Sample records for accelerator controls network

  1. Neural Networks for Modeling and Control of Particle Accelerators

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

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

  2. Neural Networks for Modeling and Control of Particle Accelerators

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

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

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

  3. Neural Networks for Modeling and Control of Particle Accelerators

    DOE PAGES

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

    2016-04-01

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

  4. Network acceleration techniques

    NASA Technical Reports Server (NTRS)

    Crowley, Patricia (Inventor); Maccabe, Arthur Barney (Inventor); Awrach, James Michael (Inventor)

    2012-01-01

    Splintered offloading techniques with receive batch processing are described for network acceleration. Such techniques offload specific functionality to a NIC while maintaining the bulk of the protocol processing in the host operating system ("OS"). The resulting protocol implementation allows the application to bypass the protocol processing of the received data. Such can be accomplished this by moving data from the NIC directly to the application through direct memory access ("DMA") and batch processing the receive headers in the host OS when the host OS is interrupted to perform other work. Batch processing receive headers allows the data path to be separated from the control path. Unlike operating system bypass, however, the operating system still fully manages the network resource and has relevant feedback about traffic and flows. Embodiments of the present disclosure can therefore address the challenges of networks with extreme bandwidth delay products (BWDP).

  5. First Steps Toward Incorporating Image Based Diagnostics Into Particle Accelerator Control Systems Using Convolutional Neural Networks

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

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

    At present, a variety of image-based diagnostics are used in particle accelerator systems. Often times, these are viewed by a human operator who then makes appropriate adjustments to the machine. Given recent advances in using convolutional neural networks (CNNs) for image processing, it should be possible to use image diagnostics directly in control routines (NN-based or otherwise). This is especially appealing for non-intercepting diagnostics that could run continuously during beam operation. Here, we show results of a first step toward implementing such a controller: our trained CNN can predict multiple simulated downstream beam parameters at the Fermilab Accelerator Science andmore » Technology (FAST) facility's low energy beamline using simulated virtual cathode laser images, gun phases, and solenoid strengths.« less

  6. Application of Fuzzy-Logic Controller and Neural Networks Controller in Gas Turbine Speed Control and Overheating Control and Surge Control on Transient Performance

    NASA Astrophysics Data System (ADS)

    Torghabeh, A. A.; Tousi, A. M.

    2007-08-01

    This paper presents Fuzzy Logic and Neural Networks approach to Gas Turbine Fuel schedules. Modeling of non-linear system using feed forward artificial Neural Networks using data generated by a simulated gas turbine program is introduced. Two artificial Neural Networks are used , depicting the non-linear relationship between gas generator speed and fuel flow, and turbine inlet temperature and fuel flow respectively . Off-line fast simulations are used for engine controller design for turbojet engine based on repeated simulation. The Mamdani and Sugeno models are used to expression the Fuzzy system . The linguistic Fuzzy rules and membership functions are presents and a Fuzzy controller will be proposed to provide an Open-Loop control for the gas turbine engine during acceleration and deceleration . MATLAB Simulink was used to apply the Fuzzy Logic and Neural Networks analysis. Both systems were able to approximate functions characterizing the acceleration and deceleration schedules . Surge and Flame-out avoidance during acceleration and deceleration phases are then checked . Turbine Inlet Temperature also checked and controls by Neural Networks controller. This Fuzzy Logic and Neural Network Controllers output results are validated and evaluated by GSP software . The validation results are used to evaluate the generalization ability of these artificial Neural Networks and Fuzzy Logic controllers.

  7. Impact of the Cancer Prevention and Control Research Network: Accelerating the Translation of Research Into Practice.

    PubMed

    Ribisl, Kurt M; Fernandez, Maria E; Friedman, Daniela B; Hannon, Peggy A; Leeman, Jennifer; Moore, Alexis; Olson, Lindsay; Ory, Marcia; Risendal, Betsy; Sheble, Laura; Taylor, Vicky M; Williams, Rebecca S; Weiner, Bryan J

    2017-03-01

    The Cancer Prevention and Control Research Network (CPCRN) is a thematic network dedicated to accelerating the adoption of evidence-based cancer prevention and control practices in communities by advancing dissemination and implementation science. Funded by the Centers for Disease Control and Prevention and National Cancer Institute, CPCRN has operated at two levels: Each participating network center conducts research projects with primarily local partners as well as multicenter collaborative research projects with state and national partners. Through multicenter collaboration, thematic networks leverage the expertise, resources, and partnerships of participating centers to conduct research projects collectively that might not be feasible individually. Although multicenter collaboration is often advocated, it is challenging to promote and assess. Using bibliometric network analysis and other graphical methods, this paper describes CPCRN's multicenter publication progression from 2004 to 2014. Searching PubMed, Scopus, and Web of Science in 2014 identified 249 peer-reviewed CPCRN publications involving two or more centers out of 6,534 total. The research and public health impact of these multicenter collaborative projects initiated by CPCRN during that 10-year period were then examined. CPCRN established numerous workgroups around topics such as: 2-1-1, training and technical assistance, colorectal cancer control, federally qualified health centers, cancer survivorship, and human papillomavirus. This paper discusses the challenges that arise in promoting multicenter collaboration and the strategies that CPCRN uses to address those challenges. The lessons learned should broadly interest those seeking to promote multisite collaboration to address public health problems, such as cancer prevention and control. Copyright © 2016 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

  8. ISHN Ion Source Control System. First Steps Toward an EPICS Based ESS-Bilbao Accelerator Control System

    NASA Astrophysics Data System (ADS)

    Eguiraun, M.; Jugo, J.; Arredondo, I.; del Campo, M.; Feuchtwanger, J.; Etxebarria, V.; Bermejo, F. J.

    2013-04-01

    ISHN (Ion Source Hydrogen Negative) consists of a Penning type ion source in operation at ESS-Bilbao facilities. From the control point of view, this source is representative of the first steps and decisions taken towards the general control architecture of the whole accelerator to be built. The ISHN main control system is based on a PXI architecture, under a real-time controller which is programmed using LabVIEW. This system, with additional elements, is connected to the general control system. The whole system is based on EPICS for the control network, and the modularization of the communication layers of the accelerator plays an important role in the proposed control architecture.

  9. To Break or to Brake Neuronal Network Accelerated by Ammonium Ions?

    PubMed Central

    Dynnik, Vladimir V.; Kononov, Alexey V.; Sergeev, Alexander I.; Teplov, Iliya Y.; Tankanag, Arina V.; Zinchenko, Valery P.

    2015-01-01

    Purpose The aim of present study was to investigate the effects of ammonium ions on in vitro neuronal network activity and to search alternative methods of acute ammonia neurotoxicity prevention. Methods Rat hippocampal neuronal and astrocytes co-cultures in vitro, fluorescent microscopy and perforated patch clamp were used to monitor the changes in intracellular Ca2+- and membrane potential produced by ammonium ions and various modulators in the cells implicated in neural networks. Results Low concentrations of NH4Cl (0.1–4 mM) produce short temporal effects on network activity. Application of 5–8 mM NH4Cl: invariably transforms diverse network firing regimen to identical burst patterns, characterized by substantial neuronal membrane depolarization at plateau phase of potential and high-amplitude Ca2+-oscillations; raises frequency and average for period of oscillations Ca2+-level in all cells implicated in network; results in the appearance of group of «run out» cells with high intracellular Ca2+ and steadily diminished amplitudes of oscillations; increases astrocyte Ca2+-signalling, characterized by the appearance of groups of cells with increased intracellular Ca2+-level and/or chaotic Ca2+-oscillations. Accelerated network activity may be suppressed by the blockade of NMDA or AMPA/kainate-receptors or by overactivation of AMPA/kainite-receptors. Ammonia still activate neuronal firing in the presence of GABA(A) receptors antagonist bicuculline, indicating that «disinhibition phenomenon» is not implicated in the mechanisms of networks acceleration. Network activity may also be slowed down by glycine, agonists of metabotropic inhibitory receptors, betaine, L-carnitine, L-arginine, etc. Conclusions Obtained results demonstrate that ammonium ions accelerate neuronal networks firing, implicating ionotropic glutamate receptors, having preserved the activities of group of inhibitory ionotropic and metabotropic receptors. This may mean, that ammonia

  10. Trunk acceleration for neuroprosthetic control of standing: a pilot study.

    PubMed

    Nataraj, Raviraj; Audu, Musa L; Kirsch, Robert F; Triolo, Ronald J

    2012-02-01

    This pilot study investigated the potential of using trunk acceleration feedback control of center of pressure (COP) against postural disturbances with a standing neuroprosthesis following paralysis. Artificial neural networks (ANNs) were trained to use three-dimensional trunk acceleration as input to predict changes in COP for able-bodied subjects undergoing perturbations during bipedal stance. Correlation coefficients between ANN predictions and actual COP ranged from 0.67 to 0.77. An ANN trained across all subject-normalized data was used to drive feedback control of ankle muscle excitation levels for a computer model representing a standing neuroprosthesis user. Feedback control reduced average upper-body loading during perturbation onset and recovery by 42% and peak loading by 29% compared with optimal, constant excitation.

  11. Knowledge engineering for PACES, the particle accelerator control expert system

    NASA Astrophysics Data System (ADS)

    Lind, P. C.; Poehlman, W. F. S.; Stark, J. W.; Cousins, T.

    1992-04-01

    The KN-3000 used at Defense Research Establishment Ottawa is a Van de Graaff particle accelerator employed primarily to produce monoenergetic neutrons for calibrating radiation detectors. To provide training and assistance for new operators, it was decided to develop an expert system for accelerator operation. Knowledge engineering aspects of the expert system are reviewed. Two important issues are involved: the need to encapsulate expert knowledge into the system in a form that facilitates automatic accelerator operation and to partition the system so that time-consuming inferencing is minimized in favor of faster, more algorithmic control. It is seen that accelerator control will require fast, narrowminded decision making for rapid fine tuning, but slower and broader reasoning for machine startup, shutdown, fault diagnosis, and correction. It is also important to render the knowledge base in a form conducive to operator training. A promising form of the expert system involves a hybrid system in which high level reasoning is performed on the host machine that interacts with the user, while an embedded controller employs neural networks for fast but limited adjustment of accelerator performance. This partitioning of duty facilitates a hierarchical chain of command yielding an effective mixture of speed and reasoning ability.

  12. Modeling of UH-60A Hub Accelerations with Neural Networks

    NASA Technical Reports Server (NTRS)

    Kottapalli, Sesi

    2002-01-01

    Neural network relationships between the full-scale, flight test hub accelerations and the corresponding three N/rev pilot floor vibration components (vertical, lateral, and longitudinal) are studied. The present quantitative effort on the UH-60A Black Hawk hub accelerations considers the lateral and longitudinal vibrations. An earlier study had considered the vertical vibration. The NASA/Army UH-60A Airloads Program flight test database is used. A physics based "maneuver-effect-factor (MEF)", derived using the roll-angle and the pitch-rate, is used. Fundamentally, the lateral vibration data show high vibration levels (up to 0.3 g's) at low airspeeds (for example, during landing flares) and at high airspeeds (for example, during turns). The results show that the advance ratio and the gross weight together can predict the vertical and the longitudinal vibration. However, the advance ratio and the gross weight together cannot predict the lateral vibration. The hub accelerations and the advance ratio can be used to satisfactorily predict the vertical, lateral, and longitudinal vibration. The present study shows that neural network based representations of all three UH-60A pilot floor vibration components (vertical, lateral, and longitudinal) can be obtained using the hub accelerations along with the gross weight and the advance ratio. The hub accelerations are clearly a factor in determining the pilot vibration. The present conclusions potentially allow for the identification of neural network relationships between the experimental hub accelerations obtained from wind tunnel testing and the experimental pilot vibration data obtained from flight testing. A successful establishment of the above neural network based link between the wind tunnel hub accelerations and the flight test vibration data can increase the value of wind tunnel testing.

  13. Trunk Acceleration for Neuroprosthetic Control of Standing – a Pilot Study

    PubMed Central

    Audu, Musa L.; Kirsch, Robert F.; Triolo, Ronald J.

    2013-01-01

    This pilot study investigated the potential of using trunk acceleration feedback control of center of pressure (COP) against postural disturbances with a standing neuroprosthesis following paralysis. Artificial neural networks (ANNs) were trained to use three-dimensional trunk acceleration as input to predict changes in COP for able-bodied subjects undergoing perturbations during bipedal stance. Correlation coefficients between ANN predictions and actual COP ranged from 0.67 to 0.77. An ANN trained across all subject-normalized data was used to drive feedback control of ankle muscle excitation levels for a computer model representing a standing neuroprosthesis user. Feedback control reduced average upper-body loading during perturbation onset and recovery by 42% and peak loading by 29% compared to optimal, constant excitation. PMID:21975251

  14. An adaptive cryptographic accelerator for network storage security on dynamically reconfigurable platform

    NASA Astrophysics Data System (ADS)

    Tang, Li; Liu, Jing-Ning; Feng, Dan; Tong, Wei

    2008-12-01

    Existing security solutions in network storage environment perform poorly because cryptographic operations (encryption and decryption) implemented in software can dramatically reduce system performance. In this paper we propose a cryptographic hardware accelerator on dynamically reconfigurable platform for the security of high performance network storage system. We employ a dynamic reconfigurable platform based on a FPGA to implement a PowerPCbased embedded system, which executes cryptographic algorithms. To reduce the reconfiguration latency, we apply prefetch scheduling. Moreover, the processing elements could be dynamically configured to support different cryptographic algorithms according to the request received by the accelerator. In the experiment, we have implemented AES (Rijndael) and 3DES cryptographic algorithms in the reconfigurable accelerator. Our proposed reconfigurable cryptographic accelerator could dramatically increase the performance comparing with the traditional software-based network storage systems.

  15. Implementation of the beamline controls at the Florence accelerator laboratory

    NASA Astrophysics Data System (ADS)

    Carraresi, L.; Mirto, F. A.

    2008-05-01

    The new Tandetron accelerator in Florence, with many different beamlines, has required a new organization of all the control signals of the used equipment (slow control). We present our solution, which allows us the control of all the employed instruments simultaneously from a number of different workplaces. All of our equipment has been designed to be Ethernet based and this is the key to accomplish two very important requirements: simultaneous remote control from many computers and electrical isolation to achieve a lower noise level. The control of the instruments requires only one Ethernet network and no particular interfaces or drivers on the computers.

  16. Protecting Accelerator Control Systems in the Face of Sophisticated Cyber Attacks

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

    Hartman, Steven M

    2012-01-01

    Cyber security for industrial control systems has received significant attention in the past two years. The news coverage of the Stuxnet attack, believed to be targeted at the control system for a uranium enrichment plant, brought the issue to the attention of news media and policy makers. This has led to increased scrutiny of control systems for critical infrastructure such as power generation and distribution, and industrial systems such as chemical plants and petroleum refineries. The past two years have also seen targeted network attacks aimed at corporate and government entities including US Department of Energy National Laboratories. Both ofmore » these developments have potential repercussions for the control systems of particle accelerators. The need to balance risks from potential attacks with the operational needs of an accelerator present a unique challenge for the system architecture and access model.« less

  17. Accelerated Biofluid Filling in Complex Microfluidic Networks by Vacuum-Pressure Accelerated Movement (V-PAM).

    PubMed

    Yu, Zeta Tak For; Cheung, Mei Ki; Liu, Shirley Xiaosu; Fu, Jianping

    2016-09-01

    Rapid fluid transport and exchange are critical operations involved in many microfluidic applications. However, conventional mechanisms used for driving fluid transport in microfluidics, such as micropumping and high pressure, can be inaccurate and difficult for implementation for integrated microfluidics containing control components and closed compartments. Here, a technology has been developed termed Vacuum-Pressure Accelerated Movement (V-PAM) capable of significantly enhancing biofluid transport in complex microfluidic environments containing dead-end channels and closed chambers. Operation of the V-PAM entails a pressurized fluid loading into microfluidic channels where gas confined inside can rapidly be dissipated through permeation through a thin, gas-permeable membrane sandwiched between microfluidic channels and a network of vacuum channels. Effects of different structural and operational parameters of the V-PAM for promoting fluid filling in microfluidic environments have been studied systematically. This work further demonstrates the applicability of V-PAM for rapid filling of temperature-sensitive hydrogels and unprocessed whole blood into complex irregular microfluidic networks such as microfluidic leaf venation patterns and blood circulatory systems. Together, the V-PAM technology provides a promising generic microfluidic tool for advanced fluid control and transport in integrated microfluidics for different microfluidic diagnosis, organs-on-chips, and biomimetic studies. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  18. The Fermilab Accelerator control system

    NASA Astrophysics Data System (ADS)

    Bogert, Dixon

    1986-06-01

    With the advent of the Tevatron, considerable upgrades have been made to the controls of all the Fermilab Accelerators. The current system is based on making as large an amount of data as possible available to many operators or end-users. Specifically there are about 100 000 separate readings, settings, and status and control registers in the various machines, all of which can be accessed by seventeen consoles, some in the Main Control Room and others distributed throughout the complex. A "Host" computer network of approximately eighteen PDP-11/34's, seven PDP-11/44's, and three VAX-11/785's supports a distributed data acquisition system including Lockheed MAC-16's left from the original Main Ring and Booster instrumentation and upwards of 1000 Z80, Z8002, and M68000 microprocessors in dozens of configurations. Interaction of the various parts of the system is via a central data base stored on the disk of one of the VAXes. The primary computer-hardware communication is via CAMAC for the new Tevatron and Antiproton Source; certain subsystems, among them vacuum, refrigeration, and quench protection, reside in the distributed microprocessors and communicate via GAS, an in-house protocol. An important hardware feature is an accurate clock system making a large number of encoded "events" in the accelerator supercycle available for both hardware modules and computers. System software features include the ability to save the current state of the machine or any subsystem and later restore it or compare it with the state at another time, a general logging facility to keep track of specific variables over long periods of time, detection of "exception conditions" and the posting of alarms, and a central filesharing capability in which files on VAX disks are available for access by any of the "Host" processors.

  19. Torque-based optimal acceleration control for electric vehicle

    NASA Astrophysics Data System (ADS)

    Lu, Dongbin; Ouyang, Minggao

    2014-03-01

    The existing research of the acceleration control mainly focuses on an optimization of the velocity trajectory with respect to a criterion formulation that weights acceleration time and fuel consumption. The minimum-fuel acceleration problem in conventional vehicle has been solved by Pontryagin's maximum principle and dynamic programming algorithm, respectively. The acceleration control with minimum energy consumption for battery electric vehicle(EV) has not been reported. In this paper, the permanent magnet synchronous motor(PMSM) is controlled by the field oriented control(FOC) method and the electric drive system for the EV(including the PMSM, the inverter and the battery) is modeled to favor over a detailed consumption map. The analytical algorithm is proposed to analyze the optimal acceleration control and the optimal torque versus speed curve in the acceleration process is obtained. Considering the acceleration time, a penalty function is introduced to realize a fast vehicle speed tracking. The optimal acceleration control is also addressed with dynamic programming(DP). This method can solve the optimal acceleration problem with precise time constraint, but it consumes a large amount of computation time. The EV used in simulation and experiment is a four-wheel hub motor drive electric vehicle. The simulation and experimental results show that the required battery energy has little difference between the acceleration control solved by analytical algorithm and that solved by DP, and is greatly reduced comparing with the constant pedal opening acceleration. The proposed analytical and DP algorithms can minimize the energy consumption in EV's acceleration process and the analytical algorithm is easy to be implemented in real-time control.

  20. Collective behavior of large-scale neural networks with GPU acceleration.

    PubMed

    Qu, Jingyi; Wang, Rubin

    2017-12-01

    In this paper, the collective behaviors of a small-world neuronal network motivated by the anatomy of a mammalian cortex based on both Izhikevich model and Rulkov model are studied. The Izhikevich model can not only reproduce the rich behaviors of biological neurons but also has only two equations and one nonlinear term. Rulkov model is in the form of difference equations that generate a sequence of membrane potential samples in discrete moments of time to improve computational efficiency. These two models are suitable for the construction of large scale neural networks. By varying some key parameters, such as the connection probability and the number of nearest neighbor of each node, the coupled neurons will exhibit types of temporal and spatial characteristics. It is demonstrated that the implementation of GPU can achieve more and more acceleration than CPU with the increasing of neuron number and iterations. These two small-world network models and GPU acceleration give us a new opportunity to reproduce the real biological network containing a large number of neurons.

  1. Explicit integration with GPU acceleration for large kinetic networks

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

    Brock, Benjamin; Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830; Belt, Andrew

    2015-12-01

    We demonstrate the first implementation of recently-developed fast explicit kinetic integration algorithms on modern graphics processing unit (GPU) accelerators. Taking as a generic test case a Type Ia supernova explosion with an extremely stiff thermonuclear network having 150 isotopic species and 1604 reactions coupled to hydrodynamics using operator splitting, we demonstrate the capability to solve of order 100 realistic kinetic networks in parallel in the same time that standard implicit methods can solve a single such network on a CPU. This orders-of-magnitude decrease in computation time for solving systems of realistic kinetic networks implies that important coupled, multiphysics problems inmore » various scientific and technical fields that were intractable, or could be simulated only with highly schematic kinetic networks, are now computationally feasible.« less

  2. Explicit integration with GPU acceleration for large kinetic networks

    DOE PAGES

    Brock, Benjamin; Belt, Andrew; Billings, Jay Jay; ...

    2015-09-15

    In this study, we demonstrate the first implementation of recently-developed fast explicit kinetic integration algorithms on modern graphics processing unit (GPU) accelerators. Taking as a generic test case a Type Ia supernova explosion with an extremely stiff thermonuclear network having 150 isotopic species and 1604 reactions coupled to hydrodynamics using operator splitting, we demonstrate the capability to solve of order 100 realistic kinetic networks in parallel in the same time that standard implicit methods can solve a single such network on a CPU. In addition, this orders-of-magnitude decrease in computation time for solving systems of realistic kinetic networks implies thatmore » important coupled, multiphysics problems in various scientific and technical fields that were intractable, or could be simulated only with highly schematic kinetic networks, are now computationally feasible.« less

  3. Enabling large-scale viscoelastic calculations via neural network acceleration

    NASA Astrophysics Data System (ADS)

    Robinson DeVries, P.; Thompson, T. B.; Meade, B. J.

    2017-12-01

    One of the most significant challenges involved in efforts to understand the effects of repeated earthquake cycle activity are the computational costs of large-scale viscoelastic earthquake cycle models. Deep artificial neural networks (ANNs) can be used to discover new, compact, and accurate computational representations of viscoelastic physics. Once found, these efficient ANN representations may replace computationally intensive viscoelastic codes and accelerate large-scale viscoelastic calculations by more than 50,000%. This magnitude of acceleration enables the modeling of geometrically complex faults over thousands of earthquake cycles across wider ranges of model parameters and at larger spatial and temporal scales than have been previously possible. Perhaps most interestingly from a scientific perspective, ANN representations of viscoelastic physics may lead to basic advances in the understanding of the underlying model phenomenology. We demonstrate the potential of artificial neural networks to illuminate fundamental physical insights with specific examples.

  4. Congestion control and routing over satellite networks

    NASA Astrophysics Data System (ADS)

    Cao, Jinhua

    Satellite networks and transmissions find their application in fields of computer communications, telephone communications, television broadcasting, transportation, space situational awareness systems and so on. This thesis mainly focuses on two networking issues affecting satellite networking: network congestion control and network routing optimization. Congestion, which leads to long queueing delays, packet losses or both, is a networking problem that has drawn the attention of many researchers. The goal of congestion control mechanisms is to ensure high bandwidth utilization while avoiding network congestion by regulating the rate at which traffic sources inject packets into a network. In this thesis, we propose a stable congestion controller using data-driven, safe switching control theory to improve the dynamic performance of satellite Transmission Control Protocol/Active Queue Management (TCP/AQM) networks. First, the stable region of the Proportional-Integral (PI) parameters for a nominal model is explored. Then, a PI controller, whose parameters are adaptively tuned by switching among members of a given candidate set, using observed plant data, is presented and compared with some classical AQM policy examples, such as Random Early Detection (RED) and fixed PI control. A new cost detectable switching law with an interval cost function switching algorithm, which improves the performance and also saves the computational cost, is developed and compared with a law commonly used in the switching control literature. Finite-gain stability of the system is proved. A fuzzy logic PI controller is incorporated as a special candidate to achieve good performance at all nominal points with the available set of candidate controllers. Simulations are presented to validate the theory. An effocient routing algorithm plays a key role in optimizing network resources. In this thesis, we briefly analyze Low Earth Orbit (LEO) satellite networks, review the Cross Entropy (CE

  5. The ADVANCE network: accelerating data value across a national community health center network

    PubMed Central

    DeVoe, Jennifer E; Gold, Rachel; Cottrell, Erika; Bauer, Vance; Brickman, Andrew; Puro, Jon; Nelson, Christine; Mayer, Kenneth H; Sears, Abigail; Burdick, Tim; Merrell, Jonathan; Matthews, Paul; Fields, Scott

    2014-01-01

    The ADVANCE (Accelerating Data Value Across a National Community Health Center Network) clinical data research network (CDRN) is led by the OCHIN Community Health Information Network in partnership with Health Choice Network and Fenway Health. The ADVANCE CDRN will ‘horizontally’ integrate outpatient electronic health record data for over one million federally qualified health center patients, and ‘vertically’ integrate hospital, health plan, and community data for these patients, often under-represented in research studies. Patient investigators, community investigators, and academic investigators with diverse expertise will work together to meet project goals related to data integration, patient engagement and recruitment, and the development of streamlined regulatory policies. By enhancing the data and research infrastructure of participating organizations, the ADVANCE CDRN will serve as a ‘community laboratory’ for including disadvantaged and vulnerable patients in patient-centered outcomes research that is aligned with the priorities of patients, clinics, and communities in our network. PMID:24821740

  6. Accelerating Innovation that Enhances Resource Recovery in the Wastewater Sector: Advancing a National Testbed Network.

    PubMed

    Mihelcic, James R; Ren, Zhiyong Jason; Cornejo, Pablo K; Fisher, Aaron; Simon, A J; Snyder, Seth W; Zhang, Qiong; Rosso, Diego; Huggins, Tyler M; Cooper, William; Moeller, Jeff; Rose, Bob; Schottel, Brandi L; Turgeon, Jason

    2017-07-18

    This Feature examines significant challenges and opportunities to spur innovation and accelerate adoption of reliable technologies that enhance integrated resource recovery in the wastewater sector through the creation of a national testbed network. The network is a virtual entity that connects appropriate physical testing facilities, and other components needed for a testbed network, with researchers, investors, technology providers, utilities, regulators, and other stakeholders to accelerate the adoption of innovative technologies and processes that are needed for the water resource recovery facility of the future. Here we summarize and extract key issues and developments, to provide a strategy for the wastewater sector to accelerate a path forward that leads to new sustainable water infrastructures.

  7. Embedded Streaming Deep Neural Networks Accelerator With Applications.

    PubMed

    Dundar, Aysegul; Jin, Jonghoon; Martini, Berin; Culurciello, Eugenio

    2017-07-01

    Deep convolutional neural networks (DCNNs) have become a very powerful tool in visual perception. DCNNs have applications in autonomous robots, security systems, mobile phones, and automobiles, where high throughput of the feedforward evaluation phase and power efficiency are important. Because of this increased usage, many field-programmable gate array (FPGA)-based accelerators have been proposed. In this paper, we present an optimized streaming method for DCNNs' hardware accelerator on an embedded platform. The streaming method acts as a compiler, transforming a high-level representation of DCNNs into operation codes to execute applications in a hardware accelerator. The proposed method utilizes maximum computational resources available based on a novel-scheduled routing topology that combines data reuse and data concatenation. It is tested with a hardware accelerator implemented on the Xilinx Kintex-7 XC7K325T FPGA. The system fully explores weight-level and node-level parallelizations of DCNNs and achieves a peak performance of 247 G-ops while consuming less than 4 W of power. We test our system with applications on object classification and object detection in real-world scenarios. Our results indicate high-performance efficiency, outperforming all other presented platforms while running these applications.

  8. APPARATUS FOR CONTROL OF HIGH-ENERGY ACCELERATORS

    DOEpatents

    Heard, H.G.

    1961-10-24

    A particle beam positioning control for a synchrotron or the like is described. The control includes means for selectively impressing a sinusoidal perturbation upon the rising voltage utilized to sweep the frequency of the f-m oscillator which is conventionally coupled to the accelerating electrode of a synchrotron. The perturbation produces a variation in the normal rate of change of frequency of the accelerating voltage applied to the accelerating electrode, resulting in an expansion or contraction of the particle beam orbit diameter during the perturbation. The beam may thus be controlled such that a portion strikes a target positioned close to the expanded or contracted orbit diameter and returns to the original orbit for further acceleration to the final energy. (AEC)

  9. A systematic FPGA acceleration design for applications based on convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Dong, Hao; Jiang, Li; Li, Tianjian; Liang, Xiaoyao

    2018-04-01

    Most FPGA accelerators for convolutional neural network are designed to optimize the inner acceleration and are ignored of the optimization for the data path between the inner accelerator and the outer system. This could lead to poor performance in applications like real time video object detection. We propose a brand new systematic FPFA acceleration design to solve this problem. This design takes the data path optimization between the inner accelerator and the outer system into consideration and optimizes the data path using techniques like hardware format transformation, frame compression. It also takes fixed-point, new pipeline technique to optimize the inner accelerator. All these make the final system's performance very good, reaching about 10 times the performance comparing with the original system.

  10. Accelerating deep neural network training with inconsistent stochastic gradient descent.

    PubMed

    Wang, Linnan; Yang, Yi; Min, Renqiang; Chakradhar, Srimat

    2017-09-01

    Stochastic Gradient Descent (SGD) updates Convolutional Neural Network (CNN) with a noisy gradient computed from a random batch, and each batch evenly updates the network once in an epoch. This model applies the same training effort to each batch, but it overlooks the fact that the gradient variance, induced by Sampling Bias and Intrinsic Image Difference, renders different training dynamics on batches. In this paper, we develop a new training strategy for SGD, referred to as Inconsistent Stochastic Gradient Descent (ISGD) to address this problem. The core concept of ISGD is the inconsistent training, which dynamically adjusts the training effort w.r.t the loss. ISGD models the training as a stochastic process that gradually reduces down the mean of batch's loss, and it utilizes a dynamic upper control limit to identify a large loss batch on the fly. ISGD stays on the identified batch to accelerate the training with additional gradient updates, and it also has a constraint to penalize drastic parameter changes. ISGD is straightforward, computationally efficient and without requiring auxiliary memories. A series of empirical evaluations on real world datasets and networks demonstrate the promising performance of inconsistent training. Copyright © 2017 Elsevier Ltd. All rights reserved.

  11. Controllability in Multi-Stage Laser Ion Acceleration

    NASA Astrophysics Data System (ADS)

    Kawata, S.; Kamiyama, D.; Ohtake, Y.; Barada, D.; Ma, Y. Y.; Kong, Q.; Wang, P. X.; Gu, Y. J.; Li, X. F.; Yu, Q.

    2015-11-01

    The present paper shows a concept for a future laser ion accelerator, which should have an ion source, ion collimators, ion beam bunchers and ion post acceleration devices. Based on the laser ion accelerator components, the ion particle energy and the ion energy spectrum are controlled, and a future compact laser ion accelerator would be designed for ion cancer therapy or for ion material treatment. In this study each component is designed to control the ion beam quality. The energy efficiency from the laser to ions is improved by using a solid target with a fine sub-wavelength structure or a near-critical density gas plasma. The ion beam collimation is performed by holes behind the solid target or a multi-layered solid target. The control of the ion energy spectrum and the ion particle energy, and the ion beam bunching are successfully realized by a multi-stage laser-target interaction. A combination of each component provides a high controllability of the ion beam quality to meet variable requirements in various purposes in the laser ion accelerator. The work was partly supported by MEXT, JSPS, ASHULA project/ ILE, Osaka University, CORE (Center for Optical Research and Education, Utsunomiya University, Japan), Fudan University and CDI (Creative Dept. for Innovation) in CCRD, Utsunomiya University.

  12. Control algorithms of SONET integrated self-healing networks

    NASA Astrophysics Data System (ADS)

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

    1994-01-01

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

  13. Subcortical electrostimulation to identify network subserving motor control.

    PubMed

    Schucht, Philippe; Moritz-Gasser, Sylvie; Herbet, Guillaume; Raabe, Andreas; Duffau, Hugues

    2013-11-01

    Recent anatomical-functional studies have transformed our understanding of cerebral motor control away from a hierarchical structure and toward parallel and interconnected specialized circuits. Subcortical electrical stimulation during awake surgery provides a unique opportunity to identify white matter tracts involved in motor control. For the first time, this study reports the findings on motor modulatory responses evoked by subcortical stimulation and investigates the cortico-subcortical connectivity of cerebral motor control. Twenty-one selected patients were operated while awake for frontal, insular, and parietal diffuse low-grade gliomas. Subcortical electrostimulation mapping was used to search for interference with voluntary movements. The corresponding stimulation sites were localized on brain schemas using the anterior and posterior commissures method. Subcortical negative motor responses were evoked in 20/21 patients, whereas acceleration of voluntary movements and positive motor responses were observed in three and five patients, respectively. The majority of the stimulation sites were detected rostral of the corticospinal tract near the vertical anterior-commissural line, and additional sites were seen in the frontal and parietal white matter. The diverse interferences with motor function resulting in inhibition and acceleration imply a modulatory influence of the detected fiber network. The subcortical stimulation sites were distributed veil-like, anterior to the primary motor fibers, suggesting descending pathways originating from premotor areas known for negative motor response characteristics. Further stimulation sites in the parietal white matter as well as in the anterior arm of the internal capsule indicate a large-scale fronto-parietal motor control network. Copyright © 2012 Wiley Periodicals, Inc.

  14. Linear induction accelerator and pulse forming networks therefor

    DOEpatents

    Buttram, Malcolm T.; Ginn, Jerry W.

    1989-01-01

    A linear induction accelerator includes a plurality of adder cavities arranged in a series and provided in a structure which is evacuated so that a vacuum inductance is provided between each adder cavity and the structure. An energy storage system for the adder cavities includes a pulsed current source and a respective plurality of bipolar converting networks connected thereto. The bipolar high-voltage, high-repetition-rate square pulse train sets and resets the cavities.

  15. Impact of the Cancer Prevention and Control Research Network

    PubMed Central

    Ribisl, Kurt M.; Fernandez, Maria E.; Friedman, Daniela B.; Hannon, Peggy; Leeman, Jennifer; Moore, Alexis; Olson, Lindsay; Ory, Marcia; Risendal, Betsy; Sheble, Laura; Taylor, Vicky; Williams, Rebecca; Weiner, Bryan J.

    2018-01-01

    The Cancer Prevention and Control Research Network (CPCRN) is a thematic network dedicated to accelerating the adoption of evidence-based cancer prevention and control practices in communities by advancing dissemination and implementation science. Funded by the Centers for Disease Control and Prevention and National Cancer Institute, CPCRN has operated at two levels: Each participating Network Center conducts research projects with primarily local partners as well as multicenter collaborative research projects with state and national partners. Through multicenter collaboration, thematic networks leverage the expertise, resources, and partnerships of participating centers to conduct research projects collectively that might not be feasible individually. Although multicenter collaboration often is advocated, it is challenging to promote and assess. Using bibliometric network analysis and other graphical methods, this paper describes CPCRN’s multicenter publication progression from 2004 to 2014. Searching PubMed, Scopus, and Web of Science in 2014 identified 249 peer-reviewed CPCRN publications involving two or more centers out of 6,534 total. The research and public health impact of these multicenter collaborative projects initiated by CPCRN during that 10-year period were then examined. CPCRN established numerous workgroups around topics such as: 2-1-1, training and technical assistance, colorectal cancer control, federally qualified health centers, cancer survivorship, and human papillomavirus. The paper discusses the challenges that arise in promoting multicenter collaboration and the strategies that CPCRN uses to address those challenges. The lessons learned should broadly interest those seeking to promote multisite collaboration to address public health problems, such as cancer prevention and control. PMID:28215371

  16. Neuromuscular Control of Rapid Linear Accelerations in Fish

    DTIC Science & Technology

    2016-06-22

    2014 30-Apr-2015 Approved for Public Release; Distribution Unlimited Final Report: Neuromuscular Control of Rapid Linear Accelerations in Fish The...it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. Tufts University Research... Control of Rapid Linear Accelerations in Fish Report Title In this project, we measured muscle activity, body movements, and flow patterns during linear

  17. Acceleration-Augmented LQG Control of an Active Magnetic Bearing

    NASA Technical Reports Server (NTRS)

    Feeley, Joseph J.

    1993-01-01

    A linear-quadratic-gaussian (LQG) regulator controller design for an acceleration-augmented active magnetic bearing (AMB) is outlined. Acceleration augmentation is a key feature in providing improved dynamic performance of the controller. The optimal control formulation provides a convenient method of trading-off fast transient response and force attenuation as control objectives.

  18. Control of Multilayer Networks

    PubMed Central

    Menichetti, Giulia; Dall’Asta, Luca; Bianconi, Ginestra

    2016-01-01

    The controllability of a network is a theoretical problem of relevance in a variety of contexts ranging from financial markets to the brain. Until now, network controllability has been characterized only on isolated networks, while the vast majority of complex systems are formed by multilayer networks. Here we build a theoretical framework for the linear controllability of multilayer networks by mapping the problem into a combinatorial matching problem. We found that correlating the external signals in the different layers can significantly reduce the multiplex network robustness to node removal, as it can be seen in conjunction with a hybrid phase transition occurring in interacting Poisson networks. Moreover we observe that multilayer networks can stabilize the fully controllable multiplex network configuration that can be stable also when the full controllability of the single network is not stable. PMID:26869210

  19. Using Equation-Free Computation to Accelerate Network-Free Stochastic Simulation of Chemical Kinetics.

    PubMed

    Lin, Yen Ting; Chylek, Lily A; Lemons, Nathan W; Hlavacek, William S

    2018-06-21

    The chemical kinetics of many complex systems can be concisely represented by reaction rules, which can be used to generate reaction events via a kinetic Monte Carlo method that has been termed network-free simulation. Here, we demonstrate accelerated network-free simulation through a novel approach to equation-free computation. In this process, variables are introduced that approximately capture system state. Derivatives of these variables are estimated using short bursts of exact stochastic simulation and finite differencing. The variables are then projected forward in time via a numerical integration scheme, after which a new exact stochastic simulation is initialized and the whole process repeats. The projection step increases efficiency by bypassing the firing of numerous individual reaction events. As we show, the projected variables may be defined as populations of building blocks of chemical species. The maximal number of connected molecules included in these building blocks determines the degree of approximation. Equation-free acceleration of network-free simulation is found to be both accurate and efficient.

  20. DeepX: Deep Learning Accelerator for Restricted Boltzmann Machine Artificial Neural Networks.

    PubMed

    Kim, Lok-Won

    2018-05-01

    Although there have been many decades of research and commercial presence on high performance general purpose processors, there are still many applications that require fully customized hardware architectures for further computational acceleration. Recently, deep learning has been successfully used to learn in a wide variety of applications, but their heavy computation demand has considerably limited their practical applications. This paper proposes a fully pipelined acceleration architecture to alleviate high computational demand of an artificial neural network (ANN) which is restricted Boltzmann machine (RBM) ANNs. The implemented RBM ANN accelerator (integrating network size, using 128 input cases per batch, and running at a 303-MHz clock frequency) integrated in a state-of-the art field-programmable gate array (FPGA) (Xilinx Virtex 7 XC7V-2000T) provides a computational performance of 301-billion connection-updates-per-second and about 193 times higher performance than a software solution running on general purpose processors. Most importantly, the architecture enables over 4 times (12 times in batch learning) higher performance compared with a previous work when both are implemented in an FPGA device (XC2VP70).

  1. Application accelerator system having bunch control

    DOEpatents

    Wang, Dunxiong; Krafft, Geoffrey Arthur

    1999-01-01

    An application accelerator system for monitoring the gain of a free electron laser. Coherent Synchrotron Radiation (CSR) detection techniques are used with a bunch length monitor for ultra short, picosec to several tens of femtosec, electron bunches. The monitor employs an application accelerator, a coherent radiation production device, an optical or beam chopping device, an infrared radiation collection device, a narrow-banding filter, an infrared detection device, and a control.

  2. FastGCN: A GPU Accelerated Tool for Fast Gene Co-Expression Networks

    PubMed Central

    Liang, Meimei; Zhang, Futao; Jin, Gulei; Zhu, Jun

    2015-01-01

    Gene co-expression networks comprise one type of valuable biological networks. Many methods and tools have been published to construct gene co-expression networks; however, most of these tools and methods are inconvenient and time consuming for large datasets. We have developed a user-friendly, accelerated and optimized tool for constructing gene co-expression networks that can fully harness the parallel nature of GPU (Graphic Processing Unit) architectures. Genetic entropies were exploited to filter out genes with no or small expression changes in the raw data preprocessing step. Pearson correlation coefficients were then calculated. After that, we normalized these coefficients and employed the False Discovery Rate to control the multiple tests. At last, modules identification was conducted to construct the co-expression networks. All of these calculations were implemented on a GPU. We also compressed the coefficient matrix to save space. We compared the performance of the GPU implementation with those of multi-core CPU implementations with 16 CPU threads, single-thread C/C++ implementation and single-thread R implementation. Our results show that GPU implementation largely outperforms single-thread C/C++ implementation and single-thread R implementation, and GPU implementation outperforms multi-core CPU implementation when the number of genes increases. With the test dataset containing 16,000 genes and 590 individuals, we can achieve greater than 63 times the speed using a GPU implementation compared with a single-thread R implementation when 50 percent of genes were filtered out and about 80 times the speed when no genes were filtered out. PMID:25602758

  3. FastGCN: a GPU accelerated tool for fast gene co-expression networks.

    PubMed

    Liang, Meimei; Zhang, Futao; Jin, Gulei; Zhu, Jun

    2015-01-01

    Gene co-expression networks comprise one type of valuable biological networks. Many methods and tools have been published to construct gene co-expression networks; however, most of these tools and methods are inconvenient and time consuming for large datasets. We have developed a user-friendly, accelerated and optimized tool for constructing gene co-expression networks that can fully harness the parallel nature of GPU (Graphic Processing Unit) architectures. Genetic entropies were exploited to filter out genes with no or small expression changes in the raw data preprocessing step. Pearson correlation coefficients were then calculated. After that, we normalized these coefficients and employed the False Discovery Rate to control the multiple tests. At last, modules identification was conducted to construct the co-expression networks. All of these calculations were implemented on a GPU. We also compressed the coefficient matrix to save space. We compared the performance of the GPU implementation with those of multi-core CPU implementations with 16 CPU threads, single-thread C/C++ implementation and single-thread R implementation. Our results show that GPU implementation largely outperforms single-thread C/C++ implementation and single-thread R implementation, and GPU implementation outperforms multi-core CPU implementation when the number of genes increases. With the test dataset containing 16,000 genes and 590 individuals, we can achieve greater than 63 times the speed using a GPU implementation compared with a single-thread R implementation when 50 percent of genes were filtered out and about 80 times the speed when no genes were filtered out.

  4. Accelerating Innovation Through Coopetition: The Innovation Learning Network Experience.

    PubMed

    McCarthy, Chris; Ford Carleton, Penny; Krumpholz, Elizabeth; Chow, Marilyn P

    Coopetition, the simultaneous pursuit of cooperation and competition, is a growing force in the innovation landscape. For some organizations, the primary mode of innovation continues to be deeply secretive and highly competitive, but for others, a new style of shared challenges, shared purpose, and shared development has become a superior, more efficient way of working to accelerate innovation capabilities and capacity. Over the last 2 decades, the literature base devoted to coopetition has gradually expanded. However, the field is still in its infancy. The majority of coopetition research is qualitative, primarily consisting of case studies. Few studies have addressed the nonprofit sector or service industries such as health care. The authors believe that this article may offer a unique perspective on coopetition in the context of a US-based national health care learning alliance designed to accelerate innovation, the Innovation Learning Network or ILN. The mission of the ILN is to "Share the joy and pain of innovation," accelerating innovation by sharing solutions, teaching techniques, and cultivating friendships. These 3 pillars (sharing, teaching, and cultivating) form the foundation for coopetition within the ILN. Through the lens of coopetition, we examine the experience of the ILN over the last 10 years and provide case examples that illustrate the benefits and challenges of coopetition in accelerating innovation in health care.

  5. Controllability of flow-conservation networks

    NASA Astrophysics Data System (ADS)

    Zhao, Chen; Zeng, An; Jiang, Rui; Yuan, Zhengzhong; Wang, Wen-Xu

    2017-07-01

    The ultimate goal of exploring complex networks is to control them. As such, controllability of complex networks has been intensively investigated. Despite recent advances in studying the impact of a network's topology on its controllability, a comprehensive understanding of the synergistic impact of network topology and dynamics on controllability is still lacking. Here, we explore the controllability of flow-conservation networks, trying to identify the minimal number of driver nodes that can guide the network to any desirable state. We develop a method to analyze the controllability on flow-conservation networks based on exact controllability theory, transforming the original analysis on adjacency matrix to Laplacian matrix. With this framework, we systematically investigate the impact of some key factors of networks, including link density, link directionality, and link polarity, on the controllability of these networks. We also obtain the analytical equations by investigating the network's structural properties approximatively and design the efficient tools. Finally, we consider some real networks with flow dynamics, finding that their controllability is significantly different from that predicted by only considering the topology. These findings deepen our understanding of network controllability with flow-conservation dynamics and provide a general framework to incorporate real dynamics in the analysis of network controllability.

  6. Dynamic boundary layer based neural network quasi-sliding mode control for soft touching down on asteroid

    NASA Astrophysics Data System (ADS)

    Liu, Xiaosong; Shan, Zebiao; Li, Yuanchun

    2017-04-01

    Pinpoint landing is a critical step in some asteroid exploring missions. This paper is concerned with the descent trajectory control for soft touching down on a small irregularly-shaped asteroid. A dynamic boundary layer based neural network quasi-sliding mode control law is proposed to track a desired descending path. The asteroid's gravitational acceleration acting on the spacecraft is described by the polyhedron method. Considering the presence of input constraint and unmodeled acceleration, the dynamic equation of relative motion is presented first. The desired descending path is planned using cubic polynomial method, and a collision detection algorithm is designed. To perform trajectory tracking, a neural network sliding mode control law is given first, where the sliding mode control is used to ensure the convergence of system states. Two radial basis function neural networks (RBFNNs) are respectively used as an approximator for the unmodeled term and a compensator for the difference between the actual control input with magnitude constraint and nominal control. To improve the chattering induced by the traditional sliding mode control and guarantee the reachability of the system, a specific saturation function with dynamic boundary layer is proposed to replace the sign function in the preceding control law. Through the Lyapunov approach, the reachability condition of the control system is given. The improved control law can guarantee the system state move within a gradually shrinking quasi-sliding mode band. Numerical simulation results demonstrate the effectiveness of the proposed control strategy.

  7. Application accelerator system having bunch control

    DOEpatents

    Wang, D.; Krafft, G.A.

    1999-06-22

    An application accelerator system for monitoring the gain of a free electron laser is disclosed. Coherent Synchrotron Radiation (CSR) detection techniques are used with a bunch length monitor for ultra short, picosec to several tens of femtosec, electron bunches. The monitor employs an application accelerator, a coherent radiation production device, an optical or beam chopping device, an infrared radiation collection device, a narrow-banding filter, an infrared detection device, and a control. 1 fig.

  8. The Interplanetary Overlay Networking Protocol Accelerator

    NASA Technical Reports Server (NTRS)

    Pang, Jackson; Torgerson, Jordan L.; Clare, Loren P.

    2008-01-01

    A document describes the Interplanetary Overlay Networking Protocol Accelerator (IONAC) an electronic apparatus, now under development, for relaying data at high rates in spacecraft and interplanetary radio-communication systems utilizing a delay-tolerant networking protocol. The protocol includes provisions for transmission and reception of data in bundles (essentially, messages), transfer of custody of a bundle to a recipient relay station at each step of a relay, and return receipts. Because of limitations on energy resources available for such relays, data rates attainable in a conventional software implementation of the protocol are lower than those needed, at any given reasonable energy-consumption rate. Therefore, a main goal in developing the IONAC is to reduce the energy consumption by an order of magnitude and the data-throughput capability by two orders of magnitude. The IONAC prototype is a field-programmable gate array that serves as a reconfigurable hybrid (hardware/ firmware) system for implementation of the protocol. The prototype can decode 108,000 bundles per second and encode 100,000 bundles per second. It includes a bundle-cache static randomaccess memory that enables maintenance of a throughput of 2.7Gb/s, and an Ethernet convergence layer that supports a duplex throughput of 1Gb/s.

  9. Control Infrastructure for a Pulsed Ion Accelerator

    NASA Astrophysics Data System (ADS)

    Persaud, A.; Regis, M. J.; Stettler, M. W.; Vytla, V. K.

    2016-10-01

    We report on updates to the accelerator controls for the Neutralized Drift Compression Experiment II, a pulsed induction-type accelerator for heavy ions. The control infrastructure is built around a LabVIEW interface combined with an Apache Cassandra backend for data archiving. Recent upgrades added the storing and retrieving of device settings into the database, as well as ZeroMQ as a message broker that replaces LabVIEW's shared variables. Converting to ZeroMQ also allows easy access via other programming languages, such as Python.

  10. Control Infrastructure for a Pulsed Ion Accelerator

    DOE PAGES

    Persaud, A.; Regis, M. J.; Stettler, M. W.; ...

    2016-07-27

    We report on updates to the accelerator controls for the Neutralized Drift Compression Experiment II, a pulsed induction-type accelerator for heavy ions. The control infrastructure is built around a LabVIEW interface combined with an Apache Cassandra backend for data archiving. Recent upgrades added the storing and retrieving of device settings into the database, as well as ZeroMQ as a message broker that replaces LabVIEW's shared variables. Converting to ZeroMQ also allows easy access via other programming languages, such as Python.

  11. ICA-based artefact and accelerated fMRI acquisition for improved Resting State Network imaging

    PubMed Central

    Griffanti, Ludovica; Salimi-Khorshidi, Gholamreza; Beckmann, Christian F.; Auerbach, Edward J.; Douaud, Gwenaëlle; Sexton, Claire E.; Zsoldos, Enikő; Ebmeier, Klaus P; Filippini, Nicola; Mackay, Clare E.; Moeller, Steen; Xu, Junqian; Yacoub, Essa; Baselli, Giuseppe; Ugurbil, Kamil; Miller, Karla L.; Smith, Stephen M.

    2014-01-01

    The identification of resting state networks (RSNs) and the quantification of their functional connectivity in resting-state fMRI (rfMRI) are seriously hindered by the presence of artefacts, many of which overlap spatially or spectrally with RSNs. Moreover, recent developments in fMRI acquisition yield data with higher spatial and temporal resolutions, but may increase artefacts both spatially and/or temporally. Hence the correct identification and removal of non-neural fluctuations is crucial, especially in accelerated acquisitions. In this paper we investigate the effectiveness of three data-driven cleaning procedures, compare standard against higher (spatial and temporal) resolution accelerated fMRI acquisitions, and investigate the combined effect of different acquisitions and different cleanup approaches. We applied single-subject independent component analysis (ICA), followed by automatic component classification with FMRIB’s ICA-based X-noiseifier (FIX) to identify artefactual components. We then compared two first-level (within-subject) cleaning approaches for removing those artefacts and motion-related fluctuations from the data. The effectiveness of the cleaning procedures were assessed using timeseries (amplitude and spectra), network matrix and spatial map analyses. For timeseries and network analyses we also tested the effect of a second-level cleaning (informed by group-level analysis). Comparing these approaches, the preferable balance between noise removal and signal loss was achieved by regressing out of the data the full space of motion-related fluctuations and only the unique variance of the artefactual ICA components. Using similar analyses, we also investigated the effects of different cleaning approaches on data from different acquisition sequences. With the optimal cleaning procedures, functional connectivity results from accelerated data were statistically comparable or significantly better than the standard (unaccelerated) acquisition

  12. Adaptive control for accelerators

    DOEpatents

    Eaton, Lawrie E.; Jachim, Stephen P.; Natter, Eckard F.

    1991-01-01

    An adaptive feedforward control loop is provided to stabilize accelerator beam loading of the radio frequency field in an accelerator cavity during successive pulses of the beam into the cavity. A digital signal processor enables an adaptive algorithm to generate a feedforward error correcting signal functionally determined by the feedback error obtained by a beam pulse loading the cavity after the previous correcting signal was applied to the cavity. Each cavity feedforward correcting signal is successively stored in the digital processor and modified by the feedback error resulting from its application to generate the next feedforward error correcting signal. A feedforward error correcting signal is generated by the digital processor in advance of the beam pulse to enable a composite correcting signal and the beam pulse to arrive concurrently at the cavity.

  13. Accelerator infrastructure in Europe: EuCARD 2011

    NASA Astrophysics Data System (ADS)

    Romaniuk, Ryszard S.

    2011-10-01

    The paper presents a digest of the research results in the domain of accelerator science and technology in Europe, shown during the annual meeting of the EuCARD - European Coordination of Accelerator Research and Development. The conference concerns building of the research infrastructure, including in this advanced photonic and electronic systems for servicing large high energy physics experiments. There are debated a few basic groups of such systems like: measurement - control networks of large geometrical extent, multichannel systems for large amounts of metrological data acquisition, precision photonic networks of reference time, frequency and phase distribution.

  14. Accelerator controls at CERN: Some converging trends

    NASA Astrophysics Data System (ADS)

    Kuiper, B.

    1990-08-01

    CERN's growing services to the high-energy physics community using frozen resources has led to the implementation of "Technical Boards", mandated to assist the management by making recommendations for rationalizations in various technological domains. The Board on Process Control and Electronics for Accelerators, TEBOCO, has emphasized four main lines which might yield economy in resources. First, a common architecture for accelerator controls has been agreed between the three accelerator divisions. Second, a common hardware/software kit has been defined, from which the large majority of future process interfacing may be composed. A support service for this kit is an essential part of the plan. Third, high-level protocols have been developed for standardizing access to process devices. They derive from agreed standard models of the devices and involve a standard control message. This should ease application development and mobility of equipment. Fourth, a common software engineering methodology and a commercial package of application development tools have been adopted. Some rationalization in the field of the man-machine interface and in matters of synchronization is also under way.

  15. EuCARD2: enhanced accelerator research and development in Europe

    NASA Astrophysics Data System (ADS)

    Romaniuk, Ryszard S.

    2013-10-01

    Accelerator science and technology is one of a key enablers of the developments in the particle physic, photon physics and also applications in medicine and industry. EuCARD2 is an European research project which will be realized during 2013-2017 inside the EC FP7 framework. The project concerns the development and coordination of European Accelerator Research and Development. The project is particularly important, to a number of domestic laboratories, due to some plans to build large accelerator infrastructure in Poland. Large accelerator infrastructure of fundamental and applied research character stimulates around it the development and industrial applications as well as biomedical of advanced accelerators, material research and engineering, cryo-technology, mechatronics, robotics, and in particular electronics - like networked measurement and control systems, sensors, computer systems, automation and control systems. The paper presents a digest of the European project EuCARD2 which is Enhanced European Coordination for Accelerator Research and Development. The paper presents a digest of the research results and assumptions in the domain of accelerator science and technology in Europe, shown during the final fourth annual meeting of the EuCARD - European Coordination of Accelerator R&D, and the kick-off meeting of the EuCARD2. There are debated a few basic groups of accelerator systems components like: measurement - control networks of large geometrical extent, multichannel systems for large amounts of metrological data acquisition, precision photonic networks of reference time, frequency and phase distribution, high field magnets, superconducting cavities, novel beam collimators, etc. The paper bases on the following materials: Internet and Intranet documents combined with EuCARD2, Description of Work FP7 EuCARD-2 DoW-312453, 2013-02-13, and discussions and preparatory materials worked on by Eucard-2 initiators.

  16. Kinematics and Dynamics of Motion Control Based on Acceleration Control

    NASA Astrophysics Data System (ADS)

    Ohishi, Kiyoshi; Ohba, Yuzuru; Katsura, Seiichiro

    The first IEEE International Workshop on Advanced Motion Control was held in 1990 pointed out the importance of physical interpretation of motion control. The software servoing technology is now common in machine tools, robotics, and mechatronics. It has been intensively developed for the numerical control (NC) machines. Recently, motion control in unknown environment will be more and more important. Conventional motion control is not always suitable due to the lack of adaptive capability to the environment. A more sophisticated ability in motion control is necessary for compliant contact with environment. Acceleration control is the key technology of motion control in unknown environment. The acceleration control can make a motion system to be a zero control stiffness system without losing the robustness. Furthermore, a realization of multi-degree-of-freedom motion is necessary for future human assistance. A human assistant motion will require various control stiffness corresponding to the task. The review paper focuses on the modal coordinate system to integrate the various control stiffness in the virtual axes. A bilateral teleoperation is a good candidate to consider the future human assistant motion and integration of decentralized systems. Thus the paper reviews and discusses the bilateral teleoperation from the control stiffness and the modal control design points of view.

  17. Attack Vulnerability of Network Controllability.

    PubMed

    Lu, Zhe-Ming; Li, Xin-Feng

    2016-01-01

    Controllability of complex networks has attracted much attention, and understanding the robustness of network controllability against potential attacks and failures is of practical significance. In this paper, we systematically investigate the attack vulnerability of network controllability for the canonical model networks as well as the real-world networks subject to attacks on nodes and edges. The attack strategies are selected based on degree and betweenness centralities calculated for either the initial network or the current network during the removal, among which random failure is as a comparison. It is found that the node-based strategies are often more harmful to the network controllability than the edge-based ones, and so are the recalculated strategies than their counterparts. The Barabási-Albert scale-free model, which has a highly biased structure, proves to be the most vulnerable of the tested model networks. In contrast, the Erdős-Rényi random model, which lacks structural bias, exhibits much better robustness to both node-based and edge-based attacks. We also survey the control robustness of 25 real-world networks, and the numerical results show that most real networks are control robust to random node failures, which has not been observed in the model networks. And the recalculated betweenness-based strategy is the most efficient way to harm the controllability of real-world networks. Besides, we find that the edge degree is not a good quantity to measure the importance of an edge in terms of network controllability.

  18. PARTICLE ACCELERATOR AND METHOD OF CONTROLLING THE TEMPERATURE THEREOF

    DOEpatents

    Neal, R.B.; Gallagher, W.J.

    1960-10-11

    A method and means for controlling the temperature of a particle accelerator and more particularly to the maintenance of a constant and uniform temperature throughout a particle accelerator is offered. The novel feature of the invention resides in the provision of two individual heating applications to the accelerator structure. The first heating application provided is substantially a duplication of the accelerator heat created from energization, this first application being employed only when the accelerator is de-energized thereby maintaining the accelerator temperature constant with regard to time whether the accelerator is energized or not. The second heating application provided is designed to add to either the first application or energization heat in a manner to create the same uniform temperature throughout all portions of the accelerator.

  19. Attack Vulnerability of Network Controllability

    PubMed Central

    2016-01-01

    Controllability of complex networks has attracted much attention, and understanding the robustness of network controllability against potential attacks and failures is of practical significance. In this paper, we systematically investigate the attack vulnerability of network controllability for the canonical model networks as well as the real-world networks subject to attacks on nodes and edges. The attack strategies are selected based on degree and betweenness centralities calculated for either the initial network or the current network during the removal, among which random failure is as a comparison. It is found that the node-based strategies are often more harmful to the network controllability than the edge-based ones, and so are the recalculated strategies than their counterparts. The Barabási-Albert scale-free model, which has a highly biased structure, proves to be the most vulnerable of the tested model networks. In contrast, the Erdős-Rényi random model, which lacks structural bias, exhibits much better robustness to both node-based and edge-based attacks. We also survey the control robustness of 25 real-world networks, and the numerical results show that most real networks are control robust to random node failures, which has not been observed in the model networks. And the recalculated betweenness-based strategy is the most efficient way to harm the controllability of real-world networks. Besides, we find that the edge degree is not a good quantity to measure the importance of an edge in terms of network controllability. PMID:27588941

  20. An accelerated training method for back propagation networks

    NASA Technical Reports Server (NTRS)

    Shelton, Robert O. (Inventor)

    1993-01-01

    The principal objective is to provide a training procedure for a feed forward, back propagation neural network which greatly accelerates the training process. A set of orthogonal singular vectors are determined from the input matrix such that the standard deviations of the projections of the input vectors along these singular vectors, as a set, are substantially maximized, thus providing an optimal means of presenting the input data. Novelty exists in the method of extracting from the set of input data, a set of features which can serve to represent the input data in a simplified manner, thus greatly reducing the time/expense to training the system.

  1. Controllability of Surface Water Networks

    NASA Astrophysics Data System (ADS)

    Riasi, M. Sadegh; Yeghiazarian, Lilit

    2017-12-01

    To sustainably manage water resources, we must understand how to control complex networked systems. In this paper, we study surface water networks from the perspective of structural controllability, a concept that integrates classical control theory with graph-theoretic formalism. We present structural controllability theory and compute four metrics: full and target controllability, control centrality and control profile (FTCP) that collectively determine the structural boundaries of the system's control space. We use these metrics to answer the following questions: How does the structure of a surface water network affect its controllability? How to efficiently control a preselected subset of the network? Which nodes have the highest control power? What types of topological structures dominate controllability? Finally, we demonstrate the structural controllability theory in the analysis of a wide range of surface water networks, such as tributary, deltaic, and braided river systems.

  2. MapReduce Based Parallel Bayesian Network for Manufacturing Quality Control

    NASA Astrophysics Data System (ADS)

    Zheng, Mao-Kuan; Ming, Xin-Guo; Zhang, Xian-Yu; Li, Guo-Ming

    2017-09-01

    Increasing complexity of industrial products and manufacturing processes have challenged conventional statistics based quality management approaches in the circumstances of dynamic production. A Bayesian network and big data analytics integrated approach for manufacturing process quality analysis and control is proposed. Based on Hadoop distributed architecture and MapReduce parallel computing model, big volume and variety quality related data generated during the manufacturing process could be dealt with. Artificial intelligent algorithms, including Bayesian network learning, classification and reasoning, are embedded into the Reduce process. Relying on the ability of the Bayesian network in dealing with dynamic and uncertain problem and the parallel computing power of MapReduce, Bayesian network of impact factors on quality are built based on prior probability distribution and modified with posterior probability distribution. A case study on hull segment manufacturing precision management for ship and offshore platform building shows that computing speed accelerates almost directly proportionally to the increase of computing nodes. It is also proved that the proposed model is feasible for locating and reasoning of root causes, forecasting of manufacturing outcome, and intelligent decision for precision problem solving. The integration of bigdata analytics and BN method offers a whole new perspective in manufacturing quality control.

  3. Structural Controllability and Controlling Centrality of Temporal Networks

    PubMed Central

    Pan, Yujian; Li, Xiang

    2014-01-01

    Temporal networks are such networks where nodes and interactions may appear and disappear at various time scales. With the evidence of ubiquity of temporal networks in our economy, nature and society, it's urgent and significant to focus on its structural controllability as well as the corresponding characteristics, which nowadays is still an untouched topic. We develop graphic tools to study the structural controllability as well as its characteristics, identifying the intrinsic mechanism of the ability of individuals in controlling a dynamic and large-scale temporal network. Classifying temporal trees of a temporal network into different types, we give (both upper and lower) analytical bounds of the controlling centrality, which are verified by numerical simulations of both artificial and empirical temporal networks. We find that the positive relationship between aggregated degree and controlling centrality as well as the scale-free distribution of node's controlling centrality are virtually independent of the time scale and types of datasets, meaning the inherent robustness and heterogeneity of the controlling centrality of nodes within temporal networks. PMID:24747676

  4. Control networks and hubs.

    PubMed

    Gratton, Caterina; Sun, Haoxin; Petersen, Steven E

    2018-03-01

    Executive control functions are associated with frontal, parietal, cingulate, and insular brain regions that interact through distributed large-scale networks. Here, we discuss how fMRI functional connectivity can shed light on the organization of control networks and how they interact with other parts of the brain. In the first section of our review, we present convergent evidence from fMRI functional connectivity, activation, and lesion studies that there are multiple dissociable control networks in the brain with distinct functional properties. In the second section, we discuss how graph theoretical concepts can help illuminate the mechanisms by which control networks interact with other brain regions to carry out goal-directed functions, focusing on the role of specialized hub regions for mediating cross-network interactions. Again, we use a combination of functional connectivity, lesion, and task activation studies to bolster this claim. We conclude that a large-scale network perspective provides important neurobiological constraints on the neural underpinnings of executive control, which will guide future basic and translational research into executive function and its disruption in disease. © 2017 Society for Psychophysiological Research.

  5. Constrained target controllability of complex networks

    NASA Astrophysics Data System (ADS)

    Guo, Wei-Feng; Zhang, Shao-Wu; Wei, Ze-Gang; Zeng, Tao; Liu, Fei; Zhang, Jingsong; Wu, Fang-Xiang; Chen, Luonan

    2017-06-01

    It is of great theoretical interest and practical significance to study how to control a system by applying perturbations to only a few driver nodes. Recently, a hot topic of modern network researches is how to determine driver nodes that allow the control of an entire network. However, in practice, to control a complex network, especially a biological network, one may know not only the set of nodes which need to be controlled (i.e. target nodes), but also the set of nodes to which only control signals can be applied (i.e. constrained control nodes). Compared to the general concept of controllability, we introduce the concept of constrained target controllability (CTC) of complex networks, which concerns the ability to drive any state of target nodes to their desirable state by applying control signals to the driver nodes from the set of constrained control nodes. To efficiently investigate the CTC of complex networks, we further design a novel graph-theoretic algorithm called CTCA to estimate the ability of a given network to control targets by choosing driver nodes from the set of constrained control nodes. We extensively evaluate the CTC of numerous real complex networks. The results indicate that biological networks with a higher average degree are easier to control than biological networks with a lower average degree, while electronic networks with a lower average degree are easier to control than web networks with a higher average degree. We also show that our CTCA can more efficiently produce driver nodes for target-controlling the networks than existing state-of-the-art methods. Moreover, we use our CTCA to analyze two expert-curated bio-molecular networks and compare to other state-of-the-art methods. The results illustrate that our CTCA can efficiently identify proven drug targets and new potentials, according to the constrained controllability of those biological networks.

  6. Acceleration and torque feedback for robotic control - Experimental results

    NASA Technical Reports Server (NTRS)

    Mclnroy, John E.; Saridis, George N.

    1990-01-01

    Gross motion control of robotic manipulators typically requires significant on-line computations to compensate for nonlinear dynamics due to gravity, Coriolis, centripetal, and friction nonlinearities. One controller proposed by Luo and Saridis avoids these computations by feeding back joint acceleration and torque. This study implements the controller on a Puma 600 robotic manipulator. Joint acceleration measurement is obtained by measuring linear accelerations of each joint, and deriving a computationally efficient transformation from the linear measurements to the angular accelerations. Torque feedback is obtained by using the previous torque sent to the joints. The implementation has stability problems on the Puma 600 due to the extremely high gains inherent in the feedback structure. Since these high gains excite frequency modes in the Puma 600, the algorithm is modified to decrease the gain inherent in the feedback structure. The resulting compensator is stable and insensitive to high frequency unmodeled dynamics. Moreover, a second compensator is proposed which uses acceleration and torque feedback, but still allows nonlinear terms to be fed forward. Thus, by feeding the increment in the easily calculated gravity terms forward, improved responses are obtained. Both proposed compensators are implemented, and the real time results are compared to those obtained with the computed torque algorithm.

  7. Network Monitor and Control of Disruption-Tolerant Networks

    NASA Technical Reports Server (NTRS)

    Torgerson, J. Leigh

    2014-01-01

    For nearly a decade, NASA and many researchers in the international community have been developing Internet-like protocols that allow for automated network operations in networks where the individual links between nodes are only sporadically connected. A family of Disruption-Tolerant Networking (DTN) protocols has been developed, and many are reaching CCSDS Blue Book status. A NASA version of DTN known as the Interplanetary Overlay Network (ION) has been flight-tested on the EPOXI spacecraft and ION is currently being tested on the International Space Station. Experience has shown that in order for a DTN service-provider to set up a large scale multi-node network, a number of network monitor and control technologies need to be fielded as well as the basic DTN protocols. The NASA DTN program is developing a standardized means of querying a DTN node to ascertain its operational status, known as the DTN Management Protocol (DTNMP), and the program has developed some prototypes of DTNMP software. While DTNMP is a necessary component, it is not sufficient to accomplish Network Monitor and Control of a DTN network. JPL is developing a suite of tools that provide for network visualization, performance monitoring and ION node control software. This suite of network monitor and control tools complements the GSFC and APL-developed DTN MP software, and the combined package can form the basis for flight operations using DTN.

  8. Effect of correlations on controllability transition in network control

    PubMed Central

    Nie, Sen; Wang, Xu-Wen; Wang, Bing-Hong; Jiang, Luo-Luo

    2016-01-01

    The network control problem has recently attracted an increasing amount of attention, owing to concerns including the avoidance of cascading failures of power-grids and the management of ecological networks. It has been proven that numerical control can be achieved if the number of control inputs exceeds a certain transition point. In the present study, we investigate the effect of degree correlation on the numerical controllability in networks whose topological structures are reconstructed from both real and modeling systems, and we find that the transition point of the number of control inputs depends strongly on the degree correlation in both undirected and directed networks with moderately sparse links. More interestingly, the effect of the degree correlation on the transition point cannot be observed in dense networks for numerical controllability, which contrasts with the corresponding result for structural controllability. In particular, for directed random networks and scale-free networks, the influence of the degree correlation is determined by the types of correlations. Our approach provides an understanding of control problems in complex sparse networks. PMID:27063294

  9. Research on NGN network control technology

    NASA Astrophysics Data System (ADS)

    Li, WenYao; Zhou, Fang; Wu, JianXue; Li, ZhiGuang

    2004-04-01

    Nowadays NGN (Next Generation Network) is the hotspot for discussion and research in IT section. The NGN core technology is the network control technology. The key goal of NGN is to realize the network convergence and evolution. Referring to overlay network model core on Softswitch technology, circuit switch network and IP network convergence realized. Referring to the optical transmission network core on ASTN/ASON, service layer (i.e. IP layer) and optical transmission convergence realized. Together with the distributing feature of NGN network control technology, on NGN platform, overview of combining Softswitch and ASTN/ASON control technology, the solution whether IP should be the NGN core carrier platform attracts general attention, and this is also a QoS problem on NGN end to end. This solution produces the significant practical meaning on equipment development, network deployment, network design and optimization, especially on realizing present network smooth evolving to the NGN. This is why this paper puts forward the research topic on the NGN network control technology. This paper introduces basics on NGN network control technology, then proposes NGN network control reference model, at the same time describes a realizable network structure of NGN. Based on above, from the view of function realization, NGN network control technology is discussed and its work mechanism is analyzed.

  10. BIOCONAID System (Bionic Control of Acceleration Induced Dimming). Final Report.

    ERIC Educational Resources Information Center

    Rogers, Dana B.; And Others

    The system described represents a new technique for enhancing the fidelity of flight simulators during high acceleration maneuvers. This technique forces the simulator pilot into active participation and energy expenditure similar to the aircraft pilot undergoing actual accelerations. The Bionic Control of Acceleration Induced Dimming (BIOCONAID)…

  11. Model-based Acceleration Control of Turbofan Engines with a Hammerstein-Wiener Representation

    NASA Astrophysics Data System (ADS)

    Wang, Jiqiang; Ye, Zhifeng; Hu, Zhongzhi; Wu, Xin; Dimirovsky, Georgi; Yue, Hong

    2017-05-01

    Acceleration control of turbofan engines is conventionally designed through either schedule-based or acceleration-based approach. With the widespread acceptance of model-based design in aviation industry, it becomes necessary to investigate the issues associated with model-based design for acceleration control. In this paper, the challenges for implementing model-based acceleration control are explained; a novel Hammerstein-Wiener representation of engine models is introduced; based on the Hammerstein-Wiener model, a nonlinear generalized minimum variance type of optimal control law is derived; the feature of the proposed approach is that it does not require the inversion operation that usually upsets those nonlinear control techniques. The effectiveness of the proposed control design method is validated through a detailed numerical study.

  12. Study on application of adaptive fuzzy control and neural network in the automatic leveling system

    NASA Astrophysics Data System (ADS)

    Xu, Xiping; Zhao, Zizhao; Lan, Weiyong; Sha, Lei; Qian, Cheng

    2015-04-01

    This paper discusses the adaptive fuzzy control and neural network BP algorithm in large flat automatic leveling control system application. The purpose is to develop a measurement system with a flat quick leveling, Make the installation on the leveling system of measurement with tablet, to be able to achieve a level in precision measurement work quickly, improve the efficiency of the precision measurement. This paper focuses on the automatic leveling system analysis based on fuzzy controller, Use of the method of combining fuzzy controller and BP neural network, using BP algorithm improve the experience rules .Construct an adaptive fuzzy control system. Meanwhile the learning rate of the BP algorithm has also been run-rate adjusted to accelerate convergence. The simulation results show that the proposed control method can effectively improve the leveling precision of automatic leveling system and shorten the time of leveling.

  13. Laser wakefield accelerated electron beam monitoring and control

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

    Koga, J. K.; Mori, M.; Kotaki, H.

    2016-03-25

    We will discuss our participation in the ImPACT project, which has as one of its goals the development of an ultra-compact electron accelerator using lasers (< 1 GeV, < 10   m) and the generation of an x-ray beam from the accelerated electrons. Within this context we will discuss our investigation into electron beam monitoring and control. Since laser accelerated electrons will be used for x-ray beam generation combined with an undulator, we will present investigation into the possibilities of the improvement of electron beam emittance through cooling.

  14. Neural networks for aircraft control

    NASA Technical Reports Server (NTRS)

    Linse, Dennis

    1990-01-01

    Current research in Artificial Neural Networks indicates that networks offer some potential advantages in adaptation and fault tolerance. This research is directed at determining the possible applicability of neural networks to aircraft control. The first application will be to aircraft trim. Neural network node characteristics, network topology and operation, neural network learning and example histories using neighboring optimal control with a neural net are discussed.

  15. ACCELERATOR TARGET POSITIONER AND CONTROL CIRCUIT THEREFOR

    DOEpatents

    Stone, K.F.; Force, R.J.; Olson, W.W.; Cagle, D.S.

    1959-12-15

    An apparatus is described for inserting and retracting a target material with respect to the internal beam of a charged particle accelerator and to circuitry for controlling the timing and motion of the target placement. Two drive coils are mounted on the shaft of a target holder arm and disposed within the accelerator magnetic field with one coil at right angles to the other. Control circuitry alternately connects each coil to a current source and to a varying shorting resistance whereby the coils interchangeably produce driving and braking forces which swing the target arm within a ninety degree arc. The target is thus moved into the beam and away from it at high speeds and is brought to rest after each movement without whiplash or vibration.

  16. ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging.

    PubMed

    Griffanti, Ludovica; Salimi-Khorshidi, Gholamreza; Beckmann, Christian F; Auerbach, Edward J; Douaud, Gwenaëlle; Sexton, Claire E; Zsoldos, Enikő; Ebmeier, Klaus P; Filippini, Nicola; Mackay, Clare E; Moeller, Steen; Xu, Junqian; Yacoub, Essa; Baselli, Giuseppe; Ugurbil, Kamil; Miller, Karla L; Smith, Stephen M

    2014-07-15

    The identification of resting state networks (RSNs) and the quantification of their functional connectivity in resting-state fMRI (rfMRI) are seriously hindered by the presence of artefacts, many of which overlap spatially or spectrally with RSNs. Moreover, recent developments in fMRI acquisition yield data with higher spatial and temporal resolutions, but may increase artefacts both spatially and/or temporally. Hence the correct identification and removal of non-neural fluctuations is crucial, especially in accelerated acquisitions. In this paper we investigate the effectiveness of three data-driven cleaning procedures, compare standard against higher (spatial and temporal) resolution accelerated fMRI acquisitions, and investigate the combined effect of different acquisitions and different cleanup approaches. We applied single-subject independent component analysis (ICA), followed by automatic component classification with FMRIB's ICA-based X-noiseifier (FIX) to identify artefactual components. We then compared two first-level (within-subject) cleaning approaches for removing those artefacts and motion-related fluctuations from the data. The effectiveness of the cleaning procedures was assessed using time series (amplitude and spectra), network matrix and spatial map analyses. For time series and network analyses we also tested the effect of a second-level cleaning (informed by group-level analysis). Comparing these approaches, the preferable balance between noise removal and signal loss was achieved by regressing out of the data the full space of motion-related fluctuations and only the unique variance of the artefactual ICA components. Using similar analyses, we also investigated the effects of different cleaning approaches on data from different acquisition sequences. With the optimal cleaning procedures, functional connectivity results from accelerated data were statistically comparable or significantly better than the standard (unaccelerated) acquisition, and

  17. A New Concept of Controller for Accelerators' Magnet Power Supplies

    NASA Astrophysics Data System (ADS)

    Visintini, Roberto; Cleva, Stefano; Cautero, Marco; Ciesla, Tomasz

    2016-04-01

    The complexity of a particle accelerator implies the remote control of very large numbers of devices, with many different typologies, either distributed along the accelerator or concentrated in locations, often far away from each other. Local and global control systems handle the devices through dedicated communication channels and interfaces. Each controlled device is practically a “smart node” performing a specific task. In addition, very often, those tasks are managed in real-time mode. The performances required to the control interface has an influence on the cost of the distributed nodes as well as on their hardware and software implementation. In large facilities (e.g. CERN) the “smart nodes” derive from specific in-house developments. Alternatively, it is possible to find on the market commercial devices, whose performances (and prices) are spread over a broad range, and spanning from proprietary design (customizable to the user's needs) to open source/design. In this paper, we will describe some applications of smart nodes in the particle accelerators field, with special focus on the power supplies for magnets. In modern accelerators, in fact, magnets and their associated power supplies constitute systems distributed along the accelerator itself, and strongly interfaced with the remote control system as well as with more specific (and often more demanding) orbit/trajectory feedback systems. We will give examples of actual systems, installed and operational on two light sources, Elettra and FERMI, located in the Elettra Research Center in Trieste, Italy.

  18. Controllability of structural brain networks

    NASA Astrophysics Data System (ADS)

    Gu, Shi; Pasqualetti, Fabio; Cieslak, Matthew; Telesford, Qawi K.; Yu, Alfred B.; Kahn, Ari E.; Medaglia, John D.; Vettel, Jean M.; Miller, Michael B.; Grafton, Scott T.; Bassett, Danielle S.

    2015-10-01

    Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. However, fundamental principles constraining these dynamic network processes have remained elusive. Here we use tools from control and network theories to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system, facilitate the movement of the brain to many easily reachable states. Weakly connected areas, particularly in cognitive control systems, facilitate the movement of the brain to difficult-to-reach states. Areas located on the boundary between network communities, particularly in attentional control systems, facilitate the integration or segregation of diverse cognitive systems. Our results suggest that structural network differences between cognitive circuits dictate their distinct roles in controlling trajectories of brain network function.

  19. Neural network for control of rearrangeable Clos networks.

    PubMed

    Park, Y K; Cherkassky, V

    1994-09-01

    Rapid evolution in the field of communication networks requires high speed switching technologies. This involves a high degree of parallelism in switching control and routing performed at the hardware level. The multistage crossbar networks have always been attractive to switch designers. In this paper a neural network approach to controlling a three-stage Clos network in real time is proposed. This controller provides optimal routing of communication traffic requests on a call-by-call basis by rearranging existing connections, with a minimum length of rearrangement sequence so that a new blocked call request can be accommodated. The proposed neural network controller uses Paull's rearrangement algorithm, along with the special (least used) switch selection rule in order to minimize the length of rearrangement sequences. The functional behavior of our model is verified by simulations and it is shown that the convergence time required for finding an optimal solution is constant, regardless of the switching network size. The performance is evaluated for random traffic with various traffic loads. Simulation results show that applying the least used switch selection rule increases the efficiency in switch rearrangements, reducing the network convergence time. The implementation aspects are also discussed to show the feasibility of the proposed approach.

  20. Structural Controllability of Temporal Networks with a Single Switching Controller

    PubMed Central

    Yao, Peng; Hou, Bao-Yu; Pan, Yu-Jian; Li, Xiang

    2017-01-01

    Temporal network, whose topology evolves with time, is an important class of complex networks. Temporal trees of a temporal network describe the necessary edges sustaining the network as well as their active time points. By a switching controller which properly selects its location with time, temporal trees are used to improve the controllability of the network. Therefore, more nodes are controlled within the limited time. Several switching strategies to efficiently select the location of the controller are designed, which are verified with synthetic and empirical temporal networks to achieve better control performance. PMID:28107538

  1. Accelerator science and technology in Europe: EuCARD 2012

    NASA Astrophysics Data System (ADS)

    Romaniuk, Ryszard S.

    2012-05-01

    Accelerator science and technology is one of a key enablers of the developments in the particle physic, photon physics and also applications in medicine and industry. The paper presents a digest of the research results in the domain of accelerator science and technology in Europe, shown during the third annual meeting of the EuCARD - European Coordination of Accelerator Research and Development. The conference concerns building of the research infrastructure, including in this advanced photonic and electronic systems for servicing large high energy physics experiments. There are debated a few basic groups of such systems like: measurement - control networks of large geometrical extent, multichannel systems for large amounts of metrological data acquisition, precision photonic networks of reference time, frequency and phase distribution.

  2. Low Level RF Control for the PIP-II Accelerator

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

    Edelen, J. P.; Chase, B. E.; Cullerton, E.

    The PIP-II accelerator is a proposed upgrade to the Fermilab accelerator complex that will replace the existing, 400 MeV room temperature LINAC with an 800 MeV superconducting LINAC. Part of this upgrade includes a new injection scheme into the booster that levies tight requirements on the LLRF control system for the cavities. In this paper we discuss the challenges of the PIP-II accelerator and the present status of the LLRF system for this project.

  3. FPGA-accelerated adaptive optics wavefront control

    NASA Astrophysics Data System (ADS)

    Mauch, S.; Reger, J.; Reinlein, C.; Appelfelder, M.; Goy, M.; Beckert, E.; Tünnermann, A.

    2014-03-01

    The speed of real-time adaptive optical systems is primarily restricted by the data processing hardware and computational aspects. Furthermore, the application of mirror layouts with increasing numbers of actuators reduces the bandwidth (speed) of the system and, thus, the number of applicable control algorithms. This burden turns out a key-impediment for deformable mirrors with continuous mirror surface and highly coupled actuator influence functions. In this regard, specialized hardware is necessary for high performance real-time control applications. Our approach to overcome this challenge is an adaptive optics system based on a Shack-Hartmann wavefront sensor (SHWFS) with a CameraLink interface. The data processing is based on a high performance Intel Core i7 Quadcore hard real-time Linux system. Employing a Xilinx Kintex-7 FPGA, an own developed PCie card is outlined in order to accelerate the analysis of a Shack-Hartmann Wavefront Sensor. A recently developed real-time capable spot detection algorithm evaluates the wavefront. The main features of the presented system are the reduction of latency and the acceleration of computation For example, matrix multiplications which in general are of complexity O(n3 are accelerated by using the DSP48 slices of the field-programmable gate array (FPGA) as well as a novel hardware implementation of the SHWFS algorithm. Further benefits are the Streaming SIMD Extensions (SSE) which intensively use the parallelization capability of the processor for further reducing the latency and increasing the bandwidth of the closed-loop. Due to this approach, up to 64 actuators of a deformable mirror can be handled and controlled without noticeable restriction from computational burdens.

  4. State feedback control design for Boolean networks.

    PubMed

    Liu, Rongjie; Qian, Chunjiang; Liu, Shuqian; Jin, Yu-Fang

    2016-08-26

    Driving Boolean networks to desired states is of paramount significance toward our ultimate goal of controlling the progression of biological pathways and regulatory networks. Despite recent computational development of controllability of general complex networks and structural controllability of Boolean networks, there is still a lack of bridging the mathematical condition on controllability to real boolean operations in a network. Further, no realtime control strategy has been proposed to drive a Boolean network. In this study, we applied semi-tensor product to represent boolean functions in a network and explored controllability of a boolean network based on the transition matrix and time transition diagram. We determined the necessary and sufficient condition for a controllable Boolean network and mapped this requirement in transition matrix to real boolean functions and structure property of a network. An efficient tool is offered to assess controllability of an arbitrary Boolean network and to determine all reachable and non-reachable states. We found six simplest forms of controllable 2-node Boolean networks and explored the consistency of transition matrices while extending these six forms to controllable networks with more nodes. Importantly, we proposed the first state feedback control strategy to drive the network based on the status of all nodes in the network. Finally, we applied our reachability condition to the major switch of P53 pathway to predict the progression of the pathway and validate the prediction with published experimental results. This control strategy allowed us to apply realtime control to drive Boolean networks, which could not be achieved by the current control strategy for Boolean networks. Our results enabled a more comprehensive understanding of the evolution of Boolean networks and might be extended to output feedback control design.

  5. Accelerated Sensitivity Analysis in High-Dimensional Stochastic Reaction Networks

    PubMed Central

    Arampatzis, Georgios; Katsoulakis, Markos A.; Pantazis, Yannis

    2015-01-01

    Existing sensitivity analysis approaches are not able to handle efficiently stochastic reaction networks with a large number of parameters and species, which are typical in the modeling and simulation of complex biochemical phenomena. In this paper, a two-step strategy for parametric sensitivity analysis for such systems is proposed, exploiting advantages and synergies between two recently proposed sensitivity analysis methodologies for stochastic dynamics. The first method performs sensitivity analysis of the stochastic dynamics by means of the Fisher Information Matrix on the underlying distribution of the trajectories; the second method is a reduced-variance, finite-difference, gradient-type sensitivity approach relying on stochastic coupling techniques for variance reduction. Here we demonstrate that these two methods can be combined and deployed together by means of a new sensitivity bound which incorporates the variance of the quantity of interest as well as the Fisher Information Matrix estimated from the first method. The first step of the proposed strategy labels sensitivities using the bound and screens out the insensitive parameters in a controlled manner. In the second step of the proposed strategy, a finite-difference method is applied only for the sensitivity estimation of the (potentially) sensitive parameters that have not been screened out in the first step. Results on an epidermal growth factor network with fifty parameters and on a protein homeostasis with eighty parameters demonstrate that the proposed strategy is able to quickly discover and discard the insensitive parameters and in the remaining potentially sensitive parameters it accurately estimates the sensitivities. The new sensitivity strategy can be several times faster than current state-of-the-art approaches that test all parameters, especially in “sloppy” systems. In particular, the computational acceleration is quantified by the ratio between the total number of parameters over

  6. Neuro-fuzzy control of structures using acceleration feedback

    NASA Astrophysics Data System (ADS)

    Schurter, Kyle C.; Roschke, Paul N.

    2001-08-01

    This paper described a new approach for the reduction of environmentally induced vibration in constructed facilities by way of a neuro-fuzzy technique. The new control technique is presented and tested in a numerical study that involves two types of building models. The energy of each building is dissipated through magnetorheological (MR) dampers whose damping properties are continuously updated by a fuzzy controller. This semi-active control scheme relies on the development of a correlation between the accelerations of the building (controller input) and the voltage applied to the MR damper (controller output). This correlation forms the basis for the development of an intelligent neuro-fuzzy control strategy. To establish a context for assessing the effectiveness of the semi-active control scheme, responses to earthquake excitation are compared with passive strategies that have similar authority for control. According to numerical simulation, MR dampers are less effective control mechanisms than passive dampers with respect to a single degree of freedom (DOF) building model. On the other hand, MR dampers are predicted to be superior when used with multiple DOF structures for reduction of lateral acceleration.

  7. Object-oriented design for accelerator control

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

    Stok, P.D.V. van der; Berk, F. van den; Deckers, R.

    1994-02-01

    An object-oriented design for the distributed computer control system of the accelerator ring EUTERPE is presented. Because of the experimental nature of the ring, flexibility is of the utmost importance. The object-oriented principles have contributed considerably to the flexibility of the design incorporating multiple views, multi-level access and distributed surveillance.

  8. Structural controllability of unidirectional bipartite networks

    NASA Astrophysics Data System (ADS)

    Nacher, Jose C.; Akutsu, Tatsuya

    2013-04-01

    The interactions between fundamental life molecules, people and social organisations build complex architectures that often result in undesired behaviours. Despite all of the advances made in our understanding of network structures over the past decade, similar progress has not been achieved in the controllability of real-world networks. In particular, an analytical framework to address the controllability of bipartite networks is still absent. Here, we present a dominating set (DS)-based approach to bipartite network controllability that identifies the topologies that are relatively easy to control with the minimum number of driver nodes. Our theoretical calculations, assisted by computer simulations and an evaluation of real-world networks offer a promising framework to control unidirectional bipartite networks. Our analysis should open a new approach to reverting the undesired behaviours in unidirectional bipartite networks at will.

  9. Minimum energy control and optimal-satisfactory control of Boolean control network

    NASA Astrophysics Data System (ADS)

    Li, Fangfei; Lu, Xiwen

    2013-12-01

    In the literatures, to transfer the Boolean control network from the initial state to the desired state, the expenditure of energy has been rarely considered. Motivated by this, this Letter investigates the minimum energy control and optimal-satisfactory control of Boolean control network. Based on the semi-tensor product of matrices and Floyd's algorithm, minimum energy, constrained minimum energy and optimal-satisfactory control design for Boolean control network are given respectively. A numerical example is presented to illustrate the efficiency of the obtained results.

  10. Methods of Phase and Power Control in Magnetron Transmitters for Superconducting Accelerators

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

    Kazadevich, G.; Johnson, R.; Neubauer, M.

    Various methods of phase and power control in magnetron RF sources of superconducting accelerators intended for ADS-class projects were recently developed and studied with conventional 2.45 GHz, 1 kW, CW magnetrons operating in pulsed and CW regimes. Magnetron transmitters excited by a resonant (injection-locking) phasemodulated signal can provide phase and power control with the rates required for precise stabilization of phase and amplitude of the accelerating field in Superconducting RF (SRF) cavities of the intensity-frontier accelerators. An innovative technique that can significantly increase the magnetron transmitter efficiency at the widerange power control required for superconducting accelerators was developed and verifiedmore » with the 2.45 GHz magnetrons operating in CW and pulsed regimes. High efficiency magnetron transmitters of this type can significantly reduce the capital and operation costs of the ADSclass accelerator projects.« less

  11. Missile Guidance Law Based on Robust Model Predictive Control Using Neural-Network Optimization.

    PubMed

    Li, Zhijun; Xia, Yuanqing; Su, Chun-Yi; Deng, Jun; Fu, Jun; He, Wei

    2015-08-01

    In this brief, the utilization of robust model-based predictive control is investigated for the problem of missile interception. Treating the target acceleration as a bounded disturbance, novel guidance law using model predictive control is developed by incorporating missile inside constraints. The combined model predictive approach could be transformed as a constrained quadratic programming (QP) problem, which may be solved using a linear variational inequality-based primal-dual neural network over a finite receding horizon. Online solutions to multiple parametric QP problems are used so that constrained optimal control decisions can be made in real time. Simulation studies are conducted to illustrate the effectiveness and performance of the proposed guidance control law for missile interception.

  12. Evolution on neutral networks accelerates the ticking rate of the molecular clock

    PubMed Central

    Manrubia, Susanna; Cuesta, José A.

    2015-01-01

    Large sets of genotypes give rise to the same phenotype, because phenotypic expression is highly redundant. Accordingly, a population can accept mutations without altering its phenotype, as long as the genotype mutates into another one on the same set. By linking every pair of genotypes that are mutually accessible through mutation, genotypes organize themselves into neutral networks (NNs). These networks are known to be heterogeneous and assortative, and these properties affect the evolutionary dynamics of the population. By studying the dynamics of populations on NNs with arbitrary topology, we analyse the effect of assortativity, of NN (phenotype) fitness and of network size. We find that the probability that the population leaves the network is smaller the longer the time spent on it. This progressive ‘phenotypic entrapment’ entails a systematic increase in the overdispersion of the process with time and an acceleration in the fixation rate of neutral mutations. We also quantify the variation of these effects with the size of the phenotype and with its fitness relative to that of neighbouring alternatives. PMID:25392402

  13. A geometrical approach to control and controllability of nonlinear dynamical networks

    PubMed Central

    Wang, Le-Zhi; Su, Ri-Qi; Huang, Zi-Gang; Wang, Xiao; Wang, Wen-Xu; Grebogi, Celso; Lai, Ying-Cheng

    2016-01-01

    In spite of the recent interest and advances in linear controllability of complex networks, controlling nonlinear network dynamics remains an outstanding problem. Here we develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective is to apply parameter perturbation to drive the system from one attractor to another, assuming that the former is undesired and the latter is desired. To make our framework practically meaningful, we consider restricted parameter perturbation by imposing two constraints: it must be experimentally realizable and applied only temporarily. We introduce the concept of attractor network, which allows us to formulate a quantifiable controllability framework for nonlinear dynamical networks: a network is more controllable if the attractor network is more strongly connected. We test our control framework using examples from various models of experimental gene regulatory networks and demonstrate the beneficial role of noise in facilitating control. PMID:27076273

  14. A neural network based methodology to predict site-specific spectral acceleration values

    NASA Astrophysics Data System (ADS)

    Kamatchi, P.; Rajasankar, J.; Ramana, G. V.; Nagpal, A. K.

    2010-12-01

    A general neural network based methodology that has the potential to replace the computationally-intensive site-specific seismic analysis of structures is proposed in this paper. The basic framework of the methodology consists of a feed forward back propagation neural network algorithm with one hidden layer to represent the seismic potential of a region and soil amplification effects. The methodology is implemented and verified with parameters corresponding to Delhi city in India. For this purpose, strong ground motions are generated at bedrock level for a chosen site in Delhi due to earthquakes considered to originate from the central seismic gap of the Himalayan belt using necessary geological as well as geotechnical data. Surface level ground motions and corresponding site-specific response spectra are obtained by using a one-dimensional equivalent linear wave propagation model. Spectral acceleration values are considered as a target parameter to verify the performance of the methodology. Numerical studies carried out to validate the proposed methodology show that the errors in predicted spectral acceleration values are within acceptable limits for design purposes. The methodology is general in the sense that it can be applied to other seismically vulnerable regions and also can be updated by including more parameters depending on the state-of-the-art in the subject.

  15. Launch Control Network Engineer

    NASA Technical Reports Server (NTRS)

    Medeiros, Samantha

    2017-01-01

    The Spaceport Command and Control System (SCCS) is being built at the Kennedy Space Center in order to successfully launch NASA’s revolutionary vehicle that allows humans to explore further into space than ever before. During my internship, I worked with the Network, Firewall, and Hardware teams that are all contributing to the huge SCCS network project effort. I learned the SCCS network design and the several concepts that are running in the background. I also updated and designed documentation for physical networks that are part of SCCS. This includes being able to assist and build physical installations as well as configurations. I worked with the network design for vehicle telemetry interfaces to the Launch Control System (LCS); this allows the interface to interact with other systems at other NASA locations. This network design includes the Space Launch System (SLS), Interim Cryogenic Propulsion Stage (ICPS), and the Orion Multipurpose Crew Vehicle (MPCV). I worked on the network design and implementation in the Customer Avionics Interface Development and Analysis (CAIDA) lab.

  16. Interorganizational relationships within state tobacco control networks: a social network analysis.

    PubMed

    Krauss, Melissa; Mueller, Nancy; Luke, Douglas

    2004-10-01

    State tobacco control programs are implemented by networks of public and private agencies with a common goal to reduce tobacco use. The degree of a program's comprehensiveness depends on the scope of its activities and the variety of agencies involved in the network. Structural aspects of these networks could help describe the process of implementing a state's tobacco control program, but have not yet been examined. Social network analysis was used to examine the structure of five state tobacco control networks. Semi-structured interviews with key agencies collected quantitative and qualitative data on frequency of contact among network partners, money flow, relationship productivity, level of network effectiveness, and methods for improvement. Most states had hierarchical communication structures in which partner agencies had frequent contact with one or two central agencies. Lead agencies had the highest control over network communication. Networks with denser communication structures had denser productivity structures. Lead agencies had the highest financial influence within the networks, while statewide coalitions were financially influenced by others. Lead agencies had highly productive relationships with others, while agencies with narrow roles had fewer productive relationships. Statewide coalitions that received Robert Wood Johnson Foundation funding had more highly productive relationships than coalitions that did not receive the funding. Results suggest that frequent communication among network partners is related to more highly productive relationships. Results also highlight the importance of lead agencies and statewide coalitions in implementing a comprehensive state tobacco control program. Network analysis could be useful in developing process indicators for state tobacco control programs.

  17. Engineering survey planning for the alignment of a particle accelerator: part II. Design of a reference network and measurement strategy

    NASA Astrophysics Data System (ADS)

    Junqueira Leão, Rodrigo; Raffaelo Baldo, Crhistian; Collucci da Costa Reis, Maria Luisa; Alves Trabanco, Jorge Luiz

    2018-03-01

    The building blocks of particle accelerators are magnets responsible for keeping beams of charged particles at a desired trajectory. Magnets are commonly grouped in support structures named girders, which are mounted on vertical and horizontal stages. The performance of this type of machine is highly dependent on the relative alignment between its main components. The length of particle accelerators ranges from small machines to large-scale national or international facilities, with typical lengths of hundreds of meters to a few kilometers. This relatively large volume together with micrometric positioning tolerances make the alignment activity a classical large-scale dimensional metrology problem. The alignment concept relies on networks of fixed monuments installed on the building structure to which all accelerator components are referred. In this work, the Sirius accelerator is taken as a case study, and an alignment network is optimized via computational methods in terms of geometry, densification, and surveying procedure. Laser trackers are employed to guide the installation and measure the girders’ positions, using the optimized network as a reference and applying the metric developed in part I of this paper. Simulations demonstrate the feasibility of aligning the 220 girders of the Sirius synchrotron to better than 0.080 mm, at a coverage probability of 95%.

  18. Acceleration constraints in modeling and control of nonholonomic systems

    NASA Astrophysics Data System (ADS)

    Bajodah, Abdulrahman H.

    2003-10-01

    Acceleration constraints are used to enhance modeling techniques for dynamical systems. In particular, Kane's equations of motion subjected to bilateral constraints, unilateral constraints, and servo-constraints are modified by utilizing acceleration constraints for the purpose of simplifying the equations and increasing their applicability. The tangential properties of Kane's method provide relationships between the holonomic and the nonholonomic partial velocities, and hence allow one to describe nonholonomic generalized active and inertia forces in terms of their holonomic counterparts, i.e., those which correspond to the system without constraints. Therefore, based on the modeling process objectives, the holonomic and the nonholonomic vector entities in Kane's approach are used interchangeably to model holonomic and nonholonomic systems. When the holonomic partial velocities are used to model nonholonomic systems, the resulting models are full-order (also called nonminimal or unreduced) and separated in accelerations. As a consequence, they are readily integrable and can be used for generic system analysis. Other related topics are constraint forces, numerical stability of the nonminimal equations of motion, and numerical constraint stabilization. Two types of unilateral constraints considered are impulsive and friction constraints. Impulsive constraints are modeled by means of a continuous-in-velocities and impulse-momentum approaches. In controlled motion, the acceleration form of constraints is utilized with the Moore-Penrose generalized inverse of the corresponding constraint matrix to solve for the inverse dynamics of servo-constraints, and for the redundancy resolution of overactuated manipulators. If control variables are involved in the algebraic constraint equations, then these tools are used to modify the controlled equations of motion in order to facilitate control system design. An illustrative example of spacecraft stabilization is presented.

  19. Research on Acceleration Compensation Strategy of Electric Vehicle Based on Fuzzy Control Theory

    NASA Astrophysics Data System (ADS)

    Zhu, Tianjun; Li, Bin; Zong, Changfu; Wei, Zhicheng

    2017-09-01

    Nowadays, the driving technology of electric vehicle is developing rapidly. There are many kinds of methods in driving performance control technology. The paper studies the acceleration performance of electric vehicle. Under the premise of energy management, an acceleration power compensation method by fuzzy control theory based on driver intention recognition is proposed, which can meet the driver’s subjective feelings better. It avoids the problem that the pedal opening and power output are single correspondence when the traditional vehicle accelerates. Through the simulation test, this method can significantly improve the performance of acceleration and output torque smoothly in non-emergency acceleration to ensure vehicle comfortable and stable.

  20. Control of collective network chaos.

    PubMed

    Wagemakers, Alexandre; Barreto, Ernest; Sanjuán, Miguel A F; So, Paul

    2014-06-01

    Under certain conditions, the collective behavior of a large globally-coupled heterogeneous network of coupled oscillators, as quantified by the macroscopic mean field or order parameter, can exhibit low-dimensional chaotic behavior. Recent advances describe how a small set of "reduced" ordinary differential equations can be derived that captures this mean field behavior. Here, we show that chaos control algorithms designed using the reduced equations can be successfully applied to imperfect realizations of the full network. To systematically study the effectiveness of this technique, we measure the quality of control as we relax conditions that are required for the strict accuracy of the reduced equations, and hence, the controller. Although the effects are network-dependent, we show that the method is effective for surprisingly small networks, for modest departures from global coupling, and even with mild inaccuracy in the estimate of network heterogeneity.

  1. Evolution on neutral networks accelerates the ticking rate of the molecular clock.

    PubMed

    Manrubia, Susanna; Cuesta, José A

    2015-01-06

    Large sets of genotypes give rise to the same phenotype, because phenotypic expression is highly redundant. Accordingly, a population can accept mutations without altering its phenotype, as long as the genotype mutates into another one on the same set. By linking every pair of genotypes that are mutually accessible through mutation, genotypes organize themselves into neutral networks (NNs). These networks are known to be heterogeneous and assortative, and these properties affect the evolutionary dynamics of the population. By studying the dynamics of populations on NNs with arbitrary topology, we analyse the effect of assortativity, of NN (phenotype) fitness and of network size. We find that the probability that the population leaves the network is smaller the longer the time spent on it. This progressive 'phenotypic entrapment' entails a systematic increase in the overdispersion of the process with time and an acceleration in the fixation rate of neutral mutations. We also quantify the variation of these effects with the size of the phenotype and with its fitness relative to that of neighbouring alternatives. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

  2. Cooperative wireless network control based health and activity monitoring system.

    PubMed

    Prakash, R; Ganesh, A Balaji; Girish, Siva V

    2016-10-01

    A real-time cooperative communication based wireless network is presented for monitoring health and activity of an end-user in their environment. The cooperative communication offers better energy consumption and also an opportunity to aware the current location of a user non-intrusively. The link between mobile sensor node and relay node is dynamically established by using Received Signal Strength Indicator (RSSI) and Link Quality Indicator (LQI) based on adaptive relay selection scheme. The study proposes a Linear Acceleration based Transmission Power Decision Control (LA-TPDC) algorithm to further enhance the energy efficiency of cooperative communication. Further, the occurrences of false alarms are carefully prevented by introducing three stages of sequential warning system. The real-time experiments are carried-out by using the nodes, namely mobile sensor node, relay nodes and a destination node which are indigenously developed by using a CC430 microcontroller integrated with an in-built transceiver at 868 MHz. The wireless node performance characteristics, such as energy consumption, Signal-Noise ratio (SNR), Bit Error Rate (BER), Packet Delivery Ratio (PDR) and transmission offset are evaluated for all the participated nodes. The experimental results observed that the proposed linear acceleration based transmission power decision control algorithm almost doubles the battery life time than energy efficient conventional cooperative communication.

  3. Stabilization of model-based networked control systems

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

    Miranda, Francisco; Instituto Politécnico de Viana do Castelo, Viana do Castelo; Abreu, Carlos

    2016-06-08

    A class of networked control systems called Model-Based Networked Control Systems (MB-NCSs) is considered. Stabilization of MB-NCSs is studied using feedback controls and simulation of stabilization for different feedbacks is made with the purpose to reduce the network trafic. The feedback control input is applied in a compensated model of the plant that approximates the plant dynamics and stabilizes the plant even under slow network conditions. Conditions for global exponential stabilizability and for the choosing of a feedback control input for a given constant time between the information moments of the network are derived. An optimal control problem to obtainmore » an optimal feedback control is also presented.« less

  4. Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control

    USGS Publications Warehouse

    Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.

    1997-01-01

    One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.

  5. Lessons learned on the Ground Test Accelerator control system

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

    Kozubal, A.J.; Weiss, R.E.

    1994-09-01

    When we initiated the control system design for the Ground Test Accelerator (GTA), we envisioned a system that would be flexible enough to handle the changing requirements of an experimental project. This control system would use a developers` toolkit to reduce the cost and time to develop applications for GTA, and through the use of open standards, the system would accommodate unforeseen requirements as they arose. Furthermore, we would attempt to demonstrate on GTA a level of automation far beyond that achieved by existing accelerator control systems. How well did we achieve these goals? What were the stumbling blocks tomore » deploying the control system, and what assumptions did we make about requirements that turned out to be incorrect? In this paper we look at the process of developing a control system that evolved into what is now the ``Experimental Physics and Industrial Control System`` (EPICS). Also, we assess the impact of this system on the GTA project, as well as the impact of GTA on EPICS. The lessons learned on GTA will be valuable for future projects.« less

  6. Distributed controller clustering in software defined networks.

    PubMed

    Abdelaziz, Ahmed; Fong, Ang Tan; Gani, Abdullah; Garba, Usman; Khan, Suleman; Akhunzada, Adnan; Talebian, Hamid; Choo, Kim-Kwang Raymond

    2017-01-01

    Software Defined Networking (SDN) is an emerging promising paradigm for network management because of its centralized network intelligence. However, the centralized control architecture of the software-defined networks (SDNs) brings novel challenges of reliability, scalability, fault tolerance and interoperability. In this paper, we proposed a novel clustered distributed controller architecture in the real setting of SDNs. The distributed cluster implementation comprises of multiple popular SDN controllers. The proposed mechanism is evaluated using a real world network topology running on top of an emulated SDN environment. The result shows that the proposed distributed controller clustering mechanism is able to significantly reduce the average latency from 8.1% to 1.6%, the packet loss from 5.22% to 4.15%, compared to distributed controller without clustering running on HP Virtual Application Network (VAN) SDN and Open Network Operating System (ONOS) controllers respectively. Moreover, proposed method also shows reasonable CPU utilization results. Furthermore, the proposed mechanism makes possible to handle unexpected load fluctuations while maintaining a continuous network operation, even when there is a controller failure. The paper is a potential contribution stepping towards addressing the issues of reliability, scalability, fault tolerance, and inter-operability.

  7. Distributed controller clustering in software defined networks

    PubMed Central

    Gani, Abdullah; Akhunzada, Adnan; Talebian, Hamid; Choo, Kim-Kwang Raymond

    2017-01-01

    Software Defined Networking (SDN) is an emerging promising paradigm for network management because of its centralized network intelligence. However, the centralized control architecture of the software-defined networks (SDNs) brings novel challenges of reliability, scalability, fault tolerance and interoperability. In this paper, we proposed a novel clustered distributed controller architecture in the real setting of SDNs. The distributed cluster implementation comprises of multiple popular SDN controllers. The proposed mechanism is evaluated using a real world network topology running on top of an emulated SDN environment. The result shows that the proposed distributed controller clustering mechanism is able to significantly reduce the average latency from 8.1% to 1.6%, the packet loss from 5.22% to 4.15%, compared to distributed controller without clustering running on HP Virtual Application Network (VAN) SDN and Open Network Operating System (ONOS) controllers respectively. Moreover, proposed method also shows reasonable CPU utilization results. Furthermore, the proposed mechanism makes possible to handle unexpected load fluctuations while maintaining a continuous network operation, even when there is a controller failure. The paper is a potential contribution stepping towards addressing the issues of reliability, scalability, fault tolerance, and inter-operability. PMID:28384312

  8. Plug-in module acceleration feedback control for fast steering mirror-based beam stabilization systems

    NASA Astrophysics Data System (ADS)

    Deng, Chao; Ren, Wei; Mao, Yao; Ren, Ge

    2017-08-01

    A plug-in module acceleration feedback control (Plug-In AFC) strategy based on the disturbance observer (DOB) principle is proposed for charge-coupled device (CCD)-based fast steering mirror (FSM) stabilization systems. In classical FSM tracking systems, dual-loop control (DLC), including velocity feedback and position feedback, is usually utilized to enhance the closed-loop performance. Due to the mechanical resonance of the system and CCD time delay, the closed-loop bandwidth is severely restricted. To solve this problem, cascade acceleration feedback control (AFC), which is a kind of high-precision robust control method, is introduced to strengthen the disturbance rejection property. However, in practical applications, it is difficult to realize an integral algorithm in an acceleration controller to compensate for the quadratic differential contained in the FSM acceleration model, resulting in a challenging controller design and a limited improvement. To optimize the acceleration feedback framework in the FSM system, different from the cascade AFC, the accelerometers are used to construct DOB to compensate for the platform vibrations directly. The acceleration nested loop can be plugged into the velocity loop without changing the system stability, and the controller design is quite simple. A series of comparative experimental results demonstrate that the disturbance rejection property of the CCD-based FSM can be effectively improved by the proposed approach.

  9. Pinning impulsive control algorithms for complex network

    NASA Astrophysics Data System (ADS)

    Sun, Wen; Lü, Jinhu; Chen, Shihua; Yu, Xinghuo

    2014-03-01

    In this paper, we further investigate the synchronization of complex dynamical network via pinning control in which a selection of nodes are controlled at discrete times. Different from most existing work, the pinning control algorithms utilize only the impulsive signals at discrete time instants, which may greatly improve the communication channel efficiency and reduce control cost. Two classes of algorithms are designed, one for strongly connected complex network and another for non-strongly connected complex network. It is suggested that in the strongly connected network with suitable coupling strength, a single controller at any one of the network's nodes can always pin the network to its homogeneous solution. In the non-strongly connected case, the location and minimum number of nodes needed to pin the network are determined by the Frobenius normal form of the coupling matrix. In addition, the coupling matrix is not necessarily symmetric or irreducible. Illustrative examples are then given to validate the proposed pinning impulsive control algorithms.

  10. Distributed intelligent control and status networking

    NASA Technical Reports Server (NTRS)

    Fortin, Andre; Patel, Manoj

    1993-01-01

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

  11. Interdependent networks: the fragility of control

    PubMed Central

    Morris, Richard G.; Barthelemy, Marc

    2013-01-01

    Recent work in the area of interdependent networks has focused on interactions between two systems of the same type. However, an important and ubiquitous class of systems are those involving monitoring and control, an example of interdependence between processes that are very different. In this Article, we introduce a framework for modelling ‘distributed supervisory control' in the guise of an electrical network supervised by a distributed system of control devices. The system is characterised by degrees of freedom salient to real-world systems— namely, the number of control devices, their inherent reliability, and the topology of the control network. Surprisingly, the behavior of the system depends crucially on the reliability of control devices. When devices are completely reliable, cascade sizes are percolation controlled; the number of devices being the relevant parameter. For unreliable devices, the topology of the control network is important and can dramatically reduce the resilience of the system. PMID:24067404

  12. Network cosmology.

    PubMed

    Krioukov, Dmitri; Kitsak, Maksim; Sinkovits, Robert S; Rideout, David; Meyer, David; Boguñá, Marián

    2012-01-01

    Prediction and control of the dynamics of complex networks is a central problem in network science. Structural and dynamical similarities of different real networks suggest that some universal laws might accurately describe the dynamics of these networks, albeit the nature and common origin of such laws remain elusive. Here we show that the causal network representing the large-scale structure of spacetime in our accelerating universe is a power-law graph with strong clustering, similar to many complex networks such as the Internet, social, or biological networks. We prove that this structural similarity is a consequence of the asymptotic equivalence between the large-scale growth dynamics of complex networks and causal networks. This equivalence suggests that unexpectedly similar laws govern the dynamics of complex networks and spacetime in the universe, with implications to network science and cosmology.

  13. Network Cosmology

    PubMed Central

    Krioukov, Dmitri; Kitsak, Maksim; Sinkovits, Robert S.; Rideout, David; Meyer, David; Boguñá, Marián

    2012-01-01

    Prediction and control of the dynamics of complex networks is a central problem in network science. Structural and dynamical similarities of different real networks suggest that some universal laws might accurately describe the dynamics of these networks, albeit the nature and common origin of such laws remain elusive. Here we show that the causal network representing the large-scale structure of spacetime in our accelerating universe is a power-law graph with strong clustering, similar to many complex networks such as the Internet, social, or biological networks. We prove that this structural similarity is a consequence of the asymptotic equivalence between the large-scale growth dynamics of complex networks and causal networks. This equivalence suggests that unexpectedly similar laws govern the dynamics of complex networks and spacetime in the universe, with implications to network science and cosmology. PMID:23162688

  14. Enabling Controlling Complex Networks with Local Topological Information.

    PubMed

    Li, Guoqi; Deng, Lei; Xiao, Gaoxi; Tang, Pei; Wen, Changyun; Hu, Wuhua; Pei, Jing; Shi, Luping; Stanley, H Eugene

    2018-03-15

    Complex networks characterize the nature of internal/external interactions in real-world systems including social, economic, biological, ecological, and technological networks. Two issues keep as obstacles to fulfilling control of large-scale networks: structural controllability which describes the ability to guide a dynamical system from any initial state to any desired final state in finite time, with a suitable choice of inputs; and optimal control, which is a typical control approach to minimize the cost for driving the network to a predefined state with a given number of control inputs. For large complex networks without global information of network topology, both problems remain essentially open. Here we combine graph theory and control theory for tackling the two problems in one go, using only local network topology information. For the structural controllability problem, a distributed local-game matching method is proposed, where every node plays a simple Bayesian game with local information and local interactions with adjacent nodes, ensuring a suboptimal solution at a linear complexity. Starring from any structural controllability solution, a minimizing longest control path method can efficiently reach a good solution for the optimal control in large networks. Our results provide solutions for distributed complex network control and demonstrate a way to link the structural controllability and optimal control together.

  15. Control range: a controllability-based index for node significance in directed networks

    NASA Astrophysics Data System (ADS)

    Wang, Bingbo; Gao, Lin; Gao, Yong

    2012-04-01

    While a large number of methods for module detection have been developed for undirected networks, it is difficult to adapt them to handle directed networks due to the lack of consensus criteria for measuring the node significance in a directed network. In this paper, we propose a novel structural index, the control range, motivated by recent studies on the structural controllability of large-scale directed networks. The control range of a node quantifies the size of the subnetwork that the node can effectively control. A related index, called the control range similarity, is also introduced to measure the structural similarity between two nodes. When applying the index of control range to several real-world and synthetic directed networks, it is observed that the control range of the nodes is mainly influenced by the network's degree distribution and that nodes with a low degree may have a high control range. We use the index of control range similarity to detect and analyze functional modules in glossary networks and the enzyme-centric network of homo sapiens. Our results, as compared with other approaches to module detection such as modularity optimization algorithm, dynamic algorithm and clique percolation method, indicate that the proposed indices are effective and practical in depicting structural and modular characteristics of sparse directed networks.

  16. Wideband Motion Control by Position and Acceleration Input Based Disturbance Observer

    NASA Astrophysics Data System (ADS)

    Irie, Kouhei; Katsura, Seiichiro; Ohishi, Kiyoshi

    The disturbance observer can observe and suppress the disturbance torque within its bandwidth. Recent motion systems begin to spread in the society and they are required to have ability to contact with unknown environment. Such a haptic motion requires much wider bandwidth. However, since the conventional disturbance observer attains the acceleration response by the second order derivative of position response, the bandwidth is limited due to the derivative noise. This paper proposes a novel structure of a disturbance observer. The proposed disturbance observer uses an acceleration sensor for enlargement of bandwidth. Generally, the bandwidth of an acceleration sensor is from 1Hz to more than 1kHz. To cover DC range, the conventional position sensor based disturbance observer is integrated. Thus, the performance of the proposed Position and Acceleration input based disturbance observer (PADO) is superior to the conventional one. The PADO is applied to position control (infinity stiffness) and force control (zero stiffness). The numerical and experimental results show viability of the proposed method.

  17. Control Centrality and Hierarchical Structure in Complex Networks

    PubMed Central

    Liu, Yang-Yu; Slotine, Jean-Jacques; Barabási, Albert-László

    2012-01-01

    We introduce the concept of control centrality to quantify the ability of a single node to control a directed weighted network. We calculate the distribution of control centrality for several real networks and find that it is mainly determined by the network’s degree distribution. We show that in a directed network without loops the control centrality of a node is uniquely determined by its layer index or topological position in the underlying hierarchical structure of the network. Inspired by the deep relation between control centrality and hierarchical structure in a general directed network, we design an efficient attack strategy against the controllability of malicious networks. PMID:23028542

  18. Neural networks for self-learning control systems

    NASA Technical Reports Server (NTRS)

    Nguyen, Derrick H.; Widrow, Bernard

    1990-01-01

    It is shown how a neural network can learn of its own accord to control a nonlinear dynamic system. An emulator, a multilayered neural network, learns to identify the system's dynamic characteristics. The controller, another multilayered neural network, next learns to control the emulator. The self-trained controller is then used to control the actual dynamic system. The learning process continues as the emulator and controller improve and track the physical process. An example is given to illustrate these ideas. The 'truck backer-upper,' a neural network controller that steers a trailer truck while the truck is backing up to a loading dock, is demonstrated. The controller is able to guide the truck to the dock from almost any initial position. The technique explored should be applicable to a wide variety of nonlinear control problems.

  19. Robust distributed control of spacecraft formation flying with adaptive network topology

    NASA Astrophysics Data System (ADS)

    Shasti, Behrouz; Alasty, Aria; Assadian, Nima

    2017-07-01

    In this study, the distributed six degree-of-freedom (6-DOF) coordinated control of spacecraft formation flying in low earth orbit (LEO) has been investigated. For this purpose, an accurate coupled translational and attitude relative dynamics model of the spacecraft with respect to the reference orbit (virtual leader) is presented by considering the most effective perturbation acceleration forces on LEO satellites, i.e. the second zonal harmonic and the atmospheric drag. Subsequently, the 6-DOF coordinated control of spacecraft in formation is studied. During the mission, the spacecraft communicate with each other through a switching network topology in which the weights of its graph Laplacian matrix change adaptively based on a distance-based connectivity function between neighboring agents. Because some of the dynamical system parameters such as spacecraft masses and moments of inertia may vary with time, an adaptive law is developed to estimate the parameter values during the mission. Furthermore, for the case that there is no knowledge of the unknown and time-varying parameters of the system, a robust controller has been developed. It is proved that the stability of the closed-loop system coupled with adaptation in network topology structure and optimality and robustness in control is guaranteed by the robust contraction analysis as an incremental stability method for multiple synchronized systems. The simulation results show the effectiveness of each control method in the presence of uncertainties and parameter variations. The adaptive and robust controllers show their superiority in reducing the state error integral as well as decreasing the control effort and settling time.

  20. Genomic and Network Patterns of Schizophrenia Genetic Variation in Human Evolutionary Accelerated Regions

    PubMed Central

    Xu, Ke; Schadt, Eric E.; Pollard, Katherine S.; Roussos, Panos; Dudley, Joel T.

    2015-01-01

    The population persistence of schizophrenia despite associated reductions in fitness and fecundity suggests that the genetic basis of schizophrenia has a complex evolutionary history. A recent meta-analysis of schizophrenia genome-wide association studies offers novel opportunities for assessment of the evolutionary trajectories of schizophrenia-associated loci. In this study, we hypothesize that components of the genetic architecture of schizophrenia are attributable to human lineage-specific evolution. Our results suggest that schizophrenia-associated loci enrich in genes near previously identified human accelerated regions (HARs). Specifically, we find that genes near HARs conserved in nonhuman primates (pHARs) are enriched for schizophrenia-associated loci, and that pHAR-associated schizophrenia genes are under stronger selective pressure than other schizophrenia genes and other pHAR-associated genes. We further evaluate pHAR-associated schizophrenia genes in regulatory network contexts to investigate associated molecular functions and mechanisms. We find that pHAR-associated schizophrenia genes significantly enrich in a GABA-related coexpression module that was previously found to be differentially regulated in schizophrenia affected individuals versus healthy controls. In another two independent networks constructed from gene expression profiles from prefrontal cortex samples, we find that pHAR-associated schizophrenia genes are located in more central positions and their average path lengths to the other nodes are significantly shorter than those of other schizophrenia genes. Together, our results suggest that HARs are associated with potentially important functional roles in the genetic architecture of schizophrenia. PMID:25681384

  1. Controllability of Deterministic Networks with the Identical Degree Sequence

    PubMed Central

    Ma, Xiujuan; Zhao, Haixing; Wang, Binghong

    2015-01-01

    Controlling complex network is an essential problem in network science and engineering. Recent advances indicate that the controllability of complex network is dependent on the network's topology. Liu and Barabási, et.al speculated that the degree distribution was one of the most important factors affecting controllability for arbitrary complex directed network with random link weights. In this paper, we analysed the effect of degree distribution to the controllability for the deterministic networks with unweighted and undirected. We introduce a class of deterministic networks with identical degree sequence, called (x,y)-flower. We analysed controllability of the two deterministic networks ((1, 3)-flower and (2, 2)-flower) by exact controllability theory in detail and give accurate results of the minimum number of driver nodes for the two networks. In simulation, we compare the controllability of (x,y)-flower networks. Our results show that the family of (x,y)-flower networks have the same degree sequence, but their controllability is totally different. So the degree distribution itself is not sufficient to characterize the controllability of deterministic networks with unweighted and undirected. PMID:26020920

  2. Measurement of performance using acceleration control and pulse control in simulated spacecraft docking operations

    NASA Technical Reports Server (NTRS)

    Brody, Adam R.; Ellis, Stephen R.

    1992-01-01

    Nine commercial airline pilots served as test subjects in a study to compare acceleration control with pulse control in simulated spacecraft maneuvers. Simulated remote dockings of an orbital maneuvering vehicle (OMV) to a space station were initiated from 50, 100, and 150 meters along the station's -V-bar (minus velocity vector). All unsuccessful missions were reflown. Five way mixed analysis of variance (ANOVA) with one between factor, first mode, and four within factors (mode, bloch, range, and trial) were performed on the data. Recorded performance measures included mission duration and fuel consumption along each of the three coordinate axes. Mission duration was lower with pulse mode, while delta V (fuel consumption) was lower with acceleration mode. Subjects used more fuel to travel faster with pulse mode than with acceleration mode. Mission duration, delta V, X delta V, Y delta V., and Z delta V all increased with range. Subjects commanded the OMV to 'fly' at faster rates from further distances. These higher average velocities were paid for with increased fuel consumption. Asymmetrical transfer was found in that the mode transitions could not be predicted solely from the mission duration main effect. More testing is advised to understand the manual control aspects of spaceflight maneuvers better.

  3. Controlling Contagion Processes in Activity Driven Networks

    NASA Astrophysics Data System (ADS)

    Liu, Suyu; Perra, Nicola; Karsai, Márton; Vespignani, Alessandro

    2014-03-01

    The vast majority of strategies aimed at controlling contagion processes on networks consider the connectivity pattern of the system either quenched or annealed. However, in the real world, many networks are highly dynamical and evolve, in time, concurrently with the contagion process. Here, we derive an analytical framework for the study of control strategies specifically devised for a class of time-varying networks, namely activity-driven networks. We develop a block variable mean-field approach that allows the derivation of the equations describing the coevolution of the contagion process and the network dynamic. We derive the critical immunization threshold and assess the effectiveness of three different control strategies. Finally, we validate the theoretical picture by simulating numerically the spreading process and control strategies in both synthetic networks and a large-scale, real-world, mobile telephone call data set.

  4. Neural network-based model reference adaptive control system.

    PubMed

    Patino, H D; Liu, D

    2000-01-01

    In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a sigma-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.

  5. Parallel processing data network of master and slave transputers controlled by a serial control network

    DOEpatents

    Crosetto, D.B.

    1996-12-31

    The present device provides for a dynamically configurable communication network having a multi-processor parallel processing system having a serial communication network and a high speed parallel communication network. The serial communication network is used to disseminate commands from a master processor to a plurality of slave processors to effect communication protocol, to control transmission of high density data among nodes and to monitor each slave processor`s status. The high speed parallel processing network is used to effect the transmission of high density data among nodes in the parallel processing system. Each node comprises a transputer, a digital signal processor, a parallel transfer controller, and two three-port memory devices. A communication switch within each node connects it to a fast parallel hardware channel through which all high density data arrives or leaves the node. 6 figs.

  6. Parallel processing data network of master and slave transputers controlled by a serial control network

    DOEpatents

    Crosetto, Dario B.

    1996-01-01

    The present device provides for a dynamically configurable communication network having a multi-processor parallel processing system having a serial communication network and a high speed parallel communication network. The serial communication network is used to disseminate commands from a master processor (100) to a plurality of slave processors (200) to effect communication protocol, to control transmission of high density data among nodes and to monitor each slave processor's status. The high speed parallel processing network is used to effect the transmission of high density data among nodes in the parallel processing system. Each node comprises a transputer (104), a digital signal processor (114), a parallel transfer controller (106), and two three-port memory devices. A communication switch (108) within each node (100) connects it to a fast parallel hardware channel (70) through which all high density data arrives or leaves the node.

  7. A control network of Triton

    NASA Astrophysics Data System (ADS)

    Davies, Merton E.; Rogers, Patricia G.; Colvin, Tim R.

    1991-08-01

    A control network for Triton has been computed using a bundle-type analytical triangulation program. The network contains 105 points that were measured on 57 Voyager-2 pictures. The adjustment contained 1010 observation equations and 382 normal equations and resulted in a standard measurement error of 13.36 microns. The coordinates of the control points, the camera orientation angles at the times when the pictures were taken, and Triton's mean radius were determined. A separate statistical analysis confirmed Triton's radius to be 1352.6 + or - 2.4 km. Attempts to tie the control network around the satellite were unsuccessful because discontinuities exist in high-resolution coverage between 66 deg and 289 deg longitude, north of 38 deg latitude, and south of 78 deg latitude.

  8. Detecting Anomalies in Process Control Networks

    NASA Astrophysics Data System (ADS)

    Rrushi, Julian; Kang, Kyoung-Don

    This paper presents the estimation-inspection algorithm, a statistical algorithm for anomaly detection in process control networks. The algorithm determines if the payload of a network packet that is about to be processed by a control system is normal or abnormal based on the effect that the packet will have on a variable stored in control system memory. The estimation part of the algorithm uses logistic regression integrated with maximum likelihood estimation in an inductive machine learning process to estimate a series of statistical parameters; these parameters are used in conjunction with logistic regression formulas to form a probability mass function for each variable stored in control system memory. The inspection part of the algorithm uses the probability mass functions to estimate the normalcy probability of a specific value that a network packet writes to a variable. Experimental results demonstrate that the algorithm is very effective at detecting anomalies in process control networks.

  9. Accelerating Learning By Neural Networks

    NASA Technical Reports Server (NTRS)

    Toomarian, Nikzad; Barhen, Jacob

    1992-01-01

    Electronic neural networks made to learn faster by use of terminal teacher forcing. Method of supervised learning involves addition of teacher forcing functions to excitations fed as inputs to output neurons. Initially, teacher forcing functions are strong enough to force outputs to desired values; subsequently, these functions decay with time. When learning successfully completed, terminal teacher forcing vanishes, and dynamics or neural network become equivalent to those of conventional neural network. Simulated neural network with terminal teacher forcing learned to produce close approximation of circular trajectory in 400 iterations.

  10. Adaptive lyapunov control and artificial neural networks for spacecraft relative maneuvering using atmospheric differential drag

    NASA Astrophysics Data System (ADS)

    Perez Chaparro, David Andres

    At low Earth orbits, a differential in the drag acceleration between spacecraft can be used to control their relative motion. This drag differential allows for a propellant-free alternative to thrusters for performing relative maneuvers in these orbits. The interest in autonomous propellant-less maneuvering comes from the desire to reduce the costs of spacecraft formations. Formation maneuvering opens up a wide variety of new applications for spacecraft missions, such as on-orbit maintenance and refueling. In this work atmospheric differential drag based nonlinear controllers are presented that can be used for virtually any planar relative maneuver of two spacecraft, provided that there is enough atmospheric density and that the spacecraft can change their ballistic coefficients by sufficient amounts to generate the necessary differential accelerations. The control techniques are successfully tested using high fidelity Satellite Tool Kit simulations for re-phase, fly-around, and rendezvous maneuvers, proving the feasibility of the proposed approach for a real flight. Furthermore, the atmospheric density varies in time and in space as the spacecraft travel along their orbits. The ability to accurately forecast the density allows for accurate onboard orbit propagation and for creating realistic guidance trajectories for maneuvers that rely on the differential drag. In this work a localized density predictor based on artificial neural networks is also presented. The predictor uses density measurements or estimates along the past orbits and can use a set of proxies for solar and geomagnetic activities to predict the value of the density along the future orbits of the spacecraft. The performance of the localized predictor is studied for different neural network structures, testing periods of high and low solar and geomagnetic activities and different prediction windows. Comparison with previously developed methods show substantial benefits in using neural networks, both

  11. Network Access Control List Situation Awareness

    ERIC Educational Resources Information Center

    Reifers, Andrew

    2010-01-01

    Network security is a large and complex problem being addressed by multiple communities. Nevertheless, current theories in networking security appear to overestimate network administrators' ability to understand network access control lists (NACLs), providing few context specific user analyses. Consequently, the current research generally seems to…

  12. Feasibility of Using Neural Network Models to Accelerate the Testing of Mechanical Systems

    NASA Technical Reports Server (NTRS)

    Fusaro, Robert L.

    1998-01-01

    Verification testing is an important aspect of the design process for mechanical mechanisms, and full-scale, full-length life testing is typically used to qualify any new component for use in space. However, as the required life specification is increased, full-length life tests become more costly and lengthen the development time. At the NASA Lewis Research Center, we theorized that neural network systems may be able to model the operation of a mechanical device. If so, the resulting neural network models could simulate long-term mechanical testing with data from a short-term test. This combination of computer modeling and short-term mechanical testing could then be used to verify the reliability of mechanical systems, thereby eliminating the costs associated with long-term testing. Neural network models could also enable designers to predict the performance of mechanisms at the conceptual design stage by entering the critical parameters as input and running the model to predict performance. The purpose of this study was to assess the potential of using neural networks to predict the performance and life of mechanical systems. To do this, we generated a neural network system to model wear obtained from three accelerated testing devices: 1) A pin-on-disk tribometer; 2) A line-contact rub-shoe tribometer; 3) A four-ball tribometer.

  13. Electron beam accelerator with magnetic pulse compression and accelerator switching

    DOEpatents

    Birx, Daniel L.; Reginato, Louis L.

    1988-01-01

    An electron beam accelerator comprising an electron beam generator-injector to produce a focused beam of .gtoreq.0.1 MeV energy electrons; a plurality of substantially identical, aligned accelerator modules to sequentially receive and increase the kinetic energies of the beam electrons by about 0.1-1 MeV per module. Each accelerator module includes a pulse-forming network that delivers a voltage pulse to the module of substantially .gtoreq.0.1-1 MeV maximum energy over a time duration of .ltoreq.1 .mu.sec.

  14. Electron beam accelerator with magnetic pulse compression and accelerator switching

    DOEpatents

    Birx, Daniel L.; Reginato, Louis L.

    1987-01-01

    An electron beam accelerator comprising an electron beam generator-injector to produce a focused beam of .gtoreq.0.1 MeV energy electrons; a plurality of substantially identical, aligned accelerator modules to sequentially receive and increase the kinetic energies of the beam electrons by about 0.1-1 MeV per module. Each accelerator module includes a pulse-forming network that delivers a voltage pulse to the module of substantially 0.1-1 MeV maximum energy over a time duration of .ltoreq.1 .mu.sec.

  15. The Accuracy and Correction of Fuel Consumption from Controller Area Network Broadcast

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

    Wang, Lijuan; Gonder, Jeffrey D; Wood, Eric W

    Fuel consumption (FC) has always been an important factor in vehicle cost. With the advent of electronically controlled engines, the controller area network (CAN) broadcasts information about engine and vehicle performance, including fuel use. However, the accuracy of the FC estimates is uncertain. In this study, the researchers first compared CAN-broadcasted FC against physically measured fuel use for three different types of trucks, which revealed the inaccuracies of CAN-broadcast fueling estimates. To match precise gravimetric fuel-scale measurements, polynomial models were developed to correct the CAN-broadcasted FC. Lastly, the robustness testing of the correction models was performed. The training cycles inmore » this section included a variety of drive characteristics, such as high speed, acceleration, idling, and deceleration. The mean relative differences were reduced noticeably.« less

  16. Realizing actual feedback control of complex network

    NASA Astrophysics Data System (ADS)

    Tu, Chengyi; Cheng, Yuhua

    2014-06-01

    In this paper, we present the concept of feedbackability and how to identify the Minimum Feedbackability Set of an arbitrary complex directed network. Furthermore, we design an estimator and a feedback controller accessing one MFS to realize actual feedback control, i.e. control the system to our desired state according to the estimated system internal state from the output of estimator. Last but not least, we perform numerical simulations of a small linear time-invariant dynamics network and a real simple food network to verify the theoretical results. The framework presented here could make an arbitrary complex directed network realize actual feedback control and deepen our understanding of complex systems.

  17. Interaction Control to Synchronize Non-synchronizable Networks.

    PubMed

    Schröder, Malte; Chakraborty, Sagar; Witthaut, Dirk; Nagler, Jan; Timme, Marc

    2016-11-17

    Synchronization constitutes one of the most fundamental collective dynamics across networked systems and often underlies their function. Whether a system may synchronize depends on the internal unit dynamics as well as the topology and strength of their interactions. For chaotic units with certain interaction topologies synchronization might be impossible across all interaction strengths, meaning that these networks are non-synchronizable. Here we propose the concept of interaction control, generalizing transient uncoupling, to induce desired collective dynamics in complex networks and apply it to synchronize even such non-synchronizable systems. After highlighting that non-synchronizability prevails for a wide range of networks of arbitrary size, we explain how a simple binary control may localize interactions in state space and thereby synchronize networks. Intriguingly, localizing interactions by a fixed control scheme enables stable synchronization across all connected networks regardless of topological constraints. Interaction control may thus ease the design of desired collective dynamics even without knowledge of the networks' exact interaction topology and consequently have implications for biological and self-organizing technical systems.

  18. New Insights into Auroral Particle Acceleration via Coordinated Optical-Radar Networks

    NASA Astrophysics Data System (ADS)

    Hirsch, M.

    2016-12-01

    The efficacy of instruments synthesized from heterogeneous sensor networks is increasingly being realized in fielded science observation systems. New insights into the finest spatio-temporal scales of ground-observable ionospheric physics are realized by coupling low-level data from fixed legacy instruments with mobile and portable sensors. In particular, turbulent ionospheric events give enhanced radar returns more than three orders of magnitude larger than typical incoherent plasma observations. Radar integration times for the Poker Flat Incoherent Scatter Radar (PFISR) can thereby be shrunk from order 100 second integration time down to order 100 millisecond integration time for the ion line. Auroral optical observations with 20 millisecond cadence synchronized in absolute time with the radar help uncover plausible particle acceleration processes for the highly dynamic aurora often associated with Langmuir turbulence. Quantitative analysis of coherent radar returns combined with a physics-based model yielding optical volume emission rate profiles vs. differential number flux input of precipitating particles into the ionosphere yield plausibility estimates for a particular auroral acceleration process type. Tabulated results from a survey of auroral events where the Boston University High Speed Auroral Tomography system operated simultaneously with PFISR are presented. Context is given to the narrow-field HiST observations by the Poker Flat Digital All-Sky Camera and THEMIS GBO ASI network. Recent advances in high-rate (order 100 millisecond) plasma line ISR observations (100x improvement in temporal resolution) will contribute to future coordinated observations. ISR beam pattern and pulse parameter configurations favorable for future coordinated optical-ISR experiments are proposed in light of recent research uncovering the criticality of aspect angle to ISR-observable physics. High-rate scientist-developed GPS TEC receivers are expected to contribute additional

  19. Enhanced vaccine control of epidemics in adaptive networks

    NASA Astrophysics Data System (ADS)

    Shaw, Leah B.; Schwartz, Ira B.

    2010-04-01

    We study vaccine control for disease spread on an adaptive network modeling disease avoidance behavior. Control is implemented by adding Poisson-distributed vaccination of susceptibles. We show that vaccine control is much more effective in adaptive networks than in static networks due to feedback interaction between the adaptive network rewiring and the vaccine application. When compared to extinction rates in static social networks, we find that the amount of vaccine resources required to sustain similar rates of extinction are as much as two orders of magnitude lower in adaptive networks.

  20. Enhanced vaccine control of epidemics in adaptive networks.

    PubMed

    Shaw, Leah B; Schwartz, Ira B

    2010-04-01

    We study vaccine control for disease spread on an adaptive network modeling disease avoidance behavior. Control is implemented by adding Poisson-distributed vaccination of susceptibles. We show that vaccine control is much more effective in adaptive networks than in static networks due to feedback interaction between the adaptive network rewiring and the vaccine application. When compared to extinction rates in static social networks, we find that the amount of vaccine resources required to sustain similar rates of extinction are as much as two orders of magnitude lower in adaptive networks.

  1. Neural Networks for Flight Control

    NASA Technical Reports Server (NTRS)

    Jorgensen, Charles C.

    1996-01-01

    Neural networks are being developed at NASA Ames Research Center to permit real-time adaptive control of time varying nonlinear systems, enhance the fault-tolerance of mission hardware, and permit online system reconfiguration. In general, the problem of controlling time varying nonlinear systems with unknown structures has not been solved. Adaptive neural control techniques show considerable promise and are being applied to technical challenges including automated docking of spacecraft, dynamic balancing of the space station centrifuge, online reconfiguration of damaged aircraft, and reducing cost of new air and spacecraft designs. Our experiences have shown that neural network algorithms solved certain problems that conventional control methods have been unable to effectively address. These include damage mitigation in nonlinear reconfiguration flight control, early performance estimation of new aircraft designs, compensation for damaged planetary mission hardware by using redundant manipulator capability, and space sensor platform stabilization. This presentation explored these developments in the context of neural network control theory. The discussion began with an overview of why neural control has proven attractive for NASA application domains. The more important issues in control system development were then discussed with references to significant technical advances in the literature. Examples of how these methods have been applied were given, followed by projections of emerging application needs and directions.

  2. A Network Access Control Framework for 6LoWPAN Networks

    PubMed Central

    Oliveira, Luís M. L.; Rodrigues, Joel J. P. C.; de Sousa, Amaro F.; Lloret, Jaime

    2013-01-01

    Low power over wireless personal area networks (LoWPAN), in particular wireless sensor networks, represent an emerging technology with high potential to be employed in critical situations like security surveillance, battlefields, smart-grids, and in e-health applications. The support of security services in LoWPAN is considered a challenge. First, this type of networks is usually deployed in unattended environments, making them vulnerable to security attacks. Second, the constraints inherent to LoWPAN, such as scarce resources and limited battery capacity, impose a careful planning on how and where the security services should be deployed. Besides protecting the network from some well-known threats, it is important that security mechanisms be able to withstand attacks that have not been identified before. One way of reaching this goal is to control, at the network access level, which nodes can be attached to the network and to enforce their security compliance. This paper presents a network access security framework that can be used to control the nodes that have access to the network, based on administrative approval, and to enforce security compliance to the authorized nodes. PMID:23334610

  3. Unified Lunar Control Network 2005 and Topographic Model

    NASA Technical Reports Server (NTRS)

    Archinal, B. A.; Rosiek, M. R.; Redding, B. L.

    2005-01-01

    There are currently two generally accepted lunar control networks. These are the Unified Lunar Control Network (ULCN) and the Clementine Lunar Control Network (CLCN), both derived by M. Davies and T. Colvin at RAND. We address here our efforts to merge and improve these networks into a new ULCN. The ULCN was described in the last major publication about a lunar control network. The statistics on this and the other networks discussed here. Images for this network are from the Apollo, Mariner 10, and Galileo missions, and Earth-based photographs. The importance of this network is that its accuracy is relatively well quantified and published information on the network is available. The CLCN includes measurements on 43,871 Clementine 750-nm images - the largest planetary control network ever computed. This purpose of this network was to determine the geometry for the Clementine Basemap Mosiac (CBM). The geometry of that mosaic was used to produce the Clementine UVVIS digital image model and the Near-Infrared Global Multispectral Map of the Moon from Clementine. Through the extensive use of these products, they and the underlying CLCN in effect define the generally accepted current coordinate system for reporting and describing the location of lunar coordinates. However, no publication describes the CLCN itself.

  4. Electron beam accelerator with magnetic pulse compression and accelerator switching

    DOEpatents

    Birx, D.L.; Reginato, L.L.

    1984-03-22

    An electron beam accelerator is described comprising an electron beam generator-injector to produce a focused beam of greater than or equal to .1 MeV energy electrons; a plurality of substantially identical, aligned accelerator modules to sequentially receive and increase the kinetic energies of the beam electron by about .1-1 MeV per module. Each accelerator module includes a pulse-forming network that delivers a voltage pulse to the module of substantially .1-1 MeV maximum energy over a time duration of less than or equal to 1 ..mu..sec.

  5. Topological Principles of Control in Dynamical Networks

    NASA Astrophysics Data System (ADS)

    Kim, Jason; Pasqualetti, Fabio; Bassett, Danielle

    Networked biological systems, such as the brain, feature complex patterns of interactions. To predict and correct the dynamic behavior of such systems, it is imperative to understand how the underlying topological structure affects and limits the function of the system. Here, we use network control theory to extract topological features that favor or prevent network controllability, and to understand the network-wide effect of external stimuli on large-scale brain systems. Specifically, we treat each brain region as a dynamic entity with real-valued state, and model the time evolution of all interconnected regions using linear, time-invariant dynamics. We propose a simplified feed-forward scheme where the effect of upstream regions (drivers) on the connected downstream regions (non-drivers) is characterized in closed-form. Leveraging this characterization of the simplified model, we derive topological features that predict the controllability properties of non-simplified networks. We show analytically and numerically that these predictors are accurate across a large range of parameters. Among other contributions, our analysis shows that heterogeneity in the network weights facilitate controllability, and allows us to implement targeted interventions that profoundly improve controllability. By assuming an underlying dynamical mechanism, we are able to understand the complex topology of networked biological systems in a functionally meaningful way.

  6. Effective augmentation of networked systems and enhancing pinning controllability

    NASA Astrophysics Data System (ADS)

    Jalili, Mahdi

    2018-06-01

    Controlling dynamics of networked systems to a reference state, known as pinning control, has many applications in science and engineering. In this paper, we introduce a method for effective augmentation of networked systems, while also providing high levels of pinning controllability for the final augmented network. The problem is how to connect a sub-network to an already existing network such that the pinning controllability is maximised. We consider the eigenratio of the augmented Laplacian matrix as a pinning controllability metric, and use graph perturbation theory to approximate the influence of edge addition on the eigenratio. The proposed metric can be effectively used to find the inter-network links connecting the disjoint networks. Also, an efficient link rewiring approach is proposed to further optimise the pinning controllability of the augmented network. We provide numerical simulations on synthetic networks and show that the proposed method is more effective than heuristic ones.

  7. Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices: Design Considerations

    PubMed Central

    Gokmen, Tayfun; Vlasov, Yurii

    2016-01-01

    In recent years, deep neural networks (DNN) have demonstrated significant business impact in large scale analysis and classification tasks such as speech recognition, visual object detection, pattern extraction, etc. Training of large DNNs, however, is universally considered as time consuming and computationally intensive task that demands datacenter-scale computational resources recruited for many days. Here we propose a concept of resistive processing unit (RPU) devices that can potentially accelerate DNN training by orders of magnitude while using much less power. The proposed RPU device can store and update the weight values locally thus minimizing data movement during training and allowing to fully exploit the locality and the parallelism of the training algorithm. We evaluate the effect of various RPU device features/non-idealities and system parameters on performance in order to derive the device and system level specifications for implementation of an accelerator chip for DNN training in a realistic CMOS-compatible technology. For large DNNs with about 1 billion weights this massively parallel RPU architecture can achieve acceleration factors of 30, 000 × compared to state-of-the-art microprocessors while providing power efficiency of 84, 000 GigaOps∕s∕W. Problems that currently require days of training on a datacenter-size cluster with thousands of machines can be addressed within hours on a single RPU accelerator. A system consisting of a cluster of RPU accelerators will be able to tackle Big Data problems with trillions of parameters that is impossible to address today like, for example, natural speech recognition and translation between all world languages, real-time analytics on large streams of business and scientific data, integration, and analysis of multimodal sensory data flows from a massive number of IoT (Internet of Things) sensors. PMID:27493624

  8. Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices: Design Considerations.

    PubMed

    Gokmen, Tayfun; Vlasov, Yurii

    2016-01-01

    In recent years, deep neural networks (DNN) have demonstrated significant business impact in large scale analysis and classification tasks such as speech recognition, visual object detection, pattern extraction, etc. Training of large DNNs, however, is universally considered as time consuming and computationally intensive task that demands datacenter-scale computational resources recruited for many days. Here we propose a concept of resistive processing unit (RPU) devices that can potentially accelerate DNN training by orders of magnitude while using much less power. The proposed RPU device can store and update the weight values locally thus minimizing data movement during training and allowing to fully exploit the locality and the parallelism of the training algorithm. We evaluate the effect of various RPU device features/non-idealities and system parameters on performance in order to derive the device and system level specifications for implementation of an accelerator chip for DNN training in a realistic CMOS-compatible technology. For large DNNs with about 1 billion weights this massively parallel RPU architecture can achieve acceleration factors of 30, 000 × compared to state-of-the-art microprocessors while providing power efficiency of 84, 000 GigaOps∕s∕W. Problems that currently require days of training on a datacenter-size cluster with thousands of machines can be addressed within hours on a single RPU accelerator. A system consisting of a cluster of RPU accelerators will be able to tackle Big Data problems with trillions of parameters that is impossible to address today like, for example, natural speech recognition and translation between all world languages, real-time analytics on large streams of business and scientific data, integration, and analysis of multimodal sensory data flows from a massive number of IoT (Internet of Things) sensors.

  9. Constrained off-line synthesis approach of model predictive control for networked control systems with network-induced delays.

    PubMed

    Tang, Xiaoming; Qu, Hongchun; Wang, Ping; Zhao, Meng

    2015-03-01

    This paper investigates the off-line synthesis approach of model predictive control (MPC) for a class of networked control systems (NCSs) with network-induced delays. A new augmented model which can be readily applied to time-varying control law, is proposed to describe the NCS where bounded deterministic network-induced delays may occur in both sensor to controller (S-A) and controller to actuator (C-A) links. Based on this augmented model, a sufficient condition of the closed-loop stability is derived by applying the Lyapunov method. The off-line synthesis approach of model predictive control is addressed using the stability results of the system, which explicitly considers the satisfaction of input and state constraints. Numerical example is given to illustrate the effectiveness of the proposed method. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

  10. Control of autonomous robot using neural networks

    NASA Astrophysics Data System (ADS)

    Barton, Adam; Volna, Eva

    2017-07-01

    The aim of the article is to design a method of control of an autonomous robot using artificial neural networks. The introductory part describes control issues from the perspective of autonomous robot navigation and the current mobile robots controlled by neural networks. The core of the article is the design of the controlling neural network, and generation and filtration of the training set using ART1 (Adaptive Resonance Theory). The outcome of the practical part is an assembled Lego Mindstorms EV3 robot solving the problem of avoiding obstacles in space. To verify models of an autonomous robot behavior, a set of experiments was created as well as evaluation criteria. The speed of each motor was adjusted by the controlling neural network with respect to the situation in which the robot was found.

  11. Controllability of multiplex, multi-time-scale networks

    NASA Astrophysics Data System (ADS)

    Pósfai, Márton; Gao, Jianxi; Cornelius, Sean P.; Barabási, Albert-László; D'Souza, Raissa M.

    2016-09-01

    The paradigm of layered networks is used to describe many real-world systems, from biological networks to social organizations and transportation systems. While recently there has been much progress in understanding the general properties of multilayer networks, our understanding of how to control such systems remains limited. One fundamental aspect that makes this endeavor challenging is that each layer can operate at a different time scale; thus, we cannot directly apply standard ideas from structural control theory of individual networks. Here we address the problem of controlling multilayer and multi-time-scale networks focusing on two-layer multiplex networks with one-to-one interlayer coupling. We investigate the practically relevant case when the control signal is applied to the nodes of one layer. We develop a theory based on disjoint path covers to determine the minimum number of inputs (Ni) necessary for full control. We show that if both layers operate on the same time scale, then the network structure of both layers equally affect controllability. In the presence of time-scale separation, controllability is enhanced if the controller interacts with the faster layer: Ni decreases as the time-scale difference increases up to a critical time-scale difference, above which Ni remains constant and is completely determined by the faster layer. We show that the critical time-scale difference is large if layer I is easy and layer II is hard to control in isolation. In contrast, control becomes increasingly difficult if the controller interacts with the layer operating on the slower time scale and increasing time-scale separation leads to increased Ni, again up to a critical value, above which Ni still depends on the structure of both layers. This critical value is largely determined by the longest path in the faster layer that does not involve cycles. By identifying the underlying mechanisms that connect time-scale difference and controllability for a simplified

  12. The Unified Lunar Control Network 2005

    USGS Publications Warehouse

    Archinal, Brent A.; Rosiek, Mark R.; Kirk, Randolph L.; Redding, Bonnie L.

    2006-01-01

    This report documents a new general unified lunar control network and lunar topographic model based on a combination of Clementine images and a previous network derived from Earth-based & Apollo photographs, and Mariner 10, & Galileo images. This photogrammetric network solution is the largest planetary control network ever completed. It includes the determination of the 3-D positions of 272,931 points on the lunar surface and the correction of the camera angles for 43,866 Clementine images, using 546,126 tie point measurements. The solution RMS is 20 ?m (= 0.9 pixels) in the image plane, with the largest residual of 6.4 pixels. The explanation given here, along with the accompanying files, comprises the release of the network information and of global lunar digital elevation models (DEMs) derived from the network. A paper that will describe the solution and network in further detail will be submitted to a refereed journal, and will include additional background information, solution details, discussion of accuracy and precision, and explanatory figures.

  13. Flight control with adaptive critic neural network

    NASA Astrophysics Data System (ADS)

    Han, Dongchen

    2001-10-01

    In this dissertation, the adaptive critic neural network technique is applied to solve complex nonlinear system control problems. Based on dynamic programming, the adaptive critic neural network can embed the optimal solution into a neural network. Though trained off-line, the neural network forms a real-time feedback controller. Because of its general interpolation properties, the neurocontroller has inherit robustness. The problems solved here are an agile missile control for U.S. Air Force and a midcourse guidance law for U.S. Navy. In the first three papers, the neural network was used to control an air-to-air agile missile to implement a minimum-time heading-reverse in a vertical plane corresponding to following conditions: a system without constraint, a system with control inequality constraint, and a system with state inequality constraint. While the agile missile is a one-dimensional problem, the midcourse guidance law is the first test-bed for multiple-dimensional problem. In the fourth paper, the neurocontroller is synthesized to guide a surface-to-air missile to a fixed final condition, and to a flexible final condition from a variable initial condition. In order to evaluate the adaptive critic neural network approach, the numerical solutions for these cases are also obtained by solving two-point boundary value problem with a shooting method. All of the results showed that the adaptive critic neural network could solve complex nonlinear system control problems.

  14. Topology Control in Aerial Multi-Beam Directional Networks

    DTIC Science & Technology

    2017-04-24

    underlying challenges to topology control in multi -beam direction networks. Two topology control algorithms are developed: a centralized algorithm...main beam, the gain is negligible. Thus, for topology control in a multi -beam system, two nodes that are being simultaneously transmitted to or...the network. As the network size is larger than the communication range, even the original network will require some multi -hop traffic. The second two

  15. Acceleration of spiking neural network based pattern recognition on NVIDIA graphics processors.

    PubMed

    Han, Bing; Taha, Tarek M

    2010-04-01

    There is currently a strong push in the research community to develop biological scale implementations of neuron based vision models. Systems at this scale are computationally demanding and generally utilize more accurate neuron models, such as the Izhikevich and the Hodgkin-Huxley models, in favor of the more popular integrate and fire model. We examine the feasibility of using graphics processing units (GPUs) to accelerate a spiking neural network based character recognition network to enable such large scale systems. Two versions of the network utilizing the Izhikevich and Hodgkin-Huxley models are implemented. Three NVIDIA general-purpose (GP) GPU platforms are examined, including the GeForce 9800 GX2, the Tesla C1060, and the Tesla S1070. Our results show that the GPGPUs can provide significant speedup over conventional processors. In particular, the fastest GPGPU utilized, the Tesla S1070, provided a speedup of 5.6 and 84.4 over highly optimized implementations on the fastest central processing unit (CPU) tested, a quadcore 2.67 GHz Xeon processor, for the Izhikevich and the Hodgkin-Huxley models, respectively. The CPU implementation utilized all four cores and the vector data parallelism offered by the processor. The results indicate that GPUs are well suited for this application domain.

  16. Interaction Control to Synchronize Non-synchronizable Networks

    PubMed Central

    Schröder, Malte; Chakraborty, Sagar; Witthaut, Dirk; Nagler, Jan; Timme, Marc

    2016-01-01

    Synchronization constitutes one of the most fundamental collective dynamics across networked systems and often underlies their function. Whether a system may synchronize depends on the internal unit dynamics as well as the topology and strength of their interactions. For chaotic units with certain interaction topologies synchronization might be impossible across all interaction strengths, meaning that these networks are non-synchronizable. Here we propose the concept of interaction control, generalizing transient uncoupling, to induce desired collective dynamics in complex networks and apply it to synchronize even such non-synchronizable systems. After highlighting that non-synchronizability prevails for a wide range of networks of arbitrary size, we explain how a simple binary control may localize interactions in state space and thereby synchronize networks. Intriguingly, localizing interactions by a fixed control scheme enables stable synchronization across all connected networks regardless of topological constraints. Interaction control may thus ease the design of desired collective dynamics even without knowledge of the networks’ exact interaction topology and consequently have implications for biological and self-organizing technical systems. PMID:27853266

  17. Autonomous Congestion Control in Delay-Tolerant Networks

    NASA Technical Reports Server (NTRS)

    Burleigh, Scott C.; Jennings, Esther H.

    2005-01-01

    Congestion control is an important feature that directly affects network performance. Network congestion may cause loss of data or long delays. Although this problem has been studied extensively in the Internet, the solutions for Internet congestion control do not apply readily to challenged network environments such as Delay Tolerant Networks (DTN) where end-to-end connectivity may not exist continuously and latency can be high. In DTN, end-to-end rate control is not feasible. This calls for congestion control mechanisms where the decisions can be made autonomously with local information only. We use an economic pricing model and propose a rule-based congestion control mechanism where each router can autonomously decide on whether to accept a bundle (data) based on local information such as available storage and the value and risk of accepting the bundle (derived from historical statistics).

  18. Controlled Microwave Heating Accelerates Rolling Circle Amplification

    PubMed Central

    Yoshimura, Takeo; Suzuki, Takamasa; Mineki, Shigeru; Ohuchi, Shokichi

    2015-01-01

    Rolling circle amplification (RCA) generates single-stranded DNAs or RNA, and the diverse applications of this isothermal technique range from the sensitive detection of nucleic acids to analysis of single nucleotide polymorphisms. Microwave chemistry is widely applied to increase reaction rate as well as product yield and purity. The objectives of the present research were to apply microwave heating to RCA and indicate factors that contribute to the microwave selective heating effect. The microwave reaction temperature was strictly controlled using a microwave applicator optimized for enzymatic-scale reactions. Here, we showed that microwave-assisted RCA reactions catalyzed by either of the four thermostable DNA polymerases were accelerated over 4-folds compared with conventional RCA. Furthermore, the temperatures of the individual buffer components were specifically influenced by microwave heating. We concluded that microwave heating accelerated isothermal RCA of DNA because of the differential heating mechanisms of microwaves on the temperatures of reaction components, although the overall reaction temperatures were the same. PMID:26348227

  19. Controlled Microwave Heating Accelerates Rolling Circle Amplification.

    PubMed

    Yoshimura, Takeo; Suzuki, Takamasa; Mineki, Shigeru; Ohuchi, Shokichi

    2015-01-01

    Rolling circle amplification (RCA) generates single-stranded DNAs or RNA, and the diverse applications of this isothermal technique range from the sensitive detection of nucleic acids to analysis of single nucleotide polymorphisms. Microwave chemistry is widely applied to increase reaction rate as well as product yield and purity. The objectives of the present research were to apply microwave heating to RCA and indicate factors that contribute to the microwave selective heating effect. The microwave reaction temperature was strictly controlled using a microwave applicator optimized for enzymatic-scale reactions. Here, we showed that microwave-assisted RCA reactions catalyzed by either of the four thermostable DNA polymerases were accelerated over 4-folds compared with conventional RCA. Furthermore, the temperatures of the individual buffer components were specifically influenced by microwave heating. We concluded that microwave heating accelerated isothermal RCA of DNA because of the differential heating mechanisms of microwaves on the temperatures of reaction components, although the overall reaction temperatures were the same.

  20. Autonomous Congestion Control in Delay-Tolerant Networks

    NASA Technical Reports Server (NTRS)

    Burleigh, Scott; Jennings, Esther; Schoolcraft, Joshua

    2006-01-01

    Congestion control is an important feature that directly affects network performance. Network congestion may cause loss of data or long delays. Although this problem has been studied extensively in the Internet, the solutions for Internet congestion control do not apply readily to challenged network environments such as Delay Tolerant Networks (DTN) where end-to-end connectivity may not exist continuously and latency can be high. In DTN, end-to-end rate control is not feasible. This calls for congestion control mechanisms where the decisions can be made autonomously with local information only. We use an economic pricing model and propose a rule-based congestion control mechanism where each router can autonomously decide on whether to accept a bundle (data) based on local information such as available storage and the value and risk of accepting the bundle (derived from historical statistics). Preliminary experimental results show that this congestion control mechanism can protect routers from resource depletion without loss of data.

  1. Connecting Core Percolation and Controllability of Complex Networks

    PubMed Central

    Jia, Tao; Pósfai, Márton

    2014-01-01

    Core percolation is a fundamental structural transition in complex networks related to a wide range of important problems. Recent advances have provided us an analytical framework of core percolation in uncorrelated random networks with arbitrary degree distributions. Here we apply the tools in analysis of network controllability. We confirm analytically that the emergence of the bifurcation in control coincides with the formation of the core and the structure of the core determines the control mode of the network. We also derive the analytical expression related to the controllability robustness by extending the deduction in core percolation. These findings help us better understand the interesting interplay between the structural and dynamical properties of complex networks. PMID:24946797

  2. Network device interface for digitally interfacing data channels to a controller a via network

    NASA Technical Reports Server (NTRS)

    Konz, Daniel W. (Inventor); Ellerbrock, Philip J. (Inventor); Grant, Robert L. (Inventor); Winkelmann, Joseph P. (Inventor)

    2006-01-01

    The present invention provides a network device interface and method for digitally connecting a plurality of data channels to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. In one embodiment, the bus controller transmits messages to the network device interface containing a plurality of bits having a value defined by a transition between first and second states in the bits. The network device interface determines timing of the data sequence of the message and uses the determined timing to communicate with the bus controller.

  3. Network device interface for digitally interfacing data channels to a controller via a network

    NASA Technical Reports Server (NTRS)

    Ellerbrock, Philip J. (Inventor); Winkelmann, Joseph P. (Inventor); Grant, Robert L. (Inventor); Konz, Daniel W. (Inventor)

    2006-01-01

    The present invention provides a network device interface and method for digitally connecting a plurality of data channels, such as sensors, actuators, and subsystems, to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. Data retrieved from the sensor is then converted by the network device interface into digital signals and transmitted back to the controller. In one advantageous embodiment, the network device interface is a state machine, such as an ASIC, that operates independent of a processor in communicating with the bus controller and data channels.

  4. Neural-tree call admission controller for ATM networks

    NASA Astrophysics Data System (ADS)

    Rughooputh, Harry C. S.

    1999-03-01

    Asynchronous Transfer Mode (ATM) has been recommended by ITU-T as the transport method for broadband integrated services digital networks. In high-speed ATM networks different types of multimedia traffic streams with widely varying traffic characteristics and Quality of Service (QoS) are asynchronously multiplexed on transmission links and switched without window flow control as found in X.25. In such an environment, a traffic control scheme is required to manage the required QoS of each class individually. To meet the QoS requirements, Bandwidth Allocation and Call Admission Control (CAC) in ATM networks must be able to adapt gracefully to the dynamic behavior of traffic and the time-varying nature of the network condition. In this paper, a Neural Network approach for CAC is proposed. The call admission problem is addressed by designing controllers based on Neural Tree Networks. Simulations reveal that the proposed scheme is not only simple but it also offers faster response than conventional neural/neuro-fuzzy controllers.

  5. Network device interface for digitally interfacing data channels to a controller via a network

    NASA Technical Reports Server (NTRS)

    Ellerbrock, Philip J. (Inventor); Grant, Robert L. (Inventor); Konz, Daniel W. (Inventor); Winkelmann, Joseph P. (Inventor)

    2005-01-01

    The present invention provides a network device interface and method for digitally connecting a plurality of data channels, such as sensors, actuators, and subsystems, to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. Data retrieved from the sensor is then converted by the network device interface into digital signals and transmitted back to the controller. In one advantageous embodiment, the network device interface uses a specialized protocol for communicating across the network bus that uses a low-level instruction set and has low overhead for data communication.

  6. Network device interface for digitally interfacing data channels to a controller via a network

    NASA Technical Reports Server (NTRS)

    Ellerbrock, Philip J. (Inventor); Konz, Daniel W. (Inventor); Winkelmann, Joseph P. (Inventor); Grant, Robert L. (Inventor)

    2004-01-01

    The present invention provides a network device interface and method for digitally connecting a plurality of data channels, such as sensors, actuators, and subsystems, to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. Data retrieved from the sensor is then converted by the network device interface into digital signals and transmitted back to the controller. In one advantageous embodiment, the network device interface uses a specialized protocol for communicating across the network bus that uses a low-level instruction set and has low overhead for data communication.

  7. Adaptive mechanism-based congestion control for networked systems

    NASA Astrophysics Data System (ADS)

    Liu, Zhi; Zhang, Yun; Chen, C. L. Philip

    2013-03-01

    In order to assure the communication quality in network systems with heavy traffic and limited bandwidth, a new ATRED (adaptive thresholds random early detection) congestion control algorithm is proposed for the congestion avoidance and resource management of network systems. Different to the traditional AQM (active queue management) algorithms, the control parameters of ATRED are not configured statically, but dynamically adjusted by the adaptive mechanism. By integrating with the adaptive strategy, ATRED alleviates the tuning difficulty of RED (random early detection) and shows a better control on the queue management, and achieve a more robust performance than RED under varying network conditions. Furthermore, a dynamic transmission control protocol-AQM control system using ATRED controller is introduced for the systematic analysis. It is proved that the stability of the network system can be guaranteed when the adaptive mechanism is finely designed. Simulation studies show the proposed ATRED algorithm achieves a good performance in varying network environments, which is superior to the RED and Gentle-RED algorithm, and providing more reliable service under varying network conditions.

  8. Accelerated spike resampling for accurate multiple testing controls.

    PubMed

    Harrison, Matthew T

    2013-02-01

    Controlling for multiple hypothesis tests using standard spike resampling techniques often requires prohibitive amounts of computation. Importance sampling techniques can be used to accelerate the computation. The general theory is presented, along with specific examples for testing differences across conditions using permutation tests and for testing pairwise synchrony and precise lagged-correlation between many simultaneously recorded spike trains using interval jitter.

  9. Network device interface for digitally interfacing data channels to a controller via a network

    NASA Technical Reports Server (NTRS)

    Konz, Daniel W. (Inventor); Ellerbrock, Philip J. (Inventor); Grant, Robert L. (Inventor); Winkelmann, Joseph P. (Inventor)

    2006-01-01

    The present invention provides a network device interface and method for digitally connecting a plurality of data channels, such as sensors, actuators, and subsystems, to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. Data retrieved from the sensor is then converted into digital signals and transmitted back to the controller. In one embodiment, the bus controller sends commands and data a defined bit rate, and the network device interface senses this bit rate and sends data back to the bus controller using the defined bit rate.

  10. A source-controlled data center network model

    PubMed Central

    Yu, Yang; Liang, Mangui; Wang, Zhe

    2017-01-01

    The construction of data center network by applying SDN technology has become a hot research topic. The SDN architecture has innovatively separated the control plane from the data plane which makes the network more software-oriented and agile. Moreover, it provides virtual multi-tenancy, effective scheduling resources and centralized control strategies to meet the demand for cloud computing data center. However, the explosion of network information is facing severe challenges for SDN controller. The flow storage and lookup mechanisms based on TCAM device have led to the restriction of scalability, high cost and energy consumption. In view of this, a source-controlled data center network (SCDCN) model is proposed herein. The SCDCN model applies a new type of source routing address named the vector address (VA) as the packet-switching label. The VA completely defines the communication path and the data forwarding process can be finished solely relying on VA. There are four advantages in the SCDCN architecture. 1) The model adopts hierarchical multi-controllers and abstracts large-scale data center network into some small network domains that has solved the restriction for the processing ability of single controller and reduced the computational complexity. 2) Vector switches (VS) developed in the core network no longer apply TCAM for table storage and lookup that has significantly cut down the cost and complexity for switches. Meanwhile, the problem of scalability can be solved effectively. 3) The SCDCN model simplifies the establishment process for new flows and there is no need to download flow tables to VS. The amount of control signaling consumed when establishing new flows can be significantly decreased. 4) We design the VS on the NetFPGA platform. The statistical results show that the hardware resource consumption in a VS is about 27% of that in an OFS. PMID:28328925

  11. A source-controlled data center network model.

    PubMed

    Yu, Yang; Liang, Mangui; Wang, Zhe

    2017-01-01

    The construction of data center network by applying SDN technology has become a hot research topic. The SDN architecture has innovatively separated the control plane from the data plane which makes the network more software-oriented and agile. Moreover, it provides virtual multi-tenancy, effective scheduling resources and centralized control strategies to meet the demand for cloud computing data center. However, the explosion of network information is facing severe challenges for SDN controller. The flow storage and lookup mechanisms based on TCAM device have led to the restriction of scalability, high cost and energy consumption. In view of this, a source-controlled data center network (SCDCN) model is proposed herein. The SCDCN model applies a new type of source routing address named the vector address (VA) as the packet-switching label. The VA completely defines the communication path and the data forwarding process can be finished solely relying on VA. There are four advantages in the SCDCN architecture. 1) The model adopts hierarchical multi-controllers and abstracts large-scale data center network into some small network domains that has solved the restriction for the processing ability of single controller and reduced the computational complexity. 2) Vector switches (VS) developed in the core network no longer apply TCAM for table storage and lookup that has significantly cut down the cost and complexity for switches. Meanwhile, the problem of scalability can be solved effectively. 3) The SCDCN model simplifies the establishment process for new flows and there is no need to download flow tables to VS. The amount of control signaling consumed when establishing new flows can be significantly decreased. 4) We design the VS on the NetFPGA platform. The statistical results show that the hardware resource consumption in a VS is about 27% of that in an OFS.

  12. Flexible brain network reconfiguration supporting inhibitory control.

    PubMed

    Spielberg, Jeffrey M; Miller, Gregory A; Heller, Wendy; Banich, Marie T

    2015-08-11

    The ability to inhibit distracting stimuli from interfering with goal-directed behavior is crucial for success in most spheres of life. Despite an abundance of studies examining regional brain activation, knowledge of the brain networks involved in inhibitory control remains quite limited. To address this critical gap, we applied graph theory tools to functional magnetic resonance imaging data collected while a large sample of adults (n = 101) performed a color-word Stroop task. Higher demand for inhibitory control was associated with restructuring of the global network into a configuration that was more optimized for specialized processing (functional segregation), more efficient at communicating the output of such processing across the network (functional integration), and more resilient to potential interruption (resilience). In addition, there were regional changes with right inferior frontal sulcus and right anterior insula occupying more central positions as network hubs, and dorsal anterior cingulate cortex becoming more tightly coupled with its regional subnetwork. Given the crucial role of inhibitory control in goal-directed behavior, present findings identifying functional network organization supporting inhibitory control have the potential to provide additional insights into how inhibitory control may break down in a wide variety of individuals with neurological or psychiatric difficulties.

  13. Coactivation of cognitive control networks during task switching.

    PubMed

    Yin, Shouhang; Deák, Gedeon; Chen, Antao

    2018-01-01

    The ability to flexibly switch between tasks is considered an important component of cognitive control that involves frontal and parietal cortical areas. The present study was designed to characterize network dynamics across multiple brain regions during task switching. Functional magnetic resonance images (fMRI) were captured during a standard rule-switching task to identify switching-related brain regions. Multiregional psychophysiological interaction (PPI) analysis was used to examine effective connectivity between these regions. During switching trials, behavioral performance declined and activation of a generic cognitive control network increased. Concurrently, task-related connectivity increased within and between cingulo-opercular and fronto-parietal cognitive control networks. Notably, the left inferior frontal junction (IFJ) was most consistently coactivated with the 2 cognitive control networks. Furthermore, switching-dependent effective connectivity was negatively correlated with behavioral switch costs. The strength of effective connectivity between left IFJ and other regions in the networks predicted individual differences in switch costs. Task switching was supported by coactivated connections within cognitive control networks, with left IFJ potentially acting as a key hub between the fronto-parietal and cingulo-opercular networks. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  14. Using a commercial mathematics software package for on-line analysis at the BNL Accelerator Test Facility

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

    Malone, R.; Wang, X.J.

    BY WRITING BOTH A CUSTOM WINDOWS(NTTM) DYNAMIC LINK LIBRARY AND GENERIC COMPANION SERVER SOFTWARE, THE INTRINSIC FUNCTIONS OF MATHSOFT MATHCAD(TM) HAVE BEEN EXTENDED WITH NEW CAPABILITIES WHICH PERMIT DIRECT ACCESS TO THE CONTROL SYSTEM DATABASES OF BROOKHAVEN NATIONAL LABORATORY ACCELERATOR TEST FACILITY. UNDER THIS SCHEME, A MATHCAD WORKSHEET EXECUTING ON A PERSONAL COMPUTER BECOMES A CLIENT WHICH CAN BOTH IMPORT AND EXPORT DATA TO A CONTROL SYSTEM SERVER VIA A NETWORK STREAM SOCKET CONNECTION. THE RESULT IS AN ALTERNATIVE, MATHEMATICALLY ORIENTED VIEW OF CONTROLLING THE ACCELERATOR INTERACTIVELY.

  15. Controllability and observability of Boolean networks arising from biology

    NASA Astrophysics Data System (ADS)

    Li, Rui; Yang, Meng; Chu, Tianguang

    2015-02-01

    Boolean networks are currently receiving considerable attention as a computational scheme for system level analysis and modeling of biological systems. Studying control-related problems in Boolean networks may reveal new insights into the intrinsic control in complex biological systems and enable us to develop strategies for manipulating biological systems using exogenous inputs. This paper considers controllability and observability of Boolean biological networks. We propose a new approach, which draws from the rich theory of symbolic computation, to solve the problems. Consequently, simple necessary and sufficient conditions for reachability, controllability, and observability are obtained, and algorithmic tests for controllability and observability which are based on the Gröbner basis method are presented. As practical applications, we apply the proposed approach to several different biological systems, namely, the mammalian cell-cycle network, the T-cell activation network, the large granular lymphocyte survival signaling network, and the Drosophila segment polarity network, gaining novel insights into the control and/or monitoring of the specific biological systems.

  16. Practical synchronization on complex dynamical networks via optimal pinning control

    NASA Astrophysics Data System (ADS)

    Li, Kezan; Sun, Weigang; Small, Michael; Fu, Xinchu

    2015-07-01

    We consider practical synchronization on complex dynamical networks under linear feedback control designed by optimal control theory. The control goal is to minimize global synchronization error and control strength over a given finite time interval, and synchronization error at terminal time. By utilizing the Pontryagin's minimum principle, and based on a general complex dynamical network, we obtain an optimal system to achieve the control goal. The result is verified by performing some numerical simulations on Star networks, Watts-Strogatz networks, and Barabási-Albert networks. Moreover, by combining optimal control and traditional pinning control, we propose an optimal pinning control strategy which depends on the network's topological structure. Obtained results show that optimal pinning control is very effective for synchronization control in real applications.

  17. Feed Forward Neural Network and Optimal Control Problem with Control and State Constraints

    NASA Astrophysics Data System (ADS)

    Kmet', Tibor; Kmet'ová, Mária

    2009-09-01

    A feed forward neural network based optimal control synthesis is presented for solving optimal control problems with control and state constraints. The paper extends adaptive critic neural network architecture proposed by [5] to the optimal control problems with control and state constraints. The optimal control problem is transcribed into a nonlinear programming problem which is implemented with adaptive critic neural network. The proposed simulation method is illustrated by the optimal control problem of nitrogen transformation cycle model. Results show that adaptive critic based systematic approach holds promise for obtaining the optimal control with control and state constraints.

  18. A neural-network approach to robotic control

    NASA Technical Reports Server (NTRS)

    Graham, D. P. W.; Deleuterio, G. M. T.

    1993-01-01

    An artificial neural-network paradigm for the control of robotic systems is presented. The approach is based on the Cerebellar Model Articulation Controller created by James Albus and incorporates several extensions. First, recognizing the essential structure of multibody equations of motion, two parallel modules are used that directly reflect the dynamical characteristics of multibody systems. Second, the architecture of the proposed network is imbued with a self-organizational capability which improves efficiency and accuracy. Also, the networks can be arranged in hierarchical fashion with each subsequent network providing finer and finer resolution.

  19. The Undiagnosed Diseases Network: Accelerating Discovery about Health and Disease.

    PubMed

    Ramoni, Rachel B; Mulvihill, John J; Adams, David R; Allard, Patrick; Ashley, Euan A; Bernstein, Jonathan A; Gahl, William A; Hamid, Rizwan; Loscalzo, Joseph; McCray, Alexa T; Shashi, Vandana; Tifft, Cynthia J; Wise, Anastasia L

    2017-02-02

    Diagnosis at the edges of our knowledge calls upon clinicians to be data driven, cross-disciplinary, and collaborative in unprecedented ways. Exact disease recognition, an element of the concept of precision in medicine, requires new infrastructure that spans geography, institutional boundaries, and the divide between clinical care and research. The National Institutes of Health (NIH) Common Fund supports the Undiagnosed Diseases Network (UDN) as an exemplar of this model of precise diagnosis. Its goals are to forge a strategy to accelerate the diagnosis of rare or previously unrecognized diseases, to improve recommendations for clinical management, and to advance research, especially into disease mechanisms. The network will achieve these objectives by evaluating patients with undiagnosed diseases, fostering a breadth of expert collaborations, determining best practices for translating the strategy into medical centers nationwide, and sharing findings, data, specimens, and approaches with the scientific and medical communities. Building the UDN has already brought insights to human and medical geneticists. The initial focus has been on data sharing, establishing common protocols for institutional review boards and data sharing, creating protocols for referring and evaluating patients, and providing DNA sequencing, metabolomic analysis, and functional studies in model organisms. By extending this precision diagnostic model nationally, we strive to meld clinical and research objectives, improve patient outcomes, and contribute to medical science. Copyright © 2017 American Society of Human Genetics. All rights reserved.

  20. The application of neural network PID controller to control the light gasoline etherification

    NASA Astrophysics Data System (ADS)

    Cheng, Huanxin; Zhang, Yimin; Kong, Lingling; Meng, Xiangyong

    2017-06-01

    Light gasoline etherification technology can effectively improve the quality of gasoline, which is environmental- friendly and economical. By combining BP neural network and PID control and using BP neural network self-learning ability for online parameter tuning, this method optimizes the parameters of PID controller and applies this to the Fcc gas flow control to achieve the control of the final product- heavy oil concentration. Finally, through MATLAB simulation, it is found that the PID control based on BP neural network has better controlling effect than traditional PID control.

  1. Optimal control of epidemic information dissemination over networks.

    PubMed

    Chen, Pin-Yu; Cheng, Shin-Ming; Chen, Kwang-Cheng

    2014-12-01

    Information dissemination control is of crucial importance to facilitate reliable and efficient data delivery, especially in networks consisting of time-varying links or heterogeneous links. Since the abstraction of information dissemination much resembles the spread of epidemics, epidemic models are utilized to characterize the collective dynamics of information dissemination over networks. From a systematic point of view, we aim to explore the optimal control policy for information dissemination given that the control capability is a function of its distribution time, which is a more realistic model in many applications. The main contributions of this paper are to provide an analytically tractable model for information dissemination over networks, to solve the optimal control signal distribution time for minimizing the accumulated network cost via dynamic programming, and to establish a parametric plug-in model for information dissemination control. In particular, we evaluate its performance in mobile and generalized social networks as typical examples.

  2. Bio-inspired spiking neural network for nonlinear systems control.

    PubMed

    Pérez, Javier; Cabrera, Juan A; Castillo, Juan J; Velasco, Juan M

    2018-08-01

    Spiking neural networks (SNN) are the third generation of artificial neural networks. SNN are the closest approximation to biological neural networks. SNNs make use of temporal spike trains to command inputs and outputs, allowing a faster and more complex computation. As demonstrated by biological organisms, they are a potentially good approach to designing controllers for highly nonlinear dynamic systems in which the performance of controllers developed by conventional techniques is not satisfactory or difficult to implement. SNN-based controllers exploit their ability for online learning and self-adaptation to evolve when transferred from simulations to the real world. SNN's inherent binary and temporary way of information codification facilitates their hardware implementation compared to analog neurons. Biological neural networks often require a lower number of neurons compared to other controllers based on artificial neural networks. In this work, these neuronal systems are imitated to perform the control of non-linear dynamic systems. For this purpose, a control structure based on spiking neural networks has been designed. Particular attention has been paid to optimizing the structure and size of the neural network. The proposed structure is able to control dynamic systems with a reduced number of neurons and connections. A supervised learning process using evolutionary algorithms has been carried out to perform controller training. The efficiency of the proposed network has been verified in two examples of dynamic systems control. Simulations show that the proposed control based on SNN exhibits superior performance compared to other approaches based on Neural Networks and SNNs. Copyright © 2018 Elsevier Ltd. All rights reserved.

  3. Software Defined Networking (SDN) controlled all optical switching networks with multi-dimensional switching architecture

    NASA Astrophysics Data System (ADS)

    Zhao, Yongli; Ji, Yuefeng; Zhang, Jie; Li, Hui; Xiong, Qianjin; Qiu, Shaofeng

    2014-08-01

    Ultrahigh throughout capacity requirement is challenging the current optical switching nodes with the fast development of data center networks. Pbit/s level all optical switching networks need to be deployed soon, which will cause the high complexity of node architecture. How to control the future network and node equipment together will become a new problem. An enhanced Software Defined Networking (eSDN) control architecture is proposed in the paper, which consists of Provider NOX (P-NOX) and Node NOX (N-NOX). With the cooperation of P-NOX and N-NOX, the flexible control of the entire network can be achieved. All optical switching network testbed has been experimentally demonstrated with efficient control of enhanced Software Defined Networking (eSDN). Pbit/s level all optical switching nodes in the testbed are implemented based on multi-dimensional switching architecture, i.e. multi-level and multi-planar. Due to the space and cost limitation, each optical switching node is only equipped with four input line boxes and four output line boxes respectively. Experimental results are given to verify the performance of our proposed control and switching architecture.

  4. A Network Scheduling Model for Distributed Control Simulation

    NASA Technical Reports Server (NTRS)

    Culley, Dennis; Thomas, George; Aretskin-Hariton, Eliot

    2016-01-01

    Distributed engine control is a hardware technology that radically alters the architecture for aircraft engine control systems. Of its own accord, it does not change the function of control, rather it seeks to address the implementation issues for weight-constrained vehicles that can limit overall system performance and increase life-cycle cost. However, an inherent feature of this technology, digital communication networks, alters the flow of information between critical elements of the closed-loop control. Whereas control information has been available continuously in conventional centralized control architectures through virtue of analog signaling, moving forward, it will be transmitted digitally in serial fashion over the network(s) in distributed control architectures. An underlying effect is that all of the control information arrives asynchronously and may not be available every loop interval of the controller, therefore it must be scheduled. This paper proposes a methodology for modeling the nominal data flow over these networks and examines the resulting impact for an aero turbine engine system simulation.

  5. On controlling networks of limit-cycle oscillators

    NASA Astrophysics Data System (ADS)

    Skardal, Per Sebastian; Arenas, Alex

    2016-09-01

    The control of network-coupled nonlinear dynamical systems is an active area of research in the nonlinear science community. Coupled oscillator networks represent a particularly important family of nonlinear systems, with applications ranging from the power grid to cardiac excitation. Here, we study the control of network-coupled limit cycle oscillators, extending the previous work that focused on phase oscillators. Based on stabilizing a target fixed point, our method aims to attain complete frequency synchronization, i.e., consensus, by applying control to as few oscillators as possible. We develop two types of controls. The first type directs oscillators towards larger amplitudes, while the second does not. We present numerical examples of both control types and comment on the potential failures of the method.

  6. Positive train control shared network.

    DOT National Transportation Integrated Search

    2015-05-01

    The Interoperable Train Control (ITC) Positive : Train Control (PTC) Shared Network (IPSN) : project investigated anticipated industry benefits : and the level of support for the development of : a hosted technological platform for PTC : messaging ac...

  7. Network device interface for digitally interfacing data channels to a controller via a network

    NASA Technical Reports Server (NTRS)

    Konz, Daniel W. (Inventor); Winkelmann, Joseph P. (Inventor); Ellerbrock, Philip J. (Inventor); Grant, Robert L. (Inventor)

    2007-01-01

    The present invention provides a network device interface and method for digitally connecting a plurality of data channels, such as sensors, actuators, and subsystems, to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. Data retrieved from the sensor is converted into digital signals and transmitted to the controller. In some embodiments, network device interfaces associated with different data channels coordinate communications with the other interfaces based on either a transition in a command message sent by the bus controller or a synchronous clock signal.

  8. Cell fate reprogramming by control of intracellular network dynamics

    NASA Astrophysics Data System (ADS)

    Zanudo, Jorge G. T.; Albert, Reka

    Identifying control strategies for biological networks is paramount for practical applications that involve reprogramming a cell's fate, such as disease therapeutics and stem cell reprogramming. Although the topic of controlling the dynamics of a system has a long history in control theory, most of this work is not directly applicable to intracellular networks. Here we present a network control method that integrates the structural and functional information available for intracellular networks to predict control targets. Formulated in a logical dynamic scheme, our control method takes advantage of certain function-dependent network components and their relation to steady states in order to identify control targets, which are guaranteed to drive any initial state to the target state with 100% effectiveness and need to be applied only transiently for the system to reach and stay in the desired state. We illustrate our method's potential to find intervention targets for cancer treatment and cell differentiation by applying it to a leukemia signaling network and to the network controlling the differentiation of T cells. We find that the predicted control targets are effective in a broad dynamic framework. Moreover, several of the predicted interventions are supported by experiments. This work was supported by NSF Grant PHY 1205840.

  9. The Cancer Prevention and Control Research Network: An Interactive Systems Approach to Advancing Cancer Control Implementation Research and Practice

    PubMed Central

    Fernández, María E.; Melvin, Cathy L.; Leeman, Jennifer; Ribisl, Kurt M.; Allen, Jennifer D.; Kegler, Michelle C.; Bastani, Roshan; Ory, Marcia G.; Risendal, Betsy C.; Hannon, Peggy A.; Kreuter, Matthew W.; Hebert, James R.

    2018-01-01

    Background Although cancer research has advanced at a rapid pace, a gap remains between what is known about how to improve cancer prevention and control (CPC) and what is implemented as best practices within health care systems and communities. The Cancer Prevention and Control Research Network (CPCRN), with more than 10 years of dissemination and implementation research experience, aims to accelerate the uptake and use of evidence-based CPC interventions. Methods The collective work of the CPCRN has facilitated the analysis and categorization of research and implementation efforts according to the Interactive Systems Framework for Dissemination and Implementation (ISF), providing a useful heuristic for bridging the gap between prevention research and practice. The ISF authors have called for examples of its application as input to help refine the model. Results We provide examples of how the collaborative activities supported by the CPCRN, using community-engaged processes, accelerated the synthesis and translation of evidence, built both general and innovation-specific capacity, and worked with delivery systems to advance cancer control research and practice. Conclusions The work of the CPCRN has provided real-world examples of the application of the ISF and demonstrated that synthesizing and translating evidence can increase the potential that evidence-based CPC programs will be used and that capacity building for both the support system and the delivery system is crucial for the successful implementation and maintenance of evidence-based cancer control. Impact Adoption and implementation of CPC can be enhanced by better understanding ISF systems and intervening to improve them. PMID:25155759

  10. Systems and methods for cylindrical hall thrusters with independently controllable ionization and acceleration stages

    DOEpatents

    Diamant, Kevin David; Raitses, Yevgeny; Fisch, Nathaniel Joseph

    2014-05-13

    Systems and methods may be provided for cylindrical Hall thrusters with independently controllable ionization and acceleration stages. The systems and methods may include a cylindrical channel having a center axial direction, a gas inlet for directing ionizable gas to an ionization section of the cylindrical channel, an ionization device that ionizes at least a portion of the ionizable gas within the ionization section to generate ionized gas, and an acceleration device distinct from the ionization device. The acceleration device may provide an axial electric field for an acceleration section of the cylindrical channel to accelerate the ionized gas through the acceleration section, where the axial electric field has an axial direction in relation to the center axial direction. The ionization section and the acceleration section of the cylindrical channel may be substantially non-overlapping.

  11. Development of wide area environment accelerator operation and diagnostics method

    NASA Astrophysics Data System (ADS)

    Uchiyama, Akito; Furukawa, Kazuro

    2015-08-01

    Remote operation and diagnostic systems for particle accelerators have been developed for beam operation and maintenance in various situations. Even though fully remote experiments are not necessary, the remote diagnosis and maintenance of the accelerator is required. Considering remote-operation operator interfaces (OPIs), the use of standard protocols such as the hypertext transfer protocol (HTTP) is advantageous, because system-dependent protocols are unnecessary between the remote client and the on-site server. Here, we have developed a client system based on WebSocket, which is a new protocol provided by the Internet Engineering Task Force for Web-based systems, as a next-generation Web-based OPI using the Experimental Physics and Industrial Control System Channel Access protocol. As a result of this implementation, WebSocket-based client systems have become available for remote operation. Also, as regards practical application, the remote operation of an accelerator via a wide area network (WAN) faces a number of challenges, e.g., the accelerator has both experimental device and radiation generator characteristics. Any error in remote control system operation could result in an immediate breakdown. Therefore, we propose the implementation of an operator intervention system for remote accelerator diagnostics and support that can obviate any differences between the local control room and remote locations. Here, remote-operation Web-based OPIs, which resolve security issues, are developed.

  12. Accelerated intoxication of GABAergic synapses by botulinum neurotoxin A disinhibits stem cell-derived neuron networks prior to network silencing

    PubMed Central

    Beske, Phillip H.; Scheeler, Stephen M.; Adler, Michael; McNutt, Patrick M.

    2015-01-01

    Botulinum neurotoxins (BoNTs) are extremely potent toxins that specifically cleave SNARE proteins in peripheral synapses, preventing neurotransmitter release. Neuronal responses to BoNT intoxication are traditionally studied by quantifying SNARE protein cleavage in vitro or monitoring physiological paralysis in vivo. Consequently, the dynamic effects of intoxication on synaptic behaviors are not well-understood. We have reported that mouse embryonic stem cell-derived neurons (ESNs) are highly sensitive to BoNT based on molecular readouts of intoxication. Here we study the time-dependent changes in synapse- and network-level behaviors following addition of BoNT/A to spontaneously active networks of glutamatergic and GABAergic ESNs. Whole-cell patch-clamp recordings indicated that BoNT/A rapidly blocked synaptic neurotransmission, confirming that ESNs replicate the functional pathophysiology responsible for clinical botulism. Quantitation of spontaneous neurotransmission in pharmacologically isolated synapses revealed accelerated silencing of GABAergic synapses compared to glutamatergic synapses, which was consistent with the selective accumulation of cleaved SNAP-25 at GAD1+ pre-synaptic terminals at early timepoints. Different latencies of intoxication resulted in complex network responses to BoNT/A addition, involving rapid disinhibition of stochastic firing followed by network silencing. Synaptic activity was found to be highly sensitive to SNAP-25 cleavage, reflecting the functional consequences of the localized cleavage of the small subpopulation of SNAP-25 that is engaged in neurotransmitter release in the nerve terminal. Collectively these findings illustrate that use of synaptic function assays in networked neurons cultures offers a novel and highly sensitive approach for mechanistic studies of toxin:neuron interactions and synaptic responses to BoNT. PMID:25954159

  13. Asynchronous data change notification between database server and accelerator controls system

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

    Fu, W.; Morris, J.; Nemesure, S.

    2011-10-10

    Database data change notification (DCN) is a commonly used feature. Not all database management systems (DBMS) provide an explicit DCN mechanism. Even for those DBMS's which support DCN (such as Oracle and MS SQL server), some server side and/or client side programming may be required to make the DCN system work. This makes the setup of DCN between database server and interested clients tedious and time consuming. In accelerator control systems, there are many well established software client/server architectures (such as CDEV, EPICS, and ADO) that can be used to implement data reflection servers that transfer data asynchronously to anymore » client using the standard SET/GET API. This paper describes a method for using such a data reflection server to set up asynchronous DCN (ADCN) between a DBMS and clients. This method works well for all DBMS systems which provide database trigger functionality. Asynchronous data change notification (ADCN) between database server and clients can be realized by combining the use of a database trigger mechanism, which is supported by major DBMS systems, with server processes that use client/server software architectures that are familiar in the accelerator controls community (such as EPICS, CDEV or ADO). This approach makes the ADCN system easy to set up and integrate into an accelerator controls system. Several ADCN systems have been set up and used in the RHIC-AGS controls system.« less

  14. Optimization of robustness of interdependent network controllability by redundant design

    PubMed Central

    2018-01-01

    Controllability of complex networks has been a hot topic in recent years. Real networks regarded as interdependent networks are always coupled together by multiple networks. The cascading process of interdependent networks including interdependent failure and overload failure will destroy the robustness of controllability for the whole network. Therefore, the optimization of the robustness of interdependent network controllability is of great importance in the research area of complex networks. In this paper, based on the model of interdependent networks constructed first, we determine the cascading process under different proportions of node attacks. Then, the structural controllability of interdependent networks is measured by the minimum driver nodes. Furthermore, we propose a parameter which can be obtained by the structure and minimum driver set of interdependent networks under different proportions of node attacks and analyze the robustness for interdependent network controllability. Finally, we optimize the robustness of interdependent network controllability by redundant design including node backup and redundancy edge backup and improve the redundant design by proposing different strategies according to their cost. Comparative strategies of redundant design are conducted to find the best strategy. Results shows that node backup and redundancy edge backup can indeed decrease those nodes suffering from failure and improve the robustness of controllability. Considering the cost of redundant design, we should choose BBS (betweenness-based strategy) or DBS (degree based strategy) for node backup and HDF(high degree first) for redundancy edge backup. Above all, our proposed strategies are feasible and effective at improving the robustness of interdependent network controllability. PMID:29438426

  15. Control of fluxes in metabolic networks

    PubMed Central

    Basler, Georg; Nikoloski, Zoran; Larhlimi, Abdelhalim; Barabási, Albert-László; Liu, Yang-Yu

    2016-01-01

    Understanding the control of large-scale metabolic networks is central to biology and medicine. However, existing approaches either require specifying a cellular objective or can only be used for small networks. We introduce new coupling types describing the relations between reaction activities, and develop an efficient computational framework, which does not require any cellular objective for systematic studies of large-scale metabolism. We identify the driver reactions facilitating control of 23 metabolic networks from all kingdoms of life. We find that unicellular organisms require a smaller degree of control than multicellular organisms. Driver reactions are under complex cellular regulation in Escherichia coli, indicating their preeminent role in facilitating cellular control. In human cancer cells, driver reactions play pivotal roles in malignancy and represent potential therapeutic targets. The developed framework helps us gain insights into regulatory principles of diseases and facilitates design of engineering strategies at the interface of gene regulation, signaling, and metabolism. PMID:27197218

  16. Control of fluxes in metabolic networks.

    PubMed

    Basler, Georg; Nikoloski, Zoran; Larhlimi, Abdelhalim; Barabási, Albert-László; Liu, Yang-Yu

    2016-07-01

    Understanding the control of large-scale metabolic networks is central to biology and medicine. However, existing approaches either require specifying a cellular objective or can only be used for small networks. We introduce new coupling types describing the relations between reaction activities, and develop an efficient computational framework, which does not require any cellular objective for systematic studies of large-scale metabolism. We identify the driver reactions facilitating control of 23 metabolic networks from all kingdoms of life. We find that unicellular organisms require a smaller degree of control than multicellular organisms. Driver reactions are under complex cellular regulation in Escherichia coli, indicating their preeminent role in facilitating cellular control. In human cancer cells, driver reactions play pivotal roles in malignancy and represent potential therapeutic targets. The developed framework helps us gain insights into regulatory principles of diseases and facilitates design of engineering strategies at the interface of gene regulation, signaling, and metabolism. © 2016 Basler et al.; Published by Cold Spring Harbor Laboratory Press.

  17. Optimizing Dynamical Network Structure for Pinning Control

    NASA Astrophysics Data System (ADS)

    Orouskhani, Yasin; Jalili, Mahdi; Yu, Xinghuo

    2016-04-01

    Controlling dynamics of a network from any initial state to a final desired state has many applications in different disciplines from engineering to biology and social sciences. In this work, we optimize the network structure for pinning control. The problem is formulated as four optimization tasks: i) optimizing the locations of driver nodes, ii) optimizing the feedback gains, iii) optimizing simultaneously the locations of driver nodes and feedback gains, and iv) optimizing the connection weights. A newly developed population-based optimization technique (cat swarm optimization) is used as the optimization method. In order to verify the methods, we use both real-world networks, and model scale-free and small-world networks. Extensive simulation results show that the optimal placement of driver nodes significantly outperforms heuristic methods including placing drivers based on various centrality measures (degree, betweenness, closeness and clustering coefficient). The pinning controllability is further improved by optimizing the feedback gains. We also show that one can significantly improve the controllability by optimizing the connection weights.

  18. Finite-horizon control-constrained nonlinear optimal control using single network adaptive critics.

    PubMed

    Heydari, Ali; Balakrishnan, Sivasubramanya N

    2013-01-01

    To synthesize fixed-final-time control-constrained optimal controllers for discrete-time nonlinear control-affine systems, a single neural network (NN)-based controller called the Finite-horizon Single Network Adaptive Critic is developed in this paper. Inputs to the NN are the current system states and the time-to-go, and the network outputs are the costates that are used to compute optimal feedback control. Control constraints are handled through a nonquadratic cost function. Convergence proofs of: 1) the reinforcement learning-based training method to the optimal solution; 2) the training error; and 3) the network weights are provided. The resulting controller is shown to solve the associated time-varying Hamilton-Jacobi-Bellman equation and provide the fixed-final-time optimal solution. Performance of the new synthesis technique is demonstrated through different examples including an attitude control problem wherein a rigid spacecraft performs a finite-time attitude maneuver subject to control bounds. The new formulation has great potential for implementation since it consists of only one NN with single set of weights and it provides comprehensive feedback solutions online, though it is trained offline.

  19. Minimizing communication cost among distributed controllers in software defined networks

    NASA Astrophysics Data System (ADS)

    Arlimatti, Shivaleela; Elbreiki, Walid; Hassan, Suhaidi; Habbal, Adib; Elshaikh, Mohamed

    2016-08-01

    Software Defined Networking (SDN) is a new paradigm to increase the flexibility of today's network by promising for a programmable network. The fundamental idea behind this new architecture is to simplify network complexity by decoupling control plane and data plane of the network devices, and by making the control plane centralized. Recently controllers have distributed to solve the problem of single point of failure, and to increase scalability and flexibility during workload distribution. Even though, controllers are flexible and scalable to accommodate more number of network switches, yet the problem of intercommunication cost between distributed controllers is still challenging issue in the Software Defined Network environment. This paper, aims to fill the gap by proposing a new mechanism, which minimizes intercommunication cost with graph partitioning algorithm, an NP hard problem. The methodology proposed in this paper is, swapping of network elements between controller domains to minimize communication cost by calculating communication gain. The swapping of elements minimizes inter and intra communication cost among network domains. We validate our work with the OMNeT++ simulation environment tool. Simulation results show that the proposed mechanism minimizes the inter domain communication cost among controllers compared to traditional distributed controllers.

  20. Neural networks for function approximation in nonlinear control

    NASA Technical Reports Server (NTRS)

    Linse, Dennis J.; Stengel, Robert F.

    1990-01-01

    Two neural network architectures are compared with a classical spline interpolation technique for the approximation of functions useful in a nonlinear control system. A standard back-propagation feedforward neural network and a cerebellar model articulation controller (CMAC) neural network are presented, and their results are compared with a B-spline interpolation procedure that is updated using recursive least-squares parameter identification. Each method is able to accurately represent a one-dimensional test function. Tradeoffs between size requirements, speed of operation, and speed of learning indicate that neural networks may be practical for identification and adaptation in a nonlinear control environment.

  1. Predictive Control of Networked Multiagent Systems via Cloud Computing.

    PubMed

    Liu, Guo-Ping

    2017-01-18

    This paper studies the design and analysis of networked multiagent predictive control systems via cloud computing. A cloud predictive control scheme for networked multiagent systems (NMASs) is proposed to achieve consensus and stability simultaneously and to compensate for network delays actively. The design of the cloud predictive controller for NMASs is detailed. The analysis of the cloud predictive control scheme gives the necessary and sufficient conditions of stability and consensus of closed-loop networked multiagent control systems. The proposed scheme is verified to characterize the dynamical behavior and control performance of NMASs through simulations. The outcome provides a foundation for the development of cooperative and coordinative control of NMASs and its applications.

  2. Controlling extreme events on complex networks

    NASA Astrophysics Data System (ADS)

    Chen, Yu-Zhong; Huang, Zi-Gang; Lai, Ying-Cheng

    2014-08-01

    Extreme events, a type of collective behavior in complex networked dynamical systems, often can have catastrophic consequences. To develop effective strategies to control extreme events is of fundamental importance and practical interest. Utilizing transportation dynamics on complex networks as a prototypical setting, we find that making the network ``mobile'' can effectively suppress extreme events. A striking, resonance-like phenomenon is uncovered, where an optimal degree of mobility exists for which the probability of extreme events is minimized. We derive an analytic theory to understand the mechanism of control at a detailed and quantitative level, and validate the theory numerically. Implications of our finding to current areas such as cybersecurity are discussed.

  3. Neural networks for continuous online learning and control.

    PubMed

    Choy, Min Chee; Srinivasan, Dipti; Cheu, Ruey Long

    2006-11-01

    This paper proposes a new hybrid neural network (NN) model that employs a multistage online learning process to solve the distributed control problem with an infinite horizon. Various techniques such as reinforcement learning and evolutionary algorithm are used to design the multistage online learning process. For this paper, the infinite horizon distributed control problem is implemented in the form of real-time distributed traffic signal control for intersections in a large-scale traffic network. The hybrid neural network model is used to design each of the local traffic signal controllers at the respective intersections. As the state of the traffic network changes due to random fluctuation of traffic volumes, the NN-based local controllers will need to adapt to the changing dynamics in order to provide effective traffic signal control and to prevent the traffic network from becoming overcongested. Such a problem is especially challenging if the local controllers are used for an infinite horizon problem where online learning has to take place continuously once the controllers are implemented into the traffic network. A comprehensive simulation model of a section of the Central Business District (CBD) of Singapore has been developed using PARAMICS microscopic simulation program. As the complexity of the simulation increases, results show that the hybrid NN model provides significant improvement in traffic conditions when evaluated against an existing traffic signal control algorithm as well as a new, continuously updated simultaneous perturbation stochastic approximation-based neural network (SPSA-NN). Using the hybrid NN model, the total mean delay of each vehicle has been reduced by 78% and the total mean stoppage time of each vehicle has been reduced by 84% compared to the existing traffic signal control algorithm. This shows the efficacy of the hybrid NN model in solving large-scale traffic signal control problem in a distributed manner. Also, it indicates the

  4. The Network of Global Corporate Control

    PubMed Central

    Vitali, Stefania; Glattfelder, James B.; Battiston, Stefano

    2011-01-01

    The structure of the control network of transnational corporations affects global market competition and financial stability. So far, only small national samples were studied and there was no appropriate methodology to assess control globally. We present the first investigation of the architecture of the international ownership network, along with the computation of the control held by each global player. We find that transnational corporations form a giant bow-tie structure and that a large portion of control flows to a small tightly-knit core of financial institutions. This core can be seen as an economic “super-entity” that raises new important issues both for researchers and policy makers. PMID:22046252

  5. Cell Fate Reprogramming by Control of Intracellular Network Dynamics

    PubMed Central

    Zañudo, Jorge G. T.; Albert, Réka

    2015-01-01

    Identifying control strategies for biological networks is paramount for practical applications that involve reprogramming a cell’s fate, such as disease therapeutics and stem cell reprogramming. Here we develop a novel network control framework that integrates the structural and functional information available for intracellular networks to predict control targets. Formulated in a logical dynamic scheme, our approach drives any initial state to the target state with 100% effectiveness and needs to be applied only transiently for the network to reach and stay in the desired state. We illustrate our method’s potential to find intervention targets for cancer treatment and cell differentiation by applying it to a leukemia signaling network and to the network controlling the differentiation of helper T cells. We find that the predicted control targets are effective in a broad dynamic framework. Moreover, several of the predicted interventions are supported by experiments. PMID:25849586

  6. Toward controlling perturbations in robotic sensor networks

    NASA Astrophysics Data System (ADS)

    Banerjee, Ashis G.; Majumder, Saikat R.

    2014-06-01

    Robotic sensor networks (RSNs), which consist of networks of sensors placed on mobile robots, are being increasingly used for environment monitoring applications. In particular, a lot of work has been done on simultaneous localization and mapping of the robots, and optimal sensor placement for environment state estimation1. The deployment of RSNs, however, remains challenging in harsh environments where the RSNs have to deal with significant perturbations in the forms of wind gusts, turbulent water flows, sand storms, or blizzards that disrupt inter-robot communication and individual robot stability. Hence, there is a need to be able to control such perturbations and bring the networks to desirable states with stable nodes (robots) and minimal operational performance (environment sensing). Recent work has demonstrated the feasibility of controlling the non-linear dynamics in other communication networks like emergency management systems and power grids by introducing compensatory perturbations to restore network stability and operation2. In this paper, we develop a computational framework to investigate the usefulness of this approach for RSNs in marine environments. Preliminary analysis shows promising performance and identifies bounds on the original perturbations within which it is possible to control the networks.

  7. On the Control of Consensus Networks: Theory and Applications

    NASA Astrophysics Data System (ADS)

    Hudoba de Badyn, Mathias

    Signed networks allow the study of positive and negative interactions between agents. In this thesis, three papers are presented that address controllability of networked dynamics. First, controllability of signed consensus networks is approached from a symmetry perspective, for both linear and nonlinear consensus protocols. It is shown that the graph-theoretic property of signed networks known as structural balance renders the consensus protocol uncontrollable when coupled with a certain type of symmetry. Stabilizability and output controllability of signed linear consensus is also examined, as well as a data-driven approach to finding bipartite consensus stemming from structural balance for signed nonlinear consensus. Second, an algorithm is constructed that allows one to grow a network while preserving controllability, and some generalizations of this algorithm are presented. Submodular optimization is used to analyze a second algorithm that adds nodes to a network to maximize the network connectivity.

  8. A Combined Adaptive Neural Network and Nonlinear Model Predictive Control for Multirate Networked Industrial Process Control.

    PubMed

    Wang, Tong; Gao, Huijun; Qiu, Jianbin

    2016-02-01

    This paper investigates the multirate networked industrial process control problem in double-layer architecture. First, the output tracking problem for sampled-data nonlinear plant at device layer with sampling period T(d) is investigated using adaptive neural network (NN) control, and it is shown that the outputs of subsystems at device layer can track the decomposed setpoints. Then, the outputs and inputs of the device layer subsystems are sampled with sampling period T(u) at operation layer to form the index prediction, which is used to predict the overall performance index at lower frequency. Radial basis function NN is utilized as the prediction function due to its approximation ability. Then, considering the dynamics of the overall closed-loop system, nonlinear model predictive control method is proposed to guarantee the system stability and compensate the network-induced delays and packet dropouts. Finally, a continuous stirred tank reactor system is given in the simulation part to demonstrate the effectiveness of the proposed method.

  9. Optimization of controllability and robustness of complex networks by edge directionality

    NASA Astrophysics Data System (ADS)

    Liang, Man; Jin, Suoqin; Wang, Dingjie; Zou, Xiufen

    2016-09-01

    Recently, controllability of complex networks has attracted enormous attention in various fields of science and engineering. How to optimize structural controllability has also become a significant issue. Previous studies have shown that an appropriate directional assignment can improve structural controllability; however, the evolution of the structural controllability of complex networks under attacks and cascading has always been ignored. To address this problem, this study proposes a new edge orientation method (NEOM) based on residual degree that changes the link direction while conserving topology and directionality. By comparing the results with those of previous methods in two random graph models and several realistic networks, our proposed approach is demonstrated to be an effective and competitive method for improving the structural controllability of complex networks. Moreover, numerical simulations show that our method is near-optimal in optimizing structural controllability. Strikingly, compared to the original network, our method maintains the structural controllability of the network under attacks and cascading, indicating that the NEOM can also enhance the robustness of controllability of networks. These results alter the view of the nature of controllability in complex networks, change the understanding of structural controllability and affect the design of network models to control such networks.

  10. 49 CFR 571.124 - Standard No. 124; Accelerator control systems.

    Code of Federal Regulations, 2013 CFR

    2013-10-01

    ... and injuries resulting from engine overspeed caused by malfunctions in the accelerator control system. S3. Application. This standard applies to passenger cars, multi-purpose passenger vehicles, trucks... components, except the fuel metering device, that regulate engine speed in direct response to movement of the...

  11. NASA Integrated Network Monitor and Control Software Architecture

    NASA Technical Reports Server (NTRS)

    Shames, Peter; Anderson, Michael; Kowal, Steve; Levesque, Michael; Sindiy, Oleg; Donahue, Kenneth; Barnes, Patrick

    2012-01-01

    The National Aeronautics and Space Administration (NASA) Space Communications and Navigation office (SCaN) has commissioned a series of trade studies to define a new architecture intended to integrate the three existing networks that it operates, the Deep Space Network (DSN), Space Network (SN), and Near Earth Network (NEN), into one integrated network that offers users a set of common, standardized, services and interfaces. The integrated monitor and control architecture utilizes common software and common operator interfaces that can be deployed at all three network elements. This software uses state-of-the-art concepts such as a pool of re-programmable equipment that acts like a configurable software radio, distributed hierarchical control, and centralized management of the whole SCaN integrated network. For this trade space study a model-based approach using SysML was adopted to describe and analyze several possible options for the integrated network monitor and control architecture. This model was used to refine the design and to drive the costing of the four different software options. This trade study modeled the three existing self standing network elements at point of departure, and then described how to integrate them using variations of new and existing monitor and control system components for the different proposed deployments under consideration. This paper will describe the trade space explored, the selected system architecture, the modeling and trade study methods, and some observations on useful approaches to implementing such model based trade space representation and analysis.

  12. Development of distinct control networks through segregation and integration

    PubMed Central

    Fair, Damien A.; Dosenbach, Nico U. F.; Church, Jessica A.; Cohen, Alexander L.; Brahmbhatt, Shefali; Miezin, Francis M.; Barch, Deanna M.; Raichle, Marcus E.; Petersen, Steven E.; Schlaggar, Bradley L.

    2007-01-01

    Human attentional control is unrivaled. We recently proposed that adults depend on distinct frontoparietal and cinguloopercular networks for adaptive online task control versus more stable set control, respectively. During development, both experience-dependent evoked activity and spontaneous waves of synchronized cortical activity are thought to support the formation and maintenance of neural networks. Such mechanisms may encourage tighter “integration” of some regions into networks over time while “segregating” other sets of regions into separate networks. Here we use resting state functional connectivity MRI, which measures correlations in spontaneous blood oxygenation level-dependent signal fluctuations between brain regions to compare previously identified control networks between children and adults. We find that development of the proposed adult control networks involves both segregation (i.e., decreased short-range connections) and integration (i.e., increased long-range connections) of the brain regions that comprise them. Delay/disruption in the developmental processes of segregation and integration may play a role in disorders of control, such as autism, attention deficit hyperactivity disorder, and Tourette's syndrome. PMID:17679691

  13. Network device interface for digitally interfacing data channels to a controller via a network

    NASA Technical Reports Server (NTRS)

    Ellerbrock, Philip J. (Inventor); Grant, Robert L. (Inventor); Winkelmann, Joseph P. (Inventor); Konz, Daniel W. (Inventor)

    2009-01-01

    A communications system and method are provided for digitally connecting a plurality of data channels, such as sensors, actuators, and subsystems, to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. Data retrieved from the sensor is converted into digital signals and transmitted to the controller. Network device interfaces associated with different data channels can coordinate communications with the other interfaces based on either a transition in a command message sent by the bus controller or a synchronous clock signal.

  14. Energy scaling and reduction in controlling complex networks

    PubMed Central

    Chen, Yu-Zhong; Wang, Le-Zhi; Wang, Wen-Xu; Lai, Ying-Cheng

    2016-01-01

    Recent works revealed that the energy required to control a complex network depends on the number of driving signals and the energy distribution follows an algebraic scaling law. If one implements control using a small number of drivers, e.g. as determined by the structural controllability theory, there is a high probability that the energy will diverge. We develop a physical theory to explain the scaling behaviour through identification of the fundamental structural elements, the longest control chains (LCCs), that dominate the control energy. Based on the LCCs, we articulate a strategy to drastically reduce the control energy (e.g. in a large number of real-world networks). Owing to their structural nature, the LCCs may shed light on energy issues associated with control of nonlinear dynamical networks. PMID:27152220

  15. System Identification for Nonlinear Control Using Neural Networks

    NASA Technical Reports Server (NTRS)

    Stengel, Robert F.; Linse, Dennis J.

    1990-01-01

    An approach to incorporating artificial neural networks in nonlinear, adaptive control systems is described. The controller contains three principal elements: a nonlinear inverse dynamic control law whose coefficients depend on a comprehensive model of the plant, a neural network that models system dynamics, and a state estimator whose outputs drive the control law and train the neural network. Attention is focused on the system identification task, which combines an extended Kalman filter with generalized spline function approximation. Continual learning is possible during normal operation, without taking the system off line for specialized training. Nonlinear inverse dynamic control requires smooth derivatives as well as function estimates, imposing stringent goals on the approximating technique.

  16. Observability and Controllability of Networks: Symmetry in Representations of Brains and Controllers for Epidemics

    NASA Astrophysics Data System (ADS)

    Schiff, Steven

    Observability and controllability are essential concepts to the design of predictive observer models and feedback controllers of networked systems. We present a numerical and group representational framework, to quantify the observability and controllability of nonlinear networks with explicit symmetries that shows the connection between symmetries and nonlinear measures of observability and controllability. In addition to the topology of brain networks, we have advanced our ability to represent network nodes within the brain using conservation principles and more accurate biophysics that unifies the dynamics of spikes, seizures, and spreading depression. Lastly, we show how symmetries in controller design can be applied to infectious disease epidemics. NIH Grants 1R01EB014641, 1DP1HD086071.

  17. The Louisiana Accelerated Schools Project First Year Evaluation Report.

    ERIC Educational Resources Information Center

    St. John, Edward P.; And Others

    The Louisiana Accelerated Schools Project (LASP) is a statewide network of schools that are changing from the traditional mode of schooling for at-risk students, which stresses remediation, to one of acceleration, which stresses accelerated learning for all students. The accelerated schools process provides a systematic approach to the…

  18. Network congestion control algorithm based on Actor-Critic reinforcement learning model

    NASA Astrophysics Data System (ADS)

    Xu, Tao; Gong, Lina; Zhang, Wei; Li, Xuhong; Wang, Xia; Pan, Wenwen

    2018-04-01

    Aiming at the network congestion control problem, a congestion control algorithm based on Actor-Critic reinforcement learning model is designed. Through the genetic algorithm in the congestion control strategy, the network congestion problems can be better found and prevented. According to Actor-Critic reinforcement learning, the simulation experiment of network congestion control algorithm is designed. The simulation experiments verify that the AQM controller can predict the dynamic characteristics of the network system. Moreover, the learning strategy is adopted to optimize the network performance, and the dropping probability of packets is adaptively adjusted so as to improve the network performance and avoid congestion. Based on the above finding, it is concluded that the network congestion control algorithm based on Actor-Critic reinforcement learning model can effectively avoid the occurrence of TCP network congestion.

  19. A neural network controller for automated composite manufacturing

    NASA Technical Reports Server (NTRS)

    Lichtenwalner, Peter F.

    1994-01-01

    At McDonnell Douglas Aerospace (MDA), an artificial neural network based control system has been developed and implemented to control laser heating for the fiber placement composite manufacturing process. This neurocontroller learns an approximate inverse model of the process on-line to provide performance that improves with experience and exceeds that of conventional feedback control techniques. When untrained, the control system behaves as a proportional plus integral (PI) controller. However after learning from experience, the neural network feedforward control module provides control signals that greatly improve temperature tracking performance. Faster convergence to new temperature set points and reduced temperature deviation due to changing feed rate have been demonstrated on the machine. A Cerebellar Model Articulation Controller (CMAC) network is used for inverse modeling because of its rapid learning performance. This control system is implemented in an IBM compatible 386 PC with an A/D board interface to the machine.

  20. Adaptive Control Parameters for Dispersal of Multi-Agent Mobile Ad Hoc Network (MANET) Swarms

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

    Kurt Derr; Milos Manic

    A mobile ad hoc network is a collection of independent nodes that communicate wirelessly with one another. This paper investigates nodes that are swarm robots with communications and sensing capabilities. Each robot in the swarm may operate in a distributed and decentralized manner to achieve some goal. This paper presents a novel approach to dynamically adapting control parameters to achieve mesh configuration stability. The presented approach to robot interaction is based on spring force laws (attraction and repulsion laws) to create near-optimal mesh like configurations. In prior work, we presented the extended virtual spring mesh (EVSM) algorithm for the dispersionmore » of robot swarms. This paper extends the EVSM framework by providing the first known study on the effects of adaptive versus static control parameters on robot swarm stability. The EVSM algorithm provides the following novelties: 1) improved performance with adaptive control parameters and 2) accelerated convergence with high formation effectiveness. Simulation results show that 120 robots reach convergence using adaptive control parameters more than twice as fast as with static control parameters in a multiple obstacle environment.« less

  1. Distributed synchronization control of complex networks with communication constraints.

    PubMed

    Xu, Zhenhua; Zhang, Dan; Song, Hongbo

    2016-11-01

    This paper is concerned with the distributed synchronization control of complex networks with communication constraints. In this work, the controllers communicate with each other through the wireless network, acting as a controller network. Due to the constrained transmission power, techniques such as the packet size reduction and transmission rate reduction schemes are proposed which could help reduce communication load of the controller network. The packet dropout problem is also considered in the controller design since it is often encountered in networked control systems. We show that the closed-loop system can be modeled as a switched system with uncertainties and random variables. By resorting to the switched system approach and some stochastic system analysis method, a new sufficient condition is firstly proposed such that the exponential synchronization is guaranteed in the mean-square sense. The controller gains are determined by using the well-known cone complementarity linearization (CCL) algorithm. Finally, a simulation study is performed, which demonstrates the effectiveness of the proposed design algorithm. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  2. Opinion control in complex networks

    NASA Astrophysics Data System (ADS)

    Masuda, Naoki

    2015-03-01

    In many political elections, the electorate appears to be a composite of partisan and independent voters. Given that partisans are not likely to convert to a different party, an important goal for a political party could be to mobilize independent voters toward the party with the help of strong leadership, mass media, partisans, and the effects of peer-to-peer influence. Based on the exact solution of classical voter model dynamics in the presence of perfectly partisan voters (i.e., zealots), we propose a computational method that uses pinning control strategy to maximize the share of a party in a social network of independent voters. The party, corresponding to the controller or zealots, optimizes the nodes to be controlled given the information about the connectivity of independent voters and the set of nodes that the opposing party controls. We show that controlling hubs is generally a good strategy, but the optimized strategy is even better. The superiority of the optimized strategy is particularly eminent when the independent voters are connected as directed (rather than undirected) networks.

  3. Resolved motion rate and resolved acceleration servo-control of wheeled mobile robots

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

    Muir, P.F.; Neuman, C.P.; Carnegie-Mellon Univ., Pittsburgh, PA

    1989-01-01

    Accurate motion control of wheeled mobile robots (WMRs) is required for their application to autonomous, semi-autonomous and teleoperated tasks. The similarities between WMRs and stationary manipulators suggest that current, successful, model-based manipulator control algorithms may be applied to WMRs. Special characteristics of WMRs including higher-pairs, closed-chains, friction and unactuated and unsensed joints require innovative modeling methodologies. The WMR modeling challenge has been recently overcome, thus enabling the application of manipulator control algorithms to WMRs. This realization lays the foundation for significant technology transfer from manipulator control to WMR control. We apply two Cartesian-space manipulator control algorithms: resolved motion rate (kinematics-based)more » and resolved acceleration (dynamics-based) control to WMR servo-control. We evaluate simulation studies of two exemplary WMRs: Uranus (a three degree-of-freedom WMR constructed at Carnegie Mellon University), and Bicsun-Bicas (a two degree-of-freedom WMR being constructed at Sandia National Laboratories) under the control of these algorithms. Although resolved motion rate servo-control is adequate for the control of Uranus, resolved acceleration servo-control is required for the control of the mechanically simpler Bicsun-Bicas because it exhibits more dynamic coupling and nonlinearities. Successful accurate motion control of these WMRs in simulation is driving current experimental research studies. 18 refs., 7 figs., 5 tabs.« less

  4. A microcomputer network for control of a continuous mining machine

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

    Schiffbauer, W.H.

    1993-12-31

    This report details a microcomputer-based control and monitoring network that was developed in-house by the U.S. Bureau of Mines and installed on a continuous mining machine. The network consists of microcomputers that are connected together via a single twisted-pair cable. Each microcomputer was developed to provide a particular function in the control process. Machine-mounted microcomputers, in conjunction with the appropriate sensors, provide closed-loop control of the machine, navigation, and environmental monitoring. Off-the-machine microcomputers provide remote control of the machine, sensor status, and a connection to the network so that external computers can access network data and control the continuous miningmore » machine. Because of the network`s generic structure, it can be installed on most mining machines.« less

  5. Treelike networks accelerating capillary flow.

    PubMed

    Shou, Dahua; Ye, Lin; Fan, Jintu

    2014-05-01

    Transport in treelike networks has received wide attention in natural systems, oil recovery, microelectronic cooling systems, and textiles. Existing studies are focused on transport behaviors under a constant potential difference (including pressure, temperature, and voltage) in a steady state [B. Yu and B. Li, Phys. Rev. E 73, 066302 (2006); J. Chen, B. Yu, P. Xu, and Y. Li, Phys. Rev. E 75, 056301 (2007)]. However, dynamic (time-dependent) transport in such systems has rarely been concerned. In this work, we theoretically investigate the dynamics of capillary flow in treelike networks and design the distribution of radius and length of local branches for the fastest capillary flow. It is demonstrated that capillary flow in the optimized tree networks is faster than in traditional parallel tube nets under fixed constraints. As well, the flow time of the liquid is found to increase approximately linearly with penetration distance, which differs from Washburn's classic description that flow time increases as the square of penetration distance in a uniform tube.

  6. Design of Accelerator Online Simulator Server Using Structured Data

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

    Shen, Guobao; /Brookhaven; Chu, Chungming

    2012-07-06

    Model based control plays an important role for a modern accelerator during beam commissioning, beam study, and even daily operation. With a realistic model, beam behaviour can be predicted and therefore effectively controlled. The approach used by most current high level application environments is to use a built-in simulation engine and feed a realistic model into that simulation engine. Instead of this traditional monolithic structure, a new approach using a client-server architecture is under development. An on-line simulator server is accessed via network accessible structured data. With this approach, a user can easily access multiple simulation codes. This paper describesmore » the design, implementation, and current status of PVData, which defines the structured data, and PVAccess, which provides network access to the structured data.« less

  7. A parallel adaptive quantum genetic algorithm for the controllability of arbitrary networks.

    PubMed

    Li, Yuhong; Gong, Guanghong; Li, Ni

    2018-01-01

    In this paper, we propose a novel algorithm-parallel adaptive quantum genetic algorithm-which can rapidly determine the minimum control nodes of arbitrary networks with both control nodes and state nodes. The corresponding network can be fully controlled with the obtained control scheme. We transformed the network controllability issue into a combinational optimization problem based on the Popov-Belevitch-Hautus rank condition. A set of canonical networks and a list of real-world networks were experimented. Comparison results demonstrated that the algorithm was more ideal to optimize the controllability of networks, especially those larger-size networks. We demonstrated subsequently that there were links between the optimal control nodes and some network statistical characteristics. The proposed algorithm provides an effective approach to improve the controllability optimization of large networks or even extra-large networks with hundreds of thousands nodes.

  8. Dissipative rendering and neural network control system design

    NASA Technical Reports Server (NTRS)

    Gonzalez, Oscar R.

    1995-01-01

    Model-based control system designs are limited by the accuracy of the models of the plant, plant uncertainty, and exogenous signals. Although better models can be obtained with system identification, the models and control designs still have limitations. One approach to reduce the dependency on particular models is to design a set of compensators that will guarantee robust stability to a set of plants. Optimization over the compensator parameters can then be used to get the desired performance. Conservativeness of this approach can be reduced by integrating fundamental properties of the plant models. This is the approach of dissipative control design. Dissipative control designs are based on several variations of the Passivity Theorem, which have been proven for nonlinear/linear and continuous-time/discrete-time systems. These theorems depend not on a specific model of a plant, but on its general dissipative properties. Dissipative control design has found wide applicability in flexible space structures and robotic systems that can be configured to be dissipative. Currently, there is ongoing research to improve the performance of dissipative control designs. For aircraft systems that are not dissipative active control may be used to make them dissipative and then a dissipative control design technique can be used. It is also possible that rendering a system dissipative and dissipative control design may be combined into one step. Furthermore, the transformation of a non-dissipative system to dissipative can be done robustly. One sequential design procedure for finite dimensional linear time-invariant systems has been developed. For nonlinear plants that cannot be controlled adequately with a single linear controller, model-based techniques have additional problems. Nonlinear system identification is still a research topic. Lacking analytical models for model-based design, artificial neural network algorithms have recently received considerable attention. Using

  9. Accelerated dissolution testing for controlled release microspheres using the flow-through dissolution apparatus.

    PubMed

    Collier, Jarrod W; Thakare, Mohan; Garner, Solomon T; Israel, Bridg'ette; Ahmed, Hisham; Granade, Saundra; Strong, Deborah L; Price, James C; Capomacchia, A C

    2009-01-01

    Theophylline controlled release capsules (THEO-24 CR) were used as a model system to evaluate accelerated dissolution tests for process and quality control and formulation development of controlled release formulations. Dissolution test acceleration was provided by increasing temperature, pH, flow rate, or adding surfactant. Electron microscope studies on the theophylline microspheres subsequent to each experiment showed that at pH values of 6.6 and 7.6 the microspheres remained intact, but at pH 8.6 they showed deterioration. As temperature was increased from 37-57 degrees C, no change in microsphere integrity was noted. Increased flow rate also showed no detrimental effect on integrity. The effect of increased temperature was determined to be the statistically significant variable.

  10. Robustness of Controllability for Networks Based on Edge-Attack

    PubMed Central

    Nie, Sen; Wang, Xuwen; Zhang, Haifeng; Li, Qilang; Wang, Binghong

    2014-01-01

    We study the controllability of networks in the process of cascading failures under two different attacking strategies, random and intentional attack, respectively. For the highest-load edge attack, it is found that the controllability of Erdős-Rényi network, that with moderate average degree, is less robust, whereas the Scale-free network with moderate power-law exponent shows strong robustness of controllability under the same attack strategy. The vulnerability of controllability under random and intentional attacks behave differently with the increasing of removal fraction, especially, we find that the robustness of control has important role in cascades for large removal fraction. The simulation results show that for Scale-free networks with various power-law exponents, the network has larger scale of cascades do not mean that there will be more increments of driver nodes. Meanwhile, the number of driver nodes in cascading failures is also related to the edges amount in strongly connected components. PMID:24586507

  11. Robustness of controllability for networks based on edge-attack.

    PubMed

    Nie, Sen; Wang, Xuwen; Zhang, Haifeng; Li, Qilang; Wang, Binghong

    2014-01-01

    We study the controllability of networks in the process of cascading failures under two different attacking strategies, random and intentional attack, respectively. For the highest-load edge attack, it is found that the controllability of Erdős-Rényi network, that with moderate average degree, is less robust, whereas the Scale-free network with moderate power-law exponent shows strong robustness of controllability under the same attack strategy. The vulnerability of controllability under random and intentional attacks behave differently with the increasing of removal fraction, especially, we find that the robustness of control has important role in cascades for large removal fraction. The simulation results show that for Scale-free networks with various power-law exponents, the network has larger scale of cascades do not mean that there will be more increments of driver nodes. Meanwhile, the number of driver nodes in cascading failures is also related to the edges amount in strongly connected components.

  12. Observability of Boolean multiplex control networks

    NASA Astrophysics Data System (ADS)

    Wu, Yuhu; Xu, Jingxue; Sun, Xi-Ming; Wang, Wei

    2017-04-01

    Boolean multiplex (multilevel) networks (BMNs) are currently receiving considerable attention as theoretical arguments for modeling of biological systems and system level analysis. Studying control-related problems in BMNs may not only provide new views into the intrinsic control in complex biological systems, but also enable us to develop a method for manipulating biological systems using exogenous inputs. In this article, the observability of the Boolean multiplex control networks (BMCNs) are studied. First, the dynamical model and structure of BMCNs with control inputs and outputs are constructed. By using of Semi-Tensor Product (STP) approach, the logical dynamics of BMCNs is converted into an equivalent algebraic representation. Then, the observability of the BMCNs with two different kinds of control inputs is investigated by giving necessary and sufficient conditions. Finally, examples are given to illustrate the efficiency of the obtained theoretical results.

  13. Thermoelastic steam turbine rotor control based on neural network

    NASA Astrophysics Data System (ADS)

    Rzadkowski, Romuald; Dominiczak, Krzysztof; Radulski, Wojciech; Szczepanik, R.

    2015-12-01

    Considered here are Nonlinear Auto-Regressive neural networks with eXogenous inputs (NARX) as a mathematical model of a steam turbine rotor for controlling steam turbine stress on-line. In order to obtain neural networks that locate critical stress and temperature points in the steam turbine during transient states, an FE rotor model was built. This model was used to train the neural networks on the basis of steam turbine transient operating data. The training included nonlinearity related to steam turbine expansion, heat exchange and rotor material properties during transients. Simultaneous neural networks are algorithms which can be implemented on PLC controllers. This allows for the application neural networks to control steam turbine stress in industrial power plants.

  14. A Clementine Derived Control Network and Topographic Model - The Unified Lunar Control Network 2005

    NASA Astrophysics Data System (ADS)

    Archinal, B. A.; Rosiek, M. R.; Kirk, R. L.; Redding, B. L.

    2006-08-01

    U. S. Geological Survey, Astrogeology Team, Flagstaff, AZ, United States Introduction: We have completed a new general unified lunar control network and lunar topographic model based on Clementine images. It includes the determination, in the lunar mean Earth/polar axis system, of the 3-D positions of 272,931 points on the lunar surface and the correction of the camera angles for 43,866 Clementine images, using 546,126 tie point measurements. The solution RMS is 0.9 pixels in the image plane, with the largest residual of 6.4 pixels. We are now documenting our solution and plan to release the solution results soon, initially as a USGS Open File report. ULCN 2005 Features: The new network is a combination of the Unified Lunar Control Network (ULCN), derived from the Apollo, Mariner 10, and Galileo missions, and Earth-based photographs, [1] and the Clementine Lunar Control Network (CLCN) [2], both derived from (mostly 750-nm) Clementine images, by M. Davies and T. Colvin at RAND. The primary difference between our new network and the previous ones is that we solve for the radii of the control points. This avoids (~7 km) distortion of horizontal positions present in the CLCN. The expected precision of such information is on the order of several hundred m, and compatible with Clementine LIDAR [3]. Thus, a by-product of this network is a global lunar topographic model that is denser than that provided by LIDAR and of similar accuracy, and denser than any other lunar topography information except that provided in limited areas ([4-7]). This is the only lunar topographic model positioning where both heights and horizontal positions are estimated in a globally-consistent system. Other features of the ULCN 2005 are that the camera angles to their values as measured during the mission, supposedly with an accuracy of 0.03º [8], and we have identified a majority of the original ULCN points on Clementine images. Future Work: The Lunar Orbiter (LO) digital mosaics now being

  15. A remote control console for the HHIRF 25-MV Tandem Accelerator

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

    Hasanul Basher, A.M.

    The CAMAC-based control system for the 25-MV Tandem Accelerator at HHIRF uses two Perkin-Elmer, 32-bit minicomputers: a message-switching computer and a supervisory computer. Two operator consoles are located on one of the six serial highways. Operator control is provided by means of a console CRT, trackball, assignable shaft encoders, and meters. The message-switching computer transmits and receives control information on the serial highways. At present, the CRT pages with updated parameters can be displayed and parameters can be controlled only from the two existing consoles, one in the Tandem control room and the other in the ORIC control room. Itmore » has become necessary to expand the control capability to several other locations in the building. With the expansion of control and monitoring capability of accelerator parameters to other locations, the operators will be able to control and observe the result of the control action at the same time. This capability will be useful in the new Radioactive Ion Beam project of the division. Since the new control console will be PC-based, the existing page format will be changed. The PC will be communicating with the Perkin-Elmer through RS-232 with the aid of a communication protocol. Hardware configuration has been established, a software program that reads the pages from the shared memory, and a communication protocol have been developed. The following sections present the implementation strategy, work completed, future action plans, and the functional details of the communication protocol.« less

  16. Adaptive Control for Microgravity Vibration Isolation System

    NASA Technical Reports Server (NTRS)

    Yang, Bong-Jun; Calise, Anthony J.; Craig, James I.; Whorton, Mark S.

    2005-01-01

    Most active vibration isolation systems that try to a provide quiescent acceleration environment for space science experiments have utilized linear design methods. In this paper, we address adaptive control augmentation of an existing classical controller that employs a high-gain acceleration feedback together with a low-gain position feedback to center the isolated platform. The control design feature includes parametric and dynamic uncertainties because the hardware of the isolation system is built as a payload-level isolator, and the acceleration Sensor exhibits a significant bias. A neural network is incorporated to adaptively compensate for the system uncertainties, and a high-pass filter is introduced to mitigate the effect of the measurement bias. Simulations show that the adaptive control improves the performance of the existing acceleration controller and keep the level of the isolated platform deviation to that of the existing control system.

  17. Fair and efficient network congestion control based on minority game

    NASA Astrophysics Data System (ADS)

    Wang, Zuxi; Wang, Wen; Hu, Hanping; Deng, Zhaozhang

    2011-12-01

    Low link utility, RTT unfairness and unfairness of Multi-Bottleneck network are the existing problems in the present network congestion control algorithms at large. Through the analogy of network congestion control with the "El Farol Bar" problem, we establish a congestion control model based on minority game(MG), and then present a novel network congestion control algorithm based on the model. The result of simulations indicates that the proposed algorithm can make the achievements of link utility closing to 100%, zero packet lose rate, and small of queue size. Besides, the RTT unfairness and the unfairness of Multi-Bottleneck network can be solved, to achieve the max-min fairness in Multi-Bottleneck network, while efficiently weaken the "ping-pong" oscillation caused by the overall synchronization.

  18. Quality of service policy control in virtual private networks

    NASA Astrophysics Data System (ADS)

    Yu, Yiqing; Wang, Hongbin; Zhou, Zhi; Zhou, Dongru

    2004-04-01

    This paper studies the QoS of VPN in an environment where the public network prices connection-oriented services based on source, destination and grade of service, and advertises these prices to its VPN customers (users). As different QoS technologies can produce different QoS, there are according different traffic classification rules and priority rules. The internet service provider (ISP) may need to build complex mechanisms separately for each node. In order to reduce the burden of network configuration, we need to design policy control technologies. We considers mainly directory server, policy server, policy manager and policy enforcers. Policy decision point (PDP) decide its control according to policy rules. In network, policy enforce point (PEP) decide its network controlled unit. For InterServ and DiffServ, we will adopt different policy control methods as following: (1) In InterServ, traffic uses resource reservation protocol (RSVP) to guarantee the network resource. (2) In DiffServ, policy server controls the DiffServ code points and per hop behavior (PHB), its PDP distributes information to each network node. Policy server will function as following: information searching; decision mechanism; decision delivering; auto-configuration. In order to prove the effectiveness of QoS policy control, we make the corrective simulation.

  19. Adaptive Neural Network Based Control of Noncanonical Nonlinear Systems.

    PubMed

    Zhang, Yanjun; Tao, Gang; Chen, Mou

    2016-09-01

    This paper presents a new study on the adaptive neural network-based control of a class of noncanonical nonlinear systems with large parametric uncertainties. Unlike commonly studied canonical form nonlinear systems whose neural network approximation system models have explicit relative degree structures, which can directly be used to derive parameterized controllers for adaptation, noncanonical form nonlinear systems usually do not have explicit relative degrees, and thus their approximation system models are also in noncanonical forms. It is well-known that the adaptive control of noncanonical form nonlinear systems involves the parameterization of system dynamics. As demonstrated in this paper, it is also the case for noncanonical neural network approximation system models. Effective control of such systems is an open research problem, especially in the presence of uncertain parameters. This paper shows that it is necessary to reparameterize such neural network system models for adaptive control design, and that such reparameterization can be realized using a relative degree formulation, a concept yet to be studied for general neural network system models. This paper then derives the parameterized controllers that guarantee closed-loop stability and asymptotic output tracking for noncanonical form neural network system models. An illustrative example is presented with the simulation results to demonstrate the control design procedure, and to verify the effectiveness of such a new design method.

  20. Accelerator diagnosis and control by Neural Nets

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

    Spencer, J.E.

    1989-01-01

    Neural Nets (NN) have been described as a solution looking for a problem. In the last conference, Artificial Intelligence (AI) was considered in the accelerator context. While good for local surveillance and control, its use for large complex systems (LCS) was much more restricted. By contrast, NN provide a good metaphor for LCS. It can be argued that they are logically equivalent to multi-loop feedback/forward control of faulty systems, and therefore provide an ideal adaptive control system. Thus, where AI may be good for maintaining a 'golden orbit,' NN should be good for obtaining it via a quantitative approach tomore » 'look and adjust' methods like operator tweaking which use pattern recognition to deal with hardware and software limitations, inaccuracies or errors as well as imprecise knowledge or understanding of effects like annealing and hysteresis. Further, insights from NN allow one to define feasibility conditions for LCS in terms of design constraints and tolerances. Hardware and software implications are discussed and several LCS of current interest are compared and contrasted. 15 refs., 5 figs.« less

  1. Network control processor for a TDMA system

    NASA Astrophysics Data System (ADS)

    Suryadevara, Omkarmurthy; Debettencourt, Thomas J.; Shulman, R. B.

    Two unique aspects of designing a network control processor (NCP) to monitor and control a demand-assigned, time-division multiple-access (TDMA) network are described. The first involves the implementation of redundancy by synchronizing the databases of two geographically remote NCPs. The two sets of databases are kept in synchronization by collecting data on both systems, transferring databases, sending incremental updates, and the parallel updating of databases. A periodic audit compares the checksums of the databases to ensure synchronization. The second aspect involves the use of a tracking algorithm to dynamically reallocate TDMA frame space. This algorithm detects and tracks current and long-term load changes in the network. When some portions of the network are overloaded while others have excess capacity, the algorithm automatically calculates and implements a new burst time plan.

  2. Controllability of switched singular mix-valued logical control networks with constraints

    NASA Astrophysics Data System (ADS)

    Deng, Lei; Gong, Mengmeng; Zhu, Peiyong

    2018-03-01

    The present paper investigates the controllability problem of switched singular mix-valued logical control networks (SSMLCNs) with constraints on states and controls. First, using the semi-tenser product (STP) of matrices, the SSMLCN is expressed in an algebraic form, based on which a necessary and sufficient condition is given for the uniqueness of solution of SSMLCNs. Second, a necessary and sufficient criteria is derived for the controllability of constrained SSMLCNs, by converting a constrained SSMLCN into a parallel constrained switched mix-valued logical control network. Third, an algorithm is presented to design a proper switching sequence and a control scheme which force a state to a reachable state. Finally, a numerical example is given to demonstrate the efficiency of the results obtained in this paper.

  3. Decay-Accelerating Factor Mitigates Controlled Hemorrhage-Instigated Intestinal and Lung Tissue Damage and Hyperkalemia in Swine

    DTIC Science & Technology

    2011-07-01

    Decay-Accelerating Factor Mitigates Controlled Hemorrhage- Instigated Intestinal and Lung Tissue Damage and Hyperkalemia in Swine Jurandir J. Dalle...DAF treatment improved hemorrhage- induced hyperkalemia . The protective effects of DAF appear to be related to its ability to reduce tissue complement...Decay-accelerating factor mitigates controlled hemorrhage-instigated intestinal and lung tissue damage and hyperkalemia in swine 5a. CONTRACT NUMBER

  4. The Longitudinal Effect of Self-Monitoring and Locus of Control on Social Network Position in Friendship Networks

    DTIC Science & Technology

    2006-03-01

    equally essential to examine the antecedents that bring a person to a particular network location. The previous body of knowledge in social networks...Locus of Control on Social Network Position in Friendship Networks THESIS Gary J. Moore, Captain, USAF AFIT/GEM/ENV/06M-11 DEPARTMENT OF THE AIR...THE LONGITUDINAL EFFECTS OF SELF-MONITORING AND LOCUS OF CONTROL ON SOCIAL NETWORK POSITION IN FRIENDSHIP NETWORKS THESIS Presented to the

  5. SDN-Enabled Dynamic Feedback Control and Sensing in Agile Optical Networks

    NASA Astrophysics Data System (ADS)

    Lin, Likun

    Fiber optic networks are no longer just pipelines for transporting data in the long haul backbone. Exponential growth in traffic in metro-regional areas has pushed higher capacity fiber toward the edge of the network, and highly dynamic patterns of heterogeneous traffic have emerged that are often bursty, severely stressing the historical "fat and dumb pipe" static optical network, which would need to be massively over-provisioned to deal with these loads. What is required is a more intelligent network with a span of control over the optical as well as electrical transport mechanisms which enables handling of service requests in a fast and efficient way that guarantees quality of service (QoS) while optimizing capacity efficiency. An "agile" optical network is a reconfigurable optical network comprised of high speed intelligent control system fed by real-time in situ network sensing. It provides fast response in the control and switching of optical signals in response to changing traffic demands and network conditions. This agile control of optical signals is enabled by pushing switching decisions downward in the network stack to the physical layer. Implementing such agility is challenging due to the response dynamics and interactions of signals in the physical layer. Control schemes must deal with issues such as dynamic power equalization, EDFA transients and cascaded noise effects, impairments due to self-phase modulation and dispersion, and channel-to-channel cross talk. If these issues are not properly predicted and mitigated, attempts at dynamic control can drive the optical network into an unstable state. In order to enable high speed actuation of signal modulators and switches, the network controller must be able to make decisions based on predictive models. In this thesis, we consider how to take advantage of Software Defined Networking (SDN) capabilities for network reconfiguration, combined with embedded models that access updates from deployed network

  6. Imbalanced functional link between executive control network and reward network explain the online-game seeking behaviors in Internet gaming disorder.

    PubMed

    Dong, Guangheng; Lin, Xiao; Hu, Yanbo; Xie, Chunming; Du, Xiaoxia

    2015-03-17

    Literatures have shown that Internet gaming disorder (IGD) subjects show impaired executive control and enhanced reward sensitivities than healthy controls. However, how these two networks jointly affect the valuation process and drive IGD subjects' online-game-seeking behaviors remains unknown. Thirty-five IGD and 36 healthy controls underwent a resting-states scan in the MRI scanner. Functional connectivity (FC) was examined within control and reward network seeds regions, respectively. Nucleus accumbens (NAcc) was selected as the node to find the interactions between these two networks. IGD subjects show decreased FC in the executive control network and increased FC in the reward network when comparing with the healthy controls. When examining the correlations between the NAcc and the executive control/reward networks, the link between the NAcc - executive control network is negatively related with the link between NAcc - reward network. The changes (decrease/increase) in IGD subjects' brain synchrony in control/reward networks suggest the inefficient/overly processing within neural circuitry underlying these processes. The inverse proportion between control network and reward network in IGD suggest that impairments in executive control lead to inefficient inhibition of enhanced cravings to excessive online game playing. This might shed light on the mechanistic understanding of IGD.

  7. Structure-based control of complex networks with nonlinear dynamics.

    PubMed

    Zañudo, Jorge Gomez Tejeda; Yang, Gang; Albert, Réka

    2017-07-11

    What can we learn about controlling a system solely from its underlying network structure? Here we adapt a recently developed framework for control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This feedback-based framework provides realizable node overrides that steer a system toward any of its natural long-term dynamic behaviors, regardless of the specific functional forms and system parameters. We use this framework on several real networks, identify the topological characteristics that underlie the predicted node overrides, and compare its predictions to those of structural controllability in control theory. Finally, we demonstrate this framework's applicability in dynamic models of gene regulatory networks and identify nodes whose override is necessary for control in the general case but not in specific model instances.

  8. Self-teaching neural network learns difficult reactor control problem

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

    Jouse, W.C.

    1989-01-01

    A self-teaching neural network used as an adaptive controller quickly learns to control an unstable reactor configuration. The network models the behavior of a human operator. It is trained by allowing it to operate the reactivity control impulsively. It is punished whenever either the power or fuel temperature stray outside technical limits. Using a simple paradigm, the network constructs an internal representation of the punishment and of the reactor system. The reactor is constrained to small power orbits.

  9. Alternative Dual Mode Network Control Strategies

    DOT National Transportation Integrated Search

    1972-03-01

    From a literature survey a qualitative evaluation was made of four network control strategies for the fundamental control philosophy of the moving synchronous slot. In the literature concerning automated transportation systems, such as dual mode, a g...

  10. Output-feedback control of combined sewer networks through receding horizon control with moving horizon estimation

    NASA Astrophysics Data System (ADS)

    Joseph-Duran, Bernat; Ocampo-Martinez, Carlos; Cembrano, Gabriela

    2015-10-01

    An output-feedback control strategy for pollution mitigation in combined sewer networks is presented. The proposed strategy provides means to apply model-based predictive control to large-scale sewer networks, in-spite of the lack of measurements at most of the network sewers. In previous works, the authors presented a hybrid linear control-oriented model for sewer networks together with the formulation of Optimal Control Problems (OCP) and State Estimation Problems (SEP). By iteratively solving these problems, preliminary Receding Horizon Control with Moving Horizon Estimation (RHC/MHE) results, based on flow measurements, were also obtained. In this work, the RHC/MHE algorithm has been extended to take into account both flow and water level measurements and the resulting control loop has been extensively simulated to assess the system performance according different measurement availability scenarios and rain events. All simulations have been carried out using a detailed physically based model of a real case-study network as virtual reality.

  11. Quantized Synchronization of Chaotic Neural Networks With Scheduled Output Feedback Control.

    PubMed

    Wan, Ying; Cao, Jinde; Wen, Guanghui

    In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control gain matrix, allowable length of sampling intervals, and upper bound of network-induced delays are derived to ensure the quantized synchronization of master-slave chaotic neural networks. Lastly, Chua's circuit system and 4-D Hopfield neural network are simulated to validate the effectiveness of the main results.In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control

  12. Increased Resting-State Functional Connectivity in the Cingulo-Opercular Cognitive-Control Network after Intervention in Children with Reading Difficulties

    PubMed Central

    Horowitz-Kraus, Tzipi; Toro-Serey, Claudio; DiFrancesco, Mark

    2015-01-01

    Dyslexia, or reading difficulty, is characterized by slow, inaccurate reading accompanied by executive dysfunction. Reading training using the Reading Acceleration Program improves reading and executive functions in both children with dyslexia and typical readers. This improvement is associated with increased activation in and functional connectivity between the anterior cingulate cortex, part of the cingulo-opercular cognitive-control network, and the fusiform gyrus during a reading task after training. The objective of the current study was to determine whether the training also has an effect on functional connectivity of the cingulo-opercular and fronto-parietal cognitive-control networks during rest in children with dyslexia and typical readers. Fifteen children with reading difficulty and 17 typical readers (8-12 years old) were included in the study. Reading and executive functions behavioral measures and resting-state functional magnetic resonance imaging data were collected before and after reading training. Imaging data were analyzed using a graphical network-modeling tool. Both reading groups had increased reading and executive-functions scores after training, with greater gains among the dyslexia group. Training may have less effect on cognitive control in typical readers and a more direct effect on the visual area, as previously reported. Statistical analysis revealed that compared to typical readers, children with reading difficulty had significantly greater functional connectivity in the cingulo-opercular network after training, which may demonstrate the importance of cognitive control during reading in this population. These results support previous findings of increased error-monitoring activation after reading training in children with dyslexia and confirm greater gains with training in this group. PMID:26197049

  13. Resource Allocation and Cross Layer Control in Wireless Networks

    DTIC Science & Technology

    2006-08-25

    arrival rates lies within the capacity region of the network. The notion of controlling the system to maximize its stability region and the following...optimization problem (4.5) that must be solved at the beginning of 48 Dynamic Control for Network Stability each time slot requires in general knowledge...Dynamic Control for Network Stability ~ (c) ab (t) those of any other feasible algorithm, then for any time t 0; X ic U (c) i (t) "X b ~ (c) ab (t) X

  14. The Life-Changing Magic of Nonlinearity in Network Control

    NASA Astrophysics Data System (ADS)

    Cornelius, Sean

    The proper functioning and reliability of many man-made and natural systems is fundamentally tied to our ability to control them. Indeed, applications as diverse as ecosystem management, emergency response and cell reprogramming all, at their heart, require us to drive a system to--or keep it in--a desired state. This process is complicated by the nonlinear dynamics inherent to most real systems, which has traditionally been viewed as the principle obstacle to their control. In this talk, I will discuss two ways in which nonlinearity turns this view on its head, in fact representing an asset to the control of complex systems. First, I will show how nonlinearity in the form of multistability allows one to systematically design control interventions that can deliberately induce ``reverse cascading failures'', in which a network spontaneously evolves to a desirable (rather than a failed) state. Second, I will show that nonlinearity in the form of time-varying dynamics unexpectedly makes temporal networks easier to control than their static counterparts, with the former enjoying dramatic and simultaneous reductions in all costs of control. This is true despite the fact that temporality tends to fragment a network's structure, disrupting the paths that allow the directly-controlled or ``driver'' nodes to communicate with the rest of the network. Taken together, these studies shed new light on the crucial role of nonlinearity in network control, and provide support to the idea we can control nonlinearity, rather than letting nonlinearity control us.

  15. Information spread in networks: Games, optimal control, and stabilization

    NASA Astrophysics Data System (ADS)

    Khanafer, Ali

    This thesis focuses on designing efficient mechanisms for controlling information spread in networks. We consider two models for information spread. The first one is the well-known distributed averaging dynamics. The second model is a nonlinear one that describes virus spread in computer and biological networks. We seek to design optimal, robust, and stabilizing controllers under practical constraints. For distributed averaging networks, we study the interaction between a network designer and an adversary. We consider two types of attacks on the network. In Attack-I, the adversary strategically disconnects a set of links to prevent the nodes from reaching consensus. Meanwhile, the network designer assists the nodes in reaching consensus by changing the weights of a limited number of links in the network. We formulate two problems to describe this competition where the order in which the players act is reversed in the two problems. Although the canonical equations provided by the Pontryagin's Maximum Principle (MP) seem to be intractable, we provide an alternative characterization for the optimal strategies that makes connection to potential theory. Further, we provide a sufficient condition for the existence of a saddle-point equilibrium (SPE) for the underlying zero-sum game. In Attack-II, the designer and the adversary are both capable of altering the measurements of all nodes in the network by injecting global signals. We impose two constraints on both players: a power constraint and an energy constraint. We assume that the available energy to each player is not sufficient to operate at maximum power throughout the horizon of the game. We show the existence of an SPE and derive the optimal strategies in closed form for this attack scenario. As an alternative to the "network designer vs. adversary" framework, we investigate the possibility of stabilizing unknown network diffusion processes using a distributed mechanism, where the uncertainty is due to an attack

  16. Resilient distributed control in the presence of misbehaving agents in networked control systems.

    PubMed

    Zeng, Wente; Chow, Mo-Yuen

    2014-11-01

    In this paper, we study the problem of reaching a consensus among all the agents in the networked control systems (NCS) in the presence of misbehaving agents. A reputation-based resilient distributed control algorithm is first proposed for the leader-follower consensus network. The proposed algorithm embeds a resilience mechanism that includes four phases (detection, mitigation, identification, and update), into the control process in a distributed manner. At each phase, every agent only uses local and one-hop neighbors' information to identify and isolate the misbehaving agents, and even compensate their effect on the system. We then extend the proposed algorithm to the leaderless consensus network by introducing and adding two recovery schemes (rollback and excitation recovery) into the current framework to guarantee the accurate convergence of the well-behaving agents in NCS. The effectiveness of the proposed method is demonstrated through case studies in multirobot formation control and wireless sensor networks.

  17. Onboard connectivity network for command-and-control aircraft

    NASA Astrophysics Data System (ADS)

    Artz, Timothy J.

    1993-02-01

    Command and control (C2) aircraft are host to an array of communications, information processing, and electronic control systems. The previous method of interconnecting this equipment involves point-to-point wiring harnesses between devices. A fiber optic broadband bus can be used to improve this situation by consolidating equipment connections on a shared medium. This network, known as the Onboard Connectivity Network (OCN), is being prototypes for application on the U.S. Government's Special Air Mission aircraft. Significant weight reduction and simplified future systems integration are the primary benefits of the OCN. The OCN design integrates voice, data, control, and video communications on a 3GHZ single mode fiber backbone. Communications within the aircraft use 500 MHz coaxial cable subnetworks connected to the backbone. The entire network is a dual redundant system for enhanced reliability. Node topologies are based on VMEbus to encourage use of commercial products and facilitate future evolution of the backbone topology. Network encryption technologies are being developed for OCN communications security. Automated workstations will be implemented to control and switch communications assets and to provide a technical control, test, and monitoring function.

  18. Can We Control Contaminant Transport In Hydrologic Networks? Application Of Control Theory Concepts To Watershed Management

    NASA Astrophysics Data System (ADS)

    Yeghiazarian, L.; Riasi, M. S.

    2016-12-01

    Although controlling the level of contamination everywhere in the surface water network may not be feasible, it is vital to maintain safe water quality levels in specific areas, e.g. recreational waters. The question then is "what is the most efficient way to fully/partially control water quality in surface water networks?". This can be posed as a control problem where the goal is to efficiently drive the system to a desired state by manipulating few input variables. Such problems reduce to (1) finding the best control locations in the network to influence the state of the system; and (2) choosing the time-variant inputs at the control locations to achieve the desired state of the system with minimum effort. We demonstrate that the optimal solution to control the level of contamination in the network can be found through application of control theory concepts to transport in dendritic surface water networks.

  19. Benchmarking Measures of Network Controllability on Canonical Graph Models

    NASA Astrophysics Data System (ADS)

    Wu-Yan, Elena; Betzel, Richard F.; Tang, Evelyn; Gu, Shi; Pasqualetti, Fabio; Bassett, Danielle S.

    2018-03-01

    The control of networked dynamical systems opens the possibility for new discoveries and therapies in systems biology and neuroscience. Recent theoretical advances provide candidate mechanisms by which a system can be driven from one pre-specified state to another, and computational approaches provide tools to test those mechanisms in real-world systems. Despite already having been applied to study network systems in biology and neuroscience, the practical performance of these tools and associated measures on simple networks with pre-specified structure has yet to be assessed. Here, we study the behavior of four control metrics (global, average, modal, and boundary controllability) on eight canonical graphs (including Erdős-Rényi, regular, small-world, random geometric, Barábasi-Albert preferential attachment, and several modular networks) with different edge weighting schemes (Gaussian, power-law, and two nonparametric distributions from brain networks, as examples of real-world systems). We observe that differences in global controllability across graph models are more salient when edge weight distributions are heavy-tailed as opposed to normal. In contrast, differences in average, modal, and boundary controllability across graph models (as well as across nodes in the graph) are more salient when edge weight distributions are less heavy-tailed. Across graph models and edge weighting schemes, average and modal controllability are negatively correlated with one another across nodes; yet, across graph instances, the relation between average and modal controllability can be positive, negative, or nonsignificant. Collectively, these findings demonstrate that controllability statistics (and their relations) differ across graphs with different topologies and that these differences can be muted or accentuated by differences in the edge weight distributions. More generally, our numerical studies motivate future analytical efforts to better understand the mathematical

  20. Neural-Network Control Of Prosthetic And Robotic Hands

    NASA Technical Reports Server (NTRS)

    Buckley, Theresa M.

    1991-01-01

    Electronic neural networks proposed for use in controlling robotic and prosthetic hands and exoskeletal or glovelike electromechanical devices aiding intact but nonfunctional hands. Specific to patient, who activates grasping motion by voice command, by mechanical switch, or by myoelectric impulse. Patient retains higher-level control, while lower-level control provided by neural network analogous to that of miniature brain. During training, patient teaches miniature brain to perform specialized, anthropomorphic movements unique to himself or herself.

  1. Distributed Interplanetary Delay/Disruption Tolerant Network (DTN) Monitor and Control System

    NASA Technical Reports Server (NTRS)

    Wang, Shin-Ywan

    2012-01-01

    The main purpose of Distributed interplanetary Delay Tolerant Network Monitor and Control System as a DTN system network management implementation in JPL is defined to provide methods and tools that can monitor the DTN operation status, detect and resolve DTN operation failures in some automated style while either space network or some heterogeneous network is infused with DTN capability. In this paper, "DTN Monitor and Control system in Deep Space Network (DSN)" exemplifies a case how DTN Monitor and Control system can be adapted into a space network as it is DTN enabled.

  2. Chaotification of complex networks with impulsive control.

    PubMed

    Guan, Zhi-Hong; Liu, Feng; Li, Juan; Wang, Yan-Wu

    2012-06-01

    This paper investigates the chaotification problem of complex dynamical networks (CDN) with impulsive control. Both the discrete and continuous cases are studied. The method is presented to drive all states of every node in CDN to chaos. The proposed impulsive control strategy is effective for both the originally stable and unstable CDN. The upper bound of the impulse intervals for originally stable networks is derived. Finally, the effectiveness of the theoretical results is verified by numerical examples.

  3. Software-defined networking control plane for seamless integration of multiple silicon photonic switches in Datacom networks.

    PubMed

    Shen, Yiwen; Hattink, Maarten H N; Samadi, Payman; Cheng, Qixiang; Hu, Ziyiz; Gazman, Alexander; Bergman, Keren

    2018-04-16

    Silicon photonics based switches offer an effective option for the delivery of dynamic bandwidth for future large-scale Datacom systems while maintaining scalable energy efficiency. The integration of a silicon photonics-based optical switching fabric within electronic Datacom architectures requires novel network topologies and arbitration strategies to effectively manage the active elements in the network. We present a scalable software-defined networking control plane to integrate silicon photonic based switches with conventional Ethernet or InfiniBand networks. Our software-defined control plane manages both electronic packet switches and multiple silicon photonic switches for simultaneous packet and circuit switching. We built an experimental Dragonfly network testbed with 16 electronic packet switches and 2 silicon photonic switches to evaluate our control plane. Observed latencies occupied by each step of the switching procedure demonstrate a total of 344 µs control plane latency for data-center and high performance computing platforms.

  4. A Hybrid Authentication and Authorization Process for Control System Networks

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

    Manz, David O.; Edgar, Thomas W.; Fink, Glenn A.

    2010-08-25

    Convergence of control system and IT networks require that security, privacy, and trust be addressed. Trust management continues to plague traditional IT managers and is even more complex when extended into control system networks, with potentially millions of entities, a mission that requires 100% availability. Yet these very networks necessitate a trusted secure environment where controllers and managers can be assured that the systems are secure and functioning properly. We propose a hybrid authentication management protocol that addresses the unique issues inherent within control system networks, while leveraging the considerable research and momentum in existing IT authentication schemes. Our hybridmore » authentication protocol for control systems provides end device to end device authentication within a remote station and between remote stations and control centers. Additionally, the hybrid protocol is failsafe and will not interrupt communication or control of vital systems in a network partition or device failure. Finally, the hybrid protocol is resilient to transitory link loss and can operate in an island mode until connectivity is reestablished.« less

  5. Transcriptional network control of normal and leukaemic haematopoiesis

    PubMed Central

    Sive, Jonathan I.; Göttgens, Berthold

    2014-01-01

    Transcription factors (TFs) play a key role in determining the gene expression profiles of stem/progenitor cells, and defining their potential to differentiate into mature cell lineages. TF interactions within gene-regulatory networks are vital to these processes, and dysregulation of these networks by TF overexpression, deletion or abnormal gene fusions have been shown to cause malignancy. While investigation of these processes remains a challenge, advances in genome-wide technologies and growing interactions between laboratory and computational science are starting to produce increasingly accurate network models. The haematopoietic system provides an attractive experimental system to elucidate gene regulatory mechanisms, and allows experimental investigation of both normal and dysregulated networks. In this review we examine the principles of TF-controlled gene regulatory networks and the key experimental techniques used to investigate them. We look in detail at examples of how these approaches can be used to dissect out the regulatory mechanisms controlling normal haematopoiesis, as well as the dysregulated networks associated with haematological malignancies. PMID:25014893

  6. Controlled Electron Injection into Plasma Accelerators and SpaceCharge Estimates

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

    Fubiani, Gwenael G.J.

    2005-09-01

    Plasma based accelerators are capable of producing electron sources which are ultra-compact (a few microns) and high energies (up to hundreds of MeVs) in much shorter distances than conventional accelerators. This is due to the large longitudinal electric field that can be excited without the limitation of breakdown as in RF structures.The characteristic scale length of the accelerating field is the plasma wavelength and for typical densities ranging from 10 18 - 10 19 cm -3, the accelerating fields and scale length can hence be on the order of 10-100GV/m and 10-40 μm, respectively. The production of quasimonoenergetic beams wasmore » recently obtained in a regime relying on self-trapping of background plasma electrons, using a single laser pulse for wakefield generation. In this dissertation, we study the controlled injection via the beating of two lasers (the pump laser pulse creating the plasma wave and a second beam being propagated in opposite direction) which induce a localized injection of background plasma electrons. The aim of this dissertation is to describe in detail the physics of optical injection using two lasers, the characteristics of the electron beams produced (the micrometer scale plasma wavelength can result in femtosecond and even attosecond bunches) as well as a concise estimate of the effects of space charge on the dynamics of an ultra-dense electron bunch with a large energy spread.« less

  7. Energy management and multi-layer control of networked microgrids

    NASA Astrophysics Data System (ADS)

    Zamora, Ramon

    Networked microgrids is a group of neighboring microgrids that has ability to interchange power when required in order to increase reliability and resiliency. Networked microgrid can operate in different possible configurations including: islanded microgrid, a grid-connected microgrid without a tie-line converter, a grid-connected microgrid with a tie-line converter, and networked microgrids. These possible configurations and specific characteristics of renewable energy offer challenges in designing control and management algorithms for voltage, frequency and power in all possible operating scenarios. In this work, control algorithm is designed based on large-signal model that enables microgrid to operate in wide range of operating points. A combination between PI controller and feed-forward measured system responses will compensate for the changes in operating points. The control architecture developed in this work has multi-layers and the outer layer is slower than the inner layer in time response. The main responsibility of the designed controls are to regulate voltage magnitude and frequency, as well as output power of the DG(s). These local controls also integrate with a microgrid level energy management system or microgrid central controller (MGCC) for power and energy balance for. the entire microgrid in islanded, grid-connected, or networked microgid mode. The MGCC is responsible to coordinate the lower level controls to have reliable and resilient operation. In case of communication network failure, the decentralized energy management will operate locally and will activate droop control. Simulation results indicate the superiority of designed control algorithms compared to existing ones.

  8. Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks

    NASA Technical Reports Server (NTRS)

    Jorgensen, Charles C.

    1997-01-01

    A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNS) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off- nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear Virtual Reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence.

  9. Effect of edge pruning on structural controllability and observability of complex networks

    PubMed Central

    Mengiste, Simachew Abebe; Aertsen, Ad; Kumar, Arvind

    2015-01-01

    Controllability and observability of complex systems are vital concepts in many fields of science. The network structure of the system plays a crucial role in determining its controllability and observability. Because most naturally occurring complex systems show dynamic changes in their network connectivity, it is important to understand how perturbations in the connectivity affect the controllability of the system. To this end, we studied the control structure of different types of artificial, social and biological neuronal networks (BNN) as their connections were progressively pruned using four different pruning strategies. We show that the BNNs are more similar to scale-free networks than to small-world networks, when comparing the robustness of their control structure to structural perturbations. We introduce a new graph descriptor, ‘the cardinality curve’, to quantify the robustness of the control structure of a network to progressive edge pruning. Knowing the susceptibility of control structures to different pruning methods could help design strategies to destroy the control structures of dangerous networks such as epidemic networks. On the other hand, it could help make useful networks more resistant to edge attacks. PMID:26674854

  10. Video-based peer feedback through social networking for robotic surgery simulation: a multicenter randomized controlled trial.

    PubMed

    Carter, Stacey C; Chiang, Alexander; Shah, Galaxy; Kwan, Lorna; Montgomery, Jeffrey S; Karam, Amer; Tarnay, Christopher; Guru, Khurshid A; Hu, Jim C

    2015-05-01

    To examine the feasibility and outcomes of video-based peer feedback through social networking to facilitate robotic surgical skill acquisition. The acquisition of surgical skills may be challenging for novel techniques and/or those with prolonged learning curves. Randomized controlled trial involving 41 resident physicians performing the Tubes (Da Vinci Intuitive Surgical, Sunnyvale, CA) simulator exercise with versus without peer feedback of video-recorded performance through a social networking Web page. Data collected included simulator exercise score, time to completion, and comfort and satisfaction with robotic surgery simulation. There were no baseline differences between the intervention group (n = 20) and controls (n = 21). The intervention group showed improvement in mean scores from session 1 to sessions 2 and 3 (60.7 vs 75.5, P < 0.001, and 60.7 vs 80.1, P < 0.001, respectively). The intervention group scored significantly higher than controls at sessions 2 and 3 (75.5 vs 59.6, P = 0.009, and 80.1 vs 65.9, P = 0.019, respectively). The mean time (seconds) to complete the task was shorter for the intervention group than for controls during sessions 2 and 3 (217.4 vs 279.0, P = 0.004, and 201.4 vs 261.9, P = 0.006, respectively). At the study conclusion, feedback subjects were more comfortable with robotic surgery than controls (90% vs 62%, P = 0.021) and expressed greater satisfaction with the learning experience (100% vs 67%, P = 0.014). Of the intervention subjects, 85% found that peer feedback was useful and 100% found it effective. Video-based peer feedback through social networking appears to be an effective paradigm for surgical education and accelerates the robotic surgery learning curve during simulation.

  11. Distributed Coordinated Control of Large-Scale Nonlinear Networks

    DOE PAGES

    Kundu, Soumya; Anghel, Marian

    2015-11-08

    We provide a distributed coordinated approach to the stability analysis and control design of largescale nonlinear dynamical systems by using a vector Lyapunov functions approach. In this formulation the large-scale system is decomposed into a network of interacting subsystems and the stability of the system is analyzed through a comparison system. However finding such comparison system is not trivial. In this work, we propose a sum-of-squares based completely decentralized approach for computing the comparison systems for networks of nonlinear systems. Moreover, based on the comparison systems, we introduce a distributed optimal control strategy in which the individual subsystems (agents) coordinatemore » with their immediate neighbors to design local control policies that can exponentially stabilize the full system under initial disturbances.We illustrate the control algorithm on a network of interacting Van der Pol systems.« less

  12. Comparing joint kinematics and center of mass acceleration as feedback for control of standing balance by functional neuromuscular stimulation.

    PubMed

    Nataraj, Raviraj; Audu, Musa L; Triolo, Ronald J

    2012-05-06

    The purpose of this study was to determine the comparative effectiveness of feedback control systems for maintaining standing balance based on joint kinematics or total body center of mass (COM) acceleration, and assess their clinical practicality for standing neuroprostheses after spinal cord injury (SCI). In simulation, controller performance was measured according to the upper extremity effort required to stabilize a three-dimensional model of bipedal standing against a variety of postural disturbances. Three cases were investigated: proportional-derivative control based on joint kinematics alone, COM acceleration feedback alone, and combined joint kinematics and COM acceleration feedback. Additionally, pilot data was collected during external perturbations of an individual with SCI standing with functional neuromuscular stimulation (FNS), and the resulting joint kinematics and COM acceleration data was analyzed. Compared to the baseline case of maximal constant muscle excitations, the three control systems reduced the mean upper extremity loading by 51%, 43% and 56%, respectively against external force-pulse perturbations. Controller robustness was defined as the degradation in performance with increasing levels of input errors expected with clinical deployment of sensor-based feedback. At error levels typical for body-mounted inertial sensors, performance degradation due to sensor noise and placement were negligible. However, at typical tracking error levels, performance could degrade as much as 86% for joint kinematics feedback and 35% for COM acceleration feedback. Pilot data indicated that COM acceleration could be estimated with a few well-placed sensors and efficiently captures information related to movement synergies observed during perturbed bipedal standing following SCI. Overall, COM acceleration feedback may be a more feasible solution for control of standing with FNS given its superior robustness and small number of inputs required.

  13. GMPLS-based control plane for optical networks: early implementation experience

    NASA Astrophysics Data System (ADS)

    Liu, Hang; Pendarakis, Dimitrios; Komaee, Nooshin; Saha, Debanjan

    2002-07-01

    Generalized Multi-Protocol Label Switching (GMPLS) extends MPLS signaling and Internet routing protocols to provide a scalable, interoperable, distributed control plane, which is applicable to multiple network technologies such as optical cross connects (OXCs), photonic switches, IP routers, ATM switches, SONET and DWDM systems. It is intended to facilitate automatic service provisioning and dynamic neighbor and topology discovery across multi-vendor intelligent transport networks, as well as their clients. Efforts to standardize such a distributed common control plane have reached various stages in several bodies such as the IETF, ITU and OIF. This paper describes the design considerations and architecture of a GMPLS-based control plane that we have prototyped for core optical networks. Functional components of GMPLS signaling and routing are integrated in this architecture with an application layer controller module. Various requirements including bandwidth, network protection and survivability, traffic engineering, optimal utilization of network resources, and etc. are taken into consideration during path computation and provisioning. Initial experiments with our prototype demonstrate the feasibility and main benefits of GMPLS as a distributed control plane for core optical networks. In addition to such feasibility results, actual adoption and deployment of GMPLS as a common control plane for intelligent transport networks will depend on the successful completion of relevant standardization activities, extensive interoperability testing as well as the strengthening of appropriate business drivers.

  14. Neural network application to aircraft control system design

    NASA Technical Reports Server (NTRS)

    Troudet, Terry; Garg, Sanjay; Merrill, Walter C.

    1991-01-01

    The feasibility of using artificial neural networks as control systems for modern, complex aerospace vehicles is investigated via an example aircraft control design study. The problem considered is that of designing a controller for an integrated airframe/propulsion longitudinal dynamics model of a modern fighter aircraft to provide independent control of pitch rate and airspeed responses to pilot command inputs. An explicit model following controller using H infinity control design techniques is first designed to gain insight into the control problem as well as to provide a baseline for evaluation of the neurocontroller. Using the model of the desired dynamics as a command generator, a multilayer feedforward neural network is trained to control the vehicle model within the physical limitations of the actuator dynamics. This is achieved by minimizing an objective function which is a weighted sum of tracking errors and control input commands and rates. To gain insight in the neurocontrol, linearized representations of the nonlinear neurocontroller are analyzed along a commanded trajectory. Linear robustness analysis tools are then applied to the linearized neurocontroller models and to the baseline H infinity based controller. Future areas of research are identified to enhance the practical applicability of neural networks to flight control design.

  15. Neural network application to aircraft control system design

    NASA Technical Reports Server (NTRS)

    Troudet, Terry; Garg, Sanjay; Merrill, Walter C.

    1991-01-01

    The feasibility of using artificial neural network as control systems for modern, complex aerospace vehicles is investigated via an example aircraft control design study. The problem considered is that of designing a controller for an integrated airframe/propulsion longitudinal dynamics model of a modern fighter aircraft to provide independent control of pitch rate and airspeed responses to pilot command inputs. An explicit model following controller using H infinity control design techniques is first designed to gain insight into the control problem as well as to provide a baseline for evaluation of the neurocontroller. Using the model of the desired dynamics as a command generator, a multilayer feedforward neural network is trained to control the vehicle model within the physical limitations of the actuator dynamics. This is achieved by minimizing an objective function which is a weighted sum of tracking errors and control input commands and rates. To gain insight in the neurocontrol, linearized representations of the nonlinear neurocontroller are analyzed along a commanded trajectory. Linear robustness analysis tools are then applied to the linearized neurocontroller models and to the baseline H infinity based controller. Future areas of research identified to enhance the practical applicability of neural networks to flight control design.

  16. LMI designmethod for networked-based PID control

    NASA Astrophysics Data System (ADS)

    Souza, Fernando de Oliveira; Mozelli, Leonardo Amaral; de Oliveira, Maurício Carvalho; Palhares, Reinaldo Martinez

    2016-10-01

    In this paper, we propose a methodology for the design of networked PID controllers for second-order delayed processes using linear matrix inequalities. The proposed procedure takes into account time-varying delay on the plant, time-varying delays induced by the network and packed dropouts. The design is carried on entirely using a continuous-time model of the closed-loop system where time-varying delays are used to represent sampling and holding occurring in a discrete-time digital PID controller.

  17. Active Control of Wind-Tunnel Model Aeroelastic Response Using Neural Networks

    NASA Technical Reports Server (NTRS)

    Scott, Robert C.

    2000-01-01

    NASA Langley Research Center, Hampton, VA 23681 Under a joint research and development effort conducted by the National Aeronautics and Space Administration and The Boeing Company (formerly McDonnell Douglas) three neural-network based control systems were developed and tested. The control systems were experimentally evaluated using a transonic wind-tunnel model in the Langley Transonic Dynamics Tunnel. One system used a neural network to schedule flutter suppression control laws, another employed a neural network in a predictive control scheme, and the third employed a neural network in an inverse model control scheme. All three of these control schemes successfully suppressed flutter to or near the limits of the testing apparatus, and represent the first experimental applications of neural networks to flutter suppression. This paper will summarize the findings of this project.

  18. Neural Network Control of a Magnetically Suspended Rotor System

    NASA Technical Reports Server (NTRS)

    Choi, Benjamin; Brown, Gerald; Johnson, Dexter

    1997-01-01

    Abstract Magnetic bearings offer significant advantages because of their noncontact operation, which can reduce maintenance. Higher speeds, no friction, no lubrication, weight reduction, precise position control, and active damping make them far superior to conventional contact bearings. However, there are technical barriers that limit the application of this technology in industry. One of them is the need for a nonlinear controller that can overcome the system nonlinearity and uncertainty inherent in magnetic bearings. This paper discusses the use of a neural network as a nonlinear controller that circumvents system nonlinearity. A neural network controller was well trained and successfully demonstrated on a small magnetic bearing rig. This work demonstrated the feasibility of using a neural network to control nonlinear magnetic bearings and systems with unknown dynamics.

  19. A MOLA-controlled RAND-USGS Control Network for Mars

    NASA Technical Reports Server (NTRS)

    Archinal, B. A.; Colvin, T. R.; Davies, M. E.; Kirk, R. L.; Duxbury, T. C.; Lee, E. M.; Cook, D.; Gitlin, A. R.

    2002-01-01

    We are undertaking, in support of the Mars Digital Image Mosaic (MDIM) 2.1, many improvements in the RAND-USGS photogrammetric control network for Mars, primarily involving the use of Mars Orbiter Laser Altimeter (MOLA)-derived radii and DIMs to improve control point absolute radii and horizontal positions. Additional information is contained in the original extended abstract.

  20. Control of coupled oscillator networks with application to microgrid technologies.

    PubMed

    Skardal, Per Sebastian; Arenas, Alex

    2015-08-01

    The control of complex systems and network-coupled dynamical systems is a topic of vital theoretical importance in mathematics and physics with a wide range of applications in engineering and various other sciences. Motivated by recent research into smart grid technologies, we study the control of synchronization and consider the important case of networks of coupled phase oscillators with nonlinear interactions-a paradigmatic example that has guided our understanding of self-organization for decades. We develop a method for control based on identifying and stabilizing problematic oscillators, resulting in a stable spectrum of eigenvalues, and in turn a linearly stable synchronized state. The amount of control, that is, number of oscillators, required to stabilize the network is primarily dictated by the coupling strength, dynamical heterogeneity, and mean degree of the network, and depends little on the structural heterogeneity of the network itself.

  1. Control of coupled oscillator networks with application to microgrid technologies

    NASA Astrophysics Data System (ADS)

    Arenas, Alex

    The control of complex systems and network-coupled dynamical systems is a topic of vital theoretical importance in mathematics and physics with a wide range of applications in engineering and various other sciences. Motivated by recent research into smart grid technologies, we study the control of synchronization and consider the important case of networks of coupled phase oscillators with nonlinear interactions-a paradigmatic example that has guided our understanding of self-organization for decades. We develop a method for control based on identifying and stabilizing problematic oscillators, resulting in a stable spectrum of eigenvalues, and in turn a linearly stable syn- chronized state. The amount of control, that is, number of oscillators, required to stabilize the network is primarily dictated by the coupling strength, dynamical heterogeneity, and mean degree of the network, and depends little on the structural heterogeneity of the network itself.

  2. Emulating RRTMG Radiation with Deep Neural Networks for the Accelerated Model for Climate and Energy

    NASA Astrophysics Data System (ADS)

    Pal, A.; Norman, M. R.

    2017-12-01

    The RRTMG radiation scheme in the Accelerated Model for Climate and Energy Multi-scale Model Framework (ACME-MMF), is a bottleneck and consumes approximately 50% of the computational time. To simulate a case using RRTMG radiation scheme in ACME-MMF with high throughput and high resolution will therefore require a speed-up of this calculation while retaining physical fidelity. In this study, RRTMG radiation is emulated with Deep Neural Networks (DNNs). The first step towards this goal is to run a case with ACME-MMF and generate input data sets for the DNNs. A principal component analysis of these input data sets are carried out. Artificial data sets are created using the previous data sets to cover a wider space. These artificial data sets are used in a standalone RRTMG radiation scheme to generate outputs in a cost effective manner. These input-output pairs are used to train multiple architectures DNNs(1). Another DNN(2) is trained using the inputs to predict the error. A reverse emulation is trained to map the output to input. An error controlled code is developed with the two DNNs (1 and 2) and will determine when/if the original parameterization needs to be used.

  3. The 1982 control network of Mars

    NASA Technical Reports Server (NTRS)

    Davies, M. E.; Katayama, F. Y.

    1983-01-01

    Attention is given to a planet-wide control network of Mars that was computed in September 1982 using a large single-block analytical triangulation with 47,524 measurements of 6853 control points on 1054 Mariner 9 and 757 Viking pictures. In all, 19,139 normal equations were solved, with a resulting standard error of measurement of 18.06 microns. The control points identified by name and letter designation are given, as are the aerographic coordinates of the control points. In addition, the coordinates of the Viking I lander site are given: latitude, 22.480 deg; longitude, 47.962 deg (radius, 3389.32 km). This study expands and updates the previously published network (1978). It is noted that the computation differs in many respects from standard aerial mapping photogrammetric practice. In comparison with aerial mapping photography, the television formats are small and the focal lengths are long; stereo coverage is rare, the scale of the pictures varies greatly, and the residual camera distortions are large.

  4. Hexacopter trajectory control using a neural network

    NASA Astrophysics Data System (ADS)

    Artale, V.; Collotta, M.; Pau, G.; Ricciardello, A.

    2013-10-01

    The modern flight control systems are complex due to their non-linear nature. In fact, modern aerospace vehicles are expected to have non-conventional flight envelopes and, then, they must guarantee a high level of robustness and adaptability in order to operate in uncertain environments. Neural Networks (NN), with real-time learning capability, for flight control can be used in applications with manned or unmanned aerial vehicles. Indeed, using proven lower level control algorithms with adaptive elements that exhibit long term learning could help in achieving better adaptation performance while performing aggressive maneuvers. In this paper we show a mathematical modeling and a Neural Network for a hexacopter dynamics in order to develop proper methods for stabilization and trajectory control.

  5. Neural Networks for Signal Processing and Control

    NASA Astrophysics Data System (ADS)

    Hesselroth, Ted Daniel

    Neural networks are developed for controlling a robot-arm and camera system and for processing images. The networks are based upon computational schemes that may be found in the brain. In the first network, a neural map algorithm is employed to control a five-joint pneumatic robot arm and gripper through feedback from two video cameras. The pneumatically driven robot arm employed shares essential mechanical characteristics with skeletal muscle systems. To control the position of the arm, 200 neurons formed a network representing the three-dimensional workspace embedded in a four-dimensional system of coordinates from the two cameras, and learned a set of pressures corresponding to the end effector positions, as well as a set of Jacobian matrices for interpolating between these positions. Because of the properties of the rubber-tube actuators of the arm, the position as a function of supplied pressure is nonlinear, nonseparable, and exhibits hysteresis. Nevertheless, through the neural network learning algorithm the position could be controlled to an accuracy of about one pixel (~3 mm) after two hundred learning steps. Applications of repeated corrections in each step via the Jacobian matrices leads to a very robust control algorithm since the Jacobians learned by the network have to satisfy the weak requirement that they yield a reduction of the distance between gripper and target. The second network is proposed as a model for the mammalian vision system in which backward connections from the primary visual cortex (V1) to the lateral geniculate nucleus play a key role. The application of hebbian learning to the forward and backward connections causes the formation of receptive fields which are sensitive to edges, bars, and spatial frequencies of preferred orientations. The receptive fields are learned in such a way as to maximize the rate of transfer of information from the LGN to V1. Orientational preferences are organized into a feature map in the primary visual

  6. Autonomous control of production networks using a pheromone approach

    NASA Astrophysics Data System (ADS)

    Armbruster, D.; de Beer, C.; Freitag, M.; Jagalski, T.; Ringhofer, C.

    2006-04-01

    The flow of parts through a production network is usually pre-planned by a central control system. Such central control fails in presence of highly fluctuating demand and/or unforeseen disturbances. To manage such dynamic networks according to low work-in-progress and short throughput times, an autonomous control approach is proposed. Autonomous control means a decentralized routing of the autonomous parts themselves. The parts’ decisions base on backward propagated information about the throughput times of finished parts for different routes. So, routes with shorter throughput times attract parts to use this route again. This process can be compared to ants leaving pheromones on their way to communicate with following ants. The paper focuses on a mathematical description of such autonomously controlled production networks. A fluid model with limited service rates in a general network topology is derived and compared to a discrete-event simulation model. Whereas the discrete-event simulation of production networks is straightforward, the formulation of the addressed scenario in terms of a fluid model is challenging. Here it is shown, how several problems in a fluid model formulation (e.g. discontinuities) can be handled mathematically. Finally, some simulation results for the pheromone-based control with both the discrete-event simulation model and the fluid model are presented for a time-dependent influx.

  7. Cherenkov Radiation Control via Self-accelerating Wave-packets.

    PubMed

    Hu, Yi; Li, Zhili; Wetzel, Benjamin; Morandotti, Roberto; Chen, Zhigang; Xu, Jingjun

    2017-08-18

    Cherenkov radiation is a ubiquitous phenomenon in nature. It describes electromagnetic radiation from a charged particle moving in a medium with a uniform velocity larger than the phase velocity of light in the same medium. Such a picture is typically adopted in the investigation of traditional Cherenkov radiation as well as its counterparts in different branches of physics, including nonlinear optics, spintronics and plasmonics. In these cases, the radiation emitted spreads along a "cone", making it impractical for most applications. Here, we employ a self-accelerating optical pump wave-packet to demonstrate controlled shaping of one type of generalized Cherenkov radiation - dispersive waves in optical fibers. We show that, by tuning the parameters of the wave-packet, the emitted waves can be judiciously compressed and focused at desired locations, paving the way to such control in any physical system.

  8. ACTS TDMA network control. [Advanced Communication Technology Satellite

    NASA Technical Reports Server (NTRS)

    Inukai, T.; Campanella, S. J.

    1984-01-01

    This paper presents basic network control concepts for the Advanced Communications Technology Satellite (ACTS) System. Two experimental systems, called the low-burst-rate and high-burst-rate systems, along with ACTS ground system features, are described. The network control issues addressed include frame structures, acquisition and synchronization procedures, coordinated station burst-time plan and satellite-time plan changes, on-board clock control based on ground drift measurements, rain fade control by means of adaptive forward-error-correction (FEC) coding and transmit power augmentation, and reassignment of channel capacities on demand. The NASA ground system, which includes a primary station, diversity station, and master control station, is also described.

  9. Network Controllability in the Inferior Frontal Gyrus Relates to Controlled Language Variability and Susceptibility to TMS.

    PubMed

    Medaglia, John D; Harvey, Denise Y; White, Nicole; Kelkar, Apoorva; Zimmerman, Jared; Bassett, Danielle S; Hamilton, Roy H

    2018-06-08

    In language production, humans are confronted with considerable word selection demands. Often, we must select a word from among similar, acceptable, and competing alternative words in order to construct a sentence that conveys an intended meaning. In recent years, the left inferior frontal gyrus (LIFG) has been identified as critical to this ability. Despite a recent emphasis on network approaches to understanding language, how the LIFG interacts with the brain's complex networks to facilitate controlled language performance remains unknown. Here, we take a novel approach to understand word selection as a network control process in the brain. Using an anatomical brain network derived from high-resolution diffusion spectrum imaging (DSI), we computed network controllability underlying the site of transcranial magnetic stimulation in the LIFG between administrations of language tasks that vary in response (cognitive control) demands: open-response (word generation) vs. closed-response (number naming) tasks. We find that a statistic that quantifies the LIFG's theoretically predicted control of communication across modules in the human connectome explains TMS-induced changes in open-response language task performance only. Moreover, we find that a statistic that quantifies the LIFG's theoretically predicted control of difficult-to-reach states explains vulnerability to TMS in the closed-ended (but not open-ended) response task. These findings establish a link between network controllability, cognitive function, and TMS effects. SIGNIFICANCE STATEMENT This work illustrates that network control statistics applied to anatomical connectivity data demonstrate relationships with cognitive variability during controlled language tasks and TMS effects. Copyright © 2018 the authors.

  10. Developing Flexible Networked Lighting Control Systems

    Science.gov Websites

    , Bluetooth, ZigBee and others are increasingly used for building control purposes. Low-cost computation : Bundling digital intelligence at the sensors and lights adds virtually no incremental cost. Coupled with cost. Research Goals and Objectives This project "Developing Flexible, Networked Lighting Control

  11. Centralized and distributed control architectures under Foundation Fieldbus network.

    PubMed

    Persechini, Maria Auxiliadora Muanis; Jota, Fábio Gonçalves

    2013-01-01

    This paper aims at discussing possible automation and control system architectures based on fieldbus networks in which the controllers can be implemented either in a centralized or in a distributed form. An experimental setup is used to demonstrate some of the addressed issues. The control and automation architecture is composed of a supervisory system, a programmable logic controller and various other devices connected to a Foundation Fieldbus H1 network. The procedures used in the network configuration, in the process modelling and in the design and implementation of controllers are described. The specificities of each one of the considered logical organizations are also discussed. Finally, experimental results are analysed using an algorithm for the assessment of control loops to compare the performances between the centralized and the distributed implementations. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

  12. An Artificial Neural Network Control System for Spacecraft Attitude Stabilization

    DTIC Science & Technology

    1990-06-01

    NAVAL POSTGRADUATE SCHOOL Monterey, California ’-DTIC 0 ELECT f NMARO 5 191 N S, U, THESIS B . AN ARTIFICIAL NEURAL NETWORK CONTROL SYSTEM FOR...NO. NO. NO ACCESSION NO 11. TITLE (Include Security Classification) AN ARTIFICIAL NEURAL NETWORK CONTROL SYSTEM FOR SPACECRAFT ATTITUDE STABILIZATION...obsolete a U.S. G v pi.. iim n P.. oiice! toog-eo.5s43 i Approved for public release; distribution is unlimited. AN ARTIFICIAL NEURAL NETWORK CONTROL

  13. Epigenetics and Why Biological Networks are More Controllable than Expected

    NASA Astrophysics Data System (ADS)

    Motter, Adilson

    2013-03-01

    A fundamental property of networks is that perturbations to one node can affect other nodes, potentially causing the entire system to change behavior or fail. In this talk, I will show that it is possible to exploit this same principle to control network behavior. This approach takes advantage of the nonlinear dynamics inherent to real networks, and allows bringing the system to a desired target state even when this state is not directly accessible or the linear counterpart is not controllable. Applications show that this framework permits both reprogramming a network to a desired task as well as rescuing networks from the brink of failure, which I will illustrate through various biological problems. I will also briefly review the progress our group has made over the past 5 years on related control of complex networks in non-biological domains.

  14. Time Course of Brain Network Reconfiguration Supporting Inhibitory Control.

    PubMed

    Popov, Tzvetan; Westner, Britta U; Silton, Rebecca L; Sass, Sarah M; Spielberg, Jeffrey M; Rockstroh, Brigitte; Heller, Wendy; Miller, Gregory A

    2018-05-02

    Hemodynamic research has recently clarified key nodes and links in brain networks implementing inhibitory control. Although fMRI methods are optimized for identifying the structure of brain networks, the relatively slow temporal course of fMRI limits the ability to characterize network operation. The latter is crucial for developing a mechanistic understanding of how brain networks shift dynamically to support inhibitory control. To address this critical gap, we applied spectrally resolved Granger causality (GC) and random forest machine learning tools to human EEG data in two large samples of adults (test sample n = 96, replication sample n = 237, total N = 333, both sexes) who performed a color-word Stroop task. Time-frequency analysis confirmed that recruitment of inhibitory control accompanied by slower behavioral responses was related to changes in theta and alpha/beta power. GC analyses revealed directionally asymmetric exchanges within frontal and between frontal and parietal brain areas: top-down influence of superior frontal gyrus (SFG) over both dorsal ACC (dACC) and inferior frontal gyrus (IFG), dACC control over middle frontal gyrus (MFG), and frontal-parietal exchanges (IFG, precuneus, MFG). Predictive analytics confirmed a combination of behavioral and brain-derived variables as the best set of predictors of inhibitory control demands, with SFG theta bearing higher classification importance than dACC theta and posterior beta tracking the onset of behavioral response. The present results provide mechanistic insight into the biological implementation of a psychological phenomenon: inhibitory control is implemented by dynamic routing processes during which the target response is upregulated via theta-mediated effective connectivity within key PFC nodes and via beta-mediated motor preparation. SIGNIFICANCE STATEMENT Hemodynamic neuroimaging research has recently clarified regional structures in brain networks supporting inhibitory control. However, due to

  15. Transcriptional network control of normal and leukaemic haematopoiesis.

    PubMed

    Sive, Jonathan I; Göttgens, Berthold

    2014-12-10

    Transcription factors (TFs) play a key role in determining the gene expression profiles of stem/progenitor cells, and defining their potential to differentiate into mature cell lineages. TF interactions within gene-regulatory networks are vital to these processes, and dysregulation of these networks by TF overexpression, deletion or abnormal gene fusions have been shown to cause malignancy. While investigation of these processes remains a challenge, advances in genome-wide technologies and growing interactions between laboratory and computational science are starting to produce increasingly accurate network models. The haematopoietic system provides an attractive experimental system to elucidate gene regulatory mechanisms, and allows experimental investigation of both normal and dysregulated networks. In this review we examine the principles of TF-controlled gene regulatory networks and the key experimental techniques used to investigate them. We look in detail at examples of how these approaches can be used to dissect out the regulatory mechanisms controlling normal haematopoiesis, as well as the dysregulated networks associated with haematological malignancies. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

  16. Implementing controlled-unitary operations over the butterfly network

    NASA Astrophysics Data System (ADS)

    Soeda, Akihito; Kinjo, Yoshiyuki; Turner, Peter S.; Murao, Mio

    2014-12-01

    We introduce a multiparty quantum computation task over a network in a situation where the capacities of both the quantum and classical communication channels of the network are limited and a bottleneck occurs. Using a resource setting introduced by Hayashi [1], we present an efficient protocol for performing controlled-unitary operations between two input nodes and two output nodes over the butterfly network, one of the most fundamental networks exhibiting the bottleneck problem. This result opens the possibility of developing a theory of quantum network coding for multiparty quantum computation, whereas the conventional network coding only treats multiparty quantum communication.

  17. Implementing controlled-unitary operations over the butterfly network

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

    Soeda, Akihito; Kinjo, Yoshiyuki; Turner, Peter S.

    2014-12-04

    We introduce a multiparty quantum computation task over a network in a situation where the capacities of both the quantum and classical communication channels of the network are limited and a bottleneck occurs. Using a resource setting introduced by Hayashi [1], we present an efficient protocol for performing controlled-unitary operations between two input nodes and two output nodes over the butterfly network, one of the most fundamental networks exhibiting the bottleneck problem. This result opens the possibility of developing a theory of quantum network coding for multiparty quantum computation, whereas the conventional network coding only treats multiparty quantum communication.

  18. Comparing joint kinematics and center of mass acceleration as feedback for control of standing balance by functional neuromuscular stimulation

    PubMed Central

    2012-01-01

    Background The purpose of this study was to determine the comparative effectiveness of feedback control systems for maintaining standing balance based on joint kinematics or total body center of mass (COM) acceleration, and assess their clinical practicality for standing neuroprostheses after spinal cord injury (SCI). Methods In simulation, controller performance was measured according to the upper extremity effort required to stabilize a three-dimensional model of bipedal standing against a variety of postural disturbances. Three cases were investigated: proportional-derivative control based on joint kinematics alone, COM acceleration feedback alone, and combined joint kinematics and COM acceleration feedback. Additionally, pilot data was collected during external perturbations of an individual with SCI standing with functional neuromuscular stimulation (FNS), and the resulting joint kinematics and COM acceleration data was analyzed. Results Compared to the baseline case of maximal constant muscle excitations, the three control systems reduced the mean upper extremity loading by 51%, 43% and 56%, respectively against external force-pulse perturbations. Controller robustness was defined as the degradation in performance with increasing levels of input errors expected with clinical deployment of sensor-based feedback. At error levels typical for body-mounted inertial sensors, performance degradation due to sensor noise and placement were negligible. However, at typical tracking error levels, performance could degrade as much as 86% for joint kinematics feedback and 35% for COM acceleration feedback. Pilot data indicated that COM acceleration could be estimated with a few well-placed sensors and efficiently captures information related to movement synergies observed during perturbed bipedal standing following SCI. Conclusions Overall, COM acceleration feedback may be a more feasible solution for control of standing with FNS given its superior robustness and small

  19. Control of coupled oscillator networks with application to microgrid technologies

    PubMed Central

    Skardal, Per Sebastian; Arenas, Alex

    2015-01-01

    The control of complex systems and network-coupled dynamical systems is a topic of vital theoretical importance in mathematics and physics with a wide range of applications in engineering and various other sciences. Motivated by recent research into smart grid technologies, we study the control of synchronization and consider the important case of networks of coupled phase oscillators with nonlinear interactions—a paradigmatic example that has guided our understanding of self-organization for decades. We develop a method for control based on identifying and stabilizing problematic oscillators, resulting in a stable spectrum of eigenvalues, and in turn a linearly stable synchronized state. The amount of control, that is, number of oscillators, required to stabilize the network is primarily dictated by the coupling strength, dynamical heterogeneity, and mean degree of the network, and depends little on the structural heterogeneity of the network itself. PMID:26601231

  20. Control of complex networks requires both structure and dynamics

    NASA Astrophysics Data System (ADS)

    Gates, Alexander J.; Rocha, Luis M.

    2016-04-01

    The study of network structure has uncovered signatures of the organization of complex systems. However, there is also a need to understand how to control them; for example, identifying strategies to revert a diseased cell to a healthy state, or a mature cell to a pluripotent state. Two recent methodologies suggest that the controllability of complex systems can be predicted solely from the graph of interactions between variables, without considering their dynamics: structural controllability and minimum dominating sets. We demonstrate that such structure-only methods fail to characterize controllability when dynamics are introduced. We study Boolean network ensembles of network motifs as well as three models of biochemical regulation: the segment polarity network in Drosophila melanogaster, the cell cycle of budding yeast Saccharomyces cerevisiae, and the floral organ arrangement in Arabidopsis thaliana. We demonstrate that structure-only methods both undershoot and overshoot the number and which sets of critical variables best control the dynamics of these models, highlighting the importance of the actual system dynamics in determining control. Our analysis further shows that the logic of automata transition functions, namely how canalizing they are, plays an important role in the extent to which structure predicts dynamics.

  1. Neural Network Model Of The PXIE RFQ Cooling System and Resonant Frequency Response

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

    Edelen, Auralee; Biedron, Sandra; Bowring, Daniel

    2016-06-01

    As part of the PIP-II Injector Experiment (PXIE) accel-erator, a four-vane radio frequency quadrupole (RFQ) accelerates a 30-keV, 1-mA to 10-mA H' ion beam to 2.1 MeV. It is designed to operate at a frequency of 162.5 MHz with arbitrary duty factor, including continuous wave (CW) mode. The resonant frequency is controlled solely by a water-cooling system. We present an initial neural network model of the RFQ frequency response to changes in the cooling system and RF power conditions during pulsed operation. A neural network model will be used in a model predictive control scheme to regulate the resonant frequencymore » of the RFQ.« less

  2. Structure-based control of complex networks with nonlinear dynamics

    NASA Astrophysics Data System (ADS)

    Zanudo, Jorge G. T.; Yang, Gang; Albert, Reka

    What can we learn about controlling a system solely from its underlying network structure? Here we use a framework for control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This feedback-based framework provides realizable node overrides that steer a system towards any of its natural long term dynamic behaviors, regardless of the dynamic details and system parameters. We use this framework on several real networks, identify the topological characteristics that underlie the predicted node overrides, and compare its predictions to those of classical structural control theory. Finally, we demonstrate this framework's applicability in dynamic models of gene regulatory networks and identify nodes whose override is necessary for control in the general case, but not in specific model instances. This work was supported by NSF Grants PHY 1205840 and IIS 1160995. JGTZ is a recipient of a Stand Up To Cancer - The V Foundation Convergence Scholar Award.

  3. Experience with ActiveX control for simple channel access

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

    Timossi, C.; Nishimura, H.; McDonald, J.

    2003-05-15

    Accelerator control system applications at Berkeley Lab's Advanced Light Source (ALS) are typically deployed on operator consoles running Microsoft Windows 2000 and utilize EPICS[2]channel access for data access. In an effort to accommodate the wide variety of Windows based development tools and developers with little experience in network programming, ActiveX controls have been deployed on the operator stations. Use of ActiveX controls for use in the accelerator control environment has been presented previously[1]. Here we report on some of our experiences with the use and development of these controls.

  4. Simultaneous Multislice Echo Planar Imaging With Blipped Controlled Aliasing in Parallel Imaging Results in Higher Acceleration: A Promising Technique for Accelerated Diffusion Tensor Imaging of Skeletal Muscle.

    PubMed

    Filli, Lukas; Piccirelli, Marco; Kenkel, David; Guggenberger, Roman; Andreisek, Gustav; Beck, Thomas; Runge, Val M; Boss, Andreas

    2015-07-01

    The aim of this study was to investigate the feasibility of accelerated diffusion tensor imaging (DTI) of skeletal muscle using echo planar imaging (EPI) applying simultaneous multislice excitation with a blipped controlled aliasing in parallel imaging results in higher acceleration unaliasing technique. After federal ethics board approval, the lower leg muscles of 8 healthy volunteers (mean [SD] age, 29.4 [2.9] years) were examined in a clinical 3-T magnetic resonance scanner using a 15-channel knee coil. The EPI was performed at a b value of 500 s/mm2 without slice acceleration (conventional DTI) as well as with 2-fold and 3-fold acceleration. Fractional anisotropy (FA) and mean diffusivity (MD) were measured in all 3 acquisitions. Fiber tracking performance was compared between the acquisitions regarding the number of tracks, average track length, and anatomical precision using multivariate analysis of variance and Mann-Whitney U tests. Acquisition time was 7:24 minutes for conventional DTI, 3:53 minutes for 2-fold acceleration, and 2:38 minutes for 3-fold acceleration. Overall FA and MD values ranged from 0.220 to 0.378 and 1.595 to 1.829 mm2/s, respectively. Two-fold acceleration yielded similar FA and MD values (P ≥ 0.901) and similar fiber tracking performance compared with conventional DTI. Three-fold acceleration resulted in comparable MD (P = 0.199) but higher FA values (P = 0.006) and significantly impaired fiber tracking in the soleus and tibialis anterior muscles (number of tracks, P < 0.001; anatomical precision, P ≤ 0.005). Simultaneous multislice EPI with blipped controlled aliasing in parallel imaging results in higher acceleration can remarkably reduce acquisition time in DTI of skeletal muscle with similar image quality and quantification accuracy of diffusion parameters. This may increase the clinical applicability of muscle anisotropy measurements.

  5. On neural networks in identification and control of dynamic systems

    NASA Technical Reports Server (NTRS)

    Phan, Minh; Juang, Jer-Nan; Hyland, David C.

    1993-01-01

    This paper presents a discussion of the applicability of neural networks in the identification and control of dynamic systems. Emphasis is placed on the understanding of how the neural networks handle linear systems and how the new approach is related to conventional system identification and control methods. Extensions of the approach to nonlinear systems are then made. The paper explains the fundamental concepts of neural networks in their simplest terms. Among the topics discussed are feed forward and recurrent networks in relation to the standard state-space and observer models, linear and nonlinear auto-regressive models, linear, predictors, one-step ahead control, and model reference adaptive control for linear and nonlinear systems. Numerical examples are presented to illustrate the application of these important concepts.

  6. Structure, function, and control of the human musculoskeletal network

    PubMed Central

    Murphy, Andrew C.; Muldoon, Sarah F.; Baker, David; Lastowka, Adam; Bennett, Brittany; Yang, Muzhi

    2018-01-01

    The human body is a complex organism, the gross mechanical properties of which are enabled by an interconnected musculoskeletal network controlled by the nervous system. The nature of musculoskeletal interconnection facilitates stability, voluntary movement, and robustness to injury. However, a fundamental understanding of this network and its control by neural systems has remained elusive. Here we address this gap in knowledge by utilizing medical databases and mathematical modeling to reveal the organizational structure, predicted function, and neural control of the musculoskeletal system. We constructed a highly simplified whole-body musculoskeletal network in which single muscles connect to multiple bones via both origin and insertion points. We demonstrated that, using this simplified model, a muscle’s role in this network could offer a theoretical prediction of the susceptibility of surrounding components to secondary injury. Finally, we illustrated that sets of muscles cluster into network communities that mimic the organization of control modules in primary motor cortex. This novel formalism for describing interactions between the muscular and skeletal systems serves as a foundation to develop and test therapeutic responses to injury, inspiring future advances in clinical treatments. PMID:29346370

  7. Inferring Network Controls from Topology Using the Chomp Database

    DTIC Science & Technology

    2015-12-03

    AFRL-AFOSR-VA-TR-2016-0033 INFERRING NETWORK CONTROLS FROM TOPOLOGY USING THE CHOMP DATABASE John Harer DUKE UNIVERSITY Final Report 12/03/2015...INFERRING NETWORK CONTROLS FROM TOPOLOGY USING THE CHOMP DATABASE 5a. CONTRACT NUMBER 5b. GRANT NUMBER FA9550-10-1-0436 5c. PROGRAM ELEMENT NUMBER 6...area of Topological Data Analysis (TDA) and it’s application to dynamical systems. The role of this work in the Complex Networks program is based on

  8. Distinct brain networks for adaptive and stable task control in humans

    PubMed Central

    Dosenbach, Nico U. F.; Fair, Damien A.; Miezin, Francis M.; Cohen, Alexander L.; Wenger, Kristin K.; Dosenbach, Ronny A. T.; Fox, Michael D.; Snyder, Abraham Z.; Vincent, Justin L.; Raichle, Marcus E.; Schlaggar, Bradley L.; Petersen, Steven E.

    2007-01-01

    Control regions in the brain are thought to provide signals that configure the brain's moment-to-moment information processing. Previously, we identified regions that carried signals related to task-control initiation, maintenance, and adjustment. Here we characterize the interactions of these regions by applying graph theory to resting state functional connectivity MRI data. In contrast to previous, more unitary models of control, this approach suggests the presence of two distinct task-control networks. A frontoparietal network included the dorsolateral prefrontal cortex and intraparietal sulcus. This network emphasized start-cue and error-related activity and may initiate and adapt control on a trial-by-trial basis. The second network included dorsal anterior cingulate/medial superior frontal cortex, anterior insula/frontal operculum, and anterior prefrontal cortex. Among other signals, these regions showed activity sustained across the entire task epoch, suggesting that this network may control goal-directed behavior through the stable maintenance of task sets. These two independent networks appear to operate on different time scales and affect downstream processing via dissociable mechanisms. PMID:17576922

  9. Brain and Cognitive Reserve: Translation via Network Control Theory

    PubMed Central

    Medaglia, John Dominic; Pasqualetti, Fabio; Hamilton, Roy H.; Thompson-Schill, Sharon L.; Bassett, Danielle S.

    2017-01-01

    Traditional approaches to understanding the brain’s resilience to neuropathology have identified neurophysiological variables, often described as brain or cognitive “reserve,” associated with better outcomes. However, mechanisms of function and resilience in large-scale brain networks remain poorly understood. Dynamic network theory may provide a basis for substantive advances in understanding functional resilience in the human brain. In this perspective, we describe recent theoretical approaches from network control theory as a framework for investigating network level mechanisms underlying cognitive function and the dynamics of neuroplasticity in the human brain. We describe the theoretical opportunities offered by the application of network control theory at the level of the human connectome to understand cognitive resilience and inform translational intervention. PMID:28104411

  10. Control Networks and Neuromodulators of Early Development

    ERIC Educational Resources Information Center

    Posner, Michael I.; Rothbart, Mary K.; Sheese, Brad E.; Voelker, Pascale

    2012-01-01

    In adults, most cognitive and emotional self-regulation is carried out by a network of brain regions, including the anterior cingulate, insula, and areas of the basal ganglia, related to executive attention. We propose that during infancy, control systems depend primarily upon a brain network involved in orienting to sensory events that includes…

  11. Control of cancer-related signal transduction networks

    NASA Astrophysics Data System (ADS)

    Albert, Reka

    2013-03-01

    Intra-cellular signaling networks are crucial to the maintenance of cellular homeostasis and for cell behavior (growth, survival, apoptosis, movement). Mutations or alterations in the expression of elements of cellular signaling networks can lead to incorrect behavioral decisions that could result in tumor development and/or the promotion of cell migration and metastasis. Thus, mitigation of the cascading effects of such dysregulations is an important control objective. My group at Penn State is collaborating with wet-bench biologists to develop and validate predictive models of various biological systems. Over the years we found that discrete dynamic modeling is very useful in molding qualitative interaction information into a predictive model. We recently demonstrated the effectiveness of network-based targeted manipulations on mitigating the disease T cell large granular lymphocyte (T-LGL) leukemia. The root of this disease is the abnormal survival of T cells which, after successfully fighting an infection, should undergo programmed cell death. We synthesized the relevant network of within-T-cell interactions from the literature, integrated it with qualitative knowledge of the dysregulated (abnormal) states of several network components, and formulated a Boolean dynamic model. The model indicated that the system possesses a steady state corresponding to the normal cell death state and a T-LGL steady state corresponding to the abnormal survival state. For each node, we evaluated the restorative manipulation consisting of maintaining the node in the state that is the opposite of its T-LGL state, e.g. knocking it out if it is overexpressed in the T-LGL state. We found that such control of any of 15 nodes led to the disappearance of the T-LGL steady state, leaving cell death as the only potential outcome from any initial condition. In four additional cases the probability of reaching the T-LGL state decreased dramatically, thus these nodes are also possible control

  12. Global Efficiency of Structural Networks Mediates Cognitive Control in Mild Cognitive Impairment

    PubMed Central

    Berlot, Rok; Metzler-Baddeley, Claudia; Ikram, M. Arfan; Jones, Derek K.; O’Sullivan, Michael J.

    2016-01-01

    Background: Cognitive control has been linked to both the microstructure of individual tracts and the structure of whole-brain networks, but their relative contributions in health and disease remain unclear. Objective: To determine the contribution of both localized white matter tract damage and disruption of global network architecture to cognitive control, in older age and Mild Cognitive Impairment (MCI). Materials and Methods: Twenty-five patients with MCI and 20 age, sex, and intelligence-matched healthy volunteers were investigated with 3 Tesla structural magnetic resonance imaging (MRI). Cognitive control and episodic memory were evaluated with established tests. Structural network graphs were constructed from diffusion MRI-based whole-brain tractography. Their global measures were calculated using graph theory. Regression models utilized both global network metrics and microstructure of specific connections, known to be critical for each domain, to predict cognitive scores. Results: Global efficiency and the mean clustering coefficient of networks were reduced in MCI. Cognitive control was associated with global network topology. Episodic memory, in contrast, correlated with individual temporal tracts only. Relationships between cognitive control and network topology were attenuated by addition of single tract measures to regression models, consistent with a partial mediation effect. The mediation effect was stronger in MCI than healthy volunteers, explaining 23-36% of the effect of cingulum microstructure on cognitive control performance. Network clustering was a significant mediator in the relationship between tract microstructure and cognitive control in both groups. Conclusion: The status of critical connections and large-scale network topology are both important for maintenance of cognitive control in MCI. Mediation via large-scale networks is more important in patients with MCI than healthy volunteers. This effect is domain-specific, and true for

  13. Nonlinear adaptive inverse control via the unified model neural network

    NASA Astrophysics Data System (ADS)

    Jeng, Jin-Tsong; Lee, Tsu-Tian

    1999-03-01

    In this paper, we propose a new nonlinear adaptive inverse control via a unified model neural network. In order to overcome nonsystematic design and long training time in nonlinear adaptive inverse control, we propose the approximate transformable technique to obtain a Chebyshev Polynomials Based Unified Model (CPBUM) neural network for the feedforward/recurrent neural networks. It turns out that the proposed method can use less training time to get an inverse model. Finally, we apply this proposed method to control magnetic bearing system. The experimental results show that the proposed nonlinear adaptive inverse control architecture provides a greater flexibility and better performance in controlling magnetic bearing systems.

  14. Fast packet switching algorithms for dynamic resource control over ATM networks

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

    Tsang, R.P.; Keattihananant, P.; Chang, T.

    1996-12-01

    Real-time continuous media traffic, such as digital video and audio, is expected to comprise a large percentage of the network load on future high speed packet switch networks such as ATM. A major feature which distinguishes high speed networks from traditional slower speed networks is the large amount of data the network must process very quickly. For efficient network usage, traffic control mechanisms are essential. Currently, most mechanisms for traffic control (such as flow control) have centered on the support of Available Bit Rate (ABR), i.e., non real-time, traffic. With regard to ATM, for ABR traffic, two major types ofmore » schemes which have been proposed are rate- control and credit-control schemes. Neither of these schemes are directly applicable to Real-time Variable Bit Rate (VBR) traffic such as continuous media traffic. Traffic control for continuous media traffic is an inherently difficult problem due to the time- sensitive nature of the traffic and its unpredictable burstiness. In this study, we present a scheme which controls traffic by dynamically allocating/de- allocating resources among competing VCs based upon their real-time requirements. This scheme incorporates a form of rate- control, real-time burst-level scheduling and link-link flow control. We show analytically potential performance improvements of our rate- control scheme and present a scheme for buffer dimensioning. We also present simulation results of our schemes and discuss the tradeoffs inherent in maintaining high network utilization and statistically guaranteeing many users` Quality of Service.« less

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

    DOT National Transportation Integrated Search

    1996-01-01

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

  16. Performance evaluation of power control algorithms in wireless cellular networks

    NASA Astrophysics Data System (ADS)

    Temaneh-Nyah, C.; Iita, V.

    2014-10-01

    Power control in a mobile communication network intents to control the transmission power levels in such a way that the required quality of service (QoS) for the users is guaranteed with lowest possible transmission powers. Most of the studies of power control algorithms in the literature are based on some kind of simplified assumptions which leads to compromise in the validity of the results when applied in a real environment. In this paper, a CDMA network was simulated. The real environment was accounted for by defining the analysis area and the network base stations and mobile stations are defined by their geographical coordinates, the mobility of the mobile stations is accounted for. The simulation also allowed for a number of network parameters including the network traffic, and the wireless channel models to be modified. Finally, we present the simulation results of a convergence speed based comparative analysis of three uplink power control algorithms.

  17. Complex Dynamical Networks Constructed with Fully Controllable Nonlinear Nanomechanical Oscillators.

    PubMed

    Fon, Warren; Matheny, Matthew H; Li, Jarvis; Krayzman, Lev; Cross, Michael C; D'Souza, Raissa M; Crutchfield, James P; Roukes, Michael L

    2017-10-11

    Control of the global parameters of complex networks has been explored experimentally in a variety of contexts. Yet, the more difficult prospect of realizing arbitrary network architectures, especially analog physical networks that provide dynamical control of individual nodes and edges, has remained elusive. Given the vast hierarchy of time scales involved, it also proves challenging to measure a complex network's full internal dynamics. These span from the fastest nodal dynamics to very slow epochs over which emergent global phenomena, including network synchronization and the manifestation of exotic steady states, eventually emerge. Here, we demonstrate an experimental system that satisfies these requirements. It is based upon modular, fully controllable, nonlinear radio frequency nanomechanical oscillators, designed to form the nodes of complex dynamical networks with edges of arbitrary topology. The dynamics of these oscillators and their surrounding network are analog and continuous-valued and can be fully interrogated in real time. They comprise a piezoelectric nanomechanical membrane resonator, which serves as the frequency-determining element within an electrical feedback circuit. This embodiment permits network interconnections entirely within the electrical domain and provides unprecedented node and edge control over a vast region of parameter space. Continuous measurement of the instantaneous amplitudes and phases of every constituent oscillator node are enabled, yielding full and detailed network data without reliance upon statistical quantities. We demonstrate the operation of this platform through the real-time capture of the dynamics of a three-node ring network as it evolves from the uncoupled state to full synchronization.

  18. A global spacecraft control network for spacecraft autonomy research

    NASA Technical Reports Server (NTRS)

    Kitts, Christopher A.

    1996-01-01

    The development and implementation of the Automated Space System Experimental Testbed (ASSET) space operations and control network, is reported on. This network will serve as a command and control architecture for spacecraft operations and will offer a real testbed for the application and validation of advanced autonomous spacecraft operations strategies. The proposed network will initially consist of globally distributed amateur radio ground stations at locations throughout North America and Europe. These stations will be linked via Internet to various control centers. The Stanford (CA) control center will be capable of human and computer based decision making for the coordination of user experiments, resource scheduling and fault management. The project's system architecture is described together with its proposed use as a command and control system, its value as a testbed for spacecraft autonomy research, and its current implementation.

  19. Neural networks applications to control and computations

    NASA Technical Reports Server (NTRS)

    Luxemburg, Leon A.

    1994-01-01

    Several interrelated problems in the area of neural network computations are described. First an interpolation problem is considered, then a control problem is reduced to a problem of interpolation by a neural network via Lyapunov function approach, and finally a new, faster method of learning as compared with the gradient descent method, was introduced.

  20. Development of a remote control console for the HHIRF 25-MV tandem accelerator

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

    Hasanul Basher, A.M.

    1991-09-01

    The CAMAC-based control system for the 25-MV Tandem Accelerator at HHIRF uses two Perkin-Elmer, 32-bit minicomputers: a message-switching computer and a supervisory computer. Two operator consoles are located on one of the six serial highways. Operator control is provided by means of a console CRT, trackball, assignable shaft encoders and meters. The message-switching computer transmits and receives control information on the serial highways. At present, the CRT pages with updated parameters can be displayed and parameters can be controlled only from the two existing consoles, one in the Tandem control room and the other in the ORIC control room. Itmore » has become necessary to expand the control capability to several other locations in the building. With the expansion of control and monitoring capability of accelerator parameters to other locations, the operators will be able to control and observe the result of the control action at the same time. Since the new control console will be PC-based, the existing page format will be changed. The PC will be communicating with the Perkin-Elmer through RS-232 and a communication software package. Hardware configuration has been established, a communication software program that reads the pages from the shared memory has been developed. In this paper, we present the implementation strategy, works completed, existing and new page format, future action plans, explanation of pages and use of related global variables, a sample session, and flowcharts.« less

  1. Laser driven ion accelerator

    DOEpatents

    Tajima, Toshiki

    2006-04-18

    A system and method of accelerating ions in an accelerator to optimize the energy produced by a light source. Several parameters may be controlled in constructing a target used in the accelerator system to adjust performance of the accelerator system. These parameters include the material, thickness, geometry and surface of the target.

  2. Functional brain networks associated with cognitive control, cocaine dependence, and treatment outcome.

    PubMed

    Worhunsky, Patrick D; Stevens, Michael C; Carroll, Kathleen M; Rounsaville, Bruce J; Calhoun, Vince D; Pearlson, Godfrey D; Potenza, Marc N

    2013-06-01

    Individuals with cocaine dependence often evidence poor cognitive control. The purpose of this exploratory study was to investigate networks of functional connectivity underlying cognitive control in cocaine dependence and examine the relationship of the networks to the disorder and its treatment. Independent component analysis (ICA) was applied to fMRI data to investigate if regional activations underlying cognitive control processes operate in functional networks, and whether these networks relate to performance and treatment outcome measures in cocaine dependence. Twenty patients completed a Stroop task during fMRI prior to entering outpatient treatment and were compared to 20 control participants. ICA identified five distinct functional networks related to cognitive control interference events. Cocaine-dependent patients displayed differences in performance-related recruitment of three networks. Reduced involvement of a "top-down" fronto-cingular network contributing to conflict monitoring correlated with better treatment retention. Greater engagement of two "bottom-up" subcortical and ventral prefrontal networks related to cue-elicited motivational processing correlated with abstinence during treatment. The identification of subcortical networks linked to cocaine abstinence and cortical networks to treatment retention suggests that specific circuits may represent important, complementary targets in treatment development for cocaine dependence. 2013 APA, all rights reserved

  3. Designing communication and remote controlling of virtual instrument network system

    NASA Astrophysics Data System (ADS)

    Lei, Lin; Wang, Houjun; Zhou, Xue; Zhou, Wenjian

    2005-01-01

    In this paper, a virtual instrument network through the LAN and finally remote control of virtual instruments is realized based on virtual instrument and LabWindows/CVI software platform. The virtual instrument network system is made up of three subsystems. There are server subsystem, telnet client subsystem and local instrument control subsystem. This paper introduced virtual instrument network structure in detail based on LabWindows. Application procedure design of virtual instrument network communication, the Client/the programming mode of the server, remote PC and server communication far realizing, the control power of the workstation is transmitted, server program and so on essential technical were introduced. And virtual instruments network may connect to entire Internet on. Above-mentioned technology, through measuring the application in the electronic measurement virtual instrument network that is already built up, has verified the actual using value of the technology. Experiment and application validate that this design is resultful.

  4. Modeling Aircraft Wing Loads from Flight Data Using Neural Networks

    NASA Technical Reports Server (NTRS)

    Allen, Michael J.; Dibley, Ryan P.

    2003-01-01

    Neural networks were used to model wing bending-moment loads, torsion loads, and control surface hinge-moments of the Active Aeroelastic Wing (AAW) aircraft. Accurate loads models are required for the development of control laws designed to increase roll performance through wing twist while not exceeding load limits. Inputs to the model include aircraft rates, accelerations, and control surface positions. Neural networks were chosen to model aircraft loads because they can account for uncharacterized nonlinear effects while retaining the capability to generalize. The accuracy of the neural network models was improved by first developing linear loads models to use as starting points for network training. Neural networks were then trained with flight data for rolls, loaded reversals, wind-up-turns, and individual control surface doublets for load excitation. Generalization was improved by using gain weighting and early stopping. Results are presented for neural network loads models of four wing loads and four control surface hinge moments at Mach 0.90 and an altitude of 15,000 ft. An average model prediction error reduction of 18.6 percent was calculated for the neural network models when compared to the linear models. This paper documents the input data conditioning, input parameter selection, structure, training, and validation of the neural network models.

  5. Improved transmission of electrostatic accelerator in a wide range of terminal voltages by controlling the focal strength of entrance acceleration tube

    NASA Astrophysics Data System (ADS)

    Lobanov, Nikolai R.; Tunningley, Thomas; Linardakis, Peter

    2018-04-01

    Tandem electrostatic accelerators often require the flexibility to operate at a variety of terminal voltages to accommodate various user requirements. However, the ion beam transmission will only be optimal for a limited range of terminal voltages. This paper describes the operational performance of a novel focusing system that expands the range of terminal voltages for optimal transmission. This is accomplished by controlling the gradient of the entrance of the low-energy tube, providing an additional focusing element. In this specific case it is achieved by applying up to 150 kV to the fifth electrode of the first unit of the accelerator tube. Numerical simulations and beam transmission tests have been performed to confirm the effectiveness of the lens. An analytical expression has been derived describing its focal properties. These tests demonstrate that the entrance lens control eliminates the need to short out sections of the tube for operation at low terminal voltage.

  6. Software-defined networking control plane for seamless integration of multiple silicon photonic switches in Datacom networks

    DOE PAGES

    Shen, Yiwen; Hattink, Maarten; Samadi, Payman; ...

    2018-04-13

    Silicon photonics based switches offer an effective option for the delivery of dynamic bandwidth for future large-scale Datacom systems while maintaining scalable energy efficiency. The integration of a silicon photonics-based optical switching fabric within electronic Datacom architectures requires novel network topologies and arbitration strategies to effectively manage the active elements in the network. Here, we present a scalable software-defined networking control plane to integrate silicon photonic based switches with conventional Ethernet or InfiniBand networks. Our software-defined control plane manages both electronic packet switches and multiple silicon photonic switches for simultaneous packet and circuit switching. We built an experimental Dragonfly networkmore » testbed with 16 electronic packet switches and 2 silicon photonic switches to evaluate our control plane. Observed latencies occupied by each step of the switching procedure demonstrate a total of 344 microsecond control plane latency for data-center and high performance computing platforms.« less

  7. Software-defined networking control plane for seamless integration of multiple silicon photonic switches in Datacom networks

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

    Shen, Yiwen; Hattink, Maarten; Samadi, Payman

    Silicon photonics based switches offer an effective option for the delivery of dynamic bandwidth for future large-scale Datacom systems while maintaining scalable energy efficiency. The integration of a silicon photonics-based optical switching fabric within electronic Datacom architectures requires novel network topologies and arbitration strategies to effectively manage the active elements in the network. Here, we present a scalable software-defined networking control plane to integrate silicon photonic based switches with conventional Ethernet or InfiniBand networks. Our software-defined control plane manages both electronic packet switches and multiple silicon photonic switches for simultaneous packet and circuit switching. We built an experimental Dragonfly networkmore » testbed with 16 electronic packet switches and 2 silicon photonic switches to evaluate our control plane. Observed latencies occupied by each step of the switching procedure demonstrate a total of 344 microsecond control plane latency for data-center and high performance computing platforms.« less

  8. Accelerating gradient improvement using shape-tailor laser front in radiation pressure acceleration progress

    NASA Astrophysics Data System (ADS)

    Wang, W. P.; Shen, B. F.; Xu, Z. Z.

    2017-05-01

    The accelerating gradient of a proton beam is crucial for stable radiation pressure acceleration (RPA) because the multi-dimensional instabilities increase γ times slower in the relativistic region. In this paper, a shape-tailored laser is proposed to significantly accelerate the ions in a controllable high accelerating gradient. In this method, the fastest ions initially rest in the middle of the foil are controlled to catch the compressed electron layer at the end of the hole-boring stage, thus the light-sail stage can start as soon as possible. Then the compressed electron layer is accelerated tightly together with the fastest ions by the shaped laser intensity, which further increases the accelerating gradient in the light-sail stage. Such tailored pulse may be beneficial for the RPA driven by the 10-fs 10 petawatt laser in the future.

  9. Effective connectivity within the frontoparietal control network differentiates cognitive control and working memory.

    PubMed

    Harding, Ian H; Yücel, Murat; Harrison, Ben J; Pantelis, Christos; Breakspear, Michael

    2015-02-01

    Cognitive control and working memory rely upon a common fronto-parietal network that includes the inferior frontal junction (IFJ), dorsolateral prefrontal cortex (dlPFC), pre-supplementary motor area/dorsal anterior cingulate cortex (pSMA/dACC), and intraparietal sulcus (IPS). This network is able to flexibly adapt its function in response to changing behavioral goals, mediating a wide range of cognitive demands. Here we apply dynamic causal modeling to functional magnetic resonance imaging data to characterize task-related alterations in the strength of network interactions across distinct cognitive processes. Evidence in favor of task-related connectivity dynamics was accrued across a very large space of possible network structures. Cognitive control and working memory demands were manipulated using a factorial combination of the multi-source interference task and a verbal 2-back working memory task, respectively. Both were found to alter the sensitivity of the IFJ to perceptual information, and to increase IFJ-to-pSMA/dACC connectivity. In contrast, increased connectivity from the pSMA/dACC to the IPS, as well as from the dlPFC to the IFJ, was uniquely driven by cognitive control demands; a task-induced negative influence of the dlPFC on the pSMA/dACC was specific to working memory demands. These results reflect a system of both shared and unique context-dependent dynamics within the fronto-parietal network. Mechanisms supporting cognitive engagement, response selection, and action evaluation may be shared across cognitive domains, while dynamic updating of task and context representations within this network are potentially specific to changing demands on cognitive control. Copyright © 2014 Elsevier Inc. All rights reserved.

  10. 1985 Particle Accelerator Conference: Accelerator Engineering and Technology, 11th, Vancouver, Canada, May 13-16, 1985, Proceedings

    NASA Astrophysics Data System (ADS)

    Strathdee, A.

    1985-10-01

    The topics discussed are related to high-energy accelerators and colliders, particle sources and electrostatic accelerators, controls, instrumentation and feedback, beam dynamics, low- and intermediate-energy circular accelerators and rings, RF and other acceleration systems, beam injection, extraction and transport, operations and safety, linear accelerators, applications of accelerators, radiation sources, superconducting supercolliders, new acceleration techniques, superconducting components, cryogenics, and vacuum. Accelerator and storage ring control systems are considered along with linear and nonlinear orbit theory, transverse and longitudinal instabilities and cures, beam cooling, injection and extraction orbit theory, high current dynamics, general beam dynamics, and medical and radioisotope applications. Attention is given to superconducting RF structures, magnet technology, superconducting magnets, and physics opportunities with relativistic heavy ion accelerators.

  11. Active control of an innovative seat suspension system with acceleration measurement based friction estimation

    NASA Astrophysics Data System (ADS)

    Ning, Donghong; Sun, Shuaishuai; Li, Hongyi; Du, Haiping; Li, Weihua

    2016-12-01

    In this paper, an innovative active seat suspension system for vehicles is presented. This seat suspension prototype is built with two low cost actuators each of which has one rotary motor and one gear reducer. A H∞ controller with friction compensation is designed for the seat suspension control system where the friction is estimated and compensated based on the measurement of seat acceleration. This principal aim of this research was to control the low frequency vibration transferred or amplified by the vehicle (chassis) suspension, and to maintain the passivity of the seat suspension at high frequency (isolation vibration) while taking into consideration the trade-off between the active seat suspension cost and its high frequency performance. Sinusoidal excitations of 1-4.5 Hz were applied to test the active seat suspension both when controlled and when uncontrolled and this is compared with a well-tuned passive heavy duty vehicle seat suspension. The results indicate the effectiveness of the proposed control algorithm within the tested frequencies. Further tests were conducted using the excitations generated from a quarter-car model under bump and random road profiles. The bump road tests indicate the controlled active seat suspension has good transient response performance. The Power Spectral Density (PSD) method and ISO 2631-1 standards were applied to analyse the seat suspension's acceleration under random road conditions. Although some low magnitude and high frequency noise will inevitably be introduced by the active system, the weighted-frequency Root Mean Square (RMS) acceleration shows that this may not have a large effect on ride comfort. In fact, the ride comfort is improved from being an 'a little uncomfortable' to a 'not uncomfortable' level when compared with the well-tuned passive seat suspension. This low cost active seat suspension design and the proposed controller with the easily measured feedback signals are very practical for real

  12. Public authority control strategy for opinion evolution in social networks

    NASA Astrophysics Data System (ADS)

    Chen, Xi; Xiong, Xi; Zhang, Minghong; Li, Wei

    2016-08-01

    This paper addresses the need to deal with and control public opinion and rumors. Existing strategies to control public opinion include degree, random, and adaptive bridge control strategies. In this paper, we use the HK model to present a public opinion control strategy based on public authority (PA). This means utilizing the influence of expert or high authority individuals whose opinions we control to obtain the optimum effect in the shortest time possible and thus reach a consensus of public opinion. Public authority (PA) is only influenced by individuals' attributes (age, economic status, and education level) and not their degree distribution; hence, in this paper, we assume that PA complies with two types of public authority distribution (normal and power-law). According to the proposed control strategy, our experiment is based on random, degree, and public authority control strategies in three different social networks (small-world, scale-free, and random) and we compare and analyze the strategies in terms of convergence time (T), final number of controlled agents (C), and comprehensive efficiency (E). We find that different network topologies and the distribution of the PA in the network can influence the final controlling effect. While the effect of PA strategy differs in different network topology structures, all structures achieve comprehensive efficiency with any kind of public authority distribution in any network. Our findings are consistent with several current sociological phenomena and show that in the process of public opinion/rumor control, considerable attention should be paid to high authority individuals.

  13. Public authority control strategy for opinion evolution in social networks.

    PubMed

    Chen, Xi; Xiong, Xi; Zhang, Minghong; Li, Wei

    2016-08-01

    This paper addresses the need to deal with and control public opinion and rumors. Existing strategies to control public opinion include degree, random, and adaptive bridge control strategies. In this paper, we use the HK model to present a public opinion control strategy based on public authority (PA). This means utilizing the influence of expert or high authority individuals whose opinions we control to obtain the optimum effect in the shortest time possible and thus reach a consensus of public opinion. Public authority (PA) is only influenced by individuals' attributes (age, economic status, and education level) and not their degree distribution; hence, in this paper, we assume that PA complies with two types of public authority distribution (normal and power-law). According to the proposed control strategy, our experiment is based on random, degree, and public authority control strategies in three different social networks (small-world, scale-free, and random) and we compare and analyze the strategies in terms of convergence time (T), final number of controlled agents (C), and comprehensive efficiency (E). We find that different network topologies and the distribution of the PA in the network can influence the final controlling effect. While the effect of PA strategy differs in different network topology structures, all structures achieve comprehensive efficiency with any kind of public authority distribution in any network. Our findings are consistent with several current sociological phenomena and show that in the process of public opinion/rumor control, considerable attention should be paid to high authority individuals.

  14. Modular networks with delayed coupling: Synchronization and frequency control

    NASA Astrophysics Data System (ADS)

    Maslennikov, Oleg V.; Nekorkin, Vladimir I.

    2014-07-01

    We study the collective dynamics of modular networks consisting of map-based neurons which generate irregular spike sequences. Three types of intramodule topology are considered: a random Erdös-Rényi network, a small-world Watts-Strogatz network, and a scale-free Barabási-Albert network. The interaction between the neurons of different modules is organized by relatively sparse connections with time delay. For all the types of the network topology considered, we found that with increasing delay two regimes of module synchronization alternate with each other: inphase and antiphase. At the same time, the average rate of collective oscillations decreases within each of the time-delay intervals corresponding to a particular synchronization regime. A dual role of the time delay is thus established: controlling a synchronization mode and degree and controlling an average network frequency. Furthermore, we investigate the influence on the modular synchronization by other parameters: the strength of intermodule coupling and the individual firing rate.

  15. Teleportation with Multiple Accelerated Partners

    NASA Astrophysics Data System (ADS)

    Sagheer, A.; Hamdoun, H.; Metwally, N.

    2015-09-01

    As the current revolution in communication is underway, quantum teleportation can increase the level of security in quantum communication applications. In this paper, we present a quantum teleportation procedure that capable to teleport either accelerated or non-accelerated information through different quantum channels. These quantum channels are based on accelerated multi-qubit states, where each qubit of each of these channels represents a partner. Namely, these states are the W state, Greenberger-Horne-Zeilinger (GHZ) state, and the GHZ-like state. Here, we show that the fidelity of teleporting accelerated information is higher than the fidelity of teleporting non-accelerated information, both through a quantum channel that is based on accelerated state. Also, the comparison among the performance of these three channels shows that the degree of fidelity depends on type of the used channel, type of the measurement, and value of the acceleration. The result of comparison concludes that teleporting information through channel that is based on the GHZ state is more robust than teleporting information through channels that are based on the other two states. For future work, the proposed procedure can be generalized later to achieve communication through a wider quantum network.

  16. Topology Control and Routing in Ad Hoc Networks

    NASA Astrophysics Data System (ADS)

    Carr-Motyckova, Lenka; Navarra, Alfredo; Johansson, Tomas; Unger, Walter

    Mobile nodes with the ability to communicate with radio signals may form an ad hoc network. In this chapter special problems arising for these ad hoc networks are considered. These include range control, the reduction of interferences, regulation of power consumption, and localization.

  17. General visual robot controller networks via artificial evolution

    NASA Astrophysics Data System (ADS)

    Cliff, David; Harvey, Inman; Husbands, Philip

    1993-08-01

    We discuss recent results from our ongoing research concerning the application of artificial evolution techniques (i.e., an extended form of genetic algorithm) to the problem of developing `neural' network controllers for visually guided robots. The robot is a small autonomous vehicle with extremely low-resolution vision, employing visual sensors which could readily be constructed from discrete analog components. In addition to visual sensing, the robot is equipped with a small number of mechanical tactile sensors. Activity from the sensors is fed to a recurrent dynamical artificial `neural' network, which acts as the robot controller, providing signals to motors governing the robot's motion. Prior to presentation of new results, this paper summarizes our rationale and past work, which has demonstrated that visually guided control networks can arise without any explicit specification that visual processing should be employed: the evolutionary process opportunistically makes use of visual information if it is available.

  18. Effects of Edge Directions on the Structural Controllability of Complex Networks.

    PubMed

    Xiao, Yandong; Lao, Songyang; Hou, Lvlin; Small, Michael; Bai, Liang

    2015-01-01

    Recent advances indicate that assigning or reversing edge direction can significantly improve the structural controllability of complex networks. For directed networks, approaching the optimal structural controllability can be achieved by detecting and reversing certain "inappropriate" edge directions. However, the existence of multiple sets of "inappropriate" edge directions suggests that different edges have different effects on optimal controllability-that is, different combinations of edges can be reversed to achieve the same structural controllability. Therefore, we classify edges into three categories based on their direction: critical, redundant and intermittent. We then investigate the effects of changing these edge directions on network controllability, and demonstrate that the existence of more critical edge directions implies not only a lower cost of modifying inappropriate edges but also better controllability. Motivated by this finding, we present a simple edge orientation method aimed at producing more critical edge directions-utilizing only local information-which achieves near optimal controllability. Furthermore, we explore the effects of edge direction on the controllability of several real networks.

  19. Network efficient power control for wireless communication systems.

    PubMed

    Campos-Delgado, Daniel U; Luna-Rivera, Jose Martin; Martinez-Sánchez, C J; Gutierrez, Carlos A; Tecpanecatl-Xihuitl, J L

    2014-01-01

    We introduce a two-loop power control that allows an efficient use of the overall power resources for commercial wireless networks based on cross-layer optimization. This approach maximizes the network's utility in the outer-loop as a function of the averaged signal to interference-plus-noise ratio (SINR) by considering adaptively the changes in the network characteristics. For this purpose, the concavity property of the utility function was verified with respect to the SINR, and an iterative search was proposed with guaranteed convergence. In addition, the outer-loop is in charge of selecting the detector that minimizes the overall power consumption (transmission and detection). Next the inner-loop implements a feedback power control in order to achieve the optimal SINR in the transmissions despite channel variations and roundtrip delays. In our proposal, the utility maximization process and detector selection and feedback power control are decoupled problems, and as a result, these strategies are implemented at two different time scales in the two-loop framework. Simulation results show that substantial utility gains may be achieved by improving the power management in the wireless network.

  20. Network Efficient Power Control for Wireless Communication Systems

    PubMed Central

    Campos-Delgado, Daniel U.; Luna-Rivera, Jose Martin; Martinez-Sánchez, C. J.; Gutierrez, Carlos A.; Tecpanecatl-Xihuitl, J. L.

    2014-01-01

    We introduce a two-loop power control that allows an efficient use of the overall power resources for commercial wireless networks based on cross-layer optimization. This approach maximizes the network's utility in the outer-loop as a function of the averaged signal to interference-plus-noise ratio (SINR) by considering adaptively the changes in the network characteristics. For this purpose, the concavity property of the utility function was verified with respect to the SINR, and an iterative search was proposed with guaranteed convergence. In addition, the outer-loop is in charge of selecting the detector that minimizes the overall power consumption (transmission and detection). Next the inner-loop implements a feedback power control in order to achieve the optimal SINR in the transmissions despite channel variations and roundtrip delays. In our proposal, the utility maximization process and detector selection and feedback power control are decoupled problems, and as a result, these strategies are implemented at two different time scales in the two-loop framework. Simulation results show that substantial utility gains may be achieved by improving the power management in the wireless network. PMID:24683350

  1. Optical control of hard X-ray polarization by electron injection in a laser wakefield accelerator

    PubMed Central

    Schnell, Michael; Sävert, Alexander; Uschmann, Ingo; Reuter, Maria; Nicolai, Maria; Kämpfer, Tino; Landgraf, Björn; Jäckel, Oliver; Jansen, Oliver; Pukhov, Alexander; Kaluza, Malte Christoph; Spielmann, Christian

    2013-01-01

    Laser-plasma particle accelerators could provide more compact sources of high-energy radiation than conventional accelerators. Moreover, because they deliver radiation in femtosecond pulses, they could improve the time resolution of X-ray absorption techniques. Here we show that we can measure and control the polarization of ultra-short, broad-band keV photon pulses emitted from a laser-plasma-based betatron source. The electron trajectories and hence the polarization of the emitted X-rays are experimentally controlled by the pulse-front tilt of the driving laser pulses. Particle-in-cell simulations show that an asymmetric plasma wave can be driven by a tilted pulse front and a non-symmetric intensity distribution of the focal spot. Both lead to a notable off-axis electron injection followed by collective electron–betatron oscillations. We expect that our method for an all-optical steering is not only useful for plasma-based X-ray sources but also has significance for future laser-based particle accelerators. PMID:24026068

  2. Stationary and structural control in gene regulatory networks: basic concepts

    NASA Astrophysics Data System (ADS)

    Dougherty, Edward R.; Pal, Ranadip; Qian, Xiaoning; Bittner, Michael L.; Datta, Aniruddha

    2010-01-01

    A major reason for constructing gene regulatory networks is to use them as models for determining therapeutic intervention strategies by deriving ways of altering their long-run dynamics in such a way as to reduce the likelihood of entering undesirable states. In general, two paradigms have been taken for gene network intervention: (1) stationary external control is based on optimally altering the status of a control gene (or genes) over time to drive network dynamics; and (2) structural intervention involves an optimal one-time change of the network structure (wiring) to beneficially alter the long-run behaviour of the network. These intervention approaches have mainly been developed within the context of the probabilistic Boolean network model for gene regulation. This article reviews both types of intervention and applies them to reducing the metastatic competence of cells via intervention in a melanoma-related network.

  3. Economical launching and accelerating control strategy for a single-shaft parallel hybrid electric bus

    NASA Astrophysics Data System (ADS)

    Yang, Chao; Song, Jian; Li, Liang; Li, Shengbo; Cao, Dongpu

    2016-08-01

    This paper presents an economical launching and accelerating mode, including four ordered phases: pure electrical driving, clutch engagement and engine start-up, engine active charging, and engine driving, which can be fit for the alternating conditions and improve the fuel economy of hybrid electric bus (HEB) during typical city-bus driving scenarios. By utilizing the fast response feature of electric motor (EM), an adaptive controller for EM is designed to realize the power demand during the pure electrical driving mode, the engine starting mode and the engine active charging mode. Concurrently, the smoothness issue induced by the sequential mode transitions is solved with a coordinated control logic for engine, EM and clutch. Simulation and experimental results show that the proposed launching and accelerating mode and its control methods are effective in improving the fuel economy and ensure the drivability during the fast transition between the operation modes of HEB.

  4. Building Cross-Country Networks for Laboratory Capacity and Improvement.

    PubMed

    Schneidman, Miriam; Matu, Martin; Nkengasong, John; Githui, Willie; Kalyesubula-Kibuuka, Simeon; Silva, Kelly Araujo

    2018-03-01

    Laboratory networks are vital to well-functioning public health systems and disease control efforts. Cross-country laboratory networks play a critical role in supporting epidemiologic surveillance, accelerating disease outbreak response, and tracking drug resistance. The East Africa Public Health Laboratory Network was established to bolster diagnostic and disease surveillance capacity. The network supports the introduction of regional quality standards; facilitates the rollout and evaluation of new diagnostic tools; and serves as a platform for training, research, and knowledge sharing. Participating facilities benefitted from state-of-the art investments, capacity building, and mentorship; conducted multicountry research studies; and contributed to disease outbreak response. Copyright © 2017 Elsevier Inc. All rights reserved.

  5. Space Network Control Conference on Resource Allocation Concepts and Approaches

    NASA Technical Reports Server (NTRS)

    Moe, Karen L. (Editor)

    1991-01-01

    The results are presented of the Space Network Control (SNC) Conference. In the late 1990s, when the Advanced Tracking and Data Relay Satellite System is operational, Space Network communication services will be supported and controlled by the SNC. The goals of the conference were to survey existing resource allocation concepts and approaches, to identify solutions applicable to the Space Network, and to identify avenues of study in support of the SNC development. The conference was divided into three sessions: (1) Concepts for Space Network Allocation; (2) SNC and User Payload Operations Control Center (POCC) Human-Computer Interface Concepts; and (3) Resource Allocation Tools, Technology, and Algorithms. Key recommendations addressed approaches to achieving higher levels of automation in the scheduling process.

  6. Effect of interaction strength on robustness of controlling edge dynamics in complex networks

    NASA Astrophysics Data System (ADS)

    Pang, Shao-Peng; Hao, Fei

    2018-05-01

    Robustness plays a critical role in the controllability of complex networks to withstand failures and perturbations. Recent advances in the edge controllability show that the interaction strength among edges plays a more important role than network structure. Therefore, we focus on the effect of interaction strength on the robustness of edge controllability. Using three categories of all edges to quantify the robustness, we develop a universal framework to evaluate and analyze the robustness in complex networks with arbitrary structures and interaction strengths. Applying our framework to a large number of model and real-world networks, we find that the interaction strength is a dominant factor for the robustness in undirected networks. Meanwhile, the strongest robustness and the optimal edge controllability in undirected networks can be achieved simultaneously. Different from the case of undirected networks, the robustness in directed networks is determined jointly by the interaction strength and the network's degree distribution. Moreover, a stronger robustness is usually associated with a larger number of driver nodes required to maintain full control in directed networks. This prompts us to provide an optimization method by adjusting the interaction strength to optimize the robustness of edge controllability.

  7. Coaching versus Direct Service Models for University Training to Accelerated Schools.

    ERIC Educational Resources Information Center

    Kirby, Peggy C.; Meza, James, Jr.

    This paper examines the changing roles and relationships of schools, central offices, and university facilitators at 11 schools that implemented the nationally recognized Accelerated Schools process. The schools joined the Louisiana Accelerated Schools Network in the summer of 1994. The paper begins with an overview of the Accelerated Schools…

  8. Improved methods in neural network-based adaptive output feedback control, with applications to flight control

    NASA Astrophysics Data System (ADS)

    Kim, Nakwan

    Utilizing the universal approximation property of neural networks, we develop several novel approaches to neural network-based adaptive output feedback control of nonlinear systems, and illustrate these approaches for several flight control applications. In particular, we address the problem of non-affine systems and eliminate the fixed point assumption present in earlier work. All of the stability proofs are carried out in a form that eliminates an algebraic loop in the neural network implementation. An approximate input/output feedback linearizing controller is augmented with a neural network using input/output sequences of the uncertain system. These approaches permit adaptation to both parametric uncertainty and unmodeled dynamics. All physical systems also have control position and rate limits, which may either deteriorate performance or cause instability for a sufficiently high control bandwidth. Here we apply a method for protecting an adaptive process from the effects of input saturation and time delays, known as "pseudo control hedging". This method was originally developed for the state feedback case, and we provide a stability analysis that extends its domain of applicability to the case of output feedback. The approach is illustrated by the design of a pitch-attitude flight control system for a linearized model of an R-50 experimental helicopter, and by the design of a pitch-rate control system for a 58-state model of a flexible aircraft consisting of rigid body dynamics coupled with actuator and flexible modes. A new approach to augmentation of an existing linear controller is introduced. It is especially useful when there is limited information concerning the plant model, and the existing controller. The approach is applied to the design of an adaptive autopilot for a guided munition. Design of a neural network adaptive control that ensures asymptotically stable tracking performance is also addressed.

  9. Robust nonlinear variable selective control for networked systems

    NASA Astrophysics Data System (ADS)

    Rahmani, Behrooz

    2016-10-01

    This paper is concerned with the networked control of a class of uncertain nonlinear systems. In this way, Takagi-Sugeno (T-S) fuzzy modelling is used to extend the previously proposed variable selective control (VSC) methodology to nonlinear systems. This extension is based upon the decomposition of the nonlinear system to a set of fuzzy-blended locally linearised subsystems and further application of the VSC methodology to each subsystem. To increase the applicability of the T-S approach for uncertain nonlinear networked control systems, this study considers the asynchronous premise variables in the plant and the controller, and then introduces a robust stability analysis and control synthesis. The resulting optimal switching-fuzzy controller provides a minimum guaranteed cost on an H2 performance index. Simulation studies on three nonlinear benchmark problems demonstrate the effectiveness of the proposed method.

  10. Adaptive model predictive process control using neural networks

    DOEpatents

    Buescher, K.L.; Baum, C.C.; Jones, R.D.

    1997-08-19

    A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data. 46 figs.

  11. Adaptive model predictive process control using neural networks

    DOEpatents

    Buescher, Kevin L.; Baum, Christopher C.; Jones, Roger D.

    1997-01-01

    A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data.

  12. Portable control device for networked mobile robots

    DOEpatents

    Feddema, John T.; Byrne, Raymond H.; Bryan, Jon R.; Harrington, John J.; Gladwell, T. Scott

    2002-01-01

    A handheld control device provides a way for controlling one or multiple mobile robotic vehicles by incorporating a handheld computer with a radio board. The device and software use a personal data organizer as the handheld computer with an additional microprocessor and communication device on a radio board for use in controlling one robot or multiple networked robots.

  13. Three Eras in Global Tobacco Control: How Global Governance Processes Influenced Online Tobacco Control Networking.

    PubMed

    Wipfli, Heather; Chu, Kar-Hai; Lancaster, Molly; Valente, Thomas

    2016-01-01

    Online networks can serve as a platform to diffuse policy innovations and enhance global health governance. This study focuses on how shifts in global health governance may influence related online networks. We compare social network metrics (average degree centrality [AVGD], density [D] and clustering coefficient [CC]) of Globalink, an online network of tobacco control advocates, across three eras in global tobacco control governance; pre-Framework Convention on Tobacco Control (FCTC) policy transfer (1992-1998), global regime formation through the FCTC negotiations (1999-2005), and philanthropic funding through the Bloomberg Initiative (2006-2012). Prior to 1999, Globalink was driven by a handful of high-income countries (AVGD=1.908 D=0.030, CC=0.215). The FCTC negotiations (1999-2005) corresponded with a rapid uptick in the number of countries represented within Globalink and new members were most often brought into the network through relationships with regional neighbors (AVGD=2.824, D=0.021, CC=0.253). Between 2006 and 2012, the centrality of the US in the network increases significantly (AVGD=3.414, D=0.023, CC=0.310). The findings suggest that global institutionalization through WHO, as with the FCTC, can lead to the rapid growth of decentralized online networks. Alternatively, private initiatives, such as the Bloomberg Initiative, can lead to clustering in which a single source of information gains increasing influence over an online network.

  14. 75 FR 57385 - Internal Control Over Financial Reporting in Exchange Act Periodic Reports of Non-Accelerated Filers

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-09-21

    ... attestation report of its registered public accounting firm on internal control over financial reporting in... definition of either an ``accelerated filer'' or a ``large accelerated filer'' under Exchange Act Rule 12b-2..., for both definitions, the issuer needs to have been subject to reporting requirements for at least...

  15. Correlations in the degeneracy of structurally controllable topologies for networks

    NASA Astrophysics Data System (ADS)

    Campbell, Colin; Aucott, Steven; Ruths, Justin; Ruths, Derek; Shea, Katriona; Albert, Réka

    2017-04-01

    Many dynamic systems display complex emergent phenomena. By directly controlling a subset of system components (nodes) via external intervention it is possible to indirectly control every other component in the system. When the system is linear or can be approximated sufficiently well by a linear model, methods exist to identify the number and connectivity of a minimum set of external inputs (constituting a so-called minimal control topology, or MCT). In general, many MCTs exist for a given network; here we characterize a broad ensemble of empirical networks in terms of the fraction of nodes and edges that are always, sometimes, or never a part of an MCT. We study the relationships between the measures, and apply the methodology to the T-LGL leukemia signaling network as a case study. We show that the properties introduced in this report can be used to predict key components of biological networks, with potentially broad applications to network medicine.

  16. Feedback Controller Design for the Synchronization of Boolean Control Networks.

    PubMed

    Liu, Yang; Sun, Liangjie; Lu, Jianquan; Liang, Jinling

    2016-09-01

    This brief investigates the partial and complete synchronization of two Boolean control networks (BCNs). Necessary and sufficient conditions for partial and complete synchronization are established by the algebraic representations of logical dynamics. An algorithm is obtained to construct the feedback controller that guarantees the synchronization of master and slave BCNs. Two biological examples are provided to illustrate the effectiveness of the obtained results.

  17. Extinction Dynamics and Control in Adaptive Networks

    NASA Astrophysics Data System (ADS)

    Schwartz, Ira; Shaw, Leah; Hindes, Jason

    Disease control is of paramount importance in public health. Moreover, models of disease spread are an important component in implementing effective vaccination and treatment campaigns. However, human behavior in response to an outbreak has only recently been included in epidemic models on networks. Here we develop the mathematical machinery to describe the dynamics of extinction in finite populations that include human adaptive behavior. The formalism enables us to compute the optimal, fluctuation-induced path to extinction, and predict the average extinction time in adaptive networks as a function of the adaptation rate. We find that both observables have several unique scalings depending on the relative speed of infection and adaptivity. Finally, we discuss how the theory can be used to design optimal control programs in general networks, by coupling the effective force of noise with treatment and human behavior. Research supported by U.S. Naval Research Laboratory funding (Grant No. N0001414WX00023) and the Office of Naval Research (Grant No. N0001414WX20610).

  18. Stimulation-Based Control of Dynamic Brain Networks

    PubMed Central

    Pasqualetti, Fabio; Gu, Shi; Cieslak, Matthew

    2016-01-01

    The ability to modulate brain states using targeted stimulation is increasingly being employed to treat neurological disorders and to enhance human performance. Despite the growing interest in brain stimulation as a form of neuromodulation, much remains unknown about the network-level impact of these focal perturbations. To study the system wide impact of regional stimulation, we employ a data-driven computational model of nonlinear brain dynamics to systematically explore the effects of targeted stimulation. Validating predictions from network control theory, we uncover the relationship between regional controllability and the focal versus global impact of stimulation, and we relate these findings to differences in the underlying network architecture. Finally, by mapping brain regions to cognitive systems, we observe that the default mode system imparts large global change despite being highly constrained by structural connectivity. This work forms an important step towards the development of personalized stimulation protocols for medical treatment or performance enhancement. PMID:27611328

  19. Controllability of symmetric spin networks

    NASA Astrophysics Data System (ADS)

    Albertini, Francesca; D'Alessandro, Domenico

    2018-05-01

    We consider a network of n spin 1/2 systems which are pairwise interacting via Ising interaction and are controlled by the same electro-magnetic control field. Such a system presents symmetries since the Hamiltonian is unchanged if we permute two spins. This prevents full (operator) controllability, in that not every unitary evolution can be obtained. We prove however that controllability is verified if we restrict ourselves to unitary evolutions which preserve the above permutation invariance. For low dimensional cases, n = 2 and n = 3, we provide an analysis of the Lie group of available evolutions and give explicit control laws to transfer between two arbitrary permutation invariant states. This class of states includes highly entangled states such as Greenberger-Horne-Zeilinger (GHZ) states and W states, which are of interest in quantum information.

  20. Adaptive Control Using Neural Network Augmentation for a Modified F-15 Aircraft

    NASA Technical Reports Server (NTRS)

    Burken, John J.; Williams-Hayes, Peggy; Karneshige, J. T.; Stachowiak, Susan J.

    2006-01-01

    Description of the performance of a simplified dynamic inversion controller with neural network augmentation follows. Simulation studies focus on the results with and without neural network adaptation through the use of an F-15 aircraft simulator that has been modified to include canards. Simulated control law performance with a surface failure, in addition to an aerodynamic failure, is presented. The aircraft, with adaptation, attempts to minimize the inertial cross-coupling effect of the failure (a control derivative anomaly associated with a jammed control surface). The dynamic inversion controller calculates necessary surface commands to achieve desired rates. The dynamic inversion controller uses approximate short period and roll axis dynamics. The yaw axis controller is a sideslip rate command system. Methods are described to reduce the cross-coupling effect and maintain adequate tracking errors for control surface failures. The aerodynamic failure destabilizes the pitching moment due to angle of attack. The results show that control of the aircraft with the neural networks is easier (more damped) than without the neural networks. Simulation results show neural network augmentation of the controller improves performance with aerodynamic and control surface failures in terms of tracking error and cross-coupling reduction.

  1. Method for neural network control of motion using real-time environmental feedback

    NASA Technical Reports Server (NTRS)

    Buckley, Theresa M. (Inventor)

    1997-01-01

    A method of motion control for robotics and other automatically controlled machinery using a neural network controller with real-time environmental feedback. The method is illustrated with a two-finger robotic hand having proximity sensors and force sensors that provide environmental feedback signals. The neural network controller is taught to control the robotic hand through training sets using back- propagation methods. The training sets are created by recording the control signals and the feedback signal as the robotic hand or a simulation of the robotic hand is moved through a representative grasping motion. The data recorded is divided into discrete increments of time and the feedback data is shifted out of phase with the control signal data so that the feedback signal data lag one time increment behind the control signal data. The modified data is presented to the neural network controller as a training set. The time lag introduced into the data allows the neural network controller to account for the temporal component of the robotic motion. Thus trained, the neural network controlled robotic hand is able to grasp a wide variety of different objects by generalizing from the training sets.

  2. Reconfigurable Control with Neural Network Augmentation for a Modified F-15 Aircraft

    NASA Technical Reports Server (NTRS)

    Burken, John J.; Williams-Hayes, Peggy; Kaneshige, John T.; Stachowiak, Susan J.

    2006-01-01

    Description of the performance of a simplified dynamic inversion controller with neural network augmentation follows. Simulation studies focus on the results with and without neural network adaptation through the use of an F-15 aircraft simulator that has been modified to include canards. Simulated control law performance with a surface failure, in addition to an aerodynamic failure, is presented. The aircraft, with adaptation, attempts to minimize the inertial cross-coupling effect of the failure (a control derivative anomaly associated with a jammed control surface). The dynamic inversion controller calculates necessary surface commands to achieve desired rates. The dynamic inversion controller uses approximate short period and roll axis dynamics. The yaw axis controller is a sideslip rate command system. Methods are described to reduce the cross-coupling effect and maintain adequate tracking errors for control surface failures. The aerodynamic failure destabilizes the pitching moment due to angle of attack. The results show that control of the aircraft with the neural networks is easier (more damped) than without the neural networks. Simulation results show neural network augmentation of the controller improves performance with aerodynamic and control surface failures in terms of tracking error and cross-coupling reduction.

  3. Development of an automatic frequency control system for an X-band (=9300 MHz) RF electron linear accelerator

    NASA Astrophysics Data System (ADS)

    Cha, Sungsu; Kim, Yujong; Lee, Byung Cheol; Park, Hyung Dal; Lee, Seung Hyun; Buaphad, Pikad

    2017-05-01

    KAERI is developing a 6 MeV X-band radio frequency (RF) electron linear accelerator for medical purposes. The proposed X-band accelerator consists of an e-gun, an accelerating structure, two solenoid magnets, two steering magnets, a magnetron, a modulator, and an automatic frequency control (AFC) system. The accelerating structure of the component consists of oxygen-free high-conductivity copper (OFHC). Therefore, the ambient temperature changes the volume, and the resonance frequency of the accelerating structure also changes. If the RF frequency of a 9300 MHz magnetron and the resonance frequency of the accelerating structure do not match, it can degrade the performance. That is, it will decrease the output power, lower the beam current, decrease the X-ray dose rate, increase the reflection power, and result in unstable operation of the accelerator. Accelerator operation should be possible at any time during all four seasons. To prevent humans from being exposed to radiation when it is operated, the accelerator should also be operable through remote monitoring and remote control. Therefore, the AFC system is designed to meet these requirements; it is configured based on the concept of a phase-locked loop (PLL) model, which includes an RF section, an intermediate frequency (IF) [1-3] section, and a local oscillator (LO) section. Some resonance frequency controllers use a DC motor, chain, and potentiometer to store the position and tune the frequency [4,5]. Our AFC system uses a step motor to tune the RF frequency of the magnetron. The maximum tuning turn number of our magnetron frequency tuning shaft is ten. Since the RF frequency of our magnetron is 9300±25 MHz, it gives 5 MHz (∵±25 MHz/10 turns → 50 MHz/10 turns =5 MHz/turn) frequency tuning per turn. The rotation angle of our step motor is 0.72° per step and the total step number per one rotation is 360°/0.72°=500 steps. Therefore, the tuning range per step is 10 kHz/step (=5 MHz per turn/500 steps per

  4. Prospects for laser-driven ion acceleration through controlled displacement of electrons by standing waves

    NASA Astrophysics Data System (ADS)

    Magnusson, J.; Mackenroth, F.; Marklund, M.; Gonoskov, A.

    2018-05-01

    During the interaction of intense femtosecond laser pulses with various targets, the natural mechanisms of laser energy transformation inherently lack temporal control and thus commonly do not provide opportunities for a controlled generation of a well-collimated, high-charge beam of ions with a given energy of particular interest. In an effort to alleviate this problem, it was recently proposed that the ions can be dragged by an electron bunch trapped in a controllably moving potential well formed by laser radiation. Such standing-wave acceleration (SWA) can be achieved through reflection of a chirped laser pulse from a mirror, which has been formulated as the concept of chirped-standing-wave acceleration (CSWA). Here, we analyse general feasibility aspects of the SWA approach and demonstrate its reasonable robustness against field structure imperfections, such as those caused by misalignment, ellipticity, and limited contrast. Using this, we also identify prospects and limitations of the CSWA concept.

  5. Distributed communications and control network for robotic mining

    NASA Technical Reports Server (NTRS)

    Schiffbauer, William H.

    1989-01-01

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

  6. Speech networks at rest and in action: interactions between functional brain networks controlling speech production.

    PubMed

    Simonyan, Kristina; Fuertinger, Stefan

    2015-04-01

    Speech production is one of the most complex human behaviors. Although brain activation during speaking has been well investigated, our understanding of interactions between the brain regions and neural networks remains scarce. We combined seed-based interregional correlation analysis with graph theoretical analysis of functional MRI data during the resting state and sentence production in healthy subjects to investigate the interface and topology of functional networks originating from the key brain regions controlling speech, i.e., the laryngeal/orofacial motor cortex, inferior frontal and superior temporal gyri, supplementary motor area, cingulate cortex, putamen, and thalamus. During both resting and speaking, the interactions between these networks were bilaterally distributed and centered on the sensorimotor brain regions. However, speech production preferentially recruited the inferior parietal lobule (IPL) and cerebellum into the large-scale network, suggesting the importance of these regions in facilitation of the transition from the resting state to speaking. Furthermore, the cerebellum (lobule VI) was the most prominent region showing functional influences on speech-network integration and segregation. Although networks were bilaterally distributed, interregional connectivity during speaking was stronger in the left vs. right hemisphere, which may have underlined a more homogeneous overlap between the examined networks in the left hemisphere. Among these, the laryngeal motor cortex (LMC) established a core network that fully overlapped with all other speech-related networks, determining the extent of network interactions. Our data demonstrate complex interactions of large-scale brain networks controlling speech production and point to the critical role of the LMC, IPL, and cerebellum in the formation of speech production network. Copyright © 2015 the American Physiological Society.

  7. Accelerating the Mining of Influential Nodes in Complex Networks through Community Detection

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

    Halappanavar, Mahantesh; Sathanur, Arun V.; Nandi, Apurba

    Computing the set of influential nodes with a given size to ensure maximal spread of influence on a complex network is a challenging problem impacting multiple applications. A rigorous approach to influence maximization involves utilization of optimization routines that comes with a high computational cost. In this work, we propose to exploit the existence of communities in complex networks to accelerate the mining of influential seeds. We provide intuitive reasoning to explain why our approach should be able to provide speedups without significantly degrading the extent of the spread of influence when compared to the case of influence maximization withoutmore » using the community information. Additionally, we have parallelized the complete workflow by leveraging an existing parallel implementation of the Louvain community detection algorithm. We then conduct a series of experiments on a dataset with three representative graphs to first verify our implementation and then demonstrate the speedups. Our method achieves speedups ranging from 3x - 28x for graphs with small number of communities while nearly matching or even exceeding the activation performance on the entire graph. Complexity analysis reveals that dramatic speedups are possible for larger graphs that contain a correspondingly larger number of communities. In addition to the speedups obtained from the utilization of the community structure, scalability results show up to 6.3x speedup on 20 cores relative to the baseline run on 2 cores. Finally, current limitations of the approach are outlined along with the planned next steps.« less

  8. Slip Ratio Estimation and Regenerative Brake Control for Decelerating Electric Vehicles without Detection of Vehicle Velocity and Acceleration

    NASA Astrophysics Data System (ADS)

    Suzuki, Toru; Fujimoto, Hiroshi

    In slip ratio control systems, it is necessary to detect the vehicle velocity in order to obtain the slip ratio. However, it is very difficult to measure this velocity directly. We have proposed slip ratio estimation and control methods that do not require the vehicle velocity with acceleration. In this paper, the slip ratio estimation and control methods are proposed without detecting the vehicle velocity and acceleration when it is decelerating. We carried out simulations and experiments by using an electric vehicle to verify the effectiveness of the proposed method.

  9. Adaptive neural network motion control of manipulators with experimental evaluations.

    PubMed

    Puga-Guzmán, S; Moreno-Valenzuela, J; Santibáñez, V

    2014-01-01

    A nonlinear proportional-derivative controller plus adaptive neuronal network compensation is proposed. With the aim of estimating the desired torque, a two-layer neural network is used. Then, adaptation laws for the neural network weights are derived. Asymptotic convergence of the position and velocity tracking errors is proven, while the neural network weights are shown to be uniformly bounded. The proposed scheme has been experimentally validated in real time. These experimental evaluations were carried in two different mechanical systems: a horizontal two degrees-of-freedom robot and a vertical one degree-of-freedom arm which is affected by the gravitational force. In each one of the two experimental set-ups, the proposed scheme was implemented without and with adaptive neural network compensation. Experimental results confirmed the tracking accuracy of the proposed adaptive neural network-based controller.

  10. Adaptive Neural Network Motion Control of Manipulators with Experimental Evaluations

    PubMed Central

    Puga-Guzmán, S.; Moreno-Valenzuela, J.; Santibáñez, V.

    2014-01-01

    A nonlinear proportional-derivative controller plus adaptive neuronal network compensation is proposed. With the aim of estimating the desired torque, a two-layer neural network is used. Then, adaptation laws for the neural network weights are derived. Asymptotic convergence of the position and velocity tracking errors is proven, while the neural network weights are shown to be uniformly bounded. The proposed scheme has been experimentally validated in real time. These experimental evaluations were carried in two different mechanical systems: a horizontal two degrees-of-freedom robot and a vertical one degree-of-freedom arm which is affected by the gravitational force. In each one of the two experimental set-ups, the proposed scheme was implemented without and with adaptive neural network compensation. Experimental results confirmed the tracking accuracy of the proposed adaptive neural network-based controller. PMID:24574910

  11. Digital Signal Processing and Control for the Study of Gene Networks

    NASA Astrophysics Data System (ADS)

    Shin, Yong-Jun

    2016-04-01

    Thanks to the digital revolution, digital signal processing and control has been widely used in many areas of science and engineering today. It provides practical and powerful tools to model, simulate, analyze, design, measure, and control complex and dynamic systems such as robots and aircrafts. Gene networks are also complex dynamic systems which can be studied via digital signal processing and control. Unlike conventional computational methods, this approach is capable of not only modeling but also controlling gene networks since the experimental environment is mostly digital today. The overall aim of this article is to introduce digital signal processing and control as a useful tool for the study of gene networks.

  12. Digital Signal Processing and Control for the Study of Gene Networks.

    PubMed

    Shin, Yong-Jun

    2016-04-22

    Thanks to the digital revolution, digital signal processing and control has been widely used in many areas of science and engineering today. It provides practical and powerful tools to model, simulate, analyze, design, measure, and control complex and dynamic systems such as robots and aircrafts. Gene networks are also complex dynamic systems which can be studied via digital signal processing and control. Unlike conventional computational methods, this approach is capable of not only modeling but also controlling gene networks since the experimental environment is mostly digital today. The overall aim of this article is to introduce digital signal processing and control as a useful tool for the study of gene networks.

  13. Prediction of force and acceleration control spectra for Space Shuttle orbiter sidewall-mounted payloads

    NASA Technical Reports Server (NTRS)

    Hipol, Philip J.

    1990-01-01

    The development of force and acceleration control spectra for vibration testing of Space Shuttle (STS) orbiter sidewall-mounted payloads requiresreliable estimates of the sidewall apparent weight and free (i.e. unloaded) vibration during lift-off. The feasibility of analytically predicting these quantities has been investigated through the development and analysis of a finite element model of the STS cargo bay. Analytical predictions of the sidewall apparent weight were compared with apparent weight measurements made on OV-101, and analytical predictions of the sidewall free vibration response during lift-off were compared with flight measurements obtained from STS-3 and STS-4. These analysis suggest that the cargo bay finite element model has potential application for the estimation of force and acceleration control spectra for STS sidewall-mounted payloads.

  14. Design and Flight Tests of an Adaptive Control System Employing Normal-Acceleration Command

    NASA Technical Reports Server (NTRS)

    McNeill, Water E.; McLean, John D.; Hegarty, Daniel M.; Heinle, Donovan R.

    1961-01-01

    An adaptive control system employing normal-acceleration command has been designed with the aid of an analog computer and has been flight tested. The design of the system was based on the concept of using a mathematical model in combination with a high gain and a limiter. The study was undertaken to investigate the application of a system of this type to the task of maintaining nearly constant dynamic longitudinal response of a piloted airplane over the flight envelope without relying on air data measurements for gain adjustment. The range of flight conditions investigated was between Mach numbers of 0.36 and 1.15 and altitudes of 10,000 and 40,000 feet. The final adaptive system configuration was derived from analog computer tests, in which the physical airplane control system and much of the control circuitry were included in the loop. The method employed to generate the feedback signals resulted in a model whose characteristics varied somewhat with changes in flight condition. Flight results showed that the system limited the variation in longitudinal natural frequency of the adaptive airplane to about half that of the basic airplane and that, for the subsonic cases, the damping ratio was maintained between 0.56 and 0.69. The system also automatically compensated for the transonic trim change. Objectionable features of the system were an exaggerated sensitivity of pitch attitude to gust disturbances, abnormally large pitch attitude response for a given pilot input at low speeds, and an initial delay in normal-acceleration response to pilot control at all flight conditions. The adaptive system chatter of +/-0.05 to +/-0.10 of elevon at about 9 cycles per second (resulting in a maximum airplane normal-acceleration response of from +/-0.025 g to +/- 0.035 g) was considered by the pilots to be mildly objectionable but tolerable.

  15. Controlling laser driven protons acceleration using a deformable mirror at a high repetition rate

    NASA Astrophysics Data System (ADS)

    Noaman-ul-Haq, M.; Sokollik, T.; Ahmed, H.; Braenzel, J.; Ehrentraut, L.; Mirzaie, M.; Yu, L.-L.; Sheng, Z. M.; Chen, L. M.; Schnürer, M.; Zhang, J.

    2018-03-01

    We present results from a proof-of-principle experiment to optimize laser driven protons acceleration by directly feeding back its spectral information to a deformable mirror (DM) controlled by evolutionary algorithms (EAs). By irradiating a stable high-repetition rate tape driven target with ultra-intense pulses of intensities ∼1020 W/ cm2, we optimize the maximum energy of the accelerated protons with a stability of less than ∼5% fluctuations near optimum value. Moreover, due to spatio-temporal development of the sheath field, modulations in the spectrum are also observed. Particularly, a prominent narrow peak is observed with a spread of ∼15% (FWHM) at low energy part of the spectrum. These results are helpful to develop high repetition rate optimization techniques required for laser-driven ion accelerators.

  16. Synchronization Control of Neural Networks With State-Dependent Coefficient Matrices.

    PubMed

    Zhang, Junfeng; Zhao, Xudong; Huang, Jun

    2016-11-01

    This brief is concerned with synchronization control of a class of neural networks with state-dependent coefficient matrices. Being different from the existing drive-response neural networks in the literature, a novel model of drive-response neural networks is established. The concepts of uniformly ultimately bounded (UUB) synchronization and convex hull Lyapunov function are introduced. Then, by using the convex hull Lyapunov function approach, the UUB synchronization design of the drive-response neural networks is proposed, and a delay-independent control law guaranteeing the bounded synchronization of the neural networks is constructed. All present conditions are formulated in terms of bilinear matrix inequalities. By comparison, it is shown that the neural networks obtained in this brief are less conservative than those ones in the literature, and the bounded synchronization is suitable for the novel drive-response neural networks. Finally, an illustrative example is given to verify the validity of the obtained results.

  17. Research and knowledge in Ontario tobacco control networks.

    PubMed

    Bickford, Julia J; Kothari, Anita R

    2008-01-01

    This study sought to better understand the role of research knowledge in Ontario tobacco control networks by asking: 1) How is research managed; 2) How is research evaluated; and 3) How is research utilized? This is a secondary analysis of a qualitative study based on individual semistructured interviews with 29 participants between January and May 2006. These participants were purposefully sampled from across four Ministries in the provincial government (n = 7), non-government (n = 15), and public health organizations (n = 7). Interviews were transcribed verbatim and coded and analyzed using QSR N7 qualitative software. This study received ethics approval from The University of Western Ontario Health Research Ethics Board. There exists a dissonance between the preference for peer-reviewed, unbiased, non-partisan knowledge to support claims and the need for fast, "real-time" information on which to base tobacco-related policy decisions. Second, there is a great deal of tacit knowledge held by experts within the Ontario tobacco control community. The networks among government, non-government, and public health organizations are the structures through which tacit knowledge is exchanged. These networks are dynamic, fluid and shifting. There exists a gap in the production and utilization of research knowledge for tobacco control policy. Tacit knowledge held by experts in Ontario tobacco control networks is an integral means of managing and evaluating research knowledge. Finally, this study builds on Weiss's concept of tactical model of evidence use by highlighting the utilization of research to enhance one's credibility.

  18. Huntington's disease accelerates epigenetic aging of human brain and disrupts DNA methylation levels.

    PubMed

    Horvath, Steve; Langfelder, Peter; Kwak, Seung; Aaronson, Jeff; Rosinski, Jim; Vogt, Thomas F; Eszes, Marika; Faull, Richard L M; Curtis, Maurice A; Waldvogel, Henry J; Choi, Oi-Wa; Tung, Spencer; Vinters, Harry V; Coppola, Giovanni; Yang, X William

    2016-07-01

    Age of Huntington's disease (HD) motoric onset is strongly related to the number of CAG trinucleotide repeats in the huntingtin gene, suggesting that biological tissue age plays an important role in disease etiology. Recently, a DNA methylation based biomarker of tissue age has been advanced as an epigenetic aging clock. We sought to inquire if HD is associated with an accelerated epigenetic age. DNA methylation data was generated for 475 brain samples from various brain regions of 26 HD cases and 39 controls. Overall, brain regions from HD cases exhibit a significant epigenetic age acceleration effect (p=0.0012). A multivariate model analysis suggests that HD status increases biological age by 3.2 years. Accelerated epigenetic age can be observed in specific brain regions (frontal lobe, parietal lobe, and cingulate gyrus). After excluding controls, we observe a negative correlation (r=-0.41, p=5.5×10-8) between HD gene CAG repeat length and the epigenetic age of HD brain samples. Using correlation network analysis, we identify 11 co-methylation modules with a significant association with HD status across 3 broad cortical regions. In conclusion, HD is associated with an accelerated epigenetic age of specific brain regions and more broadly with substantial changes in brain methylation levels.

  19. Huntington's disease accelerates epigenetic aging of human brain and disrupts DNA methylation levels

    PubMed Central

    Horvath, Steve; Langfelder, Peter; Kwak, Seung; Aaronson, Jeff; Rosinski, Jim; Vogt, Thomas F.; Eszes, Marika; Faull, Richard L.M.; Curtis, Maurice A.; Waldvogel, Henry J.; Choi, Oi-Wa; Tung, Spencer; Vinters, Harry V.; Coppola, Giovanni; Yang, X. William

    2016-01-01

    Age of Huntington's disease (HD) motoric onset is strongly related to the number of CAG trinucleotide repeats in the huntingtin gene, suggesting that biological tissue age plays an important role in disease etiology. Recently, a DNA methylation based biomarker of tissue age has been advanced as an epigenetic aging clock. We sought to inquire if HD is associated with an accelerated epigenetic age. DNA methylation data was generated for 475 brain samples from various brain regions of 26 HD cases and 39 controls. Overall, brain regions from HD cases exhibit a significant epigenetic age acceleration effect (p=0.0012). A multivariate model analysis suggests that HD status increases biological age by 3.2 years. Accelerated epigenetic age can be observed in specific brain regions (frontal lobe, parietal lobe, and cingulate gyrus). After excluding controls, we observe a negative correlation (r=−0.41, p=5.5×10−8) between HD gene CAG repeat length and the epigenetic age of HD brain samples. Using correlation network analysis, we identify 11 co-methylation modules with a significant association with HD status across 3 broad cortical regions. In conclusion, HD is associated with an accelerated epigenetic age of specific brain regions and more broadly with substantial changes in brain methylation levels. PMID:27479945

  20. fMRI reveals two distinct cerebral networks subserving speech motor control.

    PubMed

    Riecker, A; Mathiak, K; Wildgruber, D; Erb, M; Hertrich, I; Grodd, W; Ackermann, H

    2005-02-22

    There are few data on the cerebral organization of motor aspects of speech production and the pathomechanisms of dysarthric deficits subsequent to brain lesions and diseases. The authors used fMRI to further examine the neural basis of speech motor control. In eight healthy volunteers, fMRI was performed during syllable repetitions synchronized to click trains (2 to 6 Hz; vs a passive listening task). Bilateral hemodynamic responses emerged at the level of the mesiofrontal and sensorimotor cortex, putamen/pallidum, thalamus, and cerebellum (two distinct activation spots at either side). In contrast, dorsolateral premotor cortex and anterior insula showed left-sided activation. Calculation of rate/response functions revealed a negative linear relationship between repetition frequency and blood oxygen level-dependent (BOLD) signal change within the striatum, whereas both cerebellar hemispheres exhibited a step-wise increase of activation at approximately 3 Hz. Analysis of the temporal dynamics of the BOLD effect found the various cortical and subcortical brain regions engaged in speech motor control to be organized into two separate networks (medial and dorsolateral premotor cortex, anterior insula, and superior cerebellum vs sensorimotor cortex, basal ganglia, and inferior cerebellum). These data provide evidence for two levels of speech motor control bound, most presumably, to motor preparation and execution processes. They also help to explain clinical observations such as an unimpaired or even accelerated speaking rate in Parkinson disease and slowed speech tempo, which does not fall below a rate of 3 Hz, in cerebellar disorders.

  1. Three Eras in Global Tobacco Control: How Global Governance Processes Influenced Online Tobacco Control Networking

    PubMed Central

    Wipfli, Heather; Chu, Kar-Hai; Lancaster, Molly; Valente, Thomas

    2017-01-01

    Online networks can serve as a platform to diffuse policy innovations and enhance global health governance. This study focuses on how shifts in global health governance may influence related online networks. We compare social network metrics (average degree centrality [AVGD], density [D] and clustering coefficient [CC]) of Globalink, an online network of tobacco control advocates, across three eras in global tobacco control governance; pre-Framework Convention on Tobacco Control (FCTC) policy transfer (1992–1998), global regime formation through the FCTC negotiations (1999–2005), and philanthropic funding through the Bloomberg Initiative (2006–2012). Prior to 1999, Globalink was driven by a handful of high-income countries (AVGD=1.908 D=0.030, CC=0.215). The FCTC negotiations (1999–2005) corresponded with a rapid uptick in the number of countries represented within Globalink and new members were most often brought into the network through relationships with regional neighbors (AVGD=2.824, D=0.021, CC=0.253). Between 2006 and 2012, the centrality of the US in the network increases significantly (AVGD=3.414, D=0.023, CC=0.310). The findings suggest that global institutionalization through WHO, as with the FCTC, can lead to the rapid growth of decentralized online networks. Alternatively, private initiatives, such as the Bloomberg Initiative, can lead to clustering in which a single source of information gains increasing influence over an online network. PMID:28596813

  2. Network Coded Cooperative Communication in a Real-Time Wireless Hospital Sensor Network.

    PubMed

    Prakash, R; Balaji Ganesh, A; Sivabalan, Somu

    2017-05-01

    The paper presents a network coded cooperative communication (NC-CC) enabled wireless hospital sensor network architecture for monitoring health as well as postural activities of a patient. A wearable device, referred as a smartband is interfaced with pulse rate, body temperature sensors and an accelerometer along with wireless protocol services, such as Bluetooth and Radio-Frequency transceiver and Wi-Fi. The energy efficiency of wearable device is improved by embedding a linear acceleration based transmission duty cycling algorithm (NC-DRDC). The real-time demonstration is carried-out in a hospital environment to evaluate the performance characteristics, such as power spectral density, energy consumption, signal to noise ratio, packet delivery ratio and transmission offset. The resource sharing and energy efficiency features of network coding technique are improved by proposing an algorithm referred as network coding based dynamic retransmit/rebroadcast decision control (LA-TDC). From the experimental results, it is observed that the proposed LA-TDC algorithm reduces network traffic and end-to-end delay by an average of 27.8% and 21.6%, respectively than traditional network coded wireless transmission. The wireless architecture is deployed in a hospital environment and results are then successfully validated.

  3. STIMULUS: End-System Network Interface Controller for 100 Gb/s Wide Area Networks

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

    Zarkesh-Ha, Payman

    2014-09-12

    The main goal of this research grant is to develop a system-level solution leveraging novel technologies that enable network communications at 100 Gb/s or beyond. University of New Mexico in collaboration with Acadia Optronics LLC has been working on this project to develop the 100 Gb/s Network Interface Controller (NIC) under this Department of Energy (DOE) grant.

  4. Control of quasi-monoenergetic electron beams from laser-plasma accelerators with adjustable shock density profile

    NASA Astrophysics Data System (ADS)

    Tsai, Hai-En; Swanson, Kelly K.; Barber, Sam K.; Lehe, Remi; Mao, Hann-Shin; Mittelberger, Daniel E.; Steinke, Sven; Nakamura, Kei; van Tilborg, Jeroen; Schroeder, Carl; Esarey, Eric; Geddes, Cameron G. R.; Leemans, Wim

    2018-04-01

    The injection physics in a shock-induced density down-ramp injector was characterized, demonstrating precise control of a laser-plasma accelerator (LPA). Using a jet-blade assembly, experiments systematically varied the shock injector profile, including shock angle, shock position, up-ramp width, and acceleration length. Our work demonstrates that beam energy, energy spread, and pointing can be controlled by adjusting these parameters. As a result, an electron beam that was highly tunable from 25 to 300 MeV with 8% energy spread (ΔEFWHM/E), 1.5 mrad divergence, and 0.35 mrad pointing fluctuation was produced. Particle-in-cell simulation characterized how variation in the shock angle and up-ramp width impacted the injection process. This highly controllable LPA represents a suitable, compact electron beam source for LPA applications such as Thomson sources and free-electron lasers.

  5. Design of optimal nonlinear network controllers for Alzheimer's disease.

    PubMed

    Sanchez-Rodriguez, Lazaro M; Iturria-Medina, Yasser; Baines, Erica A; Mallo, Sabela C; Dousty, Mehdy; Sotero, Roberto C

    2018-05-01

    Brain stimulation can modulate the activity of neural circuits impaired by Alzheimer's disease (AD), having promising clinical benefit. However, all individuals with the same condition currently receive identical brain stimulation, with limited theoretical basis for this generic approach. In this study, we introduce a control theory framework for obtaining exogenous signals that revert pathological electroencephalographic activity in AD at a minimal energetic cost, while reflecting patients' biological variability. We used anatomical networks obtained from diffusion magnetic resonance images acquired by the Alzheimer's Disease Neuroimaging Initiative (ADNI) as mediators for the interaction between Duffing oscillators. The nonlinear nature of the brain dynamics is preserved, given that we extend the so-called state-dependent Riccati equation control to reflect the stimulation objective in the high-dimensional neural system. By considering nonlinearities in our model, we identified regions for which control inputs fail to correct abnormal activity. There are changes to the way stimulated regions are ranked in terms of the energetic cost of controlling the entire network, from a linear to a nonlinear approach. We also found that limbic system and basal ganglia structures constitute the top target locations for stimulation in AD. Patients with highly integrated anatomical networks-namely, networks having low average shortest path length, high global efficiency-are the most suitable candidates for the propagation of stimuli and consequent success on the control task. Other diseases associated with alterations in brain dynamics and the self-control mechanisms of the brain can be addressed through our framework.

  6. Wireless Sensor Networks: Monitoring and Control

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

    Hastbacka, Mildred; Ponoum, Ratcharit; Bouza, Antonio

    2013-05-31

    The article discusses wireless sensor technologies for building energy monitoring and control. This article, also, addresses wireless sensor networks as well as benefits and challenges of using wireless sensors. The energy savings and market potential of wireless sensors are reviewed.

  7. Experiments in Neural-Network Control of a Free-Flying Space Robot

    NASA Technical Reports Server (NTRS)

    Wilson, Edward

    1995-01-01

    Four important generic issues are identified and addressed in some depth in this thesis as part of the development of an adaptive neural network based control system for an experimental free flying space robot prototype. The first issue concerns the importance of true system level design of the control system. A new hybrid strategy is developed here, in depth, for the beneficial integration of neural networks into the total control system. A second important issue in neural network control concerns incorporating a priori knowledge into the neural network. In many applications, it is possible to get a reasonably accurate controller using conventional means. If this prior information is used purposefully to provide a starting point for the optimizing capabilities of the neural network, it can provide much faster initial learning. In a step towards addressing this issue, a new generic Fully Connected Architecture (FCA) is developed for use with backpropagation. A third issue is that neural networks are commonly trained using a gradient based optimization method such as backpropagation; but many real world systems have Discrete Valued Functions (DVFs) that do not permit gradient based optimization. One example is the on-off thrusters that are common on spacecraft. A new technique is developed here that now extends backpropagation learning for use with DVFs. The fourth issue is that the speed of adaptation is often a limiting factor in the implementation of a neural network control system. This issue has been strongly resolved in the research by drawing on the above new contributions.

  8. The Weight-control Information Network (WIN) | NIH MedlinePlus the Magazine

    MedlinePlus

    ... Javascript on. Feature: Reducing Childhood Obesity The Weight-control Information Network (WIN) Past Issues / Spring - Summer 2010 ... overweight children, here are tips from the Weight-control Information Network (WIN), an information service of the ...

  9. Network-targeted cerebellar transcranial magnetic stimulation improves attentional control

    PubMed Central

    Esterman, Michael; Thai, Michelle; Okabe, Hidefusa; DeGutis, Joseph; Saad, Elyana; Laganiere, Simon E.; Halko, Mark A.

    2018-01-01

    Developing non-invasive brain stimulation interventions to improve attentional control is extremely relevant to a variety of neurologic and psychiatric populations, yet few studies have identified reliable biomarkers that can be readily modified to improve attentional control. One potential biomarker of attention is functional connectivity in the core cortical network supporting attention - the dorsal attention network (DAN). We used a network-targeted cerebellar transcranial magnetic stimulation (TMS) procedure, intended to enhance cortical functional connectivity in the DAN. Specifically, in healthy young adults we administered intermittent theta burst TMS (iTBS) to the midline cerebellar node of the DAN and, as a control, the right cerebellar node of the default mode network (DMN). These cerebellar targets were localized using individual resting-state fMRI scans. Participants completed assessments of both sustained (gradual onset continuous performance task, gradCPT) and transient attentional control (attentional blink) immediately before and after stimulation, in two sessions (cerebellar DAN and DMN). Following cerebellar DAN stimulation, participants had significantly fewer attentional lapses (lower commission error rates) on the gradCPT. In contrast, stimulation to the cerebellar DMN did not affect gradCPT performance. Further, in the DAN condition, individuals with worse baseline gradCPT performance showed the greatest enhancement in gradCPT performance. These results suggest that temporarily increasing functional connectivity in the DAN via network-targeted cerebellar stimulation can enhance sustained attention, particularly in those with poor baseline performance. With regard to transient attention, TMS stimulation improved attentional blink performance across both stimulation sites, suggesting increasing functional connectivity in both networks can enhance this aspect of attention. These findings have important implications for intervention applications

  10. The Contribution of Network Organization and Integration to the Development of Cognitive Control

    PubMed Central

    Marek, Scott; Hwang, Kai; Foran, William; Hallquist, Michael N.; Luna, Beatriz

    2015-01-01

    Abstract Cognitive control, which continues to mature throughout adolescence, is supported by the ability for well-defined organized brain networks to flexibly integrate information. However, the development of intrinsic brain network organization and its relationship to observed improvements in cognitive control are not well understood. In the present study, we used resting state functional magnetic resonance imaging (RS-fMRI), graph theory, the antisaccade task, and rigorous head motion control to characterize and relate developmental changes in network organization, connectivity strength, and integration to inhibitory control development. Subjects were 192 10–26-y-olds who were imaged during 5 min of rest. In contrast to initial studies, our results indicate that network organization is stable throughout adolescence. However, cross-network integration, predominantly of the cingulo-opercular/salience network, increased with age. Importantly, this increased integration of the cingulo-opercular/salience network significantly moderated the robust effect of age on the latency to initiate a correct inhibitory control response. These results provide compelling evidence that the transition to adult-level inhibitory control is dependent upon the refinement and strengthening of integration between specialized networks. Our findings support a novel, two-stage model of neural development, in which networks stabilize prior to adolescence and subsequently increase their integration to support the cross-domain incorporation of information processing critical for mature cognitive control. PMID:26713863

  11. The Contribution of Network Organization and Integration to the Development of Cognitive Control.

    PubMed

    Marek, Scott; Hwang, Kai; Foran, William; Hallquist, Michael N; Luna, Beatriz

    2015-12-01

    Cognitive control, which continues to mature throughout adolescence, is supported by the ability for well-defined organized brain networks to flexibly integrate information. However, the development of intrinsic brain network organization and its relationship to observed improvements in cognitive control are not well understood. In the present study, we used resting state functional magnetic resonance imaging (RS-fMRI), graph theory, the antisaccade task, and rigorous head motion control to characterize and relate developmental changes in network organization, connectivity strength, and integration to inhibitory control development. Subjects were 192 10-26-y-olds who were imaged during 5 min of rest. In contrast to initial studies, our results indicate that network organization is stable throughout adolescence. However, cross-network integration, predominantly of the cingulo-opercular/salience network, increased with age. Importantly, this increased integration of the cingulo-opercular/salience network significantly moderated the robust effect of age on the latency to initiate a correct inhibitory control response. These results provide compelling evidence that the transition to adult-level inhibitory control is dependent upon the refinement and strengthening of integration between specialized networks. Our findings support a novel, two-stage model of neural development, in which networks stabilize prior to adolescence and subsequently increase their integration to support the cross-domain incorporation of information processing critical for mature cognitive control.

  12. Connected Dominating Set Based Topology Control in Wireless Sensor Networks

    ERIC Educational Resources Information Center

    He, Jing

    2012-01-01

    Wireless Sensor Networks (WSNs) are now widely used for monitoring and controlling of systems where human intervention is not desirable or possible. Connected Dominating Sets (CDSs) based topology control in WSNs is one kind of hierarchical method to ensure sufficient coverage while reducing redundant connections in a relatively crowded network.…

  13. Effects of Edge Directions on the Structural Controllability of Complex Networks

    PubMed Central

    Xiao, Yandong; Lao, Songyang; Hou, Lvlin; Small, Michael; Bai, Liang

    2015-01-01

    Recent advances indicate that assigning or reversing edge direction can significantly improve the structural controllability of complex networks. For directed networks, approaching the optimal structural controllability can be achieved by detecting and reversing certain “inappropriate” edge directions. However, the existence of multiple sets of “inappropriate” edge directions suggests that different edges have different effects on optimal controllability—that is, different combinations of edges can be reversed to achieve the same structural controllability. Therefore, we classify edges into three categories based on their direction: critical, redundant and intermittent. We then investigate the effects of changing these edge directions on network controllability, and demonstrate that the existence of more critical edge directions implies not only a lower cost of modifying inappropriate edges but also better controllability. Motivated by this finding, we present a simple edge orientation method aimed at producing more critical edge directions—utilizing only local information—which achieves near optimal controllability. Furthermore, we explore the effects of edge direction on the controllability of several real networks. PMID:26281042

  14. Neural network control of a parallel hybrid-electric propulsion system for a small unmanned aerial vehicle

    NASA Astrophysics Data System (ADS)

    Harmon, Frederick G.

    2005-11-01

    Parallel hybrid-electric propulsion systems would be beneficial for small unmanned aerial vehicles (UAVs) used for military, homeland security, and disaster-monitoring missions. The benefits, due to the hybrid and electric-only modes, include increased time-on-station and greater range as compared to electric-powered UAVs and stealth modes not available with gasoline-powered UAVs. This dissertation contributes to the research fields of small unmanned aerial vehicles, hybrid-electric propulsion system control, and intelligent control. A conceptual design of a small UAV with a parallel hybrid-electric propulsion system is provided. The UAV is intended for intelligence, surveillance, and reconnaissance (ISR) missions. A conceptual design reveals the trade-offs that must be considered to take advantage of the hybrid-electric propulsion system. The resulting hybrid-electric propulsion system is a two-point design that includes an engine primarily sized for cruise speed and an electric motor and battery pack that are primarily sized for a slower endurance speed. The electric motor provides additional power for take-off, climbing, and acceleration and also serves as a generator during charge-sustaining operation or regeneration. The intelligent control of the hybrid-electric propulsion system is based on an instantaneous optimization algorithm that generates a hyper-plane from the nonlinear efficiency maps for the internal combustion engine, electric motor, and lithium-ion battery pack. The hyper-plane incorporates charge-depletion and charge-sustaining strategies. The optimization algorithm is flexible and allows the operator/user to assign relative importance between the use of gasoline, electricity, and recharging depending on the intended mission. A MATLAB/Simulink model was developed to test the control algorithms. The Cerebellar Model Arithmetic Computer (CMAC) associative memory neural network is applied to the control of the UAVs parallel hybrid

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

    NASA Astrophysics Data System (ADS)

    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

  16. Linear control theory for gene network modeling.

    PubMed

    Shin, Yong-Jun; Bleris, Leonidas

    2010-09-16

    Systems biology is an interdisciplinary field that aims at understanding complex interactions in cells. Here we demonstrate that linear control theory can provide valuable insight and practical tools for the characterization of complex biological networks. We provide the foundation for such analyses through the study of several case studies including cascade and parallel forms, feedback and feedforward loops. We reproduce experimental results and provide rational analysis of the observed behavior. We demonstrate that methods such as the transfer function (frequency domain) and linear state-space (time domain) can be used to predict reliably the properties and transient behavior of complex network topologies and point to specific design strategies for synthetic networks.

  17. Characterization and control of small-world networks.

    PubMed

    Pandit, S A; Amritkar, R E

    1999-08-01

    Recently, Watts and Strogatz [Nature (London) 393, 440 (1998)] offered an interesting model of small-world networks. Here we concretize the concept of a "faraway" connection in a network by defining a far edge. Our definition is algorithmic and independent of any external parameters such as topology of the underlying space of the network. We show that it is possible to control the spread of an epidemic by using the knowledge of far edges. We also suggest a model for better product advertisement using the far edges. Our findings indicate that the number of far edges can be a good intrinsic parameter to characterize small-world phenomena.

  18. Characterization and control of small-world networks

    NASA Astrophysics Data System (ADS)

    Pandit, S. A.; Amritkar, R. E.

    1999-08-01

    Recently, Watts and Strogatz [Nature (London) 393, 440 (1998)] offered an interesting model of small-world networks. Here we concretize the concept of a ``faraway'' connection in a network by defining a far edge. Our definition is algorithmic and independent of any external parameters such as topology of the underlying space of the network. We show that it is possible to control the spread of an epidemic by using the knowledge of far edges. We also suggest a model for better product advertisement using the far edges. Our findings indicate that the number of far edges can be a good intrinsic parameter to characterize small-world phenomena.

  19. Muscle networks: Connectivity analysis of EMG activity during postural control

    NASA Astrophysics Data System (ADS)

    Boonstra, Tjeerd W.; Danna-Dos-Santos, Alessander; Xie, Hong-Bo; Roerdink, Melvyn; Stins, John F.; Breakspear, Michael

    2015-12-01

    Understanding the mechanisms that reduce the many degrees of freedom in the musculoskeletal system remains an outstanding challenge. Muscle synergies reduce the dimensionality and hence simplify the control problem. How this is achieved is not yet known. Here we use network theory to assess the coordination between multiple muscles and to elucidate the neural implementation of muscle synergies. We performed connectivity analysis of surface EMG from ten leg muscles to extract the muscle networks while human participants were standing upright in four different conditions. We observed widespread connectivity between muscles at multiple distinct frequency bands. The network topology differed significantly between frequencies and between conditions. These findings demonstrate how muscle networks can be used to investigate the neural circuitry of motor coordination. The presence of disparate muscle networks across frequencies suggests that the neuromuscular system is organized into a multiplex network allowing for parallel and hierarchical control structures.

  20. Synchronization criteria for generalized reaction-diffusion neural networks via periodically intermittent control.

    PubMed

    Gan, Qintao; Lv, Tianshi; Fu, Zhenhua

    2016-04-01

    In this paper, the synchronization problem for a class of generalized neural networks with time-varying delays and reaction-diffusion terms is investigated concerning Neumann boundary conditions in terms of p-norm. The proposed generalized neural networks model includes reaction-diffusion local field neural networks and reaction-diffusion static neural networks as its special cases. By establishing a new inequality, some simple and useful conditions are obtained analytically to guarantee the global exponential synchronization of the addressed neural networks under the periodically intermittent control. According to the theoretical results, the influences of diffusion coefficients, diffusion space, and control rate on synchronization are analyzed. Finally, the feasibility and effectiveness of the proposed methods are shown by simulation examples, and by choosing different diffusion coefficients, diffusion spaces, and control rates, different controlled synchronization states can be obtained.

  1. Neural network feedforward control of a closed-circuit wind tunnel

    NASA Astrophysics Data System (ADS)

    Sutcliffe, Peter

    Accurate control of wind-tunnel test conditions can be dramatically enhanced using feedforward control architectures which allow operating conditions to be maintained at a desired setpoint through the use of mathematical models as the primary source of prediction. However, as the desired accuracy of the feedforward prediction increases, the model complexity also increases, so that an ever increasing computational load is incurred. This drawback can be avoided by employing a neural network that is trained offline using the output of a high fidelity wind-tunnel mathematical model, so that the neural network can rapidly reproduce the predictions of the model with a greatly reduced computational overhead. A novel neural network database generation method, developed through the use of fractional factorial arrays, was employed such that a neural network can accurately predict wind-tunnel parameters across a wide range of operating conditions whilst trained upon a highly efficient database. The subsequent network was incorporated into a Neural Network Model Predictive Control (NNMPC) framework to allow an optimised output schedule capable of providing accurate control of the wind-tunnel operating parameters. Facilitation of an optimised path through the solution space is achieved through the use of a chaos optimisation algorithm such that a more globally optimum solution is likely to be found with less computational expense than the gradient descent method. The parameters associated with the NNMPC such as the control horizon are determined through the use of a Taguchi methodology enabling the minimum number of experiments to be carried out to determine the optimal combination. The resultant NNMPC scheme was employed upon the Hessert Low Speed Wind Tunnel at the University of Notre Dame to control the test-section temperature such that it follows a pre-determined reference trajectory during changes in the test-section velocity. Experimental testing revealed that the

  2. Application-oriented integrated control center (AICC) for heterogeneous optical networks

    NASA Astrophysics Data System (ADS)

    Zhao, Yongli; Zhang, Jie; Cao, Xuping; Wang, Dajiang; Wu, Koubo; Cai, Yinxiang; Gu, Wanyi

    2011-12-01

    Various broad bandwidth services have being swallowing the bandwidth resource of optical networks, such as the data center application and cloud computation. There are still some challenges for future optical networks although the available bandwidth is increasing with the development of transmission technologies. The relationship between upper application layer and lower network resource layer is necessary to be researched further. In order to improve the efficiency of network resources and capability of service provisioning, heterogeneous optical networks resource can be abstracted as unified Application Programming Interfaces (APIs) which can be open to various upper applications through Application-oriented Integrated Control Center (AICC) proposed in the paper. A novel Openflow-based unified control architecture is proposed for the optimization of cross layer resources. Numeric results show good performance of AICC through simulation experiments.

  3. Modeling and control of magnetorheological fluid dampers using neural networks

    NASA Astrophysics Data System (ADS)

    Wang, D. H.; Liao, W. H.

    2005-02-01

    Due to the inherent nonlinear nature of magnetorheological (MR) fluid dampers, one of the challenging aspects for utilizing these devices to achieve high system performance is the development of accurate models and control algorithms that can take advantage of their unique characteristics. In this paper, the direct identification and inverse dynamic modeling for MR fluid dampers using feedforward and recurrent neural networks are studied. The trained direct identification neural network model can be used to predict the damping force of the MR fluid damper on line, on the basis of the dynamic responses across the MR fluid damper and the command voltage, and the inverse dynamic neural network model can be used to generate the command voltage according to the desired damping force through supervised learning. The architectures and the learning methods of the dynamic neural network models and inverse neural network models for MR fluid dampers are presented, and some simulation results are discussed. Finally, the trained neural network models are applied to predict and control the damping force of the MR fluid damper. Moreover, validation methods for the neural network models developed are proposed and used to evaluate their performance. Validation results with different data sets indicate that the proposed direct identification dynamic model using the recurrent neural network can be used to predict the damping force accurately and the inverse identification dynamic model using the recurrent neural network can act as a damper controller to generate the command voltage when the MR fluid damper is used in a semi-active mode.

  4. Digital Signal Processing and Control for the Study of Gene Networks

    PubMed Central

    Shin, Yong-Jun

    2016-01-01

    Thanks to the digital revolution, digital signal processing and control has been widely used in many areas of science and engineering today. It provides practical and powerful tools to model, simulate, analyze, design, measure, and control complex and dynamic systems such as robots and aircrafts. Gene networks are also complex dynamic systems which can be studied via digital signal processing and control. Unlike conventional computational methods, this approach is capable of not only modeling but also controlling gene networks since the experimental environment is mostly digital today. The overall aim of this article is to introduce digital signal processing and control as a useful tool for the study of gene networks. PMID:27102828

  5. Congestion control strategy on complex network with privilege traffic

    NASA Astrophysics Data System (ADS)

    Li, Shi-Bao; He, Ya; Liu, Jian-Hang; Zhang, Zhi-Gang; Huang, Jun-Wei

    The congestion control of traffic is one of the most important studies in complex networks. In the previous congestion algorithms, all the network traffic is assumed to have the same priority, and the privilege of traffic is ignored. In this paper, a privilege and common traffic congestion control routing strategy (PCR) based on the different priority of traffic is proposed, which can be devised to cope with the different traffic congestion situations. We introduce the concept of privilege traffic in traffic dynamics for the first time and construct a new traffic model which taking into account requirements with different priorities. Besides, a new factor Ui is introduced by the theoretical derivation to characterize the interaction between different traffic routing selection, furthermore, Ui is related to the network throughput. Since the joint optimization among different kinds of traffic is accomplished by PCR, the maximum value of Ui can be significantly reduced and the network performance can be improved observably. The simulation results indicate that the network throughput with PCR has a better performance than the other strategies. Moreover, the network capacity is improved by 25% at least. Additionally, the network throughput is also influenced by privilege traffic number and traffic priority.

  6. Flexible body control using neural networks

    NASA Technical Reports Server (NTRS)

    Mccullough, Claire L.

    1992-01-01

    Progress is reported on the control of Control Structures Interaction suitcase demonstrator (a flexible structure) using neural networks and fuzzy logic. It is concluded that while control by neural nets alone (i.e., allowing the net to design a controller with no human intervention) has yielded less than optimal results, the neural net trained to emulate the existing fuzzy logic controller does produce acceptible system responses for the initial conditions examined. Also, a neural net was found to be very successful in performing the emulation step necessary for the anticipatory fuzzy controller for the CSI suitcase demonstrator. The fuzzy neural hybrid, which exhibits good robustness and noise rejection properties, shows promise as a controller for practical flexible systems, and should be further evaluated.

  7. High performances of dual network PVA hydrogel modified by PVP using borax as the structure-forming accelerator

    PubMed Central

    Huang, Min; Hou, Yi; Li, Yubao; Wang, Danqing; Zhang, Li

    2017-01-01

    Abstract A dual network hydrogel made up of polyvinylalcohol (PVA) crosslinked by borax and polyvinylpyrrolidone (PVP) was prepared by means of freezing-thawing circles. Here PVP was incorporated by linking with PVA to form a network structure, while the introduction of borax played the role of crosslinking PVA chains to accelerate the formation of a dual network structure in PVA/PVP composite hydrogel, thus endowing the hydrogel with high mechanical properties. The effects of both PVP and borax on the hydrogels were evaluated by comparing the two systems of PVA/PVP/borax and PVA/borax hydrogels. In the former system, adding 4.0% PVP not only increased the water content and the storage modulus but also enhanced the mechanical strength of the final hydrogel. But an overdose of PVP just as more than 4.0% tended to undermine the structure of hydrogels, and thus deteriorated hydrogels’ properties because of the weakened secondary interaction between PVP and PVA. Likewise, increasing borax could promote the gel crosslinking degree, thus making gels show a decrease in water content and swelling ratio, meanwhile shrinking the pores inside the hydrogels and finally enhancing the mechanical strength of hydrogels prominently. The developed hydrogel with high performances holds great potential for applications in biomedical and industrial fields. PMID:29491822

  8. Overlapping Networks Engaged during Spoken Language Production and Its Cognitive Control

    PubMed Central

    Wise, Richard J.S.; Mehta, Amrish; Leech, Robert

    2014-01-01

    Spoken language production is a complex brain function that relies on large-scale networks. These include domain-specific networks that mediate language-specific processes, as well as domain-general networks mediating top-down and bottom-up attentional control. Language control is thought to involve a left-lateralized fronto-temporal-parietal (FTP) system. However, these regions do not always activate for language tasks and similar regions have been implicated in nonlinguistic cognitive processes. These inconsistent findings suggest that either the left FTP is involved in multidomain cognitive control or that there are multiple spatially overlapping FTP systems. We present evidence from an fMRI study using multivariate analysis to identify spatiotemporal networks involved in spoken language production in humans. We compared spoken language production (Speech) with multiple baselines, counting (Count), nonverbal decision (Decision), and “rest,” to pull apart the multiple partially overlapping networks that are involved in speech production. A left-lateralized FTP network was activated during Speech and deactivated during Count and nonverbal Decision trials, implicating it in cognitive control specific to sentential spoken language production. A mirror right-lateralized FTP network was activated in the Count and Decision trials, but not Speech. Importantly, a second overlapping left FTP network showed relative deactivation in Speech. These three networks, with distinct time courses, overlapped in the left parietal lobe. Contrary to the standard model of the left FTP as being dominant for speech, we revealed a more complex pattern within the left FTP, including at least two left FTP networks with competing functional roles, only one of which was activated in speech production. PMID:24966373

  9. Overlapping networks engaged during spoken language production and its cognitive control.

    PubMed

    Geranmayeh, Fatemeh; Wise, Richard J S; Mehta, Amrish; Leech, Robert

    2014-06-25

    Spoken language production is a complex brain function that relies on large-scale networks. These include domain-specific networks that mediate language-specific processes, as well as domain-general networks mediating top-down and bottom-up attentional control. Language control is thought to involve a left-lateralized fronto-temporal-parietal (FTP) system. However, these regions do not always activate for language tasks and similar regions have been implicated in nonlinguistic cognitive processes. These inconsistent findings suggest that either the left FTP is involved in multidomain cognitive control or that there are multiple spatially overlapping FTP systems. We present evidence from an fMRI study using multivariate analysis to identify spatiotemporal networks involved in spoken language production in humans. We compared spoken language production (Speech) with multiple baselines, counting (Count), nonverbal decision (Decision), and "rest," to pull apart the multiple partially overlapping networks that are involved in speech production. A left-lateralized FTP network was activated during Speech and deactivated during Count and nonverbal Decision trials, implicating it in cognitive control specific to sentential spoken language production. A mirror right-lateralized FTP network was activated in the Count and Decision trials, but not Speech. Importantly, a second overlapping left FTP network showed relative deactivation in Speech. These three networks, with distinct time courses, overlapped in the left parietal lobe. Contrary to the standard model of the left FTP as being dominant for speech, we revealed a more complex pattern within the left FTP, including at least two left FTP networks with competing functional roles, only one of which was activated in speech production. Copyright © 2014 Geranmayeh et al.

  10. Symptom-specific amygdala hyperactivity modulates motor control network in conversion disorder.

    PubMed

    Hassa, Thomas; Sebastian, Alexandra; Liepert, Joachim; Weiller, Cornelius; Schmidt, Roger; Tüscher, Oliver

    2017-01-01

    Initial historical accounts as well as recent data suggest that emotion processing is dysfunctional in conversion disorder patients and that this alteration may be the pathomechanistic neurocognitive basis for symptoms in conversion disorder. However, to date evidence of direct interaction of altered negative emotion processing with motor control networks in conversion disorder is still lacking. To specifically study the neural correlates of emotion processing interacting with motor networks we used a task combining emotional and sensorimotor stimuli both separately as well as simultaneously during functional magnetic resonance imaging in a well characterized group of 13 conversion disorder patients with functional hemiparesis and 19 demographically matched healthy controls. We performed voxelwise statistical parametrical mapping for a priori regions of interest within emotion processing and motor control networks. Psychophysiological interaction (PPI) was used to test altered functional connectivity of emotion and motor control networks. Only during simultaneous emotional stimulation and passive movement of the affected hand patients displayed left amygdala hyperactivity. PPI revealed increased functional connectivity in patients between the left amygdala and the (pre-)supplemental motor area and the subthalamic nucleus, key regions within the motor control network. These findings suggest a novel mechanistic direct link between dysregulated emotion processing and motor control circuitry in conversion disorder.

  11. Formation Control over Delayed Communication Network

    NASA Astrophysics Data System (ADS)

    Secchi, Cristian; Fantuzzi, Cesare

    In this Chapter we address the problem of formation control of a group of robots that exchange information over a communication network characterized by a non negligible delay. We consider the Virtual Body Artificial Potential approach for stabilizing a group of robots at a desired formation. We show that it is possible to model the controlled group of robots as a port-Hamiltonian system and we exploit the scattering framework to achieve a passive behavior of the controlled system and to stabilize the robots in the desired formation independently of any communication delay.

  12. High Resolution Sensing and Control of Urban Water Networks

    NASA Astrophysics Data System (ADS)

    Bartos, M. D.; Wong, B. P.; Kerkez, B.

    2016-12-01

    We present a framework to enable high-resolution sensing, modeling, and control of urban watersheds using (i) a distributed sensor network based on low-cost cellular-enabled motes, (ii) hydraulic models powered by a cloud computing infrastructure, and (iii) automated actuation valves that allow infrastructure to be controlled in real time. This platform initiates two major advances. First, we achieve a high density of measurements in urban environments, with an anticipated 40+ sensors over each urban area of interest. In addition to new measurements, we also illustrate the design and evaluation of a "smart" control system for real-world hydraulic networks. This control system improves water quality and mitigates flooding by using real-time hydraulic models to adaptively control releases from retention basins. We evaluate the potential of this platform through two ongoing deployments: (i) a flood monitoring network in the Dallas-Fort Worth metropolitan area that detects and anticipates floods at the level of individual roadways, and (ii) a real-time hydraulic control system in the city of Ann Arbor, MI—soon to be one of the most densely instrumented urban watersheds in the United States. Through these applications, we demonstrate that distributed sensing and control of water infrastructure can improve flash flood predictions, emergency response, and stormwater contaminant mitigation.

  13. Extended analytical formulas for the perturbed Keplerian motion under a constant control acceleration

    NASA Astrophysics Data System (ADS)

    Zuiani, Federico; Vasile, Massimiliano

    2015-03-01

    This paper presents a set of analytical formulae for the perturbed Keplerian motion of a spacecraft under the effect of a constant control acceleration. The proposed set of formulae can treat control accelerations that are fixed in either a rotating or inertial reference frame. Moreover, the contribution of the zonal harmonic is included in the analytical formulae. It will be shown that the proposed analytical theory allows for the fast computation of long, multi-revolution spirals while maintaining good accuracy. The combined effect of different perturbations and of the shadow regions due to solar eclipse is also included. Furthermore, a simplified control parameterisation is introduced to optimise thrusting patterns with two thrust arcs and two cost arcs per revolution. This simple parameterisation is shown to ensure enough flexibility to describe complex low thrust spirals. The accuracy and speed of the proposed analytical formulae are compared against a full numerical integration with different integration schemes. An averaging technique is then proposed as an application of the analytical formulae. Finally, the paper presents an example of design of an optimal low-thrust spiral to transfer a spacecraft from an elliptical to a circular orbit around the Earth.

  14. Value of peripheral nodes in controlling multilayer scale-free networks

    NASA Astrophysics Data System (ADS)

    Zhang, Yan; Garas, Antonios; Schweitzer, Frank

    2016-01-01

    We analyze the controllability of a two-layer network, where driver nodes can be chosen randomly only from one layer. Each layer contains a scale-free network with directed links and the node dynamics depends on the incoming links from other nodes. We combine the in-degree and out-degree values to assign an importance value w to each node, and distinguish between peripheral nodes with low w and central nodes with high w . Based on numerical simulations, we find that the controllable part of the network is larger when choosing low w nodes to connect the two layers. The control is as efficient when peripheral nodes are driver nodes as it is for the case of more central nodes. However, if we assume a cost to utilize nodes that is proportional to their overall degree, utilizing peripheral nodes to connect the two layers or to act as driver nodes is not only the most cost-efficient solution, it is also the one that performs best in controlling the two-layer network among the different interconnecting strategies we have tested.

  15. Extrinsic and Intrinsic Brain Network Connectivity Maintains Cognition across the Lifespan Despite Accelerated Decay of Regional Brain Activation.

    PubMed

    Tsvetanov, Kamen A; Henson, Richard N A; Tyler, Lorraine K; Razi, Adeel; Geerligs, Linda; Ham, Timothy E; Rowe, James B

    2016-03-16

    The maintenance of wellbeing across the lifespan depends on the preservation of cognitive function. We propose that successful cognitive aging is determined by interactions both within and between large-scale functional brain networks. Such connectivity can be estimated from task-free functional magnetic resonance imaging (fMRI), also known as resting-state fMRI (rs-fMRI). However, common correlational methods are confounded by age-related changes in the neurovascular signaling. To estimate network interactions at the neuronal rather than vascular level, we used generative models that specified both the neural interactions and a flexible neurovascular forward model. The networks' parameters were optimized to explain the spectral dynamics of rs-fMRI data in 602 healthy human adults from population-based cohorts who were approximately uniformly distributed between 18 and 88 years (www.cam-can.com). We assessed directed connectivity within and between three key large-scale networks: the salience network, dorsal attention network, and default mode network. We found that age influences connectivity both within and between these networks, over and above the effects on neurovascular coupling. Canonical correlation analysis revealed that the relationship between network connectivity and cognitive function was age-dependent: cognitive performance relied on neural dynamics more strongly in older adults. These effects were driven partly by reduced stability of neural activity within all networks, as expressed by an accelerated decay of neural information. Our findings suggest that the balance of excitatory connectivity between networks, and the stability of intrinsic neural representations within networks, changes with age. The cognitive function of older adults becomes increasingly dependent on these factors. Maintaining cognitive function is critical to successful aging. To study the neural basis of cognitive function across the lifespan, we studied a large population

  16. Extrinsic and Intrinsic Brain Network Connectivity Maintains Cognition across the Lifespan Despite Accelerated Decay of Regional Brain Activation

    PubMed Central

    Henson, Richard N.A.; Tyler, Lorraine K.; Razi, Adeel; Geerligs, Linda; Ham, Timothy E.; Rowe, James B.

    2016-01-01

    The maintenance of wellbeing across the lifespan depends on the preservation of cognitive function. We propose that successful cognitive aging is determined by interactions both within and between large-scale functional brain networks. Such connectivity can be estimated from task-free functional magnetic resonance imaging (fMRI), also known as resting-state fMRI (rs-fMRI). However, common correlational methods are confounded by age-related changes in the neurovascular signaling. To estimate network interactions at the neuronal rather than vascular level, we used generative models that specified both the neural interactions and a flexible neurovascular forward model. The networks' parameters were optimized to explain the spectral dynamics of rs-fMRI data in 602 healthy human adults from population-based cohorts who were approximately uniformly distributed between 18 and 88 years (www.cam-can.com). We assessed directed connectivity within and between three key large-scale networks: the salience network, dorsal attention network, and default mode network. We found that age influences connectivity both within and between these networks, over and above the effects on neurovascular coupling. Canonical correlation analysis revealed that the relationship between network connectivity and cognitive function was age-dependent: cognitive performance relied on neural dynamics more strongly in older adults. These effects were driven partly by reduced stability of neural activity within all networks, as expressed by an accelerated decay of neural information. Our findings suggest that the balance of excitatory connectivity between networks, and the stability of intrinsic neural representations within networks, changes with age. The cognitive function of older adults becomes increasingly dependent on these factors. SIGNIFICANCE STATEMENT Maintaining cognitive function is critical to successful aging. To study the neural basis of cognitive function across the lifespan, we studied a

  17. Networked event-triggered control: an introduction and research trends

    NASA Astrophysics Data System (ADS)

    Mahmoud, Magdi S.; Sabih, Muhammad

    2014-11-01

    A physical system can be studied as either continuous time or discrete-time system depending upon the control objectives. Discrete-time control systems can be further classified into two categories based on the sampling: (1) time-triggered control systems and (2) event-triggered control systems. Time-triggered systems sample states and calculate controls at every sampling instant in a periodic fashion, even in cases when states and calculated control do not change much. This indicates unnecessary and useless data transmission and computation efforts of a time-triggered system, thus inefficiency. For networked systems, the transmission of measurement and control signals, thus, cause unnecessary network traffic. Event-triggered systems, on the other hand, have potential to reduce the communication burden in addition to reducing the computation of control signals. This paper provides an up-to-date survey on the event-triggered methods for control systems and highlights the potential research directions.

  18. Control of quasi-monoenergetic electron beams from laser-plasma accelerators with adjustable shock density profile

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

    Tsai, Hai-En; Swanson, Kelly K.; Barber, Sam K.

    The injection physics in a shock-induced density down-ramp injector was characterized, demonstrating precise control of a laser-plasma accelerator (LPA). Using a jet-blade assembly, experiments systematically v aried the shock injector profile, including shock angle, shock position, up-ramp width, and acceleration length. Our work demonstrates that beam energy, energy spread, and pointing can be controlled by adjusting these parameters. As a result, an electron beam that was highly tunable from 25 to 300 MeV with 8% energy spread (ΔE FWHM/E), 1.5 mrad divergence, and 0.35 mrad pointing fluctuation was produced. Particle-in-cell simulation characterized how variation in the shock angle and up-rampmore » width impacted the injection process. This highly controllable LPA represents a suitable, compact electron beam source for LPA applications such as Thomson sources and free-electron lasers.« less

  19. Control of quasi-monoenergetic electron beams from laser-plasma accelerators with adjustable shock density profile

    DOE PAGES

    Tsai, Hai-En; Swanson, Kelly K.; Barber, Sam K.; ...

    2018-04-13

    The injection physics in a shock-induced density down-ramp injector was characterized, demonstrating precise control of a laser-plasma accelerator (LPA). Using a jet-blade assembly, experiments systematically v aried the shock injector profile, including shock angle, shock position, up-ramp width, and acceleration length. Our work demonstrates that beam energy, energy spread, and pointing can be controlled by adjusting these parameters. As a result, an electron beam that was highly tunable from 25 to 300 MeV with 8% energy spread (ΔE FWHM/E), 1.5 mrad divergence, and 0.35 mrad pointing fluctuation was produced. Particle-in-cell simulation characterized how variation in the shock angle and up-rampmore » width impacted the injection process. This highly controllable LPA represents a suitable, compact electron beam source for LPA applications such as Thomson sources and free-electron lasers.« less

  20. Investigating the feasibility of temperature-controlled accelerated drug release testing for an intravaginal ring.

    PubMed

    Externbrink, Anna; Clark, Meredith R; Friend, David R; Klein, Sandra

    2013-11-01

    The objective of the present study was to investigate if temperature can be utilized to accelerate drug release from Nuvaring®, a reservoir type intravaginal ring based on polyethylene vinyl acetate copolymer that releases a constant dose of contraceptive steroids over a duration of 3 weeks. The reciprocating holder apparatus (USP 7) was utilized to determine real-time and accelerated etonogestrel release from ring segments. It was demonstrated that drug release increased with increasing temperature which can be attributed to enhanced drug diffusion. An Arrhenius relationship of the zero-order release constants was established, indicating that temperature is a valid parameter to accelerate drug release from this dosage form and that the release mechanism is maintained under these accelerated test conditions. Accelerated release tests are particularly useful for routine quality control to assist during batch release of extended release formulations that typically release the active over several weeks, months or even years, since they can increase the product shelf life. The accelerated method should therefore be able to discriminate between formulations with different release characteristics that can result from normal manufacturing variance. In the case of Nuvaring®, it is well known that the process parameters during the extrusion process strongly influence the polymeric structure. These changes in the polymeric structure can affect the permeability which, in turn, is reflected in the release properties. Results from this study indicate that changes in the polymeric structure can lead to a different temperature dependence of the release rate, and as a consequence, the accelerated method can become less sensitive to detect changes in the release properties. When the accelerated method is utilized during batch release, it is therefore important to take this possible restriction into account and to evaluate the accelerated method with samples from non

  1. Stochastic Characterization of Communication Network Latency for Wide Area Grid Control Applications.

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

    Ameme, Dan Selorm Kwami; Guttromson, Ross

    This report characterizes communications network latency under various network topologies and qualities of service (QoS). The characterizations are probabilistic in nature, allowing deeper analysis of stability for Internet Protocol (IP) based feedback control systems used in grid applications. The work involves the use of Raspberry Pi computers as a proxy for a controlled resource, and an ns-3 network simulator on a Linux server to create an experimental platform (testbed) that can be used to model wide-area grid control network communications in smart grid. Modbus protocol is used for information transport, and Routing Information Protocol is used for dynamic route selectionmore » within the simulated network.« less

  2. Analog neural network control method proposed for use in a backup satellite control mode

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

    Frigo, J.R.; Tilden, M.W.

    1998-03-01

    The authors propose to use an analog neural network controller implemented in hardware, independent of the active control system, for use in a satellite backup control mode. The controller uses coarse sun sensor inputs. The field of view of the sensors activate the neural controller, creating an analog dead band with respect to the direction of the sun on each axis. This network controls the orientation of the vehicle toward the sunlight to ensure adequate power for the system. The attitude of the spacecraft is stabilized with respect to the ambient magnetic field on orbit. This paper develops a modelmore » of the controller using real-time coarse sun sensor data and a dynamic model of a prototype system based on a satellite system. The simulation results and the feasibility of this control method for use in a satellite backup control mode are discussed.« less

  3. Converging Redundant Sensor Network Information for Improved Building Control

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

    Dale Tiller; D. Phil; Gregor Henze

    2007-09-30

    This project investigated the development and application of sensor networks to enhance building energy management and security. Commercial, industrial and residential buildings often incorporate systems used to determine occupancy, but current sensor technology and control algorithms limit the effectiveness of these systems. For example, most of these systems rely on single monitoring points to detect occupancy, when more than one monitoring point could improve system performance. Phase I of the project focused on instrumentation and data collection. During the initial project phase, a new occupancy detection system was developed, commissioned and installed in a sample of private offices and open-planmore » office workstations. Data acquisition systems were developed and deployed to collect data on space occupancy profiles. Phase II of the project demonstrated that a network of several sensors provides a more accurate measure of occupancy than is possible using systems based on single monitoring points. This phase also established that analysis algorithms could be applied to the sensor network data stream to improve the accuracy of system performance in energy management and security applications. In Phase III of the project, the sensor network from Phase I was complemented by a control strategy developed based on the results from the first two project phases: this controller was implemented in a small sample of work areas, and applied to lighting control. Two additional technologies were developed in the course of completing the project. A prototype web-based display that portrays the current status of each detector in a sensor network monitoring building occupancy was designed and implemented. A new capability that enables occupancy sensors in a sensor network to dynamically set the 'time delay' interval based on ongoing occupant behavior in the space was also designed and implemented.« less

  4. Identification of control targets in Boolean molecular network models via computational algebra.

    PubMed

    Murrugarra, David; Veliz-Cuba, Alan; Aguilar, Boris; Laubenbacher, Reinhard

    2016-09-23

    Many problems in biomedicine and other areas of the life sciences can be characterized as control problems, with the goal of finding strategies to change a disease or otherwise undesirable state of a biological system into another, more desirable, state through an intervention, such as a drug or other therapeutic treatment. The identification of such strategies is typically based on a mathematical model of the process to be altered through targeted control inputs. This paper focuses on processes at the molecular level that determine the state of an individual cell, involving signaling or gene regulation. The mathematical model type considered is that of Boolean networks. The potential control targets can be represented by a set of nodes and edges that can be manipulated to produce a desired effect on the system. This paper presents a method for the identification of potential intervention targets in Boolean molecular network models using algebraic techniques. The approach exploits an algebraic representation of Boolean networks to encode the control candidates in the network wiring diagram as the solutions of a system of polynomials equations, and then uses computational algebra techniques to find such controllers. The control methods in this paper are validated through the identification of combinatorial interventions in the signaling pathways of previously reported control targets in two well studied systems, a p53-mdm2 network and a blood T cell lymphocyte granular leukemia survival signaling network. Supplementary data is available online and our code in Macaulay2 and Matlab are available via http://www.ms.uky.edu/~dmu228/ControlAlg . This paper presents a novel method for the identification of intervention targets in Boolean network models. The results in this paper show that the proposed methods are useful and efficient for moderately large networks.

  5. Backstepping fuzzy-neural-network control design for hybrid maglev transportation system.

    PubMed

    Wai, Rong-Jong; Yao, Jing-Xiang; Lee, Jeng-Dao

    2015-02-01

    This paper focuses on the design of a backstepping fuzzy-neural-network control (BFNNC) for the online levitated balancing and propulsive positioning of a hybrid magnetic levitation (maglev) transportation system. The dynamic model of the hybrid maglev transportation system including levitated hybrid electromagnets to reduce the suspension power loss and the friction force during linear movement and a propulsive linear induction motor based on the concepts of mechanical geometry and motion dynamics is first constructed. The ultimate goal is to design an online fuzzy neural network (FNN) control methodology to cope with the problem of the complicated control transformation and the chattering control effort in backstepping control (BSC) design, and to directly ensure the stability of the controlled system without the requirement of strict constraints, detailed system information, and auxiliary compensated controllers despite the existence of uncertainties. In the proposed BFNNC scheme, an FNN control is utilized to be the major control role by imitating the BSC strategy, and adaptation laws for network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. The effectiveness of the proposed control strategy for the hybrid maglev transportation system is verified by experimental results, and the superiority of the BFNNC scheme is indicated in comparison with the BSC strategy and the backstepping particle-swarm-optimization control system in previous research.

  6. Receiver-Assisted Congestion Control to Achieve High Throughput in Lossy Wireless Networks

    NASA Astrophysics Data System (ADS)

    Shi, Kai; Shu, Yantai; Yang, Oliver; Luo, Jiarong

    2010-04-01

    Many applications would require fast data transfer in high-speed wireless networks nowadays. However, due to its conservative congestion control algorithm, Transmission Control Protocol (TCP) cannot effectively utilize the network capacity in lossy wireless networks. In this paper, we propose a receiver-assisted congestion control mechanism (RACC) in which the sender performs loss-based control, while the receiver is performing delay-based control. The receiver measures the network bandwidth based on the packet interarrival interval and uses it to compute a congestion window size deemed appropriate for the sender. After receiving the advertised value feedback from the receiver, the sender then uses the additive increase and multiplicative decrease (AIMD) mechanism to compute the correct congestion window size to be used. By integrating the loss-based and the delay-based congestion controls, our mechanism can mitigate the effect of wireless losses, alleviate the timeout effect, and therefore make better use of network bandwidth. Simulation and experiment results in various scenarios show that our mechanism can outperform conventional TCP in high-speed and lossy wireless environments.

  7. Web-Based Interface for Command and Control of Network Sensors

    NASA Technical Reports Server (NTRS)

    Wallick, Michael N.; Doubleday, Joshua R.; Shams, Khawaja S.

    2010-01-01

    This software allows for the visualization and control of a network of sensors through a Web browser interface. It is currently being deployed for a network of sensors monitoring Mt. Saint Helen s volcano; however, this innovation is generic enough that it can be deployed for any type of sensor Web. From this interface, the user is able to fully control and monitor the sensor Web. This includes, but is not limited to, sending "test" commands to individual sensors in the network, monitoring for real-world events, and reacting to those events

  8. Accelerators (4/5)

    ScienceCinema

    Metral, Elias

    2017-12-09

    1a) Introduction and motivation 1b) History and accelerator types 2) Transverse beam dynamics 3a) Longitudinal beam dynamics 3b) Figure of merit of a synchrotron/collider 3c) Beam control 4) Main limiting factors 5) Technical challenges Prerequisite knowledge: Previous knowledge of accelerators is not required.

  9. Accelerators (5/5)

    ScienceCinema

    None

    2018-05-16

    1a) Introduction and motivation; 1b) History and accelerator types; 2) Transverse beam dynamics; 3a) Longitudinal beam dynamics; 3b) Figure of merit of a synchrotron/collider; 3c) Beam control; 4) Main limiting factors; 5) Technical challenges Prerequisite knowledge: Previous knowledge of accelerators is not required.

  10. Hierarchical Control Using Networks Trained with Higher-Level Forward Models

    PubMed Central

    Wayne, Greg; Abbott, L.F.

    2015-01-01

    We propose and develop a hierarchical approach to network control of complex tasks. In this approach, a low-level controller directs the activity of a “plant,” the system that performs the task. However, the low-level controller may only be able to solve fairly simple problems involving the plant. To accomplish more complex tasks, we introduce a higher-level controller that controls the lower-level controller. We use this system to direct an articulated truck to a specified location through an environment filled with static or moving obstacles. The final system consists of networks that have memorized associations between the sensory data they receive and the commands they issue. These networks are trained on a set of optimal associations that are generated by minimizing cost functions. Cost function minimization requires predicting the consequences of sequences of commands, which is achieved by constructing forward models, including a model of the lower-level controller. The forward models and cost minimization are only used during training, allowing the trained networks to respond rapidly. In general, the hierarchical approach can be extended to larger numbers of levels, dividing complex tasks into more manageable sub-tasks. The optimization procedure and the construction of the forward models and controllers can be performed in similar ways at each level of the hierarchy, which allows the system to be modified to perform other tasks, or to be extended for more complex tasks without retraining lower-levels. PMID:25058706

  11. [Network Design of the Spaceport Command and Control System

    NASA Technical Reports Server (NTRS)

    Teijeiro, Antonio

    2017-01-01

    I helped the Launch Control System (LCS) hardware team sustain the network design of the Spaceport Command and Control System. I wrote the procedure that will be used to satisfy an official hardware test for the hardware carrying data from the Launch Vehicle. I installed hardware and updated design documents in support of the ongoing development of the Spaceport Command and Control System and applied firewall experience I gained during my spring 2017 semester to inspect and create firewall security policies as requested. Finally, I completed several online courses concerning networking fundamentals and Unix operating systems.

  12. State feedback controller design for the synchronization of Boolean networks with time delays

    NASA Astrophysics Data System (ADS)

    Li, Fangfei; Li, Jianning; Shen, Lijuan

    2018-01-01

    State feedback control design to make the response Boolean network synchronize with the drive Boolean network is far from being solved in the literature. Motivated by this, this paper studies the feedback control design for the complete synchronization of two coupled Boolean networks with time delays. A necessary condition for the existence of a state feedback controller is derived first. Then the feedback control design procedure for the complete synchronization of two coupled Boolean networks is provided based on the necessary condition. Finally, an example is given to illustrate the proposed design procedure.

  13. Robust optimal control of material flows in demand-driven supply networks

    NASA Astrophysics Data System (ADS)

    Laumanns, Marco; Lefeber, Erjen

    2006-04-01

    We develop a model based on stochastic discrete-time controlled dynamical systems in order to derive optimal policies for controlling the material flow in supply networks. Each node in the network is described as a transducer such that the dynamics of the material and information flows within the entire network can be expressed by a system of first-order difference equations, where some inputs to the system act as external disturbances. We apply methods from constrained robust optimal control to compute the explicit control law as a function of the current state. For the numerical examples considered, these control laws correspond to certain classes of optimal ordering policies from inventory management while avoiding, however, any a priori assumptions about the general form of the policy.

  14. How actin network dynamics control the onset of actin-based motility

    PubMed Central

    Kawska, Agnieszka; Carvalho, Kévin; Manzi, John; Boujemaa-Paterski, Rajaa; Blanchoin, Laurent; Martiel, Jean-Louis; Sykes, Cécile

    2012-01-01

    Cells use their dynamic actin network to control their mechanics and motility. These networks are made of branched actin filaments generated by the Arp2/3 complex. Here we study under which conditions the microscopic organization of branched actin networks builds up a sufficient stress to trigger sustained motility. In our experimental setup, dynamic actin networks or “gels” are grown on a hard bead in a controlled minimal protein system containing actin monomers, profilin, the Arp2/3 complex and capping protein. We vary protein concentrations and follow experimentally and through simulations the shape and mechanical properties of the actin gel growing around beads. Actin gel morphology is controlled by elementary steps including “primer” contact, growth of the network, entanglement, mechanical interaction and force production. We show that varying the biochemical orchestration of these steps can lead to the loss of network cohesion and the lack of effective force production. We propose a predictive phase diagram of actin gel fate as a function of protein concentrations. This work unveils how, in growing actin networks, a tight biochemical and physical coupling smoothens initial primer-caused heterogeneities and governs force buildup and cell motility. PMID:22908255

  15. Role of Graph Architecture in Controlling Dynamical Networks with Applications to Neural Systems.

    PubMed

    Kim, Jason Z; Soffer, Jonathan M; Kahn, Ari E; Vettel, Jean M; Pasqualetti, Fabio; Bassett, Danielle S

    2018-01-01

    Networked systems display complex patterns of interactions between components. In physical networks, these interactions often occur along structural connections that link components in a hard-wired connection topology, supporting a variety of system-wide dynamical behaviors such as synchronization. While descriptions of these behaviors are important, they are only a first step towards understanding and harnessing the relationship between network topology and system behavior. Here, we use linear network control theory to derive accurate closed-form expressions that relate the connectivity of a subset of structural connections (those linking driver nodes to non-driver nodes) to the minimum energy required to control networked systems. To illustrate the utility of the mathematics, we apply this approach to high-resolution connectomes recently reconstructed from Drosophila, mouse, and human brains. We use these principles to suggest an advantage of the human brain in supporting diverse network dynamics with small energetic costs while remaining robust to perturbations, and to perform clinically accessible targeted manipulation of the brain's control performance by removing single edges in the network. Generally, our results ground the expectation of a control system's behavior in its network architecture, and directly inspire new directions in network analysis and design via distributed control.

  16. Role of graph architecture in controlling dynamical networks with applications to neural systems

    NASA Astrophysics Data System (ADS)

    Kim, Jason Z.; Soffer, Jonathan M.; Kahn, Ari E.; Vettel, Jean M.; Pasqualetti, Fabio; Bassett, Danielle S.

    2018-01-01

    Networked systems display complex patterns of interactions between components. In physical networks, these interactions often occur along structural connections that link components in a hard-wired connection topology, supporting a variety of system-wide dynamical behaviours such as synchronization. Although descriptions of these behaviours are important, they are only a first step towards understanding and harnessing the relationship between network topology and system behaviour. Here, we use linear network control theory to derive accurate closed-form expressions that relate the connectivity of a subset of structural connections (those linking driver nodes to non-driver nodes) to the minimum energy required to control networked systems. To illustrate the utility of the mathematics, we apply this approach to high-resolution connectomes recently reconstructed from Drosophila, mouse, and human brains. We use these principles to suggest an advantage of the human brain in supporting diverse network dynamics with small energetic costs while remaining robust to perturbations, and to perform clinically accessible targeted manipulation of the brain's control performance by removing single edges in the network. Generally, our results ground the expectation of a control system's behaviour in its network architecture, and directly inspire new directions in network analysis and design via distributed control.

  17. Graphs for information security control in software defined networks

    NASA Astrophysics Data System (ADS)

    Grusho, Alexander A.; Abaev, Pavel O.; Shorgin, Sergey Ya.; Timonina, Elena E.

    2017-07-01

    Information security control in software defined networks (SDN) is connected with execution of the security policy rules regulating information accesses and protection against distribution of the malicious code and harmful influences. The paper offers a representation of a security policy in the form of hierarchical structure which in case of distribution of resources for the solution of tasks defines graphs of admissible interactions in a networks. These graphs define commutation tables of switches via the SDN controller.

  18. Distributed control network for optogenetic experiments

    NASA Astrophysics Data System (ADS)

    Kasprowicz, G.; Juszczyk, B.; Mankiewicz, L.

    2014-11-01

    Nowadays optogenetic experiments are constructed to examine social behavioural relations in groups of animals. A novel concept of implantable device with distributed control network and advanced positioning capabilities is proposed. It is based on wireless energy transfer technology, micro-power radio interface and advanced signal processing.

  19. Closed-Loop Control of Complex Networks: A Trade-Off between Time and Energy

    NASA Astrophysics Data System (ADS)

    Sun, Yong-Zheng; Leng, Si-Yang; Lai, Ying-Cheng; Grebogi, Celso; Lin, Wei

    2017-11-01

    Controlling complex nonlinear networks is largely an unsolved problem at the present. Existing works focus either on open-loop control strategies and their energy consumptions or on closed-loop control schemes with an infinite-time duration. We articulate a finite-time, closed-loop controller with an eye toward the physical and mathematical underpinnings of the trade-off between the control time and energy as well as their dependence on the network parameters and structure. The closed-loop controller is tested on a large number of real systems including stem cell differentiation, food webs, random ecosystems, and spiking neuronal networks. Our results represent a step forward in developing a rigorous and general framework to control nonlinear dynamical networks with a complex topology.

  20. Review of EuCARD project on accelerator infrastructure in Europe

    NASA Astrophysics Data System (ADS)

    Romaniuk, Ryszard S.

    2013-01-01

    The aim of big infrastructural and research programs (like pan-European Framework Programs) and individual projects realized inside these programs in Europe is to structure the European Research Area - ERA in this way as to be competitive with the leaders of the world. One of this projects in EuCARD (European Coordination of Accelerator Research and Development) with the aim to structure and modernize accelerator, (including accelerators for big free electron laser machines) research infrastructure. This article presents the periodic development of EuCARD which took place between the annual meeting, April 2012 in Warsaw and SC meeting in Uppsala, December 2012. The background of all these efforts are achievements of the LHC machine and associated detectors in the race for new physics. The LHC machine works in the regime of p-p, Pb-p, Pb-Pb (protons and lead ions). Recently, a discovery by the LHC of Higgs like boson, has started vivid debates on the further potential of this machine and the future. The periodic EuCARD conference, workshop and meetings concern building of the research infrastructure, including in this advanced photonic and electronic systems for servicing large high energy physics experiments. There are debated a few basic groups of such systems like: measurement - control networks of large geometrical extent, multichannel systems for large amounts of metrological data acquisition, precision photonic networks of reference time, frequency and phase distribution. The aim of the discussion is not only summarize the current status but make plans and prepare practically to building new infrastructures. Accelerator science and technology is one of a key enablers of the developments in the particle physic, photon physics and also applications in medicine and industry. Accelerator technology is intensely developed in all developed nations and regions of the world. The EuCARD project contains a lot of subjects related directly and indirectly to photon

  1. Camera Control and Geo-Registration for Video Sensor Networks

    NASA Astrophysics Data System (ADS)

    Davis, James W.

    With the use of large video networks, there is a need to coordinate and interpret the video imagery for decision support systems with the goal of reducing the cognitive and perceptual overload of human operators. We present computer vision strategies that enable efficient control and management of cameras to effectively monitor wide-coverage areas, and examine the framework within an actual multi-camera outdoor urban video surveillance network. First, we construct a robust and precise camera control model for commercial pan-tilt-zoom (PTZ) video cameras. In addition to providing a complete functional control mapping for PTZ repositioning, the model can be used to generate wide-view spherical panoramic viewspaces for the cameras. Using the individual camera control models, we next individually map the spherical panoramic viewspace of each camera to a large aerial orthophotograph of the scene. The result provides a unified geo-referenced map representation to permit automatic (and manual) video control and exploitation of cameras in a coordinated manner. The combined framework provides new capabilities for video sensor networks that are of significance and benefit to the broad surveillance/security community.

  2. Identification of critical regulatory genes in cancer signaling network using controllability analysis

    NASA Astrophysics Data System (ADS)

    Ravindran, Vandana; Sunitha, V.; Bagler, Ganesh

    2017-05-01

    Cancer is characterized by a complex web of regulatory mechanisms which makes it difficult to identify features that are central to its control. Molecular integrative models of cancer, generated with the help of data from experimental assays, facilitate use of control theory to probe for ways of controlling the state of such a complex dynamic network. We modeled the human cancer signaling network as a directed graph and analyzed it for its controllability, identification of driver nodes and their characterization. We identified the driver nodes using the maximum matching algorithm and classified them as backbone, peripheral and ordinary based on their role in regulatory interactions and control of the network. We found that the backbone driver nodes were key to driving the regulatory network into cancer phenotype (via mutations) as well as for steering into healthy phenotype (as drug targets). This implies that while backbone genes could lead to cancer by virtue of mutations, they are also therapeutic targets of cancer. Further, based on their impact on the size of the set of driver nodes, genes were characterized as indispensable, dispensable and neutral. Indispensable nodes within backbone of the network emerged as central to regulatory mechanisms of control of cancer. In addition to probing the cancer signaling network from the perspective of control, our findings suggest that indispensable backbone driver nodes could be potentially leveraged as therapeutic targets. This study also illustrates the application of structural controllability for studying the mechanisms underlying the regulation of complex diseases.

  3. A Study of the Effects of Fieldbus Network Induced Delays on Control Systems

    ERIC Educational Resources Information Center

    Mainoo, Joseph

    2012-01-01

    Fieldbus networks are all-digital, two-way, multi-drop communication systems that are used to connect field devices such as sensors and actuators, and controllers. These fieldbus network systems are also called networked control systems (NCS). Although, there are different varieties of fieldbus networks such as Foundation Field Bus, DeviceNet, and…

  4. EDITORIAL: Laser and plasma accelerators Laser and plasma accelerators

    NASA Astrophysics Data System (ADS)

    Bingham, Robert

    2009-02-01

    by Chen et al where the driver, instead of being a laser, is a whistler wave known as the magnetowave plasma accelerator. The application to electron--positron plasmas that are found around pulsars is studied in the paper by Shukla, and to muon acceleration by Peano et al. Electron wakefield experiments are now concentrating on control and optimisation of high-quality beams that can be used as drivers for novel radiation sources. Studies by Thomas et al show that filamentation has a deleterious effect on the production of high quality mono-energetic electron beams and is caused by non-optimal choice of focusing geometry and/or electron density. It is crucial to match the focusing with the right plasma parameters and new types of plasma channels are being developed, such as the magnetically controlled plasma waveguide reported by Froula et al. The magnetic field provides a pressure profile shaping the channel to match the guiding conditions of the incident laser, resulting in predicted electron energies of 3GeV. In the forced laser-wakefield experiment Fang et al show that pump depletion reduces or inhibits the acceleration of electrons. One of the earlier laser acceleration concepts known as the beat wave may be revived due to the work by Kalmykov et al who report on all-optical control of nonlinear focusing of laser beams, allowing for stable propagation over several Rayleigh lengths with pre-injected electrons accelerated beyond 100 MeV. With the increasing number of petawatt lasers, attention is being focused on different acceleration regimes such as stochastic acceleration by counterpropagating laser pulses, the relativistic mirror, or the snow-plough effect leading to single-step acceleration reported by Mendonca. During wakefield acceleration the leading edge of the pulse undergoes frequency downshifting and head erosion as the laser energy is transferred to the wake while the trailing edge of the laser pulse undergoes frequency up-shift. This is commonly known

  5. Stability of Control Networks in Autonomous Homeostatic Regulation of Stem Cell Lineages.

    PubMed

    Komarova, Natalia L; van den Driessche, P

    2018-05-01

    Design principles of biological networks have been studied extensively in the context of protein-protein interaction networks, metabolic networks, and regulatory (transcriptional) networks. Here we consider regulation networks that occur on larger scales, namely the cell-to-cell signaling networks that connect groups of cells in multicellular organisms. These are the feedback loops that orchestrate the complex dynamics of cell fate decisions and are necessary for the maintenance of homeostasis in stem cell lineages. We focus on "minimal" networks that are those that have the smallest possible numbers of controls. For such minimal networks, the number of controls must be equal to the number of compartments, and the reducibility/irreducibility of the network (whether or not it can be split into smaller independent sub-networks) is defined by a matrix comprised of the cell number increments induced by each of the controlled processes in each of the compartments. Using the formalism of digraphs, we show that in two-compartment lineages, reducible systems must contain two 1-cycles, and irreducible systems one 1-cycle and one 2-cycle; stability follows from the signs of the controls and does not require magnitude restrictions. In three-compartment systems, irreducible digraphs have a tree structure or have one 3-cycle and at least two more shorter cycles, at least one of which is a 1-cycle. With further work and proper biological validation, our results may serve as a first step toward an understanding of ways in which these networks become dysregulated in cancer.

  6. Development of a decentralized multi-axis synchronous control approach for real-time networks.

    PubMed

    Xu, Xiong; Gu, Guo-Ying; Xiong, Zhenhua; Sheng, Xinjun; Zhu, Xiangyang

    2017-05-01

    The message scheduling and the network-induced delays of real-time networks, together with the different inertias and disturbances in different axes, make the synchronous control of the real-time network-based systems quite challenging. To address this challenge, a decentralized multi-axis synchronous control approach is developed in this paper. Due to the limitations of message scheduling and network bandwidth, error of the position synchronization is firstly defined in the proposed control approach as a subset of preceding-axis pairs. Then, a motion message estimator is designed to reduce the effect of network delays. It is proven that position and synchronization errors asymptotically converge to zero in the proposed controller with the delay compensation. Finally, simulation and experimental results show that the developed control approach can achieve the good position synchronization performance for the multi-axis motion over the real-time network. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  7. Predictive functional control for active queue management in congested TCP/IP networks.

    PubMed

    Bigdeli, N; Haeri, M

    2009-01-01

    Predictive functional control (PFC) as a new active queue management (AQM) method in dynamic TCP networks supporting explicit congestion notification (ECN) is proposed. The ability of the controller in handling system delay along with its simplicity and low computational load makes PFC a privileged AQM method in the high speed networks. Besides, considering the disturbance term (which represents model/process mismatches, external disturbances, and existing noise) in the control formulation adds some level of robustness into the PFC-AQM controller. This is an important and desired property in the control of dynamically-varying computer networks. In this paper, the controller is designed based on a small signal linearized fluid-flow model of the TCP/AQM networks. Then, closed-loop transfer function representation of the system is derived to analyze the robustness with respect to the network and controller parameters. The analytical as well as the packet-level ns-2 simulation results show the out-performance of the developed controller for both queue regulation and resource utilization. Fast response, low queue fluctuations (and consequently low delay jitter), high link utilization, good disturbance rejection, scalability, and low packet marking probability are other features of the developed method with respect to other well-known AQM methods such as RED, PI, and REM which are also simulated for comparison.

  8. The deep space network, volume 18. [Deep Space Instrumentation Facility, Ground Communication Facility, and Network Control System

    NASA Technical Reports Server (NTRS)

    1973-01-01

    The objectives, functions, and organization of the Deep Space Network are summarized. The Deep Space Instrumentation Facility, the Ground Communications Facility, and the Network Control System are described.

  9. Evolution of Controllability in Interbank Networks

    PubMed Central

    Delpini, Danilo; Battiston, Stefano; Riccaboni, Massimo; Gabbi, Giampaolo; Pammolli, Fabio; Caldarelli, Guido

    2013-01-01

    The Statistical Physics of Complex Networks has recently provided new theoretical tools for policy makers. Here we extend the notion of network controllability to detect the financial institutions, i.e. the drivers, that are most crucial to the functioning of an interbank market. The system we investigate is a paradigmatic case study for complex networks since it undergoes dramatic structural changes over time and links among nodes can be observed at several time scales. We find a scale-free decay of the fraction of drivers with increasing time resolution, implying that policies have to be adjusted to the time scales in order to be effective. Moreover, drivers are often not the most highly connected “hub” institutions, nor the largest lenders, contrary to the results of other studies. Our findings contribute quantitative indicators which can support regulators in developing more effective supervision and intervention policies. PMID:23568033

  10. Evolution of Controllability in Interbank Networks

    NASA Astrophysics Data System (ADS)

    Delpini, Danilo; Battiston, Stefano; Riccaboni, Massimo; Gabbi, Giampaolo; Pammolli, Fabio; Caldarelli, Guido

    2013-04-01

    The Statistical Physics of Complex Networks has recently provided new theoretical tools for policy makers. Here we extend the notion of network controllability to detect the financial institutions, i.e. the drivers, that are most crucial to the functioning of an interbank market. The system we investigate is a paradigmatic case study for complex networks since it undergoes dramatic structural changes over time and links among nodes can be observed at several time scales. We find a scale-free decay of the fraction of drivers with increasing time resolution, implying that policies have to be adjusted to the time scales in order to be effective. Moreover, drivers are often not the most highly connected ``hub'' institutions, nor the largest lenders, contrary to the results of other studies. Our findings contribute quantitative indicators which can support regulators in developing more effective supervision and intervention policies.

  11. AN INTERNET RACK MONITOR-CONTROLLER FOR APS LINAC RF ELECTRONICS UPGRADE

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

    Ma, Hengjie; Smith, Terry; Nassiri, Alireza

    To support the research and development in APS LINAC area, the existing LINAC rf control performance needs to be much improved, and thus an upgrade of the legacy LINAC rf electronics becomes necessary. The proposed upgrade plan centers on the concept of using a modern, network-attached, rackmount digital electronics platform –Internet Rack Monitor-Controller (or IRMC) to achieve the goal of modernizing the rf electronics at a lower cost. The system model of the envisioned IRMC is basically a 3-tier stack with a high-performance DSP in the mid-layer to perform the core tasks of real-time rf data processing and controls. Themore » Digital Front-End (DFE) attachment layer at bottom bridges the applicationspecific rf front-ends to the DSP. A network communication gateway, together with an embedded event receiver (EVR) in the top layer merges the Internet Rack MonitorController node into the networks of the accelerator controls infrastructure. Although the concept is very much in trend with today’s Internet-of-Things (IoT), this implementation has actually been used in the accelerators for over two decades.« less

  12. Newly developed double neural network concept for reliable fast plasma position control

    NASA Astrophysics Data System (ADS)

    Jeon, Young-Mu; Na, Yong-Su; Kim, Myung-Rak; Hwang, Y. S.

    2001-01-01

    Neural network is considered as a parameter estimation tool in plasma controls for next generation tokamak such as ITER. The neural network has been reported to be so accurate and fast for plasma equilibrium identification that it may be applied to the control of complex tokamak plasmas. For this application, the reliability of the conventional neural network needs to be improved. In this study, a new idea of double neural network is developed to achieve this. The new idea has been applied to simple plasma position identification of KSTAR tokamak for feasibility test. Characteristics of the concept show higher reliability and fault tolerance even in severe faulty conditions, which may make neural network applicable to plasma control reliably and widely in future tokamaks.

  13. Experimental determination of group flux control coefficients in metabolic networks

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

    Simpson, T.W.; Shimizu, Hiroshi; Stephanopoulos, G.

    1998-04-20

    Grouping of reactions around key metabolite branch points can facilitate the study of metabolic control of complex metabolic networks. This top-down Metabolic Control Analysis is exemplified through the introduction of group control coefficients whose magnitudes provide a measure of the relative impact of each reaction group on the overall network flux, as well as on the overall network stability, following enzymatic amplification. In this article, the authors demonstrate the application of previously developed theory to the determination of group flux control coefficients. Experimental data for the changes in metabolic fluxes obtained in response to the introduction of six different environmentalmore » perturbations are used to determine the group flux control coefficients for three reaction groups formed around the phosphoenolpyruvate/pyruvate branch point. The consistency of the obtained group flux control coefficient estimates is systematically analyzed to ensure that all necessary conditions are satisfied. The magnitudes of the determined control coefficients suggest that the control of lysine production flux in Corynebacterium glutamicum cells at a growth base state resides within the lysine biosynthetic pathway that begins with the PEP/PYR carboxylation anaplorotic pathway.« less

  14. Fuzz Testing of Industrial Network Protocols in Programmable Logic Controllers

    DTIC Science & Technology

    2017-12-01

    PLCs) are vital components in these cyber-physical systems. The industrial network protocols used to communicate between nodes in a control network...AB/RA) MicroLogix 1100 PLC through its implementation of EtherNet/IP, Common Industrial Protocol (CIP), and Programmable Controller Communication ...Commands (PCCC) communication protocols. This research also examines whether cross-generational vulnerabilities exist in the more advanced AB/RA

  15. Microcomputer network for control of a continuous mining machine. Information circular/1993

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

    Schiffbauer, W.H.

    1993-01-01

    The paper details a microcomputer-based control and monitoring network that was developed in-house by the U.S. Bureau of Mines, and installed on a Joy 14 continuous mining machine. The network consists of microcomputers that are connected together via a single twisted pair cable. Each microcomputer was developed to provide a particular function in the control process. Machine-mounted microcomputers in conjunction with the appropriate sensors provide closed-loop control of the machine, navigation, and environmental monitoring. Off-the-machine microcomputers provide remote control of the machine, sensor status, and a connection to the network so that external computers can access network data and controlmore » the continuous mining machine. Although the network was installed on a Joy 14 continuous mining machine, its use extends beyond it. Its generic structure lends itself to installation onto most mining machine types.« less

  16. Reconfigurable Control with Neural Network Augmentation for a Modified F-15 Aircraft

    NASA Technical Reports Server (NTRS)

    Burken, John J.

    2007-01-01

    This paper describes the performance of a simplified dynamic inversion controller with neural network supplementation. This 6 DOF (Degree-of-Freedom) simulation study focuses on the results with and without adaptation of neural networks using a simulation of the NASA modified F-15 which has canards. One area of interest is the performance of a simulated surface failure while attempting to minimize the inertial cross coupling effect of a [B] matrix failure (a control derivative anomaly associated with a jammed or missing control surface). Another area of interest and presented is simulated aerodynamic failures ([A] matrix) such as a canard failure. The controller uses explicit models to produce desired angular rate commands. The dynamic inversion calculates the necessary surface commands to achieve the desired rates. The simplified dynamic inversion uses approximate short period and roll axis dynamics. Initial results indicated that the transient response for a [B] matrix failure using a Neural Network (NN) improved the control behavior when compared to not using a neural network for a given failure, However, further evaluation of the controller was comparable, with objections io the cross coupling effects (after changes were made to the controller). This paper describes the methods employed to reduce the cross coupling effect and maintain adequate tracking errors. The IA] matrix failure results show that control of the aircraft without adaptation is more difficult [leas damped) than with active neural networks, Simulation results show Neural Network augmentation of the controller improves performance in terms of backing error and cross coupling reduction and improved performance with aerodynamic-type failures.

  17. Controllability and observability analysis for vertex domination centrality in directed networks

    NASA Astrophysics Data System (ADS)

    Wang, Bingbo; Gao, Lin; Gao, Yong; Deng, Yue; Wang, Yu

    2014-06-01

    Topological centrality is a significant measure for characterising the relative importance of a node in a complex network. For directed networks that model dynamic processes, however, it is of more practical importance to quantify a vertex's ability to dominate (control or observe) the state of other vertices. In this paper, based on the determination of controllable and observable subspaces under the global minimum-cost condition, we introduce a novel direction-specific index, domination centrality, to assess the intervention capabilities of vertices in a directed network. Statistical studies demonstrate that the domination centrality is, to a great extent, encoded by the underlying network's degree distribution and that most network positions through which one can intervene in a system are vertices with high domination centrality rather than network hubs. To analyse the interaction and functional dependence between vertices when they are used to dominate a network, we define the domination similarity and detect significant functional modules in glossary and metabolic networks through clustering analysis. The experimental results provide strong evidence that our indices are effective and practical in accurately depicting the structure of directed networks.

  18. Two-layer wireless distributed sensor/control network based on RF

    NASA Astrophysics Data System (ADS)

    Feng, Li; Lin, Yuchi; Zhou, Jingjing; Dong, Guimei; Xia, Guisuo

    2006-11-01

    A project of embedded Wireless Distributed Sensor/Control Network (WDSCN) based on RF is presented after analyzing the disadvantages of traditional measure and control system. Because of high-cost and complexity, such wireless techniques as Bluetooth and WiFi can't meet the needs of WDSCN. The two-layer WDSCN is designed based on RF technique, which operates in the ISM free frequency channel with low power and high transmission speed. Also the network is low cost, portable and moveable, integrated with the technologies of computer network, sensor, microprocessor and wireless communications. The two-layer network topology is selected in the system; a simple but efficient self-organization net protocol is designed to fit the periodic data collection, event-driven and store-and-forward. Furthermore, adaptive frequency hopping technique is adopted for anti-jamming apparently. The problems about power reduction and synchronization of data in wireless system are solved efficiently. Based on the discussion above, a measure and control network is set up to control such typical instruments and sensors as temperature sensor and signal converter, collect data, and monitor environmental parameters around. This system works well in different rooms. Experiment results show that the system provides an efficient solution to WDSCN through wireless links, with high efficiency, low power, high stability, flexibility and wide working range.

  19. Altered resting-state frontoparietal control network in children with attention-deficit/hyperactivity disorder.

    PubMed

    Lin, Hsiang-Yuan; Tseng, Wen-Yih Isaac; Lai, Meng-Chuan; Matsuo, Kayako; Gau, Susan Shur-Fen

    2015-04-01

    The frontoparietal control network, anatomically and functionally interposed between the dorsal attention network and default mode network, underpins executive control functions. Individuals with attention-deficit/hyperactivity disorder (ADHD) commonly exhibit deficits in executive functions, which are mainly mediated by the frontoparietal control network. Involvement of the frontoparietal control network based on the anterior prefrontal cortex in neurobiological mechanisms of ADHD has yet to be tested. We used resting-state functional MRI and seed-based correlation analyses to investigate functional connectivity of the frontoparietal control network in a sample of 25 children with ADHD (7-14 years; mean 9.94 ± 1.77 years; 20 males), and 25 age-, sex-, and performance IQ-matched typically developing (TD) children. All participants had limited in-scanner head motion. Spearman's rank correlations were used to test the associations between altered patterns of functional connectivity with clinical symptoms and executive functions, measured by the Conners' Continuous Performance Test and Spatial Span in the Cambridge Neuropsychological Test Automated Battery. Compared with TD children, children with ADHD demonstrated weaker connectivity between the right anterior prefrontal cortex (PFC) and the right ventrolateral PFC, and between the left anterior PFC and the right inferior parietal lobule. Furthermore, this aberrant connectivity of the frontoparietal control network in ADHD was associated with symptoms of impulsivity and opposition-defiance, as well as impaired response inhibition and attentional control. The findings support potential integration of the disconnection model and the executive dysfunction model for ADHD. Atypical frontoparietal control network may play a pivotal role in the pathophysiology of ADHD.

  20. Dynamic Evolution Model Based on Social Network Services

    NASA Astrophysics Data System (ADS)

    Xiong, Xi; Gou, Zhi-Jian; Zhang, Shi-Bin; Zhao, Wen

    2013-11-01

    Based on the analysis of evolutionary characteristics of public opinion in social networking services (SNS), in the paper we propose a dynamic evolution model, in which opinions are coupled with topology. This model shows the clustering phenomenon of opinions in dynamic network evolution. The simulation results show that the model can fit the data from a social network site. The dynamic evolution of networks accelerates the opinion, separation and aggregation. The scale and the number of clusters are influenced by confidence limit and rewiring probability. Dynamic changes of the topology reduce the number of isolated nodes, while the increased confidence limit allows nodes to communicate more sufficiently. The two effects make the distribution of opinion more neutral. The dynamic evolution of networks generates central clusters with high connectivity and high betweenness, which make it difficult to control public opinions in SNS.

  1. Reversible large–scale modification of cortical networks during neuroprosthetic control

    PubMed Central

    Ganguly, Karunesh; Wallis, Jonathan D.

    2012-01-01

    Brain-Machine Interfaces (BMI) provide a framework to study cortical dynamics and the neural correlates of learning. Neuroprosthetic control has been associated with tuning changes in specific neurons directly projecting to the BMI (hereafter ‘direct neurons’). However, little is known about the larger network dynamics. By monitoring ensembles of neurons that were either causally linked to BMI control or indirectly involved, here we show that proficient neuroprosthetic control is associated with large-scale modifications to the cortical network in macaque monkeys. Specifically, there were changes in the preferred direction of both direct and indirect neurons. Interestingly, with learning, there was a relative decrease in the net modulation of indirect neural activity in comparison to the direct activity. These widespread differential changes in the direct and indirect population activity were remarkably stable from one day to the next and readily coexisted with the long-standing cortical network for upper limb control. Thus, the process of learning BMI control is associated with differential modification of neural populations based on their specific relation to movement control. PMID:21499255

  2. Reversible large-scale modification of cortical networks during neuroprosthetic control.

    PubMed

    Ganguly, Karunesh; Dimitrov, Dragan F; Wallis, Jonathan D; Carmena, Jose M

    2011-05-01

    Brain-machine interfaces (BMIs) provide a framework for studying cortical dynamics and the neural correlates of learning. Neuroprosthetic control has been associated with tuning changes in specific neurons directly projecting to the BMI (hereafter referred to as direct neurons). However, little is known about the larger network dynamics. By monitoring ensembles of neurons that were either causally linked to BMI control or indirectly involved, we found that proficient neuroprosthetic control is associated with large-scale modifications to the cortical network in macaque monkeys. Specifically, there were changes in the preferred direction of both direct and indirect neurons. Notably, with learning, there was a relative decrease in the net modulation of indirect neural activity in comparison with direct activity. These widespread differential changes in the direct and indirect population activity were markedly stable from one day to the next and readily coexisted with the long-standing cortical network for upper limb control. Thus, the process of learning BMI control is associated with differential modification of neural populations based on their specific relation to movement control.

  3. Resonant interaction of the electron beam with a synchronous wave in controlled magnetrons for high-current superconducting accelerators

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

    Kazakevich, G.; Johnson, R.; Lebedev, V.

    A simplified analytical model of the resonant interaction of the beam of Larmor electrons drifting in the crossed constant fields of a magnetron with a synchronous wave providing a phase grouping of the drifting charge was developed to optimize the parameters of an rf resonant injected signal driving the magnetrons for management of phase and power of rf sources with a rate required for superconducting high-current accelerators. The model, which considers the impact of the rf resonant signal injected into the magnetron on the operation of the injection-locked tube, substantiates the recently developed method of fast power control of magnetronsmore » in the range up to 10 dB at the highest generation efficiency, with low noise, precise stability of the carrier frequency, and the possibility of wideband phase control. Experiments with continuous wave 2.45 GHz, 1 kW microwave oven magnetrons have verified the correspondence of the behavior of these tubes to the analytical model. A proof of the principle of the novel method of power control in magnetrons, based on the developed model, was demonstrated in the experiments. The method is attractive for high-current superconducting rf accelerators. This study also discusses vector methods of power control with the rates required for superconducting accelerators, the impact of the rf resonant signal injected into the magnetron on the rate of phase control of the injection-locked tubes, and a conceptual scheme of the magnetron transmitter with highest efficiency for high-current accelerators.« less

  4. Resonant interaction of the electron beam with a synchronous wave in controlled magnetrons for high-current superconducting accelerators

    DOE PAGES

    Kazakevich, G.; Johnson, R.; Lebedev, V.; ...

    2018-06-14

    A simplified analytical model of the resonant interaction of the beam of Larmor electrons drifting in the crossed constant fields of a magnetron with a synchronous wave providing a phase grouping of the drifting charge was developed to optimize the parameters of an rf resonant injected signal driving the magnetrons for management of phase and power of rf sources with a rate required for superconducting high-current accelerators. The model, which considers the impact of the rf resonant signal injected into the magnetron on the operation of the injection-locked tube, substantiates the recently developed method of fast power control of magnetronsmore » in the range up to 10 dB at the highest generation efficiency, with low noise, precise stability of the carrier frequency, and the possibility of wideband phase control. Experiments with continuous wave 2.45 GHz, 1 kW microwave oven magnetrons have verified the correspondence of the behavior of these tubes to the analytical model. A proof of the principle of the novel method of power control in magnetrons, based on the developed model, was demonstrated in the experiments. The method is attractive for high-current superconducting rf accelerators. This study also discusses vector methods of power control with the rates required for superconducting accelerators, the impact of the rf resonant signal injected into the magnetron on the rate of phase control of the injection-locked tubes, and a conceptual scheme of the magnetron transmitter with highest efficiency for high-current accelerators.« less

  5. Using social network analysis to inform disease control interventions.

    PubMed

    Marquetoux, Nelly; Stevenson, Mark A; Wilson, Peter; Ridler, Anne; Heuer, Cord

    2016-04-01

    Contact patterns between individuals are an important determinant for the spread of infectious diseases in populations. Social network analysis (SNA) describes contact patterns and thus indicates how infectious pathogens may be transmitted. Here we explore network characteristics that may inform the development of disease control programes. This study applies SNA methods to describe a livestock movement network of 180 farms in New Zealand from 2006 to 2010. We found that the number of contacts was overall consistent from year to year, while the choice of trading partners tended to vary. This livestock movement network illustrated how a small number of farms central to the network could play a potentially dominant role for the spread of infection in this population. However, fragmentation of the network could easily be achieved by "removing" a small proportion of farms serving as bridges between otherwise isolated clusters, thus decreasing the probability of large epidemics. This is the first example of a comprehensive analysis of pastoral livestock movements in New Zealand. We conclude that, for our system, recording and exploiting livestock movements can contribute towards risk-based control strategies to prevent and monitor the introduction and the spread of infectious diseases in animal populations. Copyright © 2016 Elsevier B.V. All rights reserved.

  6. Attack-tolerant networked control system: an approach for detection the controller stealthy hijacking attack

    NASA Astrophysics Data System (ADS)

    Atta Yaseen, Amer; Bayart, Mireille

    2017-01-01

    In this work, a new approach will be introduced as a development for the attack-tolerant scheme in the Networked Control System (NCS). The objective is to be able to detect an attack such as the Stuxnet case where the controller is reprogrammed and hijacked. Besides the ability to detect the stealthy controller hijacking attack, the advantage of this approach is that there is no need for a priori mathematical model of the controller. In order to implement the proposed scheme, a specific detector for the controller hijacking attack is designed. The performance of this scheme is evaluated be connected the detector to NCS with basic security elements such as Data Encryption Standard (DES), Message Digest (MD5), and timestamp. The detector is tested along with networked PI controller under stealthy hijacking attack. The test results of the proposed method show that the hijacked controller can be significantly detected and recovered.

  7. Static-dynamic hybrid communication scheduling and control co-design for networked control systems.

    PubMed

    Wen, Shixi; Guo, Ge

    2017-11-01

    In this paper, the static-dynamic hybrid communication scheduling and control co-design is proposed for the networked control systems (NCSs) to solve the capacity limitation of the wireless communication network. The analytical most regular binary sequences (MRBSs) are used as the communication scheduling function for NCSs. When the communication conflicts yielded in the binary sequence MRBSs, a dynamic scheduling strategy is proposed to on-line reallocate the medium access status for each plant. Under such static-dynamic hybrid scheduling policy, plants in NCSs are described as the non-uniform sampled-control systems, whose controller have a group of controller gains and switch according to the sampling interval yielded by the binary sequence. A useful communication scheduling and control co-design framework is proposed for the NCSs to simultaneously decide the controller gains and the parameters used to generate the communication sequences MRBS. Numerical example and realistic example are respectively given to demonstrate the effectiveness of the proposed co-design method. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  8. Backbone of complex networks of corporations: the flow of control.

    PubMed

    Glattfelder, J B; Battiston, S

    2009-09-01

    We present a methodology to extract the backbone of complex networks based on the weight and direction of links, as well as on nontopological properties of nodes. We show how the methodology can be applied in general to networks in which mass or energy is flowing along the links. In particular, the procedure enables us to address important questions in economics, namely, how control and wealth are structured and concentrated across national markets. We report on the first cross-country investigation of ownership networks, focusing on the stock markets of 48 countries around the world. On the one hand, our analysis confirms results expected on the basis of the literature on corporate control, namely, that in Anglo-Saxon countries control tends to be dispersed among numerous shareholders. On the other hand, it also reveals that in the same countries, control is found to be highly concentrated at the global level, namely, lying in the hands of very few important shareholders. Interestingly, the exact opposite is observed for European countries. These results have previously not been reported as they are not observable without the kind of network analysis developed here.

  9. Backbone of complex networks of corporations: The flow of control

    NASA Astrophysics Data System (ADS)

    Glattfelder, J. B.; Battiston, S.

    2009-09-01

    We present a methodology to extract the backbone of complex networks based on the weight and direction of links, as well as on nontopological properties of nodes. We show how the methodology can be applied in general to networks in which mass or energy is flowing along the links. In particular, the procedure enables us to address important questions in economics, namely, how control and wealth are structured and concentrated across national markets. We report on the first cross-country investigation of ownership networks, focusing on the stock markets of 48 countries around the world. On the one hand, our analysis confirms results expected on the basis of the literature on corporate control, namely, that in Anglo-Saxon countries control tends to be dispersed among numerous shareholders. On the other hand, it also reveals that in the same countries, control is found to be highly concentrated at the global level, namely, lying in the hands of very few important shareholders. Interestingly, the exact opposite is observed for European countries. These results have previously not been reported as they are not observable without the kind of network analysis developed here.

  10. Prediction and control of chaotic processes using nonlinear adaptive networks

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

    Jones, R.D.; Barnes, C.W.; Flake, G.W.

    1990-01-01

    We present the theory of nonlinear adaptive networks and discuss a few applications. In particular, we review the theory of feedforward backpropagation networks. We then present the theory of the Connectionist Normalized Linear Spline network in both its feedforward and iterated modes. Also, we briefly discuss the theory of stochastic cellular automata. We then discuss applications to chaotic time series, tidal prediction in Venice lagoon, finite differencing, sonar transient detection, control of nonlinear processes, control of a negative ion source, balancing a double inverted pendulum and design advice for free electron lasers and laser fusion targets.

  11. From Cellular Attractor Selection to Adaptive Signal Control for Traffic Networks.

    PubMed

    Tian, Daxin; Zhou, Jianshan; Sheng, Zhengguo; Wang, Yunpeng; Ma, Jianming

    2016-03-14

    The management of varying traffic flows essentially depends on signal controls at intersections. However, design an optimal control that considers the dynamic nature of a traffic network and coordinates all intersections simultaneously in a centralized manner is computationally challenging. Inspired by the stable gene expressions of Escherichia coli in response to environmental changes, we explore the robustness and adaptability performance of signalized intersections by incorporating a biological mechanism in their control policies, specifically, the evolution of each intersection is induced by the dynamics governing an adaptive attractor selection in cells. We employ a mathematical model to capture such biological attractor selection and derive a generic, adaptive and distributed control algorithm which is capable of dynamically adapting signal operations for the entire dynamical traffic network. We show that the proposed scheme based on attractor selection can not only promote the balance of traffic loads on each link of the network but also allows the global network to accommodate dynamical traffic demands. Our work demonstrates the potential of bio-inspired intelligence emerging from cells and provides a deep understanding of adaptive attractor selection-based control formation that is useful to support the designs of adaptive optimization and control in other domains.

  12. Similar muscles contribute to horizontal and vertical acceleration of center of mass in forward and backward walking: implications for neural control

    PubMed Central

    Jansen, Karen; De Groote, Friedl; Massaad, Firas; Meyns, Pieter; Jonkers, Ilse

    2012-01-01

    Leg kinematics during backward walking (BW) are very similar to the time-reversed kinematics during forward walking (FW). This suggests that the underlying muscle activation pattern could originate from a simple time reversal, as well. Experimental electromyography studies have confirmed that this is the case for some muscles. Furthermore, it has been hypothesized that muscles showing a time reversal should also exhibit a reversal in function [from accelerating the body center of mass (COM) to decelerating]. However, this has not yet been verified in simulation studies. In the present study, forward simulations were used to study the effects of muscles on the acceleration of COM in FW and BW. We found that a reversal in function was indeed present in the muscle control of the horizontal movement of COM (e.g., tibialis anterior and gastrocnemius). In contrast, muscles' antigravity contributions maintained their function for both directions of movement. An important outcome of the present study is therefore that similar muscles can be used to achieve opposite functional demands at the level of control of the COM when walking direction is reversed. However, some muscles showed direction-specific contributions (i.e., dorsiflexors). We concluded that the changes in muscle contributions imply that a simple time reversal would be insufficient to produce BW from FW. We therefore propose that BW utilizes extra elements, presumably supraspinal, in addition to a common spinal drive. These additions are needed for propulsion and require a partial reconfiguration of lower level common networks. PMID:22423005

  13. Adaptively Adjusted Event-Triggering Mechanism on Fault Detection for Networked Control Systems.

    PubMed

    Wang, Yu-Long; Lim, Cheng-Chew; Shi, Peng

    2016-12-08

    This paper studies the problem of adaptively adjusted event-triggering mechanism-based fault detection for a class of discrete-time networked control system (NCS) with applications to aircraft dynamics. By taking into account the fault occurrence detection progress and the fault occurrence probability, and introducing an adaptively adjusted event-triggering parameter, a novel event-triggering mechanism is proposed to achieve the efficient utilization of the communication network bandwidth. Both the sensor-to-control station and the control station-to-actuator network-induced delays are taken into account. The event-triggered sensor and the event-triggered control station are utilized simultaneously to establish new network-based closed-loop models for the NCS subject to faults. Based on the established models, the event-triggered simultaneous design of fault detection filter (FDF) and controller is presented. A new algorithm for handling the adaptively adjusted event-triggering parameter is proposed. Performance analysis verifies the effectiveness of the adaptively adjusted event-triggering mechanism, and the simultaneous design of FDF and controller.

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

    NASA Astrophysics Data System (ADS)

    Park, Sangsoo; Miura, Yushi; Ise, Toshifumi

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

  15. Synchronization of Switched Neural Networks With Communication Delays via the Event-Triggered Control.

    PubMed

    Wen, Shiping; Zeng, Zhigang; Chen, Michael Z Q; Huang, Tingwen

    2017-10-01

    This paper addresses the issue of synchronization of switched delayed neural networks with communication delays via event-triggered control. For synchronizing coupled switched neural networks, we propose a novel event-triggered control law which could greatly reduce the number of control updates for synchronization tasks of coupled switched neural networks involving embedded microprocessors with limited on-board resources. The control signals are driven by properly defined events, which depend on the measurement errors and current-sampled states. By using a delay system method, a novel model of synchronization error system with delays is proposed with the communication delays and event-triggered control in the unified framework for coupled switched neural networks. The criteria are derived for the event-triggered synchronization analysis and control synthesis of switched neural networks via the Lyapunov-Krasovskii functional method and free weighting matrix approach. A numerical example is elaborated on to illustrate the effectiveness of the derived results.

  16. Main control computer security model of closed network systems protection against cyber attacks

    NASA Astrophysics Data System (ADS)

    Seymen, Bilal

    2014-06-01

    The model that brings the data input/output under control in closed network systems, that maintains the system securely, and that controls the flow of information through the Main Control Computer which also brings the network traffic under control against cyber-attacks. The network, which can be controlled single-handedly thanks to the system designed to enable the network users to make data entry into the system or to extract data from the system securely, intends to minimize the security gaps. Moreover, data input/output record can be kept by means of the user account assigned for each user, and it is also possible to carry out retroactive tracking, if requested. Because the measures that need to be taken for each computer on the network regarding cyber security, do require high cost; it has been intended to provide a cost-effective working environment with this model, only if the Main Control Computer has the updated hardware.

  17. Laser acceleration

    NASA Astrophysics Data System (ADS)

    Tajima, T.; Nakajima, K.; Mourou, G.

    2017-02-01

    The fundamental idea of Laser Wakefield Acceleration (LWFA) is reviewed. An ultrafast intense laser pulse drives coherent wakefield with a relativistic amplitude robustly supported by the plasma. While the large amplitude of wakefields involves collective resonant oscillations of the eigenmode of the entire plasma electrons, the wake phase velocity ˜ c and ultrafastness of the laser pulse introduce the wake stability and rigidity. A large number of worldwide experiments show a rapid progress of this concept realization toward both the high-energy accelerator prospect and broad applications. The strong interest in this has been spurring and stimulating novel laser technologies, including the Chirped Pulse Amplification, the Thin Film Compression, the Coherent Amplification Network, and the Relativistic Mirror Compression. These in turn have created a conglomerate of novel science and technology with LWFA to form a new genre of high field science with many parameters of merit in this field increasing exponentially lately. This science has triggered a number of worldwide research centers and initiatives. Associated physics of ion acceleration, X-ray generation, and astrophysical processes of ultrahigh energy cosmic rays are reviewed. Applications such as X-ray free electron laser, cancer therapy, and radioisotope production etc. are considered. A new avenue of LWFA using nanomaterials is also emerging.

  18. Using Cognitive Control in Software Defined Networking for Port Scan Detection

    DTIC Science & Technology

    2017-07-01

    ARL-TR-8059 ● July 2017 US Army Research Laboratory Using Cognitive Control in Software-Defined Networking for Port Scan...Cognitive Control in Software-Defined Networking for Port Scan Detection by Vinod K Mishra Computational and Information Sciences Directorate, ARL...currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 1. REPORT DATE (DD-MM-YYYY) July 2017 2. REPORT TYPE

  19. Mind-Body Practice Changes Fractional Amplitude of Low Frequency Fluctuations in Intrinsic Control Networks

    PubMed Central

    Wei, Gao-Xia; Gong, Zhu-Qing; Yang, Zhi; Zuo, Xi-Nian

    2017-01-01

    Cognitive control impairment is a typical symptom largely reported in populations with neurological disorders. Previous studies have provided evidence about the changes in cognitive control induced by mind-body training. However, the neural correlates underlying the effect of extensive mind-body practice on cognitive control remain largely unknown. Using resting-state functional magnetic resonance imaging, we characterized dynamic fluctuations in large-scale intrinsic connectivity networks associated with mind-body practice, and examined their differences between healthy controls and Tai Chi Chuan (TCC) practitioners. Compared with a control group, the TCC group revealed significantly decreased fractional Amplitude of Low Frequency Fluctuations (fALFF) in the bilateral frontoparietal network, default mode network and dorsal prefrontal-angular gyri network. Furthermore, we detected a significant association between mind-body practice experience and fALFF in the default mode network, as well as an association between cognitive control performance and fALFF of the frontoparietal network. This provides the first evidence of large-scale functional connectivity in brain networks associated with mind-body practice, shedding light on the neural network changes that accompany intensive mind-body training. It also highlights the functionally plastic role of the frontoparietal network in the context of the “immune system” of mental health recently developed in relation to flexible hub theory. PMID:28736535

  20. Mind-Body Practice Changes Fractional Amplitude of Low Frequency Fluctuations in Intrinsic Control Networks.

    PubMed

    Wei, Gao-Xia; Gong, Zhu-Qing; Yang, Zhi; Zuo, Xi-Nian

    2017-01-01

    Cognitive control impairment is a typical symptom largely reported in populations with neurological disorders. Previous studies have provided evidence about the changes in cognitive control induced by mind-body training. However, the neural correlates underlying the effect of extensive mind-body practice on cognitive control remain largely unknown. Using resting-state functional magnetic resonance imaging, we characterized dynamic fluctuations in large-scale intrinsic connectivity networks associated with mind-body practice, and examined their differences between healthy controls and Tai Chi Chuan (TCC) practitioners. Compared with a control group, the TCC group revealed significantly decreased fractional Amplitude of Low Frequency Fluctuations (fALFF) in the bilateral frontoparietal network, default mode network and dorsal prefrontal-angular gyri network. Furthermore, we detected a significant association between mind-body practice experience and fALFF in the default mode network, as well as an association between cognitive control performance and fALFF of the frontoparietal network. This provides the first evidence of large-scale functional connectivity in brain networks associated with mind-body practice, shedding light on the neural network changes that accompany intensive mind-body training. It also highlights the functionally plastic role of the frontoparietal network in the context of the "immune system" of mental health recently developed in relation to flexible hub theory.

  1. DCS-Neural-Network Program for Aircraft Control and Testing

    NASA Technical Reports Server (NTRS)

    Jorgensen, Charles C.

    2006-01-01

    A computer program implements a dynamic-cell-structure (DCS) artificial neural network that can perform such tasks as learning selected aerodynamic characteristics of an airplane from wind-tunnel test data and computing real-time stability and control derivatives of the airplane for use in feedback linearized control. A DCS neural network is one of several types of neural networks that can incorporate additional nodes in order to rapidly learn increasingly complex relationships between inputs and outputs. In the DCS neural network implemented by the present program, the insertion of nodes is based on accumulated error. A competitive Hebbian learning rule (a supervised-learning rule in which connection weights are adjusted to minimize differences between actual and desired outputs for training examples) is used. A Kohonen-style learning rule (derived from a relatively simple training algorithm, implements a Delaunay triangulation layout of neurons) is used to adjust node positions during training. Neighborhood topology determines which nodes are used to estimate new values. The network learns, starting with two nodes, and adds new nodes sequentially in locations chosen to maximize reductions in global error. At any given time during learning, the error becomes homogeneously distributed over all nodes.

  2. Acceleration control system for semi-active in-car crib with joint application of regular and inverted pendulum mechanisms

    NASA Astrophysics Data System (ADS)

    Kawashima, T.

    2016-09-01

    To reduce the risk of injury to an infant in an in-car crib (or in a child safety bed) collision shock during a car crash, it is necessary to maintain a constant force acting on the crib below a certain allowable value. To realize this objective, we propose a semi-active in-car crib system with the joint application of regular and inverted pendulum mechanisms. The arms of the proposed crib system support the crib like a pendulum while the pendulum system itself is supported like an inverted pendulum by the arms. In addition, the friction torque of each arm is controlled using a brake mechanism that enables the proposed in-car crib to decrease the acceleration of the crib gradually and maintain it around the target value. This system not only reduces the impulsive force but also transfers the force to the infant's back using a spin control system, i.e., the impulse force acts is made to act perpendicularly on the crib. The spin control system was developed in our previous work. This work focuses on the acceleration control system. A semi-active control law with acceleration feedback is introduced, and the effectiveness of the system is demonstrated using numerical simulation and model experiment.

  3. Frontal parietal control network regulates the anti-correlated default and dorsal attention networks.

    PubMed

    Gao, Wei; Lin, Weili

    2012-01-01

    Recent reports demonstrate the anti-correlated behaviors between the default (DF) and the dorsal attention (DA) networks. We aimed to investigate the roles of the frontal parietal control (FPC) network in regulating the two anti-correlated networks through three experimental conditions, including resting, continuous self-paced/attended sequential finger tapping (FT), and natural movie watching (MW), respectively. The two goal-directed tasks were chosen to engage either one of the two competing networks-FT for DA whereas MW for default. We hypothesized that FPC will selectively augment/suppress either network depending on how the task targets the specific network; FPC will positively correlate with the target network, but negatively correlate with the network anti-correlated with the target network. We further hypothesized that significant causal links from FPC to both DA and DF are present during all three experimental conditions, supporting the initiative regulating role of FPC over the two opposing systems. Consistent with our hypotheses, FPC exhibited a significantly higher positive correlation with DA (P = 0.0095) whereas significantly more negative correlation with default (P = 0.0025) during FT when compared to resting. Completely opposite to that observed during FT, the FPC was significantly anti-correlated with DA (P = 2.1e-6) whereas positively correlated with default (P = 0.0035) during MW. Furthermore, extensive causal links from FPC to both DA and DF were observed across all three experimental states. Together, our results strongly support the notion that the FPC regulates the anti-correlated default and DA networks. Copyright © 2011 Wiley Periodicals, Inc.

  4. Dynamics and control of diseases in networks with community structure.

    PubMed

    Salathé, Marcel; Jones, James H

    2010-04-08

    The dynamics of infectious diseases spread via direct person-to-person transmission (such as influenza, smallpox, HIV/AIDS, etc.) depends on the underlying host contact network. Human contact networks exhibit strong community structure. Understanding how such community structure affects epidemics may provide insights for preventing the spread of disease between communities by changing the structure of the contact network through pharmaceutical or non-pharmaceutical interventions. We use empirical and simulated networks to investigate the spread of disease in networks with community structure. We find that community structure has a major impact on disease dynamics, and we show that in networks with strong community structure, immunization interventions targeted at individuals bridging communities are more effective than those simply targeting highly connected individuals. Because the structure of relevant contact networks is generally not known, and vaccine supply is often limited, there is great need for efficient vaccination algorithms that do not require full knowledge of the network. We developed an algorithm that acts only on locally available network information and is able to quickly identify targets for successful immunization intervention. The algorithm generally outperforms existing algorithms when vaccine supply is limited, particularly in networks with strong community structure. Understanding the spread of infectious diseases and designing optimal control strategies is a major goal of public health. Social networks show marked patterns of community structure, and our results, based on empirical and simulated data, demonstrate that community structure strongly affects disease dynamics. These results have implications for the design of control strategies.

  5. Brain-machine interface control of a manipulator using small-world neural network and shared control strategy.

    PubMed

    Li, Ting; Hong, Jun; Zhang, Jinhua; Guo, Feng

    2014-03-15

    The improvement of the resolution of brain signal and the ability to control external device has been the most important goal in BMI research field. This paper describes a non-invasive brain-actuated manipulator experiment, which defined a paradigm for the motion control of a serial manipulator based on motor imagery and shared control. The techniques of component selection, spatial filtering and classification of motor imagery were involved. Small-world neural network (SWNN) was used to classify five brain states. To verify the effectiveness of the proposed classifier, we replace the SWNN classifier by a radial basis function (RBF) networks neural network, a standard multi-layered feed-forward backpropagation network (SMN) and a multi-SVM classifier, with the same features for the classification. The results also indicate that the proposed classifier achieves a 3.83% improvement over the best results of other classifiers. We proposed a shared control method consisting of two control patterns to expand the control of BMI from the software angle. The job of path building for reaching the 'end' point was designated as an assessment task. We recorded all paths contributed by subjects and picked up relevant parameters as evaluation coefficients. With the assistance of two control patterns and series of machine learning algorithms, the proposed BMI originally achieved the motion control of a manipulator in the whole workspace. According to experimental results, we confirmed the feasibility of the proposed BMI method for 3D motion control of a manipulator using EEG during motor imagery. Copyright © 2013 Elsevier B.V. All rights reserved.

  6. Controlled Acceleration and Inhibition of Bergman Cyclization by Metal Chlorides

    NASA Astrophysics Data System (ADS)

    Warner, Benjamin P.; Millar, Susan P.; Broene, Richard D.; Buchwald, Stephen L.

    1995-08-01

    The Bergman cyclization has been the subject of renewed interest with the discovery of naturally occurring enediyne-based antitumor agents that cleave DNA by means of an aromatic diradical. These natural substrates have a means to trigger this cycloaromatization process. Control of this reaction by substrate modification would allow aromatic diradicals to be generated selectively. In the studies presented here it is disclosed that the Bergman cyclization of 1,2-bis(diphenyl phosphinoethynyl)benzene was accelerated by a factor of >30,000 by the addition of palladium(II) chloride or platinum(II) chloride and was inhibited by the addition of mercury(II) chloride.

  7. Passivity and Dissipativity as Design and Analysis Tools for Networked Control Systems

    ERIC Educational Resources Information Center

    Yu, Han

    2012-01-01

    In this dissertation, several control problems are studied that arise when passive or dissipative systems are interconnected and controlled over a communication network. Since communication networks can impact the systems' stability and performance, there is a need to extend the results on control of passive or dissipative systems to networked…

  8. Finite-Time Stabilization and Adaptive Control of Memristor-Based Delayed Neural Networks.

    PubMed

    Wang, Leimin; Shen, Yi; Zhang, Guodong

    Finite-time stability problem has been a hot topic in control and system engineering. This paper deals with the finite-time stabilization issue of memristor-based delayed neural networks (MDNNs) via two control approaches. First, in order to realize the stabilization of MDNNs in finite time, a delayed state feedback controller is proposed. Then, a novel adaptive strategy is applied to the delayed controller, and finite-time stabilization of MDNNs can also be achieved by using the adaptive control law. Some easily verified algebraic criteria are derived to ensure the stabilization of MDNNs in finite time, and the estimation of the settling time functional is given. Moreover, several finite-time stability results as our special cases for both memristor-based neural networks (MNNs) without delays and neural networks are given. Finally, three examples are provided for the illustration of the theoretical results.Finite-time stability problem has been a hot topic in control and system engineering. This paper deals with the finite-time stabilization issue of memristor-based delayed neural networks (MDNNs) via two control approaches. First, in order to realize the stabilization of MDNNs in finite time, a delayed state feedback controller is proposed. Then, a novel adaptive strategy is applied to the delayed controller, and finite-time stabilization of MDNNs can also be achieved by using the adaptive control law. Some easily verified algebraic criteria are derived to ensure the stabilization of MDNNs in finite time, and the estimation of the settling time functional is given. Moreover, several finite-time stability results as our special cases for both memristor-based neural networks (MNNs) without delays and neural networks are given. Finally, three examples are provided for the illustration of the theoretical results.

  9. A neural network controller of a flotation process

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

    Durao, F.; Cortez, L.

    1995-12-31

    The dynamic control of a froth flotation section is simulated through a neural network feedback controller, trained in order to stabilize the concentrate metal grade and recovery by applying random step changes to the feed metal grade. The results of the application example show that this controller seems to be sufficiently robust and a good alternative to handle a non-linear process.

  10. Dual adaptive dynamic control of mobile robots using neural networks.

    PubMed

    Bugeja, Marvin K; Fabri, Simon G; Camilleri, Liberato

    2009-02-01

    This paper proposes two novel dual adaptive neural control schemes for the dynamic control of nonholonomic mobile robots. The two schemes are developed in discrete time, and the robot's nonlinear dynamic functions are assumed to be unknown. Gaussian radial basis function and sigmoidal multilayer perceptron neural networks are used for function approximation. In each scheme, the unknown network parameters are estimated stochastically in real time, and no preliminary offline neural network training is used. In contrast to other adaptive techniques hitherto proposed in the literature on mobile robots, the dual control laws presented in this paper do not rely on the heuristic certainty equivalence property but account for the uncertainty in the estimates. This results in a major improvement in tracking performance, despite the plant uncertainty and unmodeled dynamics. Monte Carlo simulation and statistical hypothesis testing are used to illustrate the effectiveness of the two proposed stochastic controllers as applied to the trajectory-tracking problem of a differentially driven wheeled mobile robot.

  11. Cyber-physical approach to the network-centric robotics control task

    NASA Astrophysics Data System (ADS)

    Muliukha, Vladimir; Ilyashenko, Alexander; Zaborovsky, Vladimir; Lukashin, Alexey

    2016-10-01

    Complex engineering tasks concerning control for groups of mobile robots are developed poorly. In our work for their formalization we use cyber-physical approach, which extends the range of engineering and physical methods for a design of complex technical objects by researching the informational aspects of communication and interaction between objects and with an external environment [1]. The paper analyzes network-centric methods for control of cyber-physical objects. Robots or cyber-physical objects interact with each other by transmitting information via computer networks using preemptive queueing system and randomized push-out mechanism [2],[3]. The main field of application for the results of our work is space robotics. The selection of cyber-physical systems as a special class of designed objects is due to the necessity of integrating various components responsible for computing, communications and control processes. Network-centric solutions allow using universal means for the organization of information exchange to integrate different technologies for the control system.

  12. Distributed dynamic simulations of networked control and building performance applications.

    PubMed

    Yahiaoui, Azzedine

    2018-02-01

    The use of computer-based automation and control systems for smart sustainable buildings, often so-called Automated Buildings (ABs), has become an effective way to automatically control, optimize, and supervise a wide range of building performance applications over a network while achieving the minimum energy consumption possible, and in doing so generally refers to Building Automation and Control Systems (BACS) architecture. Instead of costly and time-consuming experiments, this paper focuses on using distributed dynamic simulations to analyze the real-time performance of network-based building control systems in ABs and improve the functions of the BACS technology. The paper also presents the development and design of a distributed dynamic simulation environment with the capability of representing the BACS architecture in simulation by run-time coupling two or more different software tools over a network. The application and capability of this new dynamic simulation environment are demonstrated by an experimental design in this paper.

  13. Distributed dynamic simulations of networked control and building performance applications

    PubMed Central

    Yahiaoui, Azzedine

    2017-01-01

    The use of computer-based automation and control systems for smart sustainable buildings, often so-called Automated Buildings (ABs), has become an effective way to automatically control, optimize, and supervise a wide range of building performance applications over a network while achieving the minimum energy consumption possible, and in doing so generally refers to Building Automation and Control Systems (BACS) architecture. Instead of costly and time-consuming experiments, this paper focuses on using distributed dynamic simulations to analyze the real-time performance of network-based building control systems in ABs and improve the functions of the BACS technology. The paper also presents the development and design of a distributed dynamic simulation environment with the capability of representing the BACS architecture in simulation by run-time coupling two or more different software tools over a network. The application and capability of this new dynamic simulation environment are demonstrated by an experimental design in this paper. PMID:29568135

  14. Brain network alterations and vulnerability to simulated neurodegeneration in breast cancer.

    PubMed

    Kesler, Shelli R; Watson, Christa L; Blayney, Douglas W

    2015-08-01

    Breast cancer and its treatments are associated with mild cognitive impairment and brain changes that could indicate an altered or accelerated brain aging process. We applied diffusion tensor imaging and graph theory to measure white matter organization and connectivity in 34 breast cancer survivors compared with 36 matched healthy female controls. We also investigated how brain networks (connectomes) in each group responded to simulated neurodegeneration based on network attack analysis. Compared with controls, the breast cancer group demonstrated significantly lower fractional anisotropy, altered small-world connectome properties, lower brain network tolerance to systematic region (node), and connection (edge) attacks and significant cognitive impairment. Lower tolerance to network attack was associated with cognitive impairment in the breast cancer group. These findings provide further evidence of diffuse white matter pathology after breast cancer and extend the literature in this area with unique data demonstrating increased vulnerability of the post-breast cancer brain network to future neurodegenerative processes. Copyright © 2015 Elsevier Inc. All rights reserved.

  15. Controllability and observability analysis for vertex domination centrality in directed networks

    PubMed Central

    Wang, Bingbo; Gao, Lin; Gao, Yong; Deng, Yue; Wang, Yu

    2014-01-01

    Topological centrality is a significant measure for characterising the relative importance of a node in a complex network. For directed networks that model dynamic processes, however, it is of more practical importance to quantify a vertex's ability to dominate (control or observe) the state of other vertices. In this paper, based on the determination of controllable and observable subspaces under the global minimum-cost condition, we introduce a novel direction-specific index, domination centrality, to assess the intervention capabilities of vertices in a directed network. Statistical studies demonstrate that the domination centrality is, to a great extent, encoded by the underlying network's degree distribution and that most network positions through which one can intervene in a system are vertices with high domination centrality rather than network hubs. To analyse the interaction and functional dependence between vertices when they are used to dominate a network, we define the domination similarity and detect significant functional modules in glossary and metabolic networks through clustering analysis. The experimental results provide strong evidence that our indices are effective and practical in accurately depicting the structure of directed networks. PMID:24954137

  16. An architecture for designing fuzzy logic controllers using neural networks

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1991-01-01

    Described here is an architecture for designing fuzzy controllers through a hierarchical process of control rule acquisition and by using special classes of neural network learning techniques. A new method for learning to refine a fuzzy logic controller is introduced. A reinforcement learning technique is used in conjunction with a multi-layer neural network model of a fuzzy controller. The model learns by updating its prediction of the plant's behavior and is related to the Sutton's Temporal Difference (TD) method. The method proposed here has the advantage of using the control knowledge of an experienced operator and fine-tuning it through the process of learning. The approach is applied to a cart-pole balancing system.

  17. Building a Smartphone Seismic Network

    NASA Astrophysics Data System (ADS)

    Kong, Q.; Allen, R. M.

    2013-12-01

    We are exploring to build a new type of seismic network by using the smartphones. The accelerometers in smartphones can be used to record earthquakes, the GPS unit can give an accurate location, and the built-in communication unit makes the communication easier for this network. In the future, these smartphones may work as a supplement network to the current traditional network for scientific research and real-time applications. In order to build this network, we developed an application for android phones and server to record the acceleration in real time. These records can be sent back to a server in real time, and analyzed at the server. We evaluated the performance of the smartphone as a seismic recording instrument by comparing them with high quality accelerometer while located on controlled shake tables for a variety of tests, and also the noise floor test. Based on the daily human activity data recorded by the volunteers and the shake table tests data, we also developed algorithm for the smartphones to detect earthquakes from daily human activities. These all form the basis of setting up a new prototype smartphone seismic network in the near future.

  18. PeerShield: determining control and resilience criticality of collaborative cyber assets in networks

    NASA Astrophysics Data System (ADS)

    Cam, Hasan

    2012-06-01

    As attackers get more coordinated and advanced in cyber attacks, cyber assets are required to have much more resilience, control effectiveness, and collaboration in networks. Such a requirement makes it essential to take a comprehensive and objective approach for measuring the individual and relative performances of cyber security assets in network nodes. To this end, this paper presents four techniques as to how the relative importance of cyber assets can be measured more comprehensively and objectively by considering together the main variables of risk assessment (e.g., threats, vulnerabilities), multiple attributes (e.g., resilience, control, and influence), network connectivity and controllability among collaborative cyber assets in networks. In the first technique, a Bayesian network is used to include the random variables for control, recovery, and resilience attributes of nodes, in addition to the random variables of threats, vulnerabilities, and risk. The second technique shows how graph matching and coloring can be utilized to form collaborative pairs of nodes to shield together against threats and vulnerabilities. The third technique ranks the security assets of nodes by incorporating multiple weights and thresholds of attributes into a decision-making algorithm. In the fourth technique, the hierarchically well-separated tree is enhanced to first identify critical nodes of a network with respect to their attributes and network connectivity, and then selecting some nodes as driver nodes for network controllability.

  19. Controlling infectious disease through the targeted manipulation of contact network structure

    PubMed Central

    Gates, M. Carolyn; Woolhouse, Mark E.J.

    2015-01-01

    Individuals in human and animal populations are linked through dynamic contact networks with characteristic structural features that drive the epidemiology of directly transmissible infectious diseases. Using animal movement data from the British cattle industry as an example, this analysis explores whether disease dynamics can be altered by placing targeted restrictions on contact formation to reconfigure network topology. This was accomplished using a simple network generation algorithm that combined configuration wiring with stochastic block modelling techniques to preserve the weighted in- and out-degree of individual nodes (farms) as well as key demographic characteristics of the individual network connections (movement date, livestock market, and animal production type). We then tested a control strategy based on introducing additional constraints into the network generation algorithm to prevent farms with a high in-degree from selling cattle to farms with a high out-degree as these particular network connections are predicted to have a disproportionately strong role in spreading disease. Results from simple dynamic disease simulation models predicted significantly lower endemic disease prevalences on the trade restricted networks compared to the baseline generated networks. As expected, the relative magnitude of the predicted changes in endemic prevalence was greater for diseases with short infectious periods and low transmission probabilities. Overall, our study findings demonstrate that there is significant potential for controlling multiple infectious diseases simultaneously by manipulating networks to have more epidemiologically favourable topological configurations. Further research is needed to determine whether the economic and social benefits of controlling disease can justify the costs of restricting contact formation. PMID:26342238

  20. Learning Control Over Emotion Networks Through Connectivity-Based Neurofeedback.

    PubMed

    Koush, Yury; Meskaldji, Djalel-E; Pichon, Swann; Rey, Gwladys; Rieger, Sebastian W; Linden, David E J; Van De Ville, Dimitri; Vuilleumier, Patrik; Scharnowski, Frank

    2017-02-01

    Most mental functions are associated with dynamic interactions within functional brain networks. Thus, training individuals to alter functional brain networks might provide novel and powerful means to improve cognitive performance and emotions. Using a novel connectivity-neurofeedback approach based on functional magnetic resonance imaging (fMRI), we show for the first time that participants can learn to change functional brain networks. Specifically, we taught participants control over a key component of the emotion regulation network, in that they learned to increase top-down connectivity from the dorsomedial prefrontal cortex, which is involved in cognitive control, onto the amygdala, which is involved in emotion processing. After training, participants successfully self-regulated the top-down connectivity between these brain areas even without neurofeedback, and this was associated with concomitant increases in subjective valence ratings of emotional stimuli of the participants. Connectivity-based neurofeedback goes beyond previous neurofeedback approaches, which were limited to training localized activity within a brain region. It allows to noninvasively and nonpharmacologically change interconnected functional brain networks directly, thereby resulting in specific behavioral changes. Our results demonstrate that connectivity-based neurofeedback training of emotion regulation networks enhances emotion regulation capabilities. This approach can potentially lead to powerful therapeutic emotion regulation protocols for neuropsychiatric disorders. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.