Results for “air classification” — Page 3

NASA NTRS1979

Spray nozzle designs for agricultural aviation applications

Techniques of generating monodisperse sprays and information concerning chemical liquids used in agricultural aviation are surveyed. The periodic dispersion of liquid jet, the spinning disk method, and ultrasonic atomization are the techniques discussed. Conceptually designed spray nozzles for generating monodisperse sprays are assessed. These are based on the classification of the drops using centrifugal force, on using two opposing liquid laden air jets, and on operating a spinning disk at an overloaded flow. Performance requirements for the designs are described and estimates of the operational characteristics are presented.

Lee, K. W.↗

NASA NTRS1998

A Seasonal Air Transport Climatology for Kenya

A climatology of air transport to and from Kenya has been developed using kinematic trajectory modeling. Significant months for trajectory analysis have been determined from a classification of synoptic circulation fields. Five-point back and forward trajectory clusters to and from Kenya reveal that the transport corridors to Kenya are clearly bounded and well defined. Air reaching the country originates mainly from the Saharan region and northwestern Indian Ocean of the Arabian Sea in the northern hemisphere and from the Madagascan region of the Indian Ocean in the southern hemisphere. Transport from each of these source regions show distinctive annual cycles related to the northeasterly Asian monsoon and the southeasterly trade wind maximum over Kenya in May. The Saharan transport in the lower troposphere is at a maximum when the subtropical high over northern Africa is strongly developed in the boreal winter. Air reaching Kenya between 700 and 500 hPa is mainly from Sahara and northwest India Ocean flows in the months of January and March, which gives way to southwest Indian Ocean flow in May and November. In contrast, air reaching Kenya at 400 hPa is mainly from southwest Indian Ocean in January and March, which is replaced by Saharan transport in May and November. Transport of air from Kenya is invariant, both spatially and temporally, in the tropical easterlies to the Congo Basin and Atlantic Ocean in comparison to the transport to the country. Recirculation of air has also been observed, but on a limited and often local scale and not to the extent reported in southern Africa.

Gatebe, C. K.↗

NASA NTRS2005

A Concept for the Inclusion of Analytical and Computational Capability in Existing Systems for Measurement of Neutron Flux

For approximately two decades, efforts have been sponsored by NASA's Marshall Space Flight Center to make possible high-speed, automated classification and quantification of constituent materials in various harsh environments. MSFC, along with the Air Force/Arnold Engineering Development Center, has led the work, developing and implementing systems that employ principles of emission and absorption spectroscopy to monitor molecular and atomic particulates in gas plasma of rocket engine flow fields. One such system identifies species and quantifies mass loss rates in H2/O2 rocket plumes. Other gases have been examined and the physics of their detection under numerous conditions were made a part of the knowledge base for the MSFC/USAF team. Additionally, efforts are being advanced to hardware encode components of the data analysis tools in order to address real-time operational requirements for health monitoring and management. NASA has a significant investment in these systems, warranting a spiral approach that meshes current tools and experience with technological advancements. This paper addresses current systems - the Optical Plume Anomaly Detector (OPAD) and the Engine Diagnostic Filtering System (EDIFIS) - and discusses what is considered a natural progression: a concept for migrating them towards detection of high energy particles, including neutrons and gamma rays. The proposal outlines system development to date, basic concepts for future advancements, and recommendations for accomplishing them.

Patrick, Clinton↗

NASA NTRS2001

The Conference Proceedings of the 2001 Air Transport Research Society (ATRS) of the WCTR Society

The papers presented at this conference include: 1) The Global Airline Company: Agent of Market Power or Competition? 2) Airport Pavement Management; 3) Reservation System Providers and the Impact of Codeshare Arrangements on Screen Display; 4) Strategic Classification of Current Airline Alliances and Examination of Critical Factors Involving the Formations - an Explorative Perspective; 5) Airport Privatization Policy and Performance Measurement in Korea; 6) Pilot and Air Traffic Controller Relationships: The Role of Interdependence and Relative Influence; 7) Liberalization of Air Cargo Services: Background and an Economic Analysis; 8) The Implication of Hub and Spoke Network on the Airline Alliance Strategy.

Lee, Yeong-Heok↗

NASA NTRS1991

Analysis of unsteady pressures induced on a body by a rotor

An analysis was conducted on the unsteady pressures measured on a body surface in proximity to a rotor. Unsteady pressures were measured at strategic points on the body in forward flight at various combination of advance ratio, rotor thrust, and tip-path-plane angle. It was found that the local values of unsteady pressure were significantly altered by small changes in rotor thrust and advance ratio; tip-path-plane angle variations had a much smaller effect. Four characteristic pressure signatures representative of (1) blade passage, (2) close wake interactions with the body, (3) wake impingement on the body, and (4) postwake impingement were distinguished. These classifications were supported by correlations with shadowgraphic flow visualization of the rotor wake/body interaction, as well as unsteady potential flow models. This general classification of the surface pressure signatures has permitted a greater physical understanding of the overall air loads and possible mechanisms responsible for the interactional effects between a helicopter rotor and its fuselage.

Bi, Nai-Pei↗

NASA NTRS1997

Surface-Based Observations of Contrail Occurrence Over the US, Apr. 1993 to Apr. 1994

Surface observers stationed at 19 U.S. Air Force Bases and Army Air Stations recorded the daytime occurrence of contrails and cloud fraction on an hourly basis for the period April 1993 through April 1994. Each observation uses one of four main categories to report contrails as unobserved, non-persistent, persistent, and indeterminate. Additional classification includes the co-occurrence of cirrus with each report. The data cover much of the continental U.S. including locations near major commercial air routes. The mean annual frequency of occurrence in unobstructed viewing conditions is 13 percent for these sites. Contrail occurrence varied substantially with location and season. Most contrails occurred during the winter months and least during the summer with a pronounced minimum during July. Although nocturnal observations are not available, it appears that the contrails have a diurnal variation that peaks during mid morning over most areas. Contrails were most often observed in areas near major commercial air corridors and least often over areas far removed from the heaviest air traffic. A significant correlation exists between mean contrail frequency and aircraft fuel usage above 7 km suggesting predictive potential for assessing future contrail effects on climate.

Minnis, Patrick↗

NASA NTRS2014

In-Situ and Remote-Sensing Data Fusion Using Machine Learning Techniques to Infer Urban and Fire Related Pollution Plumes

Airmass type characterization is key in understanding the relative contribution of various emission sources to atmospheric composition and air quality and can be useful in bottom-up model validation and emission inventories. However, classification of pollution plumes from space is often not trivial. Sub-orbital campaigns, such as SEAC4RS (Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys) give us a unique opportunity to study atmospheric composition in detail, by using a vast suite of in-situ instruments for the detection of trace gases and aerosols. These measurements allow identification of spatial and temporal atmospheric composition changes due to various pollution plumes resulting from urban, biogenic and smoke emissions. Nevertheless, to transfer the knowledge gathered from such campaigns into a global spatial and temporal context, there is a need to develop workflow that can be applicable to measurements from space. In this work we rely on sub-orbital in-situ and total column remote sensing measurements of various pollution plumes taken aboard the NASA DC-8 during 2013 SEAC4RS campaign, linking them through a neural-network (NN) algorithm to allow inference of pollution plume types by input of columnar aerosol and trace-gas measurements. In particular, we use the 4STAR (Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research) airborne measurements of wavelength dependent aerosol optical depth (AOD), particle size proxies, O3, NO2 and water vapor to classify different pollution plumes. Our method relies on assigning a-priori ground-truth labeling to the various plumes, which include urban pollution, different fire types (i.e. forest and agriculture) and fire stage (i.e. fresh and aged) using cluster analysis of aerosol and trace-gases in-situ and auxiliary (e.g. trajectory) data and the training of a NN scheme to fit the best prediction parameters using 4STAR measurements as input. We explore our misclassification rates as related to our ground-truth labels, and with multi-layered pollution plume cases. The next step in our analysis is to optimize parameter selection for a scheme that can be applied to space-borne aerosol and trace-gas observation platforms such as OMI, and future geostationary satellites such as TEMPO and GEO-CAPE.

Neural-network↗

NASA NTRS1989

Ice-type classifications from airborne pulse-limited radar altimeter return waveform characteristics

During mid-March 1978, the NASA C-130 aircraft was deployed to Eielson Air Force Base in Fairbanks, Alaska, to make a series of flights over ice in the Beaufort Sea. The radar altimeter data analyzed were obtained northeast of Mackenzie Bay on March 14th in the vicinity of 69.9 deg N, 134.2 deg W. The data were obtained with a 13.9 GHz radar altimeter developed under the NASA Advanced Applications Flight Experiments (AAFE) Program. This airborne radar was built as a forerunner of the Seasat radar altimeter, and utilized the same pulse compression technique. Pulse-limited radar data taken with the altimeter from 1500-m altitude over sea ice are registered to high-quality photography. The backscattered power is statistically related the surface conductivity and to the number of facets whose surface normal is directed towards the radar. The variations of the radar return waveform shape and signal level are correlated with the variation of the ice type determined from photography. The AAFE altimeter has demonstrated that the return waveform shape and signal level of an airborne pulse-limited altimeter at 13.9 GHz respond to sea ice type. The signal level responded dramatically to even a very small fracture in the ice, as long as it occurred directly at the altimeter nadir point. Shear zones and regions of significant compression ridging consistently produced low signal levels. The return waveforms frequently evidenced the characteristics of both specular and diffuse scattering, and there was an indication that the power backscattered at 3 deg off-nadir in a shear zone was actually somewhat higher than that from nadir.

Fedor, L. S.↗

NASA NTRS1970

Potentially efficient forest and range applications of remote sensing using earth orbital space craft, circa 1980

Sixteen remote sensing applications or groups of related applications judged to be most important of any in the forestry and range disciplines were evaluated. In one application, major land classification, large amounts of useful data are anticipated to be contributed by space sensors in 1980. In four applications moderate amounts are anticipated to be so contributed. These are timber inventory, range inventory, fire weather forecasting, and monitoring snowfields. In the following seven applications small but significant amounts of data are anticipated to be contributed by space sensors: (1) detailed land classification; (2) inventory of wildlife habitat; (3) recreation resource inventory; (4) detecting stresses on the vegetation (5) monitoring air pollution caused by wildfires and prescribed burning; (6) monitoring water cycle, (7) pollution and erosion; and (8) evaluating damage to forests and ranges.

Wilson, R. C.↗

NASA NTRSDate not supplied

Introduction to Air Traffic Management

The presentation introduces students and faculty to air traffic management with focus on air traffic data for data-science. Starting with the common attributes of transportation systems — highway transportation, air transportation and data transportation, the initial set of slides discuss the purpose of data-science in air traffic management, reasons why air traffic management is challenging, and the multidisciplinary nature of air traffic management research. The history of flight from 1903 — Wright Flyer — to 1987 — formation of the National Air Traffic Controllers Association — is briefly discussed. The national airspace system is described in terms of airports in the U. S., air traffic control facilities (flight service stations, terminal, enroute and system command center), airspace geometry (sectors, airways and navaids), governing regulations and directives, airspace classification (Class A through G), special use airspace, visual flight rules and instrument flight rules. The contents of a flight-plan are described. Weather briefing is discussed. The surveillance equipment used for surface, terminal area and enroute are described, and the aircraft states obtained using the surveillance data are listed. Airline operations control functions — schedule development, flight planning, resource scheduling and flight following — are noted. Next, the roles and responsibilities of air traffic controllers and traffic flow managers are discussed. Separation standards and conflict resolution techniques are outlined. Finally, traffic flow management techniques are reviewed with an illustrative example.

Air Traffic Management↗

NASA NTRSDate not supplied

Introduction to Air Traffic Management

The presentation introduces students and faculty to air traffic management with focus on air traffic data for data-science. Starting with the common attributes of transportation systems — highway transportation, air transportation and data transportation, the initial set of slides discuss the purpose of data-science in air traffic management, reasons why air traffic management is challenging, and the multidisciplinary nature of air traffic management research. The history of flight from 1903 — Wright Flyer — to 1987 — formation of the National Air Traffic Controllers Association — is briefly discussed. The national airspace system is described in terms of airports in the U. S., air traffic control facilities (flight service stations, terminal, enroute and system command center), airspace geometry (sectors, airways and navaids), governing regulations and directives, airspace classification (Class A through G), special use airspace, visual flight rules and instrument flight rules. The contents of a flight-plan are described. Weather briefing is discussed. The surveillance equipment used for surface, terminal area and enroute are described, and the aircraft states obtained using the surveillance data are listed. Airline operations control functions — schedule development, flight planning, resource scheduling and flight following — are noted. Next, the roles and responsibilities of air traffic controllers and traffic flow managers are discussed. Separation standards and conflict resolution techniques are outlined. Finally, traffic flow management techniques are reviewed with an illustrative example.

Air Traffic Management↗

NASA NTRSDate not supplied

2022 Spring Internship Exit Presentation

As efforts of the National Aeronautics and Space Administration (NASA) and the Federal Aviation Administration (FAA) continue to digitize the air traffic management (ATM) domain, there is countless times of need for downstream natural language processing (NLP) tasks such as named entity recognition, text summarization, classification, and more. Although there are a plethora of open-sourced pre-trained transformer models in the NLP field such as BERT, RoBERTa, XLNet, and GPT-3, these models are trained on general corpora and perform poorly on domain-specific terminology and phraseology seen in ATM documents such as Notice to Airmen (NOTAMs) and Letters of Agreement (LoA). Our proposed research objective will be to first gather a large corpus of air traffic management related documents, orders, notices, books, technical papers, conference papers, articles, and other miscellaneous sources of text data from the FAA, NASA, and accredited conference and publication societies. After gathering this data, many steps will have to be taken to collate and preprocess the data into a format understandable by our test transformer models. Thirdly, we will set up training pipelines to train the RoBERTa model on its unsupervised training task masked language modelling (MLM) using resources provided by the NASA Advanced Supercomputing (NAS) facilities. Finally, these fine-tuned transformer models will be evaluated on their performance on down-stream NLP tasks as mentioned above, to show whether they will be effective when working with ATM related data or not. Once complete, this model could be made open-sourced on the HuggingFace website, where the rest of the ATM community can access and utilize this tool.

NLP↗

NASA NTRS1982

The ABAG biogenic emissions inventory project

The ability to identify the role of biogenic hydrocarbon emissions in contributing to overall ozone production in the Bay Area, and to identify the significance of that role, were investigated in a joint project of the Association of Bay Area Governments (ABAG) and NASA/Ames Research Center. Ozone, which is produced when nitrogen oxides and hydrocarbons combine in the presence of sunlight, is a primary factor in air quality planning. In investigating the role of biogenic emissions, this project employed a pre-existing land cover classification to define areal extent of land cover types. Emission factors were then derived for those cover types. The land cover data and emission factors were integrated into an existing geographic information system, where they were combined to form a Biogenic Hydrocarbon Emissions Inventory. The emissions inventory information was then integrated into an existing photochemical dispersion model.

Carson-Henry, C.↗

NASA NTRSDate not supplied

Contextualizing Air Traffic Management Conversations using Natural Language Understanding

Efficient management of air traffic and mitigation of delays depend on extracting actionable information from unstructured data, such as dialogues from the Federal Aviation Administration’s (FAA’s) Air Traffic Control System Command Center (ATCSCC) telecons. This study presents a pipeline utilizing Natural Language Processing (NLP) methods for Intent Classification (IC) and Slot Filling (SF) to identify and extract Traffic Management Initiatives (TMIs) from aviation-specific dialogues. We leveraged DeBERTa, a pre-trained transformer model, and fine-tuned it to the nuances of the aviation domain. Despite challenges posed by annotation complexities, the IC model achieved promising results with a weighted average F1-score of 0.81. Our results are close to those of human annotators, which demonstrates the model’s strong alignment with human-level performance. The SF model also showed strong performance, achieving a weighted F1-score of 0.97, which demonstrates its effectiveness in accurately predicting key slots. Our analysis revealed limitations in handling less frequent intents and slot labels due to data sparsity, motivating future efforts to adopt joint IC-SF modeling and data augmentation strategies. This research highlights the potential of domain-specific NLP to streamline decision-making in the aviation industry and improve the management of TMIs.

Air Traffic Control Management↗

NASA NTRSDate not supplied

Classification of Notices to Airmen using Natural Language Processing

This paper establishes the feasibility of using Natural Language Processing (NLP) to classify NOTAMs or Notices to Airmen – a pilot messaging framework to gather real-time situational awareness. Present day air mobility operations heavily rely on NOTAMs. However, pilots often have difficulty interpreting NOTAMs due to the sheer volume of inapplicable messages and unclear abbreviations. Using NLP, the presented study analyzes the accuracy of classifying NOTAMs and, thereby, the efficiency of generating actionable interpretations in real time. To this effect, efficacies of four NLP neural network architectures were analyzed, including three Recurrent Neural Networks (RNNs) with GloVe, Word2Vec, and FastText word embeddings, and one trained Bi-Directional Encoder Representations from Transformers (BERT) model. The four neural networks were trained and evaluated on three open-source datasets of varying text lengths, vocabularies, and grammars, taken from e-commerce product descriptions, social media tweets, and unstructured descriptions for data and analytics services on open data marketplaces such as NASA’s Data and Reasoning Fabric (DRF) platform. This provided cross-analysis of each neural network architecture’s performance per text type. The best performing architecture, BERT, was then fine-tuned on a collection of open-source NOTAM data. Post-training, a real-time NOTAM classification service was implemented to draw inference on new NOTAMs using the trained model, which demonstrated close to 99% accuracy in classification. This modular classification service is envisioned to be integrated with a data and analytics delivery platform, such as the DRF, thus availing real-time contextualization of NOTAMs to air mobility clients, humans, and machines for enhanced decision making.

Aiden C. Szeto↗

NASA NTRSDate not supplied

Classification of Notices to Airmen using Natural Language Processing

This paper establishes the feasibility of using Natural Language Processing (NLP) to classify NOTAMs or Notices to Airmen – a pilot messaging framework to gather real-time situational awareness. Present day air mobility operations heavily rely on NOTAMs. However, pilots often have difficulty interpreting NOTAMs due to the sheer volume of inapplicable messages and unclear abbreviations. Using NLP, the presented study analyzes the accuracy of classifying NOTAMs and, thereby, the efficiency of generating actionable interpretations in real time. To this effect, efficacies of four NLP neural network architectures were analyzed, including three Recurrent Neural Networks (RNNs) with GloVe, Word2Vec, and FastText word embeddings, and one trained Bi-Directional Encoder Representations from Transformers (BERT) model. The four neural networks were trained and evaluated on three open-source datasets of varying text lengths, vocabularies, and grammars, taken from e-commerce product descriptions, social media tweets, and unstructured descriptions for data and analytics services on open data marketplaces such as NASA’s Data and Reasoning Fabric (DRF) platform. This provided cross-analysis of each neural network architecture’s performance per text type. The best performing architecture, BERT, was then fine-tuned on a collection of open-source NOTAM data. Post-training, a real-time NOTAM classification service was implemented to draw inference on new NOTAMs using the trained model, which demonstrated close to 99% accuracy in classification. This modular classification service is envisioned to be integrated with a data and analytics delivery platform, such as the DRF, thus availing real-time contextualization of NOTAMs to air mobility clients, humans, and machines for enhanced decision making.

Aiden Szeto↗

NASA NTRS2016

Automated Segmentation and Classification of Coral using Fluid Lensing from Unmanned Airborne Platforms

In recent years, there has been a growing interest among biologists in monitoring the short and long term health of the world's coral reefs. The environmental impact of climate change poses a growing threat to these biologically diverse and fragile ecosystems, prompting scientists to use remote sensing platforms and computer vision algorithms to analyze shallow marine systems. In this study, we present a novel method for performing coral segmentation and classification from aerial data collected from small unmanned aerial vehicles (sUAV). Our method uses Fluid Lensing algorithms to remove and exploit strong optical distortions created along the air-fluid boundary to produce cm-scale resolution imagery of the ocean floor at depths up to 5 meters. A 3D model of the reef is reconstructed using structure from motion (SFM) algorithms, and the associated depth information is combined with multidimensional maximum a posteriori (MAP) estimation to separate organic from inorganic material and classify coral morphologies in the Fluid-Lensed transects. In this study, MAP estimation is performed using a set of manually classified 100 x 100 pixel training images to determine the most probable coral classification within an interrogated region of interest. Aerial footage of a coral reef was captured off the coast of American Samoa and used to test our proposed method. 90 x 20 meter transects of the Samoan coastline undergo automated classification and are manually segmented by a marine biologist for comparison, leading to success rates as high as 85%. This method has broad applications for coastal remote sensing, and will provide marine biologists access to large swaths of high resolution, segmented coral imagery.

algorithms segments classifications↗

NASA NTRS2015

Automated Segmentation and Classification of Coral Using Fluid Lensing from Unmanned Airborne Platforms

In recent years, there has been a growing interest among biologists in monitoring the short and long term health of the worlds coral reefs. The environmental impact of climate change poses a growing threat to these biologically diverse and fragile ecosystems, prompting scientists to use remote sensing platforms and computer vision algorithms to analyze shallow marine systems. In this study, we present a novel method for performing coral segmentation and classification from aerial data collected from small unmanned aerial vehicles (sUAV). Our method uses Fluid Lensing algorithms to remove and exploit strong optical distortions created along the air-fluid boundary to produce cm-scale resolution imagery of the ocean floor at depths up to 5 meters. A 3D model of the reef is reconstructed using structure from motion (SFM) algorithms, and the associated depth information is combined with multidimensional maximum a posteriori (MAP) estimation to separate organic from inorganic material and classify coral morphologies in the Fluid-Lensed transects. In this study, MAP estimation is performed using a set of manually classified 100 x 100 pixel training images to determine the most probable coral classification within an interrogated region of interest. Aerial footage of a coral reef was captured off the coast of American Samoa and used to test our proposed method. 90 x 20 meter transects of the Samoan coastline undergo automated classification and are manually segmented by a marine biologist for comparison, leading to success rates as high as 85. This method has broad applications for coastal remote sensing, and will provide marine biologists access to large swaths of high resolution, segmented coral imagery.

algorithms segments classifications↗