Low Power Multi-Hop Networking Analysis in Intelligent Environments.

PubMed

Etxaniz, Josu; Aranguren, Gerardo

2017-05-19

Intelligent systems are driven by the latest technological advances in many different areas such as sensing, embedded systems, wireless communications or context recognition. This paper focuses on some of those areas. Concretely, the paper deals with wireless communications issues in embedded systems. More precisely, the paper combines the multi-hop networking with Bluetooth technology and a quality of service (QoS) metric, the latency. Bluetooth is a radio license-free worldwide communication standard that makes low power multi-hop wireless networking available. It establishes piconets (point-to-point and point-to-multipoint links) and scatternets (multi-hop networks). As a result, many Bluetooth nodes can be interconnected to set up ambient intelligent networks. Then, this paper presents the results of the investigation on multi-hop latency with park and sniff Bluetooth low power modes conducted over the hardware test bench previously implemented. In addition, the empirical models to estimate the latency of multi-hop communications over Bluetooth Asynchronous Connectionless Links (ACL) in park and sniff mode are given. The designers of devices and networks for intelligent systems will benefit from the estimation of the latency in Bluetooth multi-hop communications that the models provide.

Low Power Multi-Hop Networking Analysis in Intelligent Environments

PubMed Central

Etxaniz, Josu; Aranguren, Gerardo

2017-01-01

Intelligent systems are driven by the latest technological advances in many different areas such as sensing, embedded systems, wireless communications or context recognition. This paper focuses on some of those areas. Concretely, the paper deals with wireless communications issues in embedded systems. More precisely, the paper combines the multi-hop networking with Bluetooth technology and a quality of service (QoS) metric, the latency. Bluetooth is a radio license-free worldwide communication standard that makes low power multi-hop wireless networking available. It establishes piconets (point-to-point and point-to-multipoint links) and scatternets (multi-hop networks). As a result, many Bluetooth nodes can be interconnected to set up ambient intelligent networks. Then, this paper presents the results of the investigation on multi-hop latency with park and sniff Bluetooth low power modes conducted over the hardware test bench previously implemented. In addition, the empirical models to estimate the latency of multi-hop communications over Bluetooth Asynchronous Connectionless Links (ACL) in park and sniff mode are given. The designers of devices and networks for intelligent systems will benefit from the estimation of the latency in Bluetooth multi-hop communications that the models provide. PMID:28534847

Energy Logic (EL): a novel fusion engine of multi-modality multi-agent data/information fusion for intelligent surveillance systems

NASA Astrophysics Data System (ADS)

Rababaah, Haroun; Shirkhodaie, Amir

2009-04-01

The rapidly advancing hardware technology, smart sensors and sensor networks are advancing environment sensing. One major potential of this technology is Large-Scale Surveillance Systems (LS3) especially for, homeland security, battlefield intelligence, facility guarding and other civilian applications. The efficient and effective deployment of LS3 requires addressing number of aspects impacting the scalability of such systems. The scalability factors are related to: computation and memory utilization efficiency, communication bandwidth utilization, network topology (e.g., centralized, ad-hoc, hierarchical or hybrid), network communication protocol and data routing schemes; and local and global data/information fusion scheme for situational awareness. Although, many models have been proposed to address one aspect or another of these issues but, few have addressed the need for a multi-modality multi-agent data/information fusion that has characteristics satisfying the requirements of current and future intelligent sensors and sensor networks. In this paper, we have presented a novel scalable fusion engine for multi-modality multi-agent information fusion for LS3. The new fusion engine is based on a concept we call: Energy Logic. Experimental results of this work as compared to a Fuzzy logic model strongly supported the validity of the new model and inspired future directions for different levels of fusion and different applications.

Intelligent manipulation technique for multi-branch robotic systems

NASA Technical Reports Server (NTRS)

Chen, Alexander Y. K.; Chen, Eugene Y. S.

1990-01-01

New analytical development in kinematics planning is reported. The INtelligent KInematics Planner (INKIP) consists of the kinematics spline theory and the adaptive logic annealing process. Also, a novel framework of robot learning mechanism is introduced. The FUzzy LOgic Self Organized Neural Networks (FULOSONN) integrates fuzzy logic in commands, control, searching, and reasoning, the embedded expert system for nominal robotics knowledge implementation, and the self organized neural networks for the dynamic knowledge evolutionary process. Progress on the mechanical construction of SRA Advanced Robotic System (SRAARS) and the real time robot vision system is also reported. A decision was made to incorporate the Local Area Network (LAN) technology in the overall communication system.

Adaptive neural network/expert system that learns fault diagnosis for different structures

NASA Astrophysics Data System (ADS)

Simon, Solomon H.

1992-08-01

Corporations need better real-time monitoring and control systems to improve productivity by watching quality and increasing production flexibility. The innovative technology to achieve this goal is evolving in the form artificial intelligence and neural networks applied to sensor processing, fusion, and interpretation. By using these advanced Al techniques, we can leverage existing systems and add value to conventional techniques. Neural networks and knowledge-based expert systems can be combined into intelligent sensor systems which provide real-time monitoring, control, evaluation, and fault diagnosis for production systems. Neural network-based intelligent sensor systems are more reliable because they can provide continuous, non-destructive monitoring and inspection. Use of neural networks can result in sensor fusion and the ability to model highly, non-linear systems. Improved models can provide a foundation for more accurate performance parameters and predictions. We discuss a research software/hardware prototype which integrates neural networks, expert systems, and sensor technologies and which can adapt across a variety of structures to perform fault diagnosis. The flexibility and adaptability of the prototype in learning two structures is presented. Potential applications are discussed.

High-autonomy control of space resource processing plants

NASA Technical Reports Server (NTRS)

Schooley, Larry C.; Zeigler, Bernard P.; Cellier, Francois E.; Wang, Fei-Yue

1993-01-01

A highly autonomous intelligent command/control architecture has been developed for planetary surface base industrial process plants and Space Station Freedom experimental facilities. The architecture makes use of a high-level task-oriented mode with supervisory control from one or several remote sites, and integrates advanced network communications concepts and state-of-the-art man/machine interfaces with the most advanced autonomous intelligent control. Attention is given to the full-dynamics model of a Martian oxygen-production plant, event-based/fuzzy-logic process control, and fault management practices.

Wireless intelligent network: infrastructure before services?

NASA Astrophysics Data System (ADS)

Chu, Narisa N.

1996-01-01

The Wireless Intelligent Network (WIN) intends to take advantage of the Advanced Intelligent Network (AIN) concepts and products developed from wireline communications. However, progress of the AIN deployment has been slow due to the many barriers that exist in the traditional wireline carriers' deployment procedures and infrastructure. The success of AIN has not been truly demonstrated. The AIN objectives and directions are applicable to the wireless industry although the plans and implementations could be significantly different. This paper points out WIN characteristics in architecture, flexibility, deployment, and value to customers. In order to succeed, the technology driven AIN concept has to be reinforced by the market driven WIN services. An infrastructure suitable for the WIN will contain elements that are foreign to the wireline network. The deployment process is expected to seed with the revenue generated services. Standardization will be achieved by simplifying and incorporating the IS-41C, AIN, and Intelligent Network CS-1 recommendations. Integration of the existing and future systems impose the biggest challenge of all. Service creation has to be complemented with service deployment process which heavily impact the carriers' infrastructure. WIN deployment will likely start from an Intelligent Peripheral, a Service Control Point and migrate to a Service Node when sufficient triggers are implemented in the mobile switch for distributed call control. The struggle to move forward will not be based on technology, but rather on the impact to existing infrastructure.

Underwater acoustic wireless sensor networks: advances and future trends in physical, MAC and routing layers.

PubMed

Climent, Salvador; Sanchez, Antonio; Capella, Juan Vicente; Meratnia, Nirvana; Serrano, Juan Jose

2014-01-06

This survey aims to provide a comprehensive overview of the current research on underwater wireless sensor networks, focusing on the lower layers of the communication stack, and envisions future trends and challenges. It analyzes the current state-of-the-art on the physical, medium access control and routing layers. It summarizes their security threads and surveys the currently proposed studies. Current envisioned niches for further advances in underwater networks research range from efficient, low-power algorithms and modulations to intelligent, energy-aware routing and medium access control protocols.

F-15 IFCS Intelligent Flight Control System

NASA Technical Reports Server (NTRS)

Bosworth, John T.

2008-01-01

This viewgraph presentation gives a detailed description of the F-15 aircraft, flight tests, aircraft performance and overall advanced neural network based flight control technologies for aerospace systems designs.

Perspectives on Research in Artificial Intelligence and Artificial General Intelligence Relevant to DoD

DTIC Science & Technology

2017-01-01

3.2 Generic Network Architecture ........................................................................................ 11 3.3 Neuron Non ...The magnitude of the importance of these technologies is hard to overstate, and we thus feel that technically minded, non -expert readers should be...The result is often billions of weights and biases which need to be calculated. 13 3.3 Neuron Non -Linearity The big, early advance in

Network traffic intelligence using a low interaction honeypot

NASA Astrophysics Data System (ADS)

Nyamugudza, Tendai; Rajasekar, Venkatesh; Sen, Prasad; Nirmala, M.; Madhu Viswanatham, V.

2017-11-01

Advancements in networking technology have seen more and more devices becoming connected day by day. This has given organizations capacity to extend their networks beyond their boundaries to remote offices and remote employees. However as the network grows security becomes a major challenge since the attack surface also increases. There is need to guard the network against different types of attacks like intrusion and malware through using different tools at different networking levels. This paper describes how network intelligence can be acquired through implementing a low-interaction honeypot which detects and track network intrusion. Honeypot allows an organization to interact and gather information about an attack earlier before it compromises the network. This process is important because it allows the organization to learn about future attacks of the same nature and allows them to develop counter measures. The paper further shows how honeypot-honey net based model for interruption detection system (IDS) can be used to get the best valuable information about the attacker and prevent unexpected harm to the network.

Modelling intelligent behavior

NASA Technical Reports Server (NTRS)

Green, H. S.; Triffet, T.

1993-01-01

An introductory discussion of the related concepts of intelligence and consciousness suggests criteria to be met in the modeling of intelligence and the development of intelligent materials. Methods for the modeling of actual structure and activity of the animal cortex have been found, based on present knowledge of the ionic and cellular constitution of the nervous system. These have led to the development of a realistic neural network model, which has been used to study the formation of memory and the process of learning. An account is given of experiments with simple materials which exhibit almost all properties of biological synapses and suggest the possibility of a new type of computer architecture to implement an advanced type of artificial intelligence.

Intelligent pump test system based on virtual instrument

NASA Astrophysics Data System (ADS)

Ma, Jungong; Wang, Shifu; Wang, Zhanlin

2003-09-01

The intelligent pump system is the key component of the aircraft hydraulic system that can solve the problem, such as the temperature sharply increasing. As the performance of the intelligent pump directly determines that of the aircraft hydraulic system and seriously affects fly security and reliability. So it is important to test all kinds of performance parameters of intelligent pump during design and development, while the advanced, reliable and complete test equipments are the necessary instruments for achieving the goal. In this paper, the application of virtual instrument and computer network technology in aircraft intelligent pump test is presented. The composition of the hardware, software, hydraulic circuit in this system are designed and implemented.

Advanced information society(2)

NASA Astrophysics Data System (ADS)

Masuyama, Keiichi

Our modern life is full of information and information infiltrates into our daily life. Networking of the telecommunication is extended to society, company, and individual level. Although we have just entered the advanced information society, business world and our daily life have been steadily transformed by the advancement of information network. This advancement of information brings a big influence on economy, and will play they the main role in the expansion of domestic demands. This paper tries to view the image of coming advanced information society, focusing on the transforming businessman's life and the situation of our daily life, which became wealthy by the spread of daily life information and the visual information by satellite system, in the development of the intelligent city.

Underwater Acoustic Wireless Sensor Networks: Advances and Future Trends in Physical, MAC and Routing Layers

PubMed Central

Climent, Salvador; Sanchez, Antonio; Capella, Juan Vicente; Meratnia, Nirvana; Serrano, Juan Jose

2014-01-01

This survey aims to provide a comprehensive overview of the current research on underwater wireless sensor networks, focusing on the lower layers of the communication stack, and envisions future trends and challenges. It analyzes the current state-of-the-art on the physical, medium access control and routing layers. It summarizes their security threads and surveys the currently proposed studies. Current envisioned niches for further advances in underwater networks research range from efficient, low-power algorithms and modulations to intelligent, energy-aware routing and medium access control protocols. PMID:24399155

F-15 837 IFCS Intelligent Flight Control System Project

NASA Technical Reports Server (NTRS)

Bosworth, John T.

2007-01-01

This viewgraph presentation reviews the use of Intelligent Flight Control System (IFCS) for the F-15. The goals of the project are: (1) Demonstrate Revolutionary Control Approaches that can Efficiently Optimize Aircraft Performance in both Normal and Failure Conditions (2) Advance Neural Network-Based Flight Control Technology for New Aerospace Systems Designs. The motivation for the development are to reduce the chance and skill required for survival.

Advanced obstacle avoidance for a laser based wheelchair using optimised Bayesian neural networks.

PubMed

Trieu, Hoang T; Nguyen, Hung T; Willey, Keith

2008-01-01

In this paper we present an advanced method of obstacle avoidance for a laser based intelligent wheelchair using optimized Bayesian neural networks. Three neural networks are designed for three separate sub-tasks: passing through a door way, corridor and wall following and general obstacle avoidance. The accurate usable accessible space is determined by including the actual wheelchair dimensions in a real-time map used as inputs to each networks. Data acquisitions are performed separately to collect the patterns required for specified sub-tasks. Bayesian frame work is used to determine the optimal neural network structure in each case. Then these networks are trained under the supervision of Bayesian rule. Experiment results showed that compare to the VFH algorithm our neural networks navigated a smoother path following a near optimum trajectory.

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1984-01-01

Activities in space communication, radio navigation, radio science, and ground-based astronomy are reported. Advanced systems for the Deep Space Network and its Ground-Communications Facility are discussed including station control and system technology. Network sustaining as well as data and information systems are covered. Studies of geodynamics, investigations of the microwave spectrum, and the search for extraterrestrial intelligence are reported.

Cooperation and the evolution of intelligence

PubMed Central

McNally, Luke; Brown, Sam P.; Jackson, Andrew L.

2012-01-01

The high levels of intelligence seen in humans, other primates, certain cetaceans and birds remain a major puzzle for evolutionary biologists, anthropologists and psychologists. It has long been held that social interactions provide the selection pressures necessary for the evolution of advanced cognitive abilities (the ‘social intelligence hypothesis’), and in recent years decision-making in the context of cooperative social interactions has been conjectured to be of particular importance. Here we use an artificial neural network model to show that selection for efficient decision-making in cooperative dilemmas can give rise to selection pressures for greater cognitive abilities, and that intelligent strategies can themselves select for greater intelligence, leading to a Machiavellian arms race. Our results provide mechanistic support for the social intelligence hypothesis, highlight the potential importance of cooperative behaviour in the evolution of intelligence and may help us to explain the distribution of cooperation with intelligence across taxa. PMID:22496188

Evaluation of the impacts of ITS technologies on the Borman Expressway Network

DOT National Transportation Integrated Search

1998-10-01

The Indiana Department of Transportation (INDOT) is currently implementing (or has implemented) several components of Intelligent Transportation Systems (ITS). This includes a mini Advanced Traffic Management Systems (ATMS) implemented on a three-mil...

Biological neural networks as model systems for designing future parallel processing computers

NASA Technical Reports Server (NTRS)

Ross, Muriel D.

1991-01-01

One of the more interesting debates of the present day centers on whether human intelligence can be simulated by computer. The author works under the premise that neurons individually are not smart at all. Rather, they are physical units which are impinged upon continuously by other matter that influences the direction of voltage shifts across the units membranes. It is only the action of a great many neurons, billions in the case of the human nervous system, that intelligent behavior emerges. What is required to understand even the simplest neural system is painstaking analysis, bit by bit, of the architecture and the physiological functioning of its various parts. The biological neural network studied, the vestibular utricular and saccular maculas of the inner ear, are among the most simple of the mammalian neural networks to understand and model. While there is still a long way to go to understand even this most simple neural network in sufficient detail for extrapolation to computers and robots, a start was made. Moreover, the insights obtained and the technologies developed help advance the understanding of the more complex neural networks that underlie human intelligence.

Distributed intelligent monitoring and reporting facilities

NASA Astrophysics Data System (ADS)

Pavlou, George; Mykoniatis, George; Sanchez-P, Jorge-A.

1996-06-01

Distributed intelligent monitoring and reporting facilities are of paramount importance in both service and network management as they provide the capability to monitor quality of service and utilization parameters and notify degradation so that corrective action can be taken. By intelligent, we refer to the capability of performing the monitoring tasks in a way that has the smallest possible impact on the managed network, facilitates the observation and summarization of information according to a number of criteria and in its most advanced form and permits the specification of these criteria dynamically to suit the particular policy in hand. In addition, intelligent monitoring facilities should minimize the design and implementation effort involved in such activities. The ISO/ITU Metric, Summarization and Performance management functions provide models that only partially satisfy the above requirements. This paper describes our extensions to the proposed models to support further capabilities, with the intention to eventually lead to fully dynamically defined monitoring policies. The concept of distributing intelligence is also discussed, including the consideration of security issues and the applicability of the model in ODP-based distributed processing environments.

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.

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

NASA Technical Reports Server (NTRS)

Broderick, Ron

1997-01-01

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

Neuroanatomical Correlates of Intelligence

PubMed Central

Luders, Eileen; Narr, Katherine L.; Thompson, Paul M.; Toga, Arthur W.

2009-01-01

With the advancement of image acquisition and analysis methods in recent decades, unique opportunities have emerged to study the neuroanatomical correlates of intelligence. Traditional approaches examining global measures have been complemented by insights from more regional analyses based on pre-defined areas. Newer state-of-the-art approaches have further enhanced our ability to localize the presence of correlations between cerebral characteristics and intelligence with high anatomic precision. These in vivo assessments have confirmed mainly positive correlations, suggesting that optimally increased brain regions are associated with better cognitive performance. Findings further suggest that the models proposed to explain the anatomical substrates of intelligence should address contributions from not only (pre)frontal regions, but also widely distributed networks throughout the whole brain. PMID:20160919

Advanced microprocessor based power protection system using artificial neural network techniques

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

Chen, Z.; Kalam, A.; Zayegh, A.

This paper describes an intelligent embedded microprocessor based system for fault classification in power system protection system using advanced 32-bit microprocessor technology. The paper demonstrates the development of protective relay to provide overcurrent protection schemes for fault detection. It also describes a method for power fault classification in three-phase system based on the use of neural network technology. The proposed design is implemented and tested on a single line three phase power system in power laboratory. Both the hardware and software development are described in detail.

Validation and augmentation of Inrix arterial travel time data using independent sources.

DOT National Transportation Integrated Search

2015-02-01

Travel time data is a key input to Intelligent Transportation Systems (ITS) applications. Advancement in vehicle : tracking and identification technologies and proliferation of location-aware and connected devices has made network-wide travel time da...

Advanced Satellite Workstation - An integrated workstation environment for operational support of satellite system planning and analysis

NASA Astrophysics Data System (ADS)

Hamilton, Marvin J.; Sutton, Stewart A.

A prototype integrated environment, the Advanced Satellite Workstation (ASW), which was developed and delivered for evaluation and operator feedback in an operational satellite control center, is described. The current ASW hardware consists of a Sun Workstation and Macintosh II Workstation connected via an ethernet Network Hardware and Software, Laser Disk System, Optical Storage System, and Telemetry Data File Interface. The central objective of ASW is to provide an intelligent decision support and training environment for operator/analysis of complex systems such as satellites. Compared to the many recent workstation implementations that incorporate graphical telemetry displays and expert systems, ASW provides a considerably broader look at intelligent, integrated environments for decision support, based on the premise that the central features of such an environment are intelligent data access and integrated toolsets.

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.

Network Structures for Distributed Situation Assessment

DTIC Science & Technology

1980-08-01

name a few-can contribute to the study of DAI. Presumably, DAI will advance these fields as well by providing a modeling technology suitable for precise...moreover, current SA sys- tems are highly centralized and vulnerable. Technological advances in cheap sen- sors, microprocessors, packet radio...communications, and artificial intelligence offer promising alternatives for performing military surveillance in many situations. A new approach now being

FPGA implementation of advanced FEC schemes for intelligent aggregation networks

NASA Astrophysics Data System (ADS)

Zou, Ding; Djordjevic, Ivan B.

2016-02-01

In state-of-the-art fiber-optics communication systems the fixed forward error correction (FEC) and constellation size are employed. While it is important to closely approach the Shannon limit by using turbo product codes (TPC) and low-density parity-check (LDPC) codes with soft-decision decoding (SDD) algorithm; rate-adaptive techniques, which enable increased information rates over short links and reliable transmission over long links, are likely to become more important with ever-increasing network traffic demands. In this invited paper, we describe a rate adaptive non-binary LDPC coding technique, and demonstrate its flexibility and good performance exhibiting no error floor at BER down to 10-15 in entire code rate range, by FPGA-based emulation, making it a viable solution in the next-generation high-speed intelligent aggregation networks.

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.

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.

Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing.

PubMed

Kriegeskorte, Nikolaus

2015-11-24

Recent advances in neural network modeling have enabled major strides in computer vision and other artificial intelligence applications. Human-level visual recognition abilities are coming within reach of artificial systems. Artificial neural networks are inspired by the brain, and their computations could be implemented in biological neurons. Convolutional feedforward networks, which now dominate computer vision, take further inspiration from the architecture of the primate visual hierarchy. However, the current models are designed with engineering goals, not to model brain computations. Nevertheless, initial studies comparing internal representations between these models and primate brains find surprisingly similar representational spaces. With human-level performance no longer out of reach, we are entering an exciting new era, in which we will be able to build biologically faithful feedforward and recurrent computational models of how biological brains perform high-level feats of intelligence, including vision.

Perspectives on driver preferences for dynamic route guidance systems

DOT National Transportation Integrated Search

1997-01-01

Insights about the design of route guidance systems based on the needs and desires of drivers who are familiar with the travel network are provided. Results from the ADVANCE Intelligent Transportation System operational test, in which more than 100 d...

An Examination of Application of Artificial Neural Network in Cognitive Radios

NASA Astrophysics Data System (ADS)

Bello Salau, H.; Onwuka, E. N.; Aibinu, A. M.

2013-12-01

Recent advancement in software radio technology has led to the development of smart device known as cognitive radio. This type of radio fuses powerful techniques taken from artificial intelligence, game theory, wideband/multiple antenna techniques, information theory and statistical signal processing to create an outstanding dynamic behavior. This cognitive radio is utilized in achieving diverse set of applications such as spectrum sensing, radio parameter adaptation and signal classification. This paper contributes by reviewing different cognitive radio implementation that uses artificial intelligence such as the hidden markov models, metaheuristic algorithm and artificial neural networks (ANNs). Furthermore, different areas of application of ANNs and their performance metrics based approach are also examined.

Reconfigurable Robust Routing for Mobile Outreach Network

NASA Technical Reports Server (NTRS)

Lin, Ching-Fang

2010-01-01

The Reconfigurable Robust Routing for Mobile Outreach Network (R3MOO N) provides advanced communications networking technologies suitable for the lunar surface environment and applications. The R3MOON techn ology is based on a detailed concept of operations tailored for luna r surface networks, and includes intelligent routing algorithms and wireless mesh network implementation on AGNC's Coremicro Robots. The product's features include an integrated communication solution inco rporating energy efficiency and disruption-tolerance in a mobile ad h oc network, and a real-time control module to provide researchers an d engineers a convenient tool for reconfiguration, investigation, an d management.

A Two-Century-Old Vision for the Future.

ERIC Educational Resources Information Center

Fuchs, Ira H.

1988-01-01

Discusses the necessity of acquiring and developing technological advances for use in the classroom to provide a vision for the future. Topics discussed include microcomputers; workstations; software; networks; cooperative endeavors in industry and academia; artificial intelligence; and the necessity for financial support. (LRW)

A survey on evolutionary algorithm based hybrid intelligence in bioinformatics.

PubMed

Li, Shan; Kang, Liying; Zhao, Xing-Ming

2014-01-01

With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs) are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks.

An Architecture for Intelligent Systems Based on Smart Sensors

NASA Technical Reports Server (NTRS)

Schmalzel, John; Figueroa, Fernando; Morris, Jon; Mandayam, Shreekanth; Polikar, Robi

2004-01-01

Based on requirements for a next-generation rocket test facility, elements of a prototype Intelligent Rocket Test Facility (IRTF) have been implemented. A key component is distributed smart sensor elements integrated using a knowledgeware environment. One of the specific goals is to imbue sensors with the intelligence needed to perform self diagnosis of health and to participate in a hierarchy of health determination at sensor, process, and system levels. The preliminary results provide the basis for future advanced development and validation using rocket test stand facilities at Stennis Space Center (SSC). We have identified issues important to further development of health-enabled networks, which should be of interest to others working with smart sensors and intelligent health management systems.

Data management system advanced development

NASA Technical Reports Server (NTRS)

Douglas, Katherine; Humphries, Terry

1990-01-01

The Data Management System (DMS) Advanced Development task provides for the development of concepts, new tools, DMS services, and for the testing of the Space Station DMS hardware and software. It also provides for the development of techniques capable of determining the effects of system changes/enhancements, additions of new technology, and/or hardware and software growth on system performance. This paper will address the built-in characteristics which will support network monitoring requirements in the design of the evolving DMS network implementation, functional and performance requirements for a real-time, multiprogramming, multiprocessor operating system, and the possible use of advanced development techniques such as expert systems and artificial intelligence tools in the DMS design.

Ethernet-based smart networked elements (sensors and actuators)

NASA Astrophysics Data System (ADS)

Mata, Carlos T.; Perotti, José M.; Oostdyk, Rebecca L.; Lucena, Angel

2006-05-01

This paper outlines the present design approach for the Ethernet-Based Smart Networked Elements (SNE) being developed by NASA's Instrumentation Branch and the Advanced Electronics and Technology Development Laboratory of ASRC Aerospace Corporation at Kennedy Space Center (KSC). The SNEs are being developed as part of the Integrated Intelligent Health Management System (IIHMS), jointly developed by Stennis Space Center (SSC), KSC, and Marshall Space Flight Center (MSFC). SNEs are sensors/actuators with embedded intelligence, capable of networking among themselves and with higher-level systems (external processors and controllers) to provide not only instrumentation data but also associated data validity qualifiers. NASA KSC has successfully developed and preliminarily demonstrated this new generation of SNEs. SNEs that collect pressure, strain, and temperature measurements (including cryogenic temperature ranges) have been developed and tested in the laboratory and are ready for demonstration in the field.

Water Intelligence and the Cyber-Infrastructure Revolution

NASA Astrophysics Data System (ADS)

Cline, D. W.

2015-12-01

As an intrinsic factor in national security, the global economy, food and energy production, and human and ecological health, fresh water resources are increasingly being considered by an ever-widening array of stakeholders. The U.S. intelligence community has identified water as a key factor in the Nation's security risk profile. Water industries are growing rapidly, and seek to revolutionize the role of water in the global economy, making water an economic value rather than a limitation on operations. Recent increased focus on the complex interrelationships and interdependencies between water, food, and energy signal a renewed effort to move towards integrated water resource management. Throughout all of this, hydrologic extremes continue to wreak havoc on communities and regions around the world, in some cases threatening long-term economic stability. This increased attention on water coincides with the "second IT revolution" of cyber-infrastructure (CI). The CI concept is a convergence of technology, data, applications and human resources, all coalescing into a tightly integrated global grid of computing, information, networking and sensor resources, and ultimately serving as an engine of change for collaboration, education and scientific discovery and innovation. In the water arena, we have unprecedented opportunities to apply the CI concept to help address complex water challenges and shape the future world of water resources - on both science and socio-economic application fronts. Providing actionable local "water intelligence" nationally or globally is now becoming feasible through high-performance computing, data technologies, and advanced hydrologic modeling. Further development on all of these fronts appears likely and will help advance this much-needed capability. Lagging behind are water observation systems, especially in situ networks, which need significant innovation to keep pace with and help fuel rapid advancements in water intelligence.

A cost-effective intelligent robotic system with dual-arm dexterous coordination and real-time vision

NASA Technical Reports Server (NTRS)

Marzwell, Neville I.; Chen, Alexander Y. K.

1991-01-01

Dexterous coordination of manipulators based on the use of redundant degrees of freedom, multiple sensors, and built-in robot intelligence represents a critical breakthrough in development of advanced manufacturing technology. A cost-effective approach for achieving this new generation of robotics has been made possible by the unprecedented growth of the latest microcomputer and network systems. The resulting flexible automation offers the opportunity to improve the product quality, increase the reliability of the manufacturing process, and augment the production procedures for optimizing the utilization of the robotic system. Moreover, the Advanced Robotic System (ARS) is modular in design and can be upgraded by closely following technological advancements as they occur in various fields. This approach to manufacturing automation enhances the financial justification and ensures the long-term profitability and most efficient implementation of robotic technology. The new system also addresses a broad spectrum of manufacturing demand and has the potential to address both complex jobs as well as highly labor-intensive tasks. The ARS prototype employs the decomposed optimization technique in spatial planning. This technique is implemented to the framework of the sensor-actuator network to establish the general-purpose geometric reasoning system. The development computer system is a multiple microcomputer network system, which provides the architecture for executing the modular network computing algorithms. The knowledge-based approach used in both the robot vision subsystem and the manipulation control subsystems results in the real-time image processing vision-based capability. The vision-based task environment analysis capability and the responsive motion capability are under the command of the local intelligence centers. An array of ultrasonic, proximity, and optoelectronic sensors is used for path planning. The ARS currently has 18 degrees of freedom made up by two articulated arms, one movable robot head, and two charged coupled device (CCD) cameras for producing the stereoscopic views, and articulated cylindrical-type lower body, and an optional mobile base. A functional prototype is demonstrated.

Artificial intelligence for analyzing orthopedic trauma radiographs.

PubMed

Olczak, Jakub; Fahlberg, Niklas; Maki, Atsuto; Razavian, Ali Sharif; Jilert, Anthony; Stark, André; Sköldenberg, Olof; Gordon, Max

2017-12-01

Background and purpose - Recent advances in artificial intelligence (deep learning) have shown remarkable performance in classifying non-medical images, and the technology is believed to be the next technological revolution. So far it has never been applied in an orthopedic setting, and in this study we sought to determine the feasibility of using deep learning for skeletal radiographs. Methods - We extracted 256,000 wrist, hand, and ankle radiographs from Danderyd's Hospital and identified 4 classes: fracture, laterality, body part, and exam view. We then selected 5 openly available deep learning networks that were adapted for these images. The most accurate network was benchmarked against a gold standard for fractures. We furthermore compared the network's performance with 2 senior orthopedic surgeons who reviewed images at the same resolution as the network. Results - All networks exhibited an accuracy of at least 90% when identifying laterality, body part, and exam view. The final accuracy for fractures was estimated at 83% for the best performing network. The network performed similarly to senior orthopedic surgeons when presented with images at the same resolution as the network. The 2 reviewer Cohen's kappa under these conditions was 0.76. Interpretation - This study supports the use for orthopedic radiographs of artificial intelligence, which can perform at a human level. While current implementation lacks important features that surgeons require, e.g. risk of dislocation, classifications, measurements, and combining multiple exam views, these problems have technical solutions that are waiting to be implemented for orthopedics.

Data mining tools for the support of traffic signal timing plan development in arterial networks

DOT National Transportation Integrated Search

2001-05-01

Intelligent transportation systems (ITS) include large numbers of traffic sensors that collect enormous quantities of data. The data provided by ITS is necessary for advanced forms of control; however, basic forms of control, primarily time-of-day (T...

Simulation and Gaming: Directions, Issues, Ponderables.

ERIC Educational Resources Information Center

Uretsky, Michael

1995-01-01

Discusses the current use of simulation and gaming in a variety of settings. Describes advances in technology that facilitate the use of simulation and gaming, including computer power, computer networks, software, object-oriented programming, video, multimedia, virtual reality, and artificial intelligence. Considers the future use of simulation…

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.

Amplify scientific discovery with artificial intelligence

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

Gil, Yolanda; Greaves, Mark T.; Hendler, James

Computing innovations have fundamentally changed many aspects of scientific inquiry. For example, advances in robotics, high-end computing, networking, and databases now underlie much of what we do in science such as gene sequencing, general number crunching, sharing information between scientists, and analyzing large amounts of data. As computing has evolved at a rapid pace, so too has its impact in science, with the most recent computing innovations repeatedly being brought to bear to facilitate new forms of inquiry. Recently, advances in Artificial Intelligence (AI) have deeply penetrated many consumer sectors, including for example Apple’s Siri™ speech recognition system, real-time automatedmore » language translation services, and a new generation of self-driving cars and self-navigating drones. However, AI has yet to achieve comparable levels of penetration in scientific inquiry, despite its tremendous potential in aiding computers to help scientists tackle tasks that require scientific reasoning. We contend that advances in AI will transform the practice of science as we are increasingly able to effectively and jointly harness human and machine intelligence in the pursuit of major scientific challenges.« less

Advances in Intelligence Research: What Should be Expected in the XXI Century (Questions & Answers).

PubMed

Colom, Roberto

2016-12-06

Here I briefly delineate my view about the main question of this International Seminar, namely, what should we expecting from the XXI Century regarding the advancements in intelligence research. This view can be summarized as 'The Brain Connection' (TBC), meaning that neuroscience will be of paramount relevance for increasing our current knowledge related to the key question: why are some people smarter than others? We need answers to the issue of what happens in our brains when the genotype and the environment are integrated. The scientific community has devoted great research efforts, ranging from observable behavior to hidden genetics, but we are still far from having a clear general picture of what it means to be more or less intelligent. After the discussion held with the panel of experts participating in the seminar, it is concluded that advancements will be more solid and safe increasing the collaboration of scientists with shared research interests worldwide. Paralleling current sophisticated analyses of how the brain computes, nowadays science may embrace a network approach.

SuperSchools: Education in the Information Age and Beyond.

ERIC Educational Resources Information Center

Ameritech Foundation, Chicago, IL.

This document discusses how improvements in the capabilities of the intelligent communications network are making new enhancements and advances available to educators, administrators, students, parents, and the community, focusing on the role of Ameritech. Modern technologies can create dynamic and appropriate learning environments for children…

The Deployment of Sensor Network Intelligent Emission Locator (SENTINEL) for Fenceline Emission Measurements

EPA Science Inventory

Abstract: As part of the Petroleum Refinery Risk and Technology Review, New Source Performance Standards rule, US EPA is proposing use of two-week passive sorbant tube fenceline monitoring for benzene. With recent technological advances, low-cost time-resolved sensors may become...

Biology-inspired Architecture for Situation Management

NASA Technical Reports Server (NTRS)

Jones, Kennie H.; Lodding, Kenneth N.; Olariu, Stephan; Wilson, Larry; Xin, Chunsheng

2006-01-01

Situation Management is a rapidly developing science combining new techniques for data collection with advanced methods of data fusion to facilitate the process leading to correct decisions prescribing action. Current research focuses on reducing increasing amounts of diverse data to knowledge used by decision makers and on reducing time between observations, decisions and actions. No new technology is more promising for increasing the diversity and fidelity of observations than sensor networks. However, current research on sensor networks concentrates on a centralized network architecture. We believe this trend will not realize the full potential of situation management. We propose a new architecture modeled after biological ecosystems where motes are autonomous and intelligent, yet cooperate with local neighborhoods. Providing a layered approach, they sense and act independently when possible, and cooperate with neighborhoods when necessary. The combination of their local actions results in global effects. While situation management research is currently dominated by military applications, advances envisioned for industrial and business applications have similar requirements. NASA has requirements for intelligent and autonomous systems in future missions that can benefit from advances in situation management. We describe requirements for the Integrated Vehicle Health Management program where our biology-inspired architecture provides a layered approach and decisions can be made at the proper level to improve safety, reduce costs, and improve efficiency in making diagnostic and prognostic assessments of the structural integrity, aerodynamic characteristics, and operation of aircraft.

Artificial intelligence for analyzing orthopedic trauma radiographs

PubMed Central

Olczak, Jakub; Fahlberg, Niklas; Maki, Atsuto; Razavian, Ali Sharif; Jilert, Anthony; Stark, André; Sköldenberg, Olof

2017-01-01

Background and purpose — Recent advances in artificial intelligence (deep learning) have shown remarkable performance in classifying non-medical images, and the technology is believed to be the next technological revolution. So far it has never been applied in an orthopedic setting, and in this study we sought to determine the feasibility of using deep learning for skeletal radiographs. Methods — We extracted 256,000 wrist, hand, and ankle radiographs from Danderyd’s Hospital and identified 4 classes: fracture, laterality, body part, and exam view. We then selected 5 openly available deep learning networks that were adapted for these images. The most accurate network was benchmarked against a gold standard for fractures. We furthermore compared the network’s performance with 2 senior orthopedic surgeons who reviewed images at the same resolution as the network. Results — All networks exhibited an accuracy of at least 90% when identifying laterality, body part, and exam view. The final accuracy for fractures was estimated at 83% for the best performing network. The network performed similarly to senior orthopedic surgeons when presented with images at the same resolution as the network. The 2 reviewer Cohen’s kappa under these conditions was 0.76. Interpretation — This study supports the use for orthopedic radiographs of artificial intelligence, which can perform at a human level. While current implementation lacks important features that surgeons require, e.g. risk of dislocation, classifications, measurements, and combining multiple exam views, these problems have technical solutions that are waiting to be implemented for orthopedics. PMID:28681679

Bio-Intelligence: A Research Program Facilitating the Development of New Paradigms for Tomorrow's Patient Care

NASA Astrophysics Data System (ADS)

Phan, Sieu; Famili, Fazel; Liu, Ziying; Peña-Castillo, Lourdes

The advancement of omics technologies in concert with the enabling information technology development has accelerated biological research to a new realm in a blazing speed and sophistication. The limited single gene assay to the high throughput microarray assay and the laborious manual count of base-pairs to the robotic assisted machinery in genome sequencing are two examples to name. Yet even more sophisticated, the recent development in literature mining and artificial intelligence has allowed researchers to construct complex gene networks unraveling many formidable biological puzzles. To harness these emerging technologies to their full potential to medical applications, the Bio-intelligence program at the Institute for Information Technology, National Research Council Canada, aims to develop and exploit artificial intelligence and bioinformatics technologies to facilitate the development of intelligent decision support tools and systems to improve patient care - for early detection, accurate diagnosis/prognosis of disease, and better personalized therapeutic management.

The Educator's Role in Preparing Visually Literate Learners

ERIC Educational Resources Information Center

Metros, Susan E.

2008-01-01

Contemporary culture has become increasingly dependent on the visual, especially for its capacity to communicate instantly and universally. Advances in technology fueled this shift. Students must learn to cope with and intelligently contribute to a culture rife with easy access to the visually rich Web, photo dependant social networks, video…

Neural Networks for Modeling and Control of Particle Accelerators

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

Edelen, A. L.; Biedron, S. G.; Chase, B. E.

Myriad nonlinear and complex physical phenomena are host to particle accelerators. They often involve a multitude of interacting systems, are subject to tight performance demands, and should be able to run for extended periods of time with minimal interruptions. Often times, traditional control techniques cannot fully meet these requirements. One promising avenue is to introduce machine learning and sophisticated control techniques inspired by artificial intelligence, particularly in light of recent theoretical and practical advances in these fields. Within machine learning and artificial intelligence, neural networks are particularly well-suited to modeling, control, and diagnostic analysis of complex, nonlinear, and time-varying systems,more » as well as systems with large parameter spaces. Consequently, the use of neural network-based modeling and control techniques could be of significant benefit to particle accelerators. For the same reasons, particle accelerators are also ideal test-beds for these techniques. Moreover, many early attempts to apply neural networks to particle accelerators yielded mixed results due to the relative immaturity of the technology for such tasks. For the purpose of this paper is to re-introduce neural networks to the particle accelerator community and report on some work in neural network control that is being conducted as part of a dedicated collaboration between Fermilab and Colorado State University (CSU). We also describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.« less

Neural Networks for Modeling and Control of Particle Accelerators

NASA Astrophysics Data System (ADS)

Edelen, A. L.; Biedron, S. G.; Chase, B. E.; Edstrom, D.; Milton, S. V.; Stabile, P.

2016-04-01

Particle accelerators are host to myriad nonlinear and complex physical phenomena. They often involve a multitude of interacting systems, are subject to tight performance demands, and should be able to run for extended periods of time with minimal interruptions. Often times, traditional control techniques cannot fully meet these requirements. One promising avenue is to introduce machine learning and sophisticated control techniques inspired by artificial intelligence, particularly in light of recent theoretical and practical advances in these fields. Within machine learning and artificial intelligence, neural networks are particularly well-suited to modeling, control, and diagnostic analysis of complex, nonlinear, and time-varying systems, as well as systems with large parameter spaces. Consequently, the use of neural network-based modeling and control techniques could be of significant benefit to particle accelerators. For the same reasons, particle accelerators are also ideal test-beds for these techniques. Many early attempts to apply neural networks to particle accelerators yielded mixed results due to the relative immaturity of the technology for such tasks. The purpose of this paper is to re-introduce neural networks to the particle accelerator community and report on some work in neural network control that is being conducted as part of a dedicated collaboration between Fermilab and Colorado State University (CSU). We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.

Neural Networks for Modeling and Control of Particle Accelerators

DOE PAGES

Edelen, A. L.; Biedron, S. G.; Chase, B. E.; ...

2016-04-01

Myriad nonlinear and complex physical phenomena are host to particle accelerators. They often involve a multitude of interacting systems, are subject to tight performance demands, and should be able to run for extended periods of time with minimal interruptions. Often times, traditional control techniques cannot fully meet these requirements. One promising avenue is to introduce machine learning and sophisticated control techniques inspired by artificial intelligence, particularly in light of recent theoretical and practical advances in these fields. Within machine learning and artificial intelligence, neural networks are particularly well-suited to modeling, control, and diagnostic analysis of complex, nonlinear, and time-varying systems,more » as well as systems with large parameter spaces. Consequently, the use of neural network-based modeling and control techniques could be of significant benefit to particle accelerators. For the same reasons, particle accelerators are also ideal test-beds for these techniques. Moreover, many early attempts to apply neural networks to particle accelerators yielded mixed results due to the relative immaturity of the technology for such tasks. For the purpose of this paper is to re-introduce neural networks to the particle accelerator community and report on some work in neural network control that is being conducted as part of a dedicated collaboration between Fermilab and Colorado State University (CSU). We also describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.« less

Some recent advances of intelligent health monitoring systems for civil infrastructures in HIT

NASA Astrophysics Data System (ADS)

Ou, Jinping

2005-06-01

The intelligent health monitoring systems more and more become a technique for ensuring the health and safety of civil infrastructures and also an important approach for research of the damage accumulation or even disaster evolving characteristics of civil infrastructures, and attracts prodigious research interests and active development interests of scientists and engineers since a great number of civil infrastructures are planning and building each year in mainland China. In this paper, some recent advances on research, development nad implementation of intelligent health monitoring systems for civil infrastructuresin mainland China, especially in Harbin Institute of Technology (HIT), P.R.China. The main contents include smart sensors such as optical fiber Bragg grating (OFBG) and polivinyllidene fluoride (PVDF) sensors, fatigue life gauges, self-sensing mortar and carbon fiber reinforced polymer (CFRP), wireless sensor networks and their implementation in practical infrastructures such as offshore platform structures, hydraulic engineering structures, large span bridges and large space structures. Finally, the relative research projects supported by the national foundation agencies of China are briefly introduced.

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.

Cellular neural network-based hybrid approach toward automatic image registration

NASA Astrophysics Data System (ADS)

Arun, Pattathal VijayaKumar; Katiyar, Sunil Kumar

2013-01-01

Image registration is a key component of various image processing operations that involve the analysis of different image data sets. Automatic image registration domains have witnessed the application of many intelligent methodologies over the past decade; however, inability to properly model object shape as well as contextual information has limited the attainable accuracy. A framework for accurate feature shape modeling and adaptive resampling using advanced techniques such as vector machines, cellular neural network (CNN), scale invariant feature transform (SIFT), coreset, and cellular automata is proposed. CNN has been found to be effective in improving feature matching as well as resampling stages of registration and complexity of the approach has been considerably reduced using coreset optimization. The salient features of this work are cellular neural network approach-based SIFT feature point optimization, adaptive resampling, and intelligent object modelling. Developed methodology has been compared with contemporary methods using different statistical measures. Investigations over various satellite images revealed that considerable success was achieved with the approach. This system has dynamically used spectral and spatial information for representing contextual knowledge using CNN-prolog approach. This methodology is also illustrated to be effective in providing intelligent interpretation and adaptive resampling.

Science of the science, drug discovery and artificial neural networks.

PubMed

Patel, Jigneshkumar

2013-03-01

Drug discovery process many times encounters complex problems, which may be difficult to solve by human intelligence. Artificial Neural Networks (ANNs) are one of the Artificial Intelligence (AI) technologies used for solving such complex problems. ANNs are widely used for primary virtual screening of compounds, quantitative structure activity relationship studies, receptor modeling, formulation development, pharmacokinetics and in all other processes involving complex mathematical modeling. Despite having such advanced technologies and enough understanding of biological systems, drug discovery is still a lengthy, expensive, difficult and inefficient process with low rate of new successful therapeutic discovery. In this paper, author has discussed the drug discovery science and ANN from very basic angle, which may be helpful to understand the application of ANN for drug discovery to improve efficiency.

Intelligent Middle-Ware Architecture for Mobile Networks

NASA Astrophysics Data System (ADS)

Rayana, Rayene Ben; Bonnin, Jean-Marie

Recent advances in electronic and automotive industries as well as in wireless telecommunication technologies have drawn a new picture where each vehicle became “fully networked”. Multiple stake-holders (network operators, drivers, car manufacturers, service providers, etc.) will participate in this emerging market, which could grow following various models. To free the market from technical constraints, it is important to return to the basics of the Internet, i.e., providing embarked devices with a fully operational Internet connectivity (IPv6).

Neural computing thermal comfort index PMV for the indoor environment intelligent control system

NASA Astrophysics Data System (ADS)

Liu, Chang; Chen, Yifei

2013-03-01

Providing indoor thermal comfort and saving energy are two main goals of indoor environmental control system. An intelligent comfort control system by combining the intelligent control and minimum power control strategies for the indoor environment is presented in this paper. In the system, for realizing the comfort control, the predicted mean vote (PMV) is designed as the control goal, and with chastening formulas of PMV, it is controlled to optimize for improving indoor comfort lever by considering six comfort related variables. On the other hand, a RBF neural network based on genetic algorithm is designed to calculate PMV for better performance and overcoming the nonlinear feature of the PMV calculation better. The formulas given in the paper are presented for calculating the expected output values basing on the input samples, and the RBF network model is trained depending on input samples and the expected output values. The simulation result is proved that the design of the intelligent calculation method is valid. Moreover, this method has a lot of advancements such as high precision, fast dynamic response and good system performance are reached, it can be used in practice with requested calculating error.

Architecture and biological applications of artificial neural networks: a tuberculosis perspective.

PubMed

Darsey, Jerry A; Griffin, William O; Joginipelli, Sravanthi; Melapu, Venkata Kiran

2015-01-01

Advancement of science and technology has prompted researchers to develop new intelligent systems that can solve a variety of problems such as pattern recognition, prediction, and optimization. The ability of the human brain to learn in a fashion that tolerates noise and error has attracted many researchers and provided the starting point for the development of artificial neural networks: the intelligent systems. Intelligent systems can acclimatize to the environment or data and can maximize the chances of success or improve the efficiency of a search. Due to massive parallelism with large numbers of interconnected processers and their ability to learn from the data, neural networks can solve a variety of challenging computational problems. Neural networks have the ability to derive meaning from complicated and imprecise data; they are used in detecting patterns, and trends that are too complex for humans, or other computer systems. Solutions to the toughest problems will not be found through one narrow specialization; therefore we need to combine interdisciplinary approaches to discover the solutions to a variety of problems. Many researchers in different disciplines such as medicine, bioinformatics, molecular biology, and pharmacology have successfully applied artificial neural networks. This chapter helps the reader in understanding the basics of artificial neural networks, their applications, and methodology; it also outlines the network learning process and architecture. We present a brief outline of the application of neural networks to medical diagnosis, drug discovery, gene identification, and protein structure prediction. We conclude with a summary of the results from our study on tuberculosis data using neural networks, in diagnosing active tuberculosis, and predicting chronic vs. infiltrative forms of tuberculosis.

Affect Recognition through Facebook for Effective Group Profiling towards Personalized Instruction

ERIC Educational Resources Information Center

Troussas, Christos; Espinosa, Kurt Junshean; Virvou, Maria

2016-01-01

Social networks are progressively being considered as an intense thought for learning. Particularly in the research area of Intelligent Tutoring Systems, they can create intuitive, versatile and customized e-learning systems which can advance the learning process by revealing the capacities and shortcomings of every learner and by customizing the…

Functional brain networks related to individual differences in human intelligence at rest.

PubMed

Hearne, Luke J; Mattingley, Jason B; Cocchi, Luca

2016-08-26

Intelligence is a fundamental ability that sets humans apart from other animal species. Despite its importance in defining human behaviour, the neural networks responsible for intelligence are not well understood. The dominant view from neuroimaging work suggests that intelligent performance on a range of tasks is underpinned by segregated interactions in a fronto-parietal network of brain regions. Here we asked whether fronto-parietal interactions associated with intelligence are ubiquitous, or emerge from more widespread associations in a task-free context. First we undertook an exploratory mapping of the existing literature on functional connectivity associated with intelligence. Next, to empirically test hypotheses derived from the exploratory mapping, we performed network analyses in a cohort of 317 unrelated participants from the Human Connectome Project. Our results revealed a novel contribution of across-network interactions between default-mode and fronto-parietal networks to individual differences in intelligence at rest. Specifically, we found that greater connectivity in the resting state was associated with higher intelligence scores. Our findings highlight the need to broaden the dominant fronto-parietal conceptualisation of intelligence to encompass more complex and context-specific network dynamics.

Where value lives in a networked world.

PubMed

Sawhney, M; Parikh, D

2001-01-01

While many management thinkers proclaim an era of radical uncertainty, authors Sawhney and Parikh assert that the seemingly endless upheavals of the digital age are more predictable than that: today's changes have a common root, and that root lies in the nature of intelligence in networks. Understanding the patterns of intelligence migration can help companies decipher and plan for the inevitable disruptions in today's business environment. Two patterns in network intelligence are reshaping industries and organizations. First, intelligence is decoupling--that is, modern high-speed networks are pushing back-end intelligence and front-end intelligence toward opposite ends of the network, making the ends the two major sources of potential profits. Second, intelligence is becoming more fluid and modular. Small units of intelligence now float freely like molecules in the ether, coalescing into temporary bundles whenever and wherever necessary to solve problems. The authors present four strategies that companies can use to profit from these patterns: arbitrage allows companies to move intelligence to new regions or countries where the cost of maintaining intelligence is lower; aggregation combines formerly isolated pieces of infrastructure intelligence into a large pool of shared infrastructure provided over a network; rewiring allows companies to connect islands of intelligence by creating common information backbones; and reassembly allows businesses to reorganize pieces of intelligence into coherent, personalized packages for customers. By being aware of patterns in network intelligence and by acting rather than reacting, companies can turn chaos into opportunity, say the authors.

Networked sensors for the combat forces

NASA Astrophysics Data System (ADS)

Klager, Gene

2004-11-01

Real-time and detailed information is critical to the success of ground combat forces. Current manned reconnaissance, surveillance, and target acquisition (RSTA) capabilities are not sufficient to cover battlefield intelligence gaps, provide Beyond-Line-of-Sight (BLOS) targeting, and the ambush avoidance information necessary for combat forces operating in hostile situations, complex terrain, and conducting military operations in urban terrain. This paper describes a current US Army program developing advanced networked unmanned/unattended sensor systems to survey these gaps and provide the Commander with real-time, pertinent information. Networked Sensors for the Combat Forces plans to develop and demonstrate a new generation of low cost distributed unmanned sensor systems organic to the RSTA Element. Networked unmanned sensors will provide remote monitoring of gaps, will increase a unit"s area of coverage, and will provide the commander organic assets to complete his Battlefield Situational Awareness (BSA) picture for direct and indirect fire weapons, early warning, and threat avoidance. Current efforts include developing sensor packages for unmanned ground vehicles, small unmanned aerial vehicles, and unattended ground sensors using advanced sensor technologies. These sensors will be integrated with robust networked communications and Battle Command tools for mission planning, intelligence "reachback", and sensor data management. The network architecture design is based on a model that identifies a three-part modular design: 1) standardized sensor message protocols, 2) Sensor Data Management, and 3) Service Oriented Architecture. This simple model provides maximum flexibility for data exchange, information management and distribution. Products include: Sensor suites optimized for unmanned platforms, stationary and mobile versions of the Sensor Data Management Center, Battle Command planning tools, networked communications, and sensor management software. Details of these products and recent test results will be presented.

Wireless Communications in Smart Grid

NASA Astrophysics Data System (ADS)

Bojkovic, Zoran; Bakmaz, Bojan

Communication networks play a crucial role in smart grid, as the intelligence of this complex system is built based on information exchange across the power grid. Wireless communications and networking are among the most economical ways to build the essential part of the scalable communication infrastructure for smart grid. In particular, wireless networks will be deployed widely in the smart grid for automatic meter reading, remote system and customer site monitoring, as well as equipment fault diagnosing. With an increasing interest from both the academic and industrial communities, this chapter systematically investigates recent advances in wireless communication technology for the smart grid.

Functional brain networks related to individual differences in human intelligence at rest

PubMed Central

Hearne, Luke J.; Mattingley, Jason B.; Cocchi, Luca

2016-01-01

Intelligence is a fundamental ability that sets humans apart from other animal species. Despite its importance in defining human behaviour, the neural networks responsible for intelligence are not well understood. The dominant view from neuroimaging work suggests that intelligent performance on a range of tasks is underpinned by segregated interactions in a fronto-parietal network of brain regions. Here we asked whether fronto-parietal interactions associated with intelligence are ubiquitous, or emerge from more widespread associations in a task-free context. First we undertook an exploratory mapping of the existing literature on functional connectivity associated with intelligence. Next, to empirically test hypotheses derived from the exploratory mapping, we performed network analyses in a cohort of 317 unrelated participants from the Human Connectome Project. Our results revealed a novel contribution of across-network interactions between default-mode and fronto-parietal networks to individual differences in intelligence at rest. Specifically, we found that greater connectivity in the resting state was associated with higher intelligence scores. Our findings highlight the need to broaden the dominant fronto-parietal conceptualisation of intelligence to encompass more complex and context-specific network dynamics. PMID:27561736

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1989-01-01

Archival reports on developments in programs managed by JPL's Office of Telecommunications and Data Acquisition (TDA) are presented. Activities of the Deep Space Network (DSN) and its associated Ground Communications Facility (GCF) related to DSN advanced systems, systems implementation, and DSN operations are addressed. In addition, recent developments in the NASA SETI (Search for Extraterrestrial Intelligence) sky survey are summarized.

Entropy based file type identification and partitioning

DTIC Science & Technology

2017-06-01

energy spectrum,” Proceedings of the Twenty-Ninth International Florida Artificial Intelligence Research Society Conference, pp. 288–293, 2016...ABBREVIATIONS AES Advanced Encryption Standard ANN Artificial Neural Network ASCII American Standard Code for Information Interchange CWT...the identification of file types and file partitioning. This approach has applications in cybersecurity as it allows for a quick determination of

GLOBECOM '87 - Global Telecommunications Conference, Tokyo, Japan, Nov. 15-18, 1987, Conference Record. Volumes 1, 2, & 3

NASA Astrophysics Data System (ADS)

The present conference on global telecommunications discusses topics in the fields of Integrated Services Digital Network (ISDN) technology field trial planning and results to date, motion video coding, ISDN networking, future network communications security, flexible and intelligent voice/data networks, Asian and Pacific lightwave and radio systems, subscriber radio systems, the performance of distributed systems, signal processing theory, satellite communications modulation and coding, and terminals for the handicapped. Also discussed are knowledge-based technologies for communications systems, future satellite transmissions, high quality image services, novel digital signal processors, broadband network access interface, traffic engineering for ISDN design and planning, telecommunications software, coherent optical communications, multimedia terminal systems, advanced speed coding, portable and mobile radio communications, multi-Gbit/second lightwave transmission systems, enhanced capability digital terminals, communications network reliability, advanced antimultipath fading techniques, undersea lightwave transmission, image coding, modulation and synchronization, adaptive signal processing, integrated optical devices, VLSI technologies for ISDN, field performance of packet switching, CSMA protocols, optical transport system architectures for broadband ISDN, mobile satellite communications, indoor wireless communication, echo cancellation in communications, and distributed network algorithms.

Lateral Prefrontal Cortex Contributes to Fluid Intelligence Through Multinetwork Connectivity.

PubMed

Cole, Michael W; Ito, Takuya; Braver, Todd S

2015-10-01

Our ability to effectively adapt to novel circumstances--as measured by general fluid intelligence--has recently been tied to the global connectivity of lateral prefrontal cortex (LPFC). Global connectivity is a broad measure that summarizes both within-network connectivity and across-network connectivity. We used additional graph theoretical measures to better characterize the nature of LPFC connectivity and its relationship with fluid intelligence. We specifically hypothesized that LPFC is a connector hub with an across-network connectivity that contributes to fluid intelligence independent of within-network connectivity. We verified that LPFC was in the top 10% of brain regions in terms of across-network connectivity, suggesting it is a strong connector hub. Importantly, we found that the LPFC across-network connectivity predicted individuals' fluid intelligence and this correlation remained statistically significant when controlling for global connectivity (which includes within-network connectivity). This supports the conclusion that across-network connectivity independently contributes to the relationship between LPFC connectivity and intelligence. These results suggest that LPFC contributes to fluid intelligence by being a connector hub with a truly global multisystem connectivity throughout the brain.

Association between resting-state coactivation in the parieto-frontal network and intelligence during late childhood and adolescence.

PubMed

Li, C; Tian, L

2014-06-01

A number of studies have associated the adult intelligence quotient with the structure and function of the bilateral parieto-frontal networks, whereas the relationship between intelligence quotient and parieto-frontal network function has been found to be relatively weak in early childhood. Because both human intelligence and brain function undergo protracted development into adulthood, the purpose of the present study was to provide a better understanding of the development of the parieto-frontal network-intelligence quotient relationship. We performed independent component analysis of resting-state fMRI data of 84 children and 50 adolescents separately and then correlated full-scale intelligence quotient with the spatial maps of the bilateral parieto-frontal networks of each group. In children, significant positive spatial-map versus intelligence quotient correlations were detected in the right angular gyrus and inferior frontal gyrus in the right parieto-frontal network, and no significant correlation was observed in the left parieto-frontal network. In adolescents, significant positive correlation was detected in the left inferior frontal gyrus in the left parieto-frontal network, and the correlations in the frontal pole in the 2 parieto-frontal networks were only marginally significant. The present findings not only support the critical role of the parieto-frontal networks for intelligence but indicate that the relationship between intelligence quotient and the parieto-frontal network in the right hemisphere has been well established in late childhood, and that the relationship in the left hemisphere was also established in adolescence. © 2014 by American Journal of Neuroradiology.

Service oriented network architecture for control and management of home appliances

NASA Astrophysics Data System (ADS)

Hayakawa, Hiroshi; Koita, Takahiro; Sato, Kenya

2005-12-01

Recent advances in multimedia network systems and mechatronics have led to the development of a new generation of applications that associate the use of various multimedia objects with the behavior of multiple robotic actors. The connection of audio and video devices through high speed multimedia networks is expected to make the system more convenient to use. For example, many home appliances, such as a video camera, a display monitor, a video recorder, an audio system and so on, are being equipped with a communication interface in the near future. Recently some platforms (i.e. UPnP1, HAVi2 and so on) are proposed for constructing home networks; however, there are some issues to be solved to realize various services by connecting different equipment via the pervasive peer-to-peer network. UPnP offers network connectivity of PCs of intelligent home appliances, practically, which means to require a PC in the network to control other devices. Meanwhile, HAVi has been developed for intelligent AV equipments with sophisticated functions using high CPU power and large memory. Considering the targets of home alliances are embedded systems, this situation raises issues of software and hardware complexity, cost, power consumption and so on. In this study, we have proposed and developed the service oriented network architecture for control and management of home appliances, named SONICA (Service Oriented Network Interoperability for Component Adaptation), to address these issues described before.

Cybercare 2.0: meeting the challenge of the global burden of disease in 2030.

PubMed

Rosen, Joseph M; Kun, Luis; Mosher, Robyn E; Grigg, Elliott; Merrell, Ronald C; Macedonia, Christian; Klaudt-Moreau, Julien; Price-Smith, Andrew; Geiling, James

In this paper, we propose to advance and transform today's healthcare system using a model of networked health care called Cybercare. Cybercare means "health care in cyberspace" - for example, doctors consulting with patients via videoconferencing across a distributed network; or patients receiving care locally - in neighborhoods, "minute clinics," and homes - using information technologies such as telemedicine, smartphones, and wearable sensors to link to tertiary medical specialists. This model contrasts with traditional health care, in which patients travel (often a great distance) to receive care from providers in a central hospital. The Cybercare model shifts health care provision from hospital to home; from specialist to generalist; and from treatment to prevention. Cybercare employs advanced technology to deliver services efficiently across the distributed network - for example, using telemedicine, wearable sensors and cell phones to link patients to specialists and upload their medical data in near-real time; using information technology (IT) to rapidly detect, track, and contain the spread of a global pandemic; or using cell phones to manage medical care in a disaster situation. Cybercare uses seven "pillars" of technology to provide medical care: genomics; telemedicine; robotics; simulation, including virtual and augmented reality; artificial intelligence (AI), including intelligent agents; the electronic medical record (EMR); and smartphones. All these technologies are evolving and blending. The technologies are integrated functionally because they underlie the Cybercare network, and/or form part of the care for patients using that distributed network. Moving health care provision to a networked, distributed model will save money, improve outcomes, facilitate access, improve security, increase patient and provider satisfaction, and may mitigate the international global burden of disease. In this paper we discuss how Cybercare is being implemented now, and envision its growth by 2030.

Sensors-network and its application in the intelligent storage security

NASA Astrophysics Data System (ADS)

Zhang, Qingying; Nicolescu, Mihai; Jiang, Xia; Zhang, Ying; Yue, Weihong; Xiao, Weihong

2004-11-01

Intelligent storage systems run on different advanced technologies, such as linear layout, business intelligence and data mining. Security, the basic desire of the storage system, has been focused on with the indraught of multimedia communication technology and sensors" network. Along with the developing of science and the social demands, multifarious alarming system has been designed and improved to be intelligentized, modularized and have network connections. It is of great moment to make the storage, and further more, the logistics system more and more efficient and perfect with modern science and technology. Diversified information on the spot should be caught by different kinds of sensors. Those signals are treated and communicated to the control center to give the further actions. For fire-proofing, broad-spectrum gas sensors, fume sensors, flame sensors and temperature sensors are used to catch the information in their own ways. Once the fire is taken somewhere, the sensors work by the fume, temperature, and flame as well as gas immediately. Meanwhile the intelligent control system starts. It passes the tidings to the center unit. At the same time, it sets those movable walls on to work quickly to obstruct the fire"s spreading. While for guarding the warehouse against theft, cut-off sensors, body sensors, photoelectric sensors, microwave sensors and closed-circuit television as well as electronic clocks are available to monitor the warehouse reasonably. All of those sensors work in a net way. The intelligent control system is made with a digital circuit instead of traditional switch one. This system can work in a better way in many cases. Its reliability is high and the cost is low.

Activities report of PTT Research

NASA Astrophysics Data System (ADS)

In the field of postal infrastructure research, activities were performed on postcode readers, radiolabels, and techniques of operations research and artificial intelligence. In the field of telecommunication, transportation, and information, research was made on multipurpose coding schemes, speech recognition, hypertext, a multimedia information server, security of electronic data interchange, document retrieval, improvement of the quality of user interfaces, domotics living support (techniques), and standardization of telecommunication prototcols. In the field of telecommunication infrastructure and provisions research, activities were performed on universal personal telecommunications, advanced broadband network technologies, coherent techniques, measurement of audio quality, near field facilities, local beam communication, local area networks, network security, coupling of broadband and narrowband integrated services digital networks, digital mapping, and standardization of protocols.

Investigation of bus transit schedule behavior modeling using advanced techniques

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

Kalaputapu, R.; Demetsky, M.J.

This research focused on investigating the application of artificial neural networks (ANN) and the Box-Jenkins technique for developing and testing schedule behavior models using data obtained for a test route from Tidewater Regional Transit`s AVL system. The three ANN architectures investigated were: Feedforward Network, Elman Network and Jordan Network. In addition, five different model structures were investigated. The time-series methodology was adopted for developing the schedule behavior models. Finally, the role of a schedule behavior model within the framework of an intelligent transit management system is defined and the potential utility of the schedule behavior model is discussed using anmore » example application.« less

An Artificial Neural Network Controller for Intelligent Transportation Systems Applications

DOT National Transportation Integrated Search

1996-01-01

An Autonomous Intelligent Cruise Control (AICC) has been designed using a feedforward artificial neural network, as an example for utilizing artificial neural networks for nonlinear control problems arising in intelligent transportation systems appli...

A Workflow-based Intelligent Network Data Movement Advisor with End-to-end Performance Optimization

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

Zhu, Michelle M.; Wu, Chase Q.

2013-11-07

Next-generation eScience applications often generate large amounts of simulation, experimental, or observational data that must be shared and managed by collaborative organizations. Advanced networking technologies and services have been rapidly developed and deployed to facilitate such massive data transfer. However, these technologies and services have not been fully utilized mainly because their use typically requires significant domain knowledge and in many cases application users are even not aware of their existence. By leveraging the functionalities of an existing Network-Aware Data Movement Advisor (NADMA) utility, we propose a new Workflow-based Intelligent Network Data Movement Advisor (WINDMA) with end-to-end performance optimization formore » this DOE funded project. This WINDMA system integrates three major components: resource discovery, data movement, and status monitoring, and supports the sharing of common data movement workflows through account and database management. This system provides a web interface and interacts with existing data/space management and discovery services such as Storage Resource Management, transport methods such as GridFTP and GlobusOnline, and network resource provisioning brokers such as ION and OSCARS. We demonstrate the efficacy of the proposed transport-support workflow system in several use cases based on its implementation and deployment in DOE wide-area networks.« less

Using Marine and Freshwater Fish Environmental Intelligence Networks Under Different Climate Change Scenarios to Evaluate the Effectiveness of the Minamata Convention on Mercury

NASA Astrophysics Data System (ADS)

Bank, M. S.

2017-12-01

The Minamata Convention on Mercury was recently ratified and will go into effect on August 16, 2017. As noted in the convention text, fish are an important source of nutrition to consumers worldwide and several marine and freshwater species represent important links in the global source-receptor dynamics of methylmercury. However, despite its importance, a coordinated global program for marine and freshwater fish species using accredited laboratories, reproducible data and reliable models is still lacking. In recent years fish mercury science has evolved significantly with its use of advanced technologies and computational models to address this complex and ubiquitous environmental and public health issue. These advances in the field have made it essential that transparency be enhanced to ensure that fish mercury studies used in support of the convention are truly reproducible and scientifically sound. One primary goal of this presentation is to evaluate fish bioinformatics and methods, results and inferential reproducibility as it relates to aggregated uncertainty in mercury fish research models, science, and biomonitoring. I use models, environmental intelligence networks and simulations of the effects of a changing climate on methylmercury in marine and freshwater fish to examine how climate change and the convention itself may create further uncertainties for policymakers to consider. Lastly, I will also present an environmental intelligence framework for fish mercury bioaccumulation models and biomonitoring in support of the evaluation of the effectiveness of the Minamata Convention on Mercury.

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.

Countering Threat Networks

DTIC Science & Technology

2016-12-21

PLANNING TO COUNTER THREAT NETWORKS  Joint Intelligence Preparation of the Operational Environment and Threat Networks...Army Expeditionary Forensic Facility in Afghanistan ........ E-9 E-4 Exploitation Support to Intelligence Fusion and Decision Making ......... E-10...Approach The groundwork for successful countering threat networks activities starts with information and intelligence to develop an understanding

Boolean logic tree of graphene-based chemical system for molecular computation and intelligent molecular search query.

PubMed

Huang, Wei Tao; Luo, Hong Qun; Li, Nian Bing

2014-05-06

The most serious, and yet unsolved, problem of constructing molecular computing devices consists in connecting all of these molecular events into a usable device. This report demonstrates the use of Boolean logic tree for analyzing the chemical event network based on graphene, organic dye, thrombin aptamer, and Fenton reaction, organizing and connecting these basic chemical events. And this chemical event network can be utilized to implement fluorescent combinatorial logic (including basic logic gates and complex integrated logic circuits) and fuzzy logic computing. On the basis of the Boolean logic tree analysis and logic computing, these basic chemical events can be considered as programmable "words" and chemical interactions as "syntax" logic rules to construct molecular search engine for performing intelligent molecular search query. Our approach is helpful in developing the advanced logic program based on molecules for application in biosensing, nanotechnology, and drug delivery.

Intelligent postoperative morbidity prediction of heart disease using artificial intelligence techniques.

PubMed

Hsieh, Nan-Chen; Hung, Lun-Ping; Shih, Chun-Che; Keh, Huan-Chao; Chan, Chien-Hui

2012-06-01

Endovascular aneurysm repair (EVAR) is an advanced minimally invasive surgical technology that is helpful for reducing patients' recovery time, postoperative morbidity and mortality. This study proposes an ensemble model to predict postoperative morbidity after EVAR. The ensemble model was developed using a training set of consecutive patients who underwent EVAR between 2000 and 2009. All data required for prediction modeling, including patient demographics, preoperative, co-morbidities, and complication as outcome variables, was collected prospectively and entered into a clinical database. A discretization approach was used to categorize numerical values into informative feature space. Then, the Bayesian network (BN), artificial neural network (ANN), and support vector machine (SVM) were adopted as base models, and stacking combined multiple models. The research outcomes consisted of an ensemble model to predict postoperative morbidity after EVAR, the occurrence of postoperative complications prospectively recorded, and the causal effect knowledge by BNs with Markov blanket concept.

Brain Anatomical Network and Intelligence

PubMed Central

Li, Jun; Qin, Wen; Li, Kuncheng; Yu, Chunshui; Jiang, Tianzi

2009-01-01

Intuitively, higher intelligence might be assumed to correspond to more efficient information transfer in the brain, but no direct evidence has been reported from the perspective of brain networks. In this study, we performed extensive analyses to test the hypothesis that individual differences in intelligence are associated with brain structural organization, and in particular that higher scores on intelligence tests are related to greater global efficiency of the brain anatomical network. We constructed binary and weighted brain anatomical networks in each of 79 healthy young adults utilizing diffusion tensor tractography and calculated topological properties of the networks using a graph theoretical method. Based on their IQ test scores, all subjects were divided into general and high intelligence groups and significantly higher global efficiencies were found in the networks of the latter group. Moreover, we showed significant correlations between IQ scores and network properties across all subjects while controlling for age and gender. Specifically, higher intelligence scores corresponded to a shorter characteristic path length and a higher global efficiency of the networks, indicating a more efficient parallel information transfer in the brain. The results were consistently observed not only in the binary but also in the weighted networks, which together provide convergent evidence for our hypothesis. Our findings suggest that the efficiency of brain structural organization may be an important biological basis for intelligence. PMID:19492086

Battery-free Wireless Sensor Network For Advanced Fossil-Fuel Based Power Generation

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

Yi Jia

2011-02-28

This report summarizes technical progress achieved during the project supported by the Department of Energy under Award Number DE-FG26-07NT4306. The aim of the project was to conduct basic research into battery-free wireless sensing mechanism in order to develop novel wireless sensors and sensor network for physical and chemical parameter monitoring in a harsh environment. Passive wireless sensing platform and five wireless sensors including temperature sensor, pressure sensor, humidity sensor, crack sensor and networked sensors developed and demonstrated in our laboratory setup have achieved the objective for the monitoring of various physical and chemical parameters in a harsh environment through remotemore » power and wireless sensor communication, which is critical to intelligent control of advanced power generation system. This report is organized by the sensors developed as detailed in each progress report.« less

Artificial intelligence in radiology.

PubMed

Hosny, Ahmed; Parmar, Chintan; Quackenbush, John; Schwartz, Lawrence H; Aerts, Hugo J W L

2018-05-17

Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this Opinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.

Self-Learning Intelligent Agents for Dynamic Traffic Routing on Transportation Networks

NASA Astrophysics Data System (ADS)

Sadek, Add; Basha, Nagi

Intelligent Transportation Systems (ITS) are designed to take advantage of recent advances in communications, electronics, and Information Technology in improving the efficiency and safety of transportation systems. Among the several ITS applications is the notion of Dynamic Traffic Routing (DTR), which involves generating "optimal" routing recommendations to drivers with the aim of maximizing network utilizing. In this paper, we demonstrate the feasibility of using a self-learning intelligent agent to solve the DTR problem to achieve traffic user equilibrium in a transportation network. The core idea is to deploy an agent to a simulation model of a highway. The agent then learns by itself by interacting with the simulation model. Once the agent reaches a satisfactory level of performance, it can then be deployed to the real-world, where it would continue to learn how to refine its control policies over time. To test this concept in this paper, the Cell Transmission Model (CTM) developed by Carlos Daganzo of the University of California at Berkeley is used to simulate a simple highway with two main alternative routes. With the model developed, a Reinforcement Learning Agent (RLA) is developed to learn how to best dynamically route traffic, so as to maximize the utilization of existing capacity. Preliminary results obtained from our experiments are promising. RL, being an adaptive online learning technique, appears to have a great potential for controlling a stochastic dynamic systems such as a transportation system. Furthermore, the approach is highly scalable and applicable to a variety of networks and roadways.

Advances in the Neuroscience of Intelligence: from Brain Connectivity to Brain Perturbation.

PubMed

Santarnecchi, Emiliano; Rossi, Simone

2016-12-06

Our view is that intelligence, as expression of the complexity of the human brain and of its evolutionary path, represents an intriguing example of "system level brain plasticity": tangible proofs of this assertion lie in the strong links intelligence has with vital brain capacities as information processing (i.e., pure, rough capacity to transfer information in an efficient way), resilience (i.e., the ability to cope with loss of efficiency and/or loss of physical elements in a network) and adaptability (i.e., being able to efficiently rearrange its dynamics in response to environmental demands). Current evidence supporting this view move from theoretical models correlating intelligence and individual response to systematic "lesions" of brain connectivity, as well as from the field of Noninvasive Brain Stimulation (NiBS). Perturbation-based approaches based on techniques as transcranial magnetic stimulation (TMS) and transcranial alternating current stimulation (tACS), are opening new in vivo scenarios which could allow to disclose more causal relationship between intelligence and brain plasticity, overcoming the limitations of brain-behavior correlational evidence.

Learning Perfectly Secure Cryptography to Protect Communications with Adversarial Neural Cryptography

PubMed Central

2018-01-01

Researches in Artificial Intelligence (AI) have achieved many important breakthroughs, especially in recent years. In some cases, AI learns alone from scratch and performs human tasks faster and better than humans. With the recent advances in AI, it is natural to wonder whether Artificial Neural Networks will be used to successfully create or break cryptographic algorithms. Bibliographic review shows the main approach to this problem have been addressed throughout complex Neural Networks, but without understanding or proving the security of the generated model. This paper presents an analysis of the security of cryptographic algorithms generated by a new technique called Adversarial Neural Cryptography (ANC). Using the proposed network, we show limitations and directions to improve the current approach of ANC. Training the proposed Artificial Neural Network with the improved model of ANC, we show that artificially intelligent agents can learn the unbreakable One-Time Pad (OTP) algorithm, without human knowledge, to communicate securely through an insecure communication channel. This paper shows in which conditions an AI agent can learn a secure encryption scheme. However, it also shows that, without a stronger adversary, it is more likely to obtain an insecure one. PMID:29695066

Learning Perfectly Secure Cryptography to Protect Communications with Adversarial Neural Cryptography.

PubMed

Coutinho, Murilo; de Oliveira Albuquerque, Robson; Borges, Fábio; García Villalba, Luis Javier; Kim, Tai-Hoon

2018-04-24

Researches in Artificial Intelligence (AI) have achieved many important breakthroughs, especially in recent years. In some cases, AI learns alone from scratch and performs human tasks faster and better than humans. With the recent advances in AI, it is natural to wonder whether Artificial Neural Networks will be used to successfully create or break cryptographic algorithms. Bibliographic review shows the main approach to this problem have been addressed throughout complex Neural Networks, but without understanding or proving the security of the generated model. This paper presents an analysis of the security of cryptographic algorithms generated by a new technique called Adversarial Neural Cryptography (ANC). Using the proposed network, we show limitations and directions to improve the current approach of ANC. Training the proposed Artificial Neural Network with the improved model of ANC, we show that artificially intelligent agents can learn the unbreakable One-Time Pad (OTP) algorithm, without human knowledge, to communicate securely through an insecure communication channel. This paper shows in which conditions an AI agent can learn a secure encryption scheme. However, it also shows that, without a stronger adversary, it is more likely to obtain an insecure one.

From neural-based object recognition toward microelectronic eyes

NASA Technical Reports Server (NTRS)

Sheu, Bing J.; Bang, Sa Hyun

1994-01-01

Engineering neural network systems are best known for their abilities to adapt to the changing characteristics of the surrounding environment by adjusting system parameter values during the learning process. Rapid advances in analog current-mode design techniques have made possible the implementation of major neural network functions in custom VLSI chips. An electrically programmable analog synapse cell with large dynamic range can be realized in a compact silicon area. New designs of the synapse cells, neurons, and analog processor are presented. A synapse cell based on Gilbert multiplier structure can perform the linear multiplication for back-propagation networks. A double differential-pair synapse cell can perform the Gaussian function for radial-basis network. The synapse cells can be biased in the strong inversion region for high-speed operation or biased in the subthreshold region for low-power operation. The voltage gain of the sigmoid-function neurons is externally adjustable which greatly facilitates the search of optimal solutions in certain networks. Various building blocks can be intelligently connected to form useful industrial applications. Efficient data communication is a key system-level design issue for large-scale networks. We also present analog neural processors based on perceptron architecture and Hopfield network for communication applications. Biologically inspired neural networks have played an important role towards the creation of powerful intelligent machines. Accuracy, limitations, and prospects of analog current-mode design of the biologically inspired vision processing chips and cellular neural network chips are key design issues.

Instrumentation for Scientific Computing in Neural Networks, Information Science, Artificial Intelligence, and Applied Mathematics.

DTIC Science & Technology

1987-10-01

include Security Classification) Instrumentation for scientific computing in neural networks, information science, artificial intelligence, and...instrumentation grant to purchase equipment for support of research in neural networks, information science, artificail intellignece , and applied mathematics...in Neural Networks, Information Science, Artificial Intelligence, and Applied Mathematics Contract AFOSR 86-0282 Principal Investigator: Stephen

A performance analysis of advanced I/O architectures for PC-based network file servers

NASA Astrophysics Data System (ADS)

Huynh, K. D.; Khoshgoftaar, T. M.

1994-12-01

In the personal computing and workstation environments, more and more I/O adapters are becoming complete functional subsystems that are intelligent enough to handle I/O operations on their own without much intervention from the host processor. The IBM Subsystem Control Block (SCB) architecture has been defined to enhance the potential of these intelligent adapters by defining services and conventions that deliver command information and data to and from the adapters. In recent years, a new storage architecture, the Redundant Array of Independent Disks (RAID), has been quickly gaining acceptance in the world of computing. In this paper, we would like to discuss critical system design issues that are important to the performance of a network file server. We then present a performance analysis of the SCB architecture and disk array technology in typical network file server environments based on personal computers (PCs). One of the key issues investigated in this paper is whether a disk array can outperform a group of disks (of same type, same data capacity, and same cost) operating independently, not in parallel as in a disk array.

Energy-efficient hierarchical processing in the network of wireless intelligent sensors (WISE)

NASA Astrophysics Data System (ADS)

Raskovic, Dejan

Sensor network nodes have benefited from technological advances in the field of wireless communication, processing, and power sources. However, the processing power of microcontrollers is often not sufficient to perform sophisticated processing, while the power requirements of digital signal processing boards or handheld computers are usually too demanding for prolonged system use. We are matching the intrinsic hierarchical nature of many digital signal-processing applications with the natural hierarchy in distributed wireless networks, and building the hierarchical system of wireless intelligent sensors. Our goal is to build a system that will exploit the hierarchical organization to optimize the power consumption and extend battery life for the given time and memory constraints, while providing real-time processing of sensor signals. In addition, we are designing our system to be able to adapt to the current state of the environment, by dynamically changing the algorithm through procedure replacement. This dissertation presents the analysis of hierarchical environment and methods for energy profiling used to evaluate different system design strategies, and to optimize time-effective and energy-efficient processing.

Human intelligence and brain networks

PubMed Central

Colom, Roberto; Karama, Sherif; Jung, Rex E.; Haier, Richard J.

2010-01-01

Intelligence can be defined as a general mental ability for reasoning, problem solving, and learning. Because of its general nature, intelligence integrates cognitive functions such as perception, attention, memory, language, or planning. On the basis of this definition, intelligence can be reliably measured by standardized tests with obtained scores predicting several broad social outcomes such as educational achievement, job performance, health, and longevity. A detailed understanding of the brain mechanisms underlying this general mental ability could provide significant individual and societal benefits. Structural and functional neuroimaging studies have generally supported a frontoparietal network relevant for intelligence. This same network has also been found to underlie cognitive functions related to perception, short-term memory storage, and language. The distributed nature of this network and its involvement in a wide range of cognitive functions fits well with the integrative nature of intelligence. A new key phase of research is beginning to investigate how functional networks relate to structural networks, with emphasis on how distributed brain areas communicate with each other. PMID:21319494

A review of evidence of health benefit from artificial neural networks in medical intervention.

PubMed

Lisboa, P J G

2002-01-01

The purpose of this review is to assess the evidence of healthcare benefits involving the application of artificial neural networks to the clinical functions of diagnosis, prognosis and survival analysis, in the medical domains of oncology, critical care and cardiovascular medicine. The primary source of publications is PUBMED listings under Randomised Controlled Trials and Clinical Trials. The rĵle of neural networks is introduced within the context of advances in medical decision support arising from parallel developments in statistics and artificial intelligence. This is followed by a survey of published Randomised Controlled Trials and Clinical Trials, leading to recommendations for good practice in the design and evaluation of neural networks for use in medical intervention.

A Risk Based Approach to Node Insertion Within Social Networks

DTIC Science & Technology

2015-03-26

changes to enemy networks, tactical involvement must evolve, beginning with the intelligent use of network infiltration through the application of the...counterterrorism begins with the intelligent use of network infiltration, or the covert insertion of assets into a network, otherwise known as node insertion. The...Federal Bureau of Intelligence (FBI) defines an undercover operation as “an investigation involving a series of related undercover activities over a

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

Common neural correlates of intertemporal choices and intelligence in adolescents.

PubMed

Ripke, Stephan; Hübner, Thomas; Mennigen, Eva; Müller, Kathrin U; Li, Shu-Chen; Smolka, Michael N

2015-02-01

Converging behavioral evidence indicates that temporal discounting, measured by intertemporal choice tasks, is inversely related to intelligence. At the neural level, the parieto-frontal network is pivotal for complex, higher-order cognitive processes. Relatedly, underrecruitment of the pFC during a working memory task has been found to be associated with steeper temporal discounting. Furthermore, this network has also been shown to be related to the consistency of intertemporal choices. Here we report an fMRI study that directly investigated the association of neural correlates of intertemporal choice behavior with intelligence in an adolescent sample (n = 206; age 13.7-15.5 years). After identifying brain regions where the BOLD response during intertemporal choice was correlated with individual differences in intelligence, we further tested whether BOLD responses in these areas would mediate the associations between intelligence, the discounting rate, and choice consistency. We found positive correlations between BOLD response in a value-independent decision network (i.e., dorsolateral pFC, precuneus, and occipital areas) and intelligence. Furthermore, BOLD response in a value-dependent decision network (i.e., perigenual ACC, inferior frontal gyrus, ventromedial pFC, ventral striatum) was positively correlated with intelligence. The mediation analysis revealed that BOLD responses in the value-independent network mediated the association between intelligence and choice consistency, whereas BOLD responses in the value-dependent network mediated the association between intelligence and the discounting rate. In summary, our findings provide evidence for common neural correlates of intertemporal choice and intelligence, possibly linked by valuation as well as executive functions.

Brain Networks for Working Memory and Factors of Intelligence Assessed in Males and Females with fMRI and DTI

ERIC Educational Resources Information Center

Tang, C. Y.; Eaves, E. L.; Ng, J. C.; Carpenter, D. M.; Mai, X.; Schroeder, D. H.; Condon, C. A.; Colom, R.; Haier, R. J.

2010-01-01

Neuro-imaging studies of intelligence implicate the importance of a parietal-frontal network. One unresolved issue is whether this network underlies a general factor of intelligence ("g") or other specific cognitive factors. A second unresolved issue is whether males and females use different parts of this network. Here we obtained intelligence…

A Deeper Level of Network Intelligence: Combating Cyber Warfare

DTIC Science & Technology

2010-04-01

A Deeper Level of Network Intelligence: Combating Cyber Warfare This information is provided for your review only and is not for any distribution...A Deeper Level of Network Intelligence: Combating Cyber Warfare 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d

2nd & 3rd Generation Vehicle Subsystems

NASA Technical Reports Server (NTRS)

2000-01-01

This paper contains viewgraph presentation on the "2nd & 3rd Generation Vehicle Subsystems" project. The objective behind this project is to design, develop and test advanced avionics, power systems, power control and distribution components and subsystems for insertion into a highly reliable and low-cost system for a Reusable Launch Vehicles (RLV). The project is divided into two sections: 3rd Generation Vehicle Subsystems and 2nd Generation Vehicle Subsystems. The following topics are discussed under the first section, 3rd Generation Vehicle Subsystems: supporting the NASA RLV program; high-performance guidance & control adaptation for future RLVs; Evolvable Hardware (EHW) for 3rd generation avionics description; Scaleable, Fault-tolerant Intelligent Network or X(trans)ducers (SFINIX); advance electric actuation devices and subsystem technology; hybrid power sources and regeneration technology for electric actuators; and intelligent internal thermal control. Topics discussed in the 2nd Generation Vehicle Subsystems program include: design, development and test of a robust, low-maintenance avionics with no active cooling requirements and autonomous rendezvous and docking systems; design and development of a low maintenance, high reliability, intelligent power systems (fuel cells and battery); and design of a low cost, low maintenance high horsepower actuation systems (actuators).

Novelty Detection in and Between Different Modalities

NASA Astrophysics Data System (ADS)

Veflingstad, Henning; Yildirim, Sule

2008-01-01

Our general aim is to reflect the advances in artificial intelligence and cognitive science fields to space exploration studies such that next generation space rovers can benefit from these advances. We believe next generation space rovers can benefit from the studies related to employing conceptual representations in generating structured thought. This way, rovers need not be equipped with all necessary steps of an action plan to execute in space exploration but they can autonomously form representations of their world and reason on them to make intelligent decision. As part of this approach, autonomous novelty detection is an important feature of next generation space rovers. This feature allows a rover to make further decisions about exploring a rock sample more closely or not and on its own. This way, a rover will use less of its time for communication between the earth and itself and more of its time for achieving its assigned tasks in space. In this paper, we propose an artificial neural network based novelty detection mechanism that next generation space rovers can employ as part of their intelligence. We also present an implementation of such a mechanism and present its reliability in detecting novelty.

Non-obvious correlations to disease management unraveled by Bayesian artificial intelligence analyses of CMS data.

PubMed

Vemulapalli, Vijetha; Qu, Jiaqi; Garren, Jeonifer M; Rodrigues, Leonardo O; Kiebish, Michael A; Sarangarajan, Rangaprasad; Narain, Niven R; Akmaev, Viatcheslav R

2016-11-01

Given the availability of extensive digitized healthcare data from medical records, claims and prescription information, it is now possible to use hypothesis-free, data-driven approaches to mine medical databases for novel insight. The goal of this analysis was to demonstrate the use of artificial intelligence based methods such as Bayesian networks to open up opportunities for creation of new knowledge in management of chronic conditions. Hospital level Medicare claims data containing discharge numbers for most common diagnoses were analyzed in a hypothesis-free manner using Bayesian networks learning methodology. While many interactions identified between discharge rates of diagnoses using this data set are supported by current medical knowledge, a novel interaction linking asthma and renal failure was discovered. This interaction is non-obvious and had not been looked at by the research and clinical communities in epidemiological or clinical data. A plausible pharmacological explanation of this link is proposed together with a verification of the risk significance by conventional statistical analysis. Potential clinical and molecular pathways defining the relationship between commonly used asthma medications and renal disease are discussed. The study underscores the need for further epidemiological research to validate this novel hypothesis. Validation will lead to advancement in clinical treatment of asthma & bronchitis, thereby, improving patient outcomes and leading to long term cost savings. In summary, this study demonstrates that application of advanced artificial intelligence methods in healthcare has the potential to enhance the quality of care by discovering non-obvious, clinically relevant relationships and enabling timely care intervention. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Verification and Validation of Adaptive and Intelligent Systems with Flight Test Results

NASA Technical Reports Server (NTRS)

Burken, John J.; Larson, Richard R.

2009-01-01

F-15 IFCS project goals are: a) Demonstrate Control Approaches that can Efficiently Optimize Aircraft Performance in both Normal and Failure Conditions [A] & [B] failures. b) Advance Neural Network-Based Flight Control Technology for New Aerospace Systems Designs with a Pilot in the Loop. Gen II objectives include; a) Implement and Fly a Direct Adaptive Neural Network Based Flight Controller; b) Demonstrate the Ability of the System to Adapt to Simulated System Failures: 1) Suppress Transients Associated with Failure; 2) Re-Establish Sufficient Control and Handling of Vehicle for Safe Recovery. c) Provide Flight Experience for Development of Verification and Validation Processes for Flight Critical Neural Network Software.

General, crystallized and fluid intelligence are not associated with functional global network efficiency: A replication study with the human connectome project 1200 data set.

PubMed

Kruschwitz, J D; Waller, L; Daedelow, L S; Walter, H; Veer, I M

2018-05-01

One hallmark example of a link between global topological network properties of complex functional brain connectivity and cognitive performance is the finding that general intelligence may depend on the efficiency of the brain's intrinsic functional network architecture. However, although this association has been featured prominently over the course of the last decade, the empirical basis for this broad association of general intelligence and global functional network efficiency is quite limited. In the current study, we set out to replicate the previously reported association between general intelligence and global functional network efficiency using the large sample size and high quality data of the Human Connectome Project, and extended the original study by testing for separate association of crystallized and fluid intelligence with global efficiency, characteristic path length, and global clustering coefficient. We were unable to provide evidence for the proposed association between general intelligence and functional brain network efficiency, as was demonstrated by van den Heuvel et al. (2009), or for any other association with the global network measures employed. More specifically, across multiple network definition schemes, ranging from voxel-level networks to networks of only 100 nodes, no robust associations and only very weak non-significant effects with a maximal R 2 of 0.01 could be observed. Notably, the strongest (non-significant) effects were observed in voxel-level networks. We discuss the possibility that the low power of previous studies and publication bias may have led to false positive results fostering the widely accepted notion of general intelligence being associated to functional global network efficiency. Copyright © 2018 Elsevier Inc. All rights reserved.

Distributed Problem Solving: Adaptive Networks with a Computer Intermediary Resource. Intelligent Executive Computer Communication

DTIC Science & Technology

1991-06-01

Proceedings of The National Conference on Artificial Intelligence , pages 181-184, The American Association for Aritificial Intelligence , Pittsburgh...Intermediary Resource: Intelligent Executive Computer Communication John Lyman and Carla J. Conaway University of California at Los Angeles for Contracting...Include Security Classification) Interim Report: Distributed Problem Solving: Adaptive Networks With a Computer Intermediary Resource: Intelligent

Advanced Twisted Pair Cables for Distributed Local Area Networks in Intelligent Structure Systems

NASA Astrophysics Data System (ADS)

Semenov, Andrey

2018-03-01

The possibility of a significant increase in the length of cable communication channels of local area networks of automation and engineering support systems of buildings in the case of their implementation on balanced twisted pair cables is shown. Assuming a direct connection scheme and an effective speed of 100 Mbit/s, analytical relationships are obtained for the calculation of the maximum communication distance. The necessity of using in the linear part of such systems of twisted pair cables with U/UTP structure and interference parameters at the level of category 5e is grounded.

Synthetic biology routes to bio-artificial intelligence

PubMed Central

Zaikin, Alexey; Saka, Yasushi; Romano, M. Carmen; Giuraniuc, Claudiu V.; Kanakov, Oleg; Laptyeva, Tetyana

2016-01-01

The design of synthetic gene networks (SGNs) has advanced to the extent that novel genetic circuits are now being tested for their ability to recapitulate archetypal learning behaviours first defined in the fields of machine and animal learning. Here, we discuss the biological implementation of a perceptron algorithm for linear classification of input data. An expansion of this biological design that encompasses cellular ‘teachers’ and ‘students’ is also examined. We also discuss implementation of Pavlovian associative learning using SGNs and present an example of such a scheme and in silico simulation of its performance. In addition to designed SGNs, we also consider the option to establish conditions in which a population of SGNs can evolve diversity in order to better contend with complex input data. Finally, we compare recent ethical concerns in the field of artificial intelligence (AI) and the future challenges raised by bio-artificial intelligence (BI). PMID:27903825

Intelligent Sensing and Classification in DSR-Based Ad Hoc Networks

NASA Astrophysics Data System (ADS)

Dempsey, Tae; Sahin, Gokhan; Morton, Yu T. (Jade

Wireless ad hoc networks have fundamentally altered today's battlefield, with applications ranging from unmanned air vehicles to randomly deployed sensor networks. Security and vulnerabilities in wireless ad hoc networks have been considered at different layers, and many attack strategies have been proposed, including denial of service (DoS) through the intelligent jamming of the most critical packet types of flows in a network. This paper investigates the effectiveness of intelligent jamming in wireless ad hoc networks using the Dynamic Source Routing (DSR) and TCP protocols and introduces an intelligent classifier to facilitate the jamming of such networks. Assuming encrypted packet headers and contents, our classifier is based solely on the observable characteristics of size, inter-arrival timing, and direction and classifies packets with up to 99.4% accuracy in our experiments.

User Needs and Advances in Space Wireless Sensing and Communications

NASA Technical Reports Server (NTRS)

Kegege, Obadiah

2017-01-01

Decades of space exploration and technology trends for future missions show the need for new approaches in space/planetary sensor networks, observatories, internetworking, and communications/data delivery to Earth. The User Needs to be discussed in this talk includes interviews with several scientists and reviews of mission concepts for the next generation of sensors, observatories, and planetary surface missions. These observatories, sensors are envisioned to operate in extreme environments, with advanced autonomy, whereby sometimes communication to Earth is intermittent and delayed. These sensor nodes require software defined networking capabilities in order to learn and adapt to the environment, collect science data, internetwork, and communicate. Also, some user cases require the level of intelligence to manage network functions (either as a host), mobility, security, and interface data to the physical radio/optical layer. For instance, on a planetary surface, autonomous sensor nodes would create their own ad-hoc network, with some nodes handling communication capabilities between the wireless sensor networks and orbiting relay satellites. A section of this talk will cover the advances in space communication and internetworking to support future space missions. NASA's Space Communications and Navigation (SCaN) program continues to evolve with the development of optical communication, a new vision of the integrated network architecture with more capabilities, and the adoption of CCSDS space internetworking protocols. Advances in wireless communications hardware and electronics have enabled software defined networking (DVB-S2, VCM, ACM, DTN, Ad hoc, etc.) protocols for improved wireless communication and network management. Developing technologies to fulfil these user needs for wireless communications and adoption of standardized communication/internetworking protocols will be a huge benefit to future planetary missions, space observatories, and manned missions to other planets.

Emotional intelligence skills for maintaining social networks in healthcare organizations.

PubMed

Freshman, Brenda; Rubino, Louis

2004-01-01

For healthcare organizations to survive in these increasingly challenging times, leadership and management must face mounting interpersonal concerns. The authors present the boundaries of internal and external social networks with respect to leadership and managerial functions: Social networks within the organization are stretched by reductions in available resources and structural ambiguity, whereas external social networks are stressed by interorganizational competitive pressures. The authors present the development of emotional intelligence skills in employees as a strategic training objective that can strengthen the internal and external social networks of healthcare organizations. The authors delineate the unique functions of leadership and management with respect to the application of emotional intelligence skills and discuss training and future research implications for emotional intelligence skill sets and social networks.

Evaluating the Effectiveness of IP Hopping via an Address Routing Gateway

DTIC Science & Technology

2013-03-01

37 DARPA Defense Advanced Research Projects Agency . . . . . . . . . . . . . . . . . . . . . . . . . . 20 DHCP Dynamic Host...Protocol ( DHCP ) to force the changes. Through the use of a slightly intelligent DHCP server that leases IPs for a only a short time frame (on the order of...tens of minutes) and only offers IPs that have not been used recently, most networks already using DHCP can quickly change to a randomized scheme. This

A generic flexible and robust approach for intelligent real-time video-surveillance systems

NASA Astrophysics Data System (ADS)

Desurmont, Xavier; Delaigle, Jean-Francois; Bastide, Arnaud; Macq, Benoit

2004-05-01

In this article we present a generic, flexible and robust approach for an intelligent real-time video-surveillance system. A previous version of the system was presented in [1]. The goal of these advanced tools is to provide help to operators by detecting events of interest in visual scenes and highlighting alarms and compute statistics. The proposed system is a multi-camera platform able to handle different standards of video inputs (composite, IP, IEEE1394 ) and which can basically compress (MPEG4), store and display them. This platform also integrates advanced video analysis tools, such as motion detection, segmentation, tracking and interpretation. The design of the architecture is optimised to playback, display, and process video flows in an efficient way for video-surveillance application. The implementation is distributed on a scalable computer cluster based on Linux and IP network. It relies on POSIX threads for multitasking scheduling. Data flows are transmitted between the different modules using multicast technology and under control of a TCP-based command network (e.g. for bandwidth occupation control). We report here some results and we show the potential use of such a flexible system in third generation video surveillance system. We illustrate the interest of the system in a real case study, which is the indoor surveillance.

77 FR 3544 - Meeting and Webinar on the Active Traffic and Demand Management and Intelligent Network Flow...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-01-24

... Intelligent Network Flow Optimization Operational Concepts; Notice of Public Meeting AGENCY: Research and... Demand Management (ADTM) and Intelligent Network Flow Optimization (INFLO) operational concepts. The ADTM... February 8, 2012, 8:30 to 4:30 p.m. The location for both meetings is the Hall of States, 444 North Capitol...

GLOBECOM '85 - Global Telecommunications Conference, New Orleans, LA, December 2-5, 1985, Conference Record. Volumes 1, 2, & 3

NASA Astrophysics Data System (ADS)

Various papers on global telecommunications are presented. The general topics addressed include: multiservice integration with optical fibers, multicompany owned telecommunication networks, softworks quality and reliability, advanced on-board processing, impact of new services and systems on operations and maintenance, analytical studies of protocols for data communication networks, topics in packet radio networking, CCITT No. 7 to support new services, document processing and communication, antenna technology and system aspects in satellite communications. Also considered are: communication systems modelling methodology, experimental integrated local area voice/data nets, spread spectrum communications, motion video at the DS-0 rate, optical and data communications, intelligent work stations, switch performance analysis, novel radio communication systems, wireless local networks, ISDN services, LAN communication protocols, user-system interface, radio propagation and performance, mobile satellite system, software for computer networks, VLSI for ISDN terminals, quality management, man-machine interfaces in switching, and local area network performance.

Improving Pattern Recognition and Neural Network Algorithms with Applications to Solar Panel Energy Optimization

NASA Astrophysics Data System (ADS)

Zamora Ramos, Ernesto

Artificial Intelligence is a big part of automation and with today's technological advances, artificial intelligence has taken great strides towards positioning itself as the technology of the future to control, enhance and perfect automation. Computer vision includes pattern recognition and classification and machine learning. Computer vision is at the core of decision making and it is a vast and fruitful branch of artificial intelligence. In this work, we expose novel algorithms and techniques built upon existing technologies to improve pattern recognition and neural network training, initially motivated by a multidisciplinary effort to build a robot that helps maintain and optimize solar panel energy production. Our contributions detail an improved non-linear pre-processing technique to enhance poorly illuminated images based on modifications to the standard histogram equalization for an image. While the original motivation was to improve nocturnal navigation, the results have applications in surveillance, search and rescue, medical imaging enhancing, and many others. We created a vision system for precise camera distance positioning motivated to correctly locate the robot for capture of solar panel images for classification. The classification algorithm marks solar panels as clean or dirty for later processing. Our algorithm extends past image classification and, based on historical and experimental data, it identifies the optimal moment in which to perform maintenance on marked solar panels as to minimize the energy and profit loss. In order to improve upon the classification algorithm, we delved into feedforward neural networks because of their recent advancements, proven universal approximation and classification capabilities, and excellent recognition rates. We explore state-of-the-art neural network training techniques offering pointers and insights, culminating on the implementation of a complete library with support for modern deep learning architectures, multilayer percepterons and convolutional neural networks. Our research with neural networks has encountered a great deal of difficulties regarding hyperparameter estimation for good training convergence rate and accuracy. Most hyperparameters, including architecture, learning rate, regularization, trainable parameters (or weights) initialization, and so on, are chosen via a trial and error process with some educated guesses. However, we developed the first quantitative method to compare weight initialization strategies, a critical hyperparameter choice during training, to estimate among a group of candidate strategies which would make the network converge to the highest classification accuracy faster with high probability. Our method provides a quick, objective measure to compare initialization strategies to select the best possible among them beforehand without having to complete multiple training sessions for each candidate strategy to compare final results.

Deep learning and the electronic structure problem

NASA Astrophysics Data System (ADS)

Mills, Kyle; Spanner, Michael; Tamblyn, Isaac

In the past decade, the fields of artificial intelligence and computer vision have progressed remarkably. Supported by the enthusiasm of large tech companies, as well as significant hardware advances and the utilization of graphical processing units to accelerate computations, deep neural networks (DNN) are gaining momentum as a robust choice for many diverse machine learning applications. We have demonstrated the ability of a DNN to solve a quantum mechanical eigenvalue equation directly, without the need to compute a wavefunction, and without knowledge of the underlying physics. We have trained a convolutional neural network to predict the total energy of an electron in a confining, 2-dimensional electrostatic potential. We numerically solved the one-electron Schrödinger equation for millions of electrostatic potentials, and used this as training data for our neural network. Four classes of potentials were assessed: the canonical cases of the harmonic oscillator and infinite well, and two types of randomly generated potentials for which no analytic solution is known. We compare the performance of the neural network and consider how these results could lead to future advances in electronic structure theory.

Passive and Active Analysis in DSR-Based Ad Hoc Networks

NASA Astrophysics Data System (ADS)

Dempsey, Tae; Sahin, Gokhan; Morton, Y. T. (Jade)

Security and vulnerabilities in wireless ad hoc networks have been considered at different layers, and many attack strategies have been proposed, including denial of service (DoS) through the intelligent jamming of the most critical packet types of flows in a network. This paper investigates the effectiveness of intelligent jamming in wireless ad hoc networks using the Dynamic Source Routing (DSR) and TCP protocols and introduces an intelligent classifier to facilitate the jamming of such networks. Assuming encrypted packet headers and contents, our classifier is based solely on the observable characteristics of size, inter-arrival timing, and direction and classifies packets with up to 99.4% accuracy in our experiments. Furthermore, we investigate active analysis, which is the combination of a classifier and intelligent jammer to invoke specific responses from a victim network.

Intelligent systems technology infrastructure for integrated systems

NASA Technical Reports Server (NTRS)

Lum, Henry, Jr.

1991-01-01

Significant advances have occurred during the last decade in intelligent systems technologies (a.k.a. knowledge-based systems, KBS) including research, feasibility demonstrations, and technology implementations in operational environments. Evaluation and simulation data obtained to date in real-time operational environments suggest that cost-effective utilization of intelligent systems technologies can be realized for Automated Rendezvous and Capture applications. The successful implementation of these technologies involve a complex system infrastructure integrating the requirements of transportation, vehicle checkout and health management, and communication systems without compromise to systems reliability and performance. The resources that must be invoked to accomplish these tasks include remote ground operations and control, built-in system fault management and control, and intelligent robotics. To ensure long-term evolution and integration of new validated technologies over the lifetime of the vehicle, system interfaces must also be addressed and integrated into the overall system interface requirements. An approach for defining and evaluating the system infrastructures including the testbed currently being used to support the on-going evaluations for the evolutionary Space Station Freedom Data Management System is presented and discussed. Intelligent system technologies discussed include artificial intelligence (real-time replanning and scheduling), high performance computational elements (parallel processors, photonic processors, and neural networks), real-time fault management and control, and system software development tools for rapid prototyping capabilities.

Distributed neural system for emotional intelligence revealed by lesion mapping.

PubMed

Barbey, Aron K; Colom, Roberto; Grafman, Jordan

2014-03-01

Cognitive neuroscience has made considerable progress in understanding the neural architecture of human intelligence, identifying a broadly distributed network of frontal and parietal regions that support goal-directed, intelligent behavior. However, the contributions of this network to social and emotional aspects of intellectual function remain to be well characterized. Here we investigated the neural basis of emotional intelligence in 152 patients with focal brain injuries using voxel-based lesion-symptom mapping. Latent variable modeling was applied to obtain measures of emotional intelligence, general intelligence and personality from the Mayer, Salovey, Caruso Emotional Intelligence Test (MSCEIT), the Wechsler Adult Intelligence Scale and the Neuroticism-Extroversion-Openness Inventory, respectively. Regression analyses revealed that latent scores for measures of general intelligence and personality reliably predicted latent scores for emotional intelligence. Lesion mapping results further indicated that these convergent processes depend on a shared network of frontal, temporal and parietal brain regions. The results support an integrative framework for understanding the architecture of executive, social and emotional processes and make specific recommendations for the interpretation and application of the MSCEIT to the study of emotional intelligence in health and disease.

Distributed neural system for emotional intelligence revealed by lesion mapping

PubMed Central

Colom, Roberto; Grafman, Jordan

2014-01-01

Cognitive neuroscience has made considerable progress in understanding the neural architecture of human intelligence, identifying a broadly distributed network of frontal and parietal regions that support goal-directed, intelligent behavior. However, the contributions of this network to social and emotional aspects of intellectual function remain to be well characterized. Here we investigated the neural basis of emotional intelligence in 152 patients with focal brain injuries using voxel-based lesion-symptom mapping. Latent variable modeling was applied to obtain measures of emotional intelligence, general intelligence and personality from the Mayer, Salovey, Caruso Emotional Intelligence Test (MSCEIT), the Wechsler Adult Intelligence Scale and the Neuroticism-Extroversion-Openness Inventory, respectively. Regression analyses revealed that latent scores for measures of general intelligence and personality reliably predicted latent scores for emotional intelligence. Lesion mapping results further indicated that these convergent processes depend on a shared network of frontal, temporal and parietal brain regions. The results support an integrative framework for understanding the architecture of executive, social and emotional processes and make specific recommendations for the interpretation and application of the MSCEIT to the study of emotional intelligence in health and disease. PMID:23171618

Predictive models in urology.

PubMed

Cestari, Andrea

2013-01-01

Predictive modeling is emerging as an important knowledge-based technology in healthcare. The interest in the use of predictive modeling reflects advances on different fronts such as the availability of health information from increasingly complex databases and electronic health records, a better understanding of causal or statistical predictors of health, disease processes and multifactorial models of ill-health and developments in nonlinear computer models using artificial intelligence or neural networks. These new computer-based forms of modeling are increasingly able to establish technical credibility in clinical contexts. The current state of knowledge is still quite young in understanding the likely future direction of how this so-called 'machine intelligence' will evolve and therefore how current relatively sophisticated predictive models will evolve in response to improvements in technology, which is advancing along a wide front. Predictive models in urology are gaining progressive popularity not only for academic and scientific purposes but also into the clinical practice with the introduction of several nomograms dealing with the main fields of onco-urology.

The paradox of intelligence: Heritability and malleability coexist in hidden gene-environment interplay.

PubMed

Sauce, Bruno; Matzel, Louis D

2018-01-01

Intelligence can have an extremely high heritability, but also be malleable; a paradox that has been the source of continuous controversy. Here we attempt to clarify the issue, and advance a frequently overlooked solution to the paradox: Intelligence is a trait with unusual properties that create a large reservoir of hidden gene-environment (GE) networks, allowing for the contribution of high genetic and environmental influences on individual differences in IQ. GE interplay is difficult to specify with current methods, and is underestimated in standard metrics of heritability (thus inflating estimates of "genetic" effects). We describe empirical evidence for GE interplay in intelligence, with malleability existing on top of heritability. The evidence covers cognitive gains consequent to adoption/immigration, changes in IQ's heritability across life span and socioeconomic status, gains in IQ over time consequent to societal development (the Flynn effect), the slowdown of age-related cognitive decline, and the gains in intelligence from early education. The GE solution has novel implications for enduring problems, including our inability to identify intelligence-related genes (also known as IQ's "missing heritability"), and the loss of initial benefits from early intervention programs (such as "Head Start"). The GE solution can be a powerful guide to future research, and may also aid policies to overcome barriers to the development of intelligence, particularly in impoverished and underprivileged populations. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

ASI aurora search: an attempt of intelligent image processing for circular fisheye lens.

PubMed

Yang, Xi; Gao, Xinbo; Song, Bin; Wang, Nannan; Yang, Dong

2018-04-02

The circular fisheye lens exhibits an approximately 180° angular field-of-view (FOV), which is much larger than that of an ordinary lens. Thus, images captured with a circular fisheye lens are distributed non-uniformly with spherical deformation. Along with the fast development of deep neural networks for normal images, how to apply it to achieve intelligent image processing for a circular fisheye lens is a new task of significant importance. In this paper, we take the aurora images captured with all-sky-imagers (ASI) as a typical example. By analyzing the imaging principle of ASI and the magnetic characteristics of the aurora, a deformed region division (DRD) scheme is proposed to replace the region proposals network (RPN) in the advanced mask regional convolutional neural network (Mask R-CNN) framework. Thus, each image can be regarded as a "bag" of deformed regions represented with CNN features. After clustering all CNN features to generate a vocabulary, each deformed region is quantified to its nearest center for indexing. On the stage of an online search, a similarity score is computed by measuring the distances between regions in the query image and all regions in the data set, and the image with the highest value is outputted as the top rank search result. Experimental results show that the proposed method greatly improves the search accuracy and efficiency, demonstrating that it is a valuable attempt of intelligent image processing for circular fisheye lenses.

Human Intelligence: An Introduction to Advances in Theory and Research.

ERIC Educational Resources Information Center

Lohman, David F.

1989-01-01

Recent advances in three research traditions are summarized: trait theories of intelligence, information-processing theories of intelligence, and general theories of thinking. Work on fluid and crystallized abilities by J. Horn and R. Snow, mental speed, spatial visualization, cognitive psychology, artificial intelligence, and the construct of…

A survey of tools and resources for the next generation analyst

NASA Astrophysics Data System (ADS)

Hall, David L.; Graham, Jake; Catherman, Emily

2015-05-01

We have previously argued that a combination of trends in information technology (IT) and changing habits of people using IT provide opportunities for the emergence of a new generation of analysts that can perform effective intelligence, surveillance and reconnaissance (ISR) on a "do it yourself" (DIY) or "armchair" approach (see D.L. Hall and J. Llinas (2014)). Key technology advances include: i) new sensing capabilities including the use of micro-scale sensors and ad hoc deployment platforms such as commercial drones, ii) advanced computing capabilities in mobile devices that allow advanced signal and image processing and modeling, iii) intelligent interconnections due to advances in "web N" capabilities, and iv) global interconnectivity and increasing bandwidth. In addition, the changing habits of the digital natives reflect new ways of collecting and reporting information, sharing information, and collaborating in dynamic teams. This paper provides a survey and assessment of tools and resources to support this emerging analysis approach. The tools range from large-scale commercial tools such as IBM i2 Analyst Notebook, Palantir, and GeoSuite to emerging open source tools such as GeoViz and DECIDE from university research centers. The tools include geospatial visualization tools, social network analysis tools and decision aids. A summary of tools is provided along with links to web sites for tool access.

Intelligence is associated with the modular structure of intrinsic brain networks.

PubMed

Hilger, Kirsten; Ekman, Matthias; Fiebach, Christian J; Basten, Ulrike

2017-11-22

General intelligence is a psychological construct that captures in a single metric the overall level of behavioural and cognitive performance in an individual. While previous research has attempted to localise intelligence in circumscribed brain regions, more recent work focuses on functional interactions between regions. However, even though brain networks are characterised by substantial modularity, it is unclear whether and how the brain's modular organisation is associated with general intelligence. Modelling subject-specific brain network graphs from functional MRI resting-state data (N = 309), we found that intelligence was not associated with global modularity features (e.g., number or size of modules) or the whole-brain proportions of different node types (e.g., connector hubs or provincial hubs). In contrast, we observed characteristic associations between intelligence and node-specific measures of within- and between-module connectivity, particularly in frontal and parietal brain regions that have previously been linked to intelligence. We propose that the connectivity profile of these regions may shape intelligence-relevant aspects of information processing. Our data demonstrate that not only region-specific differences in brain structure and function, but also the network-topological embedding of fronto-parietal as well as other cortical and subcortical brain regions is related to individual differences in higher cognitive abilities, i.e., intelligence.

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.

A survey on investigating the need for intelligent power-aware load balanced routing protocols for handling critical links in MANETs.

PubMed

Sivakumar, B; Bhalaji, N; Sivakumar, D

2014-01-01

In mobile ad hoc networks connectivity is always an issue of concern. Due to dynamism in the behavior of mobile nodes, efficiency shall be achieved only with the assumption of good network infrastructure. Presence of critical links results in deterioration which should be detected in advance to retain the prevailing communication setup. This paper discusses a short survey on the specialized algorithms and protocols related to energy efficient load balancing for critical link detection in the recent literature. This paper also suggests a machine learning based hybrid power-aware approach for handling critical nodes via load balancing.

A Survey on Investigating the Need for Intelligent Power-Aware Load Balanced Routing Protocols for Handling Critical Links in MANETs

PubMed Central

Sivakumar, B.; Bhalaji, N.; Sivakumar, D.

2014-01-01

In mobile ad hoc networks connectivity is always an issue of concern. Due to dynamism in the behavior of mobile nodes, efficiency shall be achieved only with the assumption of good network infrastructure. Presence of critical links results in deterioration which should be detected in advance to retain the prevailing communication setup. This paper discusses a short survey on the specialized algorithms and protocols related to energy efficient load balancing for critical link detection in the recent literature. This paper also suggests a machine learning based hybrid power-aware approach for handling critical nodes via load balancing. PMID:24790546

An intelligent approach to welding robot selection

NASA Astrophysics Data System (ADS)

Milano, J.; Mauk, S. D.; Flitter, L.; Morris, R.

1993-10-01

In a shipyard where multiple stationary and mobile workcells are employed in the fabrication of components of complex sub-assemblies,efficient operation requires an intelligent method of scheduling jobs and selecting workcells based on optimum throughput and cost. The achievement of this global solution requires the successful organization of resource availability,process requirements,and process constraints. The Off-line Planner (OLP) of the Programmable Automated Weld Systemd (PAWS) is capable of advanced modeling of weld processes and environments as well as the generation of complete weld procedures. These capabilities involve the integration of advanced Computer Aided Design (CAD), path planning, and obstacle detection and avoidance techniques as well as the synthesis of complex design and process information. These existing capabilities provide the basis of the functionality required for the successful implementation of an intelligent weld robot selector and material flow planner. Current efforts are focused on robot selection via the dynamic routing of components to the appropriate work cells. It is proposed that this problem is a variant of the “Traveling Salesman Problem” (TSP) that has been proven to belong to a larger set of optimization problems termed nondeterministic polynomial complete (NP complete). In this paper, a heuristic approach utilizing recurrent neural networks is explored as a rapid means of producing a near optimal, if not optimal, bdweld robot selection.

Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence.

PubMed

Li, Yongcheng; Sun, Rong; Zhang, Bin; Wang, Yuechao; Li, Hongyi

2015-01-01

Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including 'random' and '4Q' (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the '4Q' cultures presented absolutely different activities, and the robot controlled by the '4Q' network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems.

Application of Hierarchical Dissociated Neural Network in Closed-Loop Hybrid System Integrating Biological and Mechanical Intelligence

PubMed Central

Zhang, Bin; Wang, Yuechao; Li, Hongyi

2015-01-01

Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including ‘random’ and ‘4Q’ (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the ‘4Q’ cultures presented absolutely different activities, and the robot controlled by the ‘4Q’ network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems. PMID:25992579

Unattended network operations technology assessment study. Technical support for defining advanced satellite systems concepts

NASA Technical Reports Server (NTRS)

Price, Kent M.; Holdridge, Mark; Odubiyi, Jide; Jaworski, Allan; Morgan, Herbert K.

1991-01-01

The results are summarized of an unattended network operations technology assessment study for the Space Exploration Initiative (SEI). The scope of the work included: (1) identified possible enhancements due to the proposed Mars communications network; (2) identified network operations on Mars; (3) performed a technology assessment of possible supporting technologies based on current and future approaches to network operations; and (4) developed a plan for the testing and development of these technologies. The most important results obtained are as follows: (1) addition of a third Mars Relay Satellite (MRS) and MRS cross link capabilities will enhance the network's fault tolerance capabilities through improved connectivity; (2) network functions can be divided into the six basic ISO network functional groups; (3) distributed artificial intelligence technologies will augment more traditional network management technologies to form the technological infrastructure of a virtually unattended network; and (4) a great effort is required to bring the current network technology levels for manned space communications up to the level needed for an automated fault tolerance Mars communications network.

Fluid and flexible minds: Intelligence reflects synchrony in the brain’s intrinsic network architecture

PubMed Central

Ferguson, Michael A.; Anderson, Jeffrey S.; Spreng, R. Nathan

2017-01-01

Human intelligence has been conceptualized as a complex system of dissociable cognitive processes, yet studies investigating the neural basis of intelligence have typically emphasized the contributions of discrete brain regions or, more recently, of specific networks of functionally connected regions. Here we take a broader, systems perspective in order to investigate whether intelligence is an emergent property of synchrony within the brain’s intrinsic network architecture. Using a large sample of resting-state fMRI and cognitive data (n = 830), we report that the synchrony of functional interactions within and across distributed brain networks reliably predicts fluid and flexible intellectual functioning. By adopting a whole-brain, systems-level approach, we were able to reliably predict individual differences in human intelligence by characterizing features of the brain’s intrinsic network architecture. These findings hold promise for the eventual development of neural markers to predict changes in intellectual function that are associated with neurodevelopment, normal aging, and brain disease.

The intelligent user interface for NASA's advanced information management systems

NASA Technical Reports Server (NTRS)

Campbell, William J.; Short, Nicholas, Jr.; Rolofs, Larry H.; Wattawa, Scott L.

1987-01-01

NASA has initiated the Intelligent Data Management Project to design and develop advanced information management systems. The project's primary goal is to formulate, design and develop advanced information systems that are capable of supporting the agency's future space research and operational information management needs. The first effort of the project was the development of a prototype Intelligent User Interface to an operational scientific database, using expert systems and natural language processing technologies. An overview of Intelligent User Interface formulation and development is given.

A Belief-Space Approach to Integrated Intelligence - Research Area 10.3: Intelligent Networks

DTIC Science & Technology

2017-12-05

A Belief-Space Approach to Integrated Intelligence- Research Area 10.3: Intelligent Networks The views , opinions and/or findings contained in this...high dimensionality and multi -modality of their hybrid configuration spaces. Planners that perform a purely geometric search are prohibitively slow...Hamburg, January Paper Title: Hierarchical planning for multi -contact non-prehensile manipulation Publication Type: Conference Paper or Presentation

Smart-Pixel Array Processors Based on Optimal Cellular Neural Networks for Space Sensor Applications

NASA Technical Reports Server (NTRS)

Fang, Wai-Chi; Sheu, Bing J.; Venus, Holger; Sandau, Rainer

1997-01-01

A smart-pixel cellular neural network (CNN) with hardware annealing capability, digitally programmable synaptic weights, and multisensor parallel interface has been under development for advanced space sensor applications. The smart-pixel CNN architecture is a programmable multi-dimensional array of optoelectronic neurons which are locally connected with their local neurons and associated active-pixel sensors. Integration of the neuroprocessor in each processor node of a scalable multiprocessor system offers orders-of-magnitude computing performance enhancements for on-board real-time intelligent multisensor processing and control tasks of advanced small satellites. The smart-pixel CNN operation theory, architecture, design and implementation, and system applications are investigated in detail. The VLSI (Very Large Scale Integration) implementation feasibility was illustrated by a prototype smart-pixel 5x5 neuroprocessor array chip of active dimensions 1380 micron x 746 micron in a 2-micron CMOS technology.

An Advanced Orbiting Systems Approach to Quality of Service in Space-Based Intelligent Communication Networks

NASA Technical Reports Server (NTRS)

Riha, Andrew P.

2005-01-01

As humans and robotic technologies are deployed in future constellation systems, differing traffic services will arise, e.g., realtime and non-realtime. In order to provide a quality of service framework that would allow humans and robotic technologies to interoperate over a wide and dynamic range of interactions, a method of classifying data as realtime or non-realtime is needed. In our paper, we present an approach that leverages the Consultative Committee for Space Data Systems (CCSDS) Advanced Orbiting Systems (AOS) data link protocol. Specifically, we redefine the AOS Transfer Frame Replay Flag in order to provide an automated store-and-forward approach on a per-service basis for use in the next-generation Interplanetary Network. In addition to addressing the problem of intermittent connectivity and associated services, we propose a follow-on methodology for prioritizing data through further modification of the AOS Transfer Frame.

Advances in Integrating Autonomy with Acoustic Communications for Intelligent Networks of Marine Robots

DTIC Science & Technology

2013-02-01

Sonar AUV #Environmental Sampling Environmental AUV +name : string = OEX Ocean Explorer +name : string = Hammerhead Iver2 +name : string = Unicorn ...executable» Google Earth Bluefin 21 AUV ( Unicorn ) MOOS Computer GPS «serial» Bluefin 21 AUV (Macrura) MOOS Computer «acoustic» Micro-Modem «wired...Computer Bluefin 21 AUV ( Unicorn ) MOOS Computer NURC AUV (OEX) MOOS Computer Topside MOOS Computer «wifi» 5.0GHz WiLan «acoustic» Edgetech GPS

Advanced information society (11)

NASA Astrophysics Data System (ADS)

Nawa, Kotaro

Late in the 1980's the information system of Japanese corporation has been operated strategically to strengthen its competitive position in markets rather than to make corporate management efficient. Therefore, information-oriented policy in the corporation is making remarkable progress. This policy expands the intelligence activity in the corporation and also leads to the extension of the market in an information industry. In this environment closed corporate system is transformed into open one. For this system network and database are important managerial resources.

Emergency Situation Prediction Mechanism: A Novel Approach for Intelligent Transportation System Using Vehicular Ad Hoc Networks

PubMed Central

Gokulakrishnan, P.

2015-01-01

In Indian four-lane express highway, millions of vehicles are travelling every day. Accidents are unfortunate and frequently occurring in these highways causing deaths, increase in death toll, and damage to infrastructure. A mechanism is required to avoid such road accidents at the maximum to reduce the death toll. An Emergency Situation Prediction Mechanism, a novel and proactive approach, is proposed in this paper for achieving the best of Intelligent Transportation System using Vehicular Ad Hoc Network. ESPM intends to predict the possibility of occurrence of an accident in an Indian four-lane express highway. In ESPM, the emergency situation prediction is done by the Road Side Unit based on (i) the Status Report sent by the vehicles in the range of RSU and (ii) the road traffic flow analysis done by the RSU. Once the emergency situation or accident is predicted in advance, an Emergency Warning Message is constructed and disseminated to all vehicles in the area of RSU to alert and prevent the vehicles from accidents. ESPM performs well in emergency situation prediction in advance to the occurrence of an accident. ESPM predicts the emergency situation within 0.20 seconds which is comparatively less than the statistical value. The prediction accuracy of ESPM against vehicle density is found better in different traffic scenarios. PMID:26065014

Adaptive routing in wireless communication networks using swarm intelligence

NASA Technical Reports Server (NTRS)

Arabshahi, P.; Gray, A.; Kassabalidis, I.; Das, A.; Narayanan, S.; Sharkawi, M. El; Marks, R. J.

2001-01-01

In this paper we focus on the network routing problem, and survey swarm intelligent approaches for its efficient solution, after a brief overview of power-aware routing schemes, which are important in the network examples outlined above.

Fixed Point Learning Based Intelligent Traffic Control System

NASA Astrophysics Data System (ADS)

Zongyao, Wang; Cong, Sui; Cheng, Shao

2017-10-01

Fixed point learning has become an important tool to analyse large scale distributed system such as urban traffic network. This paper presents a fixed point learning based intelligence traffic network control system. The system applies convergence property of fixed point theorem to optimize the traffic flow density. The intelligence traffic control system achieves maximum road resources usage by averaging traffic flow density among the traffic network. The intelligence traffic network control system is built based on decentralized structure and intelligence cooperation. No central control is needed to manage the system. The proposed system is simple, effective and feasible for practical use. The performance of the system is tested via theoretical proof and simulations. The results demonstrate that the system can effectively solve the traffic congestion problem and increase the vehicles average speed. It also proves that the system is flexible, reliable and feasible for practical use.

Development of a Real-Time Intelligent Network Environment.

ERIC Educational Resources Information Center

Gordonov, Anatoliy; Kress, Michael; Klibaner, Roberta

This paper presents a model of an intelligent computer network that provides real-time evaluation of students' performance by incorporating intelligence into the application layer protocol. Specially designed drills allow students to independently solve a number of problems based on current lecture material; students are switched to the most…

Effect of noise in intelligent cellular decision making.

PubMed

Bates, Russell; Blyuss, Oleg; Alsaedi, Ahmed; Zaikin, Alexey

2015-01-01

Similar to intelligent multicellular neural networks controlling human brains, even single cells, surprisingly, are able to make intelligent decisions to classify several external stimuli or to associate them. This happens because of the fact that gene regulatory networks can perform as perceptrons, simple intelligent schemes known from studies on Artificial Intelligence. We study the role of genetic noise in intelligent decision making at the genetic level and show that noise can play a constructive role helping cells to make a proper decision. We show this using the example of a simple genetic classifier able to classify two external stimuli.

Intelligent Assistive Technology Applications to Dementia Care: Current Capabilities, Limitations, and Future Challenges

PubMed Central

Bharucha, Ashok J.; Anand, Vivek; Forlizzi, Jodi; Dew, Mary Amanda; Reynolds, Charles F.; Stevens, Scott; Wactlar, Howard

2009-01-01

The number of older Americans afflicted by Alzheimer disease and related dementias will triple to 13 million persons by 2050, thus greatly increasing healthcare needs. An approach to this emerging crisis is the development and deployment of intelligent assistive technologies that compensate for the specific physical and cognitive deficits of older adults with dementia, and thereby also reduce caregiver burden. The authors conducted an extensive search of the computer science, engineering, and medical databases to review intelligent cognitive devices, physiologic and environmental sensors, and advanced integrated sensor networks that may find future applications in dementia care. Review of the extant literature reveals an overwhelming focus on the physical disability of younger persons with typically nonprogressive anoxic and traumatic brain injuries, with few clinical studies specifically involving persons with dementia. A discussion of the specific capabilities, strengths, and limitations of each technology is followed by an overview of research methodological challenges that must be addressed to achieve measurable progress to meet the healthcare needs of an aging America. PMID:18849532

Collective intelligence for translational medicine: Crowdsourcing insights and innovation from an interdisciplinary biomedical research community.

PubMed

Budge, Eleanor Jane; Tsoti, Sandra Maria; Howgate, Daniel James; Sivakumar, Shivan; Jalali, Morteza

2015-01-01

Translational medicine bridges the gap between discoveries in biomedical science and their safe and effective clinical application. Despite the gross opportunity afforded by modern research for unparalleled advances in this field, the process of translation remains protracted. Efforts to expedite science translation have included the facilitation of interdisciplinary collaboration within both academic and clinical environments in order to generate integrated working platforms fuelling the sharing of knowledge, expertise, and tools to align biomedical research with clinical need. However, barriers to scientific translation remain, and further progress is urgently required. Collective intelligence and crowdsourcing applications offer the potential for global online networks, allowing connection and collaboration between a wide variety of fields. This would drive the alignment of biomedical science with biotechnology, clinical need, and patient experience, in order to deliver evidence-based innovation which can revolutionize medical care worldwide. Here we discuss the critical steps towards implementing collective intelligence in translational medicine using the experience of those in other fields of science and public health.

FPGA Based "Intelligent Tap" Device for Real-Time Ethernet Network Monitoring

NASA Astrophysics Data System (ADS)

Cupek, Rafał; Piękoś, Piotr; Poczobutt, Marcin; Ziębiński, Adam

This paper describes an "Intelligent Tap" - hardware device dedicated to support real-time Ethernet networks monitoring. Presented solution was created as a student project realized in Institute of Informatics, Silesian University of Technology with support from Softing A.G company. Authors provide description of realized FPGA based "Intelligent Tap" architecture dedicated for Real-Time Ethernet network monitoring systems. The practical device realization and feasibility study conclusions are presented also.

Investigation of a Neural Network Implementation of a TCP Packet Anomaly Detection System

DTIC Science & Technology

2004-05-01

reconnatre les nouvelles variantes d’attaque. Les réseaux de neurones artificiels (ANN) ont les capacités d’apprendre à partir de schémas et de...Computational Intelligence Techniques in Intrusion Detection Systems. In IASTED International Conference on Neural Networks and Computational Intelligence , pp...Neural Network Training: Overfitting May be Harder than Expected. In Proceedings of the Fourteenth National Conference on Artificial Intelligence , AAAI-97

Dynamic mobility applications policy analysis : policy and institutional issues for intelligent network flow optimization (INFLO).

DOT National Transportation Integrated Search

2014-12-01

The report documents policy considerations for the Intelligent Network Flow Optimization (INFLO) connected vehicle applications bundle. INFLO aims to optimize network flow on freeways and arterials by informing motorists of existing and impendi...

Intelligentization: an efficient means to get more from optical networking

NASA Astrophysics Data System (ADS)

Chen, Zhi Yun

2001-10-01

Infocom is a term used to describe the merger of Information and Communications and is used to show the radical changes in today's network traffic. The continuous growth of Infocom traffic, especially that of Internet, is driving Infocom networks to expand rapidly. To service providers, the traffic is consuming the bandwidth of their network. Simultaneously, users are complaining too slow, the net never stopped in China. It is the reality faced by both the service providers and equipment vendors. Demands from both the customers and competition in market call for an efficient network infrastructure. What should a Service Provider do? This paper will first analyze the development trends of optical networking and the formation of the concepts of Intelligent Optical Network (ION) and Automatic Switched Optical Network (ASON) as a solution to this problem. Next it will look at the ways to bring intelligence into optical networks, discussing the benefits to service providers by showing some application examples. Finally, it concludes that the development of optical networking has arrived at a point of introducing intelligence into optical networks. The intelligent optical networks and Automatic Switched Optical Networks will immediately bring a wide range of benefit to service providers, equipment vendors, and, of course, the end users.

Relationship between Social Networks Adoption and Social Intelligence

ERIC Educational Resources Information Center

Gunduz, Semseddin

2017-01-01

The purpose of this study was to set forth the relationship between the individuals' states to adopt social networks and social intelligence and analyze both concepts according to various variables. Research data were collected from 1145 social network users in the online media by using the Adoption of Social Network Scale and Social Intelligence…

Advanced, Adaptive, Modular, Distributed, Generic Universal FADEC Framework for Intelligent Propulsion Control Systems (Preprint)

DTIC Science & Technology

2007-09-01

AFRL-RZ-WP-TP-2008-2044 ADVANCED, ADAPTIVE, MODULAR, DISTRIBUTED, GENERIC UNIVERSAL FADEC FRAMEWORK FOR INTELLIGENT PROPULSION CONTROL...GRANT NUMBER 4. TITLE AND SUBTITLE ADVANCED, ADAPTIVE, MODULAR, DISTRIBUTED, GENERIC UNIVERSAL FADEC FRAMEWORK FOR INTELLIGENT PROPULSION... FADEC is unique and expensive to develop, produce, maintain, and upgrade for its particular application. Each FADEC is a centralized system, with a

Worldwide Intelligent Systems: Approaches to Telecommunications and Network Management. Frontiers in Artificial Intelligence and Applications, Volume 24.

ERIC Educational Resources Information Center

Liebowitz, Jay, Ed.; Prerau, David S., Ed.

This is an international collection of 12 papers addressing artificial intelligence (AI) and knowledge technology applications in telecommunications and network management. It covers the latest and emerging AI technologies as applied to the telecommunications field. The papers are: "The Potential for Knowledge Technology in…

Topology of genetic associations between regional gray matter volume and intellectual ability: Evidence for a high capacity network.

PubMed

Bohlken, Marc M; Brouwer, Rachel M; Mandl, René C W; Hedman, Anna M; van den Heuvel, Martijn P; van Haren, Neeltje E M; Kahn, René S; Hulshoff Pol, Hilleke E

2016-01-01

Intelligence is associated with a network of distributed gray matter areas including the frontal and parietal higher association cortices and primary processing areas of the temporal and occipital lobes. Efficient information transfer between gray matter regions implicated in intelligence is thought to be critical for this trait to emerge. Genetic factors implicated in intelligence and gray matter may promote a high capacity for information transfer. Whether these genetic factors act globally or on local gray matter areas separately is not known. Brain maps of phenotypic and genetic associations between gray matter volume and intelligence were made using structural equation modeling of 3T MRI T1-weighted scans acquired in 167 adult twins of the newly acquired U-TWIN cohort. Subsequently, structural connectivity analyses (DTI) were performed to test the hypothesis that gray matter regions associated with intellectual ability form a densely connected core. Gray matter regions associated with intellectual ability were situated in the right prefrontal, bilateral temporal, bilateral parietal, right occipital and subcortical regions. Regions implicated in intelligence had high structural connectivity density compared to 10,000 reference networks (p=0.031). The genetic association with intelligence was for 39% explained by a genetic source unique to these regions (independent of total brain volume), this source specifically implicated the right supramarginal gyrus. Using a twin design, we show that intelligence is genetically represented in a spatially distributed and densely connected network of gray matter regions providing a high capacity infrastructure. Although genes for intelligence have overlap with those for total brain volume, we present evidence that there are genes for intelligence that act specifically on the subset of brain areas that form an efficient brain network. Copyright © 2015 Elsevier Inc. All rights reserved.

Global connectivity of prefrontal cortex predicts cognitive control and intelligence

PubMed Central

Cole, Michael W.; Yarkoni, Tal; Repovs, Grega; Anticevic, Alan; Braver, Todd S.

2012-01-01

Control of thought and behavior is fundamental to human intelligence. Evidence suggests a fronto-parietal brain network implements such cognitive control across diverse contexts. We identify a mechanism – global connectivity – by which components of this network might coordinate control of other networks. A lateral prefrontal cortex (LPFC) region’s activity was found to predict performance in a high control demand working memory task, and also to exhibit high global connectivity. Critically, global connectivity in this LPFC region, involving connections both within and outside the fronto-parietal network, showed a highly selective relationship with individual differences in fluid intelligence. These findings suggest LPFC is a global hub with a brain-wide influence that facilitates the ability to implement control processes central to human intelligence. PMID:22745498

Creative-Dynamics Approach To Neural Intelligence

NASA Technical Reports Server (NTRS)

Zak, Michail A.

1992-01-01

Paper discusses approach to mathematical modeling of artificial neural networks exhibiting complicated behaviors reminiscent of creativity and intelligence of biological neural networks. Neural network treated as non-Lipschitzian dynamical system - as described in "Non-Lipschitzian Dynamics For Modeling Neural Networks" (NPO-17814). System serves as tool for modeling of temporal-pattern memories and recognition of complicated spatial patterns.

PILOT: An intelligent distributed operations support system

NASA Technical Reports Server (NTRS)

Rasmussen, Arthur N.

1993-01-01

The Real-Time Data System (RTDS) project is exploring the application of advanced technologies to the real-time flight operations environment of the Mission Control Centers at NASA's Johnson Space Center. The system, based on a network of engineering workstations, provides services such as delivery of real time telemetry data to flight control applications. To automate the operation of this complex distributed environment, a facility called PILOT (Process Integrity Level and Operation Tracker) is being developed. PILOT comprises a set of distributed agents cooperating with a rule-based expert system; together they monitor process operation and data flows throughout the RTDS network. The goal of PILOT is to provide unattended management and automated operation under user control.

ConvNetQuake: Convolutional Neural Network for Earthquake Detection and Location

NASA Astrophysics Data System (ADS)

Denolle, M.; Perol, T.; Gharbi, M.

2017-12-01

Over the last decades, the volume of seismic data has increased exponentially, creating a need for efficient algorithms to reliably detect and locate earthquakes. Today's most elaborate methods scan through the plethora of continuous seismic records, searching for repeating seismic signals. In this work, we leverage the recent advances in artificial intelligence and present ConvNetQuake, a highly scalable convolutional neural network for probabilistic earthquake detection and location from single stations. We apply our technique to study two years of induced seismicity in Oklahoma (USA). We detect 20 times more earthquakes than previously cataloged by the Oklahoma Geological Survey. Our algorithm detection performances are at least one order of magnitude faster than other established methods.

Smart sensorless prediction diagnosis of electric drives

NASA Astrophysics Data System (ADS)

Kruglova, TN; Glebov, NA; Shoshiashvili, ME

2017-10-01

In this paper, the discuss diagnostic method and prediction of the technical condition of an electrical motor using artificial intelligent method, based on the combination of fuzzy logic and neural networks, are discussed. The fuzzy sub-model determines the degree of development of each fault. The neural network determines the state of the object as a whole and the number of serviceable work periods for motors actuator. The combination of advanced techniques reduces the learning time and increases the forecasting accuracy. The experimental implementation of the method for electric drive diagnosis and associated equipment is carried out at different speeds. As a result, it was found that this method allows troubleshooting the drive at any given speed.

A Survey of Computational Intelligence Techniques in Protein Function Prediction

PubMed Central

Tiwari, Arvind Kumar; Srivastava, Rajeev

2014-01-01

During the past, there was a massive growth of knowledge of unknown proteins with the advancement of high throughput microarray technologies. Protein function prediction is the most challenging problem in bioinformatics. In the past, the homology based approaches were used to predict the protein function, but they failed when a new protein was different from the previous one. Therefore, to alleviate the problems associated with homology based traditional approaches, numerous computational intelligence techniques have been proposed in the recent past. This paper presents a state-of-the-art comprehensive review of various computational intelligence techniques for protein function predictions using sequence, structure, protein-protein interaction network, and gene expression data used in wide areas of applications such as prediction of DNA and RNA binding sites, subcellular localization, enzyme functions, signal peptides, catalytic residues, nuclear/G-protein coupled receptors, membrane proteins, and pathway analysis from gene expression datasets. This paper also summarizes the result obtained by many researchers to solve these problems by using computational intelligence techniques with appropriate datasets to improve the prediction performance. The summary shows that ensemble classifiers and integration of multiple heterogeneous data are useful for protein function prediction. PMID:25574395

Strategic Computing. New-Generation Computing Technology: A Strategic Plan for Its Development and Application to Critical Problems in Defense

DTIC Science & Technology

1983-10-28

Computing. By seizing an opportunity to leverage recent advances in artificial intelligence, computer science, and microelectronics, the Agency plans...occurred in many separated areas of artificial intelligence, computer science, and microelectronics. Advances in "expert system" technology now...and expert knowledge o Advances in Artificial Intelligence: Mechanization of speech recognition, vision, and natural language understanding. o

A Research Program on Artificial Intelligence in Process Engineering.

ERIC Educational Resources Information Center

Stephanopoulos, George

1986-01-01

Discusses the use of artificial intelligence systems in process engineering. Describes a new program at the Massachusetts Institute of Technology which attempts to advance process engineering through technological advances in the areas of artificial intelligence and computers. Identifies the program's hardware facilities, software support,…

An Intelligent Pattern Recognition System Based on Neural Network and Wavelet Decomposition for Interpretation of Heart Sounds

DTIC Science & Technology

2001-10-25

wavelet decomposition of signals and classification using neural network. Inputs to the system are the heart sound signals acquired by a stethoscope in a...Proceedings. pp. 415–418, 1990. [3] G. Ergun, “An intelligent diagnostic system for interpretation of arterpartum fetal heart rate tracings based on ANNs and...AN INTELLIGENT PATTERN RECOGNITION SYSTEM BASED ON NEURAL NETWORK AND WAVELET DECOMPOSITION FOR INTERPRETATION OF HEART SOUNDS I. TURKOGLU1, A

Fluid Intelligence Predicts Novel Rule Implementation in a Distributed Frontoparietal Control Network.

PubMed

Tschentscher, Nadja; Mitchell, Daniel; Duncan, John

2017-05-03

Fluid intelligence has been associated with a distributed cognitive control or multiple-demand (MD) network, comprising regions of lateral frontal, insular, dorsomedial frontal, and parietal cortex. Human fluid intelligence is also intimately linked to task complexity, and the process of solving complex problems in a sequence of simpler, more focused parts. Here, a complex target detection task included multiple independent rules, applied one at a time in successive task epochs. Although only one rule was applied at a time, increasing task complexity (i.e., the number of rules) impaired performance in participants of lower fluid intelligence. Accompanying this loss of performance was reduced response to rule-critical events across the distributed MD network. The results link fluid intelligence and MD function to a process of attentional focus on the successive parts of complex behavior. SIGNIFICANCE STATEMENT Fluid intelligence is intimately linked to the ability to structure complex problems in a sequence of simpler, more focused parts. We examine the basis for this link in the functions of a distributed frontoparietal or multiple-demand (MD) network. With increased task complexity, participants of lower fluid intelligence showed reduced responses to task-critical events. Reduced responses in the MD system were accompanied by impaired behavioral performance. Low fluid intelligence is linked to poor foregrounding of task-critical information across a distributed MD system. Copyright © 2017 Tschentscher et al.

Smart Collision Avoidance and Hazard Routing Mechanism for Intelligent Transport Network

NASA Astrophysics Data System (ADS)

Singh, Gurpreet; Gupta, Pooja; Wahab, Mohd Helmy Abd

2017-08-01

The smart vehicular ad-hoc network is the network that consists of vehicles for smooth movement and better management of the vehicular connectivity across the given network. This research paper aims to propose a set of solution for the VANETs consisting of the automatic driven vehicles, also called as the autonomous car. Such vehicular networks are always prone to collision due to the natural or un-natural reasons which must be solved before the large-scale deployment of the autonomous transport systems. The newly designed intelligent transport movement control mechanism is based upon the intelligent data propagation along with the vehicle collision and traffic jam prevention schema [8], which may help the future designs of smart cities to become more robust and less error-prone. In the proposed model, the focus is on designing a new dynamic and robust hazard routing protocol for intelligent vehicular networks for improvement of the overall performance in various aspects. It is expected to improve the overall transmission delay as well as the number of collisions or adversaries across the vehicular network zone.

Development and application of an optogenetic platform for controlling and imaging a large number of individual neurons

NASA Astrophysics Data System (ADS)

Mohammed, Ali Ibrahim Ali

The understanding and treatment of brain disorders as well as the development of intelligent machines is hampered by the lack of knowledge of how the brain fundamentally functions. Over the past century, we have learned much about how individual neurons and neural networks behave, however new tools are critically needed to interrogate how neural networks give rise to complex brain processes and disease conditions. Recent innovations in molecular techniques, such as optogenetics, have enabled neuroscientists unprecedented precision to excite, inhibit and record defined neurons. The impressive sensitivity of currently available optogenetic sensors and actuators has now enabled the possibility of analyzing a large number of individual neurons in the brains of behaving animals. To promote the use of these optogenetic tools, this thesis integrates cutting edge optogenetic molecular sensors which is ultrasensitive for imaging neuronal activity with custom wide field optical microscope to analyze a large number of individual neurons in living brains. Wide-field microscopy provides a large field of view and better spatial resolution approaching the Abbe diffraction limit of fluorescent microscope. To demonstrate the advantages of this optical platform, we imaged a deep brain structure, the Hippocampus, and tracked hundreds of neurons over time while mouse was performing a memory task to investigate how those individual neurons related to behavior. In addition, we tested our optical platform in investigating transient neural network changes upon mechanical perturbation related to blast injuries. In this experiment, all blasted mice show a consistent change in neural network. A small portion of neurons showed a sustained calcium increase for an extended period of time, whereas the majority lost their activities. Finally, using optogenetic silencer to control selective motor cortex neurons, we examined their contributions to the network pathology of basal ganglia related to Parkinson's disease. We found that inhibition of motor cortex does not alter exaggerated beta oscillations in the striatum that are associated with parkinsonianism. Together, these results demonstrate the potential of developing integrated optogenetic system to advance our understanding of the principles underlying neural network computation, which would have broad applications from advancing artificial intelligence to disease diagnosis and treatment.

Artificial Neural Networks and Instructional Technology.

ERIC Educational Resources Information Center

Carlson, Patricia A.

1991-01-01

Artificial neural networks (ANN), part of artificial intelligence, are discussed. Such networks are fed sample cases (training sets), learn how to recognize patterns in the sample data, and use this experience in handling new cases. Two cognitive roles for ANNs (intelligent filters and spreading, associative memories) are examined. Prototypes…

Applying Network Theory to Develop a Dedicated National Intelligence Network

DTIC Science & Technology

2006-09-01

Los Angeles Sheriff’s Department .133 There is an interesting difference between the Washington, D.C. Police Department mission and others in...123 Atlanta, the TEW (Terrorist Early Warning) group (which is part of the Los Angeles Police Department ), and the intelligence and counter...intelligence “fusion” centers and perhaps the Los Angeles Police Department TEW. The

MRI correlates of general intelligence in neurotypical adults.

PubMed

Malpas, Charles B; Genc, Sila; Saling, Michael M; Velakoulis, Dennis; Desmond, Patricia M; O'Brien, Terence J

2016-02-01

There is growing interest in the neurobiological substrate of general intelligence. Psychometric estimates of general intelligence are reduced in a range of neurological disorders, leading to practical application as sensitive, but non-specific, markers of cerebral disorder. This study examined estimates of general intelligence in neurotypical adults using diffusion tensor imaging and resting-state functional connectivity analysis. General intelligence was related to white matter organisation across multiple brain regions, confirming previous work in older healthy adults. We also found that variation in general intelligence was related to a large functional sub-network involving all cortical lobes of the brain. These findings confirm that individual variance in general intelligence is related to diffusely represented brain networks. Copyright © 2015 Elsevier Ltd. All rights reserved.

Development of the brain's structural network efficiency in early adolescence: A longitudinal DTI twin study.

PubMed

Koenis, Marinka M G; Brouwer, Rachel M; van den Heuvel, Martijn P; Mandl, René C W; van Soelen, Inge L C; Kahn, René S; Boomsma, Dorret I; Hulshoff Pol, Hilleke E

2015-12-01

The brain is a network and our intelligence depends in part on the efficiency of this network. The network of adolescents differs from that of adults suggesting developmental changes. However, whether the network changes over time at the individual level and, if so, how this relates to intelligence, is unresolved in adolescence. In addition, the influence of genetic factors in the developing network is not known. Therefore, in a longitudinal study of 162 healthy adolescent twins and their siblings (mean age at baseline 9.9 [range 9.0-15.0] years), we mapped local and global structural network efficiency of cerebral fiber pathways (weighted with mean FA and streamline count) and assessed intelligence over a three-year interval. We find that the efficiency of the brain's structural network is highly heritable (locally up to 74%). FA-based local and global efficiency increases during early adolescence. Streamline count based local efficiency both increases and decreases, and global efficiency reorganizes to a net decrease. Local FA-based efficiency was correlated to IQ. Moreover, increases in FA-based network efficiency (global and local) and decreases in streamline count based local efficiency are related to increases in intellectual functioning. Individual changes in intelligence and local FA-based efficiency appear to go hand in hand in frontal and temporal areas. More widespread local decreases in streamline count based efficiency (frontal cingulate and occipital) are correlated with increases in intelligence. We conclude that the teenage brain is a network in progress in which individual differences in maturation relate to level of intellectual functioning. © 2015 Wiley Periodicals, Inc.

Sniffing out the truth: clinical diagnosis using the electronic nose.

PubMed

Pavlou, A K; Turner, A P

2000-02-01

Recently the use of smell in clinical diagnosis has been rediscovered due to major advances in odour sensing technology and artificial intelligence (AI). It was well known in the past that a number of infectious or metabolic diseases could liberate specific odours characteristic of the disease stage. Later chromatographic techniques identified an enormous number of volatiles in human clinical specimens that might serve as potential disease markers. "Artificial nose" technology has been employed in several areas of medical diagnosis, including rapid detection of tuberculosis (TB), Helicobacter pylori (HP) and urinary tract infections (UTI). Preliminary results have demonstrated the possibility of identifying and characterising microbial pathogens in clinical specimens. A hybrid intelligent model of four interdependent "tools", odour generation "kits", rapid volatile delivery and recovery systems, consistent low drift sensor performance and a hybrid intelligent system of parallel neural networks (NN) and expert systems, have been applied in gastric, pulmonary and urine diagnosis. Initial clinical tests have shown that it may be possible in the near future to use electronic nose technology not only for the rapid detection of diseases such as peptic ulceration, UTI, and TB but also for the continuous dynamic monitoring of disease stages. Major advances in information and gas sensor technology could enhance the diagnostic power of future bio-electronic noses and facilitate global surveillance models of disease control and management.

An intelligent anti-jamming network system of data link

NASA Astrophysics Data System (ADS)

Fan, Xiangrui; Lin, Jingyong; Liu, Jiarun; Zhou, Chunmei

2017-10-01

Data link is the key information system for the cooperation of weapons, single physical layer anti-jamming technology has been unable to meet its requirements. High dynamic precision-guided weapon nodes like missiles, anti-jamming design of data link system need to have stronger pertinence and effectiveness: the best anti-jamming communication mode can be selected intelligently in combat environment, in real time, guarantee the continuity of communication. We discuss an anti-jamming intelligent networking technology of data link based on interference awareness, put forward a model of intelligent anti-jamming system, and introduces the cognitive node protocol stack model and intelligent anti-jamming method, in order to improve the data chain of intelligent anti-jamming ability.

A Cyber Situational Awareness Model for Network Administrators

DTIC Science & Technology

2017-03-01

environments, the Internet of Things, artificial intelligence , and so on. As users’ data requirements grow more complex, they demand information...security of systems of interest. Further, artificial intelligence is a powerful concept in information technology. Therefore, new research should...look into how to use artificial intelligence to develop CSA. Human interaction with cyber systems is not making networks and their components safer

Quantum Enhanced Inference in Markov Logic Networks

NASA Astrophysics Data System (ADS)

Wittek, Peter; Gogolin, Christian

2017-04-01

Markov logic networks (MLNs) reconcile two opposing schools in machine learning and artificial intelligence: causal networks, which account for uncertainty extremely well, and first-order logic, which allows for formal deduction. An MLN is essentially a first-order logic template to generate Markov networks. Inference in MLNs is probabilistic and it is often performed by approximate methods such as Markov chain Monte Carlo (MCMC) Gibbs sampling. An MLN has many regular, symmetric structures that can be exploited at both first-order level and in the generated Markov network. We analyze the graph structures that are produced by various lifting methods and investigate the extent to which quantum protocols can be used to speed up Gibbs sampling with state preparation and measurement schemes. We review different such approaches, discuss their advantages, theoretical limitations, and their appeal to implementations. We find that a straightforward application of a recent result yields exponential speedup compared to classical heuristics in approximate probabilistic inference, thereby demonstrating another example where advanced quantum resources can potentially prove useful in machine learning.

A Survey of Geosensor Networks: Advances in Dynamic Environmental Monitoring

PubMed Central

Nittel, Silvia

2009-01-01

In the recent decade, several technology trends have influenced the field of geosciences in significant ways. The first trend is the more readily available technology of ubiquitous wireless communication networks and progress in the development of low-power, short-range radio-based communication networks, the miniaturization of computing and storage platforms as well as the development of novel microsensors and sensor materials. All three trends have changed the type of dynamic environmental phenomena that can be detected, monitored and reacted to. Another important aspect is the real-time data delivery of novel platforms today. In this paper, I will survey the field of geosensor networks, and mainly focus on the technology of small-scale geosensor networks, example applications and their feasibility and lessons learnt as well as the current research questions posed by using this technology today. Furthermore, my objective is to investigate how this technology can be embedded in the current landscape of intelligent sensor platforms in the geosciences and identify its place and purpose. PMID:22346721

Quantum Enhanced Inference in Markov Logic Networks.

PubMed

Wittek, Peter; Gogolin, Christian

2017-04-19

Markov logic networks (MLNs) reconcile two opposing schools in machine learning and artificial intelligence: causal networks, which account for uncertainty extremely well, and first-order logic, which allows for formal deduction. An MLN is essentially a first-order logic template to generate Markov networks. Inference in MLNs is probabilistic and it is often performed by approximate methods such as Markov chain Monte Carlo (MCMC) Gibbs sampling. An MLN has many regular, symmetric structures that can be exploited at both first-order level and in the generated Markov network. We analyze the graph structures that are produced by various lifting methods and investigate the extent to which quantum protocols can be used to speed up Gibbs sampling with state preparation and measurement schemes. We review different such approaches, discuss their advantages, theoretical limitations, and their appeal to implementations. We find that a straightforward application of a recent result yields exponential speedup compared to classical heuristics in approximate probabilistic inference, thereby demonstrating another example where advanced quantum resources can potentially prove useful in machine learning.

Quantum Enhanced Inference in Markov Logic Networks

PubMed Central

Wittek, Peter; Gogolin, Christian

2017-01-01

Markov logic networks (MLNs) reconcile two opposing schools in machine learning and artificial intelligence: causal networks, which account for uncertainty extremely well, and first-order logic, which allows for formal deduction. An MLN is essentially a first-order logic template to generate Markov networks. Inference in MLNs is probabilistic and it is often performed by approximate methods such as Markov chain Monte Carlo (MCMC) Gibbs sampling. An MLN has many regular, symmetric structures that can be exploited at both first-order level and in the generated Markov network. We analyze the graph structures that are produced by various lifting methods and investigate the extent to which quantum protocols can be used to speed up Gibbs sampling with state preparation and measurement schemes. We review different such approaches, discuss their advantages, theoretical limitations, and their appeal to implementations. We find that a straightforward application of a recent result yields exponential speedup compared to classical heuristics in approximate probabilistic inference, thereby demonstrating another example where advanced quantum resources can potentially prove useful in machine learning. PMID:28422093

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

Application of sensor networks to intelligent transportation systems.

DOT National Transportation Integrated Search

2009-12-01

The objective of the research performed is the application of wireless sensor networks to intelligent transportation infrastructures, with the aim of increasing their dependability and improving the efficacy of data collection and utilization. Exampl...

Intelligent Network Flow Optimization (INFLO) prototype acceptance test summary.

DOT National Transportation Integrated Search

2015-05-01

This report summarizes the results of System Acceptance Testing for the implementation of the Intelligent Network Flow Optimization (INFLO) Prototype bundle within the Dynamic Mobility Applications (DMA) portion of the Connected Vehicle Program. This...

Touching the elephant: The search for fluid intelligence.

PubMed

Wasserman, Theodore; Wasserman, Lori Drucker

2017-01-01

Many constructs that we take for granted in modern neuropsychology, fluid intelligence among them, can best be explained by conceptionalizing them as a collection of task specific processes engaged in by an integrated recruited network involved in problem solving. Fractionalizing the network in an attempt to describe elements of its function leads to arbitrarily defined segments that may be interesting to discuss abstractly, but never occur independently in the real world operation of the system. We will seek to demonstrate that the construct of fluid intelligence is like that. It is a description of a type of operation of a network dedicated to solving problems and the composition of the network that is responsible for the activity changes in a task specific manner. As a result, fluid intelligence is not an independent skill, or a thing that lives on its own, or can be measured independently of the other things that contribute to the overall operation of the network as it seeks to solve problems.

An Intelligent Monitoring Network for Detection of Cracks in Anvils of High-Press Apparatus.

PubMed

Tian, Hao; Yan, Zhaoli; Yang, Jun

2018-04-09

Due to the endurance of alternating high pressure and temperature, the carbide anvils of the high-press apparatus, which are widely used in the synthetic diamond industry, are prone to crack. In this paper, an acoustic method is used to monitor the crack events, and the intelligent monitoring network is proposed to classify the sound samples. The pulse sound signals produced by such cracking are first extracted based on a short-time energy threshold. Then, the signals are processed with the proposed intelligent monitoring network to identify the operation condition of the anvil of the high-pressure apparatus. The monitoring network is an improved convolutional neural network that solves the problems that may occur in practice. The length of pulse sound excited by the crack growth is variable, so a spatial pyramid pooling layer is adopted to solve the variable-length input problem. An adaptive weighted algorithm for loss function is proposed in this method to handle the class imbalance problem. The good performance regarding the accuracy and balance of the proposed intelligent monitoring network is validated through the experiments finally.

A new modelling approach for zooplankton behaviour

NASA Astrophysics Data System (ADS)

Keiyu, A. Y.; Yamazaki, H.; Strickler, J. R.

We have developed a new simulation technique to model zooplankton behaviour. The approach utilizes neither the conventional artificial intelligence nor neural network methods. We have designed an adaptive behaviour network, which is similar to BEER [(1990) Intelligence as an adaptive behaviour: an experiment in computational neuroethology, Academic Press], based on observational studies of zooplankton behaviour. The proposed method is compared with non- "intelligent" models—random walk and correlated walk models—as well as observed behaviour in a laboratory tank. Although the network is simple, the model exhibits rich behavioural patterns similar to live copepods.

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.

Fluid intelligence and brain functional organization in aging yoga and meditation practitioners

PubMed Central

Gard, Tim; Taquet, Maxime; Dixit, Rohan; Hölzel, Britta K.; de Montjoye, Yves-Alexandre; Brach, Narayan; Salat, David H.; Dickerson, Bradford C.; Gray, Jeremy R.; Lazar, Sara W.

2014-01-01

Numerous studies have documented the normal age-related decline of neural structure, function, and cognitive performance. Preliminary evidence suggests that meditation may reduce decline in specific cognitive domains and in brain structure. Here we extended this research by investigating the relation between age and fluid intelligence and resting state brain functional network architecture using graph theory, in middle-aged yoga and meditation practitioners, and matched controls. Fluid intelligence declined slower in yoga practitioners and meditators combined than in controls. Resting state functional networks of yoga practitioners and meditators combined were more integrated and more resilient to damage than those of controls. Furthermore, mindfulness was positively correlated with fluid intelligence, resilience, and global network efficiency. These findings reveal the possibility to increase resilience and to slow the decline of fluid intelligence and brain functional architecture and suggest that mindfulness plays a mechanistic role in this preservation. PMID:24795629

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

Oehler, G.C.

As dramatic as are the recent changes in Eastern Europe and the Soviet Union political happenings, some other factors are having at least as important an impact on the intelligence community's business. For example, new and more global problems have arisen, such as the proliferation of advanced weapons, economic competitiveness, and environmental concerns. It is obvious that intelligence requirements are on the increase. For the intelligence community whose business is information gathering and processing, advanced information management tools are needed. Fortunately, recent technical advances offer these tools. Some of the more notable advances in information documentation, storage, and retrieval aremore » described.« less

Communications for unattended sensor networks

NASA Astrophysics Data System (ADS)

Nemeroff, Jay L.; Angelini, Paul; Orpilla, Mont; Garcia, Luis; DiPierro, Stefano

2004-07-01

The future model of the US Army's Future Combat Systems (FCS) and the Future Force reflects a combat force that utilizes lighter armor protection than the current standard. Survival on the future battlefield will be increased by the use of advanced situational awareness provided by unattended tactical and urban sensors that detect, identify, and track enemy targets and threats. Successful implementation of these critical sensor fields requires the development of advanced sensors, sensor and data-fusion processors, and a specialized communications network. To ensure warfighter and asset survivability, the communications must be capable of near real-time dissemination of the sensor data using robust, secure, stealthy, and jam resistant links so that the proper and decisive action can be taken. Communications will be provided to a wide-array of mission-specific sensors that are capable of processing data from acoustic, magnetic, seismic, and/or Chemical, Biological, Radiological, and Nuclear (CBRN) sensors. Other, more powerful, sensor node configurations will be capable of fusing sensor data and intelligently collect and process data images from infrared or visual imaging cameras. The radio waveform and networking protocols being developed under the Soldier Level Integrated Communications Environment (SLICE) Soldier Radio Waveform (SRW) and the Networked Sensors for the Future Force Advanced Technology Demonstration are part of an effort to develop a common waveform family which will operate across multiple tactical domains including dismounted soldiers, ground sensor, munitions, missiles and robotics. These waveform technologies will ultimately be transitioned to the JTRS library, specifically the Cluster 5 requirement.

Banknote recognition: investigating processing and cognition framework using competitive neural network.

PubMed

Oyedotun, Oyebade K; Khashman, Adnan

2017-02-01

Humans are apt at recognizing patterns and discovering even abstract features which are sometimes embedded therein. Our ability to use the banknotes in circulation for business transactions lies in the effortlessness with which we can recognize the different banknote denominations after seeing them over a period of time. More significant is that we can usually recognize these banknote denominations irrespective of what parts of the banknotes are exposed to us visually. Furthermore, our recognition ability is largely unaffected even when these banknotes are partially occluded. In a similar analogy, the robustness of intelligent systems to perform the task of banknote recognition should not collapse under some minimum level of partial occlusion. Artificial neural networks are intelligent systems which from inception have taken many important cues related to structure and learning rules from the human nervous/cognition processing system. Likewise, it has been shown that advances in artificial neural network simulations can help us understand the human nervous/cognition system even furthermore. In this paper, we investigate three cognition hypothetical frameworks to vision-based recognition of banknote denominations using competitive neural networks. In order to make the task more challenging and stress-test the investigated hypotheses, we also consider the recognition of occluded banknotes. The implemented hypothetical systems are tasked to perform fast recognition of banknotes with up to 75 % occlusion. The investigated hypothetical systems are trained on Nigeria's Naira banknotes and several experiments are performed to demonstrate the findings presented within this work.

MIMIC II: a massive temporal ICU patient database to support research in intelligent patient monitoring

NASA Technical Reports Server (NTRS)

Saeed, M.; Lieu, C.; Raber, G.; Mark, R. G.

2002-01-01

Development and evaluation of Intensive Care Unit (ICU) decision-support systems would be greatly facilitated by the availability of a large-scale ICU patient database. Following our previous efforts with the MIMIC (Multi-parameter Intelligent Monitoring for Intensive Care) Database, we have leveraged advances in networking and storage technologies to develop a far more massive temporal database, MIMIC II. MIMIC II is an ongoing effort: data is continuously and prospectively archived from all ICU patients in our hospital. MIMIC II now consists of over 800 ICU patient records including over 120 gigabytes of data and is growing. A customized archiving system was used to store continuously up to four waveforms and 30 different parameters from ICU patient monitors. An integrated user-friendly relational database was developed for browsing of patients' clinical information (lab results, fluid balance, medications, nurses' progress notes). Based upon its unprecedented size and scope, MIMIC II will prove to be an important resource for intelligent patient monitoring research, and will support efforts in medical data mining and knowledge-discovery.

The Virtual Environment for Rapid Prototyping of the Intelligent Environment

PubMed Central

Bouzouane, Abdenour; Gaboury, Sébastien

2017-01-01

Advances in domains such as sensor networks and electronic and ambient intelligence have allowed us to create intelligent environments (IEs). However, research in IE is being held back by the fact that researchers face major difficulties, such as a lack of resources for their experiments. Indeed, they cannot easily build IEs to evaluate their approaches. This is mainly because of economic and logistical issues. In this paper, we propose a simulator to build virtual IEs. Simulators are a good alternative to physical IEs because they are inexpensive, and experiments can be conducted easily. Our simulator is open source and it provides users with a set of virtual sensors that simulates the behavior of real sensors. This simulator gives the user the capacity to build their own environment, providing a model to edit inhabitants’ behavior and an interactive mode. In this mode, the user can directly act upon IE objects. This simulator gathers data generated by the interactions in order to produce datasets. These datasets can be used by scientists to evaluate several approaches in IEs. PMID:29112175

The Virtual Environment for Rapid Prototyping of the Intelligent Environment.

PubMed

Francillette, Yannick; Boucher, Eric; Bouzouane, Abdenour; Gaboury, Sébastien

2017-11-07

Advances in domains such as sensor networks and electronic and ambient intelligence have allowed us to create intelligent environments (IEs). However, research in IE is being held back by the fact that researchers face major difficulties, such as a lack of resources for their experiments. Indeed, they cannot easily build IEs to evaluate their approaches. This is mainly because of economic and logistical issues. In this paper, we propose a simulator to build virtual IEs. Simulators are a good alternative to physical IEs because they are inexpensive, and experiments can be conducted easily. Our simulator is open source and it provides users with a set of virtual sensors that simulates the behavior of real sensors. This simulator gives the user the capacity to build their own environment, providing a model to edit inhabitants' behavior and an interactive mode. In this mode, the user can directly act upon IE objects. This simulator gathers data generated by the interactions in order to produce datasets. These datasets can be used by scientists to evaluate several approaches in IEs.

An Analysis on a Negotiation Model Based on Multiagent Systems with Symbiotic Learning and Evolution

NASA Astrophysics Data System (ADS)

Hossain, Md. Tofazzal

This study explores an evolutionary analysis on a negotiation model based on Masbiole (Multiagent Systems with Symbiotic Learning and Evolution) which has been proposed as a new methodology of Multiagent Systems (MAS) based on symbiosis in the ecosystem. In Masbiole, agents evolve in consideration of not only their own benefits and losses, but also the benefits and losses of opponent agents. To aid effective application of Masbiole, we develop a competitive negotiation model where rigorous and advanced intelligent decision-making mechanisms are required for agents to achieve solutions. A Negotiation Protocol is devised aiming at developing a set of rules for agents' behavior during evolution. Simulations use a newly developed evolutionary computing technique, called Genetic Network Programming (GNP) which has the directed graph-type gene structure that can develop and design the required intelligent mechanisms for agents. In a typical scenario, competitive negotiation solutions are reached by concessions that are usually predetermined in the conventional MAS. In this model, however, not only concession is determined automatically by symbiotic evolution (making the system intelligent, automated, and efficient) but the solution also achieves Pareto optimal automatically.

Synthetic biology routes to bio-artificial intelligence.

PubMed

Nesbeth, Darren N; Zaikin, Alexey; Saka, Yasushi; Romano, M Carmen; Giuraniuc, Claudiu V; Kanakov, Oleg; Laptyeva, Tetyana

2016-11-30

The design of synthetic gene networks (SGNs) has advanced to the extent that novel genetic circuits are now being tested for their ability to recapitulate archetypal learning behaviours first defined in the fields of machine and animal learning. Here, we discuss the biological implementation of a perceptron algorithm for linear classification of input data. An expansion of this biological design that encompasses cellular 'teachers' and 'students' is also examined. We also discuss implementation of Pavlovian associative learning using SGNs and present an example of such a scheme and in silico simulation of its performance. In addition to designed SGNs, we also consider the option to establish conditions in which a population of SGNs can evolve diversity in order to better contend with complex input data. Finally, we compare recent ethical concerns in the field of artificial intelligence (AI) and the future challenges raised by bio-artificial intelligence (BI). © 2016 The Author(s). This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY).

An Intelligent Agent Approach for Teaching Neural Networks Using LEGO[R] Handy Board Robots

ERIC Educational Resources Information Center

Imberman, Susan P.

2004-01-01

In this article we describe a project for an undergraduate artificial intelligence class. The project teaches neural networks using LEGO[R] handy board robots. Students construct robots with two motors and two photosensors. Photosensors provide readings that act as inputs for the neural network. Output values power the motors and maintain the…

Behavioral networks as a model for intelligent agents

NASA Technical Reports Server (NTRS)

Sliwa, Nancy E.

1990-01-01

On-going work at NASA Langley Research Center in the development and demonstration of a paradigm called behavioral networks as an architecture for intelligent agents is described. This work focuses on the need to identify a methodology for smoothly integrating the characteristics of low-level robotic behavior, including actuation and sensing, with intelligent activities such as planning, scheduling, and learning. This work assumes that all these needs can be met within a single methodology, and attempts to formalize this methodology in a connectionist architecture called behavioral networks. Behavioral networks are networks of task processes arranged in a task decomposition hierarchy. These processes are connected by both command/feedback data flow, and by the forward and reverse propagation of weights which measure the dynamic utility of actions and beliefs.

Mediating Data and Building Community for Informed, Intelligent Decision Making for the Polar Regions

NASA Astrophysics Data System (ADS)

Pulsifer, P. L.; Stieglitz, M.

2017-12-01

Much has been written about the state of data and related systems for the polar regions, however work remains to be done to achieve an envisioned integrated and well-defined pan-Arctic observing and data network that enables access to high quality data, expertise and information in support of scientific understanding, stakeholder needs, and agency operations. In this paper we argue that priorities for establishing such a network are in the area of machine-enhanced data mediation and the human aspects of community building. The authors have engaged in a U.S.-based, multi-agency process with the goal of applying modern cyberinfrastructure to improve capabilities for integrating data. A particular case-study focuses on establishing a carbon budget for the Arctic region. This effort contributes to broader global efforts aimed at establishing an international observing and data network. Results are based on a series meetings, workshops, systems design activities, and publications. Analysis reveals that there are a large number of polar data resources interacting in a network that functions as a data ecosystem. Given the size and complexity of the network, achieving broad data discovery and access and meaningful data integration (i.e. developing a carbon budget) will require advanced techniques including machine learning, semantic mediation, and the use of highly connected virtual research environments. To achieve the aforementioned goal will require a community of engaged researchers, technologists, and stakeholders to establish requirements and the social and organizational context needed for effective machine-based approaches. The results imply that: i) the polar research and application community must be more aware of advances in technology; ii) funders must adopt a long-term, sustainable infrastructure approach to systems development; iii) the community must work together to enable interoperability; iv) we must recognize that the challenge is socio-technical and that social and community-building aspects are as important as technology. Establishing cyberinfrastructure for informed, intelligent decision making for the polar regions will require an innovative combination of emerging technologies and community-building across stakeholders.

DATA MAYHEM VERSUS NIMBLE INFORMATION: TRANSFORMING HECTIC IMAGERY INTELLIGENCE DATA INTO ACTIONABLE INFORMATION USING ARTIFICIAL NEURAL NETWORKS

DTIC Science & Technology

2017-10-01

AU/ACSC/MORALES/AY17 AIR COMMAND AND STAFF COLLEGE DISTANCE LEARNING AIR UNIVERSITY DATA MAYHEM VERSUS NIMBLE INFORMATION : TRANSFORMING...HECTIC IMAGERY INTELLIGENCE DATA INTO ACTIONABLE INFORMATION USING ARTIFICIAL NEURAL NETWORKS by Luis A. Morales, Major, USAF A Research...finding solutions to compliment and supplement human analysts’ capacity, so intelligence and information can reach operators and end-users at the

Extraterrestrial intelligence? Not likely.

PubMed

DeVore, I

2001-12-01

The possibility that there exist extraterrestrial creatures with advanced intelligence is considered by examining major events in mammalian, primate, and human evolution on earth. The overwhelming evidence is that the evolution of intelligence in creatures elsewhere who have the capability to communicate with us is vanishingly small. The history of the evolution of advanced forms of life on this planet is so beset by adventitious, unpredictable events and multiple contingencies that the evolution of human-level intelligence is highly unlikely on any planet, including earth.

Transition from intelligence cycle to intelligence process: the network-centric intelligence in narrow seas

NASA Astrophysics Data System (ADS)

Büker, Engin

2015-05-01

The defence technologies which have been developing and changing rapidly, today make it difficult to be able to foresee the next environment and spectrum of warfare. When said change and development is looked in specific to the naval operations, it can be said that the possible battlefield and scenarios to be developed in the near and middle terms (5-20 years) are more clarified with compare to other force components. Network Centric Naval Warfare Concept that was developed for the floating, diving and flying fleet platforms which serves away from its own mainland for miles, will keep its significance in the future. Accordingly, Network Centric Intelligence structure completely integrating with the command and control systems will have relatively more importance. This study will firstly try to figure out the transition from the traditional intelligence cycle that is still used in conventional war to Network Centric Intelligence Production Process. In the last part, the use of this new approach on the base of UAV that is alternative to satellite based command control and data transfer systems in the joint operations in narrow seas will be examined, a model suggestion for the use of operative and strategic UAVs which are assured within the scope of the NATO AGS2 for this aim will be brought.

Parallel plan execution with self-processing networks

NASA Technical Reports Server (NTRS)

Dautrechy, C. Lynne; Reggia, James A.

1989-01-01

A critical issue for space operations is how to develop and apply advanced automation techniques to reduce the cost and complexity of working in space. In this context, it is important to examine how recent advances in self-processing networks can be applied for planning and scheduling tasks. For this reason, the feasibility of applying self-processing network models to a variety of planning and control problems relevant to spacecraft activities is being explored. Goals are to demonstrate that self-processing methods are applicable to these problems, and that MIRRORS/II, a general purpose software environment for implementing self-processing models, is sufficiently robust to support development of a wide range of application prototypes. Using MIRRORS/II and marker passing modelling techniques, a model of the execution of a Spaceworld plan was implemented. This is a simplified model of the Voyager spacecraft which photographed Jupiter, Saturn, and their satellites. It is shown that plan execution, a task usually solved using traditional artificial intelligence (AI) techniques, can be accomplished using a self-processing network. The fact that self-processing networks were applied to other space-related tasks, in addition to the one discussed here, demonstrates the general applicability of this approach to planning and control problems relevant to spacecraft activities. It is also demonstrated that MIRRORS/II is a powerful environment for the development and evaluation of self-processing systems.

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.

An intelligent traffic controller

DOT National Transportation Integrated Search

1995-11-01

Advances in computing sciences have not been applied to traffic control. This paper describes the development of an intelligent controller. A controller with advanced control logic can significantly improve traffic flows at intersections. In this vei...

THE CHOICE OF OPTIMAL STRUCTURE OF ARTIFICIAL NEURAL NETWORK CLASSIFIER INTENDED FOR CLASSIFICATION OF WELDING FLAWS

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

Sikora, R.; Chady, T.; Baniukiewicz, P.

2010-02-22

Nondestructive testing and evaluation are under continuous development. Currently researches are concentrated on three main topics: advancement of existing methods, introduction of novel methods and development of artificial intelligent systems for automatic defect recognition (ADR). Automatic defect classification algorithm comprises of two main tasks: creating a defect database and preparing a defect classifier. Here, the database was built using defect features that describe all geometrical and texture properties of the defect. Almost twenty carefully selected features calculated for flaws extracted from real radiograms were used. The radiograms were obtained from shipbuilding industry and they were verified by qualified operator. Twomore » weld defect's classifiers based on artificial neural networks were proposed and compared. First model consisted of one neural network model, where each output neuron corresponded to different defect group. The second model contained five neural networks. Each neural network had one neuron on output and was responsible for detection of defects from one group. In order to evaluate the effectiveness of the neural networks classifiers, the mean square errors were calculated for test radiograms and compared.« less

The Choice of Optimal Structure of Artificial Neural Network Classifier Intended for Classification of Welding Flaws

NASA Astrophysics Data System (ADS)

Sikora, R.; Chady, T.; Baniukiewicz, P.; Caryk, M.; Piekarczyk, B.

2010-02-01

Nondestructive testing and evaluation are under continuous development. Currently researches are concentrated on three main topics: advancement of existing methods, introduction of novel methods and development of artificial intelligent systems for automatic defect recognition (ADR). Automatic defect classification algorithm comprises of two main tasks: creating a defect database and preparing a defect classifier. Here, the database was built using defect features that describe all geometrical and texture properties of the defect. Almost twenty carefully selected features calculated for flaws extracted from real radiograms were used. The radiograms were obtained from shipbuilding industry and they were verified by qualified operator. Two weld defect's classifiers based on artificial neural networks were proposed and compared. First model consisted of one neural network model, where each output neuron corresponded to different defect group. The second model contained five neural networks. Each neural network had one neuron on output and was responsible for detection of defects from one group. In order to evaluate the effectiveness of the neural networks classifiers, the mean square errors were calculated for test radiograms and compared.

Practopoiesis: or how life fosters a mind.

PubMed

Nikolić, Danko

2015-05-21

The mind is a biological phenomenon. Thus, biological principles of organization should also be the principles underlying mental operations. Practopoiesis states that the key for achieving intelligence through adaptation is an arrangement in which mechanisms laying at a lower level of organization, by their operations and interaction with the environment, enable creation of mechanisms laying at a higher level of organization. When such an organizational advance of a system occurs, it is called a traverse. A case of traverse is when plasticity mechanisms (at a lower level of organization), by their operations, create a neural network anatomy (at a higher level of organization). Another case is the actual production of behavior by that network, whereby the mechanisms of neuronal activity operate to create motor actions. Practopoietic theory explains why the adaptability of a system increases with each increase in the number of traverses. With a larger number of traverses, a system can be relatively small and yet, produce a higher degree of adaptive/intelligent behavior than a system with a lower number of traverses. The present analyses indicate that the two well-known traverses - neural plasticity and neural activity - are not sufficient to explain human mental capabilities. At least one additional traverse is needed, which is named anapoiesis for its contribution in reconstructing knowledge e.g., from long-term memory into working memory. The conclusions bear implications for brain theory, the mind-body explanatory gap, and developments of artificial intelligence technologies. Copyright © 2015 The Author. Published by Elsevier Ltd.. All rights reserved.

A review on experimental design for pollutants removal in water treatment with the aid of artificial intelligence.

PubMed

Fan, Mingyi; Hu, Jiwei; Cao, Rensheng; Ruan, Wenqian; Wei, Xionghui

2018-06-01

Water pollution occurs mainly due to inorganic and organic pollutants, such as nutrients, heavy metals and persistent organic pollutants. For the modeling and optimization of pollutants removal, artificial intelligence (AI) has been used as a major tool in the experimental design that can generate the optimal operational variables, since AI has recently gained a tremendous advance. The present review describes the fundamentals, advantages and limitations of AI tools. Artificial neural networks (ANNs) are the AI tools frequently adopted to predict the pollutants removal processes because of their capabilities of self-learning and self-adapting, while genetic algorithm (GA) and particle swarm optimization (PSO) are also useful AI methodologies in efficient search for the global optima. This article summarizes the modeling and optimization of pollutants removal processes in water treatment by using multilayer perception, fuzzy neural, radial basis function and self-organizing map networks. Furthermore, the results conclude that the hybrid models of ANNs with GA and PSO can be successfully applied in water treatment with satisfactory accuracies. Finally, the limitations of current AI tools and their new developments are also highlighted for prospective applications in the environmental protection. Copyright © 2018 Elsevier Ltd. All rights reserved.

Towards deep learning with segregated dendrites

PubMed Central

Guerguiev, Jordan; Lillicrap, Timothy P

2017-01-01

Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear whether deep learning could occur in the real brain. Here, we show that a deep learning algorithm that utilizes multi-compartment neurons might help us to understand how the neocortex optimizes cost functions. Like neocortical pyramidal neurons, neurons in our model receive sensory information and higher-order feedback in electrotonically segregated compartments. Thanks to this segregation, neurons in different layers of the network can coordinate synaptic weight updates. As a result, the network learns to categorize images better than a single layer network. Furthermore, we show that our algorithm takes advantage of multilayer architectures to identify useful higher-order representations—the hallmark of deep learning. This work demonstrates that deep learning can be achieved using segregated dendritic compartments, which may help to explain the morphology of neocortical pyramidal neurons. PMID:29205151

Towards deep learning with segregated dendrites.

PubMed

Guerguiev, Jordan; Lillicrap, Timothy P; Richards, Blake A

2017-12-05

Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear whether deep learning could occur in the real brain. Here, we show that a deep learning algorithm that utilizes multi-compartment neurons might help us to understand how the neocortex optimizes cost functions. Like neocortical pyramidal neurons, neurons in our model receive sensory information and higher-order feedback in electrotonically segregated compartments. Thanks to this segregation, neurons in different layers of the network can coordinate synaptic weight updates. As a result, the network learns to categorize images better than a single layer network. Furthermore, we show that our algorithm takes advantage of multilayer architectures to identify useful higher-order representations-the hallmark of deep learning. This work demonstrates that deep learning can be achieved using segregated dendritic compartments, which may help to explain the morphology of neocortical pyramidal neurons.

Investigation on the use of artificial neural networks to overcome the effects of environmental and operational changes on guided waves monitoring

NASA Astrophysics Data System (ADS)

El Mountassir, M.; Yaacoubi, S.; Dahmene, F.

2015-07-01

Intelligent feature extraction and advanced signal processing techniques are necessary for a better interpretation of ultrasonic guided waves signals either in structural health monitoring (SHM) or in nondestructive testing (NDT). Such signals are characterized by at least multi-modal and dispersive components. In addition, in SHM, these signals are closely vulnerable to environmental and operational conditions (EOCs), and can be severely affected. In this paper we investigate the use of Artificial Neural Network (ANN) to overcome these effects and to provide a reliable damage detection method with a minimal of false indications. An experimental case of study (full scale pipe) is presented. Damages sizes have been increased and their shapes modified in different steps. Various parameters such as the number of inputs and the number of hidden neurons were studied to find the optimal configuration of the neural network.

Socioscape: Real-Time Analysis of Dynamic Heterogeneous Networks In Complex Socio-Cultural Systems

DTIC Science & Technology

2015-10-22

Cluster Mixed-Membership Blockmodel for Time-Evolving Networks, Proceedings of the 14th International Conference on Artifical Intelligence and...Learning With Simultaneous Orthogonal Matching Pursuit, Proceedings of the 13th International Conference on Artifical Intelligence and Statistics

Technical report on prototype intelligent network flow optimization (INFLO) dynamic speed harmonization and queue warning.

DOT National Transportation Integrated Search

2015-06-01

This Technical Report on Prototype Intelligent Network Flow Optimization (INFLO) Dynamic Speed Harmonization and Queue Warning is the final report for the project. It describes the prototyping, acceptance testing and small-scale demonstration of the ...

Concept development and needs identification for intelligent network flow optimization (INFLO) : test readiness assessment.

DOT National Transportation Integrated Search

2012-11-01

The purpose of this project is to develop for the Intelligent Network Flow Optimization (INFLO), which is one collection (or bundle) of high-priority transformative applications identified by the United States Department of Transportation (USDOT) Mob...

An introduction to intelligent networks

NASA Astrophysics Data System (ADS)

Getto, Wolf

1994-02-01

Intelligent networking is a new and developing technology that is already having significant impact on telecommunications architectures. This paper offers a summary of this technology, concluding with a brief discussion of how it is likely to affect the military communications of the Australian Defence Force (ADF).

Concept development and needs identification for intelligent network flow optimization (INFLO) : concept of operations.

DOT National Transportation Integrated Search

2012-06-01

The purpose of this project is to develop for the Intelligent Network Flow Optimization (INFLO), which is one collection (or bundle) of high-priority transformative applications identified by the United States Department of Transportation (USDOT) Mob...

MicroSensors Systems: detection of a dismounted threat

NASA Astrophysics Data System (ADS)

Davis, Bill; Berglund, Victor; Falkofske, Dwight; Krantz, Brian

2005-05-01

The Micro Sensor System (MSS) is a layered sensor network with the goal of detecting dismounted threats approaching high value assets. A low power unattended ground sensor network is dependant on a network protocol for efficiency in order to minimize data transmissions after network establishment. The reduction of network 'chattiness' is a primary driver for minimizing power consumption and is a factor in establishing a low probability of detection and interception. The MSS has developed a unique protocol to meet these challenges. Unattended ground sensor systems are most likely dependant on batteries for power which due to size determines the ability of the sensor to be concealed after placement. To minimize power requirements, overcome size limitations, and maintain a low system cost the MSS utilizes advanced manufacturing processes know as Fluidic Self-Assembly and Chip Scale Packaging. The type of sensing element and the ability to sense various phenomenologies (particularly magnetic) at ranges greater than a few meters limits the effectiveness of a system. The MicroSensor System will overcome these limitations by deploying large numbers of low cost sensors, which is made possible by the advanced manufacturing process used in production of the sensors. The MSS program will provide unprecedented levels of real-time battlefield information which greatly enhances combat situational awareness when integrated with the existing Command, Control, Communications, Computers, Intelligence, Surveillance and Reconnaissance (C4ISR) infrastructure. This system will provide an important boost to realizing the information dominant, network-centric objective of Joint Vision 2020.

Groundhog Day for Medical Artificial Intelligence.

PubMed

London, Alex John

2018-05-01

Following a boom in investment and overinflated expectations in the 1980s, artificial intelligence entered a period of retrenchment known as the "AI winter." With advances in the field of machine learning and the availability of large datasets for training various types of artificial neural networks, AI is in another cycle of halcyon days. Although medicine is particularly recalcitrant to change, applications of AI in health care have professionals in fields like radiology worried about the future of their careers and have the public tittering about the prospect of soulless machines making life-and-death decisions. Medicine thus appears to be at an inflection point-a kind of Groundhog Day on which either AI will bring a springtime of improved diagnostic and predictive practices or the shadow of public and professional fear will lead to six more metaphorical weeks of winter in medical AI. © 2018 The Hastings Center.

Intelligent imaging systems for automotive applications

NASA Astrophysics Data System (ADS)

Thompson, Chris; Huang, Yingping; Fu, Shan

2004-03-01

In common with many other application areas, visual signals are becoming an increasingly important information source for many automotive applications. For several years CCD cameras have been used as research tools for a range of automotive applications. Infrared cameras, RADAR and LIDAR are other types of imaging sensors that have also been widely investigated for use in cars. This paper will describe work in this field performed in C2VIP over the last decade - starting with Night Vision Systems and looking at various other Advanced Driver Assistance Systems. Emerging from this experience, we make the following observations which are crucial for "intelligent" imaging systems: 1. Careful arrangement of sensor array. 2. Dynamic-Self-Calibration. 3. Networking and processing. 4. Fusion with other imaging sensors, both at the image level and the feature level, provides much more flexibility and reliability in complex situations. We will discuss how these problems can be addressed and what are the outstanding issues.

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.

Driving the brain towards creativity and intelligence: A network control theory analysis.

PubMed

Kenett, Yoed N; Medaglia, John D; Beaty, Roger E; Chen, Qunlin; Betzel, Richard F; Thompson-Schill, Sharon L; Qiu, Jiang

2018-01-04

High-level cognitive constructs, such as creativity and intelligence, entail complex and multiple processes, including cognitive control processes. Recent neurocognitive research on these constructs highlight the importance of dynamic interaction across neural network systems and the role of cognitive control processes in guiding such a dynamic interaction. How can we quantitatively examine the extent and ways in which cognitive control contributes to creativity and intelligence? To address this question, we apply a computational network control theory (NCT) approach to structural brain imaging data acquired via diffusion tensor imaging in a large sample of participants, to examine how NCT relates to individual differences in distinct measures of creative ability and intelligence. Recent application of this theory at the neural level is built on a model of brain dynamics, which mathematically models patterns of inter-region activity propagated along the structure of an underlying network. The strength of this approach is its ability to characterize the potential role of each brain region in regulating whole-brain network function based on its anatomical fingerprint and a simplified model of node dynamics. We find that intelligence is related to the ability to "drive" the brain system into easy to reach neural states by the right inferior parietal lobe and lower integration abilities in the left retrosplenial cortex. We also find that creativity is related to the ability to "drive" the brain system into difficult to reach states by the right dorsolateral prefrontal cortex (inferior frontal junction) and higher integration abilities in sensorimotor areas. Furthermore, we found that different facets of creativity-fluency, flexibility, and originality-relate to generally similar but not identical network controllability processes. We relate our findings to general theories on intelligence and creativity. Copyright © 2018 Elsevier Ltd. All rights reserved.

Integrated intelligent systems in advanced reactor control rooms

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

Beckmeyer, R.R.

1989-01-01

An intelligent, reactor control room, information system is designed to be an integral part of an advanced control room and will assist the reactor operator's decision making process by continuously monitoring the current plant state and providing recommended operator actions to improve that state. This intelligent system is an integral part of, as well as an extension to, the plant protection and control systems. This paper describes the interaction of several functional components (intelligent information data display, technical specifications monitoring, and dynamic procedures) of the overall system and the artificial intelligence laboratory environment assembled for testing the prototype. 10 refs.,more » 5 figs.« less

Artificial intelligence in medicine.

PubMed Central

Ramesh, A. N.; Kambhampati, C.; Monson, J. R. T.; Drew, P. J.

2004-01-01

INTRODUCTION: Artificial intelligence is a branch of computer science capable of analysing complex medical data. Their potential to exploit meaningful relationship with in a data set can be used in the diagnosis, treatment and predicting outcome in many clinical scenarios. METHODS: Medline and internet searches were carried out using the keywords 'artificial intelligence' and 'neural networks (computer)'. Further references were obtained by cross-referencing from key articles. An overview of different artificial intelligent techniques is presented in this paper along with the review of important clinical applications. RESULTS: The proficiency of artificial intelligent techniques has been explored in almost every field of medicine. Artificial neural network was the most commonly used analytical tool whilst other artificial intelligent techniques such as fuzzy expert systems, evolutionary computation and hybrid intelligent systems have all been used in different clinical settings. DISCUSSION: Artificial intelligence techniques have the potential to be applied in almost every field of medicine. There is need for further clinical trials which are appropriately designed before these emergent techniques find application in the real clinical setting. PMID:15333167

Artificial intelligence in medicine.

PubMed

Ramesh, A N; Kambhampati, C; Monson, J R T; Drew, P J

2004-09-01

Artificial intelligence is a branch of computer science capable of analysing complex medical data. Their potential to exploit meaningful relationship with in a data set can be used in the diagnosis, treatment and predicting outcome in many clinical scenarios. Medline and internet searches were carried out using the keywords 'artificial intelligence' and 'neural networks (computer)'. Further references were obtained by cross-referencing from key articles. An overview of different artificial intelligent techniques is presented in this paper along with the review of important clinical applications. The proficiency of artificial intelligent techniques has been explored in almost every field of medicine. Artificial neural network was the most commonly used analytical tool whilst other artificial intelligent techniques such as fuzzy expert systems, evolutionary computation and hybrid intelligent systems have all been used in different clinical settings. Artificial intelligence techniques have the potential to be applied in almost every field of medicine. There is need for further clinical trials which are appropriately designed before these emergent techniques find application in the real clinical setting.

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.

An approach to efficient mobility management in intelligent networks

NASA Technical Reports Server (NTRS)

Murthy, K. M. S.

1995-01-01

Providing personal communication systems supporting full mobility require intelligent networks for tracking mobile users and facilitating outgoing and incoming calls over different physical and network environments. In realizing the intelligent network functionalities, databases play a major role. Currently proposed network architectures envision using the SS7-based signaling network for linking these DB's and also for interconnecting DB's with switches. If the network has to support ubiquitous, seamless mobile services, then it has to support additionally mobile application parts, viz., mobile origination calls, mobile destination calls, mobile location updates and inter-switch handovers. These functions will generate significant amount of data and require them to be transferred between databases (HLR, VLR) and switches (MSC's) very efficiently. In the future, the users (fixed or mobile) may use and communicate with sophisticated CPE's (e.g. multimedia, multipoint and multisession calls) which may require complex signaling functions. This will generate volumness service handling data and require efficient transfer of these message between databases and switches. Consequently, the network providers would be able to add new services and capabilities to their networks incrementally, quickly and cost-effectively.

Communications and Intelligent Systems Division Overview

NASA Technical Reports Server (NTRS)

Emerson, Dawn

2016-01-01

This presentation provides an overview of the research and engineering in the competency fieldsof advanced communications and intelligent systems with emphasis on advanced technologies, architecture definitionand system development for application in current and future aeronautics and space systems.

Making the Net More Intelligent.

ERIC Educational Resources Information Center

Somers, Doug

1998-01-01

Discusses how service providers can address the challenge of costs and the need for attractive services valuable to business customers. Focuses on Internet service control; applying intelligent networking features to the internet working services dilemma; and providing access control over network-based applications for Internet virtual private…

Do Narcissism and Emotional Intelligence Win Us Friends? Modeling Dynamics of Peer Popularity Using Inferential Network Analysis.

PubMed

Czarna, Anna Z; Leifeld, Philip; Śmieja, Magdalena; Dufner, Michael; Salovey, Peter

2016-09-27

This research investigated effects of narcissism and emotional intelligence (EI) on popularity in social networks. In a longitudinal field study, we examined the dynamics of popularity in 15 peer groups in two waves (N = 273). We measured narcissism, ability EI, and explicit and implicit self-esteem. In addition, we measured popularity at zero acquaintance and 3 months later. We analyzed the data using inferential network analysis (temporal exponential random graph modeling, TERGM) accounting for self-organizing network forces. People high in narcissism were popular, but increased less in popularity over time than people lower in narcissism. In contrast, emotionally intelligent people increased more in popularity over time than less emotionally intelligent people. The effects held when we controlled for explicit and implicit self-esteem. These results suggest that narcissism is rather disadvantageous and that EI is rather advantageous for long-term popularity. © 2016 by the Society for Personality and Social Psychology, Inc.

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

Web Intelligence and Artificial Intelligence in Education

ERIC Educational Resources Information Center

Devedzic, Vladan

2004-01-01

This paper surveys important aspects of Web Intelligence (WI) in the context of Artificial Intelligence in Education (AIED) research. WI explores the fundamental roles as well as practical impacts of Artificial Intelligence (AI) and advanced Information Technology (IT) on the next generation of Web-related products, systems, services, and…

Research on Intelligent Synthesis Environments

NASA Technical Reports Server (NTRS)

Noor, Ahmed K.; Lobeck, William E.

2002-01-01

Four research activities related to Intelligent Synthesis Environment (ISE) have been performed under this grant. The four activities are: 1) non-deterministic approaches that incorporate technologies such as intelligent software agents, visual simulations and other ISE technologies; 2) virtual labs that leverage modeling, simulation and information technologies to create an immersive, highly interactive virtual environment tailored to the needs of researchers and learners; 3) advanced learning modules that incorporate advanced instructional, user interface and intelligent agent technologies; and 4) assessment and continuous improvement of engineering team effectiveness in distributed collaborative environments.

Research on Intelligent Synthesis Environments

NASA Astrophysics Data System (ADS)

Noor, Ahmed K.; Loftin, R. Bowen

2002-12-01

Four research activities related to Intelligent Synthesis Environment (ISE) have been performed under this grant. The four activities are: 1) non-deterministic approaches that incorporate technologies such as intelligent software agents, visual simulations and other ISE technologies; 2) virtual labs that leverage modeling, simulation and information technologies to create an immersive, highly interactive virtual environment tailored to the needs of researchers and learners; 3) advanced learning modules that incorporate advanced instructional, user interface and intelligent agent technologies; and 4) assessment and continuous improvement of engineering team effectiveness in distributed collaborative environments.

Lesion mapping of social problem solving

PubMed Central

Colom, Roberto; Paul, Erick J.; Chau, Aileen; Solomon, Jeffrey; Grafman, Jordan H.

2014-01-01

Accumulating neuroscience evidence indicates that human intelligence is supported by a distributed network of frontal and parietal regions that enable complex, goal-directed behaviour. However, the contributions of this network to social aspects of intellectual function remain to be well characterized. Here, we report a human lesion study (n = 144) that investigates the neural bases of social problem solving (measured by the Everyday Problem Solving Inventory) and examine the degree to which individual differences in performance are predicted by a broad spectrum of psychological variables, including psychometric intelligence (measured by the Wechsler Adult Intelligence Scale), emotional intelligence (measured by the Mayer, Salovey, Caruso Emotional Intelligence Test), and personality traits (measured by the Neuroticism-Extraversion-Openness Personality Inventory). Scores for each variable were obtained, followed by voxel-based lesion–symptom mapping. Stepwise regression analyses revealed that working memory, processing speed, and emotional intelligence predict individual differences in everyday problem solving. A targeted analysis of specific everyday problem solving domains (involving friends, home management, consumerism, work, information management, and family) revealed psychological variables that selectively contribute to each. Lesion mapping results indicated that social problem solving, psychometric intelligence, and emotional intelligence are supported by a shared network of frontal, temporal, and parietal regions, including white matter association tracts that bind these areas into a coordinated system. The results support an integrative framework for understanding social intelligence and make specific recommendations for the application of the Everyday Problem Solving Inventory to the study of social problem solving in health and disease. PMID:25070511

Resource-constrained Data Collection and Fusion for Identifying Weak Distributed Patterns in Networks

DTIC Science & Technology

2013-10-15

statistic,” in Artifical Intelligence and Statistics (AISTATS), 2013. [6] ——, “Detecting activity in graphs via the Graph Ellipsoid Scan Statistic... Artifical Intelligence and Statistics (AISTATS), 2013. [8] ——, “Near-optimal anomaly detection in graphs using Lovász Extended Scan Statistic,” in Neural...networks,” in Artificial Intelligence and Statistics (AISTATS), 2010. 11 [11] D. Aldous, “The random walk construction of uniform spanning trees and

Developing an intelligence analysis process through social network analysis

NASA Astrophysics Data System (ADS)

Waskiewicz, Todd; LaMonica, Peter

2008-04-01

Intelligence analysts are tasked with making sense of enormous amounts of data and gaining an awareness of a situation that can be acted upon. This process can be extremely difficult and time consuming. Trying to differentiate between important pieces of information and extraneous data only complicates the problem. When dealing with data containing entities and relationships, social network analysis (SNA) techniques can be employed to make this job easier. Applying network measures to social network graphs can identify the most significant nodes (entities) and edges (relationships) and help the analyst further focus on key areas of concern. Strange developed a model that identifies high value targets such as centers of gravity and critical vulnerabilities. SNA lends itself to the discovery of these high value targets and the Air Force Research Laboratory (AFRL) has investigated several network measures such as centrality, betweenness, and grouping to identify centers of gravity and critical vulnerabilities. Using these network measures, a process for the intelligence analyst has been developed to aid analysts in identifying points of tactical emphasis. Organizational Risk Analyzer (ORA) and Terrorist Modus Operandi Discovery System (TMODS) are the two applications used to compute the network measures and identify the points to be acted upon. Therefore, the result of leveraging social network analysis techniques and applications will provide the analyst and the intelligence community with more focused and concentrated analysis results allowing them to more easily exploit key attributes of a network, thus saving time, money, and manpower.

Intelligent Approaches in Improving In-vehicle Network Architecture and Minimizing Power Consumption in Combat Vehicles

DTIC Science & Technology

2011-01-01

4 . TITLE AND SUBTITLE INTELLIGENT APPROACHES IN IMPROVING IN-VEHICLE NETWORK ARCHITECTURE AND MINIMIZING POWER CONSUMPTION IN COMBAT VEHICLES 5a... 4 1.3 Organization...32 CHAPTER 4 – SOFTWARE RELIABILITY PREDICTION FOR COMBAT VEHICLES . 33 4.1 Introduction

Intelligent Systems Technologies and Utilization of Earth Observation Data

NASA Technical Reports Server (NTRS)

Ramapriyan, H. K.; McConaughy, G. R.; Morse, H. S.

2004-01-01

The addition of raw data and derived geophysical parameters from several Earth observing satellites over the last decade to the data held by NASA data centers has created a data rich environment for the Earth science research and applications communities. The data products are being distributed to a large and diverse community of users. Due to advances in computational hardware, networks and communications, information management and software technologies, significant progress has been made in the last decade in archiving and providing data to users. However, to realize the full potential of the growing data archives, further progress is necessary in the transformation of data into information, and information into knowledge that can be used in particular applications. Sponsored by NASA s Intelligent Systems Project within the Computing, Information and Communication Technology (CICT) Program, a conceptual architecture study has been conducted to examine ideas to improve data utilization through the addition of intelligence into the archives in the context of an overall knowledge building system (KBS). Potential Intelligent Archive concepts include: 1) Mining archived data holdings to improve metadata to facilitate data access and usability; 2) Building intelligence about transformations on data, information, knowledge, and accompanying services; 3) Recognizing the value of results, indexing and formatting them for easy access; 4) Interacting as a cooperative node in a web of distributed systems to perform knowledge building; and 5) Being aware of other nodes in the KBS, participating in open systems interfaces and protocols for virtualization, and achieving collaborative interoperability.

Higher Intelligence Is Associated with Less Task-Related Brain Network Reconfiguration

PubMed Central

Cole, Michael W.

2016-01-01

The human brain is able to exceed modern computers on multiple computational demands (e.g., language, planning) using a small fraction of the energy. The mystery of how the brain can be so efficient is compounded by recent evidence that all brain regions are constantly active as they interact in so-called resting-state networks (RSNs). To investigate the brain's ability to process complex cognitive demands efficiently, we compared functional connectivity (FC) during rest and multiple highly distinct tasks. We found previously that RSNs are present during a wide variety of tasks and that tasks only minimally modify FC patterns throughout the brain. Here, we tested the hypothesis that, although subtle, these task-evoked FC updates from rest nonetheless contribute strongly to behavioral performance. One might expect that larger changes in FC reflect optimization of networks for the task at hand, improving behavioral performance. Alternatively, smaller changes in FC could reflect optimization for efficient (i.e., small) network updates, reducing processing demands to improve behavioral performance. We found across three task domains that high-performing individuals exhibited more efficient brain connectivity updates in the form of smaller changes in functional network architecture between rest and task. These smaller changes suggest that individuals with an optimized intrinsic network configuration for domain-general task performance experience more efficient network updates generally. Confirming this, network update efficiency correlated with general intelligence. The brain's reconfiguration efficiency therefore appears to be a key feature contributing to both its network dynamics and general cognitive ability. SIGNIFICANCE STATEMENT The brain's network configuration varies based on current task demands. For example, functional brain connections are organized in one way when one is resting quietly but in another way if one is asked to make a decision. We found that the efficiency of these updates in brain network organization is positively related to general intelligence, the ability to perform a wide variety of cognitively challenging tasks well. Specifically, we found that brain network configuration at rest was already closer to a wide variety of task configurations in intelligent individuals. This suggests that the ability to modify network connectivity efficiently when task demands change is a hallmark of high intelligence. PMID:27535904

The TurboLAN project. Phase 1: Protocol choices for high speed local area networks. Phase 2: TurboLAN Intelligent Network Adapter Card, (TINAC) architecture

NASA Technical Reports Server (NTRS)

Alkhatib, Hasan S.

1991-01-01

The hardware and the software architecture of the TurboLAN Intelligent Network Adapter Card (TINAC) are described. A high level as well as detailed treatment of the workings of various components of the TINAC are presented. The TINAC is divided into the following four major functional units: (1) the network access unit (NAU); (2) the buffer management unit; (3) the host interface unit; and (4) the node processor unit.

Convolutional neural network for earthquake detection and location

PubMed Central

Perol, Thibaut; Gharbi, Michaël; Denolle, Marine

2018-01-01

The recent evolution of induced seismicity in Central United States calls for exhaustive catalogs to improve seismic hazard assessment. Over the last decades, the volume of seismic data has increased exponentially, creating a need for efficient algorithms to reliably detect and locate earthquakes. Today’s most elaborate methods scan through the plethora of continuous seismic records, searching for repeating seismic signals. We leverage the recent advances in artificial intelligence and present ConvNetQuake, a highly scalable convolutional neural network for earthquake detection and location from a single waveform. We apply our technique to study the induced seismicity in Oklahoma, USA. We detect more than 17 times more earthquakes than previously cataloged by the Oklahoma Geological Survey. Our algorithm is orders of magnitude faster than established methods. PMID:29487899

A radar-enabled collaborative sensor network integrating COTS technology for surveillance and tracking.

PubMed

Kozma, Robert; Wang, Lan; Iftekharuddin, Khan; McCracken, Ernest; Khan, Muhammad; Islam, Khandakar; Bhurtel, Sushil R; Demirer, R Murat

2012-01-01

The feasibility of using Commercial Off-The-Shelf (COTS) sensor nodes is studied in a distributed network, aiming at dynamic surveillance and tracking of ground targets. Data acquisition by low-cost (<$50 US) miniature low-power radar through a wireless mote is described. We demonstrate the detection, ranging and velocity estimation, classification and tracking capabilities of the mini-radar, and compare results to simulations and manual measurements. Furthermore, we supplement the radar output with other sensor modalities, such as acoustic and vibration sensors. This method provides innovative solutions for detecting, identifying, and tracking vehicles and dismounts over a wide area in noisy conditions. This study presents a step towards distributed intelligent decision support and demonstrates effectiveness of small cheap sensors, which can complement advanced technologies in certain real-life scenarios.

NASA/ARC proposed training in intelligent control

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1990-01-01

Viewgraphs on NASA Ames Research Center proposed training in intelligent control was presented. Topics covered include: fuzzy logic control; neural networks in control; artificial intelligence in control; hybrid approaches; hands on experience; and fuzzy controllers.

Advanced Analysis Cognition: Improving the Cognition of Intelligence Analysis

DTIC Science & Technology

2013-09-01

NSC-17. Reorganization of the Intelligence Community , 1977. (RW 423) Anonymous. Presidential Review Memorandum NSC- 11 . Intelligence Structure and...Tubes [Powerpoint Presentation]" Global Intelligence Forum 2010, Dungarvan, Ireland, July 11 -13, 2010 (RW 4233) Anonymous. Sinclair Community ...Israel’s Intelligence Community ," International Journal of Intelligence and Counterintelligence, Vol. 11 , No. 2, 1998, pp. 154-174. (RW 723) 72

Intelligence and childlessness.

PubMed

Kanazawa, Satoshi

2014-11-01

Demographers debate why people have children in advanced industrial societies where children are net economic costs. From an evolutionary perspective, however, the important question is why some individuals choose not to have children. Recent theoretical developments in evolutionary psychology suggest that more intelligent individuals may be more likely to prefer to remain childless than less intelligent individuals. Analyses of the National Child Development Study show that more intelligent men and women express preference to remain childless early in their reproductive careers, but only more intelligent women (not more intelligent men) are more likely to remain childless by the end of their reproductive careers. Controlling for education and earnings does not at all attenuate the association between childhood general intelligence and lifetime childlessness among women. One-standard-deviation increase in childhood general intelligence (15 IQ points) decreases women's odds of parenthood by 21-25%. Because women have a greater impact on the average intelligence of future generations, the dysgenic fertility among women is predicted to lead to a decline in the average intelligence of the population in advanced industrial nations. Copyright © 2014 Elsevier Inc. All rights reserved.

The Role of Probability-Based Inference in an Intelligent Tutoring System.

ERIC Educational Resources Information Center

Mislevy, Robert J.; Gitomer, Drew H.

Probability-based inference in complex networks of interdependent variables is an active topic in statistical research, spurred by such diverse applications as forecasting, pedigree analysis, troubleshooting, and medical diagnosis. This paper concerns the role of Bayesian inference networks for updating student models in intelligent tutoring…

Concept development and needs identification for intelligent network flow optimization (INFLO) : functional and performance requirements, and high-level data and communication needs.

DOT National Transportation Integrated Search

2012-11-01

The purpose of this project is to develop for the Intelligent Network Flow Optimization (INFLO), which is one collection (or bundle) of high-priority transformative applications identified by the United States Department of Transportation (USDOT) Mob...

On the role of the plasmodial cytoskeleton in facilitating intelligent behavior in slime mold Physarum polycephalum.

PubMed

Mayne, Richard; Adamatzky, Andrew; Jones, Jeff

2015-01-01

The plasmodium of slime mold Physarum polycephalum behaves as an amorphous reaction-diffusion computing substrate and is capable of apparently 'intelligent' behavior. But how does intelligence emerge in an acellular organism? Through a range of laboratory experiments, we visualize the plasmodial cytoskeleton-a ubiquitous cellular protein scaffold whose functions are manifold and essential to life-and discuss its putative role as a network for transducing, transmitting and structuring data streams within the plasmodium. Through a range of computer modeling techniques, we demonstrate how emergent behavior, and hence computational intelligence, may occur in cytoskeletal communications networks. Specifically, we model the topology of both the actin and tubulin cytoskeletal networks and discuss how computation may occur therein. Furthermore, we present bespoke cellular automata and particle swarm models for the computational process within the cytoskeleton and observe the incidence of emergent patterns in both. Our work grants unique insight into the origins of natural intelligence; the results presented here are therefore readily transferable to the fields of natural computation, cell biology and biomedical science. We conclude by discussing how our results may alter our biological, computational and philosophical understanding of intelligence and consciousness.

An integrative architecture for general intelligence and executive function revealed by lesion mapping

PubMed Central

Colom, Roberto; Solomon, Jeffrey; Krueger, Frank; Forbes, Chad; Grafman, Jordan

2012-01-01

Although cognitive neuroscience has made remarkable progress in understanding the involvement of the prefrontal cortex in executive control, the broader functional networks that support high-level cognition and give rise to general intelligence remain to be well characterized. Here, we investigated the neural substrates of the general factor of intelligence (g) and executive function in 182 patients with focal brain damage using voxel-based lesion–symptom mapping. The Wechsler Adult Intelligence Scale and Delis–Kaplan Executive Function System were used to derive measures of g and executive function, respectively. Impaired performance on these measures was associated with damage to a distributed network of left lateralized brain areas, including regions of frontal and parietal cortex and white matter association tracts, which bind these areas into a coordinated system. The observed findings support an integrative framework for understanding the architecture of general intelligence and executive function, supporting their reliance upon a shared fronto-parietal network for the integration and control of cognitive representations and making specific recommendations for the application of the Wechsler Adult Intelligence Scale and Delis–Kaplan Executive Function System to the study of high-level cognition in health and disease. PMID:22396393

The Complexity of Crime Network Data: A Case Study of Its Consequences for Crime Control and the Study of Networks

PubMed Central

Rostami, Amir; Mondani, Hernan

2015-01-01

The field of social network analysis has received increasing attention during the past decades and has been used to tackle a variety of research questions, from prevention of sexually transmitted diseases to humanitarian relief operations. In particular, social network analyses are becoming an important component in studies of criminal networks and in criminal intelligence analysis. At the same time, intelligence analyses and assessments have become a vital component of modern approaches in policing, with policy implications for crime prevention, especially in the fight against organized crime. In this study, we have a unique opportunity to examine one specific Swedish street gang with three different datasets. These datasets are the most common information sources in studies of criminal networks: intelligence, surveillance and co-offending data. We use the data sources to build networks, and compare them by computing distance, centrality, and clustering measures. This study shows the complexity factor by which different data sources about the same object of study have a fundamental impact on the results. The same individuals have different importance ranking depending on the dataset and measure. Consequently, the data source plays a vital role in grasping the complexity of the phenomenon under study. Researchers, policy makers, and practitioners should therefore pay greater attention to the biases affecting the sources of the analysis, and be cautious when drawing conclusions based on intelligence assessments and limited network data. This study contributes to strengthening social network analysis as a reliable tool for understanding and analyzing criminality and criminal networks. PMID:25775130

The complexity of crime network data: a case study of its consequences for crime control and the study of networks.

PubMed

Rostami, Amir; Mondani, Hernan

2015-01-01

The field of social network analysis has received increasing attention during the past decades and has been used to tackle a variety of research questions, from prevention of sexually transmitted diseases to humanitarian relief operations. In particular, social network analyses are becoming an important component in studies of criminal networks and in criminal intelligence analysis. At the same time, intelligence analyses and assessments have become a vital component of modern approaches in policing, with policy implications for crime prevention, especially in the fight against organized crime. In this study, we have a unique opportunity to examine one specific Swedish street gang with three different datasets. These datasets are the most common information sources in studies of criminal networks: intelligence, surveillance and co-offending data. We use the data sources to build networks, and compare them by computing distance, centrality, and clustering measures. This study shows the complexity factor by which different data sources about the same object of study have a fundamental impact on the results. The same individuals have different importance ranking depending on the dataset and measure. Consequently, the data source plays a vital role in grasping the complexity of the phenomenon under study. Researchers, policy makers, and practitioners should therefore pay greater attention to the biases affecting the sources of the analysis, and be cautious when drawing conclusions based on intelligence assessments and limited network data. This study contributes to strengthening social network analysis as a reliable tool for understanding and analyzing criminality and criminal networks.

Intelligent route surveillance

NASA Astrophysics Data System (ADS)

Schoemaker, Robin; Sandbrink, Rody; van Voorthuijsen, Graeme

2009-05-01

Intelligence on abnormal and suspicious behaviour along roads in operational domains is extremely valuable for countering the IED (Improvised Explosive Device) threat. Local sensor networks at strategic spots can gather data for continuous monitoring of daily vehicle activity. Unattended intelligent ground sensor networks use simple sensing nodes, e.g. seismic, magnetic, radar, or acoustic, or combinations of these in one housing. The nodes deliver rudimentary data at any time to be processed with software that filters out the required information. At TNO (Netherlands Organisation for Applied Scientific Research) research has started on how to equip a sensor network with data analysis software to determine whether behaviour is suspicious or not. Furthermore, the nodes should be expendable, if necessary, and be small in size such that they are hard to detect by adversaries. The network should be self-configuring and self-sustaining and should be reliable, efficient, and effective during operational tasks - especially route surveillance - as well as robust in time and space. If data from these networks are combined with data from other remote sensing devices (e.g. UAVs (Unmanned Aerial Vehicles)/aerostats), an even more accurate assessment of the tactical situation is possible. This paper shall focus on the concepts of operation towards a working intelligent route surveillance (IRS) research demonstrator network for monitoring suspicious behaviour in IED sensitive domains.

Leveraging Intelligent Vehicle Technologies to Maximize Fuel Economy (Presentation)

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

Gonder, J.

2011-11-01

Advancements in vehicle electronics, along with communication and sensing technologies, have led to a growing number of intelligent vehicle applications. Example systems include those for advanced driver information, route planning and prediction, driver assistance, and crash avoidance. The National Renewable Energy Laboratory is exploring ways to leverage intelligent vehicle systems to achieve fuel savings. This presentation discusses several potential applications, such as providing intelligent feedback to drivers on specific ways to improve their driving efficiency, and using information about upcoming driving to optimize electrified vehicle control strategies for maximum energy efficiency and battery life. The talk also covers the potentialmore » of Advanced Driver Assistance Systems (ADAS) and related technologies to deliver significant fuel savings in addition to providing safety and convenience benefits.« less

Six Information Technology Services Contracts for the Defense Intelligence Community

DTIC Science & Technology

2000-04-24

This category covers Defense Intelligence Community organizations whose mission is to provide for the planning, development, deployment, operation ... management , and oversight of global information networks and infrastructure supporting intelligence producers. • Information Systems. This category

Communications and Intelligent Systems Division Overview

NASA Technical Reports Server (NTRS)

Emerson, Dawn

2017-01-01

This presentation provides an overview of the research and engineering work being performed in the competency fields of advanced communications and intelligent systems with emphasis on advanced technologies, architecture definition, and systems development for application in current and future aeronautics and space communications systems.

Communications and Intelligent Systems Division - Division Overview

NASA Technical Reports Server (NTRS)

Miranda, Felix A.

2017-01-01

This presentation provides an overview of the research and engineering work being performed in the competency fields of advanced communications and intelligent systems with emphasis on advanced technologies, architecture definition,and systems development for application in current and future aeronautics and space communications systems.

Communications and Intelligent Systems Division - Division Overview

NASA Technical Reports Server (NTRS)

Miranda, Felix A.

2017-01-01

This presentation provides an overview of the research and engineering work being performed in the competency fields of advanced communications and intelligent systems with emphasis on advanced technologies, architecture definition, and systems development for application in current and future aeronautics and space communications systems.

A hybrid intelligent algorithm for portfolio selection problem with fuzzy returns

NASA Astrophysics Data System (ADS)

Li, Xiang; Zhang, Yang; Wong, Hau-San; Qin, Zhongfeng

2009-11-01

Portfolio selection theory with fuzzy returns has been well developed and widely applied. Within the framework of credibility theory, several fuzzy portfolio selection models have been proposed such as mean-variance model, entropy optimization model, chance constrained programming model and so on. In order to solve these nonlinear optimization models, a hybrid intelligent algorithm is designed by integrating simulated annealing algorithm, neural network and fuzzy simulation techniques, where the neural network is used to approximate the expected value and variance for fuzzy returns and the fuzzy simulation is used to generate the training data for neural network. Since these models are used to be solved by genetic algorithm, some comparisons between the hybrid intelligent algorithm and genetic algorithm are given in terms of numerical examples, which imply that the hybrid intelligent algorithm is robust and more effective. In particular, it reduces the running time significantly for large size problems.

An Intelligent Control for the Distributed Flexible Network Photovoltaic System using Autonomous Control and Agent

NASA Astrophysics Data System (ADS)

Park, Sangsoo; Miura, Yushi; Ise, Toshifumi

This paper proposes an intelligent control for the distributed flexible network photovoltaic system using autonomous control and agent. The distributed flexible network photovoltaic system is composed of a secondary battery bank and a number of subsystems which have a solar array, a dc/dc converter and a load. The control mode of dc/dc converter can be selected based on local information by autonomous control. However, if only autonomous control using local information is applied, there are some problems associated with several cases such as voltage drop on long power lines. To overcome these problems, the authors propose introducing agents to improve control characteristics. The autonomous control with agents is called as intelligent control in this paper. The intelligent control scheme that employs the communication between agents is applied for the model system and proved with simulation using PSCAD/EMTDC.

Classification of intelligence quotient via brainwave sub-band power ratio features and artificial neural network.

PubMed

Jahidin, A H; Megat Ali, M S A; Taib, M N; Tahir, N Md; Yassin, I M; Lias, S

2014-04-01

This paper elaborates on the novel intelligence assessment method using the brainwave sub-band power ratio features. The study focuses only on the left hemisphere brainwave in its relaxed state. Distinct intelligence quotient groups have been established earlier from the score of the Raven Progressive Matrices. Sub-band power ratios are calculated from energy spectral density of theta, alpha and beta frequency bands. Synthetic data have been generated to increase dataset from 50 to 120. The features are used as input to the artificial neural network. Subsequently, the brain behaviour model has been developed using an artificial neural network that is trained with optimized learning rate, momentum constant and hidden nodes. Findings indicate that the distinct intelligence quotient groups can be classified from the brainwave sub-band power ratios with 100% training and 88.89% testing accuracies. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

An Intelligent Ensemble Neural Network Model for Wind Speed Prediction in Renewable Energy Systems.

PubMed

Ranganayaki, V; Deepa, S N

2016-01-01

Various criteria are proposed to select the number of hidden neurons in artificial neural network (ANN) models and based on the criterion evolved an intelligent ensemble neural network model is proposed to predict wind speed in renewable energy applications. The intelligent ensemble neural model based wind speed forecasting is designed by averaging the forecasted values from multiple neural network models which includes multilayer perceptron (MLP), multilayer adaptive linear neuron (Madaline), back propagation neural network (BPN), and probabilistic neural network (PNN) so as to obtain better accuracy in wind speed prediction with minimum error. The random selection of hidden neurons numbers in artificial neural network results in overfitting or underfitting problem. This paper aims to avoid the occurrence of overfitting and underfitting problems. The selection of number of hidden neurons is done in this paper employing 102 criteria; these evolved criteria are verified by the computed various error values. The proposed criteria for fixing hidden neurons are validated employing the convergence theorem. The proposed intelligent ensemble neural model is applied for wind speed prediction application considering the real time wind data collected from the nearby locations. The obtained simulation results substantiate that the proposed ensemble model reduces the error value to minimum and enhances the accuracy. The computed results prove the effectiveness of the proposed ensemble neural network (ENN) model with respect to the considered error factors in comparison with that of the earlier models available in the literature.

An Intelligent Ensemble Neural Network Model for Wind Speed Prediction in Renewable Energy Systems

PubMed Central

Ranganayaki, V.; Deepa, S. N.

2016-01-01

Various criteria are proposed to select the number of hidden neurons in artificial neural network (ANN) models and based on the criterion evolved an intelligent ensemble neural network model is proposed to predict wind speed in renewable energy applications. The intelligent ensemble neural model based wind speed forecasting is designed by averaging the forecasted values from multiple neural network models which includes multilayer perceptron (MLP), multilayer adaptive linear neuron (Madaline), back propagation neural network (BPN), and probabilistic neural network (PNN) so as to obtain better accuracy in wind speed prediction with minimum error. The random selection of hidden neurons numbers in artificial neural network results in overfitting or underfitting problem. This paper aims to avoid the occurrence of overfitting and underfitting problems. The selection of number of hidden neurons is done in this paper employing 102 criteria; these evolved criteria are verified by the computed various error values. The proposed criteria for fixing hidden neurons are validated employing the convergence theorem. The proposed intelligent ensemble neural model is applied for wind speed prediction application considering the real time wind data collected from the nearby locations. The obtained simulation results substantiate that the proposed ensemble model reduces the error value to minimum and enhances the accuracy. The computed results prove the effectiveness of the proposed ensemble neural network (ENN) model with respect to the considered error factors in comparison with that of the earlier models available in the literature. PMID:27034973

Performance evaluation of cognitive radio in advanced metering infrastructure communication

NASA Astrophysics Data System (ADS)

Hiew, Yik-Kuan; Mohd Aripin, Norazizah; Din, Norashidah Md

2016-03-01

Smart grid is an intelligent electricity grid system. A reliable two-way communication system is required to transmit both critical and non-critical smart grid data. However, it is difficult to locate a huge chunk of dedicated spectrum for smart grid communications. Hence, cognitive radio based communication is applied. Cognitive radio allows smart grid users to access licensed spectrums opportunistically with the constraint of not causing harmful interference to licensed users. In this paper, a cognitive radio based smart grid communication framework is proposed. Smart grid framework consists of Home Area Network (HAN) and Advanced Metering Infrastructure (AMI), while AMI is made up of Neighborhood Area Network (NAN) and Wide Area Network (WAN). In this paper, the authors only report the findings for AMI communication. AMI is smart grid domain that comprises smart meters, data aggregator unit, and billing center. Meter data are collected by smart meters and transmitted to data aggregator unit by using cognitive 802.11 technique; data aggregator unit then relays the data to billing center using cognitive WiMAX and TV white space. The performance of cognitive radio in AMI communication is investigated using Network Simulator 2. Simulation results show that cognitive radio improves the latency and throughput performances of AMI. Besides, cognitive radio also improves spectrum utilization efficiency of WiMAX band from 5.92% to 9.24% and duty cycle of TV band from 6.6% to 10.77%.

Characterization of computer network events through simultaneous feature selection and clustering of intrusion alerts

NASA Astrophysics Data System (ADS)

Chen, Siyue; Leung, Henry; Dondo, Maxwell

2014-05-01

As computer network security threats increase, many organizations implement multiple Network Intrusion Detection Systems (NIDS) to maximize the likelihood of intrusion detection and provide a comprehensive understanding of intrusion activities. However, NIDS trigger a massive number of alerts on a daily basis. This can be overwhelming for computer network security analysts since it is a slow and tedious process to manually analyse each alert produced. Thus, automated and intelligent clustering of alerts is important to reveal the structural correlation of events by grouping alerts with common features. As the nature of computer network attacks, and therefore alerts, is not known in advance, unsupervised alert clustering is a promising approach to achieve this goal. We propose a joint optimization technique for feature selection and clustering to aggregate similar alerts and to reduce the number of alerts that analysts have to handle individually. More precisely, each identified feature is assigned a binary value, which reflects the feature's saliency. This value is treated as a hidden variable and incorporated into a likelihood function for clustering. Since computing the optimal solution of the likelihood function directly is analytically intractable, we use the Expectation-Maximisation (EM) algorithm to iteratively update the hidden variable and use it to maximize the expected likelihood. Our empirical results, using a labelled Defense Advanced Research Projects Agency (DARPA) 2000 reference dataset, show that the proposed method gives better results than the EM clustering without feature selection in terms of the clustering accuracy.

Central and non-central networks, cognition, clinical symptoms, and polygenic risk scores in schizophrenia.

PubMed

Alloza, Clara; Bastin, Mark E; Cox, Simon R; Gibson, Jude; Duff, Barbara; Semple, Scott I; Whalley, Heather C; Lawrie, Stephen M

2017-12-01

Schizophrenia is a complex disorder that may be the result of aberrant connections between specific brain regions rather than focal brain abnormalities. Here, we investigate the relationships between brain structural connectivity as described by network analysis, intelligence, symptoms, and polygenic risk scores (PGRS) for schizophrenia in a group of patients with schizophrenia and a group of healthy controls. Recently, researchers have shown an interest in the role of high centrality networks in the disorder. However, the importance of non-central networks still remains unclear. Thus, we specifically examined network-averaged fractional anisotropy (mean edge weight) in central and non-central subnetworks. Connections with the highest betweenness centrality within the average network (>75% of centrality values) were selected to represent the central subnetwork. The remaining connections were assigned to the non-central subnetwork. Additionally, we calculated graph theory measures from the average network (connections that occur in at least 2/3 of participants). Density, strength, global efficiency, and clustering coefficient were significantly lower in patients compared with healthy controls for the average network (p FDR  < 0.05). All metrics across networks were significantly associated with intelligence (p FDR  < 0.05). There was a tendency towards significance for a correlation between intelligence and PGRS for schizophrenia (r = -0.508, p = 0.052) that was significantly mediated by central and non-central mean edge weight and every graph metric from the average network. These results are consistent with the hypothesis that intelligence deficits are associated with a genetic risk for schizophrenia, which is mediated via the disruption of distributed brain networks. Hum Brain Mapp 38:5919-5930, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Some Steps towards Intelligent Computer Tutoring Systems.

ERIC Educational Resources Information Center

Tchogovadze, Gotcha G.

1986-01-01

Describes one way of structuring an intelligent tutoring system (ITS) in light of developments in artificial intelligence. A specialized intelligent operating system (SIOS) is proposed for software for a network of microcomputers, and it is postulated that a general learning system must be used as a basic framework for the SIOS. (Author/LRW)

Intelligence Fusion for Combined Operations

DTIC Science & Technology

1994-06-03

Database ISE - Intelligence Support Element JASMIN - Joint Analysis System for Military Intelligence RC - Joint Intelligence Center JDISS - Joint Defense...has made accessable otherwise inaccessible networks such as connectivity to the German Joint Analysis System for Military Intelligence ( JASMIN ) and the...successfully any mission in the Battlespace is the essence of the C41 for the Warrior concept."’ It recognizes that the current C41 systems do not

Multi-Modal Intelligent Traffic Signal Systems (MMITSS) impacts assessment.

DOT National Transportation Integrated Search

2015-08-01

The study evaluates the potential network-wide impacts of the Multi-Modal Intelligent Transportation Signal System (MMITSS) based on a field data analysis utilizing data collected from a MMITSS prototype and a simulation analysis. The Intelligent Tra...

A Systems Engineering Survey of Artificial Intelligence and Smart Sensor Networks in a Network-Centric Environment

DTIC Science & Technology

2009-09-01

problems, to better model the problem solving of computer systems. This research brought about the intertwining of AI and cognitive psychology . Much of...where symbol sequences are sequential intelligent states of the network, and must be classified as normal, abnormal , or unknown. These symbols...is associated with abnormal behavior; and abcbc is associated with unknown behavior, as it fits no known behavior. Predicted outcomes from

An Analysis of Student Model Portability

ERIC Educational Resources Information Center

Valdés Aguirre, Benjamín; Ramírez Uresti, Jorge A.; du Boulay, Benedict

2016-01-01

Sharing user information between systems is an area of interest for every field involving personalization. Recommender Systems are more advanced in this aspect than Intelligent Tutoring Systems (ITSs) and Intelligent Learning Environments (ILEs). A reason for this is that the user models of Intelligent Tutoring Systems and Intelligent Learning…

Designing Distributed Learning Environments with Intelligent Software Agents

ERIC Educational Resources Information Center

Lin, Fuhua, Ed.

2005-01-01

"Designing Distributed Learning Environments with Intelligent Software Agents" reports on the most recent advances in agent technologies for distributed learning. Chapters are devoted to the various aspects of intelligent software agents in distributed learning, including the methodological and technical issues on where and how intelligent agents…

Use of a Deep Recurrent Neural Network to Reduce Wind Noise: Effects on Judged Speech Intelligibility and Sound Quality.

PubMed

Keshavarzi, Mahmoud; Goehring, Tobias; Zakis, Justin; Turner, Richard E; Moore, Brian C J

2018-01-01

Despite great advances in hearing-aid technology, users still experience problems with noise in windy environments. The potential benefits of using a deep recurrent neural network (RNN) for reducing wind noise were assessed. The RNN was trained using recordings of the output of the two microphones of a behind-the-ear hearing aid in response to male and female speech at various azimuths in the presence of noise produced by wind from various azimuths with a velocity of 3 m/s, using the "clean" speech as a reference. A paired-comparison procedure was used to compare all possible combinations of three conditions for subjective intelligibility and for sound quality or comfort. The conditions were unprocessed noisy speech, noisy speech processed using the RNN, and noisy speech that was high-pass filtered (which also reduced wind noise). Eighteen native English-speaking participants were tested, nine with normal hearing and nine with mild-to-moderate hearing impairment. Frequency-dependent linear amplification was provided for the latter. Processing using the RNN was significantly preferred over no processing by both subject groups for both subjective intelligibility and sound quality, although the magnitude of the preferences was small. High-pass filtering (HPF) was not significantly preferred over no processing. Although RNN was significantly preferred over HPF only for sound quality for the hearing-impaired participants, for the results as a whole, there was a preference for RNN over HPF. Overall, the results suggest that reduction of wind noise using an RNN is possible and might have beneficial effects when used in hearing aids.

Comparison of Intelligent Systems in Detecting a Child's Mathematical Gift

ERIC Educational Resources Information Center

Pavlekovic, Margita; Zekic-Susac, Marijana; Djurdjevic, Ivana

2009-01-01

This paper compares the efficiency of two intelligent methods: expert systems and neural networks, in detecting children's mathematical gift at the fourth grade of elementary school. The input space for the expert system and the neural network model consisted of 60 variables describing five basic components of a child's mathematical gift…

Distributed intelligent scheduling of FMS

NASA Astrophysics Data System (ADS)

Wu, Zuobao; Cheng, Yaodong; Pan, Xiaohong

1995-08-01

In this paper, a distributed scheduling approach of a flexible manufacturing system (FMS) is presented. A new class of Petri nets called networked time Petri nets (NTPN) for system modeling of networking environment is proposed. The distributed intelligent scheduling is implemented by three schedulers which combine NTPN models with expert system techniques. The simulation results are shown.

THRESHOLD LOGIC IN ARTIFICIAL INTELLIGENCE

DTIC Science & Technology

COMPUTER LOGIC, ARTIFICIAL INTELLIGENCE , BIONICS, GEOMETRY, INPUT OUTPUT DEVICES, LINEAR PROGRAMMING, MATHEMATICAL LOGIC, MATHEMATICAL PREDICTION, NETWORKS, PATTERN RECOGNITION, PROBABILITY, SWITCHING CIRCUITS, SYNTHESIS

An intelligent control system for failure detection and controller reconfiguration

NASA Technical Reports Server (NTRS)

Biswas, Saroj K.

1994-01-01

We present an architecture of an intelligent restructurable control system to automatically detect failure of system components, assess its impact on system performance and safety, and reconfigure the controller for performance recovery. Fault detection is based on neural network associative memories and pattern classifiers, and is implemented using a multilayer feedforward network. Details of the fault detection network along with simulation results on health monitoring of a dc motor have been presented. Conceptual developments for fault assessment using an expert system and controller reconfiguration using a neural network are outlined.

Flight Test of an Intelligent Flight-Control System

NASA Technical Reports Server (NTRS)

Davidson, Ron; Bosworth, John T.; Jacobson, Steven R.; Thomson, Michael Pl; Jorgensen, Charles C.

2003-01-01

The F-15 Advanced Controls Technology for Integrated Vehicles (ACTIVE) airplane (see figure) was the test bed for a flight test of an intelligent flight control system (IFCS). This IFCS utilizes a neural network to determine critical stability and control derivatives for a control law, the real-time gains of which are computed by an algorithm that solves the Riccati equation. These derivatives are also used to identify the parameters of a dynamic model of the airplane. The model is used in a model-following portion of the control law, in order to provide specific vehicle handling characteristics. The flight test of the IFCS marks the initiation of the Intelligent Flight Control System Advanced Concept Program (IFCS ACP), which is a collaboration between NASA and Boeing Phantom Works. The goals of the IFCS ACP are to (1) develop the concept of a flight-control system that uses neural-network technology to identify aircraft characteristics to provide optimal aircraft performance, (2) develop a self-training neural network to update estimates of aircraft properties in flight, and (3) demonstrate the aforementioned concepts on the F-15 ACTIVE airplane in flight. The activities of the initial IFCS ACP were divided into three Phases, each devoted to the attainment of a different objective. The objective of Phase I was to develop a pre-trained neural network to store and recall the wind-tunnel-based stability and control derivatives of the vehicle. The objective of Phase II was to develop a neural network that can learn how to adjust the stability and control derivatives to account for failures or modeling deficiencies. The objective of Phase III was to develop a flight control system that uses the neural network outputs as a basis for controlling the aircraft. The flight test of the IFCS was performed in stages. In the first stage, the Phase I version of the pre-trained neural network was flown in a passive mode. The neural network software was running using flight data inputs with the outputs provided to instrumentation only. The IFCS was not used to control the airplane. In another stage of the flight test, the Phase I pre-trained neural network was integrated into a Phase III version of the flight control system. The Phase I pretrained neural network provided realtime stability and control derivatives to a Phase III controller that was based on a stochastic optimal feedforward and feedback technique (SOFFT). This combined Phase I/III system was operated together with the research flight-control system (RFCS) of the F-15 ACTIVE during the flight test. The RFCS enables the pilot to switch quickly from the experimental- research flight mode back to the safe conventional mode. These initial IFCS ACP flight tests were completed in April 1999. The Phase I/III flight test milestone was to demonstrate, across a range of subsonic and supersonic flight conditions, that the pre-trained neural network could be used to supply real-time aerodynamic stability and control derivatives to the closed-loop optimal SOFFT flight controller. Additional objectives attained in the flight test included (1) flight qualification of a neural-network-based control system; (2) the use of a combined neural-network/closed-loop optimal flight-control system to obtain level-one handling qualities; and (3) demonstration, through variation of control gains, that different handling qualities can be achieved by setting new target parameters. In addition, data for the Phase-II (on-line-learning) neural network were collected, during the use of stacked-frequency- sweep excitation, for post-flight analysis. Initial analysis of these data showed the potential for future flight tests that will incorporate the real-time identification and on-line learning aspects of the IFCS.

Ortho Image and DTM Generation with Intelligent Methods

NASA Astrophysics Data System (ADS)

Bagheri, H.; Sadeghian, S.

2013-10-01

Nowadays the artificial intelligent algorithms has considered in GIS and remote sensing. Genetic algorithm and artificial neural network are two intelligent methods that are used for optimizing of image processing programs such as edge extraction and etc. these algorithms are very useful for solving of complex program. In this paper, the ability and application of genetic algorithm and artificial neural network in geospatial production process like geometric modelling of satellite images for ortho photo generation and height interpolation in raster Digital Terrain Model production process is discussed. In first, the geometric potential of Ikonos-2 and Worldview-2 with rational functions, 2D & 3D polynomials were tested. Also comprehensive experiments have been carried out to evaluate the viability of the genetic algorithm for optimization of rational function, 2D & 3D polynomials. Considering the quality of Ground Control Points, the accuracy (RMSE) with genetic algorithm and 3D polynomials method for Ikonos-2 Geo image was 0.508 pixel sizes and the accuracy (RMSE) with GA algorithm and rational function method for Worldview-2 image was 0.930 pixel sizes. For more another optimization artificial intelligent methods, neural networks were used. With the use of perceptron network in Worldview-2 image, a result of 0.84 pixel sizes with 4 neurons in middle layer was gained. The final conclusion was that with artificial intelligent algorithms it is possible to optimize the existing models and have better results than usual ones. Finally the artificial intelligence methods, like genetic algorithms as well as neural networks, were examined on sample data for optimizing interpolation and for generating Digital Terrain Models. The results then were compared with existing conventional methods and it appeared that these methods have a high capacity in heights interpolation and that using these networks for interpolating and optimizing the weighting methods based on inverse distance leads to a high accurate estimation of heights.

Sense-making for intelligence analysis on social media data

NASA Astrophysics Data System (ADS)

Pritzkau, Albert

2016-05-01

Social networks, in particular online social networks as a subset, enable the analysis of social relationships which are represented by interaction, collaboration, or other sorts of influence between people. Any set of people and their internal social relationships can be modelled as a general social graph. These relationships are formed by exchanging emails, making phone calls, or carrying out a range of other activities that build up the network. This paper presents an overview of current approaches to utilizing social media as a ubiquitous sensor network in the context of national and global security. Exploitation of social media is usually an interdisciplinary endeavour, in which the relevant technologies and methods are identified and linked in order ultimately demonstrate selected applications. Effective and efficient intelligence is usually accomplished in a combined human and computer effort. Indeed, the intelligence process heavily depends on combining a human's flexibility, creativity, and cognitive ability with the bandwidth and processing power of today's computers. To improve the usability and accuracy of the intelligence analysis we will have to rely on data-processing tools at the level of natural language. Especially the collection and transformation of unstructured data into actionable, structured data requires scalable computational algorithms ranging from Artificial Intelligence, via Machine Learning, to Natural Language Processing (NLP). To support intelligence analysis on social media data, social media analytics is concerned with developing and evaluating computational tools and frameworks to collect, monitor, analyze, summarize, and visualize social media data. Analytics methods are employed to extract of significant patterns that might not be obvious. As a result, different data representations rendering distinct aspects of content and interactions serve as a means to adapt the focus of the intelligence analysis to specific information requests.

Computational Intelligence in Early Diabetes Diagnosis: A Review

PubMed Central

Shankaracharya; Odedra, Devang; Samanta, Subir; Vidyarthi, Ambarish S.

2010-01-01

The development of an effective diabetes diagnosis system by taking advantage of computational intelligence is regarded as a primary goal nowadays. Many approaches based on artificial network and machine learning algorithms have been developed and tested against diabetes datasets, which were mostly related to individuals of Pima Indian origin. Yet, despite high accuracies of up to 99% in predicting the correct diabetes diagnosis, none of these approaches have reached clinical application so far. One reason for this failure may be that diabetologists or clinical investigators are sparsely informed about, or trained in the use of, computational diagnosis tools. Therefore, this article aims at sketching out an outline of the wide range of options, recent developments, and potentials in machine learning algorithms as diabetes diagnosis tools. One focus is on supervised and unsupervised methods, which have made significant impacts in the detection and diagnosis of diabetes at primary and advanced stages. Particular attention is paid to algorithms that show promise in improving diabetes diagnosis. A key advance has been the development of a more in-depth understanding and theoretical analysis of critical issues related to algorithmic construction and learning theory. These include trade-offs for maximizing generalization performance, use of physically realistic constraints, and incorporation of prior knowledge and uncertainty. The review presents and explains the most accurate algorithms, and discusses advantages and pitfalls of methodologies. This should provide a good resource for researchers from all backgrounds interested in computational intelligence-based diabetes diagnosis methods, and allows them to extend their knowledge into this kind of research. PMID:21713313

Computational intelligence in early diabetes diagnosis: a review.

PubMed

Shankaracharya; Odedra, Devang; Samanta, Subir; Vidyarthi, Ambarish S

2010-01-01

The development of an effective diabetes diagnosis system by taking advantage of computational intelligence is regarded as a primary goal nowadays. Many approaches based on artificial network and machine learning algorithms have been developed and tested against diabetes datasets, which were mostly related to individuals of Pima Indian origin. Yet, despite high accuracies of up to 99% in predicting the correct diabetes diagnosis, none of these approaches have reached clinical application so far. One reason for this failure may be that diabetologists or clinical investigators are sparsely informed about, or trained in the use of, computational diagnosis tools. Therefore, this article aims at sketching out an outline of the wide range of options, recent developments, and potentials in machine learning algorithms as diabetes diagnosis tools. One focus is on supervised and unsupervised methods, which have made significant impacts in the detection and diagnosis of diabetes at primary and advanced stages. Particular attention is paid to algorithms that show promise in improving diabetes diagnosis. A key advance has been the development of a more in-depth understanding and theoretical analysis of critical issues related to algorithmic construction and learning theory. These include trade-offs for maximizing generalization performance, use of physically realistic constraints, and incorporation of prior knowledge and uncertainty. The review presents and explains the most accurate algorithms, and discusses advantages and pitfalls of methodologies. This should provide a good resource for researchers from all backgrounds interested in computational intelligence-based diabetes diagnosis methods, and allows them to extend their knowledge into this kind of research.

Educational Assessment Using Intelligent Systems. Research Report. ETS RR-08-68

ERIC Educational Resources Information Center

Shute, Valerie J.; Zapata-Rivera, Diego

2008-01-01

Recent advances in educational assessment, cognitive science, and artificial intelligence have made it possible to integrate valid assessment and instruction in the form of modern computer-based intelligent systems. These intelligent systems leverage assessment information that is gathered from various sources (e.g., summative and formative). This…

Attention and Working Memory as Predictors of Intelligence

ERIC Educational Resources Information Center

Schweizer, Karl; Moosbrugger, Helfried

2004-01-01

The paper reports on an investigation of attention and working memory as sources of intelligence. The investigation was concentrated on the relatedness of attention and working memory as predictors of intelligence and on the structure underlying the prediction. In a sample of 120 participants, intelligence was assessed by the Advanced Progressive…

Intelligent Transportation Systems (ITS) plan for Canada : en route to intelligent mobility

DOT National Transportation Integrated Search

1999-11-01

Intelligent Transportation Systems (ITS) include the application of advanced information processing, communications, sensor and control technologies and management strategies in an integrated manner to improve the functioning of the transportation sy...

2017 Cybersecurity Workshop: Readouts from Working Groups - Video Text

Science.gov Websites

applicability of artificial intelligence to search for cybersecurity gaps in our existing SKATA networks. Second primarily renewable that all back each other up; that are all highly intelligent, artificial intelligence we have in cyber security, digital technologies, artificial intelligence. We think that that would

Research on Holographic Evaluation of Service Quality in Power Data Network

NASA Astrophysics Data System (ADS)

Wei, Chen; Jing, Tao; Ji, Yutong

2018-01-01

With the rapid development of power data network, the continuous development of the Power data application service system, more and more service systems are being put into operation. Following this, the higher requirements for network quality and service quality are raised, in the actual process for the network operation and maintenance. This paper describes the electricity network and data network services status. A holographic assessment model was presented to achieve a comprehensive intelligence assessment on the power data network and quality of service in the operation and maintenance on the power data network. This evaluation method avoids the problems caused by traditional means which performs a single assessment of network performance quality. This intelligent Evaluation method can improve the efficiency of network operation and maintenance guarantee the quality of real-time service in the power data network..

Efficient large-scale graph data optimization for intelligent video surveillance

NASA Astrophysics Data System (ADS)

Shang, Quanhong; Zhang, Shujun; Wang, Yanbo; Sun, Chen; Wang, Zepeng; Zhang, Luming

2017-08-01

Society is rapidly accepting the use of a wide variety of cameras Location and applications: site traffic monitoring, parking Lot surveillance, car and smart space. These ones here the camera provides data every day in an analysis Effective way. Recent advances in sensor technology Manufacturing, communications and computing are stimulating.The development of new applications that can change the traditional Vision system incorporating universal smart camera network. This Analysis of visual cues in multi camera networks makes wide Applications ranging from smart home and office automation to large area surveillance and traffic surveillance. In addition, dense Camera networks, most of which have large overlapping areas of cameras. In the view of good research, we focus on sparse camera networks. One Sparse camera network using large area surveillance. As few cameras as possible, most cameras do not overlap Each other’s field of vision. This task is challenging Lack of knowledge of topology Network, the specific changes in appearance and movement Track different opinions of the target, as well as difficulties Understanding complex events in a network. In this review in this paper, we present a comprehensive survey of recent studies Results to solve the problem of topology learning, Object appearance modeling and global activity understanding sparse camera network. In addition, some of the current open Research issues are discussed.

Intelligent distributed medical image management

NASA Astrophysics Data System (ADS)

Garcia, Hong-Mei C.; Yun, David Y.

1995-05-01

The rapid advancements in high performance global communication have accelerated cooperative image-based medical services to a new frontier. Traditional image-based medical services such as radiology and diagnostic consultation can now fully utilize multimedia technologies in order to provide novel services, including remote cooperative medical triage, distributed virtual simulation of operations, as well as cross-country collaborative medical research and training. Fast (efficient) and easy (flexible) retrieval of relevant images remains a critical requirement for the provision of remote medical services. This paper describes the database system requirements, identifies technological building blocks for meeting the requirements, and presents a system architecture for our target image database system, MISSION-DBS, which has been designed to fulfill the goals of Project MISSION (medical imaging support via satellite integrated optical network) -- an experimental high performance gigabit satellite communication network with access to remote supercomputing power, medical image databases, and 3D visualization capabilities in addition to medical expertise anywhere and anytime around the country. The MISSION-DBS design employs a synergistic fusion of techniques in distributed databases (DDB) and artificial intelligence (AI) for storing, migrating, accessing, and exploring images. The efficient storage and retrieval of voluminous image information is achieved by integrating DDB modeling and AI techniques for image processing while the flexible retrieval mechanisms are accomplished by combining attribute- based and content-based retrievals.

On the use of multi-agent systems for the monitoring of industrial systems

NASA Astrophysics Data System (ADS)

Rezki, Nafissa; Kazar, Okba; Mouss, Leila Hayet; Kahloul, Laid; Rezki, Djamil

2016-03-01

The objective of the current paper is to present an intelligent system for complex process monitoring, based on artificial intelligence technologies. This system aims to realize with success all the complex process monitoring tasks that are: detection, diagnosis, identification and reconfiguration. For this purpose, the development of a multi-agent system that combines multiple intelligences such as: multivariate control charts, neural networks, Bayesian networks and expert systems has became a necessity. The proposed system is evaluated in the monitoring of the complex process Tennessee Eastman process.

Artificial Intelligence.

ERIC Educational Resources Information Center

Wash, Darrel Patrick

1989-01-01

Making a machine seem intelligent is not easy. As a consequence, demand has been rising for computer professionals skilled in artificial intelligence and is likely to continue to go up. These workers develop expert systems and solve the mysteries of machine vision, natural language processing, and neural networks. (Editor)

Interfacing a biosurveillance portal and an international network of institutional analysts to detect biological threats.

PubMed

Riccardo, Flavia; Shigematsu, Mika; Chow, Catherine; McKnight, C Jason; Linge, Jens; Doherty, Brian; Dente, Maria Grazia; Declich, Silvia; Barker, Mike; Barboza, Philippe; Vaillant, Laetitia; Donachie, Alastair; Mawudeku, Abla; Blench, Michael; Arthur, Ray

2014-01-01

The Early Alerting and Reporting (EAR) project, launched in 2008, is aimed at improving global early alerting and risk assessment and evaluating the feasibility and opportunity of integrating the analysis of biological, chemical, radionuclear (CBRN), and pandemic influenza threats. At a time when no international collaborations existed in the field of event-based surveillance, EAR's innovative approach involved both epidemic intelligence experts and internet-based biosurveillance system providers in the framework of an international collaboration called the Global Health Security Initiative, which involved the ministries of health of the G7 countries and Mexico, the World Health Organization, and the European Commission. The EAR project pooled data from 7 major internet-based biosurveillance systems onto a common portal that was progressively optimized for biological threat detection under the guidance of epidemic intelligence experts from public health institutions in Canada, the European Centre for Disease Prevention and Control, France, Germany, Italy, Japan, the United Kingdom, and the United States. The group became the first end users of the EAR portal, constituting a network of analysts working with a common standard operating procedure and risk assessment tools on a rotation basis to constantly screen and assess public information on the web for events that could suggest an intentional release of biological agents. Following the first 2-year pilot phase, the EAR project was tested in its capacity to monitor biological threats, proving that its working model was feasible and demonstrating the high commitment of the countries and international institutions involved. During the testing period, analysts using the EAR platform did not miss intentional events of a biological nature and did not issue false alarms. Through the findings of this initial assessment, this article provides insights into how the field of epidemic intelligence can advance through an international network and, more specifically, how it was further developed in the EAR project.

Interfacing a Biosurveillance Portal and an International Network of Institutional Analysts to Detect Biological Threats

PubMed Central

Shigematsu, Mika; Chow, Catherine; McKnight, C. Jason; Linge, Jens; Doherty, Brian; Dente, Maria Grazia; Declich, Silvia; Barker, Mike; Barboza, Philippe; Vaillant, Laetitia; Donachie, Alastair; Mawudeku, Abla; Blench, Michael; Arthur, Ray

2014-01-01

The Early Alerting and Reporting (EAR) project, launched in 2008, is aimed at improving global early alerting and risk assessment and evaluating the feasibility and opportunity of integrating the analysis of biological, chemical, radionuclear (CBRN), and pandemic influenza threats. At a time when no international collaborations existed in the field of event-based surveillance, EAR's innovative approach involved both epidemic intelligence experts and internet-based biosurveillance system providers in the framework of an international collaboration called the Global Health Security Initiative, which involved the ministries of health of the G7 countries and Mexico, the World Health Organization, and the European Commission. The EAR project pooled data from 7 major internet-based biosurveillance systems onto a common portal that was progressively optimized for biological threat detection under the guidance of epidemic intelligence experts from public health institutions in Canada, the European Centre for Disease Prevention and Control, France, Germany, Italy, Japan, the United Kingdom, and the United States. The group became the first end users of the EAR portal, constituting a network of analysts working with a common standard operating procedure and risk assessment tools on a rotation basis to constantly screen and assess public information on the web for events that could suggest an intentional release of biological agents. Following the first 2-year pilot phase, the EAR project was tested in its capacity to monitor biological threats, proving that its working model was feasible and demonstrating the high commitment of the countries and international institutions involved. During the testing period, analysts using the EAR platform did not miss intentional events of a biological nature and did not issue false alarms. Through the findings of this initial assessment, this article provides insights into how the field of epidemic intelligence can advance through an international network and, more specifically, how it was further developed in the EAR project. PMID:25470464

Fuzzy Logic, Neural Networks, Genetic Algorithms: Views of Three Artificial Intelligence Concepts Used in Modeling Scientific Systems

ERIC Educational Resources Information Center

Sunal, Cynthia Szymanski; Karr, Charles L.; Sunal, Dennis W.

2003-01-01

Students' conceptions of three major artificial intelligence concepts used in the modeling of systems in science, fuzzy logic, neural networks, and genetic algorithms were investigated before and after a higher education science course. Students initially explored their prior ideas related to the three concepts through active tasks. Then,…

The Use of Artificial Neural Networks to Estimate Speech Intelligibility from Acoustic Variables: A Preliminary Analysis.

ERIC Educational Resources Information Center

Metz, Dale Evan; And Others

1992-01-01

A preliminary scheme for estimating the speech intelligibility of hearing-impaired speakers from acoustic parameters, using a computerized artificial neural network to process mathematically the acoustic input variables, is outlined. Tests with 60 hearing-impaired speakers found the scheme to be highly accurate in identifying speakers separated by…

Developing Emotion-Aware, Advanced Learning Technologies: A Taxonomy of Approaches and Features

ERIC Educational Resources Information Center

Harley, Jason M.; Lajoie, Susanne P.; Frasson, Claude; Hall, Nathan C.

2017-01-01

A growing body of work on intelligent tutoring systems, affective computing, and artificial intelligence in education is exploring creative, technology-driven approaches to enhance learners' experience of adaptive, positively-valenced emotions while interacting with advanced learning technologies. Despite this, there has been no published work to…

Entanglement-Gradient Routing for Quantum Networks.

PubMed

Gyongyosi, Laszlo; Imre, Sandor

2017-10-27

We define the entanglement-gradient routing scheme for quantum repeater networks. The routing framework fuses the fundamentals of swarm intelligence and quantum Shannon theory. Swarm intelligence provides nature-inspired solutions for problem solving. Motivated by models of social insect behavior, the routing is performed using parallel threads to determine the shortest path via the entanglement gradient coefficient, which describes the feasibility of the entangled links and paths of the network. The routing metrics are derived from the characteristics of entanglement transmission and relevant measures of entanglement distribution in quantum networks. The method allows a moderate complexity decentralized routing in quantum repeater networks. The results can be applied in experimental quantum networking, future quantum Internet, and long-distance quantum communications.

Fbis report. Science and technology: China, October 18, 1995

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

NONE

1995-10-18

;Partial Contents: Nanomaterials Fabrication, Applications Research Advances Noted; CAST Announces World`s First Space-Grown Large-Diameter GaAs Monocrystal; Assay of Antiviral Activity of Antisense Phosphorothioate Oligodeoxynucleotide Against Dengue Virus; Expression and Antigenicity of Chimeric Proteins of Cholera Toxin B Subunit With Hepatitis C Virus; CNCOFIEC Signs Agreement With IBM for New Intelligent Building; Latest Reports on Optical Computing, Memory; BIDC To Introduce S3 Company`s Multimedia Accelerator Chipset; Virtual Private PCN Ring Network Based on ATM VP Cross-Connection; Beijing Gets Nation`s First Frame Relay Network; Situation of Power Industry Development and International Cooperation; Diagrams of China`s Nuclear Waste Containment Vessels; Chinese-Developed Containment Vesselmore » Material Reaches World Standards; Second Fuel Elements for Qinshan Plant Passes Inspection; and Geothermal Deep-Well Electric Pump Technology Developed.« less

A Radar-Enabled Collaborative Sensor Network Integrating COTS Technology for Surveillance and Tracking

PubMed Central

Kozma, Robert; Wang, Lan; Iftekharuddin, Khan; McCracken, Ernest; Khan, Muhammad; Islam, Khandakar; Bhurtel, Sushil R.; Demirer, R. Murat

2012-01-01

The feasibility of using Commercial Off-The-Shelf (COTS) sensor nodes is studied in a distributed network, aiming at dynamic surveillance and tracking of ground targets. Data acquisition by low-cost (<$50 US) miniature low-power radar through a wireless mote is described. We demonstrate the detection, ranging and velocity estimation, classification and tracking capabilities of the mini-radar, and compare results to simulations and manual measurements. Furthermore, we supplement the radar output with other sensor modalities, such as acoustic and vibration sensors. This method provides innovative solutions for detecting, identifying, and tracking vehicles and dismounts over a wide area in noisy conditions. This study presents a step towards distributed intelligent decision support and demonstrates effectiveness of small cheap sensors, which can complement advanced technologies in certain real-life scenarios. PMID:22438713

Mobile Robotics Activities in DOE Laboratories

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

Ron Lujan; Jerry Harbour; John T. Feddema

This paper will briefly outline major activities in Department of Energy (DOE) Laboratories focused on mobile platforms, both Unmanned Ground Vehicles (UGV’s) as well as Unmanned Air Vehicles (UAV’s). The activities will be discussed in the context of the science and technology construct used by the DOE Technology Roadmap for Robotics and Intelligent Machines (RIM)1 published in 1998; namely, Perception, Reasoning, Action, and Integration. The activities to be discussed span from research and development to deployment in field operations. The activities support customers in other agencies. The discussion of "perception" will include hyperspectral sensors, complex patterns discrimination, multisensor fusion andmore » advances in LADAR technologies, including real-world perception. "Reasoning" activities to be covered include cooperative controls, distributed systems, ad-hoc networks, platform-centric intelligence, and adaptable communications. The paper will discuss "action" activities such as advanced mobility and various air and ground platforms. In the RIM construct, "integration" includes the Human-Machine Integration. Accordingly the paper will discuss adjustable autonomy and the collaboration of operator(s) with distributed UGV’s and UAV’s. Integration also refers to the applications of these technologies into systems to perform operations such as perimeter surveillance, large-area monitoring and reconnaissance. Unique facilities and test beds for advanced mobile systems will be described. Given that this paper is an overview, rather than delve into specific detail in these activities, other more exhaustive references and sources will be cited extensively.« less

Lesion mapping of social problem solving.

PubMed

Barbey, Aron K; Colom, Roberto; Paul, Erick J; Chau, Aileen; Solomon, Jeffrey; Grafman, Jordan H

2014-10-01

Accumulating neuroscience evidence indicates that human intelligence is supported by a distributed network of frontal and parietal regions that enable complex, goal-directed behaviour. However, the contributions of this network to social aspects of intellectual function remain to be well characterized. Here, we report a human lesion study (n = 144) that investigates the neural bases of social problem solving (measured by the Everyday Problem Solving Inventory) and examine the degree to which individual differences in performance are predicted by a broad spectrum of psychological variables, including psychometric intelligence (measured by the Wechsler Adult Intelligence Scale), emotional intelligence (measured by the Mayer, Salovey, Caruso Emotional Intelligence Test), and personality traits (measured by the Neuroticism-Extraversion-Openness Personality Inventory). Scores for each variable were obtained, followed by voxel-based lesion-symptom mapping. Stepwise regression analyses revealed that working memory, processing speed, and emotional intelligence predict individual differences in everyday problem solving. A targeted analysis of specific everyday problem solving domains (involving friends, home management, consumerism, work, information management, and family) revealed psychological variables that selectively contribute to each. Lesion mapping results indicated that social problem solving, psychometric intelligence, and emotional intelligence are supported by a shared network of frontal, temporal, and parietal regions, including white matter association tracts that bind these areas into a coordinated system. The results support an integrative framework for understanding social intelligence and make specific recommendations for the application of the Everyday Problem Solving Inventory to the study of social problem solving in health and disease. © The Author (2014). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

JPEG 2000 in advanced ground station architectures

NASA Astrophysics Data System (ADS)

Chien, Alan T.; Brower, Bernard V.; Rajan, Sreekanth D.

2000-11-01

The integration and management of information from distributed and heterogeneous information producers and providers must be a key foundation of any developing imagery intelligence system. Historically, imagery providers acted as production agencies for imagery, imagery intelligence, and geospatial information. In the future, these imagery producers will be evolving to act more like e-business information brokers. The management of imagery and geospatial information-visible, spectral, infrared (IR), radar, elevation, or other feature and foundation data-is crucial from a quality and content perspective. By 2005, there will be significantly advanced collection systems and a myriad of storage devices. There will also be a number of automated and man-in-the-loop correlation, fusion, and exploitation capabilities. All of these new imagery collection and storage systems will result in a higher volume and greater variety of imagery being disseminated and archived in the future. This paper illustrates the importance-from a collection, storage, exploitation, and dissemination perspective-of the proper selection and implementation of standards-based compression technology for ground station and dissemination/archive networks. It specifically discusses the new compression capabilities featured in JPEG 2000 and how that commercially based technology can provide significant improvements to the overall imagery and geospatial enterprise both from an architectural perspective as well as from a user's prospective.

Computational intelligence for target assessment in Parkinson's disease

NASA Astrophysics Data System (ADS)

Micheli-Tzanakou, Evangelia; Hamilton, J. L.; Zheng, J.; Lehman, Richard M.

2001-11-01

Recent advances in image and signal processing have created a new challenging environment for biomedical engineers. Methods that were developed for different fields are now finding a fertile ground in biomedicine, especially in the analysis of bio-signals and in the understanding of images. More and more, these methods are used in the operating room, helping surgeons, and in the physician's office as aids for diagnostic purposes. Neural Network (NN) research on the other hand, has gone a long way in the past decade. NNs now consist of many thousands of highly interconnected processing elements that can encode, store and recall relationships between different patterns by altering the weighting coefficients of inputs in a systematic way. Although they can generate reasonable outputs from unknown input patterns, and can tolerate a great deal of noise, they are very slow when run on a serial machine. We have used advanced signal processing and innovative image processing methods that are used along with computational intelligence for diagnostic purposes and as visualization aids inside and outside the operating room. Applications to be discussed include EEGs and field potentials in Parkinson's disease along with 3D reconstruction of MR or fMR brain images in Parkinson's patients, are currently used in the operating room for Pallidotomies and Deep Brain Stimulation (DBS).

Seismic activity prediction using computational intelligence techniques in northern Pakistan

NASA Astrophysics Data System (ADS)

Asim, Khawaja M.; Awais, Muhammad; Martínez-Álvarez, F.; Iqbal, Talat

2017-10-01

Earthquake prediction study is carried out for the region of northern Pakistan. The prediction methodology includes interdisciplinary interaction of seismology and computational intelligence. Eight seismic parameters are computed based upon the past earthquakes. Predictive ability of these eight seismic parameters is evaluated in terms of information gain, which leads to the selection of six parameters to be used in prediction. Multiple computationally intelligent models have been developed for earthquake prediction using selected seismic parameters. These models include feed-forward neural network, recurrent neural network, random forest, multi layer perceptron, radial basis neural network, and support vector machine. The performance of every prediction model is evaluated and McNemar's statistical test is applied to observe the statistical significance of computational methodologies. Feed-forward neural network shows statistically significant predictions along with accuracy of 75% and positive predictive value of 78% in context of northern Pakistan.

Autonomous Control of Nuclear Power Plants

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

Basher, H.

2003-10-20

A nuclear reactor is a complex system that requires highly sophisticated controllers to ensure that desired performance and safety can be achieved and maintained during its operations. Higher-demanding operational requirements such as reliability, lower environmental impacts, and improved performance under adverse conditions in nuclear power plants, coupled with the complexity and uncertainty of the models, necessitate the use of an increased level of autonomy in the control methods. In the opinion of many researchers, the tasks involved during nuclear reactor design and operation (e.g., design optimization, transient diagnosis, and core reload optimization) involve important human cognition and decisions that maymore » be more easily achieved with intelligent methods such as expert systems, fuzzy logic, neural networks, and genetic algorithms. Many experts in the field of control systems share the idea that a higher degree of autonomy in control of complex systems such as nuclear plants is more easily achievable through the integration of conventional control systems and the intelligent components. Researchers have investigated the feasibility of the integration of fuzzy logic, neural networks, genetic algorithms, and expert systems with the conventional control methods to achieve higher degrees of autonomy in different aspects of reactor operations such as reactor startup, shutdown in emergency situations, fault detection and diagnosis, nuclear reactor alarm processing and diagnosis, and reactor load-following operations, to name a few. With the advancement of new technologies and computing power, it is feasible to automate most of the nuclear reactor control and operation, which will result in increased safety and economical benefits. This study surveys current status, practices, and recent advances made towards developing autonomous control systems for nuclear reactors.« less

Big Data and the Global Public Health Intelligence Network (GPHIN)

PubMed Central

Dion, M; AbdelMalik, P; Mawudeku, A

2015-01-01

Background Globalization and the potential for rapid spread of emerging infectious diseases have heightened the need for ongoing surveillance and early detection. The Global Public Health Intelligence Network (GPHIN) was established to increase situational awareness and capacity for the early detection of emerging public health events. Objective To describe how the GPHIN has used Big Data as an effective early detection technique for infectious disease outbreaks worldwide and to identify potential future directions for the GPHIN. Findings Every day the GPHIN analyzes over more than 20,000 online news reports (over 30,000 sources) in nine languages worldwide. A web-based program aggregates data based on an algorithm that provides potential signals of emerging public health events which are then reviewed by a multilingual, multidisciplinary team. An alert is sent out if a potential risk is identified. This process proved useful during the Severe Acute Respiratory Syndrome (SARS) outbreak and was adopted shortly after by a number of countries to meet new International Health Regulations that require each country to have the capacity for early detection and reporting. The GPHIN identified the early SARS outbreak in China, was credited with the first alert on MERS-CoV and has played a significant role in the monitoring of the Ebola outbreak in West Africa. Future developments are being considered to advance the GPHIN’s capacity in light of other Big Data sources such as social media and its analytical capacity in terms of algorithm development. Conclusion The GPHIN’s early adoption of Big Data has increased global capacity to detect international infectious disease outbreaks and other public health events. Integration of additional Big Data sources and advances in analytical capacity could further strengthen the GPHIN’s capability for timely detection and early warning. PMID:29769954

Electronic publishing and intelligent information retrieval

NASA Technical Reports Server (NTRS)

Heck, A.

1992-01-01

Europeans are now taking steps to homogenize policies and standardize procedures in electronic publishing (EP) in astronomy and space sciences. This arose from an open meeting organized in Oct. 1991 at Strasbourg Observatory (France) and another business meeting held late Mar. 1992 with the major publishers and journal editors in astronomy and space sciences. The ultimate aim of EP might be considered as the so-called 'intelligent information retrieval' (IIR) or better named 'advanced information retrieval' (AIR), taking advantage of the fact that the material to be published appears at some stage in a machine-readable form. It is obvious that the combination of desktop and electronic publishing with networking and new structuring of knowledge bases will profoundly reshape not only our ways of publishing, but also our procedures of communicating and retrieving information. It should be noted that a world-wide survey among astronomers and space scientists carried out before the October 1991 colloquium on the various packages and machines used, indicated that TEX-related packages were already in majoritarian use in our community. It has also been stressed at each meeting that the European developments should be carried out in collaboration with what is done in the US (STELLAR project, for instance). American scientists and journal editors actually attended both meetings mentioned above. The paper will offer a review of the status of electronic publishing in astronomy and its possible contribution to advanced information retrieval in this field. It will also report on recent meetings such as the 'Astronomy from Large Databases-2 (ALD-2)' conference dealing with the latest developments in networking, in data, information, and knowledge bases, as well as in the related methodologies.

Architecture of cognitive flexibility revealed by lesion mapping

PubMed Central

Barbey, Aron K.; Colom, Roberto; Grafman, Jordan

2013-01-01

Neuroscience has made remarkable progress in understanding the architecture of human intelligence, identifying a distributed network of brain structures that support goal-directed, intelligent behavior. However, the neural foundations of cognitive flexibility and adaptive aspects of intellectual function remain to be well characterized. Here, we report a human lesion study (n = 149) that investigates the neural bases of key competencies of cognitive flexibility (i.e., mental flexibility and the fluent generation of new ideas) and systematically examine their contributions to a broad spectrum of cognitive and social processes, including psychometric intelligence (Wechsler Adult Intelligence Scale), emotional intelligence (Mayer, Salovey, Caruso Emotional Intelligence Test), and personality (Neuroticism–Extraversion–Openness Personality Inventory). Latent variable modeling was applied to obtain error-free indices of each factor, followed by voxel-based lesion-symptom mapping to elucidate their neural substrates. Regression analyses revealed that latent scores for psychometric intelligence reliably predict latent scores for cognitive flexibility (adjusted R2 = 0.94). Lesion mapping results further indicated that these convergent processes depend on a shared network of frontal, temporal, and parietal regions, including white matter association tracts, which bind these areas into an integrated system. A targeted analysis of the unique variance explained by cognitive flexibility further revealed selective damage within the right superior temporal gyrus, a region known to support insight and the recognition of novel semantic relations. The observed findings motivate an integrative framework for understanding the neural foundations of adaptive behavior, suggesting that core elements of cognitive flexibility emerge from a distributed network of brain regions that support specific competencies for human intelligence. PMID:23721727

Distributed multifunctional sensor network for composite structural state sensing

NASA Astrophysics Data System (ADS)

Qing, Xinlin P.; Wang, Yishou; Gao, Limin; Kumar, Amrita

2012-04-01

Advanced fiber reinforced composite materials are becoming the main structural materials of next generation of aircraft because of their high strength and stiffness to weight ratios, and strong designability. In order to take full advantages of composite materials, there is a need to develop an embeddable multifunctional sensing system to allow a structure to "feel" and "think" its structural state. In this paper, the concept of multifunctional sensor network integrated with a structure, similar to the human nervous system, has been developed. Different types of network sensors are permanently integrated within a composite structure to sense structural strain, temperature, moisture, aerodynamic pressure; monitor external impact on the structure; and detect structural damages. Utilizing this revolutionary concept, future composite structures can be designed and manufactured to provide multiple modes of information, so that the structures have the capabilities for intelligent sensing, environmental adaptation and multi-functionality. The challenges for building such a structural state sensing system and some solutions to address the challenges are also discussed in the paper.

Artificial Neural Network Based Mission Planning Mechanism for Spacecraft

NASA Astrophysics Data System (ADS)

Li, Zhaoyu; Xu, Rui; Cui, Pingyuan; Zhu, Shengying

2018-04-01

The ability to plan and react fast in dynamic space environments is central to intelligent behavior of spacecraft. For space and robotic applications, many planners have been used. But it is difficult to encode the domain knowledge and directly use existing techniques such as heuristic to improve the performance of the application systems. Therefore, regarding planning as an advanced control problem, this paper first proposes an autonomous mission planning and action selection mechanism through a multiple layer perceptron neural network approach to select actions in planning process and improve efficiency. To prove the availability and effectiveness, we use autonomous mission planning problems of the spacecraft, which is a sophisticated system with complex subsystems and constraints as an example. Simulation results have shown that artificial neural networks (ANNs) are usable for planning problems. Compared with the existing planning method in EUROPA, the mechanism using ANNs is more efficient and can guarantee stable performance. Therefore, the mechanism proposed in this paper is more suitable for planning problems of spacecraft that require real time and stability.

Dual-Use Space Technology Transfer Conference and Exhibition. Volume 2

NASA Technical Reports Server (NTRS)

Krishen, Kumar (Compiler)

1994-01-01

This is the second volume of papers presented at the Dual-Use Space Technology Transfer Conference and Exhibition held at the Johnson Space Center February 1-3, 1994. Possible technology transfers covered during the conference were in the areas of information access; innovative microwave and optical applications; materials and structures; marketing and barriers; intelligent systems; human factors and habitation; communications and data systems; business process and technology transfer; software engineering; biotechnology and advanced bioinstrumentation; communications signal processing and analysis; medical care; applications derived from control center data systems; human performance evaluation; technology transfer methods; mathematics, modeling, and simulation; propulsion; software analysis and decision tools; systems/processes in human support technology; networks, control centers, and distributed systems; power; rapid development; perception and vision technologies; integrated vehicle health management; automation technologies; advanced avionics; and robotics technologies.

A road map to the new frontier: finding ETI

NASA Astrophysics Data System (ADS)

Bertaux, J. L.

2014-04-01

An obvious New Frontier for humanity is to locate our nearest neighbors technically advanced (ETI, extra-terrestrial intelligence). This quest can be achieved with three steps. 1. find the nearest exoplanets in the habitable zone (HZ) 2. find biosignatures in their spectra 3. find signs of advance technology. We argue that steps 2 and 3 will require space telescopes that need to be oriented to targets already identified in step 1 as hosting exoplanets of Earth or super Earth size in the habitable zone. We show that non-transiting planets in HZ are 3 to 9 times nearer the sun than transiting planets, the gain factor being a function of star temperature. The requirement for step 1 is within the reach of a network of 2.5 m diameter ground-based automated telescopes associated with HARPS-type spectrometers.

National Geospatial-Intelligence Agency Academic Research Program

NASA Astrophysics Data System (ADS)

Loomer, S. A.

2004-12-01

"Know the Earth.Show the Way." In fulfillment of its vision, the National Geospatial-Intelligence Agency (NGA) provides geospatial intelligence in all its forms and from whatever source-imagery, imagery intelligence, and geospatial data and information-to ensure the knowledge foundation for planning, decision, and action. To achieve this, NGA conducts a multi-disciplinary program of basic research in geospatial intelligence topics through grants and fellowships to the leading investigators, research universities, and colleges of the nation. This research provides the fundamental science support to NGA's applied and advanced research programs. The major components of the NGA Academic Research Program (NARP) are: - NGA University Research Initiatives (NURI): Three-year basic research grants awarded competitively to the best investigators across the US academic community. Topics are selected to provide the scientific basis for advanced and applied research in NGA core disciplines. - Historically Black College and University - Minority Institution Research Initiatives (HBCU-MI): Two-year basic research grants awarded competitively to the best investigators at Historically Black Colleges and Universities, and Minority Institutions across the US academic community. - Director of Central Intelligence Post-Doctoral Research Fellowships: Fellowships providing access to advanced research in science and technology applicable to the intelligence community's mission. The program provides a pool of researchers to support future intelligence community needs and develops long-term relationships with researchers as they move into career positions. This paper provides information about the NGA Academic Research Program, the projects it supports and how other researchers and institutions can apply for grants under the program.

Intelligent fault diagnosis of rolling bearings using an improved deep recurrent neural network

NASA Astrophysics Data System (ADS)

Jiang, Hongkai; Li, Xingqiu; Shao, Haidong; Zhao, Ke

2018-06-01

Traditional intelligent fault diagnosis methods for rolling bearings heavily depend on manual feature extraction and feature selection. For this purpose, an intelligent deep learning method, named the improved deep recurrent neural network (DRNN), is proposed in this paper. Firstly, frequency spectrum sequences are used as inputs to reduce the input size and ensure good robustness. Secondly, DRNN is constructed by the stacks of the recurrent hidden layer to automatically extract the features from the input spectrum sequences. Thirdly, an adaptive learning rate is adopted to improve the training performance of the constructed DRNN. The proposed method is verified with experimental rolling bearing data, and the results confirm that the proposed method is more effective than traditional intelligent fault diagnosis methods.

Cloud based intelligent system for delivering health care as a service.

PubMed

Kaur, Pankaj Deep; Chana, Inderveer

2014-01-01

The promising potential of cloud computing and its convergence with technologies such as mobile computing, wireless networks, sensor technologies allows for creation and delivery of newer type of cloud services. In this paper, we advocate the use of cloud computing for the creation and management of cloud based health care services. As a representative case study, we design a Cloud Based Intelligent Health Care Service (CBIHCS) that performs real time monitoring of user health data for diagnosis of chronic illness such as diabetes. Advance body sensor components are utilized to gather user specific health data and store in cloud based storage repositories for subsequent analysis and classification. In addition, infrastructure level mechanisms are proposed to provide dynamic resource elasticity for CBIHCS. Experimental results demonstrate that classification accuracy of 92.59% is achieved with our prototype system and the predicted patterns of CPU usage offer better opportunities for adaptive resource elasticity. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Informatics and Nursing in a Post-Nursing Informatics World: Future Directions for Nurses in an Automated, Artificially Intelligent, Social-Networked Healthcare Environment.

PubMed

Booth, Richard G

2016-01-01

The increased adoption and use of technology within healthcare and society has influenced the nursing informatics specialty in a multitude of fashions. Namely, the nursing informatics specialty currently faces a range of important decisions related to its knowledge base, established values and future directions - all of which are in need of development and future-proofing. In light of the increased use of automation, artificial intelligence and big data in healthcare, the specialty must also reconceptualize the roles of both nurses and informaticians to ensure that the nursing profession is ready to operate within future digitalized healthcare ecosystems. To explore these goals, the author of this manuscript outlines an examination of technological advancements currently taking place within healthcare, and also proposes implications for the nursing role and the nursing informatics specialty. Finally, recommendations and insights towards how the roles of nurses and informaticians might evolve or be shaped in the growing post-nursing informatics era are presented. Copyright © 2016 Longwoods Publishing.

An intelligent robotic aid system for human services

NASA Technical Reports Server (NTRS)

Kawamura, K.; Bagchi, S.; Iskarous, M.; Pack, R. T.; Saad, A.

1994-01-01

The long term goal of our research at the Intelligent Robotic Laboratory at Vanderbilt University is to develop advanced intelligent robotic aid systems for human services. As a first step toward our goal, the current thrusts of our R&D are centered on the development of an intelligent robotic aid called the ISAC (Intelligent Soft Arm Control). In this paper, we describe the overall system architecture and current activities in intelligent control, adaptive/interactive control and task learning.

On the role of the plasmodial cytoskeleton in facilitating intelligent behavior in slime mold Physarum polycephalum

PubMed Central

Mayne, Richard; Adamatzky, Andrew; Jones, Jeff

2015-01-01

The plasmodium of slime mold Physarum polycephalum behaves as an amorphous reaction-diffusion computing substrate and is capable of apparently ‘intelligent’ behavior. But how does intelligence emerge in an acellular organism? Through a range of laboratory experiments, we visualize the plasmodial cytoskeleton—a ubiquitous cellular protein scaffold whose functions are manifold and essential to life—and discuss its putative role as a network for transducing, transmitting and structuring data streams within the plasmodium. Through a range of computer modeling techniques, we demonstrate how emergent behavior, and hence computational intelligence, may occur in cytoskeletal communications networks. Specifically, we model the topology of both the actin and tubulin cytoskeletal networks and discuss how computation may occur therein. Furthermore, we present bespoke cellular automata and particle swarm models for the computational process within the cytoskeleton and observe the incidence of emergent patterns in both. Our work grants unique insight into the origins of natural intelligence; the results presented here are therefore readily transferable to the fields of natural computation, cell biology and biomedical science. We conclude by discussing how our results may alter our biological, computational and philosophical understanding of intelligence and consciousness. PMID:26478782

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.…

Strategic Studies Quarterly. Volume 10, Number 2, Summer 2016

DTIC Science & Technology

2016-01-01

munitions, and networked command, control, communications , com- puters, intelligence, surveillance, and reconnaissance (C4ISR) has been a vital... communications , space capabilities, and networked intelligence, surveillance, and recon- naissance (ISR). The rapid pace of this proliferation is...intensity akin to that of the Manhattan Project or the Apollo Program. Building upon recent actions by Congress and DOD leadership, the next secretary of

Hybrid swarm intelligence optimization approach for optimal data storage position identification in wireless sensor networks.

PubMed

Mohanasundaram, Ranganathan; Periasamy, Pappampalayam Sanmugam

2015-01-01

The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches.

Saving lives through advanced vehicle safety technology : intelligent vehicle initiative

DOT National Transportation Integrated Search

2005-09-01

This final report provides an overview of the intelligent vehicle initiative's (IVI) progress and accomplishments. Authorized in the 1998 Transportation Equity Act for the 21st Century (TEA-21) as part of the U.S. DOT's Intelligent Transportation Sys...

Overview of NASA Glenn Research Center's Communications and Intelligent Systems Division

NASA Technical Reports Server (NTRS)

Miranda, Felix A.

2016-01-01

The Communications and Intelligent Systems Division provides expertise, plans, conducts and directs research and engineering development in the competency fields of advanced communications and intelligent systems technologies for application in current and future aeronautics and space systems.

[Analysis of the Characteristics of Infantile Small World Neural Network Node Properties Correlated with the Influencing Factors].

PubMed

Qu, Haibo; Lu, Su; Zhang, Wenjing; Xiao, Yuan; Ning, Gang; Sun, Huaiqiang

2016-10-01

We applied resting-state functional magnetic resonance imaging(rfMRI)combined with graph theory to analyze 90 regions of the infantile small world neural network of the whole brain.We tried to get the following two points clear:1 whether the parameters of the node property of the infantile small world neural network are correlated with the level of infantile intelligence development;2 whether the parameters of the infantile small world neural network are correlated with the children’s baseline parameters,i.e.,the demographic parameters such as gender,age,parents’ education level,etc.Twelve cases of healthy infants were included in the investigation(9males and 3females with the average age of 33.42±8.42 months.)We then evaluated the level of infantile intelligence of all the cases and graded by Gesell Development Scale Test.We used a Siemens 3.0T Trio imaging system to perform resting-state(rs)EPI scans,and collected the BOLD functional Magnetic Resonance Imaging(fMRI)data.We performed the data processing with Statistical Parametric Mapping 5(SPM5)based on Matlab environment.Furthermore,we got the attributes of the whole brain small world and node attributes of 90 encephalic regions of templates of Anatomatic Automatic Labeling(ALL).At last,we carried out correlation study between the above-mentioned attitudes,intelligence scale parameters and demographic data.The results showed that many node attributes of small world neural network were closely correlated with intelligence scale parameters.Betweeness was mainly centered in thalamus,superior frontal gyrus,and occipital lobe(negative correlation).The r value of superior occipital gyrus associated with the individual and social intelligent scale was-0.729(P=0.007);degree was mainly centered in amygdaloid nucleus,superior frontal gyrus,and inferior parietal gyrus(positive correlation).The r value of inferior parietal gyrus associated with the gross motor intelligent scale was 0.725(P=0.008);efficiency was mainly centered in inferior frontal gyrus,inferior parietal gyrus,and insular lobe(positive correlation).The r value of inferior parietal gyrus associated with the language intelligent scale was 0.738(P=0.006);Anoda cluster coefficient(anodalCp)was centered in frontal lobe,inferior parietal gyrus,and paracentral lobule(positive correlation);Node shortest path length(nlp)was centered in frontal lobe,inferior parietal gyrus,and insular lobe.The distribution of the encephalic regions in the left and right brain was different.However,no statistical significance was found between the correlation of monolithic attributes of small world and intelligence scale.The encephalic regions,in which node attributes of small world were related to other demographic indices,were mainly centered in temporal lobe,cuneus,cingulated gyrus,angular gyrus,and paracentral lobule areas.Most of them belong to the default mode network(DMN).The node attributes of small world neural network are widely related to infantile intelligence level,moreover the distribution is characteristic in different encephalic regions.The distribution of dominant encephalic is in accordance the related functions.The existing correlations reflect the ever changing small world nervous network during infantile development.

Intelligence Constraints on Terrorist Network Plots

NASA Astrophysics Data System (ADS)

Woo, Gordon

Since 9/11, the western intelligence and law enforcement services have managed to interdict the great majority of planned attacks against their home countries. Network analysis shows that there are important intelligence constraints on the number and complexity of terrorist plots. If two many terrorists are involved in plots at a given time, a tipping point is reached whereby it becomes progressively easier for the dots to be joined and for the conspirators to be arrested, and for the aggregate evidence to secure convictions. Implications of this analysis are presented for the campaign to win hearts and minds.

Coordinating complex problem-solving among distributed intelligent agents

NASA Technical Reports Server (NTRS)

Adler, Richard M.

1992-01-01

A process-oriented control model is described for distributed problem solving. The model coordinates the transfer and manipulation of information across independent networked applications, both intelligent and conventional. The model was implemented using SOCIAL, a set of object-oriented tools for distributing computing. Complex sequences of distributed tasks are specified in terms of high level scripts. Scripts are executed by SOCIAL objects called Manager Agents, which realize an intelligent coordination model that routes individual tasks to suitable server applications across the network. These tools are illustrated in a prototype distributed system for decision support of ground operations for NASA's Space Shuttle fleet.

A new intrusion prevention model using planning knowledge graph

NASA Astrophysics Data System (ADS)

Cai, Zengyu; Feng, Yuan; Liu, Shuru; Gan, Yong

2013-03-01

Intelligent plan is a very important research in artificial intelligence, which has applied in network security. This paper proposes a new intrusion prevention model base on planning knowledge graph and discuses the system architecture and characteristics of this model. The Intrusion Prevention based on plan knowledge graph is completed by plan recognition based on planning knowledge graph, and the Intrusion response strategies and actions are completed by the hierarchical task network (HTN) planner in this paper. Intrusion prevention system has the advantages of intelligent planning, which has the advantage of the knowledge-sharing, the response focused, learning autonomy and protective ability.

Control algorithms of SONET integrated self-healing networks

NASA Astrophysics Data System (ADS)

Hasegawa, Satoshi; Okaoue, Yasuyo; Egawa, Takashi; Sakauchi, Hideki

1994-01-01

As the deployment of high-speed fiber transmission systems has been accelerated, they are widely recognized as a firm infrastructure of information society. Under this circumstance, the importance of network survivability has been increasing rapidly in these days. In SONET, the self-healing networks have been highlighted as one of the most advanced mechanisms to realize SONET survivable networks. Several schemes have been proposed and studied actively due to a rapid progress on the development of highly intelligent NE's. Among them in this paper, a DCS based distributed self-healing network is discussed from a viewpoint of its control algorithms. Specifically, our self-healing algorithm called TRANS is explained in detail, which possesses such desirable features as providing fast and flexible restoration with line and path level restoration applied to an individual STS-1 channel, capability to handle multiple and even node failures, and so on. Both software simulation and hardware experiment verify that TRANS works properly in a real distributed environment, the result of which is shown in the paper. In addition, the combined use of TRANS and the ring restoration control is proposed taking into account the use in a practical SONET.

Current state and future direction of computer systems at NASA Langley Research Center

NASA Technical Reports Server (NTRS)

Rogers, James L. (Editor); Tucker, Jerry H. (Editor)

1992-01-01

Computer systems have advanced at a rate unmatched by any other area of technology. As performance has dramatically increased there has been an equally dramatic reduction in cost. This constant cost performance improvement has precipitated the pervasiveness of computer systems into virtually all areas of technology. This improvement is due primarily to advances in microelectronics. Most people are now convinced that the new generation of supercomputers will be built using a large number (possibly thousands) of high performance microprocessors. Although the spectacular improvements in computer systems have come about because of these hardware advances, there has also been a steady improvement in software techniques. In an effort to understand how these hardware and software advances will effect research at NASA LaRC, the Computer Systems Technical Committee drafted this white paper to examine the current state and possible future directions of computer systems at the Center. This paper discusses selected important areas of computer systems including real-time systems, embedded systems, high performance computing, distributed computing networks, data acquisition systems, artificial intelligence, and visualization.

Prediction of Sybil attack on WSN using Bayesian network and swarm intelligence

NASA Astrophysics Data System (ADS)

Muraleedharan, Rajani; Ye, Xiang; Osadciw, Lisa Ann

2008-04-01

Security in wireless sensor networks is typically sacrificed or kept minimal due to limited resources such as memory and battery power. Hence, the sensor nodes are prone to Denial-of-service attacks and detecting the threats is crucial in any application. In this paper, the Sybil attack is analyzed and a novel prediction method, combining Bayesian algorithm and Swarm Intelligence (SI) is proposed. Bayesian Networks (BN) is used in representing and reasoning problems, by modeling the elements of uncertainty. The decision from the BN is applied to SI forming an Hybrid Intelligence Scheme (HIS) to re-route the information and disconnecting the malicious nodes in future routes. A performance comparison based on the prediction using HIS vs. Ant System (AS) helps in prioritizing applications where decisions are time-critical.

Distributed intelligent control and status networking

NASA Technical Reports Server (NTRS)

Fortin, Andre; Patel, Manoj

1993-01-01

Over the past two years, the Network Control Systems Branch (Code 532) has been investigating control and status networking technologies. These emerging technologies use distributed processing over a network to accomplish a particular custom task. These networks consist of small intelligent 'nodes' that perform simple tasks. Containing simple, inexpensive hardware and software, these nodes can be easily developed and maintained. Once networked, the nodes can perform a complex operation without a central host. This type of system provides an alternative to more complex control and status systems which require a central computer. This paper will provide some background and discuss some applications of this technology. It will also demonstrate the suitability of one particular technology for the Space Network (SN) and discuss the prototyping activities of Code 532 utilizing this technology.

The Homeland Security Ecosystem: An Analysis of Hierarchical and Ecosystem Models and Their Influence on Decision Makers

DTIC Science & Technology

2012-12-01

flows, diversity, emergence, networks, fusion, strategic planning, information sharing, ecosystem, hierarchy, NJ Regional Operations Intelligence ...Related Information...........................................................................79 viii 3. Production of Disaster Intelligence for... Intelligence for Field Personnel .................80 5. Focused Collection Efforts to Support FEMA and NJ OEM Operations

Intelligent manufacturing: the challenge for manufacturing strategy in China in the 21st century--what we will do

NASA Astrophysics Data System (ADS)

Yang, Shuzi; Lei, Ming; Guan, Zai-Lin; Xiong, Youlun

1995-08-01

This paper first introduces the project of intelligent manufacturing in China and the research state of the IIMRC (Intelligent and Integrated Manufacturing Research Centre) of HUST (Huazhong University of Science and Technology), then reviews the recent advances in object- oriented and distributed artificial intelligence and puts forth the view that these advances open up the prospect of systems that will enable the true integration of enterprises. In an attempt to identify domain requirements and match them with research achievements, the paper examines the current literature and distinguishes 14 features that are common. It argues that effective enterprise-wide support could be greatly facilitated by the existence of intelligent software entities with autonomous processing capabilities, that possess coordination and negotiation facilities and are organized in distributed hierarchical states.

Intelligent monitoring and diagnosis systems for the Space Station Freedom ECLSS

NASA Technical Reports Server (NTRS)

Dewberry, Brandon S.; Carnes, James R.

1991-01-01

Specific activities in NASA's environmental control and life support system (ECLSS) advanced automation project that is designed to minimize the crew and ground manpower needed for operations are discussed. Various analyses and the development of intelligent software for the initial and evolutionary Space Station Freedom (SSF) ECLSS are described. The following are also discussed: (1) intelligent monitoring and diagnostics applications under development for the ECLSS domain; (2) integration into the MSFC ECLSS hardware testbed; and (3) an evolutionary path from the baseline ECLSS automation to the more advanced ECLSS automation processes.

Ramp - Metering Algorithms Evaluated within Simplified Conditions

NASA Astrophysics Data System (ADS)

Janota, Aleš; Holečko, Peter; Gregor, Michal; Hruboš, Marián

2017-12-01

Freeway networks reach their limits, since it is usually impossible to increase traffic volumes by indefinitely extending transport infrastructure through adding new traffic lanes. One of the possible solutions is to use advanced intelligent transport systems, particularly ramp metering systems. The paper shows how two particular algorithms of local and traffic-responsive control (Zone, ALINEA) can be adapted to simplified conditions corresponding to Slovak freeways. Both control strategies are modelled and simulated using PTV Vissim software, including the module VisVAP. Presented results demonstrate the properties of both control strategies, which are compared mutually as well as with the initial situation in which no control strategy is applied

Hyperspectral Polymer Solar Cells, Integrated Power for Microsystems

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

Stiebitz, Paul

2014-05-27

The purpose of this research is to address a critical technology barrier to the deployment of next generation autonomous microsystems – the availability of efficient and reliable power sources. The vast majority of research on microsystems has been directed toward the development and miniaturization of sensors and other devices that enhance their intelligence, physical, and networking capabilities. However, the research into power generating and power storage technologies has not keep pace with this development. This research leveraged the capabilities of RIT’s NanoPower Research Laboratories (NPRL) in materials for advanced lithium ion batteries, nanostructured photovoltaics, and hybrid betavoltaics to develop reliablemore » power sources for microsystems.« less

ATAC Process Proof of Concept Final Report

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

Bri Rolston; Sarah Freeman

Researchers at INL with funding from the Department of Energy’s Office of Electricity Delivery and Energy Reliability (DOE-OE) evaluated a novel approach for near real-time consumption of threat intelligence. Demonstration testing in an industry environment supported the development of this new process to assist the electric sector in securing their critical networks. This report provides the reader with an understanding of the methods used during this proof of concept project. The processes and templates were further advanced with an industry partner during an onsite assessment. This report concludes with lessons learned and a roadmap for final development of these materialsmore » for use by industry.« less

Toward intelligent information sysytem

NASA Astrophysics Data System (ADS)

Onodera, Natsuo

"Hypertext" means a concept of a novel computer-assisted tool for storage and retrieval of text information based on human association. Structure of knowledge in our idea processing is generally complicated and networked, but traditional paper documents merely express it in essentially linear and sequential forms. However, recent advances in work-station technology have allowed us to process easily electronic documents containing non-linear structure such as references or hierarchies. This paper describes concept, history and basic organization of hypertext, and shows the outline and features of existing main hypertext systems. Particularly, use of the hypertext database is illustrated by an example of Intermedia developed by Brown University.

Contribution of artificial intelligence to the knowledge of prognostic factors in Hodgkin's lymphoma.

PubMed

Buciński, Adam; Marszałł, Michał Piotr; Krysiński, Jerzy; Lemieszek, Andrzej; Załuski, Jerzy

2010-07-01

Hodgkin's lymphoma is one of the most curable malignancies and most patients achieve a lasting complete remission. In this study, artificial neural network (ANN) analysis was shown to provide significant factors with regard to 5-year recurrence after lymphoma treatment. Data from 114 patients treated for Hodgkin's disease were available for evaluation and comparison. A total of 31 variables were subjected to ANN analysis. The ANN approach as an advanced multivariate data processing method was shown to provide objective prognostic data. Some of these prognostic factors are consistent or even identical to the factors evaluated earlier by other statistical methods.

Use of a Deep Recurrent Neural Network to Reduce Wind Noise: Effects on Judged Speech Intelligibility and Sound Quality

PubMed Central

Keshavarzi, Mahmoud; Goehring, Tobias; Zakis, Justin; Turner, Richard E.; Moore, Brian C. J.

2018-01-01

Despite great advances in hearing-aid technology, users still experience problems with noise in windy environments. The potential benefits of using a deep recurrent neural network (RNN) for reducing wind noise were assessed. The RNN was trained using recordings of the output of the two microphones of a behind-the-ear hearing aid in response to male and female speech at various azimuths in the presence of noise produced by wind from various azimuths with a velocity of 3 m/s, using the “clean” speech as a reference. A paired-comparison procedure was used to compare all possible combinations of three conditions for subjective intelligibility and for sound quality or comfort. The conditions were unprocessed noisy speech, noisy speech processed using the RNN, and noisy speech that was high-pass filtered (which also reduced wind noise). Eighteen native English-speaking participants were tested, nine with normal hearing and nine with mild-to-moderate hearing impairment. Frequency-dependent linear amplification was provided for the latter. Processing using the RNN was significantly preferred over no processing by both subject groups for both subjective intelligibility and sound quality, although the magnitude of the preferences was small. High-pass filtering (HPF) was not significantly preferred over no processing. Although RNN was significantly preferred over HPF only for sound quality for the hearing-impaired participants, for the results as a whole, there was a preference for RNN over HPF. Overall, the results suggest that reduction of wind noise using an RNN is possible and might have beneficial effects when used in hearing aids. PMID:29708061

Acquaintance to Artificial Neural Networks and use of artificial intelligence as a diagnostic tool for tuberculosis: A review.

PubMed

Dande, Payal; Samant, Purva

2018-01-01

Tuberculosis [TB] has afflicted numerous nations in the world. As per a report by the World Health Organization [WHO], an estimated 1.4 million TB deaths in 2015 and an additional 0.4 million deaths resulting from TB disease among people living with HIV, were observed. Most of the TB deaths can be prevented if it is detected at an early stage. The existing processes of diagnosis like blood tests or sputum tests are not only tedious but also take a long time for analysis and cannot differentiate between different drug resistant stages of TB. The need to find newer prompt methods for disease detection has been aided by the latest Artificial Intelligence [AI] tools. Artificial Neural Network [ANN] is one of the important tools that is being used widely in diagnosis and evaluation of medical conditions. This review aims at providing brief introduction to various AI tools that are used in TB detection and gives a detailed description about the utilization of ANN as an efficient diagnostic technique. The paper also provides a critical assessment of ANN and the existing techniques for their diagnosis of TB. Researchers and Practitioners in the field are looking forward to use ANN and other upcoming AI tools such as Fuzzy-logic, genetic algorithms and artificial intelligence simulation as a promising current and future technology tools towards tackling the global menace of Tuberculosis. Latest advancements in the diagnostic field include the combined use of ANN with various other AI tools like the Fuzzy-logic, which has led to an increase in the efficacy and specificity of the diagnostic techniques. Copyright © 2017 Elsevier Ltd. All rights reserved.

An Expert System for Processing Uncorrelated Satellite Tracks

DTIC Science & Technology

1992-12-17

earthworms with much intellect e\\en though they routinely carry out this same function. One definition given artificial intelligence is "the study of mental...Networks: Benchmarking Studies ," Proceedings from the IEEE International Conference on Neural Networkv. pp. 64-65, 1988. 229 Lyddane, R., "Small...reverse if necessary and rdenqtl_ by block number, Field Group Subgroup Artificial Intelligence, Expert Systems, Neural Networks. Orbital Mechanics

Efficient Effects-Based Military Planning Final Report

DTIC Science & Technology

2010-11-13

using probabilistic infer- ence methods,” in Proc. 8th Annu. Conf. Uncertainty Artificial Intelli - gence (UAI), Stanford, CA. San Mateo, CA: Morgan...Imprecise Probabilities, the 24th Conference on Uncertainty in Artificial Intelligence (UAI), 2008. 7. Yan Tong and Qiang Ji, Learning Bayesian Networks...Bayesian Networks using Constraints Cassio P. de Campos cassiopc@acm.org Dalle Molle Institute for Artificial Intelligence Galleria 2, Manno 6928

Study of intelligent building system based on the internet of things

NASA Astrophysics Data System (ADS)

Wan, Liyong; Xu, Renbo

2017-03-01

In accordance with the problem such as isolated subsystems, weak system linkage and expansibility of the bus type buildings management system, this paper based on the modern intelligent buildings has studied some related technologies of the intelligent buildings and internet of things, and designed system architecture of the intelligent buildings based on the Internet of Things. Meanwhile, this paper has also analyzed wireless networking modes, wireless communication protocol and wireless routing protocol of the intelligent buildings based on the Internet of Things.

Air Quality Forecasting through Different Statistical and Artificial Intelligence Techniques

NASA Astrophysics Data System (ADS)

Mishra, D.; Goyal, P.

2014-12-01

Urban air pollution forecasting has emerged as an acute problem in recent years because there are sever environmental degradation due to increase in harmful air pollutants in the ambient atmosphere. In this study, there are different types of statistical as well as artificial intelligence techniques are used for forecasting and analysis of air pollution over Delhi urban area. These techniques are principle component analysis (PCA), multiple linear regression (MLR) and artificial neural network (ANN) and the forecasting are observed in good agreement with the observed concentrations through Central Pollution Control Board (CPCB) at different locations in Delhi. But such methods suffers from disadvantages like they provide limited accuracy as they are unable to predict the extreme points i.e. the pollution maximum and minimum cut-offs cannot be determined using such approach. Also, such methods are inefficient approach for better output forecasting. But with the advancement in technology and research, an alternative to the above traditional methods has been proposed i.e. the coupling of statistical techniques with artificial Intelligence (AI) can be used for forecasting purposes. The coupling of PCA, ANN and fuzzy logic is used for forecasting of air pollutant over Delhi urban area. The statistical measures e.g., correlation coefficient (R), normalized mean square error (NMSE), fractional bias (FB) and index of agreement (IOA) of the proposed model are observed in better agreement with the all other models. Hence, the coupling of statistical and artificial intelligence can be use for the forecasting of air pollutant over urban area.

Gray matter correlates of creative potential: A latent variable voxel-based morphometry study

PubMed Central

Jauk, Emanuel; Neubauer, Aljoscha C.; Dunst, Beate; Fink, Andreas; Benedek, Mathias

2015-01-01

There is increasing research interest in the structural and functional brain correlates underlying creative potential. Recent investigations found that interindividual differences in creative potential relate to volumetric differences in brain regions belonging to the default mode network, such as the precuneus. Yet, the complex interplay between creative potential, intelligence, and personality traits and their respective neural bases is still under debate. We investigated regional gray matter volume (rGMV) differences that can be associated with creative potential in a heterogeneous sample of N = 135 individuals using voxel-based morphometry (VBM). By means of latent variable modeling and consideration of recent psychometric advancements in creativity research, we sought to disentangle the effects of ideational originality and fluency as two independent indicators of creative potential. Intelligence and openness to experience were considered as common covariates of creative potential. The results confirmed and extended previous research: rGMV in the precuneus was associated with ideational originality, but not with ideational fluency. In addition, we found ideational originality to be correlated with rGMV in the caudate nucleus. The results indicate that the ability to produce original ideas is tied to default-mode as well as dopaminergic structures. These structural brain correlates of ideational originality were apparent throughout the whole range of intellectual ability and thus not moderated by intelligence. In contrast, structural correlates of ideational fluency, a quantitative marker of creative potential, were observed only in lower intelligent individuals in the cuneus/lingual gyrus. PMID:25676914

Dynamic Gaming Platform (DGP)

DTIC Science & Technology

2009-04-01

GAMING PLATFORM (DGP) Lockheed Martin Corporation...YYYY) APR 09 2. REPORT TYPE Final 3. DATES COVERED (From - To) Jul 07 – Mar 09 4. TITLE AND SUBTITLE DYNAMIC GAMING PLATFORM (DGP) 5a...CMU Carnegie Mellon University DGP Dynamic Gaming Platform GA Genetic Algorithm IARPA Intelligence Advanced Research Projects Activity LM ATL Lockheed Martin Advanced Technology Laboratories PAINT ProActive INTelligence

Neuroanatomic overlap between intelligence and cognitive factors: morphometry methods provide support for the key role of the frontal lobes.

PubMed

Colom, Roberto; Burgaleta, Miguel; Román, Francisco J; Karama, Sherif; Alvarez-Linera, Juan; Abad, Francisco J; Martínez, Kenia; Quiroga, Ma Ángeles; Haier, Richard J

2013-05-15

Evidence from neuroimaging studies suggests that intelligence differences may be supported by a parieto-frontal network. Research shows that this network is also relevant for cognitive functions such as working memory and attention. However, previous studies have not explicitly analyzed the commonality of brain areas between a broad array of intelligence factors and cognitive functions tested in the same sample. Here fluid, crystallized, and spatial intelligence, along with working memory, executive updating, attention, and processing speed were each measured by three diverse tests or tasks. These twenty-one measures were completed by a group of one hundred and four healthy young adults. Three cortical measures (cortical gray matter volume, cortical surface area, and cortical thickness) were regressed against psychological latent scores obtained from a confirmatory factor analysis for removing test and task specific variance. For cortical gray matter volume and cortical surface area, the main overlapping clusters were observed in the middle frontal gyrus and involved fluid intelligence and working memory. Crystallized intelligence showed an overlapping cluster with fluid intelligence and working memory in the middle frontal gyrus. The inferior frontal gyrus showed overlap for crystallized intelligence, spatial intelligence, attention, and processing speed. The fusiform gyrus in temporal cortex showed overlap for spatial intelligence and attention. Parietal and occipital areas did not show any overlap across intelligence and cognitive factors. Taken together, these findings underscore that structural features of gray matter in the frontal lobes support those aspects of intelligence related to basic cognitive processes. Copyright © 2013 Elsevier Inc. All rights reserved.

Machine intelligence and robotics: Report of the NASA study group

NASA Technical Reports Server (NTRS)

1980-01-01

Opportunities for the application of machine intelligence and robotics in NASA missions and systems were identified. The benefits of successful adoption of machine intelligence and robotics techniques were estimated and forecasts were prepared to show their growth potential. Program options for research, advanced development, and implementation of machine intelligence and robot technology for use in program planning are presented.

Evaluation of intelligent transportation infrastructure program (ITIP) in Pittsburgh and Philadelphia, Pennsylvania

DOT National Transportation Integrated Search

2003-03-20

The Transportation Equity Act for the 21st Century (TEA-21) Public Laws 105-178 and 105-206, Title V, Section 5117(b) (3) provides for an Intelligent Transportation Infrastructure Program (ITIP) to advance the deployment of operational intelligent tr...

Automatic panoramic thermal integrated sensor

NASA Astrophysics Data System (ADS)

Gutin, Mikhail A.; Tsui, Eddy K.; Gutin, Olga N.

2005-05-01

Historically, the US Army has recognized the advantages of panoramic imagers with high image resolution: increased area coverage with fewer cameras, instantaneous full horizon detection, location and tracking of multiple targets simultaneously, extended range, and others. The novel ViperViewTM high-resolution panoramic thermal imager is the heart of the Automatic Panoramic Thermal Integrated Sensor (APTIS), being jointly developed by Applied Science Innovative, Inc. (ASI) and the Armament Research, Development and Engineering Center (ARDEC) in support of the Future Combat Systems (FCS) and the Intelligent Munitions Systems (IMS). The APTIS is anticipated to operate as an intelligent node in a wireless network of multifunctional nodes that work together to improve situational awareness (SA) in many defense and offensive operations, as well as serve as a sensor node in tactical Intelligence Surveillance Reconnaissance (ISR). The ViperView is as an aberration-corrected omnidirectional imager with small optics designed to match the resolution of a 640x480 pixels IR camera with improved image quality for longer range target detection, classification, and tracking. The same approach is applicable to panoramic cameras working in the visible spectral range. Other components of the ATPIS sensor suite include ancillary sensors, advanced power management, and wakeup capability. This paper describes the development status of the APTIS system.

Dielectric elastomer memory

NASA Astrophysics Data System (ADS)

O'Brien, Benjamin M.; McKay, Thomas G.; Xie, Sheng Q.; Calius, Emilio P.; Anderson, Iain A.

2011-04-01

Life shows us that the distribution of intelligence throughout flexible muscular networks is a highly successful solution to a wide range of challenges, for example: human hearts, octopi, or even starfish. Recreating this success in engineered systems requires soft actuator technologies with embedded sensing and intelligence. Dielectric Elastomer Actuator(s) (DEA) are promising due to their large stresses and strains, as well as quiet flexible multimodal operation. Recently dielectric elastomer devices were presented with built in sensor, driver, and logic capability enabled by a new concept called the Dielectric Elastomer Switch(es) (DES). DES use electrode piezoresistivity to control the charge on DEA and enable the distribution of intelligence throughout a DEA device. In this paper we advance the capabilities of DES further to form volatile memory elements. A set reset flip-flop with inverted reset line was developed based on DES and DEA. With a 3200V supply the flip-flop behaved appropriately and demonstrated the creation of dielectric elastomer memory capable of changing state in response to 1 second long set and reset pulses. This memory opens up applications such as oscillator, de-bounce, timing, and sequential logic circuits; all of which could be distributed throughout biomimetic actuator arrays. Future work will include miniaturisation to improve response speed, implementation into more complex circuits, and investigation of longer lasting and more sensitive switching materials.

Prototyping and Simulation of Robot Group Intelligence using Kohonen Networks.

PubMed

Wang, Zhijun; Mirdamadi, Reza; Wang, Qing

2016-01-01

Intelligent agents such as robots can form ad hoc networks and replace human being in many dangerous scenarios such as a complicated disaster relief site. This project prototypes and builds a computer simulator to simulate robot kinetics, unsupervised learning using Kohonen networks, as well as group intelligence when an ad hoc network is formed. Each robot is modeled using an object with a simple set of attributes and methods that define its internal states and possible actions it may take under certain circumstances. As the result, simple, reliable, and affordable robots can be deployed to form the network. The simulator simulates a group of robots as an unsupervised learning unit and tests the learning results under scenarios with different complexities. The simulation results show that a group of robots could demonstrate highly collaborative behavior on a complex terrain. This study could potentially provide a software simulation platform for testing individual and group capability of robots before the design process and manufacturing of robots. Therefore, results of the project have the potential to reduce the cost and improve the efficiency of robot design and building.

TALON: the telescope alert operation network system: intelligent linking of distributed autonomous robotic telescopes

NASA Astrophysics Data System (ADS)

White, Robert R.; Wren, James; Davis, Heath R.; Galassi, Mark; Starr, Daniel; Vestrand, W. T.; Wozniak, P.

2004-09-01

The internet has brought about great change in the astronomical community, but this interconnectivity is just starting to be exploited for use in instrumentation. Utilizing the internet for communicating between distributed astronomical systems is still in its infancy, but it already shows great potential. Here we present an example of a distributed network of telescopes that performs more efficiently in synchronous operation than as individual instruments. RAPid Telescopes for Optical Response (RAPTOR) is a system of telescopes at LANL that has intelligent intercommunication, combined with wide-field optics, temporal monitoring software, and deep-field follow-up capability all working in closed-loop real-time operation. The Telescope ALert Operations Network (TALON) is a network server that allows intercommunication of alert triggers from external and internal resources and controls the distribution of these to each of the telescopes on the network. TALON is designed to grow, allowing any number of telescopes to be linked together and communicate. Coupled with an intelligent alert client at each telescope, it can analyze and respond to each distributed TALON alert based on the telescopes needs and schedule.

Prototyping and Simulation of Robot Group Intelligence using Kohonen Networks

PubMed Central

Wang, Zhijun; Mirdamadi, Reza; Wang, Qing

2016-01-01

Intelligent agents such as robots can form ad hoc networks and replace human being in many dangerous scenarios such as a complicated disaster relief site. This project prototypes and builds a computer simulator to simulate robot kinetics, unsupervised learning using Kohonen networks, as well as group intelligence when an ad hoc network is formed. Each robot is modeled using an object with a simple set of attributes and methods that define its internal states and possible actions it may take under certain circumstances. As the result, simple, reliable, and affordable robots can be deployed to form the network. The simulator simulates a group of robots as an unsupervised learning unit and tests the learning results under scenarios with different complexities. The simulation results show that a group of robots could demonstrate highly collaborative behavior on a complex terrain. This study could potentially provide a software simulation platform for testing individual and group capability of robots before the design process and manufacturing of robots. Therefore, results of the project have the potential to reduce the cost and improve the efficiency of robot design and building. PMID:28540284

Prediction of 5-year overall survival in cervical cancer patients treated with radical hysterectomy using computational intelligence methods.

PubMed

Obrzut, Bogdan; Kusy, Maciej; Semczuk, Andrzej; Obrzut, Marzanna; Kluska, Jacek

2017-12-12

Computational intelligence methods, including non-linear classification algorithms, can be used in medical research and practice as a decision making tool. This study aimed to evaluate the usefulness of artificial intelligence models for 5-year overall survival prediction in patients with cervical cancer treated by radical hysterectomy. The data set was collected from 102 patients with cervical cancer FIGO stage IA2-IIB, that underwent primary surgical treatment. Twenty-three demographic, tumor-related parameters and selected perioperative data of each patient were collected. The simulations involved six computational intelligence methods: the probabilistic neural network (PNN), multilayer perceptron network, gene expression programming classifier, support vector machines algorithm, radial basis function neural network and k-Means algorithm. The prediction ability of the models was determined based on the accuracy, sensitivity, specificity, as well as the area under the receiver operating characteristic curve. The results of the computational intelligence methods were compared with the results of linear regression analysis as a reference model. The best results were obtained by the PNN model. This neural network provided very high prediction ability with an accuracy of 0.892 and sensitivity of 0.975. The area under the receiver operating characteristics curve of PNN was also high, 0.818. The outcomes obtained by other classifiers were markedly worse. The PNN model is an effective tool for predicting 5-year overall survival in cervical cancer patients treated with radical hysterectomy.

Connectionist Models for Intelligent Computation

DTIC Science & Technology

1989-07-26

Intelligent Canputation 12. PERSONAL AUTHOR(S) H.H. Chen and Y.C. Lee 13a. o R,POT Cal 13b TIME lVD/rED 14 DATE OF REPORT (Year, Month, Day) JS PAGE...fied Project Title: Connectionist Models-for Intelligent Computation Contract/Grant No.: AFOSR-87-0388 Contract/Grant Period of Performance: Sept. 1...underlying principles, architectures and appilications of artificial neural networks for intelligent computations.o, Approach: -) We use both numerical

The role of networks and artificial intelligence in nanotechnology design and analysis.

PubMed

Hudson, D L; Cohen, M E

2004-05-01

Techniques with their origins in artificial intelligence have had a great impact on many areas of biomedicine. Expert-based systems have been used to develop computer-assisted decision aids. Neural networks have been used extensively in disease classification and more recently in many bioinformatics applications including genomics and drug design. Network theory in general has proved useful in modeling all aspects of biomedicine from healthcare organizational structure to biochemical pathways. These methods show promise in applications involving nanotechnology both in the design phase and in interpretation of system functioning.

Adaptive Neural Network Algorithm for Power Control in Nuclear Power Plants

NASA Astrophysics Data System (ADS)

Masri Husam Fayiz, Al

2017-01-01

The aim of this paper is to design, test and evaluate a prototype of an adaptive neural network algorithm for the power controlling system of a nuclear power plant. The task of power control in nuclear reactors is one of the fundamental tasks in this field. Therefore, researches are constantly conducted to ameliorate the power reactor control process. Currently, in the Department of Automation in the National Research Nuclear University (NRNU) MEPhI, numerous studies are utilizing various methodologies of artificial intelligence (expert systems, neural networks, fuzzy systems and genetic algorithms) to enhance the performance, safety, efficiency and reliability of nuclear power plants. In particular, a study of an adaptive artificial intelligent power regulator in the control systems of nuclear power reactors is being undertaken to enhance performance and to minimize the output error of the Automatic Power Controller (APC) on the grounds of a multifunctional computer analyzer (simulator) of the Water-Water Energetic Reactor known as Vodo-Vodyanoi Energetichesky Reaktor (VVER) in Russian. In this paper, a block diagram of an adaptive reactor power controller was built on the basis of an intelligent control algorithm. When implementing intelligent neural network principles, it is possible to improve the quality and dynamic of any control system in accordance with the principles of adaptive control. It is common knowledge that an adaptive control system permits adjusting the controller’s parameters according to the transitions in the characteristics of the control object or external disturbances. In this project, it is demonstrated that the propitious options for an automatic power controller in nuclear power plants is a control system constructed on intelligent neural network algorithms.

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.

Evolution of the intelligent telecommunications network

NASA Astrophysics Data System (ADS)

Mayo, J. S.

1982-02-01

The development of the U.S. telecommunications network is described and traced from the invention of the telephone by Bell in 1876 to the use of integrated circuits and the UNIX system for interactive computers. The dialing system was introduced in the 19th century, and amplifiers were invented to permit coast to coast communication by 1914. Hierarchical switching was installed in the 1930s, along with telephoto and teletype services. PCM was invented in the 1930s, but was limited to military applications until the transistorized computer was fabricated in 1958, which coincided with spaceflight and the Telstar satellite in 1962. Fiber optics systems with laser pulse transmission are now entering widespread application, following the 1976 introduction of superfast digital switches controlled by a computer and capable of handling 1/2 million calls per hour. Projected advances are in increased teleconferencing, electronic mail, and full computer terminal services.

The relationship of certified flight instructors' emotional intelligence levels on flight student advancement

NASA Astrophysics Data System (ADS)

Hokeness, Mark Merrill

Aviation researchers estimate airline companies will require nearly 500,000 pilots in the next 20 years. The role of a Certified Flight Instructor (CFI) is to move student pilots to professional pilots with training typically conducted in one-on-one student and instructor sessions. The knowledge of aviation, professionalism as a teacher, and the CFI’s interpersonal skills can directly affect the successes and advancement of a student pilot. A new and emerging assessment of people skills is known as emotional intelligence (EI). The EI of the CFI can and will affect a flight students’ learning experiences. With knowledge of emotional intelligence and its effect on flight training, student pilot dropouts from aviation may be reduced, thus helping to ensure an adequate supply of pilots. Without pilots, the growth of the commercial aviation industry will be restricted. This mixed method research study established the correlation between a CFI’s measured EI levels and the advancement of flight students. The elements contributing to a CFI’s EI level were not found to be teaching or flight-related experiences, suggesting other life factors are drawn upon by the CFI and are reflected in their emotional intelligence levels presented to flight students. Students respond positively to CFIs with higher levels of emotional intelligence. Awareness of EI skills by both the CFI and flight student contribute to flight student successes and advancement.

A multi-agent intelligent environment for medical knowledge.

PubMed

Vicari, Rosa M; Flores, Cecilia D; Silvestre, André M; Seixas, Louise J; Ladeira, Marcelo; Coelho, Helder

2003-03-01

AMPLIA is a multi-agent intelligent learning environment designed to support training of diagnostic reasoning and modelling of domains with complex and uncertain knowledge. AMPLIA focuses on the medical area. It is a system that deals with uncertainty under the Bayesian network approach, where learner-modelling tasks will consist of creating a Bayesian network for a problem the system will present. The construction of a network involves qualitative and quantitative aspects. The qualitative part concerns the network topology, that is, causal relations among the domain variables. After it is ready, the quantitative part is specified. It is composed of the distribution of conditional probability of the variables represented. A negotiation process (managed by an intelligent MediatorAgent) will treat the differences of topology and probability distribution between the model the learner built and the one built-in in the system. That negotiation process occurs between the agents that represent the expert knowledge domain (DomainAgent) and the agent that represents the learner knowledge (LearnerAgent).

Analyzing the association between functional connectivity of the brain and intellectual performance

PubMed Central

Pamplona, Gustavo S. P.; Santos Neto, Gérson S.; Rosset, Sara R. E.; Rogers, Baxter P.; Salmon, Carlos E. G.

2015-01-01

Measurements of functional connectivity support the hypothesis that the brain is composed of distinct networks with anatomically separated nodes but common functionality. A few studies have suggested that intellectual performance may be associated with greater functional connectivity in the fronto-parietal network and enhanced global efficiency. In this fMRI study, we performed an exploratory analysis of the relationship between the brain's functional connectivity and intelligence scores derived from the Portuguese language version of the Wechsler Adult Intelligence Scale (WAIS-III) in a sample of 29 people, born and raised in Brazil. We examined functional connectivity between 82 regions, including graph theoretic properties of the overall network. Some previous findings were extended to the Portuguese-speaking population, specifically the presence of small-world organization of the brain and relationships of intelligence with connectivity of frontal, pre-central, parietal, occipital, fusiform and supramarginal gyrus, and caudate nucleus. Verbal comprehension was associated with global network efficiency, a new finding. PMID:25713528

Supporting performance and configuration management of GTE cellular networks

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

Tan, Ming; Lafond, C.; Jakobson, G.

GTE Laboratories, in cooperation with GTE Mobilnet, has developed and deployed PERFFEX (PERFormance Expert), an intelligent system for performance and configuration management of cellular networks. PERFEX assists cellular network performance and radio engineers in the analysis of large volumes of cellular network performance and configuration data. It helps them locate and determine the probable causes of performance problems, and provides intelligent suggestions about how to correct them. The system combines an expert cellular network performance tuning capability with a map-based graphical user interface, data visualization programs, and a set of special cellular engineering tools. PERFEX is in daily use atmore » more than 25 GTE Mobile Switching Centers. Since the first deployment of the system in late 1993, PERFEX has become a major GTE cellular network performance optimization tool.« less

TEx-Sys Model for Building Intelligent Tutoring Systems

ERIC Educational Resources Information Center

Stankov, Slavomir; Rosic, Marko; Zitko, Branko; Grubisic, Ani

2008-01-01

Special classes of asynchronous e-learning systems are the intelligent tutoring systems which represent an advanced learning and teaching environment adaptable to individual student's characteristics. Authoring shells have an environment that enables development of the intelligent tutoring systems. In this paper we present, in entirety, for the…

Thinking like animals or thinking like colleagues?

PubMed

Dennett, Daniel C; Lambert, Enoch

2017-01-01

We comment on ways in which Lake et al. advance our understanding of the machinery of intelligence and offer suggestions. The first set concerns animal-level versus human-level intelligence. The second concerns the urgent need to address ethical issues when evaluating the state of artificial intelligence.

Advanced Communication Processing Techniques

NASA Astrophysics Data System (ADS)

Scholtz, Robert A.

This document contains the proceedings of the workshop Advanced Communication Processing Techniques, held May 14 to 17, 1989, near Ruidoso, New Mexico. Sponsored by the Army Research Office (under Contract DAAL03-89-G-0016) and organized by the Communication Sciences Institute of the University of Southern California, the workshop had as its objective to determine those applications of intelligent/adaptive communication signal processing that have been realized and to define areas of future research. We at the Communication Sciences Institute believe that there are two emerging areas which deserve considerably more study in the near future: (1) Modulation characterization, i.e., the automation of modulation format recognition so that a receiver can reliably demodulate a signal without using a priori information concerning the signal's structure, and (2) the incorporation of adaptive coding into communication links and networks. (Encoders and decoders which can operate with a wide variety of codes exist, but the way to utilize and control them in links and networks is an issue). To support these two new interest areas, one must have both a knowledge of (3) the kinds of channels and environments in which the systems must operate, and of (4) the latest adaptive equalization techniques which might be employed in these efforts.

Altered brain function in new onset childhood acute lymphoblastic leukemia before chemotherapy: A resting-state fMRI study.

PubMed

Hu, Zhanqi; Zou, Dongfang; Mai, Huirong; Yuan, Xiuli; Wang, Lihong; Li, Yue; Liao, Jianxiang; Liu, Liwei; Liu, Guosheng; Zeng, Hongwu; Wen, Feiqiu

2017-10-01

Cognitive impairments had been reported in childhood acute lymphoblastic leukemia, what caused the impairments needed to be demonstrated, chemotherapy-related or the disease itself. The primary aim of this exploratory investigation was to determine if there were changes in brain function of children with acute lymphoblastic leukemia before chemotherapy. In this study, we advanced a measure named regional homogeneity to evaluate the resting-state brain activities, intelligence quotient test was performed at same time. Using regional homogeneity, we first investigated the resting state brain function in patients with new onset childhood acute lymphoblastic leukemia before chemotherapy, healthy children as control. The decreased ReHo values were mainly founded in the default mode network and left frontal lobe, bilateral inferior parietal lobule, bilateral temporal lobe, bilateral occipital lobe, precentral gyrus, bilateral cerebellum in the newly diagnosed acute lymphoblastic leukemia patients compared with the healthy control. While in contrast, increased ReHo values were mainly shown in the right frontal lobe (language area), superior frontal gyrus-R, middle frontal gyrus-R and inferior parietal lobule-R for acute lymphoblastic leukemia patients group. There were no significant differences for intelligence quotient measurements between the acute lymphoblastic leukemia patient group and the healthy control in performance intelligence quotient, verbal intelligence quotient, total intelligence quotient. The altered brain functions are associated with cognitive change and language, it is suggested that there may be cognition impairment before the chemotherapy. Regional homogeneity by functional magnetic resonance image is a sensitive way for early detection on brain damage in childhood acute lymphoblastic leukemia. Copyright © 2017 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

Student Modeling in an Intelligent Tutoring System

DTIC Science & Technology

1996-12-17

Multi-Agent Architecture." Advances in Artificial Intelligence : Proceedings of the 12 th Brazilian Symposium on Aritificial Intelligence , edited by...STUDENT MODELING IN AN INTELLIGENT TUTORING SYSTEM THESIS Jeremy E. Thompson Captain, USAF AFIT/GCS/ENG/96D-27 DIMTVMON* fCKAJWINT A Appr"v*d t=i...Air Force Base, Ohio AFIT/GCS/ENG/96D-27 STUDENT MODELING IN AN INTELLIGENT TUTORING SYSTEM THESIS Jeremy E. Thompson Captain, USAF AFIT/GCS/ENG/96D

The Role of Intelligent Agents in Advanced Information Systems

NASA Technical Reports Server (NTRS)

Kerschberg, Larry

1999-01-01

In this presentation we review the current ongoing research within George Mason University's (GMU) Center for Information Systems Integration and Evolution (CISE). We define characteristics of advanced information systems, discuss a family of agents for such systems, and show how GMU's Domain modeling tools and techniques can be used to define a product line Architecture for configuring NASA missions. These concepts can be used to define Advanced Engineering Environments such as those envisioned for NASA's new initiative for intelligent design and synthesis environments.

Supporting tactical intelligence using collaborative environments and social networking

NASA Astrophysics Data System (ADS)

Wollocko, Arthur B.; Farry, Michael P.; Stark, Robert F.

2013-05-01

Modern military environments place an increased emphasis on the collection and analysis of intelligence at the tactical level. The deployment of analytical tools at the tactical level helps support the Warfighter's need for rapid collection, analysis, and dissemination of intelligence. However, given the lack of experience and staffing at the tactical level, most of the available intelligence is not exploited. Tactical environments are staffed by a new generation of intelligence analysts who are well-versed in modern collaboration environments and social networking. An opportunity exists to enhance tactical intelligence analysis by exploiting these personnel strengths, but is dependent on appropriately designed information sharing technologies. Existing social information sharing technologies enable users to publish information quickly, but do not unite or organize information in a manner that effectively supports intelligence analysis. In this paper, we present an alternative approach to structuring and supporting tactical intelligence analysis that combines the benefits of existing concepts, and provide detail on a prototype system embodying that approach. Since this approach employs familiar collaboration support concepts from social media, it enables new-generation analysts to identify the decision-relevant data scattered among databases and the mental models of other personnel, increasing the timeliness of collaborative analysis. Also, the approach enables analysts to collaborate visually to associate heterogeneous and uncertain data within the intelligence analysis process, increasing the robustness of collaborative analyses. Utilizing this familiar dynamic collaboration environment, we hope to achieve a significant reduction of time and skill required to glean actionable intelligence in these challenging operational environments.

Age differences and interindividual variation in cognition in community-dwelling elderly.

PubMed

Christensen, H; Mackinnon, A; Jorm, A F; Henderson, A S; Scott, L R; Korten, A E

1994-09-01

The cognitive test performance of 897 community-dwelling elderly Ss, aged 70 years and over, was examined for age trends and interindividual variation. Data were subjected to factor analysis, and 3 factors emerged (Crystallized Intelligence, Fluid Intelligence, and Memory). Over the age span sampled, Crystallized Intelligence, Fluid Intelligence, and Memory all decreased with the decrease being greatest for Fluid Intelligence and least for Crystallized Intelligence. Interindividual variation increased for Fluid Intelligence and Memory, but not for Crystallized Intelligence. These findings give support to the view that crystallized intelligence is lower in the very old and that there is a greater degree of variability in test performance with advancing age.

Research of home energy management system based on technology of PLC and ZigBee

NASA Astrophysics Data System (ADS)

Wei, Qi; Shen, Jiaojiao

2015-12-01

In view of the problem of saving effectively energy and energy management in home, this paper designs a home energy intelligent control system based on power line carrier communication and wireless ZigBee sensor networks. The system is based on ARM controller, power line carrier communication and wireless ZigBee sensor network as the terminal communication mode, and realizes the centralized and intelligent control of home appliances. Through the combination of these two technologies, the advantages of the two technologies complement each other, and provide a feasible plan for the construction of energy-efficient, intelligent home energy management system.

Processing speed in recurrent visual networks correlates with general intelligence.

PubMed

Jolij, Jacob; Huisman, Danielle; Scholte, Steven; Hamel, Ronald; Kemner, Chantal; Lamme, Victor A F

2007-01-08

Studies on the neural basis of general fluid intelligence strongly suggest that a smarter brain processes information faster. Different brain areas, however, are interconnected by both feedforward and feedback projections. Whether both types of connections or only one of the two types are faster in smarter brains remains unclear. Here we show, by measuring visual evoked potentials during a texture discrimination task, that general fluid intelligence shows a strong correlation with processing speed in recurrent visual networks, while there is no correlation with speed of feedforward connections. The hypothesis that a smarter brain runs faster may need to be refined: a smarter brain's feedback connections run faster.

Intelligent Evaluation Method of Tank Bottom Corrosion Status Based on Improved BP Artificial Neural Network

NASA Astrophysics Data System (ADS)

Qiu, Feng; Dai, Guang; Zhang, Ying

According to the acoustic emission information and the appearance inspection information of tank bottom online testing, the external factors associated with tank bottom corrosion status are confirmed. Applying artificial neural network intelligent evaluation method, three tank bottom corrosion status evaluation models based on appearance inspection information, acoustic emission information, and online testing information are established. Comparing with the result of acoustic emission online testing through the evaluation of test sample, the accuracy of the evaluation model based on online testing information is 94 %. The evaluation model can evaluate tank bottom corrosion accurately and realize acoustic emission online testing intelligent evaluation of tank bottom.

Biosensors with Built-In Biomolecular Logic Gates for Practical Applications

PubMed Central

Lai, Yu-Hsuan; Sun, Sin-Cih; Chuang, Min-Chieh

2014-01-01

Molecular logic gates, designs constructed with biological and chemical molecules, have emerged as an alternative computing approach to silicon-based logic operations. These molecular computers are capable of receiving and integrating multiple stimuli of biochemical significance to generate a definitive output, opening a new research avenue to advanced diagnostics and therapeutics which demand handling of complex factors and precise control. In molecularly gated devices, Boolean logic computations can be activated by specific inputs and accurately processed via bio-recognition, bio-catalysis, and selective chemical reactions. In this review, we survey recent advances of the molecular logic approaches to practical applications of biosensors, including designs constructed with proteins, enzymes, nucleic acids, nanomaterials, and organic compounds, as well as the research avenues for future development of digitally operating “sense and act” schemes that logically process biochemical signals through networked circuits to implement intelligent control systems. PMID:25587423

Dual-Use Space Technology Transfer Conference and Exhibition. Volume 1

NASA Technical Reports Server (NTRS)

Krishen, Kumar (Compiler)

1994-01-01

This document contains papers presented at the Dual-Use Space Technology Transfer Conference and Exhibition held at the Johnson Space Center February 1-3, 1994. Possible technology transfers covered during the conference were in the areas of information access; innovative microwave and optical applications; materials and structures; marketing and barriers; intelligent systems; human factors and habitation; communications and data systems; business process and technology transfer; software engineering; biotechnology and advanced bioinstrumentation; communications signal processing and analysis; new ways of doing business; medical care; applications derived from control center data systems; human performance evaluation; technology transfer methods; mathematics, modeling, and simulation; propulsion; software analysis and decision tools systems/processes in human support technology; networks, control centers, and distributed systems; power; rapid development perception and vision technologies; integrated vehicle health management; automation technologies; advanced avionics; ans robotics technologies. More than 77 papers, 20 presentations, and 20 exhibits covering various disciplines were presented b experts from NASA, universities, and industry.

Fluid Intelligence Allows Flexible Recruitment of the Parieto-Frontal Network in Analogical Reasoning

PubMed Central

Preusse, Franziska; Elke, van der Meer; Deshpande, Gopikrishna; Krueger, Frank; Wartenburger, Isabell

2011-01-01

Fluid intelligence is the ability to think flexibly and to understand abstract relations. People with high fluid intelligence (hi-fluIQ) perform better in analogical reasoning tasks than people with average fluid intelligence (ave-fluIQ). Although previous neuroimaging studies reported involvement of parietal and frontal brain regions in geometric analogical reasoning (which is a prototypical task for fluid intelligence), however, neuroimaging findings on geometric analogical reasoning in hi-fluIQ are sparse. Furthermore, evidence on the relation between brain activation and intelligence while solving cognitive tasks is contradictory. The present study was designed to elucidate the cerebral correlates of geometric analogical reasoning in a sample of hi-fluIQ and ave-fluIQ high school students. We employed a geometric analogical reasoning task with graded levels of task difficulty and confirmed the involvement of the parieto-frontal network in solving this task. In addition to characterizing the brain regions involved in geometric analogical reasoning in hi-fluIQ and ave-fluIQ, we found that blood oxygenation level dependency (BOLD) signal changes were greater for hi-fluIQ than for ave-fluIQ in parietal brain regions. However, ave-fluIQ showed greater BOLD signal changes in the anterior cingulate cortex and medial frontal gyrus than hi-fluIQ. Thus, we showed that a similar network of brain regions is involved in geometric analogical reasoning in both groups. Interestingly, the relation between brain activation and intelligence is not mono-directional, but rather, it is specific for each brain region. The negative brain activation–intelligence relationship in frontal brain regions in hi-fluIQ goes along with a better behavioral performance and reflects a lower demand for executive monitoring compared to ave-fluIQ individuals. In conclusion, our data indicate that flexibly modulating the extent of regional cerebral activity is characteristic for fluid intelligence. PMID:21415916

Hybrid intelligent monironing systems for thermal power plant trips

NASA Astrophysics Data System (ADS)

Barsoum, Nader; Ismail, Firas Basim

2012-11-01

Steam boiler is one of the main equipment in thermal power plants. If the steam boiler trips it may lead to entire shutdown of the plant, which is economically burdensome. Early boiler trips monitoring is crucial to maintain normal and safe operational conditions. In the present work two artificial intelligent monitoring systems specialized in boiler trips have been proposed and coded within the MATLAB environment. The training and validation of the two systems has been performed using real operational data captured from the plant control system of selected power plant. An integrated plant data preparation framework for seven boiler trips with related operational variables has been proposed for IMSs data analysis. The first IMS represents the use of pure Artificial Neural Network system for boiler trip detection. All seven boiler trips under consideration have been detected by IMSs before or at the same time of the plant control system. The second IMS represents the use of Genetic Algorithms and Artificial Neural Networks as a hybrid intelligent system. A slightly lower root mean square error was observed in the second system which reveals that the hybrid intelligent system performed better than the pure neural network system. Also, the optimal selection of the most influencing variables performed successfully by the hybrid intelligent system.

An intercomparison of artificial intelligence approaches for polar scene identification

NASA Technical Reports Server (NTRS)

Tovinkere, V. R.; Penaloza, M.; Logar, A.; Lee, J.; Weger, R. C.; Berendes, T. A.; Welch, R. M.

1993-01-01

The following six different artificial-intelligence (AI) approaches to polar scene identification are examined: (1) a feed forward back propagation neural network, (2) a probabilistic neural network, (3) a hybrid neural network, (4) a 'don't care' feed forward perception model, (5) a 'don't care' feed forward back propagation neural network, and (6) a fuzzy logic based expert system. The ten classes into which six AVHRR local-coverage arctic scenes were classified were: water, solid sea ice, broken sea ice, snow-covered mountains, land, stratus over ice, stratus over water, cirrus over water, cumulus over water, and multilayer cloudiness. It was found that 'don't care' back propagation neural network produced the highest accuracies. This approach has also low CPU requirement.

Hybrid Swarm Intelligence Optimization Approach for Optimal Data Storage Position Identification in Wireless Sensor Networks

PubMed Central

Mohanasundaram, Ranganathan; Periasamy, Pappampalayam Sanmugam

2015-01-01

The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches. PMID:25734182

Intelligent reservoir operation system based on evolving artificial neural networks

NASA Astrophysics Data System (ADS)

Chaves, Paulo; Chang, Fi-John

2008-06-01

We propose a novel intelligent reservoir operation system based on an evolving artificial neural network (ANN). Evolving means the parameters of the ANN model are identified by the GA evolutionary optimization technique. Accordingly, the ANN model should represent the operational strategies of reservoir operation. The main advantages of the Evolving ANN Intelligent System (ENNIS) are as follows: (i) only a small number of parameters to be optimized even for long optimization horizons, (ii) easy to handle multiple decision variables, and (iii) the straightforward combination of the operation model with other prediction models. The developed intelligent system was applied to the operation of the Shihmen Reservoir in North Taiwan, to investigate its applicability and practicability. The proposed method is first built to a simple formulation for the operation of the Shihmen Reservoir, with single objective and single decision. Its results were compared to those obtained by dynamic programming. The constructed network proved to be a good operational strategy. The method was then built and applied to the reservoir with multiple (five) decision variables. The results demonstrated that the developed evolving neural networks improved the operation performance of the reservoir when compared to its current operational strategy. The system was capable of successfully simultaneously handling various decision variables and provided reasonable and suitable decisions.

CATO: a CAD tool for intelligent design of optical networks and interconnects

NASA Astrophysics Data System (ADS)

Chlamtac, Imrich; Ciesielski, Maciej; Fumagalli, Andrea F.; Ruszczyk, Chester; Wedzinga, Gosse

1997-10-01

Increasing communication speed requirements have created a great interest in very high speed optical and all-optical networks and interconnects. The design of these optical systems is a highly complex task, requiring the simultaneous optimization of various parts of the system, ranging from optical components' characteristics to access protocol techniques. Currently there are no computer aided design (CAD) tools on the market to support the interrelated design of all parts of optical communication systems, thus the designer has to rely on costly and time consuming testbed evaluations. The objective of the CATO (CAD tool for optical networks and interconnects) project is to develop a prototype of an intelligent CAD tool for the specification, design, simulation and optimization of optical communication networks. CATO allows the user to build an abstract, possible incomplete, model of the system, and determine its expected performance. Based on design constraints provided by the user, CATO will automatically complete an optimum design, using mathematical programming techniques, intelligent search methods and artificial intelligence (AI). Initial design and testing of a CATO prototype (CATO-1) has been completed recently. The objective was to prove the feasibility of combining AI techniques, simulation techniques, an optical device library and a graphical user interface into a flexible CAD tool for obtaining optimal communication network designs in terms of system cost and performance. CATO-1 is an experimental tool for designing packet-switching wavelength division multiplexing all-optical communication systems using a LAN/MAN ring topology as the underlying network. The two specific AI algorithms incorporated are simulated annealing and a genetic algorithm. CATO-1 finds the optimal number of transceivers for each network node, using an objective function that includes the cost of the devices and the overall system performance.

Addressing fundamental architectural challenges of an activity-based intelligence and advanced analytics (ABIAA) system

NASA Astrophysics Data System (ADS)

Yager, Kevin; Albert, Thomas; Brower, Bernard V.; Pellechia, Matthew F.

2015-06-01

The domain of Geospatial Intelligence Analysis is rapidly shifting toward a new paradigm of Activity Based Intelligence (ABI) and information-based Tipping and Cueing. General requirements for an advanced ABIAA system present significant challenges in architectural design, computing resources, data volumes, workflow efficiency, data mining and analysis algorithms, and database structures. These sophisticated ABI software systems must include advanced algorithms that automatically flag activities of interest in less time and within larger data volumes than can be processed by human analysts. In doing this, they must also maintain the geospatial accuracy necessary for cross-correlation of multi-intelligence data sources. Historically, serial architectural workflows have been employed in ABIAA system design for tasking, collection, processing, exploitation, and dissemination. These simpler architectures may produce implementations that solve short term requirements; however, they have serious limitations that preclude them from being used effectively in an automated ABIAA system with multiple data sources. This paper discusses modern ABIAA architectural considerations providing an overview of an advanced ABIAA system and comparisons to legacy systems. It concludes with a recommended strategy and incremental approach to the research, development, and construction of a fully automated ABIAA system.

A swarm intelligence framework for reconstructing gene networks: searching for biologically plausible architectures.

PubMed

Kentzoglanakis, Kyriakos; Poole, Matthew

2012-01-01

In this paper, we investigate the problem of reverse engineering the topology of gene regulatory networks from temporal gene expression data. We adopt a computational intelligence approach comprising swarm intelligence techniques, namely particle swarm optimization (PSO) and ant colony optimization (ACO). In addition, the recurrent neural network (RNN) formalism is employed for modeling the dynamical behavior of gene regulatory systems. More specifically, ACO is used for searching the discrete space of network architectures and PSO for searching the corresponding continuous space of RNN model parameters. We propose a novel solution construction process in the context of ACO for generating biologically plausible candidate architectures. The objective is to concentrate the search effort into areas of the structure space that contain architectures which are feasible in terms of their topological resemblance to real-world networks. The proposed framework is initially applied to the reconstruction of a small artificial network that has previously been studied in the context of gene network reverse engineering. Subsequently, we consider an artificial data set with added noise for reconstructing a subnetwork of the genetic interaction network of S. cerevisiae (yeast). Finally, the framework is applied to a real-world data set for reverse engineering the SOS response system of the bacterium Escherichia coli. Results demonstrate the relative advantage of utilizing problem-specific knowledge regarding biologically plausible structural properties of gene networks over conducting a problem-agnostic search in the vast space of network architectures.

Convolutional neural network guided blue crab knuckle detection for autonomous crab meat picking machine

NASA Astrophysics Data System (ADS)

Wang, Dongyi; Vinson, Robert; Holmes, Maxwell; Seibel, Gary; Tao, Yang

2018-04-01

The Atlantic blue crab is among the highest-valued seafood found in the American Eastern Seaboard. Currently, the crab processing industry is highly dependent on manual labor. However, there is great potential for vision-guided intelligent machines to automate the meat picking process. Studies show that the back-fin knuckles are robust features containing information about a crab's size, orientation, and the position of the crab's meat compartments. Our studies also make it clear that detecting the knuckles reliably in images is challenging due to the knuckle's small size, anomalous shape, and similarity to joints in the legs and claws. An accurate and reliable computer vision algorithm was proposed to detect the crab's back-fin knuckles in digital images. Convolutional neural networks (CNNs) can localize rough knuckle positions with 97.67% accuracy, transforming a global detection problem into a local detection problem. Compared to the rough localization based on human experience or other machine learning classification methods, the CNN shows the best localization results. In the rough knuckle position, a k-means clustering method is able to further extract the exact knuckle positions based on the back-fin knuckle color features. The exact knuckle position can help us to generate a crab cutline in XY plane using a template matching method. This is a pioneering research project in crab image analysis and offers advanced machine intelligence for automated crab processing.

LTE-advanced random access mechanism for M2M communication: A review

NASA Astrophysics Data System (ADS)

Mustafa, Rashid; Sarowa, Sandeep; Jaglan, Reena Rathee; Khan, Mohammad Junaid; Agrawal, Sunil

2016-03-01

Machine Type Communications (MTC) enables one or more self-sufficient machines to communicate directly with one another without human interference. MTC applications include smart grid, security, e-Health and intelligent automation system. To support huge numbers of MTC devices, one of the challenging issues is to provide a competent way for numerous access in the network and to minimize network overload. In this article, the different control mechanisms for overload random access are reviewed to avoid congestion caused by random access channel (RACH) of MTC devices. However, past and present wireless technologies have been engineered for Human-to-Human (H2H) communications, in particular, for transmission of voice. Consequently the Long Term Evolution (LTE) -Advanced is expected to play a central role in communicating Machine to Machine (M2M) and are very optimistic about H2H communications. Distinct and unique characteristics of M2M communications create new challenges from those in H2H communications. In this article, we investigate the impact of massive M2M terminals attempting random access to LTE-Advanced all at once. We discuss and review the solutions to alleviate the overload problem by Third Generation Partnership Project (3GPP). As a result, we evaluate and compare these solutions that can effectively eliminate the congestion on the random access channel for M2M communications without affecting H2H communications.

FUDAOWANG: A Web-Based Intelligent Tutoring System Implementing Advanced Education Concepts

ERIC Educational Resources Information Center

Xu, Wei; Zhao, Ke; Li, Yatao; Yi, Zhenzhen

2012-01-01

Determining how to provide good tutoring functions is an important research direction of intelligent tutoring systems. In this study, the authors develop an intelligent tutoring system with good tutoring functions, called "FUDAOWANG." The research domain that FUDAOWANG treats is junior middle school mathematics, which belongs to the objective…

An Experiential Approach to Cultural Intelligence Education

ERIC Educational Resources Information Center

MacNab, Brent R.

2012-01-01

Cultural intelligence (CQ) represents a promising advancement in the area of cross-cultural training and management. Experiential approaches for CQ development have been proposed as highly effective; however, there is a lack of CQ-specific approaches in the management literature. This work overviews the concept of cultural intelligence and its…

The Role of the Principal's Emotional Intelligence in Primary Education Leadership

ERIC Educational Resources Information Center

Brinia, Vasiliki; Zimianiti, Lina; Panagiotopoulos, Konstantinos

2014-01-01

The development of emotional intelligence skills offers sufficient leadership qualities for advancing the organization and for achieving its objectives. In particular, the emotionally intelligent leader--principal is able to inspire and facilitate a self-conscious and organizational culture by adopting the values of understanding, trust, prospect,…

Applications of Artificial Intelligence in Education--A Personal View.

ERIC Educational Resources Information Center

Richer, Mark H.

1985-01-01

Discusses: how artificial intelligence (AI) can advance education; if the future of software lies in AI; the roots of intelligent computer-assisted instruction; protocol analysis; reactive environments; LOGO programming language; student modeling and coaching; and knowledge-based instructional programs. Numerous examples of AI programs are cited.…

Integrating Human and Computer Intelligence. Technical Report No. 32.

ERIC Educational Resources Information Center

Pea, Roy D.

This paper explores the thesis that advances in computer applications and artificial intelligence have important implications for the study of development and learning in psychology. Current approaches to the use of computers as devices for problem solving, reasoning, and thinking--i.e., expert systems and intelligent tutoring systems--are…

Neuroscience-Inspired Artificial Intelligence.

PubMed

Hassabis, Demis; Kumaran, Dharshan; Summerfield, Christopher; Botvinick, Matthew

2017-07-19

The fields of neuroscience and artificial intelligence (AI) have a long and intertwined history. In more recent times, however, communication and collaboration between the two fields has become less commonplace. In this article, we argue that better understanding biological brains could play a vital role in building intelligent machines. We survey historical interactions between the AI and neuroscience fields and emphasize current advances in AI that have been inspired by the study of neural computation in humans and other animals. We conclude by highlighting shared themes that may be key for advancing future research in both fields. Copyright © 2017. Published by Elsevier Inc.

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

Simulation Framework for Intelligent Transportation Systems

DOT National Transportation Integrated Search

1996-10-01

A simulation framework has been developed for a large-scale, comprehensive, scaleable simulation of an Intelligent Transportation System. The simulator is designed for running on parellel computers and distributed (networked) computer systems, but ca...

Infusion of innovative technologies for mission operations

NASA Astrophysics Data System (ADS)

Donati, Alessandro

2010-11-01

The Advanced Mission Concepts and Technologies Office (Mission Technologies Office, MTO for short) at the European Space Operations Centre (ESOC) of ESA is entrusted with research and development of innovative mission operations concepts systems and provides operations support to special projects. Visions of future missions and requests for improvements from currently flying missions are the two major sources of inspiration to conceptualize innovative or improved mission operations processes. They include monitoring and diagnostics, planning and scheduling, resource management and optimization. The newly identified operations concepts are then proved by means of prototypes, built with embedded, enabling technology and deployed as shadow applications in mission operations for an extended validation phase. The technology so far exploited includes informatics, artificial intelligence and operational research branches. Recent outstanding results include artificial intelligence planning and scheduling applications for Mars Express, advanced integrated space weather monitoring system for the Integral space telescope and a suite of growing client applications for MUST (Mission Utilities Support Tools). The research, development and validation activities at the Mission technologies office are performed together with a network of research institutes across Europe. The objective is narrowing the gap between enabling and innovative technology and space mission operations. The paper first addresses samples of technology infusion cases with their lessons learnt. The second part is focused on the process and the methodology used at the Mission technologies office to fulfill its objectives.

Academic research opportunities at the National Geospatial-Intelligence Agency(NGA)

NASA Astrophysics Data System (ADS)

Loomer, Scott A.

2006-05-01

The vision of the National Geospatial-Intelligence Agency (NGA) is to "Know the Earth...Show the Way." To achieve this vision, the NGA provides geospatial intelligence in all its forms and from whatever source-imagery, imagery intelligence, and geospatial data and information-to ensure the knowledge foundation for planning, decision, and action. Academia plays a key role in the NGA research and development program through the NGA Academic Research Program. This multi-disciplinary program of basic research in geospatial intelligence topics provides grants and fellowships to the leading investigators, research universities, and colleges of the nation. This research provides the fundamental science support to NGA's applied and advanced research programs. The major components of the NGA Academic Research Program are: *NGA University Research Initiatives (NURI): Three-year basic research grants awarded competitively to the best investigators across the US academic community. Topics are selected to provide the scientific basis for advanced and applied research in NGA core disciplines. *Historically Black College and University - Minority Institution Research Initiatives (HBCU-MI): Two-year basic research grants awarded competitively to the best investigators at Historically Black Colleges and Universities, and Minority Institutions across the US academic community. *Intelligence Community Post-Doctoral Research Fellowships: Fellowships providing access to advanced research in science and technology applicable to the intelligence community's mission. The program provides a pool of researchers to support future intelligence community needs and develops long-term relationships with researchers as they move into career positions. This paper provides information about the NGA Academic Research Program, the projects it supports and how researchers and institutions can apply for grants under the program. In addition, other opportunities for academia to engage with NGA through training programs and recruitment are discussed.

Hybrid Architectures and Their Impact on Intelligent Design

NASA Technical Reports Server (NTRS)

Kandel, Abe

1996-01-01

In this presentation we investigate a novel framework for the design of autonomous fuzzy intelligent systems. The system integrates the following modules into a single autonomous entity: (1) a fuzzy expert system; (2) artificial neural network; (3) genetic algorithm; and (4) case-base reasoning. We describe the integration of these units into one intelligent structure and discuss potential applications.

Smart Collections: Can Artificial Intelligence Tools and Techniques Assist with Discovering, Evaluating and Tagging Digital Learning Resources?

ERIC Educational Resources Information Center

Leibbrandt, Richard; Yang, Dongqiang; Pfitzner, Darius; Powers, David; Mitchell, Pru; Hayman, Sarah; Eddy, Helen

2010-01-01

This paper reports on a joint proof of concept project undertaken by researchers from the Flinders University Artificial Intelligence Laboratory in partnership with information managers from the Education Network Australia (edna) team at Education Services Australia to address the question of whether artificial intelligence techniques could be…

A survey on bio inspired meta heuristic based clustering protocols for wireless sensor networks

NASA Astrophysics Data System (ADS)

Datta, A.; Nandakumar, S.

2017-11-01

Recent studies have shown that utilizing a mobile sink to harvest and carry data from a Wireless Sensor Network (WSN) can improve network operational efficiency as well as maintain uniform energy consumption by the sensor nodes in the network. Due to Sink mobility, the path between two sensor nodes continuously changes and this has a profound effect on the operational longevity of the network and a need arises for a protocol which utilizes minimal resources in maintaining routes between the mobile sink and the sensor nodes. Swarm Intelligence based techniques inspired by the foraging behavior of ants, termites and honey bees can be artificially simulated and utilized to solve real wireless network problems. The author presents a brief survey on various bio inspired swarm intelligence based protocols used in routing data in wireless sensor networks while outlining their general principle and operation.

DDN (Defence Data Network) Protocol Implementations and Vendors Guide

DTIC Science & Technology

1988-08-01

Artificial Intelligence Laboratory Room NE43-723 545 Technology Square Cambridge, MA 02139 (617) 253-8843 S John Wroclawski, (JTW@AI.AJ.MIT.EDU...Massachusetts Institute of Technology Artificial Intelligence Laboratory Room NE43-743 545 Technology Square 0 Cambridge, MA 02139 (617) 253-7885 ORDERING...TCP/IP Network Software for PC-DOS Systems CPU: IBM-PC/XT/AT/compatible in conjunction with EXOS 205 Inteligent Ethernet Controller for PCbus 0/s

Real-time video streaming using H.264 scalable video coding (SVC) in multihomed mobile networks: a testbed approach

NASA Astrophysics Data System (ADS)

Nightingale, James; Wang, Qi; Grecos, Christos

2011-03-01

Users of the next generation wireless paradigm known as multihomed mobile networks expect satisfactory quality of service (QoS) when accessing streamed multimedia content. The recent H.264 Scalable Video Coding (SVC) extension to the Advanced Video Coding standard (AVC), offers the facility to adapt real-time video streams in response to the dynamic conditions of multiple network paths encountered in multihomed wireless mobile networks. Nevertheless, preexisting streaming algorithms were mainly proposed for AVC delivery over multipath wired networks and were evaluated by software simulation. This paper introduces a practical, hardware-based testbed upon which we implement and evaluate real-time H.264 SVC streaming algorithms in a realistic multihomed wireless mobile networks environment. We propose an optimised streaming algorithm with multi-fold technical contributions. Firstly, we extended the AVC packet prioritisation schemes to reflect the three-dimensional granularity of SVC. Secondly, we designed a mechanism for evaluating the effects of different streamer 'read ahead window' sizes on real-time performance. Thirdly, we took account of the previously unconsidered path switching and mobile networks tunnelling overheads encountered in real-world deployments. Finally, we implemented a path condition monitoring and reporting scheme to facilitate the intelligent path switching. The proposed system has been experimentally shown to offer a significant improvement in PSNR of the received stream compared with representative existing algorithms.

Intelligent Systems For Aerospace Engineering: An Overview

NASA Technical Reports Server (NTRS)

KrishnaKumar, K.

2003-01-01

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

Intelligent Systems for Aerospace Engineering: An Overview

NASA Technical Reports Server (NTRS)

Krishnakumar, Kalmanje

2002-01-01

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

Management and development of local area network upgrade prototype

NASA Technical Reports Server (NTRS)

Fouser, T. J.

1981-01-01

Given the situation of having management and development users accessing a central computing facility and given the fact that these same users have the need for local computation and storage, the utilization of a commercially available networking system such as CP/NET from Digital Research provides the building blocks for communicating intelligent microsystems to file and print services. The major problems to be overcome in the implementation of such a network are the dearth of intelligent communication front-ends for the microcomputers and the lack of a rich set of management and software development tools.

Using Intelligent Simulation to Enhance Human Performance in Aircraft Maintenance

NASA Technical Reports Server (NTRS)

Johnson, William B.; Norton, Jeffrey E.

1992-01-01

Human factors research and development investigates the capabilities and limitations of the human within a system. Of the many variables affecting human performance in the aviation maintenance system, training is among the most important. The advent of advanced technology hardware and software has created intelligent training simulations. This paper describes one advanced technology training system under development for the Federal Aviation Administration.

Social Media Analytics: A New Approach for Cyberspace Enabled Understanding of Operational Environments

DTIC Science & Technology

2013-12-10

NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER Major Sean Lyons 5e. TASK NUMBER 5f. WORK...Advance Research Projects Agency DOD Department of Defense FM Field Manual IC Intelligence Community IO Information Operations IP Internet...Utah, Central Intelligence Agency funding of the Recorded Future Company, and Defense Advanced Research Projects Agency, XDATA project . 2

Power system voltage stability and agent based distribution automation in smart grid

NASA Astrophysics Data System (ADS)

Nguyen, Cuong Phuc

2011-12-01

Our interconnected electric power system is presently facing many challenges that it was not originally designed and engineered to handle. The increased inter-area power transfers, aging infrastructure, and old technologies, have caused many problems including voltage instability, widespread blackouts, slow control response, among others. These problems have created an urgent need to transform the present electric power system to a highly stable, reliable, efficient, and self-healing electric power system of the future, which has been termed "smart grid". This dissertation begins with an investigation of voltage stability in bulk transmission networks. A new continuation power flow tool for studying the impacts of generator merit order based dispatch on inter-area transfer capability and static voltage stability is presented. The load demands are represented by lumped load models on the transmission system. While this representation is acceptable in traditional power system analysis, it may not be valid in the future smart grid where the distribution system will be integrated with intelligent and quick control capabilities to mitigate voltage problems before they propagate into the entire system. Therefore, before analyzing the operation of the whole smart grid, it is important to understand the distribution system first. The second part of this dissertation presents a new platform for studying and testing emerging technologies in advanced Distribution Automation (DA) within smart grids. Due to the key benefits over the traditional centralized approach, namely flexible deployment, scalability, and avoidance of single-point-of-failure, a new distributed approach is employed to design and develop all elements of the platform. A multi-agent system (MAS), which has the three key characteristics of autonomy, local view, and decentralization, is selected to implement the advanced DA functions. The intelligent agents utilize a communication network for cooperation and negotiation. Communication latency is modeled using a user-defined probability density function. Failure-tolerant communication strategies are developed for agent communications. Major elements of advanced DA are developed in a completely distributed way and successfully tested for several IEEE standard systems, including: Fault Detection, Location, Isolation, and Service Restoration (FLISR); Coordination of Distributed Energy Storage Systems (DES); Distributed Power Flow (DPF); Volt-VAR Control (VVC); and Loss Reduction (LR).

Adaptive nodes enrich nonlinear cooperative learning beyond traditional adaptation by links.

PubMed

Sardi, Shira; Vardi, Roni; Goldental, Amir; Sheinin, Anton; Uzan, Herut; Kanter, Ido

2018-03-23

Physical models typically assume time-independent interactions, whereas neural networks and machine learning incorporate interactions that function as adjustable parameters. Here we demonstrate a new type of abundant cooperative nonlinear dynamics where learning is attributed solely to the nodes, instead of the network links which their number is significantly larger. The nodal, neuronal, fast adaptation follows its relative anisotropic (dendritic) input timings, as indicated experimentally, similarly to the slow learning mechanism currently attributed to the links, synapses. It represents a non-local learning rule, where effectively many incoming links to a node concurrently undergo the same adaptation. The network dynamics is now counterintuitively governed by the weak links, which previously were assumed to be insignificant. This cooperative nonlinear dynamic adaptation presents a self-controlled mechanism to prevent divergence or vanishing of the learning parameters, as opposed to learning by links, and also supports self-oscillations of the effective learning parameters. It hints on a hierarchical computational complexity of nodes, following their number of anisotropic inputs and opens new horizons for advanced deep learning algorithms and artificial intelligence based applications, as well as a new mechanism for enhanced and fast learning by neural networks.

Reservoir characterization using core, well log, and seismic data and intelligent software

NASA Astrophysics Data System (ADS)

Soto Becerra, Rodolfo

We have developed intelligent software, Oilfield Intelligence (OI), as an engineering tool to improve the characterization of oil and gas reservoirs. OI integrates neural networks and multivariate statistical analysis. It is composed of five main subsystems: data input, preprocessing, architecture design, graphics design, and inference engine modules. More than 1,200 lines of programming code as M-files using the language MATLAB been written. The degree of success of many oil and gas drilling, completion, and production activities depends upon the accuracy of the models used in a reservoir description. Neural networks have been applied for identification of nonlinear systems in almost all scientific fields of humankind. Solving reservoir characterization problems is no exception. Neural networks have a number of attractive features that can help to extract and recognize underlying patterns, structures, and relationships among data. However, before developing a neural network model, we must solve the problem of dimensionality such as determining dominant and irrelevant variables. We can apply principal components and factor analysis to reduce the dimensionality and help the neural networks formulate more realistic models. We validated OI by obtaining confident models in three different oil field problems: (1) A neural network in-situ stress model using lithology and gamma ray logs for the Travis Peak formation of east Texas, (2) A neural network permeability model using porosity and gamma ray and a neural network pseudo-gamma ray log model using 3D seismic attributes for the reservoir VLE 196 Lamar field located in Block V of south-central Lake Maracaibo (Venezuela), and (3) Neural network primary ultimate oil recovery (PRUR), initial waterflooding ultimate oil recovery (IWUR), and infill drilling ultimate oil recovery (IDUR) models using reservoir parameters for San Andres and Clearfork carbonate formations in west Texas. In all cases, we compared the results from the neural network models with the results from regression statistical and non-parametric approach models. The results show that it is possible to obtain the highest cross-correlation coefficient between predicted and actual target variables, and the lowest average absolute errors using the integrated techniques of multivariate statistical analysis and neural networks in our intelligent software.

Visiting Scholars Program

DTIC Science & Technology

2016-09-01

other associated grants. 15. SUBJECT TERMS SUNY Poly, STEM, Artificial Intelligence , Command and Control 16. SECURITY CLASSIFICATION OF: 17...neuromorphic system has the potential to be widely used in a high-efficiency artificial intelligence system. Simulation results have indicated that the...novel multiresolution fusion and advanced fusion performance evaluation tool for an Artificial Intelligence based natural language annotation engine for

Developmental Process Model for the Java Intelligent Tutoring System

ERIC Educational Resources Information Center

Sykes, Edward

2007-01-01

The Java Intelligent Tutoring System (JITS) was designed and developed to support the growing trend of Java programming around the world. JITS is an advanced web-based personalized tutoring system that is unique in several ways. Most programming Intelligent Tutoring Systems require the teacher to author problems with corresponding solutions. JITS,…

Intelligence and Metacognition as Predictors of Foreign Language Achievement: A Structural Equation Modeling Approach

ERIC Educational Resources Information Center

Pishghadam, Reza; Khajavy, Gholam Hassan

2013-01-01

This study examined the role of metacognition and intelligence in foreign language achievement on a sample of 143 Iranian English as a Foreign Language (EFL) learners. Participants completed Raven's Advanced Progressive Matrices as a measure of intelligence, and Metacognitive Awareness Inventory as a measure of metacognition. Learners' scores at…

Intelligent Resource Management for Local Area Networks: Approach and Evolution

NASA Technical Reports Server (NTRS)

Meike, Roger

1988-01-01

The Data Management System network is a complex and important part of manned space platforms. Its efficient operation is vital to crew, subsystems and experiments. AI is being considered to aid in the initial design of the network and to augment the management of its operation. The Intelligent Resource Management for Local Area Networks (IRMA-LAN) project is concerned with the application of AI techniques to network configuration and management. A network simulation was constructed employing real time process scheduling for realistic loads, and utilizing the IEEE 802.4 token passing scheme. This simulation is an integral part of the construction of the IRMA-LAN system. From it, a causal model is being constructed for use in prediction and deep reasoning about the system configuration. An AI network design advisor is being added to help in the design of an efficient network. The AI portion of the system is planned to evolve into a dynamic network management aid. The approach, the integrated simulation, project evolution, and some initial results are described.

Dynamic clustering scheme based on the coordination of management and control in multi-layer and multi-region intelligent optical network

NASA Astrophysics Data System (ADS)

Niu, Xiaoliang; Yuan, Fen; Huang, Shanguo; Guo, Bingli; Gu, Wanyi

2011-12-01

A Dynamic clustering scheme based on coordination of management and control is proposed to reduce network congestion rate and improve the blocking performance of hierarchical routing in Multi-layer and Multi-region intelligent optical network. Its implement relies on mobile agent (MA) technology, which has the advantages of efficiency, flexibility, functional and scalability. The paper's major contribution is to adjust dynamically domain when the performance of working network isn't in ideal status. And the incorporation of centralized NMS and distributed MA control technology migrate computing process to control plane node which releases the burden of NMS and improves process efficiently. Experiments are conducted on Multi-layer and multi-region Simulation Platform for Optical Network (MSPON) to assess the performance of the scheme.

TALON - The Telescope Alert Operation Network System : intelligent linking of distributed autonomous robotic telescopes

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

White, R. R.; Wren, J.; Davis, H. R.

2004-01-01

The internet has brought about great change in the astronomical community, but this interconnectivity is just starting to be exploited for use in instrumentation. Utilizing the internet for communicating between distributed astronomical systems is still in its infancy, but it already shows great potential. Here we present an example of a distributed network of telescopes that performs more efficienfiy in synchronous operation than as individual instruments. RAPid Telescopes for Optical Response (RAPTOR) is a system of telescopes at LANL that has intelligent intercommunication, combined with wide-field optics, temporal monitoring software, and deep-field follow-up capability all working in closed-loop real-time operation.more » The Telescope ALert Operations Network (TALON) is a network server that allows intercommunication of alert triggers from external and internal resources and controls the distribution of these to each of the telescopes on the network. TALON is designed to grow, allowing any number of telescopes to be linked together and communicate. Coupled with an intelligent alert client at each telescope, it can analyze and respond to each distributed TALON alert based on the telescopes needs and schedule.« less

Application of artifical intelligence principles to the analysis of "crazy" speech.

PubMed

Garfield, D A; Rapp, C

1994-04-01

Artificial intelligence computer simulation methods can be used to investigate psychotic or "crazy" speech. Here, symbolic reasoning algorithms establish semantic networks that schematize speech. These semantic networks consist of two main structures: case frames and object taxonomies. Node-based reasoning rules apply to object taxonomies and pathway-based reasoning rules apply to case frames. Normal listeners may recognize speech as "crazy talk" based on violations of node- and pathway-based reasoning rules. In this article, three separate segments of schizophrenic speech illustrate violations of these rules. This artificial intelligence approach is compared and contrasted with other neurolinguistic approaches and is discussed as a conceptual link between neurobiological and psychodynamic understandings of psychopathology.

An "intelligent" approach based on side-by-side cascade-correlation neural networks for estimating thermophysical properties from photothermal responses

NASA Astrophysics Data System (ADS)

Grieu, Stéphane; Faugeroux, Olivier; Traoré, Adama; Claudet, Bernard; Bodnar, Jean-Luc

2015-01-01

In the present paper, an artificial-intelligence-based approach dealing with the estimation of thermophysical properties is designed and evaluated. This new and "intelligent" approach makes use of photothermal responses obtained when subjecting materials to a light flux. So, the main objective of the present work was to estimate simultaneously both the thermal diffusivity and conductivity of materials, from front-face or rear-face photothermal responses to pseudo random binary signals. To this end, we used side-by-side feedforward neural networks trained with the cascade-correlation algorithm. In addition, computation time was a key point to consider. That is why the developed algorithms are computationally tractable.

Water Level Prediction of Lake Cascade Mahakam Using Adaptive Neural Network Backpropagation (ANNBP)

NASA Astrophysics Data System (ADS)

Mislan; Gaffar, A. F. O.; Haviluddin; Puspitasari, N.

2018-04-01

A natural hazard information and flood events are indispensable as a form of prevention and improvement. One of the causes is flooding in the areas around the lake. Therefore, forecasting the surface of Lake water level to anticipate flooding is required. The purpose of this paper is implemented computational intelligence method namely Adaptive Neural Network Backpropagation (ANNBP) to forecasting the Lake Cascade Mahakam. Based on experiment, performance of ANNBP indicated that Lake water level prediction have been accurate by using mean square error (MSE) and mean absolute percentage error (MAPE). In other words, computational intelligence method can produce good accuracy. A hybrid and optimization of computational intelligence are focus in the future work.

Survivability of intelligent transportation systems

DOT National Transportation Integrated Search

1999-10-01

Intelligent Transportation Systems (ITS) are being deployed around the world to improve the safety and efficiency of surface transportation through the application of advanced information technology. The introduction of ITS exposes the transportation...

Intelligent Vehicle Highway Systems Projects

DOT National Transportation Integrated Search

1993-02-01

The Intelligent Vehicle Highway Systems (IVHS) program consists of a range of advanced technologies and concepts which, in combination, can improve mobility and transportation productivity, enhance safety, maximize the use of existing transportation ...

Intelligent composting assisted by a wireless sensing network.

PubMed

López, Marga; Martinez-Farre, Xavier; Casas, Oscar; Quilez, Marcos; Polo, Jose; Lopez, Oscar; Hornero, Gemma; Pinilla, Mirta R; Rovira, Carlos; Ramos, Pedro M; Borges, Beatriz; Marques, Hugo; Girão, Pedro Silva

2014-04-01

Monitoring of the moisture and temperature of composting process is a key factor to obtain a quality product beyond the quality of raw materials. Current methodologies for monitoring these two parameters are time consuming for workers, sometimes not sufficiently reliable to help decision-making and thus are ignored in some cases. This article describes an advance on monitoring of composting process through a Wireless Sensor Network (WSN) that allows measurement of temperature and moisture in real time in multiple points of the composting material, the Compo-ball system. To implement such measurement capabilities on-line, a WSN composed of multiple sensor nodes was designed and implemented to provide the staff with an efficient monitoring composting management tool. After framing the problem, the objectives and characteristics of the WSN are briefly discussed and a short description of the hardware and software of the network's components are presented. Presentation and discussion of practical issues and results obtained with the WSN during a demonstration stage that took place in several composting sites concludes the paper. Copyright © 2014 Elsevier Ltd. All rights reserved.

Intelligent approach to prognostic enhancements of diagnostic systems

NASA Astrophysics Data System (ADS)

Vachtsevanos, George; Wang, Peng; Khiripet, Noppadon; Thakker, Ash; Galie, Thomas R.

2001-07-01

This paper introduces a novel methodology to prognostics based on a dynamic wavelet neural network construct and notions from the virtual sensor area. This research has been motivated and supported by the U.S. Navy's active interest in integrating advanced diagnostic and prognostic algorithms in existing Naval digital control and monitoring systems. A rudimentary diagnostic platform is assumed to be available providing timely information about incipient or impending failure conditions. We focus on the development of a prognostic algorithm capable of predicting accurately and reliably the remaining useful lifetime of a failing machine or component. The prognostic module consists of a virtual sensor and a dynamic wavelet neural network as the predictor. The virtual sensor employs process data to map real measurements into difficult to monitor fault quantities. The prognosticator uses a dynamic wavelet neural network as a nonlinear predictor. Means to manage uncertainty and performance metrics are suggested for comparison purposes. An interface to an available shipboard Integrated Condition Assessment System is described and applications to shipboard equipment are discussed. Typical results from pump failures are presented to illustrate the effectiveness of the methodology.

A Federal Vision for Future Computing: A Nanotechnology-Inspired Grand Challenge

DTIC Science & Technology

2016-07-29

Science Foundation (NSF), Department of Defense (DOD), National Institute of Standards and Technology (NIST), Intelligence Community (IC) Introduction...multiple Federal agencies: • Intelligent big data sensors that act autonomously and are programmable via the network for increased flexibility, and... intelligence for scientific discovery enabled by rapid extreme-scale data analysis, capable of understanding and making sense of results and thereby

Artificial Intelligence in Astronomy

NASA Astrophysics Data System (ADS)

Devinney, E. J.; Prša, A.; Guinan, E. F.; Degeorge, M.

2010-12-01

From the perspective (and bias) as Eclipsing Binary researchers, we give a brief overview of the development of Artificial Intelligence (AI) applications, describe major application areas of AI in astronomy, and illustrate the power of an AI approach in an application developed under the EBAI (Eclipsing Binaries via Artificial Intelligence) project, which employs Artificial Neural Network technology for estimating light curve solution parameters of eclipsing binary systems.

Organisational Structure and Information Technology (IT): Exploring the Implications of IT for Future Military Structures

DTIC Science & Technology

2006-07-01

4 Abbreviations AI Artificial Intelligence AM Artificial Memory CAD Computer Aided...memory (AM), artificial intelligence (AI), and embedded knowledge systems it is possible to expand the “effective span of competence” of...Technology J Joint J2 Joint Intelligence J3 Joint Operations NATO North Atlantic Treaty Organisation NCW Network Centric Warfare NHS National Health

Decade Review (1999-2009): Artificial Intelligence Techniques in Student Modeling

NASA Astrophysics Data System (ADS)

Drigas, Athanasios S.; Argyri, Katerina; Vrettaros, John

Artificial Intelligence applications in educational field are getting more and more popular during the last decade (1999-2009) and that is why much relevant research has been conducted. In this paper, we present the most interesting attempts to apply artificial intelligence methods such as fuzzy logic, neural networks, genetic programming and hybrid approaches such as neuro - fuzzy systems and genetic programming neural networks (GPNN) in student modeling. This latest research trend is a part of every Intelligent Tutoring System and aims at generating and updating a student model in order to modify learning content to fit individual needs or to provide reliable assessment and feedback to student's answers. In this paper, we make a brief presentation of methods used to point out their qualities and then we attempt a navigation to the most representative studies sought in the decade of our interest after classifying them according to the principal aim they attempted to serve.

A threat intelligence framework for access control security in the oil industry

NASA Astrophysics Data System (ADS)

Alaskandrani, Faisal T.

The research investigates the problem raised by the rapid development in the technology industry giving security concerns in facilities built by the energy industry containing diverse platforms. The difficulty of continuous updates to network security architecture and assessment gave rise to the need to use threat intelligence frameworks to better assess and address networks security issues. Focusing on access control security to the ICS and SCADA systems that is being utilized to carry out mission critical and life threatening operations. The research evaluates different threat intelligence frameworks that can be implemented in the industry seeking the most suitable and applicable one that address the issue and provide more security measures. The validity of the result is limited to the same environment that was researched as well as the technologies being utilized. The research concludes that it is possible to utilize a Threat Intelligence framework to prioritize security in Access Control Measures in the Oil Industry.

Using CONFIG for Simulation of Operation of Water Recovery Subsystems for Advanced Control Software Evaluation

NASA Technical Reports Server (NTRS)

Malin, Jane T.; Flores, Luis; Fleming, Land; Throop, Daiv

2002-01-01

A hybrid discrete/continuous simulation tool, CONFIG, has been developed to support evaluation of the operability life support systems. CON FIG simulates operations scenarios in which flows and pressures change continuously while system reconfigurations occur as discrete events. In simulations, intelligent control software can interact dynamically with hardware system models. CONFIG simulations have been used to evaluate control software and intelligent agents for automating life support systems operations. A CON FIG model of an advanced biological water recovery system has been developed to interact with intelligent control software that is being used in a water system test at NASA Johnson Space Center

Emerging interdisciplinary fields in the coming intelligence/convergence era

NASA Astrophysics Data System (ADS)

Noor, Ahmed K.

2012-09-01

Dramatic advances are in the horizon resulting from rapid pace of development of several technologies, including, computing, communication, mobile, robotic, and interactive technologies. These advances, along with the trend towards convergence of traditional engineering disciplines with physical, life and other science disciplines will result in the development of new interdisciplinary fields, as well as in new paradigms for engineering practice in the coming intelligence/convergence era (post-information age). The interdisciplinary fields include Cyber Engineering, Living Systems Engineering, Biomechatronics/Robotics Engineering, Knowledge Engineering, Emergent/Complexity Engineering, and Multiscale Systems engineering. The paper identifies some of the characteristics of the intelligence/convergence era, gives broad definition of convergence, describes some of the emerging interdisciplinary fields, and lists some of the academic and other organizations working in these disciplines. The need is described for establishing a Hierarchical Cyber-Physical Ecosystem for facilitating interdisciplinary collaborations, and accelerating development of skilled workforce in the new fields. The major components of the ecosystem are listed. The new interdisciplinary fields will yield critical advances in engineering practice, and help in addressing future challenges in broad array of sectors, from manufacturing to energy, transportation, climate, and healthcare. They will also enable building large future complex adaptive systems-of-systems, such as intelligent multimodal transportation systems, optimized multi-energy systems, intelligent disaster prevention systems, and smart cities.

14 CFR 1245.104 - Advance waivers.

Code of Federal Regulations, 2010 CFR

2010-01-01

... of business in the United States or is subject to the control of a foreign government; (2) When a... foreign intelligence or counter-intelligence activities that the restriction or elimination of the right...

Guide to federal intelligent transportation system (ITS) research.

DOT National Transportation Integrated Search

2013-01-01

The U.S. Department of Transportations (USDOT) Intelligent Transportation System (ITS) Program aims to bring connectivity to transportation through the use of advanced wireless technologies powerful technologies that enable transformative chan...

The Intelligent Technologies of Electronic Information System

NASA Astrophysics Data System (ADS)

Li, Xianyu

2017-08-01

Based upon the synopsis of system intelligence and information services, this paper puts forward the attributes and the logic structure of information service, sets forth intelligent technology framework of electronic information system, and presents a series of measures, such as optimizing business information flow, advancing data decision capability, improving information fusion precision, strengthening deep learning application and enhancing prognostic and health management, and demonstrates system operation effectiveness. This will benefit the enhancement of system intelligence.

Simulation as an Engine of Physical Scene Understanding

DTIC Science & Technology

2013-11-05

critical to the origins of intelligence : Researchers in developmental psychology, language, animal cognition, and artificial intelligence (2–6) con- sider...implemented computationally in classic artificial intelligence systems (18–20). However, these systems have not attempted to engage with physical scene un...N00014-09-0124, N00014-07-1-0937, and 1015GNA126; by Qualcomm; and by Intelligence Advanced Research Project Activity Grant D10PC20023. 1. Marr D (1982

The benefit of combining a deep neural network architecture with ideal ratio mask estimation in computational speech segregation to improve speech intelligibility.

PubMed

Bentsen, Thomas; May, Tobias; Kressner, Abigail A; Dau, Torsten

2018-01-01

Computational speech segregation attempts to automatically separate speech from noise. This is challenging in conditions with interfering talkers and low signal-to-noise ratios. Recent approaches have adopted deep neural networks and successfully demonstrated speech intelligibility improvements. A selection of components may be responsible for the success with these state-of-the-art approaches: the system architecture, a time frame concatenation technique and the learning objective. The aim of this study was to explore the roles and the relative contributions of these components by measuring speech intelligibility in normal-hearing listeners. A substantial improvement of 25.4 percentage points in speech intelligibility scores was found going from a subband-based architecture, in which a Gaussian Mixture Model-based classifier predicts the distributions of speech and noise for each frequency channel, to a state-of-the-art deep neural network-based architecture. Another improvement of 13.9 percentage points was obtained by changing the learning objective from the ideal binary mask, in which individual time-frequency units are labeled as either speech- or noise-dominated, to the ideal ratio mask, where the units are assigned a continuous value between zero and one. Therefore, both components play significant roles and by combining them, speech intelligibility improvements were obtained in a six-talker condition at a low signal-to-noise ratio.

Wireless sensor systems for sense/decide/act/communicate.

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

Berry, Nina M.; Cushner, Adam; Baker, James A.

2003-12-01

After 9/11, the United States (U.S.) was suddenly pushed into challenging situations they could no longer ignore as simple spectators. The War on Terrorism (WoT) was suddenly ignited and no one knows when this war will end. While the government is exploring many existing and potential technologies, the area of wireless Sensor networks (WSN) has emerged as a foundation for establish future national security. Unlike other technologies, WSN could provide virtual presence capabilities needed for precision awareness and response in military, intelligence, and homeland security applications. The Advance Concept Group (ACG) vision of Sense/Decide/Act/Communicate (SDAC) sensor system is an instantiationmore » of the WSN concept that takes a 'systems of systems' view. Each sensing nodes will exhibit the ability to: Sense the environment around them, Decide as a collective what the situation of their environment is, Act in an intelligent and coordinated manner in response to this situational determination, and Communicate their actions amongst each other and to a human command. This LDRD report provides a review of the research and development done to bring the SDAC vision closer to reality.« less

New frontiers in design synthesis

NASA Technical Reports Server (NTRS)

Goldin, D. S.; Venneri, S. L.; Noor, A. K.

1999-01-01

The Intelligent Synthesis Environment (ISE), which is one of the major strategic technologies under development at NASA centers and the University of Virginia, is described. One of the major objectives of ISE is to significantly enhance the rapid creation of innovative affordable products and missions. ISE uses a synergistic combination of leading-edge technologies, including high performance computing, high capacity communications and networking, human-centered computing, knowledge-based engineering, computational intelligence, virtual product development, and product information management. The environment will link scientists, design teams, manufacturers, suppliers, and consultants who participate in the mission synthesis as well as in the creation and operation of the aerospace system. It will radically advance the process by which complex science missions are synthesized, and high-tech engineering Systems are designed, manufactured and operated. The five major components critical to ISE are human-centered computing, infrastructure for distributed collaboration, rapid synthesis and simulation tools, life cycle integration and validation, and cultural change in both the engineering and science creative process. The five components and their subelements are described. Related U.S. government programs are outlined and the future impact of ISE on engineering research and education is discussed.

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.

A Genuine TEAM Player

NASA Technical Reports Server (NTRS)

2001-01-01

Qualtech Systems, Inc. developed a complete software system with capabilities of multisignal modeling, diagnostic analysis, run-time diagnostic operations, and intelligent interactive reasoners. Commercially available as the TEAMS (Testability Engineering and Maintenance System) tool set, the software can be used to reveal unanticipated system failures. The TEAMS software package is broken down into four companion tools: TEAMS-RT, TEAMATE, TEAMS-KB, and TEAMS-RDS. TEAMS-RT identifies good, bad, and suspect components in the system in real-time. It reports system health results from onboard tests, and detects and isolates failures within the system, allowing for rapid fault isolation. TEAMATE takes over from where TEAMS-RT left off by intelligently guiding the maintenance technician through the troubleshooting procedure, repair actions, and operational checkout. TEAMS-KB serves as a model management and collection tool. TEAMS-RDS (TEAMS-Remote Diagnostic Server) has the ability to continuously assess a system and isolate any failure in that system or its components, in real time. RDS incorporates TEAMS-RT, TEAMATE, and TEAMS-KB in a large-scale server architecture capable of providing advanced diagnostic and maintenance functions over a network, such as the Internet, with a web browser user interface.

Introduction to Advanced Engine Control Concepts

NASA Technical Reports Server (NTRS)

Sanjay, Garg

2007-01-01

With the increased emphasis on aircraft safety, enhanced performance and affordability, and the need to reduce the environmental impact of aircraft, there are many new challenges being faced by the designers of aircraft propulsion systems. The Controls and Dynamics Branch at NASA (National Aeronautics and Space Administration) Glenn Research Center (GRC) in Cleveland, Ohio, is leading and participating in various projects in partnership with other organizations within GRC and across NASA, the U.S. aerospace industry, and academia to develop advanced controls and health management technologies that will help meet these challenges through the concept of Intelligent Propulsion Systems. The key enabling technologies for an Intelligent Propulsion System are the increased efficiencies of components through active control, advanced diagnostics and prognostics integrated with intelligent engine control to enhance operational reliability and component life, and distributed control with smart sensors and actuators in an adaptive fault tolerant architecture. This presentation describes the current activities of the Controls and Dynamics Branch in the areas of active component control and propulsion system intelligent control, and presents some recent analytical and experimental results in these areas.

Network-based modeling and intelligent data mining of social media for improving care.

PubMed

Akay, Altug; Dragomir, Andrei; Erlandsson, Bjorn-Erik

2015-01-01

Intelligently extracting knowledge from social media has recently attracted great interest from the Biomedical and Health Informatics community to simultaneously improve healthcare outcomes and reduce costs using consumer-generated opinion. We propose a two-step analysis framework that focuses on positive and negative sentiment, as well as the side effects of treatment, in users' forum posts, and identifies user communities (modules) and influential users for the purpose of ascertaining user opinion of cancer treatment. We used a self-organizing map to analyze word frequency data derived from users' forum posts. We then introduced a novel network-based approach for modeling users' forum interactions and employed a network partitioning method based on optimizing a stability quality measure. This allowed us to determine consumer opinion and identify influential users within the retrieved modules using information derived from both word-frequency data and network-based properties. Our approach can expand research into intelligently mining social media data for consumer opinion of various treatments to provide rapid, up-to-date information for the pharmaceutical industry, hospitals, and medical staff, on the effectiveness (or ineffectiveness) of future treatments.

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.

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.

Intelligent transportation systems benefits, costs, and lessons learned : 2014 update report.

DOT National Transportation Integrated Search

2014-06-01

Intelligent transportation systems (ITS) provide a proven set of strategies for advancing transportation safety, mobility, and environmental sustainability by integrating communication and information technology applications into the management and o...

Minimal-delay traffic grooming for WDM star networks

NASA Astrophysics Data System (ADS)

Choi, Hongsik; Garg, Nikhil; Choi, Hyeong-Ah

2003-10-01

All-optical networks face the challenge of reducing slower opto-electronic conversions by managing assignment of traffic streams to wavelengths in an intelligent manner, while at the same time utilizing bandwidth resources to the maximum. This challenge becomes harder in networks closer to the end users that have insufficient data to saturate single wavelengths as well as traffic streams outnumbering the usable wavelengths, resulting in traffic grooming which requires costly traffic analysis at access nodes. We study the problem of traffic grooming that reduces the need to analyze traffic, for a class of network architecture most used by Metropolitan Area Networks; the star network. The problem being NP-complete, we provide an efficient twice-optimal-bound greedy heuristic for the same, that can be used to intelligently groom traffic at the LANs to reduce latency at the access nodes. Simulation results show that our greedy heuristic achieves a near-optimal solution.

Intelligent transportation systems impact assessment framework : final report

DOT National Transportation Integrated Search

1995-09-30

One of the most compelling reasons for investment in Intelligent Transportation System : (ITS) services is to realize a reduction in traffic congestion. Volume on Americas : highway network is expected to double by the year 2020 from 1.9 trillion ...

R and E: Communications and Intelligent Systems Division (LC)

NASA Technical Reports Server (NTRS)

Emerson, Dawn C.; Miranda, Felix A.

2015-01-01

This presentation is intended for the Ohio Federal Research Network's Centers of Excellence. The presentation provides an overview of the Communications and Intelligent Systems Division including current research and engineering work as well as future technology needs.

Intelligent Tutoring Systems

NASA Astrophysics Data System (ADS)

Anderson, John R.; Boyle, C. Franklin; Reiser, Brian J.

1985-04-01

Cognitive psychology, artificial intelligence, and computer technology have advanced to the point where it is feasible to build computer systems that are as effective as intelligent human tutors. Computer tutors based on a set of pedagogical principles derived from the ACT theory of cognition have been developed for teaching students to do proofs in geometry and to write computer programs in the language LISP.

Intelligent tutoring systems.

PubMed

Anderson, J R; Boyle, C F; Reiser, B J

1985-04-26

Cognitive psychology, artificial intelligence, and computer technology have advanced to the point where it is feasible to build computer systems that are as effective as intelligent human tutors. Computer tutors based on a set of pedagogical principles derived from the ACT theory of cognition have been developed for teaching students to do proofs in geometry and to write computer programs in the language LISP.

Evaluation Approaches to Intelligent Computer-Assisted Instruction. Testing Study Group: The Impact of Advances in Artificial Intelligence on Test Development.

ERIC Educational Resources Information Center

Baker, Eva L.

Some special problems associated with evaluating intelligent computer-assisted instruction (ICAI) programs are addressed. This paper intends to describe alternative approaches to the assessment and improvement of such applications and to provide examples of efforts undertaken and shortfalls. Issues discussed stem chiefly from the technical demands…

Differential Effects of Intelligence, Perceptual Speed and Age on Growth in Attentional Speed and Accuracy

ERIC Educational Resources Information Center

Goldhammer, Frank; Rauch, Wolfgang A.; Schweizer, Karl; Moosbrugger, Helfried

2010-01-01

The study investigates the effects of intelligence, perceptual speed and age on intraindividual growth in attentional speed and attentional accuracy over the course of a 6-minute testing session. A sample of 193 subjects completed the Advanced Progressive Matrices and the Vienna Matrices Test representing intelligence, the tests Alertness and…

"It's Going to Kill Us!" and Other Myths about the Future of Artificial Intelligence

ERIC Educational Resources Information Center

Atkinson, Robert D.

2016-01-01

Given the promise that artificial intelligence (AI) holds for economic growth and societal advancement, it is critical that policymakers not only avoid retarding the progress of AI innovation, but also actively support its further development and use. This report provides a primer on artificial intelligence and debunks five prevailing myths that,…

Intelligent deflection routing in buffer-less networks.

PubMed

Haeri, Soroush; Trajković, Ljiljana

2015-02-01

Deflection routing is employed to ameliorate packet loss caused by contention in buffer-less architectures such as optical burst-switched networks. The main goal of deflection routing is to successfully deflect a packet based only on a limited knowledge that network nodes possess about their environment. In this paper, we present a framework that introduces intelligence to deflection routing (iDef). iDef decouples the design of the signaling infrastructure from the underlying learning algorithm. It consists of a signaling and a decision-making module. Signaling module implements a feedback management protocol while the decision-making module implements a reinforcement learning algorithm. We also propose several learning-based deflection routing protocols, implement them in iDef using the ns-3 network simulator, and compare their performance.

Network-Capable Application Process and Wireless Intelligent Sensors for ISHM

NASA Technical Reports Server (NTRS)

Figueroa, Fernando; Morris, Jon; Turowski, Mark; Wang, Ray

2011-01-01

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

The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network.

PubMed

Han, Gaining; Fu, Weiping; Wang, Wen; Wu, Zongsheng

2017-05-30

The intelligent vehicle is a complicated nonlinear system, and the design of a path tracking controller is one of the key technologies in intelligent vehicle research. This paper mainly designs a lateral control dynamic model of the intelligent vehicle, which is used for lateral tracking control. Firstly, the vehicle dynamics model (i.e., transfer function) is established according to the vehicle parameters. Secondly, according to the vehicle steering control system and the CARMA (Controlled Auto-Regression and Moving-Average) model, a second-order control system model is built. Using forgetting factor recursive least square estimation (FFRLS), the system parameters are identified. Finally, a neural network PID (Proportion Integral Derivative) controller is established for lateral path tracking control based on the vehicle model and the steering system model. Experimental simulation results show that the proposed model and algorithm have the high real-time and robustness in path tracing control. This provides a certain theoretical basis for intelligent vehicle autonomous navigation tracking control, and lays the foundation for the vertical and lateral coupling control.

The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network

PubMed Central

Han, Gaining; Fu, Weiping; Wang, Wen; Wu, Zongsheng

2017-01-01

The intelligent vehicle is a complicated nonlinear system, and the design of a path tracking controller is one of the key technologies in intelligent vehicle research. This paper mainly designs a lateral control dynamic model of the intelligent vehicle, which is used for lateral tracking control. Firstly, the vehicle dynamics model (i.e., transfer function) is established according to the vehicle parameters. Secondly, according to the vehicle steering control system and the CARMA (Controlled Auto-Regression and Moving-Average) model, a second-order control system model is built. Using forgetting factor recursive least square estimation (FFRLS), the system parameters are identified. Finally, a neural network PID (Proportion Integral Derivative) controller is established for lateral path tracking control based on the vehicle model and the steering system model. Experimental simulation results show that the proposed model and algorithm have the high real-time and robustness in path tracing control. This provides a certain theoretical basis for intelligent vehicle autonomous navigation tracking control, and lays the foundation for the vertical and lateral coupling control. PMID:28556817

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...

SNMP-SI: A Network Management Tool Based on Slow Intelligence System Approach

NASA Astrophysics Data System (ADS)

Colace, Francesco; de Santo, Massimo; Ferrandino, Salvatore

The last decade has witnessed an intense spread of computer networks that has been further accelerated with the introduction of wireless networks. Simultaneously with, this growth has increased significantly the problems of network management. Especially in small companies, where there is no provision of personnel assigned to these tasks, the management of such networks is often complex and malfunctions can have significant impacts on their businesses. A possible solution is the adoption of Simple Network Management Protocol. Simple Network Management Protocol (SNMP) is a standard protocol used to exchange network management information. It is part of the Transmission Control Protocol/Internet Protocol (TCP/IP) protocol suite. SNMP provides a tool for network administrators to manage network performance, find and solve network problems, and plan for network growth. SNMP has a big disadvantage: its simple design means that the information it deals with is neither detailed nor well organized enough to deal with the expanding modern networking requirements. Over the past years much efforts has been given to improve the lack of Simple Network Management Protocol and new frameworks has been developed: A promising approach involves the use of Ontology. This is the starting point of this paper where a novel approach to the network management based on the use of the Slow Intelligence System methodologies and Ontology based techniques is proposed. Slow Intelligence Systems is a general-purpose systems characterized by being able to improve performance over time through a process involving enumeration, propagation, adaptation, elimination and concentration. Therefore, the proposed approach aims to develop a system able to acquire, according to an SNMP standard, information from the various hosts that are in the managed networks and apply solutions in order to solve problems. To check the feasibility of this model first experimental results in a real scenario are showed.

Middleware Architecture for Ambient Intelligence in the Networked Home

NASA Astrophysics Data System (ADS)

Georgantas, Nikolaos; Issarny, Valerie; Mokhtar, Sonia Ben; Bromberg, Yerom-David; Bianco, Sebastien; Thomson, Graham; Raverdy, Pierre-Guillaume; Urbieta, Aitor; Cardoso, Roberto Speicys

With computing and communication capabilities now embedded in most physical objects of the surrounding environment and most users carrying wireless computing devices, the Ambient Intelligence (AmI) / pervasive computing vision [28] pioneered by Mark Weiser [32] is becoming a reality. Devices carried by nomadic users can seamlessly network with a variety of devices, both stationary and mobile, both nearby and remote, providing a wide range of functional capabilities, from base sensing and actuating to rich applications (e.g., smart spaces). This then allows the dynamic deployment of pervasive applications, which dynamically compose functional capabilities accessible in the pervasive network at the given time and place of an application request.

Application of the GA-BP Neural Network in Earthwork Calculation

NASA Astrophysics Data System (ADS)

Fang, Peng; Cai, Zhixiong; Zhang, Ping

2018-01-01

The calculation of earthwork quantity is the key factor to determine the project cost estimate and the optimization of the scheme. It is of great significance and function in the excavation of earth and rock works. We use optimization principle of GA-BP intelligent algorithm running process, and on the basis of earthwork quantity and cost information database, the design of the GA-BP neural network intelligent computing model, through the network training and learning, the accuracy of the results meet the actual engineering construction of gauge fan requirements, it provides a new approach for other projects the calculation, and has good popularization value.

A Wavelet Neural Network Optimal Control Model for Traffic-Flow Prediction in Intelligent Transport Systems

NASA Astrophysics Data System (ADS)

Huang, Darong; Bai, Xing-Rong

Based on wavelet transform and neural network theory, a traffic-flow prediction model, which was used in optimal control of Intelligent Traffic system, is constructed. First of all, we have extracted the scale coefficient and wavelet coefficient from the online measured raw data of traffic flow via wavelet transform; Secondly, an Artificial Neural Network model of Traffic-flow Prediction was constructed and trained using the coefficient sequences as inputs and raw data as outputs; Simultaneous, we have designed the running principium of the optimal control system of traffic-flow Forecasting model, the network topological structure and the data transmitted model; Finally, a simulated example has shown that the technique is effectively and exactly. The theoretical results indicated that the wavelet neural network prediction model and algorithms have a broad prospect for practical application.

Scalable Video Streaming in Wireless Mesh Networks for Education

ERIC Educational Resources Information Center

Liu, Yan; Wang, Xinheng; Zhao, Liqiang

2011-01-01

In this paper, a video streaming system for education based on a wireless mesh network is proposed. A wireless mesh network is a self-organizing, self-managing and reliable intelligent network, which allows educators to deploy a network quickly. Video streaming plays an important role in this system for multimedia data transmission. This new…

Intelligent Vehicle Initiative Forum : proceedings

DOT National Transportation Integrated Search

1997-08-05

This event, jointly sponsored by ITS Americas Advanced Vehicle Control and Safety Systems (AVCSS) and Safety and Human Factors (S&HF) Committees, was designed to review and discuss the U.S. Department of Transportations Intelligent Vehicle Init...

Intelligent Vehicle Initiative : needs assessment

DOT National Transportation Integrated Search

1999-11-01

The aim of the Intelligent Vehicle Initiative (IVI) is to accelerate the development and availability of advanced safety and information systems for a variety of vehicle types. Public transit, through the Federal Transit Administration (FTA), is an a...

California PATH : 1997 annual report

DOT National Transportation Integrated Search

1997-01-01

The California Partners for Advanced Transit and Highways Program (PATH) has been leading the way in ITS (Intelligent Transportation Systems) research since PATHs founding in 1986, before the term ITS or its predecessor IVHS (Intelligent Vehicle H...

California PATH : 1996 annual report

DOT National Transportation Integrated Search

1996-01-01

The California Partners for Advanced Transit and Highways Program (PATH ) has been leading the way in ITS (Intelligent Transportation Systems) research since PATH's founding in 1986, before the term ITS or its predecessor IVHS (Intelligent Vehicle Hi...

Functional design for U.S. Coast Guard intelligent communications gateway

DOT National Transportation Integrated Search

1995-02-27

This report documents the functional design of hardware and software components necessary to satisfy the U.S. Coast Guard (USCG) requirements identified for an Intelligent Gateway (IG). The IG will transfer messages from USCG shoreside networks to tr...

Intelligent Cooperative MAC Protocol for Balancing Energy Consumption

NASA Astrophysics Data System (ADS)

Wu, S.; Liu, K.; Huang, B.; Liu, F.

To extend the lifetime of wireless sensor networks, we proposed an intelligent balanced energy consumption cooperative MAC protocol (IBEC-CMAC) based on the multi-node cooperative transmission model. The protocol has priority to access high-quality channels for reducing energy consumption of each transmission. It can also balance the energy consumption among cooperative nodes by using high residual energy nodes instead of excessively consuming some node's energy. Simulation results show that IBEC-CMAC can obtain longer network lifetime and higher energy utilization than direct transmission.

Implementing and Simulating Dynamic Traffic Assignment with Intelligent Transportation Systems in Cube Avenue

NASA Technical Reports Server (NTRS)

Foytik, Peter; Robinson, Mike

2010-01-01

As urban populations and traffic congestion levels increase, effective use of information and communication tools and intelligent transportation systems as becoming increasingly important in order to maximize the efficiency of transportation networks. The appropriate placement and employment of these tools within a network is critical to their effectiveness. This presentation proposes and demonstrates the use of a commercial transportation simulation tool to simulate dynamic traffic assignment and rerouting to model route modifications as a result of traffic information.

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.