Special Issue on Expert Systems for Department of Defense Training.

ERIC Educational Resources Information Center

Ahlers, Robert H., Ed.; And Others

1986-01-01

Features articles on topics related to use of expert systems for training: machine intelligence effectiveness in military systems applications; automated maneuvering board training system; intelligent tutoring system for electronic troubleshooting; technology development for intelligent maintenance advisors; design of intelligent computer assisted…

Cooperative analysis expert situation assessment research

NASA Technical Reports Server (NTRS)

Mccown, Michael G.

1987-01-01

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.

Proceedings of the 1986 IEEE international conference on systems, man and cybernetics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1986-01-01

This book presents the papers given at a conference on man-machine systems. Topics considered at the conference included neural model-based cognitive theory and engineering, user interfaces, adaptive and learning systems, human interaction with robotics, decision making, the testing and evaluation of expert systems, software development, international conflict resolution, intelligent interfaces, automation in man-machine system design aiding, knowledge acquisition in expert systems, advanced architectures for artificial intelligence, pattern recognition, knowledge bases, and machine vision.

Defense Logistics Standard Systems Functional Requirements.

DTIC Science & Technology

1987-03-01

Artificial Intelligence - the development of a machine capability to perform functions normally concerned with human intelligence, such as learning , adapting...Basic Data Base Machine Configurations .... ......... D- 18 xx ~ ?f~~~vX PART I: MODELS - DEFENSE LOGISTICS STANDARD SYSTEMS FUNCTIONAL REQUIREMENTS...On-line, Interactive Access. Integrating user input and machine output in a dynamic, real-time, give-and- take process is considered the optimum mode

Artificial Intelligence/Robotics Applications to Navy Aircraft Maintenance.

DTIC Science & Technology

1984-06-01

other automatic machinery such as presses, molding machines , and numerically-controlled machine tools, just as people do. A-36...Robotics Technologies 3 B. Relevant AI Technologies 4 1. Expert Systems 4 2. Automatic Planning 4 3. Natural Language 5 4. Machine Vision...building machines that imitate human behavior. Artificial intelligence is concerned with the functions of the brain, whereas robotics include, in

Machine Learning-based Intelligent Formal Reasoning and Proving System

NASA Astrophysics Data System (ADS)

Chen, Shengqing; Huang, Xiaojian; Fang, Jiaze; Liang, Jia

2018-03-01

The reasoning system can be used in many fields. How to improve reasoning efficiency is the core of the design of system. Through the formal description of formal proof and the regular matching algorithm, after introducing the machine learning algorithm, the system of intelligent formal reasoning and verification has high efficiency. The experimental results show that the system can verify the correctness of propositional logic reasoning and reuse the propositional logical reasoning results, so as to obtain the implicit knowledge in the knowledge base and provide the basic reasoning model for the construction of intelligent system.

Systems autonomy

NASA Technical Reports Server (NTRS)

Lum, Henry, Jr.

1988-01-01

Information on systems autonomy is given in viewgraph form. Information is given on space systems integration, intelligent autonomous systems, automated systems for in-flight mission operations, the Systems Autonomy Demonstration Project on the Space Station Thermal Control System, the architecture of an autonomous intelligent system, artificial intelligence research issues, machine learning, and real-time image processing.

Machine intelligence and autonomy for aerospace systems

NASA Technical Reports Server (NTRS)

Heer, Ewald (Editor); Lum, Henry (Editor)

1988-01-01

The present volume discusses progress toward intelligent robot systems in aerospace applications, NASA Space Program automation and robotics efforts, the supervisory control of telerobotics in space, machine intelligence and crew/vehicle interfaces, expert-system terms and building tools, and knowledge-acquisition for autonomous systems. Also discussed are methods for validation of knowledge-based systems, a design methodology for knowledge-based management systems, knowledge-based simulation for aerospace systems, knowledge-based diagnosis, planning and scheduling methods in AI, the treatment of uncertainty in AI, vision-sensing techniques in aerospace applications, image-understanding techniques, tactile sensing for robots, distributed sensor integration, and the control of articulated and deformable space structures.

CESAR research in intelligent machines

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weisbin, C.R.

1986-01-01

The Center for Engineering Systems Advanced Research (CESAR) was established in 1983 as a national center for multidisciplinary, long-range research and development in machine intelligence and advanced control theory for energy-related applications. Intelligent machines of interest here are artificially created operational systems that are capable of autonomous decision making and action. The initial emphasis for research is remote operations, with specific application to dexterous manipulation in unstructured dangerous environments where explosives, toxic chemicals, or radioactivity may be present, or in other environments with significant risk such as coal mining or oceanographic missions. Potential benefits include reduced risk to man inmore » hazardous situations, machine replication of scarce expertise, minimization of human error due to fear or fatigue, and enhanced capability using high resolution sensors and powerful computers. A CESAR goal is to explore the interface between the advanced teleoperation capability of today, and the autonomous machines of the future.« less

An intelligent CNC machine control system architecture

DOE Office of Scientific and Technical Information (OSTI.GOV)

Miller, D.J.; Loucks, C.S.

1996-10-01

Intelligent, agile manufacturing relies on automated programming of digitally controlled processes. Currently, processes such as Computer Numerically Controlled (CNC) machining are difficult to automate because of highly restrictive controllers and poor software environments. It is also difficult to utilize sensors and process models for adaptive control, or to integrate machining processes with other tasks within a factory floor setting. As part of a Laboratory Directed Research and Development (LDRD) program, a CNC machine control system architecture based on object-oriented design and graphical programming has been developed to address some of these problems and to demonstrate automated agile machining applications usingmore » platform-independent software.« less

AAAIC '88 - Aerospace Applications of Artificial Intelligence; Proceedings of the Fourth Annual Conference, Dayton, OH, Oct. 25-27, 1988. Volumes 1 2

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, J.R.; Netrologic, Inc., San Diego, CA)

1988-01-01

Topics presented include integrating neural networks and expert systems, neural networks and signal processing, machine learning, cognition and avionics applications, artificial intelligence and man-machine interface issues, real time expert systems, artificial intelligence, and engineering applications. Also considered are advanced problem solving techniques, combinational optimization for scheduling and resource control, data fusion/sensor fusion, back propagation with momentum, shared weights and recurrency, automatic target recognition, cybernetics, optical neural networks.

Trust metrics in information fusion

NASA Astrophysics Data System (ADS)

Blasch, Erik

2014-05-01

Trust is an important concept for machine intelligence and is not consistent across many applications. In this paper, we seek to understand trust from a variety of factors: humans, sensors, communications, intelligence processing algorithms and human-machine displays of information. In modeling the various aspects of trust, we provide an example from machine intelligence that supports the various attributes of measuring trust such as sensor accuracy, communication timeliness, machine processing confidence, and display throughput to convey the various attributes that support user acceptance of machine intelligence results. The example used is fusing video and text whereby an analyst needs trust information in the identified imagery track. We use the proportional conflict redistribution rule as an information fusion technique that handles conflicting data from trusted and mistrusted sources. The discussion of the many forms of trust explored in the paper seeks to provide a systems-level design perspective for information fusion trust quantification.

Combining human and machine processes (CHAMP)

NASA Astrophysics Data System (ADS)

Sudit, Moises; Sudit, David; Hirsch, Michael

2015-05-01

Machine Reasoning and Intelligence is usually done in a vacuum, without consultation of the ultimate decision-maker. The late consideration of the human cognitive process causes some major problems in the use of automated systems to provide reliable and actionable information that users can trust and depend to make the best Course-of-Action (COA). On the other hand, if automated systems are created exclusively based on human cognition, then there is a danger of developing systems that don't push the barrier of technology and are mainly done for the comfort level of selected subject matter experts (SMEs). Our approach to combining human and machine processes (CHAMP) is based on the notion of developing optimal strategies for where, when, how, and which human intelligence should be injected within a machine reasoning and intelligence process. This combination is based on the criteria of improving the quality of the output of the automated process while maintaining the required computational efficiency for a COA to be actuated in timely fashion. This research addresses the following problem areas: • Providing consistency within a mission: Injection of human reasoning and intelligence within the reliability and temporal needs of a mission to attain situational awareness, impact assessment, and COA development. • Supporting the incorporation of data that is uncertain, incomplete, imprecise and contradictory (UIIC): Development of mathematical models to suggest the insertion of a cognitive process within a machine reasoning and intelligent system so as to minimize UIIC concerns. • Developing systems that include humans in the loop whose performance can be analyzed and understood to provide feedback to the sensors.

Toward Intelligent Machine Learning Algorithms

DTIC Science & Technology

1988-05-01

Machine learning is recognized as a tool for improving the performance of many kinds of systems, yet most machine learning systems themselves are not...directed systems, and with the addition of a knowledge store for organizing and maintaining knowledge to assist learning, a learning machine learning (L...ML) algorithm is possible. The necessary components of L-ML systems are presented along with several case descriptions of existing machine learning systems

Automation and robotics technology for intelligent mining systems

NASA Technical Reports Server (NTRS)

Welsh, Jeffrey H.

1989-01-01

The U.S. Bureau of Mines is approaching the problems of accidents and efficiency in the mining industry through the application of automation and robotics to mining systems. This technology can increase safety by removing workers from hazardous areas of the mines or from performing hazardous tasks. The short-term goal of the Automation and Robotics program is to develop technology that can be implemented in the form of an autonomous mining machine using current continuous mining machine equipment. In the longer term, the goal is to conduct research that will lead to new intelligent mining systems that capitalize on the capabilities of robotics. The Bureau of Mines Automation and Robotics program has been structured to produce the technology required for the short- and long-term goals. The short-term goal of application of automation and robotics to an existing mining machine, resulting in autonomous operation, is expected to be accomplished within five years. Key technology elements required for an autonomous continuous mining machine are well underway and include machine navigation systems, coal-rock interface detectors, machine condition monitoring, and intelligent computer systems. The Bureau of Mines program is described, including status of key technology elements for an autonomous continuous mining machine, the program schedule, and future work. Although the program is directed toward underground mining, much of the technology being developed may have applications for space systems or mining on the Moon or other planets.

Active learning machine learns to create new quantum experiments.

PubMed

Melnikov, Alexey A; Poulsen Nautrup, Hendrik; Krenn, Mario; Dunjko, Vedran; Tiersch, Markus; Zeilinger, Anton; Briegel, Hans J

2018-02-06

How useful can machine learning be in a quantum laboratory? Here we raise the question of the potential of intelligent machines in the context of scientific research. A major motivation for the present work is the unknown reachability of various entanglement classes in quantum experiments. We investigate this question by using the projective simulation model, a physics-oriented approach to artificial intelligence. In our approach, the projective simulation system is challenged to design complex photonic quantum experiments that produce high-dimensional entangled multiphoton states, which are of high interest in modern quantum experiments. The artificial intelligence system learns to create a variety of entangled states and improves the efficiency of their realization. In the process, the system autonomously (re)discovers experimental techniques which are only now becoming standard in modern quantum optical experiments-a trait which was not explicitly demanded from the system but emerged through the process of learning. Such features highlight the possibility that machines could have a significantly more creative role in future research.

Knowledge-based load leveling and task allocation in human-machine systems

NASA Technical Reports Server (NTRS)

Chignell, M. H.; Hancock, P. A.

1986-01-01

Conventional human-machine systems use task allocation policies which are based on the premise of a flexible human operator. This individual is most often required to compensate for and augment the capabilities of the machine. The development of artificial intelligence and improved technologies have allowed for a wider range of task allocation strategies. In response to these issues a Knowledge Based Adaptive Mechanism (KBAM) is proposed for assigning tasks to human and machine in real time, using a load leveling policy. This mechanism employs an online workload assessment and compensation system which is responsive to variations in load through an intelligent interface. This interface consists of a loading strategy reasoner which has access to information about the current status of the human-machine system as well as a database of admissible human/machine loading strategies. Difficulties standing in the way of successful implementation of the load leveling strategy are examined.

Intelligent Systems and Its Applications in Robotics

NASA Astrophysics Data System (ADS)

Kaynak, Okyay

The last decade of the last millennium is characterized by what might be called the intelligent systems revolution, as a result of which, it is now possible to have man made systems that exhibit ability to reason, learn from experience and make rational decisions without human intervention. Prof. Zadeh has coined the word MIQ (machine intelligence quotient) to describe a measure of intelligence of man-made systems. In this perspective, an intelligent system can be defined as a system that has a high MIQ.

The Joint Tactical Aerial Resupply Vehicle Impact on Sustainment Operations

DTIC Science & Technology

2017-06-09

Artificial Intelligence , Sustainment Operations, Rifle Company, Autonomous Aerial Resupply, Joint Tactical Autonomous Aerial Resupply System 16...Integrations and Development System AI Artificial Intelligence ARCIC Army Capabilities Integration Center ARDEC Armament Research, Development and...semi- autonomous systems, and fully autonomous systems. Autonomy of machines depends on sophisticated software, including Artificial Intelligence

Artificial Intelligence.

ERIC Educational Resources Information Center

Thornburg, David D.

1986-01-01

Overview of the artificial intelligence (AI) field provides a definition; discusses past research and areas of future research; describes the design, functions, and capabilities of expert systems and the "Turing Test" for machine intelligence; and lists additional sources for information on artificial intelligence. Languages of AI are…

Architectures for intelligent machines

NASA Technical Reports Server (NTRS)

Saridis, George N.

1991-01-01

The theory of intelligent machines has been recently reformulated to incorporate new architectures that are using neural and Petri nets. The analytic functions of an intelligent machine are implemented by intelligent controls, using entropy as a measure. The resulting hierarchical control structure is based on the principle of increasing precision with decreasing intelligence. Each of the three levels of the intelligent control is using different architectures, in order to satisfy the requirements of the principle: the organization level is moduled after a Boltzmann machine for abstract reasoning, task planning and decision making; the coordination level is composed of a number of Petri net transducers supervised, for command exchange, by a dispatcher, which also serves as an interface to the organization level; the execution level, include the sensory, planning for navigation and control hardware which interacts one-to-one with the appropriate coordinators, while a VME bus provides a channel for database exchange among the several devices. This system is currently implemented on a robotic transporter, designed for space construction at the CIRSSE laboratories at the Rensselaer Polytechnic Institute. The progress of its development is reported.

A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms

PubMed Central

Meiring, Gys Albertus Marthinus; Myburgh, Hermanus Carel

2015-01-01

In this paper the various driving style analysis solutions are investigated. An in-depth investigation is performed to identify the relevant machine learning and artificial intelligence algorithms utilised in current driver behaviour and driving style analysis systems. This review therefore serves as a trove of information, and will inform the specialist and the student regarding the current state of the art in driver style analysis systems, the application of these systems and the underlying artificial intelligence algorithms applied to these applications. The aim of the investigation is to evaluate the possibilities for unique driver identification utilizing the approaches identified in other driver behaviour studies. It was found that Fuzzy Logic inference systems, Hidden Markov Models and Support Vector Machines consist of promising capabilities to address unique driver identification algorithms if model complexity can be reduced. PMID:26690164

A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms.

PubMed

Meiring, Gys Albertus Marthinus; Myburgh, Hermanus Carel

2015-12-04

In this paper the various driving style analysis solutions are investigated. An in-depth investigation is performed to identify the relevant machine learning and artificial intelligence algorithms utilised in current driver behaviour and driving style analysis systems. This review therefore serves as a trove of information, and will inform the specialist and the student regarding the current state of the art in driver style analysis systems, the application of these systems and the underlying artificial intelligence algorithms applied to these applications. The aim of the investigation is to evaluate the possibilities for unique driver identification utilizing the approaches identified in other driver behaviour studies. It was found that Fuzzy Logic inference systems, Hidden Markov Models and Support Vector Machines consist of promising capabilities to address unique driver identification algorithms if model complexity can be reduced.

Intelligent power management in a vehicular system with multiple power sources

NASA Astrophysics Data System (ADS)

Murphey, Yi L.; Chen, ZhiHang; Kiliaris, Leonidas; Masrur, M. Abul

This paper presents an optimal online power management strategy applied to a vehicular power system that contains multiple power sources and deals with largely fluctuated load requests. The optimal online power management strategy is developed using machine learning and fuzzy logic. A machine learning algorithm has been developed to learn the knowledge about minimizing power loss in a Multiple Power Sources and Loads (M_PS&LD) system. The algorithm exploits the fact that different power sources used to deliver a load request have different power losses under different vehicle states. The machine learning algorithm is developed to train an intelligent power controller, an online fuzzy power controller, FPC_MPS, that has the capability of finding combinations of power sources that minimize power losses while satisfying a given set of system and component constraints during a drive cycle. The FPC_MPS was implemented in two simulated systems, a power system of four power sources, and a vehicle system of three power sources. Experimental results show that the proposed machine learning approach combined with fuzzy control is a promising technology for intelligent vehicle power management in a M_PS&LD power system.

Multispectral Image Processing for Plants

NASA Technical Reports Server (NTRS)

Miles, Gaines E.

1991-01-01

The development of a machine vision system to monitor plant growth and health is one of three essential steps towards establishing an intelligent system capable of accurately assessing the state of a controlled ecological life support system for long-term space travel. Besides a network of sensors, simulators are needed to predict plant features, and artificial intelligence algorithms are needed to determine the state of a plant based life support system. Multispectral machine vision and image processing can be used to sense plant features, including health and nutritional status.

Behavior Analysis and the Quest for Machine Intelligence.

ERIC Educational Resources Information Center

Stephens, Kenneth R.; Hutchison, William R.

1993-01-01

Discusses three approaches to building intelligent systems: artificial intelligence, neural networks, and behavior analysis. BANKET, an object-oriented software system, is explained; a commercial application of BANKET is described; and a collaborative effort between the academic and business communities for the use of BANKET is discussed.…

Present and Future of M2M

NASA Astrophysics Data System (ADS)

Ono, Satoru; Watanabe, Takashi

In recent years, the rapid progress in the development of hardware and software technologies enables tiny and low cost information devices hereinafter referred to as Machine to be widely available. M2M (Machine to Machine) has been of much attention where many tiny machines are connected to each other through networks with minimal human intervention to provide smooth and intelligent management. M2M is a promising core technology providing timely, flexible, efficient and comprehensive service at low cost. M2M has wide variety of applications including energy management system, environmental monitoring system, intelligent transport system, industrial automation system and other applications. M2M consists of terminals and networks that connect them. In this paper, we mainly focus on M2M networking and mention the future direction of the technology.

Intelligent fault-tolerant controllers

NASA Technical Reports Server (NTRS)

Huang, Chien Y.

1987-01-01

A system with fault tolerant controls is one that can detect, isolate, and estimate failures and perform necessary control reconfiguration based on this new information. Artificial intelligence (AI) is concerned with semantic processing, and it has evolved to include the topics of expert systems and machine learning. This research represents an attempt to apply AI to fault tolerant controls, hence, the name intelligent fault tolerant control (IFTC). A generic solution to the problem is sought, providing a system based on logic in addition to analytical tools, and offering machine learning capabilities. The advantages are that redundant system specific algorithms are no longer needed, that reasonableness is used to quickly choose the correct control strategy, and that the system can adapt to new situations by learning about its effects on system dynamics.

Study on intelligent processing system of man-machine interactive garment frame model

NASA Astrophysics Data System (ADS)

Chen, Shuwang; Yin, Xiaowei; Chang, Ruijiang; Pan, Peiyun; Wang, Xuedi; Shi, Shuze; Wei, Zhongqian

2018-05-01

A man-machine interactive garment frame model intelligent processing system is studied in this paper. The system consists of several sensor device, voice processing module, mechanical parts and data centralized acquisition devices. The sensor device is used to collect information on the environment changes brought by the body near the clothes frame model, the data collection device is used to collect the information of the environment change induced by the sensor device, voice processing module is used for speech recognition of nonspecific person to achieve human-machine interaction, mechanical moving parts are used to make corresponding mechanical responses to the information processed by data collection device.it is connected with data acquisition device by a means of one-way connection. There is a one-way connection between sensor device and data collection device, two-way connection between data acquisition device and voice processing module. The data collection device is one-way connection with mechanical movement parts. The intelligent processing system can judge whether it needs to interact with the customer, realize the man-machine interaction instead of the current rigid frame model.

Distributed communications and control network for robotic mining

NASA Technical Reports Server (NTRS)

Schiffbauer, William H.

1989-01-01

The application of robotics to coal mining machines is one approach pursued to increase productivity while providing enhanced safety for the coal miner. Toward that end, a network composed of microcontrollers, computers, expert systems, real time operating systems, and a variety of program languages are being integrated that will act as the backbone for intelligent machine operation. Actual mining machines, including a few customized ones, have been given telerobotic semiautonomous capabilities by applying the described network. Control devices, intelligent sensors and computers onboard these machines are showing promise of achieving improved mining productivity and safety benefits. Current research using these machines involves navigation, multiple machine interaction, machine diagnostics, mineral detection, and graphical machine representation. Guidance sensors and systems employed include: sonar, laser rangers, gyroscopes, magnetometers, clinometers, and accelerometers. Information on the network of hardware/software and its implementation on mining machines are presented. Anticipated coal production operations using the network are discussed. A parallelism is also drawn between the direction of present day underground coal mining research to how the lunar soil (regolith) may be mined. A conceptual lunar mining operation that employs a distributed communication and control network is detailed.

Space applications of Automation, Robotics and Machine Intelligence Systems (ARAMIS). Volume 2: Space projects overview

NASA Technical Reports Server (NTRS)

Miller, R. H.; Minsky, M. L.; Smith, D. B. S.

1982-01-01

Applications of automation, robotics, and machine intelligence systems (ARAMIS) to space activities, and their related ground support functions are studied so that informed decisions can be made on which aspects of ARAMIS to develop. The space project breakdowns, which are used to identify tasks ('functional elements'), are described. The study method concentrates on the production of a matrix relating space project tasks to pieces of ARAMIS.

Designing a holistic end-to-end intelligent network analysis and security platform

NASA Astrophysics Data System (ADS)

Alzahrani, M.

2018-03-01

Firewall protects a network from outside attacks, however, once an attack entering a network, it is difficult to detect. Recent significance accidents happened. i.e.: millions of Yahoo email account were stolen and crucial data from institutions are held for ransom. Within two year Yahoo’s system administrators were not aware that there are intruder inside the network. This happened due to the lack of intelligent tools to monitor user behaviour in internal network. This paper discusses a design of an intelligent anomaly/malware detection system with proper proactive actions. The aim is to equip the system administrator with a proper tool to battle the insider attackers. The proposed system adopts machine learning to analyse user’s behaviour through the runtime behaviour of each node in the network. The machine learning techniques include: deep learning, evolving machine learning perceptron, hybrid of Neural Network and Fuzzy, as well as predictive memory techniques. The proposed system is expanded to deal with larger network using agent techniques.

Vision Guided Intelligent Robot Design And Experiments

NASA Astrophysics Data System (ADS)

Slutzky, G. D.; Hall, E. L.

1988-02-01

The concept of an intelligent robot is an important topic combining sensors, manipulators, and artificial intelligence to design a useful machine. Vision systems, tactile sensors, proximity switches and other sensors provide the elements necessary for simple game playing as well as industrial applications. These sensors permit adaption to a changing environment. The AI techniques permit advanced forms of decision making, adaptive responses, and learning while the manipulator provides the ability to perform various tasks. Computer languages such as LISP and OPS5, have been utilized to achieve expert systems approaches in solving real world problems. The purpose of this paper is to describe several examples of visually guided intelligent robots including both stationary and mobile robots. Demonstrations will be presented of a system for constructing and solving a popular peg game, a robot lawn mower, and a box stacking robot. The experience gained from these and other systems provide insight into what may be realistically expected from the next generation of intelligent machines.

Complementary Machine Intelligence and Human Intelligence in Virtual Teaching Assistant for Tutoring Program Tracing

ERIC Educational Resources Information Center

Chou, Chih-Yueh; Huang, Bau-Hung; Lin, Chi-Jen

2011-01-01

This study proposes a virtual teaching assistant (VTA) to share teacher tutoring tasks in helping students practice program tracing and proposes two mechanisms of complementing machine intelligence and human intelligence to develop the VTA. The first mechanism applies machine intelligence to extend human intelligence (teacher answers) to evaluate…

Integrating Symbolic and Statistical Methods for Testing Intelligent Systems Applications to Machine Learning and Computer Vision

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jha, Sumit Kumar; Pullum, Laura L; Ramanathan, Arvind

Embedded intelligent systems ranging from tiny im- plantable biomedical devices to large swarms of autonomous un- manned aerial systems are becoming pervasive in our daily lives. While we depend on the flawless functioning of such intelligent systems, and often take their behavioral correctness and safety for granted, it is notoriously difficult to generate test cases that expose subtle errors in the implementations of machine learning algorithms. Hence, the validation of intelligent systems is usually achieved by studying their behavior on representative data sets, using methods such as cross-validation and bootstrapping.In this paper, we present a new testing methodology for studyingmore » the correctness of intelligent systems. Our approach uses symbolic decision procedures coupled with statistical hypothesis testing to. We also use our algorithm to analyze the robustness of a human detection algorithm built using the OpenCV open-source computer vision library. We show that the human detection implementation can fail to detect humans in perturbed video frames even when the perturbations are so small that the corresponding frames look identical to the naked eye.« less

A Concept for Optimizing Behavioural Effectiveness & Efficiency

NASA Astrophysics Data System (ADS)

Barca, Jan Carlo; Rumantir, Grace; Li, Raymond

Both humans and machines exhibit strengths and weaknesses that can be enhanced by merging the two entities. This research aims to provide a broader understanding of how closer interactions between these two entities can facilitate more optimal goal-directed performance through the use of artificial extensions of the human body. Such extensions may assist us in adapting to and manipulating our environments in a more effective way than any system known today. To demonstrate this concept, we have developed a simulation where a semi interactive virtual spider can be navigated through an environment consisting of several obstacles and a virtual predator capable of killing the spider. The virtual spider can be navigated through the use of three different control systems that can be used to assist in optimising overall goal directed performance. The first two control systems use, an onscreen button interface and a touch sensor, respectively to facilitate human navigation of the spider. The third control system is an autonomous navigation system through the use of machine intelligence embedded in the spider. This system enables the spider to navigate and react to changes in its local environment. The results of this study indicate that machines should be allowed to override human control in order to maximise the benefits of collaboration between man and machine. This research further indicates that the development of strong machine intelligence, sensor systems that engage all human senses, extra sensory input systems, physical remote manipulators, multiple intelligent extensions of the human body, as well as a tighter symbiosis between man and machine, can support an upgrade of the human form.

Space applications of Automation, Robotics and Machine Intelligence Systems (ARAMIS). Volume 4: Application of ARAMIS capabilities to space project functional elements

NASA Technical Reports Server (NTRS)

Miller, R. H.; Minsky, M. L.; Smith, D. B. S.

1982-01-01

Applications of automation, robotics, and machine intelligence systems (ARAMIS) to space activities and their related ground support functions are studied, so that informed decisions can be made on which aspects of ARAMIS to develop. The specific tasks which will be required by future space project tasks are identified and the relative merits of these options are evaluated. The ARAMIS options defined and researched span the range from fully human to fully machine, including a number of intermediate options (e.g., humans assisted by computers, and various levels of teleoperation). By including this spectrum, the study searches for the optimum mix of humans and machines for space project tasks.

Machine intelligence and robotics: Report of the NASA study group. Executive summary

NASA Technical Reports Server (NTRS)

1979-01-01

A brief overview of applications of machine intelligence and robotics in the space program is given. These space exploration robots, global service robots to collect data for public service use on soil conditions, sea states, global crop conditions, weather, geology, disasters, etc., from Earth orbit, space industrialization and processing technologies, and construction of large structures in space. Program options for research, advanced development, and implementation of machine intelligence and robot technology for use in program planning are discussed. A vigorous and long-range program to incorporate and keep pace with state of the art developments in computer technology, both in spaceborne and ground-based computer systems is recommended.

Third Conference on Artificial Intelligence for Space Applications, part 1

NASA Technical Reports Server (NTRS)

Denton, Judith S. (Compiler); Freeman, Michael S. (Compiler); Vereen, Mary (Compiler)

1987-01-01

The application of artificial intelligence to spacecraft and aerospace systems is discussed. Expert systems, robotics, space station automation, fault diagnostics, parallel processing, knowledge representation, scheduling, man-machine interfaces and neural nets are among the topics discussed.

A machine learning system to improve heart failure patient assistance.

PubMed

Guidi, Gabriele; Pettenati, Maria Chiara; Melillo, Paolo; Iadanza, Ernesto

2014-11-01

In this paper, we present a clinical decision support system (CDSS) for the analysis of heart failure (HF) patients, providing various outputs such as an HF severity evaluation, HF-type prediction, as well as a management interface that compares the different patients' follow-ups. The whole system is composed of a part of intelligent core and of an HF special-purpose management tool also providing the function to act as interface for the artificial intelligence training and use. To implement the smart intelligent functions, we adopted a machine learning approach. In this paper, we compare the performance of a neural network (NN), a support vector machine, a system with fuzzy rules genetically produced, and a classification and regression tree and its direct evolution, which is the random forest, in analyzing our database. Best performances in both HF severity evaluation and HF-type prediction functions are obtained by using the random forest algorithm. The management tool allows the cardiologist to populate a "supervised database" suitable for machine learning during his or her regular outpatient consultations. The idea comes from the fact that in literature there are a few databases of this type, and they are not scalable to our case.

Tool path strategy and cutting process monitoring in intelligent machining

NASA Astrophysics Data System (ADS)

Chen, Ming; Wang, Chengdong; An, Qinglong; Ming, Weiwei

2018-06-01

Intelligent machining is a current focus in advanced manufacturing technology, and is characterized by high accuracy and efficiency. A central technology of intelligent machining—the cutting process online monitoring and optimization—is urgently needed for mass production. In this research, the cutting process online monitoring and optimization in jet engine impeller machining, cranio-maxillofacial surgery, and hydraulic servo valve deburring are introduced as examples of intelligent machining. Results show that intelligent tool path optimization and cutting process online monitoring are efficient techniques for improving the efficiency, quality, and reliability of machining.

A proposal of an architecture for the coordination level of intelligent machines

NASA Technical Reports Server (NTRS)

Beard, Randall; Farah, Jeff; Lima, Pedro

1993-01-01

The issue of obtaining a practical, structured, and detailed description of an architecture for the Coordination Level of Center for Intelligent Robotic Systems for Sapce Exploration (CIRSSE) Testbed Intelligent Controller is addressed. Previous theoretical and implementation works were the departure point for the discussion. The document is organized as follows: after this introductory section, section 2 summarizes the overall view of the Intelligent Machine (IM) as a control system, proposing a performance measure on which to base its design. Section 3 addresses with some detail implementation issues. An hierarchic petri-net with feedback-based learning capabilities is proposed. Finally, section 4 is an attempt to address the feedback problem. Feedback is used for two functions: error recovery and reinforcement learning of the correct translations for the petri-net transitions.

Neural network expert system for X-ray analysis of welded joints

NASA Astrophysics Data System (ADS)

Kozlov, V. V.; Lapik, N. V.; Popova, N. V.

2018-03-01

The use of intelligent technologies for the automated analysis of product quality is one of the main trends in modern machine building. At the same time, rapid development in various spheres of human activity is experienced by methods associated with the use of artificial neural networks, as the basis for building automated intelligent diagnostic systems. Technologies of machine vision allow one to effectively detect the presence of certain regularities in the analyzed designation, including defects of welded joints according to radiography data.

Identification Of Cells With A Compact Microscope Imaging System With Intelligent Controls

NASA Technical Reports Server (NTRS)

McDowell, Mark (Inventor)

2006-01-01

A Microscope Imaging System (CMIS) with intelligent controls is disclosed that provides techniques for scanning, identifying, detecting and tracking mic?oscopic changes in selected characteristics or features of various surfaces including, but not limited to, cells, spheres, and manufactured products subject to difficult-to-see imperfections. The practice of the present invention provides applications that include colloidal hard spheres experiments, biological cell detection for patch clamping, cell movement and tracking, as well as defect identification in products, such as semiconductor devices, where surface damage can be significant, but difficult to detect. The CMIS system is a machine vision system, which combines intelligent image processing with remote control capabilities and provides the ability to autofocus on a microscope sample, automatically scan an image, and perform machine vision analysis on multiple samples simultaneously.

Tracking of Cells with a Compact Microscope Imaging System with Intelligent Controls

NASA Technical Reports Server (NTRS)

McDowell, Mark (Inventor)

2007-01-01

A Microscope Imaging System (CMIS) with intelligent controls is disclosed that provides techniques for scanning, identifying, detecting and tracking microscopic changes in selected characteristics or features of various surfaces including, but not limited to, cells, spheres, and manufactured products subject to difficult-to-see imperfections. The practice of the present invention provides applications that include colloidal hard spheres experiments, biological cell detection for patch clamping, cell movement and tracking, as well as defect identification in products, such as semiconductor devices, where surface damage can be significant, but difficult to detect. The CMIS system is a machine vision system, which combines intelligent image processing with remote control capabilities and provides the ability to autofocus on a microscope sample, automatically scan an image, and perform machine vision analysis on multiple samples simultaneously

Tracking of cells with a compact microscope imaging system with intelligent controls

NASA Technical Reports Server (NTRS)

McDowell, Mark (Inventor)

2007-01-01

A Microscope Imaging System (CMIS) with intelligent controls is disclosed that provides techniques for scanning, identifying, detecting and tracking microscopic changes in selected characteristics or features of various surfaces including, but not limited to, cells, spheres, and manufactured products subject to difficult-to-see imperfections. The practice of the present invention provides applications that include colloidal hard spheres experiments, biological cell detection for patch clamping, cell movement and tracking, as well as defect identification in products, such as semiconductor devices, where surface damage can be significant, but difficult to detect. The CMIS system is a machine vision system, which combines intelligent image processing with remote control capabilities and provides the ability to auto-focus on a microscope sample, automatically scan an image, and perform machine vision analysis on multiple samples simultaneously.

Operation of a Cartesian Robotic System in a Compact Microscope with Intelligent Controls

NASA Technical Reports Server (NTRS)

McDowell, Mark (Inventor)

2006-01-01

A Microscope Imaging System (CMIS) with intelligent controls is disclosed that provides techniques for scanning, identifying, detecting and tracking microscopic changes in selected characteristics or features of various surfaces including, but not limited to, cells, spheres, and manufactured products subject to difficult-to-see imperfections. The practice of the present invention provides applications that include colloidal hard spheres experiments, biological cell detection for patch clamping, cell movement and tracking, as well as defect identification in products, such as semiconductor devices, where surface damage can be significant, but difficult to detect. The CMIS system is a machine vision system, which combines intelligent image processing with remote control capabilities and provides the ability to autofocus on a microscope sample, automatically scan an image, and perform machine vision analysis on multiple samples simultaneously.

Northeast Artificial Intelligence Consortium (NAIC). Volume 12. Computer Architecture for Very Large Knowledge Bases

DTIC Science & Technology

1990-12-01

data rate to the electronics would be much lower on the average and the data much "richer" in information. Intelligent use of...system bottleneck, a high data rate should be provided by I/O systems. 2. machines with intelligent storage management specially designed for logic...management information processing, surveillance sensors, intelligence data collection and handling, solid state sciences, electromagnetics, and propagation, and electronic reliability/maintainability and compatibility.

Artificial Intelligence and Expert Systems.

ERIC Educational Resources Information Center

Lawlor, Joseph

Artificial intelligence (AI) is the field of scientific inquiry concerned with designing machine systems that can simulate human mental processes. The field draws upon theoretical constructs from a wide variety of disciplines, including mathematics, psychology, linguistics, neurophysiology, computer science, and electronic engineering. Some of the…

Space Applications of Automation, Robotics and Machine Intelligence Systems (ARAMIS). Volume 1: Executive Summary

NASA Technical Reports Server (NTRS)

Miller, R. H.; Minsky, M. L.; Smith, D. B. S.

1982-01-01

Potential applications of automation, robotics, and machine intelligence systems (ARAMIS) to space activities, and to their related ground support functions are explored. The specific tasks which will be required by future space projects are identified. ARAMIS options which are candidates for those space project tasks and the relative merits of these options are defined and evaluated. Promising applications of ARAMIS and specific areas for further research are identified. The ARAMIS options defined and researched by the study group span the range from fully human to fully machine, including a number of intermediate options (e.g., humans assisted by computers, and various levels of teleoperation). By including this spectrum, the study searches for the optimum mix of humans and machines for space project tasks.

A Rather Intelligent Language Teacher.

ERIC Educational Resources Information Center

Cerri, Stefano; Breuker, Joost

1981-01-01

Characteristics of DART (Didactic Augmented Recursive Transition), an ATN-based system for writing intelligent computer assisted instruction (ICAI) programs that is available on the PLATO system are described. DART allows writing programs in an ATN dialect, compiling them in machine code for the PLATO system, and executing them as if the original…

Robotic Technology: An Assessment and Forecast,

DTIC Science & Technology

1984-07-01

Research Associates# Inc. Dr. Roger Nagel# Lehigh University Dr. Charles Rosen# Machine Intelligence Corporations and Mr. Jack Thornton# Robot Insider...amr (Subcontractors: systems for assembly and Adopt Technology# inspection Stanford University. SRI) AFSC MANTECH o McDonnell Douglas o Machine ...supervisory controls man- machine interaction and system integration. - .. _ - Foreign R& The U.S. faces a strong technological challenge in robotics from

Artificial intelligence: Learning to see and act

NASA Astrophysics Data System (ADS)

Schölkopf, Bernhard

2015-02-01

An artificial-intelligence system uses machine learning from massive training sets to teach itself to play 49 classic computer games, demonstrating that it can adapt to a variety of tasks. See Letter p.529

Design Of An Intelligent Robotic System Organizer Via Expert System Tecniques

NASA Astrophysics Data System (ADS)

Yuan, Peter H.; Valavanis, Kimon P.

1989-02-01

Intelligent Robotic Systems are a special type of Intelligent Machines. When modeled based on Vle theory of Intelligent Controls, they are composed of three interactive levels, namely: organization, coordination, and execution, ordered according, to the ,Principle of Increasing, Intelligence with Decreasing Precl.sion. Expert System techniques, are used to design an Intelligent Robotic System Organizer with a dynamic Knowledge Base and an interactive Inference Engine. Task plans are formulated using, either or both of a Probabilistic Approach and Forward Chapling Methodology, depending on pertinent information associated with a spec;fic requested job. The Intelligent Robotic System, Organizer is implemented and tested on a prototype system operating in an uncertain environment. An evaluation of-the performance, of the prototype system is conducted based upon the probability of generating a successful task sequence versus the number of trials taken by the organizer.

An overview of the artificial intelligence and expert systems component of RICIS

NASA Technical Reports Server (NTRS)

Feagin, Terry

1987-01-01

Artificial Intelligence and Expert Systems are the important component of RICIS (Research Institute and Information Systems) research program. For space applications, a number of problem areas that should be able to make good use of the above tools include: resource allocation and management, control and monitoring, environmental control and life support, power distribution, communications scheduling, orbit and attitude maintenance, redundancy management, intelligent man-machine interfaces and fault detection, isolation and recovery.

A systematic approach to the application of Automation, Robotics, and Machine Intelligence Systems /ARAMIS/ to future space projects

NASA Technical Reports Server (NTRS)

Smith, D. B. S.

1982-01-01

The potential applications of Automation, Robotics, and Machine Intelligence Systems (ARAMIS) to space projects are investigated, through a systematic method. In this method selected space projects are broken down into space project tasks, and 69 of these tasks are selected for study. Candidate ARAMIS options are defined for each task. The relative merits of these options are evaluated according to seven indices of performance. Logical sequences of ARAMIS development are also defined. Based on this data, promising applications of ARAMIS are

Analysis in Motion Initiative – Human Machine Intelligence

DOE Office of Scientific and Technical Information (OSTI.GOV)

Blaha, Leslie

As computers and machines become more pervasive in our everyday lives, we are looking for ways for humans and machines to work more intelligently together. How can we help machines understand their users so the team can do smarter things together? The Analysis in Motion Initiative is advancing the science of human machine intelligence — creating human-machine teams that work better together to make correct, useful, and timely interpretations of data.

Configurable software for satellite graphics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hartzman, P D

An important goal in interactive computer graphics is to provide users with both quick system responses for basic graphics functions and enough computing power for complex calculations. One solution is to have a distributed graphics system in which a minicomputer and a powerful large computer share the work. The most versatile type of distributed system is an intelligent satellite system in which the minicomputer is programmable by the application user and can do most of the work while the large remote machine is used for difficult computations. At New York University, the hardware was configured from available equipment. The levelmore » of system intelligence resulted almost completely from software development. Unlike previous work with intelligent satellites, the resulting system had system control centered in the satellite. It also had the ability to reconfigure software during realtime operation. The design of the system was done at a very high level using set theoretic language. The specification clearly illustrated processor boundaries and interfaces. The high-level specification also produced a compact, machine-independent virtual graphics data structure for picture representation. The software was written in a systems implementation language; thus, only one set of programs was needed for both machines. A user can program both machines in a single language. Tests of the system with an application program indicate that is has very high potential. A major result of this work is the demonstration that a gigantic investment in new hardware is not necessary for computing facilities interested in graphics.« less

From machine learning to deep learning: progress in machine intelligence for rational drug discovery.

PubMed

Zhang, Lu; Tan, Jianjun; Han, Dan; Zhu, Hao

2017-11-01

Machine intelligence, which is normally presented as artificial intelligence, refers to the intelligence exhibited by computers. In the history of rational drug discovery, various machine intelligence approaches have been applied to guide traditional experiments, which are expensive and time-consuming. Over the past several decades, machine-learning tools, such as quantitative structure-activity relationship (QSAR) modeling, were developed that can identify potential biological active molecules from millions of candidate compounds quickly and cheaply. However, when drug discovery moved into the era of 'big' data, machine learning approaches evolved into deep learning approaches, which are a more powerful and efficient way to deal with the massive amounts of data generated from modern drug discovery approaches. Here, we summarize the history of machine learning and provide insight into recently developed deep learning approaches and their applications in rational drug discovery. We suggest that this evolution of machine intelligence now provides a guide for early-stage drug design and discovery in the current big data era. Copyright © 2017 Elsevier Ltd. All rights reserved.

Mobile robotics research at Sandia National Laboratories

DOE Office of Scientific and Technical Information (OSTI.GOV)

Morse, W.D.

Sandia is a National Security Laboratory providing scientific and engineering solutions to meet national needs for both government and industry. As part of this mission, the Intelligent Systems and Robotics Center conducts research and development in robotics and intelligent machine technologies. An overview of Sandia`s mobile robotics research is provided. Recent achievements and future directions in the areas of coordinated mobile manipulation, small smart machines, world modeling, and special application robots are presented.

A First-Order Formalization of Knowledge and Action for a Multiagent Planning System.

DTIC Science & Technology

1980-12-01

1979), pp. 176-181. Doyle, J., "Truth Maintenance Systems for Problem Solvinn,’ Memo AI-TR-419, MIT Artifcial Intelligence Laboratory, Cambridge (1978...the Standpoint of Artifcial Intelligence ," in Machine Intelligence 4, B. Meltzer and D. Michie (Edo.), Edinburgh University Press, Edinburgh (1969...A -A1R 603 SRI INTERNATIONAL MENLO PARK CA ARTIFICIAL INTELLIGE --ETC FIG 9I2 A FIRST-ORDER FORMALIZATION OF KNOWLEDGE AND ACTION FOR A MULTI--ETC(U

Computer science, artificial intelligence, and cybernetics: Applied artificial intelligence in Japan

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rubinger, B.

1988-01-01

This sourcebook provides information on the developments in artificial intelligence originating in Japan. Spanning such innovations as software productivity, natural language processing, CAD, and parallel inference machines, this volume lists leading organizations conducting research or implementing AI systems, describes AI applications being pursued, illustrates current results achieved, and highlights sources reporting progress.

Proceedings of the Intelligent Tutoring Systems Research Forum (2nd) Held in San Antonio, Texas on 6-7 April 1989

DTIC Science & Technology

1990-04-01

REFERENCES Carbonell, J.R. (1970). A] in CAI: An artifcial intelligence approach to computer-assisted instruction. IEEE Transactions on Man-Machine Systems...of intelligent systems use outcome data of any sort (Anderson, in press, is an exception). Other designers describe system goals for learners and then...5601 90 o9 3007 NOTICE When Government drawings, specifications, or other data are used for any purpose other than in connection with a definitely

Human-like machines: Transparency and comprehensibility.

PubMed

Patrzyk, Piotr M; Link, Daniela; Marewski, Julian N

2017-01-01

Artificial intelligence algorithms seek inspiration from human cognitive systems in areas where humans outperform machines. But on what level should algorithms try to approximate human cognition? We argue that human-like machines should be designed to make decisions in transparent and comprehensible ways, which can be achieved by accurately mirroring human cognitive processes.

From pilot's associate to satellite controller's associate

NASA Technical Reports Server (NTRS)

Neyland, David L.; Lizza, Carl; Merkel, Philip A.

1992-01-01

Associate technology is an emerging engineering discipline wherein intelligent automation can significantly augment the performance of man-machine systems. An associate system is one that monitors operator activity and adapts its operational behavior accordingly. Associate technology is most effectively applied when mapped into management of the human-machine interface and display-control loop in typical manned systems. This paper addresses the potential for application of associate technology into the arena of intelligent command and control of satellite systems, from diagnosis of onboard and onground of satellite systems fault conditions, to execution of nominal satellite control functions. Rather than specifying a specific solution, this paper draws parallels between the Pilot's Associate concept and the domain of satellite control.

Thermal Error Test and Intelligent Modeling Research on the Spindle of High Speed CNC Machine Tools

NASA Astrophysics Data System (ADS)

Luo, Zhonghui; Peng, Bin; Xiao, Qijun; Bai, Lu

2018-03-01

Thermal error is the main factor affecting the accuracy of precision machining. Through experiments, this paper studies the thermal error test and intelligent modeling for the spindle of vertical high speed CNC machine tools in respect of current research focuses on thermal error of machine tool. Several testing devices for thermal error are designed, of which 7 temperature sensors are used to measure the temperature of machine tool spindle system and 2 displacement sensors are used to detect the thermal error displacement. A thermal error compensation model, which has a good ability in inversion prediction, is established by applying the principal component analysis technology, optimizing the temperature measuring points, extracting the characteristic values closely associated with the thermal error displacement, and using the artificial neural network technology.

The Machine Intelligence Hex Project

ERIC Educational Resources Information Center

Chalup, Stephan K.; Mellor, Drew; Rosamond, Fran

2005-01-01

Hex is a challenging strategy board game for two players. To enhance students' progress in acquiring understanding and practical experience with complex machine intelligence and programming concepts we developed the Machine Intelligence Hex (MIHex) project. The associated undergraduate student assignment is about designing and implementing Hex…

Research on intelligent machine self-perception method based on LSTM

NASA Astrophysics Data System (ADS)

Wang, Qiang; Cheng, Tao

2018-05-01

In this paper, we use the advantages of LSTM in feature extraction and processing high-dimensional and complex nonlinear data, and apply it to the autonomous perception of intelligent machines. Compared with the traditional multi-layer neural network, this model has memory, can handle time series information of any length. Since the multi-physical domain signals of processing machines have a certain timing relationship, and there is a contextual relationship between states and states, using this deep learning method to realize the self-perception of intelligent processing machines has strong versatility and adaptability. The experiment results show that the method proposed in this paper can obviously improve the sensing accuracy under various working conditions of the intelligent machine, and also shows that the algorithm can well support the intelligent processing machine to realize self-perception.

Introduction to Fuzzy Set Theory

NASA Technical Reports Server (NTRS)

Kosko, Bart

1990-01-01

An introduction to fuzzy set theory is described. Topics covered include: neural networks and fuzzy systems; the dynamical systems approach to machine intelligence; intelligent behavior as adaptive model-free estimation; fuzziness versus probability; fuzzy sets; the entropy-subsethood theorem; adaptive fuzzy systems for backing up a truck-and-trailer; product-space clustering with differential competitive learning; and adaptive fuzzy system for target tracking.

Machine vision for digital microfluidics

NASA Astrophysics Data System (ADS)

Shin, Yong-Jun; Lee, Jeong-Bong

2010-01-01

Machine vision is widely used in an industrial environment today. It can perform various tasks, such as inspecting and controlling production processes, that may require humanlike intelligence. The importance of imaging technology for biological research or medical diagnosis is greater than ever. For example, fluorescent reporter imaging enables scientists to study the dynamics of gene networks with high spatial and temporal resolution. Such high-throughput imaging is increasingly demanding the use of machine vision for real-time analysis and control. Digital microfluidics is a relatively new technology with expectations of becoming a true lab-on-a-chip platform. Utilizing digital microfluidics, only small amounts of biological samples are required and the experimental procedures can be automatically controlled. There is a strong need for the development of a digital microfluidics system integrated with machine vision for innovative biological research today. In this paper, we show how machine vision can be applied to digital microfluidics by demonstrating two applications: machine vision-based measurement of the kinetics of biomolecular interactions and machine vision-based droplet motion control. It is expected that digital microfluidics-based machine vision system will add intelligence and automation to high-throughput biological imaging in the future.

Machine learning in updating predictive models of planning and scheduling transportation projects

DOT National Transportation Integrated Search

1997-01-01

A method combining machine learning and regression analysis to automatically and intelligently update predictive models used in the Kansas Department of Transportations (KDOTs) internal management system is presented. The predictive models used...

Space Applications of Automation, Robotics and Machine Intelligence Systems (ARAMIS). Volume 4: Supplement, Appendix 4.3: Candidate ARAMIS Capabilities

NASA Technical Reports Server (NTRS)

Miller, R. H.; Minsky, M. L.; Smith, D. B. S.

1982-01-01

Potential applications of automation, robotics, and machine intelligence systems (ARAMIS) to space activities, and to their related ground support functions, in the years 1985-2000, so that NASA may make informed decisions on which aspects of ARAMIS to develop. The study first identifies the specific tasks which will be required by future space projects. It then defines ARAMIS options which are candidates for those space project tasks, and evaluates the relative merits of these options. Finally, the study identifies promising applications of ARAMIS, and recommends specific areas for further research. The ARAMIS options defined and researched by the study group span the range from fully human to fully machine, including a number of intermediate options (e.g., humans assisted by computers, and various levels of teleoperation). By including this spectrum, the study searches for the optimum mix of humans and machines for space project tasks.

Real Time Network Monitoring and Reporting System

ERIC Educational Resources Information Center

Massengale, Ricky L., Sr.

2009-01-01

With the ability of modern system developers to develop intelligent programs that allows machines to learn, modify and evolve themselves, current trends of reactionary methods to detect and eradicate malicious software code from infected machines is proving to be too costly. Addressing malicious software after an attack is the current methodology…

[Remote intelligent Brunnstrom assessment system for upper limb rehabilitation for post-stroke based on extreme learning machine].

PubMed

Wang, Yue; Yu, Lei; Fu, Jianming; Fang, Qiang

2014-04-01

In order to realize an individualized and specialized rehabilitation assessment of remoteness and intelligence, we set up a remote intelligent assessment system of upper limb movement function of post-stroke patients during rehabilitation. By using the remote rehabilitation training sensors and client data sampling software, we collected and uploaded the gesture data from a patient's forearm and upper arm during rehabilitation training to database of the server. Then a remote intelligent assessment system, which had been developed based on the extreme learning machine (ELM) algorithm and Brunnstrom stage assessment standard, was used to evaluate the gesture data. To evaluate the reliability of the proposed method, a group of 23 stroke patients, whose upper limb movement functions were in different recovery stages, and 4 healthy people, whose upper limb movement functions were normal, were recruited to finish the same training task. The results showed that, compared to that of the experienced rehabilitation expert who used the Brunnstrom stage standard table, the accuracy of the proposed remote Brunnstrom intelligent assessment system can reach a higher level, as 92.1%. The practical effects of surgery have proved that the proposed system could realize the intelligent assessment of upper limb movement function of post-stroke patients remotely, and it could also make the rehabilitation of the post-stroke patients at home or in a community care center possible.

Design consideration in constructing high performance embedded Knowledge-Based Systems (KBS)

NASA Technical Reports Server (NTRS)

Dalton, Shelly D.; Daley, Philip C.

1988-01-01

As the hardware trends for artificial intelligence (AI) involve more and more complexity, the process of optimizing the computer system design for a particular problem will also increase in complexity. Space applications of knowledge based systems (KBS) will often require an ability to perform both numerically intensive vector computations and real time symbolic computations. Although parallel machines can theoretically achieve the speeds necessary for most of these problems, if the application itself is not highly parallel, the machine's power cannot be utilized. A scheme is presented which will provide the computer systems engineer with a tool for analyzing machines with various configurations of array, symbolic, scaler, and multiprocessors. High speed networks and interconnections make customized, distributed, intelligent systems feasible for the application of AI in space. The method presented can be used to optimize such AI system configurations and to make comparisons between existing computer systems. It is an open question whether or not, for a given mission requirement, a suitable computer system design can be constructed for any amount of money.

Teleoperators - Manual/automatic system requirements.

NASA Technical Reports Server (NTRS)

Janow, C.; Malone, T. B.

1973-01-01

The teleoperator is defined as a remotely controlled, cybernetic, man-machine system designed to extend and augment man's sensory, manipulative, and cognitive capabilities. The teleoperator system incorporates the decision making, adaptive intelligence without requiring its presence. The man and the machine work as a team, each contributing unique and significant capabilities, and each depending on the other to achieve a common goal. Some of the more significant requirements associated with the development of teleoperator systems technology for space, industry, and medicine are examined. Emphasis is placed on the requirement to more effectively use the man and the machine in any man-machine system.

Machine Methods for Acquiring, Learning, and Applying Knowledge.

ERIC Educational Resources Information Center

Hayes-Roth, Frederick; And Others

A research plan for identifying and acting upon constraints that impede the development of knowledge-based intelligent systems is described. The two primary problems identified are knowledge programming, the task of which is to create an intelligent system that does what an expert says it should, and learning, the problem requiring the criticizing…

A cloud platform for remote diagnosis of breast cancer in mammography by fusion of machine and human intelligence

NASA Astrophysics Data System (ADS)

Jiang, Guodong; Fan, Ming; Li, Lihua

2016-03-01

Mammography is the gold standard for breast cancer screening, reducing mortality by about 30%. The application of a computer-aided detection (CAD) system to assist a single radiologist is important to further improve mammographic sensitivity for breast cancer detection. In this study, a design and realization of the prototype for remote diagnosis system in mammography based on cloud platform were proposed. To build this system, technologies were utilized including medical image information construction, cloud infrastructure and human-machine diagnosis model. Specifically, on one hand, web platform for remote diagnosis was established by J2EE web technology. Moreover, background design was realized through Hadoop open-source framework. On the other hand, storage system was built up with Hadoop distributed file system (HDFS) technology which enables users to easily develop and run on massive data application, and give full play to the advantages of cloud computing which is characterized by high efficiency, scalability and low cost. In addition, the CAD system was realized through MapReduce frame. The diagnosis module in this system implemented the algorithms of fusion of machine and human intelligence. Specifically, we combined results of diagnoses from doctors' experience and traditional CAD by using the man-machine intelligent fusion model based on Alpha-Integration and multi-agent algorithm. Finally, the applications on different levels of this system in the platform were also discussed. This diagnosis system will have great importance for the balanced health resource, lower medical expense and improvement of accuracy of diagnosis in basic medical institutes.

78 FR 73883 - Notice Pursuant to the National Cooperative Research and Production Act of 1993; Members of SGIP...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-09

... Utilities System, Lafayette, LA; Machine-to- Machine Intelligence Corporation (M2Mi), Moffett Field, CA; Inman Technology, Cambridge, MA; Kkrish Energy LLC, Colorado Springs, CO; Smarthome Laboratories, Ltd...

Are we there yet?

PubMed

Cristianini, Nello

2010-05-01

Statistical approaches to Artificial Intelligence are behind most success stories of the field in the past decade. The idea of generating non-trivial behaviour by analysing vast amounts of data has enabled recommendation systems, search engines, spam filters, optical character recognition, machine translation and speech recognition, among other things. As we celebrate the spectacular achievements of this line of research, we need to assess its full potential and its limitations. What are the next steps to take towards machine intelligence? 2010 Elsevier Ltd. All rights reserved.

Artificial Intelligence in Speech Understanding: Two Applications at C.R.I.N.

ERIC Educational Resources Information Center

Carbonell, N.; And Others

1986-01-01

This article explains how techniques of artificial intelligence are applied to expert systems for acoustic-phonetic decoding, phonological interpretation, and multi-knowledge sources for man-machine dialogue implementation. The basic ideas are illustrated with short examples. (Author/JDH)

Advanced automation for space missions: Technical summary

NASA Technical Reports Server (NTRS)

1980-01-01

Several representative missions which would require extensive applications of machine intelligence were identified and analyzed. The technologies which must be developed to accomplish these types of missions are discussed. These technologies include man-machine communication, space manufacturing, teleoperators, and robot systems.

Optical Inference Machines

DTIC Science & Technology

1988-06-27

de olf nessse end Id e ;-tl Sb ieeI smleo) ,Optical Artificial Intellegence ; Optical inference engines; Optical logic; Optical informationprocessing...common. They arise in areas such as expert systems and other artificial intelligence systems. In recent years, the computer science language PROLOG has...cal processors should in principle be well suited for : I artificial intelligence applications. In recent years, symbolic logic processing. , the

Cognitive computing and eScience in health and life science research: artificial intelligence and obesity intervention programs.

PubMed

Marshall, Thomas; Champagne-Langabeer, Tiffiany; Castelli, Darla; Hoelscher, Deanna

2017-12-01

To present research models based on artificial intelligence and discuss the concept of cognitive computing and eScience as disruptive factors in health and life science research methodologies. The paper identifies big data as a catalyst to innovation and the development of artificial intelligence, presents a framework for computer-supported human problem solving and describes a transformation of research support models. This framework includes traditional computer support; federated cognition using machine learning and cognitive agents to augment human intelligence; and a semi-autonomous/autonomous cognitive model, based on deep machine learning, which supports eScience. The paper provides a forward view of the impact of artificial intelligence on our human-computer support and research methods in health and life science research. By augmenting or amplifying human task performance with artificial intelligence, cognitive computing and eScience research models are discussed as novel and innovative systems for developing more effective adaptive obesity intervention programs.

Machine learning based Intelligent cognitive network using fog computing

NASA Astrophysics Data System (ADS)

Lu, Jingyang; Li, Lun; Chen, Genshe; Shen, Dan; Pham, Khanh; Blasch, Erik

2017-05-01

In this paper, a Cognitive Radio Network (CRN) based on artificial intelligence is proposed to distribute the limited radio spectrum resources more efficiently. The CRN framework can analyze the time-sensitive signal data close to the signal source using fog computing with different types of machine learning techniques. Depending on the computational capabilities of the fog nodes, different features and machine learning techniques are chosen to optimize spectrum allocation. Also, the computing nodes send the periodic signal summary which is much smaller than the original signal to the cloud so that the overall system spectrum source allocation strategies are dynamically updated. Applying fog computing, the system is more adaptive to the local environment and robust to spectrum changes. As most of the signal data is processed at the fog level, it further strengthens the system security by reducing the communication burden of the communications network.

11th Annual Intelligent Ground Vehicle Competition: team approaches to intelligent driving and machine vision

NASA Astrophysics Data System (ADS)

Theisen, Bernard L.; Lane, Gerald R.

2003-10-01

The Intelligent Ground Vehicle Competition (IGVC) is one of three, unmanned systems, student competitions that were founded by the Association for Unmanned Vehicle Systems International (AUVSI) in the 1990's. The IGVC is a multidisciplinary exercise in product realization that challenges college engineering student teams to integrate advanced control theory, machine vision, vehicular electronics, and mobile platform fundamentals to design and build an unmanned system. Both the U.S. and international teams focus on developing a suite of dual-use technologies to equip ground vehicles of the future with intelligtent driving capabilities. Over the past 11 years, the competition has challenged both undergraduates and graduates, including Ph.D. students with real world applications in intelligent transportation systems, the military, and manufacturing automation. To date, teams from over 40 universities and colleges have participated. In this paper, we describe some of the applications of the technologies required by this competition, and discuss the educational benefits. The primary goal of the IGVC is to advance engineering education in intelligent vehicles and related technologies. The employment and professional networking opportunities created for students and industrial sponsors through a series of technical events over the three-day competition are highlighted. Finally, an assessment of the competition based on participant feedback is presented.

A Starter's Guide to Artificial Intelligence.

ERIC Educational Resources Information Center

McConnell, Barry A.; McConnell, Nancy J.

1988-01-01

Discussion of the history and development of artificial intelligence (AI) highlights a bibliography of introductory books on various aspects of AI, including AI programing; problem solving; automated reasoning; game playing; natural language; expert systems; machine learning; robotics and vision; critics of AI; and representative software. (LRW)

Virtual Neurorobotics (VNR) to Accelerate Development of Plausible Neuromorphic Brain Architectures.

PubMed

Goodman, Philip H; Buntha, Sermsak; Zou, Quan; Dascalu, Sergiu-Mihai

2007-01-01

Traditional research in artificial intelligence and machine learning has viewed the brain as a specially adapted information-processing system. More recently the field of social robotics has been advanced to capture the important dynamics of human cognition and interaction. An overarching societal goal of this research is to incorporate the resultant knowledge about intelligence into technology for prosthetic, assistive, security, and decision support applications. However, despite many decades of investment in learning and classification systems, this paradigm has yet to yield truly "intelligent" systems. For this reason, many investigators are now attempting to incorporate more realistic neuromorphic properties into machine learning systems, encouraged by over two decades of neuroscience research that has provided parameters that characterize the brain's interdependent genomic, proteomic, metabolomic, anatomic, and electrophysiological networks. Given the complexity of neural systems, developing tenable models to capture the essence of natural intelligence for real-time application requires that we discriminate features underlying information processing and intrinsic motivation from those reflecting biological constraints (such as maintaining structural integrity and transporting metabolic products). We propose herein a conceptual framework and an iterative method of virtual neurorobotics (VNR) intended to rapidly forward-engineer and test progressively more complex putative neuromorphic brain prototypes for their ability to support intrinsically intelligent, intentional interaction with humans. The VNR system is based on the viewpoint that a truly intelligent system must be driven by emotion rather than programmed tasking, incorporating intrinsic motivation and intentionality. We report pilot results of a closed-loop, real-time interactive VNR system with a spiking neural brain, and provide a video demonstration as online supplemental material.

Computers Simulate Human Experts.

ERIC Educational Resources Information Center

Roberts, Steven K.

1983-01-01

Discusses recent progress in artificial intelligence in such narrowly defined areas as medical and electronic diagnosis. Also discusses use of expert systems, man-machine communication problems, novel programing environments (including comments on LISP and LISP machines), and types of knowledge used (factual, heuristic, and meta-knowledge). (JN)

Combining human and machine intelligence to derive agents' behavioral rules for groundwater irrigation

NASA Astrophysics Data System (ADS)

Hu, Yao; Quinn, Christopher J.; Cai, Ximing; Garfinkle, Noah W.

2017-11-01

For agent-based modeling, the major challenges in deriving agents' behavioral rules arise from agents' bounded rationality and data scarcity. This study proposes a "gray box" approach to address the challenge by incorporating expert domain knowledge (i.e., human intelligence) with machine learning techniques (i.e., machine intelligence). Specifically, we propose using directed information graph (DIG), boosted regression trees (BRT), and domain knowledge to infer causal factors and identify behavioral rules from data. A case study is conducted to investigate farmers' pumping behavior in the Midwest, U.S.A. Results show that four factors identified by the DIG algorithm- corn price, underlying groundwater level, monthly mean temperature and precipitation- have main causal influences on agents' decisions on monthly groundwater irrigation depth. The agent-based model is then developed based on the behavioral rules represented by three DIGs and modeled by BRTs, and coupled with a physically-based groundwater model to investigate the impacts of agents' pumping behavior on the underlying groundwater system in the context of coupled human and environmental systems.

Parallel Algorithms for Computer Vision

DTIC Science & Technology

1990-04-01

NA86-1, Thinking Machines Corporation, Cambridge, MA, December 1986. [43] J. Little, G. Blelloch, and T. Cass. How to program the connection machine for... to program the connection machine for computer vision. In Proc. Workshop on Comp. Architecture for Pattern Analysis and Machine Intell., 1987. [92] J...In Proceedings of SPIE Conf. on Advances in Intelligent Robotics Systems, Bellingham, VA, 1987. SPIE. [91] J. Little, G. Blelloch, and T. Cass. How

Building intelligent systems: Artificial intelligence research at NASA Ames Research Center

NASA Technical Reports Server (NTRS)

Friedland, P.; Lum, H.

1987-01-01

The basic components that make up the goal of building autonomous intelligent systems are discussed, and ongoing work at the NASA Ames Research Center is described. It is noted that a clear progression of systems can be seen through research settings (both within and external to NASA) to Space Station testbeds to systems which actually fly on the Space Station. The starting point for the discussion is a truly autonomous Space Station intelligent system, responsible for a major portion of Space Station control. Attention is given to research in fiscal 1987, including reasoning under uncertainty, machine learning, causal modeling and simulation, knowledge from design through operations, advanced planning work, validation methodologies, and hierarchical control of and distributed cooperation among multiple knowledge-based systems.

Building intelligent systems - Artificial intelligence research at NASA Ames Research Center

NASA Technical Reports Server (NTRS)

Friedland, Peter; Lum, Henry

1987-01-01

The basic components that make up the goal of building autonomous intelligent systems are discussed, and ongoing work at the NASA Ames Research Center is described. It is noted that a clear progression of systems can be seen through research settings (both within and external to NASA) to Space Station testbeds to systems which actually fly on the Space Station. The starting point for the discussion is a 'truly' autonomous Space Station intelligent system, responsible for a major portion of Space Station control. Attention is given to research in fiscal 1987, including reasoning under uncertainty, machine learning, causal modeling and simulation, knowledge from design through operations, advanced planning work, validation methodologies, and hierarchical control of and distributed cooperation among multiple knowledge-based systems.

Modelling of internal architecture of kinesin nanomotor as a machine language.

PubMed

Khataee, H R; Ibrahim, M Y

2012-09-01

Kinesin is a protein-based natural nanomotor that transports molecular cargoes within cells by walking along microtubules. Kinesin nanomotor is considered as a bio-nanoagent which is able to sense the cell through its sensors (i.e. its heads and tail), make the decision internally and perform actions on the cell through its actuator (i.e. its motor domain). The study maps the agent-based architectural model of internal decision-making process of kinesin nanomotor to a machine language using an automata algorithm. The applied automata algorithm receives the internal agent-based architectural model of kinesin nanomotor as a deterministic finite automaton (DFA) model and generates a regular machine language. The generated regular machine language was acceptable by the architectural DFA model of the nanomotor and also in good agreement with its natural behaviour. The internal agent-based architectural model of kinesin nanomotor indicates the degree of autonomy and intelligence of the nanomotor interactions with its cell. Thus, our developed regular machine language can model the degree of autonomy and intelligence of kinesin nanomotor interactions with its cell as a language. Modelling of internal architectures of autonomous and intelligent bio-nanosystems as machine languages can lay the foundation towards the concept of bio-nanoswarms and next phases of the bio-nanorobotic systems development.

Proceedings of the NASA Conference on Space Telerobotics, volume 3

NASA Technical Reports Server (NTRS)

Rodriguez, Guillermo (Editor); Seraji, Homayoun (Editor)

1989-01-01

The theme of the Conference was man-machine collaboration in space. The Conference provided a forum for researchers and engineers to exchange ideas on the research and development required for application of telerobotics technology to the space systems planned for the 1990s and beyond. The Conference: (1) provided a view of current NASA telerobotic research and development; (2) stimulated technical exchange on man-machine systems, manipulator control, machine sensing, machine intelligence, concurrent computation, and system architectures; and (3) identified important unsolved problems of current interest which can be dealt with by future research.

Social Intelligence in a Human-Machine Collaboration System

NASA Astrophysics Data System (ADS)

Nakajima, Hiroshi; Morishima, Yasunori; Yamada, Ryota; Brave, Scott; Maldonado, Heidy; Nass, Clifford; Kawaji, Shigeyasu

In this information society of today, it is often argued that it is necessary to create a new way of human-machine interaction. In this paper, an agent with social response capabilities has been developed to achieve this goal. There are two kinds of information that is exchanged by two entities: objective and functional information (e.g., facts, requests, states of matters, etc.) and subjective information (e.g., feelings, sense of relationship, etc.). Traditional interactive systems have been designed to handle the former kind of information. In contrast, in this study social agents handling the latter type of information are presented. The current study focuses on sociality of the agent from the view point of Media Equation theory. This article discusses the definition, importance, and benefits of social intelligence as agent technology and argues that social intelligence has a potential to enhance the user's perception of the system, which in turn can lead to improvements of the system's performance. In order to implement social intelligence in the agent, a mind model has been developed to render affective expressions and personality of the agent. The mind model has been implemented in a human-machine collaborative learning system. One differentiating feature of the collaborative learning system is that it has an agent that performs as a co-learner with which the user interacts during the learning session. The mind model controls the social behaviors of the agent, thus making it possible for the user to have more social interactions with the agent. The experiment with the system suggested that a greater degree of learning was achieved when the students worked with the co-learner agent and that the co-learner agent with the mind model that expressed emotions resulted in a more positive attitude toward the system.

The role of automation and artificial intelligence

NASA Astrophysics Data System (ADS)

Schappell, R. T.

1983-07-01

Consideration is given to emerging technologies that are not currently in common use, yet will be mature enough for implementation in a space station. Artificial intelligence (AI) will permit more autonomous operation and improve the man-machine interfaces. Technology goals include the development of expert systems, a natural language query system, automated planning systems, and AI image understanding systems. Intelligent robots and teleoperators will be needed, together with improved sensory systems for the robotics, housekeeping, vehicle control, and spacecraft housekeeping systems. Finally, NASA is developing the ROBSIM computer program to evaluate level of automation, perform parametric studies and error analyses, optimize trajectories and control systems, and assess AI technology.

Space languages

NASA Technical Reports Server (NTRS)

Hays, Dan

1987-01-01

Applications of linguistic principles to potential problems of human and machine communication in space settings are discussed. Variations in language among speakers of different backgrounds and change in language forms resulting from new experiences or reduced contact with other groups need to be considered in the design of intelligent machine systems.

Design Guidelines and Criteria for User/Operator Transactions with Battlefield Automated Systems. Volume 5. Background Literature

DTIC Science & Technology

1981-02-01

the machine . ARI’s efforts in this area focus on human perfor- mance problems related to interactions with command and control centers, and on issues...improvement of the user- machine interface. Lacking consistent design principles, current practice results in a fragmented and unsystematic approach to system...complexity in the user- machine interface of BAS, ARI supported this effort for develop- me:nt of an online language for Army tactical intelligence

Artificial Intelligence and the High School Computer Curriculum.

ERIC Educational Resources Information Center

Dillon, Richard W.

1993-01-01

Describes a four-part curriculum that can serve as a model for incorporating artificial intelligence (AI) into the high school computer curriculum. The model includes examining questions fundamental to AI, creating and designing an expert system, language processing, and creating programs that integrate machine vision with robotics and…

Intelligent Adaptive Interface: A Design Tool for Enhancing Human-Machine System Performances

DTIC Science & Technology

2009-10-01

and customizable. Thus, an intelligent interface should tailor its parameters to certain prescribed specifications or convert itself and adjust to...Computer Interaction 3(2): 87-122. [51] Schereiber, G., Akkermans, H., Anjewierden, A., de Hoog , R., Shadbolt, N., Van de Velde, W., & Wielinga, W

Design of a real-time tax-data monitoring intelligent card system

NASA Astrophysics Data System (ADS)

Gu, Yajun; Bi, Guotang; Chen, Liwei; Wang, Zhiyuan

2009-07-01

To solve the current problem of low efficiency of domestic Oil Station's information management, Oil Station's realtime tax data monitoring system has been developed to automatically access tax data of Oil pumping machines, realizing Oil-pumping machines' real-time automatic data collection, displaying and saving. The monitoring system uses the noncontact intelligent card or network to directly collect data which can not be artificially modified and so seals the loopholes and improves the tax collection's automatic level. It can perform real-time collection and management of the Oil Station information, and find the problem promptly, achieves the automatic management for the entire process covering Oil sales accounting and reporting. It can also perform remote query to the Oil Station's operation data. This system has broad application future and economic value.

Substructure Discovery of Macro-Operators

DTIC Science & Technology

1988-05-01

Aspects of Scientific Discovery," in Machine Learning: An Artifcial Intelligence Approach, Vol. II. R. S. Michalski, J. G. Carbonell and T. M. Mitchell (ed... intelligent robot using this system could learn how to perform new tasks by watching tasks being performed by someone else. even if the robot does not possess...Substructure Discovery of Macro-Operators* Bradley L. Whitehall Artificial Intelligence Research Group Coordinated Science Laboratory ’University of Illinois at

Predicting asthma exacerbations using artificial intelligence.

PubMed

Finkelstein, Joseph; Wood, Jeffrey

2013-01-01

Modern telemonitoring systems identify a serious patient deterioration when it already occurred. It would be much more beneficial if the upcoming clinical deterioration were identified ahead of time even before a patient actually experiences it. The goal of this study was to assess artificial intelligence approaches which potentially can be used in telemonitoring systems for advance prediction of changes in disease severity before they actually occur. The study dataset was based on daily self-reports submitted by 26 adult asthma patients during home telemonitoring consisting of 7001 records. Two classification algorithms were employed for building predictive models: naïve Bayesian classifier and support vector machines. Using a 7-day window, a support vector machine was able to predict asthma exacerbation to occur on the day 8 with the accuracy of 0.80, sensitivity of 0.84 and specificity of 0.80. Our study showed that methods of artificial intelligence have significant potential in developing individualized decision support for chronic disease telemonitoring systems.

The 1991 Goddard Conference on Space Applications of Artificial Intelligence

NASA Technical Reports Server (NTRS)

Rash, James L. (Editor)

1991-01-01

The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The papers in this proceeding fall into the following areas: Planning and scheduling, fault monitoring/diagnosis/recovery, machine vision, robotics, system development, information management, knowledge acquisition and representation, distributed systems, tools, neural networks, and miscellaneous applications.

Diagnostic Assessment of Troubleshooting Skill in an Intelligent Tutoring System

DTIC Science & Technology

1994-03-01

the information that can be provided from studying gauges and indicators and conventional test equipment procedures. Experts are particularly adept at...uses the results of the strategy and action evaluator to update the student profile, represented as a network, using the ERGO ( Noetic Systems, 1993...1990). Individualized tutoring using an intelligent fuzzy temporal relational database. International Tournal of Man-Machine Studies . & 409-429. 𔄁. 34

Trends and Challenges in Neuroengineering: Toward "Intelligent" Neuroprostheses through Brain-"Brain Inspired Systems" Communication.

PubMed

Vassanelli, Stefano; Mahmud, Mufti

2016-01-01

Future technologies aiming at restoring and enhancing organs function will intimately rely on near-physiological and energy-efficient communication between living and artificial biomimetic systems. Interfacing brain-inspired devices with the real brain is at the forefront of such emerging field, with the term "neurobiohybrids" indicating all those systems where such interaction is established. We argue that achieving a "high-level" communication and functional synergy between natural and artificial neuronal networks in vivo , will allow the development of a heterogeneous world of neurobiohybrids, which will include "living robots" but will also embrace "intelligent" neuroprostheses for augmentation of brain function. The societal and economical impact of intelligent neuroprostheses is likely to be potentially strong, as they will offer novel therapeutic perspectives for a number of diseases, and going beyond classical pharmaceutical schemes. However, they will unavoidably raise fundamental ethical questions on the intermingling between man and machine and more specifically, on how deeply it should be allowed that brain processing is affected by implanted "intelligent" artificial systems. Following this perspective, we provide the reader with insights on ongoing developments and trends in the field of neurobiohybrids. We address the topic also from a "community building" perspective, showing through a quantitative bibliographic analysis, how scientists working on the engineering of brain-inspired devices and brain-machine interfaces are increasing their interactions. We foresee that such trend preludes to a formidable technological and scientific revolution in brain-machine communication and to the opening of new avenues for restoring or even augmenting brain function for therapeutic purposes.

Artificial intelligence: A joint narrative on potential use in pediatric stem and immune cell therapies and regenerative medicine.

PubMed

Sniecinski, Irena; Seghatchian, Jerard

2018-05-09

Artificial Intelligence (AI) reflects the intelligence exhibited by machines and software. It is a highly desirable academic field of many current fields of studies. Leading AI researchers describe the field as "the study and design of intelligent agents". McCarthy invented this term in 1955 and defined it as "the science and engineering of making intelligent machines". The central goals of AI research are reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects. In fact the multidisplinary AI field is considered to be rather interdisciplinary covering numerous number of sciences and professions, including computer science, psychology, linguistics, philosophy and neurosciences. The field was founded on the claim that a central intellectual property of humans, intelligence-the sapience of Homo Sapiens "can be so precisely described that a machine can be made to simulate it". This raises philosophical issues about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence. Artificial Intelligence has been the subject of tremendous optimism but has also suffered stunning setbacks. The goal of this narrative is to review the potential use of AI approaches and their integration into pediatric cellular therapies and regenerative medicine. Emphasis is placed on recognition and application of AI techniques in the development of predictive models for personalized treatments with engineered stem cells, immune cells and regenerated tissues in adults and children. These intelligent machines could dissect the whole genome and isolate the immune particularities of individual patient's disease in a matter of minutes and create the treatment that is customized to patient's genetic specificity and immune system capability. AI techniques could be used for optimization of clinical trials of innovative stem cell and gene therapies in pediatric patients by precise planning of treatments, predicting clinical outcomes, simplifying recruitment and retention of patients, learning from input data and applying to new data, thus lowering their complexity and costs. Complementing human intelligence with machine intelligence could have an exponentially high impact on continual progress in many fields of pediatrics. However how long before we could see the real impact still remains the big question. The most pertinent question that remains to be answered therefore, is can AI effectively and accurately predict properties of newer DDR strategies? The goal of this article is to review the use of AI method for cellular therapy and regenerative medicine and emphasize its potential to further the progress in these fields of medicine. Copyright © 2018. Published by Elsevier Ltd.

Artificial intelligence expert systems with neural network machine learning may assist decision-making for extractions in orthodontic treatment planning.

PubMed

Takada, Kenji

2016-09-01

New approach for the diagnosis of extractions with neural network machine learning. Seok-Ki Jung and Tae-Woo Kim. Am J Orthod Dentofacial Orthop 2016;149:127-33. Not reported. Mathematical modeling. Copyright © 2016 Elsevier Inc. All rights reserved.

Topics in programmable automation. [for materials handling, inspection, and assembly

NASA Technical Reports Server (NTRS)

Rosen, C. A.

1975-01-01

Topics explored in the development of integrated programmable automation systems include: numerically controlled and computer controlled machining; machine intelligence and the emulation of human-like capabilities; large scale semiconductor integration technology applications; and sensor technology for asynchronous local computation without burdening the executive minicomputer which controls the whole system. The role and development of training aids, and the potential application of these aids to augmented teleoperator systems are discussed.

Machine listening intelligence

NASA Astrophysics Data System (ADS)

Cella, C. E.

2017-05-01

This manifesto paper will introduce machine listening intelligence, an integrated research framework for acoustic and musical signals modelling, based on signal processing, deep learning and computational musicology.

Artificial consciousness and the consciousness-attention dissociation.

PubMed

Haladjian, Harry Haroutioun; Montemayor, Carlos

2016-10-01

Artificial Intelligence is at a turning point, with a substantial increase in projects aiming to implement sophisticated forms of human intelligence in machines. This research attempts to model specific forms of intelligence through brute-force search heuristics and also reproduce features of human perception and cognition, including emotions. Such goals have implications for artificial consciousness, with some arguing that it will be achievable once we overcome short-term engineering challenges. We believe, however, that phenomenal consciousness cannot be implemented in machines. This becomes clear when considering emotions and examining the dissociation between consciousness and attention in humans. While we may be able to program ethical behavior based on rules and machine learning, we will never be able to reproduce emotions or empathy by programming such control systems-these will be merely simulations. Arguments in favor of this claim include considerations about evolution, the neuropsychological aspects of emotions, and the dissociation between attention and consciousness found in humans. Ultimately, we are far from achieving artificial consciousness. Copyright © 2016 Elsevier Inc. All rights reserved.

Virtual collaborative environments: programming and controlling robotic devices remotely

NASA Astrophysics Data System (ADS)

Davies, Brady R.; McDonald, Michael J., Jr.; Harrigan, Raymond W.

1995-12-01

This paper describes a technology for remote sharing of intelligent electro-mechanical devices. An architecture and actual system have been developed and tested, based on the proposed National Information Infrastructure (NII) or Information Highway, to facilitate programming and control of intelligent programmable machines (like robots, machine tools, etc.). Using appropriate geometric models, integrated sensors, video systems, and computing hardware; computer controlled resources owned and operated by different (in a geographic sense as well as legal sense) entities can be individually or simultaneously programmed and controlled from one or more remote locations. Remote programming and control of intelligent machines will create significant opportunities for sharing of expensive capital equipment. Using the technology described in this paper, university researchers, manufacturing entities, automation consultants, design entities, and others can directly access robotic and machining facilities located across the country. Disparate electro-mechanical resources will be shared in a manner similar to the way supercomputers are accessed by multiple users. Using this technology, it will be possible for researchers developing new robot control algorithms to validate models and algorithms right from their university labs without ever owning a robot. Manufacturers will be able to model, simulate, and measure the performance of prospective robots before selecting robot hardware optimally suited for their intended application. Designers will be able to access CNC machining centers across the country to fabricate prototypic parts during product design validation. An existing prototype architecture and system has been developed and proven. Programming and control of a large gantry robot located at Sandia National Laboratories in Albuquerque, New Mexico, was demonstrated from such remote locations as Washington D.C., Washington State, and Southern California.

A review of intelligent systems for heart sound signal analysis.

PubMed

Nabih-Ali, Mohammed; El-Dahshan, El-Sayed A; Yahia, Ashraf S

2017-10-01

Intelligent computer-aided diagnosis (CAD) systems can enhance the diagnostic capabilities of physicians and reduce the time required for accurate diagnosis. CAD systems could provide physicians with a suggestion about the diagnostic of heart diseases. The objective of this paper is to review the recent published preprocessing, feature extraction and classification techniques and their state of the art of phonocardiogram (PCG) signal analysis. Published literature reviewed in this paper shows the potential of machine learning techniques as a design tool in PCG CAD systems and reveals that the CAD systems for PCG signal analysis are still an open problem. Related studies are compared to their datasets, feature extraction techniques and the classifiers they used. Current achievements and limitations in developing CAD systems for PCG signal analysis using machine learning techniques are presented and discussed. In the light of this review, a number of future research directions for PCG signal analysis are provided.

The ZOG Technology Demonstration Project: A System Evaluation of USS CARL VINSON (CVN 70)

DTIC Science & Technology

1984-12-01

part of a larger project involving development of a wide range of computer technologies, including artifcial intelligence and a long-range computer...shipboard manage- ment, aircraft management, expert systems, menu selection, man- machine interface, artificial intelligence , automation; shipboard It AWM...functions, planning, evaluation, training, hierarchical data bases The objective of this project was to conduct an evaluation of ZOG, a general purpose

The Naturalistic Flight Deck System: An Integrated System Concept for Improved Single-Pilot Operations

NASA Technical Reports Server (NTRS)

Schutte, Paul C.; Goodrich, Kenneth H.; Cox, David E.; Jackson, Bruce; Palmer, Michael T.; Pope, Alan T.; Schlecht, Robin W.; Tedjojuwono, Ken K.; Trujillo, Anna C.; Williams, Ralph A.;

Compact Microscope Imaging System With Intelligent Controls Improved

NASA Technical Reports Server (NTRS)

McDowell, Mark

2004-01-01

The Compact Microscope Imaging System (CMIS) with intelligent controls is a diagnostic microscope analysis tool with intelligent controls for use in space, industrial, medical, and security applications. This compact miniature microscope, which can perform tasks usually reserved for conventional microscopes, has unique advantages in the fields of microscopy, biomedical research, inline process inspection, and space science. Its unique approach integrates a machine vision technique with an instrumentation and control technique that provides intelligence via the use of adaptive neural networks. The CMIS system was developed at the NASA Glenn Research Center specifically for interface detection used for colloid hard spheres experiments; biological cell detection for patch clamping, cell movement, and tracking; and detection of anode and cathode defects for laboratory samples using microscope technology.

Intelligent Machines in the 21st Century: Automating the Processes of Inference and Inquiry

NASA Technical Reports Server (NTRS)

Knuth, Kevin H.

2003-01-01

The last century saw the application of Boolean algebra toward the construction of computing machines, which work by applying logical transformations to information contained in their memory. The development of information theory and the generalization of Boolean algebra to Bayesian inference have enabled these computing machines. in the last quarter of the twentieth century, to be endowed with the ability to learn by making inferences from data. This revolution is just beginning as new computational techniques continue to make difficult problems more accessible. However, modern intelligent machines work by inferring knowledge using only their pre-programmed prior knowledge and the data provided. They lack the ability to ask questions, or request data that would aid their inferences. Recent advances in understanding the foundations of probability theory have revealed implications for areas other than logic. Of relevance to intelligent machines, we identified the algebra of questions as the free distributive algebra, which now allows us to work with questions in a way analogous to that which Boolean algebra enables us to work with logical statements. In this paper we describe this logic of inference and inquiry using the mathematics of partially ordered sets and the scaffolding of lattice theory, discuss the far-reaching implications of the methodology, and demonstrate its application with current examples in machine learning. Automation of both inference and inquiry promises to allow robots to perform science in the far reaches of our solar system and in other star systems by enabling them to not only make inferences from data, but also decide which question to ask, experiment to perform, or measurement to take given what they have learned and what they are designed to understand.

Machine Learning. Part 1. A Historical and Methodological Analysis.

DTIC Science & Technology

1983-05-31

Machine learning has always been an integral part of artificial intelligence, and its methodology has evolved in concert with the major concerns of the field. In response to the difficulties of encoding ever-increasing volumes of knowledge in modern Al systems, many researchers have recently turned their attention to machine learning as a means to overcome the knowledge acquisition bottleneck. Part 1 of this paper presents a taxonomic analysis of machine learning organized primarily by learning strategies and secondarily by

Blindness in designing intelligent systems

NASA Technical Reports Server (NTRS)

Denning, Peter J.

1988-01-01

New investigations of the foundations of artificial intelligence are challenging the hypothesis that problem solving is the cornerstone of intelligence. New distinctions among three domains of concern for humans--description, action, and commitment--have revealed that the design process for programmable machines, such as expert systems, is based on descriptions of actions and induces blindness to nonanalytic action and commitment. Design processes focusing in the domain of description are likely to yield programs like burearcracies: rigid, obtuse, impersonal, and unable to adapt to changing circumstances. Systems that learn from their past actions, and systems that organize information for interpretation by human experts, are more likely to be successful in areas where expert systems have failed.

A hardware/software environment to support R D in intelligent machines and mobile robotic systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mann, R.C.

1990-01-01

The Center for Engineering Systems Advanced Research (CESAR) serves as a focal point at the Oak Ridge National Laboratory (ORNL) for basic and applied research in intelligent machines. R D at CESAR addresses issues related to autonomous systems, unstructured (i.e. incompletely known) operational environments, and multiple performing agents. Two mobile robot prototypes (HERMIES-IIB and HERMIES-III) are being used to test new developments in several robot component technologies. This paper briefly introduces the computing environment at CESAR which includes three hypercube concurrent computers (two on-board the mobile robots), a graphics workstation, VAX, and multiple VME-based systems (several on-board the mobile robots).more » The current software environment at CESAR is intended to satisfy several goals, e.g.: code portability, re-usability in different experimental scenarios, modularity, concurrent computer hardware transparent to applications programmer, future support for multiple mobile robots, support human-machine interface modules, and support for integration of software from other, geographically disparate laboratories with different hardware set-ups. 6 refs., 1 fig.« less

Living and working in space; IAA Man in Space Symposium, 9th, Cologne, Federal Republic of Germany, June 17-21, 1991, Selection of Papers

NASA Technical Reports Server (NTRS)

Klein, Karl E. (Editor); Contant, Jean-Michel (Editor)

1992-01-01

The present symposium on living and working in space encompasses the physiological responses of humans in space and biomedical support for the conditions associated with space travel. Specific physiological issues addressed include cerebral and sensorimotor functions, effects on the cardiovascular and respiratory system, musculoskeletal system, body fluid, hormones and electrolytes, and some orthostatic hypotension mechanisms as countermeasures. The biomedical support techniques examined include selection training, and care, teleoperation and artificial intelligence, robotic automation, bioregenerative life support, and toxic hazard risks in space habitats. Also addressed are determinants of orientation in microgravity, the hormonal control of body fluid metabolism, integrated human-machine intelligence in space machines, and material flow estimation in CELSS.

Intelligent fuzzy controller for event-driven real time systems

NASA Technical Reports Server (NTRS)

Grantner, Janos; Patyra, Marek; Stachowicz, Marian S.

1992-01-01

Most of the known linguistic models are essentially static, that is, time is not a parameter in describing the behavior of the object's model. In this paper we show a model for synchronous finite state machines based on fuzzy logic. Such finite state machines can be used to build both event-driven, time-varying, rule-based systems and the control unit section of a fuzzy logic computer. The architecture of a pipelined intelligent fuzzy controller is presented, and the linguistic model is represented by an overall fuzzy relation stored in a single rule memory. A VLSI integrated circuit implementation of the fuzzy controller is suggested. At a clock rate of 30 MHz, the controller can perform 3 MFLIPS on multi-dimensional fuzzy data.

Research and development program plan for the Center for Engineering Systems Advanced Research (CESAR)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weisbin, C.R.; Hamel, W.R.; Barhen, J.

1986-02-01

The Oak Ridge National Laboratory has established the Center for Engineering Systems Advanced Research (CESAR) for the purpose of addressing fundamental problems of intelligent machine technologies. The purpose of this document is to establish a framework and guidelines for research and development within ORNL's CESAR program in areas pertaining to intelligent machines. The specific objective is to present a CESAR Research and Development Plan for such work with a planning horizon of five to ten years, i.e., FY 1985 to FY 1990 and beyond. As much as possible, the plan is based on anticipated DOE needs in the area ofmore » productivity increase and safety to the end of this century.« less

Proceedings of the NASA Conference on Space Telerobotics, volume 2

NASA Technical Reports Server (NTRS)

Rodriguez, Guillermo (Editor); Seraji, Homayoun (Editor)

1989-01-01

These proceedings contain papers presented at the NASA Conference on Space Telerobotics held in Pasadena, January 31 to February 2, 1989. The theme of the Conference was man-machine collaboration in space. The Conference provided a forum for researchers and engineers to exchange ideas on the research and development required for application of telerobotics technology to the space systems planned for the 1990s and beyond. The Conference: (1) provided a view of current NASA telerobotic research and development; (2) stimulated technical exchange on man-machine systems, manipulator control, machine sensing, machine intelligence, concurrent computation, and system architectures; and (3) identified important unsolved problems of current interest which can be dealt with by future research.

A voyage to Mars: A challenge to collaboration between man and machines

NASA Technical Reports Server (NTRS)

Statler, Irving C.

1991-01-01

A speech addressing the design of man machine systems for exploration of space beyond Earth orbit from the human factors perspective is presented. Concerns relative to the design of automated and intelligent systems for the NASA Space Exploration Initiative (SEI) missions are largely based on experiences with integrating humans and comparable systems in aviation. The history, present status, and future prospect, of human factors in machine design are discussed in relation to a manned voyage to Mars. Three different cases for design philosophy are presented. The use of simulation is discussed. Recommendations for required research are given.

Artificial intelligence (AI) systems for interpreting complex medical datasets.

PubMed

Altman, R B

2017-05-01

Advances in machine intelligence have created powerful capabilities in algorithms that find hidden patterns in data, classify objects based on their measured characteristics, and associate similar patients/diseases/drugs based on common features. However, artificial intelligence (AI) applications in medical data have several technical challenges: complex and heterogeneous datasets, noisy medical datasets, and explaining their output to users. There are also social challenges related to intellectual property, data provenance, regulatory issues, economics, and liability. © 2017 ASCPT.

Shape Matching and Image Segmentation Using Stochastic Labeling

DTIC Science & Technology

1981-08-01

hierarchique d’Etiquetage Probabiliste," To be presented at AFCET, 3 eme Congres, Reconnaissance Des Formes et Intelligence Artificielle , Sept. 16-18...Tenenbaum, "MSYS: A System for Reasoning About Scenes," Tech. Note 121, Artificial Intelligence Center, SRI Intl., Menlo Park, CA, 1976. [1-6] D. Marr, T...Analysis and Machine Intelligence . [1-10] O.D. Faugeras and M. Berthod, "Using Context in the Global Recognition of a Set of Objects: An Optimization

Computational aerodynamics and artificial intelligence

NASA Technical Reports Server (NTRS)

Mehta, U. B.; Kutler, P.

1984-01-01

The general principles of artificial intelligence are reviewed and speculations are made concerning how knowledge based systems can accelerate the process of acquiring new knowledge in aerodynamics, how computational fluid dynamics may use expert systems, and how expert systems may speed the design and development process. In addition, the anatomy of an idealized expert system called AERODYNAMICIST is discussed. Resource requirements for using artificial intelligence in computational fluid dynamics and aerodynamics are examined. Three main conclusions are presented. First, there are two related aspects of computational aerodynamics: reasoning and calculating. Second, a substantial portion of reasoning can be achieved with artificial intelligence. It offers the opportunity of using computers as reasoning machines to set the stage for efficient calculating. Third, expert systems are likely to be new assets of institutions involved in aeronautics for various tasks of computational aerodynamics.

Intelligent machines in the twenty-first century: foundations of inference and inquiry.

PubMed

Knuth, Kevin H

2003-12-15

The last century saw the application of Boolean algebra to the construction of computing machines, which work by applying logical transformations to information contained in their memory. The development of information theory and the generalization of Boolean algebra to Bayesian inference have enabled these computing machines, in the last quarter of the twentieth century, to be endowed with the ability to learn by making inferences from data. This revolution is just beginning as new computational techniques continue to make difficult problems more accessible. Recent advances in our understanding of the foundations of probability theory have revealed implications for areas other than logic. Of relevance to intelligent machines, we recently identified the algebra of questions as the free distributive algebra, which will now allow us to work with questions in a way analogous to that which Boolean algebra enables us to work with logical statements. In this paper, we examine the foundations of inference and inquiry. We begin with a history of inferential reasoning, highlighting key concepts that have led to the automation of inference in modern machine-learning systems. We then discuss the foundations of inference in more detail using a modern viewpoint that relies on the mathematics of partially ordered sets and the scaffolding of lattice theory. This new viewpoint allows us to develop the logic of inquiry and introduce a measure describing the relevance of a proposed question to an unresolved issue. Last, we will demonstrate the automation of inference, and discuss how this new logic of inquiry will enable intelligent machines to ask questions. Automation of both inference and inquiry promises to allow robots to perform science in the far reaches of our solar system and in other star systems by enabling them not only to make inferences from data, but also to decide which question to ask, which experiment to perform, or which measurement to take given what they have learned and what they are designed to understand.

Intelligent machines in the twenty-first century: foundations of inference and inquiry

NASA Technical Reports Server (NTRS)

Knuth, Kevin H.

2003-01-01

The last century saw the application of Boolean algebra to the construction of computing machines, which work by applying logical transformations to information contained in their memory. The development of information theory and the generalization of Boolean algebra to Bayesian inference have enabled these computing machines, in the last quarter of the twentieth century, to be endowed with the ability to learn by making inferences from data. This revolution is just beginning as new computational techniques continue to make difficult problems more accessible. Recent advances in our understanding of the foundations of probability theory have revealed implications for areas other than logic. Of relevance to intelligent machines, we recently identified the algebra of questions as the free distributive algebra, which will now allow us to work with questions in a way analogous to that which Boolean algebra enables us to work with logical statements. In this paper, we examine the foundations of inference and inquiry. We begin with a history of inferential reasoning, highlighting key concepts that have led to the automation of inference in modern machine-learning systems. We then discuss the foundations of inference in more detail using a modern viewpoint that relies on the mathematics of partially ordered sets and the scaffolding of lattice theory. This new viewpoint allows us to develop the logic of inquiry and introduce a measure describing the relevance of a proposed question to an unresolved issue. Last, we will demonstrate the automation of inference, and discuss how this new logic of inquiry will enable intelligent machines to ask questions. Automation of both inference and inquiry promises to allow robots to perform science in the far reaches of our solar system and in other star systems by enabling them not only to make inferences from data, but also to decide which question to ask, which experiment to perform, or which measurement to take given what they have learned and what they are designed to understand.

Intelligent Techniques Using Molecular Data Analysis in Leukaemia: An Opportunity for Personalized Medicine Support System

PubMed Central

Adelson, David; Brown, Fred; Chaudhri, Naeem

2017-01-01

The use of intelligent techniques in medicine has brought a ray of hope in terms of treating leukaemia patients. Personalized treatment uses patient's genetic profile to select a mode of treatment. This process makes use of molecular technology and machine learning, to determine the most suitable approach to treating a leukaemia patient. Until now, no reviews have been published from a computational perspective concerning the development of personalized medicine intelligent techniques for leukaemia patients using molecular data analysis. This review studies the published empirical research on personalized medicine in leukaemia and synthesizes findings across studies related to intelligence techniques in leukaemia, with specific attention to particular categories of these studies to help identify opportunities for further research into personalized medicine support systems in chronic myeloid leukaemia. A systematic search was carried out to identify studies using intelligence techniques in leukaemia and to categorize these studies based on leukaemia type and also the task, data source, and purpose of the studies. Most studies used molecular data analysis for personalized medicine, but future advancement for leukaemia patients requires molecular models that use advanced machine-learning methods to automate decision-making in treatment management to deliver supportive medical information to the patient in clinical practice. PMID:28812013

Intelligent Techniques Using Molecular Data Analysis in Leukaemia: An Opportunity for Personalized Medicine Support System.

PubMed

Banjar, Haneen; Adelson, David; Brown, Fred; Chaudhri, Naeem

2017-01-01

The use of intelligent techniques in medicine has brought a ray of hope in terms of treating leukaemia patients. Personalized treatment uses patient's genetic profile to select a mode of treatment. This process makes use of molecular technology and machine learning, to determine the most suitable approach to treating a leukaemia patient. Until now, no reviews have been published from a computational perspective concerning the development of personalized medicine intelligent techniques for leukaemia patients using molecular data analysis. This review studies the published empirical research on personalized medicine in leukaemia and synthesizes findings across studies related to intelligence techniques in leukaemia, with specific attention to particular categories of these studies to help identify opportunities for further research into personalized medicine support systems in chronic myeloid leukaemia. A systematic search was carried out to identify studies using intelligence techniques in leukaemia and to categorize these studies based on leukaemia type and also the task, data source, and purpose of the studies. Most studies used molecular data analysis for personalized medicine, but future advancement for leukaemia patients requires molecular models that use advanced machine-learning methods to automate decision-making in treatment management to deliver supportive medical information to the patient in clinical practice.

Use seismic colored inversion and power law committee machine based on imperial competitive algorithm for improving porosity prediction in a heterogeneous reservoir

NASA Astrophysics Data System (ADS)

Ansari, Hamid Reza

2014-09-01

In this paper we propose a new method for predicting rock porosity based on a combination of several artificial intelligence systems. The method focuses on one of the Iranian carbonate fields in the Persian Gulf. Because there is strong heterogeneity in carbonate formations, estimation of rock properties experiences more challenge than sandstone. For this purpose, seismic colored inversion (SCI) and a new approach of committee machine are used in order to improve porosity estimation. The study comprises three major steps. First, a series of sample-based attributes is calculated from 3D seismic volume. Acoustic impedance is an important attribute that is obtained by the SCI method in this study. Second, porosity log is predicted from seismic attributes using common intelligent computation systems including: probabilistic neural network (PNN), radial basis function network (RBFN), multi-layer feed forward network (MLFN), ε-support vector regression (ε-SVR) and adaptive neuro-fuzzy inference system (ANFIS). Finally, a power law committee machine (PLCM) is constructed based on imperial competitive algorithm (ICA) to combine the results of all previous predictions in a single solution. This technique is called PLCM-ICA in this paper. The results show that PLCM-ICA model improved the results of neural networks, support vector machine and neuro-fuzzy system.

The Challenges of Human-Autonomy Teaming

NASA Technical Reports Server (NTRS)

Vera, Alonso

2017-01-01

Machine intelligence is improving rapidly based on advances in big data analytics, deep learning algorithms, networked operations, and continuing exponential growth in computing power (Moores Law). This growth in the power and applicability of increasingly intelligent systems will change the roles humans, shifting them to tasks where adaptive problem solving, reasoning and decision-making is required. This talk will address the challenges involved in engineering autonomous systems that function effectively with humans in aeronautics domains.

Prediction of pork loin quality using online computer vision system and artificial intelligence model.

PubMed

Sun, Xin; Young, Jennifer; Liu, Jeng-Hung; Newman, David

2018-06-01

The objective of this project was to develop a computer vision system (CVS) for objective measurement of pork loin under industry speed requirement. Color images of pork loin samples were acquired using a CVS. Subjective color and marbling scores were determined according to the National Pork Board standards by a trained evaluator. Instrument color measurement and crude fat percentage were used as control measurements. Image features (18 color features; 1 marbling feature; 88 texture features) were extracted from whole pork loin color images. Artificial intelligence prediction model (support vector machine) was established for pork color and marbling quality grades. The results showed that CVS with support vector machine modeling reached the highest prediction accuracy of 92.5% for measured pork color score and 75.0% for measured pork marbling score. This research shows that the proposed artificial intelligence prediction model with CVS can provide an effective tool for predicting color and marbling in the pork industry at online speeds. Copyright © 2018 Elsevier Ltd. All rights reserved.

Proceedings of the Seventh International Symposium on Methodologies for Intelligent Systems (Poster Session)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harber, K.S.

1993-05-01

This report contains the following papers: Implications in vivid logic; a self-learning bayesian expert system; a natural language generation system for a heterogeneous distributed database system; competence-switching'' managed by intelligent systems; strategy acquisition by an artificial neural network: Experiments in learning to play a stochastic game; viewpoints and selective inheritance in object-oriented modeling; multivariate discretization of continuous attributes for machine learning; utilization of the case-based reasoning method to resolve dynamic problems; formalization of an ontology of ceramic science in CLASSIC; linguistic tools for intelligent systems; an application of rough sets in knowledge synthesis; and a relational model for imprecise queries.more » These papers have been indexed separately.« less

Proceedings of the Seventh International Symposium on Methodologies for Intelligent Systems (Poster Session)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harber, K.S.

1993-05-01

This report contains the following papers: Implications in vivid logic; a self-learning Bayesian Expert System; a natural language generation system for a heterogeneous distributed database system; ``competence-switching`` managed by intelligent systems; strategy acquisition by an artificial neural network: Experiments in learning to play a stochastic game; viewpoints and selective inheritance in object-oriented modeling; multivariate discretization of continuous attributes for machine learning; utilization of the case-based reasoning method to resolve dynamic problems; formalization of an ontology of ceramic science in CLASSIC; linguistic tools for intelligent systems; an application of rough sets in knowledge synthesis; and a relational model for imprecise queries.more » These papers have been indexed separately.« less

System diagnostic builder: a rule-generation tool for expert systems that do intelligent data evaluation

NASA Astrophysics Data System (ADS)

Nieten, Joseph L.; Burke, Roger

1993-03-01

The system diagnostic builder (SDB) is an automated knowledge acquisition tool using state- of-the-art artificial intelligence (AI) technologies. The SDB uses an inductive machine learning technique to generate rules from data sets that are classified by a subject matter expert (SME). Thus, data is captured from the subject system, classified by an expert, and used to drive the rule generation process. These rule-bases are used to represent the observable behavior of the subject system, and to represent knowledge about this system. The rule-bases can be used in any knowledge based system which monitors or controls a physical system or simulation. The SDB has demonstrated the utility of using inductive machine learning technology to generate reliable knowledge bases. In fact, we have discovered that the knowledge captured by the SDB can be used in any number of applications. For example, the knowledge bases captured from the SMS can be used as black box simulations by intelligent computer aided training devices. We can also use the SDB to construct knowledge bases for the process control industry, such as chemical production, or oil and gas production. These knowledge bases can be used in automated advisory systems to ensure safety, productivity, and consistency.

Hybrid soft computing systems for electromyographic signals analysis: a review.

PubMed

Xie, Hong-Bo; Guo, Tianruo; Bai, Siwei; Dokos, Socrates

2014-02-03

Electromyographic (EMG) is a bio-signal collected on human skeletal muscle. Analysis of EMG signals has been widely used to detect human movement intent, control various human-machine interfaces, diagnose neuromuscular diseases, and model neuromusculoskeletal system. With the advances of artificial intelligence and soft computing, many sophisticated techniques have been proposed for such purpose. Hybrid soft computing system (HSCS), the integration of these different techniques, aims to further improve the effectiveness, efficiency, and accuracy of EMG analysis. This paper reviews and compares key combinations of neural network, support vector machine, fuzzy logic, evolutionary computing, and swarm intelligence for EMG analysis. Our suggestions on the possible future development of HSCS in EMG analysis are also given in terms of basic soft computing techniques, further combination of these techniques, and their other applications in EMG analysis.

Hybrid soft computing systems for electromyographic signals analysis: a review

PubMed Central

2014-01-01

Electromyographic (EMG) is a bio-signal collected on human skeletal muscle. Analysis of EMG signals has been widely used to detect human movement intent, control various human-machine interfaces, diagnose neuromuscular diseases, and model neuromusculoskeletal system. With the advances of artificial intelligence and soft computing, many sophisticated techniques have been proposed for such purpose. Hybrid soft computing system (HSCS), the integration of these different techniques, aims to further improve the effectiveness, efficiency, and accuracy of EMG analysis. This paper reviews and compares key combinations of neural network, support vector machine, fuzzy logic, evolutionary computing, and swarm intelligence for EMG analysis. Our suggestions on the possible future development of HSCS in EMG analysis are also given in terms of basic soft computing techniques, further combination of these techniques, and their other applications in EMG analysis. PMID:24490979

Artificial Intelligence for the Artificial Kidney: Pointers to the Future of a Personalized Hemodialysis Therapy.

PubMed

Hueso, Miguel; Vellido, Alfredo; Montero, Nuria; Barbieri, Carlo; Ramos, Rosa; Angoso, Manuel; Cruzado, Josep Maria; Jonsson, Anders

2018-02-01

Current dialysis devices are not able to react when unexpected changes occur during dialysis treatment or to learn about experience for therapy personalization. Furthermore, great efforts are dedicated to develop miniaturized artificial kidneys to achieve a continuous and personalized dialysis therapy, in order to improve the patient's quality of life. These innovative dialysis devices will require a real-time monitoring of equipment alarms, dialysis parameters, and patient-related data to ensure patient safety and to allow instantaneous changes of the dialysis prescription for the assessment of their adequacy. The analysis and evaluation of the resulting large-scale data sets enters the realm of "big data" and will require real-time predictive models. These may come from the fields of machine learning and computational intelligence, both included in artificial intelligence, a branch of engineering involved with the creation of devices that simulate intelligent behavior. The incorporation of artificial intelligence should provide a fully new approach to data analysis, enabling future advances in personalized dialysis therapies. With the purpose to learn about the present and potential future impact on medicine from experts in artificial intelligence and machine learning, a scientific meeting was organized in the Hospital Universitari Bellvitge (L'Hospitalet, Barcelona). As an outcome of that meeting, the aim of this review is to investigate artificial intel ligence experiences on dialysis, with a focus on potential barriers, challenges, and prospects for future applications of these technologies. Artificial intelligence research on dialysis is still in an early stage, and the main challenge relies on interpretability and/or comprehensibility of data models when applied to decision making. Artificial neural networks and medical decision support systems have been used to make predictions about anemia, total body water, or intradialysis hypotension and are promising approaches for the prescription and monitoring of hemodialysis therapy. Current dialysis machines are continuously improving due to innovative technological developments, but patient safety is still a key challenge. Real-time monitoring systems, coupled with automatic instantaneous biofeedback, will allow changing dialysis prescriptions continuously. The integration of vital sign monitoring with dialysis parameters will produce large data sets that will require the use of data analysis techniques, possibly from the area of machine learning, in order to make better decisions and increase the safety of patients.

The sixth generation robot in space

NASA Technical Reports Server (NTRS)

Butcher, A.; Das, A.; Reddy, Y. V.; Singh, H.

1990-01-01

The knowledge based simulator developed in the artificial intelligence laboratory has become a working test bed for experimenting with intelligent reasoning architectures. With this simulator, recently, small experiments have been done with an aim to simulate robot behavior to avoid colliding paths. An automatic extension of such experiments to intelligently planning robots in space demands advanced reasoning architectures. One such architecture for general purpose problem solving is explored. The robot, seen as a knowledge base machine, goes via predesigned abstraction mechanism for problem understanding and response generation. The three phases in one such abstraction scheme are: abstraction for representation, abstraction for evaluation, and abstraction for resolution. Such abstractions require multimodality. This multimodality requires the use of intensional variables to deal with beliefs in the system. Abstraction mechanisms help in synthesizing possible propagating lattices for such beliefs. The machine controller enters into a sixth generation paradigm.

Virtual reality for intelligent and interactive operating, training, and visualization systems

NASA Astrophysics Data System (ADS)

Freund, Eckhard; Rossmann, Juergen; Schluse, Michael

2000-10-01

Virtual Reality Methods allow a new and intuitive way of communication between man and machine. The basic idea of Virtual Reality (VR) is the generation of artificial computer simulated worlds, which the user not only can look at but also can interact with actively using data glove and data helmet. The main emphasis for the use of such techniques at the IRF is the development of a new generation of operator interfaces for the control of robots and other automation components and for intelligent training systems for complex tasks. The basic idea of the methods developed at the IRF for the realization of Projective Virtual Reality is to let the user work in the virtual world as he would act in reality. The user actions are recognized by the Virtual reality System and by means of new and intelligent control software projected onto the automation components like robots which afterwards perform the necessary actions in reality to execute the users task. In this operation mode the user no longer has to be a robot expert to generate tasks for robots or to program them, because intelligent control software recognizes the users intention and generated automatically the commands for nearly every automation component. Now, Virtual Reality Methods are ideally suited for universal man-machine-interfaces for the control and supervision of a big class of automation components, interactive training and visualization systems. The Virtual Reality System of the IRF-COSIMIR/VR- forms the basis for different projects starting with the control of space automation systems in the projects CIROS, VITAL and GETEX, the realization of a comprehensive development tool for the International Space Station and last but not least with the realistic simulation fire extinguishing, forest machines and excavators which will be presented in the final paper in addition to the key ideas of this Virtual Reality System.

Conformal Predictions in Multimedia Pattern Recognition

ERIC Educational Resources Information Center

Nallure Balasubramanian, Vineeth

2010-01-01

The fields of pattern recognition and machine learning are on a fundamental quest to design systems that can learn the way humans do. One important aspect of human intelligence that has so far not been given sufficient attention is the capability of humans to express when they are certain about a decision, or when they are not. Machine learning…

Distributed intelligence for supervisory control

NASA Technical Reports Server (NTRS)

Wolfe, W. J.; Raney, S. D.

1987-01-01

Supervisory control systems must deal with various types of intelligence distributed throughout the layers of control. Typical layers are real-time servo control, off-line planning and reasoning subsystems and finally, the human operator. Design methodologies must account for the fact that the majority of the intelligence will reside with the human operator. Hierarchical decompositions and feedback loops as conceptual building blocks that provide a common ground for man-machine interaction are discussed. Examples of types of parallelism and parallel implementation on several classes of computer architecture are also discussed.

Artificial intelligence in a mission operations and satellite test environment

NASA Technical Reports Server (NTRS)

Busse, Carl

1988-01-01

A Generic Mission Operations System using Expert System technology to demonstrate the potential of Artificial Intelligence (AI) automated monitor and control functions in a Mission Operations and Satellite Test environment will be developed at the National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL). Expert system techniques in a real time operation environment are being studied and applied to science and engineering data processing. Advanced decommutation schemes and intelligent display technology will be examined to develop imaginative improvements in rapid interpretation and distribution of information. The Generic Payload Operations Control Center (GPOCC) will demonstrate improved data handling accuracy, flexibility, and responsiveness in a complex mission environment. The ultimate goal is to automate repetitious mission operations, instrument, and satellite test functions by the applications of expert system technology and artificial intelligence resources and to enhance the level of man-machine sophistication.

Simulation research on the process of large scale ship plane segmentation intelligent workshop

NASA Astrophysics Data System (ADS)

Xu, Peng; Liao, Liangchuang; Zhou, Chao; Xue, Rui; Fu, Wei

2017-04-01

Large scale ship plane segmentation intelligent workshop is a new thing, and there is no research work in related fields at home and abroad. The mode of production should be transformed by the existing industry 2.0 or part of industry 3.0, also transformed from "human brain analysis and judgment + machine manufacturing" to "machine analysis and judgment + machine manufacturing". In this transforming process, there are a great deal of tasks need to be determined on the aspects of management and technology, such as workshop structure evolution, development of intelligent equipment and changes in business model. Along with them is the reformation of the whole workshop. Process simulation in this project would verify general layout and process flow of large scale ship plane section intelligent workshop, also would analyze intelligent workshop working efficiency, which is significant to the next step of the transformation of plane segmentation intelligent workshop.

The desktop interface in intelligent tutoring systems

NASA Technical Reports Server (NTRS)

Baudendistel, Stephen; Hua, Grace

1987-01-01

The interface between an Intelligent Tutoring System (ITS) and the person being tutored is critical to the success of the learning process. If the interface to the ITS is confusing or non-supportive of the tutored domain, the effectiveness of the instruction will be diminished or lost entirely. Consequently, the interface to an ITS should be highly integrated with the domain to provide a robust and semantically rich learning environment. In building an ITS for ZetaLISP on a LISP Machine, a Desktop Interface was designed to support a programming learning environment. Using the bitmapped display, windows, and mouse, three desktops were designed to support self-study and tutoring of ZetaLISP. Through organization, well-defined boundaries, and domain support facilities, the desktops provide substantial flexibility and power for the student and facilitate learning ZetaLISP programming while screening the student from the complex LISP Machine environment. The student can concentrate on learning ZetaLISP programming and not on how to operate the interface or a LISP Machine.

Further Structural Intelligence for Sensors Cluster Technology in Manufacturing

PubMed Central

Mekid, Samir

2006-01-01

With the ever increasing complex sensing and actuating tasks in manufacturing plants, intelligent sensors cluster in hybrid networks becomes a rapidly expanding area. They play a dominant role in many fields from macro and micro scale. Global object control and the ability to self organize into fault-tolerant and scalable systems are expected for high level applications. In this paper, new structural concepts of intelligent sensors and networks with new intelligent agents are presented. Embedding new functionalities to dynamically manage cooperative agents for autonomous machines are interesting key enabling technologies most required in manufacturing for zero defects production.

Games and Machine Learning: A Powerful Combination in an Artificial Intelligence Course

ERIC Educational Resources Information Center

Wallace, Scott A.; McCartney, Robert; Russell, Ingrid

2010-01-01

Project MLeXAI [Machine Learning eXperiences in Artificial Intelligence (AI)] seeks to build a set of reusable course curriculum and hands on laboratory projects for the artificial intelligence classroom. In this article, we describe two game-based projects from the second phase of project MLeXAI: Robot Defense--a simple real-time strategy game…

Modularity in robotic systems

NASA Technical Reports Server (NTRS)

Tesar, Delbert; Butler, Michael S.

1989-01-01

Most robotic systems today are designed one at a time, at a high cost of time and money. This wasteful approach has been necessary because the industry has not established a foundation for the continued evolution of intelligent machines. The next generation of robots will have to be generic, versatile machines capable of absorbing new technology rapidly and economically. This approach is demonstrated in the success of the personal computer, which can be upgraded or expanded with new software and hardware at virtually every level. Modularity is perceived as a major opportunity to reduce the 6 to 7 year design cycle time now required for new robotic manipulators, greatly increasing the breadth and speed of diffusion of robotic systems in manufacturing. Modularity and its crucial role in the next generation of intelligent machines are the focus of interest. The main advantages that modularity provides are examined; types of modules needed to create a generic robot are discussed. Structural modules designed by the robotics group at the University of Texas at Austin are examined to demonstrate the advantages of modular design.

Software tool for data mining and its applications

NASA Astrophysics Data System (ADS)

Yang, Jie; Ye, Chenzhou; Chen, Nianyi

2002-03-01

A software tool for data mining is introduced, which integrates pattern recognition (PCA, Fisher, clustering, hyperenvelop, regression), artificial intelligence (knowledge representation, decision trees), statistical learning (rough set, support vector machine), computational intelligence (neural network, genetic algorithm, fuzzy systems). It consists of nine function models: pattern recognition, decision trees, association rule, fuzzy rule, neural network, genetic algorithm, Hyper Envelop, support vector machine, visualization. The principle and knowledge representation of some function models of data mining are described. The software tool of data mining is realized by Visual C++ under Windows 2000. Nonmonotony in data mining is dealt with by concept hierarchy and layered mining. The software tool of data mining has satisfactorily applied in the prediction of regularities of the formation of ternary intermetallic compounds in alloy systems, and diagnosis of brain glioma.

Support vector machine in machine condition monitoring and fault diagnosis

NASA Astrophysics Data System (ADS)

Widodo, Achmad; Yang, Bo-Suk

2007-08-01

Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a survey of machine condition monitoring and fault diagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and developments of SVM in machine condition monitoring and diagnosis. Numerous methods have been developed based on intelligent systems such as artificial neural network, fuzzy expert system, condition-based reasoning, random forest, etc. However, the use of SVM for machine condition monitoring and fault diagnosis is still rare. SVM has excellent performance in generalization so it can produce high accuracy in classification for machine condition monitoring and diagnosis. Until 2006, the use of SVM in machine condition monitoring and fault diagnosis is tending to develop towards expertise orientation and problem-oriented domain. Finally, the ability to continually change and obtain a novel idea for machine condition monitoring and fault diagnosis using SVM will be future works.

Using the network to achieve energy efficiency

DOE Office of Scientific and Technical Information (OSTI.GOV)

Giglio, M.

1995-12-01

Novell, the third largest software company in the world, has developed Netware Embedded Systems Technology (NEST). NEST will take the network deeper into non-traditional computing environments and will imbed networking into more intelligent devices. Ultimately, this will lead to energy efficiencies in the office. NEST can make point-of-sale terminals, alarm systems, televisions, traffic controls, printers, lights, fax machines, copiers, HVAC controls, PBX machines, etc., either intelligent or more intelligent than they are currently. The mission statement for this particular group is to integrate over 30 million new intelligent devices into the workplace and the home with Novell networks by 1997.more » Computing trends have progressed from mainframes in the 1960s to keys, security systems, and airplanes in the year 2000. In fact, the new Boeing 777 has NEST in it, and it also has network servers on board. NEST enables the embedded network with the ability to put intelligence into devices. This gives one more control of the devices from wherever one is. For example, the pharmaceutical industry could use NEST to coordinate what the consumer is buying, what is in the warehouse, what the manufacturing plant is tooled for, and so on. Through NEST technology, the pharmaceutical industry now uses a camera that takes pictures of the pills. It can see whether an {open_quotes}overdose{close_quotes} or {open_quotes}underdose{close_quotes} of a particular type of pill is being manufactured. The plant can be shut down and corrections made immediately.« less

Intelligent Systems and Advanced User Interfaces for Design, Operation, and Maintenance of Command Management Systems

NASA Technical Reports Server (NTRS)

Mitchell, Christine M.

1998-01-01

Historically Command Management Systems (CMS) have been large, expensive, spacecraft-specific software systems that were costly to build, operate, and maintain. Current and emerging hardware, software, and user interface technologies may offer an opportunity to facilitate the initial formulation and design of a spacecraft-specific CMS as well as a to develop a more generic or a set of core components for CMS systems. Current MOC (mission operations center) hardware and software include Unix workstations, the C/C++ and Java programming languages, and X and Java window interfaces representations. This configuration provides the power and flexibility to support sophisticated systems and intelligent user interfaces that exploit state-of-the-art technologies in human-machine systems engineering, decision making, artificial intelligence, and software engineering. One of the goals of this research is to explore the extent to which technologies developed in the research laboratory can be productively applied in a complex system such as spacecraft command management. Initial examination of some of the issues in CMS design and operation suggests that application of technologies such as intelligent planning, case-based reasoning, design and analysis tools from a human-machine systems engineering point of view (e.g., operator and designer models) and human-computer interaction tools, (e.g., graphics, visualization, and animation), may provide significant savings in the design, operation, and maintenance of a spacecraft-specific CMS as well as continuity for CMS design and development across spacecraft with varying needs. The savings in this case is in software reuse at all stages of the software engineering process.

Intelligent agent-based intrusion detection system using enhanced multiclass SVM.

PubMed

Ganapathy, S; Yogesh, P; Kannan, A

2012-01-01

Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set.

Intelligent Agent-Based Intrusion Detection System Using Enhanced Multiclass SVM

PubMed Central

Ganapathy, S.; Yogesh, P.; Kannan, A.

2012-01-01

Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set. PMID:23056036

Cognitive Architectures and Autonomy: A Comparative Review

NASA Astrophysics Data System (ADS)

Thórisson, Kristinn; Helgasson, Helgi

2012-05-01

One of the original goals of artificial intelligence (AI) research was to create machines with very general cognitive capabilities and a relatively high level of autonomy. It has taken the field longer than many had expected to achieve even a fraction of this goal; the community has focused on building specific, targeted cognitive processes in isolation, and as of yet no system exists that integrates a broad range of capabilities or presents a general solution to autonomous acquisition of a large set of skills. Among the reasons for this are the highly limited machine learning and adaptation techniques available, and the inherent complexity of integrating numerous cognitive and learning capabilities in a coherent architecture. In this paper we review selected systems and architectures built expressly to address integrated skills. We highlight principles and features of these systems that seem promising for creating generally intelligent systems with some level of autonomy, and discuss them in the context of the development of future cognitive architectures. Autonomy is a key property for any system to be considered generally intelligent, in our view; we use this concept as an organizing principle for comparing the reviewed systems. Features that remain largely unaddressed in present research, but seem nevertheless necessary for such efforts to succeed, are also discussed.

The Convergence of Intelligences

NASA Astrophysics Data System (ADS)

Diederich, Joachim

Minsky (1985) argued an extraterrestrial intelligence may be similar to ours despite very different origins. ``Problem- solving'' offers evolutionary advantages and individuals who are part of a technical civilisation should have this capacity. On earth, the principles of problem-solving are the same for humans, some primates and machines based on Artificial Intelligence (AI) techniques. Intelligent systems use ``goals'' and ``sub-goals'' for problem-solving, with memories and representations of ``objects'' and ``sub-objects'' as well as knowledge of relations such as ``cause'' or ``difference.'' Some of these objects are generic and cannot easily be divided into parts. We must, therefore, assume that these objects and relations are universal, and a general property of intelligence. Minsky's arguments from 1985 are extended here. The last decade has seen the development of a general learning theory (``computational learning theory'' (CLT) or ``statistical learning theory'') which equally applies to humans, animals and machines. It is argued that basic learning laws will also apply to an evolved alien intelligence, and this includes limitations of what can be learned efficiently. An example from CLT is that the general learning problem for neural networks is intractable, i.e. it cannot be solved efficiently for all instances (it is ``NP-complete''). It is the objective of this paper to show that evolved intelligences will be constrained by general learning laws and will use task-decomposition for problem-solving. Since learning and problem-solving are core features of intelligence, it can be said that intelligences converge despite very different origins.

Proceedings of the 1984 IEEE international conference on systems, man and cybernetics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1984-01-01

This conference contains papers on artificial intelligence, pattern recognition, and man-machine systems. Topics considered include concurrent minimization, a robot programming system, system modeling and simulation, camera calibration, thermal power plants, image processing, fault diagnosis, knowledge-based systems, power systems, hydroelectric power plants, expert systems, and electrical transients.

Prediction of biochar yield from cattle manure pyrolysis via least squares support vector machine intelligent approach.

PubMed

Cao, Hongliang; Xin, Ya; Yuan, Qiaoxia

2016-02-01

To predict conveniently the biochar yield from cattle manure pyrolysis, intelligent modeling approach was introduced in this research. A traditional artificial neural networks (ANN) model and a novel least squares support vector machine (LS-SVM) model were developed. For the identification and prediction evaluation of the models, a data set with 33 experimental data was used, which were obtained using a laboratory-scale fixed bed reaction system. The results demonstrated that the intelligent modeling approach is greatly convenient and effective for the prediction of the biochar yield. In particular, the novel LS-SVM model has a more satisfying predicting performance and its robustness is better than the traditional ANN model. The introduction and application of the LS-SVM modeling method gives a successful example, which is a good reference for the modeling study of cattle manure pyrolysis process, even other similar processes. Copyright © 2015 Elsevier Ltd. All rights reserved.

Learning Companion Systems, Social Learning Systems, and the Global Social Learning Club.

ERIC Educational Resources Information Center

Chan, Tak-Wai

1996-01-01

Describes the development of learning companion systems and their contributions to the class of social learning systems that integrate artificial intelligence agents and use machine learning to tutor and interact with students. Outlines initial social learning projects, their programming languages, and weakness. Future improvements will include…

Electronic collaboration: Some effects of telecommunication media and machine intelligence on team performance

NASA Technical Reports Server (NTRS)

Wellens, A. Rodney

1991-01-01

Both NASA and DoD have had a long standing interest in teamwork, distributed decision making, and automation. While research on these topics has been pursued independently, it is becoming increasingly clear that the integration of social, cognitive, and human factors engineering principles will be necessary to meet the challenges of highly sophisticated scientific and military programs of the future. Images of human/intelligent-machine electronic collaboration were drawn from NASA and Air Force reports as well as from other sources. Here, areas of common concern are highlighted. A description of the author's research program testing a 'psychological distancing' model of electronic media effects and human/expert system collaboration is given.

Development of intelligent robots - Achievements and issues

NASA Astrophysics Data System (ADS)

Nitzan, D.

1985-03-01

A flexible, intelligent robot is regarded as a general purpose machine system that may include effectors, sensors, computers, and auxiliary equipment and, like a human, can perform a variety of tasks under unpredictable conditions. Development of intelligent robots is essential for increasing the growth rate of today's robot population in industry and elsewhere. Robotics research and development topics include manipulation, end effectors, mobility, sensing (noncontact and contact), adaptive control, robot programming languages, and manufacturing process planning. Past achievements and current issues related to each of these topics are described briefly.

Artificial Intelligence and NASA Data Used to Discover Eighth Planet Circling Distant Star

NASA Image and Video Library

2017-12-12

Our solar system now is tied for most number of planets around a single star, with the recent discovery of an eighth planet circling Kepler-90, a Sun-like star 2,545 light years from Earth. The planet was discovered in data from NASA’s Kepler space telescope. The newly-discovered Kepler-90i -- a sizzling hot, rocky planet that orbits its star once every 14.4 days -- was found by researchers from Google and The University of Texas at Austin using machine learning. Machine learning is an approach to artificial intelligence in which computers “learn.” In this case, computers learned to identify planets by finding in Kepler data instances where the telescope recorded signals from planets beyond our solar system, known as exoplanets. Video Credit: NASA Ames Research Center / Google

Intelligent, Energy Saving Power Supply and Control System of Hoisting Mine Machine with Compact and Hybrid Drive System / Inteligentne, Energooszczędne Układy Zasilania I Sterowania Górniczych Maszyn Wyciągowych Z Napędem Zintegrowanym Lub Hybrydowym

NASA Astrophysics Data System (ADS)

Szymański, Zygmunt

2015-03-01

In the paper present's an analysis of suitableness an application of compact and hybrid drive system in hoisting machine. In the paper presented the review of constructional solutions of hoisting machines drive system, driving with AC and DC motor. In the paper presented conception of modern, energy sparing hoisting machine supply system, composed with compact motor, an supplied with transistor or thyristor converter supply system, and intelligent control system composed with multilevel microprocessor controller. In the paper present's also analysis of suitableness application an selected method of artificial intelligent in hoisting machine control system, automation system, and modern diagnostic system. In the paper one limited to analysis of: fuzzy logic method, genetic algorithms method, and modern neural net II and III generation. That method enables realization of complex control algorithms of hosting machine with insurance of energy sparing exploitation conditions, monitoring of exploitation parameters, and prediction diagnostic of hoisting machine technical state, minimization a number of failure states. In the paper present's a conception of control and diagnostic system of the hoisting machine based on fuzzy logic neural set control. In the chapter presented also a selected control algorithms and results of computer simulations realized for particular mathematical models of hoisting machine. Results of theoretical investigation were partly verified in laboratory and industrial experiments. Przedstawiono analizę celowości wprowadzania, napędów zintegrowanych oraz napędów hybrydowych, do układów napędowych maszyn wyciągowych. Zamieszczono przegląd rozwiązań konstrukcyjnych wybranych hybrydowych oraz zintegrowanych napędów maszyn wyciągowych z silnikami DC i AC. Opisano koncepcję nowoczesnego, energooszczędnego układu zasilania górniczych maszyny wyciągowej, złożonego z silnika zintegrowanego, (tranzystorowego lub tyrystorowego) zasilacza przekształtnikowego, oraz inteligentnego obwodu sterowania zbudowanego na wielopoziomowych sterownikach mikroprocesorowych. Przedstawiono analizę możliwości zastosowania wybranych metod sztucznej inteligencji w układach sterowania, automatyki oraz diagnostyki maszyn wyciągowych. W referacie ograniczono się do analizy metod sterowania rozmytego, metod algorytmów genetycznych oraz nowoczesnych sieci neuronowych II oraz III generacji. Metody te zapewniają realizację złożonych algorytmów sterowania maszyną wyciągową z zapewnieniem energooszczędnych warunków eksploatacyjnych, monitoringu parametrów eksploatacyjnych oraz predykcyjną diagnostykę stanu technicznego maszyny wyciągowej, minimalizującą liczbę stanów awaryjnych. Przedstawiono koncepcję układu sterowania i diagnostyki maszyny bazującej na metodzie: fuzzy-logic neuro set control system (sterowanie rozmyte w sieciach neuronowych). Przedstawiono wybrane algorytmy sterowania oraz wyniki analiz komputerowych wybranych modeli matematycznych maszyny wyciągowej. Wyniki rozważań teoretycznych zostały częściowo sprawdzone w warunkach laboratoryjnych oraz przemysłowych.

FUZZY LOGIC BASED INTELLIGENT CONTROL OF A VARIABLE SPEED CAGE MACHINE WIND GENERATION SYSTEM

EPA Science Inventory

The paper describes a variable-speed wind generation system where fuzzy logic principles are used to optimize efficiency and enhance performance control. A squirrel cage induction generator feeds the power to a double-sided pulse width modulated converter system which either pump...

Research on intelligent monitoring technology of machining process

NASA Astrophysics Data System (ADS)

Wang, Taiyong; Meng, Changhong; Zhao, Guoli

1995-08-01

Based upon research on sound and vibration characteristics of tool condition, we explore the multigrade monitoring system which takes single-chip microcomputers as the core hardware. By using the specially designed pickup true signal devices, we can more effectively do the intelligent multigrade monitoring and forecasting, and furthermore, we can build the tool condition models adaptively. This is the key problem in FMS, CIMS, and even the IMS.

Event detection for car park entries by video-surveillance

NASA Astrophysics Data System (ADS)

Coquin, Didier; Tailland, Johan; Cintract, Michel

2007-10-01

Intelligent surveillance has become an important research issue due to the high cost and low efficiency of human supervisors, and machine intelligence is required to provide a solution for automated event detection. In this paper we describe a real-time system that has been used for detecting car park entries, using an adaptive background learning algorithm and two indicators representing activity and identity to overcome the difficulty of tracking objects.

MAS Bulletin. Papers Presented at Advisory Group for Aerospace Research and Development (AGARD) Symposium on Machine Intelligence for Aerospace Electronic Systems.

DTIC Science & Technology

1991-08-01

neural networks, and machine learning . This list ie not all 9. Future ESM Systems and the Potential for Neural Processing inclusive. This research could...U.S. CAPT James M. Skinner , USAF, Air Force Space Technology 17. Development of Tactical Doecisiont Akid. Center, and Prof. Georg* F. Luger...ntegrat11111ng Macine I~1e900enc Into the Co~pi to Aid t" Pilot 26. Integrated Communications, Navigatlion. Ideintiflocation Avionics Dr. Edward J

First Annual Workshop on Space Operations Automation and Robotics (SOAR 87)

NASA Technical Reports Server (NTRS)

Griffin, Sandy (Editor)

1987-01-01

Several topics relative to automation and robotics technology are discussed. Automation of checkout, ground support, and logistics; automated software development; man-machine interfaces; neural networks; systems engineering and distributed/parallel processing architectures; and artificial intelligence/expert systems are among the topics covered.

Interactive Relationships with Computers in Teaching Reading.

ERIC Educational Resources Information Center

Doublier, Rene M.

This study summarizes recent achievements in the expanding development of man/machine communications and reviews current technological hurdles associated with the development of artificial intelligence systems which can generate and recognize human speech patterns. With the development of such systems, one potential application would be the…

Computational Foundations of Natural Intelligence

PubMed Central

van Gerven, Marcel

2017-01-01

New developments in AI and neuroscience are revitalizing the quest to understanding natural intelligence, offering insight about how to equip machines with human-like capabilities. This paper reviews some of the computational principles relevant for understanding natural intelligence and, ultimately, achieving strong AI. After reviewing basic principles, a variety of computational modeling approaches is discussed. Subsequently, I concentrate on the use of artificial neural networks as a framework for modeling cognitive processes. This paper ends by outlining some of the challenges that remain to fulfill the promise of machines that show human-like intelligence. PMID:29375355

Artificial intelligence approaches for rational drug design and discovery.

PubMed

Duch, Włodzisław; Swaminathan, Karthikeyan; Meller, Jarosław

2007-01-01

Pattern recognition, machine learning and artificial intelligence approaches play an increasingly important role in rational drug design, screening and identification of candidate molecules and studies on quantitative structure-activity relationships (QSAR). In this review, we present an overview of basic concepts and methodology in the fields of machine learning and artificial intelligence (AI). An emphasis is put on methods that enable an intuitive interpretation of the results and facilitate gaining an insight into the structure of the problem at hand. We also discuss representative applications of AI methods to docking, screening and QSAR studies. The growing trend to integrate computational and experimental efforts in that regard and some future developments are discussed. In addition, we comment on a broader role of machine learning and artificial intelligence approaches in biomedical research.

Applications of artificial intelligence 1993: Knowledge-based systems in aerospace and industry; Proceedings of the Meeting, Orlando, FL, Apr. 13-15, 1993

NASA Technical Reports Server (NTRS)

Fayyad, Usama M. (Editor); Uthurusamy, Ramasamy (Editor)

1993-01-01

The present volume on applications of artificial intelligence with regard to knowledge-based systems in aerospace and industry discusses machine learning and clustering, expert systems and optimization techniques, monitoring and diagnosis, and automated design and expert systems. Attention is given to the integration of AI reasoning systems and hardware description languages, care-based reasoning, knowledge, retrieval, and training systems, and scheduling and planning. Topics addressed include the preprocessing of remotely sensed data for efficient analysis and classification, autonomous agents as air combat simulation adversaries, intelligent data presentation for real-time spacecraft monitoring, and an integrated reasoner for diagnosis in satellite control. Also discussed are a knowledge-based system for the design of heat exchangers, reuse of design information for model-based diagnosis, automatic compilation of expert systems, and a case-based approach to handling aircraft malfunctions.

The Society of Brains: How Alan Turing and Marvin Minsky Were Both Right

NASA Astrophysics Data System (ADS)

Struzik, Zbigniew R.

2015-04-01

In his well-known prediction, Alan Turing stated that computer intelligence would surpass human intelligence by the year 2000. Although the Turing Test, as it became known, was devised to be played by one human against one computer, this is not a fair setup. Every human is a part of a social network, and a fairer comparison would be a contest between one human at the console and a network of computers behind the console. Around the year 2000, the number of web pages on the WWW overtook the number of neurons in the human brain. But these websites would be of little use without the ability to search for knowledge. By the year 2000 Google Inc. had become the search engine of choice, and the WWW became an intelligent entity. This was not without good reason. The basis for the search engine was the analysis of the ’network of knowledge’. The PageRank algorithm, linking information on the web according to the hierarchy of ‘link popularity’, continues to provide the basis for all of Google's web search tools. While PageRank was developed by Larry Page and Sergey Brin in 1996 as part of a research project about a new kind of search engine, PageRank is in its essence the key to representing and using static knowledge in an emergent intelligent system. Here I argue that Alan Turing was right, as hybrid human-computer internet machines have already surpassed our individual intelligence - this was done around the year 2000 by the Internet - the socially-minded, human-computer hybrid Homo computabilis-socialis. Ironically, the Internet's intelligence also emerged to a large extent from ‘exploiting’ humans - the key to the emergence of machine intelligence has been discussed by Marvin Minsky in his work on the foundations of intelligence through interacting agents’ knowledge. As a consequence, a decade and a half decade into the 21st century, we appear to be much better equipped to tackle the problem of the social origins of humanity - in particular thanks to the power of the intelligent partner-in-the-quest machine, however, we should not wait too long...

A comprehensive methodology for intelligent systems life-cycle cost modelling

NASA Technical Reports Server (NTRS)

Korsmeyer, David J.; Lum, Henry, Jr.

1993-01-01

As NASA moves into the last part on the twentieth century, the desire to do 'business as usual' has been replaced with the mantra 'faster, cheaper, better'. Recently, new work has been done to show how the implementation of advanced technologies, such as intelligent systems, will impact the cost of a system design or in the operational cost for a spacecraft mission. The impact of the degree of autonomous or intelligent systems and human participation on a given program is manifested most significantly during the program operational phases, while the decision of who performs what tasks, and how much automation is incorporated into the system are all made during the design and development phases. Employing intelligent systems and automation is not an either/or question, but one of degree. The question is what level of automation and autonomy will provide the optimal trade-off between performance and cost. Conventional costing methodologies, however, are unable to show the significance of technologies like these in terms of traceable cost benefits and reductions in the various phases of the spacecraft's lifecycle. The proposed comprehensive life-cycle methodology can address intelligent system technologies as well as others that impact human-machine operational modes.

Man-machine interface requirements - advanced technology

NASA Technical Reports Server (NTRS)

Remington, R. W.; Wiener, E. L.

1984-01-01

Research issues and areas are identified where increased understanding of the human operator and the interaction between the operator and the avionics could lead to improvements in the performance of current and proposed helicopters. Both current and advanced helicopter systems and avionics are considered. Areas critical to man-machine interface requirements include: (1) artificial intelligence; (2) visual displays; (3) voice technology; (4) cockpit integration; and (5) pilot work loads and performance.

The twelfth annual Intelligent Ground Vehicle Competition: team approaches to intelligent vehicles

NASA Astrophysics Data System (ADS)

Theisen, Bernard L.; Maslach, Daniel

2004-10-01

The Intelligent Ground Vehicle Competition (IGVC) is one of three, unmanned systems, student competitions that were founded by the Association for Unmanned Vehicle Systems International (AUVSI) in the 1990s. The IGVC is a multidisciplinary exercise in product realization that challenges college engineering student teams to integrate advanced control theory, machine vision, vehicular electronics, and mobile platform fundamentals to design and build an unmanned system. Both U.S. and international teams focus on developing a suite of dual-use technologies to equip ground vehicles of the future with intelligent driving capabilities. Over the past 12 years, the competition has challenged undergraduate, graduate and Ph.D. students with real world applications in intelligent transportation systems, the military and manufacturing automation. To date, teams from over 43 universities and colleges have participated. This paper describes some of the applications of the technologies required by this competition and discusses the educational benefits. The primary goal of the IGVC is to advance engineering education in intelligent vehicles and related technologies. The employment and professional networking opportunities created for students and industrial sponsors through a series of technical events over the three-day competition are highlighted. Finally, an assessment of the competition based on participant feedback is presented.

Conference Proceedings of Machine Intelligence for Aerospace Electronic Systems Held in Lisbon, Portugal on 13-16 May 1991 (L’Intelligence Artificielle dans les Systemes Electroniques Aerospatiaux)

DTIC Science & Technology

1991-09-01

more closely matched description is that of finding the distribution with the nature of the application problem. of current flow through a nonuniform ...multiprocessors, may not be effective finding the propagation of wavefronts through a in solving traditional algorithms for these medium with a nonuniform ...properties of the paths are variable cost function is similar to the noteworthy. First, every heading from the start nonuniform resistivity of the plate

A Formal Characterization of Relevant Information in Multi-Agent Systems

DTIC Science & Technology

2009-10-01

Conference iTrust. (2004) [17] Sadek, D.: Le dialogue homme-machine : de l’ ergonomie des interfaces à l’ agent intelligent dia- loguant. In: Nouvelles interfaces hommemachine, Lavoisier Editeur, Arago 18 (1996) 277–321

Intelligent Adaptive Systems: Literature Research of Design Guidance for Intelligent Adaptive Automation and Interfaces

DTIC Science & Technology

2007-09-01

behaviour based on past experience of interacting with the operator), and mobile (i.e., can move themselves from one machine to another). Edwards argues that...Sofge, D., Bugajska, M., Adams, W., Perzanowski, D., and Schultz, A. (2003). Agent-based Multimodal Interface for Dynamically Autonomous Mobile Robots...based architecture can provide a natural and scalable approach to implementing a multimodal interface to control mobile robots through dynamic

Breast Cancer Recognition Using a Novel Hybrid Intelligent Method

PubMed Central

Addeh, Jalil; Ebrahimzadeh, Ata

2012-01-01

Breast cancer is the second largest cause of cancer deaths among women. At the same time, it is also among the most curable cancer types if it can be diagnosed early. This paper presents a novel hybrid intelligent method for recognition of breast cancer tumors. The proposed method includes three main modules: the feature extraction module, the classifier module, and the optimization module. In the feature extraction module, fuzzy features are proposed as the efficient characteristic of the patterns. In the classifier module, because of the promising generalization capability of support vector machines (SVM), a SVM-based classifier is proposed. In support vector machine training, the hyperparameters have very important roles for its recognition accuracy. Therefore, in the optimization module, the bees algorithm (BA) is proposed for selecting appropriate parameters of the classifier. The proposed system is tested on Wisconsin Breast Cancer database and simulation results show that the recommended system has a high accuracy. PMID:23626945

Towards Intelligent Environments: An Augmented Reality–Brain–Machine Interface Operated with a See-Through Head-Mount Display

PubMed Central

Takano, Kouji; Hata, Naoki; Kansaku, Kenji

2011-01-01

The brain–machine interface (BMI) or brain–computer interface is a new interface technology that uses neurophysiological signals from the brain to control external machines or computers. This technology is expected to support daily activities, especially for persons with disabilities. To expand the range of activities enabled by this type of interface, here, we added augmented reality (AR) to a P300-based BMI. In this new system, we used a see-through head-mount display (HMD) to create control panels with flicker visual stimuli to support the user in areas close to controllable devices. When the attached camera detects an AR marker, the position and orientation of the marker are calculated, and the control panel for the pre-assigned appliance is created by the AR system and superimposed on the HMD. The participants were required to control system-compatible devices, and they successfully operated them without significant training. Online performance with the HMD was not different from that using an LCD monitor. Posterior and lateral (right or left) channel selections contributed to operation of the AR–BMI with both the HMD and LCD monitor. Our results indicate that AR–BMI systems operated with a see-through HMD may be useful in building advanced intelligent environments. PMID:21541307

Adaptive pattern recognition by mini-max neural networks as a part of an intelligent processor

NASA Technical Reports Server (NTRS)

Szu, Harold H.

1990-01-01

In this decade and progressing into 21st Century, NASA will have missions including Space Station and the Earth related Planet Sciences. To support these missions, a high degree of sophistication in machine automation and an increasing amount of data processing throughput rate are necessary. Meeting these challenges requires intelligent machines, designed to support the necessary automations in a remote space and hazardous environment. There are two approaches to designing these intelligent machines. One of these is the knowledge-based expert system approach, namely AI. The other is a non-rule approach based on parallel and distributed computing for adaptive fault-tolerances, namely Neural or Natural Intelligence (NI). The union of AI and NI is the solution to the problem stated above. The NI segment of this unit extracts features automatically by applying Cauchy simulated annealing to a mini-max cost energy function. The feature discovered by NI can then be passed to the AI system for future processing, and vice versa. This passing increases reliability, for AI can follow the NI formulated algorithm exactly, and can provide the context knowledge base as the constraints of neurocomputing. The mini-max cost function that solves the unknown feature can furthermore give us a top-down architectural design of neural networks by means of Taylor series expansion of the cost function. A typical mini-max cost function consists of the sample variance of each class in the numerator, and separation of the center of each class in the denominator. Thus, when the total cost energy is minimized, the conflicting goals of intraclass clustering and interclass segregation are achieved simultaneously.

IQ Tests Are Not for Machines, Yet

ERIC Educational Resources Information Center

Dowe, David L.; Hernandez-Orallo, Jose

2012-01-01

Complex, but specific, tasks--such as chess or "Jeopardy!"--are popularly seen as milestones for artificial intelligence (AI). However, they are not appropriate for evaluating the intelligence of machines or measuring the progress in AI. Aware of this delusion, Detterman has recently raised a challenge prompting AI researchers to evaluate their…

The 13 th Annual Intelligent Ground Vehicle Competition: intelligent ground vehicles created by intelligent teams

NASA Astrophysics Data System (ADS)

Theisen, Bernard L.

2005-10-01

The Intelligent Ground Vehicle Competition (IGVC) is one of three, unmanned systems, student competitions that were founded by the Association for Unmanned Vehicle Systems International (AUVSI) in the 1990s. The IGVC is a multidisciplinary exercise in product realization that challenges college engineering student teams to integrate advanced control theory, machine vision, vehicular electronics, and mobile platform fundamentals to design and build an unmanned system. Teams from around the world focus on developing a suite of dual-use technologies to equip ground vehicles of the future with intelligent driving capabilities. Over the past 13 years, the competition has challenged undergraduate, graduate and Ph.D. students with real world applications in intelligent transportation systems, the military and manufacturing automation. To date, teams from over 50 universities and colleges have participated. This paper describes some of the applications of the technologies required by this competition and discusses the educational benefits. The primary goal of the IGVC is to advance engineering education in intelligent vehicles and related technologies. The employment and professional networking opportunities created for students and industrial sponsors through a series of technical events over the three-day competition are highlighted. Finally, an assessment of the competition based on participant feedback is presented.

What Learning Systems do Intelligent Agents Need? Complementary Learning Systems Theory Updated.

PubMed

Kumaran, Dharshan; Hassabis, Demis; McClelland, James L

2016-07-01

We update complementary learning systems (CLS) theory, which holds that intelligent agents must possess two learning systems, instantiated in mammalians in neocortex and hippocampus. The first gradually acquires structured knowledge representations while the second quickly learns the specifics of individual experiences. We broaden the role of replay of hippocampal memories in the theory, noting that replay allows goal-dependent weighting of experience statistics. We also address recent challenges to the theory and extend it by showing that recurrent activation of hippocampal traces can support some forms of generalization and that neocortical learning can be rapid for information that is consistent with known structure. Finally, we note the relevance of the theory to the design of artificial intelligent agents, highlighting connections between neuroscience and machine learning. Copyright © 2016 Elsevier Ltd. All rights reserved.

FUZZY LOGIC BASED INTELLIGENT CONTROL OF A VARIABLE SPEED CAGE MACHINE WIND GENERATION SYSTEM

EPA Science Inventory

The report gives results of a demonstration of the successful application of fuzzy logic to enhance the performance and control of a variable-speed wind generation system. A squirrel cage induction generator feeds the power to either a double-sided pulse-width modulation converte...

Forecasting municipal solid waste generation using artificial intelligence modelling approaches.

PubMed

Abbasi, Maryam; El Hanandeh, Ali

2016-10-01

Municipal solid waste (MSW) management is a major concern to local governments to protect human health, the environment and to preserve natural resources. The design and operation of an effective MSW management system requires accurate estimation of future waste generation quantities. The main objective of this study was to develop a model for accurate forecasting of MSW generation that helps waste related organizations to better design and operate effective MSW management systems. Four intelligent system algorithms including support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and k-nearest neighbours (kNN) were tested for their ability to predict monthly waste generation in the Logan City Council region in Queensland, Australia. Results showed artificial intelligence models have good prediction performance and could be successfully applied to establish municipal solid waste forecasting models. Using machine learning algorithms can reliably predict monthly MSW generation by training with waste generation time series. In addition, results suggest that ANFIS system produced the most accurate forecasts of the peaks while kNN was successful in predicting the monthly averages of waste quantities. Based on the results, the total annual MSW generated in Logan City will reach 9.4×10(7)kg by 2020 while the peak monthly waste will reach 9.37×10(6)kg. Copyright © 2016 Elsevier Ltd. All rights reserved.

Computational intelligence techniques in bioinformatics.

PubMed

Hassanien, Aboul Ella; Al-Shammari, Eiman Tamah; Ghali, Neveen I

2013-12-01

Computational intelligence (CI) is a well-established paradigm with current systems having many of the characteristics of biological computers and capable of performing a variety of tasks that are difficult to do using conventional techniques. It is a methodology involving adaptive mechanisms and/or an ability to learn that facilitate intelligent behavior in complex and changing environments, such that the system is perceived to possess one or more attributes of reason, such as generalization, discovery, association and abstraction. The objective of this article is to present to the CI and bioinformatics research communities some of the state-of-the-art in CI applications to bioinformatics and motivate research in new trend-setting directions. In this article, we present an overview of the CI techniques in bioinformatics. We will show how CI techniques including neural networks, restricted Boltzmann machine, deep belief network, fuzzy logic, rough sets, evolutionary algorithms (EA), genetic algorithms (GA), swarm intelligence, artificial immune systems and support vector machines, could be successfully employed to tackle various problems such as gene expression clustering and classification, protein sequence classification, gene selection, DNA fragment assembly, multiple sequence alignment, and protein function prediction and its structure. We discuss some representative methods to provide inspiring examples to illustrate how CI can be utilized to address these problems and how bioinformatics data can be characterized by CI. Challenges to be addressed and future directions of research are also presented and an extensive bibliography is included. Copyright © 2013 Elsevier Ltd. All rights reserved.

Paradox in AI - AI 2.0: The Way to Machine Consciousness

NASA Astrophysics Data System (ADS)

Palensky, Peter; Bruckner, Dietmar; Tmej, Anna; Deutsch, Tobias

Artificial Intelligence, the big promise of the last millennium, has apparently made its way into our daily lives. Cell phones with speech control, evolutionary computing in data mining or power grids, optimized via neural network, show its applicability in industrial environments. The original expectation of true intelligence and thinking machines lies still ahead of us. Researchers are, however, optimistic as never before. This paper tries to compare the views, challenges and approaches of several disciplines: engineering, psychology, neuroscience, philosophy. It gives a short introduction to Psychoanalysis, discusses the term consciousness, social implications of intelligent machines, related theories, and expectations and shall serve as a starting point for first attempts of combining these diverse thoughts.

Proceedings of the international conference on cybernetics and societ

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1985-01-01

This book presents the papers given at a conference on artificial intelligence, expert systems and knowledge bases. Topics considered at the conference included automating expert system development, modeling expert systems, causal maps, data covariances, robot vision, image processing, multiprocessors, parallel processing, VLSI structures, man-machine systems, human factors engineering, cognitive decision analysis, natural language, computerized control systems, and cybernetics.

Experimental Realization of a Quantum Support Vector Machine

NASA Astrophysics Data System (ADS)

Li, Zhaokai; Liu, Xiaomei; Xu, Nanyang; Du, Jiangfeng

2015-04-01

The fundamental principle of artificial intelligence is the ability of machines to learn from previous experience and do future work accordingly. In the age of big data, classical learning machines often require huge computational resources in many practical cases. Quantum machine learning algorithms, on the other hand, could be exponentially faster than their classical counterparts by utilizing quantum parallelism. Here, we demonstrate a quantum machine learning algorithm to implement handwriting recognition on a four-qubit NMR test bench. The quantum machine learns standard character fonts and then recognizes handwritten characters from a set with two candidates. Because of the wide spread importance of artificial intelligence and its tremendous consumption of computational resources, quantum speedup would be extremely attractive against the challenges of big data.

Man-Robot Symbiosis: A Framework For Cooperative Intelligence And Control

NASA Astrophysics Data System (ADS)

Parker, Lynne E.; Pin, Francois G.

1988-10-01

The man-robot symbiosis concept has the fundamental objective of bridging the gap between fully human-controlled and fully autonomous systems to achieve true man-robot cooperative control and intelligence. Such a system would allow improved speed, accuracy, and efficiency of task execution, while retaining the man in the loop for innovative reasoning and decision-making. The symbiont would have capabilities for supervised and unsupervised learning, allowing an increase of expertise in a wide task domain. This paper describes a robotic system architecture facilitating the symbiotic integration of teleoperative and automated modes of task execution. The architecture reflects a unique blend of many disciplines of artificial intelligence into a working system, including job or mission planning, dynamic task allocation, man-robot communication, automated monitoring, and machine learning. These disciplines are embodied in five major components of the symbiotic framework: the Job Planner, the Dynamic Task Allocator, the Presenter/Interpreter, the Automated Monitor, and the Learning System.

What Is Artificial Intelligence Anyway?

ERIC Educational Resources Information Center

Kurzweil, Raymond

1985-01-01

Examines the past, present, and future status of Artificial Intelligence (AI). Acknowledges the limitations of AI but proposes possible areas of application and further development. Urges a concentration on the unique strengths of machine intelligence rather than a copying of human intelligence. (ML)

Mission Profiles and Evidential Reasoning for Estimating Information Relevancy in Multi-Agent Supervisory Control Applications

DTIC Science & Technology

2010-06-01

artificial agents, their limited scope and singular purpose lead us to believe that human-machine trust will be very portable. That is, if one operator... Artificial Intelligence Review 2(2), 1988. [E88] M.R. Endsley. Situation awareness global assessment technique (SAGAT). In Proceedings of the National...1995. [F98] J. Ferber, Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence, Addison- Wesley, 1998. [NP01] I. Niles and A

Artificial intelligence in medicine.

PubMed

Hamet, Pavel; Tremblay, Johanne

2017-04-01

Artificial Intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI is generally accepted as having started with the invention of robots. The term derives from the Czech word robota, meaning biosynthetic machines used as forced labor. In this field, Leonardo Da Vinci's lasting heritage is today's burgeoning use of robotic-assisted surgery, named after him, for complex urologic and gynecologic procedures. Da Vinci's sketchbooks of robots helped set the stage for this innovation. AI, described as the science and engineering of making intelligent machines, was officially born in 1956. The term is applicable to a broad range of items in medicine such as robotics, medical diagnosis, medical statistics, and human biology-up to and including today's "omics". AI in medicine, which is the focus of this review, has two main branches: virtual and physical. The virtual branch includes informatics approaches from deep learning information management to control of health management systems, including electronic health records, and active guidance of physicians in their treatment decisions. The physical branch is best represented by robots used to assist the elderly patient or the attending surgeon. Also embodied in this branch are targeted nanorobots, a unique new drug delivery system. The societal and ethical complexities of these applications require further reflection, proof of their medical utility, economic value, and development of interdisciplinary strategies for their wider application. Copyright © 2017. Published by Elsevier Inc.

Artificial Intelligence: Threat or Boon to Radiologists?

PubMed

Recht, Michael; Bryan, R Nick

2017-11-01

The development and integration of machine learning/artificial intelligence into routine clinical practice will significantly alter the current practice of radiology. Changes in reimbursement and practice patterns will also continue to affect radiology. But rather than being a significant threat to radiologists, we believe these changes, particularly machine learning/artificial intelligence, will be a boon to radiologists by increasing their value, efficiency, accuracy, and personal satisfaction. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Experiments in Schema-Driven Interpretation of a Natural Scene

DTIC Science & Technology

1980-04-01

Intilliaence, "rbilisi, USSR; 1975, pp. 483-490. EFEL743 JzA. Feldman and Y. Yakimovsky, "Deciesion Theorg and Artificiel Int lligence:, I. A Semantics-Based...lTra. ttern i a Machine Intelligence , Vol. PAMI-., Janua’ry 1980 p’p. 16-27. CRIS743 E.M. Riseman and A.R. Hanson, "I)eign o’f a Semanticall...Machine Intelligence , School of Artificial Intelligence , University of Edinburgh, 1974. tUHR723 L. Uhr, "Layered ’Recognition Cone’ Networks That

Probabilistic machine learning and artificial intelligence.

PubMed

Ghahramani, Zoubin

2015-05-28

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

Probabilistic machine learning and artificial intelligence

NASA Astrophysics Data System (ADS)

Ghahramani, Zoubin

2015-05-01

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

Intelligent open-architecture controller using knowledge server

NASA Astrophysics Data System (ADS)

Nacsa, Janos; Kovacs, George L.; Haidegger, Geza

2001-12-01

In an ideal scenario of intelligent machine tools [22] the human mechanist was almost replaced by the controller. During the last decade many efforts have been made to get closer to this ideal scenario, but the way of information processing within the CNC did not change too much. The paper summarizes the requirements of an intelligent CNC evaluating the different research efforts done in this field using different artificial intelligence (AI) methods. The need for open CNC architecture was emerging at many places around the world. The second part of the paper introduces and shortly compares these efforts. In the third part a low cost concept for intelligent and open systems named Knowledge Server for Controllers (KSC) is introduced. It allows more devices to solve their intelligent processing needs using the same server that is capable to process intelligent data. In the final part the KSC concept is used in an open CNC environment to build up some elements of an intelligent CNC. The preliminary results of the implementation are also introduced.

Human-directed local autonomy for motion guidance and coordination in an intelligent manufacturing system

NASA Astrophysics Data System (ADS)

Alford, W. A.; Kawamura, Kazuhiko; Wilkes, Don M.

1997-12-01

This paper discusses the problem of integrating human intelligence and skills into an intelligent manufacturing system. Our center has jointed the Holonic Manufacturing Systems (HMS) Project, an international consortium dedicated to developing holonic systems technologies. One of our contributions to this effort is in Work Package 6: flexible human integration. This paper focuses on one activity, namely, human integration into motion guidance and coordination. Much research on intelligent systems focuses on creating totally autonomous agents. At the Center for Intelligent Systems (CIS), we design robots that interact directly with a human user. We focus on using the natural intelligence of the user to simplify the design of a robotic system. The problem is finding ways for the user to interact with the robot that are efficient and comfortable for the user. Manufacturing applications impose the additional constraint that the manufacturing process should not be disturbed; that is, frequent interacting with the user could degrade real-time performance. Our research in human-robot interaction is based on a concept called human directed local autonomy (HuDL). Under this paradigm, the intelligent agent selects and executes a behavior or skill, based upon directions from a human user. The user interacts with the robot via speech, gestures, or other media. Our control software is based on the intelligent machine architecture (IMA), an object-oriented architecture which facilitates cooperation and communication among intelligent agents. In this paper we describe our research testbed, a dual-arm humanoid robot and human user, and the use of this testbed for a human directed sorting task. We also discuss some proposed experiments for evaluating the integration of the human into the robot system. At the time of this writing, the experiments have not been completed.

Man/Machine Interaction Dynamics And Performance (MMIDAP) capability

NASA Technical Reports Server (NTRS)

Frisch, Harold P.

1991-01-01

The creation of an ability to study interaction dynamics between a machine and its human operator can be approached from a myriad of directions. The Man/Machine Interaction Dynamics and Performance (MMIDAP) project seeks to create an ability to study the consequences of machine design alternatives relative to the performance of both machine and operator. The class of machines to which this study is directed includes those that require the intelligent physical exertions of a human operator. While Goddard's Flight Telerobotic's program was expected to be a major user, basic engineering design and biomedical applications reach far beyond telerobotics. Ongoing efforts are outlined of the GSFC and its University and small business collaborators to integrate both human performance and musculoskeletal data bases with analysis capabilities necessary to enable the study of dynamic actions, reactions, and performance of coupled machine/operator systems.

Application of artificial intelligence to the management of urological cancer.

PubMed

Abbod, Maysam F; Catto, James W F; Linkens, Derek A; Hamdy, Freddie C

2007-10-01

Artificial intelligence techniques, such as artificial neural networks, Bayesian belief networks and neuro-fuzzy modeling systems, are complex mathematical models based on the human neuronal structure and thinking. Such tools are capable of generating data driven models of biological systems without making assumptions based on statistical distributions. A large amount of study has been reported of the use of artificial intelligence in urology. We reviewed the basic concepts behind artificial intelligence techniques and explored the applications of this new dynamic technology in various aspects of urological cancer management. A detailed and systematic review of the literature was performed using the MEDLINE and Inspec databases to discover reports using artificial intelligence in urological cancer. The characteristics of machine learning and their implementation were described and reports of artificial intelligence use in urological cancer were reviewed. While most researchers in this field were found to focus on artificial neural networks to improve the diagnosis, staging and prognostic prediction of urological cancers, some groups are exploring other techniques, such as expert systems and neuro-fuzzy modeling systems. Compared to traditional regression statistics artificial intelligence methods appear to be accurate and more explorative for analyzing large data cohorts. Furthermore, they allow individualized prediction of disease behavior. Each artificial intelligence method has characteristics that make it suitable for different tasks. The lack of transparency of artificial neural networks hinders global scientific community acceptance of this method but this can be overcome by neuro-fuzzy modeling systems.

Machine learning of parameter control doctrine for sensor and communication systems. Final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kamen, R.B.; Dillard, R.A.

Artificial-intelligence approaches to learning were reviewed for their potential contributions to the construction of a system to learn parameter-control doctrine. Separate learning tasks were isolated and several levels of related problems were distinguished. Formulas for providing the learning system with measures of its performance were derived for four kinds of targets.

Role of artificial intelligence in the care of patients with nonsmall cell lung cancer.

PubMed

Rabbani, Mohamad; Kanevsky, Jonathan; Kafi, Kamran; Chandelier, Florent; Giles, Francis J

2018-04-01

Lung cancer is the leading cause of cancer death worldwide. In up to 57% of patients, it is diagnosed at an advanced stage and the 5-year survival rate ranges between 10%-16%. There has been a significant amount of research using machine learning to generate tools using patient data to improve outcomes. This narrative review is based on research material obtained from PubMed up to Nov 2017. The search terms include "artificial intelligence," "machine learning," "lung cancer," "Nonsmall Cell Lung Cancer (NSCLC)," "diagnosis" and "treatment." Recent studies support the use of computer-aided systems and the use of radiomic features to help diagnose lung cancer earlier. Other studies have looked at machine learning (ML) methods that offer prognostic tools to doctors and help them in choosing personalized treatment options for their patients based on molecular, genetics and histological features. Combining artificial intelligence approaches into health care may serve as a beneficial tool for patients with NSCLC, and this review outlines these benefits and current shortcomings throughout the continuum of care. We present a review of the various applications of ML methods in NSCLC as it relates to improving diagnosis, treatment and outcomes. © 2018 Stichting European Society for Clinical Investigation Journal Foundation.

Technology in rural transportation. Simple solution #5, traveler information using fax machines

DOT National Transportation Integrated Search

1997-01-01

This application was identified as a promising rural Intelligent Transportation Systems (ITS) solution under a project sponsored by the Federal Highway Administration (FHWA) and the ENTERPRISE program. This summary describes the solution as well as o...

Integration of passive driver-assistance systems with on-board vehicle systems

NASA Astrophysics Data System (ADS)

Savchenko, V. V.; Poddubko, S. N.

2018-02-01

Implementation in OIAS such functions as driver’s state monitoring and high-precision calculation of the current navigation coordinates of the vehicle, modularity of the OIAS construction and the possible increase in the functionality through integration with other onboard systems has a promising development future. The development of intelligent transport systems and their components allows setting and solving fundamentally new tasks for the safety of human-to-machine transport systems, and the automatic analysis of heterogeneous information flows provides a synergistic effect. The analysis of cross-modal information exchange in human-machine transport systems, from uniform methodological points of view, will allow us, with an accuracy acceptable for solving applied problems, to form in real time an integrated assessment of the state of the basic components of the human-to-machine system and the dynamics in changing situation-centered environment, including the external environment, in their interrelations.

Performance improvement in remote manipulation with time delay by means of a learning system.

NASA Technical Reports Server (NTRS)

Freedy, A.; Weltman, G.

1973-01-01

A teleoperating system is presented that involves shared control between a human operator and a general-purpose computer-based learning machine. This setup features a trainable control network termed the autonomous control subsystem (ACS) which is able to observe the operator's control actions, learn the task at hand, and take appropriate control actions. A working ACS system is described that has been put in operation for the purpose of exploring the uses of a remote intelligence of this type. The expansion of the present system into a multifunctional learning machine capable of a greater degree of autonomy is also discussed.

Artificial intelligence applied to process signal analysis

NASA Technical Reports Server (NTRS)

Corsberg, Dan

1988-01-01

Many space station processes are highly complex systems subject to sudden, major transients. In any complex process control system, a critical aspect of the human/machine interface is the analysis and display of process information. Human operators can be overwhelmed by large clusters of alarms that inhibit their ability to diagnose and respond to a disturbance. Using artificial intelligence techniques and a knowledge base approach to this problem, the power of the computer can be used to filter and analyze plant sensor data. This will provide operators with a better description of the process state. Once a process state is recognized, automatic action could be initiated and proper system response monitored.

Extended Logic Intelligent Processing System for a Sensor Fusion Processor Hardware

NASA Technical Reports Server (NTRS)

Stoica, Adrian; Thomas, Tyson; Li, Wei-Te; Daud, Taher; Fabunmi, James

2000-01-01

The paper presents the hardware implementation and initial tests from a low-power, highspeed reconfigurable sensor fusion processor. The Extended Logic Intelligent Processing System (ELIPS) is described, which combines rule-based systems, fuzzy logic, and neural networks to achieve parallel fusion of sensor signals in compact low power VLSI. The development of the ELIPS concept is being done to demonstrate the interceptor functionality which particularly underlines the high speed and low power requirements. The hardware programmability allows the processor to reconfigure into different machines, taking the most efficient hardware implementation during each phase of information processing. Processing speeds of microseconds have been demonstrated using our test hardware.

Machine Learning and Radiology

PubMed Central

Wang, Shijun; Summers, Ronald M.

2012-01-01

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. PMID:22465077

Building machines that learn and think like people.

PubMed

Lake, Brenden M; Ullman, Tomer D; Tenenbaum, Joshua B; Gershman, Samuel J

2017-01-01

Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn and how they learn it. Specifically, we argue that these machines should (1) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (2) ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned; and (3) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes toward these goals that can combine the strengths of recent neural network advances with more structured cognitive models.

Emergence of Data Analytics in the Information Systems Curriculum

ERIC Educational Resources Information Center

Jafar, Musa J.; Babb, Jeffry; Abdullat, Amjda

2017-01-01

As a phenomenon of interest, impact, and import, there is little doubt that the pervasive expansion of data is upon us as Information Systems educators. Concerns and topics such as Data Science, Data Analytics, Machine Learning, Business Analytics, and Business Intelligence are now ubiquitous and often situated as being the "next big…

Space station data system analysis/architecture study. Task 5: Program plan

NASA Technical Reports Server (NTRS)

1985-01-01

Cost estimates for both the on-board and ground segments of the Space Station Data System (SSDS) are presented along with summary program schedules. Advanced technology development recommendations are provided in the areas of distributed data base management, end-to-end protocols, command/resource management, and flight qualified artificial intelligence machines.

Collective Machine Learning: Team Learning and Classification in Multi-Agent Systems

ERIC Educational Resources Information Center

Gifford, Christopher M.

2009-01-01

This dissertation focuses on the collaboration of multiple heterogeneous, intelligent agents (hardware or software) which collaborate to learn a task and are capable of sharing knowledge. The concept of collaborative learning in multi-agent and multi-robot systems is largely under studied, and represents an area where further research is needed to…

STANFORD ARTIFICIAL INTELLIGENCE PROJECT.

DTIC Science & Technology

ARTIFICIAL INTELLIGENCE , GAME THEORY, DECISION MAKING, BIONICS, AUTOMATA, SPEECH RECOGNITION, GEOMETRIC FORMS, LEARNING MACHINES, MATHEMATICAL MODELS, PATTERN RECOGNITION, SERVOMECHANISMS, SIMULATION, BIBLIOGRAPHIES.

Intelligent man/machine interfaces on the space station

NASA Technical Reports Server (NTRS)

Daughtrey, Rodney S.

1987-01-01

Some important topics in the development of good, intelligent, usable man/machine interfaces for the Space Station are discussed. These computer interfaces should adhere strictly to three concepts or doctrines: generality, simplicity, and elegance. The motivation for natural language interfaces and their use and value on the Space Station, both now and in the future, are discussed.

The machine intelligence Hex project

NASA Astrophysics Data System (ADS)

Chalup, Stephan K.; Mellor, Drew; Rosamond, Fran

2005-12-01

Hex is a challenging strategy board game for two players. To enhance students’ progress in acquiring understanding and practical experience with complex machine intelligence and programming concepts we developed the Machine Intelligence Hex (MIHex) project. The associated undergraduate student assignment is about designing and implementing Hex players and evaluating them in an automated tournament of all programs developed by the class. This article surveys educational aspects of the MIHex project. Additionally, fundamental techniques for game programming as well as specific concepts for Hex board evaluation are reviewed. The MIHex game server and possibilities of tournament organisation are described. We summarise and discuss our experiences from running the MIHex project assignment over four consecutive years. The impact on student motivation and learning benefits are evaluated using questionnaires and interviews.

Fundamental research in artificial intelligence at NASA

NASA Technical Reports Server (NTRS)

Friedland, Peter

1990-01-01

This paper describes basic research at NASA in the field of artificial intelligence. The work is conducted at the Ames Research Center and the Jet Propulsion Laboratory, primarily under the auspices of the NASA-wide Artificial Intelligence Program in the Office of Aeronautics, Exploration and Technology. The research is aimed at solving long-term NASA problems in missions operations, spacecraft autonomy, preservation of corporate knowledge about NASA missions and vehicles, and management/analysis of scientific and engineering data. From a scientific point of view, the research is broken into the categories of: planning and scheduling; machine learning; and design of and reasoning about large-scale physical systems.

The 21st annual intelligent ground vehicle competition: robotists for the future

NASA Astrophysics Data System (ADS)

Theisen, Bernard L.

2013-12-01

The Intelligent Ground Vehicle Competition (IGVC) is one of four, unmanned systems, student competitions that were founded by the Association for Unmanned Vehicle Systems International (AUVSI). The IGVC is a multidisciplinary exercise in product realization that challenges college engineering student teams to integrate advanced control theory, machine vision, vehicular electronics and mobile platform fundamentals to design and build an unmanned system. Teams from around the world focus on developing a suite of dual-use technologies to equip ground vehicles of the future with intelligent driving capabilities. Over the past 21 years, the competition has challenged undergraduate, graduate and Ph.D. students with real world applications in intelligent transportation systems, the military and manufacturing automation. To date, teams from over 80 universities and colleges have participated. This paper describes some of the applications of the technologies required by this competition and discusses the educational benefits. The primary goal of the IGVC is to advance engineering education in intelligent vehicles and related technologies. The employment and professional networking opportunities created for students and industrial sponsors through a series of technical events over the fourday competition are highlighted. Finally, an assessment of the competition based on participation is presented.

The 20th annual intelligent ground vehicle competition: building a generation of robotists

NASA Astrophysics Data System (ADS)

Theisen, Bernard L.; Kosinski, Andrew

2013-01-01

The Intelligent Ground Vehicle Competition (IGVC) is one of four, unmanned systems, student competitions that were founded by the Association for Unmanned Vehicle Systems International (AUVSI). The IGVC is a multidisciplinary exercise in product realization that challenges college engineering student teams to integrate advanced control theory, machine vision, vehicular electronics and mobile platform fundamentals to design and build an unmanned system. Teams from around the world focus on developing a suite of dual-use technologies to equip ground vehicles of the future with intelligent driving capabilities. Over the past 20 years, the competition has challenged undergraduate, graduate and Ph.D. students with real world applications in intelligent transportation systems, the military and manufacturing automation. To date, teams from over 80 universities and colleges have participated. This paper describes some of the applications of the technologies required by this competition and discusses the educational benefits. The primary goal of the IGVC is to advance engineering education in intelligent vehicles and related technologies. The employment and professional networking opportunities created for students and industrial sponsors through a series of technical events over the four-day competition are highlighted. Finally, an assessment of the competition based on participation is presented.

Distribution of man-machine controls in space teleoperation

NASA Technical Reports Server (NTRS)

Bejczy, A. K.

1982-01-01

The distribution of control between man and machine is dependent on the tasks, available technology, human performance characteristics and control goals. This dependency has very specific projections on systems designed for teleoperation in space. This paper gives a brief outline of the space-related issues and presents the results of advanced teleoperator research and development at the Jet Propulsion Laboratory (JPL). The research and development work includes smart sensors, flexible computer controls and intelligent man-machine interface devices in the area of visual displays and kinesthetic man-machine coupling in remote control of manipulators. Some of the development results have been tested at the Johnson Space Center (JSC) using the simulated full-scale Shuttle Remote Manipulator System (RMS). The research and development work for advanced space teleoperation is far from complete and poses many interdisciplinary challenges.

Tele-assistance for semi-autonomous robots

NASA Technical Reports Server (NTRS)

Rogers, Erika; Murphy, Robin R.

1994-01-01

This paper describes a new approach in semi-autonomous mobile robots. In this approach the robot has sufficient computerized intelligence to function autonomously under a certain set of conditions, while the local system is a cooperative decision making unit that combines human and machine intelligence. Communication is then allowed to take place in a common mode and in a common language. A number of exception-handling scenarios that were constructed as a result of experiments with actual sensor data collected from two mobile robots were presented.

A Lightweight Intelligent Virtual Cinematography System for Machinima Production

DTIC Science & Technology

2007-01-01

portmanteau of machine and cinema , machinima refers to the innovation of leveraging video game technology to greatly ease the creation of computer...selecting camera angles to capture the action of an a priori unknown script as aesthetically appropriate cinema . There are a number of challenges therein...Proc. of the 4th International Conf. on Autonomous Agents. Young, R.M. and Riedl, M.O. 2003. Towards an Architecture for Intelligent Control of Narrative in Interactive Virtual Worlds. In Proc. of IUI 2003.

Action Identity in Style Simulation Systems: Do Players Consider Machine-Generated Music As of Their Own Style?

PubMed

Khatchatourov, Armen; Pachet, François; Rowe, Victoria

2016-01-01

The generation of musical material in a given style has been the subject of many studies with the increased sophistication of artificial intelligence models of musical style. In this paper we address a question of primary importance for artificial intelligence and music psychology: can such systems generate music that users indeed consider as corresponding to their own style? We address this question through an experiment involving both performance and recognition tasks with musically naïve school-age children. We asked 56 children to perform a free-form improvisation from which two kinds of music excerpt were created. One was a mere recording of original performances. The other was created by a software program designed to simulate the participants' style, based on their original performances. Two hours after the performance task, the children completed the recognition task in two conditions, one with the original excerpts and one with machine-generated music. Results indicate that the success rate is practically equivalent in two conditions: children tended to make correct attribution of the excerpts to themselves or to others, whether the music was human-produced or machine-generated (mean accuracy = 0.75 and = 0.71, respectively). We discuss this equivalence in accuracy for machine-generated and human produced music in the light of the literature on memory effects and action identity which addresses the recognition of one's own production.

Action Identity in Style Simulation Systems: Do Players Consider Machine-Generated Music As of Their Own Style?

PubMed Central

Khatchatourov, Armen; Pachet, François; Rowe, Victoria

2016-01-01

The generation of musical material in a given style has been the subject of many studies with the increased sophistication of artificial intelligence models of musical style. In this paper we address a question of primary importance for artificial intelligence and music psychology: can such systems generate music that users indeed consider as corresponding to their own style? We address this question through an experiment involving both performance and recognition tasks with musically naïve school-age children. We asked 56 children to perform a free-form improvisation from which two kinds of music excerpt were created. One was a mere recording of original performances. The other was created by a software program designed to simulate the participants' style, based on their original performances. Two hours after the performance task, the children completed the recognition task in two conditions, one with the original excerpts and one with machine-generated music. Results indicate that the success rate is practically equivalent in two conditions: children tended to make correct attribution of the excerpts to themselves or to others, whether the music was human-produced or machine-generated (mean accuracy = 0.75 and = 0.71, respectively). We discuss this equivalence in accuracy for machine-generated and human produced music in the light of the literature on memory effects and action identity which addresses the recognition of one's own production. PMID:27199788

Before They Can Speak, They Must Know.

ERIC Educational Resources Information Center

Cromie, William J.; Edson, Lee

1984-01-01

Intelligent relationships with people are among the goals for tomorrow's computers. Knowledge-based systems used and being developed to achieve these goals are discussed. Automatic learning, producing inferences, parallelism, program languages, friendly machines, computer vision, and biomodels are among the topics considered. (JN)

ERGONOMICS ABSTRACTS 48347-48982.

ERIC Educational Resources Information Center

Ministry of Technology, London (England). Warren Spring Lab.

IN THIS COLLECTION OF ERGONOMICS ABSTRACTS AND ANNOTATIONS THE FOLLOWING AREAS OF CONCERN ARE REPRESENTED--GENERAL REFERENCES, METHODS, FACILITIES, AND EQUIPMENT RELATING TO ERGONOMICS, SYSTEMS OF MAN AND MACHINES, VISUAL, AUDITORY, AND OTHER SENSORY INPUTS AND PROCESSES (INCLUDING SPEECH AND INTELLIGIBILITY), INPUT CHANNELS, BODY MEASUREMENTS,…

Expert System Software

NASA Technical Reports Server (NTRS)

1989-01-01

C Language Integrated Production System (CLIPS) is a software shell for developing expert systems is designed to allow research and development of artificial intelligence on conventional computers. Originally developed by Johnson Space Center, it enables highly efficient pattern matching. A collection of conditions and actions to be taken if the conditions are met is built into a rule network. Additional pertinent facts are matched to the rule network. Using the program, E.I. DuPont de Nemours & Co. is monitoring chemical production machines; California Polytechnic State University is investigating artificial intelligence in computer aided design; Mentor Graphics has built a new Circuit Synthesis system, and Brooke and Brooke, a law firm, can determine which facts from a file are most important.

An immune-inspired semi-supervised algorithm for breast cancer diagnosis.

PubMed

Peng, Lingxi; Chen, Wenbin; Zhou, Wubai; Li, Fufang; Yang, Jin; Zhang, Jiandong

2016-10-01

Breast cancer is the most frequently and world widely diagnosed life-threatening cancer, which is the leading cause of cancer death among women. Early accurate diagnosis can be a big plus in treating breast cancer. Researchers have approached this problem using various data mining and machine learning techniques such as support vector machine, artificial neural network, etc. The computer immunology is also an intelligent method inspired by biological immune system, which has been successfully applied in pattern recognition, combination optimization, machine learning, etc. However, most of these diagnosis methods belong to a supervised diagnosis method. It is very expensive to obtain labeled data in biology and medicine. In this paper, we seamlessly integrate the state-of-the-art research on life science with artificial intelligence, and propose a semi-supervised learning algorithm to reduce the need for labeled data. We use two well-known benchmark breast cancer datasets in our study, which are acquired from the UCI machine learning repository. Extensive experiments are conducted and evaluated on those two datasets. Our experimental results demonstrate the effectiveness and efficiency of our proposed algorithm, which proves that our algorithm is a promising automatic diagnosis method for breast cancer. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Heart failure analysis dashboard for patient's remote monitoring combining multiple artificial intelligence technologies.

PubMed

Guidi, G; Pettenati, M C; Miniati, R; Iadanza, E

2012-01-01

In this paper we describe an Heart Failure analysis Dashboard that, combined with a handy device for the automatic acquisition of a set of patient's clinical parameters, allows to support telemonitoring functions. The Dashboard's intelligent core is a Computer Decision Support System designed to assist the clinical decision of non-specialist caring personnel, and it is based on three functional parts: Diagnosis, Prognosis, and Follow-up management. Four Artificial Intelligence-based techniques are compared for providing diagnosis function: a Neural Network, a Support Vector Machine, a Classification Tree and a Fuzzy Expert System whose rules are produced by a Genetic Algorithm. State of the art algorithms are used to support a score-based prognosis function. The patient's Follow-up is used to refine the diagnosis.

Intelligent image processing for machine safety

NASA Astrophysics Data System (ADS)

Harvey, Dennis N.

1994-10-01

This paper describes the use of intelligent image processing as a machine guarding technology. One or more color, linear array cameras are positioned to view the critical region(s) around a machine tool or other piece of manufacturing equipment. The image data is processed to provide indicators of conditions dangerous to the equipment via color content, shape content, and motion content. The data from these analyses is then sent to a threat evaluator. The purpose of the evaluator is to determine if a potentially machine-damaging condition exists based on the analyses of color, shape, and motion, and on `knowledge' of the specific environment of the machine. The threat evaluator employs fuzzy logic as a means of dealing with uncertainty in the vision data.

Intelligent systems and advanced user interfaces for design, operation, and maintenance of command management systems

NASA Technical Reports Server (NTRS)

Potter, William J.; Mitchell, Christine M.

1993-01-01

Historically, command management systems (CMS) have been large and expensive spacecraft-specific software systems that were costly to build, operate, and maintain. Current and emerging hardware, software, and user interface technologies may offer an opportunity to facilitate the initial formulation and design of a spacecraft-specific CMS as well as to develop a more generic CMS system. New technologies, in addition to a core CMS common to a range of spacecraft, may facilitate the training and enhance the efficiency of CMS operations. Current mission operations center (MOC) hardware and software include Unix workstations, the C/C++ programming languages, and an X window interface. This configuration provides the power and flexibility to support sophisticated and intelligent user interfaces that exploit state-of-the-art technologies in human-machine interaction, artificial intelligence, and software engineering. One of the goals of this research is to explore the extent to which technologies developed in the research laboratory can be productively applied in a complex system such as spacecraft command management. Initial examination of some of these issues in CMS design and operation suggests that application of technologies such as intelligent planning, case-based reasoning, human-machine systems design and analysis tools (e.g., operator and designer models), and human-computer interaction tools (e.g., graphics, visualization, and animation) may provide significant savings in the design, operation, and maintenance of the CMS for a specific spacecraft as well as continuity for CMS design and development across spacecraft. The first six months of this research saw a broad investigation by Georgia Tech researchers into the function, design, and operation of current and planned command management systems at Goddard Space Flight Center. As the first step, the researchers attempted to understand the current and anticipated horizons of command management systems at Goddard. Preliminary results are given on CMS commonalities and causes of low re-use, and methods are proposed to facilitate increased re-use.

Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization

PubMed Central

Nalluri, MadhuSudana Rao; K., Kannan; M., Manisha

2017-01-01

With the widespread adoption of e-Healthcare and telemedicine applications, accurate, intelligent disease diagnosis systems have been profoundly coveted. In recent years, numerous individual machine learning-based classifiers have been proposed and tested, and the fact that a single classifier cannot effectively classify and diagnose all diseases has been almost accorded with. This has seen a number of recent research attempts to arrive at a consensus using ensemble classification techniques. In this paper, a hybrid system is proposed to diagnose ailments using optimizing individual classifier parameters for two classifier techniques, namely, support vector machine (SVM) and multilayer perceptron (MLP) technique. We employ three recent evolutionary algorithms to optimize the parameters of the classifiers above, leading to six alternative hybrid disease diagnosis systems, also referred to as hybrid intelligent systems (HISs). Multiple objectives, namely, prediction accuracy, sensitivity, and specificity, have been considered to assess the efficacy of the proposed hybrid systems with existing ones. The proposed model is evaluated on 11 benchmark datasets, and the obtained results demonstrate that our proposed hybrid diagnosis systems perform better in terms of disease prediction accuracy, sensitivity, and specificity. Pertinent statistical tests were carried out to substantiate the efficacy of the obtained results. PMID:29065626

Intelligent Automatic Right-Left Sign Lamp Based on Brain Signal Recognition System

NASA Astrophysics Data System (ADS)

Winda, A.; Sofyan; Sthevany; Vincent, R. S.

2017-12-01

Comfort as a part of the human factor, plays important roles in nowadays advanced automotive technology. Many of the current technologies go in the direction of automotive driver assistance features. However, many of the driver assistance features still require physical movement by human to enable the features. In this work, the proposed method is used in order to make certain feature to be functioning without any physical movement, instead human just need to think about it in their mind. In this work, brain signal is recorded and processed in order to be used as input to the recognition system. Right-Left sign lamp based on the brain signal recognition system can potentially replace the button or switch of the specific device in order to make the lamp work. The system then will decide whether the signal is ‘Right’ or ‘Left’. The decision of the Right-Left side of brain signal recognition will be sent to a processing board in order to activate the automotive relay, which will be used to activate the sign lamp. Furthermore, the intelligent system approach is used to develop authorized model based on the brain signal. Particularly Support Vector Machines (SVMs)-based classification system is used in the proposed system to recognize the Left-Right of the brain signal. Experimental results confirm the effectiveness of the proposed intelligent Automatic brain signal-based Right-Left sign lamp access control system. The signal is processed by Linear Prediction Coefficient (LPC) and Support Vector Machines (SVMs), and the resulting experiment shows the training and testing accuracy of 100% and 80%, respectively.

Human factors in space telepresence

NASA Technical Reports Server (NTRS)

Akin, D. L.; Howard, R. D.; Oliveria, J. S.

1983-01-01

The problems of interfacing a human with a teleoperation system, for work in space are discussed. Much of the information presented here is the result of experience gained by the M.I.T. Space Systems Laboratory during the past two years of work on the ARAMIS (Automation, Robotics, and Machine Intelligence Systems) project. Many factors impact the design of the man-machine interface for a teleoperator. The effects of each are described in turn. An annotated bibliography gives the key references that were used. No conclusions are presented as a best design, since much depends on the particular application desired, and the relevant technology is swiftly changing.

A Novel Artificial Intelligence System for Endotracheal Intubation.

PubMed

Carlson, Jestin N; Das, Samarjit; De la Torre, Fernando; Frisch, Adam; Guyette, Francis X; Hodgins, Jessica K; Yealy, Donald M

2016-01-01

Adequate visualization of the glottic opening is a key factor to successful endotracheal intubation (ETI); however, few objective tools exist to help guide providers' ETI attempts toward the glottic opening in real-time. Machine learning/artificial intelligence has helped to automate the detection of other visual structures but its utility with ETI is unknown. We sought to test the accuracy of various computer algorithms in identifying the glottic opening, creating a tool that could aid successful intubation. We collected a convenience sample of providers who each performed ETI 10 times on a mannequin using a video laryngoscope (C-MAC, Karl Storz Corp, Tuttlingen, Germany). We recorded each attempt and reviewed one-second time intervals for the presence or absence of the glottic opening. Four different machine learning/artificial intelligence algorithms analyzed each attempt and time point: k-nearest neighbor (KNN), support vector machine (SVM), decision trees, and neural networks (NN). We used half of the videos to train the algorithms and the second half to test the accuracy, sensitivity, and specificity of each algorithm. We enrolled seven providers, three Emergency Medicine attendings, and four paramedic students. From the 70 total recorded laryngoscopic video attempts, we created 2,465 time intervals. The algorithms had the following sensitivity and specificity for detecting the glottic opening: KNN (70%, 90%), SVM (70%, 90%), decision trees (68%, 80%), and NN (72%, 78%). Initial efforts at computer algorithms using artificial intelligence are able to identify the glottic opening with over 80% accuracy. With further refinements, video laryngoscopy has the potential to provide real-time, direction feedback to the provider to help guide successful ETI.

Self-Learning Embedded System for Object Identification in Intelligent Infrastructure Sensors.

PubMed

Villaverde, Monica; Perez, David; Moreno, Felix

2015-11-17

The emergence of new horizons in the field of travel assistant management leads to the development of cutting-edge systems focused on improving the existing ones. Moreover, new opportunities are being also presented since systems trend to be more reliable and autonomous. In this paper, a self-learning embedded system for object identification based on adaptive-cooperative dynamic approaches is presented for intelligent sensor's infrastructures. The proposed system is able to detect and identify moving objects using a dynamic decision tree. Consequently, it combines machine learning algorithms and cooperative strategies in order to make the system more adaptive to changing environments. Therefore, the proposed system may be very useful for many applications like shadow tolls since several types of vehicles may be distinguished, parking optimization systems, improved traffic conditions systems, etc.

Towards a Self-Configuring Optimization System for Spacecraft Design

NASA Technical Reports Server (NTRS)

Chien, Steve

1997-01-01

In this paper, we propose the use of a set of generic, metaheuristic optimization algorithms, which is configured for a particular optimization problem by an adaptive problem solver based on artificial intelligence and machine learning techniques. We describe work in progress on these principles.

Similarities and differences of emotions in human-machine and human-human interactions: what kind of emotions are relevant for future companion systems?

PubMed

Walter, Steffen; Wendt, Cornelia; Böhnke, Jan; Crawcour, Stephen; Tan, Jun-Wen; Chan, Andre; Limbrecht, Kerstin; Gruss, Sascha; Traue, Harald C

2014-01-01

Cognitive-technical intelligence is envisioned to be constantly available and capable of adapting to the user's emotions. However, the question is: what specific emotions should be reliably recognised by intelligent systems? Hence, in this study, we have attempted to identify similarities and differences of emotions between human-human (HHI) and human-machine interactions (HMI). We focused on what emotions in the experienced scenarios of HMI are retroactively reflected as compared with HHI. The sample consisted of N = 145 participants, who were divided into two groups. Positive and negative scenario descriptions of HMI and HHI were given by the first and second groups, respectively. Subsequently, the participants evaluated their respective scenarios with the help of 94 adjectives relating to emotions. The correlations between the occurrences of emotions in the HMI versus HHI were very high. The results do not support the statement that only a few emotions in HMI are relevant.

[Technologies for Complex Intelligent Clinical Data Analysis].

PubMed

Baranov, A A; Namazova-Baranova, L S; Smirnov, I V; Devyatkin, D A; Shelmanov, A O; Vishneva, E A; Antonova, E V; Smirnov, V I

2016-01-01

The paper presents the system for intelligent analysis of clinical information. Authors describe methods implemented in the system for clinical information retrieval, intelligent diagnostics of chronic diseases, patient's features importance and for detection of hidden dependencies between features. Results of the experimental evaluation of these methods are also presented. Healthcare facilities generate a large flow of both structured and unstructured data which contain important information about patients. Test results are usually retained as structured data but some data is retained in the form of natural language texts (medical history, the results of physical examination, and the results of other examinations, such as ultrasound, ECG or X-ray studies). Many tasks arising in clinical practice can be automated applying methods for intelligent analysis of accumulated structured array and unstructured data that leads to improvement of the healthcare quality. the creation of the complex system for intelligent data analysis in the multi-disciplinary pediatric center. Authors propose methods for information extraction from clinical texts in Russian. The methods are carried out on the basis of deep linguistic analysis. They retrieve terms of diseases, symptoms, areas of the body and drugs. The methods can recognize additional attributes such as "negation" (indicates that the disease is absent), "no patient" (indicates that the disease refers to the patient's family member, but not to the patient), "severity of illness", disease course", "body region to which the disease refers". Authors use a set of hand-drawn templates and various techniques based on machine learning to retrieve information using a medical thesaurus. The extracted information is used to solve the problem of automatic diagnosis of chronic diseases. A machine learning method for classification of patients with similar nosology and the methodfor determining the most informative patients'features are also proposed. Authors have processed anonymized health records from the pediatric center to estimate the proposed methods. The results show the applicability of the information extracted from the texts for solving practical problems. The records ofpatients with allergic, glomerular and rheumatic diseases were used for experimental assessment of the method of automatic diagnostic. Authors have also determined the most appropriate machine learning methods for classification of patients for each group of diseases, as well as the most informative disease signs. It has been found that using additional information extracted from clinical texts, together with structured data helps to improve the quality of diagnosis of chronic diseases. Authors have also obtained pattern combinations of signs of diseases. The proposed methods have been implemented in the intelligent data processing system for a multidisciplinary pediatric center. The experimental results show the availability of the system to improve the quality of pediatric healthcare.

Proposal for continued research in intelligent machines at the Center for Engineering Systems Advanced Research (CESAR) for FY 1988 to FY 1991

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weisbin, C.R.

1987-03-01

This document reviews research accomplishments achieved by the staff of the Center for Engineering Systems Advanced Research (CESAR) during the fiscal years 1984 through 1987. The manuscript also describes future CESAR objectives for the 1988-1991 planning horizon, and beyond. As much as possible, the basic research goals are derived from perceived Department of Energy (DOE) needs for increased safety, productivity, and competitiveness in the United States energy producing and consuming facilities. Research areas covered include the HERMIES-II Robot, autonomous robot navigation, hypercube computers, machine vision, and manipulators.

Intelligent printing system with AMPAC: boot program for printing machine with AMPAC

NASA Astrophysics Data System (ADS)

Yuasa, Tomonori; Mishina, Hiromichi

2000-12-01

The database AMPAC proposes the simple and unified format to describe single parameter of whole field of design, production and management. The database described by the format can be used commonly in any field connected by the network production system, since the description accepts any parameter in any fields and is field independent definition.

Remotely manned systems: Exploration and operation in space; Proceedings of the First National Conference, California Institute of Technology, Pasadena, Calif., September 13-15, 1972.

NASA Technical Reports Server (NTRS)

Heer, E.

1973-01-01

Free-flying teleoperator systems are discussed, giving attention to earth-orbit mission considerations and Space Tug requirements, free-flying teleoperator requirements and conceptual design, system requirements for a free-flying teleoperator to despin, and the experimental evaluation of remote manipulator systems. Shuttle-Attached Manipulator Systems are considered, together with remote surface vehicle systems, manipulator systems technology, remote sensor and display technology, the man-machine interface, and control and machine intelligence. Nonspace applications are also explored, taking into account implications of nonspace applications, naval applications of remote manipulators, and hand tools and mechanical accessories for a deep submersible. Individual items are announced in this issue.

An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification.

PubMed

Siddiqui, Muhammad Faisal; Reza, Ahmed Wasif; Kanesan, Jeevan

2015-01-01

A wide interest has been observed in the medical health care applications that interpret neuroimaging scans by machine learning systems. This research proposes an intelligent, automatic, accurate, and robust classification technique to classify the human brain magnetic resonance image (MRI) as normal or abnormal, to cater down the human error during identifying the diseases in brain MRIs. In this study, fast discrete wavelet transform (DWT), principal component analysis (PCA), and least squares support vector machine (LS-SVM) are used as basic components. Firstly, fast DWT is employed to extract the salient features of brain MRI, followed by PCA, which reduces the dimensions of the features. These reduced feature vectors also shrink the memory storage consumption by 99.5%. At last, an advanced classification technique based on LS-SVM is applied to brain MR image classification using reduced features. For improving the efficiency, LS-SVM is used with non-linear radial basis function (RBF) kernel. The proposed algorithm intelligently determines the optimized values of the hyper-parameters of the RBF kernel and also applied k-fold stratified cross validation to enhance the generalization of the system. The method was tested by 340 patients' benchmark datasets of T1-weighted and T2-weighted scans. From the analysis of experimental results and performance comparisons, it is observed that the proposed medical decision support system outperformed all other modern classifiers and achieves 100% accuracy rate (specificity/sensitivity 100%/100%). Furthermore, in terms of computation time, the proposed technique is significantly faster than the recent well-known methods, and it improves the efficiency by 71%, 3%, and 4% on feature extraction stage, feature reduction stage, and classification stage, respectively. These results indicate that the proposed well-trained machine learning system has the potential to make accurate predictions about brain abnormalities from the individual subjects, therefore, it can be used as a significant tool in clinical practice.

Development of Android Based Powered Intelligent Wheelchair for Quadriplegic Persons

NASA Astrophysics Data System (ADS)

Gupta, Ashutosh; Ghosh, Tathagata; Kumar, Pradeep; Bhawna, Shruthi. S.

2017-08-01

Several surveys give us the view that both children and adults benefit substantially from access towards independent mobility. With the inventions of technology, no individuals are satisfied with traditional manual operated machines. To accommodate population, researchers are using technology, originally developed for mobile robots to create ‘intelligent wheelchairs’. It’s a major challenge for quadriplegic persons as they really find it difficult to manipulate powered wheelchair during the activities of their daily living. As the Smartphone era has evolved with innovative android based applications, engineers are improving and trying to make such machines simple and cheap to the next level. In this paper, we present a development of android based powered intelligent wheelchair to assist the quadriplegic person by making them self sufficient in controlling the wheelchair. The wheels of the chair can be controlled by the voice or gesture movement or by touching the screen of the android app by the challenged persons. The system uses the Bluetooth communication to interface the microcontroller and the inbuilt sensors in the android Smartphone. According to the commands received from android phone, the kinematics of the wheels are controlled.

Artificial intelligence and its impact on combat aircraft

NASA Technical Reports Server (NTRS)

Ott, Lawrence M.; Abbot, Kathy; Kleider, Alfred; Moon, D.; Retelle, John

1987-01-01

As the threat becomes more sophisticated and weapon systems more complex to meet the threat, the need for machines to assist the pilot in the assessment of information becomes paramount. This is particularly true in real-time, high stress situations. The advent of artificial intelligence (AI) technology offers the opportunity to make quantum advances in the application of machine technology. However, if AI systems are to find their way into combat aircraft, they must meet certain criteria. The systems must be responsive, reliable, easy to use, flexible, and understandable. These criteria are compared with the current status used in a combat airborne application. Current AI systems deal with nonreal time applications and require significant user interaction. On the other hand, aircraft applications require real time, minimum human interaction systems. In order to fill the gap between where technology is now and where it must be for aircraft applications, considerable government research is ongoing in NASA, DARPA, and three services. The ongoing research is briefly summarized. Finally, recognizing that AI technology is in its embryonic stage, and the aircraft needs are very demanding, a number of issues arise. These issues are delineated and findings are provided where appropriate.

A study on the applications of AI in finishing of additive manufacturing parts

NASA Astrophysics Data System (ADS)

Fathima Patham, K.

2017-06-01

Artificial intelligent and computer simulation are the technological powerful tools for solving complex problems in the manufacturing industries. Additive Manufacturing is one of the powerful manufacturing techniques that provide design flexibilities to the products. The products with complex shapes are directly manufactured without the need of any machining and tooling using Additive Manufacturing. However, the main drawback of the components produced using the Additive Manufacturing processes is the quality of the surfaces. This study aims to minimize the defects caused during Additive Manufacturing with the aid of Artificial Intelligence. The developed AI system has three layers, each layer is trying to eliminate or minimize the production errors. The first layer of the AI system optimizes the digitization of the 3D CAD model of the product and hence reduces the stair case errors. The second layer of the AI system optimizes the 3D printing machine parameters in order to eliminate the warping effect. The third layer of AI system helps to choose the surface finishing technique suitable for the printed component based on the Degree of Complexity of the product and the material. The efficiency of the developed AI system was examined on the functional parts such as gears.

Research on Intelligent Interface in Double-front Work Machines

NASA Astrophysics Data System (ADS)

Kamezaki, Mitsuhiro; Iwata, Hiroyasu; Sugano, Shigeki

This paper proposes a work state identification method with full independent of work environmental conditions and operator skill levels for construction machinery. Advanced operated-work machines, which have been designed for complicated tasks, require intelligent systems that can provide the quantitative work analysis needed to determine effective work procedures and that can provide operational and cognitive support for operators. Construction work environments are extremely complicated, however, and this makes state identification, which is a key technology for an intelligent system, difficult. We therefore defined primitive static states (PSS) that are determined using on-off information for the lever inputs and manipulator loads for each part of the grapple and front and that are completely independent of the various environmental conditions and variation in operator skill level that can cause an incorrect work state identification. To confirm the usefulness of PSS, we performed experiments with a demolition task by using our virtual reality simulator. We confirmed that PSS could robustly and accurately identify the work states and that untrained skills could be easily inferred from the results of PSS-based work analysis. We also confirmed in skill-training experiments that advice information based on PSS-based skill analysis greatly improved operator's work performance. We thus confirmed that PSS can adequately identify work states and are useful for work analysis and skill improvement.

Needed: A Clinton Crusade for Quality and Equality.

ERIC Educational Resources Information Center

Clinchy, Evans

1993-01-01

Our present factory-model educational system, now desperately undergoing "restructuring," was never intended as a social equalizer but as a great American academic and social sorting machine. The archaic, restrictive goals of America 2000 are doomed, along with narrowly defined intelligence quotients. A more revolutionary, restructuring…

Intelligent community management system based on the devicenet fieldbus

NASA Astrophysics Data System (ADS)

Wang, Yulan; Wang, Jianxiong; Liu, Jiwen

2013-03-01

With the rapid development of the national economy and the improvement of people's living standards, people are making higher demands on the living environment. And the estate management content, management efficiency and service quality have been higher required. This paper in-depth analyzes about the intelligent community of the structure and composition. According to the users' requirements and related specifications, it achieves the district management systems, which includes Basic Information Management: the management level of housing, household information management, administrator-level management, password management, etc. Service Management: standard property costs, property charges collecting, the history of arrears and other property expenses. Security Management: household gas, water, electricity and security and other security management, security management district and other public places. Systems Management: backup database, restore database, log management. This article also carries out on the Intelligent Community System analysis, proposes an architecture which is based on B / S technology system. And it has achieved a global network device management with friendly, easy to use, unified human - machine interface.

Integrated intelligent sensor for the textile industry

NASA Astrophysics Data System (ADS)

Peltie, Philippe; David, Dominique

1996-08-01

A new sensor has been developed for pantyhose inspection. Unlike a first complete inspection machine devoted to post- manufacturing control of the whole panty, this sensor will be directly integrated on currently existing manufacturing machines, and will combine advantages of miniaturization is to design an intelligent, compact and very cheap product, which should be integrated without requiring any modifications of host machines. The sensor part was designed to achieve closed acquisition, and various solutions have been explored to maintain adequate depth of field. The illumination source will be integrated in the device. The processing part will include correction facilities and electronic processing. Finally, high-level information will be output in order to interface directly with the manufacturing machine automate.

AI User Support System for SAP ERP

NASA Astrophysics Data System (ADS)

Vlasov, Vladimir; Chebotareva, Victoria; Rakhimov, Marat; Kruglikov, Sergey

2017-10-01

An intelligent system for SAP ERP user support is proposed in this paper. It enables automatic replies on users’ requests for support, saving time for problem analysis and resolution and improving responsiveness for end users. The system is based on an ensemble of machine learning algorithms of multiclass text classification, providing efficient question understanding, and a special framework for evidence retrieval, providing the best answer derivation.

Machine learning and radiology.

PubMed

Wang, Shijun; Summers, Ronald M

2012-07-01

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. Copyright © 2012. Published by Elsevier B.V.

[Artificial intelligence to assist clinical diagnosis in medicine].

PubMed

Lugo-Reyes, Saúl Oswaldo; Maldonado-Colín, Guadalupe; Murata, Chiharu

2014-01-01

Medicine is one of the fields of knowledge that would most benefit from a closer interaction with Computer studies and Mathematics by optimizing complex, imperfect processes such as differential diagnosis; this is the domain of Machine Learning, a branch of Artificial Intelligence that builds and studies systems capable of learning from a set of training data, in order to optimize classification and prediction processes. In Mexico during the last few years, progress has been made on the implementation of electronic clinical records, so that the National Institutes of Health already have accumulated a wealth of stored data. For those data to become knowledge, they need to be processed and analyzed through complex statistical methods, as it is already being done in other countries, employing: case-based reasoning, artificial neural networks, Bayesian classifiers, multivariate logistic regression, or support vector machines, among other methodologies; to assist the clinical diagnosis of acute appendicitis, breast cancer and chronic liver disease, among a wide array of maladies. In this review we shift through concepts, antecedents, current examples and methodologies of machine learning-assisted clinical diagnosis.

Integration of an intelligent systems behavior simulator and a scalable soldier-machine interface

NASA Astrophysics Data System (ADS)

Johnson, Tony; Manteuffel, Chris; Brewster, Benjamin; Tierney, Terry

2007-04-01

As the Army's Future Combat Systems (FCS) introduce emerging technologies and new force structures to the battlefield, soldiers will increasingly face new challenges in workload management. The next generation warfighter will be responsible for effectively managing robotic assets in addition to performing other missions. Studies of future battlefield operational scenarios involving the use of automation, including the specification of existing and proposed technologies, will provide significant insight into potential problem areas regarding soldier workload. The US Army Tank Automotive Research, Development, and Engineering Center (TARDEC) is currently executing an Army technology objective program to analyze and evaluate the effect of automated technologies and their associated control devices with respect to soldier workload. The Human-Robotic Interface (HRI) Intelligent Systems Behavior Simulator (ISBS) is a human performance measurement simulation system that allows modelers to develop constructive simulations of military scenarios with various deployments of interface technologies in order to evaluate operator effectiveness. One such interface is TARDEC's Scalable Soldier-Machine Interface (SMI). The scalable SMI provides a configurable machine interface application that is capable of adapting to several hardware platforms by recognizing the physical space limitations of the display device. This paper describes the integration of the ISBS and Scalable SMI applications, which will ultimately benefit both systems. The ISBS will be able to use the Scalable SMI to visualize the behaviors of virtual soldiers performing HRI tasks, such as route planning, and the scalable SMI will benefit from stimuli provided by the ISBS simulation environment. The paper describes the background of each system and details of the system integration approach.

Artificial intelligence - NASA. [robotics for Space Station

NASA Technical Reports Server (NTRS)

Erickson, J. D.

1985-01-01

Artificial Intelligence (AI) represents a vital common space support element needed to enable the civil space program and commercial space program to perform their missions successfully. It is pointed out that advances in AI stimulated by the Space Station Program could benefit the U.S. in many ways. A fundamental challenge for the civil space program is to meet the needs of the customers and users of space with facilities enabling maximum productivity and having low start-up costs, and low annual operating costs. An effective way to meet this challenge may involve a man-machine system in which artificial intelligence, robotics, and advanced automation are integrated into high reliability organizations. Attention is given to the benefits, NASA strategy for AI, candidate space station systems, the Space Station as a stepping stone, and the commercialization of space.

Design and Control of Large Collections of Learning Agents

NASA Technical Reports Server (NTRS)

Agogino, Adrian

2001-01-01

The intelligent control of multiple autonomous agents is an important yet difficult task. Previous methods used to address this problem have proved to be either too brittle, too hard to use, or not scalable to large systems. The 'Collective Intelligence' project at NASA/Ames provides an elegant, machine-learning approach to address these problems. This approach mathematically defines some essential properties that a reward system should have to promote coordinated behavior among reinforcement learners. This work has focused on creating additional key properties and algorithms within the mathematics of the Collective Intelligence framework. One of the additions will allow agents to learn more quickly, in a more coordinated manner. The other will let agents learn with less knowledge of their environment. These additions will allow the framework to be applied more easily, to a much larger domain of multi-agent problems.

Dynamism in a Semiconductor Industrial Machine Allocation Problem using a Hybrid of the Bio-inspired and Musical-Harmony Approach

NASA Astrophysics Data System (ADS)

Kalsom Yusof, Umi; Nor Akmal Khalid, Mohd

2015-05-01

Semiconductor industries need to constantly adjust to the rapid pace of change in the market. Most manufactured products usually have a very short life cycle. These scenarios imply the need to improve the efficiency of capacity planning, an important aspect of the machine allocation plan known for its complexity. Various studies have been performed to balance productivity and flexibility in the flexible manufacturing system (FMS). Many approaches have been developed by the researchers to determine the suitable balance between exploration (global improvement) and exploitation (local improvement). However, not much work has been focused on the domain of machine allocation problem that considers the effects of machine breakdowns. This paper develops a model to minimize the effect of machine breakdowns, thus increasing the productivity. The objectives are to minimize system unbalance and makespan as well as increase throughput while satisfying the technological constraints such as machine time availability. To examine the effectiveness of the proposed model, results for throughput, system unbalance and makespan on real industrial datasets were performed with applications of intelligence techniques, that is, a hybrid of genetic algorithm and harmony search. The result aims to obtain a feasible solution to the domain problem.

Automated expert modeling for automated student evaluation.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Abbott, Robert G.

The 8th International Conference on Intelligent Tutoring Systems provides a leading international forum for the dissemination of original results in the design, implementation, and evaluation of intelligent tutoring systems and related areas. The conference draws researchers from a broad spectrum of disciplines ranging from artificial intelligence and cognitive science to pedagogy and educational psychology. The conference explores intelligent tutoring systems increasing real world impact on an increasingly global scale. Improved authoring tools and learning object standards enable fielding systems and curricula in real world settings on an unprecedented scale. Researchers deploy ITS's in ever larger studies and increasingly use datamore » from real students, tasks, and settings to guide new research. With high volumes of student interaction data, data mining, and machine learning, tutoring systems can learn from experience and improve their teaching performance. The increasing number of realistic evaluation studies also broaden researchers knowledge about the educational contexts for which ITS's are best suited. At the same time, researchers explore how to expand and improve ITS/student communications, for example, how to achieve more flexible and responsive discourse with students, help students integrate Web resources into learning, use mobile technologies and games to enhance student motivation and learning, and address multicultural perspectives.« less

Research on knowledge representation, machine learning, and knowledge acquisition

NASA Technical Reports Server (NTRS)

Buchanan, Bruce G.

1987-01-01

Research in knowledge representation, machine learning, and knowledge acquisition performed at Knowledge Systems Lab. is summarized. The major goal of the research was to develop flexible, effective methods for representing the qualitative knowledge necessary for solving large problems that require symbolic reasoning as well as numerical computation. The research focused on integrating different representation methods to describe different kinds of knowledge more effectively than any one method can alone. In particular, emphasis was placed on representing and using spatial information about three dimensional objects and constraints on the arrangement of these objects in space. Another major theme is the development of robust machine learning programs that can be integrated with a variety of intelligent systems. To achieve this goal, learning methods were designed, implemented and experimented within several different problem solving environments.

A New Type of Tea Baking Machine Based on Pro/E Design

NASA Astrophysics Data System (ADS)

Lin, Xin-Ying; Wang, Wei

2017-11-01

In this paper, the production process of wulong tea was discussed, mainly the effect of baking on the quality of tea. The suitable baking temperature of different tea was introduced. Based on Pro/E, a new type of baking machine suitable for wulong tea baking was designed. The working principle, mechanical structure and constant temperature timing intelligent control system of baking machine were expounded. Finally, the characteristics and innovation of new baking machine were discussed.The mechanical structure of this baking machine is more simple and reasonable, and can use the heat of the inlet and outlet, more energy saving and environmental protection. The temperature control part adopts fuzzy PID control, which can improve the accuracy and response speed of temperature control and reduce the dependence of baking operation on skilled experience.

Neural networks with fuzzy Petri nets for modeling a machining process

NASA Astrophysics Data System (ADS)

Hanna, Moheb M.

1998-03-01

The paper presents an intelligent architecture based a feedforward neural network with fuzzy Petri nets for modeling product quality in a CNC machining center. It discusses how the proposed architecture can be used for modeling, monitoring and control a product quality specification such as surface roughness. The surface roughness represents the output quality specification manufactured by a CNC machining center as a result of a milling process. The neural network approach employed the selected input parameters which defined by the machine operator via the CNC code. The fuzzy Petri nets approach utilized the exact input milling parameters, such as spindle speed, feed rate, tool diameter and coolant (off/on), which can be obtained via the machine or sensors system. An aim of the proposed architecture is to model the demanded quality of surface roughness as high, medium or low.

Activities of the Remote Sensing Information Sciences Research Group

NASA Technical Reports Server (NTRS)

Estes, J. E.; Botkin, D.; Peuquet, D.; Smith, T.; Star, J. L. (Principal Investigator)

1984-01-01

Topics on the analysis and processing of remotely sensed data in the areas of vegetation analysis and modelling, georeferenced information systems, machine assisted information extraction from image data, and artificial intelligence are investigated. Discussions on support field data and specific applications of the proposed technologies are also included.

Sparse Distributed Representation and Hierarchy: Keys to Scalable Machine Intelligence

DTIC Science & Technology

2016-04-01

Lesher, Jasmin Leveille, and Oliver Layton Neurithmic Systems, LLC APRIL 2016 Final Report Approved for public release...61101E 6. AUTHOR(S) Gerard (Rod) Rinkus, Greg Lesher, Jasmin Leveille, and Oliver Layton 5d. PROJECT NUMBER 1000 5e. TASK NUMBER N/A 5f. WORK

Beyond Electronic Forms: E-Mail as an Institution-Wide Information Server.

ERIC Educational Resources Information Center

Jacobson, Carl

1992-01-01

The University of Delaware developed an intelligent mail server to provide easy, inexpensive access to institutional information for faculty, staff, and students on any node, machine, or operating system on the campuswide computing network. Security concerns have been addressed. The small investment has returned immediate benefits. (MSE)

Intelligent Tutoring Systems: Machine Learning Research

DTIC Science & Technology

1991-07-01

is composed of cutin . Ki: Cutin is impermeable to gases. Does the cuticle restrict water loss from the leaf? Domain Expert: Yes, that’s right. Kl...transported from inside the Leaf Epidermis to the atmosphere outside of the Leaf Epidermis. Rule 1: If an object is composed of cutin , then it is

Assessing Question Quality Using NLP

ERIC Educational Resources Information Center

Kopp, Kristopher J.; Johnson, Amy M.; Crossley, Scott A.; McNamara, Danielle S.

2017-01-01

An NLP algorithm was developed to assess question quality to inform feedback on questions generated by students within iSTART (an intelligent tutoring system that teaches reading strategies). A corpus of 4575 questions was coded using a four-level taxonomy. NLP indices were calculated for each question and machine learning was used to predict…

3D Self-Localisation From Angle of Arrival Measurements

DTIC Science & Technology

2009-04-01

systems can provide precise position information. However, there are situations where GPS is not adequate such as indoor, underwater, extraterrestrial or...Transactions on Pattern Analysis and Machine Intelligence , Vol. 22, No. 6, June 2000, pp 610-622. 7. Torrieri, D.J., "Statistical Theory of Passive Location

SHRIF, a General-Purpose System for Heuristic Retrieval of Information and Facts, Applied to Medical Knowledge Processing.

ERIC Educational Resources Information Center

Findler, Nicholas V.; And Others

1992-01-01

Describes SHRIF, a System for Heuristic Retrieval of Information and Facts, and the medical knowledge base that was used in its development. Highlights include design decisions; the user-machine interface, including the language processor; and the organization of the knowledge base in an artificial intelligence (AI) project like this one. (57…

Are human beings humean robots?

NASA Astrophysics Data System (ADS)

Génova, Gonzalo; Quintanilla Navarro, Ignacio

2018-01-01

David Hume, the Scottish philosopher, conceives reason as the slave of the passions, which implies that human reason has predetermined objectives it cannot question. An essential element of an algorithm running on a computational machine (or Logical Computing Machine, as Alan Turing calls it) is its having a predetermined purpose: an algorithm cannot question its purpose, because it would cease to be an algorithm. Therefore, if self-determination is essential to human intelligence, then human beings are neither Humean beings, nor computational machines. We examine also some objections to the Turing Test as a model to understand human intelligence.

Advanced controls for light sources

NASA Astrophysics Data System (ADS)

Biedron, S. G.; Edelen, A. L.; Milton, S. V.

2016-09-01

We present a summary of our team's recent efforts in developing adaptive, artificial intelligence-inspired techniques specifically to address several control challenges that arise in machines/systems including those in particle accelerator systems. These techniques can readily be adapted to other systems such as lasers, beamline optics, etc… We are not at all suggesting that we create an autonomous system, but create a system with an intelligent control system, that can continually use operational data to improve itself and combines both traditional and advanced techniques. We believe that the system performance and reliability can be increased based on our findings. Another related point is that the controls sub-system of an overall system is usually not the heart of the system architecture or design process. More bluntly, often times all of the peripheral systems are considered as secondary to the main system components in the architecture design process because it is assumed that the controls system will be able to "fix" challenges found later with the sub-systems for overall system operation. We will show that this is not always the case and that it took an intelligent control application to overcome a sub-system's challenges. We will provide a recent example of such a "fix" with a standard controller and with an artificial intelligence-inspired controller. A final related point to be covered is that of system adaptation for requirements not original to a system's original design.

Artificial intelligence in healthcare: past, present and future.

PubMed

Jiang, Fei; Jiang, Yong; Zhi, Hui; Dong, Yi; Li, Hao; Ma, Sufeng; Wang, Yilong; Dong, Qiang; Shen, Haipeng; Wang, Yongjun

2017-12-01

Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology. We then review in more details the AI applications in stroke, in the three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation. We conclude with discussion about pioneer AI systems, such as IBM Watson, and hurdles for real-life deployment of AI.

Artificial intelligence in healthcare: past, present and future

PubMed Central

Jiang, Fei; Jiang, Yong; Zhi, Hui; Dong, Yi; Li, Hao; Ma, Sufeng; Wang, Yilong; Dong, Qiang; Shen, Haipeng; Wang, Yongjun

2017-01-01

Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology. We then review in more details the AI applications in stroke, in the three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation. We conclude with discussion about pioneer AI systems, such as IBM Watson, and hurdles for real-life deployment of AI. PMID:29507784

Logic Learning Machine and standard supervised methods for Hodgkin's lymphoma prognosis using gene expression data and clinical variables.

PubMed

Parodi, Stefano; Manneschi, Chiara; Verda, Damiano; Ferrari, Enrico; Muselli, Marco

2018-03-01

This study evaluates the performance of a set of machine learning techniques in predicting the prognosis of Hodgkin's lymphoma using clinical factors and gene expression data. Analysed samples from 130 Hodgkin's lymphoma patients included a small set of clinical variables and more than 54,000 gene features. Machine learning classifiers included three black-box algorithms ( k-nearest neighbour, Artificial Neural Network, and Support Vector Machine) and two methods based on intelligible rules (Decision Tree and the innovative Logic Learning Machine method). Support Vector Machine clearly outperformed any of the other methods. Among the two rule-based algorithms, Logic Learning Machine performed better and identified a set of simple intelligible rules based on a combination of clinical variables and gene expressions. Decision Tree identified a non-coding gene ( XIST) involved in the early phases of X chromosome inactivation that was overexpressed in females and in non-relapsed patients. XIST expression might be responsible for the better prognosis of female Hodgkin's lymphoma patients.

Visual feature extraction and establishment of visual tags in the intelligent visual internet of things

NASA Astrophysics Data System (ADS)

Zhao, Yiqun; Wang, Zhihui

2015-12-01

The Internet of things (IOT) is a kind of intelligent networks which can be used to locate, track, identify and supervise people and objects. One of important core technologies of intelligent visual internet of things ( IVIOT) is the intelligent visual tag system. In this paper, a research is done into visual feature extraction and establishment of visual tags of the human face based on ORL face database. Firstly, we use the principal component analysis (PCA) algorithm for face feature extraction, then adopt the support vector machine (SVM) for classifying and face recognition, finally establish a visual tag for face which is already classified. We conducted a experiment focused on a group of people face images, the result show that the proposed algorithm have good performance, and can show the visual tag of objects conveniently.

Machine intelligence-based decision-making (MIND) for automatic anomaly detection

NASA Astrophysics Data System (ADS)

Prasad, Nadipuram R.; King, Jason C.; Lu, Thomas

2007-04-01

Any event deemed as being out-of-the-ordinary may be called an anomaly. Anomalies by virtue of their definition are events that occur spontaneously with no prior indication of their existence or appearance. Effects of anomalies are typically unknown until they actually occur, and their effects aggregate in time to show noticeable change from the original behavior. An evolved behavior would in general be very difficult to correct unless the anomalous event that caused such behavior can be detected early, and any consequence attributed to the specific anomaly. Substantial time and effort is required to back-track the cause for abnormal behavior and to recreate the event sequence leading to abnormal behavior. There is a critical need therefore to automatically detect anomalous behavior as and when they may occur, and to do so with the operator in the loop. Human-machine interaction results in better machine learning and a better decision-support mechanism. This is the fundamental concept of intelligent control where machine learning is enhanced by interaction with human operators, and vice versa. The paper discusses a revolutionary framework for the characterization, detection, identification, learning, and modeling of anomalous behavior in observed phenomena arising from a large class of unknown and uncertain dynamical systems.

Evolutionary and Neural Computing Based Decision Support System for Disease Diagnosis from Clinical Data Sets in Medical Practice.

PubMed

Sudha, M

2017-09-27

As a recent trend, various computational intelligence and machine learning approaches have been used for mining inferences hidden in the large clinical databases to assist the clinician in strategic decision making. In any target data the irrelevant information may be detrimental, causing confusion for the mining algorithm and degrades the prediction outcome. To address this issue, this study attempts to identify an intelligent approach to assist disease diagnostic procedure using an optimal set of attributes instead of all attributes present in the clinical data set. In this proposed Application Specific Intelligent Computing (ASIC) decision support system, a rough set based genetic algorithm is employed in pre-processing phase and a back propagation neural network is applied in training and testing phase. ASIC has two phases, the first phase handles outliers, noisy data, and missing values to obtain a qualitative target data to generate appropriate attribute reduct sets from the input data using rough computing based genetic algorithm centred on a relative fitness function measure. The succeeding phase of this system involves both training and testing of back propagation neural network classifier on the selected reducts. The model performance is evaluated with widely adopted existing classifiers. The proposed ASIC system for clinical decision support has been tested with breast cancer, fertility diagnosis and heart disease data set from the University of California at Irvine (UCI) machine learning repository. The proposed system outperformed the existing approaches attaining the accuracy rate of 95.33%, 97.61%, and 93.04% for breast cancer, fertility issue and heart disease diagnosis.

An Intelligent and Interactive Simulation and Tutoring Environment for Exploring and Learning Simple Machines

NASA Astrophysics Data System (ADS)

Myneni, Lakshman Sundeep

Students in middle school science classes have difficulty mastering physics concepts such as energy and work, taught in the context of simple machines. Moreover, students' naive conceptions of physics often remain unchanged after completing a science class. To address this problem, I developed an intelligent tutoring system, called the Virtual Physics System (ViPS), which coaches students through problem solving with one class of simple machines, pulley systems. The tutor uses a unique cognitive based approach to teaching simple machines, and includes innovations in three areas. (1) It employs a teaching strategy that focuses on highlighting links among concepts of the domain that are essential for conceptual understanding yet are seldom learned by students. (2) Concepts are taught through a combination of effective human tutoring techniques (e.g., hinting) and simulations. (3) For each student, the system identifies which misconceptions he or she has, from a common set of student misconceptions gathered from domain experts, and tailors tutoring to match the correct line of scientific reasoning regarding the misconceptions. ViPS was implemented as a platform on which students can design and simulate pulley system experiments, integrated with a constraint-based tutor that intervenes when students make errors during problem solving to teach them and to help them. ViPS has a web-based client-server architecture, and has been implemented using Java technologies. ViPS is different from existing physics simulations and tutoring systems due to several original features. (1). It is the first system to integrate a simulation based virtual experimentation platform with an intelligent tutoring component. (2) It uses a novel approach, based on Bayesian networks, to help students construct correct pulley systems for experimental simulation. (3) It identifies student misconceptions based on a novel decision tree applied to student pretest scores, and tailors tutoring to individual students based on detected misconceptions. ViPS has been evaluated through usability and usefulness experiments with undergraduate engineering students taking their first college-level engineering physics course and undergraduate pre-service teachers taking their first college-level physics course. These experiments demonstrated that ViPS is highly usable and effective. Students using ViPS reduced their misconceptions, and students conducting virtual experiments in ViPS learned more than students who conducted experiments with physical pulley systems. Interestingly, it was also found that college students exhibited many of the same misconceptions that have been identified in middle school students.

Application of machine learning and expert systems to Statistical Process Control (SPC) chart interpretation

NASA Technical Reports Server (NTRS)

Shewhart, Mark

1991-01-01

Statistical Process Control (SPC) charts are one of several tools used in quality control. Other tools include flow charts, histograms, cause and effect diagrams, check sheets, Pareto diagrams, graphs, and scatter diagrams. A control chart is simply a graph which indicates process variation over time. The purpose of drawing a control chart is to detect any changes in the process signalled by abnormal points or patterns on the graph. The Artificial Intelligence Support Center (AISC) of the Acquisition Logistics Division has developed a hybrid machine learning expert system prototype which automates the process of constructing and interpreting control charts.

NASA Ames aerospace systems directorate research

NASA Technical Reports Server (NTRS)

Albers, James A.

1991-01-01

The Aerospace Systems Directorate is one of four research directorates at the NASA Ames Research Center. The Directorate conducts research and technology development for advanced aircraft and aircraft systems in intelligent computational systems and human-machine systems for aeronautics and space. The Directorate manages research and aircraft technology development projects, and operates and maintains major wind tunnels and flight simulation facilities. The Aerospace Systems Directorate's research and technology as it relates to NASA agency goals and specific strategic thrusts are discussed.

Capabilities Assessment and Employment Recommendations for Full Motion Video Optical Navigation Exploitation (FMV-ONE)

DTIC Science & Technology

2015-06-01

GEOINT geospatial intelligence GFC ground force commander GPS global positioning system GUI graphical user interface HA/DR humanitarian...transport stream UAS unmanned aerial system . See UAV. UAV unmanned aerial vehicle. See UAS. VM virtual machine VMU Marine Unmanned Aerial Vehicle... Unmanned Air Systems (UASs). Current programs promise to dramatically increase the number of FMV feeds in the near future. However, there are too

Harnessing Disordered-Ensemble Quantum Dynamics for Machine Learning

NASA Astrophysics Data System (ADS)

Fujii, Keisuke; Nakajima, Kohei

2017-08-01

The quantum computer has an amazing potential of fast information processing. However, the realization of a digital quantum computer is still a challenging problem requiring highly accurate controls and key application strategies. Here we propose a platform, quantum reservoir computing, to solve these issues successfully by exploiting the natural quantum dynamics of ensemble systems, which are ubiquitous in laboratories nowadays, for machine learning. This framework enables ensemble quantum systems to universally emulate nonlinear dynamical systems including classical chaos. A number of numerical experiments show that quantum systems consisting of 5-7 qubits possess computational capabilities comparable to conventional recurrent neural networks of 100-500 nodes. This discovery opens up a paradigm for information processing with artificial intelligence powered by quantum physics.

Information integration and diagnosis analysis of equipment status and production quality for machining process

NASA Astrophysics Data System (ADS)

Zan, Tao; Wang, Min; Hu, Jianzhong

2010-12-01

Machining status monitoring technique by multi-sensors can acquire and analyze the machining process information to implement abnormity diagnosis and fault warning. Statistical quality control technique is normally used to distinguish abnormal fluctuations from normal fluctuations through statistical method. In this paper by comparing the advantages and disadvantages of the two methods, the necessity and feasibility of integration and fusion is introduced. Then an approach that integrates multi-sensors status monitoring and statistical process control based on artificial intelligent technique, internet technique and database technique is brought forward. Based on virtual instrument technique the author developed the machining quality assurance system - MoniSysOnline, which has been used to monitoring the grinding machining process. By analyzing the quality data and AE signal information of wheel dressing process the reason of machining quality fluctuation has been obtained. The experiment result indicates that the approach is suitable for the status monitoring and analyzing of machining process.

Techniques and potential capabilities of multi-resolutional information (knowledge) processing

NASA Technical Reports Server (NTRS)

Meystel, A.

1989-01-01

A concept of nested hierarchical (multi-resolutional, pyramidal) information (knowledge) processing is introduced for a variety of systems including data and/or knowledge bases, vision, control, and manufacturing systems, industrial automated robots, and (self-programmed) autonomous intelligent machines. A set of practical recommendations is presented using a case study of a multiresolutional object representation. It is demonstrated here that any intelligent module transforms (sometimes, irreversibly) the knowledge it deals with, and this tranformation affects the subsequent computation processes, e.g., those of decision and control. Several types of knowledge transformation are reviewed. Definite conditions are analyzed, satisfaction of which is required for organization and processing of redundant information (knowledge) in the multi-resolutional systems. Providing a definite degree of redundancy is one of these conditions.

A comparative study of machine learning models for ethnicity classification

NASA Astrophysics Data System (ADS)

Trivedi, Advait; Bessie Amali, D. Geraldine

2017-11-01

This paper endeavours to adopt a machine learning approach to solve the problem of ethnicity recognition. Ethnicity identification is an important vision problem with its use cases being extended to various domains. Despite the multitude of complexity involved, ethnicity identification comes naturally to humans. This meta information can be leveraged to make several decisions, be it in target marketing or security. With the recent development of intelligent systems a sub module to efficiently capture ethnicity would be useful in several use cases. Several attempts to identify an ideal learning model to represent a multi-ethnic dataset have been recorded. A comparative study of classifiers such as support vector machines, logistic regression has been documented. Experimental results indicate that the logical classifier provides a much accurate classification than the support vector machine.

Big Data Analytics and Machine Intelligence Capability Development at NASA Langley Research Center: Strategy, Roadmap, and Progress

NASA Technical Reports Server (NTRS)

Ambur, Manjula Y.; Yagle, Jeremy J.; Reith, William; McLarney, Edward

2016-01-01

In 2014, a team of researchers, engineers and information technology specialists at NASA Langley Research Center developed a Big Data Analytics and Machine Intelligence Strategy and Roadmap as part of Langley's Comprehensive Digital Transformation Initiative, with the goal of identifying the goals, objectives, initiatives, and recommendations need to develop near-, mid- and long-term capabilities for data analytics and machine intelligence in aerospace domains. Since that time, significant progress has been made in developing pilots and projects in several research, engineering, and scientific domains by following the original strategy of collaboration between mission support organizations, mission organizations, and external partners from universities and industry. This report summarizes the work to date in Data Intensive Scientific Discovery, Deep Content Analytics, and Deep Q&A projects, as well as the progress made in collaboration, outreach, and education. Recommendations for continuing this success into future phases of the initiative are also made.

Data Mining at NASA: From Theory to Applications

NASA Technical Reports Server (NTRS)

Srivastava, Ashok N.

2009-01-01

This slide presentation demonstrates the data mining/machine learning capabilities of NASA Ames and Intelligent Data Understanding (IDU) group. This will encompass the work done recently in the group by various group members. The IDU group develops novel algorithms to detect, classify, and predict events in large data streams for scientific and engineering systems. This presentation for Knowledge Discovery and Data Mining 2009 is to demonstrate the data mining/machine learning capabilities of NASA Ames and IDU group. This will encompass the work done re cently in the group by various group members.

Alien Mindscapes—A Perspective on the Search for Extraterrestrial Intelligence

NASA Astrophysics Data System (ADS)

Cabrol, Nathalie A.

2016-09-01

Advances in planetary and space sciences, astrobiology, and life and cognitive sciences, combined with developments in communication theory, bioneural computing, machine learning, and big data analysis, create new opportunities to explore the probabilistic nature of alien life. Brought together in a multidisciplinary approach, they have the potential to support an integrated and expanded Search for Extraterrestrial Intelligence (SETI1), a search that includes looking for life as we do not know it. This approach will augment the odds of detecting a signal by broadening our understanding of the evolutionary and systemic components in the search for extraterrestrial intelligence (ETI), provide more targets for radio and optical SETI, and identify new ways of decoding and coding messages using universal markers.

Long-Term Perspectives on Interstellar Flight

NASA Astrophysics Data System (ADS)

Michaud, M. A. G.

Realizing interstellar travel by machines or living beings will require not only scientific and technological progress, but also a shared secular belief among a determined minority that this enterprise is important for the human future. Their efforts may have to extend beyond individual human lifetimes. Historical perspectives, on both the past and the future, are proposed. Interstellar probes could be a more thorough way of searching for alien forms of life and intelligence in nearby systems, particularly if there were intelligent beings there who did not employ technologies our astronomical observing devices can detect from here. Perspectives on the ethical, policy, and design issues of such close encounters with alien life and intelligence are presented. Ways of accelerating the coming of interstellar probes are suggested.

Decision making and problem solving with computer assistance

NASA Technical Reports Server (NTRS)

Kraiss, F.

1980-01-01

In modern guidance and control systems, the human as manager, supervisor, decision maker, problem solver and trouble shooter, often has to cope with a marginal mental workload. To improve this situation, computers should be used to reduce the operator from mental stress. This should not solely be done by increased automation, but by a reasonable sharing of tasks in a human-computer team, where the computer supports the human intelligence. Recent developments in this area are summarized. It is shown that interactive support of operator by intelligent computer is feasible during information evaluation, decision making and problem solving. The applied artificial intelligence algorithms comprehend pattern recognition and classification, adaptation and machine learning as well as dynamic and heuristic programming. Elementary examples are presented to explain basic principles.

[Algorithms, machine intelligence, big data : general considerations].

PubMed

Radermacher, F J

2015-08-01

We are experiencing astonishing developments in the areas of big data and artificial intelligence. They follow a pattern that we have now been observing for decades: according to Moore's Law,the performance and efficiency in the area of elementary arithmetic operations increases a thousand-fold every 20 years. Although we have not achieved the status where in the singular sense machines have become as "intelligent" as people, machines are becoming increasingly better. The Internet of Things has again helped to massively increase the efficiency of machines. Big data and suitable analytics do the same. If we let these processes simply continue, our civilization may be endangerd in many instances. If the "containment" of these processes succeeds in the context of a reasonable political global governance, a worldwide eco-social market economy, andan economy of green and inclusive markets, many desirable developments that are advantageous for our future may result. Then, at some point in time, the constant need for more and faster innovation may even stop. However, this is anything but certain. We are facing huge challenges.

Machine Translation in Post-Contemporary Era

ERIC Educational Resources Information Center

Lin, Grace Hui Chin

2010-01-01

This article focusing on translating techniques via personal computer or laptop reports updated artificial intelligence progresses before 2010. Based on interpretations and information for field of MT [Machine Translation] by Yorick Wilks' book, "Machine Translation, Its scope and limits," this paper displays understandable theoretical frameworks…

System Diagnostic Builder - A rule generation tool for expert systems that do intelligent data evaluation. [applied to Shuttle Mission Simulator

NASA Technical Reports Server (NTRS)

Nieten, Joseph; Burke, Roger

1993-01-01

Consideration is given to the System Diagnostic Builder (SDB), an automated knowledge acquisition tool using state-of-the-art AI technologies. The SDB employs an inductive machine learning technique to generate rules from data sets that are classified by a subject matter expert. Thus, data are captured from the subject system, classified, and used to drive the rule generation process. These rule bases are used to represent the observable behavior of the subject system, and to represent knowledge about this system. The knowledge bases captured from the Shuttle Mission Simulator can be used as black box simulations by the Intelligent Computer Aided Training devices. The SDB can also be used to construct knowledge bases for the process control industry, such as chemical production or oil and gas production.

Brain Network Architecture and Global Intelligence in Children with Focal Epilepsy.

PubMed

Paldino, M J; Golriz, F; Chapieski, M L; Zhang, W; Chu, Z D

2017-02-01

The biologic basis for intelligence rests to a large degree on the capacity for efficient integration of information across the cerebral network. We aimed to measure the relationship between network architecture and intelligence in the pediatric, epileptic brain. Patients were retrospectively identified with the following: 1) focal epilepsy; 2) brain MR imaging at 3T, including resting-state functional MR imaging; and 3) full-scale intelligence quotient measured by a pediatric neuropsychologist. The cerebral cortex was parcellated into approximately 700 gray matter network "nodes." The strength of a connection between 2 nodes was defined by the correlation between their blood oxygen level-dependent time-series. We calculated the following topologic properties: clustering coefficient, transitivity, modularity, path length, and global efficiency. A machine learning algorithm was used to measure the independent contribution of each metric to the intelligence quotient after adjusting for all other metrics. Thirty patients met the criteria (4-18 years of age); 20 patients required anesthesia during MR imaging. After we accounted for age and sex, clustering coefficient and path length were independently associated with full-scale intelligence quotient. Neither motion parameters nor general anesthesia was an important variable with regard to accurate intelligence quotient prediction by the machine learning algorithm. A longer history of epilepsy was associated with shorter path lengths ( P = .008), consistent with reorganization of the network on the basis of seizures. Considering only patients receiving anesthesia during machine learning did not alter the patterns of network architecture contributing to global intelligence. These findings support the physiologic relevance of imaging-based metrics of network architecture in the pathologic, developing brain. © 2017 by American Journal of Neuroradiology.

Program Helps Decompose Complex Design Systems

NASA Technical Reports Server (NTRS)

Rogers, James L., Jr.; Hall, Laura E.

1994-01-01

DeMAID (A Design Manager's Aid for Intelligent Decomposition) computer program is knowledge-based software system for ordering sequence of modules and identifying possible multilevel structure for design problem. Groups modular subsystems on basis of interactions among them. Saves considerable money and time in total design process, particularly in new design problem in which order of modules has not been defined. Available in two machine versions: Macintosh and Sun.

Machine learning methods for the classification of gliomas: Initial results using features extracted from MR spectroscopy.

PubMed

Ranjith, G; Parvathy, R; Vikas, V; Chandrasekharan, Kesavadas; Nair, Suresh

2015-04-01

With the advent of new imaging modalities, radiologists are faced with handling increasing volumes of data for diagnosis and treatment planning. The use of automated and intelligent systems is becoming essential in such a scenario. Machine learning, a branch of artificial intelligence, is increasingly being used in medical image analysis applications such as image segmentation, registration and computer-aided diagnosis and detection. Histopathological analysis is currently the gold standard for classification of brain tumors. The use of machine learning algorithms along with extraction of relevant features from magnetic resonance imaging (MRI) holds promise of replacing conventional invasive methods of tumor classification. The aim of the study is to classify gliomas into benign and malignant types using MRI data. Retrospective data from 28 patients who were diagnosed with glioma were used for the analysis. WHO Grade II (low-grade astrocytoma) was classified as benign while Grade III (anaplastic astrocytoma) and Grade IV (glioblastoma multiforme) were classified as malignant. Features were extracted from MR spectroscopy. The classification was done using four machine learning algorithms: multilayer perceptrons, support vector machine, random forest and locally weighted learning. Three of the four machine learning algorithms gave an area under ROC curve in excess of 0.80. Random forest gave the best performance in terms of AUC (0.911) while sensitivity was best for locally weighted learning (86.1%). The performance of different machine learning algorithms in the classification of gliomas is promising. An even better performance may be expected by integrating features extracted from other MR sequences. © The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Research and Development Program Plan for the Center for Engineering Systems Advanced Research (CESAR)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weisbin, C.R.; Barhen, J.; Hamel, W.R.

1987-01-01

A general framework is provided for more detailed planning on an annual basis, both for DOE and for other sponsoring organizations. The charter for the overall intelligent machine research effort is presented along with programmatic and technological goals. A research implementation plan is provided through the FY 1989 horizon.

Investigating the Relationship between Dialogue Structure and Tutoring Effectiveness: A Hidden Markov Modeling Approach

ERIC Educational Resources Information Center

Boyer, Kristy Elizabeth; Phillips, Robert; Ingram, Amy; Ha, Eun Young; Wallis, Michael; Vouk, Mladen; Lester, James

2011-01-01

Identifying effective tutorial dialogue strategies is a key issue for intelligent tutoring systems research. Human-human tutoring offers a valuable model for identifying effective tutorial strategies, but extracting them is a challenge because of the richness of human dialogue. This article addresses that challenge through a machine learning…

The intelligent database machine

NASA Technical Reports Server (NTRS)

Yancey, K. E.

1985-01-01

The IDM data base was compared with the data base crack to determine whether IDM 500 would better serve the needs of the MSFC data base management system than Oracle. The two were compared and the performance of the IDM was studied. Implementations that work best on which database are implicated. The choice is left to the database administrator.

Ramp Technology and Intelligent Processing in Small Manufacturing

NASA Technical Reports Server (NTRS)

Rentz, Richard E.

1992-01-01

To address the issues of excessive inventories and increasing procurement lead times, the Navy is actively pursuing flexible computer integrated manufacturing (FCIM) technologies, integrated by communication networks to respond rapidly to its requirements for parts. The Rapid Acquisition of Manufactured Parts (RAMP) program, initiated in 1986, is an integral part of this effort. The RAMP program's goal is to reduce the current average production lead times experienced by the Navy's inventory control points by a factor of 90 percent. The manufacturing engineering component of the RAMP architecture utilizes an intelligent processing technology built around a knowledge-based shell provided by ICAD, Inc. Rules and data bases in the software simulate an expert manufacturing planner's knowledge of shop processes and equipment. This expert system can use Product Data Exchange using STEP (PDES) data to determine what features the required part has, what material is required to manufacture it, what machines and tools are needed, and how the part should be held (fixtured) for machining, among other factors. The program's rule base then indicates, for example, how to make each feature, in what order to make it, and to which machines on the shop floor the part should be routed for processing. This information becomes part of the shop work order. The process planning function under RAMP greatly reduces the time and effort required to complete a process plan. Since the PDES file that drives the intelligent processing is 100 percent complete and accurate to start with, the potential for costly errors is greatly diminished.

Ramp technology and intelligent processing in small manufacturing

NASA Astrophysics Data System (ADS)

Rentz, Richard E.

1992-04-01

To address the issues of excessive inventories and increasing procurement lead times, the Navy is actively pursuing flexible computer integrated manufacturing (FCIM) technologies, integrated by communication networks to respond rapidly to its requirements for parts. The Rapid Acquisition of Manufactured Parts (RAMP) program, initiated in 1986, is an integral part of this effort. The RAMP program's goal is to reduce the current average production lead times experienced by the Navy's inventory control points by a factor of 90 percent. The manufacturing engineering component of the RAMP architecture utilizes an intelligent processing technology built around a knowledge-based shell provided by ICAD, Inc. Rules and data bases in the software simulate an expert manufacturing planner's knowledge of shop processes and equipment. This expert system can use Product Data Exchange using STEP (PDES) data to determine what features the required part has, what material is required to manufacture it, what machines and tools are needed, and how the part should be held (fixtured) for machining, among other factors. The program's rule base then indicates, for example, how to make each feature, in what order to make it, and to which machines on the shop floor the part should be routed for processing. This information becomes part of the shop work order. The process planning function under RAMP greatly reduces the time and effort required to complete a process plan. Since the PDES file that drives the intelligent processing is 100 percent complete and accurate to start with, the potential for costly errors is greatly diminished.

A coordination theory for intelligent machines

NASA Technical Reports Server (NTRS)

Wang, Fei-Yue; Saridis, George N.

1990-01-01

A formal model for the coordination level of intelligent machines is established. The framework of the coordination level investigated consists of one dispatcher and a number of coordinators. The model called coordination structure has been used to describe analytically the information structure and information flow for the coordination activities in the coordination level. Specifically, the coordination structure offers a formalism to (1) describe the task translation of the dispatcher and coordinators; (2) represent the individual process within the dispatcher and coordinators; (3) specify the cooperation and connection among the dispatcher and coordinators; (4) perform the process analysis and evaluation; and (5) provide a control and communication mechanism for the real-time monitor or simulation of the coordination process. A simple procedure for the task scheduling in the coordination structure is presented. The task translation is achieved by a stochastic learning algorithm. The learning process is measured with entropy and its convergence is guaranteed. Finally, a case study of the coordination structure with three coordinators and one dispatcher for a simple intelligent manipulator system illustrates the proposed model and the simulation of the task processes performed on the model verifies the soundness of the theory.

Combining Human and Machine Intelligence to Derive Agents' Behavioral Rules for Groundwater Irrigation

NASA Astrophysics Data System (ADS)

Hu, Y.; Quinn, C.; Cai, X.

2015-12-01

One major challenge of agent-based modeling is to derive agents' behavioral rules due to behavioral uncertainty and data scarcity. This study proposes a new approach to combine a data-driven modeling based on the directed information (i.e., machine intelligence) with expert domain knowledge (i.e., human intelligence) to derive the behavioral rules of agents considering behavioral uncertainty. A directed information graph algorithm is applied to identifying the causal relationships between agents' decisions (i.e., groundwater irrigation depth) and time-series of environmental, socio-economical and institutional factors. A case study is conducted for the High Plains aquifer hydrological observatory (HO) area, U.S. Preliminary results show that four factors, corn price (CP), underlying groundwater level (GWL), monthly mean temperature (T) and precipitation (P) have causal influences on agents' decisions on groundwater irrigation depth (GWID) to various extents. Based on the similarity of the directed information graph for each agent, five clusters of graphs are further identified to represent all the agents' behaviors in the study area as shown in Figure 1. Using these five representative graphs, agents' monthly optimal groundwater pumping rates are derived through the probabilistic inference. Such data-driven relationships and probabilistic quantifications are then coupled with a physically-based groundwater model to investigate the interactions between agents' pumping behaviors and the underlying groundwater system in the context of coupled human and natural systems.

An Intelligent Sensor Array Distributed System for Vibration Analysis and Acoustic Noise Characterization of a Linear Switched Reluctance Actuator

PubMed Central

Salvado, José; Espírito-Santo, António; Calado, Maria

2012-01-01

This paper proposes a distributed system for analysis and monitoring (DSAM) of vibrations and acoustic noise, which consists of an array of intelligent modules, sensor modules, communication bus and a host PC acting as data center. The main advantages of the DSAM are its modularity, scalability, and flexibility for use of different type of sensors/transducers, with analog or digital outputs, and for signals of different nature. Its final cost is also significantly lower than other available commercial solutions. The system is reconfigurable, can operate either with synchronous or asynchronous modes, with programmable sampling frequencies, 8-bit or 12-bit resolution and a memory buffer of 15 kbyte. It allows real-time data-acquisition for signals of different nature, in applications that require a large number of sensors, thus it is suited for monitoring of vibrations in Linear Switched Reluctance Actuators (LSRAs). The acquired data allows the full characterization of the LSRA in terms of its response to vibrations of structural origins, and the vibrations and acoustic noise emitted under normal operation. The DSAM can also be used for electrical machine condition monitoring, machine fault diagnosis, structural characterization and monitoring, among other applications. PMID:22969364

An ontological knowledge framework for adaptive medical workflow.

PubMed

Dang, Jiangbo; Hedayati, Amir; Hampel, Ken; Toklu, Candemir

2008-10-01

As emerging technologies, semantic Web and SOA (Service-Oriented Architecture) allow BPMS (Business Process Management System) to automate business processes that can be described as services, which in turn can be used to wrap existing enterprise applications. BPMS provides tools and methodologies to compose Web services that can be executed as business processes and monitored by BPM (Business Process Management) consoles. Ontologies are a formal declarative knowledge representation model. It provides a foundation upon which machine understandable knowledge can be obtained, and as a result, it makes machine intelligence possible. Healthcare systems can adopt these technologies to make them ubiquitous, adaptive, and intelligent, and then serve patients better. This paper presents an ontological knowledge framework that covers healthcare domains that a hospital encompasses-from the medical or administrative tasks, to hospital assets, medical insurances, patient records, drugs, and regulations. Therefore, our ontology makes our vision of personalized healthcare possible by capturing all necessary knowledge for a complex personalized healthcare scenario involving patient care, insurance policies, and drug prescriptions, and compliances. For example, our ontology facilitates a workflow management system to allow users, from physicians to administrative assistants, to manage, even create context-aware new medical workflows and execute them on-the-fly.

The Design, Development and Testing of Complex Avionics Systems: Conference Proceedings Held at the Avionics Panel Symposium in Las Vegas, Nevada on 27 April-1 May 1987

DTIC Science & Technology

1987-12-01

Normally, the system is decomposed into manageable parts with accurately defined interfaces. By rigidly controlling this process, aerospace companies have...Reference A CHANGE IN SYSTEM DESIGN EMPHASIS: FROM MACHINE TO MAN by M.L.Metersky and J.L.Ryder 16 SESSION I1 - MANAGING THE FUl URE SYSTEM DESIGN...PROCESS MANAGING ADVANCED AVIONIC SYSTEM DESIGN by P.Simons 17 ERGONOMIE PSYCHOSENSORIELLE DES COCKPITS, INTERET DES SYSTEMES INFORMATIQUES INTELLIGENTS

Matrix Multiplication Algorithm Selection with Support Vector Machines

DTIC Science & Technology

2015-05-01

libraries that could intelligently choose the optimal algorithm for a particular set of inputs. Users would be oblivious to the underlying algorithmic...SAT.” J. Artif . Intell. Res.(JAIR), vol. 32, pp. 565–606, 2008. [9] M. G. Lagoudakis and M. L. Littman, “Algorithm selection using reinforcement...Artificial Intelligence , vol. 21, no. 05, pp. 961–976, 2007. [15] C.-C. Chang and C.-J. Lin, “LIBSVM: A library for support vector machines,” ACM

Intelligent software for laboratory automation.

PubMed

Whelan, Ken E; King, Ross D

2004-09-01

The automation of laboratory techniques has greatly increased the number of experiments that can be carried out in the chemical and biological sciences. Until recently, this automation has focused primarily on improving hardware. Here we argue that future advances will concentrate on intelligent software to integrate physical experimentation and results analysis with hypothesis formulation and experiment planning. To illustrate our thesis, we describe the 'Robot Scientist' - the first physically implemented example of such a closed loop system. In the Robot Scientist, experimentation is performed by a laboratory robot, hypotheses concerning the results are generated by machine learning and experiments are allocated and selected by a combination of techniques derived from artificial intelligence research. The performance of the Robot Scientist has been evaluated by a rediscovery task based on yeast functional genomics. The Robot Scientist is proof that the integration of programmable laboratory hardware and intelligent software can be used to develop increasingly automated laboratories.

Automation and robotics for Space Station in the twenty-first century

NASA Technical Reports Server (NTRS)

Willshire, K. F.; Pivirotto, D. L.

1986-01-01

Space Station telerobotics will evolve beyond the initial capability into a smarter and more capable system as we enter the twenty-first century. Current technology programs including several proposed ground and flight experiments to enable development of this system are described. Advancements in the areas of machine vision, smart sensors, advanced control architecture, manipulator joint design, end effector design, and artificial intelligence will provide increasingly more autonomous telerobotic systems.

Mobile robots IV; Proceedings of the Meeting, Philadelphia, PA, Nov. 6, 7, 1989

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wolfe, W.J.; Chun, W.H.

1990-01-01

The present conference on mobile robot systems discusses high-speed machine perception based on passive sensing, wide-angle optical ranging, three-dimensional path planning for flying/crawling robots, navigation of autonomous mobile intelligence in an unstructured natural environment, mechanical models for the locomotion of a four-articulated-track robot, a rule-based command language for a semiautonomous Mars rover, and a computer model of the structured light vision system for a Mars rover. Also discussed are optical flow and three-dimensional information for navigation, feature-based reasoning trail detection, a symbolic neural-net production system for obstacle avoidance and navigation, intelligent path planning for robot navigation in an unknown environment,more » behaviors from a hierarchical control system, stereoscopic TV systems, the REACT language for autonomous robots, and a man-amplifying exoskeleton.« less

Application of Artificial Intelligence technology to the analysis and synthesis of reliable software systems

NASA Technical Reports Server (NTRS)

Wild, Christian; Eckhardt, Dave

1987-01-01

The development of a methodology for the production of highly reliable software is one of the greatest challenges facing the computer industry. Meeting this challenge will undoubtably involve the integration of many technologies. This paper describes the use of Artificial Intelligence technologies in the automated analysis of the formal algebraic specifications of abstract data types. These technologies include symbolic execution of specifications using techniques of automated deduction and machine learning through the use of examples. On-going research into the role of knowledge representation and problem solving in the process of developing software is also discussed.

Kepler-90 system (Artist's Concept)

NASA Image and Video Library

2017-12-14

Our solar system now is tied for most number of planets around a single star, with the recent discovery of an eighth planet circling Kepler-90, a Sun-like star 2,545 light years from Earth. The planet was discovered in data from NASA's Kepler Space Telescope. The newly-discovered Kepler-90i -- a sizzling hot, rocky planet that orbits its star once every 14.4 days -- was found using machine learning from Google. Machine learning is an approach to artificial intelligence in which computers "learn." In this case, computers learned to identify planets by finding in Kepler data instances where the telescope recorded changes in starlight caused by planets beyond our solar system, known as exoplanets. https://photojournal.jpl.nasa.gov/catalog/PIA22192

Integrating artificial and human intelligence into tablet production process.

PubMed

Gams, Matjaž; Horvat, Matej; Ožek, Matej; Luštrek, Mitja; Gradišek, Anton

2014-12-01

We developed a new machine learning-based method in order to facilitate the manufacturing processes of pharmaceutical products, such as tablets, in accordance with the Process Analytical Technology (PAT) and Quality by Design (QbD) initiatives. Our approach combines the data, available from prior production runs, with machine learning algorithms that are assisted by a human operator with expert knowledge of the production process. The process parameters encompass those that relate to the attributes of the precursor raw materials and those that relate to the manufacturing process itself. During manufacturing, our method allows production operator to inspect the impacts of various settings of process parameters within their proven acceptable range with the purpose of choosing the most promising values in advance of the actual batch manufacture. The interaction between the human operator and the artificial intelligence system provides improved performance and quality. We successfully implemented the method on data provided by a pharmaceutical company for a particular product, a tablet, under development. We tested the accuracy of the method in comparison with some other machine learning approaches. The method is especially suitable for analyzing manufacturing processes characterized by a limited amount of data.

The Potential of Artificial Intelligence in Aids for the Disabled.

ERIC Educational Resources Information Center

Boyer, John J.

The paper explores the possibilities for applying the knowledge of artificial intelligence (AI) research to aids for the disabled. Following a definition of artificial intelligence, the paper reviews areas of basic AI research, such as computer vision, machine learning, and planning and problem solving. Among application areas relevant to the…

[Intelligent systems tools in the diagnosis of acute coronary syndromes: A systemic review].

PubMed

Sprockel, John; Tejeda, Miguel; Yate, José; Diaztagle, Juan; González, Enrique

2017-03-27

Acute myocardial infarction is the leading cause of non-communicable deaths worldwide. Its diagnosis is a highly complex task, for which modelling through automated methods has been attempted. A systematic review of the literature was performed on diagnostic tests that applied intelligent systems tools in the diagnosis of acute coronary syndromes. A systematic review of the literature is presented using Medline, Embase, Scopus, IEEE/IET Electronic Library, ISI Web of Science, Latindex and LILACS databases for articles that include the diagnostic evaluation of acute coronary syndromes using intelligent systems. The review process was conducted independently by 2 reviewers, and discrepancies were resolved through the participation of a third person. The operational characteristics of the studied tools were extracted. A total of 35 references met the inclusion criteria. In 22 (62.8%) cases, neural networks were used. In five studies, the performances of several intelligent systems tools were compared. Thirteen studies sought to perform diagnoses of all acute coronary syndromes, and in 22, only infarctions were studied. In 21 cases, clinical and electrocardiographic aspects were used as input data, and in 10, only electrocardiographic data were used. Most intelligent systems use the clinical context as a reference standard. High rates of diagnostic accuracy were found with better performance using neural networks and support vector machines, compared with statistical tools of pattern recognition and decision trees. Extensive evidence was found that shows that using intelligent systems tools achieves a greater degree of accuracy than some clinical algorithms or scales and, thus, should be considered appropriate tools for supporting diagnostic decisions of acute coronary syndromes. Copyright © 2017 Instituto Nacional de Cardiología Ignacio Chávez. Publicado por Masson Doyma México S.A. All rights reserved.

Kepler-90 System Compared to Our Solar System (Artist's Concept)

NASA Image and Video Library

2017-12-14

Our solar system now is tied for most number of planets around a single star, with the recent discovery of an eighth planet circling Kepler-90, a Sun-like star 2,545 light years from Earth. The planet was discovered in data from NASA's Kepler Space Telescope. This artist's concept depicts the Kepler-90 system compared with our own solar system. The newly-discovered Kepler-90i -- a sizzling hot, rocky planet that orbits its star once every 14.4 days -- was found using machine learning from Google. Machine learning is an approach to artificial intelligence in which computers "learn." In this case, computers learned to identify planets by finding in Kepler data instances where the telescope recorded changes in starlight caused by planets beyond our solar system, known as exoplanets. https://photojournal.jpl.nasa.gov/catalog/PIA22193

Actualities and Development of Heavy-Duty CNC Machine Tool Thermal Error Monitoring Technology

NASA Astrophysics Data System (ADS)

Zhou, Zu-De; Gui, Lin; Tan, Yue-Gang; Liu, Ming-Yao; Liu, Yi; Li, Rui-Ya

2017-09-01

Thermal error monitoring technology is the key technological support to solve the thermal error problem of heavy-duty CNC (computer numerical control) machine tools. Currently, there are many review literatures introducing the thermal error research of CNC machine tools, but those mainly focus on the thermal issues in small and medium-sized CNC machine tools and seldom introduce thermal error monitoring technologies. This paper gives an overview of the research on the thermal error of CNC machine tools and emphasizes the study of thermal error of the heavy-duty CNC machine tool in three areas. These areas are the causes of thermal error of heavy-duty CNC machine tool and the issues with the temperature monitoring technology and thermal deformation monitoring technology. A new optical measurement technology called the "fiber Bragg grating (FBG) distributed sensing technology" for heavy-duty CNC machine tools is introduced in detail. This technology forms an intelligent sensing and monitoring system for heavy-duty CNC machine tools. This paper fills in the blank of this kind of review articles to guide the development of this industry field and opens up new areas of research on the heavy-duty CNC machine tool thermal error.

Remote Sensing Information Sciences Research Group, year four

NASA Technical Reports Server (NTRS)

Estes, John E.; Smith, Terence; Star, Jeffrey L.

1987-01-01

The needs of the remote sensing research and application community which will be served by the Earth Observing System (EOS) and space station, including associated polar and co-orbiting platforms are examined. Research conducted was used to extend and expand existing remote sensing research activities in the areas of georeferenced information systems, machine assisted information extraction from image data, artificial intelligence, and vegetation analysis and modeling. Projects are discussed in detail.

Intelligent Vehicle Power Management Using Machine Learning and Fuzzy Logic

DTIC Science & Technology

2008-06-01

batteries of similar physical size. An ultracapacitor can receive regenerative energy and give power during peak periods. Moreno et al. proposed to...use an ultracapacitor as an auxiliary energy system in combination with a primary source that is unable to accept energy from the regenerative ... braking [22]. There are other power sources that are being considered in HEV research [20-22] and future vehicle systems may use combinations of

Watson and Siri: The Rise of the BI Smart Machine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Troy Hiltbrand

Over the past few years, the industry has seen significant evolution in the area of human computer interaction. The era of the smart machines is upon us, with automation taking on a more advanced role than ever before, permeating into areas that have traditionally only been fulfilled by human beings. This movement has the potential of fundamentally altering the way that business intelligence is executed across the industry and the role that business intelligence has in all aspects of decision making.

Will machines ever think

NASA Technical Reports Server (NTRS)

Denning, P. J.

1986-01-01

Artificial Intelligence research has come under fire for failing to fulfill its promises. A growing number of AI researchers are reexamining the bases of AI research and are challenging the assumption that intelligent behavior can be fully explained as manipulation of symbols by algorithms. Three recent books -- Mind over Machine (H. Dreyfus and S. Dreyfus), Understanding Computers and Cognition (T. Winograd and F. Flores), and Brains, Behavior, and Robots (J. Albus) -- explore alternatives and open the door to new architectures that may be able to learn skills.

Naval Computer-Based Instruction: Cost, Implementation and Effectiveness Issues.

DTIC Science & Technology

1988-03-01

logical follow on to MITIPAC and are an attempt to use some artificial intelligence (AI) techniques with computer-based training. A good intelligent ...principles of steam plant operation and maintenance. Steamer was written in LISP on a LISP machine in an attempt to use artificial intelligence . "What... Artificial Intelligence and Speech Technology", Electronic Learning, September 1987. Montague, William. E., code 5, Navy Personnel Research and

Knowledge-Based Software Development Tools

DTIC Science & Technology

1993-09-01

GREEN, C., AND WESTFOLD, S. Knowledge-based programming self-applied. In Machine Intelligence 10, J. E. Hayes, D. Mitchie, and Y. Pao, Eds., Wiley...Technical Report KES.U.84.2, Kestrel Institute, April 1984. [181 KORF, R. E. Toward a model of representation changes. Artificial Intelligence 14, 1...Artificial Intelligence 27, 1 (February 1985), 43-96. Replinted in Readings in Artificial Intelligence and Software Engineering, C. Rich •ad R. Waters

Development of intelligent model for personalized guidance on wheelchair tilt and recline usage for people with spinal cord injury: methodology and preliminary report.

PubMed

Fu, Jicheng; Jones, Maria; Jan, Yih-Kuen

2014-01-01

Wheelchair tilt and recline functions are two of the most desirable features for relieving seating pressure to decrease the risk of pressure ulcers. The effective guidance on wheelchair tilt and recline usage is therefore critical to pressure ulcer prevention. The aim of this study was to demonstrate the feasibility of using machine learning techniques to construct an intelligent model to provide personalized guidance to individuals with spinal cord injury (SCI). The motivation stems from the clinical evidence that the requirements of individuals vary greatly and that no universal guidance on tilt and recline usage could possibly satisfy all individuals with SCI. We explored all aspects involved in constructing the intelligent model and proposed approaches tailored to suit the characteristics of this preliminary study, such as the way of modeling research participants, using machine learning techniques to construct the intelligent model, and evaluating the performance of the intelligent model. We further improved the intelligent model's prediction accuracy by developing a two-phase feature selection algorithm to identify important attributes. Experimental results demonstrated that our approaches held the promise: they could effectively construct the intelligent model, evaluate its performance, and refine the participant model so that the intelligent model's prediction accuracy was significantly improved.

Artificial Intelligence and Semantics through the Prism of Structural, Post-Structural and Transcendental Approaches.

PubMed

Gasparyan, Diana

2016-12-01

There is a problem associated with contemporary studies of philosophy of mind, which focuses on the identification and convergence of human and machine intelligence. This is the problem of machine emulation of sense. In the present study, analysis of this problem is carried out based on concepts from structural and post-structural approaches that have been almost entirely overlooked by contemporary philosophy of mind. If we refer to the basic definitions of "sign" and "meaning" found in structuralism and post-structuralism, we see a fundamental difference between the capabilities of a machine and the human brain engaged in the processing of a sign. This research will exemplify and provide additional evidence to support distinctions between syntactic and semantic aspects of intelligence, an issue widely discussed by adepts of contemporary philosophy of mind. The research will demonstrate that some aspect of a number of ideas proposed in relation to semantics and semiosis in structuralism and post-structuralism are similar to those we find in contemporary analytical studies related to the theory and philosophy of artificial intelligence. The concluding part of the paper offers an interpretation of the problem of formalization of sense, connected to its metaphysical (transcendental) properties.

Human Centered Autonomous and Assistant Systems Testbed for Exploration Operations

NASA Technical Reports Server (NTRS)

Malin, Jane T.; Mount, Frances; Carreon, Patricia; Torney, Susan E.

2001-01-01

The Engineering and Mission Operations Directorates at NASA Johnson Space Center are combining laboratories and expertise to establish the Human Centered Autonomous and Assistant Systems Testbed for Exploration Operations. This is a testbed for human centered design, development and evaluation of intelligent autonomous and assistant systems that will be needed for human exploration and development of space. This project will improve human-centered analysis, design and evaluation methods for developing intelligent software. This software will support human-machine cognitive and collaborative activities in future interplanetary work environments where distributed computer and human agents cooperate. We are developing and evaluating prototype intelligent systems for distributed multi-agent mixed-initiative operations. The primary target domain is control of life support systems in a planetary base. Technical approaches will be evaluated for use during extended manned tests in the target domain, the Bioregenerative Advanced Life Support Systems Test Complex (BIO-Plex). A spinoff target domain is the International Space Station (ISS) Mission Control Center (MCC). Prodl}cts of this project include human-centered intelligent software technology, innovative human interface designs, and human-centered software development processes, methods and products. The testbed uses adjustable autonomy software and life support systems simulation models from the Adjustable Autonomy Testbed, to represent operations on the remote planet. Ground operations prototypes and concepts will be evaluated in the Exploration Planning and Operations Center (ExPOC) and Jupiter Facility.

Key Issues in the Analysis of Remote Sensing Data: A report on the workshop

NASA Technical Reports Server (NTRS)

Swain, P. H. (Principal Investigator)

1981-01-01

The procedures of a workshop assessing the state of the art of machine analysis of remotely sensed data are summarized. Areas discussed were: data bases, image registration, image preprocessing operations, map oriented considerations, advanced digital systems, artificial intelligence methods, image classification, and improved classifier training. Recommendations of areas for further research are presented.

A Transfer Learning Approach for Applying Matrix Factorization to Small ITS Datasets

ERIC Educational Resources Information Center

Voß, Lydia; Schatten, Carlotta; Mazziotti, Claudia; Schmidt-Thieme, Lars

2015-01-01

Machine Learning methods for Performance Prediction in Intelligent Tutoring Systems (ITS) have proven their efficacy; specific methods, e.g. Matrix Factorization (MF), however suffer from the lack of available information about new tasks or new students. In this paper we show how this problem could be solved by applying Transfer Learning (TL),…

Implications of Gendered Technology for Art Education: The Case Study of a Male Drawing Machine.

ERIC Educational Resources Information Center

Morbey, Mary Leigh

Opening with a discussion of AARON, an artificial intelligence symbol system that is used to generate computer drawings, this document makes the argument that AARON is based upon a way of knowing that is abstract, analytical, rationalist and thus representative of the dominant, western, male philosophical tradition. Male bias permeates the field…

Cognitive Support Embedded in Self-Regulated E-Learning Systems for Students with Special Learning Needs

ERIC Educational Resources Information Center

Chatzara, K.; Karagiannidis, C.; Stamatis, D.

2016-01-01

This paper presents an anthropocentric approach in human-machine interaction in the area of self-regulated e-learning. In an attempt to enhance communication mediated through computers for pedagogical use we propose the incorporation of an intelligent emotional agent that is represented by a synthetic character with multimedia capabilities,…

CESAR robotics and intelligent systems research for nuclear environments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mann, R.C.

1992-07-01

The Center for Engineering Systems Advanced Research (CESAR) at the Oak Ridge National Laboratory (ORNL) encompasses expertise and facilities to perform basic and applied research in robotics and intelligent systems in order to address a broad spectrum of problems related to nuclear and other environments. For nuclear environments, research focus is derived from applications in advanced nuclear power stations, and in environmental restoration and waste management. Several programs at CESAR emphasize the cross-cutting technology issues, and are executed in appropriate cooperation with projects that address specific problem areas. Although the main thrust of the CESAR long-term research is on developingmore » highly automated systems that can cooperate and function reliably in complex environments, the development of advanced human-machine interfaces represents a significant part of our research. 11 refs.« less

CESAR robotics and intelligent systems research for nuclear environments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mann, R.C.

1992-01-01

The Center for Engineering Systems Advanced Research (CESAR) at the Oak Ridge National Laboratory (ORNL) encompasses expertise and facilities to perform basic and applied research in robotics and intelligent systems in order to address a broad spectrum of problems related to nuclear and other environments. For nuclear environments, research focus is derived from applications in advanced nuclear power stations, and in environmental restoration and waste management. Several programs at CESAR emphasize the cross-cutting technology issues, and are executed in appropriate cooperation with projects that address specific problem areas. Although the main thrust of the CESAR long-term research is on developingmore » highly automated systems that can cooperate and function reliably in complex environments, the development of advanced human-machine interfaces represents a significant part of our research. 11 refs.« less

One Dimensional Turing-Like Handshake Test for Motor Intelligence

PubMed Central

Karniel, Amir; Avraham, Guy; Peles, Bat-Chen; Levy-Tzedek, Shelly; Nisky, Ilana

2010-01-01

In the Turing test, a computer model is deemed to "think intelligently" if it can generate answers that are not distinguishable from those of a human. However, this test is limited to the linguistic aspects of machine intelligence. A salient function of the brain is the control of movement, and the movement of the human hand is a sophisticated demonstration of this function. Therefore, we propose a Turing-like handshake test, for machine motor intelligence. We administer the test through a telerobotic system in which the interrogator is engaged in a task of holding a robotic stylus and interacting with another party (human or artificial). Instead of asking the interrogator whether the other party is a person or a computer program, we employ a two-alternative forced choice method and ask which of two systems is more human-like. We extract a quantitative grade for each model according to its resemblance to the human handshake motion and name it "Model Human-Likeness Grade" (MHLG). We present three methods to estimate the MHLG. (i) By calculating the proportion of subjects' answers that the model is more human-like than the human; (ii) By comparing two weighted sums of human and model handshakes we fit a psychometric curve and extract the point of subjective equality (PSE); (iii) By comparing a given model with a weighted sum of human and random signal, we fit a psychometric curve to the answers of the interrogator and extract the PSE for the weight of the human in the weighted sum. Altogether, we provide a protocol to test computational models of the human handshake. We believe that building a model is a necessary step in understanding any phenomenon and, in this case, in understanding the neural mechanisms responsible for the generation of the human handshake. PMID:21206462

Design and Implementation of a Wireless Sensor and Actuator Network to Support the Intelligent Control of Efficient Energy Usage.

PubMed

Blanco, Jesús; García, Andrés; Morenas, Javier de Las

2018-06-09

Energy saving has become a major concern for the developed society of our days. This paper presents a Wireless Sensor and Actuator Network (WSAN) designed to provide support to an automatic intelligent system, based on the Internet of Things (IoT), which enables a responsible consumption of energy. The proposed overall system performs an efficient energetic management of devices, machines and processes, optimizing their operation to achieve a reduction in their overall energy usage at any given time. For this purpose, relevant data is collected from intelligent sensors, which are in-stalled at the required locations, as well as from the energy market through the Internet. This information is analysed to provide knowledge about energy utilization, and to improve efficiency. The system takes autonomous decisions automatically, based on the available information and the specific requirements in each case. The proposed system has been implanted and tested in a food factory. Results show a great optimization of energy efficiency and a substantial improvement on energy and costs savings.

Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining.

PubMed

Salehi, Mojtaba; Bahreininejad, Ardeshir

2011-08-01

Optimization of process planning is considered as the key technology for computer-aided process planning which is a rather complex and difficult procedure. A good process plan of a part is built up based on two elements: (1) the optimized sequence of the operations of the part; and (2) the optimized selection of the machine, cutting tool and Tool Access Direction (TAD) for each operation. In the present work, the process planning is divided into preliminary planning, and secondary/detailed planning. In the preliminary stage, based on the analysis of order and clustering constraints as a compulsive constraint aggregation in operation sequencing and using an intelligent searching strategy, the feasible sequences are generated. Then, in the detailed planning stage, using the genetic algorithm which prunes the initial feasible sequences, the optimized operation sequence and the optimized selection of the machine, cutting tool and TAD for each operation based on optimization constraints as an additive constraint aggregation are obtained. The main contribution of this work is the optimization of sequence of the operations of the part, and optimization of machine selection, cutting tool and TAD for each operation using the intelligent search and genetic algorithm simultaneously.

Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining

PubMed Central

Salehi, Mojtaba

2010-01-01

Optimization of process planning is considered as the key technology for computer-aided process planning which is a rather complex and difficult procedure. A good process plan of a part is built up based on two elements: (1) the optimized sequence of the operations of the part; and (2) the optimized selection of the machine, cutting tool and Tool Access Direction (TAD) for each operation. In the present work, the process planning is divided into preliminary planning, and secondary/detailed planning. In the preliminary stage, based on the analysis of order and clustering constraints as a compulsive constraint aggregation in operation sequencing and using an intelligent searching strategy, the feasible sequences are generated. Then, in the detailed planning stage, using the genetic algorithm which prunes the initial feasible sequences, the optimized operation sequence and the optimized selection of the machine, cutting tool and TAD for each operation based on optimization constraints as an additive constraint aggregation are obtained. The main contribution of this work is the optimization of sequence of the operations of the part, and optimization of machine selection, cutting tool and TAD for each operation using the intelligent search and genetic algorithm simultaneously. PMID:21845020

DOE Office of Scientific and Technical Information (OSTI.GOV)

Geng, Guangchao; Abhyankar, Shrirang; Wang, Xiaoyu

Transient stability-constrained optimal power flow is an important emerging problem with power systems pushed to the limits for economic benefits, dense and larger interconnected systems, and reduced inertia due to expected proliferation of renewable energy resources. In this study, two more approaches: single machine equivalent and computational intelligence are presented. Also discussed are various application areas, and future directions in this research area. In conclusion, a comprehensive resource for the available literature, publicly available test systems, and relevant numerical libraries is also provided.

Human capabilities in space. [man machine interaction

NASA Technical Reports Server (NTRS)

Nicogossian, A. E.

1984-01-01

Man's ability to live and perform useful work in space was demonstrated throughout the history of manned space flight. Current planning envisions a multi-functional space station. Man's unique abilities to respond to the unforeseen and to operate at a level of complexity exceeding any reasonable amount of previous planning distinguish him from present day machines. His limitations, however, include his inherent inability to survive without protection, his limited strength, and his propensity to make mistakes when performing repetitive and monotonous tasks. By contrast, an automated system does routine and delicate tasks, exerts force smoothly and precisely, stores, and recalls large amounts of data, and performs deductive reasoning while maintaining a relative insensitivity to the environment. The establishment of a permanent presence of man in space demands that man and machines be appropriately combined in spaceborne systems. To achieve this optimal combination, research is needed in such diverse fields as artificial intelligence, robotics, behavioral psychology, economics, and human factors engineering.

Software design and documentation language, revision 1

NASA Technical Reports Server (NTRS)

Kleine, H.

1979-01-01

The Software Design and Documentation Language (SDDL) developed to provide an effective communications medium to support the design and documentation of complex software applications is described. Features of the system include: (1) a processor which can convert design specifications into an intelligible, informative machine-reproducible document; (2) a design and documentation language with forms and syntax that are simple, unrestrictive, and communicative; and (3) methodology for effective use of the language and processor. The SDDL processor is written in the SIMSCRIPT II programming language and is implemented on the UNIVAC 1108, the IBM 360/370, and Control Data machines.

World of intelligence defense object detection-machine learning (artificial intelligence)

NASA Astrophysics Data System (ADS)

Gupta, Anitya; Kumar, Akhilesh; Bhushan, Vinayak

2018-04-01

This paper proposes a Quick Locale based Convolutional System strategy (Quick R-CNN) for question recognition. Quick R-CNN expands on past work to effectively characterize ob-ject recommendations utilizing profound convolutional systems. Com-pared to past work, Quick R-CNN utilizes a few in-novations to enhance preparing and testing speed while likewise expanding identification precision. Quick R-CNN trains the profound VGG16 arrange 9 quicker than R-CNN, is 213 speedier at test-time, and accomplishes a higher Guide on PASCAL VOC 2012. Contrasted with SPPnet, Quick R-CNN trains VGG16 3 quicker, tests 10 speedier, and is more exact. Quick R-CNN is actualized in Python and C++ (utilizing Caffe) and is accessible under the open-source MIT Permit.

Artificial Intelligence in Cardiology.

PubMed

Johnson, Kipp W; Torres Soto, Jessica; Glicksberg, Benjamin S; Shameer, Khader; Miotto, Riccardo; Ali, Mohsin; Ashley, Euan; Dudley, Joel T

2018-06-12

Artificial intelligence and machine learning are poised to influence nearly every aspect of the human condition, and cardiology is not an exception to this trend. This paper provides a guide for clinicians on relevant aspects of artificial intelligence and machine learning, reviews selected applications of these methods in cardiology to date, and identifies how cardiovascular medicine could incorporate artificial intelligence in the future. In particular, the paper first reviews predictive modeling concepts relevant to cardiology such as feature selection and frequent pitfalls such as improper dichotomization. Second, it discusses common algorithms used in supervised learning and reviews selected applications in cardiology and related disciplines. Third, it describes the advent of deep learning and related methods collectively called unsupervised learning, provides contextual examples both in general medicine and in cardiovascular medicine, and then explains how these methods could be applied to enable precision cardiology and improve patient outcomes. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

A State Cyber Hub Operations Framework

DTIC Science & Technology

2016-06-01

to communicate and sense or interact with their internal states or the external environment. Machine Learning: A type of artificial intelligence that... artificial intelligence , and computational linguistics concerned with the interactions between computers and human (natural) languages. Patching: A piece...formalizing a proof of concept for cyber initiatives and developed frameworks for operationalizing the data and intelligence produced across state

New approach for cognitive analysis and understanding of medical patterns and visualizations

NASA Astrophysics Data System (ADS)

Ogiela, Marek R.; Tadeusiewicz, Ryszard

2003-11-01

This paper presents new opportunities for applying linguistic description of the picture merit content and AI methods to undertake tasks of the automatic understanding of images semantics in intelligent medical information systems. A successful obtaining of the crucial semantic content of the medical image may contribute considerably to the creation of new intelligent multimedia cognitive medical systems. Thanks to the new idea of cognitive resonance between stream of the data extracted from the image using linguistic methods and expectations taken from the representaion of the medical knowledge, it is possible to understand the merit content of the image even if teh form of the image is very different from any known pattern. This article proves that structural techniques of artificial intelligence may be applied in the case of tasks related to automatic classification and machine perception based on semantic pattern content in order to determine the semantic meaning of the patterns. In the paper are described some examples presenting ways of applying such techniques in the creation of cognitive vision systems for selected classes of medical images. On the base of scientific research described in the paper we try to build some new systems for collecting, storing, retrieving and intelligent interpreting selected medical images especially obtained in radiological and MRI examinations.

Fish swarm intelligent to optimize real time monitoring of chips drying using machine vision

NASA Astrophysics Data System (ADS)

Hendrawan, Y.; Hawa, L. C.; Damayanti, R.

2018-03-01

This study attempted to apply machine vision-based chips drying monitoring system which is able to optimise the drying process of cassava chips. The objective of this study is to propose fish swarm intelligent (FSI) optimization algorithms to find the most significant set of image features suitable for predicting water content of cassava chips during drying process using artificial neural network model (ANN). Feature selection entails choosing the feature subset that maximizes the prediction accuracy of ANN. Multi-Objective Optimization (MOO) was used in this study which consisted of prediction accuracy maximization and feature-subset size minimization. The results showed that the best feature subset i.e. grey mean, L(Lab) Mean, a(Lab) energy, red entropy, hue contrast, and grey homogeneity. The best feature subset has been tested successfully in ANN model to describe the relationship between image features and water content of cassava chips during drying process with R2 of real and predicted data was equal to 0.9.

Artificial Intelligence in Medical Practice: The Question to the Answer?

PubMed

Miller, D Douglas; Brown, Eric W

2018-02-01

Computer science advances and ultra-fast computing speeds find artificial intelligence (AI) broadly benefitting modern society-forecasting weather, recognizing faces, detecting fraud, and deciphering genomics. AI's future role in medical practice remains an unanswered question. Machines (computers) learn to detect patterns not decipherable using biostatistics by processing massive datasets (big data) through layered mathematical models (algorithms). Correcting algorithm mistakes (training) adds to AI predictive model confidence. AI is being successfully applied for image analysis in radiology, pathology, and dermatology, with diagnostic speed exceeding, and accuracy paralleling, medical experts. While diagnostic confidence never reaches 100%, combining machines plus physicians reliably enhances system performance. Cognitive programs are impacting medical practice by applying natural language processing to read the rapidly expanding scientific literature and collate years of diverse electronic medical records. In this and other ways, AI may optimize the care trajectory of chronic disease patients, suggest precision therapies for complex illnesses, reduce medical errors, and improve subject enrollment into clinical trials. Copyright © 2018 Elsevier Inc. All rights reserved.

The 1990 Goddard Conference on Space Applications of Artificial Intelligence

NASA Technical Reports Server (NTRS)

Rash, James L. (Editor)

1990-01-01

The papers presented at the 1990 Goddard Conference on Space Applications of Artificial Intelligence are given. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The proceedings fall into the following areas: Planning and Scheduling, Fault Monitoring/Diagnosis, Image Processing and Machine Vision, Robotics/Intelligent Control, Development Methodologies, Information Management, and Knowledge Acquisition.

Intelligent Machine Learning Approaches for Aerospace Applications

NASA Astrophysics Data System (ADS)

Sathyan, Anoop

Machine Learning is a type of artificial intelligence that provides machines or networks the ability to learn from data without the need to explicitly program them. There are different kinds of machine learning techniques. This thesis discusses the applications of two of these approaches: Genetic Fuzzy Logic and Convolutional Neural Networks (CNN). Fuzzy Logic System (FLS) is a powerful tool that can be used for a wide variety of applications. FLS is a universal approximator that reduces the need for complex mathematics and replaces it with expert knowledge of the system to produce an input-output mapping using If-Then rules. The expert knowledge of a system can help in obtaining the parameters for small-scale FLSs, but for larger networks we will need to use sophisticated approaches that can automatically train the network to meet the design requirements. This is where Genetic Algorithms (GA) and EVE come into the picture. Both GA and EVE can tune the FLS parameters to minimize a cost function that is designed to meet the requirements of the specific problem. EVE is an artificial intelligence developed by Psibernetix that is trained to tune large scale FLSs. The parameters of an FLS can include the membership functions and rulebase of the inherent Fuzzy Inference Systems (FISs). The main issue with using the GFS is that the number of parameters in a FIS increase exponentially with the number of inputs thus making it increasingly harder to tune them. To reduce this issue, the FLSs discussed in this thesis consist of 2-input-1-output FISs in cascade (Chapter 4) or as a layer of parallel FISs (Chapter 7). We have obtained extremely good results using GFS for different applications at a reduced computational cost compared to other algorithms that are commonly used to solve the corresponding problems. In this thesis, GFSs have been designed for controlling an inverted double pendulum, a task allocation problem of clustering targets amongst a set of UAVs, a fire detection problem and the aircraft conflict resolution problem. During the last decade, CNNs have become increasingly popular in the domain of image and speech processing. CNNs have a lot more parameters compared to GFSs that are tuned using the back-propagation algorithm. CNNs typically have hundreds of thousands or maybe millions of parameters that are tuned using common cost functions such as integral squared error, softmax loss etc. Chapter 5 discusses a classification problem to classify images as humans or not and Chapter 6 discusses a regression task using CNN for producing an approximate near-optimal route for the Traveling Salesman Problem (TSP) which is regarded as one of the most complicated decision making problem. Both the GFS and CNN are used to develop intelligent systems specific to the application providing them computational efficiency, robustness in the face of uncertainties and scalability.

Evolving rule-based systems in two medical domains using genetic programming.

PubMed

Tsakonas, Athanasios; Dounias, Georgios; Jantzen, Jan; Axer, Hubertus; Bjerregaard, Beth; von Keyserlingk, Diedrich Graf

2004-11-01

To demonstrate and compare the application of different genetic programming (GP) based intelligent methodologies for the construction of rule-based systems in two medical domains: the diagnosis of aphasia's subtypes and the classification of pap-smear examinations. Past data representing (a) successful diagnosis of aphasia's subtypes from collaborating medical experts through a free interview per patient, and (b) correctly classified smears (images of cells) by cyto-technologists, previously stained using the Papanicolaou method. Initially a hybrid approach is proposed, which combines standard genetic programming and heuristic hierarchical crisp rule-base construction. Then, genetic programming for the production of crisp rule based systems is attempted. Finally, another hybrid intelligent model is composed by a grammar driven genetic programming system for the generation of fuzzy rule-based systems. Results denote the effectiveness of the proposed systems, while they are also compared for their efficiency, accuracy and comprehensibility, to those of an inductive machine learning approach as well as to those of a standard genetic programming symbolic expression approach. The proposed GP-based intelligent methodologies are able to produce accurate and comprehensible results for medical experts performing competitive to other intelligent approaches. The aim of the authors was the production of accurate but also sensible decision rules that could potentially help medical doctors to extract conclusions, even at the expense of a higher classification score achievement.

Games and machine learning: a powerful combination in an artificial intelligence course

NASA Astrophysics Data System (ADS)

Wallace, Scott A.; McCartney, Robert; Russell, Ingrid

2010-03-01

Project MLeXAI (Machine Learning eXperiences in Artificial Intelligence (AI)) seeks to build a set of reusable course curriculum and hands on laboratory projects for the artificial intelligence classroom. In this article, we describe two game-based projects from the second phase of project MLeXAI: Robot Defense - a simple real-time strategy game and Checkers - a classic turn-based board game. From the instructors' prospective, we examine aspects of design and implementation as well as the challenges and rewards of using the curricula. We explore students' responses to the projects via the results of a common survey. Finally, we compare the student perceptions from the game-based projects to non-game based projects from the first phase of Project MLeXAI.

A state-based approach to trend recognition and failure prediction for the Space Station Freedom

NASA Technical Reports Server (NTRS)

Nelson, Kyle S.; Hadden, George D.

1992-01-01

A state-based reasoning approach to trend recognition and failure prediction for the Altitude Determination, and Control System (ADCS) of the Space Station Freedom (SSF) is described. The problem domain is characterized by features (e.g., trends and impending failures) that develop over a variety of time spans, anywhere from several minutes to several years. Our state-based reasoning approach, coupled with intelligent data screening, allows features to be tracked as they develop in a time-dependent manner. That is, each state machine has the ability to encode a time frame for the feature it detects. As features are detected, they are recorded and can be used as input to other state machines, creating a hierarchical feature recognition scheme. Furthermore, each machine can operate independently of the others, allowing simultaneous tracking of features. State-based reasoning was implemented in the trend recognition and the prognostic modules of a prototype Space Station Freedom Maintenance and Diagnostic System (SSFMDS) developed at Honeywell's Systems and Research Center.

The Machines Are Coming: Future Directions in Instructional Communication Research. Forum: The Future of Instructional Communication

ERIC Educational Resources Information Center

Edwards, Autumn; Edwards, Chad

2017-01-01

Educational encounters of the future (and increasingly, of the present) will involve a complex collaboration of human and machine intelligences and agents, partnering to enhance learning and growth. Increasingly, "students and instructors are not only talking 'through' machines, but also [talking] 'to them', and 'within them'" (Edwards…

An intelligent crowdsourcing system for forensic analysis of surveillance video

NASA Astrophysics Data System (ADS)

Tahboub, Khalid; Gadgil, Neeraj; Ribera, Javier; Delgado, Blanca; Delp, Edward J.

2015-03-01

Video surveillance systems are of a great value for public safety. With an exponential increase in the number of cameras, videos obtained from surveillance systems are often archived for forensic purposes. Many automatic methods have been proposed to do video analytics such as anomaly detection and human activity recognition. However, such methods face significant challenges due to object occlusions, shadows and scene illumination changes. In recent years, crowdsourcing has become an effective tool that utilizes human intelligence to perform tasks that are challenging for machines. In this paper, we present an intelligent crowdsourcing system for forensic analysis of surveillance video that includes the video recorded as a part of search and rescue missions and large-scale investigation tasks. We describe a method to enhance crowdsourcing by incorporating human detection, re-identification and tracking. At the core of our system, we use a hierarchal pyramid model to distinguish the crowd members based on their ability, experience and performance record. Our proposed system operates in an autonomous fashion and produces a final output of the crowdsourcing analysis consisting of a set of video segments detailing the events of interest as one storyline.

Knowledge Based Systems: A Critical Survey of Major Concepts, Issues, and Techniques. M.S. Thesis Final Report, 1 Jul. 1985 - 31 Dec. 1987

NASA Technical Reports Server (NTRS)

Dominick, Wayne D. (Editor); Kavi, Srinu

1984-01-01

This Working Paper Series entry presents a detailed survey of knowledge based systems. After being in a relatively dormant state for many years, only recently is Artificial Intelligence (AI) - that branch of computer science that attempts to have machines emulate intelligent behavior - accomplishing practical results. Most of these results can be attributed to the design and use of Knowledge-Based Systems, KBSs (or ecpert systems) - problem solving computer programs that can reach a level of performance comparable to that of a human expert in some specialized problem domain. These systems can act as a consultant for various requirements like medical diagnosis, military threat analysis, project risk assessment, etc. These systems possess knowledge to enable them to make intelligent desisions. They are, however, not meant to replace the human specialists in any particular domain. A critical survey of recent work in interactive KBSs is reported. A case study (MYCIN) of a KBS, a list of existing KBSs, and an introduction to the Japanese Fifth Generation Computer Project are provided as appendices. Finally, an extensive set of KBS-related references is provided at the end of the report.

A methodology for the design of experiments in computational intelligence with multiple regression models.

PubMed

Fernandez-Lozano, Carlos; Gestal, Marcos; Munteanu, Cristian R; Dorado, Julian; Pazos, Alejandro

2016-01-01

The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the field of Computational Intelligence and especially on a correct comparison between the different results provided for different methods, as those techniques are complex systems that require further study to be fully understood. A methodology commonly accepted in Computational intelligence is implemented in an R package called RRegrs. This package includes ten simple and complex regression models to carry out predictive modeling using Machine Learning and well-known regression algorithms. The framework for experimental design presented herein is evaluated and validated against RRegrs. Our results are different for three out of five state-of-the-art simple datasets and it can be stated that the selection of the best model according to our proposal is statistically significant and relevant. It is of relevance to use a statistical approach to indicate whether the differences are statistically significant using this kind of algorithms. Furthermore, our results with three real complex datasets report different best models than with the previously published methodology. Our final goal is to provide a complete methodology for the use of different steps in order to compare the results obtained in Computational Intelligence problems, as well as from other fields, such as for bioinformatics, cheminformatics, etc., given that our proposal is open and modifiable.

A methodology for the design of experiments in computational intelligence with multiple regression models

PubMed Central

Gestal, Marcos; Munteanu, Cristian R.; Dorado, Julian; Pazos, Alejandro

2016-01-01

The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the field of Computational Intelligence and especially on a correct comparison between the different results provided for different methods, as those techniques are complex systems that require further study to be fully understood. A methodology commonly accepted in Computational intelligence is implemented in an R package called RRegrs. This package includes ten simple and complex regression models to carry out predictive modeling using Machine Learning and well-known regression algorithms. The framework for experimental design presented herein is evaluated and validated against RRegrs. Our results are different for three out of five state-of-the-art simple datasets and it can be stated that the selection of the best model according to our proposal is statistically significant and relevant. It is of relevance to use a statistical approach to indicate whether the differences are statistically significant using this kind of algorithms. Furthermore, our results with three real complex datasets report different best models than with the previously published methodology. Our final goal is to provide a complete methodology for the use of different steps in order to compare the results obtained in Computational Intelligence problems, as well as from other fields, such as for bioinformatics, cheminformatics, etc., given that our proposal is open and modifiable. PMID:27920952

Investigation of Air Transportation Technology at Princeton University, 1989-1990

NASA Technical Reports Server (NTRS)

Stengel, Robert F.

1990-01-01

The Air Transportation Technology Program at Princeton University proceeded along six avenues during the past year: microburst hazards to aircraft; machine-intelligent, fault tolerant flight control; computer aided heuristics for piloted flight; stochastic robustness for flight control systems; neural networks for flight control; and computer aided control system design. These topics are briefly discussed, and an annotated bibliography of publications that appeared between January 1989 and June 1990 is given.

Intelligent excavator control system for lunar mining system

NASA Astrophysics Data System (ADS)

Lever, Paul J. A.; Wang, Fei-Yue

1995-01-01

A major benefit of utilizing local planetary resources is that it reduces the need and cost of lifting materials from the Earth's surface into Earth orbit. The location of the moon makes it an ideal site for harvesting the materials needed to assist space activities. Here, lunar excavation will take place in the dynamic unstructured lunar environment, in which conditions are highly variable and unpredictable. Autonomous mining (excavation) machines are necessary to remove human operators from this hazardous environment. This machine must use a control system structure that can identify, plan, sense, and control real-time dynamic machine movements in the lunar environment. The solution is a vision-based hierarchical control structure. However, excavation tasks require force/torque sensor feedback to control the excavation tool after it has penetrated the surface. A fuzzy logic controller (FLC) is used to interpret the forces and torques gathered from a bucket mounted force/torque sensor during excavation. Experimental results from several excavation tests using the FLC are presented here. These results represent the first step toward an integrated sensing and control system for a lunar mining system.

Compact Microscope Imaging System Developed

NASA Technical Reports Server (NTRS)

McDowell, Mark

2001-01-01

The Compact Microscope Imaging System (CMIS) is a diagnostic tool with intelligent controls for use in space, industrial, medical, and security applications. The CMIS can be used in situ with a minimum amount of user intervention. This system, which was developed at the NASA Glenn Research Center, can scan, find areas of interest, focus, and acquire images automatically. Large numbers of multiple cell experiments require microscopy for in situ observations; this is only feasible with compact microscope systems. CMIS is a miniature machine vision system that combines intelligent image processing with remote control capabilities. The software also has a user-friendly interface that can be used independently of the hardware for post-experiment analysis. CMIS has potential commercial uses in the automated online inspection of precision parts, medical imaging, security industry (examination of currency in automated teller machines and fingerprint identification in secure entry locks), environmental industry (automated examination of soil/water samples), biomedical field (automated blood/cell analysis), and microscopy community. CMIS will improve research in several ways: It will expand the capabilities of MSD experiments utilizing microscope technology. It may be used in lunar and Martian experiments (Rover Robot). Because of its reduced size, it will enable experiments that were not feasible previously. It may be incorporated into existing shuttle orbiter and space station experiments, including glove-box-sized experiments as well as ground-based experiments.

IEEE 1982. Proceedings of the international conference on cybernetics and society

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1982-01-01

The following topics were dealt with: knowledge-based systems; risk analysis; man-machine interactions; human information processing; metaphor, analogy and problem-solving; manual control modelling; transportation systems; simulation; adaptive and learning systems; biocybernetics; cybernetics; mathematical programming; robotics; decision support systems; analysis, design and validation of models; computer vision; systems science; energy systems; environmental modelling and policy; pattern recognition; nuclear warfare; technological forecasting; artificial intelligence; the Turin shroud; optimisation; workloads. Abstracts of individual papers can be found under the relevant classification codes in this or future issues.

Shared direct memory access on the Explorer 2-LX

NASA Technical Reports Server (NTRS)

Musgrave, Jeffrey L.

1990-01-01

Advances in Expert System technology and Artificial Intelligence have provided a framework for applying automated Intelligence to the solution of problems which were generally perceived as intractable using more classical approaches. As a result, hybrid architectures and parallel processing capability have become more common in computing environments. The Texas Instruments Explorer II-LX is an example of a machine which combines a symbolic processing environment, and a computationally oriented environment in a single chassis for integrated problem solutions. This user's manual is an attempt to make these capabilities more accessible to a wider range of engineers and programmers with problems well suited to solution in such an environment.

Curricular Design for Intelligent Systems in Geosciences Using Urban Groundwater Studies.

NASA Astrophysics Data System (ADS)

Cabral-Cano, E.; Pierce, S. A.; Fuentes-Pineda, G.; Arora, R.

2016-12-01

Geosciences research frequently focuses on process-centered phenomena, studying combinations of physical, geological, chemical, biological, ecological, and anthropogenic factors. These interconnected Earth systems can be best understood through the use of digital tools that should be documented as workflows. To develop intelligent systems, it is important that geoscientists and computing and information sciences experts collaborate to: (1) develop a basic understanding of the geosciences and computing and information sciences disciplines so that the problem and solution approach are clear to all stakeholders, and (2) implement the desired intelligent system with a short turnaround time. However, these interactions and techniques are seldom covered in traditional Earth Sciences curricula. We have developed an exchange course on Intelligent Systems for Geosciences to support workforce development and build capacity to facilitate skill-development at the undergraduate student-level. The first version of this course was offered jointly by the University of Texas at Austin and the Universidad Nacional Autónoma de México as an intensive, study-abroad summer course. Content included: basic Linux introduction, shell scripting and high performance computing, data management, experts systems, field data collection exercises and basics of machine learning. Additionally, student teams were tasked to develop a term projects that centered on applications of Intelligent Systems applied to urban and karst groundwater systems. Projects included expert system and reusable workflow development for subsidence hazard analysis in Celaya, Mexico, a classification model to analyze land use change over a 30 Year Period in Austin, Texas, big data processing and decision support for central Texas groundwater case studies and 3D mapping with point cloud processing at three Texas field sites. We will share experiences and pedagogical insights to improve future versions of this course.

Temporal Reasoning and Default Logics.

DTIC Science & Technology

1985-10-01

Aritificial Intelligence ", Computer Science Research Report, Yale University, forthcoming (1985). . 74 .-, A Axioms for Describing Persistences and Clipping...34Circumscription - A Form of Non-Monotonic Reasoning", Artificial Intelligence , vol. 13 (1980), pp. 27-39. [13] McCarthy, John, "Applications of...and P. J. Hayes, "Some philosophical problems from the standpoint of artificial intelligence ", in: B. Meltzer and D. Michie (eds.), Machine

Reflections on human error - Matters of life and death

NASA Technical Reports Server (NTRS)

Wiener, Earl L.

1989-01-01

The last two decades have witnessed a rapid growth in the introduction of automatic devices into aircraft cockpits, and eleswhere in human-machine systems. This was motivated in part by the assumption that when human functioning is replaced by machine functioning, human error is eliminated. Experience to date shows that this is far from true, and that automation does not replace humans, but changes their role in the system, as well as the types and severity of the errors they make. This altered role may lead to fewer, but more critical errors. Intervention strategies to prevent these errors, or ameliorate their consequences include basic human factors engineering of the interface, enhanced warning and alerting systems, and more intelligent interfaces that understand the strategic intent of the crew and can detect and trap inconsistent or erroneous input before it affects the system.

Routine human-competitive machine intelligence by means of genetic programming

NASA Astrophysics Data System (ADS)

Koza, John R.; Streeter, Matthew J.; Keane, Martin

2004-01-01

Genetic programming is a systematic method for getting computers to automatically solve a problem. Genetic programming starts from a high-level statement of what needs to be done and automatically creates a computer program to solve the problem. The paper demonstrates that genetic programming (1) now routinely delivers high-return human-competitive machine intelligence; (2) is an automated invention machine; (3) can automatically create a general solution to a problem in the form of a parameterized topology; and (4) has delivered a progression of qualitatively more substantial results in synchrony with five approximately order-of-magnitude increases in the expenditure of computer time. Recent results involving the automatic synthesis of the topology and sizing of analog electrical circuits and controllers demonstrate these points.

Enacting the Semantic Web: Ontological Orderings, Negotiated Standards, and Human-Machine Translations

ERIC Educational Resources Information Center

McCarthy, Matthew T.

2017-01-01

Artificial intelligence (AI) that is based upon semantic search has become one of the dominant means for accessing information in recent years. This is particularly the case in mobile contexts, as search-based AI are embedded in each of the major mobile operating systems. The implications are such that information is becoming less a matter of…

On the Application of Pattern Recognition and AI Technique to the Cytoscreening of Vaginal Smears by Computer

NASA Astrophysics Data System (ADS)

Bow, Sing T.; Wang, Xia-Fang

1989-05-01

In this paper the concepts of pattern recognition, image processing and artificial intelligence are applied to the development of an intelligent cytoscreening system to differentiate the abnormal cytological objects from the normal ones in vaginal smears. To achieve this goal,work listed below are involved: 1. Enhancement of the microscopic images of the smears; 2. Elevation of the qualitative differentiation under the microscope by cytologists to a quantitative differentiation plateau on the epithelial cells, ciliated cells, vacuolated cells, foreign-body-giant cells, plasma cells, lymph cells, white blood cells, red blood cells, etc. These knowledges are to be inputted into our intelligent cyto-screening system to ameliorate machine differentiation; 3. Selection of a set of effective features to characterize the cytological objects onto various regions of the multiclustered by computer algorithms; and 4. Systematical summarization of the knowledge that a gynecologist has and the way he/she follows when dealing with a case.

Human-Assisted Machine Information Exploitation: a crowdsourced investigation of information-based problem solving

NASA Astrophysics Data System (ADS)

Kase, Sue E.; Vanni, Michelle; Caylor, Justine; Hoye, Jeff

2017-05-01

The Human-Assisted Machine Information Exploitation (HAMIE) investigation utilizes large-scale online data collection for developing models of information-based problem solving (IBPS) behavior in a simulated time-critical operational environment. These types of environments are characteristic of intelligence workflow processes conducted during human-geo-political unrest situations when the ability to make the best decision at the right time ensures strategic overmatch. The project takes a systems approach to Human Information Interaction (HII) by harnessing the expertise of crowds to model the interaction of the information consumer and the information required to solve a problem at different levels of system restrictiveness and decisional guidance. The design variables derived from Decision Support Systems (DSS) research represent the experimental conditions in this online single-player against-the-clock game where the player, acting in the role of an intelligence analyst, is tasked with a Commander's Critical Information Requirement (CCIR) in an information overload scenario. The player performs a sequence of three information processing tasks (annotation, relation identification, and link diagram formation) with the assistance of `HAMIE the robot' who offers varying levels of information understanding dependent on question complexity. We provide preliminary results from a pilot study conducted with Amazon Mechanical Turk (AMT) participants on the Volunteer Science scientific research platform.

Mold Heating and Cooling Pump Package Operator Interface Controls Upgrade

DOE Office of Scientific and Technical Information (OSTI.GOV)

Josh A. Salmond

2009-08-07

The modernization of the Mold Heating and Cooling Pump Package Operator Interface (MHC PP OI) consisted of upgrading the antiquated single board computer with a proprietary operating system to off-the-shelf hardware and off-the-shelf software with customizable software options. The pump package is the machine interface between a central heating and cooling system that pumps heat transfer fluid through an injection or compression mold base on a local plastic molding machine. The operator interface provides the intelligent means of controlling this pumping process. Strict temperature control of a mold allows the production of high quality parts with tight tolerances and lowmore » residual stresses. The products fabricated are used on multiple programs.« less

Prospects of a mathematical theory of human behavior in complex man-machine systems tasks. [time sharing computer analogy of automobile driving

NASA Technical Reports Server (NTRS)

Johannsen, G.; Rouse, W. B.

1978-01-01

A hierarchy of human activities is derived by analyzing automobile driving in general terms. A structural description leads to a block diagram and a time-sharing computer analogy. The range of applicability of existing mathematical models is considered with respect to the hierarchy of human activities in actual complex tasks. Other mathematical tools so far not often applied to man machine systems are also discussed. The mathematical descriptions at least briefly considered here include utility, estimation, control, queueing, and fuzzy set theory as well as artificial intelligence techniques. Some thoughts are given as to how these methods might be integrated and how further work might be pursued.

Advances in intelligent diagnosis methods for pulmonary ground-glass opacity nodules.

PubMed

Yang, Jing; Wang, Hailin; Geng, Chen; Dai, Yakang; Ji, Jiansong

2018-02-07

Pulmonary nodule is one of the important lesions of lung cancer, mainly divided into two categories of solid nodules and ground glass nodules. The improvement of diagnosis of lung cancer has significant clinical significance, which could be realized by machine learning techniques. At present, there have been a lot of researches focusing on solid nodules. But the research on ground glass nodules started late, and lacked research results. This paper summarizes the research progress of the method of intelligent diagnosis for pulmonary nodules since 2014. It is described in details from four aspects: nodular signs, data analysis methods, prediction models and system evaluation. This paper aims to provide the research material for researchers of the clinical diagnosis and intelligent analysis of lung cancer, and further improve the precision of pulmonary ground glass nodule diagnosis.

Alien Mindscapes—A Perspective on the Search for Extraterrestrial Intelligence

PubMed Central

2016-01-01

Abstract Advances in planetary and space sciences, astrobiology, and life and cognitive sciences, combined with developments in communication theory, bioneural computing, machine learning, and big data analysis, create new opportunities to explore the probabilistic nature of alien life. Brought together in a multidisciplinary approach, they have the potential to support an integrated and expanded Search for Extraterrestrial Intelligence (SETI1), a search that includes looking for life as we do not know it. This approach will augment the odds of detecting a signal by broadening our understanding of the evolutionary and systemic components in the search for extraterrestrial intelligence (ETI), provide more targets for radio and optical SETI, and identify new ways of decoding and coding messages using universal markers. Key Words: SETI—Astrobiology—Coevolution of Earth and life—Planetary habitability and biosignatures. Astrobiology 16, 661–676. PMID:27383691

Alien Mindscapes-A Perspective on the Search for Extraterrestrial Intelligence.

PubMed

Cabrol, Nathalie A

2016-09-01

Advances in planetary and space sciences, astrobiology, and life and cognitive sciences, combined with developments in communication theory, bioneural computing, machine learning, and big data analysis, create new opportunities to explore the probabilistic nature of alien life. Brought together in a multidisciplinary approach, they have the potential to support an integrated and expanded Search for Extraterrestrial Intelligence (SETI (1) ), a search that includes looking for life as we do not know it. This approach will augment the odds of detecting a signal by broadening our understanding of the evolutionary and systemic components in the search for extraterrestrial intelligence (ETI), provide more targets for radio and optical SETI, and identify new ways of decoding and coding messages using universal markers. SETI-Astrobiology-Coevolution of Earth and life-Planetary habitability and biosignatures. Astrobiology 16, 661-676.

Artificial intelligence in diagnosis of obstructive lung disease: current status and future potential.

PubMed

Das, Nilakash; Topalovic, Marko; Janssens, Wim

2018-03-01

The application of artificial intelligence in the diagnosis of obstructive lung diseases is an exciting phenomenon. Artificial intelligence algorithms work by finding patterns in data obtained from diagnostic tests, which can be used to predict clinical outcomes or to detect obstructive phenotypes. The purpose of this review is to describe the latest trends and to discuss the future potential of artificial intelligence in the diagnosis of obstructive lung diseases. Machine learning has been successfully used in automated interpretation of pulmonary function tests for differential diagnosis of obstructive lung diseases. Deep learning models such as convolutional neural network are state-of-the art for obstructive pattern recognition in computed tomography. Machine learning has also been applied in other diagnostic approaches such as forced oscillation test, breath analysis, lung sound analysis and telemedicine with promising results in small-scale studies. Overall, the application of artificial intelligence has produced encouraging results in the diagnosis of obstructive lung diseases. However, large-scale studies are still required to validate current findings and to boost its adoption by the medical community.

Work process and task-based design of intelligent assistance systems in German textile industry

NASA Astrophysics Data System (ADS)

Löhrer, M.; Ziesen, N.; Altepost, A.; Saggiomo, M.; Gloy, Y. S.

2017-10-01

The mid-sized embossed German textile industry must face social challenges e.g. demographic change or technical changing processes. Interaction with intelligent systems (on machines) and increasing automation changes processes, working structures and employees’ tasks on all levels. Work contents are getting more complex, resulting in the necessity for diversified and enhanced competencies. Mobile devices like tablets or smartphones are increasingly finding their way into the workplace. Employees who grew up with new forms of media have certain advantages regarding the usage of modern technologies compared to older employees. Therefore, it is necessary to design new systems which help to adapt the competencies of both younger and older employees to new automated production processes in the digital work environment. The key to successful integration of technical assistance systems is user-orientated design and development that includes concepts for competency development under consideration of, e.g., ethical and legal aspects.

Risk assessment of sewer condition using artificial intelligence tools: application to the SANEST sewer system.

PubMed

Sousa, V; Matos, J P; Almeida, N; Saldanha Matos, J

2014-01-01

Operation, maintenance and rehabilitation comprise the main concerns of wastewater infrastructure asset management. Given the nature of the service provided by a wastewater system and the characteristics of the supporting infrastructure, technical issues are relevant to support asset management decisions. In particular, in densely urbanized areas served by large, complex and aging sewer networks, the sustainability of the infrastructures largely depends on the implementation of an efficient asset management system. The efficiency of such a system may be enhanced with technical decision support tools. This paper describes the role of artificial intelligence tools such as artificial neural networks and support vector machines for assisting the planning of operation and maintenance activities of wastewater infrastructures. A case study of the application of this type of tool to the wastewater infrastructures of Sistema de Saneamento da Costa do Estoril is presented.

Advanced Monitoring to Improve Combustion Turbine/Combined Cycle Reliability, Availability & Maintainability

DOE Office of Scientific and Technical Information (OSTI.GOV)

Leonard Angello

2005-09-30

Power generators are concerned with the maintenance costs associated with the advanced turbines that they are purchasing. Since these machines do not have fully established Operation and Maintenance (O&M) track records, power generators face financial risk due to uncertain future maintenance costs. This risk is of particular concern, as the electricity industry transitions to a competitive business environment in which unexpected O&M costs cannot be passed through to consumers. These concerns have accelerated the need for intelligent software-based diagnostic systems that can monitor the health of a combustion turbine in real time and provide valuable information on the machine's performancemore » to its owner/operators. EPRI, Impact Technologies, Boyce Engineering, and Progress Energy have teamed to develop a suite of intelligent software tools integrated with a diagnostic monitoring platform that, in real time, interpret data to assess the 'total health' of combustion turbines. The 'Combustion Turbine Health Management System' (CTHMS) will consist of a series of 'Dynamic Link Library' (DLL) programs residing on a diagnostic monitoring platform that accepts turbine health data from existing monitoring instrumentation. CTHMS interprets sensor and instrument outputs, correlates them to a machine's condition, provide interpretative analyses, project servicing intervals, and estimate remaining component life. In addition, the CTHMS enables real-time anomaly detection and diagnostics of performance and mechanical faults, enabling power producers to more accurately predict critical component remaining useful life and turbine degradation.« less

Man-machine interface issues in space telerobotics: A JPL research and development program

NASA Technical Reports Server (NTRS)

Bejczy, A. K.

1987-01-01

Technology issues related to the use of robots as man-extension or telerobot systems in space are discussed and exemplified. General considerations are presentd on control and information problems in space teleoperation and on the characteristics of Earth orbital teleoperation. The JPL R and D work in the area of man-machine interface devices and techniques for sensing and computer-based control is briefly summarized. The thrust of this R and D effort is to render space teleoperation efficient and safe through the use of devices and techniques which will permit integrated and task-level (intelligent) two-way control communication between human operator and telerobot machine in Earth orbit. Specific control and information display devices and techniques are discussed and exemplified with development results obtained at JPL in recent years.

Emotion detection from text

NASA Astrophysics Data System (ADS)

Ramalingam, V. V.; Pandian, A.; Jaiswal, Abhijeet; Bhatia, Nikhar

2018-04-01

This paper presents a novel method based on concept of Machine Learning for Emotion Detection using various algorithms of Support Vector Machine and major emotions described are linked to the Word-Net for enhanced accuracy. The approach proposed plays a promising role to augment the Artificial Intelligence in the near future and could be vital in optimization of Human-Machine Interface.

Motor-response learning at a process control panel by an autonomous robot

DOE Office of Scientific and Technical Information (OSTI.GOV)

Spelt, P.F.; de Saussure, G.; Lyness, E.

1988-01-01

The Center for Engineering Systems Advanced Research (CESAR) was founded at Oak Ridge National Laboratory (ORNL) by the Department of Energy's Office of Energy Research/Division of Engineering and Geoscience (DOE-OER/DEG) to conduct basic research in the area of intelligent machines. Therefore, researchers at the CESAR Laboratory are engaged in a variety of research activities in the field of machine learning. In this paper, we describe our approach to a class of machine learning which involves motor response acquisition using feedback from trial-and-error learning. Our formulation is being experimentally validated using an autonomous robot, learning tasks of control panel monitoring andmore » manipulation for effect process control. The CLIPS Expert System and the associated knowledge base used by the robot in the learning process, which reside in a hypercube computer aboard the robot, are described in detail. Benchmark testing of the learning process on a robot/control panel simulation system consisting of two intercommunicating computers is presented, along with results of sample problems used to train and test the expert system. These data illustrate machine learning and the resulting performance improvement in the robot for problems similar to, but not identical with, those on which the robot was trained. Conclusions are drawn concerning the learning problems, and implications for future work on machine learning for autonomous robots are discussed. 16 refs., 4 figs., 1 tab.« less

A computational proof of concept of a machine-intelligent artificial pancreas using Lyapunov stability and differential game theory.

PubMed

Greenwood, Nigel J C; Gunton, Jenny E

2014-07-01

This study demonstrated the novel application of a "machine-intelligent" mathematical structure, combining differential game theory and Lyapunov-based control theory, to the artificial pancreas to handle dynamic uncertainties. Realistic type 1 diabetes (T1D) models from the literature were combined into a composite system. Using a mixture of "black box" simulations and actual data from diabetic medical histories, realistic sets of diabetic time series were constructed for blood glucose (BG), interstitial fluid glucose, infused insulin, meal estimates, and sometimes plasma insulin assays. The problem of underdetermined parameters was side stepped by applying a variant of a genetic algorithm to partial information, whereby multiple candidate-personalized models were constructed and then rigorously tested using further data. These formed a "dynamic envelope" of trajectories in state space, where each trajectory was generated by a hypothesis on the hidden T1D system dynamics. This dynamic envelope was then culled to a reduced form to cover observed dynamic behavior. A machine-intelligent autonomous algorithm then implemented game theory to construct real-time insulin infusion strategies, based on the flow of these trajectories through state space and their interactions with hypoglycemic or near-hyperglycemic states. This technique was tested on 2 simulated participants over a total of fifty-five 24-hour days, with no hypoglycemic or hyperglycemic events, despite significant uncertainties from using actual diabetic meal histories with 10-minute warnings. In the main case studies, BG was steered within the desired target set for 99.8% of a 16-hour daily assessment period. Tests confirmed algorithm robustness for ±25% carbohydrate error. For over 99% of the overall 55-day simulation period, either formal controller stability was achieved to the desired target or else the trajectory was within the desired target. These results suggest that this is a stable, high-confidence way to generate closed-loop insulin infusion strategies. © 2014 Diabetes Technology Society.

Zooniverse: Combining Human and Machine Classifiers for the Big Survey Era

NASA Astrophysics Data System (ADS)

Fortson, Lucy; Wright, Darryl; Beck, Melanie; Lintott, Chris; Scarlata, Claudia; Dickinson, Hugh; Trouille, Laura; Willi, Marco; Laraia, Michael; Boyer, Amy; Veldhuis, Marten; Zooniverse

2018-01-01

Many analyses of astronomical data sets, ranging from morphological classification of galaxies to identification of supernova candidates, have relied on humans to classify data into distinct categories. Crowdsourced galaxy classifications via the Galaxy Zoo project provided a solution that scaled visual classification for extant surveys by harnessing the combined power of thousands of volunteers. However, the much larger data sets anticipated from upcoming surveys will require a different approach. Automated classifiers using supervised machine learning have improved considerably over the past decade but their increasing sophistication comes at the expense of needing ever more training data. Crowdsourced classification by human volunteers is a critical technique for obtaining these training data. But several improvements can be made on this zeroth order solution. Efficiency gains can be achieved by implementing a “cascade filtering” approach whereby the task structure is reduced to a set of binary questions that are more suited to simpler machines while demanding lower cognitive loads for humans.Intelligent subject retirement based on quantitative metrics of volunteer skill and subject label reliability also leads to dramatic improvements in efficiency. We note that human and machine classifiers may retire subjects differently leading to trade-offs in performance space. Drawing on work with several Zooniverse projects including Galaxy Zoo and Supernova Hunter, we will present recent findings from experiments that combine cohorts of human and machine classifiers. We show that the most efficient system results when appropriate subsets of the data are intelligently assigned to each group according to their particular capabilities.With sufficient online training, simple machines can quickly classify “easy” subjects, leaving more difficult (and discovery-oriented) tasks for volunteers. We also find humans achieve higher classification purity while samples produced by machines are typically more complete. These findings set the stage for further investigations, with the ultimate goal of efficiently and accurately labeling the wide range of data classes that will arise from the planned large astronomical surveys.

A rapid prototyping/artificial intelligence approach to space station-era information management and access

NASA Technical Reports Server (NTRS)

Carnahan, Richard S., Jr.; Corey, Stephen M.; Snow, John B.

1989-01-01

Applications of rapid prototyping and Artificial Intelligence techniques to problems associated with Space Station-era information management systems are described. In particular, the work is centered on issues related to: (1) intelligent man-machine interfaces applied to scientific data user support, and (2) the requirement that intelligent information management systems (IIMS) be able to efficiently process metadata updates concerning types of data handled. The advanced IIMS represents functional capabilities driven almost entirely by the needs of potential users. Space Station-era scientific data projected to be generated is likely to be significantly greater than data currently processed and analyzed. Information about scientific data must be presented clearly, concisely, and with support features to allow users at all levels of expertise efficient and cost-effective data access. Additionally, mechanisms for allowing more efficient IIMS metadata update processes must be addressed. The work reported covers the following IIMS design aspects: IIMS data and metadata modeling, including the automatic updating of IIMS-contained metadata, IIMS user-system interface considerations, including significant problems associated with remote access, user profiles, and on-line tutorial capabilities, and development of an IIMS query and browse facility, including the capability to deal with spatial information. A working prototype has been developed and is being enhanced.

The Application of Collaborative Business Intelligence Technology in the Hospital SPD Logistics Management Model.

PubMed

Liu, Tongzhu; Shen, Aizong; Hu, Xiaojian; Tong, Guixian; Gu, Wei

2017-06-01

We aimed to apply collaborative business intelligence (BI) system to hospital supply, processing and distribution (SPD) logistics management model. We searched Engineering Village database, China National Knowledge Infrastructure (CNKI) and Google for articles (Published from 2011 to 2016), books, Web pages, etc., to understand SPD and BI related theories and recent research status. For the application of collaborative BI technology in the hospital SPD logistics management model, we realized this by leveraging data mining techniques to discover knowledge from complex data and collaborative techniques to improve the theories of business process. For the application of BI system, we: (i) proposed a layered structure of collaborative BI system for intelligent management in hospital logistics; (ii) built data warehouse for the collaborative BI system; (iii) improved data mining techniques such as supporting vector machines (SVM) and swarm intelligence firefly algorithm to solve key problems in hospital logistics collaborative BI system; (iv) researched the collaborative techniques oriented to data and business process optimization to improve the business processes of hospital logistics management. Proper combination of SPD model and BI system will improve the management of logistics in the hospitals. The successful implementation of the study requires: (i) to innovate and improve the traditional SPD model and make appropriate implement plans and schedules for the application of BI system according to the actual situations of hospitals; (ii) the collaborative participation of internal departments in hospital including the department of information, logistics, nursing, medical and financial; (iii) timely response of external suppliers.

Intelligent robot trends and predictions for the new millennium

NASA Astrophysics Data System (ADS)

Hall, Ernest L.; Mundhenk, Terrell N.

1999-08-01

An intelligent robot is a remarkably useful combination of a manipulator, sensors and controls. The current use of these machines in outer space, medicine, hazardous materials, defense applications and industry is being pursued with vigor but little funding. In factory automation such robotics machines can improve productivity, increase product quality and improve competitiveness. The computer and the robot have both been developed during recent times. The intelligent robot combines both technologies and requires a thorough understanding and knowledge of mechatronics. In honor of the new millennium, this paper will present a discussion of futuristic trends and predictions. However, in keeping with technical tradition, a new technique for 'Follow the Leader' will also be presented in the hope of it becoming a new, useful and non-obvious technique.

Prediction of Human Intestinal Absorption of Compounds Using Artificial Intelligence Techniques.

PubMed

Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

2017-01-01

Information about Pharmacokinetics of compounds is an essential component of drug design and development. Modeling the pharmacokinetic properties require identification of the factors effecting absorption, distribution, metabolism and excretion of compounds. There have been continuous attempts in the prediction of intestinal absorption of compounds using various Artificial intelligence methods in the effort to reduce the attrition rate of drug candidates entering to preclinical and clinical trials. Currently, there are large numbers of individual predictive models available for absorption using machine learning approaches. Six Artificial intelligence methods namely, Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis were used for prediction of absorption of compounds. Prediction accuracy of Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis for prediction of intestinal absorption of compounds was found to be 91.54%, 88.33%, 84.30%, 86.51%, 79.07% and 80.08% respectively. Comparative analysis of all the six prediction models suggested that Support vector machine with Radial basis function based kernel is comparatively better for binary classification of compounds using human intestinal absorption and may be useful at preliminary stages of drug design and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Intelligent judgements over health risks in a spatial agent-based model.

PubMed

Abdulkareem, Shaheen A; Augustijn, Ellen-Wien; Mustafa, Yaseen T; Filatova, Tatiana

2018-03-20

Millions of people worldwide are exposed to deadly infectious diseases on a regular basis. Breaking news of the Zika outbreak for instance, made it to the main media titles internationally. Perceiving disease risks motivate people to adapt their behavior toward a safer and more protective lifestyle. Computational science is instrumental in exploring patterns of disease spread emerging from many individual decisions and interactions among agents and their environment by means of agent-based models. Yet, current disease models rarely consider simulating dynamics in risk perception and its impact on the adaptive protective behavior. Social sciences offer insights into individual risk perception and corresponding protective actions, while machine learning provides algorithms and methods to capture these learning processes. This article presents an innovative approach to extend agent-based disease models by capturing behavioral aspects of decision-making in a risky context using machine learning techniques. We illustrate it with a case of cholera in Kumasi, Ghana, accounting for spatial and social risk factors that affect intelligent behavior and corresponding disease incidents. The results of computational experiments comparing intelligent with zero-intelligent representations of agents in a spatial disease agent-based model are discussed. We present a spatial disease agent-based model (ABM) with agents' behavior grounded in Protection Motivation Theory. Spatial and temporal patterns of disease diffusion among zero-intelligent agents are compared to those produced by a population of intelligent agents. Two Bayesian Networks (BNs) designed and coded using R and are further integrated with the NetLogo-based Cholera ABM. The first is a one-tier BN1 (only risk perception), the second is a two-tier BN2 (risk and coping behavior). We run three experiments (zero-intelligent agents, BN1 intelligence and BN2 intelligence) and report the results per experiment in terms of several macro metrics of interest: an epidemic curve, a risk perception curve, and a distribution of different types of coping strategies over time. Our results emphasize the importance of integrating behavioral aspects of decision making under risk into spatial disease ABMs using machine learning algorithms. This is especially relevant when studying cumulative impacts of behavioral changes and possible intervention strategies.

Space applications of artificial intelligence; 1990 Goddard Conference, Greenbelt, MD, May 1, 2, 1990, Selected Papers

NASA Technical Reports Server (NTRS)

Rash, James L. (Editor)

1990-01-01

The papers presented at the 1990 Goddard Conference on Space Applications of Artificial Intelligence are given. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The proceedings fall into the following areas: Planning and Scheduling, Fault Monitoring/Diagnosis, Image Processing and Machine Vision, Robotics/Intelligent Control, Development Methodologies, Information Management, and Knowledge Acquisition.

Demonstration of a Semi-Autonomous Hybrid Brain-Machine Interface using Human Intracranial EEG, Eye Tracking, and Computer Vision to Control a Robotic Upper Limb Prosthetic

PubMed Central

McMullen, David P.; Hotson, Guy; Katyal, Kapil D.; Wester, Brock A.; Fifer, Matthew S.; McGee, Timothy G.; Harris, Andrew; Johannes, Matthew S.; Vogelstein, R. Jacob; Ravitz, Alan D.; Anderson, William S.; Thakor, Nitish V.; Crone, Nathan E.

2014-01-01

To increase the ability of brain-machine interfaces (BMIs) to control advanced prostheses such as the modular prosthetic limb (MPL), we are developing a novel system: the Hybrid Augmented Reality Multimodal Operation Neural Integration Environment (HARMONIE). This system utilizes hybrid input, supervisory control, and intelligent robotics to allow users to identify an object (via eye tracking and computer vision) and initiate (via brain-control) a semi-autonomous reach-grasp-and-drop of the object by the MPL. Sequential iterations of HARMONIE were tested in two pilot subjects implanted with electrocorticographic (ECoG) and depth electrodes within motor areas. The subjects performed the complex task in 71.4% (20/28) and 67.7% (21/31) of trials after minimal training. Balanced accuracy for detecting movements was 91.1% and 92.9%, significantly greater than chance accuracies (p < 0.05). After BMI-based initiation, the MPL completed the entire task 100% (one object) and 70% (three objects) of the time. The MPL took approximately 12.2 seconds for task completion after system improvements implemented for the second subject. Our hybrid-BMI design prevented all but one baseline false positive from initiating the system. The novel approach demonstrated in this proof-of-principle study, using hybrid input, supervisory control, and intelligent robotics, addresses limitations of current BMIs. PMID:24760914

Demonstration of a semi-autonomous hybrid brain-machine interface using human intracranial EEG, eye tracking, and computer vision to control a robotic upper limb prosthetic.

PubMed

McMullen, David P; Hotson, Guy; Katyal, Kapil D; Wester, Brock A; Fifer, Matthew S; McGee, Timothy G; Harris, Andrew; Johannes, Matthew S; Vogelstein, R Jacob; Ravitz, Alan D; Anderson, William S; Thakor, Nitish V; Crone, Nathan E

2014-07-01

To increase the ability of brain-machine interfaces (BMIs) to control advanced prostheses such as the modular prosthetic limb (MPL), we are developing a novel system: the Hybrid Augmented Reality Multimodal Operation Neural Integration Environment (HARMONIE). This system utilizes hybrid input, supervisory control, and intelligent robotics to allow users to identify an object (via eye tracking and computer vision) and initiate (via brain-control) a semi-autonomous reach-grasp-and-drop of the object by the MPL. Sequential iterations of HARMONIE were tested in two pilot subjects implanted with electrocorticographic (ECoG) and depth electrodes within motor areas. The subjects performed the complex task in 71.4% (20/28) and 67.7% (21/31) of trials after minimal training. Balanced accuracy for detecting movements was 91.1% and 92.9%, significantly greater than chance accuracies (p < 0.05). After BMI-based initiation, the MPL completed the entire task 100% (one object) and 70% (three objects) of the time. The MPL took approximately 12.2 s for task completion after system improvements implemented for the second subject. Our hybrid-BMI design prevented all but one baseline false positive from initiating the system. The novel approach demonstrated in this proof-of-principle study, using hybrid input, supervisory control, and intelligent robotics, addresses limitations of current BMIs.

Ultrahigh Sensitive and Flexible Magnetoelectronics with Magnetic Nanocomposites: Toward an Additional Perception of Artificial Intelligence.

PubMed

Cai, Shu-Yi; Chang, Cheng-Han; Lin, Hung-I; Huang, Yuan-Fu; Lin, Wei-Ju; Lin, Shih-Yao; Liou, Yi-Rou; Shen, Tien-Lin; Huang, Yen-Hsiang; Tsao, Po-Wei; Tzou, Chen-Yang; Liao, Yu-Ming; Chen, Yang-Fang

2018-05-23

In recent years, flexible magnetoelectronics has attracted a great attention for its intriguing functionalities and potential applications, such as healthcare, memory, soft robots, navigation, and touchless human-machine interaction systems. Here, we provide the first attempt to demonstrate a new type of magneto-piezoresistance device, which possesses an ultrahigh sensitivity with several orders of resistance change under an external magnetic field (100 mT). In our device, Fe-Ni alloy powders are embedded in the silver nanowire-coated micropyramid polydimethylsiloxane films. Our devices can not only serve as an on/off switch but also act as a sensor that can detect different magnetic fields because of its ultrahigh sensitivity, which is very useful for the application in analog signal communication. Moreover, our devices contain several key features, including large-area and easy fabrication processes, fast response time, low working voltage, low power consumption, excellent flexibility, and admirable compatibility onto a freeform surface, which are the critical criteria for the future development of touchless human-machine interaction systems. On the basis of all of these unique characteristics, we have demonstrated a nontouch piano keyboard, instantaneous magnetic field visualization, and autonomous power system, making our new devices be integrable with magnetic field and enable to be implemented into our daily life applications with unfamiliar human senses. Our approach therefore paves a useful route for the development of wearable electronics and intelligent systems.

EEG Control of a Virtual Helicopter in 3-Dimensional Space Using Intelligent Control Strategies

PubMed Central

Royer, Audrey S.; Doud, Alexander J.; Rose, Minn L.

2011-01-01

Films like Firefox, Surrogates, and Avatar have explored the possibilities of using brain-computer interfaces (BCIs) to control machines and replacement bodies with only thought. Real world BCIs have made great progress toward that end. Invasive BCIs have enabled monkeys to fully explore 3-dimensional (3D) space using neuroprosthetics. However, non-invasive BCIs have not been able to demonstrate such mastery of 3D space. Here, we report our work, which demonstrates that human subjects can use a non-invasive BCI to fly a virtual helicopter to any point in a 3D world. Through use of intelligent control strategies, we have facilitated the realization of controlled flight in 3D space. We accomplished this through a reductionist approach that assigns subject-specific control signals to the crucial components of 3D flight. Subject control of the helicopter was comparable when using either the BCI or a keyboard. By using intelligent control strategies, the strengths of both the user and the BCI system were leveraged and accentuated. Intelligent control strategies in BCI systems such as those presented here may prove to be the foundation for complex BCIs capable of doing more than we ever imagined. PMID:20876032

EEG control of a virtual helicopter in 3-dimensional space using intelligent control strategies.

PubMed

Royer, Audrey S; Doud, Alexander J; Rose, Minn L; He, Bin

2010-12-01

Films like Firefox, Surrogates, and Avatar have explored the possibilities of using brain-computer interfaces (BCIs) to control machines and replacement bodies with only thought. Real world BCIs have made great progress toward that end. Invasive BCIs have enabled monkeys to fully explore 3-D space using neuroprosthetics. However, noninvasive BCIs have not been able to demonstrate such mastery of 3-D space. Here, we report our work, which demonstrates that human subjects can use a noninvasive BCI to fly a virtual helicopter to any point in a 3-D world. Through use of intelligent control strategies, we have facilitated the realization of controlled flight in 3-D space. We accomplished this through a reductionist approach that assigns subject-specific control signals to the crucial components of 3-D flight. Subject control of the helicopter was comparable when using either the BCI or a keyboard. By using intelligent control strategies, the strengths of both the user and the BCI system were leveraged and accentuated. Intelligent control strategies in BCI systems such as those presented here may prove to be the foundation for complex BCIs capable of doing more than we ever imagined.

State of the Art and Challenges of Radio Spectrum Monitoring in China

NASA Astrophysics Data System (ADS)

Lu, Q. N.; Yang, J. J.; Jin, Z. Y.; Chen, D. Z.; Huang, M.

2017-10-01

This paper provides an overview of radio spectrum monitoring in China. First, research background, the motivation is described and then train of thought, the prototype system, and the accomplishments are presented. Current radio spectrum monitoring systems are man-machine communication systems, which are unable to detect and process the radio interference automatically. In order to realize intelligent radio monitoring and spectrum management, we proposed an Internet of Things-based spectrum sensing approach using information system architecture and implemented a pilot program; then some very interesting results were obtained.

Assessing Multi-Person and Person-Machine Distributed Decision Making Using an Extended Psychological Distancing Model

DTIC Science & Technology

1990-02-01

human-to- human communication patterns during situation assessment and cooperative problem solving tasks. The research proposed for the second URRP year...Hardware development. In order to create an environment within which to study multi-channeled human-to- human communication , a multi-media observation...that machine-to- human communication can be used to increase cohesion between humans and intelligent machines and to promote human-machine team

Black-box Brain Experiments, Causal Mathematical Logic, and the Thermodynamics of Intelligence

NASA Astrophysics Data System (ADS)

Pissanetzky, Sergio; Lanzalaco, Felix

2013-12-01

Awareness of the possible existence of a yet-unknown principle of Physics that explains cognition and intelligence does exist in several projects of emulation, simulation, and replication of the human brain currently under way. Brain simulation projects define their success partly in terms of the emergence of non-explicitly programmed biophysical signals such as self-oscillation and spreading cortical waves. We propose that a recently discovered theory of Physics known as Causal Mathematical Logic (CML) that links intelligence with causality and entropy and explains intelligent behavior from first principles, is the missing link. We further propose the theory as a roadway to understanding more complex biophysical signals, and to explain the set of intelligence principles. The new theory applies to information considered as an entity by itself. The theory proposes that any device that processes information and exhibits intelligence must satisfy certain theoretical conditions irrespective of the substrate where it is being processed. The substrate can be the human brain, a part of it, a worm's brain, a motor protein that self-locomotes in response to its environment, a computer. Here, we propose to extend the causal theory to systems in Neuroscience, because of its ability to model complex systems without heuristic approximations, and to predict emerging signals of intelligence directly from the models. The theory predicts the existence of a large number of observables (or "signals"), all of which emerge and can be directly and mathematically calculated from non-explicitly programmed detailed causal models. This approach is aiming for a universal and predictive language for Neuroscience and AGI based on causality and entropy, detailed enough to describe the finest structures and signals of the brain, yet general enough to accommodate the versatility and wholeness of intelligence. Experiments are focused on a black-box as one of the devices described above of which both the input and the output are precisely known, but not the internal implementation. The same input is separately supplied to a causal virtual machine, and the calculated output is compared with the measured output. The virtual machine, described in a previous paper, is a computer implementation of CML, fixed for all experiments and unrelated to the device in the black box. If the two outputs are equivalent, then the experiment has quantitatively succeeded and conclusions can be drawn regarding details of the internal implementation of the device. Several small black-box experiments were successfully performed and demonstrated the emergence of non-explicitly programmed cognitive function in each case

Intelligent robot trends for factory automation

NASA Astrophysics Data System (ADS)

Hall, Ernest L.

1997-09-01

An intelligent robot is a remarkably useful combination of a manipulator, sensors and controls. The use of these machines in factory automation can improve productivity, increase product quality and improve competitiveness. This paper presents a discussion of recent economic and technical trends. The robotics industry now has a billion-dollar market in the U.S. and is growing. Feasibility studies are presented which also show unaudited healthy rates of return for a variety of robotic applications. Technically, the machines are faster, cheaper, more repeatable, more reliable and safer. The knowledge base of inverse kinematic and dynamic solutions and intelligent controls is increasing. More attention is being given by industry to robots, vision and motion controls. New areas of usage are emerging for service robots, remote manipulators and automated guided vehicles. However, the road from inspiration to successful application is still long and difficult, often taking decades to achieve a new product. More cooperation between government, industry and universities is needed to speed the development of intelligent robots that will benefit both industry and society.

[INVITED] Computational intelligence for smart laser materials processing

NASA Astrophysics Data System (ADS)

Casalino, Giuseppe

2018-03-01

Computational intelligence (CI) involves using a computer algorithm to capture hidden knowledge from data and to use them for training ;intelligent machine; to make complex decisions without human intervention. As simulation is becoming more prevalent from design and planning to manufacturing and operations, laser material processing can also benefit from computer generating knowledge through soft computing. This work is a review of the state-of-the-art on the methodology and applications of CI in laser materials processing (LMP), which is nowadays receiving increasing interest from world class manufacturers and 4.0 industry. The focus is on the methods that have been proven effective and robust in solving several problems in welding, cutting, drilling, surface treating and additive manufacturing using the laser beam. After a basic description of the most common computational intelligences employed in manufacturing, four sections, namely, laser joining, machining, surface, and additive covered the most recent applications in the already extensive literature regarding the CI in LMP. Eventually, emerging trends and future challenges were identified and discussed.

Syndrome Diagnosis: Human Intuition or Machine Intelligence?

PubMed Central

Braaten, Øivind; Friestad, Johannes

2008-01-01

The aim of this study was to investigate whether artificial intelligence methods can represent objective methods that are essential in syndrome diagnosis. Most syndromes have no external criterion standard of diagnosis. The predictive value of a clinical sign used in diagnosis is dependent on the prior probability of the syndrome diagnosis. Clinicians often misjudge the probabilities involved. Syndromology needs objective methods to ensure diagnostic consistency, and take prior probabilities into account. We applied two basic artificial intelligence methods to a database of machine-generated patients - a ‘vector method’ and a set method. As reference methods we ran an ID3 algorithm, a cluster analysis and a naive Bayes’ calculation on the same patient series. The overall diagnostic error rate for the the vector algorithm was 0.93%, and for the ID3 0.97%. For the clinical signs found by the set method, the predictive values varied between 0.71 and 1.0. The artificial intelligence methods that we used, proved simple, robust and powerful, and represent objective diagnostic methods. PMID:19415142

Syndrome diagnosis: human intuition or machine intelligence?

PubMed

Braaten, Oivind; Friestad, Johannes

2008-01-01

The aim of this study was to investigate whether artificial intelligence methods can represent objective methods that are essential in syndrome diagnosis. Most syndromes have no external criterion standard of diagnosis. The predictive value of a clinical sign used in diagnosis is dependent on the prior probability of the syndrome diagnosis. Clinicians often misjudge the probabilities involved. Syndromology needs objective methods to ensure diagnostic consistency, and take prior probabilities into account. We applied two basic artificial intelligence methods to a database of machine-generated patients - a 'vector method' and a set method. As reference methods we ran an ID3 algorithm, a cluster analysis and a naive Bayes' calculation on the same patient series. The overall diagnostic error rate for the the vector algorithm was 0.93%, and for the ID3 0.97%. For the clinical signs found by the set method, the predictive values varied between 0.71 and 1.0. The artificial intelligence methods that we used, proved simple, robust and powerful, and represent objective diagnostic methods.

Neo-Symbiosis: The Next Stage in the Evolution of Human Information Interaction.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Griffith, Douglas; Greitzer, Frank L.

In his 1960 paper Man-Machine Symbiosis, Licklider predicted that human brains and computing machines will be coupled in a tight partnership that will think as no human brain has ever thought and process data in a way not approached by the information-handling machines we know today. Today we are on the threshold of resurrecting the vision of symbiosis. While Licklider’s original vision suggested a co-equal relationship, here we discuss an updated vision, neo-symbiosis, in which the human holds a superordinate position in an intelligent human-computer collaborative environment. This paper was originally published as a journal article and is being publishedmore » as a chapter in an upcoming book series, Advances in Novel Approaches in Cognitive Informatics and Natural Intelligence.« less

Machine learning research 1989-90

NASA Technical Reports Server (NTRS)

Porter, Bruce W.; Souther, Arthur

1990-01-01

Multifunctional knowledge bases offer a significant advance in artificial intelligence because they can support numerous expert tasks within a domain. As a result they amortize the costs of building a knowledge base over multiple expert systems and they reduce the brittleness of each system. Due to the inevitable size and complexity of multifunctional knowledge bases, their construction and maintenance require knowledge engineering and acquisition tools that can automatically identify interactions between new and existing knowledge. Furthermore, their use requires software for accessing those portions of the knowledge base that coherently answer questions. Considerable progress was made in developing software for building and accessing multifunctional knowledge bases. A language was developed for representing knowledge, along with software tools for editing and displaying knowledge, a machine learning program for integrating new information into existing knowledge, and a question answering system for accessing the knowledge base.

Encouraging Reactivity to Create Robust Machines

DTIC Science & Technology

2013-07-01

Performance Evaluation and Benchmarking of Intelligent Systems, 113 137. Baldwin, J. (1896). A new factor in evolution. The American Naturalist, 30(355...Once more unto the breach: Co evolving a robot and its simulator. In Proceed ings of the international conference on artifical life (alife9) (pp.57...Pfeifer, R. (2003). Evolving complete agents using artificial ontogeny. In (pp. 237 258). Springer Verlag. Brooks, R. (1994). Artifical life and

Mechatronics technology in predictive maintenance method

NASA Astrophysics Data System (ADS)

Majid, Nurul Afiqah A.; Muthalif, Asan G. A.

2017-11-01

This paper presents recent mechatronics technology that can help to implement predictive maintenance by combining intelligent and predictive maintenance instrument. Vibration Fault Simulation System (VFSS) is an example of mechatronics system. The focus of this study is the prediction on the use of critical machines to detect vibration. Vibration measurement is often used as the key indicator of the state of the machine. This paper shows the choice of the appropriate strategy in the vibration of diagnostic process of the mechanical system, especially rotating machines, in recognition of the failure during the working process. In this paper, the vibration signature analysis is implemented to detect faults in rotary machining that includes imbalance, mechanical looseness, bent shaft, misalignment, missing blade bearing fault, balancing mass and critical speed. In order to perform vibration signature analysis for rotating machinery faults, studies have been made on how mechatronics technology is used as predictive maintenance methods. Vibration Faults Simulation Rig (VFSR) is designed to simulate and understand faults signatures. These techniques are based on the processing of vibrational data in frequency-domain. The LabVIEW-based spectrum analyzer software is developed to acquire and extract frequency contents of faults signals. This system is successfully tested based on the unique vibration fault signatures that always occur in a rotating machinery.

Intelligent Mining Engineering Systems in the Structure of Industry 4.0

NASA Astrophysics Data System (ADS)

Rylnikova, Marina; Radchenko, Dmitriy; Klebanov, Dmitriy

2017-11-01

The solution of the problem of improving the human environment and working conditions at mines is based on the provision of the rationale of parameters and conditions for the implementation of an environmentally balanced cycle of comprehensive development of mineral deposits on the basis of the design of mining engineering systems characterized by the minimization of the human factor effect in danger zones of mining operations. In this area, robotized technologies are being developed, machinery and mechanisms with the elements of artificial intelligence, and mining and transport system automatic controls are being put into service throughout the world. In the upcoming decades, mining machines and mechanisms will be virtually industrial robots. The article presents the results of zoning of open-pit and underground mine production areas, as well as mining engineering system of combined development depending on the fact and periodicity of human presence in zones of mining processes. As a surface geotechnology case study, the software structure based on a modular concept is described. The performance philosophy of mining and transport equipment with the elements of artificial intelligence is shown when it is put into service in an open pit.

Testing meta tagger

DTIC Science & Technology

2017-12-21

rank , and computer vision. Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on...Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed.[1] Arthur Samuel...an American pioneer in the field of computer gaming and artificial intelligence, coined the term "Machine Learning " in 1959 while at IBM[2]. Evolved

Cells, Agents, and Support Vectors in Interaction - Modeling Urban Sprawl based on Machine Learning and Artificial Intelligence Techniques in a Post-Industrial Region

NASA Astrophysics Data System (ADS)

Rienow, A.; Menz, G.

2015-12-01

Since the beginning of the millennium, artificial intelligence techniques as cellular automata (CA) and multi-agent systems (MAS) have been incorporated into land-system simulations to address the complex challenges of transitions in urban areas as open, dynamic systems. The study presents a hybrid modeling approach for modeling the two antagonistic processes of urban sprawl and urban decline at once. The simulation power of support vector machines (SVM), cellular automata (CA) and multi-agent systems (MAS) are integrated into one modeling framework and applied to the largest agglomeration of Central Europe: the Ruhr. A modified version of SLEUTH (short for Slope, Land-use, Exclusion, Urban, Transport, and Hillshade) functions as the CA component. SLEUTH makes use of historic urban land-use data sets and growth coefficients for the purpose of modeling physical urban expansion. The machine learning algorithm of SVM is applied in order to enhance SLEUTH. Thus, the stochastic variability of the CA is reduced and information about the human and ecological forces driving the local suitability of urban sprawl is incorporated. Subsequently, the supported CA is coupled with the MAS ReHoSh (Residential Mobility and the Housing Market of Shrinking City Systems). The MAS models population patterns, housing prices, and housing demand in shrinking regions based on interactions between household and city agents. Semi-explicit urban weights are introduced as a possibility of modeling from and to the pixel simultaneously. Three scenarios of changing housing preferences reveal the urban development of the region in terms of quantity and location. They reflect the dissemination of sustainable thinking among stakeholders versus the steady dream of owning a house in sub- and exurban areas. Additionally, the outcomes are transferred into a digital petri dish reflecting a synthetic environment with perfect conditions of growth. Hence, the generic growth elements affecting the future face of post-industrial cities are revealed. Finally, the advantages and limitations of linking pixels and people by combining AI and machine learning techniques in a multi-scale geosimulation approach are to be discussed.

Computers, Nanotechnology and Mind

NASA Astrophysics Data System (ADS)

Ekdahl, Bertil

2008-10-01

In 1958, two years after the Dartmouth conference, where the term artificial intelligence was coined, Herbert Simon and Allen Newell asserted the existence of "machines that think, that learn and create." They were further prophesying that the machines' capacity would increase and be on par with the human mind. Now, 50 years later, computers perform many more tasks than one could imagine in the 1950s but, virtually, no computer can do more than could the first digital computer, developed by John von Neumann in the 1940s. Computers still follow algorithms, they do not create them. However, the development of nanotechnology seems to have given rise to new hopes. With nanotechnology two things are supposed to happen. Firstly, due to the small scale it will be possible to construct huge computer memories which are supposed to be the precondition for building an artificial brain, secondly, nanotechnology will make it possible to scan the brain which in turn will make reverse engineering possible; the mind will be decoded by studying the brain. The consequence of such a belief is that the brain is no more than a calculator, i.e., all that the mind can do is in principle the results of arithmetical operations. Computers are equivalent to formal systems which in turn was an answer to an idea by Hilbert that proofs should contain ideal statements for which operations cannot be applied in a contentual way. The advocates of artificial intelligence will place content in a machine that is developed not only to be free of content but also cannot contain content. In this paper I argue that the hope for artificial intelligence is in vain.

Hands-free human-machine interaction with voice

NASA Astrophysics Data System (ADS)

Juang, B. H.

2004-05-01

Voice is natural communication interface between a human and a machine. The machine, when placed in today's communication networks, may be configured to provide automation to save substantial operating cost, as demonstrated in AT&T's VRCP (Voice Recognition Call Processing), or to facilitate intelligent services, such as virtual personal assistants, to enhance individual productivity. These intelligent services often need to be accessible anytime, anywhere (e.g., in cars when the user is in a hands-busy-eyes-busy situation or during meetings where constantly talking to a microphone is either undersirable or impossible), and thus call for advanced signal processing and automatic speech recognition techniques which support what we call ``hands-free'' human-machine communication. These techniques entail a broad spectrum of technical ideas, ranging from use of directional microphones and acoustic echo cancellatiion to robust speech recognition. In this talk, we highlight a number of key techniques that were developed for hands-free human-machine communication in the mid-1990s after Bell Labs became a unit of Lucent Technologies. A video clip will be played to demonstrate the accomplishement.

Technologies for developing an advanced intelligent ATM with self-defence capabilities

NASA Astrophysics Data System (ADS)

Sako, Hiroshi

2010-01-01

We have developed several technologies for protecting automated teller machines. These technologies are based mainly on pattern recognition and are used to implement various self-defence functions. They include (i) banknote recognition and information retrieval for preventing machines from accepting counterfeit and damaged banknotes and for retrieving information about detected counterfeits from a relational database, (ii) form processing and character recognition for preventing machines from accepting remittance forms without due dates and/or insufficient payment, (iii) person identification to prevent machines from transacting with non-customers, and (iv) object recognition to guard machines against foreign objects such as spy cams that might be surreptitiously attached to them and to protect users against someone attempting to peek at their user information such as their personal identification number. The person identification technology has been implemented in most ATMs in Japan, and field tests have demonstrated that the banknote recognition technology can recognise more then 200 types of banknote from 30 different countries. We are developing an "advanced intelligent ATM" that incorporates all of these technologies.

Solar prediction and intelligent machines

NASA Technical Reports Server (NTRS)

Johnson, Gordon G.

1987-01-01

The solar prediction program is aimed at reducing or eliminating the need to throughly understand the process previously developed and to still be able to produce a prediction. Substantial progress was made in identifying the procedures to be coded as well as testing some of the presently coded work. Another project involves work on developing ideas and software that should result in a machine capable of learning as well as carrying on an intelligent conversation over a wide range of topics. The underlying idea is to use primitive ideas and construct higher order ideas from these, which can then be easily related one to another.

Personal mobility and manipulation using robotics, artificial intelligence and advanced control.

PubMed

Cooper, Rory A; Ding, Dan; Grindle, Garrett G; Wang, Hongwu

2007-01-01

Recent advancements of technologies, including computation, robotics, machine learning, communication, and miniaturization technologies, bring us closer to futuristic visions of compassionate intelligent devices. The missing element is a basic understanding of how to relate human functions (physiological, physical, and cognitive) to the design of intelligent devices and systems that aid and interact with people. Our stakeholder and clinician consultants identified a number of mobility barriers that have been intransigent to traditional approaches. The most important physical obstacles are stairs, steps, curbs, doorways (doors), rough/uneven surfaces, weather hazards (snow, ice), crowded/cluttered spaces, and confined spaces. Focus group participants suggested a number of ways to make interaction simpler, including natural language interfaces such as the ability to say "I want a drink", a library of high level commands (open a door, park the wheelchair, ...), and a touchscreen interface with images so the user could point and use other gestures.

An intelligent identification algorithm for the monoclonal picking instrument

NASA Astrophysics Data System (ADS)

Yan, Hua; Zhang, Rongfu; Yuan, Xujun; Wang, Qun

2017-11-01

The traditional colony selection is mainly operated by manual mode, which takes on low efficiency and strong subjectivity. Therefore, it is important to develop an automatic monoclonal-picking instrument. The critical stage of the automatic monoclonal-picking and intelligent optimal selection is intelligent identification algorithm. An auto-screening algorithm based on Support Vector Machine (SVM) is proposed in this paper, which uses the supervised learning method, which combined with the colony morphological characteristics to classify the colony accurately. Furthermore, through the basic morphological features of the colony, system can figure out a series of morphological parameters step by step. Through the establishment of maximal margin classifier, and based on the analysis of the growth trend of the colony, the selection of the monoclonal colony was carried out. The experimental results showed that the auto-screening algorithm could screen out the regular colony from the other, which meets the requirement of various parameters.

Integrated flexible manufacturing program for manufacturing automation and rapid prototyping

NASA Technical Reports Server (NTRS)

Brooks, S. L.; Brown, C. W.; King, M. S.; Simons, W. R.; Zimmerman, J. J.

1993-01-01

The Kansas City Division of Allied Signal Inc., as part of the Integrated Flexible Manufacturing Program (IFMP), is developing an integrated manufacturing environment. Several systems are being developed to produce standards and automation tools for specific activities within the manufacturing environment. The Advanced Manufacturing Development System (AMDS) is concentrating on information standards (STEP) and product data transfer; the Expert Cut Planner system (XCUT) is concentrating on machining operation process planning standards and automation capabilities; the Advanced Numerical Control system (ANC) is concentrating on NC data preparation standards and NC data generation tools; the Inspection Planning and Programming Expert system (IPPEX) is concentrating on inspection process planning, coordinate measuring machine (CMM) inspection standards and CMM part program generation tools; and the Intelligent Scheduling and Planning System (ISAPS) is concentrating on planning and scheduling tools for a flexible manufacturing system environment. All of these projects are working together to address information exchange, standardization, and information sharing to support rapid prototyping in a Flexible Manufacturing System (FMS) environment.

An experimental result of estimating an application volume by machine learning techniques.

PubMed

Hasegawa, Tatsuhito; Koshino, Makoto; Kimura, Haruhiko

2015-01-01

In this study, we improved the usability of smartphones by automating a user's operations. We developed an intelligent system using machine learning techniques that periodically detects a user's context on a smartphone. We selected the Android operating system because it has the largest market share and highest flexibility of its development environment. In this paper, we describe an application that automatically adjusts application volume. Adjusting the volume can be easily forgotten because users need to push the volume buttons to alter the volume depending on the given situation. Therefore, we developed an application that automatically adjusts the volume based on learned user settings. Application volume can be set differently from ringtone volume on Android devices, and these volume settings are associated with each specific application including games. Our application records a user's location, the volume setting, the foreground application name and other such attributes as learning data, thereby estimating whether the volume should be adjusted using machine learning techniques via Weka.

Compact Microscope Imaging System with Intelligent Controls

NASA Technical Reports Server (NTRS)

McDowell, Mark

2004-01-01

The figure presents selected views of a compact microscope imaging system (CMIS) that includes a miniature video microscope, a Cartesian robot (a computer- controlled three-dimensional translation stage), and machine-vision and control subsystems. The CMIS was built from commercial off-the-shelf instrumentation, computer hardware and software, and custom machine-vision software. The machine-vision and control subsystems include adaptive neural networks that afford a measure of artificial intelligence. The CMIS can perform several automated tasks with accuracy and repeatability . tasks that, heretofore, have required the full attention of human technicians using relatively bulky conventional microscopes. In addition, the automation and control capabilities of the system inherently include a capability for remote control. Unlike human technicians, the CMIS is not at risk of becoming fatigued or distracted: theoretically, it can perform continuously at the level of the best human technicians. In its capabilities for remote control and for relieving human technicians of tedious routine tasks, the CMIS is expected to be especially useful in biomedical research, materials science, inspection of parts on industrial production lines, and space science. The CMIS can automatically focus on and scan a microscope sample, find areas of interest, record the resulting images, and analyze images from multiple samples simultaneously. Automatic focusing is an iterative process: The translation stage is used to move the microscope along its optical axis in a succession of coarse, medium, and fine steps. A fast Fourier transform (FFT) of the image is computed at each step, and the FFT is analyzed for its spatial-frequency content. The microscope position that results in the greatest dispersal of FFT content toward high spatial frequencies (indicating that the image shows the greatest amount of detail) is deemed to be the focal position.

Emotional Intelligence: Advocating for the Softer Side of Leadership

DTIC Science & Technology

2013-03-01

handles social rejection and physical pain.30 In one study , patients in fMRI machines were told they were playing a game with two other players — a...operated more freely.”43 Yet these results do not indicate the cognitive system can be allowed to take a backseat. In another study , fMRI showed that...The roots of empathy can be found at an early age, which implies empathy is hardwired into the primitive limbic system. One study observed a toddler

Autonomous Mechanical Assembly on the Space Shuttle: An Overview

NASA Technical Reports Server (NTRS)

Raibert, M. H.

1979-01-01

The space shuttle will be equipped with a pair of 50 ft. manipulators used to handle payloads and to perform mechanical assembly operations. Although current plans call for these manipulators to be operated by a human teleoperator. The possibility of using results from robotics and machine intelligence to automate this shuttle assembly system was investigated. The major components of an autonomous mechanical assembly system are examined, along with the technology base upon which they depend. The state of the art in advanced automation is also assessed.

Advances in molecular labeling, high throughput imaging and machine intelligence portend powerful functional cellular biochemistry tools.

PubMed

Price, Jeffrey H; Goodacre, Angela; Hahn, Klaus; Hodgson, Louis; Hunter, Edward A; Krajewski, Stanislaw; Murphy, Robert F; Rabinovich, Andrew; Reed, John C; Heynen, Susanne

2002-01-01

Cellular behavior is complex. Successfully understanding systems at ever-increasing complexity is fundamental to advances in modern science and unraveling the functional details of cellular behavior is no exception. We present a collection of prospectives to provide a glimpse of the techniques that will aid in collecting, managing and utilizing information on complex cellular processes via molecular imaging tools. These include: 1) visualizing intracellular protein activity with fluorescent markers, 2) high throughput (and automated) imaging of multilabeled cells in statistically significant numbers, and 3) machine intelligence to analyze subcellular image localization and pattern. Although not addressed here, the importance of combining cell-image-based information with detailed molecular structure and ligand-receptor binding models cannot be overlooked. Advanced molecular imaging techniques have the potential to impact cellular diagnostics for cancer screening, clinical correlations of tissue molecular patterns for cancer biology, and cellular molecular interactions for accelerating drug discovery. The goal of finally understanding all cellular components and behaviors will be achieved by advances in both instrumentation engineering (software and hardware) and molecular biochemistry. Copyright 2002 Wiley-Liss, Inc.

In Praise of Robots

ERIC Educational Resources Information Center

Sagan, Carl

1975-01-01

The author of this article believes that human survival depends upon the ability to develop and work with machines of high artificial intelligence. He lists uses of such machines, including terrestrial mining, outer space exploration, and other tasks too dangerous, too expensive, or too boring for human beings. (MA)

"The Intimate Machine"--30 Years On

ERIC Educational Resources Information Center

Frude, Neil; Jandric, Petar

2015-01-01

This conversation focuses on a book published in 1983 that examined "animism," the tendency to regard non-living entities as living and sentient. "The Intimate Machine" suggested that animism will be fully exploited by artificial intelligence (AI) and robotics, generating artefacts that will engage the user in…

The IS-GEO RCN: Fostering Collaborations for Intelligent Systems Research to Support Geosciences

NASA Astrophysics Data System (ADS)

Gil, Y.; Pierce, S. A.

2016-12-01

Geoscience problems are complex and often involve data that changes across space and time. Frequently geoscience knowledge and understanding provides valuable information and insight for problems related to energy, water, climate, mineral resources, and our understanding of how the Earth evolves through time. Simultaneously, many grand challenges in the geosciences cannot be addressed without the aid of computational support and innovations. Intelligent and Information Systems (IS) research includes a broad range of computational methods and topics such as knowledge representation, information integration, machine learning, robotics, adaptive sensors, and intelligent interfaces. IS research has a very important role to play in accelerating the speed of scientific discovery in geosciences and thus in solving challenges in geosciences. Many aspects of geosciences (GEO) research pose novel open problems for intelligent systems researchers. To develop intelligent systems with sound knowledge of theory and practice, it is important that GEO and IS experts collaborate. The EarthCube Research Coordination Network for Intelligent Systems for Geosciences (IS-GEO RCN) represents an emerging community of interdisciplinary researchers producing fundamental new capabilities for understanding Earth systems. Furthermore, the educational component aims to identify new approaches to teaching students in this new interdisciplinary area, seeking to raise a new generation of scientists that are better able to apply IS methods and tools to geoscience challenges of the future. By providing avenues for IS and GEO researchers to work together, the IS-GEO RCN will serve as both a point of contact, as well as an avenue for educational outreach across the disciplines for the nascent community of research and practice. The initial efforts are focused on connecting the communities in ways that help researchers understand opportunities and challenges that can benefit from IS-GEO collaborations. The IS-GEO RCN will jumpstart interdisciplinary research collaborations in this emerging new area so that progress across both disciplines can be accelerated.

Monitoring osseointegration and developing intelligent systems (Conference Presentation)

NASA Astrophysics Data System (ADS)

Salvino, Liming W.

2017-05-01

Effective monitoring of structural and biological systems is an extremely important research area that enables technology development for future intelligent devices, platforms, and systems. This presentation provides an overview of research efforts funded by the Office of Naval Research (ONR) to establish structural health monitoring (SHM) methodologies in the human domain. Basic science efforts are needed to utilize SHM sensing, data analysis, modeling, and algorithms to obtain the relevant physiological and biological information for human-specific health and performance conditions. This overview of current research efforts is based on the Monitoring Osseointegrated Prosthesis (MOIP) program. MOIP develops implantable and intelligent prosthetics that are directly anchored to the bone of residual limbs. Through real-time monitoring, sensing, and responding to osseointegration of bones and implants as well as interface conditions and environment, our research program aims to obtain individualized actionable information for implant failure identification, load estimation, infection mitigation and treatment, as well as healing assessment. Looking ahead to achieve ultimate goals of SHM, we seek to expand our research areas to cover monitoring human, biological and engineered systems, as well as human-machine interfaces. Examples of such include 1) brainwave monitoring and neurological control, 2) detecting and evaluating brain injuries, 3) monitoring and maximizing human-technological object teaming, and 4) closed-loop setups in which actions can be triggered automatically based on sensors, actuators, and data signatures. Finally, some ongoing and future collaborations across different disciplines for the development of knowledge automation and intelligent systems will be discussed.

Cohort: critical science

NASA Astrophysics Data System (ADS)

Digney, Bruce L.

2007-04-01

Unmanned vehicle systems is an attractive technology for the military, but whose promises have remained largely undelivered. There currently exist fielded remote controlled UGVs and high altitude UAV whose benefits are based on standoff in low complexity environments with sufficiently low control reaction time requirements to allow for teleoperation. While effective within there limited operational niche such systems do not meet with the vision of future military UxV scenarios. Such scenarios envision unmanned vehicles operating effectively in complex environments and situations with high levels of independence and effective coordination with other machines and humans pursing high level, changing and sometimes conflicting goals. While these aims are clearly ambitious they do provide necessary targets and inspiration with hopes of fielding near term useful semi-autonomous unmanned systems. Autonomy involves many fields of research including machine vision, artificial intelligence, control theory, machine learning and distributed systems all of which are intertwined and have goals of creating more versatile broadly applicable algorithms. Cohort is a major Applied Research Program (ARP) led by Defence R&D Canada (DRDC) Suffield and its aim is to develop coordinated teams of unmanned vehicles (UxVs) for urban environments. This paper will discuss the critical science being addressed by DRDC developing semi-autonomous systems.

How the strengths of Lisp-family languages facilitate building complex and flexible bioinformatics applications

PubMed Central

Khomtchouk, Bohdan B; Weitz, Edmund; Karp, Peter D; Wahlestedt, Claes

2018-01-01

Abstract We present a rationale for expanding the presence of the Lisp family of programming languages in bioinformatics and computational biology research. Put simply, Lisp-family languages enable programmers to more quickly write programs that run faster than in other languages. Languages such as Common Lisp, Scheme and Clojure facilitate the creation of powerful and flexible software that is required for complex and rapidly evolving domains like biology. We will point out several important key features that distinguish languages of the Lisp family from other programming languages, and we will explain how these features can aid researchers in becoming more productive and creating better code. We will also show how these features make these languages ideal tools for artificial intelligence and machine learning applications. We will specifically stress the advantages of domain-specific languages (DSLs): languages that are specialized to a particular area, and thus not only facilitate easier research problem formulation, but also aid in the establishment of standards and best programming practices as applied to the specific research field at hand. DSLs are particularly easy to build in Common Lisp, the most comprehensive Lisp dialect, which is commonly referred to as the ‘programmable programming language’. We are convinced that Lisp grants programmers unprecedented power to build increasingly sophisticated artificial intelligence systems that may ultimately transform machine learning and artificial intelligence research in bioinformatics and computational biology. PMID:28040748

How the strengths of Lisp-family languages facilitate building complex and flexible bioinformatics applications.

PubMed

Khomtchouk, Bohdan B; Weitz, Edmund; Karp, Peter D; Wahlestedt, Claes

2018-05-01

We present a rationale for expanding the presence of the Lisp family of programming languages in bioinformatics and computational biology research. Put simply, Lisp-family languages enable programmers to more quickly write programs that run faster than in other languages. Languages such as Common Lisp, Scheme and Clojure facilitate the creation of powerful and flexible software that is required for complex and rapidly evolving domains like biology. We will point out several important key features that distinguish languages of the Lisp family from other programming languages, and we will explain how these features can aid researchers in becoming more productive and creating better code. We will also show how these features make these languages ideal tools for artificial intelligence and machine learning applications. We will specifically stress the advantages of domain-specific languages (DSLs): languages that are specialized to a particular area, and thus not only facilitate easier research problem formulation, but also aid in the establishment of standards and best programming practices as applied to the specific research field at hand. DSLs are particularly easy to build in Common Lisp, the most comprehensive Lisp dialect, which is commonly referred to as the 'programmable programming language'. We are convinced that Lisp grants programmers unprecedented power to build increasingly sophisticated artificial intelligence systems that may ultimately transform machine learning and artificial intelligence research in bioinformatics and computational biology.

Solution techniques for transient stability-constrained optimal power flow – Part II

DOE PAGES

Geng, Guangchao; Abhyankar, Shrirang; Wang, Xiaoyu; ...

2017-06-28

Transient stability-constrained optimal power flow is an important emerging problem with power systems pushed to the limits for economic benefits, dense and larger interconnected systems, and reduced inertia due to expected proliferation of renewable energy resources. In this study, two more approaches: single machine equivalent and computational intelligence are presented. Also discussed are various application areas, and future directions in this research area. In conclusion, a comprehensive resource for the available literature, publicly available test systems, and relevant numerical libraries is also provided.

Basics of robotics and manipulators in endoscopic surgery.

PubMed

Rininsland, H H

1993-06-01

The experience with sophisticated remote handling systems for nuclear operations in inaccessible rooms can to a large extent be transferred to the development of robotics and telemanipulators for endoscopic surgery. A telemanipulator system is described consisting of manipulator, endeffector and tools, 3-D video-endoscope, sensors, intelligent control system, modeling and graphic simulation and man-machine interfaces as the main components or subsystems. Such a telemanipulator seems to be medically worthwhile and technically feasible, but needs a lot of effort from different scientific disciplines to become a safe and reliable instrument for future endoscopic surgery.

Solution techniques for transient stability-constrained optimal power flow – Part II

DOE Office of Scientific and Technical Information (OSTI.GOV)

Geng, Guangchao; Abhyankar, Shrirang; Wang, Xiaoyu

Transient stability-constrained optimal power flow is an important emerging problem with power systems pushed to the limits for economic benefits, dense and larger interconnected systems, and reduced inertia due to expected proliferation of renewable energy resources. In this study, two more approaches: single machine equivalent and computational intelligence are presented. Also discussed are various application areas, and future directions in this research area. In conclusion, a comprehensive resource for the available literature, publicly available test systems, and relevant numerical libraries is also provided.

Intelligence: Real or artificial?

PubMed Central

Schlinger, Henry D.

1992-01-01

Throughout the history of the artificial intelligence movement, researchers have strived to create computers that could simulate general human intelligence. This paper argues that workers in artificial intelligence have failed to achieve this goal because they adopted the wrong model of human behavior and intelligence, namely a cognitive essentialist model with origins in the traditional philosophies of natural intelligence. An analysis of the word “intelligence” suggests that it originally referred to behavior-environment relations and not to inferred internal structures and processes. It is concluded that if workers in artificial intelligence are to succeed in their general goal, then they must design machines that are adaptive, that is, that can learn. Thus, artificial intelligence researchers must discard their essentialist model of natural intelligence and adopt a selectionist model instead. Such a strategic change should lead them to the science of behavior analysis. PMID:22477051

Artificial intelligence in the service of system administrators

NASA Astrophysics Data System (ADS)

Haen, C.; Barra, V.; Bonaccorsi, E.; Neufeld, N.

2012-12-01

The LHCb online system relies on a large and heterogeneous IT infrastructure made from thousands of servers on which many different applications are running. They run a great variety of tasks: critical ones such as data taking and secondary ones like web servers. The administration of such a system and making sure it is working properly represents a very important workload for the small expert-operator team. Research has been performed to try to automatize (some) system administration tasks, starting in 2001 when IBM defined the so-called “self objectives” supposed to lead to “autonomic computing”. In this context, we present a framework that makes use of artificial intelligence and machine learning to monitor and diagnose at a low level and in a non intrusive way Linux-based systems and their interaction with software. Moreover, the multi agent approach we use, coupled with an “object oriented paradigm” architecture should increase our learning speed a lot and highlight relations between problems.

Artificial intelligence in sports on the example of weight training.

PubMed

Novatchkov, Hristo; Baca, Arnold

2013-01-01

The overall goal of the present study was to illustrate the potential of artificial intelligence (AI) techniques in sports on the example of weight training. The research focused in particular on the implementation of pattern recognition methods for the evaluation of performed exercises on training machines. The data acquisition was carried out using way and cable force sensors attached to various weight machines, thereby enabling the measurement of essential displacement and force determinants during training. On the basis of the gathered data, it was consequently possible to deduce other significant characteristics like time periods or movement velocities. These parameters were applied for the development of intelligent methods adapted from conventional machine learning concepts, allowing an automatic assessment of the exercise technique and providing individuals with appropriate feedback. In practice, the implementation of such techniques could be crucial for the investigation of the quality of the execution, the assistance of athletes but also coaches, the training optimization and for prevention purposes. For the current study, the data was based on measurements from 15 rather inexperienced participants, performing 3-5 sets of 10-12 repetitions on a leg press machine. The initially preprocessed data was used for the extraction of significant features, on which supervised modeling methods were applied. Professional trainers were involved in the assessment and classification processes by analyzing the video recorded executions. The so far obtained modeling results showed good performance and prediction outcomes, indicating the feasibility and potency of AI techniques in assessing performances on weight training equipment automatically and providing sportsmen with prompt advice. Key pointsArtificial intelligence is a promising field for sport-related analysis.Implementations integrating pattern recognition techniques enable the automatic evaluation of data measurements.Artificial neural networks applied for the analysis of weight training data show good performance and high classification rates.

Artificial Intelligence in Sports on the Example of Weight Training

PubMed Central

Novatchkov, Hristo; Baca, Arnold

2013-01-01

The overall goal of the present study was to illustrate the potential of artificial intelligence (AI) techniques in sports on the example of weight training. The research focused in particular on the implementation of pattern recognition methods for the evaluation of performed exercises on training machines. The data acquisition was carried out using way and cable force sensors attached to various weight machines, thereby enabling the measurement of essential displacement and force determinants during training. On the basis of the gathered data, it was consequently possible to deduce other significant characteristics like time periods or movement velocities. These parameters were applied for the development of intelligent methods adapted from conventional machine learning concepts, allowing an automatic assessment of the exercise technique and providing individuals with appropriate feedback. In practice, the implementation of such techniques could be crucial for the investigation of the quality of the execution, the assistance of athletes but also coaches, the training optimization and for prevention purposes. For the current study, the data was based on measurements from 15 rather inexperienced participants, performing 3-5 sets of 10-12 repetitions on a leg press machine. The initially preprocessed data was used for the extraction of significant features, on which supervised modeling methods were applied. Professional trainers were involved in the assessment and classification processes by analyzing the video recorded executions. The so far obtained modeling results showed good performance and prediction outcomes, indicating the feasibility and potency of AI techniques in assessing performances on weight training equipment automatically and providing sportsmen with prompt advice. Key points Artificial intelligence is a promising field for sport-related analysis. Implementations integrating pattern recognition techniques enable the automatic evaluation of data measurements. Artificial neural networks applied for the analysis of weight training data show good performance and high classification rates. PMID:24149722

Comparison of display enhancement with intelligent decision-aiding

NASA Technical Reports Server (NTRS)

Kirlik, Alex; Markert, Wendy J.; Kossack, Merrick

1992-01-01

Currently, two main approaches exist for improving the human-machine interface component of a system in order to improve overall system performance, display enhancement and intelligent decision aiding. Each of these two approaches has its own set of advantages and disadvantages, as well as introduce its own set of additional performance problems. These characteristics should help identify which types of problem situations and domains are better aided by which type of strategy. The characteristic issues are described of these two decision aiding strategies. Then differences in expert and novice decision making are described in order to help determine whether a particular strategy may be better for a particular type of user. Finally, research is outlined to compare and contrast the two technologies, as well as to examine the interaction effects introduced by the different skill levels and the different methods for training operators.

Multi-intelligence critical rating assessment of fusion techniques (MiCRAFT)

NASA Astrophysics Data System (ADS)

Blasch, Erik

2015-06-01

Assessment of multi-intelligence fusion techniques includes credibility of algorithm performance, quality of results against mission needs, and usability in a work-domain context. Situation awareness (SAW) brings together low-level information fusion (tracking and identification), high-level information fusion (threat and scenario-based assessment), and information fusion level 5 user refinement (physical, cognitive, and information tasks). To measure SAW, we discuss the SAGAT (Situational Awareness Global Assessment Technique) technique for a multi-intelligence fusion (MIF) system assessment that focuses on the advantages of MIF against single intelligence sources. Building on the NASA TLX (Task Load Index), SAGAT probes, SART (Situational Awareness Rating Technique) questionnaires, and CDM (Critical Decision Method) decision points; we highlight these tools for use in a Multi-Intelligence Critical Rating Assessment of Fusion Techniques (MiCRAFT). The focus is to measure user refinement of a situation over the information fusion quality of service (QoS) metrics: timeliness, accuracy, confidence, workload (cost), and attention (throughput). A key component of any user analysis includes correlation, association, and summarization of data; so we also seek measures of product quality and QuEST of information. Building a notion of product quality from multi-intelligence tools is typically subjective which needs to be aligned with objective machine metrics.

Improved RMR Rock Mass Classification Using Artificial Intelligence Algorithms

NASA Astrophysics Data System (ADS)

Gholami, Raoof; Rasouli, Vamegh; Alimoradi, Andisheh

2013-09-01

Rock mass classification systems such as rock mass rating (RMR) are very reliable means to provide information about the quality of rocks surrounding a structure as well as to propose suitable support systems for unstable regions. Many correlations have been proposed to relate measured quantities such as wave velocity to rock mass classification systems to limit the associated time and cost of conducting the sampling and mechanical tests conventionally used to calculate RMR values. However, these empirical correlations have been found to be unreliable, as they usually overestimate or underestimate the RMR value. The aim of this paper is to compare the results of RMR classification obtained from the use of empirical correlations versus machine-learning methodologies based on artificial intelligence algorithms. The proposed methods were verified based on two case studies located in northern Iran. Relevance vector regression (RVR) and support vector regression (SVR), as two robust machine-learning methodologies, were used to predict the RMR for tunnel host rocks. RMR values already obtained by sampling and site investigation at one tunnel were taken into account as the output of the artificial networks during training and testing phases. The results reveal that use of empirical correlations overestimates the predicted RMR values. RVR and SVR, however, showed more reliable results, and are therefore suggested for use in RMR classification for design purposes of rock structures.

The Application of Collaborative Business Intelligence Technology in the Hospital SPD Logistics Management Model

PubMed Central

LIU, Tongzhu; SHEN, Aizong; HU, Xiaojian; TONG, Guixian; GU, Wei

2017-01-01

Background: We aimed to apply collaborative business intelligence (BI) system to hospital supply, processing and distribution (SPD) logistics management model. Methods: We searched Engineering Village database, China National Knowledge Infrastructure (CNKI) and Google for articles (Published from 2011 to 2016), books, Web pages, etc., to understand SPD and BI related theories and recent research status. For the application of collaborative BI technology in the hospital SPD logistics management model, we realized this by leveraging data mining techniques to discover knowledge from complex data and collaborative techniques to improve the theories of business process. Results: For the application of BI system, we: (i) proposed a layered structure of collaborative BI system for intelligent management in hospital logistics; (ii) built data warehouse for the collaborative BI system; (iii) improved data mining techniques such as supporting vector machines (SVM) and swarm intelligence firefly algorithm to solve key problems in hospital logistics collaborative BI system; (iv) researched the collaborative techniques oriented to data and business process optimization to improve the business processes of hospital logistics management. Conclusion: Proper combination of SPD model and BI system will improve the management of logistics in the hospitals. The successful implementation of the study requires: (i) to innovate and improve the traditional SPD model and make appropriate implement plans and schedules for the application of BI system according to the actual situations of hospitals; (ii) the collaborative participation of internal departments in hospital including the department of information, logistics, nursing, medical and financial; (iii) timely response of external suppliers. PMID:28828316

Cellular computational generalized neuron network for frequency situational intelligence in a multi-machine power system.

PubMed

Wei, Yawei; Venayagamoorthy, Ganesh Kumar

2017-09-01

To prevent large interconnected power system from a cascading failure, brownout or even blackout, grid operators require access to faster than real-time information to make appropriate just-in-time control decisions. However, the communication and computational system limitations of currently used supervisory control and data acquisition (SCADA) system can only deliver delayed information. However, the deployment of synchrophasor measurement devices makes it possible to capture and visualize, in near-real-time, grid operational data with extra granularity. In this paper, a cellular computational network (CCN) approach for frequency situational intelligence (FSI) in a power system is presented. The distributed and scalable computing unit of the CCN framework makes it particularly flexible for customization for a particular set of prediction requirements. Two soft-computing algorithms have been implemented in the CCN framework: a cellular generalized neuron network (CCGNN) and a cellular multi-layer perceptron network (CCMLPN), for purposes of providing multi-timescale frequency predictions, ranging from 16.67 ms to 2 s. These two developed CCGNN and CCMLPN systems were then implemented on two different scales of power systems, one of which installed a large photovoltaic plant. A real-time power system simulator at weather station within the Real-Time Power and Intelligent Systems (RTPIS) laboratory at Clemson, SC, was then used to derive typical FSI results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Use of artificial intelligence in analytical systems for the clinical laboratory

PubMed Central

Truchaud, Alain; Ozawa, Kyoichi; Pardue, Harry; Schnipelsky, Paul

1995-01-01

The incorporation of information-processing technology into analytical systems in the form of standard computing software has recently been advanced by the introduction of artificial intelligence (AI), both as expert systems and as neural networks. This paper considers the role of software in system operation, control and automation, and attempts to define intelligence. AI is characterized by its ability to deal with incomplete and imprecise information and to accumulate knowledge. Expert systems, building on standard computing techniques, depend heavily on the domain experts and knowledge engineers that have programmed them to represent the real world. Neural networks are intended to emulate the pattern-recognition and parallel processing capabilities of the human brain and are taught rather than programmed. The future may lie in a combination of the recognition ability of the neural network and the rationalization capability of the expert system. In the second part of the paper, examples are given of applications of AI in stand-alone systems for knowledge engineering and medical diagnosis and in embedded systems for failure detection, image analysis, user interfacing, natural language processing, robotics and machine learning, as related to clinical laboratories. It is concluded that AI constitutes a collective form of intellectual propery, and that there is a need for better documentation, evaluation and regulation of the systems already being used in clinical laboratories. PMID:18924784

Learning by Reading for Robust Reasoning in Intelligent Agents

DTIC Science & Technology

2018-04-24

SUPPLEMENTARY NOTES 14. ABSTRACT Our hypotheses are that analogical processing plays multiple roles in enabling machines to learn by reading, and that...systems). Our overall hypotheses are that analogical processing plays multiple roles in learning by reading, and that qualitative representations provide...from reading this text? Narrative function can be seen as a kind of communication act, but the idea goes a bit beyond that. Communication acts are

Autonomy in Materials Research: A Case Study in Carbon Nanotube Growth (Postprint)

DTIC Science & Technology

2016-10-21

built an Autonomous Research System (ARES)—an autonomous research robot capable of first-of-its-kind closed-loop iterative materials experimentation...ARES exploits advances in autonomous robotics , artificial intelligence, data sciences, and high-throughput and in situ techniques, and is able to...roles of humans and autonomous research robots , and for human-machine partnering. We believe autonomous research robots like ARES constitute a

Automated Setup Assembly Mechanisms for the Intelligent Machining Workstation

DTIC Science & Technology

1990-11-01

Autoimated analysis systems such as [36,37] use the Finite Elements Method ( FEM ) to evaluate or synthesize the structures of fixtures and workpiece...the angular orientation is not altered, and vice versa. This decoupling is accomplished by elastomers that are quite stiff in compression while being...Larger pins (#14), along with elastomers (#15), and the bolt and washer assembly (#6), provide compliance between top and bottom plate (#2), and by

NASA Center for Intelligent Robotic Systems for Space Exploration

NASA Technical Reports Server (NTRS)

1990-01-01

NASA's program for the civilian exploration of space is a challenge to scientists and engineers to help maintain and further develop the United States' position of leadership in a focused sphere of space activity. Such an ambitious plan requires the contribution and further development of many scientific and technological fields. One research area essential for the success of these space exploration programs is Intelligent Robotic Systems. These systems represent a class of autonomous and semi-autonomous machines that can perform human-like functions with or without human interaction. They are fundamental for activities too hazardous for humans or too distant or complex for remote telemanipulation. To meet this challenge, Rensselaer Polytechnic Institute (RPI) has established an Engineering Research Center for Intelligent Robotic Systems for Space Exploration (CIRSSE). The Center was created with a five year $5.5 million grant from NASA submitted by a team of the Robotics and Automation Laboratories. The Robotics and Automation Laboratories of RPI are the result of the merger of the Robotics and Automation Laboratory of the Department of Electrical, Computer, and Systems Engineering (ECSE) and the Research Laboratory for Kinematics and Robotic Mechanisms of the Department of Mechanical Engineering, Aeronautical Engineering, and Mechanics (ME,AE,&M), in 1987. This report is an examination of the activities that are centered at CIRSSE.

Multiprocessor Z-Buffer Architecture for High-Speed, High Complexity Computer Image Generation.

DTIC Science & Technology

1983-12-01

Oversampling 50 17. "Poking Through" Effects 51 18. Sampling Paths 52 19. Triangle Variables 54 20. Intelligent Tiling Algorithm 61 21. Tiler Functional Blocks...64 * 22. HSD Interface 65 23. Tiling Machine Setup 67 24. Tiling Machine 68 25. Tile Accumulate 69 26. A lx$ Sorting Machine 77 27. A 2x8 Sorting...Delay 227 87. Effect of Triangle Size on Tiler Throughput Rates 229 88. Tiling Machine Setup Stage Performance for Oversample Mode 234 89. Tiling

Intelligible machine learning with malibu.

PubMed

Langlois, Robert E; Lu, Hui

2008-01-01

malibu is an open-source machine learning work-bench developed in C/C++ for high-performance real-world applications, namely bioinformatics and medical informatics. It leverages third-party machine learning implementations for more robust bug-free software. This workbench handles several well-studied supervised machine learning problems including classification, regression, importance-weighted classification and multiple-instance learning. The malibu interface was designed to create reproducible experiments ideally run in a remote and/or command line environment. The software can be found at: http://proteomics.bioengr. uic.edu/malibu/index.html.

Learning in and from brain-based devices.

PubMed

Edelman, Gerald M

2007-11-16

Biologically based mobile devices have been constructed that differ from robots based on artificial intelligence. These brain-based devices (BBDs) contain simulated brains that autonomously categorize signals from the environment without a priori instruction. Two such BBDs, Darwin VII and Darwin X, are described here. Darwin VII recognizes objects and links categories to behavior through instrumental conditioning. Darwin X puts together the "what,"when," and "where" from cues in the environment into an episodic memory that allows it to find a desired target. Although these BBDs are designed to provide insights into how the brain works, their principles may find uses in building hybrid machines. These machines would combine the learning ability of BBDs with explicitly programmed control systems.

Role of Big Data and Machine Learning in Diagnostic Decision Support in Radiology.

PubMed

Syeda-Mahmood, Tanveer

2018-03-01

The field of diagnostic decision support in radiology is undergoing rapid transformation with the availability of large amounts of patient data and the development of new artificial intelligence methods of machine learning such as deep learning. They hold the promise of providing imaging specialists with tools for improving the accuracy and efficiency of diagnosis and treatment. In this article, we will describe the growth of this field for radiology and outline general trends highlighting progress in the field of diagnostic decision support from the early days of rule-based expert systems to cognitive assistants of the modern era. Copyright © 2018 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Product demand forecasts using wavelet kernel support vector machine and particle swarm optimization in manufacture system

NASA Astrophysics Data System (ADS)

Wu, Qi

2010-03-01

Demand forecasts play a crucial role in supply chain management. The future demand for a certain product is the basis for the respective replenishment systems. Aiming at demand series with small samples, seasonal character, nonlinearity, randomicity and fuzziness, the existing support vector kernel does not approach the random curve of the sales time series in the space (quadratic continuous integral space). In this paper, we present a hybrid intelligent system combining the wavelet kernel support vector machine and particle swarm optimization for demand forecasting. The results of application in car sale series forecasting show that the forecasting approach based on the hybrid PSOWv-SVM model is effective and feasible, the comparison between the method proposed in this paper and other ones is also given, which proves that this method is, for the discussed example, better than hybrid PSOv-SVM and other traditional methods.

Mutual Authentication Scheme in Secure Internet of Things Technology for Comfortable Lifestyle.

PubMed

Park, Namje; Kang, Namhi

2015-12-24

The Internet of Things (IoT), which can be regarded as an enhanced version of machine-to-machine communication technology, was proposed to realize intelligent thing-to-thing communications by utilizing the Internet connectivity. In the IoT, "things" are generally heterogeneous and resource constrained. In addition, such things are connected to each other over low-power and lossy networks. In this paper, we propose an inter-device authentication and session-key distribution system for devices with only encryption modules. In the proposed system, unlike existing sensor-network environments where the key distribution center distributes the key, each sensor node is involved with the generation of session keys. In addition, in the proposed scheme, the performance is improved so that the authenticated device can calculate the session key in advance. The proposed mutual authentication and session-key distribution system can withstand replay attacks, man-in-the-middle attacks, and wiretapped secret-key attacks.

Automated document analysis system

NASA Astrophysics Data System (ADS)

Black, Jeffrey D.; Dietzel, Robert; Hartnett, David

2002-08-01

A software application has been developed to aid law enforcement and government intelligence gathering organizations in the translation and analysis of foreign language documents with potential intelligence content. The Automated Document Analysis System (ADAS) provides the capability to search (data or text mine) documents in English and the most commonly encountered foreign languages, including Arabic. Hardcopy documents are scanned by a high-speed scanner and are optical character recognized (OCR). Documents obtained in an electronic format bypass the OCR and are copied directly to a working directory. For translation and analysis, the script and the language of the documents are first determined. If the document is not in English, the document is machine translated to English. The documents are searched for keywords and key features in either the native language or translated English. The user can quickly review the document to determine if it has any intelligence content and whether detailed, verbatim human translation is required. The documents and document content are cataloged for potential future analysis. The system allows non-linguists to evaluate foreign language documents and allows for the quick analysis of a large quantity of documents. All document processing can be performed manually or automatically on a single document or a batch of documents.

Groundwater vulnerability indices conditioned by Supervised Intelligence Committee Machine (SICM).

PubMed

Nadiri, Ata Allah; Gharekhani, Maryam; Khatibi, Rahman; Sadeghfam, Sina; Moghaddam, Asghar Asghari

2017-01-01

This research presents a Supervised Intelligent Committee Machine (SICM) model to assess groundwater vulnerability indices of an aquifer. SICM uses Artificial Neural Networks (ANN) to overarch three Artificial Intelligence (AI) models: Support Vector Machine (SVM), Neuro-Fuzzy (NF) and Gene Expression Programming (GEP). Each model uses the DRASTIC index, the acronym of 7 geological, hydrological and hydrogeological parameters, which collectively represents intrinsic (or natural) vulnerability and gives a sense of contaminants, such as nitrate-N, penetrating aquifers from the surface. These models are trained to modify or condition their DRASTIC index values by measured nitrate-N concentration. The three AI-techniques often perform similarly but have differences as well and therefore SICM exploits the situation to improve the modeled values by producing a hybrid modeling results through selecting better performing SVM, NF and GEP components. The models of the study area at Ardabil aquifer show that the vulnerability indices by the DRASTIC framework produces sharp fronts but AI models smoothen the fronts and reflect a better correlation with observed nitrate values; SICM improves on the performances of three AI models and cope well with heterogeneity and uncertain parameters. Copyright © 2016 Elsevier B.V. All rights reserved.

The epidural needle guidance with an intelligent and automatic identification system for epidural anesthesia

NASA Astrophysics Data System (ADS)

Kao, Meng-Chun; Ting, Chien-Kun; Kuo, Wen-Chuan

2018-02-01

Incorrect placement of the needle causes medical complications in the epidural block, such as dural puncture or spinal cord injury. This study proposes a system which combines an optical coherence tomography (OCT) imaging probe with an automatic identification (AI) system to objectively identify the position of the epidural needle tip. The automatic identification system uses three features as image parameters to distinguish the different tissue by three classifiers. Finally, we found that the support vector machine (SVM) classifier has highest accuracy, specificity, and sensitivity, which reached to 95%, 98%, and 92%, respectively.

Fantastic Journey through Minds and Machines.

ERIC Educational Resources Information Center

Muir, Michael

Intended for learners with a basic familiarity with the Logo programming language, this manual is designed to introduce them to artificial intelligence and enhance their programming capabilities. Nine chapters discuss the following features of Logo: (1) MAZE.MASTER, a look at robots and how sensors make machines aware of their environment; (2)…

Breast cancer risk assessment and diagnosis model using fuzzy support vector machine based expert system

NASA Astrophysics Data System (ADS)

Dheeba, J.; Jaya, T.; Singh, N. Albert

2017-09-01

Classification of cancerous masses is a challenging task in many computerised detection systems. Cancerous masses are difficult to detect because these masses are obscured and subtle in mammograms. This paper investigates an intelligent classifier - fuzzy support vector machine (FSVM) applied to classify the tissues containing masses on mammograms for breast cancer diagnosis. The algorithm utilises texture features extracted using Laws texture energy measures and a FSVM to classify the suspicious masses. The new FSVM treats every feature as both normal and abnormal samples, but with different membership. By this way, the new FSVM have more generalisation ability to classify the masses in mammograms. The classifier analysed 219 clinical mammograms collected from breast cancer screening laboratory. The tests made on the real clinical mammograms shows that the proposed detection system has better discriminating power than the conventional support vector machine. With the best combination of FSVM and Laws texture features, the area under the Receiver operating characteristic curve reached .95, which corresponds to a sensitivity of 93.27% with a specificity of 87.17%. The results suggest that detecting masses using FSVM contribute to computer-aided detection of breast cancer and as a decision support system for radiologists.

Social Studies and Emerging Paradigms: Artificial Intelligence and Consciousness Education.

ERIC Educational Resources Information Center

Braun, Joseph A., Jr.

1987-01-01

Asks three questions: (1) Are machines capable of thinking as people do? (2) How is the thinking of computers similar and different from human thinking? and (3) What exactly is thinking? Examines research in artificial intelligence. Describes the theory and research of consciousness education and discusses an emerging paradigm for human thinking…

Intelligent robot trends for 1998

NASA Astrophysics Data System (ADS)

Hall, Ernest L.

1998-10-01

An intelligent robot is a remarkably useful combination of a manipulator, sensors and controls. The use of these machines in factory automation can improve productivity, increase product quality and improve competitiveness. This paper presents a discussion of recent technical and economic trends. Technically, the machines are faster, cheaper, more repeatable, more reliable and safer. The knowledge base of inverse kinematic and dynamic solutions and intelligent controls is increasing. More attention is being given by industry to robots, vision and motion controls. New areas of usage are emerging for service robots, remote manipulators and automated guided vehicles. Economically, the robotics industry now has a 1.1 billion-dollar market in the U.S. and is growing. Feasibility studies results are presented which also show decreasing costs for robots and unaudited healthy rates of return for a variety of robotic applications. However, the road from inspiration to successful application can be long and difficult, often taking decades to achieve a new product. A greater emphasis on mechatronics is needed in our universities. Certainly, more cooperation between government, industry and universities is needed to speed the development of intelligent robots that will benefit industry and society.

Machine Perception (La Perception de l’Environment par Senseurs Automatiques).

DTIC Science & Technology

1992-08-01

France, January 25-27, 1984, Lectures. Volumes 1 & 2 Reconnaissance des formes et intelligence artificielle ; Congres Francais, 4th, Paris, France, January...capteurs intelligents intkgris - traitement cellulaire et neuronal - operateurs visuels de base -implantation analogique vs digitale A smart retina is a...I’I riscc Li li mttlL’ dliitrlt ITTIC dies % eiicule it dsitisitl Ct Lie"IIN10%Ct ,v,,temcs intelligents dfaidc a [a perception de lai situatiotn. O

Tethered Forth system for FPGA applications

NASA Astrophysics Data System (ADS)

Goździkowski, Paweł; Zabołotny, Wojciech M.

2013-10-01

This paper presents the tethered Forth system dedicated for testing and debugging of FPGA based electronic systems. Use of the Forth language allows to interactively develop and run complex testing or debugging routines. The solution is based on a small, 16-bit soft core CPU, used to implement the Forth Virtual Machine. Thanks to the use of the tethered Forth model it is possible to minimize usage of the internal RAM memory in the FPGA. The function of the intelligent terminal, which is an essential part of the tethered Forth system, may be fulfilled by the standard PC computer or by the smartphone. System is implemented in Python (the software for intelligent terminal), and in VHDL (the IP core for FPGA), so it can be easily ported to different hardware platforms. The connection between the terminal and FPGA may be established and disconnected many times without disturbing the state of the FPGA based system. The presented system has been verified in the hardware, and may be used as a tool for debugging, testing and even implementing of control algorithms for FPGA based systems.

m-Health 2.0: New perspectives on mobile health, machine learning and big data analytics.

PubMed

Istepanian, Robert S H; Al-Anzi, Turki

2018-06-08

Mobile health (m-Health) has been repeatedly called the biggest technological breakthrough of our modern times. Similarly, the concept of big data in the context of healthcare is considered one of the transformative drivers for intelligent healthcare delivery systems. In recent years, big data has become increasingly synonymous with mobile health, however key challenges of 'Big Data and mobile health', remain largely untackled. This is becoming particularly important with the continued deluge of the structured and unstructured data sets generated on daily basis from the proliferation of mobile health applications within different healthcare systems and products globally. The aim of this paper is of twofold. First we present the relevant big data issues from the mobile health (m-Health) perspective. In particular we discuss these issues from the technological areas and building blocks (communications, sensors and computing) of mobile health and the newly defined (m-Health 2.0) concept. The second objective is to present the relevant rapprochement issues of big m-Health data analytics with m-Health. Further, we also present the current and future roles of machine and deep learning within the current smart phone centric m-health model. The critical balance between these two important areas will depend on how different stakeholder from patients, clinicians, healthcare providers, medical and m-health market businesses and regulators will perceive these developments. These new perspectives are essential for better understanding the fine balance between the new insights of how intelligent and connected the future mobile health systems will look like and the inherent risks and clinical complexities associated with the big data sets and analytical tools used in these systems. These topics will be subject for extensive work and investigations in the foreseeable future for the areas of data analytics, computational and artificial intelligence methods applied for mobile health. Copyright © 2018 Elsevier Inc. All rights reserved.

Envisioning engineering education and practice in the coming intelligence convergence era — a complex adaptive systems approach

NASA Astrophysics Data System (ADS)

Noor, Ahmed K.

2013-12-01

Some of the recent attempts for improving and transforming engineering education are reviewed. The attempts aim at providing the entry level engineers with the skills needed to address the challenges of future large-scale complex systems and projects. Some of the frontier sectors and future challenges for engineers are outlined. The major characteristics of the coming intelligence convergence era (the post-information age) are identified. These include the prevalence of smart devices and environments, the widespread applications of anticipatory computing and predictive / prescriptive analytics, as well as a symbiotic relationship between humans and machines. Devices and machines will be able to learn from, and with, humans in a natural collaborative way. The recent game changers in learnscapes (learning paradigms, technologies, platforms, spaces, and environments) that can significantly impact engineering education in the coming era are identified. Among these are open educational resources, knowledge-rich classrooms, immersive interactive 3D learning, augmented reality, reverse instruction / flipped classroom, gamification, robots in the classroom, and adaptive personalized learning. Significant transformative changes in, and mass customization of, learning are envisioned to emerge from the synergistic combination of the game changers and other technologies. The realization of the aforementioned vision requires the development of a new multidisciplinary framework of emergent engineering for relating innovation, complexity and cybernetics, within the future learning environments. The framework can be used to treat engineering education as a complex adaptive system, with dynamically interacting and communicating components (instructors, individual, small, and large groups of learners). The emergent behavior resulting from the interactions can produce progressively better, and continuously improving, learning environment. As a first step towards the realization of the vision, intelligent adaptive cyber-physical ecosystems need to be developed to facilitate collaboration between the various stakeholders of engineering education, and to accelerate the development of a skilled engineering workforce. The major components of the ecosystems include integrated knowledge discovery and exploitation facilities, blended learning and research spaces, novel ultra-intelligent software agents, multimodal and autonomous interfaces, and networked cognitive and tele-presence robots.

Intelligent hearing aids: the next revolution.

PubMed

Tao Zhang; Mustiere, Fred; Micheyl, Christophe

2016-08-01

The first revolution in hearing aids came from nonlinear amplification, which allows better compensation for both soft and loud sounds. The second revolution stemmed from the introduction of digital signal processing, which allows better programmability and more sophisticated algorithms. The third revolution in hearing aids is wireless, which allows seamless connectivity between a pair of hearing aids and with more and more external devices. Each revolution has fundamentally transformed hearing aids and pushed the entire industry forward significantly. Machine learning has received significant attention in recent years and has been applied in many other industries, e.g., robotics, speech recognition, genetics, and crowdsourcing. We argue that the next revolution in hearing aids is machine intelligence. In fact, this revolution is already quietly happening. We will review the development in at least three major areas: applications of machine learning in speech enhancement; applications of machine learning in individualization and customization of signal processing algorithms; applications of machine learning in improving the efficiency and effectiveness of clinical tests. With the advent of the internet of things, the above developments will accelerate. This revolution will bring patient satisfactions to a new level that has never been seen before.

Pyramidal neurovision architecture for vision machines

NASA Astrophysics Data System (ADS)

Gupta, Madan M.; Knopf, George K.

1993-08-01

The vision system employed by an intelligent robot must be active; active in the sense that it must be capable of selectively acquiring the minimal amount of relevant information for a given task. An efficient active vision system architecture that is based loosely upon the parallel-hierarchical (pyramidal) structure of the biological visual pathway is presented in this paper. Although the computational architecture of the proposed pyramidal neuro-vision system is far less sophisticated than the architecture of the biological visual pathway, it does retain some essential features such as the converging multilayered structure of its biological counterpart. In terms of visual information processing, the neuro-vision system is constructed from a hierarchy of several interactive computational levels, whereupon each level contains one or more nonlinear parallel processors. Computationally efficient vision machines can be developed by utilizing both the parallel and serial information processing techniques within the pyramidal computing architecture. A computer simulation of a pyramidal vision system for active scene surveillance is presented.

The Automation System Censor Speech for the Indonesian Rude Swear Words Based on Support Vector Machine and Pitch Analysis

NASA Astrophysics Data System (ADS)

Endah, S. N.; Nugraheni, D. M. K.; Adhy, S.; Sutikno

2017-04-01

According to Law No. 32 of 2002 and the Indonesian Broadcasting Commission Regulation No. 02/P/KPI/12/2009 & No. 03/P/KPI/12/2009, stated that broadcast programs should not scold with harsh words, not harass, insult or demean minorities and marginalized groups. However, there are no suitable tools to censor those words automatically. Therefore, researches to develop a system of intelligent software to censor the words automatically are needed. To conduct censor, the system must be able to recognize the words in question. This research proposes the classification of speech divide into two classes using Support Vector Machine (SVM), first class is set of rude words and the second class is set of properly words. The speech pitch values as an input in SVM, it used for the development of the system for the Indonesian rude swear word. The results of the experiment show that SVM is good for this system.

Representing Learning With Graphical Models

NASA Technical Reports Server (NTRS)

Buntine, Wray L.; Lum, Henry, Jr. (Technical Monitor)

1994-01-01

Probabilistic graphical models are being used widely in artificial intelligence, for instance, in diagnosis and expert systems, as a unified qualitative and quantitative framework for representing and reasoning with probabilities and independencies. Their development and use spans several fields including artificial intelligence, decision theory and statistics, and provides an important bridge between these communities. This paper shows by way of example that these models can be extended to machine learning, neural networks and knowledge discovery by representing the notion of a sample on the graphical model. Not only does this allow a flexible variety of learning problems to be represented, it also provides the means for representing the goal of learning and opens the way for the automatic development of learning algorithms from specifications.

A Machine Learning Approach for Business Intelligence Analysis using Commercial Shipping Transaction Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bramer, Lisa M.; Chatterjee, Samrat; Holmes, Aimee E.

Business intelligence problems are particularly challenging due to the use of large volume and high velocity data in attempts to model and explain complex underlying phenomena. Incremental machine learning based approaches for summarizing trends and identifying anomalous behavior are often desirable in such conditions to assist domain experts in characterizing their data. The overall goal of this research is to develop a machine learning algorithm that enables predictive analysis on streaming data, detects changes and anomalies in the data, and can evolve based on the dynamic behavior of the data. Commercial shipping transaction data for the U.S. is used tomore » develop and test a Naïve Bayes model that classifies several companies into lines of businesses and demonstrates an ability to predict when the behavior of these companies changes by venturing into other lines of businesses.« less

ELIPS: Toward a Sensor Fusion Processor on a Chip

NASA Technical Reports Server (NTRS)

Daud, Taher; Stoica, Adrian; Tyson, Thomas; Li, Wei-te; Fabunmi, James

1998-01-01

The paper presents the concept and initial tests from the hardware implementation of a low-power, high-speed reconfigurable sensor fusion processor. The Extended Logic Intelligent Processing System (ELIPS) processor is developed to seamlessly combine rule-based systems, fuzzy logic, and neural networks to achieve parallel fusion of sensor in compact low power VLSI. The first demonstration of the ELIPS concept targets interceptor functionality; other applications, mainly in robotics and autonomous systems are considered for the future. The main assumption behind ELIPS is that fuzzy, rule-based and neural forms of computation can serve as the main primitives of an "intelligent" processor. Thus, in the same way classic processors are designed to optimize the hardware implementation of a set of fundamental operations, ELIPS is developed as an efficient implementation of computational intelligence primitives, and relies on a set of fuzzy set, fuzzy inference and neural modules, built in programmable analog hardware. The hardware programmability allows the processor to reconfigure into different machines, taking the most efficient hardware implementation during each phase of information processing. Following software demonstrations on several interceptor data, three important ELIPS building blocks (a fuzzy set preprocessor, a rule-based fuzzy system and a neural network) have been fabricated in analog VLSI hardware and demonstrated microsecond-processing times.

Intelligent buildings.

PubMed

Williams, W E

1987-01-01

The maturing of technologies in computer capabilities, particularly direct digital signals, has provided an exciting variety of new communication and facility control opportunities. These include telecommunications, energy management systems, security systems, office automation systems, local area networks, and video conferencing. New applications are developing continuously. The so-called "intelligent" or "smart" building concept evolves from the development of this advanced technology in building environments. Automation has had a dramatic effect on facility planning. For decades, communications were limited to the telephone, the typewritten message, and copy machines. The office itself and its functions had been essentially unchanged for decades. Office automation systems began to surface during the energy crisis and, although their newer technology was timely, they were, for the most part, designed separately from other new building systems. For example, most mainframe computer systems were originally stand-alone, as were word processing installations. In the last five years, the advances in distributive systems, networking, and personal computer capabilities have provided opportunities to make such dramatic improvements in productivity that the Selectric typewriter has gone from being the most advanced piece of office equipment to nearly total obsolescence.

Machine learning in autistic spectrum disorder behavioral research: A review and ways forward.

PubMed

Thabtah, Fadi

2018-02-13

Autistic Spectrum Disorder (ASD) is a mental disorder that retards acquisition of linguistic, communication, cognitive, and social skills and abilities. Despite being diagnosed with ASD, some individuals exhibit outstanding scholastic, non-academic, and artistic capabilities, in such cases posing a challenging task for scientists to provide answers. In the last few years, ASD has been investigated by social and computational intelligence scientists utilizing advanced technologies such as machine learning to improve diagnostic timing, precision, and quality. Machine learning is a multidisciplinary research topic that employs intelligent techniques to discover useful concealed patterns, which are utilized in prediction to improve decision making. Machine learning techniques such as support vector machines, decision trees, logistic regressions, and others, have been applied to datasets related to autism in order to construct predictive models. These models claim to enhance the ability of clinicians to provide robust diagnoses and prognoses of ASD. However, studies concerning the use of machine learning in ASD diagnosis and treatment suffer from conceptual, implementation, and data issues such as the way diagnostic codes are used, the type of feature selection employed, the evaluation measures chosen, and class imbalances in data among others. A more serious claim in recent studies is the development of a new method for ASD diagnoses based on machine learning. This article critically analyses these recent investigative studies on autism, not only articulating the aforementioned issues in these studies but also recommending paths forward that enhance machine learning use in ASD with respect to conceptualization, implementation, and data. Future studies concerning machine learning in autism research are greatly benefitted by such proposals.

Artificial intelligence techniques for embryo and oocyte classification.

PubMed

Manna, Claudio; Nanni, Loris; Lumini, Alessandra; Pappalardo, Sebastiana

2013-01-01

One of the most relevant aspects in assisted reproduction technology is the possibility of characterizing and identifying the most viable oocytes or embryos. In most cases, embryologists select them by visual examination and their evaluation is totally subjective. Recently, due to the rapid growth in the capacity to extract texture descriptors from a given image, a growing interest has been shown in the use of artificial intelligence methods for embryo or oocyte scoring/selection in IVF programmes. This work concentrates the efforts on the possible prediction of the quality of embryos and oocytes in order to improve the performance of assisted reproduction technology, starting from their images. The artificial intelligence system proposed in this work is based on a set of Levenberg-Marquardt neural networks trained using textural descriptors (the local binary patterns). The proposed system was tested on two data sets of 269 oocytes and 269 corresponding embryos from 104 women and compared with other machine learning methods already proposed in the past for similar classification problems. Although the results are only preliminary, they show an interesting classification performance. This technique may be of particular interest in those countries where legislation restricts embryo selection. One of the most relevant aspects in assisted reproduction technology is the possibility of characterizing and identifying the most viable oocytes or embryos. In most cases, embryologists select them by visual examination and their evaluation is totally subjective. Recently, due to the rapid growth in our capacity to extract texture descriptors from a given image, a growing interest has been shown in the use of artificial intelligence methods for embryo or oocyte scoring/selection in IVF programmes. In this work, we concentrate our efforts on the possible prediction of the quality of embryos and oocytes in order to improve the performance of assisted reproduction technology, starting from their images. The artificial intelligence system proposed in this work is based on a set of Levenberg-Marquardt neural networks trained using textural descriptors (the 'local binary patterns'). The proposed system is tested on two data sets, of 269 oocytes and 269 corresponding embryos from 104 women, and compared with other machine learning methods already proposed in the past for similar classification problems. Although the results are only preliminary, they showed an interesting classification performance. This technique may be of particular interest in those countries where legislation restricts embryo selection. Copyright © 2012 Reproductive Healthcare Ltd. Published by Elsevier Ltd. All rights reserved.

A Workshop on the Gathering of Information for Problem Formulation

DTIC Science & Technology

1991-06-01

the Al specialists is to design "artificially intelligent" computer environments that tutor students in much the same way that a human teacher might...tuning the interface betweeen student and machine, and are using a technique of in situ development to tune the system towaid realistic user needs. 141...of transferability to new domains, while the latter suffers from extreme fragility: the inability to cope with any input not strictly conforming with

Prognostics & Health Management: A NASA Perspective

NASA Technical Reports Server (NTRS)

Boyer, Roger L.

2015-01-01

How can advanced automation techniques developed by NASA to perform Fault Detection, Isolation, and Recovery (FDIR) in space missions be used here on Earth in the Oil & Gas industry? Whether on a Mars orbiter or an oil platform, having an intelligent machine to back up the crew/operators to help monitor and diagnose the systems for possible problems and aid in determining a corrective action/response is an important and useful attribute for multiple industries.

Robust Intelligence (RI) Under Uncertainty: Mathematical Foundations of Autonomous Hybrid (Human-Machine-Robot) Teams, Organizations and Systems

DTIC Science & Technology

2013-01-01

seeds of chaos. Stability and internal peace bring prosperity; prosperity causes population increase. Demographic growth leads to overpopulation ... overpopulation causes lower wages, higher land rents, and falling per-capita incomes … [Eventually, the] collapse of order brings … famine, war...one of the most innovative, competitive and largest firms in the world (e.g., Investors Business Daily, 2000, 10/31; Bloomberg’s Businessweek, 2001, 9

Machine Learning and Inverse Problem in Geodynamics

NASA Astrophysics Data System (ADS)

Shahnas, M. H.; Yuen, D. A.; Pysklywec, R.

2017-12-01

During the past few decades numerical modeling and traditional HPC have been widely deployed in many diverse fields for problem solutions. However, in recent years the rapid emergence of machine learning (ML), a subfield of the artificial intelligence (AI), in many fields of sciences, engineering, and finance seems to mark a turning point in the replacement of traditional modeling procedures with artificial intelligence-based techniques. The study of the circulation in the interior of Earth relies on the study of high pressure mineral physics, geochemistry, and petrology where the number of the mantle parameters is large and the thermoelastic parameters are highly pressure- and temperature-dependent. More complexity arises from the fact that many of these parameters that are incorporated in the numerical models as input parameters are not yet well established. In such complex systems the application of machine learning algorithms can play a valuable role. Our focus in this study is the application of supervised machine learning (SML) algorithms in predicting mantle properties with the emphasis on SML techniques in solving the inverse problem. As a sample problem we focus on the spin transition in ferropericlase and perovskite that may cause slab and plume stagnation at mid-mantle depths. The degree of the stagnation depends on the degree of negative density anomaly at the spin transition zone. The training and testing samples for the machine learning models are produced by the numerical convection models with known magnitudes of density anomaly (as the class labels of the samples). The volume fractions of the stagnated slabs and plumes which can be considered as measures for the degree of stagnation are assigned as sample features. The machine learning models can determine the magnitude of the spin transition-induced density anomalies that can cause flow stagnation at mid-mantle depths. Employing support vector machine (SVM) algorithms we show that SML techniques can successfully predict the magnitude of the mantle density anomalies and can also be used in characterizing mantle flow patterns. The technique can be extended to more complex problems in mantle dynamics by employing deep learning algorithms for estimation of mantle properties such as viscosity, elastic parameters, and thermal and chemical anomalies.

Neuroprotective Drug for Nerve Trauma Revealed Using Artificial Intelligence.

PubMed

Romeo-Guitart, David; Forés, Joaquim; Herrando-Grabulosa, Mireia; Valls, Raquel; Leiva-Rodríguez, Tatiana; Galea, Elena; González-Pérez, Francisco; Navarro, Xavier; Petegnief, Valerie; Bosch, Assumpció; Coma, Mireia; Mas, José Manuel; Casas, Caty

2018-01-30

Here we used a systems biology approach and artificial intelligence to identify a neuroprotective agent for the treatment of peripheral nerve root avulsion. Based on accumulated knowledge of the neurodegenerative and neuroprotective processes that occur in motoneurons after root avulsion, we built up protein networks and converted them into mathematical models. Unbiased proteomic data from our preclinical models were used for machine learning algorithms and for restrictions to be imposed on mathematical solutions. Solutions allowed us to identify combinations of repurposed drugs as potential neuroprotective agents and we validated them in our preclinical models. The best one, NeuroHeal, neuroprotected motoneurons, exerted anti-inflammatory properties and promoted functional locomotor recovery. NeuroHeal endorsed the activation of Sirtuin 1, which was essential for its neuroprotective effect. These results support the value of network-centric approaches for drug discovery and demonstrate the efficacy of NeuroHeal as adjuvant treatment with surgical repair for nervous system trauma.

Smart Building: Decision Making Architecture for Thermal Energy Management.

PubMed

Uribe, Oscar Hernández; Martin, Juan Pablo San; Garcia-Alegre, María C; Santos, Matilde; Guinea, Domingo

2015-10-30

Smart applications of the Internet of Things are improving the performance of buildings, reducing energy demand. Local and smart networks, soft computing methodologies, machine intelligence algorithms and pervasive sensors are some of the basics of energy optimization strategies developed for the benefit of environmental sustainability and user comfort. This work presents a distributed sensor-processor-communication decision-making architecture to improve the acquisition, storage and transfer of thermal energy in buildings. The developed system is implemented in a near Zero-Energy Building (nZEB) prototype equipped with a built-in thermal solar collector, where optical properties are analysed; a low enthalpy geothermal accumulation system, segmented in different temperature zones; and an envelope that includes a dynamic thermal barrier. An intelligent control of this dynamic thermal barrier is applied to reduce the thermal energy demand (heating and cooling) caused by daily and seasonal weather variations. Simulations and experimental results are presented to highlight the nZEB thermal energy reduction.

[Artificial intelligence in psychiatry-an overview].

PubMed

Meyer-Lindenberg, A

2018-06-18

Artificial intelligence and the underlying methods of machine learning and neuronal networks (NN) have made dramatic progress in recent years and have allowed computers to reach superhuman performance in domains that used to be thought of as uniquely human. In this overview, the underlying methodological developments that made this possible are briefly delineated and then the applications to psychiatry in three domains are discussed: precision medicine and biomarkers, natural language processing and artificial intelligence-based psychotherapeutic interventions. In conclusion, some of the risks of this new technology are mentioned.