Results for “SME” — Page 6

NASA NTRS1997

Microwave Observations and Modeling of O2 (1-delta(sub g)) and O3 Diurnal Variation in the Mesosphere

The first microwave measurements of an electronically excited molecular species in the Earth's atmosphere are presented. Local thermodynamic equilibrium (LTE) rotational line emission from mesospheric O2(1-del(sub g)) was observed at a frequency of 255.01794 GHz (lambda is approx. 1.2 mm), employing the National Radio Astronomy Observatory (NRAO) millimeter facility at Kitt Peak, Arizona (32 N, 111 W). The pressure broadened line shapes of the O2(1-del(sub g)) spectra, which were obtained in January and April 1992 and in January and November 1993, are inverted to retrieve O2(1-del(sub g)) mixing profiles over the 50-70 km altitude region. The observed daytime abundances exceed ozone abundances in the lower mesosphere, which are separately retrieved with coincident O3 spectral line (249.7886 GHz) observations. The January and November 1993 observations are binned into 20-60 min time intervals to study O2(1-del(sub g)) diurnal behavior. Derived abundances of O2(1-del(sub g)) between 50 and 70 km for the four observation dates are 9%, 31%, 3%, and 26%, respectively, each +/- 10% higher than predicted, based on the simple photochemistry of lower mesospheric O2(1-del(sub g)). Modeled variation of [O2(1-del(sub g))] with time of day agrees with observed variation in that the observed difference between model and data abundances is constant throughout the daylight hours of each observation date. Model underprediction Of [02(lAg)] is consistent with similar model underprediction of mesospheric [O3]. A perturbation to the photochemical model that forces decreased ozone chemical loss brings brings both model [O3] and [O2(1-del(sub g))] into agreement with the observations. O2(1-del(sub g)) abundances derived from these 1.2 mm observations agree with [O2(1-del(sub g))] values derived from comparable SME observations of the 1.27 micrometers emission, with assumption of a 3880 sec O2(1-del(sub g)) radiative lifetime. The 6800 sec O2(1-del(sub g)) radiative lifetime proposed by Mlynczak and Nesbitt is ruled out by the similar comparison.

Sandor, Brad J.↗

NASA NTRS2007

A Parametric Geometry Computational Fluid Dynamics (CFD) Study Utilizing Design of Experiments (DOE)

Design of Experiments (DOE) was applied to the LAS geometric parameter study to efficiently identify and rank primary contributors to integrated drag over the vehicles ascent trajectory in an order of magnitude fewer CFD configurations thereby reducing computational resources and solution time. SME s were able to gain a better understanding on the underlying flowphysics of different geometric parameter configurations through the identification of interaction effects. An interaction effect, which describes how the effect of one factor changes with respect to the levels of other factors, is often the key to product optimization. A DOE approach emphasizes a sequential approach to learning through successive experimentation to continuously build on previous knowledge. These studies represent a starting point for expanded experimental activities that will eventually cover the entire design space of the vehicle and flight trajectory.

Rhew, Ray D.↗

NASA NTRS2010

Pilot Aircraft Interface Objectives/Rationale

Objective: Database and proof of concept for guidelines for GCS compliance a) Rationale: 1) Provide research test-bed to develop guidelines. 2) Modify GCS for NAS Compliance to provide proof of concept. b) Approach: 1) Assess current state of GCS technology. 2) Information Requirements Definition. 3) SME Workshop. 4) Modify an Existing GCS for NAS Compliance. 5) Define exemplar UAS (choose system to develop prototype). 6) Define Candidate Displays & Controls. 7) Evaluate/ refine in Simulations. 8) Demonstrate in flight. c) Deliverables: 1) Information Requirements Report. 2) Workshop Proceedings. 3) Technical Reports/ papers on Simulations & Flight Demo. 4) Database for guidelines.

Shively, Jay↗

NASA NTRS2010

IT Research Services: Powerful Tools to Track a Fast Moving Industry

Research services change and evolve over time, sometimes suddenly, in their focus, topics, formats, service model, etc. You have to check in often. Some offer products that you can customize to your environment, others not so much. You will find a variation in customer management practices (e.g., when and how they share information) ...can sometimes be annoying. Assess your needs carefully. Each research service has a very different service model. Explore ways to share or lower the cost. Someone out there may share the expense when that is an option. Make sure you read the small print. It can work for you as well as against you. When in doubt, ask your Vendor's POC. Set up Research Service "gurus or SME's." Not to violate the copyright agreement of course, just have someone who knows what's there. After you subscribe, reassess.Get the most out of your investment. Some early enthusiasts will fade, and there will be others who don't know it's there and can use it.

Hunter, Paul↗

NASA NTRS2010

NextGen Future Safety Assessment Game

The successful implementation of the next generation infrastructure systems requires solid understanding of their technical, social, political and economic aspects along with their interactions. The lack of historical data that relate to the long-term planning of complex systems introduces unique challenges for decision makers and involved stakeholders which in turn result in unsustainable systems. Also, the need to understand the infrastructure at the societal level and capture the interaction between multiple stakeholders becomes important. This paper proposes a methodology in order to develop a holistic approach aiming to provide an alternative subject-matter expert (SME) elicitation and data collection method for future sociotechnical systems. The methodology is adapted to Next Generation Air Transportation System (NextGen) decision making environment in order to demonstrate the benefits of this holistic approach.

Ancel, Ersin↗

NASA NTRS2011

A Summary of the Rendezvous, Proximity Operations, Docking, and Undocking (RPODU) Lessons Learned from the Defense Advanced Research Project Agency (DARPA) Orbital Express (OE) Demonstration System Mission

The Guidance, Navigation, and Control (GN&C) Technical Discipline Team (TDT) sponsored Dr. J. Russell Carpenter, a Navigation and Rendezvous Subject Matter Expert (SME) from NASA's Goddard Space Flight Center (GSFC), to provide support to the Defense Advanced Research Project Agency (DARPA) Orbital Express (OE) rendezvous and docking flight test that was conducted in 2007. When that DARPA OE mission was completed, Mr. Neil Dennehy, NASA Technical Fellow for GN&C, requested Dr. Carpenter document his findings (lessons learned) and recommendations for future rendezvous missions resulting from his OE support experience. This report captures lessons specifically from anomalies that occurred during one of OE's unmated operations.

Dennehy, Cornelius J.↗

NASA NTRS2011

NextGen Future Safety Assessment Game

The successful implementation of the next generation infrastructure systems requires solid understanding of their technical, social, political and economic aspects along with their interactions. The lack of historical data that relate to the long-term planning of complex systems introduces unique challenges for decision makers and involved stakeholders which in turn result in unsustainable systems. Also, the need to understand the infrastructure at the societal level and capture the interaction between multiple stakeholders becomes important. This paper proposes a methodology in order to develop a holistic approach aiming to provide an alternative subject-matter expert (SME) elicitation and data collection method for future sociotechnical systems. The methodology is adapted to Next Generation Air Transportation System (NextGen) decision making environment in order to demonstrate the benefits of this holistic approach.

Ancel, Ersin↗

NASA NTRS2011

Direct EPP Affects on the Middle Atmosphere

Energetic precipitating particles (EPPs) can cause significant direct constituent changes in the mesosphere and stratosphere (middle atmosphere) during certain periods. Both protons and electrons can influence the polar middle atmosphere through ionization and dissociation processes. EPPs can enhance HOx (H, OH, HO2) through the formation of positive ions followed by complex ion chemistry and NOx (N, NO, NO2) through the dissociation of molecular nitrogen. The HOx increases result in direct ozone destruction in the mesosphere and upper stratosphere via several catalytic loss cycles. Such middle atmospheric HOx-caused ozone loss is rather short-lived due to the relatively short lifetime (hours) of the HOx constituents. The NOx family has a considerably longer lifetime than the HOx family and can also lead to catalytic ozone destruction. EPP-caused enhancements of the NOx family can affect ozone directly, if produced in the stratosphere. Ozone decreases from the EPPs lead to a reduction in atmospheric heating and, subsequent atmospheric cooling. Conversely, EPPs can cause direct atmospheric heating through Joule heating. Measured HOx constituents OH and HO2 showed increases due to solar protons. Observed NOx constituents NO and NO2 were enhanced due to both solar protons and precipitating electrons. Other hydrogen- and nitrogen-ocntaining constituents were also measured to be directly influenced by EPPs, including N2O, HNO3, HO2NO2, N2OS, H2O2, ClONO2, HCl, and HOCl. Observed constituents ClO and CO were directly affected by EPPs as well. Many measurements indicated significant direct ozone decreases. A significant number of satellites housed instruments, which observed direct EPP-caused atmospheric effects, including Nimbus 4 (BUV), Nimbus 7 (SBUV), several NOAA platforms (SBUV/2), SME, UARS (HALOE, CLAES), SCISAT-1 (ACE-FTS), Odin (OSIRIS), Envisat-l (GOMOS, MIPAS, SCIAMACHY), and Aura (MLS). Measurements by rockets and ground-based radar also indicated EPP direct impacts. Atmospheric models have been used with some success in predicting the direct EPP impacts on the mesosphere and stratosphere. A review of the observed direct effects of EPP on the middle atmosphere will be given in this presentation.

Jackman, Charles H.↗

NASA NTRS2013

The Effect of Pre-Stressing on the Static Indentation Load Capacity of the Superelastic 60NiTi

Superelastic nickel-titanium alloys, such as 60NiTi (60Ni-40Ti by wt.%), are under development for use in mechanical components like rolling element bearings and gears. Compared to traditional bearing steels, these intermetallic alloys, when properly heat-treated, are hard but exhibit much lower elastic modulus (approx.100 GPa) and a much broader elastic deformation range (approx.3 percent or more). These material characteristics lead to high indentation static load capacity, which is important for certain applications especially space mechanisms. To ensure the maximum degree of elastic behavior, superelastic materials must be pre-stressed, a process referred to as "training" in shape memory effect (SME) terminology, at loads and stresses beyond expected use conditions. In this paper, static indentation load capacity tests are employed to assess the effects of pre-stressing on elastic response behavior of 60NiTi. The static load capacity is measured by pressing 12.7 mm diameter ceramic Si3N4 balls into highly polished, hardened 60NiTi flat plates that have previously been exposed to varying levels of pre-stress (up to 2.7 GPa) to determine the load that results in shallow but measurable (0.6 m, 25 in. deep) permanent dents. Hertz stress calculations are used to estimate contact stress. Without exposure to pre-stress, the 60NiTi surface can withstand an approximately 3400 kN load before significant denting (>0.4 m deep) occurs. When pre-stressed to 2.7 GPa, a static load of 4900 kN is required to achieve a comparable dent, a 30 percent increase. These results suggest that stressing contact surfaces prior to use enhances the static indentation load capacity of the superelastic 60NiTi. This approach may be adaptable to the engineering and manufacture of highly resilient mechanical components such as rolling element bearings.

DellaCorte, Christopher↗

NASA NTRS2013

A Validated Set of MIDAS V5 Task Network Model Scenarios to Evaluate Nextgen Closely Spaced Parallel Operations Concepts

The Closely Spaced Parallel Operations (CSPO) scenario is a complex, human performance model scenario that tested alternate operator roles and responsibilities to a series of off-nominal operations on approach and landing (see Gore, Hooey, Mahlstedt, Foyle, 2013). The model links together the procedures, equipment, crewstation, and external environment to produce predictions of operator performance in response to Next Generation system designs, like those expected in the National Airspaces NextGen concepts. The task analysis that is contained in the present report comes from the task analysis window in the MIDAS software. These tasks link definitions and states for equipment components, environmental features as well as operational contexts. The current task analysis culminated in 3300 tasks that included over 1000 Subject Matter Expert (SME)-vetted, re-usable procedural sets for three critical phases of flight; the Descent, Approach, and Land procedural sets (see Gore et al., 2011 for a description of the development of the tasks included in the model; Gore, Hooey, Mahlstedt, Foyle, 2013 for a description of the model, and its results; Hooey, Gore, Mahlstedt, Foyle, 2013 for a description of the guidelines that were generated from the models results; Gore, Hooey, Foyle, 2012 for a description of the models implementation and its settings). The rollout, after landing checks, taxi to gate and arrive at gate illustrated in Figure 1 were not used in the approach and divert scenarios exercised. The other networks in Figure 1 set up appropriate context settings for the flight deck.The current report presents the models task decomposition from the tophighest level and decomposes it to finer-grained levels. The first task that is completed by the model is to set all of the initial settings for the scenario runs included in the model (network 75 in Figure 1). This initialization process also resets the CAD graphic files contained with MIDAS, as well as the embedded operator models that comprise MIDAS. Following the initial settings, the model progresses to begin the first tasks required of the two flight deck operators, the Captain (CA) and the First Officer (FO). The task sets will initialize operator specific settings prior to loading all of the alerts, probes, and other events that occur in the scenario. As a note, the CA and FO were terms used in developing this model but the CA can also be thought of as the Pilot Flying (PF), while the FO can be considered the Pilot-Not-Flying (PNF)or Pilot Monitoring (PM). As such, the document refers to the operators as PFCA and PNFFO respectively.

task network model↗

NASA NTRS2014

Development of a Bayesian Belief Network Runway Incursion and Excursion Model

In a previous work, a statistical analysis of runway incursion (RI) event data was conducted to ascertain the relevance of this data to the top ten Technical Challenges (TC) of the National Aeronautics and Space Administration (NASA) Aviation Safety Program (AvSP). The study revealed connections to several of the AvSP top ten TC and identified numerous primary causes and contributing factors of RI events. The statistical analysis served as the basis for developing a system-level Bayesian Belief Network (BBN) model for RI events, also previously reported. Through literature searches and data analysis, this RI event network has now been extended to also model runway excursion (RE) events. These RI and RE event networks have been further modified and vetted by a Subject Matter Expert (SME) panel. The combined system-level BBN model will allow NASA to generically model the causes of RI and RE events and to assess the effectiveness of technology products being developed under NASA funding. These products are intended to reduce the frequency of runway safety incidents/accidents, and to improve runway safety in general. The development and structure of the BBN for both RI and RE events are documented in this paper.

Green, Lawrence L.↗

NASA NTRS2016

Decision Analysis Methods Used to Make Appropriate Investments in Human Exploration Capabilities and Technologies

NASA is transforming human spaceflight. The Agency is shifting from an exploration-based program with human activities in low Earth orbit (LEO) and targeted robotic missions in deep space to a more sustainable and integrated pioneering approach. Through pioneering, NASA seeks to address national goals to develop the capacity for people to work, learn, operate, live, and thrive safely beyond Earth for extended periods of time. However, pioneering space involves daunting technical challenges of transportation, maintaining health, and enabling crew productivity for long durations in remote, hostile, and alien environments. Prudent investments in capability and technology developments, based on mission need, are critical for enabling a campaign of human exploration missions. There are a wide variety of capabilities and technologies that could enable these missions, so it is a major challenge for NASA's Human Exploration and Operations Mission Directorate (HEOMD) to make knowledgeable portfolio decisions. It is critical for this pioneering initiative that these investment decisions are informed with a prioritization process that is robust and defensible. It is NASA's role to invest in targeted technologies and capabilities that would enable exploration missions even though specific requirements have not been identified. To inform these investments decisions, NASA's HEOMD has supported a variety of analysis activities that prioritize capabilities and technologies. These activities are often based on input from subject matter experts within the NASA community who understand the technical challenges of enabling human exploration missions. This paper will review a variety of processes and methods that NASA has used to prioritize and rank capabilities and technologies applicable to human space exploration. The paper will show the similarities in the various processes and showcase instances were customer specified priorities force modifications to the process. Specifically, this paper will describe the processes that the NASA Langley Research Center (LaRC) Technology Assessment and Integration Team (TAIT) has used for several years and how those processes have been customized to meet customer needs while staying robust and defensible. This paper will show how HEOMD uses these analyses results to assist with making informed portfolio investment decisions. The paper will also highlight which human exploration capabilities and technologies typically rank high regardless of the specific design reference mission. The paper will conclude by describing future capability and technology ranking activities that will continue o leverage subject matter experts (SME) input while also incorporating more model-based analysis.

Williams-Byrd, Julie↗

NASA NTRS2016

Active Learning with Rationales for Identifying Operationally Significant Anomalies in Aviation

A major focus of the commercial aviation community is discovery of unknown safety events in flight operations data. Data-driven unsupervised anomaly detection methods are better at capturing unknown safety events compared to rule-based methods which only look for known violations. However, not all statistical anomalies that are discovered by these unsupervised anomaly detection methods are operationally significant (e.g., represent a safety concern). Subject Matter Experts (SMEs) have to spend significant time reviewing these statistical anomalies individually to identify a few operationally significant ones. In this paper we propose an active learning algorithm that incorporates SME feedback in the form of rationales to build a classifier that can distinguish between uninteresting and operationally significant anomalies. Experimental evaluation on real aviation data shows that our approach improves detection of operationally significant events by as much as 75% compared to the state-of-the-art. The learnt classifier also generalizes well to additional validation data sets.

anomaly detection↗

NASA NTRS2017

Subject Matter Expert Evaluation of Multi-Flight Common Route Advisories

Traffic flow management seeks to balance the demand for National Airspace System (NAS) flight resources, such as airspace and airports, with the available supply. When forecasted weather blocks nominal air traffic routes, traffic managers must re-route affected flights for weather avoidance. Depending on the nature and scope of the weather, traffic managers may use pre-coordinated re-routes such as Playbook Routes or Coded Departure Routes, or may design ad hoc local re-routes. The routes of affected flights are modified accordingly. These weather avoidance routes will, of course, be less efficient than the nominal routes due to increased flight time and fuel burn. In current traffic management operations, the transition into a weather avoidance re-routing initiative is typically implemented more aggressively than the transition out of that initiative after the weather has dissipated or moved away. For example, strategic large-scale Playbook re-routes are sometimes left in place (as initially implemented) for many hours before being lifted entirely when the weather dissipates. There is an opportunity to periodically modify the re-routing plan as weather evolves, thereby attenuating its adverse impact on flight time and fuel consumption; this is called delay recovery. Multi-Flight Common Routes (MFCR) is a NASA-developed operational concept and associated decision support tool for delay recovery, designed to assist traffic managers to efficiently update weather avoidance traffic routes after the original re-routes have become stale due to subsequent evolution of the convective weather system. MFCR groups multiple flights to reduce the number of advisories that the traffic manager needs to evaluate, and also merges these flights on a common route segment to provide an orderly flow of re-routed traffic. The advisory is presented to the appropriate traffic manager who evaluates it and has the option to modify it using MFCRs graphical user interface. If the traffic manager finds the advisory to be operationally appropriate, he or she would coordinate with the Area Supervisor(s) of the sectors that currently control the flights in the advisory. When the traffic manager accepts the MFCR advisory via the user interface, the corresponding flight plan amendments would be sent to the displays of the appropriate sector controllers, using the Airborne Re-Routing (ABRR) capability which is scheduled for nationwide operation in 2017. The sector controllers would then offer this time-saving route modification to the pilots of the affected flights via datalink (or voice), and implement the corresponding flight plan amendment if the pilots accept it. MFCR is implemented as an application in the software environment of the Future Air traffic management Concepts Evaluation Tool (FACET). This paper focuses on an initial subject matter expert (SME) evaluation of MFCR. The evaluation covers MFCRs operational concept, algorithm, and user interface.

Human-in-the-loop Evaluation↗

NASA NTRS2017

Subject Matter Expert Evaluation of Multi-Flight Common Route Advisories

Traffic flow management seeks to balance the demand for National Airspace System (NAS) flight resources, such as airspace and airports, with the available supply. When forecasted weather blocks nominal air traffic routes, traffic managers must re-route affected flights for weather avoidance. Depending on the nature and scope of the weather, traffic managers may use pre-coordinated re-routes such as Playbook Routes or Coded Departure Routes, or may design ad hoc local re-routes. The routes of affected flights are modified accordingly. These weather avoidance routes will, of course, be less efficient than the nominal routes due to increased flight time and fuel burn. In current traffic management operations, the transition into a weather avoidance re-routing initiative is typically implemented more aggressively than the transition out of that initiative after the weather has dissipated or moved away. For example, strategic large-scale Playbook re-routes are sometimes left in place (as initially implemented) for many hours before being lifted entirely when the weather dissipates. There is an opportunity to periodically modify the re-routing plan as weather evolves, thereby attenuating its adverse impact on flight time and fuel consumption; this is called delay recovery. Multi-Flight Common Routes (MFCR) is a NASA-developed operational concept and associated decision support tool for delay recovery, designed to assist traffic managers to efficiently update weather avoidance traffic routes after the original re-routes have become stale due to subsequent evolution of the convective weather system. MFCR groups multiple flights to reduce the number of advisories that the traffic manager needs to evaluate, and also merges these flights on a common route segment to provide an orderly flow of re-routed traffic. The advisory is presented to the appropriate traffic manager who evaluates it and has the option to modify it using MFCRs graphical user interface. If the traffic manager finds the advisory to be operationally appropriate, he or she would coordinate with the Area Supervisor(s) of the sectors that currently control the flights in the advisory. When the traffic manager accepts the MFCR advisory via the user interface, the corresponding flight plan amendments would be sent to the displays of the appropriate sector controllers, using the Airborne Re-Routing (ABRR) capability which is scheduled for nationwide operation in 2017. The sector controllers would then offer this time-saving route modification to the pilots of the affected flights via datalink (or voice), and implement the corresponding flight plan amendment if the pilots accept it. MFCR is implemented as an application in the software environment of the Future Air traffic management Concepts Evaluation Tool (FACET). This paper focuses on an initial subject matter expert (SME) evaluation of MFCR. The evaluation covers MFCRs operational concept, algorithm, and user interface.

Traffic flow management↗

NASA NTRS2017

GES DISC Datalist Improves Earth Science Data Discoverability

At American Geophysical Union(AGU) 2016 Fall Meeting, Goddard Earth Sciences Data Information Services Center (GES DISC) unveiled a novel way to access data: Datalist. Currently, datalist is a collection of predefined data variables from one or more archived datasets, curated by our subject matter expert (SME). Our science support team has curated a predefined Hurricane Datalist and received very positive feedback from the user community. Datalist uses the same architecture our new website uses and have the same look and feel as other datasets on our web site. and also provides a one-stop shopping for data, metadata, citation, documentation, visualization and other available services. Since the last AGU Meeting, we have further developed a few new datalists corresponding to the Big Earth Data Initiative (BEDI) Societal Benefit Areas and A-Train data. We now have four datalists: Hurricane, Wind Energy, Greenhouse Gas and A-Train. We have also started working with our User Working Group members to create their favorite datalists and working with other DAAC to explore the possibility to include their products in our datalists that may also lead to a future of potential federated (cross-DAAC) datalists. Since our datalist prototype effort was a success, we are planning to make datalist operational. It's extremely important to have a common metadata model to support datalist, this will also be the foundation of federated datalist. We mapped our datalist metadata model to the unpublished UMM(Universal Metadata Model)-Var (Variable) (June version) and found that the UMM-var together with UMM-C (Collection) and possible UMM-S (Service) will meet our basic requirements. For example: Dataset shortname, and version are already specified in UMM-C, variable name, long name, units, dimensions are all specified in UMM-Var. UMM-Var also facilitates Science Keywords to allow tagging at variable level and Characteristics for optional variable characteristics. Measurements is useful for grouping of the variables and Set is promising to define datalist. And finally, the UMM-Service model to specify the available services for the variable will be very beneficial. In summary, UMM-Var, UMM-C and UMM-S are the basis of federated datalist and the development and deployment of datalist will contribute to the evolution of the UMM.

datalist↗

NASA NTRS2019

Exploration Medical Capability Medical System Recommendations for Gateway

- Medical System Content Development - 2019: Develop clinical content to inform medical system design; Iterate on content with wider ExMC (Exploration Medical Capability) team; Capture processes used to perform these tasks. - Using model content to inform system design - SME (Subject Matter Expert) collaboration to refine systems using clinical content.

Hailey, Melinda↗

NASA NTRS2019

Ask-The-Expert: Minimizing Human Review for Big Data Analytics Through Active Learning

In this CIF project, we worked toward semi-automating knowledge discovery from anomaly detection algorithms through the use of active learning. Active learning is an area of research within machine learning that uses an "expert in the loop" to learn from large data sets that have very few annotations or labels available, and where providing such labels is expensive. In our case, the task can be defined as the identification of safety events from flight operational data. Since traditional anomaly detection algorithms cannot differentiate between operationally relevant and irrelevant statistical anomalies, Subject Matter Experts (SMEs) have a lengthy and expensive burden of investigating every example identified by the detection algorithm, classifying and labeling them as relevant or irrelevant. Active learningidentifies the unlabeled example for which a label would most improve the classifier, asks the domain expert for a label, and repeats this process until there are no more resources (time, budget) available for labeling or a minimum required performance is reached. A positive label indicates an operationally significant safety event whereas a negative label indicates otherwise. Based on these few labels we propose to build an active learning system that utilizes the SME's time in the most effective manner by iteratively asking for labels for as few informative instances as possible. Our work was proposed to be a stepping stone toward implementation and deployment of the system with user interface to be pursued by the Aviation Operations and Safety Program (AOSP) given its interest in safety monitoring and discovery of safety incidents.

aviation safety↗