircraft accident rate by 80% within 10 years.” In response to this presidential declaration, the administrator of NASA announced that NASA would undertake a new program in aviation safety in support of this objective. NASA quickly formed the Aviation Safety Investment Strategy Team (ASIST) in collaboration with the Federal Aviation Administration (FAA) and the National Transportation Safety Board (NTSB), which organized a series of five workshops to examine the options and recommend an approach for NASA to develop the enabling technologies that address the president's goal. The exceptional and dedicated participation from all sectors of the aviation industry in the development of NASA’s strategy was remarkable.
Aviation Safety Reporting System: Passenger Electronic Devices
A sampling of reports referencing avionics problems that may result from the influence of passenger electronic devices.
Aviation Safety Reporting System: Checklist Incidents
A sampling of reports from all aviation arenas referencing checklist issues (design, procedures, distraction, etc.).
Aviation Safety Reporting System: Inflight Weather Encounters
A sampling of reports from both air carrier flight crews and GA pilots referencing encounters with severe or unforecast weather.
Proactive Management of Aviation System Safety Risk
Aviation safety systems have undergone dramatic changes over the past fifty years. If you take a look at the early technology in this area, you'll see that there was a lot of work done in the area of so-called 'built-in-testing' (BIT) which essentially tests connectivity between different components. Technology has moved forward very far since that time. With the massive storage systems and advanced sensors and other communications systems, we are now able to capture and store vast quantities of health and control related data. This data is usually stored off-line for future analysis. In many cases, we also have an abundance of human-written text reports that relate to known safety issues. A key problem is to 'look' across all these data sources in order to find precursors to safety events. Although humans do look at many aspects of the data, it is difficult, if not impossible for them to integrate all the information available in a meaningful way. Other industries face this glut of data in their own way. Businesses have invested heavily in business intelligence products based on data mining that are designed to convert data into actionable information to maximize their profits or other metrics. The IVHM project is investing in data mining technologies to help sift through these massive data sets to uncover actionable information from a safety perspective. I've attached a presentation that Irv Statler and I gave at NASA HQ on this subject during an Aeronautics Technical Seminar. A video of the talk is also included. It's about 90 minutes long, so grab some popcorn. :)
Aviation Safety 2009 IVHM Posters
Posters form 2009 Aviation Safety Technical Conference
Fleet Level Anomaly Detection of Aviation Safety Data
For the purposes of this paper, the National Airspace System (NAS) encompasses the operations of all aircraft which are subject to air traffic control procedures. The NAS is a highly complex dynamic system that is sensitive to aeronautical decision-making and risk management skills. In order to ensure a healthy system with safe flights a systematic approach to anomaly detection is very important when evaluating a given set of circumstances and for determination of the best possible course of action. Given the fact that the NAS is a vast and loosely integrated network of systems, it requires improved safety assurance capabilities to maintain an extremely low accident rate under increasingly dense operating conditions. Data mining based tools and techniques are required to support and aid operators’ (such as pilots, management, or policy makers) overall decision-making capacity. Within the NAS, the ability to analyze fleetwide aircraft data autonomously is still considered a significantly challenging task. For our purposes a fleet is defined as a group of aircraft sharing generally compatible parameter lists. Here, in this effort, we aim at developing a system level analysis scheme. In this paper we address the capability for detection of fleetwide anomalies as they occur, which itself is an important initiative toward the safety of the real-world flight operations. The flight data recorders archive millions of data points with valuable information on flights everyday. The operational parameters consist of both continuous and discrete (binary & categorical) data from several critical subsystems and numerous complex procedures. In this paper, we discuss a system level anomaly detection approach based on the theory of kernel learning to detect potential safety anomalies in a very large data base of commercial aircraft. We also demonstrate that the proposed approach uncovers some operationally significant events due to environmental, mechanical, and human factors issues in high dimensional, multivariate Flight Operations Quality Assurance (FOQA) data. We present the results of our detection algorithms on real FOQA data from a regional carrier.
Discovering Precursors to Aviation Safety Incidents: KDD 2010
Modern aircraft are producing data at an unprecedented rate with hundreds of parameters being recorded on a second by second basis. The data can be used for studying the condition of the hardware systems of the aircraft and also for studying the complex interactions between the pilot and the aircraft. NASA is developing novel data mining algorithms to detect precursors to aviation safety incidents from these data sources. This talk will cover the theoretical aspects of the algorithms and practical aspects of implementing these techniques to study one of the most complex dynamical systems in the world: the national airspace.
Aviation Safety Reporting System: Passenger Misconduct Reports
A sampling of reports referencing disruptive passenger encounters with cabin crew or flight crew members.
ANALYZING AVIATION SAFETY REPORTS: FROM TOPIC MODELING TO SCALABLE MULTI-LABEL CLASSIFICATION
ANALYZING AVIATION SAFETY REPORTS: FROM TOPIC MODELING TO SCALABLE MULTI-LABEL CLASSIFICATION AMRUDIN AGOVIC*, HANHUAI SHAN*, AND ARINDAM BANERJEE* Abstract. The Aviation Safety Reporting System (ASRS) is used to collect voluntarily submitted aviation safety reports from pilots, controllers and others. As such it is particularly useful in researching aviation safety deficiencies. In this paper we address two challenges related to the analysis of ASRS data: (1) the unsupervised extraction of meaningful and interpretable topics from ASRS reports and (2) multi-label classification of ASRS data based on a set of predefined categories. For topic modeling we investigate the practical usefulness of Latent Dirichlet Allocation (LDA) when it comes to modeling ASRS reports in terms of interpretable topics. We also utilize LDA to generate a more compact representation of ASRS reports to be used in multi-label classification. For multi-label classification we propose a novel and highly scalable multi-label classification algorithm based on multi-variate regression. Empirical results indicate that our approach is superior to several baseline and state-of-the-art approaches.
Aviation Safety Reporting System: Pilot / Controller Communications
A sampling of reports which highlight issues involving communications between pilots and controllers.
Multiple Kernel Learning for Heterogeneous Anomaly Detection: Algorithm and Aviation Safety Case Study
The world-wide aviation system is one of the most complex dynamical systems ever developed and is generating data at an extremely rapid rate. Most modern commercial aircraft record several hundred flight parameters including information from the guidance, navigation, and control systems, the avionics and propulsion systems, and the pilot inputs into the aircraft. These parameters may be continuous measurements or binary or categorical measurements recorded in one second intervals for the duration of the flight. Currently, most approaches to aviation safety are reactive, meaning that they are designed to react to an aviation safety incident or accident. In this paper, we discuss a novel approach based on the theory of multiple kernel learning to detect potential safety anomalies in very large data bases of discrete and continuous data from world-wide operations of commercial fleets. We pose a general anomaly detection problem which includes both discrete and continuous data streams, where we assume that the discrete streams have a causal influence on the continuous streams. We also assume that atypical sequences of events in the discrete streams can lead to off-nominal system performance. We discuss the application domain, novel algorithms, and also discuss results on real-world data sets. Our algorithm uncovers operationally significant events in high dimensional data streams in the aviation industry which are not detectable using state of the art methods.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.