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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Information Fusion & Analytics for Human Lunar Exploration

The Information Fusion & Data Analytics (IFDA) project commenced in FY20, continued through FY21, and its final platform development phase continues in FY22. The objective remains the fusion and rapid accessibility of large quantities of disparate sourced human spaceflight data. IFDA is a platform tailored for NA (S&MA) to develop highly advanced operational data integration and analysis techniques. IFDA leverages the JSC ER7 modeling, simulation,and data fusion capabilities to collect, warehouse, and augment data human exploration data integration and analysis techniques. The IFDA project’s integrated data visualizations have been demonstrated in two validation scenarios in FY21, and provided the architecture and platform basis for development of a full-scale data analysis suite and storage solution useful to all JSC organizations engaged in real time operations and safety tasks. Scenarioand prototypical development including the construction of a full scale data analysis suite and storage solution, useful to all JSC organizations engaged in real time operations and safety tasks, is central to IFDA Phase 3 and provides a demonstrable pathway for the Digital Transformation Program. IFDA Phase 3 is focused on data provider, data utilizer, and SME hands-on workshops that will conclude the Dem / Valphase and deliver a program-ready data integration tool as a product.

information fusion↗
NASA NTRS2005

Intelligent Information Fusion in the Aviation Domain: A Semantic-Web based Approach

Information fusion from multiple sources is a critical requirement for System Wide Information Management in the National Airspace (NAS). NASA and the FAA envision creating an "integrated pool" of information originally coming from different sources, which users, intelligent agents and NAS decision support tools can tap into. In this paper we present the results of our initial investigations into the requirements and prototype development of such an integrated information pool for the NAS. We have attempted to ascertain key requirements for such an integrated pool based on a survey of DSS tools that will benefit from this integrated pool. We then advocate key technologies from computer science research areas such as the semantic web, information integration, and intelligent agents that we believe are well suited to achieving the envisioned system wide information management capabilities.

Ashish, Naveen↗
NASA NTRS2022

Information Fusion and Data Analytics for Human Lunar Exploration (CIF REPORT: Detailed PI Write-up)

The Information Fusion & Data Analytics (IFDA) project commenced in FY20, continued through FY21, and its final platform development phase continues in FY22. The objective remains the fusion and rapid accessibility of large quantities of disparate sourced human spaceflight data. IFDA is a platform tailored for NA (S&MA) to develop highly advanced operational data integration and analysis techniques. IFDA leverages the JSC ER7 modeling, simulation,and data fusion capabilities to collect, warehouse, and augment data human exploration data integration and analysis techniques. The IFDA project’s integrated data visualizations have been demonstrated in two validation scenarios in FY21, and provided the architecture and platform basis for development of a full-scale data analysis suite and storage solution useful to all JSC organizations engaged in real time operations and safety tasks. Scenarioand prototypical development including the construction of a full scale data analysis suite and storage solution, useful to all JSC organizations engaged in real time operations and safety tasks, is central to IFDA Phase 3 and provides a demonstrable pathway for the Digital Transformation Program. IFDA Phase 3 is focused on data provider, data utilizer, and SME hands-on workshops that will conclude the Dem / Valphase and deliver a program-ready data integration tool as a product.

information fusion↗
NASA NTRS1990

Evolution of an Intelligent Information Fusion System

Consideration is given to the hardware and software needed to manage the enormous amount and complexity of data that the next generation of space-borne sensors will provide. An anthology is presented illustrating the evolution of artificial intelligence, science data processing, and management from the 1960s to the near future. Problems and limitations of technologies, data structures, data standards, and conceptual thinking are addressed. The development of an end-to-end Intelligent Information Fusion System that embodies knowledge of the user's domain-specific goals is proposed.

Campbell, William J.↗
NASA NTRSDate not supplied

Adaptive Multi-Sensor Localization Information Fusion for Autonomous Urban Air Mobility Operations

An adaptive method is developed to iteratively fuse the information provided by multiple sensors to enable autonomous urban air mobility type operations. First, noisy and bias corrupted IMU readings are processed as soon as they arrive using kinematic equations represented in the vehicle's body frame. To correct the systems drift resulting from the integration, an information content measure is introduced to decide on the environment. For the cluttered environment the information provided by environmental sensors is counted as reliable and the drift correction as accurate. For the open space, the GPS data is counted as reliable, and the drift correction is done based on the GPS readings. The measurement noise effects are minimize using Iterated Extended Kalman Filter framework. The algorithm is implemented in the in-house developed FlightDeckz simulation environment using an IMU model, simulated video recorded from a camera mounted on the vehicle (for the purpose of this study, outside scenery was generated with XPlane), which flies in an urban environment, and GPS data generated from the environment's digital map.

Onboard Perception↗
NASA NTRS2021

CIF Report - Information Fusion and Data Analytics for Human Lunar Exploration

This project leverages the Concept Exploration Laboratory (CEL) to collect, warehouse, and augment data relevant to human lunar exploration as a platform for NA (S&MA) to develop operational data integration techniques. The project capitalizes on 16+ years of CEL experience applied to NASA, DoD, the City of Houston, the State of Texas, and private industry. The integrated data will be utilized in the two scenarios described in a definition of concept for development of a full scale data analysis suite and storage solution, useful to all JSC organizations engaged in real time operations and safety tasks, and may be useful as pathfinders for the Digital Transformation Program.

information fusion↗
NASA NTRS2004

Development of an Information Fusion System for Engine Diagnostics and Health Management

Aircraft gas-turbine engine data are available from a variety of sources including on-board sensor measurements, maintenance histories, and component models. An ultimate goal of Propulsion Health Management (PHM) is to maximize the amount of meaningful information that can be extracted from disparate data sources to obtain comprehensive diagnostic and prognostic knowledge regarding the health of the engine. Data Fusion is the integration of data or information from multiple sources, to achieve improved accuracy and more specific inferences than can be obtained from the use of a single sensor alone. The basic tenet underlying the data/information fusion concept is to leverage all available information to enhance diagnostic visibility, increase diagnostic reliability and reduce the number of diagnostic false alarms. This paper describes a basic PHM Data Fusion architecture being developed in alignment with the NASA C17 Propulsion Health Management (PHM) Flight Test program. The challenge of how to maximize the meaningful information extracted from disparate data sources to obtain enhanced diagnostic and prognostic information regarding the health and condition of the engine is the primary goal of this endeavor. To address this challenge, NASA Glenn Research Center (GRC), NASA Dryden Flight Research Center (DFRC) and Pratt & Whitney (P&W) have formed a team with several small innovative technology companies to plan and conduct a research project in the area of data fusion as applied to PHM. Methodologies being developed and evaluated have been drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation, and fuzzy logic. This paper will provide a broad overview of this work, discuss some of the methodologies employed and give some illustrative examples.

Volponi, Allan J.↗
NASA NTRS2024

Effective structural impact detection and localization using convolutional neural network and Bayesian information fusion with limited sensors

Due to their unpredictable nature, many impact events (e.g., overheight vehicles striking on bridges) go unnoticed or get reported many hours later. However, they can induce structural failures or hidden damage that accelerates the structure’s long-term degradation. Therefore, prompt impact detection and localization strategies are essential for early warning of impact events and rapid maintenance of structures. Most existing impact detection strategies are developed for aircraft composite panels utilizing high-rate synchronized measurement from densely deployed sensors. Limited efforts have been made for infrastructure or human habitats which generally require large-scale but low-rate measurement. In particular, due to harsh environments (e.g., deep space habitats under meteoroids), structural impact localization must be robust to limited sensors (e.g., sensor damage during impacts) and multi-source errors (e.g., measurement errors). In this study, an effective impact detection and localization strategy is proposed using a limited number of vibration measurements, especially in harsh environments (e.g. in deep space). Convolutional neural networks are trained for each sensor node and are fused using Bayesian theory to improve the accuracy of impact localization. Special considerations are paid to evaluate the effect of both measurement error and modeling error in the analysis. The proposed strategy is illustrated using 1D structure, and further validated in 3D geodesic dome structure numerically. The results demonstrate that it can detect and localize impact events accurately and robustly on structures.

Yuguang Fu↗
NASA NTRS1992

The fusion of information via fuzzy integration

Multisensor fusion is becoming increasingly important in intelligent computer vision systems. In this paper we present the generalized fuzzy integral with respect to an S-decomposable measure as a tool for fusing information from multiple sensors in an object recognition problem. Results from an experiment with automatic target recognition imagery are provided.

Keller, James M.↗
NASA NTRSDate not supplied

Understanding the Impact of Unobservable Variables on the Performance of Predictive Models: The Need for Feature Space Partitioning and Fusion

When developing predictive models over a dataset, the model is globally optimized across the entire feature space to learn a decision boundary. However, when unobservable variables—which cannot be measured or estimated—interact with the observable variables, this can negatively impact the optimization applied to the decision boundary since the data samples introduced by unobservable variables may have little to no association with the applied global optimization. This, consequently, penalizes the entire decision boundary and model performance. This paper examines some of the detrimental effects of unobservable variables, particularly their role in creating new modes in the distribution of observable variables and reducing the separability of class distributions. Such challenges result in skewed or warped decision boundaries and decreased accuracy of model predictions, particularly for interpretable models like logistic regression and decision trees. Through two illustrative case examples, we highlight the need to address the challenges imposed by unobservable variables. We propose a strategy to mitigate these challenges by creating local regions within the feature space through partitioning. This enables the optimization of local models within the regions to overcome the impact of unobservability in different feature space localities. Research into a more sophisticated partitioning strategy and where the partition should be relative to the sample of interest is left as future work. Through the analysis of the impact of unobservability and the development of a partitioning method, we demonstrate the clear need for a partitioning strategy that integrates knowledge from multiple local models to estimate risk factors using information fusion. Thus, we establish the foundation and motivation for using partitioning and information fusion to overcome the effects of unobservability in predictive models. Formal fusion methods, such as Dempster-Shafer theory, can better leverage the information from local regions to improve the performance of interpretable predictive models in the presence of unobservable variables.

Time Series Data↗
NASA NTRS2010

Spatial Statistical Data Fusion for Remote Sensing Applications

Data fusion is the process of combining information from heterogeneous sources into a single composite picture of the relevant process, such that the composite picture is generally more accurate and complete than that derived from any single source alone. Data collection is often incomplete, sparse, and yields incompatible information. Fusion techniques can make optimal use of such data. When investment in data collection is high, fusion gives the best return. Our study uses data from two satellites: (1) Multiangle Imaging SpectroRadiometer (MISR), (2) Moderate Resolution Imaging Spectroradiometer (MODIS).

fusion techniques↗
NASA NTRSDate not supplied

Aggregate Risk Tool for Human Space Flight

This project utilized Data fusion capabilities developed from the 2020-2022 Information Fusion (IFDA) IRAD to enhance functionality of a Human Spaceflight (HSF) Aggregate Risk Product developed by the JSC Flight Safety Office. The tool compiles and presents risk data from multiple Program and Commercial Provider vehicles to provide a holistic assessment of residual risk to Program Managers and Technical Authorities prior to HSF Launches. The aggregation or coupling of risks from multiple sources (Examples: PRA, variances, anomaly data bases, Program Risk Management data bases, technical review board minutes, Hazard Analysis, etc.) when analyzed, identify potential areas of elevated risk in components, systems or mission phases that would otherwise be undetected by nonintegrated program risk management tools and possesses.

Patrick Huckaby↗
NASA NTRS2022

Aggregate Risk Tool for Human Space Flight

This tool utilizes Data fusion capabilities developed from the 2020-2022 Information Fusion (IFDA) IRAD to enhance functionality of a Human Spaceflight (HSF) Aggregate Risk Product developed by the JSC Flight Safety Office. The tool compiles and present risk data from multiple Program and Commercial Provider sources to provide a holistic assessment for Program Managers and Technical Authorities to evaluate residual risk prior to HSF Launches. The aggregation, or coupling of risks from multiple sources (Ex: PRA, variances, anomaly data bases, CRM data, technical review board minutes, Hazard Analysis, etc.) identifies areas of elevated risk in components, systems or mission phases that would otherwise be undetected by non-integrated program risk management tools and possesses. Data is then displayed in an interactive user interface/dashboard that enables users to see potential areas of concern requiring more in-depth analysis.

Patrick M Huckaby↗
NASA NTRS1991

Sensor data validation and reconstruction. Phase 1: System architecture study

The sensor validation and data reconstruction task reviewed relevant literature and selected applicable validation and reconstruction techniques for further study; analyzed the selected techniques and emphasized those which could be used for both validation and reconstruction; analyzed Space Shuttle Main Engine (SSME) hot fire test data to determine statistical and physical relationships between various parameters; developed statistical and empirical correlations between parameters to perform validation and reconstruction tasks, using a computer aided engineering (CAE) package; and conceptually designed an expert system based knowledge fusion tool, which allows the user to relate diverse types of information when validating sensor data. The host hardware for the system is intended to be a Sun SPARCstation, but could be any RISC workstation with a UNIX operating system and a windowing/graphics system such as Motif or Dataviews. The information fusion tool is intended to be developed using the NEXPERT Object expert system shell, and the C programming language.

Source record↗