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At least 505 records · Page 28

Multi-Objective Reinforcement Learning for Cognitive Radio Based Satellite Communications

Previous research on cognitive radios has addressed the performance of various machine learning and optimization techniques for decision making of terrestrial link properties. In this paper, we present our recent investigations with respect to reinforcement learning that potentially can be employed by future cognitive radios installed onboard satellite communications systems specifically tasked with radio resource management. This work analyzes the performance of learning, reasoning, and decision making while considering multiple objectives for time-varying communications channels, as well as different crosslayer requirements. Based on the urgent demand for increased bandwidth, which is being addressed by the next generation of high-throughput satellites, the performance of cognitive radio is assessed considering links between a geostationary satellite and a fixed ground station operating at Ka-band (26 GHz). Simulation results show multiple objective performance improvements of more than 3:5 times for clear sky conditions and 6:8 times for rain conditions.

ionosphere↗

An Overview of NASA's IM&S Verification and Validation Process Plan and Specification for Space Exploration

NASA's Exploration Systems Mission Directorate (ESMD) is implementing a management approach for modeling and simulation (M&S) that will provide decision-makers information on the model's fidelity, credibility, and quality. This information will allow the decision-maker to understand the risks involved in using a model's results in the decision-making process. This presentation will discuss NASA's approach for verification and validation (V&V) of its models or simulations supporting space exploration. This presentation will describe NASA's V&V process and the associated M&S verification and validation (V&V) activities required to support the decision-making process. The M&S V&V Plan and V&V Report templates for ESMD will also be illustrated.

Gravitz, Robert M.↗

PRA and Conceptual Design

Once a project obtains approval, decision makers have to consider a variety of alternative paths for completing the project and meeting the project objectives. How decisions are made involves a variety of elements including: cost, experience, current technology, ideologies, politics, future needs and desires, capabilities, manpower, timing, available information, and for many ventures management needs to assess the elements of risk versus reward. The use of high level Probabilistic Risk Assessment (PRA) Models during conceptual design phases provides management with additional information during the decision making process regarding the risk potential for proposed operations and design prototypes. The methodology can be used as a tool to: 1) allow trade studies to compare alternatives based on risk, 2) determine which elements (equipment, process or operational parameters) drives the risk, and 3) provide information to mitigate or eliminate risks early in the conceptual design to lower costs. Creating system models using conceptual design proposals and generic key systems based on what is known today can provide an understanding of the magnitudes of proposed systems and operational risks and facilitates trade study comparisons early in the decision making process. Identifying the "best" way to achieve the desired results is difficult, and generally occurs based on limited information. PRA provides a tool for decision makers to explore how some decisions will affect risk before the project is committed to that path, which can ultimately save time and money.

DeMott, Diana↗

The Application of Remotely Sensed Data and Models to Benefit Conservation and Restoration Along the Northern Gulf of Mexico Coast

New data, tools, and capabilities for decision making are significant needs in the northern Gulf of Mexico and other coastal areas. The goal of this project is to support NASA s Earth Science Mission Directorate and its Applied Science Program and the Gulf of Mexico Alliance by producing and providing NASA data and products that will benefit decision making by coastal resource managers and other end users in the Gulf region. Data and research products are being developed to assist coastal resource managers adapt and plan for changing conditions by evaluating how climate changes and urban expansion will impact land cover/land use (LCLU), hydrodynamics, water properties, and shallow water habitats; to identify priority areas for conservation and restoration; and to distribute datasets to end-users and facilitating user interaction with models. The proposed host sites for data products are NOAA s National Coastal Data Development Center Regional Ecosystem Data Management, and Mississippi-Alabama Habitat Database. Tools will be available on the Gulf of Mexico Regional Collaborative website with links to data portals to enable end users to employ models and datasets to develop and evaluate LCLU and climate scenarios of particular interest. These data will benefit the Mobile Bay National Estuary Program in ongoing efforts to protect and restore the Fish River watershed and around Weeks Bay National Estuarine Research Reserve. The usefulness of data products and tools will be demonstrated at an end-user workshop.

Quattrochi, Dale↗

3D-CHESS: Decentralized, Distributed, Dynamic, and Context-aware Heterogeneous Sensor Systems

This paper describes the objectives and current status of the 3D-CHESS project which aims to demonstrate a new Earth observing strategy based on a context-aware Earth observing sensor web. This sensor web consists of a set of nodes with a knowledge base, heterogeneous sensors, edge computing, and autonomous decision-making capabilities. Context awareness is defined as the ability for the nodes to gather, exchange, and leverage contextual information (e.g., state of the Earth system, state and capabilities of itself and of other nodes in the network, and how those states relate to the dy- namic mission objectives) to improve decision making and planning. The current goal of the project is to demonstrate proof of concept by comparing the performance of a 3D- CHESS sensor web with that of status quo architectures in the context of a multi-sensor inland hydrologic and ecologic monitoring system.

David, Cedric H.↗

Form and Objective of the Decision Rule in Absolute Identification

In several conditions of a line length identification experiment, the subjects' decision making strategies were systematically biased against the responses on the edges of the stimulus range. When the range and number of the stimuli were small, the bias caused the percentage of correct responses to be highest in the center and lowest on the extremes of the range. Two general classes of decision rules that would explain these results are considered. The first class assumes that subjects intend to adopt an optimal decision rule, but systematically misrepresent one or more parameters of the decision making context. The second class assumes that subjects use a different measure of performance than the one assumed by the experimenter: instead of maximizing the chances of a correct response, the subject attempts to minimize the expected size of the response error (a "fidelity criterion"). In a second experiment, extended experience and feedback did not diminish the bias effect, but explicitly penalizing all response errors equally, regardless of their size, did reduce or eliminate it in some subjects. Both results favor the fidelity criterion over the optimal rule.

Balakrishnan, J. D.↗

Shuttle Transportation System Case-Study Development

A case-study collection was developed for NASA's Space Shuttle Program. Using lessons learned and documented by NASA KSC engineers, analysts, and contractors, decades of information related to processing and launching the Space Shuttle was gathered into a single database. The goal was to provide educators with an alternative means to teach real-world engineering processes and to enhance critical thinking, decision making, and problem solving skills. Suggested formats were created to assist both external educators and internal NASA employees to develop and contribute their own case-study reports to share with other educators and students. Via group project, class discussion, or open-ended research format, students will be introduced to the unique decision making process related to Shuttle missions and development. Teaching notes, images, and related documents will be made accessible to the public for presentation of Space Shuttle reports. Lessons investigated included the engine cutoff (ECO) sensor anomaly which occurred during mission STS-114. Students will be presented with general mission infom1ation as well as an explanation of ECO sensors. The project will conclude with the design of a website that allows for distribution of information to the public as well as case-study report submissions from other educators online.

Ransom, Khadijah↗

BUILD: A community development simulation game, appendix A

The computer based urban decision-making game BUILD is described. BUILD is aimed at: (1) allowing maximum expression of value positions by participants through resolution of intense, task-oriented conflicts: (2) heuristically gathering information on both the technical and social functioning of the city through feedback from participants: (3) providing community participants with access to technical expertise in urban decision making, and to expose professionals to the value positions of the community: and (4) laying the groundwork for eventual development of an actual policy making tool. A brief description of the roles, sample input/output formats, an initial scenario, and information on accessing the game through a time-sharing system are included.

Orlando, J. A.↗

RECOVER: An Automated Cloud-Based Decision Support System for Post-fire Rehabilitation Planning

RECOVER is a site-specific decision support system that automatically brings together in a single analysis environment the information necessary for post-fire rehabilitation decision-making. After a major wildfire, law requires that the federal land management agencies certify a comprehensive plan for public safety, burned area stabilization, resource protection, and site recovery. These burned area emergency response (BAER) plans are a crucial part of our national response to wildfire disasters and depend heavily on data acquired from a variety of sources. Final plans are due within 21 days of control of a major wildfire and become the guiding document for managing the activities and budgets for all subsequent remediation efforts. There are few instances in the federal government where plans of such wide-ranging scope and importance are assembled on such short notice and translated into action more quickly. RECOVER has been designed in close collaboration with our agency partners and directly addresses their high-priority decision-making requirements. In response to a fire detection event, RECOVER uses the rapid resource allocation capabilities of cloud computing to automatically collect Earth observational data, derived decision products, and historic biophysical data so that when the fire is contained, BAER teams will have a complete and ready-to-use RECOVER dataset and GIS analysis environment customized for the target wildfire. Initial studies suggest that RECOVER can transform this information-intensive process by reducing from days to a matter of minutes the time required to assemble and deliver crucial wildfire-related data.

cloud computing↗

Initial Integration of a Conflict Probabilities Service for Upper Class E Traffic Management

This paper outlines the development and integration of a conflict probability calculation-based service designed for Upper Class E Airspace operations. A key aspect of this effort is the integration of the conflict probability calculation algorithm into a simulated situational awareness client application. This application serves as a tool for flight operators to submit operations for Upper Class E Airspace within a simulated environment. The focus is on validating the effectiveness of the service through simulated intentional conflicting scenarios. The paper delves into the practicalities of integrating the conflict probability service into the user interface of the simulated situational awareness client application. This integration aims to enhance the decision-making capabilities of ETM operators within the simulated environment. The experiments and observations conducted in this simulated facility provide insights into the operational effectiveness of the proposed approach. In conclusion, this paper emphasizes the development, integration, and simulation-based validation of a conflict probability calculation service for Upper Class E Airspace. The findings underscore the potential impact of our approach on decision-making within a simulated setting, contributing to the understanding of its practical applications in operational scenarios.

Upper Class E Traffic Management↗

NASA POWER: Providing Analysis-Ready, Cloud-Optimized Data for AI /ML Training and Applications in Earth Science

As global demand for sustainable development grows, the integration of Earth Observation (EO) data into decision making frameworks has become a primary objective for the scientific community. The NASA Prediction of Worldwide Energy Resources (POWER) project serves as a bridge between NASA EO data and the specialized needs of the renewable energy, sustainable infrastructure and agroclimatology communities. In this poster presentation we will present an overview of POWER data products and services along with its use in diverse research to decision-making workflows. By providing over 40 years of high-resolution historical, hourly and daily solar and meteorological data, POWER transforms satellite observations and global model reanalysis into actionable, Analysis-Ready Dataset (ARD). Currently, the project delivers over 250 industry-friendly parameters to the users from different NASA datasets like CERES SYN1Deg, MERRA-2, and IMERG alongside downscaled CMIP6 climate model data, fulfilling over 16 million requests from 50,000 unique users monthly. To ensure data quality and traceability, these parameters are rigorously validated against the ground-based observations from the Baseline Surface Radiation Network (BSRN) and the Global Surface Summary of the Day (GSOD) – these results will be discussed in the presentation. A newly introduced web-based PaRameter Uncertainty ViEwer (PRUVE) tool will be presented that provides an online validation platform to the users that benchmarks satellite-based and assimilation data products against these surface measurements. To reduce technical barriers to data adoption, POWER data is accessible through RESTful APIs, ESRI ArcGIS Image Services, a web-based Data Access Viewer tool, allowing users to visualize, validate and apply the dataset. For efficient data delivery POWER data is cloud-optimized into Zarr datastore accessible through NASA managed Amazon S3 ensures high-performance allowing users to integrate EO directly into operational pipelines. These customized services will be presented. Use cases from application will be presented from the energy sector - such as for design of generation systems, performance monitoring of solar power plants, in infrastructure sector- optimizing building energy efficiency and thermal comfort, in agriculture – such as driving crop simulation and yield forecasting models to enable climate resilient farming. Furthermore, the shift toward machine learning (ML) in EO research that has positioned POWER as a key provider for training datasets which will be discussed. Use-cases will be presented to showcase how NASA data is enabling the development of predictive tools for climate variability and resource management. The poster will present POWER’s future plans including technology development to enhance data traceability and reproducibility and improving I/O performance to support the rapid integration of new EO products, ensuring that POWER remains a robust scalable backend for the evolving landscape of AI-driven Earth Science. Additionally, POWER is developing an AI Agent and an MCP-Server to enable industry AI-Agentic workflows.

Neha Khadka↗

Identification and Reconfigurable Control of Impaired Multi-Rotor Drones

The paper presents an algorithm for control and safe landing of impaired multi-rotor drones when one or more motors fail simultaneously or in any sequence. It includes three main components: an identification block, a reconfigurable control block, and a decisions making block. The identification block monitors each motor load characteristics and the current drawn, based on which the failures are detected. The control block generates the required total thrust and three axis torques for the altitude, horizontal position and/or orientation control of the drone based on the time scale separation and nonlinear dynamic inversion. The horizontal displacement is controlled by modulating the roll and pitch angles. The decision making algorithm maps the total thrust and three torques into the individual motor thrusts based on the information provided by the identification block. The drone continues the mission execution as long as the number of functioning motors provide controllability of it. Otherwise, the controller is switched to the safe mode, which gives up the yaw control, commands a safe landing spot and descent rate while maintaining the horizontal attitude.

Unmanned aerial system↗

Applying Machine Learning Tools for Runway Configuration Decision Support

Determining optimal runway configurations at airports, a responsibility assigned to air traffic controllers, is a challenging task. The decision-making process is intricate and involves consideration of many factors such as prevailing wind condition, convective weather, visibility, cloud ceilings, departure and arrival demand, traffic flow, equipment status, and other airport constraints. In a previous work, we developed a Runway Configuration Assistance tool using an offline reinforcement learning method called conservative Q-learning. In this paper, we evaluate and validate our Runway Configuration Assistance tool as a decision support for air traffic controllers. We validated our tool using three airports with differing levels of complexity: Charlotte Douglas International Airport, Denver International Airport, and Dallas Fort Worth International Airport. We quantified the performance of the Runway Configuration Assistance tool based on (1) agreement with historical air traffic controller decisions and (2) violation of decisions that would be obvious to subject-matter experts. Our tool showed promising results in both performance metrics for the three airports, despite the complexities in the runway configuration decision-making process. We also discuss challenges in using machine learning in general to aid air traffic management and identify deployment considerations for the Runway Configuration Assistance tool.

Runway Configuration Management↗

Machine Learning Tools for Runway Configuration Decision Support

Determining optimal runway configurations at airports, a responsibility assigned to air traffic controllers, is a challenging task. The decision-making process is intricate and involves consideration of many factors such as prevailing wind condition, convective weather, visibility, cloud ceilings, departure and arrival demand, traffic flow, equipment status, and other airport constraints. In a previous work, we developed a Runway Configuration Assistance tool using an offline reinforcement learning method called conservative Q-learning. In this paper, we evaluate and validate our Runway Configuration Assistance tool as a decision support for air traffic controllers. We validated our tool using three airports with differing levels of complexity: Charlotte Douglas International Airport, Denver International Airport, and Dallas Fort Worth International Airport. We quantified the performance of the Runway Configuration Assistance tool based on (1) agreement with historical air traffic controller decisions and (2) violation of decisions that would be obvious to subject-matter experts. Our tool showed promising results in both performance metrics for the three airports, despite the complexities in the runway configuration decision-making process. We also discuss challenges in using machine learning in general to aid air traffic management and identify deployment considerations for the Runway Configuration Assistance tool.

Runway Configuration Management↗

Space Environments and Spacecraft Effects Concept: Transitioning Research to Operations and Applications

The National Aeronautics and Space Administration (NASA) is embarking on a course to expand human presence beyond Low Earth Orbit (LEO) while expanding its mission to explore the solar system. Destinations such as Near Earth Asteroids (NEA), Mars and its moons, and the outer planets are but a few of the mission targets. NASA has established numerous organizations specializing in specific space environments disciplines that will serve to enable these missions. To complement these existing discipline organizations, a concept is presented focusing on the development of a space environment and spacecraft effects organization. This includes space climate, space weather, natural and induced space environments, and effects on spacecraft materials and systems. This space environment and spacecraft effects organization would be comprised of Technical Working Groups (TWG) focusing on, for example: a) Charged Particles (CP), b) Space Environmental Effects (SEE), and c) Interplanetary and Extraterrestrial Environments (IEE). These technical working groups will generate products and provide knowledge supporting four functional areas: design environments, environment effects, operational support, and programmatic support. The four functional areas align with phases in the program mission lifecycle and are briefly described below. Design environments are used primarily in the mission concept and design phases of a program. Environment effects focuses on the material, component, sub-system and system-level selection and the testing to verify design and operational performance. Operational support provides products based on real time or near real time space weather observations to mission operators to aid in real time and near-term decision-making. The programmatic support function maintains an interface with the numerous programs within NASA and other federal agencies to ensure that communications are well established and the needs of the programs are being met. The programmatic support function also includes working in coordination with the program in anomaly resolution and generation of lesson learned documentation. The goal of this space environment and spacecraft effects organization is to develop decision-making tools and engineering products to support the mission phases of mission concept through operations by focusing on transitioning research to application. Products generated by this space environments and spacecraft effects organization are suitable for use in anomaly investigations. This paper will describe the organizational structure for this space environments and spacecraft effects organization, and outline the scope of conceptual TWG's and their relationship to the functional areas.

Edwards, D. L.↗

'And we were tired' fatigue and aircrew errors

Cooper, White, and Lauber analyzed jet-transport accidents worldwide for the period 1968 to 1976 and found more than 60 in which breakdowns of the crew-performance and decision-making process played a pivotal role. It is the contention of this paper that the results reported by Cooper, et al., are related to fatigue and that fatigue is related to sleep deprivation, circadian desynchronosis, and lack of good nutrition. It is further argued, and supporting research is cited, that fatigue does not cause loss of skill in flying the aircraft but may have disastrous effects on judgmental and decision-making functions.

Ritter, Richard D.↗

Technology Alignment and Portfolio Prioritization (TAPP): Advanced Methods in Strategic Analysis, Technology Forecasting and Long Term Planning for Human Exploration and Operations, Advanced Exploration Systems and Advanced Concepts

The Advanced Concepts Office (ACO) at NASA, Marshall Space Flight Center is expanding its current technology assessment methodologies. ACO is developing a framework called TAPP that uses a variety of methods, such as association mining and rule learning from data mining, structure development using a Technological Innovation System (TIS), and social network modeling to measure structural relationships. The role of ACO is to 1) produce a broad spectrum of ideas and alternatives for a variety of NASA's missions, 2) determine mission architecture feasibility and appropriateness to NASA's strategic plans, and 3) define a project in enough detail to establish an initial baseline capable of meeting mission objectives ACO's role supports the decision­-making process associated with the maturation of concepts for traveling through, living in, and understanding space. ACO performs concept studies and technology assessments to determine the degree of alignment between mission objectives and new technologies. The first step in technology assessment is to identify the current technology maturity in terms of a technology readiness level (TRL). The second step is to determine the difficulty associated with advancing a technology from one state to the next state. NASA has used TRLs since 1970 and ACO formalized them in 1995. The DoD, ESA, Oil & Gas, and DoE have adopted TRLs as a means to assess technology maturity. However, "with the emergence of more complex systems and system of systems, it has been increasingly recognized that TRL assessments have limitations, especially when considering [the] integration of complex systems." When performing the second step in a technology assessment, NASA requires that an Advancement Degree of Difficulty (AD2) method be utilized. NASA has used and developed or used a variety of methods to perform this step: Expert Opinion or Delphi Approach, Value Engineering or Value Stream, Analytical Hierarchy Process (AHP), Technique for the Order of Prioritization by Similarity to Ideal Solution (TOPSIS), and other multi­‐criteria decision-making methods. These methods can be labor-intensive, often contain cognitive or parochial bias, and do not consider the competing prioritization between mission architectures. Strategic Decision-Making (SDM) processes cannot be properly understood unless the context of the technology is understood. This makes assessing technological change particularly challenging due to the relationships "between incumbent technology and the incumbent (innovation) system in relation to the emerging technology and the emerging innovation system." The central idea in technology dynamics is to consider all activities that contribute to the development, diffusion, and use of innovations as system functions. Bergek defines system functions within a TIS to address what is actually happening and has a direct influence on the ultimate performance of the system and technology development. ACO uses similar metrics and is expanding these metrics to account for the structure and context of the technology. At NASA technology and strategy is strongly interrelated. NASA's Strategic Space Technology Investment Plan (SSTIP) prioritizes those technologies essential to the pursuit of NASA's missions and national interests. The SSTIP is strongly coupled with NASA's Technology Roadmaps to provide investment guidance during the next four years, within a twenty-year horizon. This paper discusses the methods ACO is currently developing to better perform technology assessments while taking into consideration Strategic Alignment, Technology Forecasting, and Long Term Planning.

Funaro, Gregory V.↗

TIES for Dummies 3rd Edition (Technology Identification, Evaluation, and Selection) Basic how to's to implement the TIES method

The TIES method is a forecasting environment whereby the decision-maker has the ability to easily assess and trade-off the impact of various technologies without sophisticated and time-consuming mathematical formulations. TIES provides a methodical approach where technically feasible alternatives can be identified with accuracy and speed to reduce design cycle time, and subsequently, life cycle costs, and was achieved through the use of various probabilistic methods, such as Response Surface Methodology and Monte Carlo Simulations. Furthermore, structured and systematic techniques are utilized from other fields to identify possible concepts and evaluation criteria by which comparisons can be made. This objective is achieved by employing the use of Morphological Matrices and Multi-Attribute Decision Making techniques. Through the execution of each step, a family of design alternatives for a given set of customer requirements can be identified and assessed subjectively or objectively. This methodology allows for more information (knowledge) to be brought into the earlier phases of the design process and will have direct implications on the affordability of the system. The increased knowledge allows for optimum allocation of company resources and quantitative justification for program decisions. Finally, the TIES method provided novel results and quantitative justification to facilitate decision making in the early stages of design so as to produce affordable and quality products.

Kirby, Michelle R.↗