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At least 91 records · Page 5

Hierarchical Mixture of Experts for Advanced Air Mobility Flight Phase Classification

Advanced Air Mobility (AAM) and Urban Air Mobility (UAM) operations will have numerous vehicles and aircraft flying in the airspace, which poses safety and security concerns. Commercial airlines utilize Air Traffic Management (ATM) and Air Traffic Control (ATC) for real-time monitoring, surveillance, traffic coordination, and rerouting to maintain safe and efficient flight patterns. Transferring ATM and ATC architectures to AAM/UAM will be difficult to implement since AAM/UAM aircraft fly at lower altitudes, have more static and dynamic obstacles, operate in highly dense environments, and have several more aircraft to monitor for a given volume of the national airspace (NAS). Automatic flight phase classification will enhance efficiencies of ATM/ATC-like architectures for AAM/UAM. Classifying the main flight phases (takeoff, climb, cruise, descent, and landing) provides insight to ensure safe operations, provide situational awareness of the NAS, and monitor flights in case there are any emergencies. Typical flight phase classification methods are all-or-nothing, which will not capture or accurately classify the transitions between flight phases. Utilizing hierarchical mixture of experts (HME) provides a flight phase classification solution that includes transitions between the flight phases by assigning weights based on ground-based distributed sensor readings from cameras and radar. Adding the transitions between flight phases increases the fidelity of flight phase classification and provides deeper insight for flight phase classification by leveraging distributed sensing concepts.

distributed sensing↗

Hierarchical Mixture of Experts for Advanced Air Mobility Flight Phase Classification

Advanced Air Mobility (AAM) and Urban Air Mobility (UAM) operations will have numerous vehicles and aircraft flying in the airspace, which poses safety and security concerns. Commercial airlines utilize Air Traffic Management (ATM) and Air Traffic Control (ATC) for real-time monitoring, surveillance, traffic coordination, and rerouting to maintain safe and efficient flight patterns. Transferring ATM and ATC architectures to AAM/UAM will be challenging to implement since AAM/UAM aircraft fly at lower altitudes, have more static and dynamic obstacles, operate in highly dense environments, and have several more aircraft to monitor for a given volume of the national airspace (NAS). Aircraft typically have the following flight phases: takeoff, climb, cruise, descent, and landing. Classifying these flight phases provides insight into ensuring safe operations, providing situational awareness of the NAS, and monitoring flights in emergencies. Automatic flight phase classification will enhance the efficiencies of ATM/ATC-like architectures for AAM/UAM, especially since numerous aircraft will be flying in highly dense urban environments. Typical flight phase classification methods are all-or-nothing, which will not capture or accurately classify the transitions between flight phases. Utilizing hierarchical mixture of experts (HME) provides a flight phase classification solution that includes transitions between the flight phases by assigning weights based on ground-based distributed sensor readings from cameras and radar. Adding the transitions between flight phases increases the fidelity of flight phase classification and provides deeper insight into flight phase classification by leveraging distributed sensing concepts. Simulation results and post-processed flight test results demonstrate the utility of HME for automatic and robust flight phase classification for real-time AAM operations.

distributed sensing↗

Application of the H-Mode, a Design and Interaction Concept for Highly Automated Vehicles, to Aircraft

Driven by increased safety, efficiency, and airspace capacity, automation is playing an increasing role in aircraft operations. As aircraft become increasingly able to autonomously respond to a range of situations with performance surpassing human operators, we are compelled to look for new methods that help us understand their use and guide their design using new forms of automation and interaction. We propose a novel design metaphor to aid the conceptualization, design, and operation of highly-automated aircraft. Design metaphors transfer meaning from common experiences to less familiar applications or functions. A notable example is the "Desktop metaphor" for manipulating files on a computer. This paper describes a metaphor for highly automated vehicles known as the H-metaphor and a specific embodiment of the metaphor known as the H-mode as applied to aircraft. The fundamentals of the H-metaphor are reviewed followed by an overview of an exploratory usability study investigating human-automation interaction issues for a simple H-mode implementation. The envisioned application of the H-mode concept to aircraft is then described as are two planned evaluations.

Goodrich, Kenneth H.↗

[NASA] in the 21st Century

This viewgraph presentation reviews the NASA programs in support of Aeronautical and Space research. This research involves imagining the future of air travel. There are three major Aeronautics technology programs: (1) Fundamental Aeronautics, (2) Aviation Safety and (3) Airspace Systems. The aim of exploring the depths of the universe through earth based and space based assets. Other Space programs include the plans for exploration of the moon and Mars.

Horn, Thomas J.↗

Air Breathing Propulsion Controls and Diagnostics Research at NASA Glenn Under NASA Aeronautics Research Mission Programs

The Intelligent Control and Autonomy Branch (ICA) at NASA (National Aeronautics and Space Administration) Glenn Research Center (GRC) in Cleveland, Ohio, is leading and participating in various projects in partnership with other organizations within GRC and across NASA, the U.S. aerospace industry, and academia to develop advanced controls and health management technologies that will help meet the goals of the NASA Aeronautics Research Mission Directorate (ARMD) Programs. These efforts are primarily under the various projects under the Advanced Air Vehicles Program (AAVP), Airspace Operations and Safety Program (AOSP) and Transformative Aeronautics Concepts Program (TAC). The ICA Branch is focused on advancing the state-of-the-art of aero-engine control and diagnostics technologies to help improve aviation safety, increase efficiency, and enable operation with reduced emissions. This paper describes the various ICA research efforts under the NASA Aeronautics Research Mission Programs with a summary of motivation, background, technical approach, and recent accomplishments for each of the research tasks.

Engine Control↗

BBN-Based Portfolio Risk Assessment for NASA Technology R&D Outcome

The NASA Aeronautics Research Mission Directorate (ARMD) vision falls into six strategic thrusts that are aimed to support the challenges of the Next Generation Air Transportation System (NextGen). In order to achieve the goals of the ARMD vision, the Airspace Operations and Safety Program (AOSP) is committed to developing and delivering new technologies. To meet the dual challenges of constrained resources and timely technology delivery, program portfolio risk assessment is critical for communication and decision-making. This paper describes how Bayesian Belief Network (BBN) is applied to assess the probability of a technology meeting the expected outcome. The network takes into account the different risk factors of technology development and implementation phases. The use of BBNs allows for all technologies of projects in a program portfolio to be separately examined and compared. In addition, the technology interaction effects are modeled through the application of object-oriented BBNs. The paper discusses the development of simplified project risk BBNs and presents various risk results. The results presented include the probability of project risks not meeting success criteria, the risk drivers under uncertainty via sensitivity analysis, and what-if analysis. Finally, the paper shows how program portfolio risk can be assessed using risk results from BBNs of projects in the portfolio.

Geuther, Steven C.↗

ATD-1 Avionics Phase 2 Flight Test: Flight Test Operations and Saftey Report (FTOSR)

The Air Traffic Management Technology Demonstration-1 (ATD-1) is a major applied research and development activity of NASA's Airspace Operations and Safety Program (AOSP). The demonstration is the first of an envisioned series of Air Traffic Management (ATM) Technology Demonstration sub-projects that will demonstrate innovative NASA technologies that have attained a sufficient level of maturity to merit more in-depth research and evaluation at the system level in relevant environments.

Boyle, Dan↗

Flight Test Assessments of Pilot Workload, System Usability, and Situation Awareness of TASAR

Traffic Aware Strategic Aircrew Requests (TASAR) is an onboard automation concept intended to identify trajectory optimizations, in terms of fuel and time saving objectives, clear of known traffic, weather, and airspace restrictions prior to the aircrew initiating a route-change request to Air Traffic Control (ATC). The software implementation of the TASAR concept is the Traffic Aware Planner (TAP). TASAR analysis and development is being executed by the NASA Langley Research Center's Crew Systems and Aviation Operations Branch (CSAOB) under the sponsorship of the Airspace Technology Demonstration (ATD) Project of the NASA Airspace Operations and Safety Program (AOSP). The TASAR Flight Trial-2 (FT-2) was conducted in June, 2015 out of the Newport News/Williamsburg International Airport. This flight trial was conducted using a Piaggio Avanti flight test aircraft and consisted of 12 Evaluation Flights with airline commercial pilots participating as the Evaluation Pilots, three destination airports in Atlanta and Jacksonville Air Route Traffic Control Centers, and one pair of flight plans associated with each destination airport. The primary goal of FT-2 was to reduce risk for upcoming operational trials with NASA partner airlines, Alaska Airlines and Virgin America. To accomplish this primary goal, six independent objectives were conducted during FT-2, however, this paper will report only the findings of Objective 5; the assessment of system usability, pilot perceived workload, and the degree of pilot acceptability of the TAP Human Machine Interface (HMI) during flight operations, via the administration of several subjective measures.

TASAR↗

Aero-Propulsion Control Research in Support of NASA Aeronautics Research Strategic Thrusts

In the past few years, NASA (National Aeronautics and Space Administration) Aeronautics Research Mission Directorate (ARMD) has introduced and updated a “New Blueprint for Transforming Global Aviation†. This blueprint consists of six NASA Aeronautics Research Strategic Thrusts â€" “The updated vision is designed to ensure that through NASA's aeronautical research the United States will maintain its leadership in the sky and sustain aviation so that it remains a key economic driver and cultural touchstone for the nation.†In mid-2016, technology development roadmaps were developed by ARMD for each of the strategic research thrusts and these roadmaps are continually being updated based on feedback from the broader aeronautics research community. The NASA Aeronautics research vision is implemented through a set of 4 programs â€" Advanced Air Vehicles Program (AAVP), Airspace Operations and Safety Program (AOSP), Integrated Aviation Systems Program (IASP), and Transformative Aeronautics Concepts Program (TACP). The Intelligent Control and Autonomy Branch (ICAB) at NASA Glenn Research Center (GRC) in Cleveland, Ohio, is leading and participating in various projects in partnership with other organizations within GRC and across NASA, the U.S. aerospace industry, and academia to develop advanced controls and health management technologies for aero-propulsion systems that will help meet the goals of the ARMD programs. These efforts are primarily under the various projects under AAVP, AOSP, and TACP. The ICAB current research tasks in support of ARMD program are described in this paper. The paper provides motivation, background, technical approach and recent accomplishments for these tasks, as well as a couple of tasks completed in the previous fiscal year.

Propulsion Control↗

Airspace Technology Demonstration 2 (ATD-2): Integrated Arrival/Departure/Surface (IADS) System Demonstration, Technology Transfer Document Summary (ReadMe)

Airspace Technology Demonstration - 2 (ATD-2) is part of NASA's Airspace Technology Demonstrations (ATD) Project under its Airspace Operations and Safety Program (AOSP). ATD-2 is a multi-year research and development effort to improve the predictability and the operational efficiency of the air traffic system in metroplex environments, through the enhancement, development, and integration of the nation's most advanced and sophisticated arrival, departure, and surface prediction, scheduling, and management systems. In order to ensure that the products of this knowledge and technology transfer are relevant and useful, NASA has created strong partnerships with the FAA and key industry stakeholders. This summary document and accompanying artifacts satisfy the first of three Research Transition Products (RTPs) defined in the IADS Research Transition Team (RTT) Plan. This transfer consists of NASA's ATD-2 Phase 1 Baseline IADS System capabilities.

IADS↗

System-Level Prognostics for the National Airspace

In the National Airspace System (NAS), safety is assured through a set of rules, regulations, and procedures to respond to unsafe events. However, safety stands to benefit immensely from the introduction of tools and methodologies from Prognostics and Health Management (PHM). PHM will enable the NAS to stochastically predict unsafe states within the NAS, enabling a proactive preventative response strategy, as opposed to a reactive mitigative one. However, current PHM methods do not directly apply to the NAS for several reasons: they typically apply only at the component level, are implemented in a centralized manner, and are focused only on predicting remaining useful life. In this paper, we extend the model-based prognostics approach to PHM in order to provide a framework that can be applied to the NAS. We offer a system-level approach that supports a distributed implementation, and provide algorithms to predict the probability of an unsafe state, either at a specific time or within a time interval, and to predict the time of an unsafe state. Experimental results in simulation demonstrate the new approach.

prognostics↗

UAM Market Study - Technical Out Brief

The Urban Air Mobility (UAM) Market Study is an in-depth study for ODM market leveraging identified key technical barriers to understand community interests and market conditions from all aspects such as regulations, economics, public acceptance, airspace operations and safety.

Parcel Delivery↗

Urban Air Mobility (UAM) Market Study

The Urban Air Mobility (UAM) Market Study is an in-depth study for ODM market leveraging identified key technical barriers to understand community interests and market conditions from all aspects such as regulations, economics, public acceptance, airspace operations and safety.

Urban Air Mobility↗

Urban Air Mobility (UAM) Market Study

The Urban Air Mobility (UAM) Market Study is an in-depth study for ODM market leveraging identified key technical barriers to understand community interests and market conditions from all aspects such as regulations, economics, public acceptance, airspace operations and safety.

Urban Air Mobility Parcel Delivery Air Taxi Air Me↗

Urban Air Mobility (UAM) Market Study

The Urban Air Mobility (UAM) Market Study is an in-depth study for ODM market leveraging identified key technical barriers to understand community interests and market conditions from all aspects such as regulations, economics, public acceptance, airspace operations and safety.

Hasan, Shahab↗

Urban Air Mobility (UAM) Market Study

The Urban Air Mobility (UAM) Market Study is an in-depth study for ODM market leveraging identified key technicalbarriers to understand community interests and market conditions from all aspects such as regulations, economics,public acceptance, airspace operations and safety.

Hasan, Shahab↗

Airspace Technology Demonstration 2 (ATD-2): Integrated Arrival/ Departure/Surface (IADS) System Demonstration Technology Transfer Document Summary (ReadMe) Version 2 - Phase 2 Fused IADS

Airspace Technology Demonstration 2 (ATD-2) is part of NASA's Airspace Technology Demonstrations (ATD) Project under its Airspace Operations and Safety Program (AOSP). ATD-2 is a multi-year research and development effort to improve the predictability and operational efficiency of the air traffic system in metroplex environments while maintaining or improving throughput by enhancing and integrating arrival, departure, and surface prediction, scheduling, and management systems. In order to ensure that the products of this knowledge and technology transfer are relevant and useful, NASA has created strong partnerships with the FAA and industry stakeholders. This summary document and accompanying artifacts satisfy the second of three research transition products (RTPs) defined in the IADS research transition team (RTT) plan. This transfer consists of NASA's ATD-2 Phase 1 Baseline IADS and Phase 2 Fused IADS system capabilities

Capps, Alan↗

Frequency Domain Functional Near-Infrared Spectrometer (fNIRS) for Crew State Monitoring

A frequency domain functional near-infrared spectrometer (fNIRS) and accompanying software have been developed by the NASA Glenn Research Center as part of the Airspace Operations and Safety Program (AOSP) Technologies for Airplane State Awareness (TASA)—SE211 Crew State Monitoring (CSM) Project. The goal of CSM was to develop a suite of instruments to measure the cognitive state of operators while performing operational activities. The fNIRS was one of the instruments intended for the CSM, developed to measure changes in oxygen levels in the brain noninvasively.

Jeffrey R Mackey↗