A novel information fusion methodology for intelligent terrain analysis
This paper presents a novel information fusion methodology for intelligent terrain analysis.
information fusion terrain analysis↗
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Intelligent terrain analysis and information fusion for safe spacecraft landing
This paper presents a novel multi-sensor information fusion methodology for intelligent terrain characterization.
terrain analysis↗
Reliability analysis in intelligent machines
Given an explicit task to be executed, an intelligent machine must be able to find the probability of success, or reliability, of alternative control and sensing strategies. By using concepts for information theory and reliability theory, new techniques for finding the reliability corresponding to alternative subsets of control and sensing strategies are proposed such that a desired set of specifications can be satisfied. The analysis is straightforward, provided that a set of Gaussian random state variables is available. An example problem illustrates the technique, and general reliability results are presented for visual servoing with a computed torque-control algorithm. Moreover, the example illustrates the principle of increasing precision with decreasing intelligence at the execution level of an intelligent machine.
Mcinroy, John E.↗
A Probabilistic System Analysis of Intelligent Propulsion System Technologies
NASA s Intelligent Propulsion System Technology (Propulsion 21) project focuses on developing adaptive technologies that will enable commercial gas turbine engines to produce fewer emissions and less noise while increasing reliability. It features adaptive technologies that have included active tip-clearance control for turbine and compressor, active combustion control, turbine aero-thermal and flow control, and enabling technologies such as sensors which are reliable at high operating temperatures and are minimally intrusive. A probabilistic system analysis is performed to evaluate the impact of these technologies on aircraft CO2 (directly proportional to fuel burn) and LTO (landing and takeoff) NO(x) reductions. A 300-passenger aircraft, with two 396-kN thrust (85,000-pound) engines is chosen for the study. The results show that NASA s Intelligent Propulsion System technologies have the potential to significantly reduce the CO2 and NO(x) emissions. The results are used to support informed decisionmaking on the development of the intelligent propulsion system technology portfolio for CO2 and NO(x) reductions.
Tong, Michael T.↗
Intelligent signal analysis and recognition
Progress in the research and development of self-organizing database system that can support the identification and characterization of signals in an RF environment is described. As the radio frequency spectrum becomes more crowded, there are a number of situations that require a characterization of the RF environment. This database system is designed to be practical in applications where communications and other instruments encounter a time varying and complex RF environment. The primary application of this system is the guidance and control of NASA's SETI Microwave Observing Project. Other possible applications include selection of telemety bands for communication with spacecraft, and the scheduling of antenna for radio astronomy are two examples where characterization of the RF environment is required. In these applications, the RF environment is constantly changing, and even experienced operators cannot quickly identify the multitude of signals that can be encountered. Some of these signals are repetitive, others appear to occur sporadically.
Levinson, Robert↗
Intelligent Image Analysis for Image-Guided Laser Hair Removal and Skin Therapy
We present the development of advanced automatic target recognition (ATR) algorithms for the hair follicles identification in digital skin images to accurately direct the laser beam to remove the hair. The ATR system first performs a wavelet filtering to enhance the contrast of the hair features in the image. The system then extracts the unique features of the targets and sends the features to an Adaboost based classifier for training and recognition operations. The ATR system automatically classifies the hair, moles, or other skin lesion and provides the accurate coordinates of the intended hair follicle locations. The coordinates can be used to guide a scanning laser to focus energy only on the hair follicles. The intended benefit would be to protect the skin from unwanted laser exposure and to provide more effective skin therapy.
laser hair removal↗
Cooperative analysis expert situation assessment research
For the past few decades, Rome Air Development Center (RADC) has been conducting research in Artificial Intelligence (AI). When the recent advances in hardware technology made many AI techniques practical, the Intelligence and Reconnaissance Directorate of RADC initiated an applications program entitled Knowledge Based Intelligence Systems (KBIS). The goal of the program is the development of a generic Intelligent Analyst System, an open machine with the framework for intelligence analysis, natural language processing, and man-machine interface techniques, needing only the specific problem domain knowledge to be operationally useful. The development of KBIS is described.
Mccown, Michael G.↗
The Intelligent Data Understanding Element of NASA's Intelligent Systems Program
Within the NASA Intelligent Systems Program, the Intelligent Data Understanding (IDU) element develops techniques for transforming data into scientific understanding. Automating such tools is critical for space science, space-based earth science, and planetary exploration with onboard scientific data analysis. Intelligent data understanding (IDU) is about extracting meaning from large, diverse science and engineering databases, via autonomous techniques that transform very large datasets into understanding. The earth science community in particular needs new tools for analyzing multi-formatted and geographically distributed datasets and for identifying cause-effect relationships in the complex data. Research within the IDU program element seeks to automate data analysis tasks so that humans can focus on creative hypothesis generation and knowledge synthesis. It may also enable NASA space missions in which autonomous agents must generate knowledge and take actions, and missions where limited bandwidth permits transmission of only the most interesting scientific observations, summaries, and conclusions. Twenty-seven research projects are-currently funded.
Coughlan, Joseph C.↗
Quantitative Phenotypic Analysis of Arabidopsis Thaliana Grown in Microgravity Using Soap, an Applied Artificial Intelligence
Phenotypic analysis is an essential step in studying the gravitropic responses and gravitational stress experienced by plants grown in microgravity. Many of the phenotypic traits analyzed in gravitropism studies, such as root length, leaf area, secondary root count, and number of root hairs, currently rely upon manual measurement methods for quantification. However, new advances in data analysis technology using artificial intelligence offer an opportunity for more efficient phenotypic analysis and a reduction of time spent in the data collection phase. In this project, the ability of a new artificially intelligent data collection software, SOAP (Simple Object Access Protocol), to collect and quantify phenotypic traits of Arabidopsis thaliana will be assessed. This project will test the measurements taken by an initial draft of the software. SOAP will take measurements of shoot length, a key phenotype used to assess A. thaliana stress response when grown in microgravity conditions. Shoot length measurements made by SOAP will be compared against a series of manual shoot length measurements. The comparison between the two methods of data measurement will provide valuable insight into the relative accuracy of SOAP and the margin of human error when conducting lab measurements.
Arabidopsis↗
Intelligent Elements for ISHM
There are a number of architecture models for implementing Integrated Systems Health Management (ISHM) capabilities. For example, approaches based on the OSA-CBM and OSA-EAI models, or specific architectures developed in response to local needs. NASA s John C. Stennis Space Center (SSC) has developed one such version of an extensible architecture in support of rocket engine testing that integrates a palette of functions in order to achieve an ISHM capability. Among the functional capabilities that are supported by the framework are: prognostic models, anomaly detection, a data base of supporting health information, root cause analysis, intelligent elements, and integrated awareness. This paper focuses on the role that intelligent elements can play in ISHM architectures. We define an intelligent element as a smart element with sufficient computing capacity to support anomaly detection or other algorithms in support of ISHM functions. A smart element has the capabilities of supporting networked implementations of IEEE 1451.x smart sensor and actuator protocols. The ISHM group at SSC has been actively developing intelligent elements in conjunction with several partners at other Centers, universities, and companies as part of our ISHM approach for better supporting rocket engine testing. We have developed several implementations. Among the key features for these intelligent sensors is support for IEEE 1451.1 and incorporation of a suite of algorithms for determination of sensor health. Regardless of the potential advantages that can be achieved using intelligent sensors, existing large-scale systems are still based on conventional sensors and data acquisition systems. In order to bring the benefits of intelligent sensors to these environments, we have also developed virtual implementations of intelligent sensors.
Schmalzel, John L.↗
The Test Analysis Retrieval System (TARS): Meeting the challenges of the network's test processes
The Networks Systems Test Section (GSFC 531.4) is responsible for managing a variety of engineering and operational tests used to assess the status of the Network elements relative to readiness certification for new and ongoing mission support and for performance trending. To conduct analysis of data collected during these tests, to disseminate and share the information, and to catalog and create reports based on the analysis is currently a cumbersome and inefficient task due primarily to the manual handling of paper products and the inability to easily exchange information between the various Networks elements. The Test Analysis and Retrieval System (TARS) is being implemented to promote concise data analysis, intelligible reporting of test results, to minimize test duplication by fostering a broad sharing of test data, and perhaps most importantly, to provide significantly improved response to the Network's internal and external customers. This paper outlines the intended application, architecture, and benefits of the TARS.
Stelmaszek, Robert L.↗
Presound: UAV Diagnostic System Enabled by Vibration-Based Machine Learning
A low-weight, inexpensive small unmanned aerial system (sUAS) that takes off, performs a mission, lands, and safely stows and recharges itself has myriad future applications ranging from agricultural imaging to last-mile package delivery. Likewise, Urban Air Mobility (UAM) systems will enable people to take air taxis from point to point in cities, rapidly moving commuters long distances without concern for road traffic and congestion. Fully electric aviation systems will be cleaner and quieter than ground transport. Cities could eliminate cars and buses, and convert roads to higher capacity bike and pedestrian throughways. Yet, for sUAS as well as UAM, system reliability and assurance is a limiting factor to deploying affordable autonomous flight systems. For this bright future of aviation to be realized, aircraft must be able to autonomously and accurately self-diagnose health issues both before takeoff and during flight. The GreenSight PreSound system is designed to identify defects on aircraft through intelligent analysis of vibration. It accomplishes this by measuring structural vibrations induced by the vehicle’s own propellers, and analyzing that data using a machine learning model that determines whether a defect is present. The PreSound system is designed to require no human oversight, and to operate across a wide array of vehicles through re-training of the model for each target aircraft. PreSound has been developed and seen limited early success using data collected from the GreenSight Dreamer sUAS, a 5lb quadrotor vehicle designed for aerial imaging applications. The final detection model, trained on data with props spinning at 50% throttle, achieves excellent performance with over 99% average accuracy in detecting blade damage using a single FFT vector input. It demonstrates the ability to generalize to new types of blade damage, correctly classifying a different type of blade damage with 98% accuracy. Full test pulses were classified with 100% accuracy, and in live testing, all sets of data during blade movement were classified accurately with over 95% confidence. When trained on in-flight data, the same model achieves an average accuracy of 85% in distinguishing between undamaged and blade-damaged states in flight. The authors believe that these accuracies show significant potential of this approach to expand unmanned flight safety, with significant potential benefits in accelerating Advanced Aerial Mobility (AAM) and UAM aviation applications.
UAS↗
ISS Propulsion Module Crew Systems Interface Analysis in the Intelligent Synthesis Environment
ERGO, a human modeling software for ergonomic assessment and task analysis, was used for the crew systems interface analysis of the International Space Station (ISS) Propulsion Module (PM). The objective of analysis was to alleviate passageway size concerns. Three basic passageway configuration concepts: (1) 45" clear passageway without centerline offset (2) 50" clear passageway, 12" centerline offset, (3) 50" clear passageway, no centerline offset, and were reviewed. 95 percentile male and female models which were provided by the software performed crew system analysis from an anthropometric point of view. Four scenarios in which the crew floats in microgravity through a 50" no-offset passageway as they carry a 16" x 20" x 30" avionics box were simulated in the 10-weeks of intensive study. From the results of the analysis, concept (3) was the preferred option. A full scale, three-dimensional virtual model of the ISS Propulsion Module was created to experience the sense of the Intelligent Synthesis Environment and to evaluate the usability and applicability of the software.
Chen, Di-Wen↗
Mission Evaluation Room Intelligent Diagnostic and Analysis System (MIDAS)
The role of Mission Evaluation Room (MER) engineers is to provide engineering support during Space Shuttle missions, for Space Shuttle systems. These engineers are concerned with ensuring that the systems for which they are responsible function reliably, and as intended. The MER is a central facility from which engineers may work, in fulfilling this obligation. Engineers participate in real-time monitoring of shuttle telemetry data and provide a variety of analyses associated with the operation of the shuttle. The Johnson Space Center's Automation and Robotics Division is working to transfer advances in intelligent systems technology to NASA's operational environment. Specifically, the MER Intelligent Diagnostic and Analysis System (MIDAS) project provides MER engineers with software to assist them with monitoring, filtering and analyzing Shuttle telemetry data, during and after Shuttle missions. MIDAS off-loads to computers and software, the tasks of data gathering, filtering, and analysis, and provides the engineers with information which is in a more concise and usable form needed to support decision making and engineering evaluation. Engineers are then able to concentrate on more difficult problems as they arise. This paper describes some, but not all of the applications that have been developed for MER engineers, under the MIDAS Project. The sampling described herewith was selected to show the range of tasks that engineers must perform for mission support, and to show the various levels of automation that have been applied to assist their efforts.
Pack, Ginger L.↗
ISTAR: Intelligent System for Telemetry Analysis in Real-time
The intelligent system for telemetry analysis in real-time (ISTAR) is an advanced vehicle monitoring environment incorporating expert systems, analysis tools, and on-line hypermedia documentation. The system was developed for the Air Force Space and Missile Systems Center (SMC) in Los Angeles, California, in support of the inertial upper stage (IUS) booster vehicle. Over a five year period the system progressed from rapid prototype to operational system. ISTAR has been used to support five IUS missions and countless mission simulations. There were a significant number of lessons learned with respect to integrating an expert system capability into an existing ground system.
Simmons, Charles↗
An expert system for integrated structural analysis and design optimization for aerospace structures
The results of a research study on the development of an expert system for integrated structural analysis and design optimization is presented. An Object Representation Language (ORL) was developed first in conjunction with a rule-based system. This ORL/AI shell was then used to develop expert systems to provide assistance with a variety of structural analysis and design optimization tasks, in conjunction with procedural modules for finite element structural analysis and design optimization. The main goal of the research study was to provide expertise, judgment, and reasoning capabilities in the aerospace structural design process. This will allow engineers performing structural analysis and design, even without extensive experience in the field, to develop error-free, efficient and reliable structural designs very rapidly and cost-effectively. This would not only improve the productivity of design engineers and analysts, but also significantly reduce time to completion of structural design. An extensive literature survey in the field of structural analysis, design optimization, artificial intelligence, and database management systems and their application to the structural design process was first performed. A feasibility study was then performed, and the architecture and the conceptual design for the integrated 'intelligent' structural analysis and design optimization software was then developed. An Object Representation Language (ORL), in conjunction with a rule-based system, was then developed using C++. Such an approach would improve the expressiveness for knowledge representation (especially for structural analysis and design applications), provide ability to build very large and practical expert systems, and provide an efficient way for storing knowledge. Functional specifications for the expert systems were then developed. The ORL/AI shell was then used to develop a variety of modules of expert systems for a variety of modeling, finite element analysis, and design optimization tasks in the integrated aerospace structural design process. These expert systems were developed to work in conjunction with procedural finite element structural analysis and design optimization modules (developed in-house at SAT, Inc.). The complete software, AutoDesign, so developed, can be used for integrated 'intelligent' structural analysis and design optimization. The software was beta-tested at a variety of companies, used by a range of engineers with different levels of background and expertise. Based on the feedback obtained by such users, conclusions were developed and are provided.
Source record↗
Requirement analysis of an intelligent, redundant, actuation system
The reliability and fault tolerance requirements of integrated, critical, digital fly-by-wire control systems for advanced military and civil aircraft requires redundant, reconfigurable implementations of the actuation system. An effective way for controlling the actuators and implementing the required fault detection and reconfiguration strategies is by means of dedicated microprocessors. This paper describes a laboratory implementation of a flexible intelligent redundant actuation system capable of demonstrating the concept and analyzing a variety of configurations and technical issues.
De Feo, P.↗
Operator function modeling: Cognitive task analysis, modeling and intelligent aiding in supervisory control systems
The design, implementation, and empirical evaluation of task-analytic models and intelligent aids for operators in the control of complex dynamic systems, specifically aerospace systems, are studied. Three related activities are included: (1) the models of operator decision making in complex and predominantly automated space systems were used and developed; (2) the Operator Function Model (OFM) was used to represent operator activities; and (3) Operator Function Model Expert System (OFMspert), a stand-alone knowledge-based system was developed, that interacts with a human operator in a manner similar to a human assistant in the control of aerospace systems. OFMspert is an architecture for an operator's assistant that uses the OFM as its system and operator knowledge base and a blackboard paradigm of problem solving to dynamically generate expectations about upcoming operator activities and interpreting actual operator actions. An experiment validated the OFMspert's intent inferencing capability and showed that it inferred the intentions of operators in ways comparable to both a human expert and operators themselves. OFMspert was also augmented with control capabilities. An interface allowed the operator to interact with OFMspert, delegating as much or as little control responsibility as the operator chose. With its design based on the OFM, OFMspert's control capabilities were available at multiple levels of abstraction and allowed the operator a great deal of discretion over the amount and level of delegated control. An experiment showed that overall system performance was comparable for teams consisting of two human operators versus a human operator and OFMspert team.
Mitchell, Christine M.↗