WorldWideScience

Sample records for networked intelligent luminaire

  1. High Flux Commercial Illumination Solution with Intelligent Controls

    Energy Technology Data Exchange (ETDEWEB)

    Camil Ghiu

    2012-04-30

    This report summarizes the work performed at OSRAM SYLVANIA under US Department of Energy contract DE-EE0003241 for developing a high efficiency LED-based luminaire. A novel light engine module (two versions: standard and super), power supply and luminaire mechanical parts were designed and tested. At steady-state, the luminaire luminous flux is 3156 lumens (lm), luminous efficacy 97.4 LPW and CRI (Ra) 88 at a correlated color temperature (CCT) of 3507K. When the luminaire is fitted with the super version of the light engine the efficacy reaches 130 LPW. In addition, the luminaire is provided with an intelligent control network capable of additional energy savings. The technology developed during the course of this project has been incorporated into a family of products. Recently, the first product in the family has been launched.

  2. Brain anatomical network and intelligence.

    Directory of Open Access Journals (Sweden)

    Yonghui Li

    2009-05-01

    Full Text Available 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.

  3. Colorimetric characterization of LED luminaires

    International Nuclear Information System (INIS)

    Costa, C L M; Vieira, R R; Pereira, R C; Silva, P V M; Oliveira, I A A; Sardinha, A S; Viana, D D; Barbosa, A H; Souza, L P; Alvarenga, A D

    2015-01-01

    The Optical Metrology Division of Inmetro – National Institute of Metrology, Quality and Technology has recently started the colorimetric characterization of lamps by implementing Correlated Color Temperature (CCT) and Color Rendering Index (CRI) measurements of incandescent lamps, followed by the CFL, and LED lamps and luminaires. Here we present the results for the verification of the color characterization of samples of SSL luminaires for public as well as indoor illumination that are sold in Brazil

  4. Collective intelligent wireless sensor networks

    NARCIS (Netherlands)

    Mihaylov, M.; Nowe, A.; Tuyls, K.P.; Nijholt, A.; Pantic, M.

    2008-01-01

    In this paper we apply the COllective INtelligence (COIN) framework ofWolpert et al. toWireless Sensor Networks (WSNs) with the aim to increase the autonomous lifetime of the network in a decentralized manner. COIN describes how selfish agents can learn to optimize their own performance, so that the

  5. Intelligent networked teleoperation control

    CERN Document Server

    Li, Zhijun; Su, Chun-Yi

    2015-01-01

    This book describes a unified framework for networked teleoperation systems involving multiple research fields: networked control systems for linear and nonlinear forms, bilateral teleoperation, trilateral teleoperation, multilateral teleoperation and cooperative teleoperation. It closely examines networked control as a field at the intersection of systems & control and robotics and presents a number of experimental case studies on testbeds for robotic systems, including networked haptic devices, robotic network systems and sensor network systems. The concepts and results outlined are easy to understand, even for readers fairly new to the subject. As such, the book offers a valuable reference work for researchers and engineers in the fields of systems & control and robotics.

  6. Intelligent distribution network design

    NARCIS (Netherlands)

    Provoost, F.

    2009-01-01

    Distribution networks (medium voltage and low voltage) are subject to changes caused by re-regulation of the energy supply, economical and environmental constraints more sensitive equipment, power quality requirements and the increasing penetration of distributed generation. The latter is seen as

  7. Intelligent Wireless Sensor Network

    OpenAIRE

    Saeed, Bakhtiar I.; Mehrdadi, Bruce

    2010-01-01

    In recent years, there has been significant increase in utilisation of embedded-microcontrollers in broad range of applications extending from commercial products to industrial process system monitoring. Furthermore, improvements in speed, size and power consumption of microcontrollers with added wireless capabilities has provided new generation of applications. These include versatile and\\ud low cost solutions in wireless sensor networking applications such as wireless system monitoring and ...

  8. Advanced dimming strategy for solid state luminaires

    DEFF Research Database (Denmark)

    Beczkowski, Szymon

    2010-01-01

    Increased luminaire gamut as well as increase in luminous efficiency is found when the LED based luminaire is driven by a hybrid PWM/AM dimming technique. This technique is applicable to any n-chromaticity luminaire but it is especially suitable for trichromatic RGB luminaires that do not have...... inherent degrees of freedom. Diodes' properties under the hybrid dimming method were measured and used in nonlinear optimization routine to find the driving conditions that yield e.g. the highest possible efficacy for a given color point....

  9. Trilateral interlaboratory with SSL (WLEDi) luminaire

    Science.gov (United States)

    Burini Junior, E. C.; Santos, E. R.; Assaf, L. O.

    2018-03-01

    The IEE/USP laboratory and two others, all belonging to RBLE (Brazilian Network of Test Laboratories) participated in a trilateral comparison performed from measurement independently of participants interaction. The results from electric and photometric measurements carried out on samples of Solid State Lighting - SSL, Inorganic White Light Emitting Diode (WLEDi) luminaires by three accredited laboratories were considered in order to point out mutual deviations and to verify the confidence in a bilateral comparison. The first analysis revealed a maximum deviation of 4.2 % between the luminous intensity attributed by one laboratory and the arithmetic mean value from three laboratories. The largest standard uncertainty value of 1.9 % was estimated for Total Harmonic Distortion of electric current THDi and the lowest value, 0.4 %, to the luminous flux. The extreme deviation for one parameter results was 7.2 % at maximum luminous intensity and the lowest was 1.7 % for luminous flux.

  10. Preparation of heterogeneous networks for ambient intelligence

    NARCIS (Netherlands)

    Stok, van der P.D.V.; Basten, A.A.; Geilen, M.C.W.; Groot, de H.W.H.

    2003-01-01

    Ambient Intelligence assumes the existence of ubiquitous networked computing. Networking is supported in the home by a so-called home network. Currently, the home network emerges from the purchase of a second PC in the home. Extending the home network confronts the prospective buyer with a multitude

  11. Efficient Networks Communication Routing Using Swarm Intelligence

    OpenAIRE

    Koushal Kumar

    2012-01-01

    As demonstrated by natural biological swarm’s collective intelligence has an abundance of desirable properties for problem-solving like in network routing. The focus of this paper is in the applications of swarm based intelligence in information routing for communication networks. As we know networks are growing and adopting new platforms as new technologies comes. Also according to new demands and requirements networks topologies and its complexity is increasing with time. Thus it is becomin...

  12. An Architectural Modelfor Intelligent Network Management

    Institute of Scientific and Technical Information of China (English)

    罗军舟; 顾冠群; 费翔

    2000-01-01

    Traditional network management approach involves the management of each vendor's equipment and network segment in isolation through its own proprietary element management system. It is necessary to set up a new network management architecture that calls for operation consolidation across vendor and technology boundaries. In this paper, an architectural model for Intelligent Network Management (INM) is presented. The INM system includes a manager system, which controls all subsystems and coordinates different management tasks; an expert system, which is responsible for handling particularly difficult problems, and intelligent agents, which bring the management closer to applications and user requirements by spreading intelligent agents through network segments or domain. In the expert system model proposed, especially an intelligent fault management system is given.The architectural model is to build the INM system to meet the need of managing modern network systems.

  13. Techniques for Intelligence Analysis of Networks

    National Research Council Canada - National Science Library

    Cares, Jeffrey R

    2005-01-01

    ...) there are significant intelligence analysis manifestations of these properties; and (4) a more satisfying theory of Networked Competition than currently exists for NCW/NCO is emerging from this research...

  14. Intelligent Network in the Republic of Croatia

    Directory of Open Access Journals (Sweden)

    Slavko Šarić

    2012-10-01

    Full Text Available The introduction of intelligence into the telecommunicationnetwork of Croatia is becoming one of the key elements ofthe future development of the network. The paper describes there-organisation of the telecommunication network in Croatia,the process of creating and managing the intelligent services,the initial situation and the feasible plan of introducing the INconcepts. The paper is concluded with a list of IN services thatare being introduced in the initial phase of the Croatian telecommunicationnetwork.

  15. CALiPER Report 24: OLED Luminaires

    Energy Technology Data Exchange (ETDEWEB)

    Miller, N. J. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Leon, F. A. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Davis, J. L. [RTI International, Research Triangle Park, NC (United States)

    2017-08-01

    This report documents an initial investigation of OLED luminaires and summarizes the key features of those products. In addition to photometric testing of four commercial products in independent laboratories, PNNL examined many of the products through teardown testing (disassemblies to identify parts and functionality) in PNNL laboratories. Results of these tests as well as results of stress testing of several OLED luminaires at RTI International have been included.

  16. Advantages of Intelligent SDH/SONET Networks

    Indian Academy of Sciences (India)

    First page Back Continue Last page Overview Graphics. Advantages of Intelligent SDH/SONET Networks. GMPLS simplifies network management, thus reducing operating expenses (“staff”) and minimizing errors. Same benefits as computerization of banking, accounts, etc. Auto-discovery simplifies equipment installation ...

  17. Luminaire layout: Design and implementation

    Science.gov (United States)

    Both, A. J.

    1994-01-01

    The information contained in this report was presented during the discussion regarding guidelines for PAR uniformity in greenhouses. The data shows a lighting uniformity analysis in a research greenhouse for rose production at the Cornell University campus. The luminaire layout was designed using the computer program Lumen-Micro. After implementation of the design, accurate measurements were taken in the greenhouse and the uniformity analysis for both the design and implementation were compared. A study of several supplemental lighting installations resulted in the following recommendations: include only the actual growing area in the lighting uniformity analysis; for growing areas up to 20 square meters, take four measurements per square meter; for growing areas above 20 square meters, take one measurement per square meter; use one of the uniformity criteria and frequency graphs to compare lighting uniformity amongst designs; and design for uniformity criterion of a least 0.75 and the fraction within +/- 15% of the average PAR value should be close to one.

  18. Social networks a framework of computational intelligence

    CERN Document Server

    Chen, Shyi-Ming

    2014-01-01

    This volume provides the audience with an updated, in-depth and highly coherent material on the conceptually appealing and practically sound information technology of Computational Intelligence applied to the analysis, synthesis and evaluation of social networks. The volume involves studies devoted to key issues of social networks including community structure detection in networks, online social networks, knowledge growth and evaluation, and diversity of collaboration mechanisms.  The book engages a wealth of methods of Computational Intelligence along with well-known techniques of linear programming, Formal Concept Analysis, machine learning, and agent modeling.  Human-centricity is of paramount relevance and this facet manifests in many ways including personalized semantics, trust metric, and personal knowledge management; just to highlight a few of these aspects. The contributors to this volume report on various essential applications including cyber attacks detection, building enterprise social network...

  19. An Artificial Neural Network Controller for Intelligent Transportation Systems Applications

    Science.gov (United States)

    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...

  20. The Cyber Intelligence Challenge of Asyngnotic Networks

    Directory of Open Access Journals (Sweden)

    Edward M. Roche

    2015-09-01

    Full Text Available The intelligence community is facing a new type of organization, one enabled by the world’s information and communications infrastructure. These asyngnotic networks operate without leadership and are self-organizing in nature. They pose a threat to national security because they are difficult to detect in time for intelligence to provide adequate warning. Social network analysis and link analysis are important tools but can be supplemented by application of neuroscience principles to understand the forces that drive asyngnotic self-organization and triggering of terrorist events. Applying Living Systems Theory (LST to a terrorist attack provides a useful framework to identify hidden asyngnotic networks. There is some antecedent work in propaganda analysis that may help uncover hidden asyngnotic networks, but computerized SIGINT methods face a number of challenges.

  1. Wireless intelligent network: infrastructure before services?

    Science.gov (United States)

    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.

  2. Constructing an Intelligent Patent Network Analysis Method

    Directory of Open Access Journals (Sweden)

    Chao-Chan Wu

    2012-11-01

    Full Text Available Patent network analysis, an advanced method of patent analysis, is a useful tool for technology management. This method visually displays all the relationships among the patents and enables the analysts to intuitively comprehend the overview of a set of patents in the field of the technology being studied. Although patent network analysis possesses relative advantages different from traditional methods of patent analysis, it is subject to several crucial limitations. To overcome the drawbacks of the current method, this study proposes a novel patent analysis method, called the intelligent patent network analysis method, to make a visual network with great precision. Based on artificial intelligence techniques, the proposed method provides an automated procedure for searching patent documents, extracting patent keywords, and determining the weight of each patent keyword in order to generate a sophisticated visualization of the patent network. This study proposes a detailed procedure for generating an intelligent patent network that is helpful for improving the efficiency and quality of patent analysis. Furthermore, patents in the field of Carbon Nanotube Backlight Unit (CNT-BLU were analyzed to verify the utility of the proposed method.

  3. Insights into accelerated aging of SSL luminaires

    Science.gov (United States)

    Davis, J. Lynn; Lamvik, Michael; Bittle, James; Shepherd, Sarah; Yaga, Robert; Baldasaro, Nick; Solano, Eric; Bobashev, Georgiy

    2013-09-01

    Although solid-state lighting (SSL) products are often intended to have product lifetimes of 15 years or more, the rapid change in technology has created a need for accelerated life tests (ALTs) that can be performed in the span of several months. A critical element of interpreting results from any systems-level ALT is understanding of the impact of the test environment on each component. Because of its ubiquity in electronics, the use of temperature-humidity environments as potential ALTs for SSL luminaires was investigated. Results from testing of populations of three commercial 6" downlights in environments of 85°C and 85% relative humidity (RH) and 75°C and 75% RH are reported. These test environments were found to accelerate lumen depreciation of the entire luminaire optical system, including LEDs, lenses, and reflectors. The effects of aging were found to depend strongly on both the optical materials that were used and the design of the luminaire; this shows that the lumen maintenance behavior of SSL luminaires must be addressed at the optical systems level. Temperature-Humidity ALTs can be a useful test in understand lumainaire depreciation provided that proper consideration is given to the different aging rates of various materials. Since the impact of the temperature-humidity environment varies among components of the optical system, uniform aging of all system components in a single test is difficult to achieve.

  4. Intelligence Constraints on Terrorist Network Plots

    Science.gov (United States)

    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.

  5. Capacity of Intelligent Underlay and Overlay Network

    DEFF Research Database (Denmark)

    Ling, Yim; Elling, Jan; Nielsen, Thomas Toftegaard

    1996-01-01

    traffic. The formulas of the model have been implemented with the use of MatLab. To verify the model, measurement methods have been developed to collect the teletraffic information in a real-live GSM network. The measured data indicates that the teletraffic model describes the capacity with high accuracy...... and therefore can be used to dimension the network. The model shows that the increase of capacity for a GSM network with 34 frequencies is about 30%. Further capacity enhancement can be achieved by intelligent frequency planning method which is currently being developed...

  6. Comparisons of luminaires: Efficacies and system design

    Energy Technology Data Exchange (ETDEWEB)

    Albright, L.D.; Both, A.J. [Cornell Univ., Ithaca, NY (United States)

    1994-12-31

    After reviewing basic information, three design examples have been presented to demonstrate a process of supplemental lighting design. The sequences of each example suggest careful thought and analysis are required to obtain supplemental lighting designs that provide both high levels of PAR and suitable uniformity. The end results should suggest how an analysis can evolve to achieve desired results, and the types of tools and adjustments required. It appears possible to design research greenhouses and plant growth chambers to achieve a {+-}10% PAR uniformity using HIPS luminaires. Further, HPS luminaires (and, by extension, NEHD, etc.) are required to achieve high PAR levels and have the decided advantage of providing the possibility of aiming, which reduces the region of the {open_quotes}edge effect{close_quotes}. Further, for designing plant lighting systems, a modification of the standard IES luminaire data file structure is potentially useful. Luminaire installation is an important factor to obtain PAR uniformity. Spacing and mounting height are critically important. Additionally, the mounting angle of each luminaire must be carefully adjusted to conform with design assumptions. This is true for both plant growth chambers and greenhouses. Surface reflectances are particularly important when designing for small lighted regions such as plant growth chambers and research greenhouses. It is not obvious, just from looking at a surface, what its reflectance is. It is suggested that an effort be mounted to develop valid surface reflectance data to be used by designers. The importance of the surfaces (particularly the walls) in achieving PAR uniformity suggests the importance of periodic cleaning/maintenance to retain initial reflectance values.

  7. Adaptive intelligent power systems: Active distribution networks

    International Nuclear Information System (INIS)

    McDonald, Jim

    2008-01-01

    Electricity networks are extensive and well established. They form a key part of the infrastructure that supports industrialised society. These networks are moving from a period of stability to a time of potentially major transition, driven by a need for old equipment to be replaced, by government policy commitments to cleaner and renewable sources of electricity generation, and by change in the power industry. This paper looks at moves towards active distribution networks. The novel transmission and distribution systems of the future will challenge today's system designs. They will cope with variable voltages and frequencies, and will offer more flexible, sustainable options. Intelligent power networks will need innovation in several key areas of information technology. Active control of flexible, large-scale electrical power systems is required. Protection and control systems will have to react to faults and unusual transient behaviour and ensure recovery after such events. Real-time network simulation and performance analysis will be needed to provide decision support for system operators, and the inputs to energy and distribution management systems. Advanced sensors and measurement will be used to achieve higher degrees of network automation and better system control, while pervasive communications will allow networks to be reconfigured by intelligent systems

  8. Intelligent neural network diagnostic system

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2010-01-01

    Recently, artificial neural network (ANN) has made a significant mark in the domain of diagnostic applications. Neural networks are used to implement complex non-linear mappings (functions) using simple elementary units interrelated through connections with adaptive weights. The performance of the ANN is mainly depending on their topology structure and weights. Some systems have been developed using genetic algorithm (GA) to optimize the topology of the ANN. But, they suffer from some limitations. They are : (1) The computation time requires for training the ANN several time reaching for the average weight required, (2) Slowness of GA for optimization process and (3) Fitness noise appeared in the optimization of ANN. This research suggests new issues to overcome these limitations for finding optimal neural network architectures to learn particular problems. This proposed methodology is used to develop a diagnostic neural network system. It has been applied for a 600 MW turbo-generator as a case of real complex systems. The proposed system has proved its significant performance compared to two common methods used in the diagnostic applications.

  9. Tomorrow's energy needs require intelligent networks

    International Nuclear Information System (INIS)

    Bitsch, R.

    1998-01-01

    With the European wide move towards increased competition and greater deregulation of the energy industry, has come a thrust for greater efficiency and understanding customer needs and external constraints such as the environment. This, in turn, has led to solutions which take advantage of the tremendous developments in information technology and on-line control systems which are described in this paper. Topics include intelligent networks, decentralised energy supplies and decentralised energy management. (UK)

  10. Advanced Applications of Neural Networks and Artificial Intelligence: A Review

    OpenAIRE

    Koushal Kumar; Gour Sundar Mitra Thakur

    2012-01-01

    Artificial Neural Network is a branch of Artificial intelligence and has been accepted as a new computing technology in computer science fields. This paper reviews the field of Artificial intelligence and focusing on recent applications which uses Artificial Neural Networks (ANN’s) and Artificial Intelligence (AI). It also considers the integration of neural networks with other computing methods Such as fuzzy logic to enhance the interpretation ability of data. Artificial Neural Networks is c...

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

    Science.gov (United States)

    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...Technology (MIT) Title: A Belief-Space Approach to Integrated Intelligence- Research Area 10.3: Intelligent Networks Report Term: 0-Other Email: tlp...students presented progress and received feedback from the research group . o wrote papers on their research and submitted them to leading conferences

  12. OLED Luminaire with Panel Integrated Drivers and Advanced Controls

    Energy Technology Data Exchange (ETDEWEB)

    Lu, Michael [Acuity Brands Lighting, Inc., Atlanta, GA (United States); Spindler, Jeff [OLEDWorks, Rochester, NY (United States)

    2017-01-31

    For this DOE award, Acuity Brands Lighting developed a novel OLED luminaire system featuring panel-integrated drivers at each individual OLED panel. The luminaire has a base station that receives user commands and performs AC/DC conversion. A power line communication (PLC) protocol is used to provide both power and digital control to each panel. A 66-panel CanvisTM luminaire using state-of-art OLED panels based on this system was successfully constructed. This is a first demonstration of such a luminaire architecture. It is also the first known implementation of this number of independently addressable nodes with a PLC protocol. This luminaire system architecture has added benefits in the flexibility of using multiple panel vendors for a given product, forward compatibility with future panels, and reduced luminaire wiring complexity and assembly time.

  13. Utilising artificial intelligence in software defined wireless sensor network

    CSIR Research Space (South Africa)

    Matlou, OG

    2017-10-01

    Full Text Available Software Defined Wireless Sensor Network (SDWSN) is realised by infusing Software Defined Network (SDN) model in Wireless Sensor Network (WSN), Reason for that is to overcome the challenges of WSN. Artificial Intelligence (AI) and machine learning...

  14. A theory of intelligence: networked problem solving in animal societies

    OpenAIRE

    Shour, Robert

    2009-01-01

    A society's single emergent, increasing intelligence arises partly from the thermodynamic advantages of networking the innate intelligence of different individuals, and partly from the accumulation of solved problems. Economic growth is proportional to the square of the network entropy of a society's population times the network entropy of the number of the society's solved problems.

  15. Soft optics in intelligent optical networks

    Science.gov (United States)

    Shue, Chikong; Cao, Yang

    2001-10-01

    In addition to the recent advances in Hard-optics that pushes the optical transmission speed, distance, wave density and optical switching capacity, Soft-optics provides the necessary intelligence and control software that reduces operational costs, increase efficiency, and enhances revenue generating services by automating optimal optical circuit placement and restoration, and enabling value-added new services like Optical VPN. This paper describes the advances in 1) Overall Hard-optics and Soft-optics 2) Layered hierarchy of Soft-optics 3) Component of Soft-optics, including hard-optics drivers, Management Soft-optics, Routing Soft-optics and System Soft-optics 4) Key component of Routing and System Soft-optics, namely optical routing and signaling (including UNI/NNI and GMPLS signaling). In summary, the soft-optics on a new generation of OXC's enables Intelligent Optical Networks to provide just-in-time service delivery and fast restoration, and real-time capacity management that eliminates stranded bandwidth. It reduces operational costs and provides new revenue opportunities.

  16. Intelligent sensor networks the integration of sensor networks, signal processing and machine learning

    CERN Document Server

    Hu, Fei

    2012-01-01

    Although governments worldwide have invested significantly in intelligent sensor network research and applications, few books cover intelligent sensor networks from a machine learning and signal processing perspective. Filling this void, Intelligent Sensor Networks: The Integration of Sensor Networks, Signal Processing and Machine Learning focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on the world-class research of award-winning authors, the book provides a firm grounding in the fundamentals of intelligent sensor networks, incl

  17. Service creation and deployment on an intelligent network

    OpenAIRE

    Collins, Michael

    1999-01-01

    Active competition in the telecommunications industry has caused a dramatic shift in focus for public network operators. Service designers need to be able to easily and rapidly create services according to the customer’s requirements. This is achievable by using Intelligent Networks (INs). Two primary goals of service development under the Intelligent Network paradigm are rapid service crcation using new software technologies and the minimisation of service development costs through switch ve...

  18. THE COMPUTATIONAL INTELLIGENCE TECHNIQUES FOR PREDICTIONS - ARTIFICIAL NEURAL NETWORKS

    OpenAIRE

    Mary Violeta Bar

    2014-01-01

    The computational intelligence techniques are used in problems which can not be solved by traditional techniques when there is insufficient data to develop a model problem or when they have errors.Computational intelligence, as he called Bezdek (Bezdek, 1992) aims at modeling of biological intelligence. Artificial Neural Networks( ANNs) have been applied to an increasing number of real world problems of considerable complexity. Their most important advantage is solving problems that are too c...

  19. High Performance OLED Panel and Luminaire

    Energy Technology Data Exchange (ETDEWEB)

    Spindler, Jeffrey [OLEDWorks LLC, Rochester, NY (United States)

    2017-02-20

    In this project, OLEDWorks developed and demonstrated the technology required to produce OLED lighting panels with high energy efficiency and excellent light quality. OLED panels developed in this program produce high quality warm white light with CRI greater than 85 and efficacy up to 80 lumens per watt (LPW). An OLED luminaire employing 24 of the high performance panels produces practical levels of illumination for general lighting, with a flux of over 2200 lumens at 60 LPW. This is a significant advance in the state of the art for OLED solid-state lighting (SSL), which is expected to be a complementary light source to the more advanced LED SSL technology that is rapidly replacing all other traditional forms of lighting.

  20. Relationship between Social Networks Adoption and Social Intelligence

    Science.gov (United States)

    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…

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

    Science.gov (United States)

    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. ...

  2. High-performance LED luminaire for sports hall

    Science.gov (United States)

    Lee, Xuan-Hao; Yang, Jin-Tsung; Chien, Wei-Ting; Chang, Jung-Hsuan; Lo, Yi-Chien; Lin, Che-Chu; Sun, Ching-Cherng

    2015-09-01

    In this paper, we present a luminaire design with anti-glare and energy-saving effects for sports hall. Compared with traditional lamps using in a badminton court, the average illuminance on the ground of the proposed LED luminaire is enhanced about 300%. Besides, the uniformity is obviously enhanced and improved. The switch-on speed of lighting in sports hall is greatly reduced from 5-10 minutes to 1 second. The simulation analysis and the corresponding experiment results are demonstrated.

  3. The role of trait emotional intelligence in predicting networking behavior

    OpenAIRE

    Teresa Torres-Coronas; María-Arántzazu Vidal-Blasco

    2017-01-01

    Objective – The purpose of this paper is to obtain evidence of the relation between entrepreneur proactive networking behavior and trait emotional intelligence to support transition towards entrepreneurial careers. Design/methodology/approach – The Trait Emotional Intelligence Questionnaire-Short form (TEIQue-SF), developed by Cooper and Petrides (2010), was used to test hypotheses on the factors that define a proactive use of a professional network and their relationship with the indivi...

  4. Supporting tactical intelligence using collaborative environments and social networking

    Science.gov (United States)

    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.

  5. The role of trait emotional intelligence in predicting networking behavior

    Directory of Open Access Journals (Sweden)

    Teresa Torres-Coronas

    2017-02-01

    Full Text Available Objective – The purpose of this paper is to obtain evidence of the relation between entrepreneur proactive networking behavior and trait emotional intelligence to support transition towards entrepreneurial careers. Design/methodology/approach – The Trait Emotional Intelligence Questionnaire-Short form (TEIQue-SF, developed by Cooper and Petrides (2010, was used to test hypotheses on the factors that define a proactive use of a professional network and their relationship with the individual level of trait emotional intelligence and its four components (well-being, self-control, emotionality and sociability. A questionnaire was sent to local entrepreneurs to verify whether trait emotional intelligence act as a predictor of proactive networking behavior. Theoretical foundation – We will be using Petrides and Furnham’s (2001 trait EI definition and EI will be studied within a personality framework (Petrides, 2001, Petrides & Furnham, 2001, 2006, 2014. Findings – Final findings partially confirms research hypothesis, with some components of EI (well-being and self-control factors showing a significant positive correlation with proactive networking behavior. This indicates that entrepreneurs’ ability to regulate emotions influences their networking behavior helping them to succeed in their business relationships. Practical implications – The present study provides a clear direction for further research by focusing on how trait emotional intelligence affects social networking behavior amongst entrepreneurs, thus demonstrating the utility of using trait EI to evaluate high potential entrepreneurs.

  6. Semantic Network and Frame Knowledge Representation Formalisms in Artificial Intelligence

    OpenAIRE

    Rashid, Pshtiwan Qader

    2015-01-01

    ABSTRACT: Choosing a suitable method to represent the knowledge concerning the real world is one of the major issues involved in Artificial Intelligence. The purpose of this research is to consider the important beneficial roles of semantic network and frame formalisms for knowledge representation in Artificial Intelligence. The basic properties of the above methods for appropriate structuring and arranging the knowledge are presented. Some types of relationships, the conceptual graph...

  7. An Intelligent Alternative Approach to the efficient Network Management

    Directory of Open Access Journals (Sweden)

    MARTÍN, A.

    2012-12-01

    Full Text Available Due to the increasing complexity and heterogeneity of networks and services, many efforts have been made to develop intelligent techniques for management. Network intelligent management is a key technology for operating large heterogeneous data transmission networks. This paper presents a proposal for an architecture that integrates management object specifications and the knowledge of expert systems. We present a new approach named Integrated Expert Management, for learning objects based on expert management rules and describe the design and implementation of an integrated intelligent management platform based on OSI and Internet management models. The main contributions of our approach is the integration of both expert system and managed models, so we can make use of them to construct more flexible intelligent management network. The prototype SONAP (Software for Network Assistant and Performance is accuracy-aware since it can control and manage a network. We have tested our system on real data to the fault diagnostic in a telecommunication system of a power utility. The results validate the model and show a significant improvement with respect to the number of rules and the error rate in others systems.

  8. Achieving uniform efficient illumination with multiple asymmetric compound parabolic luminaires

    Science.gov (United States)

    Gordon, Jeffrey M.; Kashin, Peter

    1994-01-01

    Luminaire designs based on multiple asymmetric nonimaging compound parabolic reflectors are proposed for 2-D illumination applications that require highly uniform far-field illuminance, while ensuring maximal lighting efficiency and sharp angular cutoffs. The new designs derive from recent advances in nonimaging secondary concentrators for line-focus solar collectors. The light source is not treated as a single entity, but rather is divided into two or more separate adjoining sources. An asymmetric compound parabolic luminaire is then designed around each half-source. Attaining sharp cutoffs requires relatively large reflectors. However, severe truncation of the reflectors renders these devices as compact as many conventional luminaires, at the penalty of a small fraction of the radiation being emitted outside the nominal cutoff. The configurations that maximize the uniformity of far-field illuminance offer significant improvements in flux homogeneity relative to alternative designs to date.

  9. Luminaires for Advanced Lighting in Education

    Energy Technology Data Exchange (ETDEWEB)

    Davis, J. Lynn [RTI International, Research Triangle Park, NC (United States)

    2017-06-29

    Evolving education methods and greater use of technology in the classroom are dictating the need to rethink facility designs, including classroom lighting. Advances in LED-based lighting technology have created the possibility of lighting systems that are not only cost effective and energy efficient, but also color-tunable and as durable as other facility infrastructures (a 20-30 year life expectancy). Thus, there is the opportunity that the modern lighting system can be used by educators as a tool in their teaching strategy. To meet this need, RTI International and Finelite, Inc. teamed to develop and test the Next Generation Integrated Classroom Lighting System (NICLS). The NICLS technology incorporates a high performance, color-tunable light engine into new luminaire designs (e.g., pendant, direct-indirect, downlight, troffers) that are acceptable for use in classrooms. During this project, we successfully demonstrated that the NICLS technology achieves exceptional performance and exceeds all DOE goals for the classroom of the future, including: Luminous efficacy value for NICLS luminaires in excess of 125 lpw at all CCT values; TWL range of 2,700 K to 6,500 K while maintaining a CRI of 82 or higher at all values; Capability for full-range dimming (100% to 1%) at all CCT values with flicker levels below industry guidelines; Performance of the lighting system in a classroom mock-up, incorporating daylight and occupancy sensing to provide automatic control of lighting zones to further reduce energy consumption; Rated lifetime on the system exceeding 50,000 hours with a lumen maintenance of at least 85% at 50,000 hours; and Teacher-focused UI located at the front of the classroom to operate the lighting system. A smartphone-based UI is also available to accommodate teacher movement in the classroom. A critical element of developing this technology is designing the user interface to be compatible with modern teaching methods, including increased use of icons and

  10. Open Source Intelligence in a Networked World

    CERN Document Server

    Olcott, Anthony

    2012-01-01

    The amount of publicly and often freely available information is staggering. Yet, the intelligence community still continues to collect and use information in the same manner as during WWII, when the OSS set out to learn as much as possible about Nazi Germany and Imperial Japan by scrutinizing encyclopedias, guide books, and short-wave radio. Today, the supply of information is greater than any possible demand, and anyone can provide information. In effect, intelligence analysts are drowning in information. The book explains how to navigate this rising flood and make best use of these new, ric

  11. Advances in neural networks computational intelligence for ICT

    CERN Document Server

    Esposito, Anna; Morabito, Francesco; Pasero, Eros

    2016-01-01

    This carefully edited book is putting emphasis on computational and artificial intelligent methods for learning and their relative applications in robotics, embedded systems, and ICT interfaces for psychological and neurological diseases. The book is a follow-up of the scientific workshop on Neural Networks (WIRN 2015) held in Vietri sul Mare, Italy, from the 20th to the 22nd of May 2015. The workshop, at its 27th edition became a traditional scientific event that brought together scientists from many countries, and several scientific disciplines. Each chapter is an extended version of the original contribution presented at the workshop, and together with the reviewers’ peer revisions it also benefits from the live discussion during the presentation. The content of book is organized in the following sections. 1. Introduction, 2. Machine Learning, 3. Artificial Neural Networks: Algorithms and models, 4. Intelligent Cyberphysical and Embedded System, 5. Computational Intelligence Methods for Biomedical ICT in...

  12. A Low Energy Intelligent Clustering Protocol for Wireless Sensor Network

    DEFF Research Database (Denmark)

    Li, Qiao; Cui, Lingguo; Zhang, Baihai

    2010-01-01

    LEACH (low-energy adaptive clustering hierarchy) is a well-known self-organizing, adaptive clustering protocol of wireless sensor networks. However it has some shortcomings when it faces such problems as the cluster construction and energy management. In this paper, LEICP (low energy intelligent...

  13. On the synergy of network science and artificial intelligence

    NARCIS (Netherlands)

    Mocanu, D.C.

    2016-01-01

    Traditionally science is done using the reductionism paradigm. Artificial intelligence does not make an exception and it follows the same strategy. At the same time, network science tries to study complex systems as a whole. This Ph.D. research takes an alternative approach to the reductionism

  14. New understandings of failure modes in SSL luminaires

    Science.gov (United States)

    Shepherd, Sarah D.; Mills, Karmann C.; Yaga, Robert; Johnson, Cortina; Davis, J. Lynn

    2014-09-01

    As SSL products are being rapidly introduced into the market, there is a need to develop standard screening and testing protocols that can be performed quickly and provide data surrounding product lifetime and performance. These protocols, derived from standard industry tests, are known as ALTs (accelerated life tests) and can be performed in a timeframe of weeks to months instead of years. Accelerated testing utilizes a combination of elevated temperature and humidity conditions as well as electrical power cycling to control aging of the luminaires. In this study, we report on the findings of failure modes for two different luminaire products exposed to temperature-humidity ALTs. LEDs are typically considered the determining component for the rate of lumen depreciation. However, this study has shown that each luminaire component can independently or jointly influence system performance and reliability. Material choices, luminaire designs, and driver designs all have significant impacts on the system reliability of a product. From recent data, it is evident that the most common failure modes are not within the LED, but instead occur within resistors, capacitors, and other electrical components of the driver. Insights into failure modes and rates as a result of ALTs are reported with emphasis on component influence on overall system reliability.

  15. Accelerated optical polymer aging studies for LED luminaire applications

    Science.gov (United States)

    Estupiñán, Edgar; Wendling, Peter; Kostrun, Marijan; Garner, Richard

    2013-09-01

    There is a need in the lighting industry to design and implement accelerated aging methods that accurately simulate the aging process of LED luminaire components. In response to this need, we have built a flexible and reliable system to study the aging characteristics of optical polymer materials, and we have employed it to study a commercially available LED luminaire diffuser made of PMMA. The experimental system consists of a "Blue LED Emitter" and a working surface. Both the temperatures of the samples and the optical powers of the LEDs are appropriately characterized in the system. Several accelerated aging experiments are carried out at different temperatures and optical powers over a 90 hour period and the measured transmission values are used as inputs to a degradation model derived using plausibility arguments. This model seems capable of predicting the behavior of the material as a function of time, temperature and optical power. The model satisfactorily predicts the measured transmission values of diffusers aged in luminaires at two different times and thus can be used to make application recommendations for this material. Specifically, at 35000 hours (the manufacturer's stated life of the luminaire) and at the typical operational temperature of the diffuser, the model predicts a transmission loss of only a few percent over the original transmission of the material at 450 nm, which renders this material suitable for this application.

  16. Intelligent networks recent approaches and applications in medical systems

    CERN Document Server

    Ahamed, Syed V

    2013-01-01

    This textbook offers an insightful study of the intelligent Internet-driven revolutionary and fundamental forces at work in society. Readers will have access to tools and techniques to mentor and monitor these forces rather than be driven by changes in Internet technology and flow of money. These submerged social and human forces form a powerful synergistic foursome web of (a) processor technology, (b) evolving wireless networks of the next generation, (c) the intelligent Internet, and (d) the motivation that drives individuals and corporations. In unison, the technological forces can tear

  17. Low Power Multi-Hop Networking Analysis in Intelligent Environments.

    Science.gov (United States)

    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.

  18. An approach to efficient mobility management in intelligent networks

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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

  20. Artificial neural network intelligent method for prediction

    Science.gov (United States)

    Trifonov, Roumen; Yoshinov, Radoslav; Pavlova, Galya; Tsochev, Georgi

    2017-09-01

    Accounting and financial classification and prediction problems are high challenge and researchers use different methods to solve them. Methods and instruments for short time prediction of financial operations using artificial neural network are considered. The methods, used for prediction of financial data as well as the developed forecasting system with neural network are described in the paper. The architecture of a neural network used four different technical indicators, which are based on the raw data and the current day of the week is presented. The network developed is used for forecasting movement of stock prices one day ahead and consists of an input layer, one hidden layer and an output layer. The training method is algorithm with back propagation of the error. The main advantage of the developed system is self-determination of the optimal topology of neural network, due to which it becomes flexible and more precise The proposed system with neural network is universal and can be applied to various financial instruments using only basic technical indicators as input data.

  1. Intelligent Devices in Rural Wireless Networks

    Directory of Open Access Journals (Sweden)

    Daniel FUENTES

    2014-03-01

    Full Text Available The rural wireless networks are increasingly in demand by associations and autarchies to expand Internet access in this type of areas. The problem of such solutions centers not only in network deployment and its maintenance, but also in the equipment installation on clients, which always has big costs. This installation and configuration must be performed by a technician on site, so that the equipment can be integrated in the infrastructure. To try to mitigate this problem, it is presented a solution that allows the clients to install, with transparency, the device at home, reducing not only the cost for the management entity but also for the clients. This way, for info-excluded people or with new technology low experience level, it is the user that integrates himself in the network, making him part of the process, fostering the network usage.In this article are specified not only the system architecture but also the way that it works and how it obtains the desirable result. The tests made to the solution show the quickness, reliability and autonomy in the execution of the tasks, making it a benefit for rural wireless networks.This solution, by its robustness and simplicity, allowed an uptake to the IT by people who never thought to do it, namely an advanced age group (elderly who want to join the world of the new technologies

  2. Customer Intelligence Analytics on Social Networks

    Directory of Open Access Journals (Sweden)

    Brano MARKIĆ

    2016-08-01

    Full Text Available Discovering needs, habits and consumer behavior is the primary task of marketing analytics. It is necessary to integrate marketing and analytical skills with IT skills. Such knowledge integration allows access to data (structured and unstructured, their analysis and finding out information about the opinions, attitudes, needs and behavior of customers. In the paper is set the hypothesis that software tools can collect data (messages from social networks, analyze the content of messages and get to know the attitudes of customers about a product, service, tourist destination with the ultimate goal of improving customer relations. Experimental results are based on the analysis of the content of social network Facebook by using the package and function R language. This language showed a satisfactory application and development power in analysis of textual data on social networks for marketing analytics.

  3. Probabilistic reasoning in intelligent systems networks of plausible inference

    CERN Document Server

    Pearl, Judea

    1988-01-01

    Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertainty--and offers techniques, based on belief networks, that provid

  4. Automation of seismic network signal interpolation: an artificial intelligence approach

    International Nuclear Information System (INIS)

    Chiaruttini, C.; Roberto, V.

    1988-01-01

    After discussing the current status of the automation in signal interpretation from seismic networks, a new approach, based on artificial-intelligence tecniques, is proposed. The knowledge of the human expert analyst is examined, with emphasis on its objects, strategies and reasoning techniques. It is argued that knowledge-based systems (or expert systems) provide the most appropriate tools for designing an automatic system, modelled on the expert behaviour

  5. Evaluating neural networks and artificial intelligence systems

    Science.gov (United States)

    Alberts, David S.

    1994-02-01

    Systems have no intrinsic value in and of themselves, but rather derive value from the contributions they make to the missions, decisions, and tasks they are intended to support. The estimation of the cost-effectiveness of systems is a prerequisite for rational planning, budgeting, and investment documents. Neural network and expert system applications, although similar in their incorporation of a significant amount of decision-making capability, differ from each other in ways that affect the manner in which they can be evaluated. Both these types of systems are, by definition, evolutionary systems, which also impacts their evaluation. This paper discusses key aspects of neural network and expert system applications and their impact on the evaluation process. A practical approach or methodology for evaluating a certain class of expert systems that are particularly difficult to measure using traditional evaluation approaches is presented.

  6. Intelligent Network Management and Functional Cerebellum Synthesis

    Science.gov (United States)

    Loebner, Egon E.

    1989-01-01

    Transdisciplinary modeling of the cerebellum across histology, physiology, and network engineering provides preliminary results at three organization levels: input/output links to central nervous system networks; links between the six neuron populations in the cerebellum; and computation among the neurons of the populations. Older models probably underestimated the importance and role of climbing fiber input which seems to supply write as well as read signals, not just to Purkinje but also to basket and stellate neurons. The well-known mossy fiber-granule cell-Golgi cell system should also respond to inputs originating from climbing fibers. Corticonuclear microcomplexing might be aided by stellate and basket computation and associate processing. Technological and scientific implications of the proposed cerebellum model are discussed.

  7. Middleware Architecture for Ambient Intelligence in the Networked Home

    Science.gov (United States)

    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.

  8. INTELLIGENT TRAFFIC-SAFETY MIRROR BY USING WIRELESS SENSOR NETWORK

    Directory of Open Access Journals (Sweden)

    Peter Danišovič

    2014-03-01

    Full Text Available This article is focused on the problematic of traffic safety, dealing with the problem of car intersections with blocked view crossing by a special wireless sensor network (WSN proposed for the traffic monitoring, concretely for vehicle’s detection at places, where it is necessary. Some ultra-low-power TI products were developed due to this reason: microcontroller MSP430F2232, 868MHz RF transceiver CC1101 and LDO voltage regulator TPS7033. The WSN consist of four network nodes supplied with the special safety lightings which serve the function of intelligent traffic safety mirror.

  9. Detection of Intelligent Intruders in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yun Wang

    2016-01-01

    Full Text Available Most of the existing research works on the intrusion detection problem in a wireless sensor network (WSN assume linear or random mobility patterns in abstracting intruders’ models in traversing the WSN field. However, in real-life WSN applications, an intruder is usually an intelligent mobile robot with environment learning and detection avoidance capability (i.e., the capability to avoid surrounding sensors. Due to this, the literature results based on the linear or random mobility models may not be applied to the real-life WSN design and deployment for efficient and effective intrusion detection in practice. This motivates us to investigate the impact of intruder’s intelligence on the intrusion detection problem in a WSN for various applications. To be specific, we propose two intrusion algorithms, the pinball and flood-fill algorithms, to mimic the intelligent motion and behaviors of a mobile intruder in detecting and circumventing nearby sensors for detection avoidance while heading for its destination. The two proposed algorithms are integrated into a WSN framework for intrusion detection analysis in various circumstances. Monte Carlo simulations are conducted, and the results indicate that: (1 the performance of a WSN drastically changes as a result of the intruder’s intelligence in avoiding sensor detections and intrusion algorithms; (2 network parameters, including node density, sensing range and communication range, play a crucial part in the effectiveness of the intruder’s intrusion algorithms; and (3 it is imperative to integrate intruder’s intelligence in the WSN research for intruder detection problems under various application circumstances.

  10. An Intelligent technical analysis using neural network

    Directory of Open Access Journals (Sweden)

    Reza Raei

    2011-07-01

    Full Text Available Technical analysis has been one of the most popular methods for stock market predictions for the past few decades. There have been enormous technical analysis methods to study the behavior of stock market for different kinds of trading markets such as currency, commodity or stock. In this paper, we propose two different methods based on volume adjusted moving average and ease of movement for stock trading. These methods are used with and without generalized regression neural network methods and the results are compared with each other. The preliminary results on historical stock price of 20 firms indicate that there is no meaningful difference between various proposed models of this paper.

  11. Network traffic intelligence using a low interaction honeypot

    Science.gov (United States)

    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.

  12. Resource Optimization of Mobile Intelligent System with heart MPLS network

    Directory of Open Access Journals (Sweden)

    Mohammed Elkoutbi

    2009-10-01

    Full Text Available In this paper, we introduce the original Mobile Intelligent System (MIS in embeded FPGA architecture. This node will allow the construction of autonomous mobile network units which can move in unknowns, inaccessible or hostile environnement for human being, in order to collect data by various sensors and transmits them by routing to a unit of distant process. In the sake of improving the performance of transmission, we propose a global schema of QoS management using DiffServ/MPLS backbones. We provide an evaluation of several scenarios for combining QoS IP networks with MIS access network. We conclude with a study on interoperability between QoS patterns in access and backbone networks.

  13. An Intelligent Approach to Observability of Distribution Networks

    DEFF Research Database (Denmark)

    Pokhrel, Basanta Raj; Nainar, Karthikeyan; Bak-Jensen, Birgitte

    2018-01-01

    This paper presents a novel intelligent observability approach for active distribution systems. Observability assessment of the measured power system network, which is a preliminary task in state estimation, is handled via an algebraic method that uses the triangular factors of singular, symmetric...... gain matrix accompanied by a minimum meter placement technique. In available literature, large numbers of pseudo measurements are used to cover the scarcity of sufficient real measurements in distribution systems; the values of these virtual meters are calculated value based on the available real...... measurements, network topology, and network parameters. However, since there are large margin of errors exist in the calculation phase, estimated states may be significantly differed from the actual values though network is classified as observable. Hence, an approach based on numerical observability analysis...

  14. Dynamic Intelligent Feedback Scheduling in Networked Control Systems

    Directory of Open Access Journals (Sweden)

    Hui-ying Chen

    2013-01-01

    Full Text Available For the networked control system with limited bandwidth and flexible workload, a dynamic intelligent feedback scheduling strategy is proposed. Firstly, a monitor is used to acquire the current available network bandwidth. Then, the new available bandwidth in the next interval is predicted by using LS_SVM approach. At the same time, the dynamic performance indices of all control loops are obtained with a two-dimensional fuzzy logic modulator. Finally, the predicted network bandwidth is dynamically allocated by the bandwidth manager and the priority allocator in terms of the loops' dynamic performance indices. Simulation results show that the sampling periods and priorities of control loops are adjusted timely according to the network workload condition and the dynamic performance of control loops, which make the system running in the optimal state all the time.

  15. Digital intelligent booster for DCC miniature train networks

    Science.gov (United States)

    Ursu, M. P.; Condruz, D. A.

    2017-08-01

    Modern miniature trains are now driven by means of the DCC (Digital Command and Control) system, which allows the human operator or a personal computer to launch commands to each individual train or even to control different features of the same train. The digital command station encodes these commands and sends them to the trains by means of electrical pulses via the rails of the railway network. Due to the development of the miniature railway network, it may happen that the power requirement of the increasing number of digital locomotives, carriages and accessories exceeds the nominal output power of the digital command station. This digital intelligent booster relieves the digital command station from powering the entire railway network all by itself, and it automatically handles the multiple powered sections of the network. This electronic device is also able to detect and process short-circuits and overload conditions, without the intervention of the digital command station.

  16. Intelligent Wireless Sensor Networks for System Health Monitoring

    Science.gov (United States)

    Alena, Rick

    2011-01-01

    Wireless sensor networks (WSN) based on the IEEE 802.15.4 Personal Area Network (PAN) standard are finding increasing use in the home automation and emerging smart energy markets. The network and application layers, based on the ZigBee 2007 Standard, provide a convenient framework for component-based software that supports customer solutions from multiple vendors. WSNs provide the inherent fault tolerance required for aerospace applications. The Discovery and Systems Health Group at NASA Ames Research Center has been developing WSN technology for use aboard aircraft and spacecraft for System Health Monitoring of structures and life support systems using funding from the NASA Engineering and Safety Center and Exploration Technology Development and Demonstration Program. This technology provides key advantages for low-power, low-cost ancillary sensing systems particularly across pressure interfaces and in areas where it is difficult to run wires. Intelligence for sensor networks could be defined as the capability of forming dynamic sensor networks, allowing high-level application software to identify and address any sensor that joined the network without the use of any centralized database defining the sensors characteristics. The IEEE 1451 Standard defines methods for the management of intelligent sensor systems and the IEEE 1451.4 section defines Transducer Electronic Datasheets (TEDS), which contain key information regarding the sensor characteristics such as name, description, serial number, calibration information and user information such as location within a vehicle. By locating the TEDS information on the wireless sensor itself and enabling access to this information base from the application software, the application can identify the sensor unambiguously and interpret and present the sensor data stream without reference to any other information. The application software is able to read the status of each sensor module, responding in real-time to changes of

  17. Intelligent Resource Management for Local Area Networks: Approach and Evolution

    Science.gov (United States)

    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.

  18. Prototyping and Simulation of Robot Group Intelligence using Kohonen Networks.

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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

  20. Adaptive Moving Object Tracking Integrating Neural Networks And Intelligent Processing

    Science.gov (United States)

    Lee, James S. J.; Nguyen, Dziem D.; Lin, C.

    1989-03-01

    A real-time adaptive scheme is introduced to detect and track moving objects under noisy, dynamic conditions including moving sensors. This approach integrates the adaptiveness and incremental learning characteristics of neural networks with intelligent reasoning and process control. Spatiotemporal filtering is used to detect and analyze motion, exploiting the speed and accuracy of multiresolution processing. A neural network algorithm constitutes the basic computational structure for classification. A recognition and learning controller guides the on-line training of the network, and invokes pattern recognition to determine processing parameters dynamically and to verify detection results. A tracking controller acts as the central control unit, so that tracking goals direct the over-all system. Performance is benchmarked against the Widrow-Hoff algorithm, for target detection scenarios presented in diverse FLIR image sequences. Efficient algorithm design ensures that this recognition and control scheme, implemented in software and commercially available image processing hardware, meets the real-time requirements of tracking applications.

  1. An Intelligent Handover Management System for Future Generation Wireless Networks

    Directory of Open Access Journals (Sweden)

    Kassar Meriem

    2008-01-01

    Full Text Available Abstract Future generation wireless networks should provide to mobile users the best connectivity to services anywhere at anytime. The most challenging problem is the seamless intersystem/vertical mobility across heterogeneous wireless networks. In order to answer it, a vertical handover management system is needed. In our paper, we propose an intelligent solution answering user requirements and ensuring service continuity. We focus on a vertical handover decision strategy based on the context-awareness concept. The given strategy chooses the appropriate time and the most suitable access network among those available to perform a handover. It uses advanced decision algorithms (for more efficiency and intelligence and it is governed by handover policies as decision rules (for more flexibility and optimization. To maintain a seamless service continuity, handover execution is based on mobile IP functionalities. We study our decision system in a case of a 3G/UMTS-WLAN scenario and we discuss all the handover decision issues in our solution.

  2. Artificial organic networks artificial intelligence based on carbon networks

    CERN Document Server

    Ponce-Espinosa, Hiram; Molina, Arturo

    2014-01-01

    This monograph describes the synthesis and use of biologically-inspired artificial hydrocarbon networks (AHNs) for approximation models associated with machine learning and a novel computational algorithm with which to exploit them. The reader is first introduced to various kinds of algorithms designed to deal with approximation problems and then, via some conventional ideas of organic chemistry, to the creation and characterization of artificial organic networks and AHNs in particular. The advantages of using organic networks are discussed with the rules to be followed to adapt the network to its objectives. Graph theory is used as the basis of the necessary formalism. Simulated and experimental examples of the use of fuzzy logic and genetic algorithms with organic neural networks are presented and a number of modeling problems suitable for treatment by AHNs are described: ·        approximation; ·        inference; ·        clustering; ·        control; ·        class...

  3. Intelligence Control System for Landfills Based on Wireless Sensor Network

    Science.gov (United States)

    Zhang, Qian; Huang, Chuan; Gong, Jian

    2018-06-01

    This paper put forward an intelligence system for controlling the landfill gas in landfills to make the landfill gas (LFG) exhaust controllably and actively. The system, which is assigned by the wireless sensor network, were developed and supervised by remote applications in workshop instead of manual work. An automatic valve control depending on the sensor units embedded is installed in tube, the air pressure and concentration of LFG are detected to decide the level of the valve switch. The paper also proposed a modified algorithm to solve transmission problem, so that the system can keep a high efficiency and long service life.

  4. Intelligent Surveillance Robot with Obstacle Avoidance Capabilities Using Neural Network

    Directory of Open Access Journals (Sweden)

    Widodo Budiharto

    2015-01-01

    Full Text Available For specific purpose, vision-based surveillance robot that can be run autonomously and able to acquire images from its dynamic environment is very important, for example, in rescuing disaster victims in Indonesia. In this paper, we propose architecture for intelligent surveillance robot that is able to avoid obstacles using 3 ultrasonic distance sensors based on backpropagation neural network and a camera for face recognition. 2.4 GHz transmitter for transmitting video is used by the operator/user to direct the robot to the desired area. Results show the effectiveness of our method and we evaluate the performance of the system.

  5. ARTIFICIAL NEURAL NETWORKS BASED GEARS MATERIAL SELECTION HYBRID INTELLIGENT SYSTEM

    Institute of Scientific and Technical Information of China (English)

    X.C. Li; W.X. Zhu; G. Chen; D.S. Mei; J. Zhang; K.M. Chen

    2003-01-01

    An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples,the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.

  6. Intelligence Control System for Landfills Based on Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Zhang Qian

    2018-01-01

    Full Text Available This paper put forward an intelligence system for controlling the landfill gas in landfills to make the landfill gas (LFG exhaust controllably and actively. The system, which is assigned by the wireless sensor network, were developed and supervised by remote applications in workshop instead of manual work. An automatic valve control depending on the sensor units embedded is installed in tube, the air pressure and concentration of LFG are detected to decide the level of the valve switch. The paper also proposed a modified algorithm to solve transmission problem, so that the system can keep a high efficiency and long service life.

  7. Network Edge Intelligence for the Emerging Next-Generation Internet

    Directory of Open Access Journals (Sweden)

    Salekul Islam

    2010-11-01

    Full Text Available The success of the Content Delivery Networks (CDN in the recent years has demonstrated the increased benefits of the deployment of some form of “intelligence” within the network. Cloud computing, on the other hand, has shown the benefits of economies of scale and the use of a generic infrastructure to support a variety of services. Following that trend, we propose to move away from the smart terminal-dumb network dichotomy to a model where some degree of intelligence is put back into the network, specifically at the edge, with the support of Cloud technology. In this paper, we propose the deployment of an Edge Cloud, which integrates a variety of user-side and server-side services. On the user side, surrogate, an application running on top of the Cloud, supports a virtual client. The surrogate hides the underlying network infrastructure from the user, thus allowing for simpler, more easily managed terminals. Network side services supporting delivery of and exploiting content are also deployed on this infrastructure, giving the Internet Service Providers (ISP many opportunities to become directly involved in content and service delivery.

  8. 76 FR 47178 - Energy Efficiency Program: Test Procedure for Lighting Systems (Luminaires)

    Science.gov (United States)

    2011-08-04

    ... DEPARTMENT OF ENERGY [Docket Number EERE-2011-BT-TP-0041] RIN 1904-AC50 Energy Efficiency Program: Test Procedure for Lighting Systems (Luminaires) AGENCY: Office of Energy Efficiency and Renewable... (``DOE'' or the ``Department'') is currently evaluating energy efficiency test procedures for luminaires...

  9. CALiPER Report 23: Photometric Testing of White Tunable LED Luminaires

    Energy Technology Data Exchange (ETDEWEB)

    None, None

    2016-01-01

    This report documents an initial investigation of photometric testing procedures for white-tunable LED luminaires and summarizes the key features of those products. Goals of the study include understanding the amount of testing required to characterize a white-tunable product, and documenting the performance of available color-tunable luminaires that are intended for architectural lighting.

  10. FPGA implementation of advanced FEC schemes for intelligent aggregation networks

    Science.gov (United States)

    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.

  11. Machine learning based Intelligent cognitive network using fog computing

    Science.gov (United States)

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

    2017-05-01

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

  12. An Intelligent Pinger Network for Solid Glacier Environments

    Science.gov (United States)

    Schönitz, S.; Reuter, S.; Henke, C.; Jeschke, S.; Ewert, D.; Eliseev, D.; Heinen, D.; Linder, P.; Scholz, F.; Weinstock, L.; Wickmann, S.; Wiebusch, C.; Zierke, S.

    2016-12-01

    This talk presents a novel approach for an intelligent, agent-based pinger network in an extraterrestrial glacier environment. Because of recent findings of the Cassini spacecraft, a mission to Saturn's moon Enceladus is planned in order search for extraterrestrial life within the ocean beneath Enceladus' ice crust. Therefore, a maneuverable melting probe, the EnEx probe, was developed to melt into Enceladus' ice and take liquid samples from water-filled crevasses. Hence, the probe collecting the samples has to be able to navigate in ice which is a hard problem, because neither visual nor gravitational methods can be used. To enhance the navigability of the probe, a network of autonomous pinger units (APU) is in development that is able to extract a map of the ice environment via ultrasonic soundwaves. A network of these APUs will be deployed on the surface of Enceladus, melt into the ice and form a network to help guide the probe safely to its destination. The APU network is able to form itself fully autonomously and to compensate system failures of individual APUs. The agents controlling the single APU are realized by rule-based expert systems implemented in CLIPS. The rule-based expert system evaluates available information of the environment, decides for actions to take to achieve the desired goal (e.g. a specific network topology), and executes and monitors such actions. In general, it encodes certain situations that are evaluated whenever an APU is currently idle, and then decides for a next action to take. It bases this decision on its internal world model that is shared with the other APUs. The optimal network topology that defines each agents position is iteratively determined by mixed-integer nonlinear programming. Extensive simulations studies show that the proposed agent design enables the APUs to form a robust network topology that is suited to create a reliable 3D map of the ice environment.

  13. Acoustic richness modulates the neural networks supporting intelligible speech processing.

    Science.gov (United States)

    Lee, Yune-Sang; Min, Nam Eun; Wingfield, Arthur; Grossman, Murray; Peelle, Jonathan E

    2016-03-01

    The information contained in a sensory signal plays a critical role in determining what neural processes are engaged. Here we used interleaved silent steady-state (ISSS) functional magnetic resonance imaging (fMRI) to explore how human listeners cope with different degrees of acoustic richness during auditory sentence comprehension. Twenty-six healthy young adults underwent scanning while hearing sentences that varied in acoustic richness (high vs. low spectral detail) and syntactic complexity (subject-relative vs. object-relative center-embedded clause structures). We manipulated acoustic richness by presenting the stimuli as unprocessed full-spectrum speech, or noise-vocoded with 24 channels. Importantly, although the vocoded sentences were spectrally impoverished, all sentences were highly intelligible. These manipulations allowed us to test how intelligible speech processing was affected by orthogonal linguistic and acoustic demands. Acoustically rich speech showed stronger activation than acoustically less-detailed speech in a bilateral temporoparietal network with more pronounced activity in the right hemisphere. By contrast, listening to sentences with greater syntactic complexity resulted in increased activation of a left-lateralized network including left posterior lateral temporal cortex, left inferior frontal gyrus, and left dorsolateral prefrontal cortex. Significant interactions between acoustic richness and syntactic complexity occurred in left supramarginal gyrus, right superior temporal gyrus, and right inferior frontal gyrus, indicating that the regions recruited for syntactic challenge differed as a function of acoustic properties of the speech. Our findings suggest that the neural systems involved in speech perception are finely tuned to the type of information available, and that reducing the richness of the acoustic signal dramatically alters the brain's response to spoken language, even when intelligibility is high. Copyright © 2015 Elsevier

  14. Next Generation Luminaire (NGL) Downlight Demonstration Project, Hilton Columbus Downtown

    Energy Technology Data Exchange (ETDEWEB)

    Davis, R. G. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Perrin, T. E. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

    2014-09-30

    At the Hilton Columbus Downtown hotel in Ohio, DOE's Better Buildings Alliance conducted a demonstration of Next Generation Luminaires-winning downlights installed in all guest rooms and suites prior to the hotel's 2012 opening. After a post-occupancy assessment, the LED downlights not only provided the aesthetic appearance and dimming functionality desired, but also provided 50% energy savings relative to a comparable CFL downlight and enabled the lighting power to be more than 20% below that allowed by code.

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

    Science.gov (United States)

    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 impen...

  16. Computational intelligence synergies of fuzzy logic, neural networks and evolutionary computing

    CERN Document Server

    Siddique, Nazmul

    2013-01-01

    Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella of computational intelligence. Computational intelligence schemes are investigated with the development of a suitable framework for fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems and evolutionary neural systems. Applications to linear and non-linear systems are discussed with examples. Key features: Covers all the aspect

  17. Association of structural global brain network properties with intelligence in normal aging.

    Directory of Open Access Journals (Sweden)

    Florian U Fischer

    Full Text Available Higher general intelligence attenuates age-associated cognitive decline and the risk of dementia. Thus, intelligence has been associated with cognitive reserve or resilience in normal aging. Neurophysiologically, intelligence is considered as a complex capacity that is dependent on a global cognitive network rather than isolated brain areas. An association of structural as well as functional brain network characteristics with intelligence has already been reported in young adults. We investigated the relationship between global structural brain network properties, general intelligence and age in a group of 43 cognitively healthy elderly, age 60-85 years. Individuals were assessed cross-sectionally using Wechsler Adult Intelligence Scale-Revised (WAIS-R and diffusion-tensor imaging. Structural brain networks were reconstructed individually using deterministic tractography, global network properties (global efficiency, mean shortest path length, and clustering coefficient were determined by graph theory and correlated to intelligence scores within both age groups. Network properties were significantly correlated to age, whereas no significant correlation to WAIS-R was observed. However, in a subgroup of 15 individuals aged 75 and above, the network properties were significantly correlated to WAIS-R. Our findings suggest that general intelligence and global properties of structural brain networks may not be generally associated in cognitively healthy elderly. However, we provide first evidence of an association between global structural brain network properties and general intelligence in advanced elderly. Intelligence might be affected by age-associated network deterioration only if a certain threshold of structural degeneration is exceeded. Thus, age-associated brain structural changes seem to be partially compensated by the network and the range of this compensation might be a surrogate of cognitive reserve or brain resilience.

  18. Association of Structural Global Brain Network Properties with Intelligence in Normal Aging

    Science.gov (United States)

    Fischer, Florian U.; Wolf, Dominik; Scheurich, Armin; Fellgiebel, Andreas

    2014-01-01

    Higher general intelligence attenuates age-associated cognitive decline and the risk of dementia. Thus, intelligence has been associated with cognitive reserve or resilience in normal aging. Neurophysiologically, intelligence is considered as a complex capacity that is dependent on a global cognitive network rather than isolated brain areas. An association of structural as well as functional brain network characteristics with intelligence has already been reported in young adults. We investigated the relationship between global structural brain network properties, general intelligence and age in a group of 43 cognitively healthy elderly, age 60–85 years. Individuals were assessed cross-sectionally using Wechsler Adult Intelligence Scale-Revised (WAIS-R) and diffusion-tensor imaging. Structural brain networks were reconstructed individually using deterministic tractography, global network properties (global efficiency, mean shortest path length, and clustering coefficient) were determined by graph theory and correlated to intelligence scores within both age groups. Network properties were significantly correlated to age, whereas no significant correlation to WAIS-R was observed. However, in a subgroup of 15 individuals aged 75 and above, the network properties were significantly correlated to WAIS-R. Our findings suggest that general intelligence and global properties of structural brain networks may not be generally associated in cognitively healthy elderly. However, we provide first evidence of an association between global structural brain network properties and general intelligence in advanced elderly. Intelligence might be affected by age-associated network deterioration only if a certain threshold of structural degeneration is exceeded. Thus, age-associated brain structural changes seem to be partially compensated by the network and the range of this compensation might be a surrogate of cognitive reserve or brain resilience. PMID:24465994

  19. An Intelligent Cooperative Visual Sensor Network for Urban Mobility.

    Science.gov (United States)

    Leone, Giuseppe Riccardo; Moroni, Davide; Pieri, Gabriele; Petracca, Matteo; Salvetti, Ovidio; Azzarà, Andrea; Marino, Francesco

    2017-11-10

    Smart cities are demanding solutions for improved traffic efficiency, in order to guarantee optimal access to mobility resources available in urban areas. Intelligent video analytics deployed directly on board embedded sensors offers great opportunities to gather highly informative data about traffic and transport, allowing reconstruction of a real-time neat picture of urban mobility patterns. In this paper, we present a visual sensor network in which each node embeds computer vision logics for analyzing in real time urban traffic. The nodes in the network share their perceptions and build a global and comprehensive interpretation of the analyzed scenes in a cooperative and adaptive fashion. This is possible thanks to an especially designed Internet of Things (IoT) compliant middleware which encompasses in-network event composition as well as full support of Machine-2-Machine (M2M) communication mechanism. The potential of the proposed cooperative visual sensor network is shown with two sample applications in urban mobility connected to the estimation of vehicular flows and parking management. Besides providing detailed results of each key component of the proposed solution, the validity of the approach is demonstrated by extensive field tests that proved the suitability of the system in providing a scalable, adaptable and extensible data collection layer for managing and understanding mobility in smart cities.

  20. An Intelligent Cooperative Visual Sensor Network for Urban Mobility

    Directory of Open Access Journals (Sweden)

    Giuseppe Riccardo Leone

    2017-11-01

    Full Text Available Smart cities are demanding solutions for improved traffic efficiency, in order to guarantee optimal access to mobility resources available in urban areas. Intelligent video analytics deployed directly on board embedded sensors offers great opportunities to gather highly informative data about traffic and transport, allowing reconstruction of a real-time neat picture of urban mobility patterns. In this paper, we present a visual sensor network in which each node embeds computer vision logics for analyzing in real time urban traffic. The nodes in the network share their perceptions and build a global and comprehensive interpretation of the analyzed scenes in a cooperative and adaptive fashion. This is possible thanks to an especially designed Internet of Things (IoT compliant middleware which encompasses in-network event composition as well as full support of Machine-2-Machine (M2M communication mechanism. The potential of the proposed cooperative visual sensor network is shown with two sample applications in urban mobility connected to the estimation of vehicular flows and parking management. Besides providing detailed results of each key component of the proposed solution, the validity of the approach is demonstrated by extensive field tests that proved the suitability of the system in providing a scalable, adaptable and extensible data collection layer for managing and understanding mobility in smart cities.

  1. An Intelligent Cooperative Visual Sensor Network for Urban Mobility

    Science.gov (United States)

    Leone, Giuseppe Riccardo; Petracca, Matteo; Salvetti, Ovidio; Azzarà, Andrea

    2017-01-01

    Smart cities are demanding solutions for improved traffic efficiency, in order to guarantee optimal access to mobility resources available in urban areas. Intelligent video analytics deployed directly on board embedded sensors offers great opportunities to gather highly informative data about traffic and transport, allowing reconstruction of a real-time neat picture of urban mobility patterns. In this paper, we present a visual sensor network in which each node embeds computer vision logics for analyzing in real time urban traffic. The nodes in the network share their perceptions and build a global and comprehensive interpretation of the analyzed scenes in a cooperative and adaptive fashion. This is possible thanks to an especially designed Internet of Things (IoT) compliant middleware which encompasses in-network event composition as well as full support of Machine-2-Machine (M2M) communication mechanism. The potential of the proposed cooperative visual sensor network is shown with two sample applications in urban mobility connected to the estimation of vehicular flows and parking management. Besides providing detailed results of each key component of the proposed solution, the validity of the approach is demonstrated by extensive field tests that proved the suitability of the system in providing a scalable, adaptable and extensible data collection layer for managing and understanding mobility in smart cities. PMID:29125535

  2. SNMS: an intelligent transportation system network architecture based on WSN and P2P network

    Institute of Scientific and Technical Information of China (English)

    LI Li; LIU Yuan-an; TANG Bi-hua

    2007-01-01

    With the development of city road networks, the question of how to obtain information about the roads is becoming more and more important. In this article, sensor network with mobile station (SNMS), a novel two-tiered intelligent transportation system (ITS) network architecture based on wireless sensor network (WSN) and peer-to-peer (P2P) network, is proposed to provide significant traffic information about the road and thereby, assist travelers to take optimum decisions when they are driving. A detailed explanation with regard to the strategy of each level as well as the design of two main components in the network, sensor unit (SU) and mobile station (MS), is presented. Finally, a representative scenario is described to display the operation of the system.

  3. Distributed intelligent sensor network for the rehabilitation of Parkinson's patients.

    Science.gov (United States)

    Ying, Hong; Schlösser, Mario; Schnitzer, Andreas; Schäfer, Thorsten; Schläfke, Marianne E; Leonhardt, Steffen; Schiek, Michael

    2011-03-01

    The coordination between locomotion and respiration of Parkinson's disease (PD) patients is reduced or even absent. The degree of this disturbance is assumed to be associated with the disease severity [S. Schiermeier, D. Schäfer, T. Schäfer, W. Greulich, and M. E. Schläfke, "Breathing and locomotion in patients with Parkinson's disease," Eur. J. Physiol., vol. 443, No. 1, pp. 67-71, Jul. 2001]. To enable a long-term and online analysis of the locomotion-respiration coordination for scientific purpose, we have developed a distributed wireless communicating network. We aim to integrate biofeedback protocols with the real-time analysis of the locomotion-respiration coordination in the system to aid rehabilitation of PD patients. The network of sensor nodes is composed of intelligent network operating devices (iNODEs). The miniaturized iNODE contains a continuous data acquisition system based on microcontroller, local data storage, capability of on-sensor digital signal processing in real time, and wireless communication based on IEEE 802.15.4. Force sensing resistors and respiratory inductive plethysmography are applied for motion and respiration sensing, respectively. A number of experiments have been undertaken in clinic and laboratory to test the system. It shall facilitate identification of therapeutic effects on PD, allowing to measure the patients' health status, and to aid in the rehabilitation of PD patients.

  4. TRACKING VEHICLE IN GSM NETWORK TO SUPPORT INTELLIGENT TRANSPORTATION SYSTEMS

    Directory of Open Access Journals (Sweden)

    Z. Koppanyi

    2012-07-01

    Full Text Available The penetration of GSM capable devices is very high, especially in Europe. To exploit the potential of turning these mobile devices into dynamic data acquisition nodes that provides valuable data for Intelligent Transportation Systems (ITS, position information is needed. The paper describes the basic operation principles of the GSM system and provides an overview on the existing methods for deriving location data in the network. A novel positioning solution is presented that rely on handover (HO zone measurements; the zone geometry properties are also discussed. A new concept of HO zone sequence recognition is introduced that involves application of Probabilistic Deterministic Finite State Automata (PDFA. Both the potential commercial applications and the use of the derived position data in ITS is discussed for tracking vehicles and monitoring traffic flow. As a practical cutting edge example, the integration possibility of the technology in the SafeTRIP platform (developed in an EC FP7 project is presented.

  5. Network resilience against intelligent attacks constrained by the degree-dependent node removal cost

    International Nuclear Information System (INIS)

    Annibale, A; Coolen, A C C; Bianconi, G

    2010-01-01

    We study the resilience of complex networks against attacks in which nodes are targeted intelligently, but where disabling a node has a cost to the attacker which depends on its degree. Attackers have to meet these costs with limited resources, which constrains their actions. A network's integrity is quantified in terms of the efficacy of the process that it supports. We calculate how the optimal attack strategy and the most attack-resistant network degree statistics depend on the node removal cost function and the attack resources. The resilience of networks against intelligent attacks is found to depend strongly on the node removal cost function faced by the attacker. In particular, if node removal costs increase sufficiently fast with the node degree, power law networks are found to be more resilient than Poissonian ones, even against optimized intelligent attacks. For cost functions increasing quadratically in the node degrees, intelligent attackers cannot damage the network more than random damages would.

  6. Final Report: System Reliability Model for Solid-State Lighting (SSL) Luminaires

    Energy Technology Data Exchange (ETDEWEB)

    Davis, J. Lynn [RTI International, Research Triangle Park, NC (United States)

    2017-05-31

    The primary objectives of this project was to develop and validate reliability models and accelerated stress testing (AST) methodologies for predicting the lifetime of integrated SSL luminaires. This study examined the likely failure modes for SSL luminaires including abrupt failure, excessive lumen depreciation, unacceptable color shifts, and increased power consumption. Data on the relative distribution of these failure modes were acquired through extensive accelerated stress tests and combined with industry data and other source of information on LED lighting. This data was compiled and utilized to build models of the aging behavior of key luminaire optical and electrical components.

  7. The application of neural networks with artificial intelligence technique in the modeling of industrial processes

    International Nuclear Information System (INIS)

    Saini, K. K.; Saini, Sanju

    2008-01-01

    Neural networks are a relatively new artificial intelligence technique that emulates the behavior of biological neural systems in digital software or hardware. These networks can 'learn', automatically, complex relationships among data. This feature makes the technique very useful in modeling processes for which mathematical modeling is difficult or impossible. The work described here outlines some examples of the application of neural networks with artificial intelligence technique in the modeling of industrial processes.

  8. Cultural intelligence and network organizations in society: Case of Tehran neighborhood councils

    Directory of Open Access Journals (Sweden)

    Salamzadeh Yashar

    2016-01-01

    Full Text Available Network communications is one of the modern ideas in the field of organizational behavior. On the other hand, the ability to communicate with employees and understand the cultural differences between them in a multicultural environment is one of the key skills that managers and employees need them in the nowadays organizations. These skills are introduced as cultural intelligence in organizations that have ability to respond to many challenges in multicultural environments. This article was aimed to analysis the relationship between cultural intelligence and network communication. These questionnaires were distributed between 134 members at the Tehran neighborhood councils. In order to analyzing data and concluding results, SPSS, and then Pearson correlation test were used. The research was done based on structural equation modeling (SEM. The result indicated that there was significant positive relationship between cultural intelligence and network communication. Also there was significant positive relationship between each dimension of cultural intelligence and network communication. Findings show that cultural intelligence is a basic factor in network communication and confirm the main hypothesis of this study which represents the existence of a positive and meaningful relation between cultural intelligence and network communication. Furthermore, the results show that considering this kind of intelligence, especially in network organizations which has a high ethnic and cultural variety, could be very useful for improve employees and managers communications.

  9. Resource Aware Intelligent Network Services (RAINS) Final Technical Report

    Energy Technology Data Exchange (ETDEWEB)

    Lehman, Tom; Yang, Xi

    2018-01-16

    The Resource Aware Intelligent Network Services (RAINS) project conducted research and developed technologies in the area of cyberinfrastructure resource modeling and computation. The goal of this work was to provide a foundation to enable intelligent, software defined services which spanned the network AND the resources which connect to the network. A Multi-Resource Service Plane (MRSP) was defined, which allows resource owners/managers to locate and place themselves from a topology and service availability perspective within the dynamic networked cyberinfrastructure ecosystem. The MRSP enables the presentation of integrated topology views and computation results which can include resources across the spectrum of compute, storage, and networks. The RAINS project developed MSRP includes the following key components: i) Multi-Resource Service (MRS) Ontology/Multi-Resource Markup Language (MRML), ii) Resource Computation Engine (RCE), iii) Modular Driver Framework (to allow integration of a variety of external resources). The MRS/MRML is a general and extensible modeling framework that allows for resource owners to model, or describe, a wide variety of resource types. All resources are described using three categories of elements: Resources, Services, and Relationships between the elements. This modeling framework defines a common method for the transformation of cyberinfrastructure resources into data in the form of MRML models. In order to realize this infrastructure datification, the RAINS project developed a model based computation system, i.e. “RAINS Computation Engine (RCE)”. The RCE has the ability to ingest, process, integrate, and compute based on automatically generated MRML models. The RCE interacts with the resources thru system drivers which are specific to the type of external network or resource controller. The RAINS project developed a modular and pluggable driver system which facilities a variety of resource controllers to automatically generate

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

    Science.gov (United States)

    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…

  11. Intelligent routing protocol for ad hoc wireless network

    Science.gov (United States)

    Peng, Chaorong; Chen, Chang Wen

    2006-05-01

    A novel routing scheme for mobile ad hoc networks (MANETs), which combines hybrid and multi-inter-routing path properties with a distributed topology discovery route mechanism using control agents is proposed in this paper. In recent years, a variety of hybrid routing protocols for Mobile Ad hoc wireless networks (MANETs) have been developed. Which is proactively maintains routing information for a local neighborhood, while reactively acquiring routes to destinations beyond the global. The hybrid protocol reduces routing discovery latency and the end-to-end delay by providing high connectivity without requiring much of the scarce network capacity. On the other side the hybrid routing protocols in MANETs likes Zone Routing Protocol still need route "re-discover" time when a route between zones link break. Sine the topology update information needs to be broadcast routing request on local zone. Due to this delay, the routing protocol may not be applicable for real-time data and multimedia communication. We utilize the advantages of a clustering organization and multi-routing path in routing protocol to achieve several goals at the same time. Firstly, IRP efficiently saves network bandwidth and reduces route reconstruction time when a routing path fails. The IRP protocol does not require global periodic routing advertisements, local control agents will automatically monitor and repair broke links. Secondly, it efficiently reduces congestion and traffic "bottlenecks" for ClusterHeads in clustering network. Thirdly, it reduces significant overheads associated with maintaining clusters. Fourthly, it improves clusters stability due to dynamic topology changing frequently. In this paper, we present the Intelligent Routing Protocol. First, we discuss the problem of routing in ad hoc networks and the motivation of IRP. We describe the hierarchical architecture of IRP. We describe the routing process and illustrate it with an example. Further, we describe the control manage

  12. Intercluster Connection in Cognitive Wireless Mesh Networks Based on Intelligent Network Coding

    Science.gov (United States)

    Chen, Xianfu; Zhao, Zhifeng; Jiang, Tao; Grace, David; Zhang, Honggang

    2009-12-01

    Cognitive wireless mesh networks have great flexibility to improve spectrum resource utilization, within which secondary users (SUs) can opportunistically access the authorized frequency bands while being complying with the interference constraint as well as the QoS (Quality-of-Service) requirement of primary users (PUs). In this paper, we consider intercluster connection between the neighboring clusters under the framework of cognitive wireless mesh networks. Corresponding to the collocated clusters, data flow which includes the exchanging of control channel messages usually needs four time slots in traditional relaying schemes since all involved nodes operate in half-duplex mode, resulting in significant bandwidth efficiency loss. The situation is even worse at the gateway node connecting the two colocated clusters. A novel scheme based on network coding is proposed in this paper, which needs only two time slots to exchange the same amount of information mentioned above. Our simulation shows that the network coding-based intercluster connection has the advantage of higher bandwidth efficiency compared with the traditional strategy. Furthermore, how to choose an optimal relaying transmission power level at the gateway node in an environment of coexisting primary and secondary users is discussed. We present intelligent approaches based on reinforcement learning to solve the problem. Theoretical analysis and simulation results both show that the intelligent approaches can achieve optimal throughput for the intercluster relaying in the long run.

  13. Development of constant-power driving control for light-emitting-diode (LED) luminaire

    International Nuclear Information System (INIS)

    Huang, Bin-Juine; Chen, Chun-Wei; Ong, Chin-Dian; Du, Bo-Han; Hsu, Po-Chien

    2013-01-01

    The illumination of an LED may be affected by operating temperature even under constant-current condition. A constant-power driving technique is proposed in the present study for LED luminaire. A linear system dynamics model of LED luminaire is first derived and used in the design of the feedback control system. The PI controller was designed and tuned taking into account the control accuracy and robust properties with respect to plant uncertainty and variation of operating conditions. The control system was implemented on a microprocessor and used to control a 150W LED luminaire. The test result shows that the feedback system accurately controls the input power of LED luminaire to within 1.3 per cent error. As the ambient temperature changes from 0 to 40 °C, the LED illumination varies slightly (−1.7%) for constant-power driving, as compared to that of constant-current driving (−12%) and constant-voltage driving (+50%). The constant-power driving has revealed advantage in stabilizing the illumination of LED under large temperature variation. - Highlights: ► A constant-power driving technique is proposed for LED luminaire. ► A linear system dynamics model of LED luminaire is used in the control system design. ► The test shows that the feedback system accurately controls the input power. ► The LED illumination varies slightly (−1.7%) for constant-power driving.

  14. The artificial neural networks: An approach to artificial intelligence; Un approccio ``biologico`` all`intelligenza artificiale

    Energy Technology Data Exchange (ETDEWEB)

    Taraglio, Sergio; Zanela, Andrea [ENEA, Casaccia (Italy). Dipt. Innovazione

    1997-05-01

    The artificial neural networks try to simulate the functionalities of the nervous system through a complex network of simple computing elements. In this work is presented an introduction to the neural networks and some of their possible applications, especially in the field of Artificial Intelligence.

  15. Intelligent IPv6 based iot network monitoring and altering system on ...

    African Journals Online (AJOL)

    Intelligent IPv6 based iot network monitoring and altering system on Cooja framework. ... Journal of Fundamental and Applied Sciences. Journal Home · ABOUT THIS ... Keywords: IoT; Cooja framework; Contiki OS; packet monitoring.

  16. Intelligent condition monitoring of railway catenary systems : A Bayesian Network approach

    NARCIS (Netherlands)

    Wang, H.; Nunez Vicencio, Alfredo; Dollevoet, R.P.B.J.; Liu, Zhigang; Chen, Junwen; Spiryagin, Maksym; Gordon, Timothy; Cole, Colin; McSweeney, Tim

    2017-01-01

    This study proposes a Bayesian network (BN) dedicated for the intelligent condition monitoring of railway catenary systems. It combines five types of measurements related to catenary condition, namely the contact wire stagger, contact wire height, pantograph head displacement, pantograph head

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

    Science.gov (United States)

    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...

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

    Science.gov (United States)

    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...

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

    Science.gov (United States)

    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 ...

  20. On-line Configuration of Network Emulator for Intelligent Energy System Testbed Applications

    DEFF Research Database (Denmark)

    Kemal, Mohammed Seifu; Iov, Florin; Olsen, Rasmus Løvenstein

    2015-01-01

    Intelligent energy networks (or Smart Grids) provide efficient solutions for a grid integrated with near-real-time communication technologies between various grid assets in power generation, transmission and distribution systems. The design of a communication network associated with intelligent...... power system involves detailed analysis of its communication requirements, a proposal of the appropriate protocol architecture, the choice of appropriate communication technologies for each case study, and a means to support heterogeneous communication technology management system. This paper discuses...

  1. Macroeconomic potentials of intelligent networks in Germany. Abstract; Gesamtwirtschaftliche Potenziale intelligenter Netze in Deutschland. Kurzfassung

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2012-07-01

    Within the contribution under consideration the Fraunhofer Institute for Systems and Innovation Research ISI (Karlsruhe, Federal Republic of Germany) determines the total economic effects of intelligent networks for the German economy. For the first time, the contributions of the different areas (energy, health, traffic, education, administration) are shown detailed. On the whole, it arises a societal total benefit of intelligent networks of 55.7 billion Euro per year.

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

    Science.gov (United States)

    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.

  3. Intelligent Packet Shaper to Avoid Network Congestion for Improved Streaming Video Quality at Clients

    DEFF Research Database (Denmark)

    Kaul, Manohar; Khosla, Rajiv; Mitsukura, Y

    2003-01-01

    of this intelligent traffic-shaping algorithm on the underlying network real time packet traffic and the eradication of unwanted abruption in the streaming video qualiy. This paper concluded from the end results of the simulation that neural networks are a very superior means of modeling real-time traffic......This paper proposes a traffic shaping algorithm based on neural networks, which adapts to a network over which streaming video is being transmitted. The purpose of this intelligent shaper is to eradicate all traffic congestion and improve the end-user's video quality. It possesses the capability...

  4. Optimisation of Software-Defined Networks Performance Using a Hybrid Intelligent System

    Directory of Open Access Journals (Sweden)

    Ann Sabih

    2017-06-01

    Full Text Available This paper proposes a novel intelligent technique that has been designed to optimise the performance of Software Defined Networks (SDN. The proposed hybrid intelligent system has employed integration of intelligence-based optimisation approaches with the artificial neural network. These heuristic optimisation methods include Genetic Algorithms (GA and Particle Swarm Optimisation (PSO. These methods were utilised separately in order to select the best inputs to maximise SDN performance. In order to identify SDN behaviour, the neural network model is trained and applied. The maximal optimisation approach has been identified using an analytical approach that considered SDN performance and the computational time as objective functions. Initially, the general model of the neural network was tested with unseen data before implementing the model using GA and PSO to determine the optimal performance of SDN. The results showed that the SDN represented by Artificial Neural Network ANN, and optmised by PSO, generated a better configuration with regards to computational efficiency and performance index.

  5. Construction of Gene Regulatory Networks Using Recurrent Neural Networks and Swarm Intelligence.

    Science.gov (United States)

    Khan, Abhinandan; Mandal, Sudip; Pal, Rajat Kumar; Saha, Goutam

    2016-01-01

    We have proposed a methodology for the reverse engineering of biologically plausible gene regulatory networks from temporal genetic expression data. We have used established information and the fundamental mathematical theory for this purpose. We have employed the Recurrent Neural Network formalism to extract the underlying dynamics present in the time series expression data accurately. We have introduced a new hybrid swarm intelligence framework for the accurate training of the model parameters. The proposed methodology has been first applied to a small artificial network, and the results obtained suggest that it can produce the best results available in the contemporary literature, to the best of our knowledge. Subsequently, we have implemented our proposed framework on experimental (in vivo) datasets. Finally, we have investigated two medium sized genetic networks (in silico) extracted from GeneNetWeaver, to understand how the proposed algorithm scales up with network size. Additionally, we have implemented our proposed algorithm with half the number of time points. The results indicate that a reduction of 50% in the number of time points does not have an effect on the accuracy of the proposed methodology significantly, with a maximum of just over 15% deterioration in the worst case.

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

    Science.gov (United States)

    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…

  7. Stress Testing of Organic Light- Emitting Diode Panels and Luminaires

    Energy Technology Data Exchange (ETDEWEB)

    Lynn Davis, Kelley Rountree, Karmann Mills

    2018-01-31

    This report builds on previous DOE efforts with OLED technology by updating information on a previously benchmarked OLED product (the Chalina luminaire from Acuity Brands) and provides new benchmarks on the performance of Brite 2 and Brite Amber OLED panels from OLEDWorks. During the tests described here, samples of these devices were subjected to continuous operation in stress tests at elevated ambient temperature environments of 35°C or 45°C. In addition, samples were also operated continuously at room temperature in a room temperature operational life test (RTOL). One goal of this study was to investigate whether these test conditions can accelerate failure of OLED panels, either through panel shorting or an open circuit in the panel. These stress tests are shown to provide meaningful acceleration of OLED failure modes, and an acceleration factor of 2.6 was calculated at 45°C for some test conditions. In addition, changes in the photometric properties of the emitted light (e.g., luminous flux and chromaticity maintenance) was also evaluated for insights into the long-term stability of these products compared to earlier generations. Because OLEDs are a lighting system, electrical testing was also performed on the panel-driver pairs to provide insights into the impact of the driver on long-term panel performance.

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

    Directory of Open Access Journals (Sweden)

    Michael A. Ferguson

    2017-06-01

    Full Text Available 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. In our study, we aimed to understand how individual differences in intellectual functioning are reflected in the intrinsic network architecture of the human brain. We applied statistical methods, known as spectral decompositions, in order to identify individual differences in the synchronous patterns of spontaneous brain activity that reliably predict core aspects of human intelligence. The synchrony of brain activity at rest across multiple discrete neural networks demonstrated positive relationships with fluid intelligence. In contrast, global synchrony within the brain’s network architecture reliably, and inversely, predicted mental flexibility, a core facet of intellectual functioning. The multinetwork systems approach described here represents a methodological and conceptual extension of earlier efforts that related differences in

  9. Modeling the impact of thermal effects on luminous flux maintenance for SSL luminaires

    Energy Technology Data Exchange (ETDEWEB)

    Davis, Lynn [RTI International, Research Triangle Park, NC (United States); Mills, Karmann [RTI International, Research Triangle Park, NC (United States); Lamvik, Michael [RTI International, Research Triangle Park, NC (United States); Solano, Eric [RTI International, Research Triangle Park, NC (United States); Bobashev, Georgiy [RTI International, Research Triangle Park, NC (United States); Perkins, Curtis [RTI International, Research Triangle Park, NC (United States)

    2017-05-30

    Meeting the longevity requirements of solid-state lighting (SSL) devices places extreme demands on the materials and designs that are used in SSL luminaires. Therefore, understanding the aging characteristics of lens, reflectors, and other materials is essential to projecting the long-term performance of LED-based lighting systems. Overlooking these factors at either the design or product specification stage can result in premature failure of the device due to poor luminous flux maintenance and/or excessive chromaticity shifts. This paper describes a methodology for performing accelerated stress testing (AST) on materials intended for use in SSL luminaires. This test methodology, which consists of elevated temperature and humidity conditions, produces accelerated aging data that can be correlated to expected performance under normal luminaire operating conditions. The correlations can then be leveraged to produce models of the changes in the optical properties of key materials including transmittance versus wavelength of lenses and reflectance versus wavelength for housings and other reflectors. This information has been collected into a lumen maintenance decision support tool (LM-DST) and together with user supplied inputs (e.g., expected operation conditions) can provide guidance on lifetime expectations of SSL luminaires. This approach has been applied to a variety of materials commonly found in SSL luminaires including acrylics, polycarbonates, and silicones used for lenses and paints, coatings, films, and composites used for reflectors.

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

    Science.gov (United States)

    2017-10-01

    organized intelligence with a comprehensive account of the information derived, validated by intelligence requirements tasking. Third Phase...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

  11. An Intelligent QoS Identification for Untrustworthy Web Services Via Two-phase Neural Networks

    OpenAIRE

    Wang, Weidong; Wang, Liqiang; Lu, Wei

    2016-01-01

    QoS identification for untrustworthy Web services is critical in QoS management in the service computing since the performance of untrustworthy Web services may result in QoS downgrade. The key issue is to intelligently learn the characteristics of trustworthy Web services from different QoS levels, then to identify the untrustworthy ones according to the characteristics of QoS metrics. As one of the intelligent identification approaches, deep neural network has emerged as a powerful techniqu...

  12. A new type of intelligent wireless sensing network for health monitoring of large-size structures

    Science.gov (United States)

    Lei, Ying; Liu, Ch.; Wu, D. T.; Tang, Y. L.; Wang, J. X.; Wu, L. J.; Jiang, X. D.

    2009-07-01

    In recent years, some innovative wireless sensing systems have been proposed. However, more exploration and research on wireless sensing systems are required before wireless systems can substitute for the traditional wire-based systems. In this paper, a new type of intelligent wireless sensing network is proposed for the heath monitoring of large-size structures. Hardware design of the new wireless sensing units is first studied. The wireless sensing unit mainly consists of functional modules of: sensing interface, signal conditioning, signal digitization, computational core, wireless communication and battery management. Then, software architecture of the unit is introduced. The sensing network has a two-level cluster-tree architecture with Zigbee communication protocol. Important issues such as power saving and fault tolerance are considered in the designs of the new wireless sensing units and sensing network. Each cluster head in the network is characterized by its computational capabilities that can be used to implement the computational methodologies of structural health monitoring; making the wireless sensing units and sensing network have "intelligent" characteristics. Primary tests on the measurement data collected by the wireless system are performed. The distributed computational capacity of the intelligent sensing network is also demonstrated. It is shown that the new type of intelligent wireless sensing network provides an efficient tool for structural health monitoring of large-size structures.

  13. Distributed illumination control with local sensing and actuation in networked lighting systems

    NARCIS (Netherlands)

    Caicedo Fernandez, D.R.; Pandharipande, A.

    2013-01-01

    We consider the problem of illumination control in a networked lighting system wherein luminaires have local sensing and actuation capabilities. Each luminaire (i) consists of a light emitting diode (LED) based light source dimmable by a local controller, (ii) is actuated based on sensing

  14. Intelligence

    Science.gov (United States)

    Sternberg, Robert J.

    2012-01-01

    Intelligence is the ability to learn from experience and to adapt to, shape, and select environments. Intelligence as measured by (raw scores on) conventional standardized tests varies across the lifespan, and also across generations. Intelligence can be understood in part in terms of the biology of the brain—especially with regard to the functioning in the prefrontal cortex—and also correlates with brain size, at least within humans. Studies of the effects of genes and environment suggest that the heritability coefficient (ratio of genetic to phenotypic variation) is between .4 and .8, although heritability varies as a function of socioeconomic status and other factors. Racial differences in measured intelligence have been observed, but race is a socially constructed rather than biological variable, so such differences are difficult to interpret. PMID:22577301

  15. Intelligence.

    Science.gov (United States)

    Sternberg, Robert J

    2012-03-01

    Intelligence is the ability to learn from experience and to adapt to, shape, and select environments. Intelligence as measured by (raw scores on) conventional standardized tests varies across the lifespan, and also across generations. Intelligence can be understood in part in terms of the biology of the brain-especially with regard to the functioning in the prefrontal cortex-and also correlates with brain size, at least within humans. Studies of the effects of genes and environment suggest that the heritability coefficient (ratio of genetic to phenotypic variation) is between .4 and .8, although heritability varies as a function of socioeconomic status and other factors. Racial differences in measured intelligence have been observed, but race is a socially constructed rather than biological variable, so such differences are difficult to interpret.

  16. An intelligent and networking solution of radiation monitoring system for LHC

    International Nuclear Information System (INIS)

    Shao Beibei; Gong Guanghua

    2001-01-01

    The LHC (the Large Hadron Collider), the largest accelerator in the world, is under designing and construction at CERN. It shares the 27 km LEP tunnel and is expected to be on the air in 2005. The Radiation Monitoring System of LEP was a central system with non-intelligent detectors. While as the proposed new RMS for LHC is a distributing intelligent networked system. Around 350 detectors will be employed. To save the cost, the design should make the old LEP's non-intelligent detectors reusable. To allow the detector controller automatic reports the detector database and net location through the world Fip bus, 1 wire components are embedded into the detectors and the network sockets. The radiation tolerance and the reliability of the communication of the wire components have been tested in a strong radiation field at CERN. The low cost components based position detection technique is valuable for most networked control system

  17. Event building in an intelligent network interface card for the LHCb readout network

    CERN Document Server

    Dufey, J P; Neufeld, N; Zuin, M

    2001-01-01

    LHCb is an experiment being constructed at CERN's LHC accelerator for the purpose of studying precisely the CP violation parameters in the B-B system. Triggering poses special problems since the interesting events containing B-mesons are immersed in a large background of inelastic p-p reactions. Therefore, a 4 Level Triggering scheme (Level 0 to Level 3) has been implemented. Powerful embedded processors, used in modern intelligent Network Interface Cards (smart NICs), make it attractive to use them to handle the event building protocol in the high-speed data acquisition system of the LHCb experiment. The implementation of an event building algorithm developed for a specific Gigabit Ethernet NIC is presented and performance data are discussed. 5 Refs.

  18. Fundamentals of computational intelligence neural networks, fuzzy systems, and evolutionary computation

    CERN Document Server

    Keller, James M; Fogel, David B

    2016-01-01

    This book covers the three fundamental topics that form the basis of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation. Discusses single-layer and multilayer neural networks, radial-basi function networks, and recurrent neural networks Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzz...

  19. 77 FR 3544 - Meeting and Webinar on the Active Traffic and Demand Management and Intelligent Network Flow...

    Science.gov (United States)

    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... infrastructure. The vision for ATDM research is to allow transportation agencies to increase traffic flow...

  20. Integrated Multimedia Based Intelligent Group Decision Support System for Electrical Power Network

    Directory of Open Access Journals (Sweden)

    Ajay Kumar Saxena

    2002-05-01

    Full Text Available Electrical Power Network in recent time requires an intelligent, virtual environment based decision process for the coordination of all its individual elements and the interrelated tasks. Its ultimate goal is to achieve maximum productivity and efficiency through the efficient and effective application of generation, transmission, distribution, pricing and regulatory systems. However, the complexity of electrical power network and the presence of conflicting multiple goals and objectives postulated by various groups emphasized the need of an intelligent group decision support system approach in this field. In this paper, an Integrated Multimedia based Intelligent Group Decision Support System (IM1GDSS is presented, and its main components are analyzed and discussed. In particular attention is focused on the Data Base, Model Base, Central Black Board (CBB and Multicriteria Futuristic Decision Process (MFDP module. The model base interacts with Electrical Power Network Load Forecasting and Planning (EPNLFP Module; Resource Optimization, Modeling and Simulation (ROMAS Module; Electrical Power Network Control and Evaluation Process (EPNCAEP Module, and MFDP Module through CBB for strategic planning, management control, operational planning and transaction processing. The richness of multimedia channels adds a totally new dimension in a group decision making for Electrical Power Network. The proposed IMIGDSS is a user friendly, highly interactive group decision making system, based on efficient intelligent and multimedia communication support for group discussions, retrieval of content and multi criteria decision analysis.

  1. Fluid intelligence allows flexible recruitment of the parieto-frontal network in analogical reasoning.

    Science.gov (United States)

    Preusse, Franziska; van der Meer Elke; 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.

  2. Fluid intelligence allows flexible recruitment of the parieto-frontal network in analogical reasoning.

    Directory of Open Access Journals (Sweden)

    Franziska ePreusse

    2011-03-01

    Full Text Available 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

  3. Artificial intelligence based event detection in wireless sensor networks

    NARCIS (Netherlands)

    Bahrepour, M.

    2013-01-01

    Wireless sensor networks (WSNs) are composed of large number of small, inexpensive devices, called sensor nodes, which are equipped with sensing, processing, and communication capabilities. While traditional applications of wireless sensor networks focused on periodic monitoring, the focus of more

  4. Amigo - Ambient Intelligence for the networked home environment

    NARCIS (Netherlands)

    Janse, M.D.

    2008-01-01

    The Amigo project develops open, standardized, interoperable middleware and attractive user services for the networked home environment. Fifteen of Europe's leading companies and research organizations in mobile and home networking, software development, consumer electronics and domestic appliances

  5. Artificial intelligence in diagnosis and supply restoration for a distribution network

    Energy Technology Data Exchange (ETDEWEB)

    Teo, C.Y.; Gooi, H.B. [Nanyang Technological University (Singapore). School of Electrical and Electronic Engineering

    1998-07-01

    The development of a PC-based integrated system, to illustrate the application of artificial intelligence in the fault diagnosis and supply restoration for an interconnected distribution network is described. The intelligent process utilises the post-fault network status, a list of the tripped breakers, main protection alarm, and the conventional event log. The fault diagnostic system is implemented by three independent mechanisms, namely the generic core rule, specific post-fault network matching, and generic relay inference rules. The intelligent restoration process is implemented by the switching check, the dynamic restoration algorithm and the mechanism for restoration by record matching and learning. By linking to a PC-based distribution simulator it has been demonstrated that the developed mechanisms enable the correct deduction of fault under different network configurations. The appropriate restoration plan can also be generated to restore supply to the entire restorable load for various post-fault networks. This system is currently used for undergraduate teaching and will be ideal for the training of network operation engineers. As the system developed is generic and can be used for a general network, it can be further developed for practical operation in a subtransmission system or an urban distribution system operated in any configuration. (author)

  6. Intelligent battery energy management and control for vehicle-to-grid via cloud computing network

    International Nuclear Information System (INIS)

    Khayyam, Hamid; Abawajy, Jemal; Javadi, Bahman; Goscinski, Andrzej; Stojcevski, Alex; Bab-Hadiashar, Alireza

    2013-01-01

    Highlights: • The intelligent battery energy management substantially reduces the interactions of PEV with parking lots. • The intelligent battery energy management improves the energy efficiency. • The intelligent battery energy management predicts the road load demand for vehicles. - Abstract: Plug-in Electric Vehicles (PEVs) provide new opportunities to reduce fuel consumption and exhaust emission. PEVs need to draw and store energy from an electrical grid to supply propulsive energy for the vehicle. As a result, it is important to know when PEVs batteries are available for charging and discharging. Furthermore, battery energy management and control is imperative for PEVs as the vehicle operation and even the safety of passengers depend on the battery system. Thus, scheduling the grid power electricity with parking lots would be needed for efficient charging and discharging of PEV batteries. This paper aims to propose a new intelligent battery energy management and control scheduling service charging that utilize Cloud computing networks. The proposed intelligent vehicle-to-grid scheduling service offers the computational scalability required to make decisions necessary to allow PEVs battery energy management systems to operate efficiently when the number of PEVs and charging devices are large. Experimental analyses of the proposed scheduling service as compared to a traditional scheduling service are conducted through simulations. The results show that the proposed intelligent battery energy management scheduling service substantially reduces the required number of interactions of PEV with parking lots and grid as well as predicting the load demand calculated in advance with regards to their limitations. Also it shows that the intelligent scheduling service charging using Cloud computing network is more efficient than the traditional scheduling service network for battery energy management and control

  7. Modern computer networks and distributed intelligence in accelerator controls

    International Nuclear Information System (INIS)

    Briegel, C.

    1991-01-01

    Appropriate hardware and software network protocols are surveyed for accelerator control environments. Accelerator controls network topologies are discussed with respect to the following criteria: vertical versus horizontal and distributed versus centralized. Decision-making considerations are provided for accelerator network architecture specification. Current trends and implementations at Fermilab are discussed

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

    Science.gov (United States)

    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.

  9. Using Wireless Sensor Networks to Achieve Intelligent Monitoring for High-Temperature Gas-Cooled Reactor

    Directory of Open Access Journals (Sweden)

    Jianghai Li

    2017-01-01

    Full Text Available High-temperature gas-cooled reactors (HTGR can incorporate wireless sensor network (WSN technology to improve safety and economic competitiveness. WSN has great potential in monitoring the equipment and processes within nuclear power plants (NPPs. This technology not only reduces the cost of regular monitoring but also enables intelligent monitoring. In intelligent monitoring, large sets of heterogeneous data collected by the WSN can be used to optimize the operation and maintenance of the HTGR. In this paper, WSN-based intelligent monitoring schemes that are specific for applications of HTGR are proposed. Three major concerns regarding wireless technology in HTGR are addressed: wireless devices interference, cybersecurity of wireless networks, and wireless standards selected for wireless platform. To process nonlinear and non-Gaussian data obtained by WSN for fault diagnosis, novel algorithms combining Kernel Entropy Component Analysis (KECA and support vector machine (SVM are developed.

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

    Science.gov (United States)

    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.

  11. Realization of Intelligent Household Appliance Wireless Monitoring Network Based on LEACH Protocol

    Directory of Open Access Journals (Sweden)

    Weilong ZHOU

    2014-06-01

    Full Text Available The intelligent household appliance wireless monitoring network can real-time monitor the apparent power and power factor of various household appliances in different indoor regions, and can realize the real-time monitoring on the household appliance working status and performance. The household appliance wireless monitoring network based on LEACH protocol is designed in the paper. Firstly, the basic idea of LEACH routing algorithm is proposed. Aiming at the node-distribution feature of intelligent home, the selection of cluster head in the routing algorithm and the data transmission method at the stable communication phase is modified. Moreover, the hardware circuit of power acquisition and power factor measurement is designed. The realization of wireless monitoring network based on CC2530 is described, each module and the whole system were conducted the on-line debugging. Finally, the system is proved to meet the practical requirement through the networking test.

  12. Computing Active Power Losses Using a Mathematical Model of a Regulated Street Luminaire

    Directory of Open Access Journals (Sweden)

    Roman Sikora

    2018-05-01

    Full Text Available Before the use of regulated street luminaires with variable power and luminous flux, computations were performed using constant values for their electrical and photometric parameters. At present, where such lighting is in use, it is no longer possible to base calculations on such assumptions. Computations of energy and power losses, for example, need to be performed for all dimming levels and based on the applied regulation algorithm. Based on measurements carried out on regulated luminaires, it was found that certain electrical parameters have a nonlinear dependence on the dimming level. Electrical parameters were also observed to depend on the value of the supply voltage. The results of the measurements are presented in this article. Failure to take account of power losses in computations of the energy efficiency of street lighting in accordance with the applicable EN 13201 standard causes values of energy efficiency indicators to be overstated. Power loss computations are presented in this article for a sample street lighting system with regulated luminaires, for the whole range of dimming levels and additionally for fluctuations of ±10% in the supply voltage. In addition, a mathematical model of a regulated luminaire is constructed with the use of regression methods, and a practical application of that model is described.

  13. An accelerated test method of luminous flux depreciation for LED luminaires and lamps

    International Nuclear Information System (INIS)

    Qian, C.; Fan, X.J.; Fan, J.J.; Yuan, C.A.; Zhang, G.Q.

    2016-01-01

    Light Emitting Diode (LED) luminaires and lamps are energy-saving and environmental friendly alternatives to traditional lighting products. However, current luminous flux depreciation test at luminaire and lamp level requires a minimum of 6000 h testing, which is even longer than the product development cycle time. This paper develops an accelerated test method for luminous flux depreciation to reduce the test time within 2000 h at an elevated temperature. The method is based on lumen maintenance boundary curve, obtained from a collection of LED source lumen depreciation data, known as LM-80 data. The exponential decay model and Arrhenius acceleration relationship are used to determine the new threshold of lumen maintenance and acceleration factor. The proposed method has been verified by a number of simulation studies and experimental data for a wide range of LED luminaire and lamp types from both internal and external experiments. The qualification results obtained by the accelerated test method agree well with traditional 6000 h tests. - Highlights: • We develop an accelerated test method for LED luminaires and lamps. • The method is proposed based on a “Boundary Curve” concept. • The parameters of the boundary curve are extracted from LM-80 test reports. • Qualification results from the proposed method agree with ES requirements.

  14. Development of constant-power driving control for light-emitting-diode (LED) luminaire

    KAUST Repository

    Huang, Bin-Juine

    2013-01-01

    The illumination of an LED may be affected by operating temperature even under constant-current condition. A constant-power driving technique is proposed in the present study for LED luminaire. A linear system dynamics model of LED luminaire is first derived and used in the design of the feedback control system. The PI controller was designed and tuned taking into account the control accuracy and robust properties with respect to plant uncertainty and variation of operating conditions. The control system was implemented on a microprocessor and used to control a 150W LED luminaire. The test result shows that the feedback system accurately controls the input power of LED luminaire to within 1.3 per cent error. As the ambient temperature changes from 0 to 40 °C, the LED illumination varies slightly (-1.7%) for constant-power driving, as compared to that of constant-current driving (-12%) and constant-voltage driving (+50%). The constant-power driving has revealed advantage in stabilizing the illumination of LED under large temperature variation. © 2012 Elsevier Ltd. All rights reserved.

  15. Integrated Multimedia Based Intelligent Group Decision Support System for Electrical Power Network

    OpenAIRE

    Ajay Kumar Saxena; S. 0. Bhatnagar; P. K Saxena

    2002-01-01

    Electrical Power Network in recent time requires an intelligent, virtual environment based decision process for the coordination of all its individual elements and the interrelated tasks. Its ultimate goal is to achieve maximum productivity and efficiency through the efficient and effective application of generation, transmission, distribution, pricing and regulatory systems. However, the complexity of electrical power network and the presence of conflicting multiple goals and objectives p...

  16. Smart Collision Avoidance and Hazard Routing Mechanism for Intelligent Transport Network

    Science.gov (United States)

    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.

  17. SNMP-SI: A Network Management Tool Based on Slow Intelligence System Approach

    Science.gov (United States)

    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.

  18. Scalable Light Module for Low-Cost, High-Efficiency Light- Emitting Diode Luminaires

    Energy Technology Data Exchange (ETDEWEB)

    Tarsa, Eric [Cree, Inc., Goleta, CA (United States)

    2015-08-31

    During this two-year program Cree developed a scalable, modular optical architecture for low-cost, high-efficacy light emitting diode (LED) luminaires. Stated simply, the goal of this architecture was to efficiently and cost-effectively convey light from LEDs (point sources) to broad luminaire surfaces (area sources). By simultaneously developing warm-white LED components and low-cost, scalable optical elements, a high system optical efficiency resulted. To meet program goals, Cree evaluated novel approaches to improve LED component efficacy at high color quality while not sacrificing LED optical efficiency relative to conventional packages. Meanwhile, efficiently coupling light from LEDs into modular optical elements, followed by optimally distributing and extracting this light, were challenges that were addressed via novel optical design coupled with frequent experimental evaluations. Minimizing luminaire bill of materials and assembly costs were two guiding principles for all design work, in the effort to achieve luminaires with significantly lower normalized cost ($/klm) than existing LED fixtures. Chief project accomplishments included the achievement of >150 lm/W warm-white LEDs having primary optics compatible with low-cost modular optical elements. In addition, a prototype Light Module optical efficiency of over 90% was measured, demonstrating the potential of this scalable architecture for ultra-high-efficacy LED luminaires. Since the project ended, Cree has continued to evaluate optical element fabrication and assembly methods in an effort to rapidly transfer this scalable, cost-effective technology to Cree production development groups. The Light Module concept is likely to make a strong contribution to the development of new cost-effective, high-efficacy luminaries, thereby accelerating widespread adoption of energy-saving SSL in the U.S.

  19. Intelligent Multi-Agent Middleware for Ubiquitous Home Networking Environments

    OpenAIRE

    Minwoo Son; Seung-Hun Lee; Dongkyoo Shin; Dongil Shin

    2008-01-01

    The next stage of the home networking environment is supposed to be ubiquitous, where each piece of material is equipped with an RFID (Radio Frequency Identification) tag. To fully support the ubiquitous environment, home networking middleware should be able to recommend home services based on a user-s interests and efficiently manage information on service usage profiles for the users. Therefore, USN (Ubiquitous Sensor Network) technology, which recognizes and manages a ...

  20. Amigo - Ambient Intelligence for the networked home environment

    OpenAIRE

    Janse, M.D.

    2008-01-01

    The Amigo project develops open, standardized, interoperable middleware and attractive user services for the networked home environment. Fifteen of Europe's leading companies and research organizations in mobile and home networking, software development, consumer electronics and domestic appliances have joined together in the Amigo project to develop an integrated interoperable home networking framework. Amigo is an IST-funded IP project. This report is the final report providing an overview ...

  1. Social Networks and Collective Intelligence: A Return to the Agora

    DEFF Research Database (Denmark)

    Mazzara, Manuel; Biselli, Luca; Greco, Pier Paolo

    2013-01-01

    backgrounds and institutes with significantly different agendas. Polidoxa aims at offering: 1) a trust-based search engine algorithm, which exploits stigmergic behaviours of users? network, 2) a trust-based social network, where the notion of trust derives from network activity and 3) a holonic system...... for bottom-up self-protection and social privacy. By presenting the Polidoxa solution, this work also describes the current state of traditional media as well as newer ones, providing an accurate analysis of major search engines such as Google and social network (e.g., Facebook). The advantages that Polidoxa...

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  4. Functional brain networks contributing to the Parieto-Frontal Integration Theory of Intelligence.

    Science.gov (United States)

    Vakhtin, Andrei A; Ryman, Sephira G; Flores, Ranee A; Jung, Rex E

    2014-12-01

    The refinement of localization of intelligence in the human brain is converging onto a distributed network that broadly conforms to the Parieto-Frontal Integration Theory (P-FIT). While this theory has received support in the neuroimaging literature, no functional magnetic resonance imaging study to date has conducted a whole-brain network-wise examination of the changes during engagement in tasks that are reliable measures of general intelligence (e.g., Raven's Progressive Matrices Test; RPM). Seventy-nine healthy subjects were scanned while solving RPM problems and during rest. Functional networks were extracted from the RPM and resting state data using Independent Component Analysis. Twenty-nine networks were identified, 26 of which were detected in both conditions. Fourteen networks were significantly correlated with the RPM task. The networks' spatial maps and functional connectivity measures at 3 frequency levels (low, medium, & high) were compared between the RPM and rest conditions. The regions involved in the networks that were found to be task related were consistent with the P-FIT, localizing to the bilateral medial frontal and parietal regions, right superior frontal lobule, and the right cingulate gyrus. Functional connectivity in multiple component pairs was differentially affected across all frequency levels during the RPM task. Our findings demonstrate that functional brain networks are more stable than previously thought, and maintain their general features across resting state and engagement in a complex cognitive task. The described spatial and functional connectivity alterations that such components undergo during fluid reasoning provide a network-wise framework of the P-FIT that can be valuable for further, network based, neuroimaging inquiries regarding the neural underpinnings of intelligence. Published by Elsevier Inc.

  5. A Novel Intelligent Transportation Control Supported by Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Zhe Qian

    2013-05-01

    Full Text Available With the development of wireless sensor unit, and improvement of real-time and quality of wireless communication, the intelligent transportation control system employ these technologies to realize sensing, positioning, computing, and communication for voiding collisions. This paper discusses the framework of transportation control system, and emphases TDOA positioning algorithm and the new weighted least square optimization method. The simulation result shows that, our method achieves high-accuracy of positioning, which can satisfy the need of transportation control. Finally, we outline the urgent work need to address in the future.

  6. Design on intelligent gateway technique in home network

    Science.gov (United States)

    Hu, Zhonggong; Feng, Xiancheng

    2008-12-01

    Based on digitization, multimedia, mobility, wide band, real-time interaction and so on,family networks, because can provide diverse and personalized synthesis service in information, correspondence work, entertainment, education and health care and so on, are more and more paid attention by the market. The family network product development has become the focus of the related industry. In this paper,the concept of the family network and the overall reference model of the family network are introduced firstly.Then the core techniques and the correspondence standard related with the family network are proposed.The key analysis is made for the function of family gateway, the function module of the software,the key technologies to client side software architecture and the trend of development of the family network entertainment seeing and hearing service and so on. Product present situation of the family gateway and the future trend of development, application solution of the digital family service are introduced. The development of the family network product bringing about the digital family network industry is introduced finally.It causes the development of software industries,such as communication industry,electrical appliances industry, computer and game and so on.It also causes the development of estate industry.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  9. LOCATING HUBS IN TRANSPORT NETWORKS: AN ARTIFICIAL INTELLIGENCE APPROACH

    OpenAIRE

    Dušan Teodorović; Milica Šelmić; Ivana Vukićević

    2014-01-01

    Hub facilities serve as switching and transshipment points in transportation and communication networks as well as in logistic systems. Hub networks have an influence on flows on the hub-to-hub links and ensure benefit from economies of scale in inter-hub transportation. The key factors for designing a successful hub-and-spoke network are to determine the optimal number of hubs, to properly locate hubs, and to allocate the non-hubs to the hubs. This paper presents the model to determine the l...

  10. Artificial intelligence for networks recognition in remote sensing images

    Science.gov (United States)

    Gilliot, Jean-Marc; Amat, Jean-Louis

    1993-12-01

    We describe here a knowledge-based system, NEXSYS (Nextwork EXtraction SYStem) which was designed for the recognition of communication networks in SPOT satellite images. NEXSYS is a frame-based system and uses a co-operative and distributed structure based on a blackboard architecture. Communication networks in SPOT images are composed of thin linear segments. Segments are extracted using mathematical morphology and a Hough transform. An intermediate image representation composed of geometric primitives is obtained. Then an expert module is able to process the segments at the symbolic level trying to recognize networks.

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

    Directory of Open Access Journals (Sweden)

    Yongcheng Li

    Full Text Available 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.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  15. Analysis and application of intelligence network based on FTTH

    Science.gov (United States)

    Feng, Xiancheng; Yun, Xiang

    2008-12-01

    With the continued rapid growth of Internet, new network service emerges in endless stream, especially the increase of network game, meeting TV, video on demand, etc. The bandwidth requirement increase continuously. Network technique, optical device technical development is swift and violent. FTTH supports all present and future service with enormous bandwidth, including traditional telecommunication service, traditional data service and traditional TV service, and the future digital TV and VOD. With huge bandwidth of FTTH, it wins the final solution of broadband network, becomes the final goal of development of optical access network. Firstly, it introduces the main service which FTTH supports, main analysis key technology such as FTTH system composition way, topological structure, multiplexing, optical cable and device. It focus two kinds of realization methods - PON, P2P technology. Then it proposed that the solution of FTTH can support comprehensive access (service such as broadband data, voice, video and narrowband private line). Finally, it shows the engineering application for FTTH in the district and building. It brings enormous economic benefits and social benefit.

  16. Neural network-based control of an intelligent solar Stirling pump

    International Nuclear Information System (INIS)

    Tavakolpour-Saleh, A.R.; Jokar, H.

    2016-01-01

    In this paper, an ANN (artificial neural network) control system is applied to a novel solar-powered active LTD (low temperature differential) Stirling pump. First, a mathematical description of the proposed Stirling pump is presented. Then, optimum operating frequencies of the converter corresponding to different operating conditions (i.e. different sink and source temperatures and water heads) are investigated using the proposed mathematical framework. It is found that the proposed complex mathematical scheme has a very slow convergence and thus, is not appropriate for real-time implementation of the model-based controller. Consequently, a NN (neural network) model with a lower complexity is proposed to learn the simulation data obtained from the mathematical model. The designed neural network controller is thus applied to a digital processor to effectively tune the converter frequency so that a maximum output power is acquired. Finally, the performance of the proposed mechatronic system is evaluated experimentally. The experimental results clearly demonstrate the feasibility of pumping water at low temperature difference under variable operating conditions using the proposed intelligent Stirling converter. - Highlights: • A novel intelligent solar-powered active LTD Stirling pump was introduced. • A neural network controller was used to tune the converter speed. • The intelligent converter was able to adapt itself to different operating conditions. • It was possible to excite the water column with its resonance mode. • Experimental results showed the effectiveness of the proposed converter.

  17. Exploiting Mobile Ad Hoc Networking and Knowledge Generation to Achieve Ambient Intelligence

    Directory of Open Access Journals (Sweden)

    Anna Lekova

    2012-01-01

    Full Text Available Ambient Intelligence (AmI joins together the fields of ubiquitous computing and communications, context awareness, and intelligent user interfaces. Energy, fault-tolerance, and mobility are newly added dimensions of AmI. Within the context of AmI the concept of mobile ad hoc networks (MANETs for “anytime and anywhere” is likely to play larger roles in the future in which people are surrounded and supported by small context-aware, cooperative, and nonobtrusive devices that will aid our everyday life. The connection between knowledge generation and communication ad hoc networking is symbiotic—knowledge generation utilizes ad hoc networking to perform their communication needs, and MANETs will utilize the knowledge generation to enhance their network services. The contribution of the present study is a distributed evolving fuzzy modeling framework (EFMF to observe and categorize relationships and activities in the user and application level and based on that social context to take intelligent decisions about MANETs service management. EFMF employs unsupervised online one-pass fuzzy clustering method to recognize nodes' mobility context from social scenario traces and ubiquitously learn “friends” and “strangers” indirectly and anonymously.

  18. Aspects concerning power distribution networks planning using artificial intelligence

    Energy Technology Data Exchange (ETDEWEB)

    Georgescu, Gh.; Gavrilas, M.; Cartina, Gh. [Gh. Asachi Technical Univ. of Iasi, Iasi (Romania)

    1997-12-31

    This paper presents the application of AI tools for the on-line identification of load structure in distribution networks. The authors have considered Artificial Neural Networks (ANN) which are known as valuable and fast tools for pattern identification or completion. This approach to the load model allows a more detailed analysis directed towards the optimization of system structure and working conditions. Traditional methods produce good results but raise the processing time problem, especially when applied to large systems. For such cases another approach appeal to the Genetic Algorithms, which are frequently referenced in the literature concerned with PDS (reconfiguration of open loop radial networks, optimal var-sources distribution, optimal selection of transformer tap position). (author)

  19. Sensor fusion in smart camera networks for ambient Intelligence

    NARCIS (Netherlands)

    Maatta, T.T.

    2013-01-01

    This short report introduces the topics of PhD research that was conducted on 2008-2013 and was defended on July 2013. The PhD thesis covers sensor fusion theory, gathers it into a framework with design rules for fusion-friendly design of vision networks, and elaborates on the rules through fusion

  20. Intelligent networks and energy storage; Intelligente netten en energieopslag

    Energy Technology Data Exchange (ETDEWEB)

    Ongkiehong, O.; Van den Berg, P.

    2012-11-15

    The emergence of decentralized generators cause electricity networks to become more complex and technically difficult to control. This article highlights the role of ICT [Dutch] Door de opkomst van decentrale opwekkers worden elektriciteitsnetten steeds complexer en moeilijker om technisch te beheersen. In dit artikel wordt de rol van ICT belicht.

  1. Intelligent Configuration of Social Support Networks around Depressed Persons

    NARCIS (Netherlands)

    Aziz, A.A.; Klein, M.C.A.; Treur, J.

    2011-01-01

    Helping someone who is depressed can be very important to the depressed person. A number of supportive family members or friends can often make a big difference. This paper addresses how a social support network can be formed, taking the needs of the support recipient and the possibilities of the

  2. Intelligence is differentially related to neural effort in the task-positive and the task-negative brain network

    NARCIS (Netherlands)

    Basten, U.; Stelzel, C.; Fiebach, C.J.

    2013-01-01

    Previous studies on individual differences in intelligence and brain activation during cognitive processing focused on brain regions where activation increases with task demands (task-positive network, TPN). Our study additionally considers brain regions where activation decreases with task demands

  3. Control of framed structures using intelligent monitoring networks

    Directory of Open Access Journals (Sweden)

    Foti Dora

    2017-01-01

    Full Text Available The paper proposes the integration of structural monitoring with Building Management Systems for electricity and gas distributions. To assess the state of damage of existing buildings the technics of Structural Health Monitoring (SHM is adopted. SHM as well as to record the occurrence of sudden structural damage resulting from exceptional events (earthquakes, explosions, shocks and collisions with vehicles, etc., allows the monitoring of the progressive damage and structural performance under operating conditions through the extraction of the modal parameters of the structure. This approach requires time to process acquired data that, depending on the size of the building and the number of monitored points, varies from minutes to hours. In this paper, an intelligent system is proposed to immediately communicate during an earthquake the overrun of a certain ground shaking threshold so that gas delivery and selected power loads are interrupted, as suggested by current national regulations on structures. The use of low-cost and reduced size accelerometric sensors integrated with Energy Monitoring Systems is proposed in both highrisk earthquake centers and in all “strategic” buildings that must ensure their operation use immediately after the earthquake. The procedure for calibrating the horizontal and vertical acceleration threshold is also sketched.

  4. Intelligent system to control electric power distribution networks

    Directory of Open Access Journals (Sweden)

    Pablo CHAMOSO

    2016-07-01

    Full Text Available The use of high voltage power lines transport involves some risks that may be avoided with periodic reviews as imposed by law in most countries. The objective of this work is to reduce the number of these periodic reviews so that the maintenance cost of power lines is also reduced. To reduce the number of transmission towers (TT to be reviewed, a virtual organization (VO based system of agents is proposed in conjunction with different artificial intelligence methods and algorithms. This system is able to propose a sample of TT from a selected set to be reviewed and to ensure that the whole set will have similar values without needing to review all the TT. As a result, the system provides a software solution to manage all the review processes and all the TT of Spain, allowing the review companies to use the application either when they initiate a new review process for a whole line or area of TT, or when they want to place an entirely new set of TT, in which case the system would recommend the best place and the best type of structure to use.

  5. A Lateral Control Method of Intelligent Vehicle Based on Fuzzy Neural Network

    Directory of Open Access Journals (Sweden)

    Linhui Li

    2015-01-01

    Full Text Available A lateral control method is proposed for intelligent vehicle to track the desired trajectory. Firstly, a lateral control model is established based on the visual preview and dynamic characteristics of intelligent vehicle. Then, the lateral error and orientation error are melded into an integrated error. Considering the system parameter perturbation and the external interference, a sliding model control is introduced in this paper. In order to design a sliding surface, the integrated error is chosen as the parameter of the sliding mode switching function. The sliding mode switching function and its derivative are selected as two inputs of the controller, and the front wheel angle is selected as the output. Next, a fuzzy neural network is established, and the self-learning functions of neural network is utilized to construct the fuzzy rules. Finally, the simulation results demonstrate the effectiveness and robustness of the proposed method.

  6. Intelligent Electric Power Systems with Active-Adaptive Electric Networks: Challenges for Simulation Tools

    Directory of Open Access Journals (Sweden)

    Ufa Ruslan A.

    2015-01-01

    Full Text Available The motivation of the presented research is based on the needs for development of new methods and tools for adequate simulation of intelligent electric power systems with active-adaptive electric networks (IES including Flexible Alternating Current Transmission System (FACTS devices. The key requirements for the simulation were formed. The presented analysis of simulation results of IES confirms the need to use a hybrid modelling approach.

  7. Intelligent Middle-Ware Architecture for Mobile Networks

    Science.gov (United States)

    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).

  8. Swarm intelligence techniques for optimization and management tasks insensor networks

    OpenAIRE

    Hernández Pibernat, Hugo

    2012-01-01

    Premi extraordinari doctorat curs 2011-2012, àmbit Enginyeria de les TIC The main contributions of this thesis are located in the domain of wireless sensor netorks. More in detail, we introduce energyaware algorithms and protocols in the context of the following topics: self-synchronized duty-cycling in networks with energy harvesting capabilities, distributed graph coloring and minimum energy broadcasting with realistic antennas. In the following, we review the research conducted...

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

    Science.gov (United States)

    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

  10. Neural-Network-Based Fuzzy Logic Navigation Control for Intelligent Vehicles

    Directory of Open Access Journals (Sweden)

    Ahcene Farah

    2002-06-01

    Full Text Available This paper proposes a Neural-Network-Based Fuzzy logic system for navigation control of intelligent vehicles. First, the use of Neural Networks and Fuzzy Logic to provide intelligent vehicles  with more autonomy and intelligence is discussed. Second, the system  for the obstacle avoidance behavior is developed. Fuzzy Logic improves Neural Networks (NN obstacle avoidance approach by handling imprecision and rule-based approximate reasoning. This system must make the vehicle able, after supervised learning, to achieve two tasks: 1- to make one’s way towards its target by a NN, and 2- to avoid static or dynamic obstacles by a Fuzzy NN capturing the behavior of a human expert. Afterwards, two association phases between each task and the appropriate actions are carried out by Trial and Error learning and their coordination allows to decide the appropriate action. Finally, the simulation results display the generalization and adaptation abilities of the system by testing it in new unexplored environments.

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

    Science.gov (United States)

    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.

  12. Study of system dynamics model and control of a high-power LED lighting luminaire

    International Nuclear Information System (INIS)

    Huang, B.-J.; Hsu, P.-C.; Wu, M.-S.; Tang, C.-W.

    2007-01-01

    The purpose of the present study is to design a current control system which is robust to the system dynamics uncertainty and the disturbance of ambient temperature to assure a stable optical output property of LED. The system dynamics model of the LED lighting system was first derived. A 96 W high-power LED luminaire was designed and built in the present study. The linearly perturbed system dynamics model for the LED luminaire is derived experimentally. The dynamics model of LED lighting system is of a multiple-input-multiple-output (MIMO) system with two inputs (applied voltage and ambient temperature) and two outputs (forward current and heat conducting body temperature). A step response test method was employed to the 96 W LED luminaire to identify the system dynamics model. It is found that the current model is just a constant gain (resistance) and the disturbance model is of first order, both changing with operating conditions (voltage and ambient temperature). A feedback control system using PI algorithm was designed using the results of the system dynamics model. The control system was implemented on a PIC microprocessor. Experimental results show that the control system can stably and accurately control the LED current to a constant value at the variation of ambient temperature up to 40 o C. The control system is shown to have a robust property with respect to the plant uncertainty and the ambient temperature disturbance

  13. ELiXIR—Solid-State Luminaire With Enhanced Light Extraction by Internal Reflection

    Science.gov (United States)

    Allen, Steven C.; Steckl, Andrew J.

    2007-06-01

    A phosphor-converted light-emitting diode (pcLED) luminaire featuring enhanced light extraction by internal reflection (ELiXIR) with efficacy of 60 lm/W producing 18 lumens of yellowish green light at 100 mA is presented. The luminaire consists of a commercial blue high power LED, a polymer hemispherical shell lens with interior phosphor coating, and planar aluminized reflector. High extraction efficiency of the phosphor-converted light is achieved by separating the phosphor from the LED and using internal reflection to steer the light away from lossy reflectors and the LED package and out of the device. At 10 and 500 mA, the luminaire produces 2.1 and 66 lumens with efficacies of 80 and 37 lm/W, respectively. Technological improvements over existing commercial LEDs, such as more efficient pcLED packages or, alternatively, higher efficiency green or yellow for color mixing, will be essential to achieving 150 200 lm/W solid-state lighting. Advances in both areas are demonstrated.

  14. Generalized nonimaging compound elliptical and compound hyperbolic luminaire designs for pair-overlap illumination applications.

    Science.gov (United States)

    Georlette, O; Gordon, J M

    1994-07-01

    Generalized nonimaging compound elliptical luminaires (CEL's) and compound hyperbolic luminaires (CHL's) are developed for pair-overlap illumination applications. A comprehensive analysis of CEL's and CHL's is presented. This includes the possibility of reflector truncation, as well as the extreme direction that spans the full range from positive to negative. Negative extreme direction devices have been overlooked in earlier studies and are shown to be well suited to illumination problems for which large cutoff angles are required. Flux maps can be calculated analytically without the need for computer ray tracing. It is demonstrated that, for a broad range of cutoff angles, adjacent pairs of CEL's and CHL's can generate highly uniform far-field illuminance while maintaining maximal lighting efficiency and excellent glare control. The trade-off between luminaire compactness and flux homogeneity is also illustrated. For V troughs, being a special case of CHL's and being well suited to simple, inexpensive fabri ation, we identify geometries that closely approach the performance characteristics of the optimized CEL's and CHL's.

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

    Science.gov (United States)

    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.

  16. Artificial intelligence. Application of the Statistical Neural Networks computer program in nuclear medicine

    International Nuclear Information System (INIS)

    Stefaniak, B.; Cholewinski, W.; Tarkowska, A.

    2005-01-01

    Artificial Neural Networks (ANN) may be a tool alternative and complementary to typical statistical analysis. However, in spite of many computer application of various ANN algorithms ready for use, artificial intelligence is relatively rarely applied to data processing. In this paper practical aspects of scientific application of ANN in medicine using the Statistical Neural Networks Computer program, were presented. Several steps of data analysis with the above ANN software package were discussed shortly, from material selection and its dividing into groups to the types of obtained results. The typical problems connected with assessing scintigrams by ANN were also described. (author)

  17. Intelligent composting assisted by a wireless sensing network.

    Science.gov (United States)

    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.

  18. A Wavelet Neural Network Optimal Control Model for Traffic-Flow Prediction in Intelligent Transport Systems

    Science.gov (United States)

    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.

  19. Intelligent harmonic load model based on neural networks

    Science.gov (United States)

    Ji, Pyeong-Shik; Lee, Dae-Jong; Lee, Jong-Pil; Park, Jae-Won; Lim, Jae-Yoon

    2007-12-01

    In this study, we developed a RBFNs(Radial Basis Function Networks) based load modeling method with harmonic components. The developed method implemented by using harmonic information as well as fundamental frequency and voltage which are essential input factors in conventional method. Thus, the proposed method makes it possible to effectively estimate load characteristics in power lines with harmonics. The RBFNs have certain advantage such as simple structure and rapid computation ability compared with multilayer perceptron which is extensively applied for load modeling. To show the effectiveness, the proposed method has been intensively tested with various dataset acquired under the different frequency and voltage and compared it with conventional methods such as polynominal 2nd equation method, MLP and RBF without considering harmonic components.

  20. The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network.

    Science.gov (United States)

    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.

  1. The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network

    Directory of Open Access Journals (Sweden)

    Gaining Han

    2017-05-01

    Full Text Available 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.

  2. Computational Intelligence based techniques for islanding detection of distributed generation in distribution network: A review

    International Nuclear Information System (INIS)

    Laghari, J.A.; Mokhlis, H.; Karimi, M.; Bakar, A.H.A.; Mohamad, Hasmaini

    2014-01-01

    Highlights: • Unintentional and intentional islanding, their causes, and solutions are presented. • Remote, passive, active and hybrid islanding detection techniques are discussed. • The limitation of these techniques in accurately detect islanding are discussed. • Computational intelligence techniques ability in detecting islanding is discussed. • Review of ANN, fuzzy logic control, ANFIS, Decision tree techniques is provided. - Abstract: Accurate and fast islanding detection of distributed generation is highly important for its successful operation in distribution networks. Up to now, various islanding detection technique based on communication, passive, active and hybrid methods have been proposed. However, each technique suffers from certain demerits that cause inaccuracies in islanding detection. Computational intelligence based techniques, due to their robustness and flexibility in dealing with complex nonlinear systems, is an option that might solve this problem. This paper aims to provide a comprehensive review of computational intelligence based techniques applied for islanding detection of distributed generation. Moreover, the paper compares the accuracies of computational intelligence based techniques over existing techniques to provide a handful of information for industries and utility researchers to determine the best method for their respective system

  3. Network-Capable Application Process and Wireless Intelligent Sensors for ISHM

    Science.gov (United States)

    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

  4. Generative Adversarial Networks Based Heterogeneous Data Integration and Its Application for Intelligent Power Distribution and Utilization

    Directory of Open Access Journals (Sweden)

    Yuanpeng Tan

    2018-01-01

    Full Text Available Heterogeneous characteristics of a big data system for intelligent power distribution and utilization have already become more and more prominent, which brings new challenges for the traditional data analysis technologies and restricts the comprehensive management of distribution network assets. In order to solve the problem that heterogeneous data resources of power distribution systems are difficult to be effectively utilized, a novel generative adversarial networks (GANs based heterogeneous data integration method for intelligent power distribution and utilization is proposed. In the proposed method, GANs theory is introduced to expand the distribution of completed data samples. Then, a so-called peak clustering algorithm is proposed to realize the finite open coverage of the expanded sample space, and repair those incomplete samples to eliminate the heterogeneous characteristics. Finally, in order to realize the integration of the heterogeneous data for intelligent power distribution and utilization, the well-trained discriminator model of GANs is employed to check the restored data samples. The simulation experiments verified the validity and stability of the proposed heterogeneous data integration method, which provides a novel perspective for the further data quality management of power distribution systems.

  5. Self-Calibration and Optimal Response in Intelligent Sensors Design Based on Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Gilberto Bojorquez

    2007-08-01

    Full Text Available The development of smart sensors involves the design of reconfigurable systemscapable of working with different input sensors. Reconfigurable systems ideally shouldspend the least possible amount of time in their calibration. An autocalibration algorithmfor intelligent sensors should be able to fix major problems such as offset, variation of gainand lack of linearity, as accurately as possible. This paper describes a new autocalibrationmethodology for nonlinear intelligent sensors based on artificial neural networks, ANN.The methodology involves analysis of several network topologies and training algorithms.The proposed method was compared against the piecewise and polynomial linearizationmethods. Method comparison was achieved using different number of calibration points,and several nonlinear levels of the input signal. This paper also shows that the proposedmethod turned out to have a better overall accuracy than the other two methods. Besides,experimentation results and analysis of the complete study, the paper describes theimplementation of the ANN in a microcontroller unit, MCU. In order to illustrate themethod capability to build autocalibration and reconfigurable systems, a temperaturemeasurement system was designed and tested. The proposed method is an improvement over the classic autocalibration methodologies, because it impacts on the design process of intelligent sensors, autocalibration methodologies and their associated factors, like time and cost.

  6. Intelligent Stale-Frame Discards for Real-Time Video Streaming over Wireless Ad Hoc Networks

    Directory of Open Access Journals (Sweden)

    Sheu Tsang-Ling

    2009-01-01

    Full Text Available Abstract This paper presents intelligent early packet discards (I-EPD for real-time video streaming over a multihop wireless ad hoc network. In a multihop wireless ad hoc network, the quality of transferring real-time video streams could be seriously degraded, since every intermediate node (IN functionally like relay device does not possess large buffer and sufficient bandwidth. Even worse, a selected relay node could leave or power off unexpectedly, which breaks the route to destination. Thus, a stale video frame is useless even if it can reach destination after network traffic becomes smooth or failed route is reconfigured. In the proposed I-EPD, an IN can intelligently determine whether a buffered video packet should be early discarded. For the purpose of validation, we implement the I-EPD on Linux-based embedded systems. Via the comparisons of performance metrics (packet/frame discards ratios, PSNR, etc., we demonstrate that video quality over a wireless ad hoc network can be substantially improved and unnecessary bandwidth wastage is greatly reduced.

  7. Improved Space Surveillance Network (SSN) Scheduling using Artificial Intelligence Techniques

    Science.gov (United States)

    Stottler, D.

    There are close to 20,000 cataloged manmade objects in space, the large majority of which are not active, functioning satellites. These are tracked by phased array and mechanical radars and ground and space-based optical telescopes, collectively known as the Space Surveillance Network (SSN). A better SSN schedule of observations could, using exactly the same legacy sensor resources, improve space catalog accuracy through more complementary tracking, provide better responsiveness to real-time changes, better track small debris in low earth orbit (LEO) through efficient use of applicable sensors, efficiently track deep space (DS) frequent revisit objects, handle increased numbers of objects and new types of sensors, and take advantage of future improved communication and control to globally optimize the SSN schedule. We have developed a scheduling algorithm that takes as input the space catalog and the associated covariance matrices and produces a globally optimized schedule for each sensor site as to what objects to observe and when. This algorithm is able to schedule more observations with the same sensor resources and have those observations be more complementary, in terms of the precision with which each orbit metric is known, to produce a satellite observation schedule that, when executed, minimizes the covariances across the entire space object catalog. If used operationally, the results would be significantly increased accuracy of the space catalog with fewer lost objects with the same set of sensor resources. This approach inherently can also trade-off fewer high priority tasks against more lower-priority tasks, when there is benefit in doing so. Currently the project has completed a prototyping and feasibility study, using open source data on the SSN's sensors, that showed significant reduction in orbit metric covariances. The algorithm techniques and results will be discussed along with future directions for the research.

  8. Intelligent Networks Data Fusion Web-based Services for Ad-hoc Integrated WSNs-RFID

    Directory of Open Access Journals (Sweden)

    Falah Alshahrany

    2016-01-01

    Full Text Available The use of variety of data fusion tools and techniques for big data processing poses the problem of the data and information integration called data fusion having objectives which can differ from one application to another. The design of network data fusion systems aimed at meeting these objectives, need to take into account of the necessary synergy that can result from distributed data processing within the data networks and data centres, involving increased computation and communication. This papers reports on how this processing distribution is functionally structured as configurable integrated web-based support services, in the context of an ad-hoc wireless sensor network used for sensing and tracking, in the context of distributed detection based on complete observations to support real rime decision making. The interrelated functional and hardware RFID-WSN integration is an essential aspect of the data fusion framework that focuses on multi-sensor collaboration as an innovative approach to extend the heterogeneity of the devices and sensor nodes of ad-hoc networks generating a huge amount of heterogeneous soft and hard raw data. The deployment and configuration of these networks require data fusion processing that includes network and service management and enhances the performance and reliability of networks data fusion support systems providing intelligent capabilities for real-time control access and fire detection.

  9. Cost/worth assessment of reliability improvement in distribution networks by means of artificial intelligence

    Energy Technology Data Exchange (ETDEWEB)

    Bouhouras, Aggelos S.; Labridis, Dimitris P.; Bakirtzis, Anastasios G. [Power Systems Laboratory, Aristotle University of Thessaloniki, Dept. of Electrical and Computer Engineering, 54124 Thessaloniki (Greece)

    2010-06-15

    A major challenge for the power utilities today is to ensure a high level of reliability of supply to customers. Two main factors determine the feasibility of a project that improves the reliability of supply: the project cost (investment and operational) and the benefits that result from the implementation of the project. This paper examines the implementation of an Artificial Intelligence System in an urban distribution network, capable to locate and isolate short circuit faults in the feeder, thus accomplishing immediate restoration of electric supply to the customers. The paper describes the benefits of the project, which are supply reliability improvement and distribution network loss reduction through network reconfigurations. By comparison of the project benefits and costs the economic feasibility of such a project for an underground distribution feeder in Greece is demonstrated. (author)

  10. Artificial intelligence methods applied in the controlled synthesis of polydimethilsiloxane - poly (methacrylic acid) copolymer networks with imposed properties

    Science.gov (United States)

    Rusu, Teodora; Gogan, Oana Marilena

    2016-05-01

    This paper describes the use of artificial intelligence method in copolymer networks design. In the present study, we pursue a hybrid algorithm composed from two research themes in the genetic design framework: a Kohonen neural network (KNN), path (forward problem) combined with a genetic algorithm path (backward problem). The Tabu Search Method is used to improve the performance of the genetic algorithm path.

  11. Network-based modeling and intelligent data mining of social media for improving care.

    Science.gov (United States)

    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.

  12. Cellular computational generalized neuron network for frequency situational intelligence in a multi-machine power system.

    Science.gov (United States)

    Wei, Yawei; Venayagamoorthy, Ganesh Kumar

    2017-09-01

    To prevent large interconnected power system from a cascading failure, brownout or even blackout, grid operators require access to faster than real-time information to make appropriate just-in-time control decisions. However, the communication and computational system limitations of currently used supervisory control and data acquisition (SCADA) system can only deliver delayed information. However, the deployment of synchrophasor measurement devices makes it possible to capture and visualize, in near-real-time, grid operational data with extra granularity. In this paper, a cellular computational network (CCN) approach for frequency situational intelligence (FSI) in a power system is presented. The distributed and scalable computing unit of the CCN framework makes it particularly flexible for customization for a particular set of prediction requirements. Two soft-computing algorithms have been implemented in the CCN framework: a cellular generalized neuron network (CCGNN) and a cellular multi-layer perceptron network (CCMLPN), for purposes of providing multi-timescale frequency predictions, ranging from 16.67 ms to 2 s. These two developed CCGNN and CCMLPN systems were then implemented on two different scales of power systems, one of which installed a large photovoltaic plant. A real-time power system simulator at weather station within the Real-Time Power and Intelligent Systems (RTPIS) laboratory at Clemson, SC, was then used to derive typical FSI results. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. An intelligent switch with back-propagation neural network based hybrid power system

    Science.gov (United States)

    Perdana, R. H. Y.; Fibriana, F.

    2018-03-01

    The consumption of conventional energy such as fossil fuels plays the critical role in the global warming issues. The carbon dioxide, methane, nitrous oxide, etc. could lead the greenhouse effects and change the climate pattern. In fact, 77% of the electrical energy is generated from fossil fuels combustion. Therefore, it is necessary to use the renewable energy sources for reducing the conventional energy consumption regarding electricity generation. This paper presents an intelligent switch to combine both energy resources, i.e., the solar panels as the renewable energy with the conventional energy from the State Electricity Enterprise (PLN). The artificial intelligence technology with the back-propagation neural network was designed to control the flow of energy that is distributed dynamically based on renewable energy generation. By the continuous monitoring on each load and source, the dynamic pattern of the intelligent switch was better than the conventional switching method. The first experimental results for 60 W solar panels showed the standard deviation of the trial at 0.7 and standard deviation of the experiment at 0.28. The second operation for a 900 W of solar panel obtained the standard deviation of the trial at 0.05 and 0.18 for the standard deviation of the experiment. Moreover, the accuracy reached 83% using this method. By the combination of the back-propagation neural network with the observation of energy usage of the load using wireless sensor network, each load can be evenly distributed and will impact on the reduction of conventional energy usage.

  14. Intelligible Artificial Intelligence

    OpenAIRE

    Weld, Daniel S.; Bansal, Gagan

    2018-01-01

    Since Artificial Intelligence (AI) software uses techniques like deep lookahead search and stochastic optimization of huge neural networks to fit mammoth datasets, it often results in complex behavior that is difficult for people to understand. Yet organizations are deploying AI algorithms in many mission-critical settings. In order to trust their behavior, we must make it intelligible --- either by using inherently interpretable models or by developing methods for explaining otherwise overwh...

  15. Intelligent self-organization methods for wireless ad hoc sensor networks based on limited resources

    Science.gov (United States)

    Hortos, William S.

    2006-05-01

    A wireless ad hoc sensor network (WSN) is a configuration for area surveillance that affords rapid, flexible deployment in arbitrary threat environments. There is no infrastructure support and sensor nodes communicate with each other only when they are in transmission range. To a greater degree than the terminals found in mobile ad hoc networks (MANETs) for communications, sensor nodes are resource-constrained, with limited computational processing, bandwidth, memory, and power, and are typically unattended once in operation. Consequently, the level of information exchange among nodes, to support any complex adaptive algorithms to establish network connectivity and optimize throughput, not only deplete those limited resources and creates high overhead in narrowband communications, but also increase network vulnerability to eavesdropping by malicious nodes. Cooperation among nodes, critical to the mission of sensor networks, can thus be disrupted by the inappropriate choice of the method for self-organization. Recent published contributions to the self-configuration of ad hoc sensor networks, e.g., self-organizing mapping and swarm intelligence techniques, have been based on the adaptive control of the cross-layer interactions found in MANET protocols to achieve one or more performance objectives: connectivity, intrusion resistance, power control, throughput, and delay. However, few studies have examined the performance of these algorithms when implemented with the limited resources of WSNs. In this paper, self-organization algorithms for the initiation, operation and maintenance of a network topology from a collection of wireless sensor nodes are proposed that improve the performance metrics significant to WSNs. The intelligent algorithm approach emphasizes low computational complexity, energy efficiency and robust adaptation to change, allowing distributed implementation with the actual limited resources of the cooperative nodes of the network. Extensions of the

  16. Intelligent control of robotic arm/hand systems for the NASA EVA retriever using neural networks

    Science.gov (United States)

    Mclauchlan, Robert A.

    1989-01-01

    Adaptive/general learning algorithms using varying neural network models are considered for the intelligent control of robotic arm plus dextrous hand/manipulator systems. Results are summarized and discussed for the use of the Barto/Sutton/Anderson neuronlike, unsupervised learning controller as applied to the stabilization of an inverted pendulum on a cart system. Recommendations are made for the application of the controller and a kinematic analysis for trajectory planning to simple object retrieval (chase/approach and capture/grasp) scenarios in two dimensions.

  17. Fuzzy mobile-robot positioning in intelligent spaces using wireless sensor networks.

    Science.gov (United States)

    Herrero, David; Martínez, Humberto

    2011-01-01

    This work presents the development and experimental evaluation of a method based on fuzzy logic to locate mobile robots in an Intelligent Space using wireless sensor networks (WSNs). The problem consists of locating a mobile node using only inter-node range measurements, which are estimated by radio frequency signal strength attenuation. The sensor model of these measurements is very noisy and unreliable. The proposed method makes use of fuzzy logic for modeling and dealing with such uncertain information. Besides, the proposed approach is compared with a probabilistic technique showing that the fuzzy approach is able to handle highly uncertain situations that are difficult to manage by well-known localization methods.

  18. Research and Development of a Network-Based Intelligent Maintenance Information System

    Institute of Scientific and Technical Information of China (English)

    ZANG Tie-gang; YANG Ming-zhong; GUO Shun-sheng

    2003-01-01

    A maintenance information system is an important part of equipment management. An intelli gent maintenance information system ( IMIS) is a synthesis of network technology, information technology and intelligent technology. The IMIS is used to finish flexible maintenance decision-making and fast maintenance planning, which helps enterprises to effectively reduce maintenance cost and increase working efficiency. Because the IMIS integrates advanced technologies, its performance is better than a traditional one.The difference between an IMIS and a traditional maintenance information system, and the functions, structure, important realizations, and application of an IMIS are discussed in this paper.

  19. Prognostics of damage accural in SSL luminaires and drivers subjected to HTSL accelerated aging

    Energy Technology Data Exchange (ETDEWEB)

    Lall, Pradeep [Auburn Univ., AL (United States); Sakalaukus, Peter [Auburn Univ., AL (United States); Davis, Lynn [RTI International, Research Triangle Park, NC (United States)

    2013-07-16

    This paper will show an investigation of off-the-shelf luminaires with the focus on the LED electronic drivers, specifically the aluminum electrolytic capacitors (AECs), that have been aged using high temperature shelf life (HTSL) testing of 135°C in order to prognosticate the remaining useful life of the luminaires. Luminaires have the potential of seeing excessive temperatures when being transported across country or being stored in non-climate controlled warehouses. They are also being used in outdoor applications in desert environments that see little or no humidity but will experience extremely high temperatures during the day. This makes it important to increase our understanding of what effects being stored at high temperatures for a prolonged period of time will have on the usability and survivability of these devices. The U.S. Department of Energy has made a long term commitment to advance the efficiency, understanding and development of solid-state lighting (SSL) and is making a strong push for the acceptance and use of SSL products. In this work, the four AECs of three different types inside each LED electronic driver were studied. The change in capacitance and the change in equivalent series resistance (ESR) of the AECs were measured and considered to be a leading indication of failure of the LED system. These indicators were used to make remaining useful life predictions to develop an algorithm to predict the end of life of the AECs. The luminous flux of a pristine downlight module was also monitored using each LED electronic driver that was subjected to HTSL through the progression of the testing to determine a correlation between the light output of the lamp and the failing components of the LED electronic driver. Prognostic and Health Management (PHM) is a useful tool for assessment of the remaining life of electrical components and is demonstrated for AECs in this work.

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

    Science.gov (United States)

    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.

  1. Voltage Stability Control of Electrical Network Using Intelligent Load Shedding Strategy Based on Fuzzy Logic

    Directory of Open Access Journals (Sweden)

    Houda Jouini

    2010-01-01

    Full Text Available As a perspective to ensure the power system stability and to avoid the vulnerability leading to the blackouts, several preventive and curative means are adopted. In order to avoid the voltage collapse, load shedding schemes represent a suitable action to maintain the power system service quality and to control its vulnerability. In this paper, we try to propose an intelligent load shedding strategy as a new approach based on fuzzy controllers. This strategy was founded on the calculation of generated power sensitivity degree related to those injected at different network buses. During the fault phase, fuzzy controller algorithms generate monitor vectors ensuring a precalculated load shedding ratio in the purpose to reestablish the power balance and conduct the network to a new steady state.

  2. Computational intelligence in wireless sensor networks recent advances and future challenges

    CERN Document Server

    Falcon, Rafael; Koeppen, Mario

    2017-01-01

    This book emphasizes the increasingly important role that Computational Intelligence (CI) methods are playing in solving a myriad of entangled Wireless Sensor Networks (WSN) related problems. The book serves as a guide for surveying several state-of-the-art WSN scenarios in which CI approaches have been employed. The reader finds in this book how CI has contributed to solve a wide range of challenging problems, ranging from balancing the cost and accuracy of heterogeneous sensor deployments to recovering from real-time sensor failures to detecting attacks launched by malicious sensor nodes and enacting CI-based security schemes. Network managers, industry experts, academicians and practitioners alike (mostly in computer engineering, computer science or applied mathematics) benefit from the spectrum of successful applications reported in this book. Senior undergraduate or graduate students may discover in this book some problems well suited for their own research endeavors. USP: Presents recent advances and fu...

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

    Energy Technology Data Exchange (ETDEWEB)

    Zhu, Michelle M. [Southern Illinois Univ., Carbondale, IL (United States); Wu, Chase Q. [Univ. of Memphis, TN (United States)

    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 for 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.

  4. THE INTEREST OF GEOGRAPHICAL INFORMATION, ARTIFICIAL INTELLIGENCE AND VIRTUAL REALITY FOR THE UNDERGROUND NETWORK REPRESENTATION

    Directory of Open Access Journals (Sweden)

    M. Lacroix

    2016-01-01

    Full Text Available Two years ago, 63 people died and more than 150 were seriously injured in Beijing (China because of damage to a hydrocarbon pipeline. Urban networks are invisible because usually buried between 1 and 1,5 meters underground. They should be identified to prevent such accidents which involve workers as well as the public. Rural and urban districts, network concessionaries and contractors: everyone could benefit from their networks becoming safer. To prevent such accidents and protect workers and the public as well, some new regulations propose to identify and secure the buried networks. That’s why it is important to develop a software which deals with the risk management process and also about the risk visualization. This work is structured around three major sections:– the utility of the Geographical Information to determine the minimal distances and the topological relations between the networks themselves, and also with the other element in their vicinity;– the use of some Artificial Intelligence tools, and more particularly of Expert System, to take the current regulation into account and determine the accident risk probability;– the contribution of virtual reality to perceive the underground world.

  5. Defining a convergence network platform framework for smart grid and intelligent transport systems

    International Nuclear Information System (INIS)

    Coronado Mondragon, Adrian E.; Coronado, Etienne S.; Coronado Mondragon, Christian E.

    2015-01-01

    The challenges faced by electricity grids suggest smart grids will have to coordinate its operation with other important initiatives in areas such as transportation. The smart grid relies on the use of network platforms where meter readings and data can be transmitted. On the other hand, concerning transportation systems the need to achieve a reduction of road congestion and traffic accidents among the increasing use of electric vehicles has consolidated the importance of ITS (intelligent transport systems). Given the magnitude of the challenges faced by both the smart grid and ITS, the aim of this work is to identify the elements comprising a convergence platform capable of supporting future services for data traffic associated to smart grid operations as well as ITS-related commercial service applications and road traffic safety messaging. A seaport terminal scenario is used to present a convergence network platform incorporating WSN (wireless sensor network) theory. The results of the simulation of the proposed network confirms the suitability of WSN to be used in the transmission of data traffic associated to meter readings which is required for effective energy consumption and management policies in industrial environments comprising equipment with high energy demands. - Highlights: • Common needs/challenges of smart grid/ITS can be addressed by a convergence network platform. • VANETs are identified as key components of the smart grid/ITS convergence network platform. • WSN (Wireless Sensor Network) theory is suitable for the transmission of data traffic associated to meter readings. • The amount of energy supplied to the network is low but enough to support data traffic required in industrial environments. • WSN supports the steady exchange of packets as characterized in industrial environments like seaports

  6. PENINGKATAN KUALITAS JARINGAN KOMUNIKASI SEKTOR INDUSTRI DENGAN INTELLIGENT NETWORK SEBAGAI UPAYA PERLUASAN DAERAH PEMASARAN

    Directory of Open Access Journals (Sweden)

    Haryanto Haryanto

    2005-08-01

    Full Text Available Satu hal yang paling penting dalam upaya peningkatan profit adalah dengan penambahan dan perluasan daerah pemasaran, serta adanya sistem informasi yang terpadu dari pusat ke tiap unit yang dimiliki. Perluasan daerah pemasaran dapat diwujudkan dengan adanya sistem telekomunikasi yang lancar dan dapat menjangkau di tiap daerah yang akan dijadikan sebagai daerah pemasaran. Sehingga hal ini diharapkan tidak akan menghambat proses pendistribusian produk maupun informasi dari tiap daerah tujuan. Intelligent Network (Jaringan Cerdas adalah suatu arsitektur jaringan telekomunikasi yang memiliki tujuan untuk memberikan framework sehingga kerja dari jaringan untuk implementasi, kontrol, dan management menjadi lebih efektif serta lebih ekonomis, dan lebih cepat proses kerjanya dibandingkan arsitektur jaringan yang digunakan saat ini. Untuk meningkatkan kualitas jaringan komunikasi memerlukan infrastruktur yang memadai. Tidak luput dari standar mengenai spesifikasi yang menyangkut mobile system yang akan digunakan. Sehingga perkembangan suatu teknologi akan dapat berefek terjangkaunya komunikasi di daerah-daerah yang terisolasi, sehingga meningkatkan perkembangan daerah tersebut, bahkan tidak menutup kemungkinan dengan adanya peningkatan jaringan komunikasi di daerah maka akan memunculkan daerah-daerah sentra produksi di bidang industri. Peningkatan mobilitas komponen usaha yang tidak diperkirakan sebelumnya, semakin menuntut kemampuan sistem jaringan komunikasi dan data, serta pengembangan jenis-jenis pelanggan baru. Dengan adanya peranan Intelligent Network sebagai konsep arsitektur jaringan, maka akan mendapatkan solusi yang lebih baik untuk memenuhi segala kebutuhan.

  7. Intelligent PV Power Smoothing Control Using Probabilistic Fuzzy Neural Network with Asymmetric Membership Function

    Directory of Open Access Journals (Sweden)

    Faa-Jeng Lin

    2017-01-01

    Full Text Available An intelligent PV power smoothing control using probabilistic fuzzy neural network with asymmetric membership function (PFNN-AMF is proposed in this study. First, a photovoltaic (PV power plant with a battery energy storage system (BESS is introduced. The BESS consisted of a bidirectional DC/AC 3-phase inverter and LiFePO4 batteries. Then, the difference of the actual PV power and smoothed power is supplied by the BESS. Moreover, the network structure of the PFNN-AMF and its online learning algorithms are described in detail. Furthermore, the three-phase output currents of the PV power plant are converted to the dq-axis current components. The resulted q-axis current is the input of the PFNN-AMF power smoothing control, and the output is a smoothing PV power curve to achieve the effect of PV power smoothing. Comparing to the other smoothing methods, a minimum energy capacity of the BESS with a small fluctuation of the grid power can be achieved by the PV power smoothing control using PFNN-AMF. In addition, a personal computer- (PC- based PV power plant emulator and BESS are built for the experimentation. From the experimental results of various irradiance variation conditions, the effectiveness of the proposed intelligent PV power smoothing control can be verified.

  8. DESIGN OF AN INTELLIGENT SYSTEM TO DETECT TYPE OF PAIN USING ARTIFICIAL NEURAL NETWORK FOR PATIENTS WITH SPINAL CORD INJURY IN SHEFA NEUROSCIENCE RESEARCH CENTER

    OpenAIRE

    Nasrolah Nasr HeidarAbadi, Reza Safdari, Peirhossein Kolivand, Amir Javadi, Azimeh Danesh Shahraki1, Marjan Ghazi Saeidi*

    2017-01-01

    Using artificial intelligence in computerized clinical systems helps physicians diagnose disease or choose treatment. Intelligent methods are constantly changed to be more effective and accurate for quick medical diagnosis. Neural networks are a powerful tool to help physicians. The tools can process a high number of data and minimize errors in ignoring patients' information. Intelligent system design based on artificial neural network was performed in 3 phases. Phase1: Designing the data rec...

  9. Assessment of LED Technology in Ornamental Post-Top Luminaires (Host Site: Sacramento, CA)

    Energy Technology Data Exchange (ETDEWEB)

    Tuenge, Jason R.

    2011-12-01

    The DOE Municipal Solid-State Street Lighting Consortium has evaluated four different LED replacements for existing ornamental post-top street lights in Sacramento, California. The project team was composed of the City and its consultant, PNNL (representing the Consortium), and the Sacramento Municipal Utility District. Product selection was finalized in March 2011, yielding one complete luminaire replacement and three lamp-ballast retrofit kits. Computer simulations, field measurements, and laboratory testing were performed to compare the performance and cost-effectiveness of the LED products relative to the existing luminaire with 100 W high-pressure sodium lamp. After it was confirmed the LED products were not equivalent to HPS in terms of initial photopic illumination, the following parameters were scaled proportionally to enable equitable (albeit hypothetical) comparisons: light output, input wattage, and pricing. Four replacement scenarios were considered for each LED product, incorporating new IES guidance for mesopic multipliers and lumen maintenance extrapolation, but life cycle analysis indicated cost effectiveness was also unacceptable. Although LED efficacy and pricing continue to improve, this project serves as a timely and objective notice that LED technology may not be quite ready yet for such applications.

  10. A Computationally Intelligent Approach to the Detection of Wormhole Attacks in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Mohammad Nurul Afsar Shaon

    2017-05-01

    Full Text Available A wormhole attack is one of the most critical and challenging security threats for wireless sensor networks because of its nature and ability to perform concealed malicious activities. This paper proposes an innovative wormhole detection scheme to detect wormhole attacks using computational intelligence and an artificial neural network (ANN. Most wormhole detection schemes reported in the literature assume the sensors are uniformly distributed in a network, and, furthermore, they use statistical and topological information and special hardware for their detection. However, these schemes may perform poorly in non-uniformly distributed networks, and, moreover, they may fail to defend against “out of band” and “in band” wormhole attacks. The aim of the proposed research is to develop a detection scheme that is able to detect all kinds of wormhole attacks in both uniformly and non-uniformly distributed sensor networks. Furthermore, the proposed research does not require any special hardware and causes no significant network overhead throughout the network. Most importantly, the probable location of the malicious nodes can be identified by the proposed ANN based detection scheme. We evaluate the efficacy of the proposed detection scheme in terms of detection accuracy, false positive rate, and false negative rate. The performance of the proposed algorithm is also compared with other machine learning techniques (i.e. SVM and regularized nonlinear logistic regression (LR based detection models. The simulation results show that proposed ANN based algorithm outperforms the SVM or LR based detection schemes in terms of detection accuracy, false positive rate, and false negative rates.

  11. Intelligent neural network and fuzzy logic control of industrial and power systems

    Science.gov (United States)

    Kuljaca, Ognjen

    The main role played by neural network and fuzzy logic intelligent control algorithms today is to identify and compensate unknown nonlinear system dynamics. There are a number of methods developed, but often the stability analysis of neural network and fuzzy control systems was not provided. This work will meet those problems for the several algorithms. Some more complicated control algorithms included backstepping and adaptive critics will be designed. Nonlinear fuzzy control with nonadaptive fuzzy controllers is also analyzed. An experimental method for determining describing function of SISO fuzzy controller is given. The adaptive neural network tracking controller for an autonomous underwater vehicle is analyzed. A novel stability proof is provided. The implementation of the backstepping neural network controller for the coupled motor drives is described. Analysis and synthesis of adaptive critic neural network control is also provided in the work. Novel tuning laws for the system with action generating neural network and adaptive fuzzy critic are given. Stability proofs are derived for all those control methods. It is shown how these control algorithms and approaches can be used in practical engineering control. Stability proofs are given. Adaptive fuzzy logic control is analyzed. Simulation study is conducted to analyze the behavior of the adaptive fuzzy system on the different environment changes. A novel stability proof for adaptive fuzzy logic systems is given. Also, adaptive elastic fuzzy logic control architecture is described and analyzed. A novel membership function is used for elastic fuzzy logic system. The stability proof is proffered. Adaptive elastic fuzzy logic control is compared with the adaptive nonelastic fuzzy logic control. The work described in this dissertation serves as foundation on which analysis of particular representative industrial systems will be conducted. Also, it gives a good starting point for analysis of learning abilities of

  12. Information Dissemination of Public Health Emergency on Social Networks and Intelligent Computation

    Directory of Open Access Journals (Sweden)

    Hongzhi Hu

    2015-01-01

    Full Text Available Due to the extensive social influence, public health emergency has attracted great attention in today’s society. The booming social network is becoming a main information dissemination platform of those events and caused high concerns in emergency management, among which a good prediction of information dissemination in social networks is necessary for estimating the event’s social impacts and making a proper strategy. However, information dissemination is largely affected by complex interactive activities and group behaviors in social network; the existing methods and models are limited to achieve a satisfactory prediction result due to the open changeable social connections and uncertain information processing behaviors. ACP (artificial societies, computational experiments, and parallel execution provides an effective way to simulate the real situation. In order to obtain better information dissemination prediction in social networks, this paper proposes an intelligent computation method under the framework of TDF (Theory-Data-Feedback based on ACP simulation system which was successfully applied to the analysis of A (H1N1 Flu emergency.

  13. Universal Intelligent Small Cell (UnISCell for next generation cellular networks

    Directory of Open Access Journals (Sweden)

    Mohammad Patwary

    2016-11-01

    Full Text Available Exploring innovative cellular architectures to achieve enhanced system capacity and good coverage has become a critical issue towards realizing the next generation of wireless communications. In this context, this paper proposes a novel concept of Universal Intelligent Small Cell (UnISCell for enabling the densification of the next generation of cellular networks. The proposed novel concept envisions an integrated platform of providing a strong linkage between different stakeholders such as street lighting networks, landline telephone networks and future wireless networks, and is universal in nature being independent of the operating frequency bands and traffic types. The main motivating factors for the proposed small cell concept are the need of public infrastructure re-engineering, and the recent advances in several enabling technologies. First, we highlight the main concepts of the proposed UnISCell platform. Subsequently, we present two deployment scenarios for the proposed UnISCell concept considering infrastructure sharing and service sharing as important aspects. We then describe the key future technologies for enabling the proposed UnISCell concept and present a use case example with the help of numerical results. Finally, we conclude this article by providing some interesting future recommendations.

  14. Information Dissemination of Public Health Emergency on Social Networks and Intelligent Computation.

    Science.gov (United States)

    Hu, Hongzhi; Mao, Huajuan; Hu, Xiaohua; Hu, Feng; Sun, Xuemin; Jing, Zaiping; Duan, Yunsuo

    2015-01-01

    Due to the extensive social influence, public health emergency has attracted great attention in today's society. The booming social network is becoming a main information dissemination platform of those events and caused high concerns in emergency management, among which a good prediction of information dissemination in social networks is necessary for estimating the event's social impacts and making a proper strategy. However, information dissemination is largely affected by complex interactive activities and group behaviors in social network; the existing methods and models are limited to achieve a satisfactory prediction result due to the open changeable social connections and uncertain information processing behaviors. ACP (artificial societies, computational experiments, and parallel execution) provides an effective way to simulate the real situation. In order to obtain better information dissemination prediction in social networks, this paper proposes an intelligent computation method under the framework of TDF (Theory-Data-Feedback) based on ACP simulation system which was successfully applied to the analysis of A (H1N1) Flu emergency.

  15. Location Prediction-Based Data Dissemination Using Swarm Intelligence in Opportunistic Cognitive Networks

    Directory of Open Access Journals (Sweden)

    Jie Li

    2014-01-01

    Full Text Available Swarm intelligence is widely used in the application of communication networks. In this paper we adopt a biologically inspired strategy to investigate the data dissemination problem in the opportunistic cognitive networks (OCNs. We model the system as a centralized and distributed hybrid system including a location prediction server and a pervasive environment deploying the large-scale human-centric devices. To exploit such environment, data gathering and dissemination are fundamentally based on the contact opportunities. To tackle the lack of contemporaneous end-to-end connectivity in opportunistic networks, we apply ant colony optimization as a cognitive heuristic technology to formulate a self-adaptive dissemination-based routing scheme in opportunistic cognitive networks. This routing strategy has attempted to find the most appropriate nodes conveying messages to the destination node based on the location prediction information and intimacy between nodes, which uses the online unsupervised learning on geographical locations and the biologically inspired algorithm on the relationship of nodes to estimate the delivery probability. Extensive simulation is carried out on the real-world traces to evaluate the accuracy of the location prediction and the proposed scheme in terms of transmission cost, delivery ratio, average hops, and delivery latency, which achieves better routing performances compared to the typical routing schemes in OCNs.

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

    Science.gov (United States)

    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

  17. Interoperable Cloud Networking for intelligent power supply; Interoperables Cloud Networking fuer intelligente Energieversorgung

    Energy Technology Data Exchange (ETDEWEB)

    Hardin, Dave [Invensys Operations Management, Foxboro, MA (United States)

    2010-09-15

    Intelligent power supply by a so-called Smart Grid will make it possible to control consumption by market-based pricing and signals for load reduction. This necessitates that both the energy rates and the energy information are distributed reliably and in real time to automation systems in domestic and other buildings and in industrial plants over a wide geographic range and across the most varied grid infrastructures. Effective communication at this level of complexity necessitates computer and grid resources that are normally only available in the computer centers of big industries. The cloud computing technology, which is described here in some detail, has all features to provide reliability, interoperability and efficiency for large-scale smart grid applications, at lower cost than traditional computer centers. (orig.)

  18. Intelligent Mobile Sensing and Analysis Research Network in South Africa – Building a base at the CSIR

    CSIR Research Space (South Africa)

    Mekuria, F

    2010-09-01

    Full Text Available Intelligent capturing and secure communication of physiological and environmental data to central point of presence in large magnitude is crucial in the design of accurate models of natural and man-made systems or processes. The research network...

  19. Do narcissism and emotional intelligence win us friends? : modeling dynamics of peer popularity using inferential network analysis

    OpenAIRE

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

    2016-01-01

    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...

  20. Intelligent Smoke Alarm System with Wireless Sensor Network Using ZigBee

    Directory of Open Access Journals (Sweden)

    Qin Wu

    2018-01-01

    Full Text Available The conflagration of fire is still a serious problem caused by humans, and houses are at a high risk of fire. Recently, people have used smoke alarms which only have one sensor to detect fire. Smoke is emitted in several forms in daily life. A single sensor is not a reliable way to detect fire. With the rapid advancement in Internet technology, people can monitor their houses remotely to determine the current condition of the house. This paper introduces an intelligent smoke alarm system that uses ZigBee transmission technology to build a wireless network, uses random forest to identify smoke, and uses E-charts for data visualization. By combining the real-time dynamic changes of various environmental factors, compared to the traditional smoke alarm, the accuracy and controllability of the fire warning are increased, and the visualization of the data enables users to monitor the room environment more intuitively. The proposed system consists of a smoke detection module, a wireless communication module, and intelligent identification and data visualization module. At present, the collected environmental data can be classified into four statuses, that is, normal air, water mist, kitchen cooking, and fire smoke. Reducing the frequency of miscalculations also means improving the safety of the person and property of the user.

  1. Introducing a Novel Hybrid Artificial Intelligence Algorithm to Optimize Network of Industrial Applications in Modern Manufacturing

    Directory of Open Access Journals (Sweden)

    Aydin Azizi

    2017-01-01

    Full Text Available Recent advances in modern manufacturing industries have created a great need to track and identify objects and parts by obtaining real-time information. One of the main technologies which has been utilized for this need is the Radio Frequency Identification (RFID system. As a result of adopting this technology to the manufacturing industry environment, RFID Network Planning (RNP has become a challenge. Mainly RNP deals with calculating the number and position of antennas which should be deployed in the RFID network to achieve full coverage of the tags that need to be read. The ultimate goal of this paper is to present and evaluate a way of modelling and optimizing nonlinear RNP problems utilizing artificial intelligence (AI techniques. This effort has led the author to propose a novel AI algorithm, which has been named “hybrid AI optimization technique,” to perform optimization of RNP as a hard learning problem. The proposed algorithm is composed of two different optimization algorithms: Redundant Antenna Elimination (RAE and Ring Probabilistic Logic Neural Networks (RPLNN. The proposed hybrid paradigm has been explored using a flexible manufacturing system (FMS, and results have been compared with Genetic Algorithm (GA that demonstrates the feasibility of the proposed architecture successfully.

  2. Multiobjective RFID Network Optimization Using Multiobjective Evolutionary and Swarm Intelligence Approaches

    Directory of Open Access Journals (Sweden)

    Hanning Chen

    2014-01-01

    Full Text Available The development of radio frequency identification (RFID technology generates the most challenging RFID network planning (RNP problem, which needs to be solved in order to operate the large-scale RFID network in an optimal fashion. RNP involves many objectives and constraints and has been proven to be a NP-hard multi-objective problem. The application of evolutionary algorithm (EA and swarm intelligence (SI for solving multiobjective RNP (MORNP has gained significant attention in the literature, but these algorithms always transform multiple objectives into a single objective by weighted coefficient approach. In this paper, we use multiobjective EA and SI algorithms to find all the Pareto optimal solutions and to achieve the optimal planning solutions by simultaneously optimizing four conflicting objectives in MORNP, instead of transforming multiobjective functions into a single objective function. The experiment presents an exhaustive comparison of three successful multiobjective EA and SI, namely, the recently developed multiobjective artificial bee colony algorithm (MOABC, the nondominated sorting genetic algorithm II (NSGA-II, and the multiobjective particle swarm optimization (MOPSO, on MORNP instances of different nature, namely, the two-objective and three-objective MORNP. Simulation results show that MOABC proves to be more superior for planning RFID networks than NSGA-II and MOPSO in terms of optimization accuracy and computation robustness.

  3. Hopfield neural network and optical fiber sensor as intelligent heart rate monitor

    Science.gov (United States)

    Mutter, Kussay Nugamesh

    2018-01-01

    This paper presents a design and fabrication of an intelligent fiber-optic sensor used for examining and monitoring heart rate activity. It is found in the literature that the use of fiber sensors as heart rate sensor is widely studied. However, the use of smart sensors based on Hopfield neural networks is very low. In this work, the sensor is a three fibers without cladding of about 1 cm, fed by laser light of 1550 nm of wavelength. The sensing portions are mounted with a micro sensitive diaphragm to transfer the pulse pressure on the left radial wrist. The influenced light intensity will be detected by a three photodetectors as inputs into the Hopfield neural network algorithm. The latter is a singlelayer auto-associative memory structure with a same input and output layers. The prior training weights are stored in the net memory for the standard recorded normal heart rate signals. The sensors' heads work on the reflection intensity basis. The novelty here is that the sensor uses a pulse pressure and Hopfield neural network in an integrity approach. The results showed a significant output measurements of heart rate and counting with a plausible error rate.

  4. Intelligent Intrusion Detection of Grey Hole and Rushing Attacks in Self-Driving Vehicular Networks

    Directory of Open Access Journals (Sweden)

    Khattab M. Ali Alheeti

    2016-07-01

    Full Text Available Vehicular ad hoc networks (VANETs play a vital role in the success of self-driving and semi self-driving vehicles, where they improve safety and comfort. Such vehicles depend heavily on external communication with the surrounding environment via data control and Cooperative Awareness Messages (CAMs exchanges. VANETs are potentially exposed to a number of attacks, such as grey hole, black hole, wormhole and rushing attacks. This work presents an intelligent Intrusion Detection System (IDS that relies on anomaly detection to protect the external communication system from grey hole and rushing attacks. These attacks aim to disrupt the transmission between vehicles and roadside units. The IDS uses features obtained from a trace file generated in a network simulator and consists of a feed-forward neural network and a support vector machine. Additionally, the paper studies the use of a novel systematic response, employed to protect the vehicle when it encounters malicious behaviour. Our simulations of the proposed detection system show that the proposed schemes possess outstanding detection rates with a reduction in false alarms. This safe mode response system has been evaluated using four performance metrics, namely, received packets, packet delivery ratio, dropped packets and the average end to end delay, under both normal and abnormal conditions.

  5. Application of Swarm Intelligence Based Routingprotocols for Wireless Adhoc Sensor Network

    Directory of Open Access Journals (Sweden)

    Mrutyunjaya PANDA

    2011-07-01

    Full Text Available The enormous growth of wireless sensor network (WSN research has opined challenges about their ease in implementation and performance evaluation. Efficient swarm intelligence based routing protocols that can be used to obtain the application specific service guarantee are the key design issues in designing a WSN model. In this paper, an experimental testbed is designed with 100 sensor nodes deployed in a dense environment to address the scalability and performance issues of WSN. In this paper, we use Flooded Piggyback (FP and SC-MCBR ant colony based routing along with AODV and MCBR Tree in order to design an efficient WSN model. Finally, simulation results are presented with various performance measures to understand the efficacy of the proposed WSN design.

  6. An Intelligent Gear Fault Diagnosis Methodology Using a Complex Wavelet Enhanced Convolutional Neural Network.

    Science.gov (United States)

    Sun, Weifang; Yao, Bin; Zeng, Nianyin; Chen, Binqiang; He, Yuchao; Cao, Xincheng; He, Wangpeng

    2017-07-12

    As a typical example of large and complex mechanical systems, rotating machinery is prone to diversified sorts of mechanical faults. Among these faults, one of the prominent causes of malfunction is generated in gear transmission chains. Although they can be collected via vibration signals, the fault signatures are always submerged in overwhelming interfering contents. Therefore, identifying the critical fault's characteristic signal is far from an easy task. In order to improve the recognition accuracy of a fault's characteristic signal, a novel intelligent fault diagnosis method is presented. In this method, a dual-tree complex wavelet transform (DTCWT) is employed to acquire the multiscale signal's features. In addition, a convolutional neural network (CNN) approach is utilized to automatically recognise a fault feature from the multiscale signal features. The experiment results of the recognition for gear faults show the feasibility and effectiveness of the proposed method, especially in the gear's weak fault features.

  7. Fuzzy Mobile-Robot Positioning in Intelligent Spaces Using Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    David Herrero

    2011-11-01

    Full Text Available This work presents the development and experimental evaluation of a method based on fuzzy logic to locate mobile robots in an Intelligent Space using Wireless Sensor Networks (WSNs. The problem consists of locating a mobile node using only inter-node range measurements, which are estimated by radio frequency signal strength attenuation. The sensor model of these measurements is very noisy and unreliable. The proposed method makes use of fuzzy logic for modeling and dealing with such uncertain information. Besides, the proposed approach is compared with a probabilistic technique showing that the fuzzy approach is able to handle highly uncertain situations that are difficult to manage by well-known localization methods.

  8. An Intelligent Gear Fault Diagnosis Methodology Using a Complex Wavelet Enhanced Convolutional Neural Network

    Science.gov (United States)

    Sun, Weifang; Yao, Bin; Zeng, Nianyin; He, Yuchao; Cao, Xincheng; He, Wangpeng

    2017-01-01

    As a typical example of large and complex mechanical systems, rotating machinery is prone to diversified sorts of mechanical faults. Among these faults, one of the prominent causes of malfunction is generated in gear transmission chains. Although they can be collected via vibration signals, the fault signatures are always submerged in overwhelming interfering contents. Therefore, identifying the critical fault’s characteristic signal is far from an easy task. In order to improve the recognition accuracy of a fault’s characteristic signal, a novel intelligent fault diagnosis method is presented. In this method, a dual-tree complex wavelet transform (DTCWT) is employed to acquire the multiscale signal’s features. In addition, a convolutional neural network (CNN) approach is utilized to automatically recognise a fault feature from the multiscale signal features. The experiment results of the recognition for gear faults show the feasibility and effectiveness of the proposed method, especially in the gear’s weak fault features. PMID:28773148

  9. Artificial Intelligence.

    Science.gov (United States)

    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)

  10. A Quantitative Methodology for Vetting Dark Network Intelligence Sources for Social Network Analysis

    Science.gov (United States)

    2012-06-01

    Figure V-7 Source Stress Contributions for the Example ............................................ V-24  Figure V-8 ROC Curve for the Example...resilience is the ability of the organization “to avoid disintegration when coming under stress (Milward & Raab, 2006, p. 351).” Despite numerous...members of the network. Examples such as subordinates directed to meetings in place of their superiors, virtual participation via telecommuting

  11. Intelligent mechatronics; Intelligent mechatronics

    Energy Technology Data Exchange (ETDEWEB)

    Hashimoto, H. [The University of Tokyo, Tokyo (Japan). Institute of Industrial Science

    1995-10-01

    Intelligent mechatronics (IM) was explained as follows: a study of IM essentially targets realization of a robot namely, but in the present stage the target is a creation of new values by intellectualization of machine, that is, a combination of the information infrastructure and the intelligent machine system. IM is also thought to be constituted of computers positively used and micromechatronics. The paper next introduces examples of IM study, mainly those the author is concerned with as shown below: sensor gloves, robot hands, robot eyes, tele operation, three-dimensional object recognition, mobile robot, magnetic bearing, construction of remote controlled unmanned dam, robot network, sensitivity communication using neuro baby, etc. 27 figs.

  12. Intelligent control a hybrid approach based on fuzzy logic, neural networks and genetic algorithms

    CERN Document Server

    Siddique, Nazmul

    2014-01-01

    Intelligent Control considers non-traditional modelling and control approaches to nonlinear systems. Fuzzy logic, neural networks and evolutionary computing techniques are the main tools used. The book presents a modular switching fuzzy logic controller where a PD-type fuzzy controller is executed first followed by a PI-type fuzzy controller thus improving the performance of the controller compared with a PID-type fuzzy controller.  The advantage of the switching-type fuzzy controller is that it uses one rule-base thus minimises the rule-base during execution. A single rule-base is developed by merging the membership functions for change of error of the PD-type controller and sum of error of the PI-type controller. Membership functions are then optimized using evolutionary algorithms. Since the two fuzzy controllers were executed in series, necessary further tuning of the differential and integral scaling factors of the controller is then performed. Neural-network-based tuning for the scaling parameters of t...

  13. HIGH: A Hexagon-based Intelligent Grouping Approach in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    FAN, C.-S.

    2016-02-01

    Full Text Available In a random deployment or uniform deployment strategy, sensor nodes are scattered randomly or uniformly in the sensing field, respectively. Hence, the coverage ratio cannot be guaranteed. The coverage ratio of uniform deployment, in general, is larger than that of the random deployment strategy. However, a random deployment or uniform deployment strategy may cause unbalanced traffic pattern in wireless sensor networks (WSNs. Therefore, cluster heads (CHs around the sink have larger loads than those farther away from the sink. That is, CHs close to the sink exhaust their energy earlier. In order to overcome the above problem, we propose a Hexagon-based Intelligent Grouping approacH in WSNs (called HIGH. The coverage, energy consumption and data routing issues are well investigated and taken into consideration in the proposed HIGH scheme. The simulation results validate our theoretical analysis and show that the proposed HIGH scheme achieves a satisfactory coverage ratio, balances the energy consumption among sensor nodes, and extends network lifetime significantly.

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

    Science.gov (United States)

    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.

  15. Intelligent Energy Management Control for Extended Range Electric Vehicles Based on Dynamic Programming and Neural Network

    Directory of Open Access Journals (Sweden)

    Lihe Xi

    2017-11-01

    Full Text Available The extended range electric vehicle (EREV can store much clean energy from the electric grid when it arrives at the charging station with lower battery energy. Consuming minimum gasoline during the trip is a common goal for most energy management controllers. To achieve these objectives, an intelligent energy management controller for EREV based on dynamic programming and neural networks (IEMC_NN is proposed. The power demand split ratio between the extender and battery are optimized by DP, and the control objectives are presented as a cost function. The online controller is trained by neural networks. Three trained controllers, constructing the controller library in IEMC_NN, are obtained from training three typical lengths of the driving cycle. To determine an appropriate NN controller for different driving distance purposes, the selection module in IEMC_NN is developed based on the remaining battery energy and the driving distance to the charging station. Three simulation conditions are adopted to validate the performance of IEMC_NN. They are target driving distance information, known and unknown, changing the destination during the trip. Simulation results using these simulation conditions show that the IEMC_NN had better fuel economy than the charging deplete/charging sustain (CD/CS algorithm. More significantly, with known driving distance information, the battery SOC controlled by IEMC_NN can just reach the lower bound as the EREV arrives at the charging station, which was also feasible when the driver changed the destination during the trip.

  16. Matrix Completion Optimization for Localization in Wireless Sensor Networks for Intelligent IoT

    Directory of Open Access Journals (Sweden)

    Thu L. N. Nguyen

    2016-05-01

    Full Text Available Localization in wireless sensor networks (WSNs is one of the primary functions of the intelligent Internet of Things (IoT that offers automatically discoverable services, while the localization accuracy is a key issue to evaluate the quality of those services. In this paper, we develop a framework to solve the Euclidean distance matrix completion problem, which is an important technical problem for distance-based localization in WSNs. The sensor network localization problem is described as a low-rank dimensional Euclidean distance completion problem with known nodes. The task is to find the sensor locations through recovery of missing entries of a squared distance matrix when the dimension of the data is small compared to the number of data points. We solve a relaxation optimization problem using a modification of Newton’s method, where the cost function depends on the squared distance matrix. The solution obtained in our scheme achieves a lower complexity and can perform better if we use it as an initial guess for an interactive local search of other higher precision localization scheme. Simulation results show the effectiveness of our approach.

  17. Hybrid Swarm Intelligence Energy Efficient Clustered Routing Algorithm for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Rajeev Kumar

    2016-01-01

    Full Text Available Currently, wireless sensor networks (WSNs are used in many applications, namely, environment monitoring, disaster management, industrial automation, and medical electronics. Sensor nodes carry many limitations like low battery life, small memory space, and limited computing capability. To create a wireless sensor network more energy efficient, swarm intelligence technique has been applied to resolve many optimization issues in WSNs. In many existing clustering techniques an artificial bee colony (ABC algorithm is utilized to collect information from the field periodically. Nevertheless, in the event based applications, an ant colony optimization (ACO is a good solution to enhance the network lifespan. In this paper, we combine both algorithms (i.e., ABC and ACO and propose a new hybrid ABCACO algorithm to solve a Nondeterministic Polynomial (NP hard and finite problem of WSNs. ABCACO algorithm is divided into three main parts: (i selection of optimal number of subregions and further subregion parts, (ii cluster head selection using ABC algorithm, and (iii efficient data transmission using ACO algorithm. We use a hierarchical clustering technique for data transmission; the data is transmitted from member nodes to the subcluster heads and then from subcluster heads to the elected cluster heads based on some threshold value. Cluster heads use an ACO algorithm to discover the best route for data transmission to the base station (BS. The proposed approach is very useful in designing the framework for forest fire detection and monitoring. The simulation results show that the ABCACO algorithm enhances the stability period by 60% and also improves the goodput by 31% against LEACH and WSNCABC, respectively.

  18. The “Wireless Sensor Networks for City-Wide Ambient Intelligence (WISE-WAI” Project

    Directory of Open Access Journals (Sweden)

    Michele Zorzi

    2009-05-01

    Full Text Available This paper gives a detailed technical overview of some of the activities carried out in the context of the “Wireless Sensor networks for city-Wide Ambient Intelligence (WISEWAI” project, funded by the Cassa di Risparmio di Padova e Rovigo Foundation, Italy. The main aim of the project is to demonstrate the feasibility of large-scale wireless sensor network deployments, whereby tiny objects integrating one or more environmental sensors (humidity, temperature, light intensity, a microcontroller and a wireless transceiver are deployed over a large area, which in this case involves the buildings of the Department of Information Engineering at the University of Padova. We will describe how the network is organized to provide full-scale automated functions, and which services and applications it is configured to provide. These applications include long-term environmental monitoring, alarm event detection and propagation, single-sensor interrogation, localization and tracking of objects, assisted navigation, as well as fast data dissemination services to be used, e.g., to rapidly re-program all sensors over-the-air. The organization of such a large testbed requires notable efforts in terms of communication protocols and strategies, whose design must pursue scalability, energy efficiency (while sensors are connected through USB cables for logging and debugging purposes, most of them will be battery-operated, as well as the capability to support applications with diverse requirements. These efforts, the description of a subset of the results obtained so far, and of the final objectives to be met are the scope of the present paper.

  19. An intelligent nuclear reactor core controller for load following operations, using recurrent neural networks and fuzzy systems

    International Nuclear Information System (INIS)

    Boroushaki, M.; Ghofrani, M.B.; Lucas, C.; Yazdanpanah, M.J.

    2003-01-01

    In the last decade, the intelligent control community has paid great attention to the topic of intelligent control systems for nuclear plants (core, steam generator...). Papers mostly used approximate and simple mathematical SISO (single-input-single-output) model of nuclear plants for testing and/or tuning of the control systems. They also tried to generalize theses models to a real MIMO (multi-input-multi-output) plant, while nuclear plants are typically of complex nonlinear and multivariable nature with high interactions between their state variables and therefore, many of these proposed intelligent control systems are not appropriate for real cases. In this paper, we designed an on-line intelligent core controller for load following operations, based on a heuristic control algorithm, using a valid and updatable recurrent neural network (RNN). We have used an accurate 3-dimensional core calculation code to represent the real plant and to train the RNN. The results of simulation show that this intelligent controller can control the reactor core during load following operations, using optimum control rod groups manoeuvre and variable overlapping strategy. This methodology represents a simple and reliable procedure for controlling other complex nonlinear MIMO plants, and may improve the responses, comparing to other control systems

  20. A Review of Fuzzy Logic and Neural Network Based Intelligent Control Design for Discrete-Time Systems

    Directory of Open Access Journals (Sweden)

    Yiming Jiang

    2016-01-01

    Full Text Available Over the last few decades, the intelligent control methods such as fuzzy logic control (FLC and neural network (NN control have been successfully used in various applications. The rapid development of digital computer based control systems requires control signals to be calculated in a digital or discrete-time form. In this background, the intelligent control methods developed for discrete-time systems have drawn great attentions. This survey aims to present a summary of the state of the art of the design of FLC and NN-based intelligent control for discrete-time systems. For discrete-time FLC systems, numerous remarkable design approaches are introduced and a series of efficient methods to deal with the robustness, stability, and time delay of FLC discrete-time systems are recommended. Techniques for NN-based intelligent control for discrete-time systems, such as adaptive methods and adaptive dynamic programming approaches, are also reviewed. Overall, this paper is devoted to make a brief summary for recent progresses in FLC and NN-based intelligent control design for discrete-time systems as well as to present our thoughts and considerations of recent trends and potential research directions in this area.

  1. An Intelligent Network Proposed for Assessing Seismic Vulnerability Index of Sewerage Networks within a GIS Framework (A Case Study of Shahr-e-Kord

    Directory of Open Access Journals (Sweden)

    Mohamadali Rahgozar

    2016-01-01

    Full Text Available Due to their vast spread, sewerage networks are exposed to considerable damages during severe earthquakes, which may lead to catastrophic environmental contamination. Multiple repairs in the pipelines, including pipe and joint fractures, could be costly and time-consuming. In seismic risk management, it is of utmost importance to have an intelligent tool for assessing seismic vulnerability index at any given point in time for such important utilities as sewerage networks. This study uses a weight-factor methodology and proposes an online GIS-based intelligent algorithm to evaluate the seismic vulnerability index (VI for metropolitan sewerage networks. The proposed intelligent tool is capable of updating VI as the sewerage network conditions may change with time and at different locations. The city of Shahr-e-Kord located on the high risk seismic belt is selected for a case study to which the proposed methodology is applied for zoning the vulnerability index in GIS. Results show that the overall seismic vulnerability index for the selected study area ranges from low to medium but that it increases in the southern parts of the city, especially in the old town where brittle pipes have been laid

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

    Science.gov (United States)

    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...

  3. Planning pesticides usage for herbal and animal pests based on intelligent classification system with image processing and neural networks

    Directory of Open Access Journals (Sweden)

    Dimililer Kamil

    2018-01-01

    Full Text Available Pests are divided into two as herbal and animal pests in agriculture, and detection and use of minimum pesticides are quite challenging task. Last three decades, researchers have been improving their studies on these manners. Therefore, effective, efficient, and as well as intelligent systems are designed and modelled. In this paper, an intelligent classification system is designed for detecting pests as herbal or animal to use of proper pesticides accordingly. The designed system suggests two main stages. Firstly, images are processed using different image processing techniques that images have specific distinguishing geometric patterns. The second stage is neural network phase for classification. A backpropagation neural network is used for training and testing with processed images. System is tested, and experiment results show efficiency and effective classification rate. Autonomy and time efficiency within the pesticide usage are also discussed.

  4. Intelligent and robust prediction of short term wind power using genetic programming based ensemble of neural networks

    International Nuclear Information System (INIS)

    Zameer, Aneela; Arshad, Junaid; Khan, Asifullah; Raja, Muhammad Asif Zahoor

    2017-01-01

    Highlights: • Genetic programming based ensemble of neural networks is employed for short term wind power prediction. • Proposed predictor shows resilience against abrupt changes in weather. • Genetic programming evolves nonlinear mapping between meteorological measures and wind-power. • Proposed approach gives mathematical expressions of wind power to its independent variables. • Proposed model shows relatively accurate and steady wind-power prediction performance. - Abstract: The inherent instability of wind power production leads to critical problems for smooth power generation from wind turbines, which then requires an accurate forecast of wind power. In this study, an effective short term wind power prediction methodology is presented, which uses an intelligent ensemble regressor that comprises Artificial Neural Networks and Genetic Programming. In contrast to existing series based combination of wind power predictors, whereby the error or variation in the leading predictor is propagated down the stream to the next predictors, the proposed intelligent ensemble predictor avoids this shortcoming by introducing Genetical Programming based semi-stochastic combination of neural networks. It is observed that the decision of the individual base regressors may vary due to the frequent and inherent fluctuations in the atmospheric conditions and thus meteorological properties. The novelty of the reported work lies in creating ensemble to generate an intelligent, collective and robust decision space and thereby avoiding large errors due to the sensitivity of the individual wind predictors. The proposed ensemble based regressor, Genetic Programming based ensemble of Artificial Neural Networks, has been implemented and tested on data taken from five different wind farms located in Europe. Obtained numerical results of the proposed model in terms of various error measures are compared with the recent artificial intelligence based strategies to demonstrate the

  5. A Neural Network Based Intelligent Predictive Sensor for Cloudiness, Solar Radiation and Air Temperature

    Science.gov (United States)

    Ferreira, Pedro M.; Gomes, João M.; Martins, Igor A. C.; Ruano, António E.

    2012-01-01

    Accurate measurements of global solar radiation and atmospheric temperature, as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight and portable sensor was developed, using artificial neural network models as the time-series predictor mechanisms. These have been identified with the aid of a procedure based on the multi-objective genetic algorithm. As cloudiness is the most significant factor affecting the solar radiation reaching a particular location on the Earth surface, it has great impact on the performance of predictive solar radiation models for that location. This work also represents one step towards the improvement of such models by using ground-to-sky hemispherical colour digital images as a means to estimate cloudiness by the fraction of visible sky corresponding to clouds and to clear sky. The implementation of predictive models in the prototype has been validated and the system is able to function reliably, providing measurements and four-hour forecasts of cloudiness, solar radiation and air temperature. PMID:23202230

  6. Ontology-Based Architecture for Intelligent Transportation Systems Using a Traffic Sensor Network

    Directory of Open Access Journals (Sweden)

    Susel Fernandez

    2016-08-01

    Full Text Available Intelligent transportation systems are a set of technological solutions used to improve the performance and safety of road transportation. A crucial element for the success of these systems is the exchange of information, not only between vehicles, but also among other components in the road infrastructure through different applications. One of the most important information sources in this kind of systems is sensors. Sensors can be within vehicles or as part of the infrastructure, such as bridges, roads or traffic signs. Sensors can provide information related to weather conditions and traffic situation, which is useful to improve the driving process. To facilitate the exchange of information between the different applications that use sensor data, a common framework of knowledge is needed to allow interoperability. In this paper an ontology-driven architecture to improve the driving environment through a traffic sensor network is proposed. The system performs different tasks automatically to increase driver safety and comfort using the information provided by the sensors.

  7. Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend

    Directory of Open Access Journals (Sweden)

    Montri Inthachot

    2016-01-01

    Full Text Available This study investigated the use of Artificial Neural Network (ANN and Genetic Algorithm (GA for prediction of Thailand’s SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid’s prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span.

  8. Sustainable Technology Analysis of Artificial Intelligence Using Bayesian and Social Network Models

    Directory of Open Access Journals (Sweden)

    Juhwan Kim

    2018-01-01

    Full Text Available Recent developments in artificial intelligence (AI have led to a significant increase in the use of AI technologies. Many experts are researching and developing AI technologies in their respective fields, often submitting papers and patent applications as a result. In particular, owing to the characteristics of the patent system that is used to protect the exclusive rights to registered technology, patent documents contain detailed information on the developed technology. Therefore, in this study, we propose a statistical method for analyzing patent data on AI technology to improve our understanding of sustainable technology in the field of AI. We collect patent documents that are related to AI technology, and then analyze the patent data to identify sustainable AI technology. In our analysis, we develop a statistical method that combines social network analysis and Bayesian modeling. Based on the results of the proposed method, we provide a technological structure that can be applied to understand the sustainability of AI technology. To show how the proposed method can be applied to a practical problem, we apply the technological structure to a case study in order to analyze sustainable AI technology.

  9. An intelligent signal processing and pattern recognition technique for defect identification using an active sensor network

    Science.gov (United States)

    Su, Zhongqing; Ye, Lin

    2004-08-01

    The practical utilization of elastic waves, e.g. Rayleigh-Lamb waves, in high-performance structural health monitoring techniques is somewhat impeded due to the complicated wave dispersion phenomena, the existence of multiple wave modes, the high susceptibility to diverse interferences, the bulky sampled data and the difficulty in signal interpretation. An intelligent signal processing and pattern recognition (ISPPR) approach using the wavelet transform and artificial neural network algorithms was developed; this was actualized in a signal processing package (SPP). The ISPPR technique comprehensively functions as signal filtration, data compression, characteristic extraction, information mapping and pattern recognition, capable of extracting essential yet concise features from acquired raw wave signals and further assisting in structural health evaluation. For validation, the SPP was applied to the prediction of crack growth in an alloy structural beam and construction of a damage parameter database for defect identification in CF/EP composite structures. It was clearly apparent that the elastic wave propagation-based damage assessment could be dramatically streamlined by introduction of the ISPPR technique.

  10. Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend

    Science.gov (United States)

    Boonjing, Veera; Intakosum, Sarun

    2016-01-01

    This study investigated the use of Artificial Neural Network (ANN) and Genetic Algorithm (GA) for prediction of Thailand's SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid's prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span. PMID:27974883

  11. Intelligent Management System of Power Network Information Collection Under Big Data Storage

    Directory of Open Access Journals (Sweden)

    Qin Yingying

    2017-01-01

    Full Text Available With the development of economy and society, big data storage in enterprise management has become a problem that can’t be ignored. How to manage and optimize the allocation of tasks better is an important factor in the sustainable development of an enterprise. Now the enterprise information intelligent management has become a hot spot of management mode and concept in the information age. It presents information to the business managers in a more efficient, lower cost, and global form. The system uses the SG-UAP development tools, which is based on Eclipse development environment, and suits for Windows operating system, with Oracle as database development platform, Tomcat network information service for application server. The system uses SOA service-oriented architecture, provides RESTful style service, and HTTP(S as the communication protocol, and JSON as the data format. The system is divided into two parts, the front-end and the backs-end, achieved functions like user login, registration, password retrieving, enterprise internal personnel information management and internal data display and other functions.

  12. Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data

    Science.gov (United States)

    Jia, Feng; Lei, Yaguo; Lin, Jing; Zhou, Xin; Lu, Na

    2016-05-01

    Aiming to promptly process the massive fault data and automatically provide accurate diagnosis results, numerous studies have been conducted on intelligent fault diagnosis of rotating machinery. Among these studies, the methods based on artificial neural networks (ANNs) are commonly used, which employ signal processing techniques for extracting features and further input the features to ANNs for classifying faults. Though these methods did work in intelligent fault diagnosis of rotating machinery, they still have two deficiencies. (1) The features are manually extracted depending on much prior knowledge about signal processing techniques and diagnostic expertise. In addition, these manual features are extracted according to a specific diagnosis issue and probably unsuitable for other issues. (2) The ANNs adopted in these methods have shallow architectures, which limits the capacity of ANNs to learn the complex non-linear relationships in fault diagnosis issues. As a breakthrough in artificial intelligence, deep learning holds the potential to overcome the aforementioned deficiencies. Through deep learning, deep neural networks (DNNs) with deep architectures, instead of shallow ones, could be established to mine the useful information from raw data and approximate complex non-linear functions. Based on DNNs, a novel intelligent method is proposed in this paper to overcome the deficiencies of the aforementioned intelligent diagnosis methods. The effectiveness of the proposed method is validated using datasets from rolling element bearings and planetary gearboxes. These datasets contain massive measured signals involving different health conditions under various operating conditions. The diagnosis results show that the proposed method is able to not only adaptively mine available fault characteristics from the measured signals, but also obtain superior diagnosis accuracy compared with the existing methods.

  13. PIYAS-Proceeding to Intelligent Service Oriented Memory Allocation for Flash Based Data Centric Sensor Devices in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Sanam Shahla Rizvi

    2009-12-01

    Full Text Available Flash memory has become a more widespread storage medium for modern wireless devices because of its effective characteristics like non-volatility, small size, light weight, fast access speed, shock resistance, high reliability and low power consumption. Sensor nodes are highly resource constrained in terms of limited processing speed, runtime memory, persistent storage, communication bandwidth and finite energy. Therefore, for wireless sensor networks supporting sense, store, merge and send schemes, an efficient and reliable file system is highly required with consideration of sensor node constraints. In this paper, we propose a novel log structured external NAND flash memory based file system, called Proceeding to Intelligent service oriented memorY Allocation for flash based data centric Sensor devices in wireless sensor networks (PIYAS. This is the extended version of our previously proposed PIYA [1]. The main goals of the PIYAS scheme are to achieve instant mounting and reduced SRAM space by keeping memory mapping information to a very low size of and to provide high query response throughput by allocation of memory to the sensor data by network business rules. The scheme intelligently samples and stores the raw data and provides high in-network data availability by keeping the aggregate data for a longer period of time than any other scheme has done before. We propose effective garbage collection and wear-leveling schemes as well. The experimental results show that PIYAS is an optimized memory management scheme allowing high performance for wireless sensor networks.

  14. PIYAS-proceeding to intelligent service oriented memory allocation for flash based data centric sensor devices in wireless sensor networks.

    Science.gov (United States)

    Rizvi, Sanam Shahla; Chung, Tae-Sun

    2010-01-01

    Flash memory has become a more widespread storage medium for modern wireless devices because of its effective characteristics like non-volatility, small size, light weight, fast access speed, shock resistance, high reliability and low power consumption. Sensor nodes are highly resource constrained in terms of limited processing speed, runtime memory, persistent storage, communication bandwidth and finite energy. Therefore, for wireless sensor networks supporting sense, store, merge and send schemes, an efficient and reliable file system is highly required with consideration of sensor node constraints. In this paper, we propose a novel log structured external NAND flash memory based file system, called Proceeding to Intelligent service oriented memorY Allocation for flash based data centric Sensor devices in wireless sensor networks (PIYAS). This is the extended version of our previously proposed PIYA [1]. The main goals of the PIYAS scheme are to achieve instant mounting and reduced SRAM space by keeping memory mapping information to a very low size of and to provide high query response throughput by allocation of memory to the sensor data by network business rules. The scheme intelligently samples and stores the raw data and provides high in-network data availability by keeping the aggregate data for a longer period of time than any other scheme has done before. We propose effective garbage collection and wear-leveling schemes as well. The experimental results show that PIYAS is an optimized memory management scheme allowing high performance for wireless sensor networks.

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

    Science.gov (United States)

    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.

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

    NARCIS (Netherlands)

    Hilger, K.; Ekman, M.; Fiebach, C.J.; Basten, U.

    2017-01-01

    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

  17. Ad hoc networking and ambient intelligence to support future disaster response

    NARCIS (Netherlands)

    Jones, Valerie M.; Karagiannis, Georgios; Heemstra de Groot, S.M.

    2005-01-01

    We present a vision of how ambient intelligent environments may be used in future to support the emergency services during first response to a major incident. A futuristic scenario is presented where, for each of the emergency services, Ambient Intelligence (AmI) technologies are used to support

  18. Ad hoc networking and ambient intelligence to support future disaster response

    NARCIS (Netherlands)

    Jones, Valerie M.; Karagiannis, Georgios; Heemstra de Groot, S.M.; Afifi, H.; Zeghlache, D.

    We present a vision of how ambient intelligent environments may be used in future to support the emergency services during first response to a major incident. A futuristic scenario is presented where, for each of the emergency services, Ambient Intelligence (AmI) technologies are used to support

  19. Modeling and simulation of adaptive Neuro-fuzzy based intelligent system for predictive stabilization in structured overlay networks

    Directory of Open Access Journals (Sweden)

    Ramanpreet Kaur

    2017-02-01

    Full Text Available Intelligent prediction of neighboring node (k well defined neighbors as specified by the dht protocol dynamism is helpful to improve the resilience and can reduce the overhead associated with topology maintenance of structured overlay networks. The dynamic behavior of overlay nodes depends on many factors such as underlying user’s online behavior, geographical position, time of the day, day of the week etc. as reported in many applications. We can exploit these characteristics for efficient maintenance of structured overlay networks by implementing an intelligent predictive framework for setting stabilization parameters appropriately. Considering the fact that human driven behavior usually goes beyond intermittent availability patterns, we use a hybrid Neuro-fuzzy based predictor to enhance the accuracy of the predictions. In this paper, we discuss our predictive stabilization approach, implement Neuro-fuzzy based prediction in MATLAB simulation and apply this predictive stabilization model in a chord based overlay network using OverSim as a simulation tool. The MATLAB simulation results present that the behavior of neighboring nodes is predictable to a large extent as indicated by the very small RMSE. The OverSim based simulation results also observe significant improvements in the performance of chord based overlay network in terms of lookup success ratio, lookup hop count and maintenance overhead as compared to periodic stabilization approach.

  20. An intelligent service matching method for mechanical equipment condition monitoring using the fibre Bragg grating sensor network

    Science.gov (United States)

    Zhang, Fan; Zhou, Zude; Liu, Quan; Xu, Wenjun

    2017-02-01

    Due to the advantages of being able to function under harsh environmental conditions and serving as a distributed condition information source in a networked monitoring system, the fibre Bragg grating (FBG) sensor network has attracted considerable attention for equipment online condition monitoring. To provide an overall conditional view of the mechanical equipment operation, a networked service-oriented condition monitoring framework based on FBG sensing is proposed, together with an intelligent matching method for supporting monitoring service management. In the novel framework, three classes of progressive service matching approaches, including service-chain knowledge database service matching, multi-objective constrained service matching and workflow-driven human-interactive service matching, are developed and integrated with an enhanced particle swarm optimisation (PSO) algorithm as well as a workflow-driven mechanism. Moreover, the manufacturing domain ontology, FBG sensor network structure and monitoring object are considered to facilitate the automatic matching of condition monitoring services to overcome the limitations of traditional service processing methods. The experimental results demonstrate that FBG monitoring services can be selected intelligently, and the developed condition monitoring system can be re-built rapidly as new equipment joins the framework. The effectiveness of the service matching method is also verified by implementing a prototype system together with its performance analysis.

  1. Artificial intelligence: Neural network model as the multidisciplinary team member in clinical decision support to avoid medical mistakes

    Directory of Open Access Journals (Sweden)

    Igor Vyacheslavovich Buzaev

    2016-09-01

    Full Text Available Objective: The continuous uninterrupted feedback system is the essential part of any well-organized system. We propose aLYNX concept that is a possibility to use an artificial intelligence algorithm or a neural network model in decision-making system so as to avoid possible mistakes and to remind the doctors to review tactics once more in selected cases. Method: aLYNX system includes: registry with significant factors, decisions and results; machine learning process based on this registry data; the use of the machine learning results as the adviser. We show a possibility to build a computer adviser with a neural network model for making a choice between coronary aortic bypass surgery (CABG and percutaneous coronary intervention (PCI in order to achieve a higher 5-year survival rate in patients with angina based on the experience of 5107 patients. Results: The neural network was trained by 4679 patients who achieved 5-year survival. Among them, 2390 patients underwent PCI and 2289 CABG. After training, the correlation coefficient (r of the network was 0.74 for training, 0.67 for validation, 0.71 for test and 0.73 for total. Simulation of the neural network function has been performed after training in the two groups of patients with known 5-year outcome. The disagreement rate was significantly higher in the dead patient group than that in the survivor group between neural network model and heart team [16.8% (787/4679 vs. 20.3% (87/428, P = 0.065]. Conclusion: The study shows the possibility to build a computer adviser with a neural network model for making a choice between CABG and PCI in order to achieve a higher 5-year survival rate in patients with angina. Keywords: Coronary artery bypass grafting, Percutaneous coronary intervention, Artificial intelligence, Decision making

  2. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images.

    Science.gov (United States)

    Hirasawa, Toshiaki; Aoyama, Kazuharu; Tanimoto, Tetsuya; Ishihara, Soichiro; Shichijo, Satoki; Ozawa, Tsuyoshi; Ohnishi, Tatsuya; Fujishiro, Mitsuhiro; Matsuo, Keigo; Fujisaki, Junko; Tada, Tomohiro

    2018-07-01

    Image recognition using artificial intelligence with deep learning through convolutional neural networks (CNNs) has dramatically improved and been increasingly applied to medical fields for diagnostic imaging. We developed a CNN that can automatically detect gastric cancer in endoscopic images. A CNN-based diagnostic system was constructed based on Single Shot MultiBox Detector architecture and trained using 13,584 endoscopic images of gastric cancer. To evaluate the diagnostic accuracy, an independent test set of 2296 stomach images collected from 69 consecutive patients with 77 gastric cancer lesions was applied to the constructed CNN. The CNN required 47 s to analyze 2296 test images. The CNN correctly diagnosed 71 of 77 gastric cancer lesions with an overall sensitivity of 92.2%, and 161 non-cancerous lesions were detected as gastric cancer, resulting in a positive predictive value of 30.6%. Seventy of the 71 lesions (98.6%) with a diameter of 6 mm or more as well as all invasive cancers were correctly detected. All missed lesions were superficially depressed and differentiated-type intramucosal cancers that were difficult to distinguish from gastritis even for experienced endoscopists. Nearly half of the false-positive lesions were gastritis with changes in color tone or an irregular mucosal surface. The constructed CNN system for detecting gastric cancer could process numerous stored endoscopic images in a very short time with a clinically relevant diagnostic ability. It may be well applicable to daily clinical practice to reduce the burden of endoscopists.

  3. Study on intelligence fault diagnosis method for nuclear power plant equipment based on rough set and fuzzy neural network

    International Nuclear Information System (INIS)

    Liu Yongkuo; Xia Hong; Xie Chunli; Chen Zhihui; Chen Hongxia

    2007-01-01

    Rough set theory and fuzzy neural network are combined, to take full advantages of the two of them. Based on the reduction technology to knowledge of Rough set method, and by drawing the simple rule from a large number of initial data, the fuzzy neural network was set up, which was with better topological structure, improved study speed, accurate judgment, strong fault-tolerant ability, and more practical. In order to test the validity of the method, the inverted U-tubes break accident of Steam Generator and etc are used as examples, and many simulation experiments are performed. The test result shows that it is feasible to incorporate the fault intelligence diagnosis method based on rough set and fuzzy neural network in the nuclear power plant equipment, and the method is simple and convenience, with small calculation amount and reliable result. (authors)

  4. A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment.

    Directory of Open Access Journals (Sweden)

    Yongcheng Li

    Full Text Available We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning. Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot's performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks.

  5. A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment.

    Science.gov (United States)

    Li, Yongcheng; Sun, Rong; Wang, Yuechao; Li, Hongyi; Zheng, Xiongfei

    2016-01-01

    We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning). Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle) to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot's performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks.

  6. An Effective Wormhole Attack Defence Method for a Smart Meter Mesh Network in an Intelligent Power Grid

    Directory of Open Access Journals (Sweden)

    Jungtaek Seo

    2012-08-01

    Full Text Available Smart meters are one of the key components of intelligent power grids. Wireless mesh networks based on smart meters could provide customer-oriented information on electricity use to the operational control systems, which monitor power grid status and estimate electric power demand. Using this information, an operational control system could regulate devices within the smart grid in order to provide electricity in a cost-efficient manner. Ensuring the availability of the smart meter mesh network is therefore a critical factor in securing the soundness of an intelligent power system. Wormhole attacks can be one of the most difficult-to-address threats to the availability of mesh networks, and although many methods to nullify wormhole attacks have been tried, these have been limited by high computational resource requirements and unnecessary overhead, as well as by the lack of ability of such methods to respond to attacks. In this paper, an effective defense mechanism that both detects and responds to wormhole attacks is proposed. In the proposed system, each device maintains information on its neighbors, allowing each node to identify replayed packets. The effectiveness and efficiency of the proposed method is analyzed in light of additional computational message and memory complexities.

  7. 14th ACIS/IEEE International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing

    CERN Document Server

    Studies in Computational Intelligence : Volume 492

    2013-01-01

    This edited book presents scientific results of the 14th ACIS/IEEE International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2013), held in Honolulu, Hawaii, USA on July 1-3, 2013. The aim of this conference was to bring together scientists, engineers, computer users, and students to share their experiences and exchange new ideas, research results about all aspects (theory, applications and tools) of computer and information science, and to discuss the practical challenges encountered along the way and the solutions adopted to solve them. The conference organizers selected the 17 outstanding papers from those papers accepted for presentation at the conference.  

  8. 15th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing

    CERN Document Server

    2015-01-01

    This edited book presents scientific results of 15th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2014) held on June 30 – July 2, 2014 in Las Vegas Nevada, USA. The aim of this conference was to bring together scientists, engineers, computer users, and students to share their experiences and exchange new ideas, research results about all aspects (theory, applications and tools) of computer and information science, and to discuss the practical challenges encountered along the way and the solutions adopted to solve them. The conference organizers selected the 13 outstanding papers from those papers accepted for presentation at the conference.

  9. Indirect intelligent sliding mode control of a shape memory alloy actuated flexible beam using hysteretic recurrent neural networks

    International Nuclear Information System (INIS)

    Hannen, Jennifer C; Buckner, Gregory D; Crews, John H

    2012-01-01

    This paper introduces an indirect intelligent sliding mode controller (IISMC) for shape memory alloy (SMA) actuators, specifically a flexible beam deflected by a single offset SMA tendon. The controller manipulates applied voltage, which alters SMA tendon temperature to track reference bending angles. A hysteretic recurrent neural network (HRNN) captures the nonlinear, hysteretic relationship between SMA temperature and bending angle. The variable structure control strategy provides robustness to model uncertainties and parameter variations, while effectively compensating for system nonlinearities, achieving superior tracking compared to an optimized PI controller. (paper)

  10. Intelligent quotient estimation of mental retarded people from different psychometric instruments using artificial neural networks.

    Science.gov (United States)

    Di Nuovo, Alessandro G; Di Nuovo, Santo; Buono, Serafino

    2012-02-01

    The estimation of a person's intelligence quotient (IQ) by means of psychometric tests is indispensable in the application of psychological assessment to several fields. When complex tests as the Wechsler scales, which are the most commonly used and universally recognized parameter for the diagnosis of degrees of retardation, are not applicable, it is necessary to use other psycho-diagnostic tools more suited for the subject's specific condition. But to ensure a homogeneous diagnosis it is necessary to reach a common metric, thus, the aim of our work is to build models able to estimate accurately and reliably the Wechsler IQ, starting from different psycho-diagnostic tools. Four different psychometric tests (Leiter international performance scale; coloured progressive matrices test; the mental development scale; psycho educational profile), along with the Wechsler scale, were administered to a group of 40 mentally retarded subjects, with various pathologies, and control persons. The obtained database is used to evaluate Wechsler IQ estimation models starting from the scores obtained in the other tests. Five modelling methods, two statistical and three from machine learning, that belong to the family of artificial neural networks (ANNs) are employed to build the estimator. Several error metrics for estimated IQ and for retardation level classification are defined to compare the performance of the various models with univariate and multivariate analyses. Eight empirical studies show that, after ten-fold cross-validation, best average estimation error is of 3.37 IQ points and mental retardation level classification error of 7.5%. Furthermore our experiments prove the superior performance of ANN methods over statistical regression ones, because in all cases considered ANN models show the lowest estimation error (from 0.12 to 0.9 IQ points) and the lowest classification error (from 2.5% to 10%). Since the estimation performance is better than the confidence interval of

  11. Accurate Traffic Flow Prediction in Heterogeneous Vehicular Networks in an Intelligent Transport System Using a Supervised Non-Parametric Classifier

    Directory of Open Access Journals (Sweden)

    Hesham El-Sayed

    2018-05-01

    Full Text Available Heterogeneous vehicular networks (HETVNETs evolve from vehicular ad hoc networks (VANETs, which allow vehicles to always be connected so as to obtain safety services within intelligent transportation systems (ITSs. The services and data provided by HETVNETs should be neither interrupted nor delayed. Therefore, Quality of Service (QoS improvement of HETVNETs is one of the topics attracting the attention of researchers and the manufacturing community. Several methodologies and frameworks have been devised by researchers to address QoS-prediction service issues. In this paper, to improve QoS, we evaluate various traffic characteristics of HETVNETs and propose a new supervised learning model to capture knowledge on all possible traffic patterns. This model is a refinement of support vector machine (SVM kernels with a radial basis function (RBF. The proposed model produces better results than SVMs, and outperforms other prediction methods used in a traffic context, as it has lower computational complexity and higher prediction accuracy.

  12. Artificial intelligence, neural network, and Internet tool integration in a pathology workstation to improve information access

    Science.gov (United States)

    Sargis, J. C.; Gray, W. A.

    1999-03-01

    The APWS allows user friendly access to several legacy systems which would normally each demand domain expertise for proper utilization. The generalized model, including objects, classes, strategies and patterns is presented. The core components of the APWS are the Microsoft Windows 95 Operating System, Oracle, Oracle Power Objects, Artificial Intelligence tools, a medical hyperlibrary and a web site. The paper includes a discussion of how could be automated by taking advantage of the expert system, object oriented programming and intelligent relational database tools within the APWS.

  13. Intelligent Control of Welding Gun Pose for Pipeline Welding Robot Based on Improved Radial Basis Function Network and Expert System

    Directory of Open Access Journals (Sweden)

    Jingwen Tian

    2013-02-01

    Full Text Available Since the control system of the welding gun pose in whole-position welding is complicated and nonlinear, an intelligent control system of welding gun pose for a pipeline welding robot based on an improved radial basis function neural network (IRBFNN and expert system (ES is presented in this paper. The structure of the IRBFNN is constructed and the improved genetic algorithm is adopted to optimize the network structure. This control system makes full use of the characteristics of the IRBFNN and the ES. The ADXRS300 micro-mechanical gyro is used as the welding gun position sensor in this system. When the welding gun position is obtained, an appropriate pitch angle can be obtained through expert knowledge and the numeric reasoning capacity of the IRBFNN. ARM is used as the controller to drive the welding gun pitch angle step motor in order to adjust the pitch angle of the welding gun in real-time. The experiment results show that the intelligent control system of the welding gun pose using the IRBFNN and expert system is feasible and it enhances the welding quality. This system has wide prospects for application.

  14. Intelligent Tutoring Systems: Formalization as Automata and Interface Design Using Neural Networks

    Science.gov (United States)

    Curilem, S. Gloria; Barbosa, Andrea R.; de Azevedo, Fernando M.

    2007-01-01

    This article proposes a mathematical model of Intelligent Tutoring Systems (ITS), based on observations of the behaviour of these systems. One of the most important problems of pedagogical software is to establish a common language between the knowledge areas involved in their development, basically pedagogical, computing and domain areas. A…

  15. Presenting the networked home: a content analysis of promotion material of Ambient Intelligence applications

    NARCIS (Netherlands)

    Ben Allouch, Soumaya; van Dijk, Johannes A.G.M.; Peters, O.

    2006-01-01

    Ambient Intelligence (AmI) for the home uses information and communication technologies to make users’ everyday life more comfortable. AmI is still in its developmental phase and is headed towards the first stages of diffusion. Characteristics of AmI design can be observed, among others, in the

  16. Artificial intelligence: Neural network model as the multidisciplinary team member in clinical decision support to avoid medical mistakes.

    Science.gov (United States)

    Buzaev, Igor Vyacheslavovich; Plechev, Vladimir Vyacheslavovich; Nikolaeva, Irina Evgenievna; Galimova, Rezida Maratovna

    2016-09-01

    The continuous uninterrupted feedback system is the essential part of any well-organized system. We propose aLYNX concept that is a possibility to use an artificial intelligence algorithm or a neural network model in decision-making system so as to avoid possible mistakes and to remind the doctors to review tactics once more in selected cases. aLYNX system includes: registry with significant factors, decisions and results; machine learning process based on this registry data; the use of the machine learning results as the adviser. We show a possibility to build a computer adviser with a neural network model for making a choice between coronary aortic bypass surgery (CABG) and percutaneous coronary intervention (PCI) in order to achieve a higher 5-year survival rate in patients with angina based on the experience of 5107 patients. The neural network was trained by 4679 patients who achieved 5-year survival. Among them, 2390 patients underwent PCI and 2289 CABG. After training, the correlation coefficient ( r ) of the network was 0.74 for training, 0.67 for validation, 0.71 for test and 0.73 for total. Simulation of the neural network function has been performed after training in the two groups of patients with known 5-year outcome. The disagreement rate was significantly higher in the dead patient group than that in the survivor group between neural network model and heart team [16.8% (787/4679) vs. 20.3% (87/428), P  = 0.065)]. The study shows the possibility to build a computer adviser with a neural network model for making a choice between CABG and PCI in order to achieve a higher 5-year survival rate in patients with angina.

  17. Comparing detection and disclosure of traffic incidents in social networks: an intelligent approach based on Twitter vs. Waze

    Directory of Open Access Journals (Sweden)

    Sebastián Vallejos

    2018-03-01

    Full Text Available Nowadays, social networks have become  in a  communication  medium widely  used to disseminate any type  of  information. In  particular,  the  shared  information  in  social  networks  usually  includes  a  considerable number of traffic incidents reports of specific cities. In light of this, specialized social networks have emerged for detecting and disseminating traffic incidents, differentiating from generic social networks in which a wide variety of  topics  are  communicated.  In this  context,  Twitter  is  a  case  in  point  of  a  generic  social  network  in  which  its users often share information about traffic incidents, while Waze is a social network specialized in traffic. In this paper we present a comparative study between Waze and an intelligent approach that detects traffic incidents by analyzing publications shared in Twitter. The comparative study was carried out considering Ciudad Autónoma de Buenos  Aires  (CABA,  Argentina,  as  the  region  of  interest.  The results of this work suggest that both social networks should be considered as complementary sources of information. This conclusion is based on the fact that the proportion of mutual detections, i.e. traffic incidents detected by both approaches, was considerably low since it did not exceed 6% of the cases. Moreover, the results do not show that any of the approaches tend to anticipate in time to the other one in the detection of traffic incidents.

  18. Intelligent systems

    CERN Document Server

    Irwin, J David

    2011-01-01

    Technology has now progressed to the point that intelligent systems are replacing humans in the decision making processes as well as aiding in the solution of very complex problems. In many cases intelligent systems are already outperforming human activities. Artificial neural networks are not only capable of learning how to classify patterns, such images or sequence of events, but they can also effectively model complex nonlinear systems. Their ability to classify sequences of events is probably more popular in industrial applications where there is an inherent need to model nonlinear system

  19. Augmented Teams -- Assembling Smart Sensors, Intelligent Networks and Humans into Agile Task Groups

    NARCIS (Netherlands)

    Neef, R.M.; Rijn, M. van; Marck, J.W.; Keus, D.

    2009-01-01

    Safety and security environments are full of networked devices. Despite ample research on sensor networks and network technology, there is little practical comprehensive work on how to incorporate such technologies effectively into human-centered teams. This paper discusses the challenge of

  20. Intelligence in Artificial Intelligence

    OpenAIRE

    Datta, Shoumen Palit Austin

    2016-01-01

    The elusive quest for intelligence in artificial intelligence prompts us to consider that instituting human-level intelligence in systems may be (still) in the realm of utopia. In about a quarter century, we have witnessed the winter of AI (1990) being transformed and transported to the zenith of tabloid fodder about AI (2015). The discussion at hand is about the elements that constitute the canonical idea of intelligence. The delivery of intelligence as a pay-per-use-service, popping out of ...

  1. Advanced intelligent systems

    CERN Document Server

    Ryoo, Young; Jang, Moon-soo; Bae, Young-Chul

    2014-01-01

    Intelligent systems have been initiated with the attempt to imitate the human brain. People wish to let machines perform intelligent works. Many techniques of intelligent systems are based on artificial intelligence. According to changing and novel requirements, the advanced intelligent systems cover a wide spectrum: big data processing, intelligent control, advanced robotics, artificial intelligence and machine learning. This book focuses on coordinating intelligent systems with highly integrated and foundationally functional components. The book consists of 19 contributions that features social network-based recommender systems, application of fuzzy enforcement, energy visualization, ultrasonic muscular thickness measurement, regional analysis and predictive modeling, analysis of 3D polygon data, blood pressure estimation system, fuzzy human model, fuzzy ultrasonic imaging method, ultrasonic mobile smart technology, pseudo-normal image synthesis, subspace classifier, mobile object tracking, standing-up moti...

  2. Intelligent control for modeling of real-time reservoir operation, part II: artificial neural network with operating rule curves

    Science.gov (United States)

    Chang, Ya-Ting; Chang, Li-Chiu; Chang, Fi-John

    2005-04-01

    To bridge the gap between academic research and actual operation, we propose an intelligent control system for reservoir operation. The methodology includes two major processes, the knowledge acquired and implemented, and the inference system. In this study, a genetic algorithm (GA) and a fuzzy rule base (FRB) are used to extract knowledge based on the historical inflow data with a design objective function and on the operating rule curves respectively. The adaptive network-based fuzzy inference system (ANFIS) is then used to implement the knowledge, to create the fuzzy inference system, and then to estimate the optimal reservoir operation. To investigate its applicability and practicability, the Shihmen reservoir, Taiwan, is used as a case study. For the purpose of comparison, a simulation of the currently used M-5 operating rule curve is also performed. The results demonstrate that (1) the GA is an efficient way to search the optimal input-output patterns, (2) the FRB can extract the knowledge from the operating rule curves, and (3) the ANFIS models built on different types of knowledge can produce much better performance than the traditional M-5 curves in real-time reservoir operation. Moreover, we show that the model can be more intelligent for reservoir operation if more information (or knowledge) is involved.

  3. Application of Wireless Sensor and Actuator Networks to Achieve Intelligent Microgrids: A Promising Approach towards a Global Smart Grid Deployment

    Directory of Open Access Journals (Sweden)

    Alvaro Llaria

    2016-02-01

    Full Text Available Smart Grids (SGs constitute the evolution of the traditional electrical grid towards a new paradigm, which should increase the reliability, the security and, at the same time, reduce the costs of energy generation, distribution and consumption. Electrical microgrids (MGs can be considered the first stage of this evolution of the grid, because of the intelligent management techniques that must be applied to assure their correct operation. To accomplish this task, sensors and actuators will be necessary, along with wireless communication technologies to transmit the measured data and the command messages. Wireless Sensor and Actuator Networks (WSANs are therefore a promising solution to achieve an intelligent management of MGs and, by extension, the SG. In this frame, this paper surveys several aspects concerning the application of WSANs to manage MGs and the electrical grid, as well as the communication protocols that could be applied. The main concerns regarding the SG deployment are also presented, including future scenarios where the interoperability of different generation technologies must be assured.

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

    Science.gov (United States)

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

    2018-06-09

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

  5. 10th International Symposium on Intelligent Distributed Computing

    CERN Document Server

    Seghrouchni, Amal; Beynier, Aurélie; Camacho, David; Herpson, Cédric; Hindriks, Koen; Novais, Paulo

    2017-01-01

    This book presents the combined peer-reviewed proceedings of the tenth International Symposium on Intelligent Distributed Computing (IDC’2016), which was held in Paris, France from October 10th to 12th, 2016. The 23 contributions address a range of topics related to theory and application of intelligent distributed computing, including: Intelligent Distributed Agent-Based Systems, Ambient Intelligence and Social Networks, Computational Sustainability, Intelligent Distributed Knowledge Representation and Processing, Smart Networks, Networked Intelligence and Intelligent Distributed Applications, amongst others.

  6. 6th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing

    CERN Document Server

    2016-01-01

    This edited book presents scientific results of the 16th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2015) which was held on June 1 – 3, 2015 in Takamatsu, Japan. The aim of this conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the numerous fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way. Research results about all aspects (theory, applications and tools) of computer and information science, and to discuss the practical challenges encountered along the way and the solutions adopted to solve them.

  7. 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing

    CERN Document Server

    SNPD 2016

    2016-01-01

    This edited book presents scientific results of the 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2016) which was held on May 30 - June 1, 2016 in Shanghai, China. The aim of this conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the numerous fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way. Research results about all aspects (theory, applications and tools) of computer and information science, and to discuss the practical challenges encountered along the way and the solutions adopted to solve them.

  8. Future Internet as a Driver for Virtualization, Connectivity and Intelligence of Agri‐Food Supply Chain Networks

    Directory of Open Access Journals (Sweden)

    Cor N. Verdouw

    2014-03-01

    Full Text Available The food and agribusiness is an important sector in European logistics with a share in the EU road transport of about 20%. One of the main logistic challenges in this sector is to deal with the high dynamics and uncertainty in supply and demand. This paper discusses the opportunities of Future Internet (FI technologies to addresses the specific demands on information systems for logistics in the food and agribusiness domain. More specifically, it presents a Future Internet (FI based design for smart agri‐food logistic information systems. This design aims to enable new types of efficient and responsive logistics networks with flexible chain‐encompassing tracking and tracing systems and decision support based on that information. These systems effectively virtualise the logistics flows from farm to fork, support a timely and error‐free exchange of logistics information and provide functionality for intelligent analysis and reporting of exchanged data to enable early warning and advanced forecasting.

  9. From the shadows into the limelight: Intelligent local network stations; Vom Schattendasein ins Rampenlicht. Intelligente Ortsnetzstationen

    Energy Technology Data Exchange (ETDEWEB)

    Opitsch, Bruno [Siemens AG, Energy Sektor, Nuernberg (Germany)

    2011-03-21

    Local network stations ought to receive greater attention in view of increasing consumption, increasing supply from decentral power stations, load management and new functions relating to increasing electromobility in individual traffic. Higher efficiency should be the central goal in making local network stations fit for the 21st century.

  10. Association between structural brain network efficiency and intelligence increases during adolescence

    NARCIS (Netherlands)

    Koenis, Marinka M G; Brouwer, Rachel M; Swagerman, Suzanne C; van Soelen, Inge L C; Boomsma, Dorret I; Hulshoff Pol, Hilleke E

    2018-01-01

    Adolescence represents an important period during which considerable changes in the brain take place, including increases in integrity of white matter bundles, and increasing efficiency of the structural brain network. A more efficient structural brain network has been associated with higher

  11. On the Capacity of a GSM Frequency Hopping network with Intelligent Underlayer-Overlayer

    DEFF Research Database (Denmark)

    Nielsen, Thomas Toftegaard; Wigard, Jeroen; Mogensen, Preben Elgaard

    1997-01-01

    . By combining this reuse partitioning with frequency hopping, an increase in the network capacity in terms of carried traffic per cell is achieved. Simulations have indicated that for slow moving mobiles a gain of approximately 35% is achieved by this new feature when compared with a frequency hopping network...

  12. An application of neural networks and artificial intelligence for in-core fuel management

    International Nuclear Information System (INIS)

    Miller, L.F.; Algutifan, F.; Uhrig, R.E.

    1992-01-01

    This paper reports the feasibility of using expert systems in combination with neural networks and neutronics calculations to improve the efficiency for obtaining optimal candidate reload core designs. The general objectives of this research are as follows: (1) generate a suitable data base and ancillary software for training neural networks that duplicate neutronics calculations. (2) develop a graphical interface with neutronics software and neural networks for manual shuffling of reload cores. (3) construct an expert system for shuffling reload cores with specified rules. (4) develp neural networks that capture the nonlinear behavior of fuel depletion. (5) integrate the neural networks and neutronics software with an expert system to specify reload cores that obtain appropriate figure of merit

  13. Congestion and flow control in signaling system no. 7: Impacts of intelligent networks and new services

    Science.gov (United States)

    Zepf, Joachim; Rufa, Gerhard

    1994-04-01

    This paper focuses on the transient performance analysis of the congestion and flow control mechanisms in CCITT Signaling System No. 7 (SS7). Special attention is directed to the impacts of the introduction of intelligent services and new applications, e.g., Freephone, credit card services, user-to-user signaling, etc. In particular, we show that signaling traffic characteristics like signaling scenarios or signaling message length as well as end-to-end signaling capabilities have a significant influence on the congestion and flow control and, therefore, on the real-time signaling performance. One important result of our performance studies is that if, e.g., intelligent services are introduced, the SS7 congestion and flow control does not work correctly. To solve this problem, some reinvestigations into these mechanisms would be necessary. Therefore, some approaches, e.g., modification of the Signaling Connection Control Part (SCCP) congestion control, usage of the SCCP relay function, or a redesign of the MTP flow control procedures are discussed in order to guarantee the efficacy of the congestion and flow control mechanisms also in the future.

  14. Systematic construction and control of stereo nerve vision network in intelligent manufacturing

    Science.gov (United States)

    Liu, Hua; Wang, Helong; Guo, Chunjie; Ding, Quanxin; Zhou, Liwei

    2017-10-01

    A system method of constructing stereo vision by using neural network is proposed, and the operation and control mechanism in actual operation are proposed. This method makes effective use of the neural network in learning and memory function, by after training with samples. Moreover, the neural network can learn the nonlinear relationship in the stereoscopic vision system and the internal and external orientation elements. These considerations are Worthy of attention, which includes limited constraints, the scientific of critical group, the operating speed and the operability in technical aspects. The results support our theoretical forecast.

  15. Intelligent QoS routing algorithm based on improved AODV protocol for Ad Hoc networks

    Science.gov (United States)

    Huibin, Liu; Jun, Zhang

    2016-04-01

    Mobile Ad Hoc Networks were playing an increasingly important part in disaster reliefs, military battlefields and scientific explorations. However, networks routing difficulties are more and more outstanding due to inherent structures. This paper proposed an improved cuckoo searching-based Ad hoc On-Demand Distance Vector Routing protocol (CSAODV). It elaborately designs the calculation methods of optimal routing algorithm used by protocol and transmission mechanism of communication-package. In calculation of optimal routing algorithm by CS Algorithm, by increasing QoS constraint, the found optimal routing algorithm can conform to the requirements of specified bandwidth and time delay, and a certain balance can be obtained among computation spending, bandwidth and time delay. Take advantage of NS2 simulation software to take performance test on protocol in three circumstances and validate the feasibility and validity of CSAODV protocol. In results, CSAODV routing protocol is more adapt to the change of network topological structure than AODV protocol, which improves package delivery fraction of protocol effectively, reduce the transmission time delay of network, reduce the extra burden to network brought by controlling information, and improve the routing efficiency of network.

  16. Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network.

    Science.gov (United States)

    Falat, Lukas; Marcek, Dusan; Durisova, Maria

    2016-01-01

    This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.

  17. Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

    Directory of Open Access Journals (Sweden)

    Lukas Falat

    2016-01-01

    Full Text Available This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.

  18. Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

    Science.gov (United States)

    Marcek, Dusan; Durisova, Maria

    2016-01-01

    This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process. PMID:26977450

  19. Data-Driven Design of Intelligent Wireless Networks: An Overview and Tutorial

    Directory of Open Access Journals (Sweden)

    Merima Kulin

    2016-06-01

    Full Text Available Data science or “data-driven research” is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i clarifies when, why and how to use data science in wireless network research; (ii provides a generic framework for applying data science in wireless networks; (iii gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v provides the reader the necessary datasets and scripts to go through the tutorial steps themselves.

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

    Science.gov (United States)

    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

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

    Science.gov (United States)

    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.

  2. A novel framework for intelligent signal detection via artificial neural networks for cyclic voltammetry in pyroprocessing technology

    International Nuclear Information System (INIS)

    Rakhshan Pouri, Samaneh; Manic, Milos; Phongikaroon, Supathorn

    2018-01-01

    Highlights: •First time ANN implementation toward pyroprocessing safeguards. •Real time monitoring in terms of intelligent materials detection and accountability. •CV simulation via ANN showing a high accuracy of prediction for the unseen situation. •Elimination of trial and error approach to avoid overfitting in learning. -- Abstract: Electrorefiner (ER) is the heart of pyroprocessing technology which contains different fission, rare-earth, and transuranic chloride compositions during the operation. This is still a developing technology that needs to be advanced for the commercial reprocessing design of used nuclear fuel (UNF) in terms of intelligent materials detection and accountability towards safeguards. A novel signal detection, artificial neural network (ANN), has been proposed in this study to apply on massive ER systemic parameters to simulate cyclic voltammetry (CV) graphs for the unseen situation. ANN could be trained to mimic the system by driving the data sets interrelation between variables to provide current and potential simulated data sets with a high accuracy of prediction. For this purpose, over 230,000 experimental data points reported in literature have been explored—0.5–5 wt% of zirconium chloride (ZrCl 4 ) in LiCl-KCl molten salt with different scan rates at 773 K. This study has illustrated a new framework of ANN implementation to eliminate trial and error approach by comparing the average error of one to three hidden layers with different number of neurons. In addition, this framework results in finding a preferable balance between underfitting and overfitting in deep learning. Furthermore, simulated CV graphs were compared with the experimental data and illustrated a reasonable prediction. The results reveal two structures with three hidden layers providing a good prediction with a low average error. The outcomes indicate that ANN has a strong potential in applying toward safeguards for pyroprocessing technology.

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

    Science.gov (United States)

    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.

  4. Comparison of Back propagation neural network and Back propagation neural network Based Particle Swarm intelligence in Diagnostic Breast Cancer

    Directory of Open Access Journals (Sweden)

    Farahnaz SADOUGHI

    2014-03-01

    Full Text Available Breast cancer is the most commonly diagnosed cancer and the most common cause of death in women all over the world. Use of computer technology supporting breast cancer diagnosing is now widespread and pervasive across a broad range of medical areas. Early diagnosis of this disease can greatly enhance the chances of long-term survival of breast cancer victims. Artificial Neural Networks (ANN as mainly method play important role in early diagnoses breast cancer. This paper studies Levenberg Marquardet Backpropagation (LMBP neural network and Levenberg Marquardet Backpropagation based Particle Swarm Optimization(LMBP-PSO for the diagnosis of breast cancer. The obtained results show that LMBP and LMBP based PSO system provides higher classification efficiency. But LMBP based PSO needs minimum training and testing time. It helps in developing Medical Decision System (MDS for breast cancer diagnosing. It can also be used as secondary observer in clinical decision making.

  5. The “Wireless Sensor Networks for City-Wide Ambient Intelligence (WISE-WAI)” Project

    Science.gov (United States)

    Casari, Paolo; Castellani, Angelo P.; Cenedese, Angelo; Lora, Claudio; Rossi, Michele; Schenato, Luca; Zorzi, Michele

    2009-01-01

    This paper gives a detailed technical overview of some of the activities carried out in the context of the “Wireless Sensor networks for city-Wide Ambient Intelligence (WISE-WAI)” project, funded by the Cassa di Risparmio di Padova e Rovigo Foundation, Italy. The main aim of the project is to demonstrate the feasibility of large-scale wireless sensor network deployments, whereby tiny objects integrating one or more environmental sensors (humidity, temperature, light intensity), a microcontroller and a wireless transceiver are deployed over a large area, which in this case involves the buildings of the Department of Information Engineering at the University of Padova. We will describe how the network is organized to provide full-scale automated functions, and which services and applications it is configured to provide. These applications include long-term environmental monitoring, alarm event detection and propagation, single-sensor interrogation, localization and tracking of objects, assisted navigation, as well as fast data dissemination services to be used, e.g., to rapidly re-program all sensors over-the-air. The organization of such a large testbed requires notable efforts in terms of communication protocols and strategies, whose design must pursue scalability, energy efficiency (while sensors are connected through USB cables for logging and debugging purposes, most of them will be battery-operated), as well as the capability to support applications with diverse requirements. These efforts, the description of a subset of the results obtained so far, and of the final objectives to be met are the scope of the present paper. PMID:22408513

  6. On a new concept of community: social networks, personal communities and collective intelligence

    Directory of Open Access Journals (Sweden)

    Rogério da Costa

    2006-01-01

    Full Text Available This text essentially deals with the transmutation of the concept of "community" into "social networks". This change is due largely to the boom of virtual communities in cyberspace, a fact that has generated a number of studies not only on this new way of weaving a society, but also on the dynamic structure of communication networks. At the core of this transformation, concepts such as social capital, trust and partial sympathy are called upon, to enable us to think about the new forms of association that regulate human activity in our time.

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

    Science.gov (United States)

    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.

  8. THRESHOLD LOGIC IN ARTIFICIAL INTELLIGENCE

    Science.gov (United States)

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

  9. Fuzzy logic and neural networks in artificial intelligence and pattern recognition

    Science.gov (United States)

    Sanchez, Elie

    1991-10-01

    With the use of fuzzy logic techniques, neural computing can be integrated in symbolic reasoning to solve complex real world problems. In fact, artificial neural networks, expert systems, and fuzzy logic systems, in the context of approximate reasoning, share common features and techniques. A model of Fuzzy Connectionist Expert System is introduced, in which an artificial neural network is designed to construct the knowledge base of an expert system from, training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the synaptic connections in an AND-OR structure: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through min-max fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feedforward network is described and first illustrated in a biomedical application (medical diagnosis assistance from inflammatory-syndromes/proteins profiles). Then, it is shown how this methodology can be utilized for handwritten pattern recognition (characters play the role of diagnoses): in a fuzzy neuron describing a number for example, the linguistic weights represent fuzzy sets on cross-detecting lines and the numerical weights reflect the importance (or weakness) of connections between cross-detecting lines and characters.

  10. Intelligent Broadcasting in Mobile Ad Hoc Networks: Three Classes of Adaptive Protocols

    Directory of Open Access Journals (Sweden)

    Michael D. Colagrosso

    2006-11-01

    Full Text Available Because adaptability greatly improves the performance of a broadcast protocol, we identify three ways in which machine learning can be applied to broadcasting in a mobile ad hoc network (MANET. We chose broadcasting because it functions as a foundation of MANET communication. Unicast, multicast, and geocast protocols utilize broadcasting as a building block, providing important control and route establishment functionality. Therefore, any improvements to the process of broadcasting can be immediately realized by higher-level MANET functionality and applications. While efficient broadcast protocols have been proposed, no single broadcasting protocol works well in all possible MANET conditions. Furthermore, protocols tend to fail catastrophically in severe network environments. Our three classes of adaptive protocols are pure machine learning, intra-protocol learning, and inter-protocol learning. In the pure machine learning approach, we exhibit a new approach to the design of a broadcast protocol: the decision of whether to rebroadcast a packet is cast as a classification problem. Each mobile node (MN builds a classifier and trains it on data collected from the network environment. Using intra-protocol learning, each MN consults a simple machine model for the optimal value of one of its free parameters. Lastly, in inter-protocol learning, MNs learn to switch between different broadcasting protocols based on network conditions. For each class of learning method, we create a prototypical protocol and examine its performance in simulation.

  11. Intelligent Broadcasting in Mobile Ad Hoc Networks: Three Classes of Adaptive Protocols

    Directory of Open Access Journals (Sweden)

    Colagrosso Michael D

    2007-01-01

    Full Text Available Because adaptability greatly improves the performance of a broadcast protocol, we identify three ways in which machine learning can be applied to broadcasting in a mobile ad hoc network (MANET. We chose broadcasting because it functions as a foundation of MANET communication. Unicast, multicast, and geocast protocols utilize broadcasting as a building block, providing important control and route establishment functionality. Therefore, any improvements to the process of broadcasting can be immediately realized by higher-level MANET functionality and applications. While efficient broadcast protocols have been proposed, no single broadcasting protocol works well in all possible MANET conditions. Furthermore, protocols tend to fail catastrophically in severe network environments. Our three classes of adaptive protocols are pure machine learning, intra-protocol learning, and inter-protocol learning. In the pure machine learning approach, we exhibit a new approach to the design of a broadcast protocol: the decision of whether to rebroadcast a packet is cast as a classification problem. Each mobile node (MN builds a classifier and trains it on data collected from the network environment. Using intra-protocol learning, each MN consults a simple machine model for the optimal value of one of its free parameters. Lastly, in inter-protocol learning, MNs learn to switch between different broadcasting protocols based on network conditions. For each class of learning method, we create a prototypical protocol and examine its performance in simulation.

  12. Intelligent Data Transfer for Multiple Sensor Networks over a Broad Temperature Range

    Science.gov (United States)

    Krasowski, Michael (Inventor)

    2018-01-01

    A sensor network may be configured to operate in extreme temperature environments. A sensor may be configured to generate a frequency carrier, and transmit the frequency carrier to a node. The node may be configured to amplitude modulate the frequency carrier, and transmit the amplitude modulated frequency carrier to a receiver.

  13. How Wireless Sensor Networks Can Benefit from Brain Emotional Learning Based Intelligent Controller (BELBIC)

    NARCIS (Netherlands)

    Kalayci, Tahir Emre; Bahrepour, M.; Meratnia, Nirvana; Havinga, Paul J.M.

    2011-01-01

    Wireless sensor networks (WSNs) are composed of small sensing and actuating devices that collaboratively monitor a phenomena, process and reason about sensor measurements, and provide adequate feedback or take actions. One of WSNs tasks is event detection, in which occurrence of events of interest

  14. Path planning in GPS-denied environments via collective intelligence of distributed sensor networks

    Science.gov (United States)

    Jha, Devesh K.; Chattopadhyay, Pritthi; Sarkar, Soumik; Ray, Asok

    2016-05-01

    This paper proposes a framework for reactive goal-directed navigation without global positioning facilities in unknown dynamic environments. A mobile sensor network is used for localising regions of interest for path planning of an autonomous mobile robot. The underlying theory is an extension of a generalised gossip algorithm that has been recently developed in a language-measure-theoretic setting. The algorithm has been used to propagate local decisions of target detection over a mobile sensor network and thus, it generates a belief map for the detected target over the network. In this setting, an autonomous mobile robot may communicate only with a few mobile sensing nodes in its own neighbourhood and localise itself relative to the communicating nodes with bounded uncertainties. The robot makes use of the knowledge based on the belief of the mobile sensors to generate a sequence of way-points, leading to a possible goal. The estimated way-points are used by a sampling-based motion planning algorithm to generate feasible trajectories for the robot. The proposed concept has been validated by numerical simulation on a mobile sensor network test-bed and a Dubin's car-like robot.

  15. Intelligent future wireless networks for energy efficiency: overall analysis and standardization activities

    CSIR Research Space (South Africa)

    Kliks, A

    2013-10-01

    Full Text Available solutions supporting sophisticated energy management within each layer of the OSI stack model in the context of wireless networking, as well as the overview of cross-layer energy-efficient applications. All parts of the chapter have adequate references...

  16. Intelligent Local Avoided Collision (iLAC) MAC Protocol for Very High Speed Wireless Network

    Science.gov (United States)

    Hieu, Dinh Chi; Masuda, Akeo; Rabarijaona, Verotiana Hanitriniala; Shimamoto, Shigeru

    Future wireless communication systems aim at very high data rates. As the medium access control (MAC) protocol plays the central role in determining the overall performance of the wireless system, designing a suitable MAC protocol is critical to fully exploit the benefit of high speed transmission that the physical layer (PHY) offers. In the latest 802.11n standard [2], the problem of long overhead has been addressed adequately but the issue of excessive colliding transmissions, especially in congested situation, remains untouched. The procedure of setting the backoff value is the heart of the 802.11 distributed coordination function (DCF) to avoid collision in which each station makes its own decision on how to avoid collision in the next transmission. However, collision avoidance is a problem that can not be solved by a single station. In this paper, we introduce a new MAC protocol called Intelligent Local Avoided Collision (iLAC) that redefines individual rationality in choosing the backoff counter value to avoid a colliding transmission. The distinguishing feature of iLAC is that it fundamentally changes this decision making process from collision avoidance to collaborative collision prevention. As a result, stations can avoid colliding transmissions with much greater precision. Analytical solution confirms the validity of this proposal and simulation results show that the proposed algorithm outperforms the conventional algorithms by a large margin.

  17. An intelligent sales forecasting system through integration of artificial neural networks and fuzzy neural networks with fuzzy weight elimination.

    Science.gov (United States)

    Kuo, R J; Wu, P; Wang, C P

    2002-09-01

    Sales forecasting plays a very prominent role in business strategy. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average (ARMA). However, sales forecasting is very complicated owing to influence by internal and external environments. Recently, artificial neural networks (ANNs) have also been applied in sales forecasting since their promising performances in the areas of control and pattern recognition. However, further improvement is still necessary since unique circumstances, e.g. promotion, cause a sudden change in the sales pattern. Thus, this study utilizes a proposed fuzzy neural network (FNN), which is able to eliminate the unimportant weights, for the sake of learning fuzzy IF-THEN rules obtained from the marketing experts with respect to promotion. The result from FNN is further integrated with the time series data through an ANN. Both the simulated and real-world problem results show that FNN with weight elimination can have lower training error compared with the regular FNN. Besides, real-world problem results also indicate that the proposed estimation system outperforms the conventional statistical method and single ANN in accuracy.

  18. Trends in ambient intelligent systems the role of computational intelligence

    CERN Document Server

    Khan, Mohammad; Abraham, Ajith

    2016-01-01

    This book demonstrates the success of Ambient Intelligence in providing possible solutions for the daily needs of humans. The book addresses implications of ambient intelligence in areas of domestic living, elderly care, robotics, communication, philosophy and others. The objective of this edited volume is to show that Ambient Intelligence is a boon to humanity with conceptual, philosophical, methodical and applicative understanding. The book also aims to schematically demonstrate developments in the direction of augmented sensors, embedded systems and behavioral intelligence towards Ambient Intelligent Networks or Smart Living Technology. It contains chapters in the field of Ambient Intelligent Networks, which received highly positive feedback during the review process. The book contains research work, with in-depth state of the art from augmented sensors, embedded technology and artificial intelligence along with cutting-edge research and development of technologies and applications of Ambient Intelligent N...

  19. "Intelligent" design of molecular materials: Understanding the concepts of design in supramolecular synthesis of network solids

    Science.gov (United States)

    Moulton, Brian D.

    This work endeavors to delineate modern paradigms for crystal engineering, i.e. the design and supramolecular synthesis of functional molecular materials. Paradigms predicated on an understanding of the geometry of polygons and polyhedra are developed. The primary focus is on structural determination by single crystal X-ray crystallography, structural interpretation using a suite of graphical visualization and molecular modeling software, and on the importance of proper graphical representation in the presentation and explanation of crystal structures. A detailed analysis of a selected series of crystal structures is presented. The reduction of these molecular networks to schematic representations that illustrate their fundamental connectivity facilitates the understanding of otherwise complex supramolecular solids. Circuit symbols and Schlafli notation are used to describe the network topologies, which enables networks of different composition and metrics to be easily compared. This reveals that molecular orientations in the crystals and networks are commensurate with networks that can be derived from spherical close packed lattices. The development of a logical design strategy for a new class of materials based on our understanding of the chemical composition and topology of these networks is described. The synthesis and crystal structure of a series of new materials generated by exploitation of this design strategy is presented, in addition to a detailed analysis of the topology of these materials and their relationship to a 'parent' structure. In summary, this dissertation demonstrates that molecular polygons can self-assemble at their vertexes to produce molecular architectures and crystal structures that are consistent with long established geometric dogma. The design strategy represents a potentially broad ranging approach to the design of nanoporous structures from a wide range of chemical components that are based on molecular shape rather than chemical

  20. Intelligent Image Recognition System for Marine Fouling Using Softmax Transfer Learning and Deep Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    C. S. Chin

    2017-01-01

    Full Text Available The control of biofouling on marine vessels is challenging and costly. Early detection before hull performance is significantly affected is desirable, especially if “grooming” is an option. Here, a system is described to detect marine fouling at an early stage of development. In this study, an image of fouling can be transferred wirelessly via a mobile network for analysis. The proposed system utilizes transfer learning and deep convolutional neural network (CNN to perform image recognition on the fouling image by classifying the detected fouling species and the density of fouling on the surface. Transfer learning using Google’s Inception V3 model with Softmax at last layer was carried out on a fouling database of 10 categories and 1825 images. Experimental results gave acceptable accuracies for fouling detection and recognition.

  1. A proposition of a manufactronic network approach for intelligent and flexible manufacturing systems

    Directory of Open Access Journals (Sweden)

    Fernando Almeida

    2011-10-01

    Full Text Available The XPRESS project introduces a completely new scalable concept of a manufactronic networked factory, which is composed by a co-ordinated team of specialized autonomous objects (Manufactrons, each knowing how to do a certain process optimally. This knowledge based concept integrated the complete chain: production configuration (decrease of ramp-up time of at least 50%, multi-variant production line (varying types and volumes on a single line and 100% quality monitoring. The manufactronic networked architecture allows continuous process improvement, and will be able to anticipate and to respond to rapidly changing consumer needs, producing high-quality products in adequate quantities while reducing costs. This concept is demonstrated in the automotive, aeronautics and electrical industry but can be transferred to nearly all production processes.

  2. Intelligent Security Auditing Based on Access Control of Devices in Ad Hoc Network

    Institute of Scientific and Technical Information of China (English)

    XU Guang-wei; SHI You-qun; ZHU Ming; WU Guo-wen; CAO Qi-ying

    2006-01-01

    Security in Ad Hoc network is an important issue under the opening circumstance of application service. Some protocols and models of security auditing have been proposed to ensure rationality of contracting strategy and operating regulation and used to identify abnormal operation. Model of security auditing based on access control of devices will be advanced to register sign of devices and property of event of access control and to audit those actions. In the end, the model is analyzed and simulated.

  3. Intelligent Spectrum Handoff via Docitive Learning in Cognitive Radio Networks (CRNs)

    Science.gov (United States)

    2017-03-01

    Release; Distribution Unlimited. 22 error style to seek proper MDP settings, it may find a neighboring SU with similar traffic QoS demands, and...for CRN testbed implementation (Figure 20). USRP products are a family of computer-hosted hardware units offered by Ettus Research LLC and its parent ...Networking Technologies for Software-Defined Radio and White Space, 2010. Boston, MA, USA, 2010, pp. 1-6. [26] T. R. Newman and T. Bose, “A cognitive radio

  4. Inferring Social Isolation in Older Adults through Ambient Intelligence and Social Networking Sites

    OpenAIRE

    Campos, Wilfrido; Martinez, Alicia; Sanchez, Wendy; Estrada, Hugo; Favela, Jesus; Perez, Joaquin

    2016-01-01

    Abstract Early diagnosis of social isolation in older adults can prevent physical and cognitive impairment or further impoverishment of their social network. This diagnosis is usually performed by personal and periodic application of psychological assessment instruments. This situation encourages the development of novel approaches able to monitor risk situations in social interactions to obtain early diagnosis and implement appropriate measures. This paper presents the development of a predi...

  5. Botanical drugs, synergy, and network pharmacology: forth and back to intelligent mixtures.

    Science.gov (United States)

    Gertsch, Jürg

    2011-07-01

    For centuries the science of pharmacognosy has dominated rational drug development until it was gradually substituted by target-based drug discovery in the last fifty years. Pharmacognosy stems from the different systems of traditional herbal medicine and its "reverse pharmacology" approach has led to the discovery of numerous pharmacologically active molecules and drug leads for humankind. But do botanical drugs also provide effective mixtures? Nature has evolved distinct strategies to modulate biological processes, either by selectively targeting biological macromolecules or by creating molecular promiscuity or polypharmacology (one molecule binds to different targets). Widely claimed to be superior over monosubstances, mixtures of bioactive compounds in botanical drugs allegedly exert synergistic therapeutic effects. Despite evolutionary clues to molecular synergism in nature, sound experimental data are still widely lacking to support this assumption. In this short review, the emerging concept of network pharmacology is highlighted, and the importance of studying ligand-target networks for botanical drugs is emphasized. Furthermore, problems associated with studying mixtures of molecules with distinctly different pharmacodynamic properties are addressed. It is concluded that a better understanding of the polypharmacology and potential network pharmacology of botanical drugs is fundamental in the ongoing rationalization of phytotherapy. © Georg Thieme Verlag KG Stuttgart · New York.

  6. Intelligent Noise Removal from EMG Signal Using Focused Time-Lagged Recurrent Neural Network

    Directory of Open Access Journals (Sweden)

    S. N. Kale

    2009-01-01

    Full Text Available Electromyography (EMG signals can be used for clinical/biomedical application and modern human computer interaction. EMG signals acquire noise while traveling through tissue, inherent noise in electronics equipment, ambient noise, and so forth. ANN approach is studied for reduction of noise in EMG signal. In this paper, it is shown that Focused Time-Lagged Recurrent Neural Network (FTLRNN can elegantly solve to reduce the noise from EMG signal. After rigorous computer simulations, authors developed an optimal FTLRNN model, which removes the noise from the EMG signal. Results show that the proposed optimal FTLRNN model has an MSE (Mean Square Error as low as 0.000067 and 0.000048, correlation coefficient as high as 0.99950 and 0.99939 for noise signal and EMG signal, respectively, when validated on the test dataset. It is also noticed that the output of the estimated FTLRNN model closely follows the real one. This network is indeed robust as EMG signal tolerates the noise variance from 0.1 to 0.4 for uniform noise and 0.30 for Gaussian noise. It is clear that the training of the network is independent of specific partitioning of dataset. It is seen that the performance of the proposed FTLRNN model clearly outperforms the best Multilayer perceptron (MLP and Radial Basis Function NN (RBF models. The simple NN model such as the FTLRNN with single-hidden layer can be employed to remove noise from EMG signal.

  7. Stability of Intelligent Transportation Network Dynamics: A Daily Path Flow Adjustment considering Travel Time Differentiation

    Directory of Open Access Journals (Sweden)

    Ming-Chorng Hwang

    2015-01-01

    Full Text Available A theoretic formulation on how traffic time information distributed by ITS operations influences the trajectory of network flows is presented in this paper. The interactions between users and ITS operator are decomposed into three parts: (i travel time induced path flow dynamics (PFDTT; (ii demand induced path flow dynamics (PFDD; and (iii predicted travel time dynamics for an origin-destination (OD pair (PTTDOD. PFDTT describes the collective results of user’s daily route selection by pairwise comparison of path travel time provided by ITS services. The other two components, PTTDOD and PFDD, are concentrated on the evolutions of system variables which are predicted and observed, respectively, by ITS operators to act as a benchmark in guiding the target system towards an expected status faster. In addition to the delivered modelings, the stability theorem of the equilibrium solution in the sense of Lyapunov stability is also provided. A Lyapunov function is developed and employed to the proof of stability theorem to show the asymptotic behavior of the aimed system. The information of network flow dynamics plays a key role in traffic control policy-making. The evaluation of ITS-based strategies will not be reasonable without a well-established modeling of network flow evolutions.

  8. Symbiotic intelligence: Self-organizing knowledge on distributed networks, driven by human interaction

    Energy Technology Data Exchange (ETDEWEB)

    Johnson, N.; Joslyn, C.; Rocha, L.; Smith, S.; Kantor, M. [Los Alamos National Lab., NM (United States); Rasmussen, S. [Los Alamos National Lab., NM (United States)]|[Santa Fe Inst., NM (United States)

    1998-07-01

    -organizing knowledge formation from this symbiotic intelligence exemplifies a new type of self-organizing system, one without dissipation and not constrained by limited resources.

  9. The Global Public Health Intelligence Network and early warning outbreak detection: a Canadian contribution to global public health.

    Science.gov (United States)

    Mykhalovskiy, Eric; Weir, Lorna

    2006-01-01

    The recent SARS epidemic has renewed widespread concerns about the global transmission of infectious diseases. In this commentary, we explore novel approaches to global infectious disease surveillance through a focus on an important Canadian contribution to the area--the Global Public Health Intelligence Network (GPHIN). GPHIN is a cutting-edge initiative that draws on the capacity of the Internet and newly available 24/7 global news coverage of health events to create a unique form of early warning outbreak detection. This commentary outlines the operation and development of GPHIN and compares it to ProMED-mail, another Internet-based approach to global health surveillance. We argue that GPHIN has created an important shift in the relationship of public health and news information. By exiting the pyramid of official reporting, GPHIN has created a new monitoring technique that has disrupted national boundaries of outbreak notification, while creating new possibilities for global outbreak response. By incorporating news within the emerging apparatus of global infectious disease surveillance, GPHIN has effectively responded to the global media's challenge to official country reporting of outbreak and enhanced the effectiveness and credibility of international public health.

  10. Intelligent distributed computing

    CERN Document Server

    Thampi, Sabu

    2015-01-01

    This book contains a selection of refereed and revised papers of the Intelligent Distributed Computing Track originally presented at the third International Symposium on Intelligent Informatics (ISI-2014), September 24-27, 2014, Delhi, India.  The papers selected for this Track cover several Distributed Computing and related topics including Peer-to-Peer Networks, Cloud Computing, Mobile Clouds, Wireless Sensor Networks, and their applications.

  11. Hybrid intelligence systems and artificial neural network (ANN approach for modeling of surface roughness in drilling

    Directory of Open Access Journals (Sweden)

    Ch. Sanjay

    2014-12-01

    Full Text Available In machining processes, drilling operation is material removal process that has been widely used in manufacturing since industrial revolution. The useful life of cutting tool and its operating conditions largely controls the economics of machining operations. Drilling is most frequently performed material removing process and is used as a preliminary step for many operations, such as reaming, tapping, and boring. Drill wear has a bad effect on the surface finish and dimensional accuracy of the work piece. The surface finish of a machined part is one of the most important quality characteristics in manufacturing industries. The primary objective of this research is the prediction of suitable parameters for surface roughness in drilling. Cutting speed, cutting force, and machining time were given as inputs to the adaptive fuzzy neural network and neuro-fuzzy analysis for estimating the values of surface roughness by using 2, 3, 4, and 5 membership functions. The best structures were selected based on minimum of summation of square with the actual values with the estimated values by artificial neural fuzzy inference system (ANFIS and neuro-fuzzy systems. For artificial neural network (ANN analysis, the number of neurons was selected from 1, 2, 3, … , 20. The learning rate was selected as .5 and .5 smoothing factor was used. The inputs were selected as cutting speed, feed, machining time, and thrust force. The best structures of neural networks were selected based on the criteria as the minimum of summation of square with the actual value of surface roughness. Drilling experiments with 10 mm size were performed at two cutting speeds and feeds. Comparative analysis has been done between the actual values and the estimated values obtained by ANFIS, neuro-fuzzy, and ANN analysis.

  12. Intelligent Services in Converged Networks - Evolution steps in the signalling arena

    DEFF Research Database (Denmark)

    Soler-Lucas, José; Fosgerau, Anders; Grabner, Boris

    2003-01-01

    The paper aims to present the authors' view of the future of telephony. While voice transport over IP is no longer a dream but a reality, the capacity to offer IN-like services, as value added services within VoIP environments, has still been rarely treated and implemented. We present an overview...... on the subject and the work currently in development, within the IST project GEMINI, towards the implementation of IN IP-based services and its interoperability with traditional PSTN-SS7-IN networks....

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

    Science.gov (United States)

    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.

  14. An Improvement of E-Systems over Networks via the use of Artificial Intelligence

    Directory of Open Access Journals (Sweden)

    Yaser A. Jasim

    2018-03-01

    Full Text Available Nowadays, many research has been dedicated to the distribution of expert systems; unfortunately, few have deliberated the study of Dynamic Host Configuration Protocol (DHCP. In this paper, the researcher will demonstrate the construction of vacuum tubes, which embodies the principles of software engineering. In order to classify this challenge, the researcher argues not only that local-area networks (LAN can be made event-driven, stable, and random but the same is true for the Universal Automatic Computer (UNIVAC. 

  15. Forming of the regional core transport network taking into account the allocation of alternative energy sources based on artificial intelligence methods

    Directory of Open Access Journals (Sweden)

    Marina ZHURAVSKAYA

    2014-12-01

    Full Text Available In the modern world the alternative energy sources, which considerably depend on a region, play more and more significant role. However, the transition of regions to new energy sources lead to the change of transport and logistic network configuration. The formation of optimal core transport network today is a guarantee of the successful economic development of a region tomorrow. The present article studies the issue of advanced core transport network development in a region based on the experience of European and Asian countries and the opportunity to adapt the best foreign experience to Russian conditions. On the basis of artificial intelligence methods for forest industry complex of Sverdlovskaya Oblast the algorithm of problem solution of an optimal logistic infrastructure allocation is offered and some results of a regional transport network are presented. These methods allowed to solve the set task in the conditions of information uncertainty. There are suggestions on the improvement of transport and logistic network in the territory of Sverdlovskaya Oblast. Traditionally the logistics of mineral fuel plays main role in regions development. Actually it is required to develop logistic strategic plans to be able to provide different possibilities of power-supply, flexible enough to change with the population density, transport infrastructure and demographics of different regions. The problem of logistic centers allocation was studied by many authors. The approach, offered by the authors of this paper is to solve the set of tasks by applying artificial intelligence methods, such as fuzzy set theory and genetic algorithms.

  16. Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network.

    Science.gov (United States)

    Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing

    2016-01-08

    A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method.

  17. Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network

    Directory of Open Access Journals (Sweden)

    Ke Li

    2016-01-01

    Full Text Available A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF and Diagnostic Bayesian Network (DBN is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO. To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA is proposed to evaluate the sensitiveness of symptom parameters (SPs for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method.

  18. Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network

    Science.gov (United States)

    Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing

    2016-01-01

    A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method. PMID:26761006

  19. Intelligent environmental sensing

    CERN Document Server

    Mukhopadhyay, Subhas

    2015-01-01

    Developing environmental sensing and monitoring technologies become essential especially for industries that may cause severe contamination. Intelligent environmental sensing uses novel sensor techniques, intelligent signal and data processing algorithms, and wireless sensor networks to enhance environmental sensing and monitoring. It finds applications in many environmental problems such as oil and gas, water quality, and agriculture. This book addresses issues related to three main approaches to intelligent environmental sensing and discusses their latest technological developments. Key contents of the book include:   Agricultural monitoring Classification, detection, and estimation Data fusion Geological monitoring Motor monitoring Multi-sensor systems Oil reservoirs monitoring Sensor motes Water quality monitoring Wireless sensor network protocol  

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

    Science.gov (United States)

    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.

  1. Speech Intelligibility Potential of General and Specialized Deep Neural Network Based Speech Enhancement Systems

    DEFF Research Database (Denmark)

    Kolbæk, Morten; Tan, Zheng-Hua; Jensen, Jesper

    2017-01-01

    In this paper, we study aspects of single microphone speech enhancement (SE) based on deep neural networks (DNNs). Specifically, we explore the generalizability capabilities of state-of-the-art DNN-based SE systems with respect to the background noise type, the gender of the target speaker...... general. Finally, we compare how a DNN-based SE system trained to be noise type general, speaker general, and SNR general performs relative to a state-of-the-art short-time spectral amplitude minimum mean square error (STSA-MMSE) based SE algorithm. We show that DNN-based SE systems, when trained...... a state-of-the-art STSA-MMSE based SE method, when tested using a range of unseen speakers and noise types. Finally, a listening test using several DNN-based SE systems tested in unseen speaker conditions show that these systems can improve SI for some SNR and noise type configurations but degrade SI...

  2. COGNITIVE LEARNING OF INTELLIGENCE SYSTEMS USING NEURAL NETWORKS: EVIDENCE FROM THE AUSTRALIAN CAPITAL MARKETS

    Directory of Open Access Journals (Sweden)

    Joachim Tan

    2002-01-01

    Full Text Available Artificial neural networks (ANNs allow users to improve forecasts through pattern recognition. The purpose of this paper is to validate ANNs as a detection tool in four financial markets. This study investigates whether market inefficiencies exist using ANN as a model. It also investigates whether additional publicly available information can provide investors with a trading advantage. In finance, any forecasting advantage obtained through the use of publicly available information albeit internal or/and external market factors suggest inefficiencies in the financial markets. In this paper, we explore the efficiency of the United States, Japan, Hong Kong and Australia. In Australia, using the ASX 200 index, we demonstrate how the inclusion of external information to our ANN improves our forecasting. Our results show accounting for external market signals significantly improves forecasts of the ASX200 index by an additional 10 percent. This suggests the inclusion of publicly available information from other markets, can improve predictions and returns for investors.

  3. Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks.

    Science.gov (United States)

    Kim, D H; MacKinnon, T

    2018-05-01

    To identify the extent to which transfer learning from deep convolutional neural networks (CNNs), pre-trained on non-medical images, can be used for automated fracture detection on plain radiographs. The top layer of the Inception v3 network was re-trained using lateral wrist radiographs to produce a model for the classification of new studies as either "fracture" or "no fracture". The model was trained on a total of 11,112 images, after an eightfold data augmentation technique, from an initial set of 1,389 radiographs (695 "fracture" and 694 "no fracture"). The training data set was split 80:10:10 into training, validation, and test groups, respectively. An additional 100 wrist radiographs, comprising 50 "fracture" and 50 "no fracture" images, were used for final testing and statistical analysis. The area under the receiver operator characteristic curve (AUC) for this test was 0.954. Setting the diagnostic cut-off at a threshold designed to maximise both sensitivity and specificity resulted in values of 0.9 and 0.88, respectively. The AUC scores for this test were comparable to state-of-the-art providing proof of concept for transfer learning from CNNs in fracture detection on plain radiographs. This was achieved using only a moderate sample size. This technique is largely transferable, and therefore, has many potential applications in medical imaging, which may lead to significant improvements in workflow productivity and in clinical risk reduction. Copyright © 2017 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

  4. Intelligent microchip networks: an agent-on-chip synthesis framework for the design of smart and robust sensor networks

    Science.gov (United States)

    Bosse, Stefan

    2013-05-01

    Sensorial materials consisting of high-density, miniaturized, and embedded sensor networks require new robust and reliable data processing and communication approaches. Structural health monitoring is one major field of application for sensorial materials. Each sensor node provides some kind of sensor, electronics, data processing, and communication with a strong focus on microchip-level implementation to meet the goals of miniaturization and low-power energy environments, a prerequisite for autonomous behaviour and operation. Reliability requires robustness of the entire system in the presence of node, link, data processing, and communication failures. Interaction between nodes is required to manage and distribute information. One common interaction model is the mobile agent. An agent approach provides stronger autonomy than a traditional object or remote-procedure-call based approach. Agents can decide for themselves, which actions are performed, and they are capable of flexible behaviour, reacting on the environment and other agents, providing some degree of robustness. Traditionally multi-agent systems are abstract programming models which are implemented in software and executed on program controlled computer architectures. This approach does not well scale to micro-chip level and requires full equipped computers and communication structures, and the hardware architecture does not consider and reflect the requirements for agent processing and interaction. We propose and demonstrate a novel design paradigm for reliable distributed data processing systems and a synthesis methodology and framework for multi-agent systems implementable entirely on microchip-level with resource and power constrained digital logic supporting Agent-On-Chip architectures (AoC). The agent behaviour and mobility is fully integrated on the micro-chip using pipelined communicating processes implemented with finite-state machines and register-transfer logic. The agent behaviour

  5. Accident Management System Based on Vehicular Network for an Intelligent Transportation System in Urban Environments

    Directory of Open Access Journals (Sweden)

    Yusor Rafid Bahar Al-Mayouf

    2018-01-01

    Full Text Available As cities across the world grow and the mobility of populations increases, there has also been a corresponding increase in the number of vehicles on roads. The result of this has been a proliferation of challenges for authorities with regard to road traffic management. A consequence of this has been congestion of traffic, more accidents, and pollution. Accidents are a still major cause of death, despite the development of sophisticated systems for traffic management and other technologies linked with vehicles. Hence, it is necessary that a common system for accident management is developed. For instance, traffic congestion in most urban areas can be alleviated by the real-time planning of routes. However, the designing of an efficient route planning algorithm to attain a globally optimal vehicle control is still a challenge that needs to be solved, especially when the unique preferences of drivers are considered. The aim of this paper is to establish an accident management system that makes use of vehicular ad hoc networks coupled with systems that employ cellular technology in public transport. This system ensures the possibility of real-time communication among vehicles, ambulances, hospitals, roadside units, and central servers. In addition, the accident management system is able to lessen the amount of time required to alert an ambulance that it is required at an accident scene by using a multihop optimal forwarding algorithm. Moreover, an optimal route planning algorithm (ORPA is proposed in this system to improve the aggregate spatial use of a road network, at the same time bringing down the travel cost of operating a vehicle. This can reduce the incidence of vehicles being stuck on congested roads. Simulations are performed to evaluate ORPA, and the results are compared with existing algorithms. The evaluation results provided evidence that ORPA outperformed others in terms of average ambulance speed and travelling time. Finally, our

  6. Robot Advanced Intelligent Control developed through Versatile ...

    African Journals Online (AJOL)

    pc

    2018-03-05

    Mar 5, 2018 ... environments of human life exposed to great dangers such as support and repair in .... intelligent control interfaces, network quality of service, shared resources and ..... Artificial Intelligence series, volume 6556, p. 336-349 ...

  7. An infrastructure with a unified control plane to integrate IP into optical metro networks to provide flexible and intelligent bandwidth on demand for cloud computing

    Science.gov (United States)

    Yang, Wei; Hall, Trevor

    2012-12-01

    The Internet is entering an era of cloud computing to provide more cost effective, eco-friendly and reliable services to consumer and business users and the nature of the Internet traffic will undertake a fundamental transformation. Consequently, the current Internet will no longer suffice for serving cloud traffic in metro areas. This work proposes an infrastructure with a unified control plane that integrates simple packet aggregation technology with optical express through the interoperation between IP routers and electrical traffic controllers in optical metro networks. The proposed infrastructure provides flexible, intelligent, and eco-friendly bandwidth on demand for cloud computing in metro areas.

  8. Artificial intelligence in medicine.

    OpenAIRE

    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 ...

  9. Artificial Intelligence in Astronomy

    Science.gov (United States)

    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.

  10. Intelligent Lighting Control System

    OpenAIRE

    García, Elena; Rodríguez González, Sara; de Paz Santana, Juan F.; Bajo Pérez, Javier

    2014-01-01

    This paper presents an adaptive architecture that allows centralized control of public lighting and intelligent management, in order to economise on lighting and maintain maximum comfort status of the illuminated areas. To carry out this management, architecture merges various techniques of artificial intelligence (AI) and statistics such as artificial neural networks (ANN), multi-agent systems (MAS), EM algorithm, methods based on ANOVA and a Service Oriented Aproach (SOA). It performs optim...

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

    DEFF Research Database (Denmark)

    Bentsen, Thomas; May, Tobias; Kressner, Abigail Anne

    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......, 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...

  12. Artificial intelligence

    CERN Document Server

    Hunt, Earl B

    1975-01-01

    Artificial Intelligence provides information pertinent to the fundamental aspects of artificial intelligence. This book presents the basic mathematical and computational approaches to problems in the artificial intelligence field.Organized into four parts encompassing 16 chapters, this book begins with an overview of the various fields of artificial intelligence. This text then attempts to connect artificial intelligence problems to some of the notions of computability and abstract computing devices. Other chapters consider the general notion of computability, with focus on the interaction bet

  13. Evaluation of Uniformity and Glare Improvement with Low Energy Efficiency Losses in Street Lighting LED Luminaires Using Laser-Sintered Polyamide-Based Diffuse Covers

    Directory of Open Access Journals (Sweden)

    Alfonso Gago-Calderón

    2018-04-01

    Full Text Available Energy saving in street lighting is garnering more interest and has become a priority in municipal management. Therefore, LED luminaires are gradually becoming prevalent in our cities. Beyond their energy/economic saving potential, quality in public lighting installations concerns aspects such as uniformity and glare which must be maintained if not improved in any installation renewal project using this technology. The high light intensity generated in a discrete point in LED packages and its directional nature result in significant deficiencies in these last two parameters. To soften these effects, translucent covers are being used as one of the most common solutions with the drawback of significant light intensity losses. The objective of this paper is to evaluate the behavior of LED luminaire’s polyamide-based optical covers manufactured with a laser-sintered process. These are designed to improve glare and uniformity output, to minimize light output reductions, and to be industrially manufactured with no increment of cost for their lighting equipment compared to conventional transparent polycarbonate solutions. A laboratory and field lighting test study has been applied to different covers with the same LED lamp and luminaire to compare the performance of three different solutions built with different polymeric materials and with different light transmission surface textures. The photometric results have been observed and discussed to demonstrate the ability to significantly improve the lighting performance of LED luminaires—illuminance and uniformity levels and discomfort and disability glare indexes— using an improved optic cover.

  14. Network control stations in the smart grid. Process and information knots for business intelligence applications; Netzleitstellen im Smart Grid. Prozess- und Informationsknoten fuer Business Intelligence Applikationen

    Energy Technology Data Exchange (ETDEWEB)

    Kautsch, Stephan; Kroll, Meinhard [ABB AG, Mannheim (Germany); Schoellhorn, Daniel [EnBW Regional AG, Stuttgart (Germany)

    2012-07-01

    The degree of automation in the distribution will increase, whereas a more extensive monitoring is possible. Smart metering in the local network station replaces the drag pointers. This allows the pre-determined load flows to be precise and it can be determined and valuable data can be collected about how resources, for example the transformers in the secondary substations, are actually utilized. The amount of information available is increasing steadily, not least because of the increasing expansion of smart meters, that also provide valuable information for the operation of the distribution networks. This ''flood'' of data that is processed by the system, filtered, and analyzed must be prepared for the user in order to make sense, but can also be used to support and optimize many business processes. Although these tasks mentioned are usually not yet allocated within the grid operator organization, they offer themselves to be placed close to the network control centers as they propose new challenges but also opportunities. (orig.)

  15. 2015 Chinese Intelligent Automation Conference

    CERN Document Server

    Li, Hongbo

    2015-01-01

    Proceedings of the 2015 Chinese Intelligent Automation Conference presents selected research papers from the CIAC’15, held in Fuzhou, China. The topics include adaptive control, fuzzy control, neural network based control, knowledge based control, hybrid intelligent control, learning control, evolutionary mechanism based control, multi-sensor integration, failure diagnosis, reconfigurable control, etc. Engineers and researchers from academia, industry and the government can gain valuable insights into interdisciplinary solutions in the field of intelligent automation.

  16. Bayesian artificial intelligence

    CERN Document Server

    Korb, Kevin B

    2010-01-01

    Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Bayesian networks, the authors discuss the use of Bayesian networks for causal modeling. They also draw on their own applied research to illustrate various applications of the technology.New to the Second EditionNew chapter on Bayesian network classifiersNew section on object-oriente

  17. Artificial Intelligence and Moral intelligence

    OpenAIRE

    Laura Pana

    2008-01-01

    We discuss the thesis that the implementation of a moral code in the behaviour of artificial intelligent systems needs a specific form of human and artificial intelligence, not just an abstract intelligence. We present intelligence as a system with an internal structure and the structural levels of the moral system, as well as certain characteristics of artificial intelligent agents which can/must be treated as 1- individual entities (with a complex, specialized, autonomous or selfdetermined,...

  18. Topological Organization of Functional Brain Networks in Healthy Children: Differences in Relation to Age, Sex, and Intelligence

    OpenAIRE

    Wu, Kai; Taki, Yasuyuki; Sato, Kazunori; Hashizume, Hiroshi; Sassa, Yuko; Takeuchi, Hikaru; Thyreau, Benjamin; He, Yong; Evans, Alan C.; Li, Xiaobo; Kawashima, Ryuta; Fukuda, Hiroshi

    2013-01-01

    Recent studies have demonstrated developmental changes of functional brain networks derived from functional connectivity using graph theoretical analysis, which has been rapidly translated to studies of brain network organization. However, little is known about sex- and IQ-related differences in the topological organization of functional brain networks during development. In this study, resting-state fMRI (rs-fMRI) was used to map the functional brain networks in 51 healthy children. We then ...

  19. Modelling intelligent behavior

    Science.gov (United States)

    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.

  20. Computational Intelligence in Intelligent Data Analysis

    CERN Document Server

    Nürnberger, Andreas

    2013-01-01

    Complex systems and their phenomena are ubiquitous as they can be found in biology, finance, the humanities, management sciences, medicine, physics and similar fields. For many problems in these fields, there are no conventional ways to mathematically or analytically solve them completely at low cost. On the other hand, nature already solved many optimization problems efficiently. Computational intelligence attempts to mimic nature-inspired problem-solving strategies and methods. These strategies can be used to study, model and analyze complex systems such that it becomes feasible to handle them. Key areas of computational intelligence are artificial neural networks, evolutionary computation and fuzzy systems. As only a few researchers in that field, Rudolf Kruse has contributed in many important ways to the understanding, modeling and application of computational intelligence methods. On occasion of his 60th birthday, a collection of original papers of leading researchers in the field of computational intell...

  1. Some Steps towards Intelligent Computer Tutoring Systems.

    Science.gov (United States)

    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)

  2. Development and evaluation of a novel lossless image compression method (AIC: artificial intelligence compression method) using neural networks as artificial intelligence

    International Nuclear Information System (INIS)

    Fukatsu, Hiroshi; Naganawa, Shinji; Yumura, Shinnichiro

    2008-01-01

    This study was aimed to validate the performance of a novel image compression method using a neural network to achieve a lossless compression. The encoding consists of the following blocks: a prediction block; a residual data calculation block; a transformation and quantization block; an organization and modification block; and an entropy encoding block. The predicted image is divided into four macro-blocks using the original image for teaching; and then redivided into sixteen sub-blocks. The predicted image is compared to the original image to create the residual image. The spatial and frequency data of the residual image are compared and transformed. Chest radiography, computed tomography (CT), magnetic resonance imaging, positron emission tomography, radioisotope mammography, ultrasonography, and digital subtraction angiography images were compressed using the AIC lossless compression method; and the compression rates were calculated. The compression rates were around 15:1 for chest radiography and mammography, 12:1 for CT, and around 6:1 for other images. This method thus enables greater lossless compression than the conventional methods. This novel method should improve the efficiency of handling of the increasing volume of medical imaging data. (author)

  3. Artificial Intelligence.

    Science.gov (United States)

    Information Technology Quarterly, 1985

    1985-01-01

    This issue of "Information Technology Quarterly" is devoted to the theme of "Artificial Intelligence." It contains two major articles: (1) Artificial Intelligence and Law" (D. Peter O'Neill and George D. Wood); (2) "Artificial Intelligence: A Long and Winding Road" (John J. Simon, Jr.). In addition, it contains two sidebars: (1) "Calculating and…

  4. Competitive Intelligence.

    Science.gov (United States)

    Bergeron, Pierrette; Hiller, Christine A.

    2002-01-01

    Reviews the evolution of competitive intelligence since 1994, including terminology and definitions and analytical techniques. Addresses the issue of ethics; explores how information technology supports the competitive intelligence process; and discusses education and training opportunities for competitive intelligence, including core competencies…

  5. Design of AN Intelligent Individual Evacuation Model for High Rise Building Fires Based on Neural Network Within the Scope of 3d GIS

    Science.gov (United States)

    Atila, U.; Karas, I. R.; Turan, M. K.; Rahman, A. A.

    2013-09-01

    One of the most dangerous disaster threatening the high rise and complex buildings of today's world including thousands of occupants inside is fire with no doubt. When we consider high population and the complexity of such buildings it is clear to see that performing a rapid and safe evacuation seems hard and human being does not have good memories in case of such disasters like world trade center 9/11. Therefore, it is very important to design knowledge based realtime interactive evacuation methods instead of classical strategies which lack of flexibility. This paper presents a 3D-GIS implementation which simulates the behaviour of an intelligent indoor pedestrian navigation model proposed for a self -evacuation of a person in case of fire. The model is based on Multilayer Perceptron (MLP) which is one of the most preferred artificial neural network architecture in classification and prediction problems. A sample fire scenario following through predefined instructions has been performed on 3D model of the Corporation Complex in Putrajaya (Malaysia) and the intelligent evacuation process has been realized within a proposed 3D-GIS based simulation.

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

    Science.gov (United States)

    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.

  7. Data transfer based on intelligent ethernet card

    International Nuclear Information System (INIS)

    Zhu Haitao; Chinese Academy of Sciences, Beijing; Chu Yuanping; Zhao Jingwei

    2007-01-01

    Intelligent Ethernet Cards are widely used in systems where the network throughout is very large, such as the DAQ systems for modern high energy physics experiments, web service. With the example of a commercial intelligent Ethernet card, this paper introduces the architecture, the principle and the process of intelligent Ethernet cards. In addition, the results of several experiments showing the differences between intelligent Ethernet cards and general ones are also presented. (authors)

  8. A hybrid optical switch architecture to integrate IP into optical networks to provide flexible and intelligent bandwidth on demand for cloud computing

    Science.gov (United States)

    Yang, Wei; Hall, Trevor J.

    2013-12-01

    The Internet is entering an era of cloud computing to provide more cost effective, eco-friendly and reliable services to consumer and business users. As a consequence, the nature of the Internet traffic has been fundamentally transformed from a pure packet-based pattern to today's predominantly flow-based pattern. Cloud computing has also brought about an unprecedented growth in the Internet traffic. In this paper, a hybrid optical switch architecture is presented to deal with the flow-based Internet traffic, aiming to offer flexible and intelligent bandwidth on demand to improve fiber capacity utilization. The hybrid optical switch is capable of integrating IP into optical networks for cloud-based traffic with predictable performance, for which the delay performance of the electronic module in the hybrid optical switch architecture is evaluated through simulation.

  9. Hybrid intelligent engineering systems

    CERN Document Server

    Jain, L C; Adelaide, Australia University of

    1997-01-01

    This book on hybrid intelligent engineering systems is unique, in the sense that it presents the integration of expert systems, neural networks, fuzzy systems, genetic algorithms, and chaos engineering. It shows that these new techniques enhance the capabilities of one another. A number of hybrid systems for solving engineering problems are presented.

  10. Soft computing in artificial intelligence

    CERN Document Server

    Matson, Eric

    2014-01-01

    This book explores the concept of artificial intelligence based on knowledge-based algorithms. Given the current hardware and software technologies and artificial intelligence theories, we can think of how efficient to provide a solution, how best to implement a model and how successful to achieve it. This edition provides readers with the most recent progress and novel solutions in artificial intelligence. This book aims at presenting the research results and solutions of applications in relevance with artificial intelligence technologies. We propose to researchers and practitioners some methods to advance the intelligent systems and apply artificial intelligence to specific or general purpose. This book consists of 13 contributions that feature fuzzy (r, s)-minimal pre- and β-open sets, handling big coocurrence matrices, Xie-Beni-type fuzzy cluster validation, fuzzy c-regression models, combination of genetic algorithm and ant colony optimization, building expert system, fuzzy logic and neural network, ind...

  11. Intelligence Ethics:

    DEFF Research Database (Denmark)

    Rønn, Kira Vrist

    2016-01-01

    Questions concerning what constitutes a morally justified conduct of intelligence activities have received increased attention in recent decades. However, intelligence ethics is not yet homogeneous or embedded as a solid research field. The aim of this article is to sketch the state of the art...... of intelligence ethics and point out subjects for further scrutiny in future research. The review clusters the literature on intelligence ethics into two groups: respectively, contributions on external topics (i.e., the accountability of and the public trust in intelligence agencies) and internal topics (i.......e., the search for an ideal ethical framework for intelligence actions). The article concludes that there are many holes to fill for future studies on intelligence ethics both in external and internal discussions. Thus, the article is an invitation – especially, to moral philosophers and political theorists...

  12. A Multi-Agent-Based Intelligent Sensor and Actuator Network Design for Smart House and Home Automation

    Directory of Open Access Journals (Sweden)

    Fei Hu

    2013-08-01

    Full Text Available The smart-house technology aims to increase home automation and security with reduced energy consumption. A smart house consists of various intelligent sensors and actuators operating on different platforms with conflicting objectives. This paper proposes a multi-agent system (MAS design framework to achieve smart house automation. The novelties of this work include the developments of (1 belief, desire and intention (BDI agent behavior models; (2 a regulation policy-based multi-agent collaboration mechanism; and (3 a set of metrics for MAS performance evaluation. Simulations of case studies are performed using the Java Agent Development Environment (JADE to demonstrate the advantages of the proposed method.

  13. Intelligence Naturelle et Intelligence Artificielle

    OpenAIRE

    Dubois, Daniel

    2011-01-01

    Cet article présente une approche systémique du concept d’intelligence naturelle en ayant pour objectif de créer une intelligence artificielle. Ainsi, l’intelligence naturelle, humaine et animale non-humaine, est une fonction composée de facultés permettant de connaître et de comprendre. De plus, l'intelligence naturelle reste indissociable de la structure, à savoir les organes du cerveau et du corps. La tentation est grande de doter les systèmes informatiques d’une intelligence artificielle ...

  14. Application of RBF neural network improved by peak density function in intelligent color matching of wood dyeing

    International Nuclear Information System (INIS)

    Guan, Xuemei; Zhu, Yuren; Song, Wenlong

    2016-01-01

    According to the characteristics of wood dyeing, we propose a predictive model of pigment formula for wood dyeing based on Radial Basis Function (RBF) neural network. In practical application, however, it is found that the number of neurons in the hidden layer of RBF neural network is difficult to determine. In general, we need to test several times according to experience and prior knowledge, which is lack of a strict design procedure on theoretical basis. And we also don’t know whether the RBF neural network is convergent. This paper proposes a peak density function to determine the number of neurons in the hidden layer. In contrast to existing approaches, the centers and the widths of the radial basis function are initialized by extracting the features of samples. So the uncertainty caused by random number when initializing the training parameters and the topology of RBF neural network is eliminated. The average relative error of the original RBF neural network is 1.55% in 158 epochs. However, the average relative error of the RBF neural network which is improved by peak density function is only 0.62% in 50 epochs. Therefore, the convergence rate and approximation precision of the RBF neural network are improved significantly.

  15. Test and Evaluation of a Prototyped Sensor-Camera Network for Persistent Intelligence, Surveillance, and Reconnaissance in Support of Tactical Coalition Networking Environments

    Science.gov (United States)

    2006-06-01

    networks is home automation . Wireless sensor networks can be employed in a home environment similar to the ways they are deployed in environmental...and industrial settings. Home automation provides increased control of home appliances and security. Climate control and security systems are the...most common types of home automation applications. However, as technology 12 has increased, new applications are emerging. For example

  16. Topological organization of functional brain networks in healthy children: differences in relation to age, sex, and intelligence.

    Science.gov (United States)

    Wu, Kai; Taki, Yasuyuki; Sato, Kazunori; Hashizume, Hiroshi; Sassa, Yuko; Takeuchi, Hikaru; Thyreau, Benjamin; He, Yong; Evans, Alan C; Li, Xiaobo; Kawashima, Ryuta; Fukuda, Hiroshi

    2013-01-01

    Recent studies have demonstrated developmental changes of functional brain networks derived from functional connectivity using graph theoretical analysis, which has been rapidly translated to studies of brain network organization. However, little is known about sex- and IQ-related differences in the topological organization of functional brain networks during development. In this study, resting-state fMRI (rs-fMRI) was used to map the functional brain networks in 51 healthy children. We then investigated the effects of age, sex, and IQ on economic small-world properties and regional nodal properties of the functional brain networks. At a global level of whole networks, we found significant age-related increases in the small-worldness and local efficiency, significant higher values of the global efficiency in boys compared with girls, and no significant IQ-related difference. Age-related increases in the regional nodal properties were found predominately in the frontal brain regions, whereas the parietal, temporal, and occipital brain regions showed age-related decreases. Significant sex-related differences in the regional nodal properties were found in various brain regions, primarily related to the default mode, language, and vision systems. Positive correlations between IQ and the regional nodal properties were found in several brain regions related to the attention system, whereas negative correlations were found in various brain regions primarily involved in the default mode, emotion, and language systems. Together, our findings of the network topology of the functional brain networks in healthy children and its relationship with age, sex, and IQ bring new insights into the understanding of brain maturation and cognitive development during childhood and adolescence.

  17. Topological organization of functional brain networks in healthy children: differences in relation to age, sex, and intelligence.

    Directory of Open Access Journals (Sweden)

    Kai Wu

    Full Text Available Recent studies have demonstrated developmental changes of functional brain networks derived from functional connectivity using graph theoretical analysis, which has been rapidly translated to studies of brain network organization. However, little is known about sex- and IQ-related differences in the topological organization of functional brain networks during development. In this study, resting-state fMRI (rs-fMRI was used to map the functional brain networks in 51 healthy children. We then investigated the effects of age, sex, and IQ on economic small-world properties and regional nodal properties of the functional brain networks. At a global level of whole networks, we found significant age-related increases in the small-worldness and local efficiency, significant higher values of the global efficiency in boys compared with girls, and no significant IQ-related difference. Age-related increases in the regional nodal properties were found predominately in the frontal brain regions, whereas the parietal, temporal, and occipital brain regions showed age-related decreases. Significant sex-related differences in the regional nodal properties were found in various brain regions, primarily related to the default mode, language, and vision systems. Positive correlations between IQ and the regional nodal properties were found in several brain regions related to the attention system, whereas negative correlations were found in various brain regions primarily involved in the default mode, emotion, and language systems. Together, our findings of the network topology of the functional brain networks in healthy children and its relationship with age, sex, and IQ bring new insights into the understanding of brain maturation and cognitive development during childhood and adolescence.

  18. Intelligent Pressure Management to Reduce Leakage in Urban Water Supply Networks, A Case Study of Sarafrazan District, Mashhad

    OpenAIRE

    Mohammad Soltani Asl; Mahmoud Faghfour Maghrebi

    2009-01-01

    Water losses are inevitable in urban water distribution systems. The two approaches adopted nowadays to combat this problem include management of hydraulic parameters such as pressure and leakage detection in the network. Intellitgent pressure management is a suitable technique for controlling leakage and reducing damages due to high operating pressures in a network. This paper aims to investigate the effects of pressure reduction on leakage. The EPANET 2.10 software is used to simulate the w...

  19. A Novel Intelligent Method for the State of Charge Estimation of Lithium-Ion Batteries Using a Discrete Wavelet Transform-Based Wavelet Neural Network

    Directory of Open Access Journals (Sweden)

    Deyu Cui

    2018-04-01

    Full Text Available State of charge (SOC estimation is becoming increasingly important, along with electric vehicle (EV rapid development, while SOC is one of the most significant parameters for the battery management system, indicating remaining energy and ensuring the safety and reliability of EV. In this paper, a hybrid wavelet neural network (WNN model combining the discrete wavelet transform (DWT method and adaptive WNN is proposed to estimate the SOC of lithium-ion batteries. The WNN model is trained by Levenberg-Marquardt (L-M algorithm, whose inputs are processed by discrete wavelet decomposition and reconstitution. Compared with back-propagation neural network (BPNN, L-M based BPNN (LMBPNN, L-M based WNN (LMWNN, DWT with L-M based BPNN (DWTLMBPNN and extend Kalman filter (EKF, the proposed intelligent SOC estimation method is validated and proved to be effective. Under the New European Driving Cycle (NEDC, the mean absolute error and maximum error can be reduced to 0.59% and 3.13%, respectively. The characteristics of high accuracy and strong robustness of the proposed method are verified by comparison study and robustness evaluation results (e.g., measurement noise test and untrained driving cycle test.

  20. Staging computed tomography in upper GI malignancy. A survey of the 5 cancer networks covered by the South West Cancer Intelligence Service

    International Nuclear Information System (INIS)

    Callaway, M.P.; Bailey, D.

    2005-01-01

    AIM: To identify the methods and protocols of staging CT scans performed for upper GI malignancy throughout the region covered by the South West Cancer Intelligence Service. MATERIALS AND METHODS: A questionnaire relating to the protocols used in the CT staging of upper GI cancer was circulated to all the Cancer Leads and Clinical Directors of Radiology throughout the network covered by the South West Cancer Intelligence Service (SWCIS). Information about the type of scanner, the provision of protocols and the staging of oesophageal, gastric and pancreatic carcinoma was obtained. RESULTS: Twenty one of the twenty six departments contacted responded (81%). Ninety percent of departments perform staging CT scans to a departmental protocol but these protocols vary throughout the region. Most centres have multislice CT technology and all use intravenous contrast media administered via a pump. All centres us a portal venous phase to exclude liver metastasis in all cancers. Thirty-eight to forty percent of centres use an arterial phase of enhancement when examining the oesophagus and stomach. Sixty one percent of centres use an arterial phase and seventy percent of centres use a pancreatic phase of enhancement in addition to a portal venous phase when staging pancreatic carcinoma. Addition imaging of the chest to identify disseminated disease is often performed, 100% of centres include the chest when staging oesophageal malignancy, 87% include the chest in gastric staging and 51% include this additional scan when staging pancreatic carcinoma. The staging scans were reported in 80% of centres by radiologists with a sub-speciality interest in GI malignancy. CONCLUSION: Whilst nearly all centres perform staging CT scans for upper GI malignancy to a departmental protocol there is much variability in the protocols used throughout the South West region

  1. 2nd International Conference on Intelligent Computing, Communication & Devices

    CERN Document Server

    Popentiu-Vladicescu, Florin

    2017-01-01

    The book presents high quality papers presented at 2nd International Conference on Intelligent Computing, Communication & Devices (ICCD 2016) organized by Interscience Institute of Management and Technology (IIMT), Bhubaneswar, Odisha, India, during 13 and 14 August, 2016. The book covers all dimensions of intelligent sciences in its three tracks, namely, intelligent computing, intelligent communication and intelligent devices. intelligent computing track covers areas such as intelligent and distributed computing, intelligent grid and cloud computing, internet of things, soft computing and engineering applications, data mining and knowledge discovery, semantic and web technology, hybrid systems, agent computing, bioinformatics, and recommendation systems. Intelligent communication covers communication and network technologies, including mobile broadband and all optical networks that are the key to groundbreaking inventions of intelligent communication technologies. This covers communication hardware, soft...

  2. Integrating RFID Technology and EPC Network into a B2B Retail Supply Chain: A Step Toward Intelligent Business Processes

    Directory of Open Access Journals (Sweden)

    Samuel Fosso Wamba

    2007-06-01

    Full Text Available This article introduces RFID technology and the EPC Network and investigates their potential for B-to-B eCommerce supply chain management. Based on empirical data gathered from four tightly interrelated firms from three layers of a supply chain, several scenarios integrating RFID and the EPC Network have been tested and evaluated. In the context of warehousing activities in one specific retail supply chain, the results indicate that i the business process approach seems quite appropriate to capture the real potential of RFID and the EPC Network; ii RFID technology and the EPC Network can improve the “shipping” and the “receiving” processes; iii they can automatically trigger some business processes; iv they foster a higher level of information sharing between supply chain members; and v they promote the emergence of new business processes such as “process-to-process,” “process-to-machine,” and “machine-to-machine.” The paper helps to improve our understanding of the real potential of RFID and the EPC Network for business processes.

  3. Intelligent Pressure Management to Reduce Leakage in Urban Water Supply Networks, A Case Study of Sarafrazan District, Mashhad

    Directory of Open Access Journals (Sweden)

    Mohammad Soltani Asl

    2009-09-01

    Full Text Available Water losses are inevitable in urban water distribution systems. The two approaches adopted nowadays to combat this problem include management of hydraulic parameters such as pressure and leakage detection in the network. Intellitgent pressure management is a suitable technique for controlling leakage and reducing damages due to high operating pressures in a network. This paper aims to investigate the effects of pressure reduction on leakage. The EPANET 2.10 software is used to simulate the water distribution network in the Sarafrazan District,Mashhad, assuming leakage from network nodes. The results are then used to develop a pressure variation program based on the patterns obtained from the simulation, which is applied to the pressure reducing valve. The results show that pressure management can reduce nightly leakage by up to 35% while maintaining a more uniform pressure distribution. Implementation of the time-dependent pressure pattern by applying programmable pressure reducing valves in a real urban water distribution network is feasible and plays a key role in reducing water losses to leakage.

  4. Intelligent Design

    DEFF Research Database (Denmark)

    Hjorth, Poul G.

    2005-01-01

    Forestillingen om at naturen er designet af en guddommelig 'intelligens' er et smukt filosofisk princip. Teorier om Intelligent Design som en naturvidenskabeligt baseret teori er derimod helt forfærdelig.......Forestillingen om at naturen er designet af en guddommelig 'intelligens' er et smukt filosofisk princip. Teorier om Intelligent Design som en naturvidenskabeligt baseret teori er derimod helt forfærdelig....

  5. Graph metrics of structural brain networks in individuals with schizophrenia and healthy controls : Group differences, relationships with intelligence, and genetics

    NARCIS (Netherlands)

    Yeo, Ronald A.; Ryman, Sephira G.; Van Den Heuvel, Martijn P.; De Reus, Marcel A.; Jung, Rex E.; Pommy, Jessica; Mayer, Andrew R.; Ehrlich, Stefan; Schulz, S. Charles; Morrow, Eric M.; Manoach, Dara; Ho, Beng Choon; Sponheim, Scott R.; Calhoun, Vince D.

    2016-01-01

    Objectives: One of the most prominent features of schizophrenia is relatively lower general cognitive ability (GCA). An emerging approach to understanding the roots of variation in GCA relies on network properties of the brain. In this multi-center study, we determined global characteristics of

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

    Science.gov (United States)

    Takada, Kenji

    2016-09-01

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

  7. An Intelligent Approach to Educational Data: Performance Comparison of the Multilayer Perceptron and the Radial Basis Function Artificial Neural Networks

    Science.gov (United States)

    Kayri, Murat

    2015-01-01

    The objective of this study is twofold: (1) to investigate the factors that affect the success of university students by employing two artificial neural network methods (i.e., multilayer perceptron [MLP] and radial basis function [RBF]); and (2) to compare the effects of these methods on educational data in terms of predictive ability. The…

  8. Artificial Intelligence in Civil Engineering

    Directory of Open Access Journals (Sweden)

    Pengzhen Lu

    2012-01-01

    Full Text Available Artificial intelligence is a branch of computer science, involved in the research, design, and application of intelligent computer. Traditional methods for modeling and optimizing complex structure systems require huge amounts of computing resources, and artificial-intelligence-based solutions can often provide valuable alternatives for efficiently solving problems in the civil engineering. This paper summarizes recently developed methods and theories in the developing direction for applications of artificial intelligence in civil engineering, including evolutionary computation, neural networks, fuzzy systems, expert system, reasoning, classification, and learning, as well as others like chaos theory, cuckoo search, firefly algorithm, knowledge-based engineering, and simulated annealing. The main research trends are also pointed out in the end. The paper provides an overview of the advances of artificial intelligence applied in civil engineering.

  9. Creative-Dynamics Approach To Neural Intelligence

    Science.gov (United States)

    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.

  10. Application of Intelligent Dynamic Bayesian Network with Wavelet Analysis for Probabilistic Prediction of Storm Track Intensity Index

    Directory of Open Access Journals (Sweden)

    Ming Li

    2018-06-01

    Full Text Available The effective prediction of storm track (ST is greatly beneficial for analyzing the development and anomalies of mid-latitude weather systems. For the non-stationarity, nonlinearity, and uncertainty of ST intensity index (STII, a new probabilistic prediction model was proposed based on dynamic Bayesian network (DBN and wavelet analysis (WA. We introduced probability theory and graph theory for the first time to quantitatively describe the nonlinear relationship and uncertain interaction of the ST system. Then a casual prediction network (i.e., DBN was constructed through wavelet decomposition, structural learning, parameter learning, and probabilistic inference, which was used for expression of relation among predictors and probabilistic prediction of STII. The intensity prediction of the North Pacific ST with data from 1961–2010 showed that the new model was able to give more comprehensive prediction information and higher prediction accuracy and had strong generalization ability and good stability.

  11. Structure and weights optimisation of a modified Elman network emotion classifier using hybrid computational intelligence algorithms: a comparative study

    Science.gov (United States)

    Sheikhan, Mansour; Abbasnezhad Arabi, Mahdi; Gharavian, Davood

    2015-10-01

    Artificial neural networks are efficient models in pattern recognition applications, but their performance is dependent on employing suitable structure and connection weights. This study used a hybrid method for obtaining the optimal weight set and architecture of a recurrent neural emotion classifier based on gravitational search algorithm (GSA) and its binary version (BGSA), respectively. By considering the features of speech signal that were related to prosody, voice quality, and spectrum, a rich feature set was constructed. To select more efficient features, a fast feature selection method was employed. The performance of the proposed hybrid GSA-BGSA method was compared with similar hybrid methods based on particle swarm optimisation (PSO) algorithm and its binary version, PSO and discrete firefly algorithm, and hybrid of error back-propagation and genetic algorithm that were used for optimisation. Experimental tests on Berlin emotional database demonstrated the superior performance of the proposed method using a lighter network structure.

  12. Intelligent Prediction of Soccer Technical Skill on Youth Soccer Player's Relative Performance Using Multivariate Analysis and Artificial Neural Network Techniques

    OpenAIRE

    Abdullah, M. R; Maliki, A. B. H. M; Musa, R. M; Kosni, N. A; Juahir, H

    2016-01-01

    This study aims to predict the potential pattern of soccer technical skill on Malaysia youth soccer players relative performance using multivariate analysis and artificial neural network techniques. 184 male youth soccer players were recruited in Malaysia soccer academy (average age = 15.2±2.0) underwent to, physical fitness test, anthropometric, maturity, motivation and the level of skill related soccer. Unsupervised pattern recognition of principal component analysis (PCA) was used to ident...

  13. 2013 Chinese Intelligent Automation Conference

    CERN Document Server

    Deng, Zhidong

    2013-01-01

    Proceedings of the 2013 Chinese Intelligent Automation Conference presents selected research papers from the CIAC’13, held in Yangzhou, China. The topics include e.g. adaptive control, fuzzy control, neural network based control, knowledge based control, hybrid intelligent control, learning control, evolutionary mechanism based control, multi-sensor integration, failure diagnosis, and reconfigurable control. Engineers and researchers from academia, industry, and government can gain an inside view of new solutions combining ideas from multiple disciplines in the field of intelligent automation.   Zengqi Sun and Zhidong Deng are professors at the Department of Computer Science, Tsinghua University, China.

  14. 2013 Chinese Intelligent Automation Conference

    CERN Document Server

    Deng, Zhidong

    2013-01-01

    Proceedings of the 2013 Chinese Intelligent Automation Conference presents selected research papers from the CIAC’13, held in Yangzhou, China. The topics include e.g. adaptive control, fuzzy control, neural network based control, knowledge based control, hybrid intelligent control, learning control, evolutionary mechanism based control, multi-sensor integration, failure diagnosis, and reconfigurable control. Engineers and researchers from academia, industry, and government can gain an inside view of new solutions combining ideas from multiple disciplines in the field of intelligent automation. Zengqi Sun and Zhidong Deng are professors at the Department of Computer Science, Tsinghua University, China.

  15. Computational Foundations of Natural Intelligence.

    Science.gov (United States)

    van Gerven, Marcel

    2017-01-01

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

  16. Computational Foundations of Natural Intelligence

    Directory of Open Access Journals (Sweden)

    Marcel van Gerven

    2017-12-01

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

  17. The unknown-unknowns: Revealing the hidden insights in massive biomedical data using combined artificial intelligence and knowledge networks

    Directory of Open Access Journals (Sweden)

    Chris Yoo

    2017-12-01

    Full Text Available Genomic data is estimated to be doubling every seven months with over 2 trillion bases from whole genome sequence studies deposited in Genbank in just the last 15 years alone. Recent advances in compute and storage have enabled the use of artificial intelligence techniques in areas such as feature recognition in digital pathology and chemical synthesis for drug development. To apply A.I. productively to multidimensional data such as cellular processes and their dysregulation, the data must be transformed into a structured format, using prior knowledge to create contextual relationships and hierarchies upon which computational analysis can be performed. Here we present the organization of complex data into hypergraphs that facilitate the application of A.I. We provide an example use case of a hypergraph containing hundreds of biological data values and the results of several classes of A.I. algorithms applied in a popular compute cloud. While multiple, biologically insightful correlations between disease states, behavior, and molecular features were identified, the insights of scientific import were revealed only when exploration of the data included visualization of subgraphs of represented knowledge. The results suggest that while machine learning can identify known correlations and suggest testable ones, the greater probability of discovering unexpected relationships between seemingly independent variables (unknown-unknowns requires a context-aware system – hypergraphs that impart biological meaning in nodes and edges. We discuss the implications of a combined hypergraph-A.I. analysis approach to multidimensional data and the pre-processing requirements for such a system.

  18. Using Artificial Intelligence to Retrieve the Optimal Parameters and Structures of Adaptive Network-Based Fuzzy Inference System for Typhoon Precipitation Forecast Modeling

    Directory of Open Access Journals (Sweden)

    Chien-Lin Huang

    2015-01-01

    Full Text Available This study aims to construct a typhoon precipitation forecast model providing forecasts one to six hours in advance using optimal model parameters and structures retrieved from a combination of the adaptive network-based fuzzy inference system (ANFIS and artificial intelligence. To enhance the accuracy of the precipitation forecast, two structures were then used to establish the precipitation forecast model for a specific lead-time: a single-model structure and a dual-model hybrid structure where the forecast models of higher and lower precipitation were integrated. In order to rapidly, automatically, and accurately retrieve the optimal parameters and structures of the ANFIS-based precipitation forecast model, a tabu search was applied to identify the adjacent radius in subtractive clustering when constructing the ANFIS structure. The coupled structure was also employed to establish a precipitation forecast model across short and long lead-times in order to improve the accuracy of long-term precipitation forecasts. The study area is the Shimen Reservoir, and the analyzed period is from 2001 to 2009. Results showed that the optimal initial ANFIS parameters selected by the tabu search, combined with the dual-model hybrid method and the coupled structure, provided the favors in computation efficiency and high-reliability predictions in typhoon precipitation forecasts regarding short to long lead-time forecasting horizons.

  19. Artificial intelligence approach with the use of artificial neural networks for the creation of a forecasting model of Plasmopara viticola infection.

    Science.gov (United States)

    Bugliosi, R; Spera, G; La Torre, A; Campoli, L; Scaglione, M

    2006-01-01

    Most of the forecasting models of Plasmopara viticola infections are based upon empiric correlations between meteorological/environmental data and pathogen outbreak. These models generally overestimate the risk of infections and induce to treat the vineyard even if it should be not necessary. In rare cases they underrate the risk of infection leaving the pathogen to breakout. Starting from these considerations we have decided to approach the problem from another point of view utilizing Artificial Intelligence techniques for data elaboration and analysis. Meanwhile the same data have been studied with a more classic approach with statistical tools to verify the impact of a large data collection on the standard data analysis methods. A network of RTUs (Remote Terminal Units) distributed all over the Italian national territory transmits 12 environmental parameters every 15 minutes via radio or via GPRS to a centralized Data Base. Other pedologic data is collected directly from the field and sent via Internet to the centralized data base utilizing Personal Digital Assistants (PDAs) running a specific software. Data is stored after having been preprocessed, to guarantee the quality of the information. The subsequent analysis has been realized mostly with Artificial Neural Networks (ANNs). Collecting and analizing data in this way will probably bring us to the possibility of preventing Plasmospara viticola infection starting from the environmental conditions in this very complex context. The aim of this work is to forecast the infection avoiding the ineffective use of the plant protection products in agriculture. Applying different analysis models we will try to find the best ANN capable of forecasting with an high level of affordability.

  20. Intelligent playgrounds

    DEFF Research Database (Denmark)

    Larsen, Lasse Juel

    2009-01-01

    This paper examines play, gaming and learning in regard to intelligent playware developed for outdoor use. The key questions are how does these novel artefacts influence the concept of play, gaming and learning. Up until now play and game have been understood as different activities. This paper...... examines if the sharp differentiation between the two can be uphold in regard to intelligent playware for outdoor use. Play and game activities will be analysed and viewed in conjunction with learning contexts. This paper will stipulate that intelligent playware facilitates rapid shifts in contexts...

  1. Artificial intelligence

    CERN Document Server

    Ennals, J R

    1987-01-01

    Artificial Intelligence: State of the Art Report is a two-part report consisting of the invited papers and the analysis. The editor first gives an introduction to the invited papers before presenting each paper and the analysis, and then concludes with the list of references related to the study. The invited papers explore the various aspects of artificial intelligence. The analysis part assesses the major advances in artificial intelligence and provides a balanced analysis of the state of the art in this field. The Bibliography compiles the most important published material on the subject of

  2. Artificial Intelligence

    CERN Document Server

    Warwick, Kevin

    2011-01-01

    if AI is outside your field, or you know something of the subject and would like to know more then Artificial Intelligence: The Basics is a brilliant primer.' - Nick Smith, Engineering and Technology Magazine November 2011 Artificial Intelligence: The Basics is a concise and cutting-edge introduction to the fast moving world of AI. The author Kevin Warwick, a pioneer in the field, examines issues of what it means to be man or machine and looks at advances in robotics which have blurred the boundaries. Topics covered include: how intelligence can be defined whether machines can 'think' sensory

  3. A Secure, Intelligent, and Smart-Sensing Approach for Industrial System Automation and Transmission over Unsecured Wireless Networks.

    Science.gov (United States)

    Shahzad, Aamir; Lee, Malrey; Xiong, Neal Naixue; Jeong, Gisung; Lee, Young-Keun; Choi, Jae-Young; Mahesar, Abdul Wheed; Ahmad, Iftikhar

    2016-03-03

    In Industrial systems, Supervisory control and data acquisition (SCADA) system, the pseudo-transport layer of the distributed network protocol (DNP3) performs the functions of the transport layer and network layer of the open systems interconnection (OSI) model. This study used a simulation design of water pumping system, in-which the network nodes are directly and wirelessly connected with sensors, and are monitored by the main controller, as part of the wireless SCADA system. This study also intends to focus on the security issues inherent in the pseudo-transport layer of the DNP3 protocol. During disassembly and reassembling processes, the pseudo-transport layer keeps track of the bytes sequence. However, no mechanism is available that can verify the message or maintain the integrity of the bytes in the bytes received/transmitted from/to the data link layer or in the send/respond from the main controller/sensors. To properly and sequentially keep track of the bytes, a mechanism is required that can perform verification while bytes are received/transmitted from/to the lower layer of the DNP3 protocol or the send/respond to/from field sensors. For security and byte verification purposes, a mechanism needs to be proposed for the pseudo-transport layer, by employing cryptography algorithm. A dynamic choice security buffer (SB) is designed and employed during the security development. To achieve the desired goals of the proposed study, a pseudo-transport layer stack model is designed using the DNP3 protocol open library and the security is deployed and tested, without changing the original design.

  4. A Secure, Intelligent, and Smart-Sensing Approach for Industrial System Automation and Transmission over Unsecured Wireless Networks

    Science.gov (United States)

    Shahzad, Aamir; Lee, Malrey; Xiong, Neal Naixue; Jeong, Gisung; Lee, Young-Keun; Choi, Jae-Young; Mahesar, Abdul Wheed; Ahmad, Iftikhar

    2016-01-01

    In Industrial systems, Supervisory control and data acquisition (SCADA) system, the pseudo-transport layer of the distributed network protocol (DNP3) performs the functions of the transport layer and network layer of the open systems interconnection (OSI) model. This study used a simulation design of water pumping system, in-which the network nodes are directly and wirelessly connected with sensors, and are monitored by the main controller, as part of the wireless SCADA system. This study also intends to focus on the security issues inherent in the pseudo-transport layer of the DNP3 protocol. During disassembly and reassembling processes, the pseudo-transport layer keeps track of the bytes sequence. However, no mechanism is available that can verify the message or maintain the integrity of the bytes in the bytes received/transmitted from/to the data link layer or in the send/respond from the main controller/sensors. To properly and sequentially keep track of the bytes, a mechanism is required that can perform verification while bytes are received/transmitted from/to the lower layer of the DNP3 protocol or the send/respond to/from field sensors. For security and byte verification purposes, a mechanism needs to be proposed for the pseudo-transport layer, by employing cryptography algorithm. A dynamic choice security buffer (SB) is designed and employed during the security development. To achieve the desired goals of the proposed study, a pseudo-transport layer stack model is designed using the DNP3 protocol open library and the security is deployed and tested, without changing the original design. PMID:26950129

  5. A Secure, Intelligent, and Smart-Sensing Approach for Industrial System Automation and Transmission over Unsecured Wireless Networks

    Directory of Open Access Journals (Sweden)

    Aamir Shahzad

    2016-03-01

    Full Text Available In Industrial systems, Supervisory control and data acquisition (SCADA system, the pseudo-transport layer of the distributed network protocol (DNP3 performs the functions of the transport layer and network layer of the open systems interconnection (OSI model. This study used a simulation design of water pumping system, in-which the network nodes are directly and wirelessly connected with sensors, and are monitored by the main controller, as part of the wireless SCADA system. This study also intends to focus on the security issues inherent in the pseudo-transport layer of the DNP3 protocol. During disassembly and reassembling processes, the pseudo-transport layer keeps track of the bytes sequence. However, no mechanism is available that can verify the message or maintain the integrity of the bytes in the bytes received/transmitted from/to the data link layer or in the send/respond from the main controller/sensors. To properly and sequentially keep track of the bytes, a mechanism is required that can perform verification while bytes are received/transmitted from/to the lower layer of the DNP3 protocol or the send/respond to/from field sensors. For security and byte verification purposes, a mechanism needs to be proposed for the pseudo-transport layer, by employing cryptography algorithm. A dynamic choice security buffer (SB is designed and employed during the security development. To achieve the desired goals of the proposed study, a pseudo-transport layer stack model is designed using the DNP3 protocol open library and the security is deployed and tested, without changing the original design.

  6. France bundles knowledge and expertise in intelligent energy networks; Frankrijk bundelt kennis en expertise in intelligente energienetwerken

    Energy Technology Data Exchange (ETDEWEB)

    Van Kooij, E.

    2012-03-15

    The networks that provides cities, office buildings, houses, cars and mobile phones with energy, is expected in the coming years to be digitized. The French innovation cluster Systematic has recently taken the initiative to set up a knowledge partnership which should connect knowledge and expertise with regard to future smart grids [Dutch] Het netwerk dat onze steden, kantoren, huizen, auto's en mobieltjes van energie voorziet, zal naar verwachting in de komende jaren een digitalisering ondergaan. Het Franse innovatiecluster Systematic heeft onlangs het initiatief genomen een samenwerkingsverband op te richten die kennis en expertise op het gebied van toekomstige intelligente energienetwerken bij elkaar brengt.

  7. Simulation of CO2 Solubility in Polystyrene-b-Polybutadieneb-Polystyrene (SEBS) by artificial intelligence network (ANN) method

    Science.gov (United States)

    Sharudin, R. W.; AbdulBari Ali, S.; Zulkarnain, M.; Shukri, M. A.

    2018-05-01

    This study reports on the integration of Artificial Neural Network (ANNs) with experimental data in predicting the solubility of carbon dioxide (CO2) blowing agent in SEBS by generating highest possible value for Regression coefficient (R2). Basically, foaming of thermoplastic elastomer with CO2 is highly affected by the CO2 solubility. The ability of ANN in predicting interpolated data of CO2 solubility was investigated by comparing training results via different method of network training. Regards to the final prediction result for CO2 solubility by ANN, the prediction trend (output generate) was corroborated with the experimental results. The obtained result of different method of training showed the trend of output generated by Gradient Descent with Momentum & Adaptive LR (traingdx) required longer training time and required more accurate input to produce better output with final Regression Value of 0.88. However, it goes vice versa with Levenberg-Marquardt (trainlm) technique as it produced better output in quick detention time with final Regression Value of 0.91.

  8. Intelligent Advertising

    OpenAIRE

    Díaz Pinedo, Edilfredo Eliot

    2012-01-01

    Intelligent Advertisement diseña e implementa un sistema de publicidad para dispositivos móviles en un centro comercial, donde los clientes reciben publicidad de forma pasiva en sus dispositivos mientras están dentro.

  9. Intelligent Traffic Quantification System

    Science.gov (United States)

    Mohanty, Anita; Bhanja, Urmila; Mahapatra, Sudipta

    2017-08-01

    Currently, city traffic monitoring and controlling is a big issue in almost all cities worldwide. Vehicular ad-hoc Network (VANET) technique is an efficient tool to minimize this problem. Usually, different types of on board sensors are installed in vehicles to generate messages characterized by different vehicle parameters. In this work, an intelligent system based on fuzzy clustering technique is developed to reduce the number of individual messages by extracting important features from the messages of a vehicle. Therefore, the proposed fuzzy clustering technique reduces the traffic load of the network. The technique also reduces congestion and quantifies congestion.

  10. Recent Advances on Hybrid Intelligent Systems

    CERN Document Server

    Melin, Patricia; Kacprzyk, Janusz

    2013-01-01

    This book presents recent advances on hybrid intelligent systems using soft computing techniques for intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. Soft Computing (SC) consists of several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful hybrid intelligent systems. The book is organized in five main parts, which contain groups of papers around a similar subject. The first part consists of papers with the main theme of hybrid intelligent systems for control and robotics, which are basically state of the art papers that propose new models and concepts, which can be the basis for achieving intelligent control and mobile robotics. The second part contains papers with the main theme of hybrid intelligent systems for pattern recognition and time series prediction, which are basically papers using nature-inspired techniques, like evolutionary algo...

  11. Cooperation and the evolution of intelligence.

    Science.gov (United States)

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

    2012-08-07

    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.

  12. BUSINESS INTELLIGENCE

    OpenAIRE

    Bogdan Mohor Dumitrita

    2011-01-01

    The purpose of this work is to present business intelligence systems. These systems can be extremely complex and important in modern market competition. Its effectiveness also reflects in price, so we have to exlore their financial potential before investment. The systems have 20 years long history and during that time many of such tools have been developed, but they are rarely still in use. Business intelligence system consists of three main areas: Data Warehouse, ETL tools and tools f...

  13. Intelligent indexing

    International Nuclear Information System (INIS)

    Farkas, J.

    1992-01-01

    In this paper we discuss the relevance of artificial intelligence to the automatic indexing of natural language text. We describe the use of domain-specific semantically-based thesauruses and address the problem of creating adequate knowledge bases for intelligent indexing systems. We also discuss the relevance of the Hilbert space ι 2 to the compact representation of documents and to the definition of the similarity of natural language texts. (author). 17 refs., 2 figs

  14. Intelligent indexing

    Energy Technology Data Exchange (ETDEWEB)

    Farkas, J

    1993-12-31

    In this paper we discuss the relevance of artificial intelligence to the automatic indexing of natural language text. We describe the use of domain-specific semantically-based thesauruses and address the problem of creating adequate knowledge bases for intelligent indexing systems. We also discuss the relevance of the Hilbert space {iota}{sup 2} to the compact representation of documents and to the definition of the similarity of natural language texts. (author). 17 refs., 2 figs.

  15. Intelligent Decision Technologies : Proceedings of the 4th International Conference on Intelligent Decision Technologies

    CERN Document Server

    Watanabe, Toyohide; Phillips-Wren, Gloria; Howlett, Robert; Jain, Lakhmi

    2012-01-01

    The Intelligent Decision Technologies (IDT) International Conference encourages an interchange of research on intelligent systems and intelligent technologies that enhance or improve decision making. The focus of IDT is interdisciplinary and includes research on all aspects of intelligent decision technologies, from fundamental development to real applications. IDT has the potential to expand their support of decision making in such areas as finance, accounting, marketing, healthcare, medical and diagnostic systems, military decisions, production and operation, networks, traffic management, crisis response, human-machine interfaces, financial and stock market monitoring and prediction, and robotics. Intelligent decision systems implement advances in intelligent agents, fuzzy logic, multi-agent systems, artificial neural networks, and genetic algorithms, among others.  Emerging areas of active research include virtual decision environments, social networking, 3D human-machine interfaces, cognitive interfaces,...

  16. Advances in Intelligence and Security Informatics

    CERN Document Server

    Mao, Wenji

    2012-01-01

    The Intelligent Systems Series comprises titles that present state of the art knowledge and the latest advances in intelligent systems. Its scope includes theoretical studies, design methods, and real-world implementations and applications. Traditionally, Intelligence and Security Informatics (ISI) research and applications have focused on information sharing and data mining, social network analysis, infrastructure protection and emergency responses for security informatics. With the continuous advance of IT technologies and the increasing sophistication of national and international securi

  17. Unsupervised learning of mixture models based on swarm intelligence and neural networks with optimal completion using incomplete data

    Directory of Open Access Journals (Sweden)

    Ahmed R. Abas

    2012-07-01

    Full Text Available In this paper, a new algorithm is presented for unsupervised learning of finite mixture models (FMMs using data set with missing values. This algorithm overcomes the local optima problem of the Expectation-Maximization (EM algorithm via integrating the EM algorithm with Particle Swarm Optimization (PSO. In addition, the proposed algorithm overcomes the problem of biased estimation due to overlapping clusters in estimating missing values in the input data set by integrating locally-tuned general regression neural networks with Optimal Completion Strategy (OCS. A comparison study shows the superiority of the proposed algorithm over other algorithms commonly used in the literature in unsupervised learning of FMM parameters that result in minimum mis-classification errors when used in clustering incomplete data set that is generated from overlapping clusters and these clusters are largely different in their sizes.

  18. 9th International Symposium on Intelligent Distributed Computing

    CERN Document Server

    Camacho, David; Analide, Cesar; Seghrouchni, Amal; Badica, Costin

    2016-01-01

    This book represents the combined peer-reviewed proceedings of the ninth International Symposium on Intelligent Distributed Computing – IDC’2015, of the Workshop on Cyber Security and Resilience of Large-Scale Systems – WSRL’2015, and of the International Workshop on Future Internet and Smart Networks – FI&SN’2015. All the events were held in Guimarães, Portugal during October 7th-9th, 2015. The 46 contributions published in this book address many topics related to theory and applications of intelligent distributed computing, including: Intelligent Distributed Agent-Based Systems, Ambient Intelligence and Social Networks, Computational Sustainability, Intelligent Distributed Knowledge Representation and Processing, Smart Networks, Networked Intelligence and Intelligent Distributed Applications, amongst others.

  19. Recent Advances in Intelligent Engineering Systems

    CERN Document Server

    Klempous, Ryszard; Araujo, Carmen

    2012-01-01

    This volume is a collection of 19 chapters on intelligent engineering systems written by respectable experts of the fields. The book consists of three parts. The first part is devoted to the foundational aspects of computational intelligence. It consists of 8 chapters that include studies in genetic algorithms, fuzzy logic connectives, enhanced intelligence in product models, nature-inspired optimization technologies, particle swarm optimization, evolution algorithms, model complexity of neural networks, and fitness landscape analysis. The second part contains contributions to intelligent computation in networks, presented in 5 chapters. The covered subjects include the application of self-organizing maps for early detection of denial of service attacks, combating security threats via immunity and adaptability in cognitive radio networks, novel modifications in WSN network design for improved SNR and reliability, a conceptual framework for the design of audio based cognitive infocommunication channels, and a ...

  20. Artificial Intelligence and Public Healthcare Service Innovation

    DEFF Research Database (Denmark)

    Sun, Tara Qian; Medaglia, Rony

    Public healthcare ecosystems are complex networks of diverse actors that are subject to pressures to innovate, also a result of technological advancements. Artificial Intelligence (AI), in particular, has the potential to transform the way hospitals, doctors, patients, government agencies...

  1. Intelligent Universe

    Energy Technology Data Exchange (ETDEWEB)

    Hoyle, F

    1983-01-01

    The subject is covered in chapters, entitled: chance and the universe (synthesis of proteins; the primordial soup); the gospel according to Darwin (discussion of Darwin theory of evolution); life did not originate on earth (fossils from space; life in space); the interstellar connection (living dust between the stars; bacteria in space falling to the earth; interplanetary dust); evolution by cosmic control (microorganisms; genetics); why aren't the others here (a cosmic origin of life); after the big bang (big bang and steady state); the information rich universe; what is intelligence up to; the intelligent universe.

  2. Artificial intelligence

    International Nuclear Information System (INIS)

    Perret-Galix, D.

    1992-01-01

    A vivid example of the growing need for frontier physics experiments to make use of frontier technology is in the field of artificial intelligence and related themes. This was reflected in the second international workshop on 'Software Engineering, Artificial Intelligence and Expert Systems in High Energy and Nuclear Physics' which took place from 13-18 January at France Telecom's Agelonde site at La Londe des Maures, Provence. It was the second in a series, the first having been held at Lyon in 1990

  3. Artificial Intelligence and Moral intelligence

    Directory of Open Access Journals (Sweden)

    Laura Pana

    2008-07-01

    Full Text Available We discuss the thesis that the implementation of a moral code in the behaviour of artificial intelligent systems needs a specific form of human and artificial intelligence, not just an abstract intelligence. We present intelligence as a system with an internal structure and the structural levels of the moral system, as well as certain characteristics of artificial intelligent agents which can/must be treated as 1- individual entities (with a complex, specialized, autonomous or selfdetermined, even unpredictable conduct, 2- entities endowed with diverse or even multiple intelligence forms, like moral intelligence, 3- open and, even, free-conduct performing systems (with specific, flexible and heuristic mechanisms and procedures of decision, 4 – systems which are open to education, not just to instruction, 5- entities with “lifegraphy”, not just “stategraphy”, 6- equipped not just with automatisms but with beliefs (cognitive and affective complexes, 7- capable even of reflection (“moral life” is a form of spiritual, not just of conscious activity, 8 – elements/members of some real (corporal or virtual community, 9 – cultural beings: free conduct gives cultural value to the action of a ”natural” or artificial being. Implementation of such characteristics does not necessarily suppose efforts to design, construct and educate machines like human beings. The human moral code is irremediably imperfect: it is a morality of preference, of accountability (not of responsibility and a morality of non-liberty, which cannot be remedied by the invention of ethical systems, by the circulation of ideal values and by ethical (even computing education. But such an imperfect morality needs perfect instruments for its implementation: applications of special logic fields; efficient psychological (theoretical and technical attainments to endow the machine not just with intelligence, but with conscience and even spirit; comprehensive technical

  4. 1st International Conference on Intelligent Computing, Communication and Devices

    CERN Document Server

    Patnaik, Srikanta; Ichalkaranje, Nikhil

    2015-01-01

    In the history of mankind, three revolutions which impact the human life are the tool-making revolution, agricultural revolution and industrial revolution. They have transformed not only the economy and civilization but the overall development of the society. Probably, intelligence revolution is the next revolution, which the society will perceive in the next 10 years. ICCD-2014 covers all dimensions of intelligent sciences, i.e. Intelligent Computing, Intelligent Communication and Intelligent Devices. This volume covers contributions from Intelligent Communication which are from the areas such as Communications and Wireless Ad Hoc & Sensor Networks, Speech & Natural Language Processing, including Signal, Image and Video Processing and Mobile broadband and Optical networks, which are the key to the ground-breaking inventions to intelligent communication technologies. Secondly, Intelligent Device is any type of equipment, instrument, or machine that has its own computing capability. Contributions from ...

  5. Building an intelligence-led security program

    CERN Document Server

    Liska, Allan

    2014-01-01

    As recently as five years ago, securing a network meant putting in a firewall, intrusion detection system, and installing antivirus software on the desktop. Unfortunately, attackers have grown more nimble and effective, meaning that traditional security programs are no longer effective. Today's effective cyber security programs take these best practices and overlay them with intelligence. Adding cyber threat intelligence can help security teams uncover events not detected by traditional security platforms and correlate seemingly disparate events across the network. Properly-implemented inte

  6. International Conference on Intelligent Technologies and Engineering System (ICITES 2012)

    CERN Document Server

    Huang, Yi-Cheng; Intelligent Technologies and Engineering Systems

    2013-01-01

    This book concentrates on intelligent technologies as it relates to engineering systems. The book covers the following topics: networking, signal processing, artificial intelligence, control and software engineering, intelligent electronic circuits and systems, communications, and materials and mechanical engineering. The book is a collection of original papers that have been reviewed by technical editors. These papers were presented at the International Conference on Intelligent Technologies and Engineering Systems, held Dec. 13-15, 2012.

  7. Comparison and Evaluation of the Performance of Various Types of Neural Networks for Planning Issues Related to Optimal Management of Charging and Discharging Electric Cars in Intelligent Power Grids

    Directory of Open Access Journals (Sweden)

    Arash Moradzaeh

    2018-01-01

    Full Text Available The use of electric vehicles in addition to reducing environmental concerns can play a significant role in reducing the peak and filling the characteristic valleys of the daily network load. In other words, in the context of smart grids, it is possible to improve the battery of electric vehicles by scheduling charging and discharging processes. In this research, the issue of controlling the charge and discharge of electric vehicles was evaluated using a variety of neural models, until the by examining the effect of the growth rate of the penetration level of electric vehicles of the hybrid type that can be connected to the distribution network, the results of the charge management and discharge model of the proposed response are examined. The results indicate that due to increased penetration of these cars is increased the amount of responses to charge and discharge management. In this research, a variety of neural network methods, a neural network method using Multilayer Perceptron Training (MLP, b neural network method using Jordan Education (RNN, c neural network method using training (RBF Was evaluated based on parameters such as reduction of training error, reduction of network testing error, duration of run and number of replications for each one. The final results indicate that electric vehicles can be used as scattered power plants, and can be useful for regulating the frequency and regulation of network voltages and the supply of peak traffic. This also reduces peak charges and incidental costs, which ultimately helps to further network stability. Finally, the charge and discharge management response reflects the fact that intelligent network-based models have the ability to manage the charge and discharge of electric vehicles, and among the models the amount of error reduction training and testing is very favourable for both RNN, MLP.

  8. Algorithms for Efficient Intelligence Collection

    Science.gov (United States)

    2013-09-01

    2006. Cortical substrates for exploratory decisions in humans. Nature 441(7095) 876–879. Deitchman, S. J. 1962. A lanchester model of guerrilla...Monterey, CA. Pearl, J. 1986. Fusion, propagation and structuring in belief networks. Artificial Intelligence 29 241–288. Schaffer, M. B. 1968. Lanchester

  9. Ambient intelligence, ethics and privacy

    NARCIS (Netherlands)

    Hoof, van J.; Kort, H.S.M.; Markopoulos, P.; Soede, M.

    2007-01-01

    Networked and ubiquitous information and communication technologies (ICTs) and ambient intelligence are increasingly used in the home environment to facilitate independent living for older adults. These systems collect and disperse a high volume of personal data, which is used for assistance and

  10. Plant intelligence

    Science.gov (United States)

    Lipavská, Helena; Žárský, Viktor

    2009-01-01

    The concept of plant intelligence, as proposed by Anthony Trewavas, has raised considerable discussion. However, plant intelligence remains loosely defined; often it is either perceived as practically synonymous to Darwinian fitness, or reduced to a mere decorative metaphor. A more strict view can be taken, emphasizing necessary prerequisites such as memory and learning, which requires clarifying the definition of memory itself. To qualify as memories, traces of past events have to be not only stored, but also actively accessed. We propose a criterion for eliminating false candidates of possible plant intelligence phenomena in this stricter sense: an “intelligent” behavior must involve a component that can be approximated by a plausible algorithmic model involving recourse to stored information about past states of the individual or its environment. Re-evaluation of previously presented examples of plant intelligence shows that only some of them pass our test. “You were hurt?” Kumiko said, looking at the scar. Sally looked down. “Yeah.” “Why didn't you have it removed?” “Sometimes it's good to remember.” “Being hurt?” “Being stupid.”—(W. Gibson: Mona Lisa Overdrive) PMID:19816094

  11. Speech Intelligibility

    Science.gov (United States)

    Brand, Thomas

    Speech intelligibility (SI) is important for different fields of research, engineering and diagnostics in order to quantify very different phenomena like the quality of recordings, communication and playback devices, the reverberation of auditoria, characteristics of hearing impairment, benefit using hearing aids or combinations of these things.

  12. International Conference on Computational Intelligence 2015

    CERN Document Server

    Saha, Sujan

    2017-01-01

    This volume comprises the proceedings of the International Conference on Computational Intelligence 2015 (ICCI15). This book aims to bring together work from leading academicians, scientists, researchers and research scholars from across the globe on all aspects of computational intelligence. The work is composed mainly of original and unpublished results of conceptual, constructive, empirical, experimental, or theoretical work in all areas of computational intelligence. Specifically, the major topics covered include classical computational intelligence models and artificial intelligence, neural networks and deep learning, evolutionary swarm and particle algorithms, hybrid systems optimization, constraint programming, human-machine interaction, computational intelligence for the web analytics, robotics, computational neurosciences, neurodynamics, bioinspired and biomorphic algorithms, cross disciplinary topics and applications. The contents of this volume will be of use to researchers and professionals alike....

  13. Artificial Intelligence.

    Science.gov (United States)

    Lawrence, David R; Palacios-González, César; Harris, John

    2016-04-01

    It seems natural to think that the same prudential and ethical reasons for mutual respect and tolerance that one has vis-à-vis other human persons would hold toward newly encountered paradigmatic but nonhuman biological persons. One also tends to think that they would have similar reasons for treating we humans as creatures that count morally in our own right. This line of thought transcends biological boundaries-namely, with regard to artificially (super)intelligent persons-but is this a safe assumption? The issue concerns ultimate moral significance: the significance possessed by human persons, persons from other planets, and hypothetical nonorganic persons in the form of artificial intelligence (AI). This article investigates why our possible relations to AI persons could be more complicated than they first might appear, given that they might possess a radically different nature to us, to the point that civilized or peaceful coexistence in a determinate geographical space could be impossible to achieve.

  14. Intelligent Tutor

    Science.gov (United States)

    1990-01-01

    NASA also seeks to advance American education by employing the technology utilization process to develop a computerized, artificial intelligence-based Intelligent Tutoring System (ITS) to help high school and college physics students. The tutoring system is designed for use with the lecture and laboratory portions of a typical physics instructional program. Its importance lies in its ability to observe continually as a student develops problem solutions and to intervene when appropriate with assistance specifically directed at the student's difficulty and tailored to his skill level and learning style. ITS originated as a project of the Johnson Space Center (JSC). It is being developed by JSC's Software Technology Branch in cooperation with Dr. R. Bowen Loftin at the University of Houston-Downtown. Program is jointly sponsored by NASA and ACOT (Apple Classrooms of Tomorrow). Other organizations providing support include Texas Higher Education Coordinating Board, the National Research Council, Pennzoil Products Company and the George R. Brown Foundation. The Physics I class of Clear Creek High School, League City, Texas are providing the classroom environment for test and evaluation of the system. The ITS is a spinoff product developed earlier to integrate artificial intelligence into training/tutoring systems for NASA astronauts flight controllers and engineers.

  15. Heterodox networks

    DEFF Research Database (Denmark)

    Lala, Purnima; Kumar, Ambuj

    2016-01-01

    It is imperative for the service providers to bring innovation in the network design to meet the exponential growth of mobile subscribers for multi-technology future wireless networks. As a matter of research, studies on providing services to moving subscriber groups aka ‘Place Time Capacity (PTC......)’ have not been considered much in the literature. In this article we present Heterodox networks as an innovative and alternate approach to handle the PTC congestion. We describe two different approaches to combat the PTC congestion where the traditional terrestrial infrastructure fails to provide......-Configurable Intelligent Distributed Antenna System (SCIDAS)’ that overlays intelligence over the conventional DAS architecture and latter is in the form of a swarm of intelligent hovering base stations working in a team to cooperatively resolve the PTC congestion at the Area of Event (AoE). A suitable network...

  16. Intelligent Design and Intelligent Failure

    Science.gov (United States)

    Jerman, Gregory

    2015-01-01

    Good Evening, my name is Greg Jerman and for nearly a quarter century I have been performing failure analysis on NASA's aerospace hardware. During that time I had the distinct privilege of keeping the Space Shuttle flying for two thirds of its history. I have analyzed a wide variety of failed hardware from simple electrical cables to cryogenic fuel tanks to high temperature turbine blades. During this time I have found that for all the time we spend intelligently designing things, we need to be equally intelligent about understanding why things fail. The NASA Flight Director for Apollo 13, Gene Kranz, is best known for the expression "Failure is not an option." However, NASA history is filled with failures both large and small, so it might be more accurate to say failure is inevitable. It is how we react and learn from our failures that makes the difference.

  17. Toward New-Generation Intelligent Manufacturing

    Directory of Open Access Journals (Sweden)

    Ji Zhou

    2018-02-01

    Full Text Available Intelligent manufacturing is a general concept that is under continuous development. It can be categorized into three basic paradigms: digital manufacturing, digital-networked manufacturing, and new-generation intelligent manufacturing. New-generation intelligent manufacturing represents an in-depth integration of new-generation artificial intelligence (AI technology and advanced manufacturing technology. It runs through every link in the full life-cycle of design, production, product, and service. The concept also relates to the optimization and integration of corresponding systems; the continuous improvement of enterprises’ product quality, performance, and service levels; and reduction in resources consumption. New-generation intelligent manufacturing acts as the core driving force of the new industrial revolution and will continue to be the main pathway for the transformation and upgrading of the manufacturing industry in the decades to come. Human-cyber-physical systems (HCPSs reveal the technological mechanisms of new-generation intelligent manufacturing and can effectively guide related theoretical research and engineering practice. Given the sequential development, cross interaction, and iterative upgrading characteristics of the three basic paradigms of intelligent manufacturing, a technology roadmap for “parallel promotion and integrated development” should be developed in order to drive forward the intelligent transformation of the manufacturing industry in China. Keywords: Advanced manufacturing, New-generation intelligent manufacturing, Human-cyber-physical system, New-generation AI, Basic paradigms, Parallel promotion, Integrated development

  18. Soft computing for business intelligence

    CERN Document Server

    Pérez, Rafael; Cobo, Angel; Marx, Jorge; Valdés, Ariel

    2014-01-01

    The book Soft Computing for Business Intelligence is the remarkable output of a program based on the idea of joint trans-disciplinary research as supported by the Eureka Iberoamerica Network and the University of Oldenburg. It contains twenty-seven papers allocated to three sections: Soft Computing, Business Intelligence and Knowledge Discovery, and Knowledge Management and Decision Making. Although the contents touch different domains they are similar in so far as they follow the BI principle “Observation and Analysis” while keeping a practical oriented theoretical eye on sound methodologies, like Fuzzy Logic, Compensatory Fuzzy Logic (CFL), Rough Sets and other softcomputing elements. The book tears down the traditional focus on business, and extends Business Intelligence techniques in an impressive way to a broad range of fields like medicine, environment, wind farming, social collaboration and interaction, car sharing and sustainability.

  19. Map Matching for Intelligent Speed Adaptation

    DEFF Research Database (Denmark)

    Tradisauskas, Nerius; Juhl, Jens; Lahrmann, Harry

    2007-01-01

    The availability of Global Navigation Satellite Systems enables sophisticated vehicle guidance and advisory systems such as Intelligent Speed Adaptation (ISA) systems. In ISA systems, it is essential to be able to position vehicles within a road network. Because digital road networks as well as G...

  20. Intelligent computing systems emerging application areas

    CERN Document Server

    Virvou, Maria; Jain, Lakhmi

    2016-01-01

    This book at hand explores emerging scientific and technological areas in which Intelligent Computing Systems provide efficient solutions and, thus, may play a role in the years to come. It demonstrates how Intelligent Computing Systems make use of computational methodologies that mimic nature-inspired processes to address real world problems of high complexity for which exact mathematical solutions, based on physical and statistical modelling, are intractable. Common intelligent computational methodologies are presented including artificial neural networks, evolutionary computation, genetic algorithms, artificial immune systems, fuzzy logic, swarm intelligence, artificial life, virtual worlds and hybrid methodologies based on combinations of the previous. The book will be useful to researchers, practitioners and graduate students dealing with mathematically-intractable problems. It is intended for both the expert/researcher in the field of Intelligent Computing Systems, as well as for the general reader in t...

  1. New trends in computational collective intelligence

    CERN Document Server

    Kim, Sang-Wook; Trawiński, Bogdan

    2015-01-01

    This book consists of 20 chapters in which the authors deal with different theoretical and practical aspects of new trends in Collective Computational Intelligence techniques. Computational Collective Intelligence methods and algorithms are one the current trending research topics from areas related to Artificial Intelligence, Soft Computing or Data Mining among others. Computational Collective Intelligence is a rapidly growing field that is most often understood as an AI sub-field dealing with soft computing methods which enable making group decisions and processing knowledge among autonomous units acting in distributed environments. Web-based Systems, Social Networks, and Multi-Agent Systems very often need these tools for working out consistent knowledge states, resolving conflicts and making decisions. The chapters included in this volume cover a selection of topics and new trends in several domains related to Collective Computational Intelligence: Language and Knowledge Processing, Data Mining Methods an...

  2. Where value lives in a networked world.

    Science.gov (United States)

    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.

  3. Employing the intelligence cycle process model within the Homeland Security Enterprise

    OpenAIRE

    Stokes, Roger L.

    2013-01-01

    CHDS State/Local The purpose of this thesis was to examine the employment and adherence of the intelligence cycle process model within the National Network of Fusion Centers and the greater Homeland Security Enterprise by exploring the customary intelligence cycle process model established by the United States Intelligence Community (USIC). This thesis revealed there are various intelligence cycle process models used by the USIC and taught to the National Network. Given the numerous differ...

  4. Business Intelligence

    OpenAIRE

    Petersen, Anders

    2001-01-01

    Cílem této bakalářské práce je seznámení s Business Intelligence a zpracování vývojového trendu, který ovlivňuje podobu řešení Business Intelligence v podniku ? Business Activity Monitoring. Pro zpracování tohoto tématu byla použita metoda studia odborných pramenů, a to jak v českém, tak v anglickém jazyce. Hlavním přínosem práce je ucelený, v českém jazyce zpracovaný materiál pojednávající o Business Activity Monitoring. Práce je rozdělena do šesti hlavních kapitol. Prvních pět je věnováno p...

  5. 基于类脑智能的复杂网络机会数据竞争规避机制%Opportunity competition and evasion mechanism data with brain intelligent in complex networks

    Institute of Scientific and Technical Information of China (English)

    魏霖静; 宁璐璐; 代永强; 侯振兴

    2017-01-01

    为了解决复杂网络的数据竞争和规避问题,基于类脑智能与复杂网络相结合提出了一种机会数据竞争规避机制.首先,针对大规模多变和动态复杂网络,基于拓扑演变建立了复杂网络机会数据竞争模型;其次,在已发生竞争的数据集合与复杂子网之间建立映射矩阵,通过类脑智能优化数据竞争序列,机会控制复杂网络大规模信息处理和数据规避.实验结果表明,所建立的数据竞争规避算法对于数据集损耗较低且具有较高的数据竞争检测效率;同时,所提出的算法与插入随机延迟规避机制相比具有低能耗优势.%In order to solve the problem of data competition and avoidance of complex networks,this paper proposed a kind of opportunity data competition avoidance mechanism based on the combination of brain like intelligence and complex network.Firstly,according to the large-scale and dynamic complex network,it established the competition model of complex network opportunity data based on the topological evolution.Secondly,it established the mapping between data matrix and complex networks in the competition,through brain intelligent optimization competition data sequence,opportunity to control the largescale complex network information processing and data to avoid.The experimental results show that the efficiency of the data race detection data competition avoidance algorithm for data sets with low loss and high.At the same time,the proposed algorithm and insert random delay mechanism to avoid advantages compared with low energy consumption

  6. Web Intelligence and Artificial Intelligence in Education

    Science.gov (United States)

    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…

  7. New challenges in computational collective intelligence

    Energy Technology Data Exchange (ETDEWEB)

    Nguyen, Ngoc Thanh; Katarzyniak, Radoslaw Piotr [Wroclaw Univ. of Technology (Poland). Inst. of Informatics; Janiak, Adam (eds.) [Wroclaw Univ. of Technology (Poland). Inst. of Computer Engineering, Control and Robotics

    2009-07-01

    The book consists of 29 chapters which have been selected and invited from the submissions to the 1{sup st} International Conference on Collective Intelligence - Semantic Web, Social Networks and Multiagent Systems (ICCCI 2009). All chapters in the book discuss various examples of applications of computational collective intelligence and related technologies to such fields as semantic web, information systems ontologies, social networks, agent and multiagent systems. The editors hope that the book can be useful for graduate and Ph.D. students in Computer Science, in particular participants to courses on Soft Computing, Multi-Agent Systems and Robotics. This book can also be useful for researchers working on the concept of computational collective intelligence in artificial populations. It is the hope of the editors that readers of this volume can find many inspiring ideas and use them to create new cases intelligent collectives. Many such challenges are suggested by particular approaches and models presented in particular chapters of this book. (orig.)

  8. Application of artificial intelligence to electrofacies identification: neural networks versus discriminant analysis; Aplicacao de inteligencia artificial na identificacao de eletrofacies redes neuroniais versus analise discriminante

    Energy Technology Data Exchange (ETDEWEB)

    Silva Rodrigues, F da [PETROBRAS, Rio de Janeiro, RJ (Brazil); Queiroz Neto, I.A. de [PETROBRAS, Rio de Janeiro, RJ (Brazil). Centro de Pesquisas

    1992-07-01

    Electro-facies are identified by neural network trained with well log and core data. Differences between neural network and expert system are discussed. According the author, the combination of neural network computing and traditional computing methods, like discriminant analysis, can help in the solution of many problems in electro-facies identification. 5 figs., 1 tab., 11 refs.

  9. Distributed neural system for emotional intelligence revealed by lesion mapping.

    Science.gov (United States)

    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.

  10. Distributed neural system for emotional intelligence revealed by lesion mapping

    Science.gov (United States)

    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

  11. 16th UK Workshop on Computational Intelligence

    CERN Document Server

    Gegov, Alexander; Jayne, Chrisina; Shen, Qiang

    2017-01-01

    The book is a timely report on advanced methods and applications of computational intelligence systems. It covers a long list of interconnected research areas, such as fuzzy systems, neural networks, evolutionary computation, evolving systems and machine learning. The individual chapters are based on peer-reviewed contributions presented at the 16th Annual UK Workshop on Computational Intelligence, held on September 7-9, 2016, in Lancaster, UK. The book puts a special emphasis on novels methods and reports on their use in a wide range of applications areas, thus providing both academics and professionals with a comprehensive and timely overview of new trends in computational intelligence.

  12. Internet-based intelligent information processing systems

    CERN Document Server

    Tonfoni, G; Ichalkaranje, N S

    2003-01-01

    The Internet/WWW has made it possible to easily access quantities of information never available before. However, both the amount of information and the variation in quality pose obstacles to the efficient use of the medium. Artificial intelligence techniques can be useful tools in this context. Intelligent systems can be applied to searching the Internet and data-mining, interpreting Internet-derived material, the human-Web interface, remote condition monitoring and many other areas. This volume presents the latest research on the interaction between intelligent systems (neural networks, adap

  13. Self-Adaptive Systems for Machine Intelligence

    CERN Document Server

    He, Haibo

    2011-01-01

    This book will advance the understanding and application of self-adaptive intelligent systems; therefore it will potentially benefit the long-term goal of replicating certain levels of brain-like intelligence in complex and networked engineering systems. It will provide new approaches for adaptive systems within uncertain environments. This will provide an opportunity to evaluate the strengths and weaknesses of the current state-of-the-art of knowledge, give rise to new research directions, and educate future professionals in this domain. Self-adaptive intelligent systems have wide application

  14. Intelligence and negotiating

    International Nuclear Information System (INIS)

    George, D.G.

    1990-01-01

    This paper discusses the role of US intelligence during arms control negotiations between 1982 and 1987. It also covers : the orchestration of intelligence projects; an evaluation of the performance of intelligence activities; the effect intelligence work had on actual arms negotiations; and suggestions for improvements in the future

  15. Intelligent products : A survey

    NARCIS (Netherlands)

    Meyer, G.G.; Främling, K.; Holmström, J.

    This paper presents an overview of the field of Intelligent Products. As Intelligent Products have many facets, this paper is mainly focused on the concept behind Intelligent Products, the technical foundations, and the achievable practical goals of Intelligent Products. A novel classification of

  16. Intelligence Issues for Congress

    Science.gov (United States)

    2013-04-23

    open source information— osint (newspapers...by user agencies. Section 1052 of the Intelligence Reform Act expressed the sense of Congress that there should be an open source intelligence ...center to coordinate the collection, analysis, production, and dissemination of open source intelligence to other intelligence agencies. An Open Source

  17. Intelligent Governmentality

    Directory of Open Access Journals (Sweden)

    Willem de Lint

    2008-10-01

    Full Text Available Recently, within liberal democracies, the post-Westphalian consolidation of security and intelligence has ushered in the normalization not only of security in ‘securitization’ but also of intelligence in what is proposed here as ‘intelligencification.’ In outlining the features of intelligencified governance, my aim is to interrogate the view that effects or traces, and productivity rather than negation is as persuasive as commonly thought by the constructivists. After all, counter-intelligence is both about purging and reconstructing the archive for undisclosed values. In practice, what is being normalized is the authorized and legalized use of release and retention protocols of politically actionable information. The intelligencification of governmentality affords a sovereignty shell-game or the instrumentalization of sovereign power by interests that are dependent on, yet often inimical to, the power of state, national, and popular sovereignty. On voit le politique et le social comme dépendant de contingences exclusives. Récemment, au sein des démocraties libérales, la consolidation de la sécurité et des services de renseignements de sécurité qui a suivi les traités de la Westphalie a donné lieu à la normalisation non seulement de la sécurité en «sécurisation» mais aussi des services de renseignements de sécurité en ce qui est proposé ici comme «intelligencification» [terme anglais créé par l’auteur, dérivé du mot anglais «intelligence» dans le sens de renseignements des écurité]. En particulier, ce que l’on normalise dans le but de contourner des contingences exclusives est l’utilisation autorisée et légalisée de protocoles de communication et de rétention d’information qui, politiquement, pourrait mener à des poursuites. En esquissant les traits de la gouvernance «intelligencifiée», mon but est d’interroger le point de vue que les effets ou les traces, et la productivité plutôt que la

  18. Pathogen intelligence

    Directory of Open Access Journals (Sweden)

    Michael eSteinert

    2014-01-01

    Full Text Available Different species inhabit different sensory worlds and thus have evolved diverse means of processing information, learning and memory. In the escalated arms race with host defense, each pathogenic bacterium not only has evolved its individual cellular sensing and behaviour, but also collective sensing, interbacterial communication, distributed information processing, joint decision making, dissociative behaviour, and the phenotypic and genotypic heterogeneity necessary for epidemiologic success. Moreover, pathogenic populations take advantage of dormancy strategies and rapid evolutionary speed, which allow them to save co-generated intelligent traits in a collective genomic memory. This review discusses how these mechanisms add further levels of complexity to bacterial pathogenicity and transmission, and how mining for these mechanisms could help to develop new anti-infective strategies.

  19. Intelligent Routines

    CERN Document Server

    Anastassiou, George A

    Intelligent Routines II: Solving Linear Algebra and Differential Geometry with Sage” contains numerous of examples and problems as well as many unsolved problems. This book extensively applies the successful software Sage, which can be found free online http://www.sagemath.org/. Sage is a recent and popular software for mathematical computation, available freely and simple to use. This book is useful to all applied scientists in mathematics, statistics and engineering, as well for late undergraduate and graduate students of above subjects. It is the first such book in solving symbolically with Sage problems in Linear Algebra and Differential Geometry. Plenty of SAGE applications are given at each step of the exposition.

  20. Intelligence: Real or artificial?

    OpenAIRE

    Schlinger, Henry D.

    1992-01-01

    Throughout the history of the artificial intelligence movement, researchers have strived to create computers that could simulate general human intelligence. This paper argues that workers in artificial intelligence have failed to achieve this goal because they adopted the wrong model of human behavior and intelligence, namely a cognitive essentialist model with origins in the traditional philosophies of natural intelligence. An analysis of the word “intelligence” suggests that it originally r...

  1. The Advantages of Human Milk Recognize the Spatiotemporal Locations of Toxins and Intelligently Bypass Them by Forming a Hummingbird-Like Hovering Neural Network Circuitry Based on an Organic Biomimetic Choline Acetyltransferase Memristor/Memcapacitor Prosthesis

    Directory of Open Access Journals (Sweden)

    E. T. CHEN

    2016-08-01

    Full Text Available We have demonstrated a unique approach to study human milk’s advantage in promoting and protecting infant early brain cognitive development by recognizing toxins and intelligently bypassing the toxin by forming high frequency oscillation (HFO in the brain circuitry when compared with organic cow milk samples based on an organic memristor/memcapacitor biomimetic Choline Acetyltransferase (CHAT neural network circuitry prosthesis along with a 3D Energy-sensory dynamic mapping method under antibody- free, radiolabeling-free, and reagent-less conditions. We also demonstrated cow milk is unfit for infant cognitive development, and it is actually harmful in terms of mutating infant brain synapse circuitry conformation, current flow direction, and energy output that lead to multiple Pathological High Frequency Oscillation (pHFO formations, and further, it led to sudden infant death syndrome (SIDS based on our prediction.

  2. The role of soft computing in intelligent machines.

    Science.gov (United States)

    de Silva, Clarence W

    2003-08-15

    An intelligent machine relies on computational intelligence in generating its intelligent behaviour. This requires a knowledge system in which representation and processing of knowledge are central functions. Approximation is a 'soft' concept, and the capability to approximate for the purposes of comparison, pattern recognition, reasoning, and decision making is a manifestation of intelligence. This paper examines the use of soft computing in intelligent machines. Soft computing is an important branch of computational intelligence, where fuzzy logic, probability theory, neural networks, and genetic algorithms are synergistically used to mimic the reasoning and decision making of a human. This paper explores several important characteristics and capabilities of machines that exhibit intelligent behaviour. Approaches that are useful in the development of an intelligent machine are introduced. The paper presents a general structure for an intelligent machine, giving particular emphasis to its primary components, such as sensors, actuators, controllers, and the communication backbone, and their interaction. The role of soft computing within the overall system is discussed. Common techniques and approaches that will be useful in the development of an intelligent machine are introduced, and the main steps in the development of an intelligent machine for practical use are given. An industrial machine, which employs the concepts of soft computing in its operation, is presented, and one aspect of intelligent tuning, which is incorporated into the machine, is illustrated.

  3. MRI correlates of general intelligence in neurotypical adults.

    Science.gov (United States)

    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.

  4. Inter-cooperative collective intelligence techniques and applications

    CERN Document Server

    Bessis, Nik

    2014-01-01

    This book covers the latest advances in the rapid growing field of inter-cooperative collective intelligence aiming the integration and cooperation of various computational resources, networks and intelligent processing paradigms to collectively build intelligence and advanced decision support and interfaces for end-users. The book brings a comprehensive view of the state-of-the-art in the field of integration of sensor networks, IoT and Cloud computing, massive and intelligent querying and processing of data. As a result, the book presents lessons learned so far and identifies new research issues, challenges and opportunities for further research and development agendas. Emerging areas of applications are also identified and usefulness of inter-cooperative collective intelligence is envisaged.   Researchers, software developers, practitioners and students interested in the field of inter-cooperative collective intelligence will find the comprehensive coverage of this book useful for their research, academic...

  5. Forming of the regional core transport network taking into account the allocation of alternative energy sources based on artificial intelligence methods

    OpenAIRE

    Marina ZHURAVSKAYA; Vladimir TARASYAN

    2014-01-01

    In the modern world the alternative energy sources, which considerably depend on a region, play more and more significant role. However, the transition of regions to new energy sources lead to the change of transport and logistic network configuration. The formation of optimal core transport network today is a guarantee of the successful economic development of a region tomorrow. The present article studies the issue of advanced core transport network development in a region based on the ...

  6. Intelligent nesting system

    Directory of Open Access Journals (Sweden)

    Đuričić Zoran

    2003-01-01

    Full Text Available The economy of the process for the manufacture of parts from sheet metal plates depends on successful solution of the process of cutting various parts from sheet metal plates. Essentially, the problem is to arrange contours within a defined space so that they take up minimal surface. When taken in this way, the considered problem assumes a more general nature; it refers to the utilization of a flat surface, and it can represent a general principle of arranging 2D contours on a certain surface. The paper presents a conceptual solution and a prototypal intelligent nesting system for optimal cutting. The problem of nesting can generally be divided into two intellectual phases: recognition and classification of shapes, and arrangement of recognized shapes on a given surface. In solving these problems, methods of artificial intelligence are applied. In the paper, trained neural network is used for recognition of shapes; on the basis of raster record of a part's drawing, it recognizes the part's shape and which class it belongs to. By means of the expert system, based on rules defined on the basis of acquisition of knowledge from manufacturing sections, as well as on the basis of certain mathematical algorithms, parts are arranged on the arrangement surface. Both systems can also work independently, having been built on the modular principle. The system uses various product models as elements of integration for the entire system. .

  7. 2nd International Symposium on Intelligent Systems Technologies and Applications

    CERN Document Server

    Mitra, Sushmita; Thampi, Sabu; El-Alfy, El-Sayed

    2016-01-01

    This book constitutes the thoroughly refereed proceedings of the second International Symposium on Intelligent Systems Technologies and Applications (ISTA’16), held on September 21–24, 2016 in Jaipur, India. The 80 revised papers presented were carefully reviewed and selected from 210 initial submissions and are organized in topical sections on image processing and artificial vision, computer networks and distributed systems, intelligent tools and techniques and applications using intelligent techniques.

  8. Memory Based Machine Intelligence Techniques in VLSI hardware

    OpenAIRE

    James, Alex Pappachen

    2012-01-01

    We briefly introduce the memory based approaches to emulate machine intelligence in VLSI hardware, describing the challenges and advantages. Implementation of artificial intelligence techniques in VLSI hardware is a practical and difficult problem. Deep architectures, hierarchical temporal memories and memory networks are some of the contemporary approaches in this area of research. The techniques attempt to emulate low level intelligence tasks and aim at providing scalable solutions to high ...

  9. An Intelligent Weather Station

    Science.gov (United States)

    Mestre, Gonçalo; Ruano, Antonio; Duarte, Helder; Silva, Sergio; Khosravani, Hamid; Pesteh, Shabnam; Ferreira, Pedro M.; Horta, Ricardo

    2015-01-01

    Accurate measurements of global solar radiation, atmospheric temperature and relative humidity, as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight, self-powered and portable sensor was developed, using a nearest-neighbors (NEN) algorithm and artificial neural network (ANN) models as the time-series predictor mechanisms. The hardware and software design of the implemented prototype are described, as well as the forecasting performance related to the three atmospheric variables, using both approaches, over a prediction horizon of 48-steps-ahead. PMID:26690433

  10. Artificial intelligence in radiology.

    Science.gov (United States)

    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.

  11. An Intelligent Weather Station

    Directory of Open Access Journals (Sweden)

    Gonçalo Mestre

    2015-12-01

    Full Text Available Accurate measurements of global solar radiation, atmospheric temperature and relative humidity, as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight, self-powered and portable sensor was developed, using a nearest-neighbors (NEN algorithm and artificial neural network (ANN models as the time-series predictor mechanisms. The hardware and software design of the implemented prototype are described, as well as the forecasting performance related to the three atmospheric variables, using both approaches, over a prediction horizon of 48-steps-ahead.

  12. Networking

    OpenAIRE

    Rauno Lindholm, Daniel; Boisen Devantier, Lykke; Nyborg, Karoline Lykke; Høgsbro, Andreas; Fries, de; Skovlund, Louise

    2016-01-01

    The purpose of this project was to examine what influencing factor that has had an impact on the presumed increasement of the use of networking among academics on the labour market and how it is expressed. On the basis of the influence from globalization on the labour market it can be concluded that the globalization has transformed the labour market into a market based on the organization of networks. In this new organization there is a greater emphasis on employees having social qualificati...

  13. Software and Network Engineering

    CERN Document Server

    2012-01-01

    The series "Studies in Computational Intelligence" (SCI) publishes new developments and advances in the various areas of computational intelligence – quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life science, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Critical to both contributors and readers are the short publication time and world-wide distribution - this permits a rapid and broad dissemination of research results.   The purpose of the first ACIS International Symposium on Software and Network Engineering held on Decembe...

  14. Network on Target: Remotely Configured Adaptive Tactical Networks

    National Research Council Canada - National Science Library

    Bordetsky, Alex; Bourakov, Eugene

    2006-01-01

    .... The node mobility as well as ad hoc network topology reconfiguration becomes a powerful control option, which network operators or intelligent management agents could apply to provide for self...

  15. Evolution Engines and Artificial Intelligence

    Science.gov (United States)

    Hemker, Andreas; Becks, Karl-Heinz

    In the last years artificial intelligence has achieved great successes, mainly in the field of expert systems and neural networks. Nevertheless the road to truly intelligent systems is still obscured. Artificial intelligence systems with a broad range of cognitive abilities are not within sight. The limited competence of such systems (brittleness) is identified as a consequence of the top-down design process. The evolution principle of nature on the other hand shows an alternative and elegant way to build intelligent systems. We propose to take an evolution engine as the driving force for the bottom-up development of knowledge bases and for the optimization of the problem-solving process. A novel data analysis system for the high energy physics experiment DELPHI at CERN shows the practical relevance of this idea. The system is able to reconstruct the physical processes after the collision of particles by making use of the underlying standard model of elementary particle physics. The evolution engine acts as a global controller of a population of inference engines working on the reconstruction task. By implementing the system on the Connection Machine (Model CM-2) we use the full advantage of the inherent parallelization potential of the evolutionary approach.

  16. Behavior Analysis and the Quest for Machine Intelligence.

    Science.gov (United States)

    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.…

  17. 1st International Conference on Computational Intelligence and Informatics

    CERN Document Server

    Prasad, V; Rani, B; Udgata, Siba; Raju, K

    2017-01-01

    The book covers a variety of topics which include data mining and data warehousing, high performance computing, parallel and distributed computing, computational intelligence, soft computing, big data, cloud computing, grid computing, cognitive computing, image processing, computer networks, wireless networks, social networks, wireless sensor networks, information and network security, web security, internet of things, bioinformatics and geoinformatics. The book is a collection of best papers submitted in the First International Conference on Computational Intelligence and Informatics (ICCII 2016) held during 28-30 May 2016 at JNTUH CEH, Hyderabad, India. It was hosted by Department of Computer Science and Engineering, JNTUH College of Engineering in association with Division V (Education & Research) CSI, India. .

  18. Educational Programs for Intelligence Professionals.

    Science.gov (United States)

    Miller, Jerry P.

    1994-01-01

    Discusses the need for education programs for competitive intelligence professionals. Highlights include definitions of intelligence functions, focusing on business intelligence; information utilization by decision makers; information sources; competencies for intelligence professionals; and the development of formal education programs. (38…

  19. A New Dimension of Business Intelligence: Location-based Intelligence

    OpenAIRE

    Zeljko Panian

    2012-01-01

    Through the course of this paper we define Locationbased Intelligence (LBI) which is outgrowing from process of amalgamation of geolocation and Business Intelligence. Amalgamating geolocation with traditional Business Intelligence (BI) results in a new dimension of BI named Location-based Intelligence. LBI is defined as leveraging unified location information for business intelligence. Collectively, enterprises can transform location data into business intelligence applic...

  20. Intelligent Extruder

    Energy Technology Data Exchange (ETDEWEB)

    AlperEker; Mark Giammattia; Paul Houpt; Aditya Kumar; Oscar Montero; Minesh Shah; Norberto Silvi; Timothy Cribbs

    2003-04-24

    ''Intelligent Extruder'' described in this report is a software system and associated support services for monitoring and control of compounding extruders to improve material quality, reduce waste and energy use, with minimal addition of new sensors or changes to the factory floor system components. Emphasis is on process improvements to the mixing, melting and de-volatilization of base resins, fillers, pigments, fire retardants and other additives in the :finishing'' stage of high value added engineering polymer materials. While GE Plastics materials were used for experimental studies throughout the program, the concepts and principles are broadly applicable to other manufacturers materials. The project involved a joint collaboration among GE Global Research, GE Industrial Systems and Coperion Werner & Pleiderer, USA, a major manufacturer of compounding equipment. Scope of the program included development of a algorithms for monitoring process material viscosity without rheological sensors or generating waste streams, a novel detection scheme for rapid detection of process upsets and an adaptive feedback control system to compensate for process upsets where at line adjustments are feasible. Software algorithms were implemented and tested on a laboratory scale extruder (50 lb/hr) at GE Global Research and data from a production scale system (2000 lb/hr) at GE Plastics was used to validate the monitoring and detection software. Although not evaluated experimentally, a new concept for extruder process monitoring through estimation of high frequency drive torque without strain gauges is developed and demonstrated in simulation. A plan to commercialize the software system is outlined, but commercialization has not been completed.