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At least 127 records · Page 7

Real-Time Simulation Clock

Fiber-optic network delivers time signals to widely separated simulator sites. Real-time digital flight simulation used to support variety of research programs. Majority of research involves man-in-the-loop simulation requiring interconnection of digital computers, simulation cockpits, control consoles, graphics subsystems, and related equipment. Real-time simulation subsystem consists of several CYBER(R) computers and over 20 simulation sites. Allows for several simultaneous simulation jobs on one CYBER(R) as well as any combination of sites in each integrated simulation job.

Bennington, Donald R.↗

Pigs in cyberspace

Exploration and colonization of the universe awaits, but Earth-adapted biological humans are ill-equipped to respond to the challenge. Machines have gone farther and seen more, limited though they presently are by insect-like behavior inflexibility. As they become smarter over the coming decades, space will be theirs. Organizations of robots of ever increasing intelligence and sensory and motor ability will expand and transform what they occupy, working with matter, space and time. As they grow, a smaller and smaller fraction of their territory will be undeveloped frontier. Competitive success will depend more and more on using already available matter and space in ever more refined and useful forms. The process, analogous to the miniaturization that makes today's computers a trillion times more powerful than the mechanical calculators of the past, will gradually transform all activity from grossly physical homesteading of raw nature, to minimum-energy quantum transactions of computation. The final frontier will be urbanized, ultimately into an arena where every bit of activity is a meaningful computation: the inhabited portion of the universe will be transformed into a cyberspace. Because it will use resources more efficiently, a mature cyberspace of the distant future will be effectively much bigger than the present physical universe. While only an infinitesimal fraction of existing matter and space is doing interesting work, in a well developed cyberspace every bit will be part of a relevant computation or storing a useful datum. Over time, more compact and faster ways of using space and matter will be invented, and used to restructure the cyberspace, effectively increasing the amount of computational spacetime per unit of physical spacetime. Computational speed-ups will affect the subjective experience of entities in the cyberspace in a paradoxical way. At first glimpse, there is no subjective effect, because everything, inside and outside the individual, speeds up equally. But, more subtly, speed-up produces an expansion of the cyber universe, because, as thought accelerates, more subjective time passes during the fixed (probably lightspeed) physical transit time of a message between a given pair of locations - so those fixed locations seem to grow farther apart. Also, as information storage is made continually more efficient through both denser utilization of matter and more efficient encodings, there will be increasingly more cyber-stuff between any two points. The effect may somewhat resemble the continuous-creation process in the old steady-state theory of the physical universe of Hoyle, Bondi and Gold, where hydrogen atoms appear just fast enough throughout the expanding cosmos to maintain a constant density.

Moravec, Hans↗

Considerations for an Integrated UAS CNS Architecture

The National Aeronautics and Space Administration (NASA) Glenn Research Center (GRC) is investigating revolutionary and advanced universal, reliable, always available, cyber secure and affordable Communication, Navigation, Surveillance (CNS) options for all altitudes of UAS operations. In Spring 2015, NASA issued a Call for Proposals under NASA Research Announcements (NRA) NNH15ZEA001N, Amendment 7 Subtopic 2.4. Boeing was selected to conduct a study with the objective to determine the most promising candidate technologies for Unmanned Air Systems (UAS) air-to-air and air-to-ground data exchange and analyze their suitability in a post-NextGen NAS environment. The overall objectives are to develop UAS CNS requirements and then develop architectures that satisfy the requirements for UAS in both controlled and uncontrolled air space. This contract is funded under NASAs Aeronautics Research Mission Directorates (ARMD) Aviation Operations and Safety Program (AOSP) Safe Autonomous Systems Operations (SASO) project and proposes technologies for the Unmanned Air Systems Traffic Management (UTM) service.There is a need for accommodating large-scale populations of Unmanned Air Systems (UAS) in the national air space. Scale obviously impacts capacity planning for Communication, Navitation, and Surveillance (CNS) technologies. For example, can wireless communications data links provide the necessary capacity for accommodating millions of small UASs (sUAS) nationwide? Does the communications network provide sufficient Internet Protocol (IP) address space to allow air traffic control to securely address both UAS teams as a whole as well as individual UAS within each team? Can navigation and surveillance approaches assure safe route planning and safe separation of vehicles even in crowded skies?Our objective is to identify revolutionary and advanced CNS alternatives supporting UASs operating at all altitudes and in all airspace while accurately navigating in the absence of navigational aids. These CNS alternatives must be reliable, redundant, always available, cyber-secure, and affordable for all types of vehicles including small UAS to large transport category aircraft. The approach will identify CNS technology candidates that can meet the needs of the range of UAS missions to specific air traffic management applications where they will be most beneficial and cost effective.

Templin, Fred L.↗

Considerations for an Integrated UAS CNS Architecture

The National Aeronautics and Space Administration (NASA) Glenn Research Center (GRC) is investigating revolutionary and advanced universal, reliable, always available, cyber secure and affordable Communication, Navigation, Surveillance (CNS) options for all altitudes of UAS operations. In Spring 2015, NASA issued a Call for Proposals under NASA Research Announcements (NRA) NNH15ZEA001N, Amendment 7 Subtopic 2.4. Boeing was selected to conduct a study with the objective to determine the most promising candidate technologies for Unmanned Air Systems (UAS) air-to-air and air-to-ground data exchange and analyze their suitability in a post-NextGen NAS environment. The overall objectives are to develop UAS CNS requirements and then develop architectures that satisfy the requirements for UAS in both controlled and uncontrolled air space. This contract is funded under NASAs Aeronautics Research Mission Directorates (ARMD) Aviation Operations and Safety Program (AOSP) Safe Autonomous Systems Operations (SASO) project and proposes technologies for the Unmanned Air Systems Traffic Management (UTM) service.There is a need for accommodating large-scale populations of Unmanned Air Systems (UAS) in the national air space. Scale obviously impacts capacity planning for Communication, Navigation, and Surveillance (CNS) technologies. For example, can wireless communications data links provide the necessary capacity for accommodating millions of small UASs (sUAS) nationwide? Does the communications network provide sufficient Internet Protocol (IP) address space to allow air traffic control to securely address both UAS teams as a whole as well as individual UAS within each team? Can navigation and surveillance approaches assure safe route planning and safe separation of vehicles even in crowded skies?Our objective is to identify revolutionary and advanced CNS alternatives supporting UASs operating at all altitudes and in all airspace while accurately navigating in the absence of navigational aids. These CNS alternatives must be reliable, redundant, always available, cyber-secure, and affordable for all types of vehicles including small UAS to large transport category aircraft. The approach will identify CNS technology candidates that can meet the needs of the range of UAS missions to specific air traffic management applications where they will be most beneficial and cost effective.

Templin, Fred L.↗

Reliable and Secure Surveillance, Communications and Navigation (RSCAN) for Unmanned Air Systems (UAS) in Controlled Airspace

The aviation industry faces a rapidly-emerging need for integrating Unmanned Air Systems (UAS) into the national airspace (NAS). This trend will present challenging questions for the safe operation of UAS in controlled and uncontrolled airspaces based on new Communications, Navigation and Surveillance (CNS) technologies. For example, can wireless communications data links provide the necessary capacity for accommodating ever increasing numbers of UAS worldwide? Does the communications network provide ample Internet Protocol (IP) address space to allow Air Traffic Control (ATC) to securely address each UAS? Can navigation and surveillance approaches assure safe route planning and safe separation of vehicles even in crowded skies?Under NASA contract NNA16BD84C, Boeing is developing an integrated CNS architecture to enable UAS operations in the NAS. Revolutionary and advanced CNS alternatives are needed to support UAS operations at all altitudes and in all airspaces, including both controlled and uncontrolled. These CNS alternatives must be reliable, redundant, always available, cyber-secure, and affordable for all types of vehicles including small UAS to large transport category aircraft. Our approach considers CNS requirements that address the range of UAS missions where they will be most beneficial and cost-effective.A cybersecure future UAS CNS architecture is needed to support the NASA vision for an Unmanned Air Traffic Management (UTM) system in uncontrolled airspace and a cooperative operation of manned and unmanned aircraft in the controlled global Air Traffic Management (ATM) system. The architecture must, therefore, support always-available and cyber secure operations. This paper presents UAS CNS architecture concepts for large UAS operating in the ATM system in controlled airspace. Future companion works will consider small UAS operating in the UTM system in uncontrolled airspace.

Ponchak, Denise S.↗

Software and System Health Management with R2U2

R2U2 (Realizable, Responsive, Unobtrusive Unit) is a hardware-supported tool and framework for the real-time system and software health management of cyber-physical systems. R2U2 continuously monitors properties about safety, performance, and security of the vehicle and can perform diagnostic reasoning. Efficient observers for past-time and future-time Metric Temporal Logic, reasoners for Bayesian Networks, and model-based prognostics algorithms are major components of R2U2. Their combination makes it possible to design powerful models for system runtime monitoring, diagnostics, software health management, prognostics, and security monitoring. The R2U2 monitoring engine is designed for minimal runtime overhead and is available as Simulink block or as a software component for integration into the flight software stack, and enables R2U2 to monitor complex cyber-physical systems without any instrumentation of the flight software. In this presentation, we give an overview of R2U2 architecture and reasoning algorithms, present its features, and give a life demo of the tool.

Schumann, Johann↗

Hazard Contribution Modes of Machine Learning Components

Amongst the essential steps to be taken towards developing and deploying safe systems with embedded learning-enabled components (LECs) i.e., software components that use ma- chine learning (ML)—are to analyze and understand the con- tribution of the constituent LECs to safety, and to assure that those contributions have been appropriately managed. This paper addresses both steps by, first, introducing the notion of hazard contribution modes (HCMs) a categorization of the ways in which the ML elements of LECs can contribute to hazardous system states; and, second, describing how argumentation patterns can capture the reasoning that can be used to assure HCM mitigation. Our framework is generic in the sense that the categories of HCMs developed i) can admit different learning schemes, i.e., supervised, unsupervised, and reinforcement learning, and ii) are not dependent on the type of system in which the LECs are embedded, i.e., both cyber and cyber-physical systems. One of the goals of this work is to serve a starting point for systematizing L analysis towards eventually automating it in a tool.

Smith, Colin↗

Runtime Analysis with R2U2: A Tool Exhibition Report

We present R2U2 (Realizable, Responsive, Unobtrusive Unit), a hardware- supported tool and framework for the continuous monitoring of safetycritical and embedded cyber-physical systems.With the widespread advent of autonomous systems such as Unmanned Aerial Systems (UAS), satellites, rovers, and cars, real-time, on-board decision making requires unobtrusive monitoring of properties for safety, performance, security, and system health. R2U2 models combine past-time and future-time Metric Temporal Logic, “mission time” Linear Temporal Logic, probabilistic reasoning with Bayesian Networks, and modelbased prognostics. The R2U2 monitoring engine can be instantiated as a hardware solution, running on an FPGA, or as a software component. The FPGA realization enables R2U2 to monitor complex cyber-physical systems without any overhead or instrumentation of the flight software. In this tool exhibition report, we present R2U2 and demonstrate applications on system runtime monitoring, diagnostics, software health management, and security monitoring for a UAS. Our tool demonstration uses a hardware-based processor-in-the-loop “iron-bird” configuration.

Johann Martin Schumann↗

The Spaceport Command and Control System Security Assessor Project

This Summer, I worked as a National Aeronautics and Space Administration (NASA) Internships and Fellowships (NIF) intern under my mentor, Jill Giles within the Software Engineering Branch. Within this project, I worked alongside the Cyber Security branch to identify a list of Commercial Off the Shelf (COTS) software to analyze, research, and gain insight about potential vulnerabilities within the software that could become a threat of attack. After identifying the list of COTS software, my team and I used Microsoft Excel to create a worksheet to easily organize and design a questionnaire about the software. Security reports weregiven to us to identify the software used on the machines in the firing rooms. With these reports, we created a script that would populate the database with the software information to identify potential security weaknesses of COTS software.The goal of the project was to produce a final report, summarizing the most vulnerable launch control system servers and configurations and document vulnerabilities, residual risk, likelihood, and consequence. This project is important for the Cyber Security and Information Technology branches because it will identify security weaknesses and help to mitigate risk. From the Spaceport Command and Control System Security Assessor Project, I learned how to properly identify weaknesses and vulnerabilities within software and how to mitigate the risks within the software. This project also taught me how to create databases using scripts and input files.

Destani Satora Van Arsdalen↗

Enabling Real-time Multi-messenger Astrophysics Discoveries with Deep Learning

Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos. In this Expert Recommendation, we review the key challenges of real-time observations of gravitational wave sources and their electromagnetic and astroparticle counterparts, and make a number of recommendations to maximize their potential for scientific discovery. These recommendations refer to the design of scalable and computationally efficient machine learning algorithms; the cyber-infrastructure to numerically simulate astrophysical sources, and to process and interpret multi-messenger astrophysics data; the management of gravitational wave detections to trigger real-time alerts for electromagnetic and astroparticle follow-ups; a vision to harness future developments of machine learning and cyber-infrastructure resources to cope with the big-data requirements; and the need to build a community of experts to realize the goals of multi-messenger astrophysics.

E A Huerta↗

Partially Observable Games for Secure Autonomy

Technology development efforts in autonomy and cyber-defense have been evolving independently of each other, over the past decade. In this paper, we report our ongoing effort to integrate these two presently distinct areas into a single framework. To this end, we propose the two-player partially observable stochastic game formalism to capture both high-level autonomous mission planning under uncertainty and adversarial decision making subject to imperfect information. We show that synthesizing sub-optimal strategies for such games is possible under finite-memory assumptions for both the autonomous decision maker and the cyber-adversary. We then describe an experimental testbed to evaluate the efficacy of the proposed framework.

Ames, Aaron D.↗

SafeMAP: Safe Multi-Agent Planning Framework Based on Dynamic Probabilistic Risk Assessment

This paper proposes a risk-aware framework for Safe Multi-Agent Planning (SafeMAP) that unifies disparate models for multi-agent systems in a Markovian process that allows for simultaneous system health monitoring, decision making under uncertainty, and multi-agent system collaboration. As operations beyond low earth orbit mature, there is an increased need for autonomous cyber-physical systems with onboard decision making capabilities. Multi-agent cyber-physical systems in particular offer the potential of increased efficiency, resiliency, and mission capabilities for future applications such as multi-rover terrain operations, distributed satellite operations, and management of smart lunar habitats. SafeMAP utilizes physics-based models of each agent and the relevant components, probability models of the environment and component operational states, and reward models for mission-specific objectives such as scientific task completion or resource consumption. The output of SafeMAP is a set of mission plans that satisfy the mission objective under specified risk/reward constraints. A readable interpretation of each of these generated mission plans is provided as an additional output. SafeMAP has been demonstrated on a simulated case study involving a four-rover system performing surface mapping operations and science tasks. Results of this paper demonstrate SafeMAP’s ability to generate explainable mission plans that satisfy the mission objective while minimizing risk under nominal and off-nominal conditions.

Mohammad Hejase↗

SafeMAP: Safe Multi-Agent Planning framework based on Dynamic Probabilistic Risk Assessment

This paper proposes a risk-aware framework for Safe Multi-Agent Planning (SafeMAP) that unifies disparate models for multi-agent systems in a Markovian process that allows for simultaneous system health monitoring, decision making under uncertainty, and multi-agent system collaboration. As operations beyond low earth orbit mature, there is an increased need for autonomous cyber-physical systems with onboard decision making capabilities. Multi-agent cyber-physical systems in particular offer the potential of increased efficiency, resiliency, and mission capabilities for future applications such as multi-rover terrain operations, distributed satellite operations, and management of smart lunar habitats. SafeMAP utilizes physics-based models of each agent and the relevant components, probability models of the environment and component operational states, and reward models for mission-specific objectives such as scientific task completion or resource consumption. The output of SafeMAP is a set of mission plans that satisfy the mission objective under specified risk/reward constraints. A readable interpretation of each of these generated mission plans is provided as an additional output. SafeMAP has been demonstrated on a simulated case study involving a four-rover system performing surface mapping operations and science tasks. Results of this paper demonstrate SafeMAP’s ability to generate explainable mission plans that satisfy the mission objective while minimizing risk under nominal and off-nominal conditions.

Mohammad Hejase↗

Developing Deep Learning Models for System Remaining Useful Life Predictions: Application to Aircraft Engines

Prognostics and health management (PHM) is an important part of ensuring reliable operations of complex safety- critical systems. System-level remaining useful life (RUL) estimation is a much more complex problem than making estimations at the component level, and system-level RUL methodologies remain sparse in the literature. Model-based approaches have traditionally worked in the past for components such as capacitors, MOSFETs, batteries, or hard-drives (to name a few examples), but developing high fidelity dynamics models of cyber physical systems that can be used to study the effects of multiple degrading components in the system remains a challenging task. Some initial work on model-based System RUL predictions was demonstrated in Khorasgani, et al [1], but, to generalize the system-level prognostics problem, we have to resort to pure data driven and hybrid approaches. In this work, we propose an end-to-end data- driven framework for developing deep learning models to predict remaining useful life of cyber physical systems operating under unknown faulty conditions. The raw data is organized with a data schema that improves the model development process and down stream data analysis tasks. Due to the unknown faulty conditions, the raw sensor data is transformed into signals that expose the underlying degradation processes, which are then used for model development. Bayesian Optimization is used to tune the model parameters prior to training and validation. We show that this approach results in accurate predictions within 3 cycles to end of life (EOL). We demonstrate the effectiveness of our approach by applying it to the N-CMAPSS turbofan engine dataset recently released by NASA, which includes high fidelity degradation modeling, real world operating conditions, and a large set of fault operating modes.

Prognostics↗

Gateway Implementation of Cybersecurity Requirements

Cybersecurity threats are a constant present-day reality for any type of business -- Space exploration is not excluded from these threats either. The Gateway Program is one of NASA’s latest initiatives that extend space exploration beyond low earth orbit. Gateway allows for NASA to prove technologies and mature systems necessary to live and work on another celestial body before embarking on multi-year missions to Mars. The Gateway is a small, human-tended space station in orbit around the Moon. With the increased autonomy, distance and criticality of systems, cybersecurity is a critical discipline that touches and integrates with most if not all subsystems of the Gateway. Building a gateway to the lunar orbit is no simple task. In this presentation, we outline an approach that the Gateway team adopted in creating a cyber safe and robust vehicle to support operations and assure protection of the critical functions. Gateway Program is required to implement National Institute of Standards and Technology (NIST) guidelines to adhere to the Federal Information Security Modernization Act (FISMA). NIST provides a framework for managing and controlling cybersecurity risks by defining cybersecurity controls and methodologies for implementation. The NIST framework is based upon the system, data within the system, integrations with external systems, and risk assessments to determine impacts for each of those systems. The goals and objectives are to identify appropriate security controls that fulfil and map to the NIST 800-53 framework. The implementation process involves developing an organizational understanding to manage cybersecurity risk to systems, people, assets, data, and capabilities. NIST Security controls are interpreted and defined within the Gateway vehicle requirements subsystems specifications. This paper details the approach, implementation, and challenges faced during the development and design phases to address cyber threats during the Gateway vehicle operations.

Cybersecurity↗

Development of a Computational Framework for the Design of Resilient Space Structures

Cyber-physical testing provides a unique platform to enable the design of resilient space structures. This hybrid approach requires the development of a structural model that accounts for various hazards (e.g., micrometeorite and debris impact) and interacts with physical tests and other sub-system models (e.g., thermal) of the space habitat. A two-dimensional finite element analysis code was developed in MATLAB to facilitate the evaluation of potential designs under operating and unexpected loads and prepare the computational framework for eventually performing cyber-physical testing. The code’s efficiency was enhanced by using an object-oriented programming approach that reduced data transfer between functions. In this study, the code is implemented to predict the response of a dome-style structure made of regolith concrete to impact loading and identify the force magnitude that will cause the tensile strength to be exceeded in domes with different thicknesses.

Tensile strength↗

Quantifying Cybersecurity Risk for NASA Missions

An end-to-end cyber risk assessment process is presented that is based on the combination of guidelines from the National Institute of Standards & Technology (NIST), the standard 5x5 risk matrix, and quantitative methods for generating loss exceedance curves. The NIST guidelines provide a framework for cyber risk assessment, and the standard 5x5 matrix is widely used across the industry for the representation of risk across multiple disciplines. Loss exceedance curves are a means of quantitatively assessing the loss that occurs due to a given risk profile. Combining these different techniques enables us to follow the guidelines, adhere to standard 5x5 risk management practices and develop quantitative metrics simultaneously. Our quantification process is based on the consideration of the NASA and JPL Cost Risk assessment modeling techniques as we define the cost associated with the cybersecurity risk profile of a mission as a function of the mission cost.

Miller, Robert L.↗

Differential Equation Approximation Using Gradient-Boosted Quantile Regression

The operation of cyber-physical-human (CPH) systems is subject to various epistemic and aleatory uncertainties. Overall trustworthiness of CPH systems relies on the trustworthiness of its components and their interactions. It is important that computational models comprising the cyber component of CPH provide predictions accompanied by a measure of confidence in model outcomes. Uncertainty quantification (UQ) and propagation are especially important in safety critical CPH systems. Gradient-boosted trees is a modeling approach capable both of learning the dynamics of a system and performing UQ. In this paper, we devise a method for using gradient boosting to learn the dynamics of a second order differential equation and estimate uncertainty at the same time. We do this by creating a custom loss function that trains the model to approximate the second derivative of a noisy time series, and to penalize based on a parameter that corresponds to the desired quantile. The resulting gradient boosting model can simulate stochastic trajectories of the system given a single starting point, that is, it can estimate both the expected trajectory and its uncertainty. We show that the uncertainty estimation is well calibrated and that the model can learn the dynamics even in the presence of noise. We demonstrate the approach on a simple cartpole system.

Autonomous systems↗