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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Intelligent Buildings of the Future: Cyberaware, Deep Learning Powered, and Human Interacting

This study discuss, intelligent buildings are quickly becoming cohesive and integral entities of cyber-physical ecosystems. Modern buildings adapt to internal and external elements and thrive on ever increasing data sources such as ubiquitous smart devices and sensors while mimicking various approaches previously known in software, hardware, and bio inspired systems. This article provides an overview of intelligent buildings of the future from a range of perspectives. First, a brief overview of prospects of U.S. and world energy consumption, concepts of smart buildings as integral entities of smart grids is presented. This is followed by a discussion of the role of connectivity and interoperability as well as current issues and solutions in the areas of security, resilience, and humans in realm of intelligent buildings. The article dives into detail about the intelligence aspect of intelligent buildings of the future. The learning and predictive Artificial Intelligence (AI) techniques behind many of the concepts of intelligent buildings are reviewed, including the latest techniques such as deep learning. As a concrete application of AI in the realm of intelligent buildings, prediction of energy consumption/load/demand, which is a much researched application of AI in the realm of intelligent buildings, is elaborated. The discussion is focused on most recent advancements of AI based demand prediction and the overview consists of existing research and a case study conducted by the authors for deep learning based demand prediction. Then, the article discusses the role of humans in intelligent buildings. Different types of human-building interactions are identified. Further, the article presents how each type of human-building interaction can benefit the overall control of intelligent buildings. The section is concluded by elaborating a case study conducted by the authors to achieve effective and efficient human-building interaction. The article is concluded with compilation of future insights based on latest technological advancements in U.S. industry and government.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2008

Intelligent Integrated Health Management for a System of Systems

An intelligent integrated health management system (IIHMS) incorporates major improvements over prior such systems. The particular IIHMS is implemented for any system defined as a hierarchical distributed network of intelligent elements (HDNIE), comprising primarily: (1) an architecture (Figure 1), (2) intelligent elements, (3) a conceptual framework and taxonomy (Figure 2), and (4) and ontology that defines standards and protocols. Some definitions of terms are prerequisite to a further brief description of this innovation: A system-of-systems (SoS) is an engineering system that comprises multiple subsystems (e.g., a system of multiple possibly interacting flow subsystems that include pumps, valves, tanks, ducts, sensors, and the like); 'Intelligent' is used here in the sense of artificial intelligence. An intelligent element may be physical or virtual, it is network enabled, and it is able to manage data, information, and knowledge (DIaK) focused on determining its condition in the context of the entire SoS; As used here, 'health' signifies the functionality and/or structural integrity of an engineering system, subsystem, or process (leading to determination of the health of components); 'Process' can signify either a physical process in the usual sense of the word or an element into which functionally related sensors are grouped; 'Element' can signify a component (e.g., an actuator, a valve), a process, a controller, an actuator, a subsystem, or a system; The term Integrated System Health Management (ISHM) is used to describe a capability that focuses on determining the condition (health) of every element in a complex system (detect anomalies, diagnose causes, prognosis of future anomalies), and provide data, information, and knowledge (DIaK) not just data to control systems for safe and effective operation. A major novel aspect of the present development is the concept of intelligent integration. The purpose of intelligent integration, as defined and implemented in the present IIHMS, is to enable automated analysis of physical phenomena in imitation of human reasoning, including the use of qualitative methods. Intelligent integration is said to occur in a system in which all elements are intelligent and can acquire, maintain, and share knowledge and information. In the HDNIE of the present IIHMS, an SoS is represented as being operationally organized in a hierarchical-distributed format. The elements of the SoS are considered to be intelligent in that they determine their own conditions within an integrated scheme that involves consideration of data, information, knowledge bases, and methods that reside in all elements of the system. The conceptual framework of the HDNIE and the methodologies of implementing it enable the flow of information and knowledge among the elements so as to make possible the determination of the condition of each element. The necessary information and knowledge is made available to each affected element at the desired time, satisfying a need to prevent information overload while providing context-sensitive information at the proper level of detail. Provision of high-quality data is a central goal in designing this or any IIHMS. In pursuit of this goal, functionally related sensors are logically assigned to groups denoted processes. An aggregate of processes is considered to form a system. Alternatively or in addition to what has been said thus far, the HDNIE of this IIHMS can be regarded as consisting of a framework containing object models that encapsulate all elements of the system, their individual and relational knowledge bases, generic methods and procedures based on models of the applicable physics, and communication processes (Figure 2). The framework enables implementation of a paradigm inspired by how expert operators monitor the health of systems with the help of (1) DIaK from various sources, (2) software tools that assist in rapid visualization of the condition of the system, (3) analical software tools that assist in reasoning about the condition, (4) sharing of information via network communication hardware and software, and (5) software tools that aid in making decisions to remedy unacceptable conditions or improve performance.

Smith, Harvey↗
DOE OSTI2023

Explainable Artificial Intelligence Technology for Predictive Maintenance

The domestic nuclear power plant fleet has relied on labor-intensive and time-consuming preventive maintenance programs, thus driving up operation and maintenance costs to achieve high-capacity factors. Artificial intelligence and machine learning can help simplify complex problems, such as diagnosing equipment degradation, to enable more effective decision-making. Benefits will be felt not only within existing analog and digital instrumentation and control, but also work processes, the integration of people with technology, and most importantly, the business case. Together, these hold promise to make nuclear power more efficient and reduce costs associated with operation and maintenance. While the artificial intelligence and machine learning technologies hold significant promise in the nuclear industry, there are challenges or barriers to their adoption. This report outlines the those different machine learning adoption barriers (categorized as historical, technical, economic, regulatory, and user) that the industry must overcome to realize the full benefits of artificial intelligence and machine learning capabilities for long-term economic sustainability. This report also provides solutions for some of these barriers by focusing on improving the explainability of machine learning to encourage trust from the end-user. Trust and explainability are essential to machine learning adoption. This report focuses on research-developed solutions to some of these barriers while analyzing a non-safety-related system, namely the circulating water system. This system frequently experiences waterbox fouling which our models preemptively diagnoses then explains to the operator how those conclusions were reached. This report presents and discusses the inherent trade-off between machine learning performance (in terms of accuracy) and explainability, where highly accurate machine learning methods (such as deep-learning) are the least explainable, and the most explainable methods (such as decision trees) are the least accurate. In addition, explainability of artificial intelligence techniques in terms of transparency and post-hoc metrics are discussed. This report outlines the importance of data novelty and value of new information in evaluating both the explainability and trustworthiness. Novelty detection helps to establish consistency or inconsistency of the new data with respect to the training data. On the other hand, value of information could be a part of the user-centric visualization recommendation system that request additional information to be collected, thereby strengthening the machine learning outcomes. During this project, a copyrighted user-centric visualization that aligns with a human-in-the-loop approach was developed. The user-centric visualization presents different levels of information and can be tailored as per user credentials to gain user confidence. One of the salient features of the user-centric visualization is it presents machine learning methods with explainability metrics. A simplified version of the user-centric visualization was presented to 32 users with varying levels of machine learning expertise. Feedback was solicited to test the hypothesis that the app contained sufficient explainability and that the users would trust the algorithm. Overall, the app was positively received, and the hypothesis was supported. This report discusses the trust-but-verify framework – a potential approach to build user trust artificial intelligence. The framework discusses trust from the human level to artificial intelligence level. The fundamental premise of the trust but verify framework is derived from an observation of nuclear safety culture (i.e., nuclear power plant personnel do not rely on a singular source of data to make a decision). This also ties back to the user-centric visualization that presents different levels of information to achieve both explainability and trustworthiness of artificial intelligence. Even so, the adoption of artificial intelligence and machine learning in the nuclear industry faces additional barriers, namely regulatory and stakeholder readiness. To overcome these challenges, new solutions must gain regulatory approval and cater to stakeholder needs. The Nuclear Regulatory Committee has a 5-year strategic plan which prepares them for reviewing artificial intelligence technologies in licensee submissions. Early and frequent engagement with the regulator is encouraged. Additionally, artificial intelligence solutions should incorporate human-in-the-loop considerations and offer explainability. Stakeholders must prepare by hiring or training staff to adapt to advancing technology in everyday plant tasks.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1989

A Boltzmann machine for the organization of intelligent machines

In the present technological society, there is a major need to build machines that would execute intelligent tasks operating in uncertain environments with minimum interaction with a human operator. Although some designers have built smart robots, utilizing heuristic ideas, there is no systematic approach to design such machines in an engineering manner. Recently, cross-disciplinary research from the fields of computers, systems AI and information theory has served to set the foundations of the emerging area of the design of intelligent machines. Since 1977 Saridis has been developing an approach, defined as Hierarchical Intelligent Control, designed to organize, coordinate and execute anthropomorphic tasks by a machine with minimum interaction with a human operator. This approach utilizes analytical (probabilistic) models to describe and control the various functions of the intelligent machine structured by the intuitively defined principle of Increasing Precision with Decreasing Intelligence (IPDI) (Saridis 1979). This principle, even though resembles the managerial structure of organizational systems (Levis 1988), has been derived on an analytic basis by Saridis (1988). The purpose is to derive analytically a Boltzmann machine suitable for optimal connection of nodes in a neural net (Fahlman, Hinton, Sejnowski, 1985). Then this machine will serve to search for the optimal design of the organization level of an intelligent machine. In order to accomplish this, some mathematical theory of the intelligent machines will be first outlined. Then some definitions of the variables associated with the principle, like machine intelligence, machine knowledge, and precision will be made (Saridis, Valavanis 1988). Then a procedure to establish the Boltzmann machine on an analytic basis will be presented and illustrated by an example in designing the organization level of an Intelligent Machine. A new search technique, the Modified Genetic Algorithm, is presented and proved to converge to the minimum of a cost function. Finally, simulations will show the effectiveness of a variety of search techniques for the intelligent machine.

Moed, Michael C.↗
NASA NTRS1994

DYNACLIPS (DYNAmic CLIPS): A dynamic knowledge exchange tool for intelligent agents

In a dynamic environment, intelligent agents must be responsive to unanticipated conditions. When such conditions occur, an intelligent agent may have to stop a previously planned and scheduled course of actions and replan, reschedule, start new activities and initiate a new problem solving process to successfully respond to the new conditions. Problems occur when an intelligent agent does not have enough knowledge to properly respond to the new situation. DYNACLIPS is an implementation of a framework for dynamic knowledge exchange among intelligent agents. Each intelligent agent is a CLIPS shell and runs a separate process under SunOS operating system. Intelligent agents can exchange facts, rules, and CLIPS commands at run time. Knowledge exchange among intelligent agents at run times does not effect execution of either sender and receiver intelligent agent. Intelligent agents can keep the knowledge temporarily or permanently. In other words, knowledge exchange among intelligent agents would allow for a form of learning to be accomplished.

Cengeloglu, Yilmaz↗
NASA NTRS2005

Intelligent Sensors: An Integrated Systems Approach

The need for intelligent sensors as a critical component for Integrated System Health Management (ISHM) is fairly well recognized by now. Even the definition of what constitutes an intelligent sensor (or smart sensor) is well documented and stems from an intuitive desire to get the best quality measurement data that forms the basis of any complex health monitoring and/or management system. If the sensors, i.e. the elements closest to the measurand, are unreliable then the whole system works with a tremendous handicap. Hence, there has always been a desire to distribute intelligence down to the sensor level, and give it the ability to assess its own health thereby improving the confidence in the quality of the data at all times. This paper proposes the development of intelligent sensors as an integrated systems approach, i.e. one treats the sensors as a complete system with its own sensing hardware (the traditional sensor), A/D converters, processing and storage capabilities, software drivers, self-assessment algorithms, communication protocols and evolutionary methodologies that allow them to get better with time. Under a project being undertaken at the NASA Stennis Space Center, an integrated framework is being developed for the intelligent monitoring of smart elements. These smart elements can be sensors, actuators or other devices. The immediate application is the monitoring of the rocket test stands, but the technology should be generally applicable to the Intelligent Systems Health Monitoring (ISHM) vision. This paper outlines some fundamental issues in the development of intelligent sensors under the following two categories: Physical Intelligent Sensors (PIS) and Virtual Intelligent Sensors (VIS).

Mahajan, Ajay↗
NASA NTRS1993

Information for the user in design of intelligent systems

Recommendations are made for improving intelligent system reliability and usability based on the use of information requirements in system development. Information requirements define the task-relevant messages exchanged between the intelligent system and the user by means of the user interface medium. Thus, these requirements affect the design of both the intelligent system and its user interface. Many difficulties that users have in interacting with intelligent systems are caused by information problems. These information problems result from the following: (1) not providing the right information to support domain tasks; and (2) not recognizing that using an intelligent system introduces new user supervisory tasks that require new types of information. These problems are especially prevalent in intelligent systems used for real-time space operations, where data problems and unexpected situations are common. Information problems can be solved by deriving information requirements from a description of user tasks. Using information requirements embeds human-computer interaction design into intelligent system prototyping, resulting in intelligent systems that are more robust and easier to use.

Malin, Jane T.↗
NASA NTRS1987

Analytical design of intelligent machines

The problem of designing 'intelligent machines' to operate in uncertain environments with minimum supervision or interaction with a human operator is examined. The structure of an 'intelligent machine' is defined to be the structure of a Hierarchically Intelligent Control System, composed of three levels hierarchically ordered according to the principle of 'increasing precision with decreasing intelligence', namely: the organizational level, performing general information processing tasks in association with a long-term memory; the coordination level, dealing with specific information processing tasks with a short-term memory; and the control level, which performs the execution of various tasks through hardware using feedback control methods. The behavior of such a machine may be managed by controls with special considerations and its 'intelligence' is directly related to the derivation of a compatible measure that associates the intelligence of the higher levels with the concept of entropy, which is a sufficient analytic measure that unifies the treatment of all the levels of an 'intelligent machine' as the mathematical problem of finding the right sequence of internal decisions and controls for a system structured in the order of intelligence and inverse order of precision such that it minimizes its total entropy. A case study on the automatic maintenance of a nuclear plant illustrates the proposed approach.

Saridis, George N.↗
NASA NTRS1997

Knowledge-Based Aircraft Automation: Managers Guide on the use of Artificial Intelligence for Aircraft Automation and Verification and Validation Approach for a Neural-Based Flight Controller

The ultimate goal of this report was to integrate the powerful tools of artificial intelligence into the traditional process of software development. To maintain the US aerospace competitive advantage, traditional aerospace and software engineers need to more easily incorporate the technology of artificial intelligence into the advanced aerospace systems being designed today. The future goal was to transition artificial intelligence from an emerging technology to a standard technology that is considered early in the life cycle process to develop state-of-the-art aircraft automation systems. This report addressed the future goal in two ways. First, it provided a matrix that identified typical aircraft automation applications conducive to various artificial intelligence methods. The purpose of this matrix was to provide top-level guidance to managers contemplating the possible use of artificial intelligence in the development of aircraft automation. Second, the report provided a methodology to formally evaluate neural networks as part of the traditional process of software development. The matrix was developed by organizing the discipline of artificial intelligence into the following six methods: logical, object representation-based, distributed, uncertainty management, temporal and neurocomputing. Next, a study of existing aircraft automation applications that have been conducive to artificial intelligence implementation resulted in the following five categories: pilot-vehicle interface, system status and diagnosis, situation assessment, automatic flight planning, and aircraft flight control. The resulting matrix provided management guidance to understand artificial intelligence as it applied to aircraft automation. The approach taken to develop a methodology to formally evaluate neural networks as part of the software engineering life cycle was to start with the existing software quality assurance standards and to change these standards to include neural network development. The changes were to include evaluation tools that can be applied to neural networks at each phase of the software engineering life cycle. The result was a formal evaluation approach to increase the product quality of systems that use neural networks for their implementation.

Broderick, Ron↗
NASA NTRS2008

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.↗
NASA NTRS2011

Network-Capable Application Process and Wireless Intelligent Sensors for ISHM

Intelligent sensor technology and systems are increasingly becoming attractive means to serve as frameworks for intelligent rocket test facilities with embedded intelligent sensor elements, distributed data acquisition elements, and onboard data acquisition elements. Networked intelligent processors enable users and systems integrators to automatically configure their measurement automation systems for analog sensors. NASA and leading sensor vendors are working together to apply the IEEE 1451 standard for adding plug-and-play capabilities for wireless analog transducers through the use of a Transducer Electronic Data Sheet (TEDS) in order to simplify sensor setup, use, and maintenance, to automatically obtain calibration data, and to eliminate manual data entry and error. A TEDS contains the critical information needed by an instrument or measurement system to identify, characterize, interface, and properly use the signal from an analog sensor. A TEDS is deployed for a sensor in one of two ways. First, the TEDS can reside in embedded, nonvolatile memory (typically flash memory) within the intelligent processor. Second, a virtual TEDS can exist as a separate file, downloadable from the Internet. This concept of virtual TEDS extends the benefits of the standardized TEDS to legacy sensors and applications where the embedded memory is not available. An HTML-based user interface provides a visual tool to interface with those distributed sensors that a TEDS is associated with, to automate the sensor management process. Implementing and deploying the IEEE 1451.1-based Network-Capable Application Process (NCAP) can achieve support for intelligent process in Integrated Systems Health Management (ISHM) for the purpose of monitoring, detection of anomalies, diagnosis of causes of anomalies, prediction of future anomalies, mitigation to maintain operability, and integrated awareness of system health by the operator. It can also support local data collection and storage. This invention enables wide-area sensing and employs numerous globally distributed sensing devices that observe the physical world through the existing sensor network. This innovation enables distributed storage, distributed processing, distributed intelligence, and the availability of DiaK (Data, Information, and Knowledge) to any element as needed. It also enables the simultaneous execution of multiple processes, and represents models that contribute to the determination of the condition and health of each element in the system. The NCAP (intelligent process) can configure data-collection and filtering processes in reaction to sensed data, allowing it to decide when and how to adapt collection and processing with regard to sophisticated analysis of data derived from multiple sensors. The user will be able to view the sensing device network as a single unit that supports a high-level query language. Each query would be able to operate over data collected from across the global sensor network just as a search query encompasses millions of Web pages. The sensor web can preserve ubiquitous information access between the querier and the queried data. Pervasive monitoring of the physical world raises significant data and privacy concerns. This innovation enables different authorities to control portions of the sensing infrastructure, and sensor service authors may wish to compose services across authority boundaries.

Figueroa, Fernando↗
NASA NTRS2005

Artificial Intelligence for Controlling Robotic Aircraft

A document consisting mostly of lecture slides presents overviews of artificial-intelligence-based control methods now under development for application to robotic aircraft [called Unmanned Aerial Vehicles (UAVs) in the paper] and spacecraft and to the next generation of flight controllers for piloted aircraft. Following brief introductory remarks, the paper presents background information on intelligent control, including basic characteristics defining intelligent systems and intelligent control and the concept of levels of intelligent control. Next, the paper addresses several concepts in intelligent flight control. The document ends with some concluding remarks, including statements to the effect that (1) intelligent control architectures can guarantee stability of inner control loops and (2) for UAVs, intelligent control provides a robust way to accommodate an outer-loop control architecture for planning and/or related purposes.

Krishnakumar, Kalmanje↗
NASA NTRS1993

Making intelligent systems team players: Additional case studies

Observations from a case study of intelligent systems are reported as part of a multi-year interdisciplinary effort to provide guidance and assistance for designers of intelligent systems and their user interfaces. A series of studies were conducted to investigate issues in designing intelligent fault management systems in aerospace applications for effective human-computer interaction. The results of the initial study are documented in two NASA technical memoranda: TM 104738 Making Intelligent Systems Team Players: Case Studies and Design Issues, Volumes 1 and 2; and TM 104751, Making Intelligent Systems Team Players: Overview for Designers. The objective of this additional study was to broaden the investigation of human-computer interaction design issues beyond the focus on monitoring and fault detection in the initial study. The results of this second study are documented which is intended as a supplement to the original design guidance documents. These results should be of interest to designers of intelligent systems for use in real-time operations, and to researchers in the areas of human-computer interaction and artificial intelligence.

Malin, Jane T.↗
NASA NTRS1994

Intelligent robotics can boost America's economic growth

A case is made for strategic investment in intelligent robotics as a part of the solution to the problem of improved global competitiveness for U.S. manufacturing, a critical industrial sector. Similar cases are made for strategic investments in intelligent robotics for field applications, construction, and service industries such as health care. The scope of the country's problems and needs is beyond the capability of the private sector alone, government alone, or academia alone to solve independently of the others. National cooperative programs in intelligent robotics are needed with the private sector supplying leadership direction and aerospace and non-aerospace industries conducting the development. Some necessary elements of such programs are outlined. The National Aeronautics and Space Administration (NASA) and the Lyndon B. Johnson Space Center (JSC) can be key players in such national cooperative programs in intelligent robotics for several reasons: (1) human space exploration missions require supervised intelligent robotics as enabling tools and, hence must develop supervised intelligent robotic systems; (2) intelligent robotic technology is being developed for space applications at JSC (but has a strong crosscutting or generic flavor) that is advancing the state of the art and is producing both skilled personnel and adaptable developmental infrastructure such as integrated testbeds; and (3) a NASA JSC Technology Investment Program in Robotics has been proposed based on commercial partnerships and collaborations for precompetitive, dual-use developments.

Erickson, Jon D.↗
DOE OSTI2017

Intelligent Weld Manufacturing: Role of Integrated Computational Welding Engineering

A master welder uses his sensory perceptions to evaluate the process and connect them with his/her knowledge base to take the necessary corrective measures with his/her acquired skills to make a good weld. All these actions must take place in real time. Success depends on intuition and skills, and the procedure is labor-intensive and frequently unreliable. The solution is intelligent weld manufacturing. The ultimate goal of intelligent weld manufacturing would involve sensing and control of heat source position, weld temperature, weld penetration, defect formation and ultimately control of microstructure and properties. This involves a solution to a problem (welding) with many highly coupled and nonlinear variables. The trend is to use an emerging tool known as intelligent control. This approach enables the user to choose a desirable end factor such as properties, defect control, or productivity to derive the selection of process parameters such as current, voltage, or speed to provide for appropriate control of the process. Important elements of intelligent manufacturing are sensing and control theory and design, process modeling, and artificial intelligence. Significant progress has been made in all these areas. Integrated computational welding engineering (ICWE) is an emerging field that will aid in the realization of intelligent weld manufacturing. The paper will discuss the progress in process modeling, microstructure, properties, and process control and automation and the importance of ICWE. Also, control and automation strategies for friction stir welding will be discussed.

42 ENGINEERING↗
NASA NTRS1983

Human evolution in the age of the intelligent machine

A systems analysis of the future evolution of man can be conducted by analyzing the biological material of the galaxy into three subsystems: man, intelligent machines, and intelligent extraterrestrial organisms. A binomial interpretation is applied to this system wherein each of the subsystems is assigned a designation of success or failure. For man the two alternatives are, respectively, 'decline' or 'flourish', for machine they are 'become intelligent' or 'stay dumb', while for extraterrestrial intelligence the dichotomy is that of 'existence' or 'nonexistence'. The choices for each of three subsystems yield a total of eight possible states for the system. The relative lack of integration between brain components makes man a weak evolutionary contestant compared to machines. It is judged that machines should become dominant on earth within 100 years, probably by means of continuing development of existing man-machine systems. Advanced forms of extraterrestrial intelligence may exist but are too difficult to observe. The prospects for communication with extraterrestrial intelligence are reviewed.

Mclaughlin, W. I.↗
NASA NTRS2002

Intelligent Systems for Aerospace Engineering: An Overview

Intelligent systems are nature-inspired, mathematically sound, computationally intensive problem solving tools and methodologies that have become extremely important for advancing the current trends in information technology. Artificially intelligent systems currently utilize computers to emulate various faculties of human intelligence and biological metaphors. They use a combination of symbolic and sub-symbolic systems capable of evolving human cognitive skills and intelligence, not just systems capable of doing things humans do not do well. Intelligent systems are ideally suited for tasks such as search and optimization, pattern recognition and matching, planning, uncertainty management, control, and adaptation. In this paper, the intelligent system technologies and their application potential are highlighted via several examples.

Krishnakumar, Kalmanje↗
NASA NTRS2003

Intelligent Systems Technologies to Assist in Utilization of Earth Observation Data

With the launch of several Earth observing satellites over the last decade, we are now in a data rich environment. From NASA's Earth Observing System (EOS) satellites alone, we are accumulating more than 3 TB per day of raw data and derived geophysical parameters. The data products are being distributed to a large user community comprising scientific researchers, educators and operational government agencies. Notable progress has been made in the last decade in facilitating access to data. However, to realize the full potential of the growing archives of valuable scientific data, further progress is necessary in the transformation of data into information, and information into knowledge that can be used in particular applications. Sponsored by NASA s Intelligent Systems Project within the Computing, Information and Communication Technology (CICT) Program, a conceptual architecture study has been conducted to examine ideas to improve data utilization through the addition of intelligence into the archives in the context of an overall knowledge building system. Potential Intelligent Archive concepts include: 1) Mining archived data holdings using Intelligent Data Understanding algorithms to improve metadata to facilitate data access and usability; 2) Building intelligence about transformations on data, information, knowledge, and accompanying services involved in a scientific enterprise; 3) Recognizing the value of results, indexing and formatting them for easy access, and delivering them to concerned individuals; 4) Interacting as a cooperative node in a web of distributed systems to perform knowledge building (i.e., the transformations from data to information to knowledge) instead of just data pipelining; and 5) Being aware of other nodes in the knowledge building system, participating in open systems interfaces and protocols for virtualization, and collaborative interoperability. This paper presents some of these concepts and identifies issues to be addressed by research in future intelligent systems technology.

Ramapriyan, Hampapuram K.↗