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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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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.
A parallel version of CLIPS 5.1 has been developed to run on Intel Hypercubes. The user interface is the same as that for CLIPS with some added commands to allow for parallel calls. A complete version of CLIPS runs on each node of the hypercube. The system has been instrumented to display the time spent in the match, recognize, and act cycles on each node. Only rule-level parallelism is supported. Parallel commands enable the assertion and retraction of facts to/from remote nodes working memory. Parallel CLIPS was used to implement a knowledge-based command, control, communications, and intelligence (C(sup 3)I) system to demonstrate the fusion of high-level, disparate sources. We discuss the nature of the information fusion problem, our approach, and implementation. Parallel CLIPS has also be used to run several benchmark parallel knowledge bases such as one to set up a cafeteria. Results show from running Parallel CLIPS with parallel knowledge base partitions indicate that significant speed increases, including superlinear in some cases, are possible.
A process was designed to fuse data from multiple sensors in order to make a more accurate estimation of the environment and overall health in an intelligent rocket test facility (IRTF), to provide reliable, high-confidence measurements for a variety of propulsion test articles. The object of the technology is to provide sensor fusion based on a distributed architecture. Specifically, the fusion technology is intended to succeed in providing health condition monitoring capability at the intelligent transceiver, such as RF signal strength, battery reading, computing resource monitoring, and sensor data reading. The technology also provides analytic and diagnostic intelligence at the intelligent transceiver, enhancing the IEEE 1451.x-based standard for sensor data management and distributions, as well as providing appropriate communications protocols to enable complex interactions to support timely and high-quality flow of information among the system elements.
For practical industrial applications, the development of trainable robots is an important and immediate objective. Therefore, the developing of flexible intelligence directly applicable to training is emphasized. It is generally agreed upon by the AI community that the fusion of expert systems, neural networks, and conventionally programmed modules (e.g., a trajectory generator) is promising in the quest for autonomous robotic intelligence. Autonomous robot development is hindered by integration and architectural problems. Some obstacles towards the construction of more general robot control systems are as follows: (1) Growth problem; (2) Software generation; (3) Interaction with environment; (4) Reliability; and (5) Resource limitation. Neural networks can be successfully applied to some of these problems. However, current implementations of neural networks are hampered by the resource limitation problem and must be trained extensively to produce computationally accurate output. A generalization of conventional neural nets is proposed, and an architecture is offered in an attempt to address the above problems.
Intelligence robotic system design involves sensor allocation and sensor data fusion. In the field of geometric reasoning, a fast and effective representation method is needed to determine the physical dimensions of the manipulator as well as the motion environment in sufficient detail. Due to the capability of current sensor technology, the integrity of the sensor information is location dependent and point-wise, line-wise and plane-wise in nature. In order to maneuver the robot by reasoning the environment, there is a minimum requirement of prior information for integrating the allocated sensor feedback. Some new theoretical developments of how to associate the number and the locations of the sensors with three-dimensional geometry of the robot and the motion environment is presented. The related collision avoidance problem (CAP) will be addressed as well.
The goal of this research is to explore and prove out robust concepts for telerobotic support of space servicing, assembly, maintenance, and telescience tasks. This goal is being addressed through a program of coordinated work in artificial intelligence, robotics, and human factors. The general research objective is the fusion of robot sensing and manipulation, teleoperation, and human and machine cognitive skills into an effective architecture for supervised task automation. NASA is evaluating results of this research program in a ground laboratory telerobot testbed under development at JPL. The testbed development activity includes integrated technology demonstrations. The demonstrations will show telerobot capabilities to perform tasks of increasing complexity, and duration in increasingly unstructured environments. The first such demonstration is the ground-based grappling, dockling, and servicing of a satellite taskboard.
This is a listing of recent unclassified RTO technical publications for April 1, 2004 through June 30, 2004, processed by the NASA Center for AeroSpace Information. Topics covered include: heat transfer and cooling in propulsion and power systems; assessment of operator functional state; microwaves; aerodynamics in solid rocket propulsion; command, control, communications and intelligence modeling; personal protective equipment against anti-personnel mine blast; and data fusion and visualization.
A theoretical framework is presented for a 'sensing-knowledge command-fusion' paradigm of interactive and cooperative sensing and control in advanced teleoperators, which takes advantage of both current and projected robotic dexterousness and sensor-based autonomy capabilities. Attention is given to (1) a method for the achievement of a sensing-knowledge-command computational mechanism that implements the intended cooperative/interactive system, and (2) the system architecture and man/machine-interface protocols entailed by this implementation.
The U.S. Air Force and National Oceanic Atmospheric Agency (NOAA) space environmental operations centers are facing increasingly complex challenges meeting the needs of their growing user community. These centers provide current space environmental information and short term forecasts of geomagnetic activity. Recent advances in modeling and data access have provided sophisticated tools for making accurate and timely forecasts, but have introduced new problems associated with handling and analyzing large quantities of complex data. AI (Artificial Intelligence) techniques have been considered as potential solutions to some of these problems. Fielding AI systems has proven more difficult than expected, in part because of operational constraints. Using systems which have been demonstrated successfully in the operational environment will provide a basis for a useful data fusion and analysis capability. Our approach uses a general purpose AI system already in operational use within the military intelligence community, called the Temporal Analysis System (TAS). TAS is an operational suite of tools supporting data processing, data visualization, historical analysis, situation assessment and predictive analysis. TAS includes expert system tools to analyze incoming events for indications of particular situations and predicts future activity. The expert system operates on a knowledge base of temporal patterns encoded using a knowledge representation called Temporal Transition Models (TTM's) and an event database maintained by the other TAS tools. The system also includes a robust knowledge acquisition and maintenance tool for creating TTM's using a graphical specification language. The ability to manipulate TTM's in a graphical format gives non-computer specialists an intuitive way of accessing and editing the knowledge base. To support space environmental analyses, we used TAS's ability to define domain specific event analysis abstractions. The prototype system defines events covering reports of natural phenomena such as solar flares, bursts, geomagnetic storms, and five others pertinent to space environmental analysis. With our preliminary event definitions we experimented with TAS's support for temporal pattern analysis using X-ray flare and geomagnetic storm forecasts as case studies. We are currently working on a framework for integrating advanced graphics and space environmental models into this analytical environment.
Various papers on remote sensing (RS) for the nineties are presented. The general topics addressed include: subsurface methods, radar scattering, oceanography, microwave models, atmospheric correction, passive microwave systems, RS in tropical forests, moderate resolution land analysis, SAR geometry and SNR improvement, image analysis, inversion and signal processing for geoscience, surface scattering, rain measurements, sensor calibration, wind measurements, terrestrial ecology, agriculture, geometric registration, subsurface sediment geology, radar modulation mechanisms, radar ocean scattering, SAR calibration, airborne radar systems, water vapor retrieval, forest ecosystem dynamics, land analysis, multisensor data fusion. Also considered are: geologic RS, RS sensor optical measurements, RS of snow, temperature retrieval, vegetation structure, global change, artificial intelligence, SAR processing techniques, geologic RS field experiment, stochastic modeling, topography and Digital Elevation model, SAR ocean waves, spaceborne lidar and optical, sea ice field measurements, millimeter waves, advanced spectroscopy, spatial analysis and data compression, SAR polarimetry techniques. Also discussed are: plant canopy modeling, optical RS techniques, optical and IR oceanography, soil moisture, sea ice back scattering, lightning cloud measurements, spatial textural analysis, SAR systems and techniques, active microwave sensing, lidar and optical, radar scatterometry, RS of estuaries, vegetation modeling, RS systems, EOS/SAR Alaska, applications for developing countries, SAR speckle and texture.
Exponential growth of data from Earth Observation (EO) assets has necessitated the development of sophisticated methods for data interpretation and management. NASA’s New Observing Strategy (NOS) approach aims to coordinate operations among complex heterogenous systems of constellations, requiring advanced Artificial Intelligence and Machine Learning (AI/ML) techniques. Despite significant advancements in AI/ML across various domains, the EO and machine learning for satellite (SatML) fields remain fragmented, often relying on adapted techniques rather than domain-specific solutions. We present a novel end-to-end data fusion framework tailored specifically for EO and SatML, addressing this gap by facilitating rapid development of AI/ML applications. This framework, called, Multimodal Earth Observation Workflow for Machine Learning (MEOW-ML), sup- ports the entire AI/ML lifecycle, from dataset manipulation, to model training, evaluation, and logging, and is designed to expedite the development of next-generation NOS deployments and SOTA in EO. We apply our framework to predict canopy height model (CHM) derived from lidar data. We integrate multiple data modalities through a hierarchical, multi-path model architecture, effectively identifying and leveraging the unique strengths of each data source to enhance predictive accuracy. Our experiments demonstrate that the multi-path architecture outperforms traditional single-path models and provides significant advantages in both accuracy and computational efficiency.
A system for automated fusion and interpretation of image data from multiple sensors, including multispectral data from an imaging spectrometer is being developed. Classical artificial intelligence techniques and artificial neural networks are employed to make real time decision based on current input and known scientific goals. Emphasis is placed on identifying minerals which could indicate past life activity or an environment supportive of life. Multispectral data can be used for geological analysis because different minerals have characteristic spectral reflectance in the visible and near infrared range. Classification of each spectrum into a broad class, based on overall spectral shape and locations of absorption bands is possible in real time using artificial neural networks. The goal of the system is twofold: multisensor and multispectral data must be interpreted in real time so that potentially interesting sites can be flagged and investigated in more detail while the rover is near those sites; and the sensed data must be reduced to the most compact form possible without loss of crucial information. Autonomous decision making will allow a rover to achieve maximum scientific benefit from a mission. Both a classical rule based approach and a decision neural network for making real time choices are being considered. Neural nets may work well for adaptive decision making. A neural net can be trained to work in two steps. First, the actual input state is mapped to the closest of a number of memorized states. After weighing the importance of various input parameters, the net produces an output decision based on the matched memory state. Real time, autonomous image data analysis and decision making capabilities are required for achieving maximum scientific benefit from a rover mission. The system under development will enhance the chances of identifying fossils or environments capable of supporting life on Mars
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.
Aircraft gas-turbine engine data is 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 for the achievement of 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 report describes a basic PHM data fusion architecture being developed in alignment with the NASA C-17 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, NASA Dryden Flight Research Center, and Pratt & Whitney have formed a team with several small innovative technology companies to plan and conduct a research project in the area of data fusion, as it applies 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 report will provide a chronology and summary of the work accomplished under this research contract.
A novel cognitive computing architecture is conceptualized for processing multiple channels of multi-modal sensory data streams simultaneously, and fusing the information in real time to generate intelligent reaction sequences. This unique architecture is capable of assimilating parallel data streams that could be analog, digital, synchronous/asynchronous, and could be programmed to act as a knowledge synthesizer and/or an "intelligent perception" processor. In this architecture, the bio-inspired models of visual pathway and olfactory receptor processing are combined as processing components, to achieve the composite function of "searching for a source of food while avoiding the predator." The architecture is particularly suited for scene analysis from visual data and odorant.
Data fusion and sensor management approaches have largely been implemented with centralized and hierarchical architectures. Numerical and statistical methods are the most common data fusion methods found in these systems. Given the proliferation and low cost of processing power, there is now an emphasis on designing distributed and decentralized systems. These systems use analytical/quantitative techniques or qualitative reasoning methods for date fusion.Based on other work by the author, a sensor may be treated as a highly autonomous (decentralized) unit. Each highly autonomous sensor (HAS) is capable of extracting qualitative behaviours from its data. For example, it detects spikes, disturbances, noise levels, off-limit excursions, step changes, drift, and other typical measured trends. In this context, this paper describes a distributed sensor fusion paradigm and theory where each sensor in the system is a HAS. Hence, given the reach qualitative information from each HAS, a paradigm and formal definitions are given so that sensors and processes can reason and make decisions at the qualitative level. This approach to sensor fusion makes it possible the implementation of intuitive (effective) methods to monitor, diagnose, and compensate processes/systems and their sensors. This paradigm facilitates a balanced distribution of intelligence (code and/or hardware) to the sensor level, the process/system level, and a higher controller level. The primary application of interest is in intelligent health management of rocket engine test stands.
At the core of ISHM is the ability to extract information and knowledge from raw data. Conventional data acquisition systems sample and convert physical measurements to engineering units, which higher-level systems use to derive health and information about processes and systems. Although health management is essential at the top level, there are considerable advantages to implementing health-related functions at the sensor level. The distribution of processing to lower levels reduces bandwidth requirements, enhances data fusion, and improves the resolution for detection and isolation of failures in a system, subsystem, component, or process. The Smart Networked Element (SNE) has been developed to implement intelligent functions and algorithms at the sensor level in support of ISHM.
In this paper, we describe the architecture of both the PATS and SAP systems and how these two systems interoperate with each other forming a unified capability for deploying intelligence in hostile environments with the objective of providing actionable situational awareness of individuals. The SAP system works in concert with the UICDS information sharing middleware to provide data fusion from multiple sources. UICDS can then publish the sensor data using the OGC's Web Mapping Service, Web Feature Service, and Sensor Observation Service standards. The system described in the paper is able to integrate a spatially distributed sensor system, operating without the benefit of the Web infrastructure, with a remote monitoring and control system that is equipped to take advantage of SWE.