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Sample records for genetic network controlling

  1. Genetic control of functional brain network efficiency in children

    NARCIS (Netherlands)

    Heuvel, M.P.; van Soelen, I.L.C.; Stam, C.J.; Kahn, R.S.; Boomsma, D.I.; Hulshoff Pol, H.E.

    2013-01-01

    The human brain is a complex network of interconnected brain regions. In adulthood, the brain's network was recently found to be under genetic influence. However, the extent to which genes influence the functional brain network early in development is not yet known. We report on the heritability of

  2. Predictive Control of Hydronic Floor Heating Systems using Neural Networks and Genetic Algorithms

    DEFF Research Database (Denmark)

    Vinther, Kasper; Green, Torben; Østergaard, Søren

    2017-01-01

    This paper presents the use a neural network and a micro genetic algorithm to optimize future set-points in existing hydronic floor heating systems for improved energy efficiency. The neural network can be trained to predict the impact of changes in set-points on future room temperatures. Additio...... space is not guaranteed. Evaluation of the performance of multiple neural networks is performed, using different levels of information, and optimization results are presented on a detailed house simulation model.......This paper presents the use a neural network and a micro genetic algorithm to optimize future set-points in existing hydronic floor heating systems for improved energy efficiency. The neural network can be trained to predict the impact of changes in set-points on future room temperatures...

  3. Predictive Control of Hydronic Floor Heating Systems using Neural Networks and Genetic Algorithms

    DEFF Research Database (Denmark)

    Vinther, Kasper; Green, Torben; Østergaard, Søren

    2017-01-01

    . Additionally, weather disturbances such as solar heat gain can be anticipated and compensated for, while taking into account the slow dynamics of the floor. Together with a genetic algorithm, they provide a way to search for optimal future set-point sequences, when convexity and continuity in the solution......This paper presents the use a neural network and a micro genetic algorithm to optimize future set-points in existing hydronic floor heating systems for improved energy efficiency. The neural network can be trained to predict the impact of changes in set-points on future room temperatures...

  4. Elevator Group Supervisory Control System Using Genetic Network Programming with Macro Nodes and Reinforcement Learning

    Science.gov (United States)

    Zhou, Jin; Yu, Lu; Mabu, Shingo; Hirasawa, Kotaro; Hu, Jinglu; Markon, Sandor

    Elevator Group Supervisory Control System (EGSCS) is a very large scale stochastic dynamic optimization problem. Due to its vast state space, significant uncertainty and numerous resource constraints such as finite car capacities and registered hall/car calls, it is hard to manage EGSCS using conventional control methods. Recently, many solutions for EGSCS using Artificial Intelligence (AI) technologies have been reported. Genetic Network Programming (GNP), which is proposed as a new evolutionary computation method several years ago, is also proved to be efficient when applied to EGSCS problem. In this paper, we propose an extended algorithm for EGSCS by introducing Reinforcement Learning (RL) into GNP framework, and an improvement of the EGSCS' performances is expected since the efficiency of GNP with RL has been clarified in some other studies like tile-world problem. Simulation tests using traffic flows in a typical office building have been made, and the results show an actual improvement of the EGSCS' performances comparing to the algorithms using original GNP and conventional control methods. Furthermore, as a further study, an importance weight optimization algorithm is employed based on GNP with RL and its efficiency is also verified with the better performances.

  5. Dynamics and genetic fuzzy neural network vibration control design of a smart flexible four-bar linkage mechanism

    Energy Technology Data Exchange (ETDEWEB)

    Rong Bao, E-mail: rongbao_nust@sina.com; Rui Xiaoting [Nanjing University of Science and Technology, Institute of Launch Dynamics (China); Tao Ling [Chinese Academy of Sciences (ASIPP), Institute of Plasma Physics (China)

    2012-11-15

    In this paper, a dynamic modeling method and an active vibration control scheme for a smart flexible four-bar linkage mechanism featuring piezoelectric actuators and strain gauge sensors are presented. The dynamics of this smart mechanism is described by the Discrete Time Transfer Matrix Method of Multibody System (MS-DTTMM). Then a nonlinear fuzzy neural network control is employed to suppress the vibration of this smart mechanism. For improving the dynamic performance of the fuzzy neural network, a genetic algorithm based on the MS-DTTMM is designed offline to tune the initial parameters of the fuzzy neural network. The MS-DTTMM avoids the global dynamics equations of the system, which results in the matrices involved are always very small, so the computational efficiency of the dynamic analysis and control system optimization can be greatly improved. Formulations of the method as well as a numerical simulation are given to demonstrate the proposed dynamic method and control scheme.

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

    CERN Document Server

    Siddique, Nazmul

    2014-01-01

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

  7. PREDICTIVE CONTROL OF A BATCH POLYMERIZATION SYSTEM USING A FEEDFORWARD NEURAL NETWORK WITH ONLINE ADAPTATION BY GENETIC ALGORITHM

    Directory of Open Access Journals (Sweden)

    A. Cancelier

    Full Text Available Abstract This study used a predictive controller based on an empirical nonlinear model comprising a three-layer feedforward neural network for temperature control of the suspension polymerization process. In addition to the offline training technique, an algorithm was also analyzed for online adaptation of its parameters. For the offline training, the network was statically trained and the genetic algorithm technique was used in combination with the least squares method. For online training, the network was trained on a recurring basis and only the technique of genetic algorithms was used. In this case, only the weights and bias of the output layer neuron were modified, starting from the parameters obtained from the offline training. From the experimental results obtained in a pilot plant, a good performance was observed for the proposed control system, with superior performance for the control algorithm with online adaptation of the model, particularly with respect to the presence of off-set for the case of the fixed parameters model.

  8. Control of Stochastic Master Equation Models of Genetic Regulatory Networks by Approximating Their Average Behavior

    Science.gov (United States)

    Umut Caglar, Mehmet; Pal, Ranadip

    2010-10-01

    The central dogma of molecular biology states that ``information cannot be transferred back from protein to either protein or nucleic acid.'' However, this assumption is not exactly correct in most of the cases. There are a lot of feedback loops and interactions between different levels of systems. These types of interactions are hard to analyze due to the lack of data in the cellular level and probabilistic nature of interactions. Probabilistic models like Stochastic Master Equation (SME) or deterministic models like differential equations (DE) can be used to analyze these types of interactions. SME models based on chemical master equation (CME) can provide detailed representation of genetic regulatory system, but their use is restricted by the large data requirements and computational costs of calculations. The differential equations models on the other hand, have low calculation costs and much more adequate to generate control procedures on the system; but they are not adequate to investigate the probabilistic nature of interactions. In this work the success of the mapping between SME and DE is analyzed, and the success of a control policy generated by DE model with respect to SME model is examined. Index Terms--- Stochastic Master Equation models, Differential Equation Models, Control Policy Design, Systems biology

  9. A Double-Deck Elevator Group Supervisory Control System with Destination Floor Guidance System Using Genetic Network Programming

    Science.gov (United States)

    Yu, Lu; Zhou, Jin; Mabu, Shingo; Hirasawa, Kotaro; Hu, Jinglu; Markon, Sandor

    The Elevator Group Supervisory Control Systems (EGSCS) are the control systems that systematically manage three or more elevators in order to efficiently transport the passengers in buildings. Double-deck elevators, where two elevators are connected with each other, serve passengers at two consecutive floors simultaneously. Double-deck Elevator systems (DDES) become more complex in their behavior than conventional single-deck elevator systems (SDES). Recently, Artificial Intelligence (AI) technology has been used in such complex systems. Genetic Network Programming (GNP), a graph-based evolutionary method, has been applied to EGSCS and its advantages are shown in some papers. GNP can obtain the strategy of a new hall call assignment to the optimal elevator when it performs crossover and mutation operations to judgment nodes and processing nodes. Meanwhile, Destination Floor Guidance System (DFGS) is installed in DDES, so that passengers can also input their destinations at elevator halls. In this paper, we have applied GNP to DDES and compared DFGS with normal systems. The waiting time and traveling time of DFGS are all improved because of getting more information from DFGS. The simulations showed the effectiveness of the double-deck elevators with DFGS in different building traffics.

  10. Comparison of Control Approaches in Genetic Regulatory Networks by Using Stochastic Master Equation Models, Probabilistic Boolean Network Models and Differential Equation Models and Estimated Error Analyzes

    Science.gov (United States)

    Caglar, Mehmet Umut; Pal, Ranadip

    2011-03-01

    Central dogma of molecular biology states that ``information cannot be transferred back from protein to either protein or nucleic acid''. However, this assumption is not exactly correct in most of the cases. There are a lot of feedback loops and interactions between different levels of systems. These types of interactions are hard to analyze due to the lack of cell level data and probabilistic - nonlinear nature of interactions. Several models widely used to analyze and simulate these types of nonlinear interactions. Stochastic Master Equation (SME) models give probabilistic nature of the interactions in a detailed manner, with a high calculation cost. On the other hand Probabilistic Boolean Network (PBN) models give a coarse scale picture of the stochastic processes, with a less calculation cost. Differential Equation (DE) models give the time evolution of mean values of processes in a highly cost effective way. The understanding of the relations between the predictions of these models is important to understand the reliability of the simulations of genetic regulatory networks. In this work the success of the mapping between SME, PBN and DE models is analyzed and the accuracy and affectivity of the control policies generated by using PBN and DE models is compared.

  11. Intelligent networked teleoperation control

    CERN Document Server

    Li, Zhijun; Su, Chun-Yi

    2015-01-01

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

  12. Temporal Genetic Modifications after Controlled Cortical Impact—Understanding Traumatic Brain Injury through a Systematic Network Approach

    Directory of Open Access Journals (Sweden)

    Yung-Hao Wong

    2016-02-01

    Full Text Available Traumatic brain injury (TBI is a primary injury caused by external physical force and also a secondary injury caused by biological processes such as metabolic, cellular, and other molecular events that eventually lead to brain cell death, tissue and nerve damage, and atrophy. It is a common disease process (as opposed to an event that causes disabilities and high death rates. In order to treat all the repercussions of this injury, treatment becomes increasingly complex and difficult throughout the evolution of a TBI. Using high-throughput microarray data, we developed a systems biology approach to explore potential molecular mechanisms at four time points post-TBI (4, 8, 24, and 72 h, using a controlled cortical impact (CCI model. We identified 27, 50, 48, and 59 significant proteins as network biomarkers at these four time points, respectively. We present their network structures to illustrate the protein–protein interactions (PPIs. We also identified UBC (Ubiquitin C, SUMO1, CDKN1A (cyclindependent kinase inhibitor 1A, and MYC as the core network biomarkers at the four time points, respectively. Using the functional analytical tool MetaCore™, we explored regulatory mechanisms and biological processes and conducted a statistical analysis of the four networks. The analytical results support some recent findings regarding TBI and provide additional guidance and directions for future research.

  13. Virtualized Network Control (VNC)

    Energy Technology Data Exchange (ETDEWEB)

    Lehman, Thomas [Univ. of Southern California, Los Angeles, CA (United States); Guok, Chin [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Ghani, Nasir [Univ. of New Mexico, Albuquerque, NM (United States)

    2013-01-31

    The focus of this project was on the development of a "Network Service Plane" as an abstraction model for the control and provisioning of multi-layer networks. The primary motivation for this work were the requirements of next generation networked applications which will need to access advanced networking as a first class resource at the same level as compute and storage resources. A new class of "Intelligent Network Services" were defined in order to facilitate the integration of advanced network services into application specific workflows. This new class of network services are intended to enable real-time interaction between the application co-scheduling algorithms and the network for the purposes of workflow planning, real-time resource availability identification, scheduling, and provisioning actions.

  14. Controllability of Complex Networks

    Science.gov (United States)

    Liu, Yang; Slotine, Jean-Jacques; Barabasi, Albert-Laszlo

    2011-03-01

    The ultimate proof of our understanding of natural or technological systems is reflected in our ability to control them. While control theory offers mathematical tools to steer engineered systems towards a desired state, we lack a general framework to control complex self-organized systems, like the regulatory network of a cell or the Internet. Here we develop analytical tools to study the controllability of an arbitrary complex directed network, identifying the set of driver nodes whose time-dependent control can guide the system's dynamics. We apply these tools to real and model networks, finding that sparse inhomogeneous networks, which emerge in many real complex systems, are the most difficult to control. In contrast, dense and homogeneous networks can be controlled via a few driver nodes. Counterintuitively, we find that in both model and real systems the driver nodes tend to avoid the hubs. We show that the robustness of control to link failure is determined by a core percolation problem, helping us understand why many complex systems are relatively insensitive to link deletion. The developed approach offers a framework to address the controllability of an arbitrary network, representing a key step towards the eventual control of complex systems.

  15. Introduction to Focus Issue: Quantitative Approaches to Genetic Networks

    Science.gov (United States)

    Albert, Réka; Collins, James J.; Glass, Leon

    2013-06-01

    All cells of living organisms contain similar genetic instructions encoded in the organism's DNA. In any particular cell, the control of the expression of each different gene is regulated, in part, by binding of molecular complexes to specific regions of the DNA. The molecular complexes are composed of protein molecules, called transcription factors, combined with various other molecules such as hormones and drugs. Since transcription factors are coded by genes, cellular function is partially determined by genetic networks. Recent research is making large strides to understand both the structure and the function of these networks. Further, the emerging discipline of synthetic biology is engineering novel gene circuits with specific dynamic properties to advance both basic science and potential practical applications. Although there is not yet a universally accepted mathematical framework for studying the properties of genetic networks, the strong analogies between the activation and inhibition of gene expression and electric circuits suggest frameworks based on logical switching circuits. This focus issue provides a selection of papers reflecting current research directions in the quantitative analysis of genetic networks. The work extends from molecular models for the binding of proteins, to realistic detailed models of cellular metabolism. Between these extremes are simplified models in which genetic dynamics are modeled using classical methods of systems engineering, Boolean switching networks, differential equations that are continuous analogues of Boolean switching networks, and differential equations in which control is based on power law functions. The mathematical techniques are applied to study: (i) naturally occurring gene networks in living organisms including: cyanobacteria, Mycoplasma genitalium, fruit flies, immune cells in mammals; (ii) synthetic gene circuits in Escherichia coli and yeast; and (iii) electronic circuits modeling genetic networks

  16. Optimized intelligent control of a 2-degree of freedom robot for rehabilitation of lower limbs using neural network and genetic algorithm.

    Science.gov (United States)

    Aminiazar, Wahab; Najafi, Farid; Nekoui, Mohammad Ali

    2013-08-14

    There is an increasing trend in using robots for medical purposes. One specific area is rehabilitation. Rehabilitation is one of the non-drug treatments in community health which means the restoration of the abilities to maximize independence. It is a prolonged work and costly labor. On the other hand, by using the flexible and efficient robots in rehabilitation area, this process will be more useful for handicapped patients. In this study, a rule-based intelligent control methodology is proposed to mimic the behavior of a healthy limb in a satisfactory way by a 2-DOF planar robot. Inverse kinematic of the planar robot will be solved by neural networks and control parameters will be optimized by genetic algorithm, as rehabilitation progress. The results of simulations are presented by defining a physiotherapy simple mode on desired trajectory. MATLAB/Simulink is used for simulations. The system is capable of learning the action of the physiotherapist for each patient and imitating this behaviour in the absence of a physiotherapist that can be called robotherapy. In this study, a therapeutic exercise planar 2-DOF robot is designed and controlled for lower-limb rehabilitation. The robot manipulator is controlled by combination of hybrid and adaptive controls. Some safety factors and stability constraints are defined and obtained. The robot is stopped when the safety factors are not satisfied. Kinematics of robot is estimated by an MLP neural network and proper control parameters are achieved using GA optimization.

  17. Optimization of controlled release nanoparticle formulation of verapamil hydrochloride using artificial neural networks with genetic algorithm and response surface methodology.

    Science.gov (United States)

    Li, Yongqiang; Abbaspour, Mohammadreza R; Grootendorst, Paul V; Rauth, Andrew M; Wu, Xiao Yu

    2015-08-01

    This study was performed to optimize the formulation of polymer-lipid hybrid nanoparticles (PLN) for the delivery of an ionic water-soluble drug, verapamil hydrochloride (VRP) and to investigate the roles of formulation factors. Modeling and optimization were conducted based on a spherical central composite design. Three formulation factors, i.e., weight ratio of drug to lipid (X1), and concentrations of Tween 80 (X2) and Pluronic F68 (X3), were chosen as independent variables. Drug loading efficiency (Y1) and mean particle size (Y2) of PLN were selected as dependent variables. The predictive performance of artificial neural networks (ANN) and the response surface methodology (RSM) were compared. As ANN was found to exhibit better recognition and generalization capability over RSM, multi-objective optimization of PLN was then conducted based upon the validated ANN models and continuous genetic algorithms (GA). The optimal PLN possess a high drug loading efficiency (92.4%, w/w) and a small mean particle size (∼100nm). The predicted response variables matched well with the observed results. The three formulation factors exhibited different effects on the properties of PLN. ANN in coordination with continuous GA represent an effective and efficient approach to optimize the PLN formulation of VRP with desired properties. Copyright © 2015 Elsevier B.V. All rights reserved.

  18. Operation and Power Flow Control of Multi-Terminal DC Networks for Grid Integration of Offshore Wind Farms Using Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Rodrigo Teixeira Pinto

    2012-12-01

    Full Text Available For achieving the European renewable electricity targets, a significant contribution is foreseen to come from offshore wind energy. Considering the large scale of the future planned offshore wind farms and the increasing distances to shore, grid integration through a transnational DC network is desirable for several reasons. This article investigates a nine-node DC grid connecting three northern European countries — namely UK, The Netherlands and Germany. The power-flow control inside the multi-terminal DC grid based on voltage-source converters is achieved through a novel method, called distributed voltage control (DVC. In this method, an optimal power flow (OPF is solved in order to minimize the transmission losses in the network. The main contribution of the paper is the utilization of a genetic algorithm (GA to solve the OPF problem while maintaining an N-1 security constraint. After describing main DC network component models, several case studies illustrate the dynamic behavior of the proposed control method.

  19. Using previously genotyped controls in genome-wide association studies (GWAS: application to the Stroke Genetics Network (SiGN

    Directory of Open Access Journals (Sweden)

    Braxton D Mitchell

    2014-04-01

    Full Text Available Genome-wide association studies (GWAS are widely applied to identify susceptibility loci for a variety of diseases using genotyping arrays that interrogate known polymorphisms throughout the genome. A particular strength of GWAS is that it is unbiased with respect to specific genomic elements (e.g., coding or regulatory regions of genes, and it has revealed important associations that would have never been suspected based on prior knowledge or assumptions. To date, the discovered SNPs associated with complex human traits tend to have small effect sizes, requiring very large sample sizes to achieve robust statistical power. To address these issues, a number of efficient strategies have emerged for conducting GWAS, including combining study results across multiple studies using meta-analysis, collecting cases through electronic health records, and using samples collected from other studies as controls that have already been genotyped and made publicly available (e.g., through deposition of de-identified data into dbGaP or EGA.In certain scenarios, it may be attractive to use already genotyped controls and divert resources to standardized collection, phenotyping, and genotyping of cases only. This strategy, however, requires that careful attention be paid to the choice of public controls and to the comparability of genetic data between cases and the public controls to ensure that any allele frequency differences observed between groups is attributable to locus-specific effects rather than to a systematic bias due to poor matching (population stratification or differential genotype calling (batch effects.The goal of this paper is to describe some of the potential pitfalls in using previously genotyped control data. We focus on considerations related to the choice of control groups, the use of different genotyping platforms, and approaches to deal with population stratification when cases and controls are genotyped across different platforms.

  20. Study on the idity fuzzy neural network controller based on improved genetic algorithm of intelligent temperature control system in vegetable greenhouse

    Science.gov (United States)

    Zhang, Su; Yuan, Hongbo; Zhou, Yuhong; Wang, Nan

    2009-07-01

    In order to create the environment that the suitable crop grows, direct against the characteristic of the system of the greenhouse. The aim of the research was to study the intelligent temperature control system in vegetable greenhouse. Based on computer automatic control ,a kind of intelligent temperature control system in vegetable greenhouse was designed. The design thought of systematic hardwares such as temperature collection system, temperature display, control system, heater control circuit in the heater were expounded in detail The control algorithm of the system was improved and system simulation was made by using MATLAB finally. The control algorithm of the system was improved by a new fuzzy neural network controller. The stimulation curve showed that the system had better controlling and tracking performances ,higher accuracy of controlling the temperature. And this system and host epigyny computer could constitute the secondary computer control system which was favorable for realizing the centralized management of the production.

  1. Genetic Algorithm Optimized Neural Networks Ensemble as ...

    African Journals Online (AJOL)

    NJD

    Genetic Algorithm Optimized Neural Networks Ensemble as. Calibration Model for Simultaneous Spectrophotometric. Estimation of Atenolol and Losartan Potassium in Tablets. Dondeti Satyanarayana*, Kamarajan Kannan and Rajappan Manavalan. Department of Pharmacy, Annamalai University, Annamalainagar, Tamil ...

  2. Quantifying and analyzing the network basis of genetic complexity.

    Directory of Open Access Journals (Sweden)

    Ethan G Thompson

    Full Text Available Genotype-to-phenotype maps exhibit complexity. This genetic complexity is mentioned frequently in the literature, but a consistent and quantitative definition is lacking. Here, we derive such a definition and investigate its consequences for model genetic systems. The definition equates genetic complexity with a surplus of genotypic diversity over phenotypic diversity. Applying this definition to ensembles of Boolean network models, we found that the in-degree distribution and the number of periodic attractors produced determine the relative complexity of different topology classes. We found evidence that networks that are difficult to control, or that exhibit a hierarchical structure, are genetically complex. We analyzed the complexity of the cell cycle network of Sacchoromyces cerevisiae and pinpointed genes and interactions that are most important for its high genetic complexity. The rigorous definition of genetic complexity is a tool for unraveling the structure and properties of genotype-to-phenotype maps by enabling the quantitative comparison of the relative complexities of different genetic systems. The definition also allows the identification of specific network elements and subnetworks that have the greatest effects on genetic complexity. Moreover, it suggests ways to engineer biological systems with desired genetic properties.

  3. Launch Control Network Engineer

    Science.gov (United States)

    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.

  4. Genetic algorithm for neural networks optimization

    Science.gov (United States)

    Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta

    2004-11-01

    This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB«.

  5. Bayesian neural networks for detecting epistasis in genetic association studies.

    Science.gov (United States)

    Beam, Andrew L; Motsinger-Reif, Alison; Doyle, Jon

    2014-11-21

    Discovering causal genetic variants from large genetic association studies poses many difficult challenges. Assessing which genetic markers are involved in determining trait status is a computationally demanding task, especially in the presence of gene-gene interactions. A non-parametric Bayesian approach in the form of a Bayesian neural network is proposed for use in analyzing genetic association studies. Demonstrations on synthetic and real data reveal they are able to efficiently and accurately determine which variants are involved in determining case-control status. By using graphics processing units (GPUs) the time needed to build these models is decreased by several orders of magnitude. In comparison with commonly used approaches for detecting interactions, Bayesian neural networks perform very well across a broad spectrum of possible genetic relationships. The proposed framework is shown to be a powerful method for detecting causal SNPs while being computationally efficient enough to handle large datasets.

  6. Material procedure quality forecast based on genetic BP neural network

    Science.gov (United States)

    Zheng, Bao-Hua

    2017-07-01

    Material procedure quality forecast plays an important role in quality control. This paper proposes a prediction model based on genetic algorithm (GA) and back propagation (BP) neural network. It can obtain the initial weights and thresholds of optimized BP neural network with the GA global search ability. A material process quality prediction model with the optimized BP neural network is adopted to predict the error of future process to measure the accuracy of process quality. The results show that the proposed method has the advantages of high accuracy and fast convergence rate compared with BP neural network.

  7. Assessing the molecular genetics of attention networks

    Directory of Open Access Journals (Sweden)

    Pfaff Donald W

    2002-10-01

    Full Text Available Abstract Background Current efforts to study the genetic underpinnings of higher brain functions have been lacking appropriate phenotypes to describe cognition. One of the problems is that many cognitive concepts for which there is a single word (e.g. attention have been shown to be related to several anatomical networks. Recently, we have developed an Attention Network Test (ANT that provides a separate measure for each of three anatomically defined attention networks. Results In this study we have measured the efficiency of neural networks related to aspects of attention using the ANT in a population of 200 adult subjects. We then examined genetic polymorphisms in four candidate genes (DRD4, DAT, COMT and MAOA that have been shown to contribute to the risk of developing various psychiatric disorders where attention is disrupted. We find modest associations of several polymorphisms with the efficiency of executive attention but not with overall performance measures such as reaction time. Conclusions These results suggest that genetic variation may underlie inter-subject variation in the efficiency of executive attention. This study also shows that genetic influences on executive attention may be specific to certain anatomical networks rather than affecting performance in a global or non-specific manner. Lastly, this study further validates the ANT as an endophenotypic assay suitable for assessing how genes influence certain anatomical networks that may be disrupted in various psychiatric disorders.

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

    NARCIS (Netherlands)

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

    2016-01-01

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

  9. Meta-analysis of Metabolome QTLs in Arabidopsis: trying to estimate the network size controlling genetic variation of the metabolome.

    OpenAIRE

    Bindu eJoseph; Susanna eAtwell; Jason eCorwin; Baohua eLi; Daniel J Kliebenstein; Daniel J Kliebenstein

    2014-01-01

    A central goal of systems biology is to develop models that are both predictive and accurately describe the biological system. One complexity to this endeavor is that it is possible to develop models that appear predictive even if they use far fewer components than the biological system itself uses for the same process. This problem also occurs in quantitative genetics where it is often possible to describe the variation in a system using fewer genes than are actually variable due to the comp...

  10. Structural systems identification of genetic regulatory networks.

    Science.gov (United States)

    Xiong, Hao; Choe, Yoonsuck

    2008-02-15

    Reverse engineering of genetic regulatory networks from experimental data is the first step toward the modeling of genetic networks. Linear state-space models, also known as linear dynamical models, have been applied to model genetic networks from gene expression time series data, but existing works have not taken into account available structural information. Without structural constraints, estimated models may contradict biological knowledge and estimation methods may over-fit. In this report, we extended expectation-maximization (EM) algorithms to incorporate prior network structure and to estimate genetic regulatory networks that can track and predict gene expression profiles. We applied our method to synthetic data and to SOS data and showed that our method significantly outperforms the regular EM without structural constraints. The Matlab code is available upon request and the SOS data can be downloaded from http://www.weizmann.ac.il/mcb/UriAlon/Papers/SOSData/, courtesy of Uri Alon. Zak's data is available from his website, http://www.che.udel.edu/systems/people/zak.

  11. Mining protein networks for synthetic genetic interactions

    Directory of Open Access Journals (Sweden)

    Zhao Shan

    2008-10-01

    Full Text Available Abstract Background The local connectivity and global position of a protein in a protein interaction network are known to correlate with some of its functional properties, including its essentiality or dispensability. It is therefore of interest to extend this observation and examine whether network properties of two proteins considered simultaneously can determine their joint dispensability, i.e., their propensity for synthetic sick/lethal interaction. Accordingly, we examine the predictive power of protein interaction networks for synthetic genetic interaction in Saccharomyces cerevisiae, an organism in which high confidence protein interaction networks are available and synthetic sick/lethal gene pairs have been extensively identified. Results We design a support vector machine system that uses graph-theoretic properties of two proteins in a protein interaction network as input features for prediction of synthetic sick/lethal interactions. The system is trained on interacting and non-interacting gene pairs culled from large scale genetic screens as well as literature-curated data. We find that the method is capable of predicting synthetic genetic interactions with sensitivity and specificity both exceeding 85%. We further find that the prediction performance is reasonably robust with respect to errors in the protein interaction network and with respect to changes in the features of test datasets. Using the prediction system, we carried out novel predictions of synthetic sick/lethal gene pairs at a genome-wide scale. These pairs appear to have functional properties that are similar to those that characterize the known synthetic lethal gene pairs. Conclusion Our analysis shows that protein interaction networks can be used to predict synthetic lethal interactions with accuracies on par with or exceeding that of other computational methods that use a variety of input features, including functional annotations. This indicates that protein

  12. Broadband accelerator control network

    Energy Technology Data Exchange (ETDEWEB)

    Skelly, J.; Clifford, T.; Frankel, R.

    1983-01-01

    A broadband data communications network has been implemented at BNL for control of the Alternating Gradient Synchrotron (AG) proton accelerator, using commercial CATV hardware, dual coaxial cables as the communications medium, and spanning 2.0 km. A 4 MHz bandwidth Digital Control channel using CSMA-CA protocol is provided for digital data transmission, with 8 access nodes available over the length of the RELWAY. Each node consists of an rf modem and a microprocessor-based store-and-forward message handler which interfaces the RELWAY to a branch line implemented in GPIB. A gateway to the RELWAY control channel for the (preexisting) AGS Computerized Accelerator Operating system has been constructed using an LSI-11/23 microprocessor as a device in a GPIB branch line. A multilayer communications protocol has been defined for the Digital Control Channel, based on the ISO Open Systems Interconnect layered model, and a RELWAY Device Language defined as the required universal language for device control on this channel.

  13. Neural Networks for Optimal Control

    DEFF Research Database (Denmark)

    Sørensen, O.

    1995-01-01

    Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process.......Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process....

  14. GKIN: a tool for drawing genetic networks

    Directory of Open Access Journals (Sweden)

    Jonathan Arnold

    2012-03-01

    Full Text Available We present GKIN, a simulator and a comprehensive graphical interface where one can draw the model specification of reactions between hypothesized molecular participants in a gene regulatory and biochemical reaction network (or genetic network for short. The solver is written in C++ in a nearly platform independentmanner to simulate large ensembles of models, which can run on PCs, Macintoshes, and UNIX machines, and its graphical user interface is written in Java which can run as a standalone or WebStart application. The drawing capability for rendering a network significantly enhances the ease of use of other reaction network simulators, such as KINSOLVER (Aleman-Meza et al., 2009 and enforces a correct semantic specification of the network. In a usability study with novice users, drawing the network with GKIN was preferred and faster in comparison with entry with a dialog-box guided interface in COPASI (Hoops, et al., 2006 with no difference in error rates between GKIN and COPASI in specifying the network. GKIN is freely available at http://faculty.cs.wit.edu/~ldeligia/PROJECTS/GKIN/.

  15. Robust Multiobjective Controllability of Complex Neuronal Networks.

    Science.gov (United States)

    Tang, Yang; Gao, Huijun; Du, Wei; Lu, Jianquan; Vasilakos, Athanasios V; Kurths, Jurgen

    2016-01-01

    This paper addresses robust multiobjective identification of driver nodes in the neuronal network of a cat's brain, in which uncertainties in determination of driver nodes and control gains are considered. A framework for robust multiobjective controllability is proposed by introducing interval uncertainties and optimization algorithms. By appropriate definitions of robust multiobjective controllability, a robust nondominated sorting adaptive differential evolution (NSJaDE) is presented by means of the nondominated sorting mechanism and the adaptive differential evolution (JaDE). The simulation experimental results illustrate the satisfactory performance of NSJaDE for robust multiobjective controllability, in comparison with six statistical methods and two multiobjective evolutionary algorithms (MOEAs): nondominated sorting genetic algorithms II (NSGA-II) and nondominated sorting composite differential evolution. It is revealed that the existence of uncertainties in choosing driver nodes and designing control gains heavily affects the controllability of neuronal networks. We also unveil that driver nodes play a more drastic role than control gains in robust controllability. The developed NSJaDE and obtained results will shed light on the understanding of robustness in controlling realistic complex networks such as transportation networks, power grid networks, biological networks, etc.

  16. Controlling Congestion on Complex Networks

    CERN Document Server

    Buzna, Lubos

    2016-01-01

    From the Internet to road networks and the power grid, modern life depends on controlling flows on critical infrastructure networks that often operate in a congested state. Yet, we have a limited understanding of the relative performance of the control mechanisms available to manage congestion and of the interplay between network topology, path layout and congestion control algorithms. Here, we consider two flow algorithms (max-flow and uniform-flow), and two more realistic congestion control schemes (max-min fairness and proportional fairness). We analyse how the algorithms and network topology affect throughput, fairness and the location of bottleneck edges. Our results show that on large random networks a network operator can implement the trade-off (proportional fairness) instead of the fair allocation (max-min fairness) with little sacrifice in throughput. We illustrate how the previously studied uniform-flow approach leaves networks severely underutilised in comparison with congestion control algorithms...

  17. Stochastic dynamics of genetic broadcasting networks

    Science.gov (United States)

    Potoyan, Davit A.; Wolynes, Peter G.

    2017-11-01

    The complex genetic programs of eukaryotic cells are often regulated by key transcription factors occupying or clearing out of a large number of genomic locations. Orchestrating the residence times of these factors is therefore important for the well organized functioning of a large network. The classic models of genetic switches sidestep this timing issue by assuming the binding of transcription factors to be governed entirely by thermodynamic protein-DNA affinities. Here we show that relying on passive thermodynamics and random release times can lead to a "time-scale crisis" for master genes that broadcast their signals to a large number of binding sites. We demonstrate that this time-scale crisis for clearance in a large broadcasting network can be resolved by actively regulating residence times through molecular stripping. We illustrate these ideas by studying a model of the stochastic dynamics of the genetic network of the central eukaryotic master regulator NFκ B which broadcasts its signals to many downstream genes that regulate immune response, apoptosis, etc.

  18. Asynchronous control for networked systems

    CERN Document Server

    Rubio, Francisco; Bencomo, Sebastián

    2015-01-01

    This book sheds light on networked control systems; it describes different techniques for asynchronous control, moving away from the periodic actions of classical control, replacing them with state-based decisions and reducing the frequency with which communication between subsystems is required. The text focuses specially on event-based control. Split into two parts, Asynchronous Control for Networked Systems begins by addressing the problems of single-loop networked control systems, laying out various solutions which include two alternative model-based control schemes (anticipatory and predictive) and the use of H2/H∞ robust control to deal with network delays and packet losses. Results on self-triggering and send-on-delta sampling are presented to reduce the need for feedback in the loop. In Part II, the authors present solutions for distributed estimation and control. They deal first with reliable networks and then extend their results to scenarios in which delays and packet losses may occur. The novel ...

  19. Cybersecurity of Critical Control Networks

    Science.gov (United States)

    2015-07-14

    AFRL-OSR-VA-TR-2015-0173 CONGRESSIONAL) CYBERSECURITY OF CRITICAL CONTROL NETWORKS William Mahoney UNIVERSITY OF NEBRASKA Final Report 07/14/2015...Congressional) Cybersecurity of Critical Control Networks 5a. CONTRACT NUMBER 5b. GRANT NUMBER FA9550-10-1-0341 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR...FA9550-10-1-0341 Cybersecurity of Critical Control Networks Report Type Final Report Primary Contact E-mail wmahoney@unomaha.edu Primary

  20. Performance of Networked Control Systems

    Directory of Open Access Journals (Sweden)

    Yingwei Zhang

    2013-01-01

    Full Text Available Data packet dropout is a special kind of time delay problem. In this paper, predictive controllers for networked control systems (NCSs with dual-network are designed by model predictive control method. The contributions are as follows. (1 The predictive control problem of the dual-network is considered. (2 The predictive performance of the dual-network is evaluated. (3 Compared to the popular networked control systems, the optimal controller of the new NCSs with data packets dropout is designed, which can minimize infinite performance index at each sampling time and guarantee the closed-loop system stability. Finally, the simulation results show the feasibility and effectiveness of the controllers designed.

  1. Network Access Control For Dummies

    CERN Document Server

    Kelley, Jay; Wessels, Denzil

    2009-01-01

    Network access control (NAC) is how you manage network security when your employees, partners, and guests need to access your network using laptops and mobile devices. Network Access Control For Dummies is where you learn how NAC works, how to implement a program, and how to take real-world challenges in stride. You'll learn how to deploy and maintain NAC in your environment, identify and apply NAC standards, and extend NAC for greater network security. Along the way you'll become familiar with what NAC is (and what it isn't) as well as the key business drivers for deploying NAC.Learn the step

  2. Character Recognition Using Genetically Trained Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Diniz, C.; Stantz, K.M.; Trahan, M.W.; Wagner, J.S.

    1998-10-01

    Computationally intelligent recognition of characters and symbols addresses a wide range of applications including foreign language translation and chemical formula identification. The combination of intelligent learning and optimization algorithms with layered neural structures offers powerful techniques for character recognition. These techniques were originally developed by Sandia National Laboratories for pattern and spectral analysis; however, their ability to optimize vast amounts of data make them ideal for character recognition. An adaptation of the Neural Network Designer soflsvare allows the user to create a neural network (NN_) trained by a genetic algorithm (GA) that correctly identifies multiple distinct characters. The initial successfid recognition of standard capital letters can be expanded to include chemical and mathematical symbols and alphabets of foreign languages, especially Arabic and Chinese. The FIN model constructed for this project uses a three layer feed-forward architecture. To facilitate the input of characters and symbols, a graphic user interface (GUI) has been developed to convert the traditional representation of each character or symbol to a bitmap. The 8 x 8 bitmap representations used for these tests are mapped onto the input nodes of the feed-forward neural network (FFNN) in a one-to-one correspondence. The input nodes feed forward into a hidden layer, and the hidden layer feeds into five output nodes correlated to possible character outcomes. During the training period the GA optimizes the weights of the NN until it can successfully recognize distinct characters. Systematic deviations from the base design test the network's range of applicability. Increasing capacity, the number of letters to be recognized, requires a nonlinear increase in the number of hidden layer neurodes. Optimal character recognition performance necessitates a minimum threshold for the number of cases when genetically training the net. And, the

  3. Neural Networks in Control Applications

    DEFF Research Database (Denmark)

    Sørensen, O.

    The intention of this report is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... examined, and it appears that considering 'normal' neural network models with, say, 500 samples, the problem of over-fitting is neglible, and therefore it is not taken into consideration afterwards. Numerous model types, often met in control applications, are implemented as neural network models...... Kalmann filter) representing state space description. The potentials of neural networks for control of non-linear processes are also examined, focusing on three different groups of control concepts, all considered as generalizations of known linear control concepts to handle also non-linear processes...

  4. System control fuzzy neural sewage pumping stations using genetic algorithms

    Directory of Open Access Journals (Sweden)

    Владлен Николаевич Кузнецов

    2015-06-01

    Full Text Available It is considered the system of management of sewage pumping station with regulators based on a neuron network with fuzzy logic. Linguistic rules for the controller based on fuzzy logic, maintaining the level of effluent in the receiving tank within the prescribed limits are developed. The use of genetic algorithms for neuron network training is shown.

  5. Positive train control shared network.

    Science.gov (United States)

    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...

  6. Neural Networks in Control Applications

    DEFF Research Database (Denmark)

    Sørensen, O.

    simulated process and compared. The closing chapter describes some practical experiments, where the different control concepts and training methods are tested on the same practical process operating in very noisy environments. All tests confirm that neural networks also have the potential to be trained......The intention of this report is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: - Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. - Amongst numerous training algorithms, only four algorithms are examined, all...

  7. HSUPA Transport Network Congestion Control

    Directory of Open Access Journals (Sweden)

    Nádas Szilveszter

    2009-01-01

    Full Text Available The introduction of High Speed Uplink Packet Access (HSUPA greatly improves achievable uplink bitrate but it presents new challenges to be solved in the WCDMA radio access network. In the transport network, bandwidth reservation for HSUPA is not efficient and TCP cannot efficiently resolve congestion because of lower layer retransmissions. This paper proposes an HSUPA transport network flow control algorithm that handles congestion situations efficiently and supports Quality of Service differentiation. In the Radio Network Controller (RNC, transport network congestion is detected. Relying on the standardized control frame, the RNC notifies the Node B about transport network congestion. In case of transport network congestion, the Node B part of the HSUPA flow control instructs the air interface scheduler to reduce the bitrate of the flow to eliminate congestion. The performance analysis concentrates on transport network limited scenarios. It is shown that TCP cannot provide efficient congestion control. The proposed algorithm can achieve high end-user perceived throughput, while maintaining low delay, loss, and good fairness in the transport network.

  8. MAC Protocol for Ad Hoc Networks Using a Genetic Algorithm

    Science.gov (United States)

    Elizarraras, Omar; Panduro, Marco; Méndez, Aldo L.

    2014-01-01

    The problem of obtaining the transmission rate in an ad hoc network consists in adjusting the power of each node to ensure the signal to interference ratio (SIR) and the energy required to transmit from one node to another is obtained at the same time. Therefore, an optimal transmission rate for each node in a medium access control (MAC) protocol based on CSMA-CDMA (carrier sense multiple access-code division multiple access) for ad hoc networks can be obtained using evolutionary optimization. This work proposes a genetic algorithm for the transmission rate election considering a perfect power control, and our proposition achieves improvement of 10% compared with the scheme that handles the handshaking phase to adjust the transmission rate. Furthermore, this paper proposes a genetic algorithm that solves the problem of power combining, interference, data rate, and energy ensuring the signal to interference ratio in an ad hoc network. The result of the proposed genetic algorithm has a better performance (15%) compared to the CSMA-CDMA protocol without optimizing. Therefore, we show by simulation the effectiveness of the proposed protocol in terms of the throughput. PMID:25140339

  9. Delays and networked control systems

    CERN Document Server

    Hetel, Laurentiu; Daafouz, Jamal; Johansson, Karl

    2016-01-01

    This edited monograph includes state-of-the-art contributions on continuous time dynamical networks with delays. The book is divided into four parts. The first part presents tools and methods for the analysis of time-delay systems with a particular attention on control problems of large scale or infinite-dimensional systems with delays. The second part of the book is dedicated to the use of time-delay models for the analysis and design of Networked Control Systems. The third part of the book focuses on the analysis and design of systems with asynchronous sampling intervals which occur in Networked Control Systems. The last part of the book exposes several contributions dealing with the design of cooperative control and observation laws for networked control systems. The target audience primarily comprises researchers and experts in the field of control theory, but the book may also be beneficial for graduate students. .

  10. Understanding control of network spreading from network controllability

    Science.gov (United States)

    Sun, Peng Gang; Ma, Xiaoke

    2017-09-01

    How to control the spread of an epidemic or information is a great challenge for us. A dynamic network-based system’s structural controllability provides a new way to control spreading with the minimum input of external signals, and the dynamic system is controllable if the signals can drive it from any initial state to any desired final state in finite time. Therefore, we are motivated to develop a new framework by introducing spreading networks (SNs) to describe the spreading pathways from a global view, and we try to understand the control of the spreading by the structural controllability of the SNs. The SNs are transformed from original networks, in which each node is considered as a single spreading origin. The weights of directed links pointing at its direct contacts in the SNs denote the spreading abilities, which can be determined by a new probability function. Furthermore, we also investigate the impact of the dynamics of network structures on the framework. The results show that sparse homogeneous networks with a higher transmission probability tend to trigger a larger scale of diffusion, which is easier to control. We can also see that an epidemic or information is inclined to diffuse easily on the networks with strong community strengths and heterogeneous community sizes. From the structural controllability of the SNs, we observe that driver nodes for the control of the spread tend not to be the nodes located within the core of original networks or those with high-degree. In addition, the scale of diffusion, the number of driver nodes and positions of nodes are highly associated with the degree distribution of the original networks.

  11. Neural Networks in Control Applications

    DEFF Research Database (Denmark)

    Sørensen, O.

    The intention of this report is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: - Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. - Amongst numerous training algorithms, only four algorithms are examined, all...... in a recursive form (sample updating). The simplest is the Back Probagation Error Algorithm, and the most complex is the recursive Prediction Error Method using a Gauss-Newton search direction. - Over-fitting is often considered to be a serious problem when training neural networks. This problem is specifically...

  12. Training product unit neural networks with genetic algorithms

    Science.gov (United States)

    Janson, D. J.; Frenzel, J. F.; Thelen, D. C.

    1991-01-01

    The training of product neural networks using genetic algorithms is discussed. Two unusual neural network techniques are combined; product units are employed instead of the traditional summing units and genetic algorithms train the network rather than backpropagation. As an example, a neural netork is trained to calculate the optimum width of transistors in a CMOS switch. It is shown how local minima affect the performance of a genetic algorithm, and one method of overcoming this is presented.

  13. Control and Optimization of Network in Networked Control System

    Directory of Open Access Journals (Sweden)

    Wang Zhiwen

    2014-01-01

    Full Text Available In order to avoid quality of performance (QoP degradation resulting from quality of service (QoS, the solution to network congestion from the point of control theory, which marks departure of our results from the existing methods, is proposed in this paper. The congestion and bandwidth are regarded as state and control variables, respectively; then, the linear time-invariant (LTI model between congestion state and bandwidth of network is established. Consequently, linear quadratic method is used to eliminate the network congestion by allocating bandwidth dynamically. At last, numerical simulation results are given to illustrate the effectiveness of this modeling approach.

  14. A Model of Genetic Variation in Human Social Networks

    CERN Document Server

    Fowler, James H; Christakis, Nicholas A

    2008-01-01

    Social networks influence the evolution of cooperation and they exhibit strikingly systematic patterns across a wide range of human contexts. Both of these facts suggest that variation in the topological attributes of human social networks might have a genetic basis. While genetic variation accounts for a significant portion of the variation in many complex social behaviors, the heritability of egocentric social network attributes is unknown. Here we show that three of these attributes (in-degree, transitivity, and centrality) are heritable. We then develop a "mirror network" method to test extant network models and show that none accounts for observed genetic variation in human social networks. We propose an alternative "attract and introduce" model that generates significant heritability as well as other important network features, and we show that this model with two simple forms of heterogeneity is well suited to the modeling of real social networks in humans. These results suggest that natural selection ...

  15. A Network Traffic Control Enhancement Approach over Bluetooth Networks

    DEFF Research Database (Denmark)

    Son, L.T.; Schiøler, Henrik; Madsen, Ole Brun

    2003-01-01

    This paper analyzes network traffic control issues in Bluetooth data networks as convex optimization problem. We formulate the problem of maximizing of total network flows and minimizing the costs of flows. An adaptive distributed network traffic control scheme is proposed as an approximated...... solution of the stated optimization problem that satisfies quality of service requirements and topologically induced constraints in Bluetooth networks, such as link capacity and node resource limitations. The proposed scheme is decentralized and complies with frequent changes of topology as well...... as capacity limitations and flow requirements in the network. Simulation shows that the performance of Bluetooth networks could be improved by applying the adaptive distributed network traffic control scheme...

  16. Computational Genetic Regulatory Networks Evolvable, Self-organizing Systems

    CERN Document Server

    Knabe, Johannes F

    2013-01-01

    Genetic Regulatory Networks (GRNs) in biological organisms are primary engines for cells to enact their engagements with environments, via incessant, continually active coupling. In differentiated multicellular organisms, tremendous complexity has arisen in the course of evolution of life on earth. Engineering and science have so far achieved no working system that can compare with this complexity, depth and scope of organization. Abstracting the dynamics of genetic regulatory control to a computational framework in which artificial GRNs in artificial simulated cells differentiate while connected in a changing topology, it is possible to apply Darwinian evolution in silico to study the capacity of such developmental/differentiated GRNs to evolve. In this volume an evolutionary GRN paradigm is investigated for its evolvability and robustness in models of biological clocks, in simple differentiated multicellularity, and in evolving artificial developing 'organisms' which grow and express an ontogeny starting fr...

  17. Multilayer Optimization of Heterogeneous Networks Using Grammatical Genetic Programming.

    Science.gov (United States)

    Fenton, Michael; Lynch, David; Kucera, Stepan; Claussen, Holger; O'Neill, Michael

    2017-09-01

    Heterogeneous cellular networks are composed of macro cells (MCs) and small cells (SCs) in which all cells occupy the same bandwidth. Provision has been made under the third generation partnership project-long term evolution framework for enhanced intercell interference coordination (eICIC) between cell tiers. Expanding on previous works, this paper instruments grammatical genetic programming to evolve control heuristics for heterogeneous networks. Three aspects of the eICIC framework are addressed including setting SC powers and selection biases, MC duty cycles, and scheduling of user equipments (UEs) at SCs. The evolved heuristics yield minimum downlink rates three times higher than a baseline method, and twice that of a state-of-the-art benchmark. Furthermore, a greater number of UEs receive transmissions under the proposed scheme than in either the baseline or benchmark cases.

  18. Information and control in networks

    CERN Document Server

    Bernhardsson, Bo; Rantzer, Anders

    2014-01-01

    Information and Control in Networks demonstrates the way in which system dynamics and information flows intertwine as they evolve, and the central role played by information in the control of complex networked systems. It is a milestone on the road to that convergence from traditionally independent development of control theory and information theory which has emerged strongly in the last fifteen years, and is now a very active research field. In addition to efforts in control and information theory, the text is witness to strong research in such diverse fields as computer science, mathematics, and statistics. Aspects that are given specialist treatment include: ·                 data-rate theorems; ·                 computation and control over communication networks; ·                 decentralized stochastic control; ·                 Gaussian networks and Gaussian–Markov random fields; and ·                 routability ...

  19. State Observer Design for Delayed Genetic Regulatory Networks

    Directory of Open Access Journals (Sweden)

    Li-Ping Tian

    2014-01-01

    Full Text Available Genetic regulatory networks are dynamic systems which describe the interactions among gene products (mRNAs and proteins. The internal states of a genetic regulatory network consist of the concentrations of mRNA and proteins involved in it, which are very helpful in understanding its dynamic behaviors. However, because of some limitations such as experiment techniques, not all internal states of genetic regulatory network can be effectively measured. Therefore it becomes an important issue to estimate the unmeasured states via the available measurements. In this study, we design a state observer to estimate the states of genetic regulatory networks with time delays from available measurements. Furthermore, based on linear matrix inequality (LMI approach, a criterion is established to guarantee that the dynamic of estimation error is globally asymptotically stable. A gene repressillatory network is employed to illustrate the effectiveness of our design approach.

  20. Optimization of PID Controllers Using Ant Colony and Genetic Algorithms

    CERN Document Server

    Ünal, Muhammet; Topuz, Vedat; Erdal, Hasan

    2013-01-01

    Artificial neural networks, genetic algorithms and the ant colony optimization algorithm have become a highly effective tool for solving hard optimization problems. As their popularity has increased, applications of these algorithms have grown in more than equal measure. While many of the books available on these subjects only provide a cursory discussion of theory, the present book gives special emphasis to the theoretical background that is behind these algorithms and their applications. Moreover, this book introduces a novel real time control algorithm, that uses genetic algorithm and ant colony optimization algorithms for optimizing PID controller parameters. In general, the present book represents a solid survey on artificial neural networks, genetic algorithms and the ant colony optimization algorithm and introduces novel practical elements related to the application of these methods to  process system control.

  1. Dynamic Network Security Control Using Software Defined Networking

    Science.gov (United States)

    2016-03-24

    not subject to copyright protection in the United States. AFIT-ENG-MS-16-M-049 DYNAMIC NETWORK SECURITY CONTROL USING SOFTWARE DEFINED NETWORKING... software and tools vetted by industry leaders in networking and security. After considering the technologies previously discussed, the four components...DYNAMIC NETWORK SECURITY CONTROL USING SOFTWARE DEFINED NETWORKING THESIS Michael C. Todd, Captain, USAF AFIT-ENG-MS-16-M-049 DEPARTMENT OF THE AIR

  2. Solving Hub Network Problem Using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Mursyid Hasan Basri

    2012-01-01

    Full Text Available This paper addresses a network problem that described as follows. There are n ports that interact, and p of those will be designated as hubs. All hubs are fully interconnected. Each spoke will be allocated to only one of available hubs. Direct connection between two spokes is allowed only if they are allocated to the same hub. The latter is a distinct characteristic that differs it from pure hub-and-spoke system. In case of pure hub-and-spoke system, direct connection between two spokes is not allowed. The problem is where to locate hub ports and to which hub a spoke should be allocated so that total transportation cost is minimum. In the first model, there are some additional aspects are taken into consideration in order to achieve a better representation of the problem. The first, weekly service should be accomplished. Secondly, various vessel types should be considered. The last, a concept of inter-hub discount factor is introduced. Regarding the last aspect, it represents cost reduction factor at hub ports due to economies of scale. In practice, it is common that the cost rate for inter-hub movement is less than the cost rate for movement between hub and origin/destination. In this first model, inter-hub discount factor is assumed independent with amount of flows on inter-hub links (denoted as flow-independent discount policy. The results indicated that the patterns of enlargement of container ship size, to some degree, are similar with those in Kurokawa study. However, with regard to hub locations, the results have not represented the real practice. In the proposed model, unsatisfactory result on hub locations is addressed. One aspect that could possibly be improved to find better hub locations is inter-hub discount factor. Then inter-hub discount factor is assumed to depend on amount of inter-hub flows (denoted as flow-dependent discount policy. There are two discount functions examined in this paper. Both functions are characterized by

  3. Spectral coarse grained controllability of complex networks

    Science.gov (United States)

    Wang, Pei; Xu, Shuang

    2017-07-01

    With the accumulation of interaction data from various systems, a fundamental question in network science is how to reduce the sizes while keeping certain properties of complex networks. Combined the spectral coarse graining theory and the structural controllability of complex networks, we explore the structural controllability of undirected complex networks during coarse graining processes. We evidence that the spectral coarse grained controllability (SCGC) properties for the Erdös-Rényi (ER) random networks, the scale-free (SF) random networks and the small-world (SW) random networks are distinct from each other. The SW networks are very robust, while the SF networks are sensitive during the coarse graining processes. As an emergent properties for the dense ER networks, during the coarse graining processes, there exists a threshold value of the coarse grained sizes, which separates the controllability of the reduced networks into robust and sensitive to coarse graining. Investigations on some real-world complex networks indicate that the SCGC properties are varied among different categories and different kinds of networks, some highly organized social or biological networks are more difficult to be controlled, while many man-made power networks and infrastructure networks can keep the controllability properties during the coarse graining processes. Furthermore, we speculate that the SCGC properties of complex networks may depend on their degree distributions. The associated investigations have potential implications in the control of large-scale complex networks, as well as in the understanding of the organization of complex networks.

  4. Virtualized Network Control. Final Report

    Energy Technology Data Exchange (ETDEWEB)

    Ghani, Nasir [Univ. of New Mexico, Albuquerque, NM (United States)

    2013-02-01

    This document is the final report for the Virtualized Network Control (VNC) project, which was funded by the United States Department of Energy (DOE) Office of Science. This project was also informally referred to as Advanced Resource Computation for Hybrid Service and TOpology NEtworks (ARCHSTONE). This report provides a summary of the project's activities, tasks, deliverable, and accomplishments. It also provides a summary of the documents, software, and presentations generated as part of this projects activities. Namely, the Appendix contains an archive of the deliverables, documents, and presentations generated a part of this project.

  5. Identification of genetic interaction networks via an evolutionary algorithm evolved Bayesian network.

    Science.gov (United States)

    Li, Ruowang; Dudek, Scott M; Kim, Dokyoon; Hall, Molly A; Bradford, Yuki; Peissig, Peggy L; Brilliant, Murray H; Linneman, James G; McCarty, Catherine A; Bao, Le; Ritchie, Marylyn D

    2016-01-01

    The future of medicine is moving towards the phase of precision medicine, with the goal to prevent and treat diseases by taking inter-individual variability into account. A large part of the variability lies in our genetic makeup. With the fast paced improvement of high-throughput methods for genome sequencing, a tremendous amount of genetics data have already been generated. The next hurdle for precision medicine is to have sufficient computational tools for analyzing large sets of data. Genome-Wide Association Studies (GWAS) have been the primary method to assess the relationship between single nucleotide polymorphisms (SNPs) and disease traits. While GWAS is sufficient in finding individual SNPs with strong main effects, it does not capture potential interactions among multiple SNPs. In many traits, a large proportion of variation remain unexplained by using main effects alone, leaving the door open for exploring the role of genetic interactions. However, identifying genetic interactions in large-scale genomics data poses a challenge even for modern computing. For this study, we present a new algorithm, Grammatical Evolution Bayesian Network (GEBN) that utilizes Bayesian Networks to identify interactions in the data, and at the same time, uses an evolutionary algorithm to reduce the computational cost associated with network optimization. GEBN excelled in simulation studies where the data contained main effects and interaction effects. We also applied GEBN to a Type 2 diabetes (T2D) dataset obtained from the Marshfield Personalized Medicine Research Project (PMRP). We were able to identify genetic interactions for T2D cases and controls and use information from those interactions to classify T2D samples. We obtained an average testing area under the curve (AUC) of 86.8 %. We also identified several interacting genes such as INADL and LPP that are known to be associated with T2D. Developing the computational tools to explore genetic associations beyond main

  6. Smart Business Networks Design and Business Genetics

    NARCIS (Netherlands)

    L-F. Pau (Louis-François)

    2006-01-01

    textabstractWith the emergence of smart business networks, agile networks, etc. as important research areas in management, for all the attractiveness of these concepts, a major issue remains around their design and the selection rules. While smart business networks should provide advantages due to

  7. Information transmission in genetic regulatory networks: a review

    CERN Document Server

    Walczak, Aleksandra M

    2011-01-01

    Genetic regulatory networks enable cells to respond to the changes in internal and external conditions by dynamically coordinating their gene expression profiles. Our ability to make quantitative measurements in these biochemical circuits has deepened our understanding of what kinds of computations genetic regulatory networks can perform and with what reliability. These advances have motivated researchers to look for connections between the architecture and function of genetic regulatory networks. Transmitting information between network's inputs and its outputs has been proposed as one such possible measure of function, relevant in certain biological contexts. Here we summarize recent developments in the application of information theory to gene regulatory networks. We first review basic concepts in information theory necessary to understand recent work. We then discuss the functional complexity of gene regulation which arrises from the molecular nature of the regulatory interactions. We end by reviewing som...

  8. Controllability of the better chosen partial networks

    Science.gov (United States)

    Liu, Xueming; Pan, Linqiang

    2016-08-01

    How to control large complex networks is a great challenge. Recent studies have proved that the whole network can be sufficiently steered by injecting control signals into a minimum set of driver nodes, and the minimum numbers of driver nodes for many real networks are high, indicating that it is difficult to control them. For some large natural and technological networks, it is impossible and not feasible to control the full network. For example, in biological networks like large-scale gene regulatory networks it is impossible to control all the genes. This prompts us to explore the question how to choose partial networks that are easy for controlling and important in networked systems. In this work, we propose a method to achieve this goal. By computing the minimum driver nodes densities of the partial networks of Erdös-Rényi (ER) networks, scale-free (SF) networks and 23 real networks, we find that our method performs better than random method that chooses nodes randomly. Moreover, we find that the nodes chosen by our method tend to be the essential elements of the whole systems, via studying the nodes chosen by our method of a real human signaling network and a human protein interaction network and discovering that the chosen nodes from these networks tend to be cancer-associated genes. The implementation of our method shows some interesting connections between the structure and the controllability of networks, improving our understanding of the control principles of complex systems.

  9. Robustness of network controllability in cascading failure

    Science.gov (United States)

    Chen, Shi-Ming; Xu, Yun-Fei; Nie, Sen

    2017-04-01

    It is demonstrated that controlling complex networks in practice needs more inputs than that predicted by the structural controllability framework. Besides, considering the networks usually faces to the external or internal failure, we define parameters to evaluate the control cost and the variation of controllability after cascades, exploring the effect of number of control inputs on the controllability for random networks and scale-free networks in the process of cascading failure. For different topological networks, the results show that the robustness of controllability will be stronger through allocating different control inputs and edge capacity.

  10. District Heating Network Design and Configuration Optimization with Genetic Algorithm

    DEFF Research Database (Denmark)

    Li, Hongwei; Svendsen, Svend

    2013-01-01

    and the pipe friction and heat loss formulations are non-linear. In order to find the optimal district heating network configuration, genetic algorithm which handles the mixed integer nonlinear programming problem is chosen. The network configuration is represented with binary and integer encoding...

  11. Congestion control in satellite networks

    Science.gov (United States)

    Byun, Do Jun

    Due to exponential increases in internet traffic, Active Queue Management (AQM) has been heavily studied by numerous researchers. However, little is known about AQM in satellite networks. A microscopic examination of queueing behavior in satellite networks is conducted to identify problems with applying existing AQM methods. A new AQM method is proposed to overcome the problems and it is validated using a realistic emulation environment and a mathematical model. Three problems that were discovered during the research are discussed in this dissertation. The first problem is oscillatory queueing, which is caused by high buffering due to Performance Enhancing Proxy (PEP) in satellite networks where congestion control after the PEP buffering does not effectively control traffic senders. Existing AQMs that can solve this problem have tail drop queueing that results in consecutive packet drops (global synchronization). A new AQM method called Adaptive Virtual Queue Random Early Detection (AVQRED) is proposed to solve this problem. The second problem is unfair bandwidth sharing caused by inaccurate measurements of per-flow bandwidth usage. AVQRED is enhanced to accurately measure per-flow bandwidth usage to solve this problem without adding much complexity to the algorithm. The third problem is queueing instability caused by buffer flow control where TCP receive windows are adjusted to flow control traffic senders instead of dropping received packets during congestion. Although buffer flow control is quite attractive to satellite networks, queueing becomes unstable because accepting packets instead of dropping them aggravates the congestion level. Furthermore, buffer flow control has abrupt reductions in the TCP receive window size due to high PEP buffering causing more instability. AVQRED with packet drop is proposed to solve this problem. Networks with scarce bandwidth and high propagation delays can not afford to have an unstable AQM. In this research, three problems

  12. Proxy SDN Controller for Wireless Networks

    Directory of Open Access Journals (Sweden)

    Won-Suk Kim

    2016-01-01

    Full Text Available Management of wireless networks as well as wired networks by using software-defined networking (SDN has been highlighted continually. However, control features of a wireless network differ from those of a wired network in several aspects. In this study, we identify the various inefficient points when controlling and managing wireless networks by using SDN and propose SDN-based control architecture called Proxcon to resolve these problems. Proxcon introduces the concept of a proxy SDN controller (PSC for the wireless network control, and the PSC entrusted with the role of a main controller performs control operations and provides the latest network state for a network administrator. To address the control inefficiency, Proxcon supports offloaded SDN operations for controlling wireless networks by utilizing the PSC, such as local control by each PSC, hybrid control utilizing the PSC and the main controller, and locally cooperative control utilizing the PSCs. The proposed architecture and the newly supported control operations can enhance scalability and response time when the logically centralized control plane responds to the various wireless network events. Through actual experiments, we verified that the proposed architecture could address the various control issues such as scalability, response time, and control overhead.

  13. Genetic Algorithm Optimization of Artificial Neural Networks for Hydrological Modelling

    Science.gov (United States)

    Abrahart, R. J.

    2004-05-01

    This paper will consider the case for genetic algorithm optimization in the development of an artificial neural network model. It will provide a methodological evaluation of reported investigations with respect to hydrological forecasting and prediction. The intention in such operations is to develop a superior modelling solution that will be: \\begin{itemize} more accurate in terms of output precision and model estimation skill; more tractable in terms of personal requirements and end-user control; and/or more robust in terms of conceptual and mechanical power with respect to adverse conditions. The genetic algorithm optimization toolbox could be used to perform a number of specific roles or purposes and it is the harmonious and supportive relationship between neural networks and genetic algorithms that will be highlighted and assessed. There are several neural network mechanisms and procedures that could be enhanced and potential benefits are possible at different stages in the design and construction of an operational hydrological model e.g. division of inputs; identification of structure; initialization of connection weights; calibration of connection weights; breeding operations between successful models; and output fusion associated with the development of ensemble solutions. Each set of opportunities will be discussed and evaluated. Two strategic questions will also be considered: [i] should optimization be conducted as a set of small individual procedures or as one large holistic operation; [ii] what specific function or set of weighted vectors should be optimized in a complex software product e.g. timings, volumes, or quintessential hydrological attributes related to the 'problem situation' - that might require the development flood forecasting, drought estimation, or record infilling applications. The paper will conclude with a consideration of hydrological forecasting solutions developed on the combined methodologies of co-operative co-evolution and

  14. Opinion control in complex networks

    Science.gov (United States)

    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.

  15. Flexible Tube-Based Network Control Project

    Data.gov (United States)

    National Aeronautics and Space Administration — The Innovation Laboratory, Inc. builds a control system which controls the topology of an air traffic flow network and the network flow properties which enables Air...

  16. Controllability of flow-conservation networks

    Science.gov (United States)

    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.

  17. Genetic Algorithm Optimized Neural Networks Ensemble as ...

    African Journals Online (AJOL)

    Improvements in neural network calibration models by a novel approach using neural network ensemble (NNE) for the simultaneous spectrophotometric multicomponent analysis are suggested, with a study on the estimation of the components of an antihypertensive combination, namely, atenolol and losartan potassium.

  18. Event-based cluster synchronization of coupled genetic regulatory networks

    Science.gov (United States)

    Yue, Dandan; Guan, Zhi-Hong; Li, Tao; Liao, Rui-Quan; Liu, Feng; Lai, Qiang

    2017-09-01

    In this paper, the cluster synchronization of coupled genetic regulatory networks with a directed topology is studied by using the event-based strategy and pinning control. An event-triggered condition with a threshold consisting of the neighbors' discrete states at their own event time instants and a state-independent exponential decay function is proposed. The intra-cluster states information and extra-cluster states information are involved in the threshold in different ways. By using the Lyapunov function approach and the theories of matrices and inequalities, we establish the cluster synchronization criterion. It is shown that both the avoidance of continuous transmission of information and the exclusion of the Zeno behavior are ensured under the presented triggering condition. Explicit conditions on the parameters in the threshold are obtained for synchronization. The stability criterion of a single GRN is also given under the reduced triggering condition. Numerical examples are provided to validate the theoretical results.

  19. Operation and Power Flow Control of Multi-Terminal DC Networks for Grid Integration of Offshore Wind Farms Using Genetic Algorithms

    National Research Council Canada - National Science Library

    Teixeira Pinto, R; Fragoso Roigues, S; Wiggelinkhuizen, E; Scherrer, R; Bauer, P; Pierik, P

    .... Considering the large scale of the future planned offshore wind farms and the increasing distances to shore, grid integration through a transnational DC network is desirable for several reasons...

  20. Seeded Bayesian Networks: Constructing genetic networks from microarray data

    Directory of Open Access Journals (Sweden)

    Quackenbush John

    2008-07-01

    Full Text Available Abstract Background DNA microarrays and other genomics-inspired technologies provide large datasets that often include hidden patterns of correlation between genes reflecting the complex processes that underlie cellular metabolism and physiology. The challenge in analyzing large-scale expression data has been to extract biologically meaningful inferences regarding these processes – often represented as networks – in an environment where the datasets are often imperfect and biological noise can obscure the actual signal. Although many techniques have been developed in an attempt to address these issues, to date their ability to extract meaningful and predictive network relationships has been limited. Here we describe a method that draws on prior information about gene-gene interactions to infer biologically relevant pathways from microarray data. Our approach consists of using preliminary networks derived from the literature and/or protein-protein interaction data as seeds for a Bayesian network analysis of microarray results. Results Through a bootstrap analysis of gene expression data derived from a number of leukemia studies, we demonstrate that seeded Bayesian Networks have the ability to identify high-confidence gene-gene interactions which can then be validated by comparison to other sources of pathway data. Conclusion The use of network seeds greatly improves the ability of Bayesian Network analysis to learn gene interaction networks from gene expression data. We demonstrate that the use of seeds derived from the biomedical literature or high-throughput protein-protein interaction data, or the combination, provides improvement over a standard Bayesian Network analysis, allowing networks involving dynamic processes to be deduced from the static snapshots of biological systems that represent the most common source of microarray data. Software implementing these methods has been included in the widely used TM4 microarray analysis package.

  1. Constrained target controllability of complex networks

    Science.gov (United States)

    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.

  2. Acoustic Performance of Exhaust Muffler based Genetic Algorithms and Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Wang Xiao Li

    2013-07-01

    Full Text Available The noise level was one of the important indicators as a measure of the quality and performance of the diesel engine, exhaust noise in diesel engines machine noise accounted for an important proportion of installed performance exhaust mufflerwas an effective way to control exhaust noise. This article using orthogonal test program was to the muffler structure parameters as input to the sound pressure level and diesel fuel each output artificial neural network (BP network learning sample. Matlab artificial neural network toolbox to complete the training of the network, and better noise performance and fuel consumption rate performance muffler internal structure parameters combination was obtained through genetic algorithm gifted collaborative validation of artificial neural networks and genetic algorithms to optimize application exhaust muffler design is entirely feasible

  3. Combination of uniform design with artificial neural network coupling genetic algorithm: an effective way to obtain high yield of biomass and algicidal compound of a novel HABs control actinomycete.

    Science.gov (United States)

    Cai, Guanjing; Zheng, Wei; Yang, Xujun; Zhang, Bangzhou; Zheng, Tianling

    2014-05-24

    Controlling harmful algae blooms (HABs) using microbial algicides is cheap, efficient and environmental-friendly. However, obtaining high yield of algicidal microbes to meet the need of field test is still a big challenge since qualitative and quantitative analysis of algicidal compounds is difficult. In this study, we developed a protocol to increase the yield of both biomass and algicidal compound present in a novel algicidal actinomycete Streptomyces alboflavus RPS, which kills Phaeocystis globosa. To overcome the problem in algicidal compound quantification, we chose algicidal ratio as the index and used artificial neural network to fit the data, which was appropriate for this nonlinear situation. In this protocol, we firstly determined five main influencing factors through single factor experiments and generated the multifactorial experimental groups with a U15(155) uniform-design-table. Then, we used the traditional quadratic polynomial stepwise regression model and an accurate, fully optimized BP-neural network to simulate the fermentation. Optimized with genetic algorithm and verified using experiments, we successfully increased the algicidal ratio of the fermentation broth by 16.90% and the dry mycelial weight by 69.27%. These results suggested that this newly developed approach is a viable and easy way to optimize the fermentation conditions for algicidal microorganisms.

  4. Adaptive optimization and control using neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.

    1993-10-22

    Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.

  5. Operation and Power Flow Control of Multi-Terminal DC Networks for Grid Integration of Offshore Wind Farms Using Genetic Algorithms

    NARCIS (Netherlands)

    Teixeira Pinto, R.; Fragoso Rodrigues, S.; Wiggelinkhuizen, E.; Scherrer, R.; Bauer, P.; Pierik, P.

    2012-01-01

    For achieving the European renewable electricity targets, a significant contribution is foreseen to come from offshore wind energy. Considering the large scale of the future planned offshore wind farms and the increasing distances to shore, grid integration through a transnational DC network is

  6. Genetic noise control via protein oligomerization

    Energy Technology Data Exchange (ETDEWEB)

    Ghim, C; Almaas, E

    2008-06-12

    Gene expression in a cell entails random reaction events occurring over disparate time scales. Thus, molecular noise that often results in phenotypic and population-dynamic consequences sets a fundamental limit to biochemical signaling. While there have been numerous studies correlating the architecture of cellular reaction networks with noise tolerance, only a limited effort has been made to understand the dynamical role of protein-protein associations. We have developed a fully stochastic model for the positive feedback control of a single gene, as well as a pair of genes (toggle switch), integrating quantitative results from previous in vivo and in vitro studies. In particular, we explicitly account for the fast protein binding-unbinding kinetics, RNA polymerases, and the promoter/operator sequences of DNA. We find that the overall noise-level is reduced and the frequency content of the noise is dramatically shifted to the physiologically irrelevant high-frequency regime in the presence of protein dimerization. This is independent of the choice of monomer or dimer as transcription factor and persists throughout the multiple model topologies considered. For the toggle switch, we additionally find that the presence of a protein dimer, either homodimer or heterodimer, may significantly reduce its intrinsic switching rate. Hence, the dimer promotes the robust function of bistable switches by preventing the uninduced (induced) state from randomly being induced (uninduced). The specific binding between regulatory proteins provides a buffer that may prevent the propagation of fluctuations in genetic activity. The capacity of the buffer is a non-monotonic function of association-dissociation rates. Since the protein oligomerization per se does not require extra protein components to be expressed, it provides a basis for the rapid control of intrinsic or extrinsic noise. The stabilization of phenotypically important toggle switches, and nested positive feedback loops in

  7. Simplified LQG Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1997-01-01

    A new neural network application for non-linear state control is described. One neural network is modelled to form a Kalmann predictor and trained to act as an optimal state observer for a non-linear process. Another neural network is modelled to form a state controller and trained to produce...

  8. Synchronizability on complex networks via pinning control

    Indian Academy of Sciences (India)

    pinning strategies have different pinning synchronizability on the same complex network, and the synchronizability with pinning control is consistent with one without pinning control in various complex networks. Keywords. Complex network; the pinning synchronization; synchronizability. PACS Nos 05.45.Xt; 89.75.−k; 05.45.

  9. Opinion control in complex networks

    CERN Document Server

    Masuda, Naoki

    2014-01-01

    In many instances of election, the electorate appears to be a composite of partisan and independent voters. Given that partisans are not likely to convert to a different party, a main goal for a party could be to mobilize independent voters toward the party with the help of strong leadership, mass media, partisans, and effects of peer-to-peer influence. Based on the exact solution of the classical voter model dynamics in the presence of perfectly partisan voters (i.e., zealots), we propose a computational method to maximize the share of the party in a social network of independent voters by pinning control strategy. 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 opponent party controls. We show that controlling hubs is generally a good strategy, whereas the optimized strategy is even better. The superiority of the optimized strategy is particularly eminent when the inde...

  10. Neural Network Predictive Control for Vanadium Redox Flow Battery

    Directory of Open Access Journals (Sweden)

    Hai-Feng Shen

    2013-01-01

    Full Text Available The vanadium redox flow battery (VRB is a nonlinear system with unknown dynamics and disturbances. The flowrate of the electrolyte is an important control mechanism in the operation of a VRB system. Too low or too high flowrate is unfavorable for the safety and performance of VRB. This paper presents a neural network predictive control scheme to enhance the overall performance of the battery. A radial basis function (RBF network is employed to approximate the dynamics of the VRB system. The genetic algorithm (GA is used to obtain the optimum initial values of the RBF network parameters. The gradient descent algorithm is used to optimize the objective function of the predictive controller. Compared with the constant flowrate, the simulation results show that the flowrate optimized by neural network predictive controller can increase the power delivered by the battery during the discharge and decrease the power consumed during the charge.

  11. Access Network Selection Based on Fuzzy Logic and Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Mohammed Alkhawlani

    2008-01-01

    Full Text Available In the next generation of heterogeneous wireless networks (HWNs, a large number of different radio access technologies (RATs will be integrated into a common network. In this type of networks, selecting the most optimal and promising access network (AN is an important consideration for overall networks stability, resource utilization, user satisfaction, and quality of service (QoS provisioning. This paper proposes a general scheme to solve the access network selection (ANS problem in the HWN. The proposed scheme has been used to present and design a general multicriteria software assistant (SA that can consider the user, operator, and/or the QoS view points. Combined fuzzy logic (FL and genetic algorithms (GAs have been used to give the proposed scheme the required scalability, flexibility, and simplicity. The simulation results show that the proposed scheme and SA have better and more robust performance over the random-based selection.

  12. District Heating Network Design and Configuration Optimization with Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Hongwei Li

    2013-12-01

    Full Text Available In this paper, the configuration of a district heating network which connects from the heating plant to the end users is optimized. Each end user in the network represents a building block. The connections between the heat generation plant and the end users are represented with mixed integer and the pipe friction and heat loss formulations are non-linear. In order to find the optimal district heating network configuration, genetic algorithm which handles the mixed integer nonlinear programming problem is chosen. The network configuration is represented with binary and integer encoding and is optimized in terms of the net present cost. The optimization results indicates that the optimal DH network configuration is determined by multiple factors such as the consumer heating load, the distance between the heating plant to the consumer, the design criteria regarding the pressure and temperature limitation, as well as the corresponding network heat loss.

  13. A full bayesian approach for boolean genetic network inference.

    Directory of Open Access Journals (Sweden)

    Shengtong Han

    Full Text Available Boolean networks are a simple but efficient model for describing gene regulatory systems. A number of algorithms have been proposed to infer Boolean networks. However, these methods do not take full consideration of the effects of noise and model uncertainty. In this paper, we propose a full Bayesian approach to infer Boolean genetic networks. Markov chain Monte Carlo algorithms are used to obtain the posterior samples of both the network structure and the related parameters. In addition to regular link addition and removal moves, which can guarantee the irreducibility of the Markov chain for traversing the whole network space, carefully constructed mixture proposals are used to improve the Markov chain Monte Carlo convergence. Both simulations and a real application on cell-cycle data show that our method is more powerful than existing methods for the inference of both the topology and logic relations of the Boolean network from observed data.

  14. Ripple-Spreading Network Model Optimization by Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Xiao-Bing Hu

    2013-01-01

    Full Text Available Small-world and scale-free properties are widely acknowledged in many real-world complex network systems, and many network models have been developed to capture these network properties. The ripple-spreading network model (RSNM is a newly reported complex network model, which is inspired by the natural ripple-spreading phenomenon on clam water surface. The RSNM exhibits good potential for describing both spatial and temporal features in the development of many real-world networks where the influence of a few local events spreads out through nodes and then largely determines the final network topology. However, the relationships between ripple-spreading related parameters (RSRPs of RSNM and small-world and scale-free topologies are not as obvious or straightforward as in many other network models. This paper attempts to apply genetic algorithm (GA to tune the values of RSRPs, so that the RSNM may generate these two most important network topologies. The study demonstrates that, once RSRPs are properly tuned by GA, the RSNM is capable of generating both network topologies and therefore has a great flexibility to study many real-world complex network systems.

  15. Genetical Swarm Optimization of Multihop Routes in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Davide Caputo

    2010-01-01

    Full Text Available In recent years, wireless sensor networks have been attracting considerable research attention for a wide range of applications, but they still present significant network communication challenges, involving essentially the use of large numbers of resource-constrained nodes operating unattended and exposed to potential local failures. In order to maximize the network lifespan, in this paper, genetical swarm optimization (GSO is applied, a class of hybrid evolutionary techniques developed in order to exploit in the most effective way the uniqueness and peculiarities of two classical optimization approaches; particle swarm optimization (PSO and genetic algorithms (GA. This procedure is here implemented to optimize the communication energy consumption in a wireless network by selecting the optimal multihop routing schemes, with a suitable hybridization of different routing criteria, confirming itself as a flexible and useful tool for engineering applications.

  16. Controlling complex networks with conformity behavior

    Science.gov (United States)

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

    2015-09-01

    Controlling complex networks accompanied by common conformity behavior is a fundamental problem in social and physical science. Conformity behavior that individuals tend to follow the majority in their neighborhood is common in human society and animal communities. Despite recent progress in understanding controllability of complex networks, the existent controllability theories cannot be directly applied to networks associated with conformity. Here we propose a simple model to incorporate conformity-based decision making into the evolution of a network system, which allows us to employ the exact controllability theory to explore the controllability of such systems. We offer rigorous theoretical results of controllability for representative regular networks. We also explore real networks in different fields and some typical model networks, finding some interesting results that are different from the predictions of structural and exact controllability theory in the absence of conformity. We finally present an example of steering a real social network to some target states to further validate our controllability theory and tools. Our work offers a more realistic understanding of network controllability with conformity behavior and can have potential applications in networked evolutionary games, opinion dynamics and many other complex networked systems.

  17. Model-based control of networked systems

    CERN Document Server

    Garcia, Eloy; Montestruque, Luis A

    2014-01-01

    This monograph introduces a class of networked control systems (NCS) called model-based networked control systems (MB-NCS) and presents various architectures and control strategies designed to improve the performance of NCS. The overall performance of NCS considers the appropriate use of network resources, particularly network bandwidth, in conjunction with the desired response of the system being controlled.   The book begins with a detailed description of the basic MB-NCS architecture that provides stability conditions in terms of state feedback updates . It also covers typical problems in NCS such as network delays, network scheduling, and data quantization, as well as more general control problems such as output feedback control, nonlinear systems stabilization, and tracking control.   Key features and topics include: Time-triggered and event-triggered feedback updates Stabilization of uncertain systems subject to time delays, quantization, and extended absence of feedback Optimal control analysis and ...

  18. Logistic control in automated transportation networks

    NARCIS (Netherlands)

    Ebben, Mark

    2001-01-01

    Increasing congestion problems lead to a search for alternative transportation systems. Automated transportation networks, possibly underground, are an option. Logistic control systems are essential for future implementations of such automated transportation networks. This book contributes to the

  19. Genetic algorithm based PID controller design for a multi-area AGC ...

    African Journals Online (AJOL)

    user

    SMES) units to inject or absorb the active power of an interconnected power system. The functioning of Genetic Algorithm based PID controller has been tested on a 39-bus New England system and 75-bus Indian power system network.

  20. Genetic Algorithms in Wireless Networking: Techniques, Applications, and Issues

    OpenAIRE

    Mehboob, Usama; Qadir, Junaid; Ali, Salman; Vasilakos, Athanasios

    2014-01-01

    In recent times, wireless access technology is becoming increasingly commonplace due to the ease of operation and installation of untethered wireless media. The design of wireless networking is challenging due to the highly dynamic environmental condition that makes parameter optimization a complex task. Due to the dynamic, and often unknown, operating conditions, modern wireless networking standards increasingly rely on machine learning and artificial intelligence algorithms. Genetic algorit...

  1. Controller placement problem in industrial networks

    OpenAIRE

    Macián Ribera, Sergi

    2016-01-01

    SDN is the new trend in networks, for next Mobile and optical networks. Dimensioning, design and optimization of Software Defined Optical Networks. To be done at Technical University Munich (TUM) In this work the Controller Placement Problem (CPP) for SDN architecture is studied when it is applied to industrial networks. En este trabajo se estudia el problema CPP (controller placement problem) para la arquitectura SDN, aplicado a redes industriales. En aquest treball s'estudia el pro...

  2. A hybrid Genetic and Simulated Annealing Algorithm for Chordal Ring implementation in large-scale networks

    DEFF Research Database (Denmark)

    Riaz, M. Tahir; Gutierrez Lopez, Jose Manuel; Pedersen, Jens Myrup

    2011-01-01

    of the networks. There have been many use of evolutionary algorithms to solve the problems which are in combinatory complexity nature, and extremely hard to solve by exact approaches. Both Genetic and Simulated annealing algorithms are similar in using controlled stochastic method to search the solution...

  3. GeneNetwork: A Toolbox for Systems Genetics.

    Science.gov (United States)

    Mulligan, Megan K; Mozhui, Khyobeni; Prins, Pjotr; Williams, Robert W

    2017-01-01

    The goal of systems genetics is to understand the impact of genetic variation across all levels of biological organization, from mRNAs, proteins, and metabolites, to higher-order physiological and behavioral traits. This approach requires the accumulation and integration of many types of data, and also requires the use of many types of statistical tools to extract relevant patterns of covariation and causal relations as a function of genetics, environment, stage, and treatment. In this protocol we explain how to use the GeneNetwork web service, a powerful and free online resource for systems genetics. We provide workflows and methods to navigate massive multiscalar data sets and we explain how to use an extensive systems genetics toolkit for analysis and synthesis. Finally, we provide two detailed case studies that take advantage of human and mouse cohorts to evaluate linkage between gene variants, addiction, and aging.

  4. Combining epidemiological and genetic networks signifies the importance of early treatment in HIV-1 transmission.

    Directory of Open Access Journals (Sweden)

    Narges Zarrabi

    Full Text Available Inferring disease transmission networks is important in epidemiology in order to understand and prevent the spread of infectious diseases. Reconstruction of the infection transmission networks requires insight into viral genome data as well as social interactions. For the HIV-1 epidemic, current research either uses genetic information of patients' virus to infer the past infection events or uses statistics of sexual interactions to model the network structure of viral spreading. Methods for a reliable reconstruction of HIV-1 transmission dynamics, taking into account both molecular and societal data are still lacking. The aim of this study is to combine information from both genetic and epidemiological scales to characterize and analyse a transmission network of the HIV-1 epidemic in central Italy.We introduce a novel filter-reduction method to build a network of HIV infected patients based on their social and treatment information. The network is then combined with a genetic network, to infer a hypothetical infection transmission network. We apply this method to a cohort study of HIV-1 infected patients in central Italy and find that patients who are highly connected in the network have longer untreated infection periods. We also find that the network structures for homosexual males and heterosexual populations are heterogeneous, consisting of a majority of 'peripheral nodes' that have only a few sexual interactions and a minority of 'hub nodes' that have many sexual interactions. Inferring HIV-1 transmission networks using this novel combined approach reveals remarkable correlations between high out-degree individuals and longer untreated infection periods. These findings signify the importance of early treatment and support the potential benefit of wide population screening, management of early diagnoses and anticipated antiretroviral treatment to prevent viral transmission and spread. The approach presented here for reconstructing HIV-1

  5. 2016 Network Games, Control, and Optimization Conference

    CERN Document Server

    Jimenez, Tania; Solan, Eilon

    2017-01-01

    This contributed volume offers a collection of papers presented at the 2016 Network Games, Control, and Optimization conference (NETGCOOP), held at the University of Avignon in France, November 23-25, 2016. These papers highlight the increasing importance of network control and optimization in many networking application domains, such as mobile and fixed access networks, computer networks, social networks, transportation networks, and, more recently, electricity grids and biological networks. Covering a wide variety of both theoretical and applied topics in the areas listed above, the authors explore several conceptual and algorithmic tools that are needed for efficient and robust control operation, performance optimization, and better understanding the relationships between entities that may be acting cooperatively or selfishly in uncertain and possibly adversarial environments. As such, this volume will be of interest to applied mathematicians, computer scientists, engineers, and researchers in other relate...

  6. Filtering and control of wireless networked systems

    CERN Document Server

    Zhang, Dan; Yu, Li

    2017-01-01

    This self-contained book, written by leading experts, offers a cutting-edge, in-depth overview of the filtering and control of wireless networked systems. It addresses the energy constraint and filter/controller gain variation problems, and presents both the centralized and the distributed solutions. The first two chapters provide an introduction to networked control systems and basic information on system analysis. Chapters (3–6) then discuss the centralized filtering of wireless networked systems, presenting different approaches to deal with energy efficiency and filter/controller gain variation problems. The next part (chapters 7–10) explores the distributed filtering of wireless networked systems, addressing the main problems of energy constraint and filter gain variation. The final part (chapters 11–14) focuses on the distributed control of wireless networked systems. networked systems for communication and control applications, the bo...

  7. Sparse time series chain graphical models for reconstructing genetic networks

    NARCIS (Netherlands)

    Abegaz, Fentaw; Wit, Ernst

    We propose a sparse high-dimensional time series chain graphical model for reconstructing genetic networks from gene expression data parametrized by a precision matrix and autoregressive coefficient matrix. We consider the time steps as blocks or chains. The proposed approach explores patterns of

  8. GENETIC ALGORITHM BASED CONCEPT DESIGN TO OPTIMIZE NETWORK LOAD BALANCE

    Directory of Open Access Journals (Sweden)

    Ashish Jain

    2012-07-01

    Full Text Available Multiconstraints optimal network load balancing is an NP-hard problem and it is an important part of traffic engineering. In this research we balance the network load using classical method (brute force approach and dynamic programming is used but result shows the limitation of this method but at a certain level we recognized that the optimization of balanced network load with increased number of nodes and demands is intractable using the classical method because the solution set increases exponentially. In such case the optimization techniques like evolutionary techniques can employ for optimizing network load balance. In this paper we analyzed proposed classical algorithm and evolutionary based genetic approach is devise as well as proposed in this paper for optimizing the balance network load.

  9. A network characteristic that correlates environmental and genetic robustness.

    Directory of Open Access Journals (Sweden)

    Zeina Shreif

    2014-02-01

    Full Text Available As scientific advances in perturbing biological systems and technological advances in data acquisition allow the large-scale quantitative analysis of biological function, the robustness of organisms to both transient environmental stresses and inter-generational genetic changes is a fundamental impediment to the identifiability of mathematical models of these functions. An approach to overcoming this impediment is to reduce the space of possible models to take into account both types of robustness. However, the relationship between the two is still controversial. This work uncovers a network characteristic, transient responsiveness, for a specific function that correlates environmental imperturbability and genetic robustness. We test this characteristic extensively for dynamic networks of ordinary differential equations ranging up to 30 interacting nodes and find that there is a power-law relating environmental imperturbability and genetic robustness that tends to linearity as the number of nodes increases. Using our methods, we refine the classification of known 3-node motifs in terms of their environmental and genetic robustness. We demonstrate our approach by applying it to the chemotaxis signaling network. In particular, we investigate plausible models for the role of CheV protein in biochemical adaptation via a phosphorylation pathway, testing modifications that could improve the robustness of the system to environmental and/or genetic perturbation.

  10. PID Controller Based on Memristive CMAC Network

    Directory of Open Access Journals (Sweden)

    Lidan Wang

    2013-01-01

    Full Text Available Compound controller which consists of CMAC network and PID network is mainly used in control system, especially in robot control. It can realize nonlinear tracking control of the real-time dynamic trajectory and possesses good approximation effect. According to the structure and principle of the compound controller, memristor is introduced to CMAC network to be a compound controller in this paper. The new PID controller based on memristive CMAC network is built up by replacing the synapse in the original controller by memristors. The effect of curve approximation is obtained by MATLAB simulation experiments. This network improves the response and learning speed of the system and processes better robustness and antidisturbance performance.

  11. Genetic dissection of acute ethanol responsive gene networks in prefrontal cortex: functional and mechanistic implications.

    Directory of Open Access Journals (Sweden)

    Aaron R Wolen

    Full Text Available Individual differences in initial sensitivity to ethanol are strongly related to the heritable risk of alcoholism in humans. To elucidate key molecular networks that modulate ethanol sensitivity we performed the first systems genetics analysis of ethanol-responsive gene expression in brain regions of the mesocorticolimbic reward circuit (prefrontal cortex, nucleus accumbens, and ventral midbrain across a highly diverse family of 27 isogenic mouse strains (BXD panel before and after treatment with ethanol.Acute ethanol altered the expression of ~2,750 genes in one or more regions and 400 transcripts were jointly modulated in all three. Ethanol-responsive gene networks were extracted with a powerful graph theoretical method that efficiently summarized ethanol's effects. These networks correlated with acute behavioral responses to ethanol and other drugs of abuse. As predicted, networks were heavily populated by genes controlling synaptic transmission and neuroplasticity. Several of the most densely interconnected network hubs, including Kcnma1 and Gsk3β, are known to influence behavioral or physiological responses to ethanol, validating our overall approach. Other major hub genes like Grm3, Pten and Nrg3 represent novel targets of ethanol effects. Networks were under strong genetic control by variants that we mapped to a small number of chromosomal loci. Using a novel combination of genetic, bioinformatic and network-based approaches, we identified high priority cis-regulatory candidate genes, including Scn1b, Gria1, Sncb and Nell2.The ethanol-responsive gene networks identified here represent a previously uncharacterized intermediate phenotype between DNA variation and ethanol sensitivity in mice. Networks involved in synaptic transmission were strongly regulated by ethanol and could contribute to behavioral plasticity seen with chronic ethanol. Our novel finding that hub genes and a small number of loci exert major influence over the ethanol

  12. Control theory of digitally networked dynamic systems

    CERN Document Server

    Lunze, Jan

    2013-01-01

    The book gives an introduction to networked control systems and describes new modeling paradigms, analysis methods for event-driven, digitally networked systems, and design methods for distributed estimation and control. Networked model predictive control is developed as a means to tolerate time delays and packet loss brought about by the communication network. In event-based control the traditional periodic sampling is replaced by state-dependent triggering schemes. Novel methods for multi-agent systems ensure complete or clustered synchrony of agents with identical or with individual dynamic

  13. Scheduling maintenance of electrical power transmission networks using genetic programming

    Energy Technology Data Exchange (ETDEWEB)

    Langdon, W.B.; Treleaven, P.C.

    1997-12-31

    The National Grid Company plc is responsible for the maintenance of the high voltage electricity transmission network in England and Wales. It must plan maintenance so as to minimise costs taking into account: (i) location and size of demand, (ii) generator capacities and availabilities, (iii) electricity carrying capacity of the remainder of the network, i.e. that part not undergoing maintenance. Previous work showed the combination of a genetic algorithm using an order or permutation chromosome combined with hand coded ``greedy`` optimisers can readily produce an optimal schedule for a four node test problem [10]. Following this the same GA has been used to find low cost schedules for the South Wales region of the UK high voltage power network. This paper describes the evolution of the best known schedule for the base South Wales problem using genetic programming starting from the hand coded heuristics used with the GA. (Author)

  14. Genetic algorithm application in optimization of wireless sensor networks.

    Science.gov (United States)

    Norouzi, Ali; Zaim, A Halim

    2014-01-01

    There are several applications known for wireless sensor networks (WSN), and such variety demands improvement of the currently available protocols and the specific parameters. Some notable parameters are lifetime of network and energy consumption for routing which play key role in every application. Genetic algorithm is one of the nonlinear optimization methods and relatively better option thanks to its efficiency for large scale applications and that the final formula can be modified by operators. The present survey tries to exert a comprehensive improvement in all operational stages of a WSN including node placement, network coverage, clustering, and data aggregation and achieve an ideal set of parameters of routing and application based WSN. Using genetic algorithm and based on the results of simulations in NS, a specific fitness function was achieved, optimized, and customized for all the operational stages of WSNs.

  15. Classifying epilepsy diseases using artificial neural networks and genetic algorithm.

    Science.gov (United States)

    Koçer, Sabri; Canal, M Rahmi

    2011-08-01

    In this study, FFT analysis is applied to the EEG signals of the normal and patient subjects and the obtained FFT coefficients are used as inputs in Artificial Neural Network (ANN). The differences shown by the non-stationary random signals such as EEG signals in cases of health and sickness (epilepsy) were evaluated and tried to be analyzed under computer-supported conditions by using artificial neural networks. Multi-Layer Perceptron (MLP) architecture is used Levenberg-Marquardt (LM), Quickprop (QP), Delta-bar delta (DBD), Momentum and Conjugate gradient (CG) learning algorithms, and the best performance was tried to be attained by ensuring the optimization with the use of genetic algorithms of the weights, learning rates, neuron numbers of hidden layer in the training process. This study shows that the artificial neural network increases the classification performance using genetic algorithm.

  16. Electronic Circuit Analog of Synthetic Genetic Networks: Revisited

    CERN Document Server

    Hellen, Edward H

    2016-01-01

    Electronic circuits are useful tools for studying potential dynamical behaviors of synthetic genetic networks. The circuit models are complementary to numerical simulations of the networks, especially providing a framework for verification of dynamical behaviors in the presence of intrinsic and extrinsic noise of the electrical systems. Here we present an improved version of our previous design of an electronic analog of genetic networks that includes the 3-gene Repressilator and we show conversions between model parameters and real circuit component values to mimic the numerical results in experiments. Important features of the circuit design include the incorporation of chemical kinetics representing Hill function inhibition, quorum sensing coupling, and additive noise. Especially, we make a circuit design for a systematic change of initial conditions in experiment, which is critically important for studies of dynamical systems' behavior, particularly, when it shows multistability. This improved electronic ...

  17. Evolving neural networks using a genetic algorithm for heartbeat classification.

    Science.gov (United States)

    Sekkal, Mansouria; Chikh, Mohamed Amine; Settouti, Nesma

    2011-07-01

    This study investigates the effectiveness of a genetic algorithm (GA) evolved neural network (NN) classifier and its application to the classification of premature ventricular contraction (PVC) beats. As there is no standard procedure to determine the network structure for complicated cases, generally the design of the NN would be dependent on the user's experience. To prevent this problem, we propose a neural classifier that uses a GA for the determination of optimal connections between neurons for better recognition. The MIT-BIH arrhythmia database is employed to evaluate its accuracy. First, the topology of the NN was determined using the trial and error method. Second, the genetic operators were carefully designed to optimize the neural network structure. Performance and accuracy of the two techniques are presented and compared. Copyright © 2011 Informa UK, Ltd.

  18. Information theory and the ethylene genetic network.

    Science.gov (United States)

    González-García, José S; Díaz, José

    2011-10-01

    The original aim of the Information Theory (IT) was to solve a purely technical problem: to increase the performance of communication systems, which are constantly affected by interferences that diminish the quality of the transmitted information. That is, the theory deals only with the problem of transmitting with the maximal precision the symbols constituting a message. In Shannon's theory messages are characterized only by their probabilities, regardless of their value or meaning. As for its present day status, it is generally acknowledged that Information Theory has solid mathematical foundations and has fruitful strong links with Physics in both theoretical and experimental areas. However, many applications of Information Theory to Biology are limited to using it as a technical tool to analyze biopolymers, such as DNA, RNA or protein sequences. The main point of discussion about the applicability of IT to explain the information flow in biological systems is that in a classic communication channel, the symbols that conform the coded message are transmitted one by one in an independent form through a noisy communication channel, and noise can alter each of the symbols, distorting the message; in contrast, in a genetic communication channel the coded messages are not transmitted in the form of symbols but signaling cascades transmit them. Consequently, the information flow from the emitter to the effector is due to a series of coupled physicochemical processes that must ensure the accurate transmission of the message. In this review we discussed a novel proposal to overcome this difficulty, which consists of the modeling of gene expression with a stochastic approach that allows Shannon entropy (H) to be directly used to measure the amount of uncertainty that the genetic machinery has in relation to the correct decoding of a message transmitted into the nucleus by a signaling pathway. From the value of H we can define a function I that measures the amount of

  19. Additive Feed Forward Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1999-01-01

    This paper demonstrates a method to control a non-linear, multivariable, noisy process using trained neural networks. The basis for the method is a trained neural network controller acting as the inverse process model. A training method for obtaining such an inverse process model is applied....... A suitable 'shaped' (low-pass filtered) reference is used to overcome problems with excessive control action when using a controller acting as the inverse process model. The control concept is Additive Feed Forward Control, where the trained neural network controller, acting as the inverse process model......, is placed in a supplementary pure feed-forward path to an existing feedback controller. This concept benefits from the fact, that an existing, traditional designed, feedback controller can be retained without any modifications, and after training the connection of the neural network feed-forward controller...

  20. Genetic algorithms in adaptive fuzzy control

    Science.gov (United States)

    Karr, C. Lucas; Harper, Tony R.

    1992-01-01

    Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust fuzzy membership functions in response to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific computer-simulated chemical system is used to demonstrate the ideas presented.

  1. Distributed medium access control in wireless networks

    CERN Document Server

    Wang, Ping

    2013-01-01

    This brief investigates distributed medium access control (MAC) with QoS provisioning for both single- and multi-hop wireless networks including wireless local area networks (WLANs), wireless ad hoc networks, and wireless mesh networks. For WLANs, an efficient MAC scheme and a call admission control algorithm are presented to provide guaranteed QoS for voice traffic and, at the same time, increase the voice capacity significantly compared with the current WLAN standard. In addition, a novel token-based scheduling scheme is proposed to provide great flexibility and facility to the network servi

  2. Predicting genetic interactions with random walks on biological networks

    Directory of Open Access Journals (Sweden)

    Singh Ambuj K

    2009-01-01

    Full Text Available Abstract Background Several studies have demonstrated that synthetic lethal genetic interactions between gene mutations provide an indication of functional redundancy between molecular complexes and pathways. These observations help explain the finding that organisms are able to tolerate single gene deletions for a large majority of genes. For example, system-wide gene knockout/knockdown studies in S. cerevisiae and C. elegans revealed non-viable phenotypes for a mere 18% and 10% of the genome, respectively. It has been postulated that the low percentage of essential genes reflects the extensive amount of genetic buffering that occurs within genomes. Consistent with this hypothesis, systematic double-knockout screens in S. cerevisiae and C. elegans show that, on average, 0.5% of tested gene pairs are synthetic sick or synthetic lethal. While knowledge of synthetic lethal interactions provides valuable insight into molecular functionality, testing all combinations of gene pairs represents a daunting task for molecular biologists, as the combinatorial nature of these relationships imposes a large experimental burden. Still, the task of mapping pairwise interactions between genes is essential to discovering functional relationships between molecular complexes and pathways, as they form the basis of genetic robustness. Towards the goal of alleviating the experimental workload, computational techniques that accurately predict genetic interactions can potentially aid in targeting the most likely candidate interactions. Building on previous studies that analyzed properties of network topology to predict genetic interactions, we apply random walks on biological networks to accurately predict pairwise genetic interactions. Furthermore, we incorporate all published non-interactions into our algorithm for measuring the topological relatedness between two genes. We apply our method to S. cerevisiae and C. elegans datasets and, using a decision tree

  3. Cloud-based Networked Visual Servo Control

    DEFF Research Database (Denmark)

    Wu, Haiyan; Lu, Lei; Chen, Chih-Chung

    2013-01-01

    feedback, ii) a stabilizing control law for the networked visual servo control system with time-varying feedback time delay, and iii) a sending rate scheduling strategy aiming at reducing the communication network load. The performance of the networked visual servo control system with sending rate......The performance of vision-based control systems, in particular of highly dynamic vision-based motion control systems, is often limited by the low sampling rate of the visual feedback caused by the long image processing time. In order to overcome this problem, the networked visual servo control......, which integrates networked computational resources for cloud image processing, is considered in this article. The main contributions of this article are i) a real-time transport protocol for transmitting large volume image data on a cloud computing platform, which enables high sampling rate visual...

  4. Patterns of genetic connectivity in invertebrates of temperate MPA networks

    Directory of Open Access Journals (Sweden)

    Patricia Marti-Puig

    2013-11-01

    Full Text Available Temperate reefs are among the most threatened marine habitats due to impacts caused by high density of human settlements, coastal development, pollution, fisheries and tourism. Networks of marine protected areas (MPAs are an important tool for ensuring long-term health and conservation of ecological processes in the marine environment. Design of the MPA network has to be based on deep understanding of spatial patterns of species distribution, and on the make-up of connectivity among populations. Most benthic invertebrates are sessile and/or sedentary in the adult phase, and their dispersal relies mainly on the gametes and/or larval behaviours. Genetic markers allow us to quantify gene flow and structuring among populations, and to infer patterns of genetic connectivity. Based on the information available in the peer reviewed literature on genetic connectivity in benthic invertebrates of temperate MPAs, we provide a comment about the gaps and the needs. Moreover, we propose a rationale to plan and optimise future studies on this topic. A conceptual framework for planning effective studies on genetic connectivity in an MPAs network is provided, including general recommendations on sampling design, key species and molecular markers to use.

  5. Trends in Integrated Ship Control Networking

    DEFF Research Database (Denmark)

    Jørgensen, N.; Nielsen, Jens Frederik Dalsgaard

    1997-01-01

    Integrated Ship Control systems can be designed as robust, distributed, autonomous control systems. The EU funded ATOMOS and ATOMOS II projects involves both technical and non technical aspects of this process. A reference modelling concept giving an outline of a generic ISC system covering...... the network and the equipment connected to it, a framework for verification of network functionality and performance by simulation and a general distribution platform for ISC systems, The ATOMOS Network, are results of this work....

  6. Neural Networks for Non-linear Control

    DEFF Research Database (Denmark)

    Sørensen, O.

    1994-01-01

    This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process.......This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process....

  7. Precise Network Modeling of Systems Genetics Data Using the Bayesian Network Webserver.

    Science.gov (United States)

    Ziebarth, Jesse D; Cui, Yan

    2017-01-01

    The Bayesian Network Webserver (BNW, http://compbio.uthsc.edu/BNW ) is an integrated platform for Bayesian network modeling of biological datasets. It provides a web-based network modeling environment that seamlessly integrates advanced algorithms for probabilistic causal modeling and reasoning with Bayesian networks. BNW is designed for precise modeling of relatively small networks that contain less than 20 nodes. The structure learning algorithms used by BNW guarantee the discovery of the best (most probable) network structure given the data. To facilitate network modeling across multiple biological levels, BNW provides a very flexible interface that allows users to assign network nodes into different tiers and define the relationships between and within the tiers. This function is particularly useful for modeling systems genetics datasets that often consist of multiscalar heterogeneous genotype-to-phenotype data. BNW enables users to, within seconds or minutes, go from having a simply formatted input file containing a dataset to using a network model to make predictions about the interactions between variables and the potential effects of experimental interventions. In this chapter, we will introduce the functions of BNW and show how to model systems genetics datasets with BNW.

  8. Stress controls the mechanics of collagen networks

    Science.gov (United States)

    Licup, Albert James; Münster, Stefan; Sharma, Abhinav; Sheinman, Michael; Jawerth, Louise M.; Fabry, Ben; Weitz, David A.; MacKintosh, Fred C.

    2015-01-01

    Collagen is the main structural and load-bearing element of various connective tissues, where it forms the extracellular matrix that supports cells. It has long been known that collagenous tissues exhibit a highly nonlinear stress–strain relationship, although the origins of this nonlinearity remain unknown. Here, we show that the nonlinear stiffening of reconstituted type I collagen networks is controlled by the applied stress and that the network stiffness becomes surprisingly insensitive to network concentration. We demonstrate how a simple model for networks of elastic fibers can quantitatively account for the mechanics of reconstituted collagen networks. Our model points to the important role of normal stresses in determining the nonlinear shear elastic response, which can explain the approximate exponential relationship between stress and strain reported for collagenous tissues. This further suggests principles for the design of synthetic fiber networks with collagen-like properties, as well as a mechanism for the control of the mechanics of such networks. PMID:26195769

  9. Critical dynamics in genetic regulatory networks: examples from four kingdoms.

    Directory of Open Access Journals (Sweden)

    Enrique Balleza

    2008-06-01

    Full Text Available The coordinated expression of the different genes in an organism is essential to sustain functionality under the random external perturbations to which the organism might be subjected. To cope with such external variability, the global dynamics of the genetic network must possess two central properties. (a It must be robust enough as to guarantee stability under a broad range of external conditions, and (b it must be flexible enough to recognize and integrate specific external signals that may help the organism to change and adapt to different environments. This compromise between robustness and adaptability has been observed in dynamical systems operating at the brink of a phase transition between order and chaos. Such systems are termed critical. Thus, criticality, a precise, measurable, and well characterized property of dynamical systems, makes it possible for robustness and adaptability to coexist in living organisms. In this work we investigate the dynamical properties of the gene transcription networks reported for S. cerevisiae, E. coli, and B. subtilis, as well as the network of segment polarity genes of D. melanogaster, and the network of flower development of A. thaliana. We use hundreds of microarray experiments to infer the nature of the regulatory interactions among genes, and implement these data into the Boolean models of the genetic networks. Our results show that, to the best of the current experimental data available, the five networks under study indeed operate close to criticality. The generality of this result suggests that criticality at the genetic level might constitute a fundamental evolutionary mechanism that generates the great diversity of dynamically robust living forms that we observe around us.

  10. Performance of artificial neural networks and genetical evolved artificial neural networks unfolding techniques

    Energy Technology Data Exchange (ETDEWEB)

    Ortiz R, J. M. [Escuela Politecnica Superior, Departamento de Electrotecnia y Electronica, Avda. Menendez Pidal s/n, Cordoba (Spain); Martinez B, M. R.; Vega C, H. R. [Universidad Autonoma de Zacatecas, Unidad Academica de Estudios Nucleares, Calle Cipres No. 10, Fracc. La Penuela, 98068 Zacatecas (Mexico); Gallego D, E.; Lorente F, A. [Universidad Politecnica de Madrid, Departamento de Ingenieria Nuclear, ETSI Industriales, C. Jose Gutierrez Abascal 2, 28006 Madrid (Spain); Mendez V, R.; Los Arcos M, J. M.; Guerrero A, J. E., E-mail: morvymm@yahoo.com.m [CIEMAT, Laboratorio de Metrologia de Radiaciones Ionizantes, Avda. Complutense 22, 28040 Madrid (Spain)

    2011-02-15

    With the Bonner spheres spectrometer neutron spectrum is obtained through an unfolding procedure. Monte Carlo methods, Regularization, Parametrization, Least-squares, and Maximum Entropy are some of the techniques utilized for unfolding. In the last decade methods based on Artificial Intelligence Technology have been used. Approaches based on Genetic Algorithms and Artificial Neural Networks (Ann) have been developed in order to overcome the drawbacks of previous techniques. Nevertheless the advantages of Ann still it has some drawbacks mainly in the design process of the network, vg the optimum selection of the architectural and learning Ann parameters. In recent years the use of hybrid technologies, combining Ann and genetic algorithms, has been utilized to. In this work, several Ann topologies were trained and tested using Ann and Genetically Evolved Artificial Neural Networks in the aim to unfold neutron spectra using the count rates of a Bonner sphere spectrometer. Here, a comparative study of both procedures has been carried out. (Author)

  11. Modeling, Optimization & Control of Hydraulic Networks

    DEFF Research Database (Denmark)

    Tahavori, Maryamsadat

    2014-01-01

    in water network is pressure management. By reducing the pressure in the water network, the leakage can be reduced significantly. Also it reduces the amount of energy consumption in water networks. The primary purpose of this work is to develop control algorithms for pressure control in water supply....... The nonlinear network model is derived based on the circuit theory. A suitable projection is used to reduce the state vector and to express the model in standard state-space form. Then, the controllability of nonlinear nonaffine hydraulic networks is studied. The Lie algebra-based controllability matrix is used...... to solve nonlinear optimal control problems. In the water supply system model, the hydraulic resistance of the valve is estimated by real data and it is considered to be a disturbance. The disturbance in our system is updated every 24 hours based on the amount of water usage by consumers every day. Model...

  12. Genetic noise control via protein oligomerization

    Directory of Open Access Journals (Sweden)

    Almaas Eivind

    2008-11-01

    Full Text Available Abstract Background Gene expression in a cell entails random reaction events occurring over disparate time scales. Thus, molecular noise that often results in phenotypic and population-dynamic consequences sets a fundamental limit to biochemical signaling. While there have been numerous studies correlating the architecture of cellular reaction networks with noise tolerance, only a limited effort has been made to understand the dynamic role of protein-protein interactions. Results We have developed a fully stochastic model for the positive feedback control of a single gene, as well as a pair of genes (toggle switch, integrating quantitative results from previous in vivo and in vitro studies. In particular, we explicitly account for the fast binding-unbinding kinetics among proteins, RNA polymerases, and the promoter/operator sequences of DNA. We find that the overall noise-level is reduced and the frequency content of the noise is dramatically shifted to the physiologically irrelevant high-frequency regime in the presence of protein dimerization. This is independent of the choice of monomer or dimer as transcription factor and persists throughout the multiple model topologies considered. For the toggle switch, we additionally find that the presence of a protein dimer, either homodimer or heterodimer, may significantly reduce its random switching rate. Hence, the dimer promotes the robust function of bistable switches by preventing the uninduced (induced state from randomly being induced (uninduced. Conclusion The specific binding between regulatory proteins provides a buffer that may prevent the propagation of fluctuations in genetic activity. The capacity of the buffer is a non-monotonic function of association-dissociation rates. Since the protein oligomerization per se does not require extra protein components to be expressed, it provides a basis for the rapid control of intrinsic or extrinsic noise. The stabilization of regulatory circuits

  13. District Heating Network Design and Configuration Optimization with Genetic Algorithm

    DEFF Research Database (Denmark)

    Li, Hongwei; Svendsen, Svend

    2011-01-01

    the heating plant location is allowed to vary. The connection between the heat generation plant and the end users can be represented with mixed integer and the pipe friction and heat loss formulations are non-linear. In order to find the optimal DH distribution pipeline configuration, the genetic algorithm...... which handles the mixed integer nonlinear programming problem was chosen. The network configuration was represented through binary and integer encoding and was optimized in terms of the net present cost (NPC). The optimization results indicated that the optimal DH network configuration is determined...

  14. Distributed controller clustering in software defined networks.

    Directory of Open Access Journals (Sweden)

    Ahmed Abdelaziz

    Full Text Available 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.

  15. Distributed controller clustering in software defined networks.

    Science.gov (United States)

    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.

  16. Genetic Control of Root System Development in Maize.

    Science.gov (United States)

    Hochholdinger, Frank; Yu, Peng; Marcon, Caroline

    2017-11-20

    The maize root system comprises structurally and functionally different root types. Mutant analyses have revealed that root-type-specific genetic regulators intrinsically determine the maize root system architecture. Molecular cloning of these genes has demonstrated that key elements of auxin signal transduction, such as LOB domain (LBD) and Aux/IAA proteins, are instrumental for seminal, shoot-borne, and lateral root initiation. Moreover, genetic analyses have demonstrated that genes related to exocytotic vesicle docking, cell wall loosening, and cellulose synthesis and organization control root hair elongation. The identification of upstream regulators, protein interaction partners, and downstream targets of these genes together with cell-type-specific transcriptome analyses have provided novel insights into the regulatory networks controlling root development and architecture in maize. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. Detecting controlling nodes of boolean regulatory networks.

    Science.gov (United States)

    Schober, Steffen; Kracht, David; Heckel, Reinhard; Bossert, Martin

    2011-10-11

    Boolean models of regulatory networks are assumed to be tolerant to perturbations. That qualitatively implies that each function can only depend on a few nodes. Biologically motivated constraints further show that functions found in Boolean regulatory networks belong to certain classes of functions, for example, the unate functions. It turns out that these classes have specific properties in the Fourier domain. That motivates us to study the problem of detecting controlling nodes in classes of Boolean networks using spectral techniques. We consider networks with unbalanced functions and functions of an average sensitivity less than 23k, where k is the number of controlling variables for a function. Further, we consider the class of 1-low networks which include unate networks, linear threshold networks, and networks with nested canalyzing functions. We show that the application of spectral learning algorithms leads to both better time and sample complexity for the detection of controlling nodes compared with algorithms based on exhaustive search. For a particular algorithm, we state analytical upper bounds on the number of samples needed to find the controlling nodes of the Boolean functions. Further, improved algorithms for detecting controlling nodes in large-scale unate networks are given and numerically studied.

  18. Controlling congestion on complex networks: fairness, efficiency and network structure.

    Science.gov (United States)

    Buzna, Ľuboš; Carvalho, Rui

    2017-08-22

    We consider two elementary (max-flow and uniform-flow) and two realistic (max-min fairness and proportional fairness) congestion control schemes, and analyse how the algorithms and network structure affect throughput, the fairness of flow allocation, and the location of bottleneck edges. The more realistic proportional fairness and max-min fairness algorithms have similar throughput, but path flow allocations are more unequal in scale-free than in random regular networks. Scale-free networks have lower throughput than their random regular counterparts in the uniform-flow algorithm, which is favoured in the complex networks literature. We show, however, that this relation is reversed on all other congestion control algorithms for a region of the parameter space given by the degree exponent γ and average degree 〈k〉. Moreover, the uniform-flow algorithm severely underestimates the network throughput of congested networks, and a rich phenomenology of path flow allocations is only present in the more realistic α-fair family of algorithms. Finally, we show that the number of paths passing through an edge characterises the location of a wide range of bottleneck edges in these algorithms. Such identification of bottlenecks could provide a bridge between the two fields of complex networks and congestion control.

  19. MPC control of water supply networks

    DEFF Research Database (Denmark)

    Baunsgaard, Kenneth Marx Hoe; Ravn, Ole; Kallesoe, Carsten Skovmose

    2016-01-01

    This paper investigates the modelling and predictive control of a drinking water supply network with the aim of minimising the energy and economic cost. A model predictive controller, MPC, is applied to a nonlinear model of a drinking water network that follows certain constraints to maintain......, controlling the drinking water supply network with the MPC showed reduction of the energy and the economic cost of running the system. This has been achieved by minimising actuator control effort and by shifting the actuator use towards the night time, where energy prices are lower. Along with energy cost...

  20. Gene networks controlling the initiation of flower development.

    Science.gov (United States)

    Wellmer, Frank; Riechmann, José L

    2010-12-01

    The onset of flower formation is a key regulatory event during the life cycle of angiosperm plants, which marks the beginning of the reproductive phase of development. It has been shown that floral initiation is under tight genetic control, and deciphering the underlying molecular mechanisms has been a main area of interest in plant biology for the past two decades. Here, we provide an overview of the developmental and genetic processes that occur during floral initiation. We further review recent studies that have led to the genome-wide identification of target genes of key floral regulators and discuss how they have contributed to an in-depth understanding of the gene regulatory networks controlling early flower development. We focus especially on a master regulator of floral initiation in Arabidopsis thaliana APETALA1 (AP1), but also outline what is known about the AP1 network in other plant species and the evolutionary implications. Copyright © 2010 Elsevier Ltd. All rights reserved.

  1. Emergence of bimodality in controlling complex networks

    CERN Document Server

    Jia, Tao; Csóka, Endre; Pósfai, Márton; Slotine, Jean-Jacques; Barabási, Albert-László

    2015-01-01

    Our ability to control complex systems is a fundamental challenge of contemporary science. Recently introduced tools to identify the driver nodes, nodes through which we can achieve full control, predict the existence of multiple control configurations, prompting us to classify each node in a network based on their role in control. Accordingly a node is critical, intermittent or redundant if it acts as a driver node in all, some or none of the control configurations. Here we develop an analytical framework to identify the category of each node, leading to the discovery of two distinct control modes in complex systems: centralized vs distributed control. We predict the control mode for an arbitrary network and show that one can alter it through small structural perturbations. The uncovered bimodality has implications from network security to organizational research and offers new insights into the dynamics and control of complex systems.

  2. Protein complexes predictions within protein interaction networks using genetic algorithms.

    Science.gov (United States)

    Ramadan, Emad; Naef, Ahmed; Ahmed, Moataz

    2016-07-25

    Protein-protein interaction networks are receiving increased attention due to their importance in understanding life at the cellular level. A major challenge in systems biology is to understand the modular structure of such biological networks. Although clustering techniques have been proposed for clustering protein-protein interaction networks, those techniques suffer from some drawbacks. The application of earlier clustering techniques to protein-protein interaction networks in order to predict protein complexes within the networks does not yield good results due to the small-world and power-law properties of these networks. In this paper, we construct a new clustering algorithm for predicting protein complexes through the use of genetic algorithms. We design an objective function for exclusive clustering and overlapping clustering. We assess the quality of our proposed clustering algorithm using two gold-standard data sets. Our algorithm can identify protein complexes that are significantly enriched in the gold-standard data sets. Furthermore, our method surpasses three competing methods: MCL, ClusterOne, and MCODE in terms of the quality of the predicted complexes. The source code and accompanying examples are freely available at http://faculty.kfupm.edu.sa/ics/eramadan/GACluster.zip .

  3. Advanced mobile networking, sensing, and controls.

    Energy Technology Data Exchange (ETDEWEB)

    Feddema, John Todd; Kilman, Dominique Marie; Byrne, Raymond Harry; Young, Joseph G.; Lewis, Christopher L.; Van Leeuwen, Brian P.; Robinett, Rush D. III; Harrington, John J.

    2005-03-01

    This report describes an integrated approach for designing communication, sensing, and control systems for mobile distributed systems. Graph theoretic methods are used to analyze the input/output reachability and structural controllability and observability of a decentralized system. Embedded in each network node, this analysis will automatically reconfigure an ad hoc communication network for the sensing and control task at hand. The graph analysis can also be used to create the optimal communication flow control based upon the spatial distribution of the network nodes. Edge coloring algorithms tell us that the minimum number of time slots in a planar network is equal to either the maximum number of adjacent nodes (or degree) of the undirected graph plus some small number. Therefore, the more spread out that the nodes are, the fewer number of time slots are needed for communication, and the smaller the latency between nodes. In a coupled system, this results in a more responsive sensor network and control system. Network protocols are developed to propagate this information, and distributed algorithms are developed to automatically adjust the number of time slots available for communication. These protocols and algorithms must be extremely efficient and only updated as network nodes move. In addition, queuing theory is used to analyze the delay characteristics of Carrier Sense Multiple Access (CSMA) networks. This report documents the analysis, simulation, and implementation of these algorithms performed under this Laboratory Directed Research and Development (LDRD) effort.

  4. Bayesian network structure learning using chaos hybrid genetic algorithm

    Science.gov (United States)

    Shen, Jiajie; Lin, Feng; Sun, Wei; Chang, KC

    2012-06-01

    A new Bayesian network (BN) learning method using a hybrid algorithm and chaos theory is proposed. The principles of mutation and crossover in genetic algorithm and the cloud-based adaptive inertia weight were incorporated into the proposed simple particle swarm optimization (sPSO) algorithm to achieve better diversity, and improve the convergence speed. By means of ergodicity and randomicity of chaos algorithm, the initial network structure population is generated by using chaotic mapping with uniform search under structure constraints. When the algorithm converges to a local minimal, a chaotic searching is started to skip the local minima and to identify a potentially better network structure. The experiment results show that this algorithm can be effectively used for BN structure learning.

  5. Reconstruction and analysis of the genetic and metabolic regulatory networks of the central metabolism of Bacillus subtilis

    Directory of Open Access Journals (Sweden)

    Aymerich Stéphane

    2008-02-01

    Full Text Available Abstract Background Few genome-scale models of organisms focus on the regulatory networks and none of them integrates all known levels of regulation. In particular, the regulations involving metabolite pools are often neglected. However, metabolite pools link the metabolic to the genetic network through genetic regulations, including those involving effectors of transcription factors or riboswitches. Consequently, they play pivotal roles in the global organization of the genetic and metabolic regulatory networks. Results We report the manually curated reconstruction of the genetic and metabolic regulatory networks of the central metabolism of Bacillus subtilis (transcriptional, translational and post-translational regulations and modulation of enzymatic activities. We provide a systematic graphic representation of regulations of each metabolic pathway based on the central role of metabolites in regulation. We show that the complex regulatory network of B. subtilis can be decomposed as sets of locally regulated modules, which are coordinated by global regulators. Conclusion This work reveals the strong involvement of metabolite pools in the general regulation of the metabolic network. Breaking the metabolic network down into modules based on the control of metabolite pools reveals the functional organization of the genetic and metabolic regulatory networks of B. subtilis.

  6. Congestion control and routing over satellite networks

    Science.gov (United States)

    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

  7. Control of autonomous robot using neural networks

    Science.gov (United States)

    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.

  8. Comparison and evaluation of network clustering algorithms applied to genetic interaction networks.

    Science.gov (United States)

    Hou, Lin; Wang, Lin; Berg, Arthur; Qian, Minping; Zhu, Yunping; Li, Fangting; Deng, Minghua

    2012-01-01

    The goal of network clustering algorithms detect dense clusters in a network, and provide a first step towards the understanding of large scale biological networks. With numerous recent advances in biotechnologies, large-scale genetic interactions are widely available, but there is a limited understanding of which clustering algorithms may be most effective. In order to address this problem, we conducted a systematic study to compare and evaluate six clustering algorithms in analyzing genetic interaction networks, and investigated influencing factors in choosing algorithms. The algorithms considered in this comparison include hierarchical clustering, topological overlap matrix, bi-clustering, Markov clustering, Bayesian discriminant analysis based community detection, and variational Bayes approach to modularity. Both experimentally identified and synthetically constructed networks were used in this comparison. The accuracy of the algorithms is measured by the Jaccard index in comparing predicted gene modules with benchmark gene sets. The results suggest that the choice differs according to the network topology and evaluation criteria. Hierarchical clustering showed to be best at predicting protein complexes; Bayesian discriminant analysis based community detection proved best under epistatic miniarray profile (EMAP) datasets; the variational Bayes approach to modularity was noticeably better than the other algorithms in the genome-scale networks.

  9. Gene networks controlling Arabidopsis thaliana flower development.

    Science.gov (United States)

    Ó'Maoiléidigh, Diarmuid Seosamh; Graciet, Emmanuelle; Wellmer, Frank

    2014-01-01

    The formation of flowers is one of the main models for studying the regulatory mechanisms that underlie plant development and evolution. Over the past three decades, extensive genetic and molecular analyses have led to the identification of a large number of key floral regulators and to detailed insights into how they control flower morphogenesis. In recent years, genome-wide approaches have been applied to obtaining a global view of the gene regulatory networks underlying flower formation. Furthermore, mathematical models have been developed that can simulate certain aspects of this process and drive further experimentation. Here, we review some of the main findings made in the field of Arabidopsis thaliana flower development, with an emphasis on recent advances. In particular, we discuss the activities of the floral organ identity factors, which are pivotal for the specification of the different types of floral organs, and explore the experimental avenues that may elucidate the molecular mechanisms and gene expression programs through which these master regulators of flower development act. © 2013 The Authors. New Phytologist © 2013 New Phytologist Trust.

  10. Self-Control in Sparsely Coded Networks

    Science.gov (United States)

    Dominguez, D. R. C.; Bollé, D.

    1998-03-01

    A complete self-control mechanism is proposed in the dynamics of neural networks through the introduction of a time-dependent threshold, determined in function of both the noise and the pattern activity in the network. Especially for sparsely coded models this mechanism is shown to considerably improve the storage capacity, the basins of attraction, and the mutual information content.

  11. Evolution of networks and sequences in eukaryotic cell cycle control.

    Science.gov (United States)

    Cross, Frederick R; Buchler, Nicolas E; Skotheim, Jan M

    2011-12-27

    The molecular networks regulating the G1-S transition in budding yeast and mammals are strikingly similar in network structure. However, many of the individual proteins performing similar network roles appear to have unrelated amino acid sequences, suggesting either extremely rapid sequence evolution, or true polyphyly of proteins carrying out identical network roles. A yeast/mammal comparison suggests that network topology, and its associated dynamic properties, rather than regulatory proteins themselves may be the most important elements conserved through evolution. However, recent deep phylogenetic studies show that fungal and animal lineages are relatively closely related in the opisthokont branch of eukaryotes. The presence in plants of cell cycle regulators such as Rb, E2F and cyclins A and D, that appear lost in yeast, suggests cell cycle control in the last common ancestor of the eukaryotes was implemented with this set of regulatory proteins. Forward genetics in non-opisthokonts, such as plants or their green algal relatives, will provide direct information on cell cycle control in these organisms, and may elucidate the potentially more complex cell cycle control network of the last common eukaryotic ancestor.

  12. Neural PID Control Strategy for Networked Process Control

    Directory of Open Access Journals (Sweden)

    Jianhua Zhang

    2013-01-01

    Full Text Available A new method with a two-layer hierarchy is presented based on a neural proportional-integral-derivative (PID iterative learning method over the communication network for the closed-loop automatic tuning of a PID controller. It can enhance the performance of the well-known simple PID feedback control loop in the local field when real networked process control applied to systems with uncertain factors, such as external disturbance or randomly delayed measurements. The proposed PID iterative learning method is implemented by backpropagation neural networks whose weights are updated via minimizing tracking error entropy of closed-loop systems. The convergence in the mean square sense is analysed for closed-loop networked control systems. To demonstrate the potential applications of the proposed strategies, a pressure-tank experiment is provided to show the usefulness and effectiveness of the proposed design method in network process control systems.

  13. Bioelectric gene and reaction networks: computational modelling of genetic, biochemical and bioelectrical dynamics in pattern regulation.

    Science.gov (United States)

    Pietak, Alexis; Levin, Michael

    2017-09-01

    Gene regulatory networks (GRNs) describe interactions between gene products and transcription factors that control gene expression. In combination with reaction-diffusion models, GRNs have enhanced comprehension of biological pattern formation. However, although it is well known that biological systems exploit an interplay of genetic and physical mechanisms, instructive factors such as transmembrane potential (Vmem) have not been integrated into full GRN models. Here we extend regulatory networks to include bioelectric signalling, developing a novel synthesis: the bioelectricity-integrated gene and reaction (BIGR) network. Using in silico simulations, we highlight the capacity for Vmem to alter steady-state concentrations of key signalling molecules inside and out of cells. We characterize fundamental feedbacks where Vmem both controls, and is in turn regulated by, biochemical signals and thereby demonstrate Vmem homeostatic control, Vmem memory and Vmem controlled state switching. BIGR networks demonstrating hysteresis are identified as a mechanisms through which more complex patterns of stable Vmem spots and stripes, along with correlated concentration patterns, can spontaneously emerge. As further proof of principle, we present and analyse a BIGR network model that mechanistically explains key aspects of the remarkable regenerative powers of creatures such as planarian flatworms. The functional properties of BIGR networks generate the first testable, quantitative hypotheses for biophysical mechanisms underlying the stability and adaptive regulation of anatomical bioelectric pattern. © 2017 The Author(s).

  14. Optical network control plane for multi-domain networking

    DEFF Research Database (Denmark)

    Manolova, Anna Vasileva

    This thesis focuses on multi-domain routing for traffice engineering and survivability support in optical transport networks under the Generalized Multi-Protocol Label Switching (GMPLS) control framework. First, different extensions to the Border Gateway Protocol for multi-domain Traffic Engineer......This thesis focuses on multi-domain routing for traffice engineering and survivability support in optical transport networks under the Generalized Multi-Protocol Label Switching (GMPLS) control framework. First, different extensions to the Border Gateway Protocol for multi-domain Traffic...... process are not enough for efficient TE in mesh multi-domain networks. Enhancing the protocol with multi-path dissemination capability, combined with the employment of an end-to-end TE metric proves to be a highly efficient solution. Simulation results show good performance characteristics of the proposed...... is not as essential for improved network performance as the length of the provided paths. Second, the issue of multi-domain survivability support is analyzed. An AS-disjoint paths is beneficial not only for resilience support, but also for facilitating adequate network reactions to changes in the network, which...

  15. Genetic network properties of the human cortex based on regional thickness and surface area measures

    Directory of Open Access Journals (Sweden)

    Anna R. Docherty

    2015-08-01

    Full Text Available We examined network properties of genetic covariance between average cortical thickness (CT and surface area (SA within genetically-identified cortical parcellations that we previously derived from human cortical genetic maps using vertex-wise fuzzy clustering analysis with high spatial resolution. There were 24 hierarchical parcellations based on vertex-wise CT and 24 based on vertex-wise SA expansion/contraction; in both cases the 12 parcellations per hemisphere were largely symmetrical. We utilized three techniques—biometrical genetic modeling, cluster analysis, and graph theory—to examine genetic relationships and network properties within and between the 48 parcellation measures. Biometrical modeling indicated significant shared genetic covariance between size of several of the genetic parcellations. Cluster analysis suggested small distinct groupings of genetic covariance; networks highlighted several significant negative and positive genetic correlations between bilateral parcellations. Graph theoretical analysis suggested that small world, but not rich club, network properties may characterize the genetic relationships between these regional size measures. These findings suggest that cortical genetic parcellations exhibit short characteristic path lengths across a broad network of connections. This property may be protective against network failure. In contrast, previous research with structural data has observed strong rich club properties with tightly interconnected hub networks. Future studies of these genetic networks might provide powerful phenotypes for genetic studies of normal and pathological brain development, aging, and function.

  16. A candidate multimodal functional genetic network for thermal adaptation

    Directory of Open Access Journals (Sweden)

    Katharina C. Wollenberg Valero

    2014-09-01

    Full Text Available Vertebrate ectotherms such as reptiles provide ideal organisms for the study of adaptation to environmental thermal change. Comparative genomic and exomic studies can recover markers that diverge between warm and cold adapted lineages, but the genes that are functionally related to thermal adaptation may be difficult to identify. We here used a bioinformatics genome-mining approach to predict and identify functions for suitable candidate markers for thermal adaptation in the chicken. We first established a framework of candidate functions for such markers, and then compiled the literature on genes known to adapt to the thermal environment in different lineages of vertebrates. We then identified them in the genomes of human, chicken, and the lizard Anolis carolinensis, and established a functional genetic interaction network in the chicken. Surprisingly, markers initially identified from diverse lineages of vertebrates such as human and fish were all in close functional relationship with each other and more associated than expected by chance. This indicates that the general genetic functional network for thermoregulation and/or thermal adaptation to the environment might be regulated via similar evolutionarily conserved pathways in different vertebrate lineages. We were able to identify seven functions that were statistically overrepresented in this network, corresponding to four of our originally predicted functions plus three unpredicted functions. We describe this network as multimodal: central regulator genes with the function of relaying thermal signal (1, affect genes with different cellular functions, namely (2 lipoprotein metabolism, (3 membrane channels, (4 stress response, (5 response to oxidative stress, (6 muscle contraction and relaxation, and (7 vasodilation, vasoconstriction and regulation of blood pressure. This network constitutes a novel resource for the study of thermal adaptation in the closely related nonavian reptiles and

  17. Evolution through genetically controlled allometry space

    Science.gov (United States)

    Langlade, Nicolas B.; Feng, Xianzhong; Dransfield, Tracy; Copsey, Lucy; Hanna, Andrew I.; Thébaud, Christophe; Bangham, Andrew; Hudson, Andrew; Coen, Enrico

    2005-01-01

    Understanding evolutionary change requires phenotypic differences between organisms to be placed in a genetic context. However, there are few cases where it has been possible to define an appropriate genotypic space for a range of species. Here we address this problem by defining a genetically controlled space that captures variation in shape and size between closely related species of Antirrhinum. The axes of the space are based on an allometric model of leaves from an F2 of an interspecific cross between Antirrhinum majus and Antirrhinum charidemi. Three principal components were found to capture most of the genetic variation in shape and size, allowing a three-dimensional allometric space to be defined. The contribution of individual genetic loci was determined from QTL analysis, allowing each locus to be represented as a vector in the allometric space. Leaf shapes and sizes of 18 different Antirrhinum taxa, encompassing a broad range of leaf morphologies, could be accurately represented as clouds within the space. Most taxa overlapped with, or were near to, at least one other species in the space, so that together they defined a largely interconnected domain of viable forms. It is likely that the pattern of evolution within this domain reflects a combination of directional selection and evolutionary tradeoffs within a high dimensional space. PMID:16009935

  18. Inferring network connectivity by delayed feedback control.

    Directory of Open Access Journals (Sweden)

    Dongchuan Yu

    Full Text Available We suggest a control based approach to topology estimation of networks with N elements. This method first drives the network to steady states by a delayed feedback control; then performs structural perturbations for shifting the steady states M times; and finally infers the connection topology from the steady states' shifts by matrix inverse algorithm (M = N or l(1-norm convex optimization strategy applicable to estimate the topology of sparse networks from M << N perturbations. We discuss as well some aspects important for applications, such as the topology reconstruction quality and error sources, advantages and disadvantages of the suggested method, and the influence of (control perturbations, inhomegenity, sparsity, coupling functions, and measurement noise. Some examples of networks with Chua's oscillators are presented to illustrate the reliability of the suggested technique.

  19. Genetic dissection of the Gpnmb network in the eye.

    Science.gov (United States)

    Lu, Hong; Wang, Xusheng; Pullen, Matthew; Guan, Huaijin; Chen, Hui; Sahu, Shwetapadma; Zhang, Bing; Chen, Hao; Williams, Robert W; Geisert, Eldon E; Lu, Lu; Jablonski, Monica M

    2011-06-13

    To use a systematic genetics approach to investigate the regulation of Gpnmb, a gene that contributes to pigmentary dispersion syndrome (PDS) and pigmentary glaucoma (PG) in the DBA/2J (D2) mouse. Global patterns of gene expression were studied in whole eyes of a large family of BXD mouse strains (n = 67) generated by crossing the PDS- and PG-prone parent (DBA/2J) with a resistant strain (C57BL/6J). Quantitative trait locus (eQTL) mapping methods and gene set analysis were used to evaluate Gpnmb coexpression networks in wild-type and mutant cohorts. The level of Gpnmb expression was associated with a highly significant cis-eQTL at the location of the gene itself. This autocontrol of Gpnmb is likely to be a direct consequence of the known premature stop codon in exon 4. Both gene ontology and coexpression network analyses demonstrated that the mutation in Gpnmb radically modified the set of genes with which Gpnmb expression is correlated. The covariates of wild-type Gpnmb are involved in biological processes including melanin synthesis and cell migration, whereas the covariates of mutant Gpnmb are involved in the biological processes of posttranslational modification, stress activation, and sensory processing. These results demonstrated that a systematic genetics approach provides a powerful tool for constructing coexpression networks that define the biological process categories within which similarly regulated genes function. The authors showed that the R150X mutation in Gpnmb dramatically modified its list of genetic covariates, which may explain the associated ocular pathology.

  20. The role of social networking sites in medical genetics research.

    Science.gov (United States)

    Reaves, Allison Cook; Bianchi, Diana W

    2013-05-01

    Social networking sites (SNS) have potential value in the field of medical genetics as a means of research subject recruitment and source of data. This article examines the current role of SNS in medical genetics research and potential applications for these sites in future studies. Facebook is the primary SNS considered, given the prevalence of its use in the United States and role in a small but growing number of studies. To date, utilization of SNS in medical genetics research has been primarily limited to three studies that recruited subjects from populations of Facebook users [McGuire et al. (2009); Am J Bioeth 9: 3-10; Janvier et al. (2012); Pediatrics 130: 293-298; Leighton et al. (2012); Public Health Genomics 15: 11-21]. These studies and a number of other medical and public health studies that have used Facebook as a context for recruiting research subjects are discussed. Approaches for Facebook-based subject recruitment are identified, including paid Facebook advertising, snowball sampling, targeted searching and posting. The use of these methods in medical genetics research has the potential to facilitate cost-effective research on both large, heterogeneous populations and small, hard-to-access sub-populations. Copyright © 2013 Wiley Periodicals, Inc.

  1. High Performance Data mining by Genetic Neural Network

    Directory of Open Access Journals (Sweden)

    Dadmehr Rahbari

    2013-10-01

    Full Text Available Data mining in computer science is the process of discovering interesting and useful patterns and relationships in large volumes of data. Most methods for mining problems is based on artificial intelligence algorithms. Neural network optimization based on three basic parameters topology, weights and the learning rate is a powerful method. We introduce optimal method for solving this problem. In this paper genetic algorithm with mutation and crossover operators change the network structure and optimized that. Dataset used for our work is stroke disease with twenty features that optimized number of that achieved by new hybrid algorithm. Result of this work is very well incomparison with other similar method. Low present of error show that our method is our new approach to efficient, high-performance data mining problems is introduced.

  2. Combining neural networks and genetic algorithms for hydrological flow forecasting

    Science.gov (United States)

    Neruda, Roman; Srejber, Jan; Neruda, Martin; Pascenko, Petr

    2010-05-01

    We present a neural network approach to rainfall-runoff modeling for small size river basins based on several time series of hourly measured data. Different neural networks are considered for short time runoff predictions (from one to six hours lead time) based on runoff and rainfall data observed in previous time steps. Correlation analysis shows that runoff data, short time rainfall history, and aggregated API values are the most significant data for the prediction. Neural models of multilayer perceptron and radial basis function networks with different numbers of units are used and compared with more traditional linear time series predictors. Out of possible 48 hours of relevant history of all the input variables, the most important ones are selected by means of input filters created by a genetic algorithm. The genetic algorithm works with population of binary encoded vectors defining input selection patterns. Standard genetic operators of two-point crossover, random bit-flipping mutation, and tournament selection were used. The evaluation of objective function of each individual consists of several rounds of building and testing a particular neural network model. The whole procedure is rather computational exacting (taking hours to days on a desktop PC), thus a high-performance mainframe computer has been used for our experiments. Results based on two years worth data from the Ploucnice river in Northern Bohemia suggest that main problems connected with this approach to modeling are ovetraining that can lead to poor generalization, and relatively small number of extreme events which makes it difficult for a model to predict the amplitude of the event. Thus, experiments with both absolute and relative runoff predictions were carried out. In general it can be concluded that the neural models show about 5 per cent improvement in terms of efficiency coefficient over liner models. Multilayer perceptrons with one hidden layer trained by back propagation algorithm and

  3. Control of Stochastic and Induced Switching in Biophysical Networks

    Science.gov (United States)

    Wells, Daniel K.; Kath, William L.; Motter, Adilson E.

    2015-07-01

    Noise caused by fluctuations at the molecular level is a fundamental part of intracellular processes. While the response of biological systems to noise has been studied extensively, there has been limited understanding of how to exploit it to induce a desired cell state. Here we present a scalable, quantitative method based on the Freidlin-Wentzell action to predict and control noise-induced switching between different states in genetic networks that, conveniently, can also control transitions between stable states in the absence of noise. We apply this methodology to models of cell differentiation and show how predicted manipulations of tunable factors can induce lineage changes, and further utilize it to identify new candidate strategies for cancer therapy in a cell death pathway model. This framework offers a systems approach to identifying the key factors for rationally manipulating biophysical dynamics, and should also find use in controlling other classes of noisy complex networks.

  4. Capturing the spectrum of interaction effects in genetic association studies by simulated evaporative cooling network analysis.

    Directory of Open Access Journals (Sweden)

    Brett A McKinney

    2009-03-01

    Full Text Available Evidence from human genetic studies of several disorders suggests that interactions between alleles at multiple genes play an important role in influencing phenotypic expression. Analytical methods for identifying Mendelian disease genes are not appropriate when applied to common multigenic diseases, because such methods investigate association with the phenotype only one genetic locus at a time. New strategies are needed that can capture the spectrum of genetic effects, from Mendelian to multifactorial epistasis. Random Forests (RF and Relief-F are two powerful machine-learning methods that have been studied as filters for genetic case-control data due to their ability to account for the context of alleles at multiple genes when scoring the relevance of individual genetic variants to the phenotype. However, when variants interact strongly, the independence assumption of RF in the tree node-splitting criterion leads to diminished importance scores for relevant variants. Relief-F, on the other hand, was designed to detect strong interactions but is sensitive to large backgrounds of variants that are irrelevant to classification of the phenotype, which is an acute problem in genome-wide association studies. To overcome the weaknesses of these data mining approaches, we develop Evaporative Cooling (EC feature selection, a flexible machine learning method that can integrate multiple importance scores while removing irrelevant genetic variants. To characterize detailed interactions, we construct a genetic-association interaction network (GAIN, whose edges quantify the synergy between variants with respect to the phenotype. We use simulation analysis to show that EC is able to identify a wide range of interaction effects in genetic association data. We apply the EC filter to a smallpox vaccine cohort study of single nucleotide polymorphisms (SNPs and infer a GAIN for a collection of SNPs associated with adverse events. Our results suggest an important

  5. Next Generation Network Routing and Control Plane

    DEFF Research Database (Denmark)

    Fu, Rong

    proved, the dominating Border Gateway Protocol (BGP) cannot address all the issues that in inter-domain QoS routing. Thus a new protocol or network architecture has to be developed to be able to carry the inter-domain traffic with the QoS and TE consideration. Moreover, the current network control also......-domain. It is a routing component that flexibly supports path computation with different requirements, constraints and areas. It is also can be seen as part of NGN transport control plane, which integrates with the other functions. In the aspect of resource control, an NGN release Resource and Admission Control Functions...... of Service classes. Under the NGN context, there are plenty of proposals intending to accommodate the issues listed above. Path Computation Elements (PCE) proposed by IETF designs suitable network architecture that aiming at compute the QoS based paths for traffic transportation through intra- and inter...

  6. On the Design of Energy Efficient Optical Networks with Software Defined Networking Control Across Core and Access Networks

    DEFF Research Database (Denmark)

    Wang, Jiayuan; Yan, Ying; Dittmann, Lars

    2013-01-01

    This paper presents a Software Defined Networking (SDN) control plane based on an overlay GMPLS control model. The SDN control platform manages optical core networks (WDM/DWDM networks) and the associated access networks (GPON networks), which makes it possible to gather global information...

  7. Tobacco Control Research, Dissemination and Networking in ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Tobacco Control Research, Dissemination and Networking in Lebanon. The Tobacco Control Research Group (TCRG), University of Beirut (AUB), is a multidisciplinary team of professionals from the health sciences, medicine, chemistry and engineering departments. The Group was established in 1999 with IDRC support ...

  8. Neural network topology design for nonlinear control

    Science.gov (United States)

    Haecker, Jens; Rudolph, Stephan

    2001-03-01

    Neural networks, especially in nonlinear system identification and control applications, are typically considered to be black-boxes which are difficult to analyze and understand mathematically. Due to this reason, an in- depth mathematical analysis offering insight into the different neural network transformation layers based on a theoretical transformation scheme is desired, but up to now neither available nor known. In previous works it has been shown how proven engineering methods such as dimensional analysis and the Laplace transform may be used to construct a neural controller topology for time-invariant systems. Using the knowledge of neural correspondences of these two classical methods, the internal nodes of the network could also be successfully interpreted after training. As further extension to these works, the paper describes the latest of a theoretical interpretation framework describing the neural network transformation sequences in nonlinear system identification and control. This can be achieved By incorporation of the method of exact input-output linearization in the above mentioned two transform sequences of dimensional analysis and the Laplace transformation. Based on these three theoretical considerations neural network topologies may be designed in special situations by pure translation in the sense of a structural compilation of the known classical solutions into their correspondent neural topology. Based on known exemplary results, the paper synthesizes the proposed approach into the visionary goals of a structural compiler for neural networks. This structural compiler for neural networks is intended to automatically convert classical control formulations into their equivalent neural network structure based on the principles of equivalence between formula and operator, and operator and structure which are discussed in detail in this work.

  9. Somatic surveillance: corporeal control through information networks

    OpenAIRE

    Monahan, Torin; Wall, Tyler

    2007-01-01

    Somatic surveillance is the increasingly invasive technological monitoring of and intervention into body functions. Within this type of surveillance regime, bodies are recast as nodes on vast information networks, enabling corporeal control through remote network commands, automated responses, or self-management practices. In this paper, we investigate three developments in somatic surveillance: nanotechnology systems for soldiers on the battlefield, commercial body-monitoring systems for hea...

  10. Social Network Privacy via Evolving Access Control

    Science.gov (United States)

    di Crescenzo, Giovanni; Lipton, Richard J.

    We study the problem of limiting privacy loss due to data shared in a social network, where the basic underlying assumptions are that users are interested in sharing data and cannot be assumed to constantly follow appropriate privacy policies. Note that if these two assumptions do not hold, social network privacy is theoretically very easy to achieve; for instance, via some form of access control and confidentiality transformation on the data.

  11. Decentralized control of ecological and biological networks through Evolutionary Network Control

    Directory of Open Access Journals (Sweden)

    Alessandro Ferrarini

    2016-09-01

    Full Text Available Evolutionary Network Control (ENC has been recently introduced to allow the control of any kind of ecological and biological networks, with an arbitrary number of nodes and links, acting from inside and/or outside. To date, ENC has been applied using a centralized approach where an arbitrary number of network nodes and links could be tamed. This approach has shown to be effective in the control of ecological and biological networks. However a decentralized control, where only one node and the correspondent input/output links are controlled, could be more economic from a computational viewpoint, in particular when the network is very large (i.e. big data. In this view, ENC is upgraded here to realize the decentralized control of ecological and biological nets.

  12. Scaling up: human genetics as a Cold War network.

    Science.gov (United States)

    Lindee, Susan

    2014-09-01

    In this commentary I explore how the papers here illuminate the processes of collection that have been so central to the history of human genetics since 1945. The development of human population genetics in the Cold War period produced databases and biobanks that have endured into the present, and that continue to be used and debated. In the decades after the bomb, scientists collected and transferred human biological materials and information from populations of interest, and as they moved these biological resources or biosocial resources acquired new meanings and uses. The papers here collate these practices and map their desires and ironies. They explore how a large international network of geneticists, biological anthropologists, virologists and other physicians and scientists interacted with local informants, research subjects and public officials. They also track the networks and standards that mobilized the transfer of information, genealogies, tissue and blood samples. As Joanna Radin suggests here, the massive collections of human biological materials and data were often understood to be resources for an "as-yet-unknown" future. The stories told here contain elements of surveillance, extraction, salvage and eschatology. Copyright © 2014 Elsevier Ltd. All rights reserved.

  13. Scheduled Controller Design of Congestion Control Considering Network Resource Constraints

    Science.gov (United States)

    Naito, Hiroyuki; Azuma, Takehito; Fujita, Masayuki

    In this paper, we consider a dynamical model of computer networks and derive a synthesis method for congestion control. First, we show a model of TCP/AQM (Transmission Control Protocol/Active Queue Management) as a dynamical model of computer networks. The dynamical model of TCP/AQM networks consists of models of TCP window size, queue length and AQM mechanisms. Second, we propose to describe the dynamical model of TCP/AQM networks as linear systems with self-scheduling parameters, which also depend on information delay. Here we focus on the constraints on the maximum queue length and TCP window-size, which are the network resources in TCP/AQM networks. We derive TCP/AQM networks as the LPV system (linear parameter varying system) with information delay and self-scheduling parameter. We design a memoryless state feedback controller of the LPV system based on a gain-scheduling method. Finally, the effectiveness of the proposed method is evaluated by using MATLAB and the well-known ns-2 (Network Simulator Ver.2) simulator.

  14. The Congenital Heart Disease Genetic Network Study: Cohort description.

    Science.gov (United States)

    Hoang, Thanh T; Goldmuntz, Elizabeth; Roberts, Amy E; Chung, Wendy K; Kline, Jennie K; Deanfield, John E; Giardini, Alessandro; Aleman, Adolfo; Gelb, Bruce D; Mac Neal, Meghan; Porter, George A; Kim, Richard; Brueckner, Martina; Lifton, Richard P; Edman, Sharon; Woyciechowski, Stacy; Mitchell, Laura E; Agopian, A J

    2018-01-01

    The Pediatric Cardiac Genomics Consortium (PCGC) designed the Congenital Heart Disease Genetic Network Study to provide phenotype and genotype data for a large congenital heart defects (CHDs) cohort. This article describes the PCGC cohort, overall and by major types of CHDs (e.g., conotruncal defects) and subtypes of conotrucal heart defects (e.g., tetralogy of Fallot) and left ventricular outflow tract obstructions (e.g., hypoplastic left heart syndrome). Cases with CHDs were recruited through ten sites, 2010-2014. Information on cases (N = 9,727) and their parents was collected through interviews and medical record abstraction. Four case characteristics, eleven parental characteristics, and thirteen parent-reported neurodevelopment outcomes were summarized using counts and frequencies and compared across CHD types and subtypes. Eleven percent of cases had a genetic diagnosis. Among cases without a genetic diagnosis, the majority had conotruncal heart defects (40%) or left ventricular outflow tract obstruction (21%). Across CHD types, there were significant differences (ptypes and subtypes, provides a reference work for investigators who are interested in collaborating with or using publically available resources from the PCGC.

  15. Adaptive Gain Scheduled Semiactive Vibration Control Using a Neural Network

    Directory of Open Access Journals (Sweden)

    Kazuhiko Hiramoto

    2018-01-01

    Full Text Available We propose an adaptive gain scheduled semiactive control method using an artificial neural network for structural systems subject to earthquake disturbance. In order to design a semiactive control system with high control performance against earthquakes with different time and/or frequency properties, multiple semiactive control laws with high performance for each of multiple earthquake disturbances are scheduled with an adaptive manner. Each semiactive control law to be scheduled is designed based on the output emulation approach that has been proposed by the authors. As the adaptive gain scheduling mechanism, we introduce an artificial neural network (ANN. Input signals of the ANN are the measured earthquake disturbance itself, for example, the acceleration, velocity, and displacement. The output of the ANN is the parameter for the scheduling of multiple semiactive control laws each of which has been optimized for a single disturbance. Parameters such as weight and bias in the ANN are optimized by the genetic algorithm (GA. The proposed design method is applied to semiactive control design of a base-isolated building with a semiactive damper. With simulation study, the proposed adaptive gain scheduling method realizes control performance exceeding single semiactive control optimizing the average of the control performance subject to various earthquake disturbances.

  16. General asymmetric neutral networks and structure design by genetic algorithms: A learning rule for temporal patterns

    Energy Technology Data Exchange (ETDEWEB)

    Bornholdt, S. [Heidelberg Univ., (Germany). Inst., fuer Theoretische Physik; Graudenz, D. [Lawrence Berkeley Lab., CA (United States)

    1993-07-01

    A learning algorithm based on genetic algorithms for asymmetric neural networks with an arbitrary structure is presented. It is suited for the learning of temporal patterns and leads to stable neural networks with feedback.

  17. Genetic Control of Phenylbutazone Metabolism in Man

    Science.gov (United States)

    Whittaker, J. A.; Evans, D. A. Price

    1970-01-01

    The purposes of the present investigation were to assess the genetic contribution to thevariability between individuals in the rate at which they metabolize phenylbutazone and to characterize the type of inheritance that controls the metabolism of the drug. The 155 persons investigated included 43 unrelated random individual subjects and the members of 28 two-generation family units. None of these subjects had taken drugs in the six months preceding the experiments. Each subject ingested an oral dose of phenylbutazone and the plasma half-life of the drug was determined. These non-pretreated plasma phenylbutazone half-lives suggest the existence of polygenic control, but the value of the data is marred by the frequency distribution being very skewed. The 142 persons given a second test, included 41 unrelated random subjects and 24 two-generation family units. A three-day course of oral phenobarbitone was followed by an oral dose of phenylbutazone and the plasma half-life of the latter determined. The phenobarbitone was given with the aim of “inducing” drugmetabolizing enzymes in the liver, thus rendering the environment more uniform. When the post-phenobarbitone half-lives were adjusted to a standard height they were approximately normally distributed. There was a significant regression of mean offspring value on mid-parent value, indicating that about 65% of the observed phenotypic variance of post-phenobarbitone plasma phenylbutazone half-lives is due to the additive effects of genes. Phenylbutazone metabolism in man is thus shown to be under polygenic control, and genetically controlled in a similar manner and to a similar degree to body height. Improved understanding of phenylbutazone metabolism may lead to improved therapeutic efficacy and a lower incidence of adverse reactions. PMID:5528753

  18. Robust dynamics in minimal hybrid models of genetic networks.

    Science.gov (United States)

    Perkins, Theodore J; Wilds, Roy; Glass, Leon

    2010-11-13

    Many gene-regulatory networks necessarily display robust dynamics that are insensitive to noise and stable under evolution. We propose that a class of hybrid systems can be used to relate the structure of these networks to their dynamics and provide insight into the origin of robustness. In these systems, the genes are represented by logical functions, and the controlling transcription factor protein molecules are real variables, which are produced and destroyed. As the transcription factor concentrations cross thresholds, they control the production of other transcription factors. We discuss mathematical analysis of these systems and show how the concepts of robustness and minimality can be used to generate putative logical organizations based on observed symbolic sequences. We apply the methods to control of the cell cycle in yeast.

  19. [The network of official medicines control laboratories].

    Science.gov (United States)

    Buchheit, K-H; Wanko, R

    2014-10-01

    Licensing, control and surveillance by competent authorities is the basis for ensuring efficacy, safety and quality of medicines in Europe. The control of the quality of medicines by national control laboratories, known as Official Medicines Control Laboratories (OMCLs) is an essential step in this process; it encompasses controls before and after granting a marketing authorisation. For certain groups of biomedical medicines (vaccines for human and veterinary use, medicines derived from human plasma) even each batch is controlled before it can be placed on the market. As single OMCLs would not be able to cope with their task, given the large number and diversity of medicines, in 1994 the OMCL network was founded upon initiative of the European Directorate for the Quality of Medicines & HealthCare, in close collaboration with the Commission of the European Union. Currently 68 OMCLs from 39 countries are part of the network. Prerequisite for the smooth operation of the OMCL network is the harmonisation of the quality management system of the individual OMCLs, based on the ISO 17025 standard, internal guidelines and the European Pharmacopoeia. Compliance with these standards is checked through regular audits, thus creating the basis for mutual recognition of test results. The collaboration in the OMCL network for the surveillance of the medicines market, the official control authority batch release and the fight against counterfeiting and illegal medicines enables OMCLs to keep pace with the developments in the field of medicines and to control the broad spectrum of medicines. In the 20 years since its start, the OMCL network has become a European success story.

  20. Controls from remote through Social networks

    Directory of Open Access Journals (Sweden)

    Alessandra Ingrao

    2016-03-01

    Full Text Available The Author focuses on the recently reformed provisions regulating the employer’s power to control from remote the employees’ activities (art. 4 of the Workers Statute, with particular regard to controls performed by means of Social networks.Such controls are in fact extremely powerful due to the versatile and multi-purpose character of Social networks, which may also be used as a working device. A widespread case law shows indeed that employer’s controls may cost a worker his job.Therefore, after the reform, all employees will have to read carefully the employer’s Privacy policies, before accessing socials during the worktime to express opinions and/or frustrations.

  1. Controllable Buoys and Networked Buoy Systems

    Science.gov (United States)

    Davoodi, Faranak (Inventor); Davoudi, Farhooman (Inventor)

    2017-01-01

    Buoyant sensor networks are described, comprising floating buoys with sensors and energy harvesting capabilities. The buoys can control their buoyancy and motion, and can organize communication in a distributed fashion. Some buoys may have tethered underwater vehicles with a smart spooling system that allows the vehicles to dive deep underwater while remaining in communication and connection with the buoys.

  2. Distributed control network for optogenetic experiments

    Science.gov (United States)

    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.

  3. Genetic Control of Mosquitoes: population suppression strategies

    Directory of Open Access Journals (Sweden)

    André Barretto Bruno Wilke

    2012-10-01

    Full Text Available Over the last two decades, morbidity and mortality from malaria and dengue fever among other pathogens are an increasing Public Health problem. The increase in the geographic distribution of vectors is accompanied by the emergence of viruses and diseases in new areas. There are insufficient specific therapeutic drugs available and there are no reliable vaccines for malaria or dengue, although some progress has been achieved, there is still a long way between its development and actual field use. Most mosquito control measures have failed to achieve their goals, mostly because of the mosquito's great reproductive capacity and genomic flexibility. Chemical control is increasingly restricted due to potential human toxicity, mortality in no target organisms, insecticide resistance, and other environmental impacts. Other strategies for mosquito control are desperately needed. The Sterile Insect Technique (SIT is a species-specific and environmentally benign method for insect population suppression, it is based on mass rearing, radiation mediated sterilization, and release of a large number of male insects. Releasing of Insects carrying a dominant lethal gene (RIDL offers a solution to many of the drawbacks of traditional SIT that have limited its application in mosquitoes while maintaining its environmentally friendly and species-specific utility. The self-limiting nature of sterile mosquitoes tends to make the issues related to field use of these somewhat less challenging than for self-spreading systems characteristic of population replacement strategies. They also are closer to field use, so might be appropriate to consider first. The prospect of genetic control methods against mosquito vectored human diseases is rapidly becoming a reality, many decisions will need to be made on a national, regional and international level regarding the biosafety, social, cultural and ethical aspects of the use and deployment of these vector control methods.

  4. Towards Controlling Latency in Wireless Networks

    KAUST Repository

    Bouacida, Nader

    2017-04-24

    Wireless networks are undergoing an unprecedented revolution in the last decade. With the explosion of delay-sensitive applications in the Internet (i.e., online gaming and VoIP), latency becomes a major issue for the development of wireless technology. Taking advantage of the significant decline in memory prices, industrialists equip the network devices with larger buffering capacities to improve the network throughput by limiting packets drops. Over-buffering results in increasing the time that packets spend in the queues and, thus, introducing more latency in networks. This phenomenon is known as “bufferbloat”. While throughput is the dominant performance metric, latency also has a huge impact on user experience not only for real-time applications but also for common applications like web browsing, which is sensitive to latencies in order of hundreds of milliseconds. Concerns have arisen about designing sophisticated queue management schemes to mitigate the effects of such phenomenon. My thesis research aims to solve bufferbloat problem in both traditional half-duplex and cutting-edge full-duplex wireless systems by reducing delay while maximizing wireless links utilization and fairness. Our work shed lights on buffer management algorithms behavior in wireless networks and their ability to reduce latency resulting from excessive queuing delays inside oversized static network buffers without a significant loss in other network metrics. First of all, we address the problem of buffer management in wireless full-duplex networks by using Wireless Queue Management (WQM), which is an active queue management technique for wireless networks. Our solution is based on Relay Full-Duplex MAC (RFD-MAC), an asynchronous media access control protocol designed for relay full-duplexing. Compared to the default case, our solution reduces the end-to-end delay by two orders of magnitude while achieving similar throughput in most of the cases. In the second part of this thesis

  5. Robust state estimation for stochastic genetic regulatory networks

    Science.gov (United States)

    Liang, Jinling; Lam, James

    2010-01-01

    In this article, the state estimation problem is investigated for genetic regulatory networks (GRNs) with parameter uncertainties and stochastic disturbances. To account for the unavoidable modelling errors and parameter fluctuations, the network parameters are assumed to be time-varying but norm-bounded. Furthermore, scalar multiplicative white noises are introduced into both the translation process and the feedback regulation process in order to reflect the inherent intracellular and extracellular noise perturbations. The purpose of the addressed problem is to design a linear state estimator that can estimate the true concentration of the mRNA and the protein of the uncertain GRNs. By resorting to the Lyapunov-Krasovskii functional method combined with the linear matrix inequality (LMI) technique, sufficient conditions are first established for ensuring the stochastic stability of the dynamics of the estimation error, and the estimator gains are then designed in terms of the solutions to some LMIs that can be easily solved by using the standard numerical software. A three-node GRN is presented to show the effectiveness of the proposed design procedures.

  6. Fusion Control of Flexible Logic Control and Neural Network

    Directory of Open Access Journals (Sweden)

    Lihua Fu

    2014-01-01

    Full Text Available Based on the basic physical meaning of error E and error variety EC, this paper analyzes the logical relationship between them and uses Universal Combinatorial Operation Model in Universal Logic to describe it. Accordingly, a flexible logic control method is put forward to realize effective control on multivariable nonlinear system. In order to implement fusion control with artificial neural network, this paper proposes a new neuron model of Zero-level Universal Combinatorial Operation in Universal Logic. And the artificial neural network of flexible logic control model is implemented based on the proposed neuron model. Finally, stability control, anti-interference control of double inverted-pendulum system, and free walking of cart pendulum system on a level track are realized, showing experimentally the feasibility and validity of this method.

  7. Population genetics of Chrysoperla externa (Neuroptera: Chrysopidae and implications for biological control

    Directory of Open Access Journals (Sweden)

    T. C. Lavagnini

    Full Text Available Abstract Green lacewings are insects with great potential to be use in the biological control of agricultural pests, but relatively few studies have attempted to understand the genetic structure of these agents, especially those of predatory insects. The purpose of this study was to characterize genetically populations of C. externa using sequences of subunit I of the cytochrome oxidase, a mitochondrial gene, and examine the population structure of this species in sampled areas in São Paulo state. The results indicate high genetic diversity but no genetic structure, detected by AMOVA analysis, and high levels of haplotype sharing in the network. These genetic patterns could be a consequence of environmental homogeneity provided by agroecosystem (citrus orchard, allowing gene flow among populations. Probably there is a unique population in the area sampled that could be used as a population (genetic source for mass-reared and posterior release in these farms.

  8. Population genetics of Chrysoperla externa (Neuroptera: Chrysopidae) and implications for biological control.

    Science.gov (United States)

    Lavagnini, T C; Morales, A C; Freitas, S

    2015-11-01

    Green lacewings are insects with great potential to be use in the biological control of agricultural pests, but relatively few studies have attempted to understand the genetic structure of these agents, especially those of predatory insects. The purpose of this study was to characterize genetically populations of C. externa using sequences of subunit I of the cytochrome oxidase, a mitochondrial gene, and examine the population structure of this species in sampled areas in São Paulo state. The results indicate high genetic diversity but no genetic structure, detected by AMOVA analysis, and high levels of haplotype sharing in the network. These genetic patterns could be a consequence of environmental homogeneity provided by agroecosystem (citrus orchard), allowing gene flow among populations. Probably there is a unique population in the area sampled that could be used as a population (genetic) source for mass-reared and posterior release in these farms.

  9. Scale-dependent genetic structure of the Idaho giant salamander (Dicamptodon aterrimus) in stream networks

    Science.gov (United States)

    Lindy B. Mullen; H. Arthur Woods; Michael K. Schwartz; Adam J. Sepulveda; Winsor H. Lowe

    2010-01-01

    The network architecture of streams and rivers constrains evolutionary, demographic and ecological processes of freshwater organisms. This consistent architecture also makes stream networks useful for testing general models of population genetic structure and the scaling of gene flow. We examined genetic structure and gene flow in the facultatively paedomorphic Idaho...

  10. Plug & Play Control of Hydraulic Networks

    DEFF Research Database (Denmark)

    Jensen, Tom Nørgaard

    2012-01-01

    Process Control research program, which the work presented here is a part of. An industrial case study involving a large-scale hydraulic network with non-linear dynamics is studied. The hydraulic network underlies a district heating system, which provides heating water to a number of end-users in a city...... district. The case study considers a novel approach to the design of district heating systems in which the diameter of the pipes used in the system is reduced in order to reduce the heat losses in the system, thereby making it profitable to provide district heating to areas with low energy demands. The new...

  11. Flexible body control using neural networks

    Science.gov (United States)

    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.

  12. Evolution of Controllability in Interbank Networks

    Science.gov (United States)

    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.

  13. Mathematical inference and control of molecular networks from perturbation experiments

    Science.gov (United States)

    Mohammed-Rasheed, Mohammed

    in order to affect the time evolution of molecular activity in a desirable manner. In this proposal, we address both the inference and control problems of GRNs. In the first part of the thesis, we consider the control problem. We assume that we are given a general topology network structure, whose dynamics follow a discrete-time Markov chain model. We subsequently develop a comprehensive framework for optimal perturbation control of the network. The aim of the perturbation is to drive the network away from undesirable steady-states and to force it to converge to a unique desirable steady-state. The proposed framework does not make any assumptions about the topology of the initial network (e.g., ergodicity, weak and strong connectivity), and is thus applicable to general topology networks. We define the optimal perturbation as the minimum-energy perturbation measured in terms of the Frobenius norm between the initial and perturbed networks. We subsequently demonstrate that there exists at most one optimal perturbation that forces the network into the desirable steady-state. In the event where the optimal perturbation does not exist, we construct a family of sub-optimal perturbations that approximate the optimal solution arbitrarily closely. In the second part of the thesis, we address the inference problem of GRNs from time series data. We model the dynamics of the molecules using a system of ordinary differential equations corrupted by additive white noise. For large-scale networks, we formulate the inference problem as a constrained maximum likelihood estimation problem. We derive the molecular interactions that maximize the likelihood function while constraining the network to be sparse. We further propose a procedure to recover weak interactions based on the Bayesian information criterion. For small-size networks, we investigated the inference of a globally stable 7-gene melanoma genetic regulatory network from genetic perturbation experiments. We considered five

  14. Networked control of microgrid system of systems

    Science.gov (United States)

    Mahmoud, Magdi S.; Rahman, Mohamed Saif Ur; AL-Sunni, Fouad M.

    2016-08-01

    The microgrid has made its mark in distributed generation and has attracted widespread research. However, microgrid is a complex system which needs to be viewed from an intelligent system of systems perspective. In this paper, a network control system of systems is designed for the islanded microgrid system consisting of three distributed generation units as three subsystems supplying a load. The controller stabilises the microgrid system in the presence of communication infractions such as packet dropouts and delays. Simulation results are included to elucidate the effectiveness of the proposed control strategy.

  15. Genetic Algorithm for Restricted Maximum k-Satisfiability in the Hopfield Network

    Directory of Open Access Journals (Sweden)

    Mohd Shareduwan Bin Mohd Kasihmuddin

    2016-12-01

    Full Text Available The restricted Maximum k-Satisfiability MAX- kSAT is an enhanced Boolean satisfiability counterpart that has attracted numerous amount of research. Genetic algorithm has been the prominent optimization heuristic algorithm to solve constraint optimization problem. The core motivation of this paper is to introduce Hopfield network incorporated with genetic algorithm in solving MAX-kSAT problem. Genetic algorithm will be integrated with Hopfield network as a single network. The proposed method will be compared with the conventional Hopfield network. The results demonstrate that Hopfield network with genetic algorithm outperforms conventional Hopfield networks. Furthermore, the outcome had provided a solid evidence of the robustness of our proposed algorithms to be used in other satisfiability problem.

  16. Steady-State Analysis of Genetic Regulatory Networks Modelled by Probabilistic Boolean Networks

    Directory of Open Access Journals (Sweden)

    Wei Zhang

    2006-04-01

    Full Text Available Probabilistic Boolean networks (PBNs have recently been introduced as a promising class of models of genetic regulatory networks. The dynamic behaviour of PBNs can be analysed in the context of Markov chains. A key goal is the determination of the steady-state (long-run behaviour of a PBN by analysing the corresponding Markov chain. This allows one to compute the long-term influence of a gene on another gene or determine the long-term joint probabilistic behaviour of a few selected genes. Because matrix-based methods quickly become prohibitive for large sizes of networks, we propose the use of Monte Carlo methods. However, the rate of convergence to the stationary distribution becomes a central issue. We discuss several approaches for determining the number of iterations necessary to achieve convergence of the Markov chain corresponding to a PBN. Using a recently introduced method based on the theory of two-state Markov chains, we illustrate the approach on a sub-network designed from human glioma gene expression data and determine the joint steadystate probabilities for several groups of genes.

  17. Neural network controller for underwater work ROV. Suichu sagyoyo ROV no neural network controller

    Energy Technology Data Exchange (ETDEWEB)

    Yoshida, Y.; Kidoshi, H.; Arahata, M.; Shoji, K.; Takahashi, Y. (Ishikawajima-Harima Heavy Industries, Co. Ltd., Tokyo (Japan))

    1993-07-01

    The previous underwater work ROV (remotely operated vehicle) has been controlled manually because its dynamic properties are changeable underwater. Ishikawajima-Harima Heavy Industries (IHI) has applied a neural network to an adaptive controller for the ROV. This paper describes objectives of the research, design of control logic, and tank experiments on a model ROV. For the neural network, manual operation was used to provide the initial learning data for the neural network in order to initialize control parameters for optimization. The model ROV was designed to achieve and maintain constant depth in normal operation. As a consequence of the tank experiments, it was demonstrated that the controller can acquire skill of operators, can further improve the acquired skill of operators, and can construct an automatic control system autonomically even if any dynamic properties are not known. 6 refs., 8 figs.

  18. Improving Control Mechanism at Routers in TCP/IP Network

    Directory of Open Access Journals (Sweden)

    Nguyen Kim Quoc

    2014-09-01

    Full Text Available The existing control mechanisms at the network nodes have a good active and very effective at each local router, but they do not still strong enough to control nonlinear and dynamical behaviour of the network. Therefore, the control system requirements must be designed to be flexible to fully grasp the important status information of the variation and intelligent control methods to control network congestion in nonlinear network. To solve this problem, we propose a solution combined fuzzy reasoning with neural network control put on active queue management mechanisms at the network nodes.

  19. Call Admission Control in Mobile Cellular Networks

    CERN Document Server

    Ghosh, Sanchita

    2013-01-01

    Call Admission Control (CAC) and Dynamic Channel Assignments (DCA) are important decision-making problems in mobile cellular communication systems. Current research in mobile communication considers them as two independent problems, although the former greatly depends on the resulting free channels obtained as the outcome of the latter. This book provides a solution to the CAC problem, considering DCA as an integral part of decision-making for call admission. Further, current technical resources ignore movement issues of mobile stations and fluctuation in network load (incoming calls) in the control strategy used for call admission. In addition, the present techniques on call admission offers solution globally for the entire network, instead of considering the cells independently.      CAC here has been formulated by two alternative approaches. The first approach aimed at handling the uncertainty in the CAC problem by employing fuzzy comparators.  The second approach is concerned with formulation of CAC ...

  20. Coordinated Voltage Control of Active Distribution Network

    Directory of Open Access Journals (Sweden)

    Xie Jiang

    2016-01-01

    Full Text Available This paper presents a centralized coordinated voltage control method for active distribution network to solve off-limit problem of voltage after incorporation of distributed generation (DG. The proposed method consists of two parts, it coordinated primal-dual interior point method-based voltage regulation schemes of DG reactive powers and capacitors with centralized on-load tap changer (OLTC controlling method which utilizes system’s maximum and minimum voltages, to improve the qualified rate of voltage and reduce the operation numbers of OLTC. The proposed coordination has considered the cost of capacitors. The method is tested using a radial edited IEEE-33 nodes distribution network which is modelled using MATLAB.

  1. Design of Supply Chain Networks with Supply Disruptions using Genetic Algorithm

    OpenAIRE

    Taha, Raghda; Abdallah, Khaled; Sadek, Yomma; El-Kharbotly, Amin; Afia, Nahid

    2014-01-01

    The design of supply chain networks subject to disruptions is tackled. A genetic algorithm with the objective of minimizing the design cost and regret cost is developed to achieve a reliable supply chain network. The improvement of supply chain network reliability is measured against the supply chain cost.

  2. NHLBI family blood pressure program: methodology and recruitment in the HyperGEN network. Hypertension genetic epidemiology network.

    Science.gov (United States)

    Williams, R R; Rao, D C; Ellison, R C; Arnett, D K; Heiss, G; Oberman, A; Eckfeldt, J H; Leppert, M F; Province, M A; Mockrin, S C; Hunt, S C

    2000-08-01

    Hypertension is a common precursor of serious disorders including stroke, myocardial infarction, congestive heart failure, and renal failure in whites and to a greater extent in African Americans. Large genetic-epidemiological studies of hypertension are needed to gain information that will improve future methods for diagnosis, treatment, and prevention of hypertension, a major contributor to cardiovascular morbidity and mortality. We report successful implementation of a new structure of research collaboration involving four NHLBI "Networks," coordinated under the Family Blood Pressure Program. The Hypertension Genetic Epidemiology Network (HyperGEN) involves scientists from six universities and the NHLBI who seek to identify and characterize genes promoting hypertension. Blood samples and clinical data were projected to be collected from a sample of 2244 hypertensive siblings diagnosed before age 60 from 960 sibships (half African-American) with two or more affected persons. Nonparametric sibship linkage analysis of over one million genotype determinations (20 candidate loci and 387 anonymous marker loci) was projected to have sufficient power for detecting genetic loci promoting hypertension. For loci showing evidence for linkage in this study and for loci reported linked or associated with hypertension by other groups, genotypes are compared in hypertensive cases versus population-based controls to identify or confirm genetic variants associated with hypertension. For some of these genetic variants associated with hypertension, detailed physiological and biochemical characterization of untreated adult offspring carriers versus non-carriers may help elucidate the pathophysiological mechanisms that promote hypertension. The projected sample size of 2244 hypertensive participants was surpassed, as 2407 hypertensive individuals (1262 African-Americans and 1145 whites) from 917 sibships were examined. Detailed consent forms were designed to offer participants

  3. Model Predictive Control of Sewer Networks

    DEFF Research Database (Denmark)

    Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik

    2016-01-01

    The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and cont...... benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control....

  4. Stochastic modelling and control of communication networks

    OpenAIRE

    Zuraniewski, P.W.

    2011-01-01

    The unprecedented growth of the Information Technologies sector observed within the past years creates an excellent opportunity to conduct new, exciting and interdisciplinary research. Increasing complexity of the communication networks calls for incorporating rigorously developed and reliable methods for traffic control and management. Mathematics may offer extremely valuable tools to achieve these goals but transforming an engineering problem into the mathematical one requires a good unders...

  5. Genetic control of biennial bearing in apple

    Science.gov (United States)

    Guitton, Baptiste; Kelner, Jean-Jacques; Velasco, Riccardo; Gardiner, Susan E.; Chagné, David; Costes, Evelyne

    2012-01-01

    Although flowering in mature fruit trees is recurrent, floral induction can be strongly inhibited by concurrent fruiting, leading to a pattern of irregular fruiting across consecutive years referred to as biennial bearing. The genetic determinants of biennial bearing in apple were investigated using the 114 flowering individuals from an F1 population of 122 genotypes, from a ‘Starkrimson’ (strong biennial bearer)בGranny Smith’ (regular bearer) cross. The number of inflorescences, and the number and the mass of harvested fruit were recorded over 6 years and used to calculate 26 variables and indices quantifying yield, precocity of production, and biennial bearing. Inflorescence traits exhibited the highest genotypic effect, and three quantitative trait loci (QTLs) on linkage group (LG) 4, LG8, and LG10 explained 50% of the phenotypic variability for biennial bearing. Apple orthologues of flowering and hormone-related genes were retrieved from the whole-genome assembly of ‘Golden Delicious’ and their position was compared with QTLs. Four main genomic regions that contain floral integrator genes, meristem identity genes, and gibberellin oxidase genes co-located with QTLs. The results indicated that flowering genes are less likely to be responsible for biennial bearing than hormone-related genes. New hypotheses for the control of biennial bearing emerged from QTL and candidate gene co-locations and suggest the involvement of different physiological processes such as the regulation of flowering genes by hormones. The correlation between tree architecture and biennial bearing is also discussed. PMID:21963613

  6. Etiologic Ischemic Stroke Phenotypes in the NINDS Stroke Genetics Network

    Science.gov (United States)

    Ay, Hakan; Arsava, Ethem Murat; Andsberg, Gunnar; Benner, Thomas; Brown, Robert D.; Chapman, Sherita N.; Cole, John W.; Delavaran, Hossein; Dichgans, Martin; Engström, Gunnar; Giralt-Steinhauer, Eva; Grewal, Raji P.; Gwinn, Katrina; Jern, Christina; Jimenez-Conde, Jordi; Jood, Katarina; Katsnelson, Michael; Kissela, Brett; Kittner, Steven J.; Kleindorfer, Dawn O.; Labovitz, Daniel L.; Lanfranconi, Silvia; Lee, Jin-Moo; Lehm, Manuel; Lemmens, Robin; Levi, Chris; Li, Linxin; Lindgren, Arne; Markus, Hugh S.; McArdle, Patrick F.; Melander, Olle; Norrving, Bo; Peddareddygari, Leema Reddy; Pedersén, Annie; Pera, Joanna; Rannikmäe, Kristiina; Rexrode, Kathryn M.; Rhodes, David; Rich, Stephen S.; Roquer, Jaume; Rosand, Jonathan; Rothwell, Peter M.; Rundek, Tatjana; Sacco, Ralph L.; Schmidt, Reinhold; Schürks, Markus; Seiler, Stephan; Sharma, Pankaj; Slowik, Agnieszka; Sudlow, Cathie; Thijs, Vincent; Woodfield, Rebecca; Worrall, Bradford B.; Meschia, James F.

    2014-01-01

    Background and Purpose NINDS Stroke Genetics Network (SiGN) is an international consortium of ischemic stroke studies that aims to generate high quality phenotype data to identify the genetic basis of etiologic stroke subtypes. This analysis characterizes the etiopathogenetic basis of ischemic stroke and reliability of stroke classification in the consortium. Methods Fifty-two trained and certified adjudicators determined both phenotypic (abnormal test findings categorized in major etiologic groups without weighting towards the most likely cause) and causative ischemic stroke subtypes in 16,954 subjects with imaging-confirmed ischemic stroke from 12 US studies and 11 studies from 8 European countries using the web-based Causative Classification of Stroke System. Classification reliability was assessed with blinded re-adjudication of 1509 randomly selected cases. Results The distribution of etiologic categories varied by study, age, sex, and race (p<0.001 for each). Overall, only 40% to 54% of cases with a given major ischemic stroke etiology (phenotypic subtype) were classified into the same final causative category with high confidence. There was good agreement for both causative (kappa 0.72, 95%CI:0.69-0.75) and phenotypic classifications (kappa 0.73, 95%CI:0.70-0.75). Conclusions This study demonstrates that etiologic subtypes can be determined with good reliability in studies that include investigators with different expertise and background, institutions with different stroke evaluation protocols and geographic location, and patient populations with different epidemiological characteristics. The discordance between phenotypic and causative stroke subtypes highlights the fact that the presence of an abnormality in a stroke patient does not necessarily mean that it is the cause of stroke. PMID:25378430

  7. Tuning of an optimal fuzzy PID controller with stochastic algorithms for networked control systems with random time delay.

    Science.gov (United States)

    Pan, Indranil; Das, Saptarshi; Gupta, Amitava

    2011-01-01

    An optimal PID and an optimal fuzzy PID have been tuned by minimizing the Integral of Time multiplied Absolute Error (ITAE) and squared controller output for a networked control system (NCS). The tuning is attempted for a higher order and a time delay system using two stochastic algorithms viz. the Genetic Algorithm (GA) and two variants of Particle Swarm Optimization (PSO) and the closed loop performances are compared. The paper shows that random variation in network delay can be handled efficiently with fuzzy logic based PID controllers over conventional PID controllers. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

  8. Genome-level analysis of genetic regulation of liver gene expression networks

    Energy Technology Data Exchange (ETDEWEB)

    Gatti, Daniel [University of North Carolina, Chapel Hill; Maki, Akira [University of North Carolina, Chapel Hill; Chesler, Elissa J [ORNL; Kirova, Roumyana [Oak Ridge National Laboratory (ORNL); Kosyk, Oksana [University of North Carolina, Chapel Hill; Lu, Lu [University of Tennessee Health Science Center, Memphis; Manly, Kenneth [University of Tennessee Health Science Center, Memphis; Matthews, Douglas B. [University of Memphis; Qu, Yanhua [University of Tennessee Health Science Center, Memphis; Williams, Robert [University of Tennessee Health Science Center, Memphis; Perkins, Andy [University of Tennessee, Knoxville (UTK); Langston, Michael A [University of Tennessee, Knoxville (UTK); Threadgill, David [University of North Carolina, Chapel Hill; Rusyn, Ivan [University of North Carolina, Chapel Hill

    2007-01-01

    Liver is the primary site for metabolism of nutrients, drugs and chemical agents. While metabolic pathways are complex and tightly regulated, genetic variation among individuals, reflected in variation in gene expression levels, introduces complexity into research on liver disease. This study aimed to dissect genetic networks that control liver gene expression by combining largescale quantitative mRNA expression analysis with genetic mapping in a reference population of BXD recombinant inbred mouse strains for which extensive SNP, haplotype and phenotypic data is publicly available. We profiled gene expression in livers of naive mice of both sexes from C57BL/6J, DBA/2J, B6D2F1, and 37 BXD strains using Agilent oligonucleotide microarrays. This data was used to map quantitative trait loci (QTLs) responsible for variation in expression of about 19,000 transcripts. We identified polymorphic cis- and trans-acting loci, including several loci that control expression of large numbers of genes in liver, by comparing the physical transcript position with the location of the controlling QTL. The data is available through a public web-based resource (www.genenetwork.org) that allows custom data mining, identification of co-regulated transcripts and correlated phenotypes, cross-tissue and -species comparisons, as well as testing of a broad array of hypotheses.

  9. EVOLUTIONARY MODELING PROBLEMS IN STRUCTURAL SYNTHESIS OF INFORMATION NETWORKS OF AUTOMATED CONTROL SYSTEMS

    Directory of Open Access Journals (Sweden)

    N.R.Yusupbekov

    2014-07-01

    Full Text Available This paper provides a new approach for solving a problem of modeling and structural syntheses of information networks of automated control systems by applying fuzzy sets theory, fuzzy logic and genetic algorithms. The procedure of formalizing structural syntheses of multi-level dispersed information networks of automated control systems is proposed. Also, the paper proposes a conceptual model of evolutionary syntheses based on genetic algorithms, which do not require additional information about the characteristics and features of target function. Modified genetic operators of crossover, mutation and algorithms of evolutionary syntheses of information networks systems are developed. Finally, the results of computational experiments on researching the influence of probability of the use of crossover and mutation operators, method of choosing parental pairs, and the size of initial population on the speed and precision of final results are provided.

  10. Maintaining genetic stability in a control flock of South African ...

    African Journals Online (AJOL)

    A genetic control flock of Merino sheep consisting of 160ewes and 16 rams, was established in 1969at the Tygerhoek ... It is concluded that genetic stability can be maintained in a. Merino control flock of this size under .... differences for individual traits (expressed as a percentage) also indicate that the replacement groups ...

  11. A comprehensive Network Security Risk Model for process control networks.

    Science.gov (United States)

    Henry, Matthew H; Haimes, Yacov Y

    2009-02-01

    The risk of cyber attacks on process control networks (PCN) is receiving significant attention due to the potentially catastrophic extent to which PCN failures can damage the infrastructures and commodity flows that they support. Risk management addresses the coupled problems of (1) reducing the likelihood that cyber attacks would succeed in disrupting PCN operation and (2) reducing the severity of consequences in the event of PCN failure or manipulation. The Network Security Risk Model (NSRM) developed in this article provides a means of evaluating the efficacy of candidate risk management policies by modeling the baseline risk and assessing expectations of risk after the implementation of candidate measures. Where existing risk models fall short of providing adequate insight into the efficacy of candidate risk management policies due to shortcomings in their structure or formulation, the NSRM provides model structure and an associated modeling methodology that captures the relevant dynamics of cyber attacks on PCN for risk analysis. This article develops the NSRM in detail in the context of an illustrative example.

  12. Epidemic Modelling by Ripple-Spreading Network and Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Jian-Qin Liao

    2013-01-01

    Full Text Available Mathematical analysis and modelling is central to infectious disease epidemiology. This paper, inspired by the natural ripple-spreading phenomenon, proposes a novel ripple-spreading network model for the study of infectious disease transmission. The new epidemic model naturally has good potential for capturing many spatial and temporal features observed in the outbreak of plagues. In particular, using a stochastic ripple-spreading process simulates the effect of random contacts and movements of individuals on the probability of infection well, which is usually a challenging issue in epidemic modeling. Some ripple-spreading related parameters such as threshold and amplifying factor of nodes are ideal to describe the importance of individuals’ physical fitness and immunity. The new model is rich in parameters to incorporate many real factors such as public health service and policies, and it is highly flexible to modifications. A genetic algorithm is used to tune the parameters of the model by referring to historic data of an epidemic. The well-tuned model can then be used for analyzing and forecasting purposes. The effectiveness of the proposed method is illustrated by simulation results.

  13. Towards structural controllability of local-world networks

    Energy Technology Data Exchange (ETDEWEB)

    Sun, Shiwen, E-mail: sunsw80@126.com [Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin 300384 (China); Key Laboratory of Computer Vision and System (Tianjin University of Technology), Ministry of Education, Tianjin 300384 (China); Ma, Yilin; Wu, Yafang; Wang, Li; Xia, Chengyi [Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin 300384 (China); Key Laboratory of Computer Vision and System (Tianjin University of Technology), Ministry of Education, Tianjin 300384 (China)

    2016-05-20

    Controlling complex networks is of vital importance in science and engineering. Meanwhile, local-world effect is an important ingredient which should be taken into consideration in the complete description of real-world complex systems. In this letter, structural controllability of a class of local-world networks is investigated. Through extensive numerical simulations, firstly, effects of local world size M and network size N on structural controllability are examined. For local-world networks with sparse topological configuration, compared to network size, local-world size can induce stronger influence on controllability, however, for dense networks, controllability is greatly affected by network size and local-world effect can be neglected. Secondly, relationships between controllability and topological properties are analyzed. Lastly, the robustness of local-world networks under targeted attacks regarding structural controllability is discussed. These results can help to deepen the understanding of structural complexity and connectivity patterns of complex systems. - Highlights: • Structural controllability of a class of local-world networks is investigated. • For sparse local-world networks, compared to network size, local-world size can bring stronger influence on controllability. • For dense networks, controllability is greatly affected by network size and the effect of local-world size can be neglected. • Structural controllability against targeted node attacks is discussed.

  14. Genetic Algorithm Optimisation of PID Controllers for a Multivariable Process

    OpenAIRE

    Wael Alharbi; Barry Gomm

    2017-01-01

    This project is about the design of PID controllers and the improvement of outputs in multivariable processes. The optimisation of PID controller for the Shell oil process is presented in this paper, using Genetic Algorithms (GAs). Genetic Algorithms (GAs) are used to automatically tune PID controllers according to given specifications. They use an objective function, which is specially formulated and measures the performance of controller in terms of time-domain bounds on the responses of cl...

  15. Analysis and design of networked control systems

    CERN Document Server

    You, Keyou; Xie, Lihua

    2015-01-01

    This monograph focuses on characterizing the stability and performance consequences of inserting limited-capacity communication networks within a control loop. The text shows how integration of the ideas of control and estimation with those of communication and information theory can be used to provide important insights concerning several fundamental problems such as: ·         minimum data rate for stabilization of linear systems over noisy channels; ·         minimum network requirement for stabilization of linear systems over fading channels; and ·         stability of Kalman filtering with intermittent observations. A fundamental link is revealed between the topological entropy of linear dynamical systems and the capacities of communication channels. The design of a logarithmic quantizer for the stabilization of linear systems under various network environments is also extensively discussed and solutions to many problems of Kalman filtering with intermittent observations are de...

  16. Maximizing lifetime of wireless sensor networks using genetic approach

    DEFF Research Database (Denmark)

    Wagh, Sanjeev; Prasad, Ramjee

    2014-01-01

    The wireless sensor networks are designed to install the smart network applications or network for emergency solutions, where human interaction is not possible. The nodes in wireless sensor networks have to self organize as per the users requirements through monitoring environments. As the sensor...

  17. On the attenuation and amplification of molecular noise in genetic regulatory networks

    Directory of Open Access Journals (Sweden)

    Wang Yu-Chao

    2006-02-01

    Full Text Available Abstract Background Noise has many important roles in cellular genetic regulatory functions at the nanomolar scale. At present, no good theory exists for identifying all possible mechanisms of genetic regulatory networks to attenuate the molecular noise to achieve regulatory ability or to amplify the molecular noise to randomize outcomes to the advantage of diversity. Therefore, the noise filtering of genetic regulatory network is an important topic for gene networks under intrinsic fluctuation and extrinsic noise. Results Based on stochastic dynamic regulation equation, the intrinsic fluctuation in reaction rates is modeled as a state-dependent stochastic process, which will influence the stability of gene regulatory network, especially, with low concentrations of reacting species. Then the mechanisms of genetic regulatory network to attenuate or amplify extrinsic fluctuation are revealed from the nonlinear stochastic filtering point of view. Furthermore, a simple measure of attenuation level or amplification level of extrinsic noise for genetic regulatory networks is also introduced by nonlinear robust filtering method. Based on the global linearization scheme, a convenient method is introduced to measure noise attenuation or amplification for each gene of the nonlinear stochastic regulatory network by solving a set of filtering problems, which correspond to a set of linearized stochastic regulatory networks. Finally, by the proposed methods, several simulation examples of genetic regulatory networks are given to measure their robust stability under intrinsic fluctuations, and to estimate the genes' attenuation and amplification levels under extrinsic noises. Conclusion In this study, a stochastic nonlinear dynamic model is developed for genetic regulatory networks under intrinsic fluctuation and extrinsic noise. By the method we proposed, we could determine the robust stability under intrinsic fluctuations and identify the genes that are

  18. Transmission Expansion Planning Considering Network Adequacy and Investment Cost Limitation using Genetic Algorithm

    OpenAIRE

    M. Mahdavi; E. Mahdavi

    2011-01-01

    In this research, STNEP is being studied considering network adequacy and limitation of investment cost by decimal codification genetic algorithm (DCGA). The goal is obtaining the maximum of network adequacy with lowest expansion cost for a specific investment. Finally, the proposed idea is applied to the Garvers 6-bus network. The results show that considering the network adequacy for solution of STNEP problem is caused that among of expansion plans for a determined investment, configuration...

  19. Genetic Optimization of Neural Networks for Person Recognition based on the Iris

    Directory of Open Access Journals (Sweden)

    Oscar Castillo

    2012-06-01

    Full Text Available This paper describes the application of modular neural network architectures for person recognition using the human iris images as a biometric measure. The iris database was obtained from the Institute of Automation of the Academy of Sciences China (CASIA. We show simulation results with the modular neural network approach, its optimization using genetic algorithms, and the integration with different methods, such as: the gating network method, type-1 fuzzy integration and optimized fuzzy integration using genetic algorithms. Simulation results show a good identification rate using fuzzy integrators and the best structure found by the genetic algorithm.

  20. neural network based load frequency control for restructuring power

    African Journals Online (AJOL)

    2012-03-01

    Mar 1, 2012 ... Abstract. In this study, an artificial neural network (ANN) application of load frequency control. (LFC) of a Multi-Area power system by using a neural network controller is presented. The comparison between a conventional Proportional Integral (PI) controller and the proposed artificial neural networks ...

  1. Efficient Access Control in Multimedia Social Networks

    Science.gov (United States)

    Sachan, Amit; Emmanuel, Sabu

    Multimedia social networks (MMSNs) have provided a convenient way to share multimedia contents such as images, videos, blogs, etc. Contents shared by a person can be easily accessed by anybody else over the Internet. However, due to various privacy, security, and legal concerns people often want to selectively share the contents only with their friends, family, colleagues, etc. Access control mechanisms play an important role in this situation. With access control mechanisms one can decide the persons who can access a shared content and who cannot. But continuously growing content uploads and accesses, fine grained access control requirements (e.g. different access control parameters for different parts in a picture), and specific access control requirements for multimedia contents can make the time complexity of access control to be very large. So, it is important to study an efficient access control mechanism suitable for MMSNs. In this chapter we present an efficient bit-vector transform based access control mechanism for MMSNs. The proposed approach is also compatible with other requirements of MMSNs, such as access rights modification, content deletion, etc. Mathematical analysis and experimental results show the effectiveness and efficiency of our proposed approach.

  2. Reveal, A General Reverse Engineering Algorithm for Inference of Genetic Network Architectures

    Science.gov (United States)

    Liang, Shoudan; Fuhrman, Stefanie; Somogyi, Roland

    1998-01-01

    Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able to infer the sets of input elements controlling each element or gene in the network. This process is unequivocal and exact for complete state transition tables. We implemented this REVerse Engineering ALgorithm (REVEAL) in a C program, and found the problem to be tractable within the conditions tested so far. For n = 50 (elements) and k = 3 (inputs per element), the analysis of incomplete state transition tables (100 state transition pairs out of a possible 10(exp 15)) reliably produced the original rule and wiring sets. While this study is limited to synchronous Boolean networks, the algorithm is generalizable to include multi-state models, essentially allowing direct application to realistic biological data sets. The ability to adequately solve the inverse problem may enable in-depth analysis of complex dynamic systems in biology and other fields.

  3. Identification and Position Control of Marine Helm using Artificial Neural Network Neural Network

    Directory of Open Access Journals (Sweden)

    Hui ZHU

    2008-02-01

    Full Text Available If nonlinearities such as saturation of the amplifier gain and motor torque, gear backlash, and shaft compliances- just to name a few - are considered in the position control system of marine helm, traditional control methods are no longer sufficient to be used to improve the performance of the system. In this paper an alternative approach to traditional control methods - a neural network reference controller - is proposed to establish an adaptive control of the position of the marine helm to achieve the controlled variable at the command position. This neural network controller comprises of two neural networks. One is the plant model network used to identify the nonlinear system and the other the controller network used to control the output to follow the reference model. The experimental results demonstrate that this adaptive neural network reference controller has much better control performance than is obtained with traditional controllers.

  4. Neural Network Control of Asymmetrical Multilevel Converters

    Directory of Open Access Journals (Sweden)

    Patrice WIRA

    2009-12-01

    Full Text Available This paper proposes a neural implementation of a harmonic eliminationstrategy (HES to control a Uniform Step Asymmetrical Multilevel Inverter(USAMI. The mapping between the modulation rate and the requiredswitching angles is learned and approximated with a Multi-Layer Perceptron(MLP neural network. After learning, appropriate switching angles can bedetermined with the neural network leading to a low-computational-costneural controller which is well suited for real-time applications. Thistechnique can be applied to multilevel inverters with any number of levels. Asan example, a nine-level inverter and an eleven-level inverter are consideredand the optimum switching angles are calculated on-line. Comparisons to thewell-known sinusoidal pulse-width modulation (SPWM have been carriedout in order to evaluate the performance of the proposed approach. Simulationresults demonstrate the technical advantages of the proposed neuralimplementation over the conventional method (SPWM in eliminatingharmonics while controlling a nine-level and eleven-level USAMI. Thisneural approach is applied for the supply of an asynchronous machine andresults show that it ensures a highest quality torque by efficiently cancelingthe harmonics generated by the inverters.

  5. Ensembles of Bayesian-regularized genetic neural networks for modeling of acetylcholinesterase inhibition by huprines.

    Science.gov (United States)

    Fernández, Michael; Caballero, Julio

    2006-10-01

    Acetylcholinesterase inhibition was modeled for a set of huprines using ensembles of Bayesian-regularized Genetic Neural Networks. In the Bayesian-regularized Genetic Neural Network approach the Bayesian regularization avoids overfitted regressions and the genetic algorithm allows exploring a wide pool of three-dimensional descriptors. The predictive capacity of our selected model was evaluated by averaging multiple validation sets generated as members of neural network ensembles. When 60 members are assembled, the neural network ensemble provides a reliable measure of training and test set R(2)-values of 0.945 and 0.850 respectively. In other respects, the ability of the nonlinear selected genetic algorithm space for differentiate the data were evidenced when total data set was well distributed in a Kohonen self-organizing map. The analysis of the self-organizing map zones allows establishing the main structural features differentiated by our vectorial space.

  6. Calibration of parameters of water supply network model using genetic algorithm

    National Research Council Canada - National Science Library

    Tomasz Boczar; Norbert Adamikiewicz; Włodzimierz Stanisławski

    2017-01-01

    ...: the pressure on the node and volume flow in the network section. The first calibration method regards to application of the genetic algorithm, which is a build in plugin - “Epanet Calibrator...

  7. Rhizobial exopolysaccharides: genetic control and symbiotic functions

    Science.gov (United States)

    Skorupska, Anna; Janczarek, Monika; Marczak, Małgorzata; Mazur, Andrzej; Król, Jarosław

    2006-01-01

    Specific complex interactions between soil bacteria belonging to Rhizobium, Sinorhizobium, Mesorhizobium, Phylorhizobium, Bradyrhizobium and Azorhizobium commonly known as rhizobia, and their host leguminous plants result in development of root nodules. Nodules are new organs that consist mainly of plant cells infected with bacteroids that provide the host plant with fixed nitrogen. Proper nodule development requires the synthesis and perception of signal molecules such as lipochitooligosaccharides, called Nod factors that are important for induction of nodule development. Bacterial surface polysaccharides are also crucial for establishment of successful symbiosis with legumes. Sugar polymers of rhizobia are composed of a number of different polysaccharides, such as lipopolysaccharides (LPS), capsular polysaccharides (CPS or K-antigens), neutral β-1, 2-glucans and acidic extracellular polysaccharides (EPS). Despite extensive research, the molecular function of the surface polysaccharides in symbiosis remains unclear. This review focuses on exopolysaccharides that are especially important for the invasion that leads to formation of indetermined (with persistent meristem) type of nodules on legumes such as clover, vetch, peas or alfalfa. The significance of EPS synthesis in symbiotic interactions of Rhizobium leguminosarum with clover is especially noticed. Accumulating data suggest that exopolysaccharides may be involved in invasion and nodule development, bacterial release from infection threads, bacteroid development, suppression of plant defense response and protection against plant antimicrobial compounds. Rhizobial exopolysaccharides are species-specific heteropolysaccharide polymers composed of common sugars that are substituted with non-carbohydrate residues. Synthesis of repeating units of exopolysaccharide, their modification, polymerization and export to the cell surface is controlled by clusters of genes, named exo/exs, exp or pss that are localized on

  8. Neural Networks for Modeling and Control of Particle Accelerators

    CERN Document Server

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

    2016-01-01

    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.

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

    Science.gov (United States)

    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.

  10. Power graph compression reveals dominant relationships in genetic transcription networks.

    Science.gov (United States)

    Ahnert, Sebastian E

    2013-11-01

    We introduce a framework for the discovery of dominant relationship patterns in transcription networks, by compressing the network into a power graph with overlapping power nodes. Our application of this approach to the transcription networks of S. cerevisiae and E. coli, paired with GO term enrichment analysis, provides a highly informative overview of the most prominent relationships in the gene regulatory networks of these two organisms.

  11. Reconstruction of switching thresholds in piecewise-affine models of genetic regulatory networks

    OpenAIRE

    Drulhe, Samuel; Ferrari-Trecate, Giancarlo; De Jong, Hidde; Viari, Alain

    2006-01-01

    http://dx.doi.org/10.1007/11730637_16; Recent advances of experimental techniques in biology have led to the production of enormous amounts of data on the dynamics of genetic regulatory networks. In this paper, we present an approach for the identification of PieceWise-Affine (PWA) models of genetic regulatory networks from experimental data, focusing on the reconstruction of switching thresholds associated with regulatory interactions. In particular, our method takes into account geometric c...

  12. Modeling the evolution of complex genetic systems: the gene network family tree.

    Science.gov (United States)

    Fierst, Janna L; Phillips, Patrick C

    2015-01-01

    In 1994 and 1996, Andreas Wagner introduced a novel model in two papers addressing the evolution of genetic regulatory networks. This work, and a suite of papers that followed using similar models, helped integrate network thinking into biology and motivate research focused on the evolution of genetic networks. The Wagner network has its mathematical roots in the Ising model, a statistical physics model describing the activity of atoms on a lattice, and in neural networks. These models have given rise to two branches of applications, one in physics and biology and one in artificial intelligence and machine learning. Here, we review development along these branches, outline similarities and differences between biological models of genetic regulatory circuits and neural circuits models used in machine learning, and identify ways in which these models can provide novel insights into biological systems. © 2014 Wiley Periodicals, Inc.

  13. Improving Roadside Unit Deployment in Vehicular Networks by Exploiting Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Manuel Fogue

    2018-01-01

    Full Text Available Vehicular networks make use of the Roadside Units (RSUs to enhance the communication capabilities of the vehicles in order to forward control messages and/or to provide Internet access to vehicles, drivers and passengers. Unfortunately, within vehicular networks, the wireless signal propagation is mostly affected by buildings and other obstacles (e.g., urban fixtures, in particular when considering the IEEE 802.11p standard. Therefore, a crowded RSU deployment may be required to ensure vehicular communications within urban environments. Furthermore, some applications, notably those applications related to safety, require a fast and reliable warning data transmission to the emergency services and traffic authorities. However, communication is not always possible in vehicular environments due to the lack of connectivity even employing multiple hops. To overcome the signal propagation problem and delayed warning notification time issues, an effective, smart, cost-effective and all-purpose RSU deployment policy should be put into place. In this paper, we propose the genetic algorithm for roadside unit deployment (GARSUD system, which uses a genetic algorithm that is capable of automatically providing an RSU deployment suitable for any given road map layout. Our simulation results show that GARSUD is able to reduce the warning notification time (the time required to inform emergency authorities in traffic danger situations and to improve vehicular communication capabilities within different density scenarios and complexity layouts.

  14. Research on PID control system based on genetic algorithm

    Science.gov (United States)

    Zhang, Dingqun; Yang, Xinfeng

    2011-12-01

    Genetic algorithms, there are many difficult issues, such as premature convergence, choice of control parameters. This combination of all improvements, the optimal preservation strategy, adaptive set the crossover probability and mutation probability, the idea of fitness scaling into the simple genetic algorithm, the algorithm is improved and used Matlab program to achieve the improved algorithm, prove the correctness and practicability of this method.

  15. Linking social and pathogen transmission networks using microbial genetics in giraffe (Giraffa camelopardalis).

    Science.gov (United States)

    VanderWaal, Kimberly L; Atwill, Edward R; Isbell, Lynne A; McCowan, Brenda

    2014-03-01

    Although network analysis has drawn considerable attention as a promising tool for disease ecology, empirical research has been hindered by limitations in detecting the occurrence of pathogen transmission (who transmitted to whom) within social networks. Using a novel approach, we utilize the genetics of a diverse microbe, Escherichia coli, to infer where direct or indirect transmission has occurred and use these data to construct transmission networks for a wild giraffe population (Giraffe camelopardalis). Individuals were considered to be a part of the same transmission chain and were interlinked in the transmission network if they shared genetic subtypes of E. coli. By using microbial genetics to quantify who transmits to whom independently from the behavioural data on who is in contact with whom, we were able to directly investigate how the structure of contact networks influences the structure of the transmission network. To distinguish between the effects of social and environmental contact on transmission dynamics, the transmission network was compared with two separate contact networks defined from the behavioural data: a social network based on association patterns, and a spatial network based on patterns of home-range overlap among individuals. We found that links in the transmission network were more likely to occur between individuals that were strongly linked in the social network. Furthermore, individuals that had more numerous connections or that occupied 'bottleneck' positions in the social network tended to occupy similar positions in the transmission network. No similar correlations were observed between the spatial and transmission networks. This indicates that an individual's social network position is predictive of transmission network position, which has implications for identifying individuals that function as super-spreaders or transmission bottlenecks in the population. These results emphasize the importance of association patterns in

  16. Network Traffic Features for Anomaly Detection in Specific Industrial Control System Network

    Directory of Open Access Journals (Sweden)

    Matti Mantere

    2013-09-01

    Full Text Available The deterministic and restricted nature of industrial control system networks sets them apart from more open networks, such as local area networks in office environments. This improves the usability of network security, monitoring approaches that would be less feasible in more open environments. One of such approaches is machine learning based anomaly detection. Without proper customization for the special requirements of the industrial control system network environment, many existing anomaly or misuse detection systems will perform sub-optimally. A machine learning based approach could reduce the amount of manual customization required for different industrial control system networks. In this paper we analyze a possible set of features to be used in a machine learning based anomaly detection system in the real world industrial control system network environment under investigation. The network under investigation is represented by architectural drawing and results derived from network trace analysis. The network trace is captured from a live running industrial process control network and includes both control data and the data flowing between the control network and the office network. We limit the investigation to the IP traffic in the traces.

  17. An Efficient Congestion Control Protocol for Wired/Wireless Networks

    OpenAIRE

    Hanaa Torkey; Gamal ATTIYA; Ahmed Abdel Nabi

    2014-01-01

    Recently, wide spectrum of heterogeneous wireless access networks integrate with high speed wired networks to deliver Internet services. End-to-end service delivery with satisfactory quality is challenging issue in such network architectures. Although the Internet transport control protocol (TCP) addresses such challenge, it has poor performance with high speed wired networks (i.e. high bandwidth-delay product). Moreover, it behaves badly with wireless access networks (i.e. misinterpretation ...

  18. Intelligent Joint Admission Control for Next Generation Wireless Networks

    OpenAIRE

    Abdulqader M. Mohsen; Al-Akwaa, Fadhl M.; Mohammed M. Alkhawlani

    2012-01-01

    The Heterogeneous Wireless Network (HWN) integrates different wireless networks into one common network. The integrated networks often overlap coverage in the same wireless service areas, leading to the availability of a great variety of innovative services based on user demands in a cost-efficient manner. Joint Admission Control (JAC) handles all new or handoff service requests in the HWN. It checks whether the incoming service request to the selected Radio Access Network (RAN) by the initia...

  19. Pinning control of complex networked systems synchronization, consensus and flocking of networked systems via pinning

    CERN Document Server

    Su, Housheng

    2013-01-01

    Synchronization, consensus and flocking are ubiquitous requirements in networked systems. Pinning Control of Complex Networked Systems investigates these requirements by using the pinning control strategy, which aims to control the whole dynamical network with huge numbers of nodes by imposing controllers for only a fraction of the nodes. As the direct control of every node in a dynamical network with huge numbers of nodes might be impossible or unnecessary, it’s then very important to use the pinning control strategy for the synchronization of complex dynamical networks. The research on pinning control strategy in consensus and flocking of multi-agent systems can not only help us to better understand the mechanisms of natural collective phenomena, but also benefit applications in mobile sensor/robot networks. This book offers a valuable resource for researchers and engineers working in the fields of control theory and control engineering.   Housheng Su is an Associate Professor at the Department of Contro...

  20. Neural Network for Optimization of Existing Control Systems

    DEFF Research Database (Denmark)

    Madsen, Per Printz

    1995-01-01

    The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems.......The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems....

  1. Intelligent Controller Design for DC Motor Speed Control based on Fuzzy Logic-Genetic Algorithms Optimization

    OpenAIRE

    Allaoua, Boumediene; Laoufi, Abdellah; Gasbaoui, Brahim; Abdelfatah NASRI; Abdessalam ABDERRAHMANI

    2008-01-01

    In this paper, an intelligent controller of the DC (Direct current) Motor drive is designed using fuzzy logic-genetic algorithms optimization. First, a controller is designed according to fuzzy rules such that the systems are fundamentally robust. To obtain the globally optimal values, parameters of the fuzzy controller are improved by genetic algorithms optimization model. Computer MATLAB work space demonstrate that the fuzzy controller associated to the genetic algorithms approach became ve...

  2. Network resource control for grid workflow management systems

    NARCIS (Netherlands)

    Strijkers, R.J.; Cristea, M.; Korkhov, V.; Marchal, D.; Belloum, A.; Laat, C.de; Meijer, R.J.

    2010-01-01

    Grid workflow management systems automate the orchestration of scientific applications with large computational and data processing needs, but lack control over network resources. Consequently, the management system cannot prevent multiple communication intensive applications to compete for network

  3. Optimization of the Compensation of a Meshed MV Network by a Modified Genetic Algorithm

    DEFF Research Database (Denmark)

    Nielsen, Hans; Paar, M.; Toman, P.

    2007-01-01

    The article discusses the utilization of a modified genetic algorithm (GA) for the optimization of the shunt compensation in meshed and radial MV distribution networks. The algorithm looks for minimum costs of the network power losses and minimum capital and operating costs of applied capacitors...

  4. Robust reactor power control system design by genetic algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Yoon Joon; Cho, Kyung Ho; Kim, Sin [Cheju National University, Cheju (Korea, Republic of)

    1997-12-31

    The H{sub {infinity}} robust controller for the reactor power control system is designed by use of the mixed weight sensitivity. The system is configured into the typical two-port model with which the weight functions are augmented. Since the solution depends on the weighting functions and the problem is of nonconvex, the genetic algorithm is used to determine the weighting functions. The cost function applied in the genetic algorithm permits the direct control of the power tracking performances. In addition, the actual operating constraints such as rod velocity and acceleration can be treated as design parameters. Compared with the conventional approach, the controller designed by the genetic algorithm results in the better performances with the realistic constraints. Also, it is found that the genetic algorithm could be used as an effective tool in the robust design. 4 refs., 6 figs. (Author)

  5. Mobility Assisted Sensor Node Self-Deployment for Maximizing the Coverage of Wireless Sensor Networks using A Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    VV Juli

    2013-12-01

    Full Text Available Wireless sensor networks extend the capability to monitor and control far-flung environments. However, sensor nodes must be deployed appropriately to reach an adequate coverage level for the successful acquisition of data. Modern sensing devices are able to move from one place to another for different purposes and constitute the mobile sensor network. This mobile sensor capability could be used to enhance the coverage of the sensor network. Since mobile sensor nodes have limited capabilities and power constraints, the algorithms which drive the sensors to optimal locations should extend the coverage. It should also reduce the power needed to move the sensors efficiently. In this paper, a genetic algorithm- (GA based sensor deployment scheme is proposed to maximize network coverage, and the performance was studied with the random deployment using a Matlab simulation.

  6. Control and estimation methods over communication networks

    CERN Document Server

    Mahmoud, Magdi S

    2014-01-01

    This book provides a rigorous framework in which to study problems in the analysis, stability and design of networked control systems. Four dominant sources of difficulty are considered: packet dropouts, communication bandwidth constraints, parametric uncertainty, and time delays. Past methods and results are reviewed from a contemporary perspective, present trends are examined, and future possibilities proposed. Emphasis is placed on robust and reliable design methods. New control strategies for improving the efficiency of sensor data processing and reducing associated time delay are presented. The coverage provided features: ·        an overall assessment of recent and current fault-tolerant control algorithms; ·        treatment of several issues arising at the junction of control and communications; ·        key concepts followed by their proofs and efficient computational methods for their implementation; and ·        simulation examples (including TrueTime simulations) to...

  7. POWER AWARE ROUTING PROTOCOLS FOR MOBILE ADHOC NETWORKS MANETS USING MODIFIED GENETIC ALGORITHM

    OpenAIRE

    Priya Mudgal *, Dushyant Singh **

    2016-01-01

    Mobile adhoc networks MANETs are very popular networks which are having many applications in science and engineering. MANETs are very dynamic networks which does not have any infrastructure for their operation. Routing in MANETs is an area of research for many authors in recent years. Devices in MANETs are battery operated so routing protocols must be power aware which consumes less battery of nodes in transferring data. Genetic algorithm (GA) is a very common optimizing algorithm which can m...

  8. Bayesian network reconstruction using systems genetics data: comparison of MCMC methods.

    Science.gov (United States)

    Tasaki, Shinya; Sauerwine, Ben; Hoff, Bruce; Toyoshiba, Hiroyoshi; Gaiteri, Chris; Chaibub Neto, Elias

    2015-04-01

    Reconstructing biological networks using high-throughput technologies has the potential to produce condition-specific interactomes. But are these reconstructed networks a reliable source of biological interactions? Do some network inference methods offer dramatically improved performance on certain types of networks? To facilitate the use of network inference methods in systems biology, we report a large-scale simulation study comparing the ability of Markov chain Monte Carlo (MCMC) samplers to reverse engineer Bayesian networks. The MCMC samplers we investigated included foundational and state-of-the-art Metropolis-Hastings and Gibbs sampling approaches, as well as novel samplers we have designed. To enable a comprehensive comparison, we simulated gene expression and genetics data from known network structures under a range of biologically plausible scenarios. We examine the overall quality of network inference via different methods, as well as how their performance is affected by network characteristics. Our simulations reveal that network size, edge density, and strength of gene-to-gene signaling are major parameters that differentiate the performance of various samplers. Specifically, more recent samplers including our novel methods outperform traditional samplers for highly interconnected large networks with strong gene-to-gene signaling. Our newly developed samplers show comparable or superior performance to the top existing methods. Moreover, this performance gain is strongest in networks with biologically oriented topology, which indicates that our novel samplers are suitable for inferring biological networks. The performance of MCMC samplers in this simulation framework can guide the choice of methods for network reconstruction using systems genetics data. Copyright © 2015 by the Genetics Society of America.

  9. Controllability of giant connected components in a directed network

    Science.gov (United States)

    Liu, Xueming; Pan, Linqiang; Stanley, H. Eugene; Gao, Jianxi

    2017-04-01

    When controlling a complex networked system it is not feasible to control the full network because many networks, including biological, technological, and social systems, are massive in size and complexity. But neither is it necessary to control the full network. In complex networks, the giant connected components provide the essential information about the entire system. How to control these giant connected components of a network remains an open question. We derive the mathematical expression of the degree distributions for four types of giant connected components and develop an analytic tool for studying the controllability of these giant connected components. We find that for both Erdős-Rényi (ER) networks and scale-free (SF) networks with p fraction of remaining nodes, the minimum driver node density to control the giant component first increases and then decreases as p increases from zero to one, showing a peak at a critical point p =pm . We find that, for ER networks, the peak value of the driver node density remains the same regardless of its average degree and that it is determined by pm . In addition, we find that for SF networks the minimum driver node densities needed to control the giant components of networks decrease as the degree distribution exponents increase. Comparing the controllability of the giant components of ER networks and SF networks, we find that when the fraction of remaining nodes p is low, the giant in-connected, out-connected, and strong-connected components in ER networks have lower controllability than those in SF networks.

  10. Fault Detection for Quantized Networked Control Systems

    Directory of Open Access Journals (Sweden)

    Wei-Wei Che

    2013-01-01

    Full Text Available The fault detection problem in the finite frequency domain for networked control systems with signal quantization is considered. With the logarithmic quantizer consideration, a quantized fault detection observer is designed by employing a performance index which is used to increase the fault sensitivity in finite frequency domain. The quantized measurement signals are dealt with by utilizing the sector bound method, in which the quantization error is treated as sector-bounded uncertainty. By using the Kalman-Yakubovich-Popov (GKYP Lemma, an iterative LMI-based optimization algorithm is developed for designing the quantized fault detection observer. And a numerical example is given to illustrate the effectiveness of the proposed method.

  11. Urgent epidemic control mechanism for aviation networks

    KAUST Repository

    Peng, Chengbin

    2011-01-01

    In the current century, the highly developed transportation system can not only boost the economy, but also greatly accelerate the spreading of epidemics. While some epidemic diseases may infect quite a number of people ahead of our awareness, the health care resources such as vaccines and the medical staff are usually locally or even globally insufficient. In this research, with the network of major aviation routes as an example, we present a method to determine the optimal locations to allocate the medical service in order to minimize the impact of the infectious disease with limited resources. Specifically, we demonstrate that when the medical resources are insufficient, we should concentrate our efforts on the travelers with the objective of effectively controlling the spreading rate of the epidemic diseases. © 2011 Springer-Verlag Berlin Heidelberg.

  12. Hybrid genetic algorithm in the Hopfield network for maximum 2-satisfiability problem

    Science.gov (United States)

    Kasihmuddin, Mohd Shareduwan Mohd; Sathasivam, Saratha; Mansor, Mohd. Asyraf

    2017-08-01

    Heuristic method was designed for finding optimal solution more quickly compared to classical methods which are too complex to comprehend. In this study, a hybrid approach that utilizes Hopfield network and genetic algorithm in doing maximum 2-Satisfiability problem (MAX-2SAT) was proposed. Hopfield neural network was used to minimize logical inconsistency in interpretations of logic clauses or program. Genetic algorithm (GA) has pioneered the implementation of methods that exploit the idea of combination and reproduce a better solution. The simulation incorporated with and without genetic algorithm will be examined by using Microsoft Visual 2013 C++ Express software. The performance of both searching techniques in doing MAX-2SAT was evaluate based on global minima ratio, ratio of satisfied clause and computation time. The result obtained form the computer simulation demonstrates the effectiveness and acceleration features of genetic algorithm in doing MAX-2SAT in Hopfield network.

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

    OpenAIRE

    Jingwen Tian; Meijuan Gao; Yonggang He

    2013-01-01

    Since the control system of the welding gun pose in whole‐position welding is complicated and nonlinear, an intelligent control system of welding gun pose for a pipeline welding robot based on an improved radial basis function neural network (IRBFNN) and expert system (ES) is presented in this paper. The structure of the IRBFNN is constructed and the improved genetic algorithm is adopted to optimize the network structure. This control system makes full use of the characteristics of the IRBFNN...

  14. Influence Maximization in Social Networks with Genetic Algorithms

    NARCIS (Netherlands)

    Bucur, Doina; Iacca, Giovanni; Squillero, Giovanni; Burelli, Paolo

    We live in a world of social networks. Our everyday choices are often influenced by social interactions. Word of mouth, meme diffusion on the Internet, and viral marketing are all examples of how social networks can affect our behaviour. In many practical applications, it is of great interest to

  15. Genetic control of active neural circuits

    Directory of Open Access Journals (Sweden)

    Leon Reijmers

    2009-12-01

    Full Text Available The use of molecular tools to study the neurobiology of complex behaviors has been hampered by an inability to target the desired changes to relevant groups of neurons. Specific memories and specific sensory representations are sparsely encoded by a small fraction of neurons embedded in a sea of morphologically and functionally similar cells. In this review we discuss genetics techniques that are being developed to address this difficulty. In several studies the use of promoter elements that are responsive to neural activity have been used to drive long lasting genetic alterations into neural ensembles that are activated by natural environmental stimuli. This approach has been used to examine neural activity patterns during learning and retrieval of a memory, to examine the regulation of receptor trafficking following learning and to functionally manipulate a specific memory trace. We suggest that these techniques will provide a general approach to experimentally investigate the link between patterns of environmentally activated neural firing and cognitive processes such as perception and memory.

  16. Prediction of Compressive Strength of Concrete Using Artificial Neural Network and Genetic Programming

    Directory of Open Access Journals (Sweden)

    Palika Chopra

    2016-01-01

    Full Text Available An effort has been made to develop concrete compressive strength prediction models with the help of two emerging data mining techniques, namely, Artificial Neural Networks (ANNs and Genetic Programming (GP. The data for analysis and model development was collected at 28-, 56-, and 91-day curing periods through experiments conducted in the laboratory under standard controlled conditions. The developed models have also been tested on in situ concrete data taken from literature. A comparison of the prediction results obtained using both the models is presented and it can be inferred that the ANN model with the training function Levenberg-Marquardt (LM for the prediction of concrete compressive strength is the best prediction tool.

  17. Tuning of active vibration controllers for ACTEX by genetic algorithm

    Science.gov (United States)

    Kwak, Moon K.; Denoyer, Keith K.

    1999-06-01

    This paper is concerned with the optimal tuning of digitally programmable analog controllers on the ACTEX-1 smart structures flight experiment. The programmable controllers for each channel include a third order Strain Rate Feedback (SRF) controller, a fifth order SRF controller, a second order Positive Position Feedback (PPF) controller, and a fourth order PPF controller. Optimal manual tuning of several control parameters can be a difficult task even though the closed-loop control characteristics of each controller are well known. Hence, the automatic tuning of individual control parameters using Genetic Algorithms is proposed in this paper. The optimal control parameters of each control law are obtained by imposing a constraint on the closed-loop frequency response functions using the ACTEX mathematical model. The tuned control parameters are then uploaded to the ACTEX electronic control electronics and experiments on the active vibration control are carried out in space. The experimental results on ACTEX will be presented.

  18. Application framework for programmable network control

    NARCIS (Netherlands)

    Strijkers, R.; Cristea, M.; de Laat, C.; Meijer, R.; Clemm, A.; Wolter, R.

    2011-01-01

    We present a framework that enables application developers to create complex and application specific network services. The essence of our approach is to utilize programmable network elements to create a software representation of network elements in the application. We show that the typical pattern

  19. Estimation of hydrogen production in genetically modified E. coli fermentations using an artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Rosales-Colunga, Luis Manuel; De Leon Rodriguez, Antonio [Division de Biologia Molecular, Instituto Potosino de Investigacion Cientifica y Tecnologica, Camino a la Presa San Jose 2055, Col. Lomas 4a secc, San Luis Potosi, SLP 78216 (Mexico); Garcia, Raul Gonzalez [Centro de Investigacion y Estudios de Posgrado, Facultad de Ciencias Quimicas, Universidad Autonoma de San Luis Potosi, Av. Dr. Manuel Nava 6, San Luis Potosi, SLP 78210 (Mexico)

    2010-12-15

    Biological hydrogen production is an active research area due to the importance of this gas as an energy carrier and the advantages of using biological systems to produce it. A cheap and practical on-line hydrogen determination is desired in those processes. In this study, an artificial neural network (ANN) was developed to estimate the hydrogen production in fermentative processes. A back propagation neural network (BPNN) of one hidden layer with 12 nodes was selected. The BPNN training was done using the conjugated gradient algorithm and on-line measurements of dissolved CO{sub 2}, pH and oxidation-reduction potential during the fermentations of cheese whey by Escherichia coli {delta}hycA {delta}lacI (WDHL) strain with or without pH control. The correlation coefficient between the hydrogen production determined by gas chromatography and the hydrogen production estimated by the BPNN was 0.955. Results showed that the BPNN successfully estimated the hydrogen production using only on-line parameters in genetically modified E. coli fermentations either with or without pH control. This approach could be used for other hydrogen production systems. (author)

  20. MQCC: Maximum Queue Congestion Control for Multipath Networks with Blockage

    Science.gov (United States)

    2015-10-19

    Design, Implementation and Evaluation of Congestion Control for Multipath TCP ,” in Proc. of USENIX Conference on Networked Systems Design and...MQCC: Maximum Queue Congestion Control for Multipath Networks with Blockage Scott Pudlewski, Brooke Shrader, Laura Herrera, Nathaniel M. Jones...queue-based (MQCC) congestion control algorithm. MQCC uses average buffer occupancy as a measure of the congestion in a network (as opposed to packet

  1. Recurrent neural network for non-smooth convex optimization problems with application to the identification of genetic regulatory networks.

    Science.gov (United States)

    Cheng, Long; Hou, Zeng-Guang; Lin, Yingzi; Tan, Min; Zhang, Wenjun Chris; Wu, Fang-Xiang

    2011-05-01

    A recurrent neural network is proposed for solving the non-smooth convex optimization problem with the convex inequality and linear equality constraints. Since the objective function and inequality constraints may not be smooth, the Clarke's generalized gradients of the objective function and inequality constraints are employed to describe the dynamics of the proposed neural network. It is proved that the equilibrium point set of the proposed neural network is equivalent to the optimal solution of the original optimization problem by using the Lagrangian saddle-point theorem. Under weak conditions, the proposed neural network is proved to be stable, and the state of the neural network is convergent to one of its equilibrium points. Compared with the existing neural network models for non-smooth optimization problems, the proposed neural network can deal with a larger class of constraints and is not based on the penalty method. Finally, the proposed neural network is used to solve the identification problem of genetic regulatory networks, which can be transformed into a non-smooth convex optimization problem. The simulation results show the satisfactory identification accuracy, which demonstrates the effectiveness and efficiency of the proposed approach.

  2. Genetic Control of Meat Quality Traits

    Science.gov (United States)

    Williams, John L.

    Meat was originally produced from non-specialized animals that were used for a variety of purposes, in addition to being a source of food. However, selective breeding has resulted in “improved” breeds of cattle that are now used to produce either milk or beef, and specialized chicken lines that produce eggs or meat. These improved breeds are very productive under appropriate management systems. The selection methods used to create these specialized breeds were based on easily measured phenotypic variations, such as growth rate or physical size. Improvement in the desired trait was achieved by breeding directly from animals displaying the desired phenotype. However, more recently sophisticated genetic models have been developed using statistical approaches that consider phenotypic information collected, not only from individual animals but also from their parents, sibs, and progeny.

  3. Geochemical, Genetic, and Community Controls on Mercury

    Energy Technology Data Exchange (ETDEWEB)

    Wall, Judy D.

    2014-11-10

    The sulfate-reducing bacteria (SRB) are soil bacteria that share two common characteristics, strict anaerobiosis and the ability to respire sulfate. The metabolic activities of these bacteria play significant roles in the global sulfur cycle, anaerobic degradation of biomass, biological metal corrosion in the environment and, recently, degradation of toxic compounds. The accumulation of evidence suggests these bacteria are also key to the production of the neurotoxin methylmercury in environmental settings. We propose to use our experience with the development of genetics in sulfate-reducing bacteria of the genus Desulfovibrio to create mutations that will eliminate the methylation of mercury, thereby identifying the genes essential for this process. This information may allow the environmental monitoring of the mercury methylation potential to learn the location and quantity of the production this toxin. From these data, more accurate predictive models of mercury cycling can be generated.

  4. Near-Minimal Node Control of Networked Evolutionary Games

    NARCIS (Netherlands)

    Riehl, James Robert; Cao, Ming

    2014-01-01

    We investigate a problem related to the controllability of networked evolutionary games, first presenting an algorithm that computes a near-minimal set of nodes to drive all nodes in a tree network to a desired strategy, and then briefly discussing an algorithm that works for arbitrary networks

  5. CFO finance network centrality, errors and internal control material weaknessess

    NARCIS (Netherlands)

    Schabus, M.

    2015-01-01

    CFOs finance networks matter in determining certain accounting and reporting outcomes. Drawing on social network theory, this study shows that CFO centrality in a network of financial experts is inversely related to the occurrence of restatements due to errors and disclosure of internal control

  6. An improved method for network congestion control

    Science.gov (United States)

    Qiao, Xiaolin

    2013-03-01

    The rapid progress of the wireless network technology has great convenience on the people's life and work. However, because of its openness, the mobility of the terminal and the changing topology, the wireless network is more susceptible to security attacks. Authentication and key agreement is the base of the network security. The authentication and key agreement mechanism can prevent the unauthorized user from accessing the network, resist malicious network to deceive the lawful user, encrypt the session data by using the exchange key and provide the identification of the data origination. Based on characteristics of the wireless network, this paper proposed a key agreement protocol for wireless network. The authentication of protocol is based on Elliptic Curve Cryptosystems and Diffie-Hellman.

  7. Genetic architecture of wood properties based on association analysis and co-expression networks in white spruce.

    Science.gov (United States)

    Lamara, Mebarek; Raherison, Elie; Lenz, Patrick; Beaulieu, Jean; Bousquet, Jean; MacKay, John

    2016-04-01

    Association studies are widely utilized to analyze complex traits but their ability to disclose genetic architectures is often limited by statistical constraints, and functional insights are usually minimal in nonmodel organisms like forest trees. We developed an approach to integrate association mapping results with co-expression networks. We tested single nucleotide polymorphisms (SNPs) in 2652 candidate genes for statistical associations with wood density, stiffness, microfibril angle and ring width in a population of 1694 white spruce trees (Picea glauca). Associations mapping identified 229-292 genes per wood trait using a statistical significance level of P wood associated genes and several known MYB and NAC regulators were identified as network hubs. The network revealed a link between the gene PgNAC8, wood stiffness and microfibril angle, as well as considerable within-season variation for both genetic control of wood traits and gene expression. Trait associations were distributed throughout the network suggesting complex interactions and pleiotropic effects. Our findings indicate that integration of association mapping and co-expression networks enhances our understanding of complex wood traits. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

  8. Complex systems and networks dynamics, controls and applications

    CERN Document Server

    Yu, Xinghuo; Chen, Guanrong; Yu, Wenwu

    2016-01-01

    This elementary book provides some state-of-the-art research results on broad disciplinary sciences on complex networks. It presents an in-depth study with detailed description of dynamics, controls and applications of complex networks. The contents of this book can be summarized as follows. First, the dynamics of complex networks, for example, the cluster dynamic analysis by using kernel spectral methods, community detection algorithms in bipartite networks, epidemiological modeling with demographics and epidemic spreading on multi-layer networks, are studied. Second, the controls of complex networks are investigated including topics like distributed finite-time cooperative control of multi-agent systems by applying homogenous-degree and Lyapunov methods, composite finite-time containment control for disturbed second-order multi-agent systems, fractional-order observer design of multi-agent systems, chaos control and anticontrol of complex systems via Parrondos game and many more. Third, the applications of ...

  9. Implementing controlled-unitary operations over the butterfly network

    Science.gov (United States)

    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.

  10. Implementing controlled-unitary operations over the butterfly network

    Energy Technology Data Exchange (ETDEWEB)

    Soeda, Akihito; Kinjo, Yoshiyuki; Turner, Peter S. [Department of Physics, Graduate School of Science, The University of Tokyo, Tokyo (Japan); Murao, Mio [Department of Physics, Graduate School of Science, The University of Tokyo, Tokyo, Japan and NanoQuine, The University of Tokyo, Tokyo (Japan)

    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.

  11. Adaptive bridge control strategy for opinion evolution on social networks.

    Science.gov (United States)

    Qian, Cheng; Cao, Jinde; Lu, Jianquan; Kurths, Jürgen

    2011-06-01

    In this paper, we present an efficient opinion control strategy for complex networks, in particular, for social networks. The proposed adaptive bridge control (ABC) strategy calls for controlling a special kind of nodes named bridge and requires no knowledge of the node degrees or any other global or local knowledge, which are necessary for some other immunization strategies including targeted immunization and acquaintance immunization. We study the efficiency of the proposed ABC strategy on random networks, small-world networks, scale-free networks, and the random networks adjusted by the edge exchanging method. Our results show that the proposed ABC strategy is efficient for all of these four kinds of networks. Through an adjusting clustering coefficient by the edge exchanging method, it is found out that the efficiency of our ABC strategy is closely related with the clustering coefficient. The main contributions of this paper can be listed as follows: (1) A new high-order social network is proposed to describe opinion dynamic. (2) An algorithm, which does not require the knowledge of the nodes' degree and other global∕local network structure information, is proposed to control the "bridges" more accurately and further control the opinion dynamics of the social networks. The efficiency of our ABC strategy is illustrated by numerical examples. (3) The numerical results indicate that our ABC strategy is more efficient for networks with higher clustering coefficient.

  12. Immunity factor contributes to altered brain functional networks in individuals at risk for Alzheimer's disease: Neuroimaging-genetic evidence.

    Science.gov (United States)

    Bai, Feng; Shi, Yongmei; Yuan, Yonggui; Xie, Chunming; Zhang, Zhijun

    2016-08-01

    Clusterin (CLU) is recognized as a secreted protein that is related to the processes of inflammation and immunity in the pathogenesis of Alzheimer's disease (AD). The effects of the risk variant of the C allele at the rs11136000 locus of the CLU gene are associated with variations in the brain structure and function. However, the relationship of the CLU-C allele to architectural disruptions in resting-state networks in amnestic mild cognitive impairment (aMCI) subjects (i.e., individuals with elevated risk of AD) remains relatively unknown. Using resting-state functional magnetic resonance imaging and an imaging genetic approach, this study investigated whether individual brain functional networks, i.e., the default mode network (DMN) and the task-positive network, were modulated by the CLU-C allele (rs11136000) in 50 elderly participants, including 26 aMCI subjects and 24 healthy controls. CLU-by-aMCI interactions were associated with the information-bridging regions between resting-state networks rather than with the DMN itself, especially in cortical midline regions. Interestingly, the complex communications between resting-state networks were enhanced in aMCI subjects with the CLU rs11136000 CC genotype and were modulated by the degree of memory impairment, suggesting a reconstructed balance of the resting-state networks in these individuals with an elevated risk of AD. The neuroimaging-genetic evidence indicates that immunity factors may contribute to alterations in brain functional networks in aMCI. These findings add to the evidence that the CLU gene may represent a potential therapeutic target for slowing disease progression in AD. Copyright © 2016 Elsevier Inc. All rights reserved.

  13. Optimization procedures for establishing reserve networks for biodiversity conservation taking into account population genetic structure

    Directory of Open Access Journals (Sweden)

    José Alexandre Felizola Diniz Filho

    2006-01-01

    Full Text Available Conservation genetics has been focused on the ecological and evolutionary persistence of targets (species or other intraspecific units, especially when dealing with narrow-ranged species, and no generalized solution regarding the problem of where to concentrate conservation efforts for multiple genetic targets has yet been achieved. Broadly distributed and abundant species allow the identification of evolutionary significant units, management units, phylogeographical units or other spatial patterns in genetic variability, including those generated by effects of habitat fragmentation caused by human activities. However, these genetic units are rarely considered as priority conservation targets in regional conservation planning procedures. In this paper, we discuss a theoretical framework in which target persistence and genetic representation of targets defined using multiple genetic criteria can be explicitly incorporated into the broad-scale reserve network models used to optimize biodiversity conservation based on multiple species data. When genetic variation can be considered discrete in geographical space, the solution is straightforward, and each spatial unit must be considered as a distinct target. But methods for dealing with continuous genetic variation in space are not trivial and optimization procedures must still be developed. We present a simple heuristic and sequential algorithm to deal with this problem by combining multiple networks of local populations of multiple species in which minimum separation distance between conserved populations is a function of spatial autocorrelation patterns of genetic variability within each species.

  14. A genetic algorithm solution for the operation of green LTE networks with energy and environment considerations

    KAUST Repository

    Ghazzai, Hakim

    2012-01-01

    The Base Station (BS) sleeping strategy has become a well-known technique to achieve energy savings in cellular networks by switching off redundant BSs mainly for lightly loaded networks. Besides, the exploitation of renewable energies, as additional power sources in smart grids, becomes a real challenge to network operators to reduce power costs. In this paper, we propose a method based on genetic algorithms that decreases the energy consumption of a Long-Term Evolution (LTE) cellular network by not only shutting down underutilized BSs but also by optimizing the amounts of energy procured from the smart grid without affecting the desired Quality of Service. © 2012 Springer-Verlag.

  15. OPTIMAL-TUNING OF PID CONTROLLER GAINS USING GENETIC ALGORITHMS

    Directory of Open Access Journals (Sweden)

    Ömer GÜNDOĞDU

    2005-01-01

    Full Text Available This paper presents a method of optimum parameter tuning of a PID controller to be used in driving an inertial load by a dc motor thorough a gearbox. Specifically, the method uses genetic algorithms to determine the optimum controller parameters by minimizing the sum of the integral of the squared error and the squared controller output deviated from its steady state value. The paper suggests the use of Ziegler-Nichols settings to form the intervals for the controller parameters in which the population to be formed. The results obtained from the genetic algorithms are compared with the ones from Ziegler-Nichols in both figures and tabular form. Comparatively better results are obtained in the genetic algorithm case.

  16. Genetic Control of Contagious Asexuality in the Pea Aphid

    Science.gov (United States)

    Jaquiéry, Julie; Stoeckel, Solenn; Larose, Chloé; Nouhaud, Pierre; Rispe, Claude; Mieuzet, Lucie; Bonhomme, Joël; Mahéo, Frédérique; Legeai, Fabrice; Gauthier, Jean-Pierre; Prunier-Leterme, Nathalie; Tagu, Denis; Simon, Jean-Christophe

    2014-01-01

    Although evolutionary transitions from sexual to asexual reproduction are frequent in eukaryotes, the genetic bases of such shifts toward asexuality remain largely unknown. We addressed this issue in an aphid species where both sexual and obligate asexual lineages coexist in natural populations. These sexual and asexual lineages may occasionally interbreed because some asexual lineages maintain a residual production of males potentially able to mate with the females produced by sexual lineages. Hence, this species is an ideal model to study the genetic basis of the loss of sexual reproduction with quantitative genetic and population genomic approaches. Our analysis of the co-segregation of ∼300 molecular markers and reproductive phenotype in experimental crosses pinpointed an X-linked region controlling obligate asexuality, this state of character being recessive. A population genetic analysis (>400-marker genome scan) on wild sexual and asexual genotypes from geographically distant populations under divergent selection for reproductive strategies detected a strong signature of divergent selection in the genomic region identified by the experimental crosses. These population genetic data confirm the implication of the candidate region in the control of reproductive mode in wild populations originating from 700 km apart. Patterns of genetic differentiation along chromosomes suggest bidirectional gene flow between populations with distinct reproductive modes, supporting contagious asexuality as a prevailing route to permanent parthenogenesis in pea aphids. This genetic system provides new insights into the mechanisms of coexistence of sexual and asexual aphid lineages. PMID:25473828

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

    Science.gov (United States)

    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.

  18. Adaptive Dynamics, Control, and Extinction in Networked Populations

    Science.gov (United States)

    2015-07-09

    Adaptive Dynamics, Control, and Extinction in Networked Populations Ira B. Schwartz US Naval Research Laboratory Code 6792 Nonlinear System Dynamics...theory of large deviations to stochastic network extinction to predict extinction times. In particular, we use the theory to find the most probable...paths leading to extinction . We then apply the methodology to network models and discover how mean extinction times scale with network parameters in Erdos

  19. Does Childhood Anxiety Evoke Maternal Control? A Genetically Informed Study

    Science.gov (United States)

    Eley, Thalia C.; Napolitano, Maria; Lau, Jennifer Y. F.; Gregory, Alice M.

    2010-01-01

    Background: Despite theoretical and empirical support for an association between maternal control and child anxiety, few studies have examined the origins of this association. Furthermore, none use observer-ratings of maternal control within a genetically informative design. This study addressed three questions: 1) do children who experience…

  20. Genetic control of postnatal human brain growth.

    Science.gov (United States)

    van Dyck, Laura I; Morrow, Eric M

    2017-02-01

    Studies investigating postnatal brain growth disorders inform the biology underlying the development of human brain circuitry. This research is becoming increasingly important for the diagnosis and treatment of childhood neurodevelopmental disorders, including autism and related disorders. Here, we review recent research on typical and abnormal postnatal brain growth and examine potential biological mechanisms. Clinically, brain growth disorders are heralded by diverging head size for a given age and sex, but are more precisely characterized by brain imaging, post-mortem analysis, and animal model studies. Recent neuroimaging and molecular biological studies on postnatal brain growth disorders have broadened our view of both typical and pathological postnatal neurodevelopment. Correlating gene and protein function with brain growth trajectories uncovers postnatal biological mechanisms, including neuronal arborization, synaptogenesis and pruning, and gliogenesis and myelination. Recent investigations of childhood neurodevelopmental and neurodegenerative disorders highlight the underlying genetic programming and experience-dependent remodeling of neural circuitry. To understand typical and abnormal postnatal brain development, clinicians and researchers should characterize brain growth trajectories in the context of neurogenetic syndromes. Understanding mechanisms and trajectories of postnatal brain growth will aid in differentiating, diagnosing, and potentially treating neurodevelopmental disorders.

  1. Genetic architecture of sex determination in fish: Applications to sex ratio control in aquaculture

    Directory of Open Access Journals (Sweden)

    Paulino eMartínez

    2014-09-01

    Full Text Available Controlling the sex ratio is essential in finfish farming. A balanced sex ratio is usually good for broodstock management, since it enables to develop appropriate breeding schemes. However, in some species the production of monosex populations is desirable because the existence of sexual dimorphism, primarily in growth or first time of sexual maturation, but also in color or shape, can render one sex more valuable. The knowledge of the genetic architecture of sex determination (SD is convenient for controlling sex ratio and for the implementation of breeding programs. Unlike mammals and birds, which show highly conserved master genes that control a conserved genetic network responsible for gonad differentiation (GD, a huge diversity of SD mechanisms has been reported in fish. Despite theory predictions, more than one gene is in many cases involved in fish SD and genetic differences have been observed in the GD network. Environmental factors also play a relevant role and epigenetic mechanisms are becoming increasingly recognized for the establishment and maintenance of the GD pathways. Although major genetic factors are frequently involved in fish SD, these observations strongly suggest that SD in this group resembles a complex trait. Accordingly, the application of quantitative genetics combined with genomic tools is desirable to address its study and in fact, when applied, it has frequently demonstrated a multigene trait interacting with environmental factors in model and cultured fish species. This scenario has notable implications for aquaculture and, depending upon the species, from chromosome manipulation or environmental control techniques up to classical selection or marker assisted selection programs, are being applied. In this review, we selected four relevant species or fish groups to illustrate this diversity and hence the technologies that can be used by the industry for the control of sex ratio: turbot and European sea bass, two

  2. Genetic control of flowering time in rice: integration of Mendelian genetics and genomics.

    Science.gov (United States)

    Hori, Kiyosumi; Matsubara, Kazuki; Yano, Masahiro

    2016-12-01

    Integration of previous Mendelian genetic analyses and recent molecular genomics approaches, such as linkage mapping and QTL cloning, dramatically strengthened our current understanding of genetic control of rice flowering time. Flowering time is one of the most important agronomic traits for seed production in rice (Oryza sativa L.). It is controlled mainly by genes associated with photoperiod sensitivity, particularly in short-day plants such as rice. Since the early twentieth century, rice breeders and researchers have been interested in elucidating the genetic basis of flowering time because its modification is important for regional adaptation and yield optimization. Although flowering time is a complex trait controlled by many quantitative trait loci (QTLs), classical genetic studies have shown that many associated genes are inherited in accordance with Mendelian laws. Decoding the rice genome sequence opened a new era in understanding the genetic control of flowering time on the basis of genome-wide mapping and gene cloning. Heading date 1 (Hd1) was the first flowering time QTL to be isolated using natural variation in rice. Recent accumulation of information on rice genome has facilitated the cloning of other QTLs, including those with minor effects on flowering time. This information has allowed us to rediscover some of the flowering genes that were identified by classical Mendelian genetics. The genes characterized so far, including Hd1, have been assigned to specific photoperiod pathways. In this review, we provide an overview of the studies that led to an in-depth understanding of the genetic control of flowering time in rice, and of the current state of improving and fine-tuning this trait for rice breeding.

  3. Genetic evaluation of Alpine goats using different milk control intervals.

    Science.gov (United States)

    Silva, F G; Torres, R A; Brito, L F; Silva, L P; Menezes, G R O; Brito, L C; Euclydes, R F; Rodrigues, M T

    2013-07-24

    The objective of this study was to compare the results of genetic evaluations by using different milk control intervals to reduce the cost of milk yield controls without harming the quality of genetic evaluation of the animals. We analyzed test day milk yield data from the Goat Sector of Universidade Federal de Viçosa. After editing and checking for errors in the database, there were 20,710 records of test day milk yield for the 667 first lactations of Alpine goats, constituting the complete file, with 7-day control intervals. Information on specific weeks was excluded from the complete file to create files with data on control intervals of 15, 21, and 28 days. The RENPED program was used to recode the pedigree and data files and correct pedigree errors; the WOMBAT program was used for genetic evaluations of the 4 files. The following comparison criteria of analysis results were used: logarithm of the function of the restricted maximum likelihood, length of the analyses in seconds, Pearson and Spearman correlations, and common elimination percentage among the areas below the regression curve of the genetic values of the animals. Overall, it is recommended that a 7-day interval among milk controls should be used in breeding programs and farms with a high technical level. Intervals of 14 and 21 days can achieve satisfactory results combined with a lower data collection cost for farms with an average-to-low technical level, less effective size, and genetic variability that depend on external genetic material for genetic improvement.

  4. Nonlinear Adaptive PID Control for Greenhouse Environment Based on RBF Network

    Science.gov (United States)

    Zeng, Songwei; Hu, Haigen; Xu, Lihong; Li, Guanghui

    2012-01-01

    This paper presents a hybrid control strategy, combining Radial Basis Function (RBF) network with conventional proportional, integral, and derivative (PID) controllers, for the greenhouse climate control. A model of nonlinear conservation laws of enthalpy and matter between numerous system variables affecting the greenhouse climate is formulated. RBF network is used to tune and identify all PID gain parameters online and adaptively. The presented Neuro-PID control scheme is validated through simulations of set-point tracking and disturbance rejection. We compare the proposed adaptive online tuning method with the offline tuning scheme that employs Genetic Algorithm (GA) to search the optimal gain parameters. The results show that the proposed strategy has good adaptability, strong robustness and real-time performance while achieving satisfactory control performance for the complex and nonlinear greenhouse climate control system, and it may provide a valuable reference to formulate environmental control strategies for actual application in greenhouse production. PMID:22778587

  5. Nonlinear adaptive PID control for greenhouse environment based on RBF network.

    Science.gov (United States)

    Zeng, Songwei; Hu, Haigen; Xu, Lihong; Li, Guanghui

    2012-01-01

    This paper presents a hybrid control strategy, combining Radial Basis Function (RBF) network with conventional proportional, integral, and derivative (PID) controllers, for the greenhouse climate control. A model of nonlinear conservation laws of enthalpy and matter between numerous system variables affecting the greenhouse climate is formulated. RBF network is used to tune and identify all PID gain parameters online and adaptively. The presented Neuro-PID control scheme is validated through simulations of set-point tracking and disturbance rejection. We compare the proposed adaptive online tuning method with the offline tuning scheme that employs Genetic Algorithm (GA) to search the optimal gain parameters. The results show that the proposed strategy has good adaptability, strong robustness and real-time performance while achieving satisfactory control performance for the complex and nonlinear greenhouse climate control system, and it may provide a valuable reference to formulate environmental control strategies for actual application in greenhouse production.

  6. Intelligent Control of Urban Road Networks: Algorithms, Systems and Communications

    Science.gov (United States)

    Smith, Mike

    This paper considers control in road networks. Using a simple example based on the well-known Braess network [1] the paper shows that reducing delay for traffic, assuming that the traffic distribution is fixed, may increase delay when travellers change their travel choices in light of changes in control settings and hence delays. It is shown that a similar effect occurs within signal controlled networks. In this case the effect appears at first sight to be much stronger: the overall capacity of a network may be substantially reduced by utilising standard responsive signal control algorithms. In seeking to reduce delays for existing flows, these policies do not allow properly for consequent routeing changes by travellers. Control methods for signal-controlled networks that do take proper account of the reactions of users are suggested; these require further research, development, and careful real-life trials.

  7. Controllability analysis of transcriptional regulatory networks reveals circular control patterns among transcription factors

    DEFF Research Database (Denmark)

    Österlund, Tobias; Bordel, Sergio; Nielsen, Jens

    2015-01-01

    we analyze the topology and organization of nine transcriptional regulatory networks for E. coli, yeast, mouse and human, and we evaluate how the structure of these networks influences two of their key properties, namely controllability and stability. We calculate the controllability for each network......% for the human network. The high controllability (low number of drivers needed to control the system) in yeast, mouse and human is due to the presence of internal loops in their regulatory networks where the TFs regulate each other in a circular fashion. We refer to these internal loops as circular control...... motifs (CCM). The E. coli transcriptional regulatory network, which does not have any CCMs, shows a hierarchical structure of the transcriptional regulatory network in contrast to the eukaryal networks. The presence of CCMs also has influence on the stability of these networks, as the presence of cycles...

  8. PGTandMe: social networking-based genetic testing and the evolving research model.

    Science.gov (United States)

    Koch, Valerie Gutmann

    2012-01-01

    The opportunity to use extensive genetic data, personal information, and family medical history for research purposes may be naturally appealing to the personal genetic testing (PGT) industry, which is already coupling direct-to-consumer (DTC) products with social networking technologies, as well as to potential industry or institutional partners. This article evaluates the transformation in research that the hybrid of PGT and social networking will bring about, and--highlighting the challenges associated with a new paradigm of "patient-driven" genomic research--focuses on the consequences of shifting the structure, locus, timing, and scope of research through genetic crowd-sourcing. This article also explores potential ethical, legal, and regulatory issues that arise from the hybrid between personal genomic research and online social networking, particularly regarding informed consent, institutional review board (IRB) oversight, and ownership/intellectual property (IP) considerations.

  9. A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics

    Science.gov (United States)

    Kobayashi, Takahisa; Simon, Donald L.

    2001-01-01

    In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.

  10. Genetic networks inducing invasive growth in Saccharomyces cerevisiae identified through systematic genome-wide overexpression.

    Science.gov (United States)

    Shively, Christian A; Eckwahl, Matthew J; Dobry, Craig J; Mellacheruvu, Dattatreya; Nesvizhskii, Alexey; Kumar, Anuj

    2013-04-01

    The budding yeast Saccharomyces cerevisiae can respond to nutritional and environmental stress by implementing a morphogenetic program wherein cells elongate and interconnect, forming pseudohyphal filaments. This growth transition has been studied extensively as a model signaling system with similarity to processes of hyphal development that are linked with virulence in related fungal pathogens. Classic studies have identified core pseudohyphal growth signaling modules in yeast; however, the scope of regulatory networks that control yeast filamentation is broad and incompletely defined. Here, we address the genetic basis of yeast pseudohyphal growth by implementing a systematic analysis of 4909 genes for overexpression phenotypes in a filamentous strain of S. cerevisiae. Our results identify 551 genes conferring exaggerated invasive growth upon overexpression under normal vegetative growth conditions. This cohort includes 79 genes lacking previous phenotypic characterization. Pathway enrichment analysis of the gene set identifies networks mediating mitogen-activated protein kinase (MAPK) signaling and cell cycle progression. In particular, overexpression screening suggests that nuclear export of the osmoresponsive MAPK Hog1p may enhance pseudohyphal growth. The function of nuclear Hog1p is unclear from previous studies, but our analysis using a nuclear-depleted form of Hog1p is consistent with a role for nuclear Hog1p in repressing pseudohyphal growth. Through epistasis and deletion studies, we also identified genetic relationships with the G2 cyclin Clb2p and phenotypes in filamentation induced by S-phase arrest. In sum, this work presents a unique and informative resource toward understanding the breadth of genes and pathways that collectively constitute the molecular basis of filamentation.

  11. Adaptive Process Control with Fuzzy Logic and Genetic Algorithms

    Science.gov (United States)

    Karr, C. L.

    1993-01-01

    Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision-making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.

  12. Adaptive process control using fuzzy logic and genetic algorithms

    Science.gov (United States)

    Karr, C. L.

    1993-01-01

    Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.

  13. Active Engine Mounting Control Algorithm Using Neural Network

    Directory of Open Access Journals (Sweden)

    Fadly Jashi Darsivan

    2009-01-01

    Full Text Available This paper proposes the application of neural network as a controller to isolate engine vibration in an active engine mounting system. It has been shown that the NARMA-L2 neurocontroller has the ability to reject disturbances from a plant. The disturbance is assumed to be both impulse and sinusoidal disturbances that are induced by the engine. The performance of the neural network controller is compared with conventional PD and PID controllers tuned using Ziegler-Nichols. From the result simulated the neural network controller has shown better ability to isolate the engine vibration than the conventional controllers.

  14. Self-organization of nodes in mobile ad hoc networks using evolutionary games and genetic algorithms

    Directory of Open Access Journals (Sweden)

    Janusz Kusyk

    2011-07-01

    Full Text Available In this paper, we present a distributed and scalable evolutionary game played by autonomous mobile ad hoc network (MANET nodes to place themselves uniformly over a dynamically changing environment without a centralized controller. A node spreading evolutionary game, called NSEG, runs at each mobile node, autonomously makes movement decisions based on localized data while the movement probabilities of possible next locations are assigned by a forced-based genetic algorithm (FGA. Because FGA takes only into account the current position of the neighboring nodes, our NSEG, combining FGA with game theory, can find better locations. In NSEG, autonomous node movement decisions are based on the outcome of the locally run FGA and the spatial game set up among it and the nodes in its neighborhood. NSEG is a good candidate for the node spreading class of applications used in both military tasks and commercial applications. We present a formal analysis of our NSEG to prove that an evolutionary stable state is its convergence point. Simulation experiments demonstrate that NSEG performs well with respect to network area coverage, uniform distribution of mobile nodes, and convergence speed.

  15. Angular Rate Sensing with GyroWheel Using Genetic Algorithm Optimized Neural Networks.

    Science.gov (United States)

    Zhao, Yuyu; Zhao, Hui; Huo, Xin; Yao, Yu

    2017-07-22

    GyroWheel is an integrated device that can provide three-axis control torques and two-axis angular rate sensing for small spacecrafts. Large tilt angle of its rotor and de-tuned spin rate lead to a complex and non-linear dynamics as well as difficulties in measuring angular rates. In this paper, the problem of angular rate sensing with the GyroWheel is investigated. Firstly, a simplified rate sensing equation is introduced, and the error characteristics of the method are analyzed. According to the analysis results, a rate sensing principle based on torque balance theory is developed, and a practical way to estimate the angular rates within the whole operating range of GyroWheel is provided by using explicit genetic algorithm optimized neural networks. The angular rates can be determined by the measurable values of the GyroWheel (including tilt angles, spin rate and torque coil currents), the weights and the biases of the neural networks. Finally, the simulation results are presented to illustrate the effectiveness of the proposed angular rate sensing method with GyroWheel.

  16. Multi-species genetic connectivity in a terrestrial habitat network.

    Science.gov (United States)

    Marrotte, Robby R; Bowman, Jeff; Brown, Michael G C; Cordes, Chad; Morris, Kimberley Y; Prentice, Melanie B; Wilson, Paul J

    2017-01-01

    Habitat fragmentation reduces genetic connectivity for multiple species, yet conservation efforts tend to rely heavily on single-species connectivity estimates to inform land-use planning. Such conservation activities may benefit from multi-species connectivity estimates, which provide a simple and practical means to mitigate the effects of habitat fragmentation for a larger number of species. To test the validity of a multi-species connectivity model, we used neutral microsatellite genetic datasets of Canada lynx (Lynx canadensis), American marten (Martes americana), fisher (Pekania pennanti), and southern flying squirrel (Glaucomys volans) to evaluate multi-species genetic connectivity across Ontario, Canada. We used linear models to compare node-based estimates of genetic connectivity for each species to point-based estimates of landscape connectivity (current density) derived from circuit theory. To our knowledge, we are the first to evaluate current density as a measure of genetic connectivity. Our results depended on landscape context: habitat amount was more important than current density in explaining multi-species genetic connectivity in the northern part of our study area, where habitat was abundant and fragmentation was low. In the south however, where fragmentation was prevalent, genetic connectivity was correlated with current density. Contrary to our expectations however, locations with a high probability of movement as reflected by high current density were negatively associated with gene flow. Subsequent analyses of circuit theory outputs showed that high current density was also associated with high effective resistance, underscoring that the presence of pinch points is not necessarily indicative of gene flow. Overall, our study appears to provide support for the hypothesis that landscape pattern is important when habitat amount is low. We also conclude that while current density is proportional to the probability of movement per unit area, this

  17. Deterministic learning enhanced neutral network control of unmanned helicopter

    Directory of Open Access Journals (Sweden)

    Yiming Jiang

    2016-11-01

    Full Text Available In this article, a neural network–based tracking controller is developed for an unmanned helicopter system with guaranteed global stability in the presence of uncertain system dynamics. Due to the coupling and modeling uncertainties of the helicopter systems, neutral networks approximation techniques are employed to compensate the unknown dynamics of each subsystem. In order to extend the semiglobal stability achieved by conventional neural control to global stability, a switching mechanism is also integrated into the control design, such that the resulted neural controller is always valid without any concern on either initial conditions or range of state variables. In addition, deterministic learning is applied to the neutral network learning control, such that the adaptive neutral networks are able to store the learned knowledge that could be reused to construct neutral network controller with improved control performance. Simulation studies are carried out on a helicopter model to illustrate the effectiveness of the proposed control design.

  18. Positive train control interoperability and networking research : final report.

    Science.gov (United States)

    2015-12-01

    This document describes the initial development of an ITC PTC Shared Network (IPSN), a hosted : environment to support the distribution, configuration management, and IT governance of Interoperable : Train Control (ITC) Positive Train Control (PTC) s...

  19. Smart Control of Energy Distribution Grids over Heterogeneous Communication Networks

    DEFF Research Database (Denmark)

    Schwefel, Hans-Peter; Silva, Nuno; Olsen, Rasmus Løvenstein

    2018-01-01

    Off-the shelf wireless communication technologies reduce infrastructure deployment costs and are thus attractive for distribution system control. Wireless communication however may lead to variable network performance. Hence the impact of this variability on overall distribution system control be...

  20. Using a Control System Ethernet Network as a Field Bus

    CERN Document Server

    De Van, William R; Lawson, Gregory S; Wagner, William H; Wantland, David M; Williams, Ernest

    2005-01-01

    A major component of a typical accelerator distributed control system (DCS) is a dedicated, large-scale local area communications network (LAN). The SNS EPICS-based control system uses a LAN based on the popular IEEE-802.3 set of standards (Ethernet). Since the control system network infrastructure is available throughout the facility, and since Ethernet-based controllers are readily available, it is tempting to use the control system LAN for "fieldbus" communications to low-level control devices (e.g. vacuum controllers; remote I/O). These devices may or may not be compatible with the high-level DCS protocols. This paper presents some of the benefits and risks of combining high-level DCS communications with low-level "field bus" communications on the same network, and describes measures taken at SNS to promote compatibility between devices connected to the control system network.

  1. Bayesian network model for identification of pathways by integrating protein interaction with genetic interaction data.

    Science.gov (United States)

    Fu, Changhe; Deng, Su; Jin, Guangxu; Wang, Xinxin; Yu, Zu-Guo

    2017-09-21

    Molecular interaction data at proteomic and genetic levels provide physical and functional insights into a molecular biosystem and are helpful for the construction of pathway structures complementarily. Despite advances in inferring biological pathways using genetic interaction data, there still exists weakness in developed models, such as, activity pathway networks (APN), when integrating the data from proteomic and genetic levels. It is necessary to develop new methods to infer pathway structure by both of interaction data. We utilized probabilistic graphical model to develop a new method that integrates genetic interaction and protein interaction data and infers exquisitely detailed pathway structure. We modeled the pathway network as Bayesian network and applied this model to infer pathways for the coherent subsets of the global genetic interaction profiles, and the available data set of endoplasmic reticulum genes. The protein interaction data were derived from the BioGRID database. Our method can accurately reconstruct known cellular pathway structures, including SWR complex, ER-Associated Degradation (ERAD) pathway, N-Glycan biosynthesis pathway, Elongator complex, Retromer complex, and Urmylation pathway. By comparing N-Glycan biosynthesis pathway and Urmylation pathway identified from our approach with that from APN, we found that our method is able to overcome its weakness (certain edges are inexplicable). According to underlying protein interaction network, we defined a simple scoring function that only adopts genetic interaction information to avoid the balance difficulty in the APN. Using the effective stochastic simulation algorithm, the performance of our proposed method is significantly high. We developed a new method based on Bayesian network to infer detailed pathway structures from interaction data at proteomic and genetic levels. The results indicate that the developed method performs better in predicting signaling pathways than previously

  2. Connected Dominating Set Based Topology Control in Wireless Sensor Networks

    Science.gov (United States)

    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.…

  3. Implementation of neural network based non-linear predictive control

    DEFF Research Database (Denmark)

    Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole

    1999-01-01

    of non-linear systems. GPC is model based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model, a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis...

  4. Mitigating the controller performance bottlenecks in Software Defined Networks

    DEFF Research Database (Denmark)

    Caba, Cosmin Marius; Soler, José

    2016-01-01

    The centralization of the control plane decision logic in Software Defined Networking (SDN) has raised concerns regarding the performance of the SDN Controller (SDNC) when the network scales up. A number of solutions have been proposed in the literature to address these concerns. This paper...

  5. A control model for district heating networks with storage

    NARCIS (Netherlands)

    Scholten, Tjeert; De Persis, Claudio; Tesi, Pietro

    2014-01-01

    In [1] pressure control of hydraulic networks is investigated. We extend this work to district heating systems with storage capabilities and derive a model taking the topology of the network into account. The goal for the derived model is that it should allow for control of the storage level and

  6. Distributed Estimation and Control for Robotic Networks

    NARCIS (Netherlands)

    Simonetto, A.

    2012-01-01

    Mobile robots that communicate and cooperate to achieve a common task have been the subject of an increasing research interest in recent years. These possibly heterogeneous groups of robots communicate locally via a communication network and therefore are usually referred to as robotic networks.

  7. Robust tests for matched case-control genetic association studies

    Directory of Open Access Journals (Sweden)

    Fung Wing

    2010-10-01

    Full Text Available Abstract Background The Cochran-Armitage trend test (CATT is powerful in detecting association between a susceptible marker and a disease. This test, however, may suffer from a substantial loss of power when the underlying genetic model is unknown and incorrectly specified. Thus, it is useful to derive tests obtaining the plausible power against all common genetic models. For this purpose, the genetic model selection (GMS and genetic model exclusion (GME methods were proposed recently. Simulation results showed that GMS and GME can obtain the plausible power against three common genetic models while the overall type I error is well controlled. Results Although GMS and GME are powerful statistically, they could be seriously affected by known confounding factors such as gender, age and race. Therefore, in this paper, via comparing the difference of Hardy-Weinberg disequilibrium coefficients between the cases and the controls within each sub-population, we propose the stratified genetic model selection (SGMS and exclusion (SGME methods which could eliminate the effect of confounding factors by adopting a matching framework. Our goal in this paper is to investigate the robustness of the proposed statistics and compare them with other commonly used efficiency robust tests such as MAX3 and χ2 with 2 degrees of freedom (df test in matched case-control association designs through simulation studies. Conclusion Simulation results showed that if the mean genetic effect of the heterozygous genotype is between those of the two homozygous genotypes, then the proposed tests and MAX3 are preferred. Otherwise, χ2 with 2 df test may be used. To illustrate the robust procedures, the proposed tests are applied to a real matched pair case-control etiologic study of sarcoidosis.

  8. Use of genetic algorithms for encoding efficient neural network architectures: neurocomputer implementation

    Science.gov (United States)

    James, Jason; Dagli, Cihan H.

    1995-04-01

    In this study an attempt is being made to encode the architecture of a neural network in a chromosome string for evolving robust, fast-learning, minimal neural network architectures through genetic algorithms. Various attributes affecting the learning of the network are represented as genes. The performance of the networks is used as the fitness value. Neural network architecture design concepts are initially demonstrated using a backpropagation architecture with the standard data set of Rosenberg and Sejnowski for text to speech conversion on Adaptive Solutions Inc.'s CNAPS Neuro-Computer. The architectures obtained are compared with the one reported in the literature for the standard data set used. The study concludes by providing some insights regarding the architecture encoding for other artificial neural network paradigms.

  9. Projection learning algorithm for threshold - controlled neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Reznik, A.M.

    1995-03-01

    The projection learning algorithm proposed in [1, 2] and further developed in [3] substantially improves the efficiency of memorizing information and accelerates the learning process in neural networks. This algorithm is compatible with the completely connected neural network architecture (the Hopfield network [4]), but its application to other networks involves a number of difficulties. The main difficulties include constraints on interconnection structure and the need to eliminate the state uncertainty of latent neurons if such are present in the network. Despite the encouraging preliminary results of [3], further extension of the applications of the projection algorithm therefore remains problematic. In this paper, which is a continuation of the work begun in [3], we consider threshold-controlled neural networks. Networks of this type are quite common. They represent the receptor neuron layers in some neurocomputer designs. A similar structure is observed in the lower divisions of biological sensory systems [5]. In multilayer projection neural networks with lateral interconnections, the neuron layers or parts of these layers may also have the structure of a threshold-controlled completely connected network. Here the thresholds are the potentials delivered through the projection connections from other parts of the network. The extension of the projection algorithm to the class of threshold-controlled networks may accordingly prove to be useful both for extending its technical applications and for better understanding of the operation of the nervous system in living organisms.

  10. MetaNetwork : A computational protocol for the genetic study of metabolic networks

    NARCIS (Netherlands)

    Fu, Jingyuan; Swertz, Morris A.; Keurentjes, Joost J. B.; Jansen, Ritsert C.

    2007-01-01

    We here describe the MetaNetwork protocol to reconstruct metabolic networks using metabolite abundance data from segregating populations. MetaNetwork maps metabolite quantitative trait loci (mQTLs) underlying variation in metabolite abundance in individuals of a segregating population using a

  11. MetaNetwork: a computational protocol for the genetic study of metabolic networks

    NARCIS (Netherlands)

    Fu, J.; Swertz, M.A.; Keurentjes, J.J.B.; Jansen, R.C.

    2007-01-01

    We here describe the MetaNetwork protocol to reconstruct metabolic networks using metabolite abundance data from segregating populations. MetaNetwork maps metabolite quantitative trait loci (mQTLs) underlying variation in metabolite abundance in individuals of a segregating population using a

  12. Prediction of Aerodynamic Coefficients for Wind Tunnel Data using a Genetic Algorithm Optimized Neural Network

    Science.gov (United States)

    Rajkumar, T.; Aragon, Cecilia; Bardina, Jorge; Britten, Roy

    2002-01-01

    A fast, reliable way of predicting aerodynamic coefficients is produced using a neural network optimized by a genetic algorithm. Basic aerodynamic coefficients (e.g. lift, drag, pitching moment) are modelled as functions of angle of attack and Mach number. The neural network is first trained on a relatively rich set of data from wind tunnel tests of numerical simulations to learn an overall model. Most of the aerodynamic parameters can be well-fitted using polynomial functions. A new set of data, which can be relatively sparse, is then supplied to the network to produce a new model consistent with the previous model and the new data. Because the new model interpolates realistically between the sparse test data points, it is suitable for use in piloted simulations. The genetic algorithm is used to choose a neural network architecture to give best results, avoiding over-and under-fitting of the test data.

  13. Hybrid Neural-Network: Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics Developed and Demonstrated

    Science.gov (United States)

    Kobayashi, Takahisa; Simon, Donald L.

    2002-01-01

    As part of the NASA Aviation Safety Program, a unique model-based diagnostics method that employs neural networks and genetic algorithms for aircraft engine performance diagnostics has been developed and demonstrated at the NASA Glenn Research Center against a nonlinear gas turbine engine model. Neural networks are applied to estimate the internal health condition of the engine, and genetic algorithms are used for sensor fault detection, isolation, and quantification. This hybrid architecture combines the excellent nonlinear estimation capabilities of neural networks with the capability to rank the likelihood of various faults given a specific sensor suite signature. The method requires a significantly smaller data training set than a neural network approach alone does, and it performs the combined engine health monitoring objectives of performance diagnostics and sensor fault detection and isolation in the presence of nominal and degraded engine health conditions.

  14. Genetic reinforcement learning through symbiotic evolution for fuzzy controller design.

    Science.gov (United States)

    Juang, C F; Lin, J Y; Lin, C T

    2000-01-01

    An efficient genetic reinforcement learning algorithm for designing fuzzy controllers is proposed in this paper. The genetic algorithm (GA) adopted in this paper is based upon symbiotic evolution which, when applied to fuzzy controller design, complements the local mapping property of a fuzzy rule. Using this Symbiotic-Evolution-based Fuzzy Controller (SEFC) design method, the number of control trials, as well as consumed CPU time, are considerably reduced when compared to traditional GA-based fuzzy controller design methods and other types of genetic reinforcement learning schemes. Moreover, unlike traditional fuzzy controllers, which partition the input space into a grid, SEFC partitions the input space in a flexible way, thus creating fewer fuzzy rules. In SEFC, different types of fuzzy rules whose consequent parts are singletons, fuzzy sets, or linear equations (TSK-type fuzzy rules) are allowed. Further, the free parameters (e.g., centers and widths of membership functions) and fuzzy rules are all tuned automatically. For the TSK-type fuzzy rule especially, which put the proposed learning algorithm in use, only the significant input variables are selected to participate in the consequent of a rule. The proposed SEFC design method has been applied to different simulated control problems, including the cart-pole balancing system, a magnetic levitation system, and a water bath temperature control system. The proposed SEFC has been verified to be efficient and superior from these control problems, and from comparisons with some traditional GA-based fuzzy systems.

  15. Information propagation within the Genetic Network of Saccharomyces cerevisiae.

    Science.gov (United States)

    Chowdhury, Sharif; Lloyd-Price, Jason; Smolander, Olli-Pekka; Baici, Wayne C V; Hughes, Timothy R; Yli-Harja, Olli; Chua, Gordon; Ribeiro, Andre S

    2010-10-26

    A gene network's capacity to process information, so as to bind past events to future actions, depends on its structure and logic. From previous and new microarray measurements in Saccharomyces cerevisiae following gene deletions and overexpressions, we identify a core gene regulatory network (GRN) of functional interactions between 328 genes and the transfer functions of each gene. Inferred connections are verified by gene enrichment. We find that this core network has a generalized clustering coefficient that is much higher than chance. The inferred Boolean transfer functions have a mean p-bias of 0.41, and thus similar amounts of activation and repression interactions. However, the distribution of p-biases differs significantly from what is expected by chance that, along with the high mean connectivity, is found to cause the core GRN of S. cerevisiae's to have an overall sensitivity similar to critical Boolean networks. In agreement, we find that the amount of information propagated between nodes in finite time series is much higher in the inferred core GRN of S. cerevisiae than what is expected by chance. We suggest that S. cerevisiae is likely to have evolved a core GRN with enhanced information propagation among its genes.

  16. Design and Simulation Analysis for Integrated Vehicle Chassis-Network Control System Based on CAN Network

    Directory of Open Access Journals (Sweden)

    Wei Yu

    2016-01-01

    Full Text Available Due to the different functions of the system used in the vehicle chassis control, the hierarchical control strategy also leads to many kinds of the network topology structure. According to the hierarchical control principle, this research puts forward the integrated control strategy of the chassis based on supervision mechanism. The purpose is to consider how the integrated control architecture affects the control performance of the system after the intervention of CAN network. Based on the principle of hierarchical control and fuzzy control, a fuzzy controller is designed, which is used to monitor and coordinate the ESP, AFS, and ARS. And the IVC system is constructed with the upper supervisory controller and three subcontrol systems on the Simulink platform. The network topology structure of IVC is proposed, and the IVC communication matrix based on CAN network communication is designed. With the common sensors and the subcontrollers as the CAN network independent nodes, the network induced delay and packet loss rate on the system control performance are studied by simulation. The results show that the simulation method can be used for designing the communication network of the vehicle.

  17. Optimal traffic control in highway transportation networks using linear programming

    KAUST Repository

    Li, Yanning

    2014-06-01

    This article presents a framework for the optimal control of boundary flows on transportation networks. The state of the system is modeled by a first order scalar conservation law (Lighthill-Whitham-Richards PDE). Based on an equivalent formulation of the Hamilton-Jacobi PDE, the problem of controlling the state of the system on a network link in a finite horizon can be posed as a Linear Program. Assuming all intersections in the network are controllable, we show that the optimization approach can be extended to an arbitrary transportation network, preserving linear constraints. Unlike previously investigated transportation network control schemes, this framework leverages the intrinsic properties of the Halmilton-Jacobi equation, and does not require any discretization or boolean variables on the link. Hence this framework is very computational efficient and provides the globally optimal solution. The feasibility of this framework is illustrated by an on-ramp metering control example.

  18. Transient and permanent error control for networks-on-chip

    CERN Document Server

    Yu, Qiaoyan

    2012-01-01

    This book addresses reliability and energy efficiency of on-chip networks using a configurable error control coding (ECC) scheme for datalink-layer transient error management. The method can adjust both error detection and correction strengths at runtime by varying the number of redundant wires for parity-check bits. Methods are also presented to tackle joint transient and permanent error correction, exploiting the redundant resources already available on-chip. A parallel and flexible network simulator is also introduced, which facilitates examining the impact of various error control methods on network-on-chip performance. Includes a complete survey of error control methods for reliable networks-on-chip, evaluated for reliability, energy and performance metrics; Provides analysis of error control in various network-on-chip layers, as well as presentation of an innovative multi-layer error control coding technique; Presents state-of-the-art solutions to address simultaneously reliability, energy and performan...

  19. Stochasticity, bistability and the wisdom of crowds: a model for associative learning in genetic regulatory networks.

    Science.gov (United States)

    Sorek, Matan; Balaban, Nathalie Q; Loewenstein, Yonatan

    2013-01-01

    It is generally believed that associative memory in the brain depends on multistable synaptic dynamics, which enable the synapses to maintain their value for extended periods of time. However, multistable dynamics are not restricted to synapses. In particular, the dynamics of some genetic regulatory networks are multistable, raising the possibility that even single cells, in the absence of a nervous system, are capable of learning associations. Here we study a standard genetic regulatory network model with bistable elements and stochastic dynamics. We demonstrate that such a genetic regulatory network model is capable of learning multiple, general, overlapping associations. The capacity of the network, defined as the number of associations that can be simultaneously stored and retrieved, is proportional to the square root of the number of bistable elements in the genetic regulatory network. Moreover, we compute the capacity of a clonal population of cells, such as in a colony of bacteria or a tissue, to store associations. We show that even if the cells do not interact, the capacity of the population to store associations substantially exceeds that of a single cell and is proportional to the number of bistable elements. Thus, we show that even single cells are endowed with the computational power to learn associations, a power that is substantially enhanced when these cells form a population.

  20. Stochasticity, bistability and the wisdom of crowds: a model for associative learning in genetic regulatory networks.

    Directory of Open Access Journals (Sweden)

    Matan Sorek

    Full Text Available It is generally believed that associative memory in the brain depends on multistable synaptic dynamics, which enable the synapses to maintain their value for extended periods of time. However, multistable dynamics are not restricted to synapses. In particular, the dynamics of some genetic regulatory networks are multistable, raising the possibility that even single cells, in the absence of a nervous system, are capable of learning associations. Here we study a standard genetic regulatory network model with bistable elements and stochastic dynamics. We demonstrate that such a genetic regulatory network model is capable of learning multiple, general, overlapping associations. The capacity of the network, defined as the number of associations that can be simultaneously stored and retrieved, is proportional to the square root of the number of bistable elements in the genetic regulatory network. Moreover, we compute the capacity of a clonal population of cells, such as in a colony of bacteria or a tissue, to store associations. We show that even if the cells do not interact, the capacity of the population to store associations substantially exceeds that of a single cell and is proportional to the number of bistable elements. Thus, we show that even single cells are endowed with the computational power to learn associations, a power that is substantially enhanced when these cells form a population.

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

    Science.gov (United States)

    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.

  2. Network statistics of genetically-driven gene co-expression modules in mouse crosses

    Directory of Open Access Journals (Sweden)

    Marie-Pier eScott-Boyer

    2013-12-01

    Full Text Available In biology, networks are used in different contexts as ways to represent relationships between entities, such as for instance interactions between genes, proteins or metabolites. Despite progress in the analysis of such networks and their potential to better understand the collective impact of genes on complex traits, one remaining challenge is to establish the biologic validity of gene co-expression networks and to determine what governs their organization. We used WGCNA to construct and analyze seven gene expression datasets from several tissues of mouse recombinant inbred strains (RIS. For six out of the 7 networks, we found that linkage to module QTLs (mQTLs could be established for 29.3% of gene co-expression modules detected in the several mouse RIS. For about 74.6% of such genetically-linked modules, the mQTL was on the same chromosome as the one contributing most genes to the module, with genes originating from that chromosome showing higher connectivity than other genes in the modules. Such modules (that we considered as genetically-driven had network statistic properties (density, centralization and heterogeneity that set them apart from other modules in the network. Altogether, a sizeable portion of gene co-expression modules detected in mouse RIS panels had genetic determinants as their main organizing principle. In addition to providing a biologic interpretation validation for these modules, these genetic determinants imparted on them particular properties that set them apart from other modules in the network, to the point that they can be predicted to a large extent on the basis of their network statistics.

  3. Simulating Visual Learning and Optical Illusions via a Network-Based Genetic Algorithm

    Science.gov (United States)

    Siu, Theodore; Vivar, Miguel; Shinbrot, Troy

    We present a neural network model that uses a genetic algorithm to identify spatial patterns. We show that the model both learns and reproduces common visual patterns and optical illusions. Surprisingly, we find that the illusions generated are a direct consequence of the network architecture used. We discuss the implications of our results and the insights that we gain on how humans fall for optical illusions

  4. BisoGenet: a new tool for gene network building, visualization and analysis.

    Science.gov (United States)

    Martin, Alexander; Ochagavia, Maria E; Rabasa, Laya C; Miranda, Jamilet; Fernandez-de-Cossio, Jorge; Bringas, Ricardo

    2010-02-17

    The increasing availability and diversity of omics data in the post-genomic era offers new perspectives in most areas of biomedical research. Graph-based biological networks models capture the topology of the functional relationships between molecular entities such as gene, protein and small compounds and provide a suitable framework for integrating and analyzing omics-data. The development of software tools capable of integrating data from different sources and to provide flexible methods to reconstruct, represent and analyze topological networks is an active field of research in bioinformatics. BisoGenet is a multi-tier application for visualization and analysis of biomolecular relationships. The system consists of three tiers. In the data tier, an in-house database stores genomics information, protein-protein interactions, protein-DNA interactions, gene ontology and metabolic pathways. In the middle tier, a global network is created at server startup, representing the whole data on bioentities and their relationships retrieved from the database. The client tier is a Cytoscape plugin, which manages user input, communication with the Web Service, visualization and analysis of the resulting network. BisoGenet is able to build and visualize biological networks in a fast and user-friendly manner. A feature of Bisogenet is the possibility to include coding relations to distinguish between genes and their products. This feature could be instrumental to achieve a finer grain representation of the bioentities and their relationships. The client application includes network analysis tools and interactive network expansion capabilities. In addition, an option is provided to allow other networks to be converted to BisoGenet. This feature facilitates the integration of our software with other tools available in the Cytoscape platform. BisoGenet is available at http://bio.cigb.edu.cu/bisogenet-cytoscape/.

  5. Genetic Evaluation of Children with Global Developmental Delay—Current Status of Network Systems in Taiwan

    Directory of Open Access Journals (Sweden)

    Yong-Lin Foo

    2015-08-01

    Full Text Available This review article aims to introduce the screening and referral network of genetic evaluation for children with developmental delay in Taiwan. For these children, integrated systems provide services from the medical, educational, and social welfare sectors. All cities and counties in Taiwan have established a network for screening, detection, referral, evaluation, and intervention services. Increased awareness improves early detection and intervention. There remains a gap between supply and demand, especially with regard to financial resources and professional manpower. Genetic etiology has a major role in prenatal causes of developmental delay. A summary of reports on some related genetic disorders in the Taiwanese population is included in this review. Genetic diagnosis allows counseling with regard to recurrence risk and prevention. Networking with neonatal screening, laboratory diagnosis, genetic counseling, and orphan drugs logistics systems can provide effective treatment for patients. In Taiwan, several laboratories provide genetic tests for clinical diagnosis. Accessibility to advanced expensive tests such as gene chips or whole exome sequencing is limited because of funding problems; however, the service system in Taiwan can still operate in a relatively cost-effective manner. This experience in Taiwan may serve as a reference for other countries.

  6. Design and Implementation of the International Genetics and Translational Research in Transplantation Network.

    Science.gov (United States)

    2015-11-01

    Genetic association studies of transplantation outcomes have been hampered by small samples and highly complex multifactorial phenotypes, hindering investigations of the genetic architecture of a range of comorbidities which significantly impact graft and recipient life expectancy. We describe here the rationale and design of the International Genetics & Translational Research in Transplantation Network. The network comprises 22 studies to date, including 16494 transplant recipients and 11669 donors, of whom more than 5000 are of non-European ancestry, all of whom have existing genomewide genotype data sets. We describe the rich genetic and phenotypic information available in this consortium comprising heart, kidney, liver, and lung transplant cohorts. We demonstrate significant power in International Genetics & Translational Research in Transplantation Network to detect main effect association signals across regions such as the MHC region as well as genomewide for transplant outcomes that span all solid organs, such as graft survival, acute rejection, new onset of diabetes after transplantation, and for delayed graft function in kidney only. This consortium is designed and statistically powered to deliver pioneering insights into the genetic architecture of transplant-related outcomes across a range of different solid-organ transplant studies. The study design allows a spectrum of analyses to be performed including recipient-only analyses, donor-recipient HLA mismatches with focus on loss-of-function variants and nonsynonymous single nucleotide polymorphisms.

  7. Control Strategy on Worms Spread in Complex Networks

    Science.gov (United States)

    Xianmei, Fang

    First, a preliminary understanding of what is meant by complex network and its features, and network worm virus that understanding and analysis of the emergence and development of the worm, the worm to understand the current situation, focus on the worm propagation model (simple propagation model, Kermack-Mckendrick model, SIS model, two-factor model, BCM model - network worms against the model). Contact the characteristics of complex networks and the worm theory, detection and prevention of worms and an important node in the network-based control strategy (target immunity, virus containment) for a simple discussion.

  8. Design of PID-type controllers using multiobjective genetic algorithms.

    Science.gov (United States)

    Herreros, Alberto; Baeyens, Enrique; Perán, José R

    2002-10-01

    The design of a PID controller is a multiobjective problem. A plant and a set of specifications to be satisfied are given. The designer has to adjust the parameters of the PID controller such that the feedback interconnection of the plant and the controller satisfies the specifications. These specifications are usually competitive and any acceptable solution requires a tradeoff among them. An approach for adjusting the parameters of a PID controller based on multiobjective optimization and genetic algorithms is presented in this paper. The MRCD (multiobjective robust control design) genetic algorithm has been employed. The approach can be easily generalized to design multivariable coupled and decentralized PID loops and has been successfully validated for a large number of experimental cases.

  9. VNEC - A Virtual Network Experiment Controller

    Science.gov (United States)

    Gagnon, François; Dej, Tomas; Esfandiari, Babak

    This paper presents VNEC, a tool to specify and execute network experiments in a virtual environment. The user first formulates the network topology and then provides the tasks that should be performed by the computers together with their execution. Next, VNEC initializes the environment by powering up and configuring the virtual machines to match the desired network topology. Finally, commands are dispatched to the right virtual machines in the specified order. VNEC provides an environment for several types of research experiments such as virus propagation patterns and reactions of different targets against a given attack.

  10. River network architecture, genetic effective size and distributional patterns predict differences in genetic structure across species in a dryland stream fish community.

    Science.gov (United States)

    Pilger, Tyler J; Gido, Keith B; Propst, David L; Whitney, James E; Turner, Thomas F

    2017-05-01

    Dendritic ecological network (DEN) architecture can be a strong predictor of spatial genetic patterns in theoretical and simulation studies. Yet, interspecific differences in dispersal capabilities and distribution within the network may equally affect species' genetic structuring. We characterized patterns of genetic variation from up to ten microsatellite loci for nine numerically dominant members of the upper Gila River fish community, New Mexico, USA. Using comparative landscape genetics, we evaluated the role of network architecture for structuring populations within species (pairwise F ST ) while explicitly accounting for intraspecific demographic influences on effective population size (N e ). Five species exhibited patterns of connectivity and/or genetic diversity gradients that were predicted by network structure. These species were generally considered to be small-bodied or habitat specialists. Spatial variation of N e was a strong predictor of pairwise F ST for two species, suggesting patterns of connectivity may also be influenced by genetic drift independent of network properties. Finally, two study species exhibited genetic patterns that were unexplained by network properties and appeared to be related to nonequilibrium processes. Properties of DENs shape community-wide genetic structure but effects are modified by intrinsic traits and nonequilibrium processes. Further theoretical development of the DEN framework should account for such cases. © 2017 John Wiley & Sons Ltd.

  11. A Method to Exploit the Structure of Genetic Ancestry Space to Enhance Case-Control Studies.

    Science.gov (United States)

    Bodea, Corneliu A; Neale, Benjamin M; Ripke, Stephan; Daly, Mark J; Devlin, Bernie; Roeder, Kathryn

    2016-05-05

    One goal of human genetics is to understand the genetic basis of disease, a challenge for diseases of complex inheritance because risk alleles are few relative to the vast set of benign variants. Risk variants are often sought by association studies in which allele frequencies in case subjects are contrasted with those from population-based samples used as control subjects. In an ideal world we would know population-level allele frequencies, releasing researchers to focus on case subjects. We argue this ideal is possible, at least theoretically, and we outline a path to achieving it in reality. If such a resource were to exist, it would yield ample savings and would facilitate the effective use of data repositories by removing administrative and technical barriers. We call this concept the Universal Control Repository Network (UNICORN), a means to perform association analyses without necessitating direct access to individual-level control data. Our approach to UNICORN uses existing genetic resources and various statistical tools to analyze these data, including hierarchical clustering with spectral analysis of ancestry; and empirical Bayesian analysis along with Gaussian spatial processes to estimate ancestry-specific allele frequencies. We demonstrate our approach using tens of thousands of control subjects from studies of Crohn disease, showing how it controls false positives, provides power similar to that achieved when all control data are directly accessible, and enhances power when control data are limiting or even imperfectly matched ancestrally. These results highlight how UNICORN can enable reliable, powerful, and convenient genetic association analyses without access to the individual-level data. Copyright © 2016 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

  12. Modeling Genetic Regulatory Networks Using First-Order Probabilistic Logic

    Science.gov (United States)

    2013-03-01

    that model GRNs from real data. PRISM, a probabilistic learning framework based on B- prolog , was used to program the Bayesian networks. Instead of...intelligence, prolog , gene regulation, “Raf” pathway 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT UU 18. NUMBER OF PAGES 28 19a...probabilistic logic paradigm. PRISM is a probabilistic logical framework based on B- prolog the language extends the Horn clauses to include random variables

  13. Stochastic modelling and control of communication networks

    NARCIS (Netherlands)

    Zuraniewski, P.W.

    2011-01-01

    The unprecedented growth of the Information Technologies sector observed within the past years creates an excellent opportunity to conduct new, exciting and interdisciplinary research. Increasing complexity of the communication networks calls for incorporating rigorously developed and reliable

  14. Dynamics and control of diseases in networks with community structure.

    Directory of Open Access Journals (Sweden)

    Marcel Salathé

    2010-04-01

    Full Text Available 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.

  15. Genetic interaction network of the Saccharomyces cerevisiae type 1 phosphatase Glc7

    Directory of Open Access Journals (Sweden)

    Neszt Michael

    2008-07-01

    Full Text Available Abstract Background Protein kinases and phosphatases regulate protein phosphorylation, a critical means of modulating protein function, stability and localization. The identification of functional networks for protein phosphatases has been slow due to their redundant nature and the lack of large-scale analyses. We hypothesized that a genome-scale analysis of genetic interactions using the Synthetic Genetic Array could reveal protein phosphatase functional networks. We apply this approach to the conserved type 1 protein phosphatase Glc7, which regulates numerous cellular processes in budding yeast. Results We created a novel glc7 catalytic mutant (glc7-E101Q. Phenotypic analysis indicates that this novel allele exhibits slow growth and defects in glucose metabolism but normal cell cycle progression and chromosome segregation. This suggests that glc7-E101Q is a hypomorphic glc7 mutant. Synthetic Genetic Array analysis of glc7-E101Q revealed a broad network of 245 synthetic sick/lethal interactions reflecting that many processes are required when Glc7 function is compromised such as histone modification, chromosome segregation and cytokinesis, nutrient sensing and DNA damage. In addition, mitochondrial activity and inheritance and lipid metabolism were identified as new processes involved in buffering Glc7 function. An interaction network among 95 genes genetically interacting with GLC7 was constructed by integration of genetic and physical interaction data. The obtained network has a modular architecture, and the interconnection among the modules reflects the cooperation of the processes buffering Glc7 function. Conclusion We found 245 genes required for the normal growth of the glc7-E101Q mutant. Functional grouping of these genes and analysis of their physical and genetic interaction patterns bring new information on Glc7-regulated processes.

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

    Science.gov (United States)

    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. Control of coupled oscillator networks with application to microgrid technologies

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  19. Landscape attributes and life history variability shape genetic structure of trout populations in a stream network

    Science.gov (United States)

    Neville, H.M.; Dunham, J.B.; Peacock, M.M.

    2006-01-01

    Spatial and temporal landscape patterns have long been recognized to influence biological processes, but these processes often operate at scales that are difficult to study by conventional means. Inferences from genetic markers can overcome some of these limitations. We used a landscape genetics approach to test hypotheses concerning landscape processes influencing the demography of Lahontan cutthroat trout in a complex stream network in the Great Basin desert of the western US. Predictions were tested with population- and individual-based analyses of microsatellite DNA variation, reflecting patterns of dispersal, population stability, and local effective population sizes. Complementary genetic inferences suggested samples from migratory corridors housed a mixture of fish from tributaries, as predicted based on assumed migratory life histories in those habitats. Also as predicted, populations presumed to have greater proportions of migratory fish or from physically connected, large, or high quality habitats had higher genetic variability and reduced genetic differentiation from other populations. Populations thought to contain largely non-migratory individuals generally showed the opposite pattern, suggesting behavioral isolation. Estimated effective sizes were small, and we identified significant and severe genetic bottlenecks in several populations that were isolated, recently founded, or that inhabit streams that desiccate frequently. Overall, this work suggested that Lahontan cutthroat trout populations in stream networks are affected by a combination of landscape and metapopulation processes. Results also demonstrated that genetic patterns can reveal unexpected processes, even within a system that is well studied from a conventional ecological perspective. ?? Springer 2006.

  20. Physiology and genetics of metabolic flux control in Zymomonas mobilis

    Energy Technology Data Exchange (ETDEWEB)

    Conway, T.

    1992-01-01

    This work seeks to understand the role of gene expression in regulating glycolytic enzyme synthesis in a balance that allows proper glycoltic flux control. The seven genes targeted for study in this laboratory have been cloned and sequenced, and molecular details of regulation have been investigated. Clear that glycolytic enzyme synthesis is coordinated to prevent the build up of toxic metabolic intermediates. The genetic mechanisms responsible for regulating balanced expression of the EntnerDoudoroff and glycolytic genes in Z. mobilis are beginning to be understood. Several layers of genetic control, perhaps in a hierarchal arrangement act in concert to determine the relative abundance of the glycolytic enzymes. These genetic controls involve differential translational efficiency, highly conserved promoter sequences, transcription factors, differential mRNA stabilities, and nucleolytic mRNA processing. The serendipitous cloning of the glucose facilitator, glf, as a result of linkage to several other genes of interest will have a significant impact on the study of Z. mobilis metabolism. The glucose facilitator is being characterized in a genetically reconstituted system in E. coli. Molecular genetic studies indicate that the ratio of glf expression to that of glk, zmf, and edd is carefully regulated, and suggests a critical role in metabolic control. Regulation of glycolytic gene expression is now sufficiently well understood to allow use of the glycolytic genes as tools to manipulate specified enzyme levels for the purpose of analyzing metabolic flux control. The critical genes have been subcloned for stable expression in Z. mobilis and placed under control of a regulated promoter system involving the tac promoter, the lacI repressor, and gene induction in by IPTG. HPLC methods have been developed that allow quantitation of virtually all of the metabolic intermediates in the cell pool.

  1. Design of PID Controller Simulator based on Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Fahri VATANSEVER

    2013-08-01

    Full Text Available PID (Proportional Integral and Derivative controllers take an important place in the field of system controlling. Various methods such as Ziegler-Nichols, Cohen-Coon, Chien Hrones Reswick (CHR and Wang-Juang-Chan are available for the design of such controllers benefiting from the system time and frequency domain data. These controllers are in compliance with system properties under certain criteria suitable to the system. Genetic algorithms have become widely used in control system applications in parallel to the advances in the field of computer and artificial intelligence. In this study, PID controller designs have been carried out by means of classical methods and genetic algorithms and comparative results have been analyzed. For this purpose, a graphical user interface program which can be used for educational purpose has been developed. For the definite (entered transfer functions, the suitable P, PI and PID controller coefficients have calculated by both classical methods and genetic algorithms and many parameters and responses of the systems have been compared and presented numerically and graphically

  2. Robust receding horizon control for networked and distributed nonlinear systems

    CERN Document Server

    Li, Huiping

    2017-01-01

    This book offers a comprehensive, easy-to-understand overview of receding-horizon control for nonlinear networks. It presents novel general strategies that can simultaneously handle general nonlinear dynamics, system constraints, and disturbances arising in networked and large-scale systems and which can be widely applied. These receding-horizon-control-based strategies can achieve sub-optimal control performance while ensuring closed-loop stability: a feature attractive to engineers. The authors address the problems of networked and distributed control step-by-step, gradually increasing the level of challenge presented. The book first introduces the state-feedback control problems of nonlinear networked systems and then studies output feedback control problems. For large-scale nonlinear systems, disturbance is considered first, then communication delay separately, and lastly the simultaneous combination of delays and disturbances. Each chapter of this easy-to-follow book not only proposes and analyzes novel ...

  3. System Identification, Prediction, Simulation and Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1997-01-01

    a Gauss-Newton search direction is applied. 3) Amongst numerous model types, often met in control applications, only the Non-linear ARMAX (NARMAX) model, representing input/output description, is examined. A simulated example confirms that a neural network has the potential to perform excellent System...... Identification, Prediction, Simulation and Control of a dynamic, non-linear and noisy process. Further, the difficulties to control a practical non-linear laboratory process in a satisfactory way by using a traditional controller are overcomed by using a trained neural network to perform non-linear System......The intention of this paper is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...

  4. Genetic algorithm based adaptive neural network ensemble and its application in predicting carbon flux

    Science.gov (United States)

    Xue, Y.; Liu, S.; Hu, Y.; Yang, J.; Chen, Q.

    2007-01-01

    To improve the accuracy in prediction, Genetic Algorithm based Adaptive Neural Network Ensemble (GA-ANNE) is presented. Intersections are allowed between different training sets based on the fuzzy clustering analysis, which ensures the diversity as well as the accuracy of individual Neural Networks (NNs). Moreover, to improve the accuracy of the adaptive weights of individual NNs, GA is used to optimize the cluster centers. Empirical results in predicting carbon flux of Duke Forest reveal that GA-ANNE can predict the carbon flux more accurately than Radial Basis Function Neural Network (RBFNN), Bagging NN ensemble, and ANNE. ?? 2007 IEEE.

  5. Maintaining genetic stability in a control flock of South African ...

    African Journals Online (AJOL)

    A genetic control flock of Merino sheep consisting of 160ewes and 16 rams, was established in 1969at the Tygerhoek Experimental Farm near. Riviersonderend in the South Western Districts of South Africa. In 1976it was extended to 200 ewes and 20 rams. Ewes are replaced by their se- cond ewe lamb when it reaches ...

  6. Operational predictive optimal control of Barcelona water transport network

    OpenAIRE

    Pascual, J.; Romera, J.; Puig, V.; Cembrano, G.; Creus, R.; Minoves, M.

    2013-01-01

    This paper describes the application of model-based predictive control (MPC) techniques to the supervisory flow management in large-scale drinking water networks including a telemetry/telecontrol system. MPC is used to generate flow control strategies (set-points for the regulatory controllers) from the sources to the consumer areas to meet future demands, optimizing performance indexes associated to operational goals such as economic cost, safety storage volumes in the network and smoothness...

  7. Neural-Network Control Of Prosthetic And Robotic Hands

    Science.gov (United States)

    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.

  8. Topology Control in Aerial Multi Beam Directional Networks

    Science.gov (United States)

    2017-04-24

    Topology Control in Aerial Multi-Beam Directional Networks Brian Proulx, Nathaniel M. Jones, Jennifer Madiedo, Greg Kuperman {brian.proulx, njones...significant interference. Topology control (i.e., selecting a subset of neighbors to communicate with) is vital to reduce the interference. Good topology... control balances the number of links utilized to achieve fewer collisions while maintaining robust network connectivity. In this work, we discuss the

  9. IDENTIFICATION AND CONTROL OF AN ASYNCHRONOUS MACHINE USING NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    A ZERGAOUI

    2000-06-01

    Full Text Available In this work, we present the application of artificial neural networks to the identification and control of the asynchronous motor, which is a complex nonlinear system with variable internal dynamics.  We show that neural networks can be applied to control the stator currents of the induction motor.  The results of the different simulations are presented to evaluate the performance of the neural controller proposed.

  10. Genetic Polymorphisms of Infectious Diseases in Case-Control Studies

    Directory of Open Access Journals (Sweden)

    Antonio G. Pacheco

    2009-01-01

    Full Text Available In the past decade, genetic epidemiological analyses in infectious diseases have increased drastically since the publication of human genome and all the subsequent projects analyzing human diversity at molecular level. The great majority of studies use classical epidemiological designs applied to genetic data, and more than 80% of published studies use population-based case-control designs with widely spread genetic markers in human genome, like short tandem repeats (STR or single nucleotide polymorphisms (SNP, in genes chosen by their physiological association with the disease (candidate genes. Even though genetic data is less prone to several bias issues inherent to case-control studies, some care has to be taken when designing, performing, analyzing and interpreting results from such studies. Here we discuss some basic concepts of genetics and epidemiology as a departure to evaluate and review every step that should be followed to design, conduct, analyze, interpret and present data from those studies, using particularities of infectious diseases, especially leprosy and tuberculosis as models.

  11. Muscle networks: Connectivity analysis of EMG activity during postural control

    Science.gov (United States)

    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.

  12. Genetic improvement of tomato by targeted control of fruit softening

    KAUST Repository

    Uluisik, Selman

    2016-07-25

    Controlling the rate of softening to extend shelf life was a key target for researchers engineering genetically modified (GM) tomatoes in the 1990s, but only modest improvements were achieved. Hybrids grown nowadays contain \\'non-ripening mutations\\' that slow ripening and improve shelf life, but adversely affect flavor and color. We report substantial, targeted control of tomato softening, without affecting other aspects of ripening, by silencing a gene encoding a pectate lyase. © 2016 Nature America, Inc. All rights reserved.

  13. Recombination networks as genetic markers in a human variation study of the Old World.

    NARCIS (Netherlands)

    Javed, A.; Mele, M.; Pybus, M.; Zalloua, P.; Haber, M.; Comas, D.; Netea, M.G.; Balanovsky, O.; Balanovska, E.; Jin, L.; Yang, Y.; Arunkumar, G.; Pitchappan, R.; Bertranpetit, J.; Calafell, F.; Parida, L.

    2012-01-01

    We have analyzed human genetic diversity in 33 Old World populations including 23 populations obtained through Genographic Project studies. A set of 1,536 SNPs in five X chromosome regions were genotyped in 1,288 individuals (mostly males). We use a novel analysis employing subARG network

  14. Machine Learning for Information Retrieval: Neural Networks, Symbolic Learning, and Genetic Algorithms.

    Science.gov (United States)

    Chen, Hsinchun

    1995-01-01

    Presents an overview of artificial-intelligence-based inductive learning techniques and their use in information science research. Three methods are discussed: the connectionist Hopfield network; the symbolic ID3/ID5R; evolution-based genetic algorithms. The knowledge representations and algorithms of these methods are examined in the context of…

  15. Feature extraction of osteoporosis risk factors using artificial neural networks and genetic algorithms.

    Science.gov (United States)

    Anastassopoulos, George; Adamopoulos, Adam; Galiatsatos, Dimitrios; Drosos, Georgios

    2013-01-01

    A hybrid model that consists of an Artificial Neural Network and Genetic Algorithm is used in order to select the most significant osteoporosis risk factors. The results indicated that just 8 to 10 parameters, out of a total 34, are essential in order of high performance to be achieved.

  16. Investigating the Relationship between Topology and Evolution in a Dynamic Nematode Odor Genetic Network

    Directory of Open Access Journals (Sweden)

    David A. Fitzpatrick

    2012-01-01

    Full Text Available The relationship between biological network architectures and evolution is unclear. Within the phylum nematoda olfaction represents a critical survival tool. For nematodes, olfaction contributes to multiple processes including the finding of food, hosts, and reproductive partners, making developmental decisions, and evading predators. Here we examine a dynamic nematode odor genetic network to investigate how divergence, diversity, and contribution are shaped by network topology. Our findings describe connectivity frameworks and characteristics that correlate with molecular evolution and contribution across the olfactory network. Our data helps guide the development of a robust evolutionary description of the nematode odor network that may eventually aid in the prediction of interactive and functional qualities of novel nodes.

  17. Finding Robust Adaptation Gene Regulatory Networks Using Multi-Objective Genetic Algorithm.

    Science.gov (United States)

    Ren, Hai-Peng; Huang, Xiao-Na; Hao, Jia-Xuan

    2016-01-01

    Robust adaptation plays a key role in gene regulatory networks, and it is thought to be an important attribute for the organic or cells to survive in fluctuating conditions. In this paper, a simplified three-node enzyme network is modeled by the Michaelis-Menten rate equations for all possible topologies, and a family of topologies and the corresponding parameter sets of the network with satisfactory adaptation are obtained using the multi-objective genetic algorithm. The proposed approach improves the computation efficiency significantly as compared to the time consuming exhaustive searching method. This approach provides a systemic way for searching the feasible topologies and the corresponding parameter sets to make the gene regulatory networks have robust adaptation. The proposed methodology, owing to its universality and simplicity, can be used to address more complex issues in biological networks.

  18. AUTOMATIC CONTROL OF INTELLECTUAL RIGHTS IN THE GLOBAL COMPUTER NETWORKS

    OpenAIRE

    Anatoly P. Yakimaho; Victoriya V. Bessarabova

    2013-01-01

    The problems of use of subjects of intellectual property in the global computer networks are stated. The main attention is focused on the ways of problems solutions arising during the work in computer networks. Legal problems of information society are considered. The analysis of global computer networks as places for the organization of collective management by copyrights in the world scale is carried out. Issues of creation of a system of automatic control of property rights of authors and ...

  19. Mission-Aware Medium Access Control in Random Access Networks

    OpenAIRE

    Park, Jaeok; Van Der Schaar, Mihaela

    2009-01-01

    We study mission-critical networking in wireless communication networks, where network users are subject to critical events such as emergencies and crises. If a critical event occurs to a user, the user needs to send necessary information for help as early as possible. However, most existing medium access control (MAC) protocols are not adequate to meet the urgent need for information transmission by users in a critical situation. In this paer, we propose a novel class of MAC protocols that u...

  20. Resource Allocation and Cross Layer Control in Wireless Networks

    Science.gov (United States)

    2006-08-25

    Modiano , and J. Tsitsiklis, �Optimal energy allocation and admis- sion control for communication satellites,�IEEE Transactions on Networking, vol. 11...E. Modiano , �Improving delay in ad-hoc mobile networks via redundant packet transfers,�in Proceedings of Conference on Information Sciences and...M. J. Neely, E. Modiano , and C.-P. Li, �Fairness and optimal stochastic con- trol for heterogeneous networks,�in Proceedings of IEEE INFOCOM, Miami

  1. Gene expression network reconstruction by convex feature selection when incorporating genetic perturbations.

    Directory of Open Access Journals (Sweden)

    Benjamin A Logsdon

    Full Text Available Cellular gene expression measurements contain regulatory information that can be used to discover novel network relationships. Here, we present a new algorithm for network reconstruction powered by the adaptive lasso, a theoretically and empirically well-behaved method for selecting the regulatory features of a network. Any algorithms designed for network discovery that make use of directed probabilistic graphs require perturbations, produced by either experiments or naturally occurring genetic variation, to successfully infer unique regulatory relationships from gene expression data. Our approach makes use of appropriately selected cis-expression Quantitative Trait Loci (cis-eQTL, which provide a sufficient set of independent perturbations for maximum network resolution. We compare the performance of our network reconstruction algorithm to four other approaches: the PC-algorithm, QTLnet, the QDG algorithm, and the NEO algorithm, all of which have been used to reconstruct directed networks among phenotypes leveraging QTL. We show that the adaptive lasso can outperform these algorithms for networks of ten genes and ten cis-eQTL, and is competitive with the QDG algorithm for networks with thirty genes and thirty cis-eQTL, with rich topologies and hundreds of samples. Using this novel approach, we identify unique sets of directed relationships in Saccharomyces cerevisiae when analyzing genome-wide gene expression data for an intercross between a wild strain and a lab strain. We recover novel putative network relationships between a tyrosine biosynthesis gene (TYR1, and genes involved in endocytosis (RCY1, the spindle checkpoint (BUB2, sulfonate catabolism (JLP1, and cell-cell communication (PRM7. Our algorithm provides a synthesis of feature selection methods and graphical model theory that has the potential to reveal new directed regulatory relationships from the analysis of population level genetic and gene expression data.

  2. Improved Cost-Base Design of Water Distribution Networks using Genetic Algorithm

    Science.gov (United States)

    Moradzadeh Azar, Foad; Abghari, Hirad; Taghi Alami, Mohammad; Weijs, Steven

    2010-05-01

    Population growth and progressive extension of urbanization in different places of Iran cause an increasing demand for primary needs. The water, this vital liquid is the most important natural need for human life. Providing this natural need is requires the design and construction of water distribution networks, that incur enormous costs on the country's budget. Any reduction in these costs enable more people from society to access extreme profit least cost. Therefore, investment of Municipal councils need to maximize benefits or minimize expenditures. To achieve this purpose, the engineering design depends on the cost optimization techniques. This paper, presents optimization models based on genetic algorithm(GA) to find out the minimum design cost Mahabad City's (North West, Iran) water distribution network. By designing two models and comparing the resulting costs, the abilities of GA were determined. the GA based model could find optimum pipe diameters to reduce the design costs of network. Results show that the water distribution network design using Genetic Algorithm could lead to reduction of at least 7% in project costs in comparison to the classic model. Keywords: Genetic Algorithm, Optimum Design of Water Distribution Network, Mahabad City, Iran.

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

    Science.gov (United States)

    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.

  4. Submodularity in dynamics and control of networked systems

    CERN Document Server

    Clark, Andrew; Bushnell, Linda; Poovendran, Radha

    2016-01-01

    This book presents a framework for the control of networked systems utilizing submodular optimization techniques. The main focus is on selecting input nodes for the control of networked systems, an inherently discrete optimization problem with applications in power system stability, social influence dynamics, and the control of vehicle formations. The first part of the book is devoted to background information on submodular functions, matroids, and submodular optimization, and presents algorithms for distributed submodular optimization that are scalable to large networked systems. In turn, the second part develops a unifying submodular optimization approach to controlling networked systems based on multiple performance and controllability criteria. Techniques are introduced for selecting input nodes to ensure smooth convergence, synchronization, and robustness to environmental and adversarial noise. Submodular optimization is the first unifying approach towards guaranteeing both performance and controllabilit...

  5. Logistics Distribution Center Location Evaluation Based on Genetic Algorithm and Fuzzy Neural Network

    Science.gov (United States)

    Shao, Yuxiang; Chen, Qing; Wei, Zhenhua

    Logistics distribution center location evaluation is a dynamic, fuzzy, open and complicated nonlinear system, which makes it difficult to evaluate the distribution center location by the traditional analysis method. The paper proposes a distribution center location evaluation system which uses the fuzzy neural network combined with the genetic algorithm. In this model, the neural network is adopted to construct the fuzzy system. By using the genetic algorithm, the parameters of the neural network are optimized and trained so as to improve the fuzzy system’s abilities of self-study and self-adaptation. At last, the sampled data are trained and tested by Matlab software. The simulation results indicate that the proposed identification model has very small errors.

  6. Input graph: the hidden geometry in controlling complex networks

    Science.gov (United States)

    Zhang, Xizhe; Lv, Tianyang; Pu, Yuanyuan

    2016-11-01

    The ability to control a complex network towards a desired behavior relies on our understanding of the complex nature of these social and technological networks. The existence of numerous control schemes in a network promotes us to wonder: what is the underlying relationship of all possible input nodes? Here we introduce input graph, a simple geometry that reveals the complex relationship between all control schemes and input nodes. We prove that the node adjacent to an input node in the input graph will appear in another control scheme, and the connected nodes in input graph have the same type in control, which they are either all possible input nodes or not. Furthermore, we find that the giant components emerge in the input graphs of many real networks, which provides a clear topological explanation of bifurcation phenomenon emerging in dense networks and promotes us to design an efficient method to alter the node type in control. The findings provide an insight into control principles of complex networks and offer a general mechanism to design a suitable control scheme for different purposes.

  7. Genetic Algorithm Optimisation of PID Controllers for a Multivariable Process

    Directory of Open Access Journals (Sweden)

    Wael Alharbi

    2017-03-01

    Full Text Available This project is about the design of PID controllers and the improvement of outputs in multivariable processes. The optimisation of PID controller for the Shell oil process is presented in this paper, using Genetic Algorithms (GAs. Genetic Algorithms (GAs are used to automatically tune PID controllers according to given specifications. They use an objective function, which is specially formulated and measures the performance of controller in terms of time-domain bounds on the responses of closed-loop process.A specific objective function is suggested that allows the designer for a single-input, single-output (SISO process to explicitly specify the process performance specifications associated with the given problem in terms of time-domain bounds, then experimentally evaluate the closed-loop responses. This is investigated using a simple two-term parametric PID controller tuning problem. The results are then analysed and compared with those obtained using a number of popular conventional controller tuning methods. The intention is to demonstrate that the proposed objective function is inherently capable of accurately quantifying complex performance specifications in the time domain. This is something that cannot normally be employed in conventional controller design or tuning methods.Finally, the recommended objective function will be used to examine the control problems of Multi-Input-Multi-Output (MIMO processes, and the results will be presented in order to determine the efficiency of the suggested control system.

  8. Neural Networks for Modeling and Control of Particle Accelerators

    Science.gov (United States)

    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.

  9. Guaranteed Cost Fault-Tolerant Control for Networked Control Systems with Sensor Faults

    Directory of Open Access Journals (Sweden)

    Qixin Zhu

    2015-01-01

    Full Text Available For the large scale and complicated structure of networked control systems, time-varying sensor faults could inevitably occur when the system works in a poor environment. Guaranteed cost fault-tolerant controller for the new networked control systems with time-varying sensor faults is designed in this paper. Based on time delay of the network transmission environment, the networked control systems with sensor faults are modeled as a discrete-time system with uncertain parameters. And the model of networked control systems is related to the boundary values of the sensor faults. Moreover, using Lyapunov stability theory and linear matrix inequalities (LMI approach, the guaranteed cost fault-tolerant controller is verified to render such networked control systems asymptotically stable. Finally, simulations are included to demonstrate the theoretical results.

  10. Analysis of Basic Transmission Networks for Integrated Ship Control Systems

    DEFF Research Database (Denmark)

    Hansen, T.N.; Granum-Jensen, M.

    1993-01-01

    Description of a computer network for Integrated Ship Control Systems which is going to be developed as part of an EC-project. Today equipment of different make are not able to communicate with each other because most often each supplier of ISC systems has got their own proprietary network.....

  11. Analysis and control of flows in pressurized hydraulic networks

    NARCIS (Netherlands)

    Gupta, R.K.

    2006-01-01

    Analysis, design and flow control problems in pressurized hydraulic networks such as water transmission and distribution systems consisting of pipes and other appurtenant components such as reservoirs, pumps, valves and surge devices are dealt with from the prospective of network synthesis aiming at

  12. Distributed control of networked Lur’e systems

    NARCIS (Netherlands)

    Zhang, Fan

    2015-01-01

    In this thesis we systematically study distributed control of networked Lur'e systems, specifically, robust synchronization problems and cooperative robust output regulation problems. In such nonlinear multi-agent networks, the model of each agent dynamics is taken as a Lur'e system that consists of

  13. Complex Dynamical Network Control for Trajectory Tracking Using Delayed Recurrent Neural Networks

    Directory of Open Access Journals (Sweden)

    Jose P. Perez

    2014-01-01

    Full Text Available In this paper, the problem of trajectory tracking is studied. Based on the V-stability and Lyapunov theory, a control law that achieves the global asymptotic stability of the tracking error between a delayed recurrent neural network and a complex dynamical network is obtained. To illustrate the analytic results, we present a tracking simulation of a dynamical network with each node being just one Lorenz’s dynamical system and three identical Chen’s dynamical systems.

  14. Overdominant Effect of a CHRNA4 Polymorphism on Cingulo-Opercular Network Activity and Cognitive Control.

    Science.gov (United States)

    Sadaghiani, Sepideh; Ng, Bernard; Altmann, Andre; Poline, Jean-Baptiste; Banaschewski, Tobias; Bokde, Arun L W; Bromberg, Uli; Büchel, Christian; Burke Quinlan, Erin; Conrod, Patricia; Desrivières, Sylvane; Flor, Herta; Frouin, Vincent; Garavan, Hugh; Gowland, Penny; Gallinat, Jürgen; Heinz, Andreas; Ittermann, Bernd; Martinot, Jean-Luc; Paillère Martinot, Marie-Laure; Lemaitre, Hervé; Nees, Frauke; Papadopoulos Orfanos, Dimitri; Paus, Tomáš; Poustka, Luise; Millenet, Sabina; Fröhner, Juliane H; Smolka, Michael N; Walter, Henrik; Whelan, Robert; Schumann, Gunter; Napolioni, Valerio; Greicius, Michael

    2017-10-04

    The nicotinic system plays an important role in cognitive control and is implicated in several neuropsychiatric conditions. However, the contributions of genetic variability in this system to individuals' cognitive control abilities are poorly understood and the brain processes that mediate such genetic contributions remain largely unidentified. In this first large-scale neuroimaging genetics study of the human nicotinic receptor system (two cohorts, males and females, fMRI total N = 1586, behavioral total N = 3650), we investigated a common polymorphism of the high-affinity nicotinic receptor α4β2 (rs1044396 on the CHRNA4 gene) previously implicated in behavioral and nicotine-related studies (albeit with inconsistent major/minor allele impacts). Based on our prior neuroimaging findings, we expected this polymorphism to affect neural activity in the cingulo-opercular (CO) network involved in core cognitive control processes including maintenance of alertness. Consistent across the cohorts, all cortical areas of the CO network showed higher activity in heterozygotes compared with both types of homozygotes during cognitive engagement. This inverted U-shaped relation reflects an overdominant effect; that is, allelic interaction (cumulative evidence p = 1.33 * 10(-5)). Furthermore, heterozygotes performed more accurately in behavioral tasks that primarily depend on sustained alertness. No effects were observed for haplotypes of the surrounding CHRNA4 region, supporting a true overdominant effect at rs1044396. As a possible mechanism, we observed that this polymorphism is an expression quantitative trait locus modulating CHRNA4 expression levels. This is the first report of overdominance in the nicotinic system. These findings connect CHRNA4 genotype, CO network activation, and sustained alertness, providing insights into how genetics shapes individuals' cognitive control abilities.SIGNIFICANCE STATEMENT The nicotinic acetylcholine system plays a central role in

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  18. Energy scaling and reduction in controlling complex networks

    Science.gov (United States)

    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

  19. PID Neural Network Based Speed Control of Asynchronous Motor Using Programmable Logic Controller

    Directory of Open Access Journals (Sweden)

    MARABA, V. A.

    2011-11-01

    Full Text Available This paper deals with the structure and characteristics of PID Neural Network controller for single input and single output systems. PID Neural Network is a new kind of controller that includes the advantages of artificial neural networks and classic PID controller. Functioning of this controller is based on the update of controller parameters according to the value extracted from system output pursuant to the rules of back propagation algorithm used in artificial neural networks. Parameters obtained from the application of PID Neural Network training algorithm on the speed model of the asynchronous motor exhibiting second order linear behavior were used in the real time speed control of the motor. Programmable logic controller (PLC was used as real time controller. The real time control results show that reference speed successfully maintained under various load conditions.

  20. Integrated control platform for converged optical and wireless networks

    DEFF Research Database (Denmark)

    Yan, Ying

    rates, whereas optical networks can offer much higher data rates but only provide fixed connection structures. Their complementary characteristics make the integration of the two networks a promising trend for next generation networks. With combined strengths, the converged network will provide both...... high data rate services and connectivity at anytime and anywhere. One major challenge in the interworking is how to achieve seamless integration. There are many aspects involved in designing an integrated control platform, such as QoS provisioning, mobility, and resiliency. This dissertation introduces...

  1. Intelligent Servo Drives Control Based on a Single Fieldbus Network

    Directory of Open Access Journals (Sweden)

    D. Puiu

    2010-11-01

    Full Text Available Due to the quick evolution of manufacturing processes, the demand for more flexible automation systems is on the rise. To answer these requirements, distributed motion control architecture based on intelligent drives tends more and more to replace the traditional solutions. This paper presents the control of an articulated arm robot with two local intelligent servo drives connected on a CAN network to a motion controller which receives the trajectory of the robot from a computer. The control structure is based on a single CAN network where local intelligent servo drives, a motion controller and a computer are connected.

  2. Designing a neural network for closed thermosyphon with nanofluid using a genetic algorithm

    Directory of Open Access Journals (Sweden)

    H. Salehi

    2011-03-01

    Full Text Available Heat transfer of a silver/water nanofluid in a two-phase closed thermosyphon that is thermally enhanced by magnetic field has been predicted by an optimized artificial Neural Network. Artificial neural network is a technique with flexible mathematical structure that is capable of identifying complex non-linear relationships between input and output data. A multi-layer perception neural network was used to estimate the thermal efficiency and resistance of a thermosyphon during application of a magnetic field and using nanoparticles in the water as the working fluid. The magnetic field strength, volume fraction of nanofluid in water and inlet power were used as input parameters and the thermal efficiency and thermal resistance were used as output parameters. The results were compared with experimental data and it was found that the thermal efficiency and resistance estimated by the multi-layer perception neural network are accurate. The GA-ANN (Genetic Algorithm-Artificial Neural network predicts the thermosyphon behavior correctly within the given range of the training data. In this study, a new approach for the auto-design of neural networks, based on a genetic algorithm, has been used to predict collection output of a closed thermosyphon.

  3. Coactivation of Cognitive Control Networks During Task Switching.

    Science.gov (United States)

    Yin, Shouhang; Deák, Gedeon; Chen, Antao

    2017-12-14

    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) 2017 APA, all rights reserved).

  4. Genetic and epigenetic control of transfer cell development in plants.

    Science.gov (United States)

    Yuan, Jing; Bateman, Perry; Gutierrez-Marcos, Jose

    2016-09-20

    The inter-cellular translocation of nutrients in plant is mediated by highly specialized transfer cells (TCs). TCs share similar functional and structural features across a wide range of plant species, including location at plant exchange surfaces, rich in secondary wall ingrowths, facilitation of nutrient flow, and passage of select molecules. The fate of endosperm TCs is determined in the TC fate acquisition stage (TCF), before the structure features are formed in the TC differentiation stage (TCD). At present, the molecular basis of TC development in plants remains largely unknown. In this review, we summarize the important roles of the signaling molecules in different development phases, such as sugars in TCF and phytohormones in TCD, and discuss the genetic and epigenetic factors, including TC-specific genes and endogenous plant peptides, and their crosstalk with these signaling molecules as a complex regulatory network in regulation of TC development in plants. Copyright © 2016 Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, and Genetics Society of China. Published by Elsevier Ltd. All rights reserved.

  5. Modeling and controlling the two-phase dynamics of the p53 network: a Boolean network approach

    Science.gov (United States)

    Lin, Guo-Qiang; Ao, Bin; Chen, Jia-Wei; Wang, Wen-Xu; Di, Zeng-Ru

    2014-12-01

    Although much empirical evidence has demonstrated that p53 plays a key role in tumor suppression, the dynamics and function of the regulatory network centered on p53 have not yet been fully understood. Here, we develop a Boolean network model to reproduce the two-phase dynamics of the p53 network in response to DNA damage. In particular, we map the fates of cells into two types of Boolean attractors, and we find that the apoptosis attractor does not exist for minor DNA damage, reflecting that the cell is reparable. As the amount of DNA damage increases, the basin of the repair attractor shrinks, accompanied by the rising of the apoptosis attractor and the expansion of its basin, indicating that the cell becomes more irreparable with more DNA damage. For severe DNA damage, the repair attractor vanishes, and the apoptosis attractor dominates the state space, accounting for the exclusive fate of death. Based on the Boolean network model, we explore the significance of links, in terms of the sensitivity of the two-phase dynamics, to perturbing the weights of links and removing them. We find that the links are either critical or ordinary, rather than redundant. This implies that the p53 network is irreducible, but tolerant of small mutations at some ordinary links, and this can be interpreted with evolutionary theory. We further devised practical control schemes for steering the system into the apoptosis attractor in the presence of DNA damage by pinning the state of a single node or perturbing the weight of a single link. Our approach offers insights into understanding and controlling the p53 network, which is of paramount importance for medical treatment and genetic engineering.

  6. Full design of fuzzy controllers using genetic algorithms

    Science.gov (United States)

    Homaifar, Abdollah; Mccormick, ED

    1992-01-01

    This paper examines the applicability of genetic algorithms (GA) in the complete design of fuzzy logic controllers. While GA has been used before in the development of rule sets or high performance membership functions, the interdependence between these two components dictates that they should be designed together simultaneously. GA is fully capable of creating complete fuzzy controllers given the equations of motion of the system, eliminating the need for human input in the design loop. We show the application of this new method to the development of a cart controller.

  7. Smart Home: Controlling and Monitoring Households Appliances Using Gsm Network

    National Research Council Canada - National Science Library

    Budi Rahmadya; Fahrul Ghazi; Derisma

    2016-01-01

    This study discussed about using the smart home automation systems for household appliances such as lights and fans, by utilizing the GSM network as a communication medium to control and monitor the household appliances...

  8. The value of peripheral nodes in controlling multilayer networks

    CERN Document Server

    Zhang, Yan; Schweitzer, Frank

    2015-01-01

    We analyze the controllability of a two-layer network, where driver nodes can be chosen only from one layer. Each layer contains a scale-free network with directed links. The dynamics of nodes depends on the incoming links from other nodes (reputation dynamics). We find that the controllable part of the network is larger when choosing peripherial nodes to connect the two layers. The control is as efficient for peripherial nodes as driver nodes as it is for more central nodes. If we assume a cost to utilize nodes which is proportional to their degree, utilizing peripherial 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 gives us the best performance in controlling the two-layer network.

  9. Neighbor-friendly autonomous power control in wireless heterogeneous networks

    National Research Council Canada - National Science Library

    Torrea-Duran, Rodolfo; Tsiaflakis, Paschalis; Vandendorpe, Luc; Moonen, Marc

    2014-01-01

    .... To solve this problem, we propose a neighbor-friendly autonomous algorithm for power control in wireless heterogeneous networks that protects victim users from neighboring cells through a penalty...

  10. Passivity-based control and estimation in networked robotics

    CERN Document Server

    Hatanaka, Takeshi; Fujita, Masayuki; Spong, Mark W

    2015-01-01

    Highlighting the control of networked robotic systems, this book synthesizes a unified passivity-based approach to an emerging cross-disciplinary subject. Thanks to this unified approach, readers can access various state-of-the-art research fields by studying only the background foundations associated with passivity. In addition to the theoretical results and techniques,  the authors provide experimental case studies on testbeds of robotic systems  including networked haptic devices, visual robotic systems,  robotic network systems and visual sensor network systems. The text begins with an introduction to passivity and passivity-based control together with the other foundations needed in this book. The main body of the book consists of three parts. The first examines how passivity can be utilized for bilateral teleoperation and demonstrates the inherent robustness of the passivity-based controller against communication delays. The second part emphasizes passivity’s usefulness for visual feedback control ...

  11. STIMULUS: End-System Network Interface Controller for 100 Gb/s Wide Area Networks

    Energy Technology Data Exchange (ETDEWEB)

    Zarkesh-Ha, Payman [University of New Mexico

    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.

  12. Context-sensitive network-based disease genetics prediction and its implications in drug discovery.

    Science.gov (United States)

    Chen, Yang; Xu, Rong

    2017-04-01

    Disease phenotype networks play an important role in computational approaches to identifying new disease-gene associations. Current disease phenotype networks often model disease relationships based on pairwise similarities, therefore ignore the specific context on how two diseases are connected. In this study, we propose a new strategy to model disease associations using context-sensitive networks (CSNs). We developed a CSN-based phenome-driven approach for disease genetics prediction, and investigated the translational potential of the predicted genes in drug discovery. We constructed CSNs by directly connecting diseases with associated phenotypes. Here, we constructed two CSNs using different data sources; the two networks contain 26 790 and 13 822 nodes respectively. We integrated the CSNs with a genetic functional relationship network and predicted disease genes using a network-based ranking algorithm. For comparison, we built Similarity-Based disease Networks (SBN) using the same disease phenotype data. In a de novo cross validation for 3324 diseases, the CSN-based approach significantly increased the average rank from top 12.6 to top 8.8% for all tested genes comparing with the SBN-based approach ( pdisease using CSNs, and demonstrated that the top-ranked genes are highly relevant to PD pathologenesis. We pin-pointed a top-ranked drug target gene for PD, and found its association with neurodegeneration supported by literature. In summary, CSNs lead to significantly improve the disease genetics prediction comparing with SBNs and provide leads for potential drug targets. nlp.case.edu/public/data/. rxx@case.edu.

  13. An Analysis of Social Seed Network and Its Contribution to On-Farm Conservation of Crop Genetic Diversity in Nepal

    Directory of Open Access Journals (Sweden)

    Diwakar Poudel

    2015-01-01

    Full Text Available Social seed systems are important for the maintenance of crop genetic diversity on farm. This is governed by local and informal system in the community through a farmers’ network. This paper analyses these local seed systems through application of social network analysis tools and mappings and examines the network member and its stability over space and time in a small rice farming community in Nepal. NetDraw software is used for data analysis and network mapping. We found that the dynamic network structure had key role in provisioning of traditional varieties and maintaining of crop genetic diversity on farm. We identify and ascertain the key network members, constituted either as nodal or bridging (connector farmers, occupying central position in the network who promote seed flow of local crop diversity, thus strengthening crop genetic resource diversity on farm.

  14. Illusions of controlling the future: Risk and genetic inheritance.

    Science.gov (United States)

    Finkler, Kaja

    2003-01-01

    The aim of this paper is to analyse the implications of current beliefs that human beings can control the risk of inheriting a genetic disease and influence their present and future health. To accomplish this goal, materials will be drawn from disparate literatures bearing on concepts of probability, risk and genetic inheritance, and on empirical data gathered on cancer survivors and healthy persons with a family history of cancer. The concept of risk has been theorised on a grand scale but, as Lupton (1999, Introduction, Risk and Sociological Theory , Cambridge University Press) correctly observes, there has been very little empirical work done on how people experience risk as part of their lived world. In this paper, notions of probabilities and risk will be examined as applied to beliefs in genetic inheritance that are shaped by historical and cultural forces and in turn how they shape people's lives. It will be proposed that the belief that knowledge of one's genetic inheritance can control one's future health and disease is an illusion, and also replicates in part a religious notion of predestination.

  15. The Vital Network: An Algorithmic Milieu of Communication and Control

    Directory of Open Access Journals (Sweden)

    Sandra Robinson

    2016-09-01

    Full Text Available The biological turn in computing has influenced the development of algorithmic control and what I call the vital network: a dynamic, relational, and generative assemblage that is self-organizing in response to the heterogeneity of contemporary network processes, connections, and communication. I discuss this biological turn in computation and control for communication alongside historically significant developments in cybernetics that set out the foundation for the development of self-regulating computer systems. Control is shifting away from models that historically relied on the human-animal model of cognition to govern communication and control, as in early cybernetics and computer science, to a decentred, nonhuman model of control by algorithm for communication and networks. To illustrate the rise of contemporary algorithmic control, I outline a particular example, that of the biologically-inspired routing algorithm known as a ‘quorum sensing’ algorithm. The increasing expansion of algorithms as a sense-making apparatus is important in the context of social media, but also in the subsystems that coordinate networked flows of information. In that domain, algorithms are not inferring categories of identity, sociality, and practice associated with Internet consumers, rather, these algorithms are designed to act on information flows as they are transmitted along the network. The development of autonomous control realized through the power of the algorithm to monitor, sort, organize, determine, and transmit communication is the form of control emerging as a postscript to Gilles Deleuze’s ‘postscript on societies of control.’

  16. Prediction of Genetic Interactions Using Machine Learning and Network Properties.

    Science.gov (United States)

    Madhukar, Neel S; Elemento, Olivier; Pandey, Gaurav

    2015-01-01

    A genetic interaction (GI) is a type of interaction where the effect of one gene is modified by the effect of one or several other genes. These interactions are important for delineating functional relationships among genes and their corresponding proteins, as well as elucidating complex biological processes and diseases. An important type of GI - synthetic sickness or synthetic lethality - involves two or more genes, where the loss of either gene alone has little impact on cell viability, but the combined loss of all genes leads to a severe decrease in fitness (sickness) or cell death (lethality). The identification of GIs is an important problem for it can help delineate pathways, protein complexes, and regulatory dependencies. Synthetic lethal interactions have important clinical and biological significance, such as providing therapeutically exploitable weaknesses in tumors. While near systematic high-content screening for GIs is possible in single cell organisms such as yeast, the systematic discovery of GIs is extremely difficult in mammalian cells. Therefore, there is a great need for computational approaches to reliably predict GIs, including synthetic lethal interactions, in these organisms. Here, we review the state-of-the-art approaches, strategies, and rigorous evaluation methods for learning and predicting GIs, both under general (healthy/standard laboratory) conditions and under specific contexts, such as diseases.

  17. Genetic Algorithm Optimizes Q-LAW Control Parameters

    Science.gov (United States)

    Lee, Seungwon; von Allmen, Paul; Petropoulos, Anastassios; Terrile, Richard

    2008-01-01

    A document discusses a multi-objective, genetic algorithm designed to optimize Lyapunov feedback control law (Q-law) parameters in order to efficiently find Pareto-optimal solutions for low-thrust trajectories for electronic propulsion systems. These would be propellant-optimal solutions for a given flight time, or flight time optimal solutions for a given propellant requirement. The approximate solutions are used as good initial solutions for high-fidelity optimization tools. When the good initial solutions are used, the high-fidelity optimization tools quickly converge to a locally optimal solution near the initial solution. Q-law control parameters are represented as real-valued genes in the genetic algorithm. The performances of the Q-law control parameters are evaluated in the multi-objective space (flight time vs. propellant mass) and sorted by the non-dominated sorting method that assigns a better fitness value to the solutions that are dominated by a fewer number of other solutions. With the ranking result, the genetic algorithm encourages the solutions with higher fitness values to participate in the reproduction process, improving the solutions in the evolution process. The population of solutions converges to the Pareto front that is permitted within the Q-law control parameter space.

  18. Graphs for information security control in software defined networks

    Science.gov (United States)

    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.

  19. Impact of SDN Controllers Deployment on Network Availability

    OpenAIRE

    Nencioni, Gianfranco; Helvik, Bjarne Emil; Gonzalez, Andres Javier; Heegaard, Poul Einar; Kamisinski, Andrzej

    2016-01-01

    Software-defined networking (SDN) promises to improve the programmability and flexibility of networks, but it may bring also new challenges that need to be explored. The purpose of this technical report is to assess how the deployment of the SDN controllers affects the overall availability of SDN. For this, we have varied the number, homing and location of SDN controllers. A two-level modelling approach that is used to evaluate the availability of the studied scenarios. Our results show how n...

  20. A hyperstable neural network for the modelling and control of ...

    Indian Academy of Sciences (India)

    A multivariable hyperstable robust adaptive decoupling control algorithm based on a neural network is presented for the control of nonlinear multivariable coupled systems with unknown parameters and structure. The Popov theorem is used in the design of the controller. The modelling errors, coupling action and other ...

  1. Brain and cognitive reserve: Translation via network control theory.

    Science.gov (United States)

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

    2017-04-01

    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. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

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

    Science.gov (United States)

    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.

  3. Controlling technology costs as integrated networks expand.

    Science.gov (United States)

    Halverson, K

    1995-01-01

    The expansion of integrated healthcare networks is changing the way radiology technology should be acquired and maintained. Some significant effects are that utilization will decline with managed care; the radiology department is now a cost center, not a source of revenue; and integrated networks find themselves with redundant technology and excess radiology equipment. While hospitals shoulder additional financial risk associated with managed care, responsibility for finding solutions to new problems falls to the radiology administrator. Administrators can take the following steps to effectively reduce expense and risk: Understand current usage. Eliminate redundancies. Prioritize modalities. Find new financing opportunities. Lease equipment. Purchase reconditioned equipment. Redeploy assets instead of buying new. Radiology administrators who view these problems as a challenging puzzle will naturally explore creative options. They will provide the greatest flexibility and best position for their department's contributions to the hospital's overall strategic goals.

  4. Controlling Intervention Hazards in the Network MNC

    OpenAIRE

    Foss, Kirsten; Foss, Nicolai J.; Nell, Phillip C.

    2011-01-01

    The MNC literature treats the (parent) HQ as entirely benevolent with respect to their perceived and actual intentions when they intervene at lower levels of the MNC. However, HQ may intervene in subsidiaries in ways that demotivate subsidiary employees and managers (and therefore harm value-creation). This may happen even if such intervention is benevolent in its intentions. We argue that the movement away from more traditional hierarchical forms of the MNC and towards network MNCs placed in...

  5. Optimal Robust Motion Controller Design Using Multiobjective Genetic Algorithm

    Science.gov (United States)

    Svečko, Rajko

    2014-01-01

    This paper describes the use of a multiobjective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance observer in an RIC framework. The RIC approach is presented in the form with internal and external feedback loops, in which an internal disturbance rejection controller and an external performance controller must be synthesised. This paper involves novel objectives for robustness and performance assessments for such an approach. Objective functions for the robustness property of RIC are based on simple even polynomials with nonnegativity conditions. Regional pole placement method is presented with the aims of controllers' structures simplification and their additional arbitrary selection. Regional pole placement involves arbitrary selection of central polynomials for both loops, with additional admissible region of the optimized pole location. Polynomial deviation between selected and optimized polynomials is measured with derived performance objective functions. A multiobjective function is composed of different unrelated criteria such as robust stability, controllers' stability, and time-performance indexes of closed loops. The design of controllers and multiobjective optimization procedure involve a set of the objectives, which are optimized simultaneously with a genetic algorithm—differential evolution. PMID:24987749

  6. Environmental noise, genetic diversity and the evolution of evolvability and robustness in model gene networks.

    Directory of Open Access Journals (Sweden)

    Christopher F Steiner

    Full Text Available The ability of organisms to adapt and persist in the face of environmental change is accepted as a fundamental feature of natural systems. More contentious is whether the capacity of organisms to adapt (or "evolvability" can itself evolve and the mechanisms underlying such responses. Using model gene networks, I provide evidence that evolvability emerges more readily when populations experience positively autocorrelated environmental noise (red noise compared to populations in stable or randomly varying (white noise environments. Evolvability was correlated with increasing genetic robustness to effects on network viability and decreasing robustness to effects on phenotypic expression; populations whose networks displayed greater viability robustness and lower phenotypic robustness produced more additive genetic variation and adapted more rapidly in novel environments. Patterns of selection for robustness varied antagonistically with epistatic effects of mutations on viability and phenotypic expression, suggesting that trade-offs between these properties may constrain their evolutionary responses. Evolution of evolvability and robustness was stronger in sexual populations compared to asexual populations indicating that enhanced genetic variation under fluctuating selection combined with recombination load is a primary driver of the emergence of evolvability. These results provide insight into the mechanisms potentially underlying rapid adaptation as well as the environmental conditions that drive the evolution of genetic interactions.

  7. Network-Informed Gene Ranking Tackles Genetic Heterogeneity in Exome-Sequencing Studies of Monogenic Disease.

    Science.gov (United States)

    Dand, Nick; Schulz, Reiner; Weale, Michael E; Southgate, Laura; Oakey, Rebecca J; Simpson, Michael A; Schlitt, Thomas

    2015-12-01

    Genetic heterogeneity presents a significant challenge for the identification of monogenic disease genes. Whole-exome sequencing generates a large number of candidate disease-causing variants and typical analyses rely on deleterious variants being observed in the same gene across several unrelated affected individuals. This is less likely to occur for genetically heterogeneous diseases, making more advanced analysis methods necessary. To address this need, we present HetRank, a flexible gene-ranking method that incorporates interaction network data. We first show that different genes underlying the same monogenic disease are frequently connected in protein interaction networks. This motivates the central premise of HetRank: those genes carrying potentially pathogenic variants and whose network neighbors do so in other affected individuals are strong candidates for follow-up study. By simulating 1,000 exome sequencing studies (20,000 exomes in total), we model varying degrees of genetic heterogeneity and show that HetRank consistently prioritizes more disease-causing genes than existing analysis methods. We also demonstrate a proof-of-principle application of the method to prioritize genes causing Adams-Oliver syndrome, a genetically heterogeneous rare disease. An implementation of HetRank in R is available via the Website http://sourceforge.net/p/hetrank/. © 2015 The Authors. **Human Mutation published by Wiley Periodicals, Inc.

  8. An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks.

    Science.gov (United States)

    Yoon, Yourim; Kim, Yong-Hyuk

    2013-10-01

    Sensor networks have a lot of applications such as battlefield surveillance, environmental monitoring, and industrial diagnostics. Coverage is one of the most important performance metrics for sensor networks since it reflects how well a sensor field is monitored. In this paper, we introduce the maximum coverage deployment problem in wireless sensor networks and analyze the properties of the problem and its solution space. Random deployment is the simplest way to deploy sensor nodes but may cause unbalanced deployment and therefore, we need a more intelligent way for sensor deployment. We found that the phenotype space of the problem is a quotient space of the genotype space in a mathematical view. Based on this property, we propose an efficient genetic algorithm using a novel normalization method. A Monte Carlo method is adopted to design an efficient evaluation function, and its computation time is decreased without loss of solution quality using a method that starts from a small number of random samples and gradually increases the number for subsequent generations. The proposed genetic algorithms could be further improved by combining with a well-designed local search. The performance of the proposed genetic algorithm is shown by a comparative experimental study. When compared with random deployment and existing methods, our genetic algorithm was not only about twice faster, but also showed significant performance improvement in quality.

  9. Wireless sensor network for streetlight monitoring and control

    Science.gov (United States)

    Huang, Xin-Ming; Ma, Jing; Leblanc, Lawrence E.

    2004-08-01

    Wireless sensor network has attracted considerable research attention as the world becomes more information oriented. This technology provides an opportunity of innovations in traditional industries. Management and control of streetlight system is a labor-intensive high-cost task for public facility operations. This paper applies wireless sensor network technology in streetlight monitoring and control. Wireless sensor networks are employed to replace traditional physical patrol maintenance and manual switching on every lamp in the street or along the highway at the aim of reducing the maintenance and management expense. Active control is used to preserve energy cost while ensuring public safety. A proof-of-concept network architecture operated at 900 MHz industrial, scientific, and medical (ISM) band is designed for a two-way wireless telemetry system in streetlight remote control and monitoring. The radio architecture, multi-hop protocol and system interface are discussed in detail. MOTES sensor nodes are used in simulation and experimental tests. Simulation results show that the sensor network approach provides an efficient solution to monitor and control lighting infrastructures through wireless links. The unique application in this paper addresses an immediate need in streetlight control and monitoring, the architecture developed in this research could also serve as a platform for many other applications and researches in wireless sensor network.

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

    Science.gov (United States)

    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.

  11. Inference of Vohradsky's models of genetic networks by solving two-dimensional function optimization problems.

    Directory of Open Access Journals (Sweden)

    Shuhei Kimura

    Full Text Available The inference of a genetic network is a problem in which mutual interactions among genes are inferred from time-series of gene expression levels. While a number of models have been proposed to describe genetic networks, this study focuses on a mathematical model proposed by Vohradský. Because of its advantageous features, several researchers have proposed the inference methods based on Vohradský's model. When trying to analyze large-scale networks consisting of dozens of genes, however, these methods must solve high-dimensional non-linear function optimization problems. In order to resolve the difficulty of estimating the parameters of the Vohradský's model, this study proposes a new method that defines the problem as several two-dimensional function optimization problems. Through numerical experiments on artificial genetic network inference problems, we showed that, although the computation time of the proposed method is not the shortest, the method has the ability to estimate parameters of Vohradský's models more effectively with sufficiently short computation times. This study then applied the proposed method to an actual inference problem of the bacterial SOS DNA repair system, and succeeded in finding several reasonable regulations.

  12. A Genetic Algorithm for the Bi-Level Topological Design of Local Area Networks.

    Directory of Open Access Journals (Sweden)

    José-Fernando Camacho-Vallejo

    Full Text Available Local access networks (LAN are commonly used as communication infrastructures which meet the demand of a set of users in the local environment. Usually these networks consist of several LAN segments connected by bridges. The topological LAN design bi-level problem consists on assigning users to clusters and the union of clusters by bridges in order to obtain a minimum response time network with minimum connection cost. Therefore, the decision of optimally assigning users to clusters will be made by the leader and the follower will make the decision of connecting all the clusters while forming a spanning tree. In this paper, we propose a genetic algorithm for solving the bi-level topological design of a Local Access Network. Our solution method considers the Stackelberg equilibrium to solve the bi-level problem. The Stackelberg-Genetic algorithm procedure deals with the fact that the follower's problem cannot be optimally solved in a straightforward manner. The computational results obtained from two different sets of instances show that the performance of the developed algorithm is efficient and that it is more suitable for solving the bi-level problem than a previous Nash-Genetic approach.

  13. Optimization of cocoa butter analog synthesis variables using neural networks and genetic algorithm.

    Science.gov (United States)

    Shekarchizadeh, Hajar; Tikani, Reza; Kadivar, Mahdi

    2014-09-01

    Cocoa butter analog was prepared from camel hump fat and tristearin by enzymatic interesterification in supercritical carbon dioxide (SC-CO2) using immobilized Thermomyces lanuginosus lipase (Lipozyme TL IM) as a biocatalyst. Optimal process conditions were determined using neural networks and genetic algorithm optimization. Response surfaces methodology was used to design the experiments to collect data for the neural network modelling. A general regression neural network model was developed to predict the response of triacylglycerol (TAG) distribution of cocoa butter analog from the process pressure, temperature, tristearin/camel hump fat ratio, water content, and incubation time. A genetic algorithm was used to search for a combination of the process variables for production of most similar cocoa butter analog to the corresponding cocoa butter. The combinations of the process variables during genetic algorithm optimization were evaluated using the neural network model. The pressure of 10 MPa; temperature of 40 °C; SSS/CHF ratio of 0.6:1; water content of 13 % (w/w); and incubation time of 4.5 h were found to be the optimum conditions to achieve the most similar cocoa butter analog to the corresponding cocoa butter.

  14. Exploitation of genetic interaction network topology for the prediction of epistatic behavior

    KAUST Repository

    Alanis Lobato, Gregorio

    2013-10-01

    Genetic interaction (GI) detection impacts the understanding of human disease and the ability to design personalized treatment. The mapping of every GI in most organisms is far from complete due to the combinatorial amount of gene deletions and knockdowns required. Computational techniques to predict new interactions based only on network topology have been developed in network science but never applied to GI networks.We show that topological prediction of GIs is possible with high precision and propose a graph dissimilarity index that is able to provide robust prediction in both dense and sparse networks.Computational prediction of GIs is a strong tool to aid high-throughput GI determination. The dissimilarity index we propose in this article is able to attain precise predictions that reduce the universe of candidate GIs to test in the lab. © 2013 Elsevier Inc.

  15. Dysfunction and dysconnection in cortical-striatal networks during sustained attention: Genetic risk for schizophrenia or bipolar disorder and its impact on brain network function

    Directory of Open Access Journals (Sweden)

    Vaibhav A. Diwadkar

    2014-05-01

    Full Text Available Abnormalities in the brain’s attention network may represent early identifiable neurobiological impairments in individuals at increased risk for schizophrenia or bipolar disorder. Here we provide evidence of dysfunctional regional and network function in adolescents at higher genetic risk for schizophrenia or bipolar disorder (henceforth HGR. During fMRI, participants engaged in a sustained attention task with variable demands. The task alternated between attention (120 s, visual control (passive viewing; 120 s and rest (20 s epochs. Low and high demand attention conditions were created using the rapid presentation of 2- or 3-digit numbers. Subjects were required to detect repeated presentation of numbers. We demonstrate that the recruitment of cortical and striatal regions are disordered in HGR: Relative to typical controls (TC, HGR showed lower recruitment of the dorsal prefrontal cortex, but higher recruitment of the superior parietal cortex. This imbalance was more dramatic in the basal ganglia. There, a group by task demand interaction was observed, such that increased attention demand led to increased engagement in TC, but disengagement in HGR. These activation studies were complemented by network analyses using Dynamic Causal Modeling. Competing model architectures were assessed across a network of cortical-striatal regions, distinguished at a second level using random effects Bayesian model selection. In the winning architecture, HGR were characterized by significant reductions in coupling across both frontal-striatal and frontal-parietal pathways. The effective connectivity analyses indicate emergent network dysconnection, consistent with findings in patients with schizophrenia. Emergent patterns of regional dysfunction and disconnection in cortical-striatal pathways may provide functional biological signatures in the adolescent risk state for psychiatric illness.

  16. Fluid Limits of Optimally Controlled Queueing Networks

    OpenAIRE

    Guodong Pang; Day, Martin V.

    2007-01-01

    We consider a class of queueing processes represented by a Skorokhod problem coupled with a controlled point process. Posing a discounted control problem for such processes, we show that the optimal value functions converge, in the fluid limit, to the value of an analogous deterministic control problem for fluid processes. Peer Reviewed

  17. Fluid Limits of Optimally Controlled Queueing Networks

    Directory of Open Access Journals (Sweden)

    Guodong Pang

    2007-01-01

    Full Text Available We consider a class of queueing processes represented by a Skorokhod problem coupled with a controlled point process. Posing a discounted control problem for such processes, we show that the optimal value functions converge, in the fluid limit, to the value of an analogous deterministic control problem for fluid processes.

  18. Scalable high-throughput identification of genetic targets by network filtering.

    Science.gov (United States)

    Bevilacqua, Vitoantonio; Pannarale, Paolo

    2013-01-01

    Discovering the molecular targets of compounds or the cause of physiological conditions, among the multitude of known genes, is one of the major challenges of bioinformatics. One of the most common approaches to this problem is finding sets of differentially expressed, and more recently differentially co-expressed, genes. Other approaches require libraries of genetic mutants or require to perform a large number of assays. Another elegant approach is the filtering of mRNA expression profiles using reverse-engineered gene network models of the target cell. This approach has the advantage of not needing control samples, libraries or numerous assays. Nevertheless, the impementations of this strategy proposed so far are computationally demanding. Moreover the user has to arbitrarily choose a threshold on the number of potentially relevant genes from the algorithm output. Our solution, while performing comparably to state of the art algorithms in terms of discovered targets, is more efficient in terms of memory and time consumption. The proposed algorithm computes the likelihood associated to each gene and outputs to the user only the list of likely perturbed genes. The proposed algorithm is a valid alternative to existing algorithms and is particularly suited to contemporary gene expression microarrays, given the number of probe sets in each chip, also when executed on common desktop computers.

  19. Generalized Mutual Synchronization between Two Controlled Interdependent Networks

    Directory of Open Access Journals (Sweden)

    Quan Xu

    2014-01-01

    Full Text Available This paper mainly focuses on the generalized mutual synchronization between two controlled interdependent networks. First, we propose the general model of controlled interdependent networks A and B with time-varying internetwork delays coupling. Then, by constructing Lyapunov functions and utilizing adaptive control technique, some sufficient conditions are established to ensure that the mutual synchronization errors between the state variables of networks A and B can asymptotically converge to zero. Finally, two numerical examples are given to illustrate the effectiveness of the theoretical results and to explore potential application in future smart grid. The simulation results also show how interdependent topologies and internetwork coupling delays influence the mutual synchronizability, which help to design interdependent networks with optimal mutual synchronizability.

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

    Science.gov (United States)

    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.

  1. Adaptive Neural Network Algorithm for Power Control in Nuclear Power Plants

    Science.gov (United States)

    Masri Husam Fayiz, Al

    2017-01-01

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

  2. Genetics

    DEFF Research Database (Denmark)

    Christensen, Kaare; McGue, Matt

    2016-01-01

    The sequenced genomes of individuals aged ≥80 years, who were highly educated, self-referred volunteers and with no self-reported chronic diseases were compared to young controls. In these data, healthy ageing is a distinct phenotype from exceptional longevity and genetic factors that protect...

  3. Genetic optimization of fuzzy fractional PD+I controllers.

    Science.gov (United States)

    Jesus, Isabel S; Barbosa, Ramiro S

    2015-07-01

    Fractional order calculus is a powerful emerging mathematical tool in science and engineering. There is currently an increasing interest in generalizing classical control theories and developing novel control strategies. The genetic algorithms (GA) are a stochastic search and optimization methods based on the reproduction processes found in biological systems, used for solving engineering problems. In the context of process control, the fuzzy logic usually means variables that are described by imprecise terms, and represented by quantities that are qualitative and vague. In this article we consider the development of an optimal fuzzy fractional PD+I controller in which the parameters are tuned by a GA. The performance of the proposed fuzzy fractional control is illustrated through some application examples. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  4. Role extraction in complex networks and its application in control of networks

    Science.gov (United States)

    Zhou, Mingyang; He, Xingsheng; Fu, Zhongqian; Zhuo, Zhao

    2016-01-01

    Given a large network, dynamics of the network are determined by both nodes' features and network connections. Some features could be extracted from node labels and other kinds of priori knowledge. But how to perform the feature classification without priori knowledge is a challenge. This paper addresses the key problem: how do we conduct role extraction in networks with only edge connections known? On the basis of behavior differences in dynamics, nodes are classified into three role groups: Leaders(L), Communicators(C) and Members(M). Unlike traditional community detections, we detect overlapping communities by link clustering first and then classify nodes according to the community entropy, which describes the disorder of how many different communities a node connects to. We propose a time saving and unsupervised learning approach for automatically discovering nodes' roles based solely on network topology. The effectiveness of this method is demonstrated on six real-world networks through pinning control. By controlling communicator nodes, the controllability is enhanced and the cost for control is reduced obviously in networks with strong community structure.

  5. Control Plane Strategies for Elastic Optical Networks

    DEFF Research Database (Denmark)

    Turus, Ioan

    Networks (EONs) concept is proposed as a solution to enable a more flexible handling of the optical capacity and allows an increase of available capacity over the existing optical infrastructure. One main requirement for enabling EONs is to have a flexible spectrum structure (i.e.Flex-Grid) which allows...... consumption. EONs offer the opportunity of deploying energy efficiency strategies, which benefit from the flexible nature of elastic optoelectronic devices. This thesis proposes and investigates different approaches for reducing power consumption based on EONs in realistic dynamic traffic scenarios....

  6. Ensemble learning of genetic networks from time-series expression data.

    Science.gov (United States)

    Nam, Dougu; Yoon, Sung Ho; Kim, Jihyun F

    2007-12-01

    Inferring genetic networks from time-series expression data has been a great deal of interest. In most cases, however, the number of genes exceeds that of data points which, in principle, makes it impossible to recover the underlying networks. To address the dimensionality problem, we apply the subset selection method to a linear system of difference equations. Previous approaches assign the single most likely combination of regulators to each target gene, which often causes over-fitting of the small number of data. Here, we propose a new algorithm, named LEARNe, which merges the predictions from all the combinations of regulators that have a certain level of likelihood. LEARNe provides more accurate and robust predictions than previous methods for the structure of genetic networks under the linear system model. We tested LEARNe for reconstructing the SOS regulatory network of Escherichia coli and the cell cycle regulatory network of yeast from real experimental data, where LEARNe also exhibited better performances than previous methods. The MATLAB codes are available upon request from the authors.

  7. Calibration of parameters of water supply network model using genetic algorithm

    Directory of Open Access Journals (Sweden)

    Boczar Tomasz

    2017-01-01

    Full Text Available Computer simulation models of water supply networks are commonly applied in the water industry. As part of the research works, results of which are presented in the paper, OFF-LINE and ON-LINE calibration of water supply network model parameters using two methods was carried out and compared. The network skeleton was developed in the Epanet software. For optimization two types of dependent variables were subjected: the pressure on the node and volume flow in the network section. The first calibration method regards to application of the genetic algorithm, which is a build in plugin - “Epanet Calibrator”. The second method was related to the use of function ga, which is implemented in the MATLAB toolbox Genetic Algorithm and Direct Search. The possibilities of application of these algorithms to solve the issue of optimizing the parameters of the created model of water supply network in both cases: OFF-LINE and ON-LINE calibration was examined. An analysis of the effectiveness of the considered algorithms for different values of configuration parameters was performed. Based on the achieved results it was stated that application of the ga algorithm gives higher correlation of the calibrated values to the empirical data.

  8. Calibration of parameters of water supply network model using genetic algorithm

    Science.gov (United States)

    Boczar, Tomasz; Adamikiewicz, Norbert; Stanisławski, Włodzimierz

    2017-10-01

    Computer simulation models of water supply networks are commonly applied in the water industry. As part of the research works, results of which are presented in the paper, OFF-LINE and ON-LINE calibration of water supply network model parameters using two methods was carried out and compared. The network skeleton was developed in the Epanet software. For optimization two types of dependent variables were subjected: the pressure on the node and volume flow in the network section. The first calibration method regards to application of the genetic algorithm, which is a build in plugin - "Epanet Calibrator". The second method was related to the use of function ga, which is implemented in the MATLAB toolbox Genetic Algorithm and Direct Search. The possibilities of application of these algorithms to solve the issue of optimizing the parameters of the created model of water supply network in both cases: OFF-LINE and ON-LINE calibration was examined. An analysis of the effectiveness of the considered algorithms for different values of configuration parameters was performed. Based on the achieved results it was stated that application of the ga algorithm gives higher correlation of the calibrated values to the empirical data.

  9. A Selfish Constraint Satisfaction Genetic Algorithms for Planning a Long-Distance Transportation Network

    Science.gov (United States)

    Onoyama, Takashi; Maekawa, Takuya; Kubota, Sen; Tsuruta, Setuso; Komoda, Norihisa

    To build a cooperative logistics network covering multiple enterprises, a planning method that can build a long-distance transportation network is required. Many strict constraints are imposed on this type of problem. To solve these strict-constraint problems, a selfish constraint satisfaction genetic algorithm (GA) is proposed. In this GA, each gene of an individual satisfies only its constraint selfishly, disregarding the constraints of other genes in the same individuals. Moreover, a constraint pre-checking method is also applied to improve the GA convergence speed. The experimental result shows the proposed method can obtain an accurate solution in a practical response time.

  10. FIPA agent based network distributed control system

    Energy Technology Data Exchange (ETDEWEB)

    D. Abbott; V. Gyurjyan; G. Heyes; E. Jastrzembski; C. Timmer; E. Wolin

    2003-03-01

    A control system with the capabilities to combine heterogeneous control systems or processes into a uniform homogeneous environment is discussed. This dynamically extensible system is an example of the software system at the agent level of abstraction. This level of abstraction considers agents as atomic entities that communicate to implement the functionality of the control system. Agents' engineering aspects are addressed by adopting the domain independent software standard, formulated by FIPA. Jade core Java classes are used as a FIPA specification implementation. A special, lightweight, XML RDFS based, control oriented, ontology markup language is developed to standardize the description of the arbitrary control system data processor. Control processes, described in this language, are integrated into the global system at runtime, without actual programming. Fault tolerance and recovery issues are also addressed.

  11. A multi-agent genetic algorithm for community detection in complex networks

    Science.gov (United States)

    Li, Zhangtao; Liu, Jing

    2016-05-01

    Complex networks are popularly used to represent a lot of practical systems in the domains of biology and sociology, and the structure of community is one of the most important network attributes which has received an enormous amount of attention. Community detection is the process of discovering the community structure hidden in complex networks, and modularity Q is one of the best known quality functions measuring the quality of communities of networks. In this paper, a multi-agent genetic algorithm, named as MAGA-Net, is proposed to optimize modularity value for the community detection. An agent, coded by a division of a network, represents a candidate solution. All agents live in a lattice-like environment, with each agent fixed on a lattice point. A series of operators are designed, namely split and merging based neighborhood competition operator, hybrid neighborhood crossover, adaptive mutation and self-learning operator, to increase modularity value. In the experiments, the performance of MAGA-Net is validated on both well-known real-world benchmark networks and large-scale synthetic LFR networks with 5000 nodes. The systematic comparisons with GA-Net and Meme-Net show that MAGA-Net outperforms these two algorithms, and can detect communities with high speed, accuracy and stability.

  12. Modeling Slump of Ready Mix Concrete Using Genetically Evolved Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Vinay Chandwani

    2014-01-01

    Full Text Available Artificial neural networks (ANNs have been the preferred choice for modeling the complex and nonlinear material behavior where conventional mathematical approaches do not yield the desired accuracy and predictability. Despite their popularity as a universal function approximator and wide range of applications, no specific rules for deciding the architecture of neural networks catering to a specific modeling task have been formulated. The research paper presents a methodology for automated design of neural network architecture, replacing the conventional trial and error technique of finding the optimal neural network. The genetic algorithms (GA stochastic search has been harnessed for evolving the optimum number of hidden layer neurons, transfer function, learning rate, and momentum coefficient for backpropagation ANN. The methodology has been applied for modeling slump of ready mix concrete based on its design mix constituents, namely, cement, fly ash, sand, coarse aggregates, admixture, and water-binder ratio. Six different statistical performance measures have been used for evaluating the performance of the trained neural networks. The study showed that, in comparison to conventional trial and error technique of deciding the neural network architecture and training parameters, the neural network architecture evolved through GA was of reduced complexity and provided better prediction performance.

  13. Genetically Controlled Fusion, Exocytosis and Fission of Artificial Vesicles

    DEFF Research Database (Denmark)

    Bönzli, Eva; Hadorn, Maik; De Lucrezia, Davide

    if a special class of viral proteins, termed fusogenic peptides, were added to the external medium. In the present work, we intend to develop genetically controlled fusion, fission and exocytosis of vesicles by the synthesis of peptides within vesicles. First, we enclosed synthesized peptides in vesicles...... to induce in a next step fusion of adjacent vesicles, fission and exocytosis of nested vesicles. Second, we will replace the peptides by an enclosed cell-free expression system to internally synthesize fusion peptides. To control the gene expression, different mechanisms are available, e.g. addition...... fusion, fission and exocytosis....

  14. Resistance to genetic insect control: Modelling the effects of space.

    Science.gov (United States)

    Watkinson-Powell, Benjamin; Alphey, Nina

    2017-01-21

    Genetic insect control, such as self-limiting RIDL(2) (Release of Insects Carrying a Dominant Lethal) technology, is a development of the sterile insect technique which is proposed to suppress wild populations of a number of major agricultural and public health insect pests. This is achieved by mass rearing and releasing male insects that are homozygous for a repressible dominant lethal genetic construct, which causes death in progeny when inherited. The released genetically engineered ('GE') insects compete for mates with wild individuals, resulting in population suppression. A previous study modelled the evolution of a hypothetical resistance to the lethal construct using a frequency-dependent population genetic and population dynamic approach. This found that proliferation of resistance is possible but can be diluted by the introgression of susceptible alleles from the released homozygous-susceptible GE males. We develop this approach within a spatial context by modelling the spread of a lethal construct and resistance trait, and the effect on population control, in a two deme metapopulation, with GE release in one deme. Results show that spatial effects can drive an increased or decreased evolution of resistance in both the target and non-target demes, depending on the effectiveness and associated costs of the resistant trait, and on the rate of dispersal. A recurrent theme is the potential for the non-target deme to act as a source of resistant or susceptible alleles for the target deme through dispersal. This can in turn have a major impact on the effectiveness of insect population control. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  15. Controllability of Boolean networks via input controls under Harvey's update scheme

    Science.gov (United States)

    Luo, Chao; Zhang, Xiaolin; Shao, Rui; Zheng, YuanJie

    2016-02-01

    In this article, the controllability of Boolean networks via input controls under Harvey's update scheme is investigated. First, the model of Boolean control networks under Harvey's stochastic update is proposed, by means of semi-tensor product approach, which is converted into discrete-time linear representation. And, a general formula of control-depending network transition matrix is provided. Second, based on discrete-time dynamics, controllability of the proposed model is analytically discussed by revealing the necessary and sufficient conditions of the reachable sets, respectively, for three kinds of controls, i.e., free Boolean control sequence, input control networks, and close-loop control. Examples are showed to demonstrate the effectiveness and feasibility of the proposed scheme.

  16. A Network Scheduling Model for Distributed Control Simulation

    Science.gov (United States)

    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.

  17. Noise Control for a Moving Evaluation Point Using Neural Networks

    Science.gov (United States)

    Maeda, Toshiki; Shiraishi, Toshihiko

    2016-09-01

    This paper describes the noise control for a moving evaluation point using neural networks by making the best use of its learning ability. Noise control is a technology which is effective on low-frequency noise. Based on the principle of superposition, a primary sound wave can be cancelled at an evaluation point by emitting a secondary opposite sound wave. To obtain good control performance, it is important to precisely identify the characteristics of all the sound paths. One of the most popular algorithms of noise control is filtered-x LMS algorithm. This algorithm can deliver a good result while all the sound paths do not change. However, the control system becomes uncontrollable while the evaluation point is moving. To solve the problem, the characteristics of all the paths are must be identified at all time. In this paper, we applied neural networks with the learning ability to the noise control system to follow the time-varying paths and verified its control performance by numerical simulations. Then, dropout technique for the networks is also applied. Dropout is a technique that prevent the network from overfitting and enables better control performance. By applying dropout for noise control, it prevents the system from diverging.

  18. A source-controlled data center network model.

    Directory of Open Access Journals (Sweden)

    Yang Yu

    Full Text Available 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.

  19. A source-controlled data center network model.

    Science.gov (United States)

    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.

  20. A source-controlled data center network model

    Science.gov (United States)

    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

  1. Microcontroller Protocol for Secure Broadcast in Controller Area Networks

    OpenAIRE

    B Vijayalakshmi; Kumar, K

    2014-01-01

    Controller Area Network is a bus commonly used by controllers inside vehicles and in various industrial control applications. In the past controllers were assumed to operate in secure perimeters, but today these environments are well connected to the outside world and recent incidents showed them extremely vulnerable to cyber-attacks. To withstand such threats, one can implement security in the application layer of CAN. Here we design, refine and implement a broadcast authenti...

  2. A Network Access Control Framework for 6LoWPAN Networks

    Science.gov (United States)

    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. Maximizing influence in a social network: Improved results using a genetic algorithm

    Science.gov (United States)

    Zhang, Kaiqi; Du, Haifeng; Feldman, Marcus W.

    2017-07-01

    The influence maximization problem focuses on finding a small subset of nodes in a social network that maximizes the spread of influence. While the greedy algorithm and some improvements to it have been applied to solve this problem, the long solution time remains a problem. Stochastic optimization algorithms, such as simulated annealing, are other choices for solving this problem, but they often become trapped in local optima. We propose a genetic algorithm to solve the influence maximization problem. Through multi-population competition, using this algorithm we achieve an optimal result while maintaining diversity of the solution. We tested our method with actual networks, and our genetic algorithm performed slightly worse than the greedy algorithm but better than other algorithms.

  4. Exploring the Link between Genetic Relatedness r and Social Contact Structure k in Animal Social Networks.

    Science.gov (United States)

    Wolf, Jochen B W; Traulsen, Arne; James, Richard

    2011-01-01

    Our understanding of how cooperation can arise in a population of selfish individuals has been greatly advanced by theory. More than one approach has been used to explore the effect of population structure. Inclusive fitness theory uses genetic relatedness r to express the role of population structure. Evolutionary graph theory models the evolution of cooperation on network structures and focuses on the number of interacting partners k as a quantity of interest. Here we use empirical data from a hierarchically structured animal contact network to examine the interplay between independent, measurable proxies for these key parameters. We find strong inverse correlations between estimates of r and k over three levels of social organization, suggesting that genetic relatedness and social contact structure capture similar structural information in a real population.

  5. Real-Coded Quantum-Inspired Genetic Algorithm-Based BP Neural Network Algorithm

    Directory of Open Access Journals (Sweden)

    Jianyong Liu

    2015-01-01

    Full Text Available The method that the real-coded quantum-inspired genetic algorithm (RQGA used to optimize the weights and threshold of BP neural network is proposed to overcome the defect that the gradient descent method makes the algorithm easily fall into local optimal value in the learning process. Quantum genetic algorithm (QGA is with good directional global optimization ability, but the conventional QGA is based on binary coding; the speed of calculation is reduced by the coding and decoding processes. So, RQGA is introduced to explore the search space, and the improved varied learning rate is adopted to train the BP neural network. Simulation test shows that the proposed algorithm is effective to rapidly converge to the solution conformed to constraint conditions.

  6. A Neural Network: Family Competition Genetic Algorithm and Its Applications in Electromagnetic Optimization

    Directory of Open Access Journals (Sweden)

    P.-Y. Chen

    2009-01-01

    Full Text Available This study proposes a neural network-family competition genetic algorithm (NN-FCGA for solving the electromagnetic (EM optimization and other general-purpose optimization problems. The NN-FCGA is a hybrid evolutionary-based algorithm, combining the good approximation performance of neural network (NN and the robust and effective optimum search ability of the family competition genetic algorithms (FCGA to accelerate the optimization process. In this study, the NN-FCGA is used to extract a set of optimal design parameters for two representative design examples: the multiple section low-pass filter and the polygonal electromagnetic absorber. Our results demonstrate that the optimal electromagnetic properties given by the NN-FCGA are comparable to those of the FCGA, but reducing a large amount of computation time and a well-trained NN model that can serve as a nonlinear approximator was developed during the optimization process of the NN-FCGA.

  7. Genetic algorithm pruning of probabilistic neural networks in medical disease estimation.

    Science.gov (United States)

    Mantzaris, Dimitrios; Anastassopoulos, George; Adamopoulos, Adam

    2011-10-01

    A hybrid model consisting of an Artificial Neural Network (ANN) and a Genetic Algorithm procedure for diagnostic risk factors selection in Medicine is proposed in this paper. A medical disease prediction may be viewed as a pattern classification problem based on a set of clinical and laboratory parameters. Probabilistic Neural Network models were assessed in terms of their classification accuracy concerning medical disease prediction. A Genetic Algorithm search was performed to examine potential redundancy in the diagnostic factors. This search led to a pruned ANN architecture, minimizing the number of diagnostic factors used during the training phase and therefore minimizing the number of nodes in the ANN input and hidden layer as well as the Mean Square Error of the trained ANN at the testing phase. As a conclusion, a number of diagnostic factors in a patient's data record can be omitted without loss of fidelity in the diagnosis procedure. Copyright © 2011 Elsevier Ltd. All rights reserved.

  8. Genetic and Environmental Control of Neurodevelopmental Robustness in Drosophila.

    Directory of Open Access Journals (Sweden)

    David J Mellert

    Full Text Available Interindividual differences in neuronal wiring may contribute to behavioral individuality and affect susceptibility to neurological disorders. To investigate the causes and potential consequences of wiring variation in Drosophila melanogaster, we focused on a hemilineage of ventral nerve cord interneurons that exhibits morphological variability. We find that late-born subclasses of the 12A hemilineage are highly sensitive to genetic and environmental variation. Neurons in the second thoracic segment are particularly variable with regard to two developmental decisions, whereas its segmental homologs are more robust. This variability "hotspot" depends on Ultrabithorax expression in the 12A neurons, indicating variability is cell-intrinsic and under genetic control. 12A development is more variable and sensitive to temperature in long-established laboratory strains than in strains recently derived from the wild. Strains with a high frequency of one of the 12A variants also showed a high frequency of animals with delayed spontaneous flight initiation, whereas other wing-related behaviors did not show such a correlation and were thus not overtly affected by 12A variation. These results show that neurodevelopmental robustness is variable and under genetic control in Drosophila and suggest that the fly may serve as a model for identifying conserved gene pathways that stabilize wiring in stressful developmental environments. Moreover, some neuronal lineages are variation hotspots and thus may be more amenable to evolutionary change.

  9. Genetic algorithms for optimal design and control of adaptive structures

    Science.gov (United States)

    Ribeiro, Rui; da Mota Silva, Suzana; Rodrigues, Jose D.; Vaz, Mario A. P.

    2000-06-01

    Future High Energy Physics experiments require the use of light and stable structures to support their most precise radiation detection elements. These large structures must be light, highly stable, stiff and radiation tolerant in an environment where external vibrations, high radiation levels, material aging, temperature and humidity gradients are not negligible. Unforeseen factors and the unknown result of the coupling of environmental conditions, together with external vibrations, may affect the position stability of the detectors and their support structures compromising their physics performance. Careful optimization of static and dynamic behavior must be an essential part of the engineering design. Genetic Algorithms (GA) belong to the group of probabilistic algorithms, combining elements of direct and stochastic search. They are more robust than existing directed search methods with the advantage of maintaining a population of potential solutions. There is a class of optimization problems for which Genetic Algorithms can be effectively applied. Among them are the ones related to shape control and optimal placement of sensors/actuators for active control of vibrations. In this paper these two problems are addressed and numerically investigated. The finite element method is used for the analysis of the dynamic characteristics. For the case of the optimal placement of sensors/actuators a performance index, proportional to the damping of the system in closed- loop, is used. Genetic algorithms prove their efficiency in this kind of optimization problems.

  10. Co-expression network analysis and genetic algorithms for gene prioritization in preeclampsia.

    Science.gov (United States)

    Tejera, Eduardo; Bernardes, João; Rebelo, Irene

    2013-11-12

    In this study, we explored the gene prioritization in preeclampsia, combining co-expression network analysis and genetic algorithms optimization approaches. We analysed five public projects obtaining 1,146 significant genes after cross-platform and processing of 81 and 149 microarrays in preeclamptic and normal conditions, respectively. After co-expression network construction, modular and node analysis were performed using several approaches. Moreover, genetic algorithms were also applied in combination with the nearest neighbour and discriminant analysis classification methods. Significant differences were found in the genes connectivity distribution, both in normal and preeclampsia conditions pointing to the need and importance of examining connectivity alongside expression for prioritization. We discuss the global as well as intra-modular connectivity for hubs detection and also the utility of genetic algorithms in combination with the network information. FLT1, LEP, INHA and ENG genes were identified according to the literature, however, we also found other genes as FLNB, INHBA, NDRG1 and LYN highly significant but underexplored during normal pregnancy or preeclampsia. Weighted genes co-expression network analysis reveals a similar distribution along the modules detected both in normal and preeclampsia conditions. However, major differences were obtained by analysing the nodes connectivity. All models obtained by genetic algorithm procedures were consistent with a correct classification, higher than 90%, restricting to 30 variables in both classification methods applied.Combining the two methods we identified well known genes related to preeclampsia, but also lead us to propose new candidates poorly explored or completely unknown in the pathogenesis of preeclampsia, which may have to be validated experimentally.

  11. Optimal Sensor Placement for Leak Location in Water Distribution Networks Using Genetic Algorithms

    OpenAIRE

    Casillas, Myrna V.; Puig, Vicenc; Garza-Castanon, Luis E.; Rosich, Albert

    2013-01-01

    This paper proposes a new sensor placement approach for leak location in water distribution networks (WDNs). The sensor placement problem is formulated as an integer optimization problem. The optimization criterion consists in minimizing the number of non-isolable leaks according to the isolability criteria introduced. Because of the large size and non-linear integer nature of the resulting optimization problem, genetic algorithms (GAs) are used as the solution approach. The obtained results ...

  12. Efficient synthesis of heat exchanger networks combining heuristic approaches with a genetic algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Brandt, Christopher; Fieg, Georg [Hamburg University of Technology, Institute of Process and Plant Engineering, Hamburg (Germany); Luo, Xing [Helmut Schmidt University, Institute of Thermodynamics, Hamburg (Germany); University of Shanghai for Science and Technology, Institute of Thermal Engineering, Shanghai (China)

    2011-08-15

    In this work an innovative method for heat exchanger network (HEN) synthesis is introduced and examined. It combines a genetic algorithm (GA) with a heuristic based optimization procedure. The novel algorithm removes appearing heat load loops from the HEN structures when profitable, throughout the evolution. Two examples were examined with the new HEN synthesis method and for both better results were obtained. Thus, a positive effect of heuristic based optimization methods on the HEN synthesis with GA could be located. (orig.)

  13. Digitally Controlled Linear Four-Port Network

    Directory of Open Access Journals (Sweden)

    V. Michalek

    1994-09-01

    Full Text Available The paper deals with the design of a universal linear multipart. The circuit is based on digitally controlled multiple voltage-controlled voltage sources (MVCVSs. The main advantages of this control are accuracy, invariability, and very small area requirements. The whole system is simply connected to a PC via its parallel port. This multipart can generally be used as a building block for any model of a nonlinear dynamic system, namely for the piecewise-linear (PWL model in both explicit and implicit forms.

  14. Some thoughts on the control of network systems

    Directory of Open Access Journals (Sweden)

    Alessandro Ferrarini

    2011-12-01

    Full Text Available The controllability of network-like systems is becoming a trendy key-issue in many disciplines, including ecology and biology. To control a biological, ecological or economic system is to make it behave according to our wishes, at the least possible cost. In this paper, I propose some ideas on networks control that do not precisely follow recent papers on the argument. By the way, since this scientific topic is still in open evolution, discordant thoughts might be helpful to the debate.

  15. Stability and synchronization control of stochastic neural networks

    CERN Document Server

    Zhou, Wuneng; Zhou, Liuwei; Tong, Dongbing

    2016-01-01

    This book reports on the latest findings in the study of Stochastic Neural Networks (SNN). The book collects the novel model of the disturbance driven by Levy process, the research method of M-matrix, and the adaptive control method of the SNN in the context of stability and synchronization control. The book will be of interest to university researchers, graduate students in control science and engineering and neural networks who wish to learn the core principles, methods, algorithms and applications of SNN.

  16. Practical Application of Neural Networks in State Space Control

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon

    theoretic notions followed by a detailed description of the topology, neuron functions and learning rules of the two types of neural networks treated in the thesis, the multilayer perceptron and the neurofuzzy networks. In both cases, a Least Squares second-order gradient method is used to train....... Then the controller is shown to work on a simulation example. We also address the potential problem of too rapidly fluctuating parameters by including regularization in the learning rule. Next we develop a direct adaptive certainty-equivalence controller based on neurofuzzy models. The control loop is proven...

  17. Contrasting network and modular perspectives on inhibitory control.

    Science.gov (United States)

    Hampshire, Adam; Sharp, David J

    2015-08-01

    A prominent theory proposes that the right inferior frontal cortex of the human brain houses a dedicated region for motor response inhibition. However, there is growing evidence to support the view that this inhibitory control hypothesis is incorrect. Here, we discuss evidence in favour of our alternative hypothesis, which states that response inhibition is one example of a broader class of control processes that are supported by the same set of frontoparietal networks. These domain-general networks exert control by modulating local lateral inhibition processes, which occur ubiquitously throughout the cortex. We propose that to fully understand the neural basis of behavioural control requires a more holistic approach that considers how common network mechanisms support diverse cognitive processes. Copyright © 2015 Elsevier Ltd. All rights reserved.

  18. Dual adaptive dynamic control of mobile robots using neural networks.

    Science.gov (United States)

    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.

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

    CERN Document Server

    Zañudo, Jorge G T; Albert, Réka

    2016-01-01

    Given the network of interactions underlying a complex system, what can we learn about controlling such a system solely from its structure? Over a century of research in control theory has given us tools to answer this question, which were widely applied in science and engineering. Yet the current tools do not always consider the inherently nonlinear dynamics of real systems and the naturally occurring system states in their definition of "control", a term whose interpretation varies across disciplines. Here we use a new mathematical framework for structure-based control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This framework provides realizable node overrides that steer a system towards any of its natural long term dynamic behaviors and which are guaranteed to be effective regardless of the dynamic details and parameters of the underlying system. We use this framework on several real networks, compar...

  20. Representational Similarity Analysis Reveals Heterogeneous Networks Supporting Speech Motor Control

    DEFF Research Database (Denmark)

    Zheng, Zane; Cusack, Rhodri; Johnsrude, Ingrid

    The everyday act of speaking involves the complex processes of speech motor control. One important feature of such control is regulation of articulation when auditory concomitants of speech do not correspond to the intended motor gesture. While theoretical accounts of speech monitoring posit...... is supported by a complex neural network that is involved in linguistic, motoric and sensory processing. With the aid of novel real-time acoustic analyses and representational similarity analyses of fMRI signals, our data show functionally differentiated networks underlying auditory feedback control of speech....... multiple functional components required for detection of errors in speech planning (e.g., Levelt, 1983), neuroimaging studies generally indicate either single brain regions sensitive to speech production errors, or small, discrete networks. Here we demonstrate that the complex system controlling speech...

  1. An artificial neural network controller for intelligent transportation systems applications

    Energy Technology Data Exchange (ETDEWEB)

    Vitela, J.E.; Hanebutte, U.R.; Reifman, J. [Argonne National Lab., IL (United States). Reactor Analysis Div.

    1996-04-01

    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 applications. The AICC is based on a simple nonlinear model of the vehicle dynamics. A Neural Network Controller (NNC) code developed at Argonne National Laboratory to control discrete dynamical systems was used for this purpose. In order to test the NNC, an AICC-simulator containing graphical displays was developed for a system of two vehicles driving in a single lane. Two simulation cases are shown, one involving a lead vehicle with constant velocity and the other a lead vehicle with varying acceleration. More realistic vehicle dynamic models will be considered in future work.

  2. Neural-Network-Biased Genetic Algorithms for Materials Design: Evolutionary Algorithms That Learn.

    Science.gov (United States)

    Patra, Tarak K; Meenakshisundaram, Venkatesh; Hung, Jui-Hsiang; Simmons, David S

    2017-02-13

    Machine learning has the potential to dramatically accelerate high-throughput approaches to materials design, as demonstrated by successes in biomolecular design and hard materials design. However, in the search for new soft materials exhibiting properties and performance beyond those previously achieved, machine learning approaches are frequently limited by two shortcomings. First, because they are intrinsically interpolative, they are better suited to the optimization of properties within the known range of accessible behavior than to the discovery of new materials with extremal behavior. Second, they require large pre-existing data sets, which are frequently unavailable and prohibitively expensive to produce. Here we describe a new strategy, the neural-network-biased genetic algorithm (NBGA), for combining genetic algorithms, machine learning, and high-throughput computation or experiment to discover materials with extremal properties in the absence of pre-existing data. Within this strategy, predictions from a progressively constructed artificial neural network are employed to bias the evolution of a genetic algorithm, with fitness evaluations performed via direct simulation or experiment. In effect, this strategy gives the evolutionary algorithm the ability to "learn" and draw inferences from its experience to accelerate the evolutionary process. We test this algorithm against several standard optimization problems and polymer design problems and demonstrate that it matches and typically exceeds the efficiency and reproducibility of standard approaches including a direct-evaluation genetic algorithm and a neural-network-evaluated genetic algorithm. The success of this algorithm in a range of test problems indicates that the NBGA provides a robust strategy for employing informatics-accelerated high-throughput methods to accelerate materials design in the absence of pre-existing data.

  3. Genetic control of lithium sensitivity and regulation of inositol biosynthetic genes.

    Directory of Open Access Journals (Sweden)

    Jason King

    2010-06-01

    Full Text Available Lithium (Li(+ is a common treatment for bipolar mood disorder, a major psychiatric illness with a lifetime prevalence of more than 1%. Risk of bipolar disorder is heavily influenced by genetic predisposition, but is a complex genetic trait and, to date, genetic studies have provided little insight into its molecular origins. An alternative approach is to investigate the genetics of Li(+ sensitivity. Using the social amoeba Dictyostelium, we previously identified prolyl oligopeptidase (PO as a modulator of Li(+ sensitivity. In a link to the clinic, PO enzyme activity is altered in bipolar disorder patients. Further studies demonstrated that PO is a negative regulator of inositol(1,4,5trisphosphate (IP(3 synthesis, a Li(+ sensitive intracellular signal. However, it was unclear how PO could influence either Li(+ sensitivity or risk of bipolar disorder. Here we show that in both Dictyostelium and cultured human cells PO acts via Multiple Inositol Polyphosphate Phosphatase (Mipp1 to control gene expression. This reveals a novel, gene regulatory network that modulates inositol metabolism and Li(+ sensitivity. Among its targets is the inositol monophosphatase gene IMPA2, which has also been associated with risk of bipolar disorder in some family studies, and our observations offer a cellular signalling pathway in which PO activity and IMPA2 gene expression converge.

  4. Epidemic Propagation of Control Plane Failures in GMPLS Controlled Optical Transport Networks

    DEFF Research Database (Denmark)

    Ruepp, Sarah Renée; Fagertun, Anna Manolova

    2013-01-01

    In this paper, we investigate the behaviour of a dataplane-decoupled GMPLS control plane, when it is affected by failures that spread in the network in an epidemic manner. In particular, we consider network nodes to be either fully functional, or having a failed control plane, or having both...... a failed control and data plane. Through large-scale network simulation, we evaluate the effect of epidemically spreading control plane failures in terms of blocked connections requests and the amount of stranded capacity due to a dysfunctional control plane. Furthermore, we investigate the effect...

  5. Trust-Based Collaborative Control for Teams on Communication Networks

    Science.gov (United States)

    2012-02-11

    year only [1] S. Ferrari, S. Jagannathan , and F.L. Lewis, “Special Issue on Approximate Dynamic Programming and Reinforcement Learning,” Journal of...to appear, 2012. [17] H. Xu, S. Jagannathan , and F.L. Lewis, “Stochastic Optimal Control of Unknown Linear Networked Control System in the Presence

  6. Neural Network Based Load Frequency Control for Restructuring ...

    African Journals Online (AJOL)

    Electric load variations can happen independently in both units. Both neural controllers are trained with the back propagation-through-time algorithm. Use of a neural network to model the dynamic system is avoided by introducing the Jacobian matrices of the system in the back propagation chain used in controller training.

  7. Synchronization of general complex networks via adaptive control ...

    Indian Academy of Sciences (India)

    2014-03-07

    Mar 7, 2014 ... 3Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science &. Engineering, Zigong, Sichuan, 643000, People's Republic of China ...... inputs ui(t) (i = 1, 2, 3) and the values of control inputs are acceptable. From figures 1–5, it is easy to see that the controlled complex network ...

  8. Distributed MPC for controlling mu-CHPs in a network

    NARCIS (Netherlands)

    Larsen, Gunn; Trip, Sebastian; van Foreest, Nicky; Scherpen, Jacquelien M.A.

    2012-01-01

    This paper describes a dynamic price mechanism to coordinate electricity generation from micro Combined Heat and Power (mu-CHP) systems in a network of households. The control is done on household level in a completely distributed manner. Distributed Model Predictive control is applied to the

  9. Four Degree Freedom Robot Arm with Fuzzy Neural Network Control

    Directory of Open Access Journals (Sweden)

    Şinasi Arslan

    2013-01-01

    Full Text Available In this study, the control of four degree freedom robot arm has been realized with the computed torque control method.. It is usually required that the four jointed robot arm has high precision capability and good maneuverability for using in industrial applications. Besides, high speed working and external applied loads have been acting as important roles. For those purposes, the computed torque control method has been developed in a good manner that the robot arm can track the given trajectory, which has been able to enhance the feedback control together with fuzzy neural network control. The simulation results have proved that the computed torque control with the neural network has been so successful in robot control.

  10. Nitrogen deprivation promotes Populus root growth through global transcriptome reprogramming and activation of hierarchical genetic networks.

    Science.gov (United States)

    Wei, Hairong; Yordanov, Yordan S; Georgieva, Tatyana; Li, Xiang; Busov, Victor

    2013-10-01

    We show a distinct and previously poorly characterized response of poplar (Populus tremula × Populus alba) roots to low nitrogen (LN), which involves activation of root growth and significant transcriptome reprogramming. Analysis of the temporal patterns of enriched ontologies among the differentially expressed genes revealed an ordered assembly of functionally cohesive biological events that aligned well with growth and morphological responses. A core set of 28 biological processes was significantly enriched across the whole studied period and 21 of these were also enriched in the roots of Arabidopsis thaliana during the LN response. More than half (15) of the 28 processes belong to gene ontology (GO) terms associated with signaling and signal transduction pathways, suggesting the presence of conserved signaling mechanisms triggered by LN. A reconstruction of genetic regulatory network analysis revealed a sub-network centered on a PtaNAC1 (P. tremula × alba NAM, ATAF, CUC 1) transcription factor. PtaNAC1 root-specific up-regulation increased root biomass and significantly changed the expression of the connected hub genes specifically under LN. Our results provide evidence that the root response to LN involves hierarchically structured genetic networks centered on key regulatory factors. Targeting these factors via genetic engineering or breeding approaches can allow dynamic adjustment of root architecture in response to variable nitrogen availabilities in the soil. © 2013 The Authors. New Phytologist © 2013 New Phytologist Trust.

  11. Genetic analysis of mitochondrial DNA control region variations in four tribes of Khyber Pakhtunkhwa, Pakistan.

    Science.gov (United States)

    Bhatti, Shahzad; Aslamkhan, M; Abbas, Sana; Attimonelli, Marcella; Aydin, Hikmet Hakan; de Souza, Erica Martinha Silva

    2017-09-01

    Due to its geo strategic position at the crossroad of Asia, Pakistan has gained crucial importance of playing its pivotal role in subsequent human migratory events, both prehistoric and historic. This human movement became possible through an ancient overland network of trails called "The Silk Route" linking Asia Minor, Middle East China, Central Asia and Southeast Asia. This study was conducted to analyze complete mitochondrial control region samples of 100 individuals of four major Pashtun tribes namely, Bangash, Khattak, Mahsuds and Orakzai in the province of Khyber Pakhtunkhwa, Pakistan. All Pashtun tribes revealed high genetic diversity which is comparable to the other Central Asian, Southeast Asian and European populations. The configuration of genetic variation and heterogeneity further unveiled through Multidimensional Scaling, Principal Component Analysis and phylogenetic analysis. The results revealed that Pashtun are the composite mosaic of West Eurasian ancestry of numerous geographic origin. They received substantial gene flow during different invasive movements and have a high element of the Western provenance. The most common haplogroups reported in this study are: South Asian haplogroups M (28%) and R (8%); whereas, West Asians haplogroups are present, albeit in high frequencies (67%) and widespread over all; HV (15%), U (17%), H (9%), J (8%), K (8%), W (4%), N (3%) and T (3%). Moreover, we linked the unexplored genetic connection between Ashkenazi Jews and Pashtun. The presence of specific haplotypes J1b (4%) and K1a1b1a (5%) pointed to a genetic connection of Jewish conglomeration in Khattak tribe. This was a result of an ancient genetic influx in the early Neolithic period that led to the formation of a diverse genetic substratum in present day Pashtun.

  12. On the Need of Novel Medium Access Control Schemes for Network Coding enabled Wireless Mesh Networks

    DEFF Research Database (Denmark)

    Paramanathan, Achuthan; Pahlevani, Peyman; Roetter, Daniel Enrique Lucani

    2013-01-01

    This paper advocates for a new Medium Access Control (MAC) strategy for wireless meshed networks by identifying overload scenarios in order to provide additional channel access priority to the relay. The key behind our MAC protocol is that the relay will adjust its back off window size according...... that network coding will improve the throughput in such systems, but our novel medium access scheme improves the performance in the cross topology by another 66 % for network coding and 150 % for classical forwarding in theory. These gains translate in a theoretical gain of 33 % of network coding over...... classical forwarding when both systems implement the improved MAC. However, our measurement results show an even larger gain for network coding, namely, up to 65 % over forwarding, as it copes better with channel losses under high load scenarios....

  13. Neuro-genetic optimization of temperature control for a continuous flow polymerase chain reaction microdevice.

    Science.gov (United States)

    Lee, Hing Wah; Arunasalam, Parthiban; Laratta, William P; Seetharamu, Kankanhalli N; Azid, Ishak A

    2007-08-01

    In this study, a hybridized neuro-genetic optimization methodology realized by embedding finite element analysis (FEA) trained artificial neural networks (ANN) into genetic algorithms (GA), is used to optimize temperature control in a ceramic based continuous flow polymerase chain reaction (CPCR) device. The CPCR device requires three thermally isolated reaction zones of 94 degrees C, 65 degrees C, and 72 degrees C for the denaturing, annealing, and extension processes, respectively, to complete a cycle of polymerase chain reaction. The most important aspect of temperature control in the CPCR is to maintain temperature distribution at each reaction zone with a precision of +/-1 degree C or better, irrespective of changing ambient conditions. Results obtained from the FEA simulation shows good comparison with published experimental work for the temperature control in each reaction zone of the microfluidic channels. The simulation data are then used to train the ANN to predict the temperature distribution of the microfluidic channel for various heater input power and fluid flow rate. Once trained, the ANN analysis is able to predict the temperature distribution in the microchannel in less than 20 min, whereas the FEA simulation takes approximately 7 h to do so. The final optimization of temperature control in the CPCR device is achieved by embedding the trained ANN results as a fitness function into GA. Finally, the GA optimized results are used to build a new FEA model for numerical simulation analysis. The simulation results for the neuro-genetic optimized CPCR model and the initial CPCR model are then compared. The neuro-genetic optimized model shows a significant improvement from the initial model, establishing the optimization method's superiority.

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

    Science.gov (United States)

    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.

  15. Predicting recurrent aphthous ulceration using genetic algorithms-optimized neural networks.

    Science.gov (United States)

    Dar-Odeh, Najla S; Alsmadi, Othman M; Bakri, Faris; Abu-Hammour, Zaer; Shehabi, Asem A; Al-Omiri, Mahmoud K; Abu-Hammad, Shatha M K; Al-Mashni, Hamzeh; Saeed, Mohammad B; Muqbil, Wael; Abu-Hammad, Osama A

    2010-01-01

    To construct and optimize a neural network that is capable of predicting the occurrence of recurrent aphthous ulceration (RAU) based on a set of appropriate input data. Artificial neural networks (ANN) software employing genetic algorithms to optimize the architecture neural networks was used. Input and output data of 86 participants (predisposing factors and status of the participants with regards to recurrent aphthous ulceration) were used to construct and train the neural networks. The optimized neural networks were then tested using untrained data of a further 10 participants. THE OPTIMIZED NEURAL NETWORK, WHICH PRODUCED THE MOST ACCURATE PREDICTIONS FOR THE PRESENCE OR ABSENCE OF RECURRENT APHTHOUS ULCERATION WAS FOUND TO EMPLOY: gender, hematological (with or without ferritin) and mycological data of the participants, frequency of tooth brushing, and consumption of vegetables and fruits. FACTORS APPEARING TO BE RELATED TO RECURRENT APHTHOUS ULCERATION AND APPROPRIATE FOR USE AS INPUT DATA TO CONSTRUCT ANNS THAT PREDICT RECURRENT APHTHOUS ULCERATION WERE FOUND TO INCLUDE THE FOLLOWING: gender, hemoglobin, serum vitamin B12, serum ferritin, red cell folate, salivary candidal colony count, frequency of tooth brushing, and the number of fruits or vegetables consumed daily.

  16. Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm–Artificial Neural Network

    Science.gov (United States)

    Ramadan Suleiman, Ahmed; Nehdi, Moncef L.

    2017-01-01

    This paper presents an approach to predicting the intrinsic self-healing in concrete using a hybrid genetic algorithm–artificial neural network (GA–ANN). A genetic algorithm was implemented in the network as a stochastic optimizing tool for the initial optimal weights and biases. This approach can assist the network in achieving a global optimum and avoid the possibility of the network getting trapped at local optima. The proposed model was trained and validated using an especially built database using various experimental studies retrieved from the open literature. The model inputs include the cement content, water-to-cement ratio (w/c), type and dosage of supplementary cementitious materials, bio-healing materials, and both expansive and crystalline additives. Self-healing indicated by means of crack width is the model output. The results showed that the proposed GA–ANN model is capable of capturing the complex effects of various self-healing agents (e.g., biochemical material, silica-based additive, expansive and crystalline components) on the self-healing performance in cement-based materials. PMID:28772495

  17. Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm–Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Ahmed Ramadan Suleiman

    2017-02-01

    Full Text Available This paper presents an approach to predicting the intrinsic self-healing in concrete using a hybrid genetic algorithm–artificial neural network (GA–ANN. A genetic algorithm was implemented in the network as a stochastic optimizing tool for the initial optimal weights and biases. This approach can assist the network in achieving a global optimum and avoid the possibility of the network getting trapped at local optima. The proposed model was trained and validated using an especially built database using various experimental studies retrieved from the open literature. The model inputs include the cement content, water-to-cement ratio (w/c, type and dosage of supplementary cementitious materials, bio-healing materials, and both expansive and crystalline additives. Self-healing indicated by means of crack width is the model output. The results showed that the proposed GA–ANN model is capable of capturing the complex effects of various self-healing agents (e.g., biochemical material, silica-based additive, expansive and crystalline components on the self-healing performance in cement-based materials.

  18. STUDY OF SENSITIVITY OF THE PARAMETERS OF A GENETIC ALGORITHM FOR DESIGN OF WATER DISTRIBUTION NETWORKS

    Directory of Open Access Journals (Sweden)

    Pedro L. Iglesias

    2007-12-01

    Full Text Available The Genetic Algorithms (GAs are a technique of optimization used for water distribution networks design. This work has been made with a modified pseudo genetic algorithm (PGA, whose main variation with a classical GA is a change in the codification of the chromosomes, which is made of numerical form instead of the binary codification. This variation entails a series of special characteristics in the codification and in the definition of the operations of mutation and crossover. Initially, the work displays the results of the PGA on a water network studied in the literature. The results show the kindness of the method. Also is made a statistical analysis of the obtained solutions. This analysis allows verifying the values of mutation and crossing probability more suitable for the proposed method. Finally, in the study of the analyzed water supply networks the concept of reliability in introduced. This concept is essential to understand the validity of the obtained results. The second part, starting with values optimized for the probability of crossing and mutation, the influence of the population size is analyzed in the final solutions on the network of Hanoi, widely studied in the bibliography. The aim is to find the most suitable configuration of the problem, so that good solutions are obtained in the less time.

  19. Genetic Algorithm-Based Artificial Neural Network for Voltage Stability Assessment

    Directory of Open Access Journals (Sweden)

    Garima Singh

    2011-01-01

    Full Text Available With the emerging trend of restructuring in the electric power industry, many transmission lines have been forced to operate at almost their full capacities worldwide. Due to this, more incidents of voltage instability and collapse are being observed throughout the world leading to major system breakdowns. To avoid these undesirable incidents, a fast and accurate estimation of voltage stability margin is required. In this paper, genetic algorithm based back propagation neural network (GABPNN has been proposed for voltage stability margin estimation which is an indication of the power system's proximity to voltage collapse. The proposed approach utilizes a hybrid algorithm that integrates genetic algorithm and the back propagation neural network. The proposed algorithm aims to combine the capacity of GAs in avoiding local minima and at the same time fast execution of the BP algorithm. Input features for GABPNN are selected on the basis of angular distance-based clustering technique. The performance of the proposed GABPNN approach has been compared with the most commonly used gradient based BP neural network by estimating the voltage stability margin at different loading conditions in 6-bus and IEEE 30-bus system. GA based neural network learns faster, at the same time it provides more accurate voltage stability margin estimation as compared to that based on BP algorithm. It is found to be suitable for online applications in energy management systems.

  20. Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm-Artificial Neural Network.

    Science.gov (United States)

    Ramadan Suleiman, Ahmed; Nehdi, Moncef L

    2017-02-07

    This paper presents an approach to predicting the intrinsic self-healing in concrete using a hybrid genetic algorithm-artificial neural network (GA-ANN). A genetic algorithm was implemented in the network as a stochastic optimizing tool for the initial optimal weights and biases. This approach can assist the network in achieving a global optimum and avoid the possibility of the network getting trapped at local optima. The proposed model was trained and validated using an especially built database using various experimental studies retrieved from the open literature. The model inputs include the cement content, water-to-cement ratio (w/c), type and dosage of supplementary cementitious materials, bio-healing materials, and both expansive and crystalline additives. Self-healing indicated by means of crack width is the model output. The results showed that the proposed GA-ANN model is capable of capturing the complex effects of various self-healing agents (e.g., biochemical material, silica-based additive, expansive and crystalline components) on the self-healing performance in cement-based materials.

  1. Modeling the cooling performance of vortex tube using a genetic algorithm-based artificial neural network

    Directory of Open Access Journals (Sweden)

    Pouraria Hassan

    2016-01-01

    Full Text Available In this study, artificial neural networks (ANNs have been used to model the effects of four important parameters consist of the ratio of the length to diameter(L/D, the ratio of the cold outlet diameter to the tube diameter(d/D, inlet pressure(P, and cold mass fraction (Y on the cooling performance of counter flow vortex tube. In this approach, experimental data have been used to train and validate the neural network model with MATLAB software. Also, genetic algorithm (GA has been used to find the optimal network architecture. In this model, temperature drop at the cold outlet has been considered as the cooling performance of the vortex tube. Based on experimental data, cooling performance of the vortex tube has been predicted by four inlet parameters (L/D, d/D, P, Y. The results of this study indicate that the genetic algorithm-based artificial neural network model is capable of predicting the cooling performance of vortex tube in a wide operating range and with satisfactory precision.

  2. Genetic optimization of neural network and fuzzy logic for oil bubble point pressure modeling

    Energy Technology Data Exchange (ETDEWEB)

    Afshar, Mohammad [Islamic Azad University, Kharg (Iran, Islamic Republic of); Gholami, Amin [Petroleum University of Technology, Abadan (Iran, Islamic Republic of); Asoodeh, Mojtaba [Islamic Azad University, Birjand (Iran, Islamic Republic of)

    2014-03-15

    Bubble point pressure is a critical pressure-volume-temperature (PVT) property of reservoir fluid, which plays an important role in almost all tasks involved in reservoir and production engineering. We developed two sophisticated models to estimate bubble point pressure from gas specific gravity, oil gravity, solution gas oil ratio, and reservoir temperature. Neural network and adaptive neuro-fuzzy inference system are powerful tools for extracting the underlying dependency of a set of input/output data. However, the mentioned tools are in danger of sticking in local minima. The present study went further by optimizing fuzzy logic and neural network models using the genetic algorithm in charge of eliminating the risk of being exposed to local minima. This strategy is capable of significantly improving the accuracy of both neural network and fuzzy logic models. The proposed methodology was successfully applied to a dataset of 153 PVT data points. Results showed that the genetic algorithm can serve the neural network and neuro-fuzzy models from local minima trapping, which might occur through back-propagation algorithm.

  3. Optimization of stochastic discrete systems and control on complex networks computational networks

    CERN Document Server

    Lozovanu, Dmitrii

    2014-01-01

    This book presents the latest findings on stochastic dynamic programming models and on solving optimal control problems in networks. It includes the authors' new findings on determining the optimal solution of discrete optimal control problems in networks and on solving game variants of Markov decision problems in the context of computational networks. First, the book studies the finite state space of Markov processes and reviews the existing methods and algorithms for determining the main characteristics in Markov chains, before proposing new approaches based on dynamic programming and combinatorial methods. Chapter two is dedicated to infinite horizon stochastic discrete optimal control models and Markov decision problems with average and expected total discounted optimization criteria, while Chapter three develops a special game-theoretical approach to Markov decision processes and stochastic discrete optimal control problems. In closing, the book's final chapter is devoted to finite horizon stochastic con...

  4. Carrier ethernet network control plane based on the Next Generation Network

    DEFF Research Database (Denmark)

    Fu, Rong; Wang, Yanmeng; Berger, Michael Stubert

    2008-01-01

    architecture. The approaches to QoS mapping, label distribution and connection and admission control (CAC) are specified here. At last, a simple T-MPLS based Carrier Ethernet network model with three kinds of users (VoIP, VoD and HTTP) and a RACE based control module is simulated in OPNET. The model is aiming...

  5. Intrusion Detection in Networked Control Systems: From System Knowledge to Network Security

    NARCIS (Netherlands)

    Caselli, M.

    2016-01-01

    Networked control system‿ (NCS) is an umbrella term encompassing a broad variety of infrastructures such as industrial control systems (ICSs) and building automation systems (BASs). Nowadays, all these infrastructures play an important role in several aspects of our daily life, from managing

  6. REAL TIME ANALYSIS OF WIRELESS CONTROLLER AREA NETWORK

    Directory of Open Access Journals (Sweden)

    Gerardine Immaculate Mary

    2014-09-01

    Full Text Available It is widely known that Control Area Networks (CAN are used in real-time, distributed and parallel processing which cover manufacture plants, humanoid robots, networking fields, etc., In applications where wireless conditions are encountered it is convenient to continue the exchange of CAN frames within the Wireless CAN (WCAN. The WCAN considered in this research is based on wireless token ring protocol (WTRP; a MAC protocol for wireless networks to reduce the number of retransmissions due to collision and the wired counterpart CAN attribute on message based communication. WCAN uses token frame method to provide channel access to the nodes in the system. This method allow all the nodes to share common broadcast channel by taken turns in transmitting upon receiving the token frame which is circulating within the network for specified amount of time. This method provides high throughput in bounded latency environment, consistent and predictable delays and good packet delivery ratio. The most important factor to consider when evaluating a control network is the end-to-end time delay between sensors, controllers, and actuators. The correct operation of a control system depends on the timeliness of the data coming over the network, and thus, a control network should be able to guarantee message delivery within a bounded transmission time. The proposed WCAN is modeled and simulated using QualNet, and its average end to end delay and packet delivery ratio (PDR are calculated. The parameters boundaries of WCAN are evaluated to guarantee a maximum throughput and a minimum latency time, in the case of wireless communications, precisely WCAN.

  7. Wireless Sensor/Actuator Network Design for Mobile Control Applications

    Directory of Open Access Journals (Sweden)

    Youxian Sung

    2007-10-01

    Full Text Available Wireless sensor/actuator networks (WSANs are emerging as a new generationof sensor networks. Serving as the backbone of control applications, WSANs will enablean unprecedented degree of distributed and mobile control. However, the unreliability ofwireless communications and the real-time requirements of control applications raise greatchallenges for WSAN design. With emphasis on the reliability issue, this paper presents anapplication-level design methodology for WSANs in mobile control applications. Thesolution is generic in that it is independent of the underlying platforms, environment,control system models, and controller design. To capture the link quality characteristics interms of packet loss rate, experiments are conducted on a real WSAN system. From theexperimental observations, a simple yet efficient method is proposed to deal withunpredictable packet loss on actuator nodes. Trace-based simulations give promisingresults, which demonstrate the effectiveness of the proposed approach.

  8. Adaptive model predictive process control using neural networks

    Science.gov (United States)

    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.

  9. Expanding the NATO Movement Control Network

    Science.gov (United States)

    2016-05-17

    mander of the 624th Movement Con- trol Team, 39th Transportation Battalion (Movement Control), 16th Sustainment Brigade, at Kleber Kaserne, Germany . He...Philip Stephens) 31 Army Sustainment November–December 2015 Challenges of Moving in Europe...have been operating in Germany and Italy since the end of World War II and understand those nations’ requirements well, but recent changes in

  10. Controlling high speed automated transport network operations

    NARCIS (Netherlands)

    de Feijter, R.

    2006-01-01

    This thesis presents a framework for the control of automated guided vehicles (AGVs). The framework implements the transport system as a community of cooperating agents. Besides the architecture and elements of the framework a wide range of infrastructure scene templates is described. These scene

  11. Modelling and control of cell reaction networks

    NARCIS (Netherlands)

    S. Jha; J.H. van Schuppen (Jan)

    2001-01-01

    textabstractThe project aims at a study of the nonlinear systems arising in the biochemical processes occuring inside a cell. The cellular regulation has been formulated in the more familiar framework used in control and system theory in terms of inputs as the variables which can be influenced

  12. Experimental control of a fluidic pinball using genetic programming

    Science.gov (United States)

    Raibaudo, Cedric; Zhong, Peng; Noack, Bernd R.; Martinuzzi, Robert J.

    2017-11-01

    The wake stabilization of a triangular cluster of three rotating cylinders was investigated in the present study. Experiments were performed at Reynolds number Re 6000, and compared with URANS-2D simulations at same flow conditions. 2D2C PIV measurements and constant temperature anemometry were used to characterize the flow without and with actuation. Open-loop actuation was first considered for the identification of particular control strategies. Machine learning control was also implemented for the experimental study. Linear genetic programming has been used for the optimization of open-loop parameters and closed-loop controllers. Considering a cost function J based on the fluctuations of the velocity measured by the hot-wire sensor, significant performances were achieved using the machine learning approach. The present work is supported by the senior author's (R. J. Martinuzzi) NSERC discovery Grant. C. Raibaudo acknowledges the financial support of the University of Calgary Eyes-High PDF program.

  13. MyGeneFriends: A Social Network Linking Genes, Genetic Diseases, and Researchers.

    Science.gov (United States)

    Allot, Alexis; Chennen, Kirsley; Nevers, Yannis; Poidevin, Laetitia; Kress, Arnaud; Ripp, Raymond; Thompson, Julie Dawn; Poch, Olivier; Lecompte, Odile

    2017-06-16

    The constant and massive increase of biological data offers unprecedented opportunities to decipher the function and evolution of genes and their roles in human diseases. However, the multiplicity of sources and flow of data mean that efficient access to useful information and knowledge production has become a major challenge. This challenge can be addressed by taking inspiration from Web 2.0 and particularly social networks, which are at the forefront of big data exploration and human-data interaction. MyGeneFriends is a Web platform inspired by social networks, devoted to genetic disease analysis, and organized around three types of proactive agents: genes, humans, and genetic diseases. The aim of this study was to improve exploration and exploitation of biological, postgenomic era big data. MyGeneFriends leverages conventions popularized by top social networks (Facebook, LinkedIn, etc), such as networks of friends, profile pages, friendship recommendations, affinity scores, news feeds, content recommendation, and data visualization. MyGeneFriends provides simple and intuitive interactions with data through evaluation and visualization of connections (friendships) between genes, humans, and diseases. The platform suggests new friends and publications and allows agents to follow the activity of their friends. It dynamically personalizes information depending on the user's specific interests and provides an efficient way to share information with collaborators. Furthermore, the user's behavior itself generates new information that constitutes an added value integrated in the network, which can be used to discover new connections between biological agents. We have developed MyGeneFriends, a Web platform leveraging conventions from popular social networks to redefine the relationship between humans and biological big data and improve human processing of biomedical data. MyGeneFriends is available at lbgi.fr/mygenefriends.

  14. Control of a hybrid compensator in a power network by an artificial neural network

    Directory of Open Access Journals (Sweden)

    I. S. Shaw

    1998-07-01

    Full Text Available Increased interest in the elimination of distortion in electrical power networks has led to the development of various compensator topologies. The increasing cost of electrical energy necessitates the cost-effective operation of any of these topologies. This paper considers the development of an artificial neural network based controller, trained by means of the backpropagation method, that ensures the cost-effective operation of the hybrid compensator consisting of various converters and filters.

  15. Genetic and epigenetic control of plant heat responses

    Directory of Open Access Journals (Sweden)

    Junzhong eLiu

    2015-04-01

    Full Text Available Plants have evolved sophisticated genetic and epigenetic regulatory systems to respond quickly to unfavorable environmental conditions such as heat, cold, drought, and pathogen infections. In particular, heat greatly affects plant growth and development, immunity and circadian rhythm, and poses a serious threat to the global food supply. According to temperatures exposing, heat can be usually classified as warm ambient temperature (about 22-27℃, high temperature (27-30℃ and extremely high temperature (37-42℃, also known as heat stress for the model plant Arabidopsis thaliana. The genetic mechanisms of plant responses to heat have been well studied, mainly focusing on elevated ambient temperature-mediated morphological acclimation and acceleration of flowering, modulation of plant immunity and circadian clock by high temperatures, and thermotolerance to heat stress. Recently, great progress has been achieved on epigenetic regulation of heat responses, including DNA methylation, histone modifications, histone variants, ATP-dependent chromatin remodeling, histone chaperones, small RNAs, long non-coding RNAs and other undefined epigenetic mechanisms. These epigenetic modifications regulate the expression of heat-responsive genes and function to prevent heat-related damage. This review focuses on recent progresses regarding the genetic and epigenetic control of heat responses in plants, and pays more attention to the role of the major epigenetic mechanisms in plant heat responses. Further research perspectives are also discussed.

  16. Control of the lighting system using a genetic algorithm

    Directory of Open Access Journals (Sweden)

    Čongradac Velimir D.

    2012-01-01

    Full Text Available The manufacturing, distribution and use of electricity are of fundamental importance for the social life and they have the biggest influence on the environment associated with any human activity. The energy needed for building lighting makes up 20-40% of the total consumption. This paper displays the development of the mathematical model and genetic algorithm for the control of dimmable lighting on problems of regulating the level of internal lighting and increase of energetic efficiency using daylight. A series of experiments using the optimization algorithm on the realized model confirmed very high savings in electricity consumption.

  17. Adaptive traffic control systems for urban networks

    Directory of Open Access Journals (Sweden)

    Radivojević Danilo

    2017-01-01

    Full Text Available Adaptive traffic control systems represent complex, but powerful tool for improvement of traffic flow conditions in locations or zones where applied. Many traffic agencies, especially those that have a large number of signalized intersections with high variability of the traffic demand, choose to apply some of the adaptive traffic control systems. However, those systems are manufactured and offered by multiple vendors (companies that are competing for the market share. Due to that fact, besides the information available from the vendors themselves, or the information from different studies conducted on different continents, very limited amount of information is available about the details how those systems are operating. The reason for that is the protecting of the intellectual property from plagiarism. The primary goal of this paper is to make a brief analysis of the functionalities, characteristics, abilities and results of the most recognized, but also less known adaptive traffic control systems to the professional public and other persons with interest in this subject.

  18. Effective feature selection of clinical and genetic to predict warfarin dose using artificial neural network

    Directory of Open Access Journals (Sweden)

    Mohammad Karim Sohrabi

    2016-03-01

    Full Text Available Background: Warfarin is one of the most common oral anticoagulant, which role is to prevent the clots. The dose of this medicine is very important because changes can be dangerous for patients. Diagnosis is difficult for physicians because increase and decrease in use of warfarin is so dangerous for patients. Identifying the clinical and genetic features involved in determining dose could be useful to predict using data mining techniques. The aim of this paper is to provide a convenient way to select the clinical and genetic features to determine the dose of warfarin using artificial neural networks (ANN and evaluate it in order to predict the dose patients. Methods: This experimental study, was investigate from April to May 2014 on 552 patients in Tehran Heart Center Hospital (THC candidates for warfarin anticoagulant therapy within the international normalized ratio (INR therapeutic target. Factors affecting the dose include clinical characteristics and genetic extracted, and different methods of feature selection based on genetic algorithm and particle swarm optimization (PSO and evaluation function neural networks in MATLAB (MathWorks, MA, USA, were performed. Results: Between algorithms used, particle swarm optimization algorithm accuracy was more appropriate, for the mean square error (MSE, root mean square error (RMSE and mean absolute error (MAE were 0.0262, 0.1621 and 0.1164, respectively. Conclusion: In this article, the most important characteristics were identified using methods of feature selection and the stable dose had been predicted based on artificial neural networks. The output is acceptable and with less features, it is possible to achieve the prediction warfarin dose accurately. Since the prescribed dose for the patients is important, the output of the obtained model can be used as a decision support system.

  19. Adaptive Reference Control for Pressure Management in Water Networks

    DEFF Research Database (Denmark)

    Kallesøe, Carsten; Jensen, Tom Nørgaard; Wisniewski, Rafal

    2015-01-01

    Water scarcity is an increasing problem worldwide and at the same time a huge amount of water is lost through leakages in the distribution network. It is well known that improved pressure control can lower the leakage problems. In this work water networks with a single pressure actuator and several....... Subsequently, these relations are exploited in an adaptive reference control scheme for the actuator pressure that ensures constant pressure at the critical points. Numerical experiments underpin the results. © Copyright IEEE - All rights reserved....

  20. Probabilistic Priority Message Checking Modeling Based on Controller Area Networks

    Science.gov (United States)

    Lin, Cheng-Min

    Although the probabilistic model checking tool called PRISM has been applied in many communication systems, such as wireless local area network, Bluetooth, and ZigBee, the technique is not used in a controller area network (CAN). In this paper, we use PRISM to model the mechanism of priority messages for CAN because the mechanism has allowed CAN to become the leader in serial communication for automobile and industry control. Through modeling CAN, it is easy to analyze the characteristic of CAN for further improving the security and efficiency of automobiles. The Markov chain model helps us to model the behaviour of priority messages.