WorldWideScience

Sample records for network distributed learning

  1. Structure Learning in Power Distribution Networks

    Energy Technology Data Exchange (ETDEWEB)

    Deka, Deepjyoti [Univ. of Texas, Austin, TX (United States); Chertkov, Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Backhaus, Scott N. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2015-01-13

    Traditionally power distribution networks are either not observable or only partially observable. This complicates development and implementation of new smart grid technologies, such as these related to demand response, outage detection and management, and improved load-monitoring. Here, inspired by proliferation of the metering technology, we discuss statistical estimation problems in structurally loopy but operationally radial distribution grids consisting in learning operational layout of the network from measurements, e.g. voltage data, which are either already available or can be made available with a relatively minor investment. Our newly suggested algorithms apply to a wide range of realistic scenarios. The algorithms are also computationally efficient – polynomial in time – which is proven theoretically and illustrated computationally on a number of test cases. The technique developed can be applied to detect line failures in real time as well as to understand the scope of possible adversarial attacks on the grid.

  2. Distributed Extreme Learning Machine for Nonlinear Learning over Network

    Directory of Open Access Journals (Sweden)

    Songyan Huang

    2015-02-01

    Full Text Available Distributed data collection and analysis over a network are ubiquitous, especially over a wireless sensor network (WSN. To our knowledge, the data model used in most of the distributed algorithms is linear. However, in real applications, the linearity of systems is not always guaranteed. In nonlinear cases, the single hidden layer feedforward neural network (SLFN with radial basis function (RBF hidden neurons has the ability to approximate any continuous functions and, thus, may be used as the nonlinear learning system. However, confined by the communication cost, using the distributed version of the conventional algorithms to train the neural network directly is usually prohibited. Fortunately, based on the theorems provided in the extreme learning machine (ELM literature, we only need to compute the output weights of the SLFN. Computing the output weights itself is a linear learning problem, although the input-output mapping of the overall SLFN is still nonlinear. Using the distributed algorithmto cooperatively compute the output weights of the SLFN, we obtain a distributed extreme learning machine (dELM for nonlinear learning in this paper. This dELM is applied to the regression problem and classification problem to demonstrate its effectiveness and advantages.

  3. Cooperative Learning for Distributed In-Network Traffic Classification

    Science.gov (United States)

    Joseph, S. B.; Loo, H. R.; Ismail, I.; Andromeda, T.; Marsono, M. N.

    2017-04-01

    Inspired by the concept of autonomic distributed/decentralized network management schemes, we consider the issue of information exchange among distributed network nodes to network performance and promote scalability for in-network monitoring. In this paper, we propose a cooperative learning algorithm for propagation and synchronization of network information among autonomic distributed network nodes for online traffic classification. The results show that network nodes with sharing capability perform better with a higher average accuracy of 89.21% (sharing data) and 88.37% (sharing clusters) compared to 88.06% for nodes without cooperative learning capability. The overall performance indicates that cooperative learning is promising for distributed in-network traffic classification.

  4. Distributed Jointly Sparse Multitask Learning Over Networks.

    Science.gov (United States)

    Li, Chunguang; Huang, Songyan; Liu, Ying; Zhang, Zhaoyang

    2018-01-01

    Distributed data processing over networks has received a lot of attention due to its wide applicability. In this paper, we consider the multitask problem of in-network distributed estimation. For the multitask problem, the unknown parameter vectors (tasks) for different nodes can be different. Moreover, considering some real application scenarios, it is also assumed that there are some similarities among the tasks. Thus, the intertask cooperation is helpful to enhance the estimation performance. In this paper, we exploit an additional special characteristic of the vectors of interest, namely, joint sparsity, aiming to further enhance the estimation performance. A distributed jointly sparse multitask algorithm for the collaborative sparse estimation problem is derived. In addition, an adaptive intertask cooperation strategy is adopted to improve the robustness against the degree of difference among the tasks. The performance of the proposed algorithm is analyzed theoretically, and its effectiveness is verified by some simulations.

  5. Social Networks and Performance in Distributed Learning Communities

    Science.gov (United States)

    Cadima, Rita; Ojeda, Jordi; Monguet, Josep M.

    2012-01-01

    Social networks play an essential role in learning environments as a key channel for knowledge sharing and students' support. In distributed learning communities, knowledge sharing does not occur as spontaneously as when a working group shares the same physical space; knowledge sharing depends even more on student informal connections. In this…

  6. Distributed reinforcement learning for adaptive and robust network intrusion response

    Science.gov (United States)

    Malialis, Kleanthis; Devlin, Sam; Kudenko, Daniel

    2015-07-01

    Distributed denial of service (DDoS) attacks constitute a rapidly evolving threat in the current Internet. Multiagent Router Throttling is a novel approach to defend against DDoS attacks where multiple reinforcement learning agents are installed on a set of routers and learn to rate-limit or throttle traffic towards a victim server. The focus of this paper is on online learning and scalability. We propose an approach that incorporates task decomposition, team rewards and a form of reward shaping called difference rewards. One of the novel characteristics of the proposed system is that it provides a decentralised coordinated response to the DDoS problem, thus being resilient to DDoS attacks themselves. The proposed system learns remarkably fast, thus being suitable for online learning. Furthermore, its scalability is successfully demonstrated in experiments involving 1000 learning agents. We compare our approach against a baseline and a popular state-of-the-art throttling technique from the network security literature and show that the proposed approach is more effective, adaptive to sophisticated attack rate dynamics and robust to agent failures.

  7. Learning Networks, Networked Learning

    NARCIS (Netherlands)

    Sloep, Peter; Berlanga, Adriana

    2010-01-01

    Sloep, P. B., & Berlanga, A. J. (2011). Learning Networks, Networked Learning [Redes de Aprendizaje, Aprendizaje en Red]. Comunicar, XIX(37), 55-63. Retrieved from http://dx.doi.org/10.3916/C37-2011-02-05

  8. Structure Learning and Statistical Estimation in Distribution Networks - Part I

    Energy Technology Data Exchange (ETDEWEB)

    Deka, Deepjyoti [Univ. of Texas, Austin, TX (United States); Backhaus, Scott N. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Chertkov, Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2015-02-13

    Traditionally power distribution networks are either not observable or only partially observable. This complicates development and implementation of new smart grid technologies, such as those related to demand response, outage detection and management, and improved load-monitoring. In this two part paper, inspired by proliferation of the metering technology, we discuss estimation problems in structurally loopy but operationally radial distribution grids from measurements, e.g. voltage data, which are either already available or can be made available with a relatively minor investment. In Part I, the objective is to learn the operational layout of the grid. Part II of this paper presents algorithms that estimate load statistics or line parameters in addition to learning the grid structure. Further, Part II discusses the problem of structure estimation for systems with incomplete measurement sets. Our newly suggested algorithms apply to a wide range of realistic scenarios. The algorithms are also computationally efficient – polynomial in time– which is proven theoretically and illustrated computationally on a number of test cases. The technique developed can be applied to detect line failures in real time as well as to understand the scope of possible adversarial attacks on the grid.

  9. Machine learning of network metrics in ATLAS Distributed Data Management

    CERN Document Server

    AUTHOR|(INSPIRE)INSPIRE-00218873; The ATLAS collaboration; Toler, Wesley; Vamosi, Ralf; Bogado Garcia, Joaquin Ignacio

    2017-01-01

    The increasing volume of physics data poses a critical challenge to the ATLAS experiment. In anticipation of high luminosity physics, automation of everyday data management tasks has become necessary. Previously many of these tasks required human decision-making and operation. Recent advances in hardware and software have made it possible to entrust more complicated duties to automated systems using models trained by machine learning algorithms. In this contribution we show results from one of our ongoing automation efforts that focuses on network metrics. First, we describe our machine learning framework built atop the ATLAS Analytics Platform. This framework can automatically extract and aggregate data, train models with various machine learning algorithms, and eventually score the resulting models and parameters. Second, we use these models to forecast metrics relevant for network-aware job scheduling and data brokering. We show the characteristics of the data and evaluate the forecasting accuracy of our m...

  10. Distributed Learning and Information Dynamics In Networked Autonomous Systems

    Science.gov (United States)

    2015-11-20

    sever edges based on a fixed set of local interaction rules described by a graph grammar . In contrast to existing re- sults, our work insist on...IMA workshop on Distributed Control and Decision Making Over Networks, September 28 October 2, 2015 • Asu Ozdaglar – Recipient of Spira teaching award

  11. A Streaming Content Distribution Network for E-Learning Support

    Science.gov (United States)

    Esteve, M.; Molina, B.; Palau, C.; Fortino, G.

    2006-01-01

    To date e-Learning material has usually been accessed and delivered through a central web server. As the number of users, the amount of information, the frequency of accesses and the volume of data increase, together with the introduction of multimedia streaming applications, a decentralized content distribution architecture is necessary. In this…

  12. Structure Learning and Statistical Estimation in Distribution Networks - Part II

    Energy Technology Data Exchange (ETDEWEB)

    Deka, Deepjyoti [Univ. of Texas, Austin, TX (United States); Backhaus, Scott N. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Chertkov, Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2015-02-13

    Limited placement of real-time monitoring devices in the distribution grid, recent trends notwithstanding, has prevented the easy implementation of demand-response and other smart grid applications. Part I of this paper discusses the problem of learning the operational structure of the grid from nodal voltage measurements. In this work (Part II), the learning of the operational radial structure is coupled with the problem of estimating nodal consumption statistics and inferring the line parameters in the grid. Based on a Linear-Coupled(LC) approximation of AC power flows equations, polynomial time algorithms are designed to identify the structure and estimate nodal load characteristics and/or line parameters in the grid using the available nodal voltage measurements. Then the structure learning algorithm is extended to cases with missing data, where available observations are limited to a fraction of the grid nodes. The efficacy of the presented algorithms are demonstrated through simulations on several distribution test cases.

  13. Machine learning of network metrics in ATLAS Distributed Data Management

    Science.gov (United States)

    Lassnig, Mario; Toler, Wesley; Vamosi, Ralf; Bogado, Joaquin; ATLAS Collaboration

    2017-10-01

    The increasing volume of physics data poses a critical challenge to the ATLAS experiment. In anticipation of high luminosity physics, automation of everyday data management tasks has become necessary. Previously many of these tasks required human decision-making and operation. Recent advances in hardware and software have made it possible to entrust more complicated duties to automated systems using models trained by machine learning algorithms. In this contribution we show results from one of our ongoing automation efforts that focuses on network metrics. First, we describe our machine learning framework built atop the ATLAS Analytics Platform. This framework can automatically extract and aggregate data, train models with various machine learning algorithms, and eventually score the resulting models and parameters. Second, we use these models to forecast metrics relevant for networkaware job scheduling and data brokering. We show the characteristics of the data and evaluate the forecasting accuracy of our models.

  14. Supervised learning of probability distributions by neural networks

    Science.gov (United States)

    Baum, Eric B.; Wilczek, Frank

    1988-01-01

    Supervised learning algorithms for feedforward neural networks are investigated analytically. The back-propagation algorithm described by Werbos (1974), Parker (1985), and Rumelhart et al. (1986) is generalized by redefining the values of the input and output neurons as probabilities. The synaptic weights are then varied to follow gradients in the logarithm of likelihood rather than in the error. This modification is shown to provide a more rigorous theoretical basis for the algorithm and to permit more accurate predictions. A typical application involving a medical-diagnosis expert system is discussed.

  15. distribution network

    African Journals Online (AJOL)

    user

    This paper examined the acidic properties of distribution transformer oil insulation in service at Jericho distribution network Ibadan, Nigeria. Five oil samples each from six distribution transformers (DT1, DT2, DT3, DT4 and DT5) making a total of thirty samples were taken from different installed distribution transformers all ...

  16. Learning Signaling Network Structures with Sparsely Distributed Data

    OpenAIRE

    Sachs, Karen; Itani, Solomon; Carlisle, Jennifer; Nolan, Garry P.; Pe'er, Dana; Lauffenburger, Douglas A.

    2009-01-01

    Flow cytometric measurement of signaling protein abundances has proved particularly useful for elucidation of signaling pathway structure. The single cell nature of the data ensures a very large dataset size, providing a statistically robust dataset for structure learning. Moreover, the approach is easily scaled to many conditions in high throughput. However, the technology suffers from a dimensionality constraint: at the cutting edge, only about 12 protein species can be measured per cell, f...

  17. Using multi-step proposal distribution for improved MCMC convergence in Bayesian network structure learning.

    Science.gov (United States)

    Larjo, Antti; Lähdesmäki, Harri

    2015-12-01

    Bayesian networks have become popular for modeling probabilistic relationships between entities. As their structure can also be given a causal interpretation about the studied system, they can be used to learn, for example, regulatory relationships of genes or proteins in biological networks and pathways. Inference of the Bayesian network structure is complicated by the size of the model structure space, necessitating the use of optimization methods or sampling techniques, such Markov Chain Monte Carlo (MCMC) methods. However, convergence of MCMC chains is in many cases slow and can become even a harder issue as the dataset size grows. We show here how to improve convergence in the Bayesian network structure space by using an adjustable proposal distribution with the possibility to propose a wide range of steps in the structure space, and demonstrate improved network structure inference by analyzing phosphoprotein data from the human primary T cell signaling network.

  18. A Distributed Learning Method for ℓ 1 -Regularized Kernel Machine over Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Xinrong Ji

    2016-07-01

    Full Text Available In wireless sensor networks, centralized learning methods have very high communication costs and energy consumption. These are caused by the need to transmit scattered training examples from various sensor nodes to the central fusion center where a classifier or a regression machine is trained. To reduce the communication cost, a distributed learning method for a kernel machine that incorporates ℓ 1 norm regularization ( ℓ 1 -regularized is investigated, and a novel distributed learning algorithm for the ℓ 1 -regularized kernel minimum mean squared error (KMSE machine is proposed. The proposed algorithm relies on in-network processing and a collaboration that transmits the sparse model only between single-hop neighboring nodes. This paper evaluates the proposed algorithm with respect to the prediction accuracy, the sparse rate of model, the communication cost and the number of iterations on synthetic and real datasets. The simulation results show that the proposed algorithm can obtain approximately the same prediction accuracy as that obtained by the batch learning method. Moreover, it is significantly superior in terms of the sparse rate of model and communication cost, and it can converge with fewer iterations. Finally, an experiment conducted on a wireless sensor network (WSN test platform further shows the advantages of the proposed algorithm with respect to communication cost.

  19. Distributed Generation Planning using Peer Enhanced Multi-objective Teaching-Learning based Optimization in Distribution Networks

    Science.gov (United States)

    Selvam, Kayalvizhi; Vinod Kumar, D. M.; Siripuram, Ramakanth

    2017-04-01

    In this paper, an optimization technique called peer enhanced teaching learning based optimization (PeTLBO) algorithm is used in multi-objective problem domain. The PeTLBO algorithm is parameter less so it reduced the computational burden. The proposed peer enhanced multi-objective based TLBO (PeMOTLBO) algorithm has been utilized to find a set of non-dominated optimal solutions [distributed generation (DG) location and sizing in distribution network]. The objectives considered are: real power loss and the voltage deviation subjected to voltage limits and maximum penetration level of DG in distribution network. Since the DG considered is capable of injecting real and reactive power to the distribution network the power factor is considered as 0.85 lead. The proposed peer enhanced multi-objective optimization technique provides different trade-off solutions in order to find the best compromise solution a fuzzy set theory approach has been used. The effectiveness of this proposed PeMOTLBO is tested on IEEE 33-bus and Indian 85-bus distribution system. The performance is validated with Pareto fronts and two performance metrics (C-metric and S-metric) by comparing with robust multi-objective technique called non-dominated sorting genetic algorithm-II and also with the basic TLBO.

  20. Learning conditional Gaussian networks

    DEFF Research Database (Denmark)

    Bøttcher, Susanne Gammelgaard

    This paper considers conditional Gaussian networks. The parameters in the network are learned by using conjugate Bayesian analysis. As conjugate local priors, we apply the Dirichlet distribution for discrete variables and the Gaussian-inverse gamma distribution for continuous variables, given...... a configuration of the discrete parents. We assume parameter independence and complete data. Further, to learn the structure of the network, the network score is deduced. We then develop a local master prior procedure, for deriving parameter priors in these networks. This procedure satisfies parameter...... independence, parameter modularity and likelihood equivalence. Bayes factors to be used in model search are introduced. Finally the methods derived are illustrated by a simple example....

  1. Routing strategy in a distribution network when the driver learning effect is considered

    DEFF Research Database (Denmark)

    Battini, Daria; Faccio, Maurizio; Persona, Alessandro

    2015-01-01

    's familiarity with his/her surroundings, customers' habits and set-ups. The final aim is to provide an effective and flexible decision-making tool to identify the best routing strategy, considering the level of learning invested by each driver. It is assumed that learning effect impacts on the service time, i......This paper faces one critical short term decision: the construction of routes for daily goods' deliveries in a distribution network. It investigates the possibility of applying a fixed routing strategy instead of a daily routing optimisation strategy, analysing the benefits derived from the driver.......e., on the time spent in service operations once arrived at the customer's site. Results demonstrate that fixed routes strategy can often be better than the daily optimised strategy and strongly depends on the parameters investigated in this work. Two case applications are provided to help the reader...

  2. Structure Learning of Bayesian Networks by Estimation of Distribution Algorithms with Transpose Mutation

    Directory of Open Access Journals (Sweden)

    D.W. Kim

    2013-08-01

    Full Text Available Estimation of distribution algorithms (EDAs constitute a new branch of evolutionary optimization algorithms that were developed as a natural alternative to genetic algorithms (GAs. Several studies have demonstrated that the heuristic scheme of EDAs is effective and efficient for many optimization problems. Recently, it has been reported that the incorporation of mutation into EDAs increases the diversity of genetic information in the population, thereby avoiding premature convergence into a suboptimal solution. In this study, we propose a new mutation operator, a transpose mutation, designed for Bayesian structure learning. It enhances the diversity of the offspring and it increases the possibility of inferring the correct arc direction by considering the arc directions in candidate solutions as bi-directional, using the matrix transpose operator. As compared to the conventional EDAs, the transpose mutation-adopted EDAs are superior and effective algorithms for learning Bayesian networks.

  3. DLTAP: A Network-efficient Scheduling Method for Distributed Deep Learning Workload in Containerized Cluster Environment

    Directory of Open Access Journals (Sweden)

    Qiao Wei

    2017-01-01

    Full Text Available Deep neural networks (DNNs have recently yielded strong results on a range of applications. Training these DNNs using a cluster of commodity machines is a promising approach since training is time consuming and compute-intensive. Furthermore, putting DNN tasks into containers of clusters would enable broader and easier deployment of DNN-based algorithms. Toward this end, this paper addresses the problem of scheduling DNN tasks in the containerized cluster environment. Efficiently scheduling data-parallel computation jobs like DNN over containerized clusters is critical for job performance, system throughput, and resource utilization. It becomes even more challenging with the complex workloads. We propose a scheduling method called Deep Learning Task Allocation Priority (DLTAP which performs scheduling decisions in a distributed manner, and each of scheduling decisions takes aggregation degree of parameter sever task and worker task into account, in particularly, to reduce cross-node network transmission traffic and, correspondingly, decrease the DNN training time. We evaluate the DLTAP scheduling method using a state-of-the-art distributed DNN training framework on 3 benchmarks. The results show that the proposed method can averagely reduce 12% cross-node network traffic, and decrease the DNN training time even with the cluster of low-end servers.

  4. Spiking Neural Network With Distributed Plasticity Reproduces Cerebellar Learning in Eye Blink Conditioning Paradigms.

    Science.gov (United States)

    Antonietti, Alberto; Casellato, Claudia; Garrido, Jesús A; Luque, Niceto R; Naveros, Francisco; Ros, Eduardo; D' Angelo, Egidio; Pedrocchi, Alessandra

    2016-01-01

    In this study, we defined a realistic cerebellar model through the use of artificial spiking neural networks, testing it in computational simulations that reproduce associative motor tasks in multiple sessions of acquisition and extinction. By evolutionary algorithms, we tuned the cerebellar microcircuit to find out the near-optimal plasticity mechanism parameters that better reproduced human-like behavior in eye blink classical conditioning, one of the most extensively studied paradigms related to the cerebellum. We used two models: one with only the cortical plasticity and another including two additional plasticity sites at nuclear level. First, both spiking cerebellar models were able to well reproduce the real human behaviors, in terms of both "timing" and "amplitude", expressing rapid acquisition, stable late acquisition, rapid extinction, and faster reacquisition of an associative motor task. Even though the model with only the cortical plasticity site showed good learning capabilities, the model with distributed plasticity produced faster and more stable acquisition of conditioned responses in the reacquisition phase. This behavior is explained by the effect of the nuclear plasticities, which have slow dynamics and can express memory consolidation and saving. We showed how the spiking dynamics of multiple interactive neural mechanisms implicitly drive multiple essential components of complex learning processes.  This study presents a very advanced computational model, developed together by biomedical engineers, computer scientists, and neuroscientists. Since its realistic features, the proposed model can provide confirmations and suggestions about neurophysiological and pathological hypotheses and can be used in challenging clinical applications.

  5. Multilayer Optical Learning Networks

    Science.gov (United States)

    Wagner, Kelvin; Psaltis, Demetri

    1987-08-01

    In this paper we present a new approach to learning in a multilayer optical neural network which is based on holographically interconnected nonlinear Fabry-Perot etalons. The network can learn the interconnections that form a distributed representation of a desired pattern transformation operation. The interconnections are formed in an adaptive and self aligning fashion, as volume holographic gratings in photorefractive crystals. Parallel arrays of globally space integrated inner products diffracted by the interconnecting hologram illuminate arrays of nonlinear Fabry-Perot etalons for fast thresholding of the transformed patterns. A phase conjugated reference wave interferes with a backwards propagating error signal to form holographic interference patterns which are time integrated in the volume of the photorefractive crystal in order to slowly modify and learn the appropriate self aligning interconnections. A holographic implementation of a single layer perceptron learning procedure is presented that can be extendept ,to a multilayer learning network through an optical implementation of the backward error propagation (BEP) algorithm.

  6. IMPLEMENTATION OF MULTIAGENT REINFORCEMENT LEARNING MECHANISM FOR OPTIMAL ISLANDING OPERATION OF DISTRIBUTION NETWORK

    DEFF Research Database (Denmark)

    Saleem, Arshad; Lind, Morten

    2008-01-01

    among electric power utilities to utilize modern information and communication technologies (ICT) in order to improve the automation of the distribution system. In this paper we present our work for the implementation of a dynamic multi-agent based distributed reinforcement learning mechanism...

  7. Learning Networks for Lifelong Learning

    OpenAIRE

    Sloep, Peter

    2008-01-01

    Presentation in a seminar organized by Christopher Hoadley at Penn State University, October 2004.Contains general introduction into the Learning Network Programme and a demonstration of the Netlogo Simulation of a Learning Network.

  8. Learning to read aloud: A neural network approach using sparse distributed memory

    Science.gov (United States)

    Joglekar, Umesh Dwarkanath

    1989-01-01

    An attempt to solve a problem of text-to-phoneme mapping is described which does not appear amenable to solution by use of standard algorithmic procedures. Experiments based on a model of distributed processing are also described. This model (sparse distributed memory (SDM)) can be used in an iterative supervised learning mode to solve the problem. Additional improvements aimed at obtaining better performance are suggested.

  9. Distributed Estimation in Sensor Networks with Imperfect Model Information: An Adaptive Learning-Based Approach

    Science.gov (United States)

    2012-05-01

    in particular, the mean- squared error (MSE) blows up with the SNR. Other than being inaccurate, since the SNR is unknown apriori , the estimate...requires per- fect knowledge of a, which is unknown apriori . In Section 3, we will introduce a learning-based distributed estimation procedure, the MDE

  10. Learning Networks for Lifelong Learning

    NARCIS (Netherlands)

    Koper, Rob

    2004-01-01

    Presentation in a seminar organized by Christopher Hoadley at Penn State University, October 2004.Contains general introduction into the Learning Network Programme and a demonstration of the Netlogo Simulation of a Learning Network.

  11. A distribution network review

    Energy Technology Data Exchange (ETDEWEB)

    Fairbairn, R.J.; Maunder, D.; Kenyon, P.

    1999-07-01

    This report summarises the findings of a study reviewing the distribution network in England, Scotland and Wales to evaluate its ability to accommodate more embedded generation from both fossil fuel and renewable energy sources. The background to the study is traced, and descriptions of the existing electricity supply system, the licence conditions relating to embedded generation, and the effects of the Review of Electricity Trading Arrangements are given. The ability of the UK distribution networks to accept embedded generation is examined, and technical benefits/drawbacks arising from embedded generation, and the potential for uptake of embedded generation technologies are considered. The distribution network capacity and the potential uptake of embedded generation are compared, and possible solutions to overcome obstacles are suggested. (UK)

  12. Distributional learning of appearance

    National Research Council Canada - National Science Library

    Griffin, Lewis D; Wahab, M Husni; Newell, Andrew J

    2013-01-01

    ... for the rate of language acquisition in children. It has been suggested that additional learning could occur in a distributional mode, where information is gleaned from the distributional statistics...

  13. Distributed Private Online Learning for Social Big Data Computing over Data Center Networks

    OpenAIRE

    Li, Chencheng; Zhou, Pan; Zhou, Yingxue; Bian, Kaigui; Jiang, Tao; Rahardja, Susanto

    2016-01-01

    With the rapid growth of Internet technologies, cloud computing and social networks have become ubiquitous. An increasing number of people participate in social networks and massive online social data are obtained. In order to exploit knowledge from copious amounts of data obtained and predict social behavior of users, we urge to realize data mining in social networks. Almost all online websites use cloud services to effectively process the large scale of social data, which are gathered from ...

  14. Distributed sensor networks

    CERN Document Server

    Rubin, Donald B; Carlin, John B; Iyengar, S Sitharama; Brooks, Richard R; University, Clemson

    2014-01-01

    An Overview, S.S. Iyengar, Ankit Tandon, and R.R. BrooksMicrosensor Applications, David ShepherdA Taxonomy of Distributed Sensor Networks, Shivakumar Sastry and S.S. IyengarContrast with Traditional Systems, R.R. BrooksDigital Signal Processing Background, Yu Hen HuImage-Processing Background Lynne Grewe and Ben ShahshahaniObject Detection and Classification, Akbar M. SayeedParameter Estimation David FriedlanderTarget Tracking with Self-Organizing Distributed Sensors R.R. Brooks, C. Griffin, D.S. Friedlander, and J.D. KochCollaborative Signal and Information Processing: AnInformation-Directed Approach Feng Zhao, Jie Liu, Juan Liu, Leonidas Guibas, and James ReichEnvironmental Effects, David C. SwansonDetecting and Counteracting Atmospheric Effects Lynne L. GreweSignal Processing and Propagation for Aeroacoustic Sensor Networks, Richard J. Kozick, Brian M. Sadler, and D. Keith WilsonDistributed Multi-Target Detection in Sensor Networks Xiaoling Wang, Hairong Qi, and Steve BeckFoundations of Data Fusion f...

  15. Distributed sensor networks

    Science.gov (United States)

    Lacoss, R. T.

    1985-09-01

    The Distributed Sensor Networks (DSN) program is aimed at developing and extending target surveillance and tracking technology in systems that employ multiple spatially distributed sensors and processing resources. Such a system would be made up of sensors, data bases, and processors distributed throughout an area and interconnected by an appropriate digital data communication system. The detection, tracking, and classification of low flying aircraft has been selected to develop and evaluate DSN concepts in the light of a specific system problem. A DSN test bed has been developed and is being used to test and demonstrate DSN techniques and technology. The overall concept calls for a mix of sensor types. The initial test-bed sensors are small arrays of microphones at each node augmented by TV sensors at some nodes. This Semiannual Technical Summary (SATS) reports results for the period 1 October 1984 through 31 March 1985. Progress in the development of distributed tracking algorithms and their implementation in the DSN test-bed system is reviewed in Section II. Test-bed versions of distributed acoustic tracking algorithms now have been implemented and tested using simulated acoustic data. This required developing a solution to a basic distributed tracking problem: the information feedback problem. Target tracks received by one node from another node often implicitly include information that originally was obtained from the receiving node.

  16. Distributed Semantic Overlay Networks

    Science.gov (United States)

    Doulkeridis, Christos; Vlachou, Akrivi; Nørvåg, Kjetil; Vazirgiannis, Michalis

    Semantic Overlay Networks (SONs) have been recently proposed as a way to organize content in peer-to-peer (P2P) networks. The main objective is to discover peers with similar content and then form thematically focused peer groups. Efficient content retrieval can be performed by having queries selectively forwarded only to relevant groups of peers to the query. As a result, less peers need to be contacted, in order to answer a query. In this context, the challenge is to generate SONs in a decentralized and distributed manner, as the centralized assembly of global information is not feasible. Different approaches for exploiting the generated SONs for content retrieval have been proposed in the literature, which are examined in this chapter, with a particular focus on SON interconnections for efficient search. Several applications, such as P2P document and image retrieval, can be deployed over generated SONs, motivating the need for distributed and truly scalable SON creation. Therefore, recently several research papers focus on SONs as stated in our comprehensive overview of related work in the field of semantic overlay networks. A classification of existing algorithms according to a set of qualitative criteria is also provided. In spite of the rich existing work in the field of SONs, several challenges have not been efficiently addressed yet, therefore, future promising research directions are pointed out and discussed at the end of this chapter.

  17. Learning Networks for Lifelong Learning

    NARCIS (Netherlands)

    Koper, Rob

    2004-01-01

    Presentation at: "Learning Designs in a Networked World A Dutch - Canada Education Seminar", October 15th, 2004, University of Alberta, Edmonton, Canada. Similar presentation as: http://hdl.handle.net/1820/278

  18. Distributional Learning of Appearance

    Science.gov (United States)

    Griffin, Lewis D.; Wahab, M. Husni; Newell, Andrew J.

    2013-01-01

    Opportunities for associationist learning of word meaning, where a word is heard or read contemperaneously with information being available on its meaning, are considered too infrequent to account for the rate of language acquisition in children. It has been suggested that additional learning could occur in a distributional mode, where information is gleaned from the distributional statistics (word co-occurrence etc.) of natural language. Such statistics are relevant to meaning because of the Distributional Principle that ‘words of similar meaning tend to occur in similar contexts’. Computational systems, such as Latent Semantic Analysis, have substantiated the viability of distributional learning of word meaning, by showing that semantic similarities between words can be accurately estimated from analysis of the distributional statistics of a natural language corpus. We consider whether appearance similarities can also be learnt in a distributional mode. As grounds for such a mode we advance the Appearance Hypothesis that ‘words with referents of similar appearance tend to occur in similar contexts’. We assess the viability of such learning by looking at the performance of a computer system that interpolates, on the basis of distributional and appearance similarity, from words that it has been explicitly taught the appearance of, in order to identify and name objects that it has not been taught about. Our experiment tests with a set of 660 simple concrete noun words. Appearance information on words is modelled using sets of images of examples of the word. Distributional similarity is computed from a standard natural language corpus. Our computation results support the viability of distributional learning of appearance. PMID:23460927

  19. Distributional learning of appearance.

    Directory of Open Access Journals (Sweden)

    Lewis D Griffin

    Full Text Available Opportunities for associationist learning of word meaning, where a word is heard or read contemperaneously with information being available on its meaning, are considered too infrequent to account for the rate of language acquisition in children. It has been suggested that additional learning could occur in a distributional mode, where information is gleaned from the distributional statistics (word co-occurrence etc. of natural language. Such statistics are relevant to meaning because of the Distributional Principle that 'words of similar meaning tend to occur in similar contexts'. Computational systems, such as Latent Semantic Analysis, have substantiated the viability of distributional learning of word meaning, by showing that semantic similarities between words can be accurately estimated from analysis of the distributional statistics of a natural language corpus. We consider whether appearance similarities can also be learnt in a distributional mode. As grounds for such a mode we advance the Appearance Hypothesis that 'words with referents of similar appearance tend to occur in similar contexts'. We assess the viability of such learning by looking at the performance of a computer system that interpolates, on the basis of distributional and appearance similarity, from words that it has been explicitly taught the appearance of, in order to identify and name objects that it has not been taught about. Our experiment tests with a set of 660 simple concrete noun words. Appearance information on words is modelled using sets of images of examples of the word. Distributional similarity is computed from a standard natural language corpus. Our computation results support the viability of distributional learning of appearance.

  20. Learning dynamic Bayesian networks with mixed variables

    DEFF Research Database (Denmark)

    Bøttcher, Susanne Gammelgaard

    This paper considers dynamic Bayesian networks for discrete and continuous variables. We only treat the case, where the distribution of the variables is conditional Gaussian. We show how to learn the parameters and structure of a dynamic Bayesian network and also how the Markov order can be learned...

  1. Protection of electricity distribution networks

    CERN Document Server

    Gers, Juan M

    2004-01-01

    Written by two practicing electrical engineers, this second edition of the bestselling Protection of Electricity Distribution Networks offers both practical and theoretical coverage of the technologies, from the classical electromechanical relays to the new numerical types, which protect equipment on networks and in electrical plants. A properly coordinated protection system is vital to ensure that an electricity distribution network can operate within preset requirements for safety for individual items of equipment, staff and public, and the network overall. Suitable and reliable equipment sh

  2. Research, Boundaries, and Policy in Networked Learning

    DEFF Research Database (Denmark)

    This book presents cutting-edge, peer reviewed research on networked learning organized by three themes: policy in networked learning, researching networked learning, and boundaries in networked learning. The "policy in networked learning" section explores networked learning in relation to policy...

  3. Learning Networks for Professional Development & Lifelong Learning

    NARCIS (Netherlands)

    Sloep, Peter

    2009-01-01

    Sloep, P. B. (2009). Learning Networks for Professional Development & Lifelong Learning. Presentation at a NeLLL seminar with Etienne Wenger held at the Open Universiteit Nederland. September, 10, 2009, Heerlen, The Netherlands.

  4. Evaluation of the information servicing in a distributed learning ...

    African Journals Online (AJOL)

    The distributed learning is an instructional model that allows instructor, students, and content to be located in different nodes in the global network. The authors' main idea is to organize a distributed learning environment (DLE) based on information and communication resources of global network in combination with the ...

  5. Redes de aprendizaje, aprendizaje en red Learning Networks, Networked Learning

    Directory of Open Access Journals (Sweden)

    Peter Sloep

    2011-10-01

    Full Text Available Las redes de aprendizaje (Learning Networks son redes sociales en línea mediante las cuales los participantes comparten información y colaboran para crear conocimiento. De esta manera, estas redes enriquecen la experiencia de aprendizaje en cualquier contexto de aprendizaje, ya sea de educación formal (en escuelas o universidades o educación no-formal (formación profesional. Aunque el concepto de aprendizaje en red suscita el interés de diferentes actores del ámbito educativo, aún existen muchos interrogantes sobre cómo debe diseñarse el aprendizaje en red para facilitar adecuadamente la educación y la formación. El artículo toma este interrogante como punto de partida, y posteriormente aborda cuestiones como la dinámica de la evolución de las redes de aprendizaje, la importancia de fomentar la confianza entre los participantes y el papel central que desempeña el perfil de usuario en la construcción de la confianza, así como el apoyo entre compañeros. Además, se elabora el proceso de diseño de una red de aprendizaje, y se describe un ejemplo en el contexto universitario. Basándonos en la investigación que actualmente se lleva a cabo en nuestro propio centro y en otros lugares, el capítulo concluye con una visión del futuro de las redes de aprendizaje.Learning Networks are on-line social networks through which users share knowledge with each other and jointly develop new knowledge. This way, Learning Networks may enrich the experience of formal, school-based learning and form a viable setting for professional development. Although networked learning enjoys an increasing interest, many questions remain on how exactly learning in such networked contexts can contribute to successful education and training. Put differently, how should networked learning be designed best to facilitate education and training? Taking this as its point of departure, the chapter addresses such issues as the dynamic evolution of Learning Networks

  6. Distributed Active Learning

    National Research Council Canada - National Science Library

    Shen, Pengcheng; Li, Chunguang; Zhang, Zhaoyang

    2016-01-01

    Active learning aims at obtaining high-accuracy models with as a few labeled data as possible, by iteratively and elaborately selecting most valuable data to query labels during the learning process...

  7. Neural networks and statistical learning

    CERN Document Server

    Du, Ke-Lin

    2014-01-01

    Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardw...

  8. Distributed Bayesian Networks for User Modeling

    DEFF Research Database (Denmark)

    Tedesco, Roberto; Dolog, Peter; Nejdl, Wolfgang

    2006-01-01

    The World Wide Web is a popular platform for providing eLearning applications to a wide spectrum of users. However – as users differ in their preferences, background, requirements, and goals – applications should provide personalization mechanisms. In the Web context, user models used...... of Web-based eLearning platforms. The scenario we are tackling assumes learners who use several systems over time, which are able to create partial Bayesian Networks for user models based on the local system context. In particular, we focus on how to merge these partial user models. Our merge mechanism...... efficiently combines distributed learner models without the need to exchange internal structure of local Bayesian networks, nor local evidence between the involved platforms....

  9. Learning Novel Musical Pitch via Distributional Learning

    Science.gov (United States)

    Ong, Jia Hoong; Burnham, Denis; Stevens, Catherine J.

    2017-01-01

    Because different musical scales use different sets of intervals and, hence, different musical pitches, how do music listeners learn those that are in their native musical system? One possibility is that musical pitches are acquired in the same way as phonemes, that is, via distributional learning, in which learners infer knowledge from the…

  10. On Network Coded Distributed Storage

    DEFF Research Database (Denmark)

    Cabrera Guerrero, Juan Alberto; Roetter, Daniel Enrique Lucani; Fitzek, Frank Hanns Paul

    2016-01-01

    This paper focuses on distributed fog storage solutions, where a number of unreliable devices organize themselves in Peer-to-Peer (P2P) networks with the purpose to store reliably their data and that of other devices and/or local users and provide lower delay and higher throughput. Cloud storage...... P2P system that achieves the predicted performance within 1 dB in measurement campaigns using commercial devices....

  11. Latent Semantic Analysis As a Tool for Learner Positioning in Learning Networks for Lifelong Learning

    Science.gov (United States)

    van Bruggen, Jan; Sloep, Peter; van Rosmalen, Peter; Brouns, Francis; Vogten, Hubert; Koper, Rob; Tattersall, Colin

    2004-01-01

    As we move towards distributed, self-organised learning networks for lifelong learning to which multiple providers contribute content, there is a need to develop new techniques to determine where learners can be positioned in these networks. Positioning requires us to map characteristics of the learner onto characteristics of learning materials…

  12. Loss optimization in distribution networks with distributed generation

    DEFF Research Database (Denmark)

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

    2017-01-01

    This paper presents a novel power loss minimization approach in distribution grids considering network reconfiguration, distributed generation and storage installation. Identification of optimum configuration in such scenario is one of the main challenges faced by distribution system operators in...

  13. Entanglement distribution in quantum networks

    Energy Technology Data Exchange (ETDEWEB)

    Perseguers, Sebastien

    2010-04-15

    This Thesis contributes to the theory of entanglement distribution in quantum networks, analyzing the generation of long-distance entanglement in particular. We consider that neighboring stations share one partially entangled pair of qubits, which emphasizes the difficulty of creating remote entanglement in realistic settings. The task is then to design local quantum operations at the stations, such that the entanglement present in the links of the whole network gets concentrated between few parties only, regardless of their spatial arrangement. First, we study quantum networks with a two-dimensional lattice structure, where quantum connections between the stations (nodes) are described by non-maximally entangled pure states (links). We show that the generation of a perfectly entangled pair of qubits over an arbitrarily long distance is possible if the initial entanglement of the links is larger than a threshold. This critical value highly depends on the geometry of the lattice, in particular on the connectivity of the nodes, and is related to a classical percolation problem. We then develop a genuine quantum strategy based on multipartite entanglement, improving both the threshold and the success probability of the generation of long-distance entanglement. Second, we consider a mixed-state definition of the connections of the quantum networks. This formalism is well-adapted for a more realistic description of systems in which noise (random errors) inevitably occurs. New techniques are required to create remote entanglement in this setting, and we show how to locally extract and globally process some error syndromes in order to create useful long-distance quantum correlations. Finally, we turn to networks that have a complex topology, which is the case for most real-world communication networks such as the Internet for instance. Besides many other characteristics, these systems have in common the small-world feature, stating that any two nodes are separated by a

  14. Collaborative learning in networks.

    Science.gov (United States)

    Mason, Winter; Watts, Duncan J

    2012-01-17

    Complex problems in science, business, and engineering typically require some tradeoff between exploitation of known solutions and exploration for novel ones, where, in many cases, information about known solutions can also disseminate among individual problem solvers through formal or informal networks. Prior research on complex problem solving by collectives has found the counterintuitive result that inefficient networks, meaning networks that disseminate information relatively slowly, can perform better than efficient networks for problems that require extended exploration. In this paper, we report on a series of 256 Web-based experiments in which groups of 16 individuals collectively solved a complex problem and shared information through different communication networks. As expected, we found that collective exploration improved average success over independent exploration because good solutions could diffuse through the network. In contrast to prior work, however, we found that efficient networks outperformed inefficient networks, even in a problem space with qualitative properties thought to favor inefficient networks. We explain this result in terms of individual-level explore-exploit decisions, which we find were influenced by the network structure as well as by strategic considerations and the relative payoff between maxima. We conclude by discussing implications for real-world problem solving and possible extensions.

  15. Spatially Distributed Social Complex Networks

    Directory of Open Access Journals (Sweden)

    Gerald F. Frasco

    2014-01-01

    Full Text Available We propose a bare-bones stochastic model that takes into account both the geographical distribution of people within a country and their complex network of connections. The model, which is designed to give rise to a scale-free network of social connections and to visually resemble the geographical spread seen in satellite pictures of the Earth at night, gives rise to a power-law distribution for the ranking of cities by population size (but for the largest cities and reflects the notion that highly connected individuals tend to live in highly populated areas. It also yields some interesting insights regarding Gibrat’s law for the rates of city growth (by population size, in partial support of the findings in a recent analysis of real data [Rozenfeld et al., Proc. Natl. Acad. Sci. U.S.A. 105, 18702 (2008.]. The model produces a nontrivial relation between city population and city population density and a superlinear relationship between social connectivity and city population, both of which seem quite in line with real data.

  16. Spatially Distributed Social Complex Networks

    Science.gov (United States)

    Frasco, Gerald F.; Sun, Jie; Rozenfeld, Hernán D.; ben-Avraham, Daniel

    2014-01-01

    We propose a bare-bones stochastic model that takes into account both the geographical distribution of people within a country and their complex network of connections. The model, which is designed to give rise to a scale-free network of social connections and to visually resemble the geographical spread seen in satellite pictures of the Earth at night, gives rise to a power-law distribution for the ranking of cities by population size (but for the largest cities) and reflects the notion that highly connected individuals tend to live in highly populated areas. It also yields some interesting insights regarding Gibrat's law for the rates of city growth (by population size), in partial support of the findings in a recent analysis of real data [Rozenfeld et al., Proc. Natl. Acad. Sci. U.S.A. 105, 18702 (2008).]. The model produces a nontrivial relation between city population and city population density and a superlinear relationship between social connectivity and city population, both of which seem quite in line with real data.

  17. The Operational Risk Assessment for Distribution Network with Distributed Generations

    Science.gov (United States)

    Hua, Xie; Yaqi, Wu; Yifan, Wang; Qian, Sun; Jianwei, Ma

    2017-05-01

    Distribution network is an important part of the power system and is connected to the consumers directly. Many distributed generations that have discontinuous output power are connected in the distribution networks, which may cause adverse impact to the distribution network. Therefore, to ensure the security and reliability of distribution network with numerous distributed generations, the risk analysis is necessary for this kind of distribution networks. After study of stochastic load flow algorithm, this paper applies it in the static security risk assessment. The wind and photovoltaic output probabilistic model are built. The voltage over-limit is chosen to calculate the risk indicators. As a case study, the IEEE 33 system is simulated for analyzing impact of distributed generations on system risk in the proposed method.

  18. Collaborative Supervised Learning for Sensor Networks

    Science.gov (United States)

    Wagstaff, Kiri L.; Rebbapragada, Umaa; Lane, Terran

    2011-01-01

    Collaboration methods for distributed machine-learning algorithms involve the specification of communication protocols for the learners, which can query other learners and/or broadcast their findings preemptively. Each learner incorporates information from its neighbors into its own training set, and they are thereby able to bootstrap each other to higher performance. Each learner resides at a different node in the sensor network and makes observations (collects data) independently of the other learners. After being seeded with an initial labeled training set, each learner proceeds to learn in an iterative fashion. New data is collected and classified. The learner can then either broadcast its most confident classifications for use by other learners, or can query neighbors for their classifications of its least confident items. As such, collaborative learning combines elements of both passive (broadcast) and active (query) learning. It also uses ideas from ensemble learning to combine the multiple responses to a given query into a single useful label. This approach has been evaluated against current non-collaborative alternatives, including training a single classifier and deploying it at all nodes with no further learning possible, and permitting learners to learn from their own most confident judgments, absent interaction with their neighbors. On several data sets, it has been consistently found that active collaboration is the best strategy for a distributed learner network. The main advantages include the ability for learning to take place autonomously by collaboration rather than by requiring intervention from an oracle (usually human), and also the ability to learn in a distributed environment, permitting decisions to be made in situ and to yield faster response time.

  19. Learning Python network programming

    CERN Document Server

    Sarker, M O Faruque

    2015-01-01

    If you're a Python developer or a system administrator with Python experience and you're looking to take your first steps in network programming, then this book is for you. Basic knowledge of Python is assumed.

  20. Language Choice & Global Learning Networks

    Directory of Open Access Journals (Sweden)

    Dennis Sayers

    1995-05-01

    Full Text Available How can other languages be used in conjunction with English to further intercultural and multilingual learning when teachers and students participate in computer-based global learning networks? Two portraits are presented of multilingual activities in the Orillas and I*EARN learning networks, and are discussed as examples of the principal modalities of communication employed in networking projects between distant classes. Next, an important historical precedent --the social controversy which accompanied the introduction of telephone technology at the end of the last century-- is examined in terms of its implications for language choice in contemporary classroom telecomputing projects. Finally, recommendations are offered to guide decision making concerning the role of language choice in promoting collaborative critical inquiry.

  1. Changing Conditions for Networked Learning?

    DEFF Research Database (Denmark)

    Ryberg, Thomas

    2011-01-01

    of social technologies. I argue that we are seeing the emergence of new architectures and scales of participation, collaboration and networking e.g. through interesting formations of learning networks at different levels of scale, for different purposes and often bridging boundaries such as formal......In this talk I should like to initially take a critical look at popular ideas and discourses related to web 2.0, social technologies and learning. I argue that many of the pedagogical ideals particularly associated with web 2.0 have a longer history and background, which is often forgotten...

  2. Hybrid E-Learning Tool TransLearning: Video Storytelling to Foster Vicarious Learning within Multi-Stakeholder Collaboration Networks

    Science.gov (United States)

    van der Meij, Marjoleine G.; Kupper, Frank; Beers, Pieter J.; Broerse, Jacqueline E. W.

    2016-01-01

    E-learning and storytelling approaches can support informal vicarious learning within geographically widely distributed multi-stakeholder collaboration networks. This case study evaluates hybrid e-learning and video-storytelling approach "TransLearning" by investigation into how its storytelling e-tool supported informal vicarious…

  3. Computational Aspects of Sensor Network Protocols (Distributed Sensor Network Simulator

    Directory of Open Access Journals (Sweden)

    Vasanth Iyer

    2009-08-01

    Full Text Available In this work, we model the sensor networks as an unsupervised learning and clustering process. We classify nodes according to its static distribution to form known class densities (CCPD. These densities are chosen from specific cross-layer features which maximizes lifetime of power-aware routing algorithms. To circumvent computational complexities of a power-ware communication STACK we introduce path-loss models at the nodes only for high density deployments. We study the cluster heads and formulate the data handling capacity for an expected deployment and use localized probability models to fuse the data with its side information before transmission. So each cluster head has a unique Pmax but not all cluster heads have the same measured value. In a lossless mode if there are no faults in the sensor network then we can show that the highest probability given by Pmax is ambiguous if its frequency is ≤ n/2 otherwise it can be determined by a local function. We further show that the event detection at the cluster heads can be modelled with a pattern 2m and m, the number of bits can be a correlated pattern of 2 bits and for a tight lower bound we use 3-bit Huffman codes which have entropy < 1. These local algorithms are further studied to optimize on power, fault detection and to maximize on the distributed routing algorithm used at the higher layers. From these bounds in large network, it is observed that the power dissipation is network size invariant. The performance of the routing algorithms solely based on success of finding healthy nodes in a large distribution. It is also observed that if the network size is kept constant and the density of the nodes is kept closer then the local pathloss model effects the performance of the routing algorithms. We also obtain the maximum intensity of transmitting nodes for a given category of routing algorithms for an outage constraint, i.e., the lifetime of sensor network.

  4. Large scale network-centric distributed systems

    CERN Document Server

    Sarbazi-Azad, Hamid

    2014-01-01

    A highly accessible reference offering a broad range of topics and insights on large scale network-centric distributed systems Evolving from the fields of high-performance computing and networking, large scale network-centric distributed systems continues to grow as one of the most important topics in computing and communication and many interdisciplinary areas. Dealing with both wired and wireless networks, this book focuses on the design and performance issues of such systems. Large Scale Network-Centric Distributed Systems provides in-depth coverage ranging from ground-level hardware issu

  5. Personalizing Access to Learning Networks

    DEFF Research Database (Denmark)

    Dolog, Peter; Simon, Bernd; Nejdl, Wolfgang

    2008-01-01

    In this article, we describe a Smart Space for Learning™ (SS4L) framework and infrastructure that enables personalized access to distributed heterogeneous knowledge repositories. Helping a learner to choose an appropriate learning resource or activity is a key problem which we address in this fra......In this article, we describe a Smart Space for Learning™ (SS4L) framework and infrastructure that enables personalized access to distributed heterogeneous knowledge repositories. Helping a learner to choose an appropriate learning resource or activity is a key problem which we address...... in this framework, enabling personalized access to federated learning repositories with a vast number of learning offers. Our infrastructure includes personalization strategies both at the query and the query results level. Query rewriting is based on learning and language preferences; rule-based and ranking...

  6. Distributed Structure-Searchable Toxicity Database Network

    Data.gov (United States)

    U.S. Environmental Protection Agency — The Distributed Structure-Searchable Toxicity (DSSTox) Database Network provides a public forum for search and publishing downloadable, structure-searchable,...

  7. Maximum Entropy Learning with Deep Belief Networks

    Directory of Open Access Journals (Sweden)

    Payton Lin

    2016-07-01

    Full Text Available Conventionally, the maximum likelihood (ML criterion is applied to train a deep belief network (DBN. We present a maximum entropy (ME learning algorithm for DBNs, designed specifically to handle limited training data. Maximizing only the entropy of parameters in the DBN allows more effective generalization capability, less bias towards data distributions, and robustness to over-fitting compared to ML learning. Results of text classification and object recognition tasks demonstrate ME-trained DBN outperforms ML-trained DBN when training data is limited.

  8. Parallel Distributed Processing Theory in the Age of Deep Networks.

    Science.gov (United States)

    Bowers, Jeffrey S

    2017-12-01

    Parallel distributed processing (PDP) models in psychology are the precursors of deep networks used in computer science. However, only PDP models are associated with two core psychological claims, namely that all knowledge is coded in a distributed format and cognition is mediated by non-symbolic computations. These claims have long been debated in cognitive science, and recent work with deep networks speaks to this debate. Specifically, single-unit recordings show that deep networks learn units that respond selectively to meaningful categories, and researchers are finding that deep networks need to be supplemented with symbolic systems to perform some tasks. Given the close links between PDP and deep networks, it is surprising that research with deep networks is challenging PDP theory. Copyright © 2017. Published by Elsevier Ltd.

  9. Neural Network for Estimating Conditional Distribution

    DEFF Research Database (Denmark)

    Schiøler, Henrik; Kulczycki, P.

    Neural networks for estimating conditional distributions and their associated quantiles are investigated in this paper. A basic network structure is developed on the basis of kernel estimation theory, and consistency is proved from a mild set of assumptions. A number of applications within...... statistcs, decision theory and signal processing are suggested, and a numerical example illustrating the capabilities of the elaborated network is given...

  10. Certainty Power Flow Calculation for Distribution Network with Distributed Generation

    Directory of Open Access Journals (Sweden)

    FU Min

    2017-06-01

    Full Text Available System nodes need to be renumbered manually when upgrading and reforming distribution network makes network topology change. Because optimization method is inapplicable to the network change,an improved forward and backward sweep algorithm is proposed which is unrelated to node label. In this paper,node type of sorts of distributed generation ( DG in power flow calculation are induced and part of new node type of DG under improved control strategy are provided. The basis of DG as active constant node in certainty power flow calculation is analyzed. Based on improved back - forward sweep algorithm,general programs of power flow calculation in power distribution network of DG are programmed by MATLAB and the impact of DG on flow calculation to distribution network is analyzed quantitatively by plenty of simulation calculation of IEEE33 test system.

  11. Mathematical theories of distributed sensor networks

    CERN Document Server

    Iyengar, Sitharama S; Balakrishnan, N

    2014-01-01

    Mathematical Theory of Distributed Sensor Networks demonstrates how mathematical theories can be used to provide distributed sensor modeling and to solve important problems such as coverage hole detection and repair. The book introduces the mathematical and computational structure by discussing what they are, their applications and how they differ from traditional systems. The text also explains how mathematics are utilized to provide efficient techniques implementing effective coverage, deployment, transmission, data processing, signal processing, and data protection within distributed sensor networks. Finally, the authors discuss some important challenges facing mathematics to get more incite to the multidisciplinary area of distributed sensor networks.

  12. Learning and coordinating in a multilayer network

    CERN Document Server

    Lugo, Haydee

    2014-01-01

    We introduce a two layer network model for social coordination incorporating two relevant ingredients: a) different networks of interaction to learn and to obtain a payoff , and b) decision making processes based both on social and strategic motivations. Two populations of agents are distributed in two layers with intralayer learning processes and playing interlayer a coordination game. We find that the skepticism about the wisdom of crowd and the local connectivity are the driving forces to accomplish full coordination of the two populations, while polarized coordinated layers are only possible for all-to-all interactions. Local interactions also allow for full coordination in the socially efficient Pareto-dominant strategy in spite of being the riskier one.

  13. Learning and coordinating in a multilayer network

    Science.gov (United States)

    Lugo, Haydée; Miguel, Maxi San

    2015-01-01

    We introduce a two layer network model for social coordination incorporating two relevant ingredients: a) different networks of interaction to learn and to obtain a pay-off, and b) decision making processes based both on social and strategic motivations. Two populations of agents are distributed in two layers with intralayer learning processes and playing interlayer a coordination game. We find that the skepticism about the wisdom of crowd and the local connectivity are the driving forces to accomplish full coordination of the two populations, while polarized coordinated layers are only possible for all-to-all interactions. Local interactions also allow for full coordination in the socially efficient Pareto-dominant strategy in spite of being the riskier one.

  14. Machine Learning for ATLAS DDM Network Metrics

    CERN Document Server

    Lassnig, Mario; The ATLAS collaboration; Vamosi, Ralf

    2016-01-01

    The increasing volume of physics data is posing a critical challenge to the ATLAS experiment. In anticipation of high luminosity physics, automation of everyday data management tasks has become necessary. Previously many of these tasks required human decision-making and operation. Recent advances in hardware and software have made it possible to entrust more complicated duties to automated systems using models trained by machine learning algorithms. In this contribution we show results from our ongoing automation efforts. First, we describe our framework for distributed data management and network metrics, automatically extract and aggregate data, train models with various machine learning algorithms, and eventually score the resulting models and parameters. Second, we use these models to forecast metrics relevant for network-aware job scheduling and data brokering. We show the characteristics of the data and evaluate the forecasting accuracy of our models.

  15. Associative learning in biochemical networks.

    Science.gov (United States)

    Gandhi, Nikhil; Ashkenasy, Gonen; Tannenbaum, Emmanuel

    2007-11-07

    It has been recently suggested that there are likely generic features characterizing the emergence of systems constructed from the self-organization of self-replicating agents acting under one or more selection pressures. Therefore, structures and behaviors at one length scale may be used to infer analogous structures and behaviors at other length scales. Motivated by this suggestion, we seek to characterize various "animate" behaviors in biochemical networks, and the influence that these behaviors have on genomic evolution. Specifically, in this paper, we develop a simple, chemostat-based model illustrating how a process analogous to associative learning can occur in a biochemical network. Associative learning is a form of learning whereby a system "learns" to associate two stimuli with one another. Associative learning, also known as conditioning, is believed to be a powerful learning process at work in the brain (associative learning is essentially "learning by analogy"). In our model, two types of replicating molecules, denoted as A and B, are present in some initial concentration in the chemostat. Molecules A and B are stimulated to replicate by some growth factors, denoted as G(A) and G(B), respectively. It is also assumed that A and B can covalently link, and that the conjugated molecule can be stimulated by either the G(A) or G(B) growth factors (and can be degraded). We show that, if the chemostat is stimulated by both growth factors for a certain time, followed by a time gap during which the chemostat is not stimulated at all, and if the chemostat is then stimulated again by only one of the growth factors, then there will be a transient increase in the number of molecules activated by the other growth factor. Therefore, the chemostat bears the imprint of earlier, simultaneous stimulation with both growth factors, which is indicative of associative learning. It is interesting to note that the dynamics of our model is consistent with certain aspects of

  16. Blending Formal and Informal Learning Networks for Online Learning

    Science.gov (United States)

    Czerkawski, Betül C.

    2016-01-01

    With the emergence of social software and the advance of web-based technologies, online learning networks provide invaluable opportunities for learning, whether formal or informal. Unlike top-down, instructor-centered, and carefully planned formal learning settings, informal learning networks offer more bottom-up, student-centered participatory…

  17. Neural networks and perceptual learning

    Science.gov (United States)

    Tsodyks, Misha; Gilbert, Charles

    2005-01-01

    Sensory perception is a learned trait. The brain strategies we use to perceive the world are constantly modified by experience. With practice, we subconsciously become better at identifying familiar objects or distinguishing fine details in our environment. Current theoretical models simulate some properties of perceptual learning, but neglect the underlying cortical circuits. Future neural network models must incorporate the top-down alteration of cortical function by expectation or perceptual tasks. These newly found dynamic processes are challenging earlier views of static and feedforward processing of sensory information. PMID:15483598

  18. Reconstructing Causal Biological Networks through Active Learning.

    Directory of Open Access Journals (Sweden)

    Hyunghoon Cho

    Full Text Available Reverse-engineering of biological networks is a central problem in systems biology. The use of intervention data, such as gene knockouts or knockdowns, is typically used for teasing apart causal relationships among genes. Under time or resource constraints, one needs to carefully choose which intervention experiments to carry out. Previous approaches for selecting most informative interventions have largely been focused on discrete Bayesian networks. However, continuous Bayesian networks are of great practical interest, especially in the study of complex biological systems and their quantitative properties. In this work, we present an efficient, information-theoretic active learning algorithm for Gaussian Bayesian networks (GBNs, which serve as important models for gene regulatory networks. In addition to providing linear-algebraic insights unique to GBNs, leading to significant runtime improvements, we demonstrate the effectiveness of our method on data simulated with GBNs and the DREAM4 network inference challenge data sets. Our method generally leads to faster recovery of underlying network structure and faster convergence to final distribution of confidence scores over candidate graph structures using the full data, in comparison to random selection of intervention experiments.

  19. Power Quality Improvement in Electrical Distribution Network

    OpenAIRE

    Oladepo Olatunde; Awofolaju Tolulope Tola

    2016-01-01

    The introduction of Distributed Generation (DG) in a distribution system offers several benefits to utilities, customers and society. However, the integration of these sources into the networks can cause some challenges regarding their expected impacts on the security and the dynamic behaviour of the entire network. This paper presents the Modified Particle Swarm Optimization algorithm (MPSOA) to determine the optimal location and size of Distributed Generation and Capacitor banks to maximizi...

  20. Scaling up: Distributed machine learning with cooperation

    Energy Technology Data Exchange (ETDEWEB)

    Provost, F.J. [NYNEX Science & Technology, White Plains, NY (United States); Hennessy, D.N. [Univ. of Pittsburgh, PA (United States)

    1996-12-31

    Machine-learning methods are becoming increasingly popular for automated data analysis. However, standard methods do not scale up to massive scientific and business data sets without expensive hardware. This paper investigates a practical alternative for scaling up: the use of distributed processing to take advantage of the often dormant PCs and workstations available on local networks. Each workstation runs a common rule-learning program on a subset of the data. We first show that for commonly used rule-evaluation criteria, a simple form of cooperation can guarantee that a rule will look good to the set of cooperating learners if and only if it would look good to a single learner operating with the entire data set. We then show how such a system can further capitalize on different perspectives by sharing learned knowledge for significant reduction in search effort. We demonstrate the power of the method by learning from a massive data set taken from the domain of cellular fraud detection. Finally, we provide an overview of other methods for scaling up machine learning.

  1. Approximating distributions in stochastic learning.

    Science.gov (United States)

    Leen, Todd K; Friel, Robert; Nielsen, David

    2012-08-01

    On-line machine learning algorithms, many biological spike-timing-dependent plasticity (STDP) learning rules, and stochastic neural dynamics evolve by Markov processes. A complete description of such systems gives the probability densities for the variables. The evolution and equilibrium state of these densities are given by a Chapman-Kolmogorov equation in discrete time, or a master equation in continuous time. These formulations are analytically intractable for most cases of interest, and to make progress a nonlinear Fokker-Planck equation (FPE) is often used in their place. The FPE is limited, and some argue that its application to describe jump processes (such as in these problems) is fundamentally flawed. We develop a well-grounded perturbation expansion that provides approximations for both the density and its moments. The approach is based on the system size expansion in statistical physics (which does not give approximations for the density), but our simple development makes the methods accessible and invites application to diverse problems. We apply the method to calculate the equilibrium distributions for two biologically-observed STDP learning rules and for a simple nonlinear machine-learning problem. In all three examples, we show that our perturbation series provides good agreement with Monte-Carlo simulations in regimes where the FPE breaks down. Copyright © 2012 Elsevier Ltd. All rights reserved.

  2. Transient stability analysis of a distribution network with distributed generators

    NARCIS (Netherlands)

    Xyngi, I.; Ishchenko, A.; Popov, M.; Van der Sluis, L.

    2009-01-01

    This letter describes the transient stability analysis of a 10-kV distribution network with wind generators, microturbines, and CHP plants. The network being modeled in Matlab/Simulink takes into account detailed dynamic models of the generators. Fault simulations at various locations are

  3. 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

  4. Structure of Small World Innovation Network and Learning Performance

    Directory of Open Access Journals (Sweden)

    Shuang Song

    2014-01-01

    Full Text Available This paper examines the differences of learning performance of 5 MNCs (multinational corporations that filed the largest number of patents in China. We establish the innovation network with the patent coauthorship data by these 5 MNCs and classify the networks by the tail of distribution curve of connections. To make a comparison of the learning performance of these 5 MNCs with differing network structures, we develop an organization learning model by regarding the reality as having m dimensions, which denotes the heterogeneous knowledge about the reality. We further set n innovative individuals that are mutually interactive and own unique knowledge about the reality. A longer (shorter distance between the knowledge of the individual and the reality denotes a lower (higher knowledge level of that individual. Individuals interact with and learn from each other within the small-world network. By making 1,000 numerical simulations and averaging the simulated results, we find that the differing structure of the small-world network leads to the differences of learning performance between these 5 MNCs. The network monopolization negatively impacts and network connectivity positively impacts learning performance. Policy implications in the conclusion section suggest that to improve firm learning performance, it is necessary to establish a flat and connective network.

  5. Optimal design of network distribution systems

    Directory of Open Access Journals (Sweden)

    U. Passy

    2003-12-01

    Full Text Available The problem of finding the optimal distribution of pressure drop over a network is solved via an unconstrained gradient type algorithm. The developed algorithm is computationally attractive. Problems with several hundred variables and constraints were solved.

  6. The Impact of Distributed Generation on Distribution Networks ...

    African Journals Online (AJOL)

    Their advantages are the ability to reduce or postpone the need for investment in the transmission and distribution infrastructure when optimally located; the ability to reduce technical losses within the transmission and distribution networks as well as general improvement in power quality and system reliability. This paper ...

  7. Distributed Task Allocation in Social Networks

    NARCIS (Netherlands)

    De Weerdt, M.M.; Zhang, Y.; Klos, T.

    2007-01-01

    This paper proposes a new variant of the task allocation problem, where the agents are connected in a social network and tasks arrive at the agents distributed over the network. We show that the complexity of this problem remains NPhard. Moreover, it is not approximable within some factor. We

  8. Neural network approach to parton distributions fitting

    CERN Document Server

    Piccione, Andrea; Forte, Stefano; Latorre, Jose I.; Rojo, Joan; Piccione, Andrea; Rojo, Joan

    2006-01-01

    We will show an application of neural networks to extract information on the structure of hadrons. A Monte Carlo over experimental data is performed to correctly reproduce data errors and correlations. A neural network is then trained on each Monte Carlo replica via a genetic algorithm. Results on the proton and deuteron structure functions, and on the nonsinglet parton distribution will be shown.

  9. 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.

  10. 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.

  11. Mobile Agents in Networking and Distributed Computing

    CERN Document Server

    Cao, Jiannong

    2012-01-01

    The book focuses on mobile agents, which are computer programs that can autonomously migrate between network sites. This text introduces the concepts and principles of mobile agents, provides an overview of mobile agent technology, and focuses on applications in networking and distributed computing.

  12. Current distribution in conducting nanowire networks

    Science.gov (United States)

    Kumar, Ankush; Vidhyadhiraja, N. S.; Kulkarni, Giridhar U.

    2017-07-01

    Conducting nanowire networks find diverse applications in solar cells, touch-screens, transparent heaters, sensors, and various related transparent conducting electrode (TCE) devices. The performances of these devices depend on effective resistance, transmittance, and local current distribution in these networks. Although, there have been rigorous studies addressing resistance and transmittance in TCE, not much attention is paid on studying the distribution of current. Present work addresses this compelling issue of understanding current distribution in TCE networks using analytical as well as Monte-Carlo approaches. We quantified the current carrying backbone region against isolated and dangling regions as a function of wire density (ranging from percolation threshold to many multiples of threshold) and compared the wired connectivity with those obtained from template-based methods. Further, the current distribution in the obtained backbone is studied using Kirchhoff's law, which reveals that a significant fraction of the backbone (which is believed to be an active current component) may not be active for end-to-end current transport due to the formation of intervening circular loops. The study shows that conducting wire based networks possess hot spots (extremely high current carrying regions) which can be potential sources of failure. The fraction of these hot spots is found to decrease with increase in wire density, while they are completely absent in template based networks. Thus, the present work discusses unexplored issues related to current distribution in conducting networks, which are necessary to choose the optimum network for best TCE applications.

  13. Learning Processes of Layered Neural Networks

    OpenAIRE

    Fujiki, Sumiyoshi; FUJIKI, Nahomi, M.

    1995-01-01

    A positive reinforcement type learning algorithm is formulated for a stochastic feed-forward neural network, and a learning equation similar to that of the Boltzmann machine algorithm is obtained. By applying a mean field approximation to the same stochastic feed-forward neural network, a deterministic analog feed-forward network is obtained and the back-propagation learning rule is re-derived.

  14. Identifying Gatekeepers in Online Learning Networks

    Science.gov (United States)

    Gursakal, Necmi; Bozkurt, Aras

    2017-01-01

    The rise of the networked society has not only changed our perceptions but also the definitions, roles, processes and dynamics of online learning networks. From offline to online worlds, networks are everywhere and gatekeepers are an important entity in these networks. In this context, the purpose of this paper is to explore gatekeeping and…

  15. Network management systems for active distribution networks. A feasibility study

    Energy Technology Data Exchange (ETDEWEB)

    Roberts, D.A.

    2004-07-01

    A technical feasibility study on network management systems for active distribution networks is reported. The study investigated the potential for modifying a Distribution Network Operator (DNO) Supervisory Control and Data Acquisition System (SCADA) to give some degree of active management. Government incentives have encouraged more and more embedded generation being connected to the UK distribution networks and further acceleration of the process should support the 2010 target for a reduction in emissions of carbon dioxide. The report lists the objectives of the study and summarises what has been achieved; it also discusses limitations, reliability and resilience of existing SCADA. Safety and operational communications are discussed under staff safety and operational safety. Recommendations that could facilitate active management through SCADA are listed, together with suggestions for further study. The work was carried out as part of the DTI New and Renewable Energy Programme managed by Future Energy Solutions.

  16. PARALLEL ALGORITHM FOR BAYESIAN NETWORK STRUCTURE LEARNING

    Directory of Open Access Journals (Sweden)

    S. A. Arustamov

    2013-03-01

    Full Text Available The article deals with implementation of a scalable parallel algorithm for structure learning of Bayesian network. Comparative analysis of sequential and parallel algorithms is done.

  17. A Learning Dashboard to Monitor an Open Networked Learning Community

    Science.gov (United States)

    Grippa, Francesca; Secundo, Giustina; de Maggio, Marco

    This chapter proposes an operational model to monitor and assess an Open Networked Learning Community. Specifically, the model is based on the Intellectual Capital framework, along the Human, Structural and Social dimensions. It relies on the social network analysis to map several and complementary perspectives of a learning network. Its application allows to observe and monitor the cognitive behaviour of a learning community, in the final perspective of tracking and obtaining precious insights for value generation.

  18. Software defined networking applications in distributed datacenters

    CERN Document Server

    Qi, Heng

    2016-01-01

    This SpringerBrief provides essential insights on the SDN application designing and deployment in distributed datacenters. In this book, three key problems are discussed: SDN application designing, SDN deployment and SDN management. This book demonstrates how to design the SDN-based request allocation application in distributed datacenters. It also presents solutions for SDN controller placement to deploy SDN in distributed datacenters. Finally, an SDN management system is proposed to guarantee the performance of datacenter networks which are covered and controlled by many heterogeneous controllers. Researchers and practitioners alike will find this book a valuable resource for further study on Software Defined Networking. .

  19. The Development of Distributed Learning Techniques in Bhutan and Nepal

    Directory of Open Access Journals (Sweden)

    Frank Rennie

    2007-03-01

    Full Text Available This paper discusses research and development work currently being conducted with universities in Bhutan and Nepal to design appropriate systems for distance and distributed learning courses among a network of campus sites. Although working from a high level of awareness of pedagogic skills, staff in the region face two significant impediments in the adoption of a more open culture of learning. Firstly, Internet access is improving rapidly, but is still generally too weak and inconsistent to allow any reliance on net-based learning solutions. Secondly, the academic culture is resistant to the recognition of the value of open-learning degrees, with subsequent difficulties in re-designing course materials for a more educationally flexible, student-centred learning environment. Some current pilot initiatives in distributed learning are described. Methods for addressing these two impediments are discussed.

  20. Distributed Wind Cost Reduction: Learning from Solar

    Energy Technology Data Exchange (ETDEWEB)

    Tegen, Suzanne

    2016-02-23

    The distributed wind energy industry can learn several lessons from the solar industry regarding reducing soft costs. Suzanne Tegen presented this overview at the 2016 Distributed Wind Energy Association Business Conference in Washington, D.C., on February 23, 2016.

  1. Network anomaly detection a machine learning perspective

    CERN Document Server

    Bhattacharyya, Dhruba Kumar

    2013-01-01

    With the rapid rise in the ubiquity and sophistication of Internet technology and the accompanying growth in the number of network attacks, network intrusion detection has become increasingly important. Anomaly-based network intrusion detection refers to finding exceptional or nonconforming patterns in network traffic data compared to normal behavior. Finding these anomalies has extensive applications in areas such as cyber security, credit card and insurance fraud detection, and military surveillance for enemy activities. Network Anomaly Detection: A Machine Learning Perspective presents mach

  2. Quantum key distribution network for multiple applications

    Science.gov (United States)

    Tajima, A.; Kondoh, T.; Ochi, T.; Fujiwara, M.; Yoshino, K.; Iizuka, H.; Sakamoto, T.; Tomita, A.; Shimamura, E.; Asami, S.; Sasaki, M.

    2017-09-01

    The fundamental architecture and functions of secure key management in a quantum key distribution (QKD) network with enhanced universal interfaces for smooth key sharing between arbitrary two nodes and enabling multiple secure communication applications are proposed. The proposed architecture consists of three layers: a quantum layer, key management layer and key supply layer. We explain the functions of each layer, the key formats in each layer and the key lifecycle for enabling a practical QKD network. A quantum key distribution-advanced encryption standard (QKD-AES) hybrid system and an encrypted smartphone system were developed as secure communication applications on our QKD network. The validity and usefulness of these systems were demonstrated on the Tokyo QKD Network testbed.

  3. Efficient Learning Strategy of Chinese Characters Based on Network Approach

    Science.gov (United States)

    Yan, Xiaoyong; Fan, Ying; Di, Zengru; Havlin, Shlomo; Wu, Jinshan

    2013-01-01

    We develop an efficient learning strategy of Chinese characters based on the network of the hierarchical structural relations between Chinese characters. A more efficient strategy is that of learning the same number of useful Chinese characters in less effort or time. We construct a node-weighted network of Chinese characters, where character usage frequencies are used as node weights. Using this hierarchical node-weighted network, we propose a new learning method, the distributed node weight (DNW) strategy, which is based on a new measure of nodes' importance that considers both the weight of the nodes and its location in the network hierarchical structure. Chinese character learning strategies, particularly their learning order, are analyzed as dynamical processes over the network. We compare the efficiency of three theoretical learning methods and two commonly used methods from mainstream Chinese textbooks, one for Chinese elementary school students and the other for students learning Chinese as a second language. We find that the DNW method significantly outperforms the others, implying that the efficiency of current learning methods of major textbooks can be greatly improved. PMID:23990887

  4. Efficient learning strategy of Chinese characters based on network approach.

    Science.gov (United States)

    Yan, Xiaoyong; Fan, Ying; Di, Zengru; Havlin, Shlomo; Wu, Jinshan

    2013-01-01

    We develop an efficient learning strategy of Chinese characters based on the network of the hierarchical structural relations between Chinese characters. A more efficient strategy is that of learning the same number of useful Chinese characters in less effort or time. We construct a node-weighted network of Chinese characters, where character usage frequencies are used as node weights. Using this hierarchical node-weighted network, we propose a new learning method, the distributed node weight (DNW) strategy, which is based on a new measure of nodes' importance that considers both the weight of the nodes and its location in the network hierarchical structure. Chinese character learning strategies, particularly their learning order, are analyzed as dynamical processes over the network. We compare the efficiency of three theoretical learning methods and two commonly used methods from mainstream Chinese textbooks, one for Chinese elementary school students and the other for students learning Chinese as a second language. We find that the DNW method significantly outperforms the others, implying that the efficiency of current learning methods of major textbooks can be greatly improved.

  5. Efficient learning strategy of Chinese characters based on network approach.

    Directory of Open Access Journals (Sweden)

    Xiaoyong Yan

    Full Text Available We develop an efficient learning strategy of Chinese characters based on the network of the hierarchical structural relations between Chinese characters. A more efficient strategy is that of learning the same number of useful Chinese characters in less effort or time. We construct a node-weighted network of Chinese characters, where character usage frequencies are used as node weights. Using this hierarchical node-weighted network, we propose a new learning method, the distributed node weight (DNW strategy, which is based on a new measure of nodes' importance that considers both the weight of the nodes and its location in the network hierarchical structure. Chinese character learning strategies, particularly their learning order, are analyzed as dynamical processes over the network. We compare the efficiency of three theoretical learning methods and two commonly used methods from mainstream Chinese textbooks, one for Chinese elementary school students and the other for students learning Chinese as a second language. We find that the DNW method significantly outperforms the others, implying that the efficiency of current learning methods of major textbooks can be greatly improved.

  6. Towards a Pattern Language for Networked Learning

    NARCIS (Netherlands)

    Goodyear, Peter; Avgeriou, Paris; Baggetun, Rune; Bartoluzzi, Sonia; Retalis, Simeon; Ronteltap, Frans; Rusman, Ellen

    2004-01-01

    The work of designing a useful, convivial networked learning environment is complex and demanding. People new to designing for networked learning face a number of major challenges when they try to draw on the experience of others – whether that experience is shared informally, in the everyday

  7. Learning and structure of neuronal networks

    Indian Academy of Sciences (India)

    We study the effect of learning dynamics on network topology. Firstly, a network of discrete dynamical systems is considered for this purpose and the coupling strengths are made to evolve according to a temporal learning rule that is based on the paradigm of spike-time-dependent plasticity (STDP). This incorporates ...

  8. Learning and structure of neuronal networks

    Indian Academy of Sciences (India)

    Corresponding author. E-mail: Kiran.Kolwankar@gmail.com. Abstract. We study the effect of learning dynamics on network topology. Firstly, a network of dis- crete dynamical systems is considered for this purpose and the coupling strengths are made to evolve according to a temporal learning rule that is based on the ...

  9. Personalized Learning Network Teaching Model

    Science.gov (United States)

    Feng, Zhou

    Adaptive learning system on the salient features, expounded personalized learning is adaptive learning system adaptive to learners key to learning. From the perspective of design theory, put forward an adaptive learning system to learn design thinking individual model, and using data mining techniques, the initial establishment of personalized adaptive systems model of learning.

  10. Exploring the Peer Interaction Effects on Learning Achievement in a Social Learning Platform Based on Social Network Analysis

    Science.gov (United States)

    Lin, Yu-Tzu; Chen, Ming-Puu; Chang, Chia-Hu; Chang, Pu-Chen

    2017-01-01

    The benefits of social learning have been recognized by existing research. To explore knowledge distribution in social learning and its effects on learning achievement, we developed a social learning platform and explored students' behaviors of peer interactions by the proposed algorithms based on social network analysis. An empirical study was…

  11. Document management guidelines for distributed project networks

    CERN Document Server

    Hameri, A P; Høimyr, Nils-Joar

    1999-01-01

    This paper provides the project engineer with guidelines or a checklist on tasks that must be considered, defined and documented before the project can successfully implement a document management system in geographically distributed project environment. Topics ranging from configuration management, approval process, document types, user administration and document naming are covered. The underlying cases of the paper are that of CERN (European Laboratory for Particle Physics) and its latest accelerator project, together with the Nordisk Industrifond -funded Connecting Distributed Competencies (NI#: 98082) project, with a focus on distributed shipbuilding processes. Keywords: distributed project management, product data management, networking, document management, virtual workspaces

  12. Communication-Free Distributed Coverage for Networked Systems

    KAUST Repository

    Yazicioglu, A. Yasin

    2016-01-15

    In this paper, we present a communication-free algorithm for distributed coverage of an arbitrary network by a group of mobile agents with local sensing capabilities. The network is represented as a graph, and the agents are arbitrarily deployed on some nodes of the graph. Any node of the graph is covered if it is within the sensing range of at least one agent. The agents are mobile devices that aim to explore the graph and to optimize their locations in a decentralized fashion by relying only on their sensory inputs. We formulate this problem in a game theoretic setting and propose a communication-free learning algorithm for maximizing the coverage.

  13. Wireless sensor networks distributed consensus estimation

    CERN Document Server

    Chen, Cailian; Guan, Xinping

    2014-01-01

    This SpringerBrief evaluates the cooperative effort of sensor nodes to accomplish high-level tasks with sensing, data processing and communication. The metrics of network-wide convergence, unbiasedness, consistency and optimality are discussed through network topology, distributed estimation algorithms and consensus strategy. Systematic analysis reveals that proper deployment of sensor nodes and a small number of low-cost relays (without sensing function) can speed up the information fusion and thus improve the estimation capability of wireless sensor networks (WSNs). This brief also investiga

  14. Learning a Probability Distribution Efficiently and Reliably

    Science.gov (United States)

    Laird, Philip; Gamble, Evan

    1988-01-01

    A new algorithm, called the CDF-Inversion Algorithm, is described. Using it, one can efficiently learn a probability distribution over a finite set to a specified accuracy and confidence. The algorithm can be extended to learn joint distributions over a vector space. Some implementation results are described.

  15. Reduction Method for Active Distribution Networks

    DEFF Research Database (Denmark)

    Raboni, Pietro; Chen, Zhe

    2013-01-01

    On-line security assessment is traditionally performed by Transmission System Operators at the transmission level, ignoring the effective response of distributed generators and small loads. On the other hand the required computation time and amount of real time data for including Distribution Net...... by comparing the results obtained in PSCAD® with the detailed network model and with the reduced one. Moreover the control schemes of a wind turbine and a photovoltaic plant included in the detailed network model are described.......On-line security assessment is traditionally performed by Transmission System Operators at the transmission level, ignoring the effective response of distributed generators and small loads. On the other hand the required computation time and amount of real time data for including Distribution...

  16. deal: A Package for Learning Bayesian Networks

    Directory of Open Access Journals (Sweden)

    Susanne G. Boettcher

    2003-12-01

    Full Text Available deal is a software package for use with R. It includes several methods for analysing data using Bayesian networks with variables of discrete and/or continuous types but restricted to conditionally Gaussian networks. Construction of priors for network parameters is supported and their parameters can be learned from data using conjugate updating. The network score is used as a metric to learn the structure of the network and forms the basis of a heuristic search strategy. deal has an interface to Hugin.

  17. Learning in the context of distribution drift

    Science.gov (United States)

    2017-05-09

    However, the world is dynamic, in a constant state of flux. Despite this changing environment , conventional machine learning algorithms derive static...Classifiers. Journal of Machine Learning Research, 17(44), 1-35. Chen, S., Martínez, A. M., Webb, G. I., & Wang, L. (2016). Selective AnDE for large...AFRL-AFOSR-JP-TR-2017-0039 Learning in the context of distribution drift Geoff Webb MONASH UNIVERSITY Final Report 05/09/2017 DISTRIBUTION A

  18. Insulation Coordination in Modern Distribution Networks

    OpenAIRE

    Tossani, Fabio

    2016-01-01

    The appropriate analysis of the response of distribution networks against Lightning Electro Magnetic Pulse (LEMP) – originated by nearby strikes – requires the availability of accurate coupling models in order to reproduce the real and complex configuration of distribution systems. The above models represent a fundamental tool for estimating the number of protective devices and their most appropriate location in order to guarantee a given minimum number of flashovers and outages per year....

  19. Conditions for Productive Learning in Network Learning Environments

    DEFF Research Database (Denmark)

    Ponti, M.; Dirckinck-Holmfeld, Lone; Lindström, B.

    2004-01-01

    The Kaleidoscope1 Jointly Executed Integrating Research Project (JEIRP) on Conditions for Productive Networked Learning Environments is developing and elaborating conceptual understandings of Computer Supported Collaborative Learning (CSCL) emphasizing the use of cross-cultural comparative......: Pedagogical design and the dialectics of the digital artefacts, the concept of collaboration, ethics/trust, identity and the role of scaffolding of networked learning environments.   The JEIRP is motivated by the fact that many networked learning environments in various European educational settings...... are designed without a deep understanding of the pedagogical, communicative and collaborative conditions embedded in networked learning. Despite the existence of good theoretical views pointing to a social understanding of learning, rather than a traditional individualistic and information processing approach...

  20. One pass learning for generalized classifier neural network.

    Science.gov (United States)

    Ozyildirim, Buse Melis; Avci, Mutlu

    2016-01-01

    Generalized classifier neural network introduced as a kind of radial basis function neural network, uses gradient descent based optimized smoothing parameter value to provide efficient classification. However, optimization consumes quite a long time and may cause a drawback. In this work, one pass learning for generalized classifier neural network is proposed to overcome this disadvantage. Proposed method utilizes standard deviation of each class to calculate corresponding smoothing parameter. Since different datasets may have different standard deviations and data distributions, proposed method tries to handle these differences by defining two functions for smoothing parameter calculation. Thresholding is applied to determine which function will be used. One of these functions is defined for datasets having different range of values. It provides balanced smoothing parameters for these datasets through logarithmic function and changing the operation range to lower boundary. On the other hand, the other function calculates smoothing parameter value for classes having standard deviation smaller than the threshold value. Proposed method is tested on 14 datasets and performance of one pass learning generalized classifier neural network is compared with that of probabilistic neural network, radial basis function neural network, extreme learning machines, and standard and logarithmic learning generalized classifier neural network in MATLAB environment. One pass learning generalized classifier neural network provides more than a thousand times faster classification than standard and logarithmic generalized classifier neural network. Due to its classification accuracy and speed, one pass generalized classifier neural network can be considered as an efficient alternative to probabilistic neural network. Test results show that proposed method overcomes computational drawback of generalized classifier neural network and may increase the classification performance. Copyright

  1. A Collaborative Learning Network Approach to Improvement: The CUSP Learning Network.

    Science.gov (United States)

    Weaver, Sallie J; Lofthus, Jennifer; Sawyer, Melinda; Greer, Lee; Opett, Kristin; Reynolds, Catherine; Wyskiel, Rhonda; Peditto, Stephanie; Pronovost, Peter J

    2015-04-01

    Collaborative improvement networks draw on the science of collaborative organizational learning and communities of practice to facilitate peer-to-peer learning, coaching, and local adaption. Although significant improvements in patient safety and quality have been achieved through collaborative methods, insight regarding how collaborative networks are used by members is needed. Improvement Strategy: The Comprehensive Unit-based Safety Program (CUSP) Learning Network is a multi-institutional collaborative network that is designed to facilitate peer-to-peer learning and coaching specifically related to CUSP. Member organizations implement all or part of the CUSP methodology to improve organizational safety culture, patient safety, and care quality. Qualitative case studies developed by participating members examine the impact of network participation across three levels of analysis (unit, hospital, health system). In addition, results of a satisfaction survey designed to evaluate member experiences were collected to inform network development. Common themes across case studies suggest that members found value in collaborative learning and sharing strategies across organizational boundaries related to a specific improvement strategy. The CUSP Learning Network is an example of network-based collaborative learning in action. Although this learning network focuses on a particular improvement methodology-CUSP-there is clear potential for member-driven learning networks to grow around other methods or topic areas. Such collaborative learning networks may offer a way to develop an infrastructure for longer-term support of improvement efforts and to more quickly diffuse creative sustainment strategies.

  2. Creating real network with expected degree distribution: A statistical simulation

    OpenAIRE

    WenJun Zhang; GuangHua Liu

    2012-01-01

    The degree distribution of known networks is one of the focuses in network analysis. However, its inverse problem, i.e., to create network from known degree distribution has not yet been reported. In present study, a statistical simulation algorithm was developed to create real network with expected degree distribution. It is aniteration procedure in which a real network, with the least deviation of actual degree distribution to expected degree distribution, was created. Random assignment was...

  3. 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.

  4. Benford’s Distribution in Complex Networks

    OpenAIRE

    Morzy, Mikołaj; Kajdanowicz, Tomasz; Szymański, Bolesław K.

    2016-01-01

    Many collections of numbers do not have a uniform distribution of the leading digit, but conform to a very particular pattern known as Benford?s distribution. This distribution has been found in numerous areas such as accounting data, voting registers, census data, and even in natural phenomena. Recently it has been reported that Benford?s law applies to online social networks. Here we introduce a set of rigorous tests for adherence to Benford?s law and apply it to verification of this claim,...

  5. MICROCONTROLLERS HYBRID NETWORK FOR DISTRIBUTED INSTRUMENTATION

    Directory of Open Access Journals (Sweden)

    F. Córdoba-Montiel

    2004-08-01

    Full Text Available In this work the proposal of an hybrid microcontroller network is presented, whose main application is orientedtowards the implementation of a functionally distributed system, which is commanded from a personal computer(PC, where a program developed for the Windows graphical environment is executed and acts as System Monitorsupervising the events related to the indebted operation of the network, unfolding numerically and graphicallyfrom the information that handles to node level and the present status of the network. The network wasimplemented under the common bus topology and the application includes the physical layer handling under theRS-485 standard used in the connection between each considered microcontroller node, as well as differentmicrocontroller families such as Motorola, Microchip and the AVR of ATMEL are used.

  6. 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.

  7. Stochastic Variational Learning in Recurrent Spiking Networks

    Directory of Open Access Journals (Sweden)

    Danilo eJimenez Rezende

    2014-04-01

    Full Text Available The ability to learn and perform statistical inference with biologically plausible recurrent network of spiking neurons is an important step towards understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent spiking networks with hidden neurons, combining principles from variational learning and reinforcement learning. Our network defines a generative model over spike train histories and the derived learning rule has the form of a local Spike Timing Dependent Plasticity rule modulated by global factors (neuromodulators conveying information about ``novelty on a statistically rigorous ground.Simulations show that our model is able to learn bothstationary and non-stationary patterns of spike trains.We also propose one experiment that could potentially be performed with animals in order to test the dynamics of the predicted novelty signal.

  8. RELIABILITY INVESTIGATION IN EXEMPLARY SEGMENT OF POLISH DISTRIBUTION NETWORK

    OpenAIRE

    Mariusz Kawecki

    2008-01-01

    Reliability of distribution network based on Correlation Critical Clusters is taken into account. Method allows to assess risk of power stoppage in analysed network. Therefore, it allows to estimate the reliability of analysed distribution network. Presented analysis is carried out on distribution network located in south Poland where data was collected.

  9. Network Learning and Innovation in SME Formal Networks

    Directory of Open Access Journals (Sweden)

    Jivka Deiters

    2013-02-01

    Full Text Available The driver for this paper is the need to better understand the potential for learning and innovation that networks canprovide especially for small and medium sized enterprises (SMEs which comprise by far the majority of enterprises in the food sector. With the challenges the food sector is facing in the near future, learning and innovation or more focused, as it is being discussed in the paper, ‘learning for innovation’ are not just opportunities but pre‐conditions for the sustainability of the sector. Network initiatives that could provide appropriate support involve social interaction and knowledge exchange, learning, competence development, and coordination (organization and management of implementation. The analysis identifies case studies in any of these orientations which serve different stages of the innovation process: invention and implementation. The variety of network case studies cover networks linked to a focus group for training, research, orconsulting, networks dealing with focused market oriented product or process development, promotional networks, and networks for open exchange and social networking.

  10. Energy cost saving strategies in distributed power networks

    Directory of Open Access Journals (Sweden)

    Tcheukam Alain

    2016-01-01

    Full Text Available In this paper we study energy cost saving strategies in power networks in presence of prosumers. Three tips are considered: (i distributed power network architecture, (ii peak energy shaving with the integration of prosumers’ contribution, (iii Electric vehicles self-charging by means of prosumers’ production. The proposed distributed power network architecture reduces significantly the transmission costs and can reduce significantly the global energy cost up to 42 percent. Different types of prosumer who use self-charging photovoltaic systems, are able to intelligently buy energy from, or sell it, to the power grid. Therein, prosumers interact in a distributed environment during the purchase or sale of electric power using a double auction with negotiation mechanism. Using a two-step combined learning and optimization scheme, each prosumer can learn its optimal bidding strategy and forecast its energy production, consumption and storage. Our simulation results, conducted for the region of Sicily in Italy, show that the integration of prosumers can reduce peak hour costs up to 19 percent and 6 percent for eligible prosumers with electric vehicles.

  11. DISTRIBUTION OF HIGH-FREQUENCY VOLTAGE IN DISTRIBUTION NETWORK

    Directory of Open Access Journals (Sweden)

    M. I. Polujanov

    2005-01-01

    Full Text Available The paper reveals a method for remote determination of a location of single-phase short circuit on the ground in distribution networks with isolated neutral point. The method is based on measurement of high-frequency (a tone  range inter-phase voltage at all transformer substations and it creates preconditions for automation of searching process.  

  12. Quantitative learning strategies based on word networks

    Science.gov (United States)

    Zhao, Yue-Tian-Yi; Jia, Zi-Yang; Tang, Yong; Xiong, Jason Jie; Zhang, Yi-Cheng

    2018-02-01

    Learning English requires a considerable effort, but the way that vocabulary is introduced in textbooks is not optimized for learning efficiency. With the increasing population of English learners, learning process optimization will have significant impact and improvement towards English learning and teaching. The recent developments of big data analysis and complex network science provide additional opportunities to design and further investigate the strategies in English learning. In this paper, quantitative English learning strategies based on word network and word usage information are proposed. The strategies integrate the words frequency with topological structural information. By analyzing the influence of connected learned words, the learning weights for the unlearned words and dynamically updating of the network are studied and analyzed. The results suggest that quantitative strategies significantly improve learning efficiency while maintaining effectiveness. Especially, the optimized-weight-first strategy and segmented strategies outperform other strategies. The results provide opportunities for researchers and practitioners to reconsider the way of English teaching and designing vocabularies quantitatively by balancing the efficiency and learning costs based on the word network.

  13. Flood impacts on a water distribution network

    Science.gov (United States)

    Arrighi, Chiara; Tarani, Fabio; Vicario, Enrico; Castelli, Fabio

    2017-12-01

    Floods cause damage to people, buildings and infrastructures. Water distribution systems are particularly exposed, since water treatment plants are often located next to the rivers. Failure of the system leads to both direct losses, for instance damage to equipment and pipework contamination, and indirect impact, since it may lead to service disruption and thus affect populations far from the event through the functional dependencies of the network. In this work, we present an analysis of direct and indirect damages on a drinking water supply system, considering the hazard of riverine flooding as well as the exposure and vulnerability of active system components. The method is based on interweaving, through a semi-automated GIS procedure, a flood model and an EPANET-based pipe network model with a pressure-driven demand approach, which is needed when modelling water distribution networks in highly off-design conditions. Impact measures are defined and estimated so as to quantify service outage and potential pipe contamination. The method is applied to the water supply system of the city of Florence, Italy, serving approximately 380 000 inhabitants. The evaluation of flood impact on the water distribution network is carried out for different events with assigned recurrence intervals. Vulnerable elements exposed to the flood are identified and analysed in order to estimate their residual functionality and to simulate failure scenarios. Results show that in the worst failure scenario (no residual functionality of the lifting station and a 500-year flood), 420 km of pipework would require disinfection with an estimated cost of EUR 21 million, which is about 0.5 % of the direct flood losses evaluated for buildings and contents. Moreover, if flood impacts on the water distribution network are considered, the population affected by the flood is up to 3 times the population directly flooded.

  14. Flood impacts on a water distribution network

    Directory of Open Access Journals (Sweden)

    C. Arrighi

    2017-12-01

    Full Text Available Floods cause damage to people, buildings and infrastructures. Water distribution systems are particularly exposed, since water treatment plants are often located next to the rivers. Failure of the system leads to both direct losses, for instance damage to equipment and pipework contamination, and indirect impact, since it may lead to service disruption and thus affect populations far from the event through the functional dependencies of the network. In this work, we present an analysis of direct and indirect damages on a drinking water supply system, considering the hazard of riverine flooding as well as the exposure and vulnerability of active system components. The method is based on interweaving, through a semi-automated GIS procedure, a flood model and an EPANET-based pipe network model with a pressure-driven demand approach, which is needed when modelling water distribution networks in highly off-design conditions. Impact measures are defined and estimated so as to quantify service outage and potential pipe contamination. The method is applied to the water supply system of the city of Florence, Italy, serving approximately 380 000 inhabitants. The evaluation of flood impact on the water distribution network is carried out for different events with assigned recurrence intervals. Vulnerable elements exposed to the flood are identified and analysed in order to estimate their residual functionality and to simulate failure scenarios. Results show that in the worst failure scenario (no residual functionality of the lifting station and a 500-year flood, 420 km of pipework would require disinfection with an estimated cost of EUR 21 million, which is about 0.5 % of the direct flood losses evaluated for buildings and contents. Moreover, if flood impacts on the water distribution network are considered, the population affected by the flood is up to 3 times the population directly flooded.

  15. Learning in innovation networks: Some simulation experiments

    Science.gov (United States)

    Gilbert, Nigel; Ahrweiler, Petra; Pyka, Andreas

    2007-05-01

    According to the organizational learning literature, the greatest competitive advantage a firm has is its ability to learn. In this paper, a framework for modeling learning competence in firms is presented to improve the understanding of managing innovation. Firms with different knowledge stocks attempt to improve their economic performance by engaging in radical or incremental innovation activities and through partnerships and networking with other firms. In trying to vary and/or to stabilize their knowledge stocks by organizational learning, they attempt to adapt to environmental requirements while the market strongly selects on the results. The simulation experiments show the impact of different learning activities, underlining the importance of innovation and learning.

  16. Advanced Distribution Network Modelling with Distributed Energy Resources

    Science.gov (United States)

    O'Connell, Alison

    The addition of new distributed energy resources, such as electric vehicles, photovoltaics, and storage, to low voltage distribution networks means that these networks will undergo major changes in the future. Traditionally, distribution systems would have been a passive part of the wider power system, delivering electricity to the customer and not needing much control or management. However, the introduction of these new technologies may cause unforeseen issues for distribution networks, due to the fact that they were not considered when the networks were originally designed. This thesis examines different types of technologies that may begin to emerge on distribution systems, as well as the resulting challenges that they may impose. Three-phase models of distribution networks are developed and subsequently utilised as test cases. Various management strategies are devised for the purposes of controlling distributed resources from a distribution network perspective. The aim of the management strategies is to mitigate those issues that distributed resources may cause, while also keeping customers' preferences in mind. A rolling optimisation formulation is proposed as an operational tool which can manage distributed resources, while also accounting for the uncertainties that these resources may present. Network sensitivities for a particular feeder are extracted from a three-phase load flow methodology and incorporated into an optimisation. Electric vehicles are the focus of the work, although the method could be applied to other types of resources. The aim is to minimise the cost of electric vehicle charging over a 24-hour time horizon by controlling the charge rates and timings of the vehicles. The results demonstrate the advantage that controlled EV charging can have over an uncontrolled case, as well as the benefits provided by the rolling formulation and updated inputs in terms of cost and energy delivered to customers. Building upon the rolling optimisation, a

  17. Learning Latent Structure in Complex Networks

    DEFF Research Database (Denmark)

    Mørup, Morten; Hansen, Lars Kai

    Latent structure in complex networks, e.g., in the form of community structure, can help understand network dynamics, identify heterogeneities in network properties, and predict ‘missing’ links. While most community detection algorithms are based on optimizing heuristic clustering objectives...... prediction performance of the learning based approaches and other widely used link prediction approaches in 14 networks ranging from medium size to large networks with more than a million nodes. While link prediction is typically well above chance for all networks, we find that the learning based mixed...... membership stochastic block model of Airoldi et al., performs well and often best in our experiments. The added complexity of the LD model improves link predictions for four of the 14 networks....

  18. A Transfer Learning Approach for Network Modeling

    Science.gov (United States)

    Huang, Shuai; Li, Jing; Chen, Kewei; Wu, Teresa; Ye, Jieping; Wu, Xia; Yao, Li

    2012-01-01

    Networks models have been widely used in many domains to characterize the interacting relationship between physical entities. A typical problem faced is to identify the networks of multiple related tasks that share some similarities. In this case, a transfer learning approach that can leverage the knowledge gained during the modeling of one task to help better model another task is highly desirable. In this paper, we propose a transfer learning approach, which adopts a Bayesian hierarchical model framework to characterize task relatedness and additionally uses the L1-regularization to ensure robust learning of the networks with limited sample sizes. A method based on the Expectation-Maximization (EM) algorithm is further developed to learn the networks from data. Simulation studies are performed, which demonstrate the superiority of the proposed transfer learning approach over single task learning that learns the network of each task in isolation. The proposed approach is also applied to identification of brain connectivity networks of Alzheimer’s disease (AD) from functional magnetic resonance image (fMRI) data. The findings are consistent with the AD literature. PMID:24526804

  19. Markov Chain Monte Carlo Bayesian Learning for Neural Networks

    Science.gov (United States)

    Goodrich, Michael S.

    2011-01-01

    Conventional training methods for neural networks involve starting al a random location in the solution space of the network weights, navigating an error hyper surface to reach a minimum, and sometime stochastic based techniques (e.g., genetic algorithms) to avoid entrapment in a local minimum. It is further typically necessary to preprocess the data (e.g., normalization) to keep the training algorithm on course. Conversely, Bayesian based learning is an epistemological approach concerned with formally updating the plausibility of competing candidate hypotheses thereby obtaining a posterior distribution for the network weights conditioned on the available data and a prior distribution. In this paper, we developed a powerful methodology for estimating the full residual uncertainty in network weights and therefore network predictions by using a modified Jeffery's prior combined with a Metropolis Markov Chain Monte Carlo method.

  20. Small Distributed Renewable Energy Generation for Low Voltage Distribution Networks

    Directory of Open Access Journals (Sweden)

    Chindris M.

    2016-08-01

    Full Text Available Driven by the existing energy policies, the use of renewable energy has increased considerably all over the world in order to respond to the increasing energy consumption and to reduce the environmental impact of the electricity generation. Although most policy makers and companies are focusing on large applications, the use of cheap small generation units, based on local renewable resources, has become increasingly attractive for the general public, small farms and remote communities. The paper presents several results of a research project aiming to identify the power quality issues and the impact of RES based distributed generation (DG or other non-linear loads on low voltage (LV distribution networks in Romania; the final goal is to develop a Universal Power Quality Conditioner (UPQC able to diminish the existing disturbances. Basically, the work analyses the existing DG technologies and identifies possible solutions for their integration in Romania; taking into account the existent state of the art, the attention was paid on small systems, using wind and solar energy, and on possibility to integrate them into suburban and rural LV distribution networks. The presence of DG units at distribution voltage level means the transition from traditional passive to active distribution networks. In general, the relatively low penetration levels of DG does not produce problems; however, the nowadays massive increase of local power generation have led to new integration challenges in order to ensure the reliability and quality of the power supply. Power quality issues are identified and their assessment is the key element in the design of measures aiming to diminish all existing disturbances.

  1. Reconfigurable real-time distributed processing network

    Science.gov (United States)

    Page, S. F.; Seely, R. D.; Hickman, D.

    2011-06-01

    This paper describes a novel real-time image and signal processing network, RONINTM, which facilitates the rapid design and deployment of systems providing advanced geospatial surveillance and situational awareness capability. RONINTM is a distributed software architecture consisting of multiple agents or nodes, which can be configured to implement a variety of state-of-the-art computer vision and signal processing algorithms. The nodes operate in an asynchronous fashion and can run on a variety of hardware platforms, thus providing a great deal of scalability and flexibility. Complex algorithmic configuration chains can be assembled using an intuitive graphical interface in a plug-and- play manner. RONINTM has been successfully exploited for a number of applications, ranging from remote event detection to complex multiple-camera real-time 3D object reconstruction. This paper describes the motivation behind the creation of the network, the core design features, and presents details of an example application. Finally, the on-going development of the network is discussed, which is focussed on dynamic network reconfiguration. This allows to the network to automatically adapt itself to node or communications failure by intelligently re-routing network communications and through adaptive resource management.

  2. Brain Networks of Explicit and Implicit Learning

    Science.gov (United States)

    Yang, Jing; Li, Ping

    2012-01-01

    Are explicit versus implicit learning mechanisms reflected in the brain as distinct neural structures, as previous research indicates, or are they distinguished by brain networks that involve overlapping systems with differential connectivity? In this functional MRI study we examined the neural correlates of explicit and implicit learning of artificial grammar sequences. Using effective connectivity analyses we found that brain networks of different connectivity underlie the two types of learning: while both processes involve activation in a set of cortical and subcortical structures, explicit learners engage a network that uses the insula as a key mediator whereas implicit learners evoke a direct frontal-striatal network. Individual differences in working memory also differentially impact the two types of sequence learning. PMID:22952624

  3. An artificial immune system algorithm approach for reconfiguring distribution network

    Science.gov (United States)

    Syahputra, Ramadoni; Soesanti, Indah

    2017-08-01

    This paper proposes an artificial immune system (AIS) algorithm approach for reconfiguring distribution network with the presence distributed generators (DG). The distribution network with high-performance is a network that has a low power loss, better voltage profile, and loading balance among feeders. The task for improving the performance of the distribution network is optimization of network configuration. The optimization has become a necessary study with the presence of DG in entire networks. In this work, optimization of network configuration is based on an AIS algorithm. The methodology has been tested in a model of 33 bus IEEE radial distribution networks with and without DG integration. The results have been showed that the optimal configuration of the distribution network is able to reduce power loss and to improve the voltage profile of the distribution network significantly.

  4. Optimal power flow for distribution networks with distributed generation

    Directory of Open Access Journals (Sweden)

    Radosavljević Jordan

    2015-01-01

    Full Text Available This paper presents a genetic algorithm (GA based approach for the solution of the optimal power flow (OPF in distribution networks with distributed generation (DG units, including fuel cells, micro turbines, diesel generators, photovoltaic systems and wind turbines. The OPF is formulated as a nonlinear multi-objective optimization problem with equality and inequality constraints. Due to the stochastic nature of energy produced from renewable sources, i.e. wind turbines and photovoltaic systems, as well as load uncertainties, a probabilisticalgorithm is introduced in the OPF analysis. The Weibull and normal distributions are employed to model the input random variables, namely the wind speed, solar irradiance and load power. The 2m+1 point estimate method and the Gram Charlier expansion theory are used to obtain the statistical moments and the probability density functions (PDFs of the OPF results. The proposed approach is examined and tested on a modified IEEE 34 node test feeder with integrated five different DG units. The obtained results prove the efficiency of the proposed approach to solve both deterministic and probabilistic OPF problems for different forms of the multi-objective function. As such, it can serve as a useful decision-making supporting tool for distribution network operators. [Projekat Ministarstva nauke Republike Srbije, br. TR33046

  5. Learning Bayesian networks for discrete data

    KAUST Repository

    Liang, Faming

    2009-02-01

    Bayesian networks have received much attention in the recent literature. In this article, we propose an approach to learn Bayesian networks using the stochastic approximation Monte Carlo (SAMC) algorithm. Our approach has two nice features. Firstly, it possesses the self-adjusting mechanism and thus avoids essentially the local-trap problem suffered by conventional MCMC simulation-based approaches in learning Bayesian networks. Secondly, it falls into the class of dynamic importance sampling algorithms; the network features can be inferred by dynamically weighted averaging the samples generated in the learning process, and the resulting estimates can have much lower variation than the single model-based estimates. The numerical results indicate that our approach can mix much faster over the space of Bayesian networks than the conventional MCMC simulation-based approaches. © 2008 Elsevier B.V. All rights reserved.

  6. Automatic balancing valves in distribution networks today

    Energy Technology Data Exchange (ETDEWEB)

    Golestan, F. [Flow Design, Inc., Dallas, TX (United States)

    1996-12-31

    Automatic flow-limiting (self-actuated) valves have been in the heating, ventilating, and air-conditioning (HVAC) market for some time now. Their principle of operation is based on fluid momentum and Bernoulli`s theorem. Basically, they absorb pressure to keep the flow rate constant. The general operation and their flow characteristics are described in the 1992 ASHRAE Handbook--Systems and Equipment, chapter 43 (ASHRAE 1992). The application and interaction of these valves with other system components, when installed in hydronic distribution networks, are outlined in this presentation. A simple, multilevel piping network is analyzed. The network consists of a pump, connecting piping, an automatic temperature control valve (ATC), a coil, and balancing valves.

  7. Self Calibrated Wireless Distributed Environmental Sensory Networks

    Science.gov (United States)

    Fishbain, Barak; Moreno-Centeno, Erick

    2016-04-01

    Recent advances in sensory and communication technologies have made Wireless Distributed Environmental Sensory Networks (WDESN) technically and economically feasible. WDESNs present an unprecedented tool for studying many environmental processes in a new way. However, the WDESNs’ calibration process is a major obstacle in them becoming the common practice. Here, we present a new, robust and efficient method for aggregating measurements acquired by an uncalibrated WDESN, and producing accurate estimates of the observed environmental variable’s true levels rendering the network as self-calibrated. The suggested method presents novelty both in group-decision-making and in environmental sensing as it offers a most valuable tool for distributed environmental monitoring data aggregation. Applying the method on an extensive real-life air-pollution dataset showed markedly more accurate results than the common practice and the state-of-the-art.

  8. Multiple brain networks underpinning word learning from fluent speech revealed by independent component analysis.

    Science.gov (United States)

    López-Barroso, Diana; Ripollés, Pablo; Marco-Pallarés, Josep; Mohammadi, Bahram; Münte, Thomas F; Bachoud-Lévi, Anne-Catherine; Rodriguez-Fornells, Antoni; de Diego-Balaguer, Ruth

    2015-04-15

    Although neuroimaging studies using standard subtraction-based analysis from functional magnetic resonance imaging (fMRI) have suggested that frontal and temporal regions are involved in word learning from fluent speech, the possible contribution of different brain networks during this type of learning is still largely unknown. Indeed, univariate fMRI analyses cannot identify the full extent of distributed networks that are engaged by a complex task such as word learning. Here we used Independent Component Analysis (ICA) to characterize the different brain networks subserving word learning from an artificial language speech stream. Results were replicated in a second cohort of participants with a different linguistic background. Four spatially independent networks were associated with the task in both cohorts: (i) a dorsal Auditory-Premotor network; (ii) a dorsal Sensory-Motor network; (iii) a dorsal Fronto-Parietal network; and (iv) a ventral Fronto-Temporal network. The level of engagement of these networks varied through the learning period with only the dorsal Auditory-Premotor network being engaged across all blocks. In addition, the connectivity strength of this network in the second block of the learning phase correlated with the individual variability in word learning performance. These findings suggest that: (i) word learning relies on segregated connectivity patterns involving dorsal and ventral networks; and (ii) specifically, the dorsal auditory-premotor network connectivity strength is directly correlated with word learning performance. Copyright © 2015 Elsevier Inc. All rights reserved.

  9. Rethinking the learning of belief network probabilities

    Energy Technology Data Exchange (ETDEWEB)

    Musick, R.

    1996-03-01

    Belief networks are a powerful tool for knowledge discovery that provide concise, understandable probabilistic models of data. There are methods grounded in probability theory to incrementally update the relationships described by the belief network when new information is seen, to perform complex inferences over any set of variables in the data, to incorporate domain expertise and prior knowledge into the model, and to automatically learn the model from data. This paper concentrates on part of the belief network induction problem, that of learning the quantitative structure (the conditional probabilities), given the qualitative structure. In particular, the current practice of rote learning the probabilities in belief networks can be significantly improved upon. We advance the idea of applying any learning algorithm to the task of conditional probability learning in belief networks, discuss potential benefits, and show results of applying neural networks and other algorithms to a medium sized car insurance belief network. The results demonstrate from 10 to 100% improvements in model error rates over the current approaches.

  10. Adaptive Learning in Weighted Network Games

    NARCIS (Netherlands)

    Bayer, Péter; Herings, P. Jean-Jacques; Peeters, Ronald; Thuijsman, Frank

    2017-01-01

    This paper studies adaptive learning in the class of weighted network games. This class of games includes applications like research and development within interlinked firms, crime within social networks, the economics of pollution, and defense expenditures within allied nations. We show that for

  11. Electronic Social Networks, Teaching, and Learning

    Science.gov (United States)

    Pidduck, Anne Banks

    2010-01-01

    This paper explores the relationship between electronic social networks, teaching, and learning. Previous studies have shown a strong positive correlation between student engagement and learning. By extending this work to engage instructors and add an electronic component, our study shows possible teaching improvement as well. In particular,…

  12. Realizing Wisdom Theory in Complex Learning Networks

    Science.gov (United States)

    Kok, Ayse

    2009-01-01

    The word "wisdom" is rarely seen in contemporary technology and learning discourse. This conceptual paper aims to provide some clear principles that answer the question: How can we establish wisdom in complex learning networks? By considering the nature of contemporary calls for wisdom the paper provides a metatheoretial framework to evaluate the…

  13. NASA Engineering Network Lessons Learned

    Data.gov (United States)

    National Aeronautics and Space Administration — The NASA Lessons Learned system provides access to official, reviewed lessons learned from NASA programs and projects. These lessons have been made available to the...

  14. Learning Orthographic Structure With Sequential Generative Neural Networks.

    Science.gov (United States)

    Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco

    2016-04-01

    Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine (RBM), a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and can encode contextual information in the form of internal, distributed representations. We assessed whether this type of network can extract the orthographic structure of English monosyllables by learning a generative model of the letter sequences forming a word training corpus. We show that the network learned an accurate probabilistic model of English graphotactics, which can be used to make predictions about the letter following a given context as well as to autonomously generate high-quality pseudowords. The model was compared to an extended version of simple recurrent networks, augmented with a stochastic process that allows autonomous generation of sequences, and to non-connectionist probabilistic models (n-grams and hidden Markov models). We conclude that sequential RBMs and stochastic simple recurrent networks are promising candidates for modeling cognition in the temporal domain. Copyright © 2015 Cognitive Science Society, Inc.

  15. Infrastructure and distributed learning methodology for privacy-preserving multi-centric rapid learning health care: euroCAT

    Directory of Open Access Journals (Sweden)

    Timo M. Deist

    2017-06-01

    The euroCAT infrastructure has been successfully implemented in five radiation clinics across three countries. SVM models can be learned on data distributed over all five clinics. Furthermore, the infrastructure provides a general framework to execute learning algorithms on distributed data. The ongoing expansion of the euroCAT network will facilitate machine learning in radiation oncology. The resulting access to larger datasets with sufficient variation will pave the way for generalizable prediction models and personalized medicine.

  16. Distribution network tariffs: A closed question?

    Energy Technology Data Exchange (ETDEWEB)

    Rodriguez Ortega, Maria Pia; Perez-Arriaga, J. Ignacio; Abbad, Juan Rivier; Gonzalez, Jesus Peco [Instituto de Investigacion Tecnologica, Escuela Tecnica Superior de Ingenieria, Universidad Pontificia Comillas, c/ Alberto Aguilera 23, 28015 Madrid (Spain)

    2008-05-15

    Electricity regulators are facing new challenges to keep the pace of the liberalization process and the revision of regulatory schemes that is taking place all over the world. The pressure is also felt by regulated activities such as distribution. One of the main objectives of this process is to improve efficiency. Electricity rates and more specifically distribution network tariffs should also be adapted to these new requirements. This paper describes the main rate design approaches that are used to recover distribution costs. Drawbacks of the current methods are highlighted, and a new tariff design methodology based on cost causality is presented. Efficiency achievement as well as compliance with legal and regulatory criteria, such as cost recovery and non-discrimination, is analyzed. (author)

  17. Convolutional neural networks for estimating spatially distributed evapotranspiration

    Science.gov (United States)

    García-Pedrero, Angel M.; Gonzalo-Martín, Consuelo; Lillo-Saavedra, Mario F.; Rodriguéz-Esparragón, Dionisio; Menasalvas, Ernestina

    2017-10-01

    Efficient water management in agriculture requires an accurate estimation of evapotranspiration (ET). There are available several balance energy surface models that provide a daily ET estimation (ETd) spatially and temporarily distributed for different crops over wide areas. These models need infrared thermal spectral band (gathered from remotely sensors) to estimate sensible heat flux from the surface temperature. However, this spectral band is not available for most current operational remote sensors. Even though the good results provided by machine learning (ML) methods in many different areas, few works have applied these approaches for forecasting distributed ETd on space and time when aforementioned information is missing. However, these methods do not exploit the land surface characteristics and the relationships among land covers producing estimation errors. In this work, we have developed and evaluated a methodology that provides spatial distributed estimates of ETd without thermal information by means of Convolutional Neural Networks.

  18. Network design for cylinder gas distribution

    Directory of Open Access Journals (Sweden)

    Tejinder Pal Singh

    2015-01-01

    Full Text Available Purpose: Network design of the supply chain is an important and strategic aspect of logistics management. In this paper, we address the network design problem specific to packaged gases (cylinder supply chain. We propose an integrated framework that allows for the determination of the optimal facility locations, the filling plant production capacities, the inventory at plants and hubs, and the number of packages to be routed in primary and secondary transportation. Design/methodology/approach: We formulate the problem as a mixed integer program and then develop a decomposition approach to solve it. We illustrate the proposed framework with numerical examples from real-life packaged gases supply chain. The results show that the decomposition approach is effective in solving a broad range of problem sizes. Findings: The main finding of this paper is that decomposing the network design problem into two sub-problems is very effective to tackle the real-life large scale network design problems occurring in cylinder gas distribution by optimizing strategic and tactical decisions and approximating the operational decisions. We also benchmark the results from the decomposition approach by solving the complete packaged gases network design model for smaller test cases. Originality/value: The main contribution of our work is that it integrates supply chain network design decisions without fixing the fillings plant locations with inventory and resource allocation decisions required at the plants. We also consider the transportation costs for the entire supply chain including the transhipment costs among different facilities by deciding the replenishment frequency.

  19. Distributed Function Calculation over Noisy Networks

    Directory of Open Access Journals (Sweden)

    Zhidun Zeng

    2016-01-01

    Full Text Available Considering any connected network with unknown initial states for all nodes, the nearest-neighbor rule is utilized for each node to update its own state at every discrete-time step. Distributed function calculation problem is defined for one node to compute some function of the initial values of all the nodes based on its own observations. In this paper, taking into account uncertainties in the network and observations, an algorithm is proposed to compute and explicitly characterize the value of the function in question when the number of successive observations is large enough. While the number of successive observations is not large enough, we provide an approach to obtain the tightest possible bounds on such function by using linear programing optimization techniques. Simulations are provided to demonstrate the theoretical results.

  20. Evolutionary epistemology and dynamical virtual learning networks.

    Science.gov (United States)

    Giani, Umberto

    2004-01-01

    This paper is an attempt to define the main features of a new educational model aimed at satisfying the needs of a rapidly changing society. The evolutionary epistemology paradigm of culture diffusion in human groups could be the conceptual ground for the development of this model. Multidimensionality, multi-disciplinarity, complexity, connectivity, critical thinking, creative thinking, constructivism, flexible learning, contextual learning, are the dimensions that should characterize distance learning models aimed at increasing the epistemological variability of learning communities. Two multimedia educational software, Dynamic Knowledge Networks (DKN) and Dynamic Virtual Learning Networks (DVLN) are described. These two complementary tools instantiate these dimensions, and were tested in almost 150 online courses. Even if the examples are framed in the medical context, the analysis of the shortcomings of the traditional educational systems and the proposed solutions can be applied to the vast majority of the educational contexts.

  1. Electronic Power Transformer for Power Distribution Networks

    Directory of Open Access Journals (Sweden)

    Ermuraсhi Iu.V.

    2017-12-01

    Full Text Available Reducing losses in electricity distribution networks is a current technical problem. This issue also has social and environmental aspects. As a promising solution one can examine the direct distribution from the medium voltage power network using new equipment based on the use of power electronics. The aim of the paper is to propose and argue an innovative technical solution for the realization of the Solid State Transformer (SST in order to decrease the number of energy transformation stages compared to the known solutions, simplifying the topology of the functional scheme with the reduction of production costs and the loss of energy in transformers used in electrical distribution networks. It is proposed the solution of simplifying the topology of the AC/AC electronic transformer by reducing the number of passive electronic components (resistors, inductors, capacitors and active (transistors. The inverter of the SST transformer ensures the switching mode of the transistors, using for this purpose the inductance of the magnetic leakage flux of the high frequency transformer. The robustness of the laboratory sample of the SST 10 / 0.22 kV transformer with the power of 20 kW was manufactured and tested. Testing of the laboratory sample confirmed the functionality of the proposed scheme and the possibility of switching of the transistors to at zero current (ZCS mode with the reduction of the energy losses. In the proposed converter a single high-frequency transformer with a simplified construction with two windings is used, which reduces its mass and the cost of making the transformer. The reduction in the manufacturing cost of the converter is also due to the decrease in the number of links between the functional elements.

  2. Peer Apprenticeship Learning in Networked Learning Communities: The Diffusion of Epistemic Learning

    Science.gov (United States)

    Jamaludin, Azilawati; Shaari, Imran

    2016-01-01

    This article discusses peer apprenticeship learning (PAL) as situated within networked learning communities (NLCs). The context revolves around the diffusion of technologically-mediated learning in Singapore schools, where teachers begin to implement inquiry-oriented learning, consistent with 21st century learning, among students. As these schools…

  3. Using Content Distribution Networks for Astronomy Outreach

    Science.gov (United States)

    Jäger, M.; Christiansen, L. L.; André, M.

    2015-09-01

    Thousands of people from all over the world search the internet on a daily basis for the newest discoveries in astronomy: be it in the form of press releases, high resolution images, videos or even planetarium fulldome content. The growing amount of data available, combined with the increasing number of media files and users distributed across the globe, leads to a significant decrease in speed for those users located furthest from the server delivering the content. One solution for bringing astronomical content to users faster is to use a content delivery network.

  4. eSTAR: a distributed telescope network

    Science.gov (United States)

    Steele, Iain A.; Naylor, Tim; Allan, Alisdair; Etherton, Jason; Mottram, C. J.

    2002-11-01

    The e-STAR (e-Science Telescopes for Astronomical Research) project uses GRID techniques to develop the software infrastructure for a global network of robotic telescopes. The basic architecture is based around Intelligent Agents which request data from Discovery Nodes that may be telescopes or databases. Communication is based on a development of the XML RTML language secured using the Globus I/O library, with status serving provided via LDAP. We describe the system architecture and protocols devised to give a distributed approach to telescope scheduling, as well as giving details of the implementation of prototype Intelligent Agent and Discovery Node systems.

  5. Distributed Rate Allocation for Wireless Networks

    CERN Document Server

    Jose, Jubin

    2010-01-01

    This paper describes a distributed algorithm for rate allocation in wireless networks. As the main result, the paper establishes that this algorithm is throughput-optimal for very general class of throughput regions. In contrast to distributed on-off scheduling algorithms, this algorithm enables optimal utilization of physical layer schemes by scheduling multiple rate levels. The algorithm is based on a Markov process on these discrete set of rates with certain transition rates. For dealing with multiple rate levels, the paper introduces an important structure for the transition rates, which enable the design of appropriate update rule for these transition rates. The update uses local queue length information alone, and thus does not require global exchange of queue length information. In addition, the algorithm requires that each link can determine the feasibility of increasing its data-rate from the current value without reducing the data-rates of other links. Determining rate feasibility does not introduce...

  6. Learning theory of distributed spectral algorithms

    Science.gov (United States)

    Guo, Zheng-Chu; Lin, Shao-Bo; Zhou, Ding-Xuan

    2017-07-01

    Spectral algorithms have been widely used and studied in learning theory and inverse problems. This paper is concerned with distributed spectral algorithms, for handling big data, based on a divide-and-conquer approach. We present a learning theory for these distributed kernel-based learning algorithms in a regression framework including nice error bounds and optimal minimax learning rates achieved by means of a novel integral operator approach and a second order decomposition of inverse operators. Our quantitative estimates are given in terms of regularity of the regression function, effective dimension of the reproducing kernel Hilbert space, and qualification of the filter function of the spectral algorithm. They do not need any eigenfunction or noise conditions and are better than the existing results even for the classical family of spectral algorithms.

  7. Social Learning Network Analysis Model to Identify Learning Patterns Using Ontology Clustering Techniques and Meaningful Learning

    Science.gov (United States)

    Firdausiah Mansur, Andi Besse; Yusof, Norazah

    2013-01-01

    Clustering on Social Learning Network still not explored widely, especially when the network focuses on e-learning system. Any conventional methods are not really suitable for the e-learning data. SNA requires content analysis, which involves human intervention and need to be carried out manually. Some of the previous clustering techniques need…

  8. A Decomposition Algorithm for Learning Bayesian Network Structures from Data

    DEFF Research Database (Denmark)

    Zeng, Yifeng; Cordero Hernandez, Jorge

    2008-01-01

    It is a challenging task of learning a large Bayesian network from a small data set. Most conventional structural learning approaches run into the computational as well as the statistical problems. We propose a decomposition algorithm for the structure construction without having to learn...... the complete network. The new learning algorithm firstly finds local components from the data, and then recover the complete network by joining the learned components. We show the empirical performance of the decomposition algorithm in several benchmark networks....

  9. Few-shot learning in deep networks through global prototyping.

    Science.gov (United States)

    Blaes, Sebastian; Burwick, Thomas

    2017-10-01

    Training a deep convolution neural network (CNN) to succeed in visual object classification usually requires a great number of examples. Here, starting from such a pre-learned CNN, we study the task of extending the network to classify additional categories on the basis of only few examples ("few-shot learning"). We find that a simple and fast prototype-based learning procedure in the global feature layers ("Global Prototype Learning", GPL) leads to some remarkably good classification results for a large portion of the new classes. It requires only up to ten examples for the new classes to reach a plateau in performance. To understand this few-shot learning performance resulting from GPL as well as the performance of the original network, we use the t-SNE method (Maaten and Hinton, 2008) to visualize clusters of object category examples. This reveals the strong connection between classification performance and data distribution and explains why some new categories only need few examples for learning while others resist good classification results even when trained with many more examples. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Diamond photonics for distributed quantum networks

    Science.gov (United States)

    Johnson, Sam; Dolan, Philip R.; Smith, Jason M.

    2017-09-01

    The distributed quantum network, in which nodes comprising small but well-controlled quantum states are entangled via photonic channels, has in recent years emerged as a strategy for delivering a range of quantum technologies including secure communications, enhanced sensing and scalable quantum computing. Colour centres in diamond are amongst the most promising candidates for nodes fabricated in the solid-state, offering potential for large scale production and for chip-scale integrated devices. In this review we consider the progress made and the remaining challenges in developing diamond-based nodes for quantum networks. We focus on the nitrogen-vacancy and silicon-vacancy colour centres, which have demonstrated many of the necessary attributes for these applications. We focus in particular on the use of waveguides and other photonic microstructures for increasing the efficiency with which photons emitted from these colour centres can be coupled into a network, and the use of microcavities for increasing the fraction of photons emitted that are suitable for generating entanglement between nodes.

  11. Game-Theoretic Learning in Distributed Control

    KAUST Repository

    Marden, Jason R.

    2018-01-05

    In distributed architecture control problems, there is a collection of interconnected decision-making components that seek to realize desirable collective behaviors through local interactions and by processing local information. Applications range from autonomous vehicles to energy to transportation. One approach to control of such distributed architectures is to view the components as players in a game. In this approach, two design considerations are the components’ incentives and the rules that dictate how components react to the decisions of other components. In game-theoretic language, the incentives are defined through utility functions, and the reaction rules are online learning dynamics. This chapter presents an overview of this approach, covering basic concepts in game theory, special game classes, measures of distributed efficiency, utility design, and online learning rules, all with the interpretation of using game theory as a prescriptive paradigm for distributed control design.

  12. Technology Acceptance and Social Networking in Distance Learning

    Directory of Open Access Journals (Sweden)

    Barry Davidson

    2003-04-01

    Full Text Available This study examines the use of integrated communication and engineering design tools in a distributed learning environment. We examined students' attitudes toward the technology using two different approaches. First, we utilized the technology acceptance model to investigate the attitude formation process. Then, to investigate how attitudes changed over time, we applied social information processing model using social network analysis method. Using the technology acceptance model, we were able to demonstrate that students’ initial expectation affected the perceptions of, attitudes toward, and use of the system. With social network analysis, we found that one’s attitude change was significantly influenced by other students’ attitude changes. We discussed the uniqueness of distance learning environments in the context of social influence research and how studies of distance learning could contribute to the research on the social influence of technology use.

  13. Maximum entropy methods for extracting the learned features of deep neural networks.

    Science.gov (United States)

    Finnegan, Alex; Song, Jun S

    2017-10-01

    New architectures of multilayer artificial neural networks and new methods for training them are rapidly revolutionizing the application of machine learning in diverse fields, including business, social science, physical sciences, and biology. Interpreting deep neural networks, however, currently remains elusive, and a critical challenge lies in understanding which meaningful features a network is actually learning. We present a general method for interpreting deep neural networks and extracting network-learned features from input data. We describe our algorithm in the context of biological sequence analysis. Our approach, based on ideas from statistical physics, samples from the maximum entropy distribution over possible sequences, anchored at an input sequence and subject to constraints implied by the empirical function learned by a network. Using our framework, we demonstrate that local transcription factor binding motifs can be identified from a network trained on ChIP-seq data and that nucleosome positioning signals are indeed learned by a network trained on chemical cleavage nucleosome maps. Imposing a further constraint on the maximum entropy distribution also allows us to probe whether a network is learning global sequence features, such as the high GC content in nucleosome-rich regions. This work thus provides valuable mathematical tools for interpreting and extracting learned features from feed-forward neural networks.

  14. On Distributed Deep Network for Processing Large-Scale Sets of Complex Data

    OpenAIRE

    Chen, D

    2016-01-01

    Recent work in unsupervised feature learning and deep learning has shown that being able to train large models can dramatically improve performance. In this paper, we consider the problem of training a deep network with hundreds of parameters using distributed CPU cores. We have developed Bagging-Down SGD algorithm to solve the distributing problems. Bagging-Down SGD introduces the parameter server adding on the several model replicas, and separates the updating and the training computing to ...

  15. Deep Learning in Neural Networks: An Overview

    OpenAIRE

    Schmidhuber, Juergen

    2014-01-01

    In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpr...

  16. Logarithmic learning for generalized classifier neural network.

    Science.gov (United States)

    Ozyildirim, Buse Melis; Avci, Mutlu

    2014-12-01

    Generalized classifier neural network is introduced as an efficient classifier among the others. Unless the initial smoothing parameter value is close to the optimal one, generalized classifier neural network suffers from convergence problem and requires quite a long time to converge. In this work, to overcome this problem, a logarithmic learning approach is proposed. The proposed method uses logarithmic cost function instead of squared error. Minimization of this cost function reduces the number of iterations used for reaching the minima. The proposed method is tested on 15 different data sets and performance of logarithmic learning generalized classifier neural network is compared with that of standard one. Thanks to operation range of radial basis function included by generalized classifier neural network, proposed logarithmic approach and its derivative has continuous values. This makes it possible to adopt the advantage of logarithmic fast convergence by the proposed learning method. Due to fast convergence ability of logarithmic cost function, training time is maximally decreased to 99.2%. In addition to decrease in training time, classification performance may also be improved till 60%. According to the test results, while the proposed method provides a solution for time requirement problem of generalized classifier neural network, it may also improve the classification accuracy. The proposed method can be considered as an efficient way for reducing the time requirement problem of generalized classifier neural network. Copyright © 2014 Elsevier Ltd. All rights reserved.

  17. Advanced Energy Storage Management in Distribution Network

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Guodong [ORNL; Ceylan, Oguzhan [ORNL; Xiao, Bailu [ORNL; Starke, Michael R [ORNL; Ollis, T Ben [ORNL; King, Daniel J [ORNL; Irminger, Philip [ORNL; Tomsovic, Kevin [University of Tennessee, Knoxville (UTK)

    2016-01-01

    With increasing penetration of distributed generation (DG) in the distribution networks (DN), the secure and optimal operation of DN has become an important concern. In this paper, an iterative mixed integer quadratic constrained quadratic programming model to optimize the operation of a three phase unbalanced distribution system with high penetration of Photovoltaic (PV) panels, DG and energy storage (ES) is developed. The proposed model minimizes not only the operating cost, including fuel cost and purchasing cost, but also voltage deviations and power loss. The optimization model is based on the linearized sensitivity coefficients between state variables (e.g., node voltages) and control variables (e.g., real and reactive power injections of DG and ES). To avoid slow convergence when close to the optimum, a golden search method is introduced to control the step size and accelerate the convergence. The proposed algorithm is demonstrated on modified IEEE 13 nodes test feeders with multiple PV panels, DG and ES. Numerical simulation results validate the proposed algorithm. Various scenarios of system configuration are studied and some critical findings are concluded.

  18. Learning curves for stochastic gradient descent in linear feedforward networks.

    Science.gov (United States)

    Werfel, Justin; Xie, Xiaohui; Seung, H Sebastian

    2005-12-01

    Gradient-following learning methods can encounter problems of implementation in many applications, and stochastic variants are sometimes used to overcome these difficulties. We analyze three online training methods used with a linear perceptron: direct gradient descent, node perturbation, and weight perturbation. Learning speed is defined as the rate of exponential decay in the learning curves. When the scalar parameter that controls the size of weight updates is chosen to maximize learning speed, node perturbation is slower than direct gradient descent by a factor equal to the number of output units; weight perturbation is slower still by an additional factor equal to the number of input units. Parallel perturbation allows faster learning than sequential perturbation, by a factor that does not depend on network size. We also characterize how uncertainty in quantities used in the stochastic updates affects the learning curves. This study suggests that in practice, weight perturbation may be slow for large networks, and node perturbation can have performance comparable to that of direct gradient descent when there are few output units. However, these statements depend on the specifics of the learning problem, such as the input distribution and the target function, and are not universally applicable.

  19. Reinforcement learning account of network reciprocity.

    Directory of Open Access Journals (Sweden)

    Takahiro Ezaki

    Full Text Available Evolutionary game theory predicts that cooperation in social dilemma games is promoted when agents are connected as a network. However, when networks are fixed over time, humans do not necessarily show enhanced mutual cooperation. Here we show that reinforcement learning (specifically, the so-called Bush-Mosteller model approximately explains the experimentally observed network reciprocity and the lack thereof in a parameter region spanned by the benefit-to-cost ratio and the node's degree. Thus, we significantly extend previously obtained numerical results.

  20. Portability and networked learning environments

    NARCIS (Netherlands)

    Collis, Betty; de Diana, I.P.F.

    1994-01-01

    Abstract The portability of educational software is defined as the likelihood of software usage, with or without adaptation, in an educational environment different from that for which it was originally designed and produced. Barriers and research relevant to the portability of electronic learning

  1. Aggregated Representation of Distribution Networks for Large-Scale Transmission Network Simulations

    DEFF Research Database (Denmark)

    Göksu, Ömer; Altin, Müfit; Sørensen, Poul Ejnar

    2014-01-01

    As a common practice of large-scale transmission network analysis the distribution networks have been represented as aggregated loads. However, with increasing share of distributed generation, especially wind and solar power, in the distribution networks, it became necessary to include the distri......As a common practice of large-scale transmission network analysis the distribution networks have been represented as aggregated loads. However, with increasing share of distributed generation, especially wind and solar power, in the distribution networks, it became necessary to include...... the distributed generation within those analysis. In this paper a practical methodology to obtain aggregated behaviour of the distributed generation is proposed. The methodology, which is based on the use of the IEC standard wind turbine models, is applied on a benchmark distribution network via simulations....

  2. Social Networking Adapted for Distributed Scientific Collaboration

    Science.gov (United States)

    Karimabadi, Homa

    2012-01-01

    Share is a social networking site with novel, specially designed feature sets to enable simultaneous remote collaboration and sharing of large data sets among scientists. The site will include not only the standard features found on popular consumer-oriented social networking sites such as Facebook and Myspace, but also a number of powerful tools to extend its functionality to a science collaboration site. A Virtual Observatory is a promising technology for making data accessible from various missions and instruments through a Web browser. Sci-Share augments services provided by Virtual Observatories by enabling distributed collaboration and sharing of downloaded and/or processed data among scientists. This will, in turn, increase science returns from NASA missions. Sci-Share also enables better utilization of NASA s high-performance computing resources by providing an easy and central mechanism to access and share large files on users space or those saved on mass storage. The most common means of remote scientific collaboration today remains the trio of e-mail for electronic communication, FTP for file sharing, and personalized Web sites for dissemination of papers and research results. Each of these tools has well-known limitations. Sci-Share transforms the social networking paradigm into a scientific collaboration environment by offering powerful tools for cooperative discourse and digital content sharing. Sci-Share differentiates itself by serving as an online repository for users digital content with the following unique features: a) Sharing of any file type, any size, from anywhere; b) Creation of projects and groups for controlled sharing; c) Module for sharing files on HPC (High Performance Computing) sites; d) Universal accessibility of staged files as embedded links on other sites (e.g. Facebook) and tools (e.g. e-mail); e) Drag-and-drop transfer of large files, replacing awkward e-mail attachments (and file size limitations); f) Enterprise-level data and

  3. Learning to trust : network effects through time.

    NARCIS (Netherlands)

    Barrera, D.; Bunt, G. van de

    2009-01-01

    This article investigates the effects of information originating from social networks on the development of interpersonal trust relations in the context of a dialysis department of a Dutch medium-sized hospital. Hypotheses on learning effects are developed from existing theories and tested using

  4. Learning to trust: network effects through time

    NARCIS (Netherlands)

    Barrera, D.; van de Bunt, G

    2009-01-01

    This article investigates the effects of information originating from social networks on the development of interpersonal trust relations in the context of a dialysis department of a Dutch medium-sized hospital. Hypotheses on learning effects are developed from existing theories and tested using

  5. Social Networking Services in E-Learning

    Science.gov (United States)

    Weber, Peter; Rothe, Hannes

    2016-01-01

    This paper is a report on the findings of a study conducted on the use of the social networking service NING in a cross-location e-learning setting named "Net Economy." We describe how we implemented NING as a fundamental part of the setting through a special phase concept and team building approach. With the help of user statistics, we…

  6. Learning chaotic attractors by neural networks

    NARCIS (Netherlands)

    Bakker, R; Schouten, JC; Giles, CL; Takens, F; van den Bleek, CM

    2000-01-01

    An algorithm is introduced that trains a neural network to identify chaotic dynamics from a single measured time series. During training, the algorithm learns to short-term predict the time series. At the same time a criterion, developed by Diks, van Zwet, Takens, and de Goede (1996) is monitored

  7. Social Networking Sites as a Learning Tool

    Science.gov (United States)

    Sanchez-Casado, Noelia; Cegarra Navarro, Juan Gabriel; Wensley, Anthony; Tomaseti-Solano, Eva

    2016-01-01

    Purpose: Over the past few years, social networking sites (SNSs) have become very useful for firms, allowing companies to manage the customer-brand relationships. In this context, SNSs can be considered as a learning tool because of the brand knowledge that customers develop from these relationships. Because of the fact that knowledge in…

  8. Learning in Networks for Sustainable Development

    NARCIS (Netherlands)

    Lansu, Angelique; Boon, Jo; Sloep, Peter; Van Dam-Mieras, Rietje

    2010-01-01

    The didactic model of remote internships described in this study provides the flexibility needed to support networked learners, i.e. to facilitate the development and subsequent assessment of their competences. The heterogeneity of the participants (students, employers, tutors) in the learning

  9. THE DISTRIBUTION NETWORK DEVELOPEMENT IN PRINT MEDIA

    Directory of Open Access Journals (Sweden)

    Loredana Iordache

    2012-09-01

    Full Text Available In this article, we identify the characteristics of the distribution networks in print media and the features ofmarketing in mass media, emphasising the attempts initiated by the press in the context of the financial crisis. Theresearch was conducted through a case study on regional newspaper,, Gazeta de Sud'' The main problems analyzedwere decreasing newspaper circulation and advertising. The research taken into account trends and developmentsworldwide print media as well as print media particularities of Romania, with a focus on identifying factors thatcontributed to the closure of a significant number of newspapers, or their transition from printed version online format.The paper is mainly focused on some practical issues related to the way of organizing the print media sales networks,the authors elaborating proposals for the implementation of certain measures to increase the circulation, on the onehand, and on the hand, to increase the sale of ad space in the newspaper. Compared with other products, thenewspaper has unique characteristics caused by daily changing content, and therefore the product itself. Having ahighly perishable, the content of media products should always seen in relation to time, which requires more rapiddistribution and continuous production.

  10. Distribution Of Wealth In A Network Model Of The Economy

    OpenAIRE

    Tao Ma; Holden, John G.; Serota, R. A.

    2012-01-01

    We show, analytically and numerically, that wealth distribution in the Bouchaud-M\\'ezard network model of the economy is described by a three-parameter generalized inverse gamma distribution. In the mean-field limit of a network with any two agents linked, it reduces to the inverse gamma distribution.

  11. Evaluating Maximum Wind Energy Exploitation in Active Distribution Networks

    DEFF Research Database (Denmark)

    Siano, Pierluigi; Chen, Peiyuan; Chen, Zhe

    2010-01-01

    The increased spreading of distributed and renewable generation requires moving towards active management of distribution networks. In this paper, in order to evaluate maximum wind energy exploitation in active distribution networks, a method based on a multi-period optimal power flow (OPF...

  12. A Cascade-Based Emergency Model for Water Distribution Network

    Directory of Open Access Journals (Sweden)

    Qing Shuang

    2015-01-01

    Full Text Available Water distribution network is important in the critical physical infrastructure systems. The paper studies the emergency resource strategies on water distribution network with the approach of complex network and cascading failures. The model of cascade-based emergency for water distribution network is built. The cascade-based model considers the network topology analysis and hydraulic analysis to provide a more realistic result. A load redistribution function with emergency recovery mechanisms is established. From the aspects of uniform distribution, node betweenness, and node pressure, six recovery strategies are given to reflect the network topology and the failure information, respectively. The recovery strategies are evaluated with the complex network indicators to describe the failure scale and failure velocity. The proposed method is applied by an illustrative example. The results showed that the recovery strategy considering the node pressure can enhance the network robustness effectively. Besides, this strategy can reduce the failure nodes and generate the least failure nodes per time.

  13. Modelling flow dynamics in water distribution networks using ...

    African Journals Online (AJOL)

    DR OKE

    Keywords: Artificial neural network; Leakage detection technique; Water distribution; Leakages ... techniques, artificial neural networks (ANNs), genetic algorithms (GA), and probabilistic and evidential reasoning. ANNs are mimicry of ..... Implementation of an online artificial intelligence district meter area flow meter data.

  14. The neighborhood MCMC sampler for learning Bayesian networks

    Science.gov (United States)

    Alyami, Salem A.; Azad, A. K. M.; Keith, Jonathan M.

    2016-07-01

    Getting stuck in local maxima is a problem that arises while learning Bayesian networks (BNs) structures. In this paper, we studied a recently proposed Markov chain Monte Carlo (MCMC) sampler, called the Neighbourhood sampler (NS), and examined how efficiently it can sample BNs when local maxima are present. We assume that a posterior distribution f(N,E|D) has been defined, where D represents data relevant to the inference, N and E are the sets of nodes and directed edges, respectively. We illustrate the new approach by sampling from such a distribution, and inferring BNs. The simulations conducted in this paper show that the new learning approach substantially avoids getting stuck in local modes of the distribution, and achieves a more rapid rate of convergence, compared to other common algorithms e.g. the MCMC Metropolis-Hastings sampler.

  15. Learning networks and communication skills

    Directory of Open Access Journals (Sweden)

    Kerry Musselbrook

    2000-12-01

    Full Text Available The increase in student numbers in further and higher education over the last decade has been dramatic, placing greater pressures on academic staff in terms of contact hours. At the same time public funding of universities has decreased. Furthermore, the current pace of technological innovation and change and the fact that there are fewer jobs for life with clear pathways for progression mean that more of us need to be engaged in learning throughout our lives in order to remain competitive in the job-market. That is the reality of lifelong learning. Students are consequently demanding (especially as they are having to meet more of the costs of education themselves a more flexible learning framework. This framework should be able to accommodate all types of learners - part-time, mature, remote and disabled students. The revised Disability Discrimination Act, which came into force in October 1999, only temporarily excludes education from its remit and has already challenged university practices. (Another JlSC-funded initiative, Disability Information Systems in Higher Education, addresses just this issue: http://www.disinhe.ac.uk. All this is set against a backdrop of the government's stated vision for a more inclusive, less elitist education system with opportunities for all, and the requirement for a professional and accountable community of university teachers.

  16. Co-Creation in Distributed Value Creation Systems and Networks

    DEFF Research Database (Denmark)

    Bang, Anne Louise; Christensen, Poul Rind

    2013-01-01

    Design and management are not performed in organizational isolation, but rather in distributed networks of actors and activities. In this contribution co-creation and relationship management are combined in a network perspective.......Design and management are not performed in organizational isolation, but rather in distributed networks of actors and activities. In this contribution co-creation and relationship management are combined in a network perspective....

  17. Asymmetric Branching in Biological Resource Distribution Networks

    Science.gov (United States)

    Brummer, Alexander Byers

    There is a remarkable relationship between an organism's metabolic rate (resting power consumption) and the organism's mass. It may be a universal law of nature that an organism's resting metabolic rate is proportional to its mass to the power of 3/4. This relationship, known as Kleiber's Law, appears to be valid for both plants and animals. This law is important because it implies that larger organisms are more efficient than smaller organisms, and knowledge regarding metabolic rates are essential to a multitude of other fields in ecology and biology. This includes modeling the interactions of many species across multiple trophic levels, distributions of species abundances across large spatial landscapes, and even medical diagnostics for respiratory and cardiovascular pathologies. Previous models of vascular networks that seek to identify the origin of metabolic scaling have all been based on the unrealistic assumption of perfectly symmetric branching. In this dissertation I will present a theory of asymmetric branching in self-similar vascular networks (published by Brummer et al. in [9]). The theory shows that there can exist a suite of vascular forms that result in the often observed 3/4 metabolic scaling exponent of Kleiber's Law. Furthermore, the theory makes predictions regarding major morphological features related to vascular branching patterns and their relationships to metabolic scaling. These predictions are suggestive of evolutionary convergence in vascular branching. To test these predictions, I will present an analysis of real mammalian and plant vascular data that shows: (i) broad patterns in vascular networks across entire animal kingdoms and (ii) within these patterns, plant and mammalian vascular networks can be uniquely distinguished from one another (publication in preparation by Brummer et al.). I will also present results from a computational study in support of point (i). Namely, that asymmetric branching may be the optimal strategy to

  18. Active Coordinated Operation of Distribution Network System for Many Connections of Distributed Generators

    Science.gov (United States)

    Hayashi, Yasuhiro; Kawasaki, Shoji; Matsuki, Junya; Wakao, Shinji; Baba, Junpei; Hojo, Masahide; Yokoyama, Akihiko; Kobayashi, Naoki; Hirai, Takao; Oishi, Kohei

    Recently, total number of distributed generators (DGS) such as photovoltaic generation system and wind turbine generation system connected to an actual distribution network increases drastically. The distribution network connected with many distributed generators must be operated keeping reliability of power supply, power quality and loss minimization. In order to accomplish active distribution network operation to take advantage of many connections of DGS, a new coordinated operation of distribution system with many connections of DGS is necessary. In this paper, the authors propose a coordinated operation of distribution network system connected with many DGS by using newly proposed sectionalizing switches control, sending voltage control and computation of available DG connection capability. In order to check validity of the proposed coordinated operation of distribution system, numerical simulations using the proposed coordinated distribution system operation are carried out in a practical distribution network model.

  19. Learning Bayesian networks from big meteorological spatial datasets. An alternative to complex network analysis

    Science.gov (United States)

    Gutiérrez, Jose Manuel; San Martín, Daniel; Herrera, Sixto; Santiago Cofiño, Antonio

    2016-04-01

    The growing availability of spatial datasets (observations, reanalysis, and regional and global climate models) demands efficient multivariate spatial modeling techniques for many problems of interest (e.g. teleconnection analysis, multi-site downscaling, etc.). Complex networks have been recently applied in this context using graphs built from pairwise correlations between the different stations (or grid boxes) forming the dataset. However, this analysis does not take into account the full dependence structure underlying the data, gien by all possible marginal and conditional dependencies among the stations, and does not allow a probabilistic analysis of the dataset. In this talk we introduce Bayesian networks as an alternative multivariate analysis and modeling data-driven technique which allows building a joint probability distribution of the stations including all relevant dependencies in the dataset. Bayesian networks is a sound machine learning technique using a graph to 1) encode the main dependencies among the variables and 2) to obtain a factorization of the joint probability distribution of the stations given by a reduced number of parameters. For a particular problem, the resulting graph provides a qualitative analysis of the spatial relationships in the dataset (alternative to complex network analysis), and the resulting model allows for a probabilistic analysis of the dataset. Bayesian networks have been widely applied in many fields, but their use in climate problems is hampered by the large number of variables (stations) involved in this field, since the complexity of the existing algorithms to learn from data the graphical structure grows nonlinearly with the number of variables. In this contribution we present a modified local learning algorithm for Bayesian networks adapted to this problem, which allows inferring the graphical structure for thousands of stations (from observations) and/or gridboxes (from model simulations) thus providing new

  20. Learning in a Network: A "Third Way" between School Learning and Workplace Learning?

    Science.gov (United States)

    Bottrup, Pernille

    2005-01-01

    Purpose--The aim of this article is to examine network-based learning and discuss how participation in network can enhance organisational learning. Design/methodology/approach--In recent years, companies have increased their collaboration with other organisations, suppliers, customers, etc., in order to meet challenges from a globalised market.…

  1. Networks and Inter-Organizational Learning: A Critical Review.

    Science.gov (United States)

    Beeby, Mick; Booth, Charles

    2000-01-01

    Reviews literature on knowledge management and organizational learning; highlights the significance of networks, alliances, and interorganizational relationships. Refines a model of organizational learning to account for different levels: individual, interdepartmental, team, organizational, and interorganizational learning. (Contains 62…

  2. 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...

  3. Machine learning based Intelligent cognitive network using fog computing

    Science.gov (United States)

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

    2017-05-01

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

  4. A Complex Network Approach to Distributional Semantic Models.

    Directory of Open Access Journals (Sweden)

    Akira Utsumi

    Full Text Available A number of studies on network analysis have focused on language networks based on free word association, which reflects human lexical knowledge, and have demonstrated the small-world and scale-free properties in the word association network. Nevertheless, there have been very few attempts at applying network analysis to distributional semantic models, despite the fact that these models have been studied extensively as computational or cognitive models of human lexical knowledge. In this paper, we analyze three network properties, namely, small-world, scale-free, and hierarchical properties, of semantic networks created by distributional semantic models. We demonstrate that the created networks generally exhibit the same properties as word association networks. In particular, we show that the distribution of the number of connections in these networks follows the truncated power law, which is also observed in an association network. This indicates that distributional semantic models can provide a plausible model of lexical knowledge. Additionally, the observed differences in the network properties of various implementations of distributional semantic models are consistently explained or predicted by considering the intrinsic semantic features of a word-context matrix and the functions of matrix weighting and smoothing. Furthermore, to simulate a semantic network with the observed network properties, we propose a new growing network model based on the model of Steyvers and Tenenbaum. The idea underlying the proposed model is that both preferential and random attachments are required to reflect different types of semantic relations in network growth process. We demonstrate that this model provides a better explanation of network behaviors generated by distributional semantic models.

  5. PARTNERS IN LEARNING NETWORK FOR UKRAINIAN TEACHERS

    Directory of Open Access Journals (Sweden)

    K. Sereda

    2011-05-01

    Full Text Available The network «Partners in Learning Network» is presented in the article – the Ukrainian segment of global educational community. PILN is created with support of the Microsoft company for teachers who use information communication technology in their professional work. The PILN's purpose and value for Ukrainian teachers, for their professional dialogue and collaboration are described in the article. Functions of PILN's communities for teacher’s cooperation, the joint decision of questions and an exchange of ideas and of technique, teaching tools for increase of level of ICT introduction in educational process are described.

  6. Removal of batch effects using distribution-matching residual networks.

    Science.gov (United States)

    Shaham, Uri; Stanton, Kelly P; Zhao, Jun; Li, Huamin; Raddassi, Khadir; Montgomery, Ruth; Kluger, Yuval

    2017-08-15

    Sources of variability in experimentally derived data include measurement error in addition to the physical phenomena of interest. This measurement error is a combination of systematic components, originating from the measuring instrument and random measurement errors. Several novel biological technologies, such as mass cytometry and single-cell RNA-seq (scRNA-seq), are plagued with systematic errors that may severely affect statistical analysis if the data are not properly calibrated. We propose a novel deep learning approach for removing systematic batch effects. Our method is based on a residual neural network, trained to minimize the Maximum Mean Discrepancy between the multivariate distributions of two replicates, measured in different batches. We apply our method to mass cytometry and scRNA-seq datasets, and demonstrate that it effectively attenuates batch effects. our codes and data are publicly available at https://github.com/ushaham/BatchEffectRemoval.git. yuval.kluger@yale.edu. Supplementary data are available at Bioinformatics online.

  7. Integrating network awareness in ATLAS distributed computing

    CERN Document Server

    De, K; The ATLAS collaboration; Klimentov, A; Maeno, T; Mckee, S; Nilsson, P; Petrosyan, A; Vukotic, I; Wenaus, T

    2014-01-01

    A crucial contributor to the success of the massively scaled global computing system that delivers the analysis needs of the LHC experiments is the networking infrastructure upon which the system is built. The experiments have been able to exploit excellent high-bandwidth networking in adapting their computing models for the most efficient utilization of resources. New advanced networking technologies now becoming available such as software defined networks hold the potential of further leveraging the network to optimize workflows and dataflows, through proactive control of the network fabric on the part of high level applications such as experiment workload management and data management systems. End to end monitoring of networking and data flow performance further allows applications to adapt based on real time conditions. We will describe efforts underway in ATLAS on integrating network awareness at the application level, particularly in workload management.

  8. Conflict free network coding for distributed storage networks

    KAUST Repository

    Al-Habob, Ahmed A.

    2015-06-01

    © 2015 IEEE. In this paper, we design a conflict free instantly decodable network coding (IDNC) solution for file download from distributed storage servers. Considering previously downloaded files at the clients from these servers as side information, IDNC can speed up the current download process. However, transmission conflicts can occur since multiple servers can simultaneously send IDNC combinations of files to the same client, which can tune to only one of them at a time. To avoid such conflicts and design more efficient coded download patterns, we propose a dual conflict IDNC graph model, which extends the conventional IDNC graph model in order to guarantee conflict free server transmissions to each of the clients. We then formulate the download time minimization problem as a stochastic shortest path problem whose action space is defined by the independent sets of this new graph. Given the intractability of the solution, we design a channel-aware heuristic algorithm and show that it achieves a considerable reduction in the file download time, compared to applying the conventional IDNC approach separately at each of the servers.

  9. Distributed Robust Optimization in Networked System.

    Science.gov (United States)

    Wang, Shengnan; Li, Chunguang

    2016-10-11

    In this paper, we consider a distributed robust optimization (DRO) problem, where multiple agents in a networked system cooperatively minimize a global convex objective function with respect to a global variable under the global constraints. The objective function can be represented by a sum of local objective functions. The global constraints contain some uncertain parameters which are partially known, and can be characterized by some inequality constraints. After problem transformation, we adopt the Lagrangian primal-dual method to solve this problem. We prove that the primal and dual optimal solutions of the problem are restricted in some specific sets, and we give a method to construct these sets. Then, we propose a DRO algorithm to find the primal-dual optimal solutions of the Lagrangian function, which consists of a subgradient step, a projection step, and a diffusion step, and in the projection step of the algorithm, the optimized variables are projected onto the specific sets to guarantee the boundedness of the subgradients. Convergence analysis and numerical simulations verifying the performance of the proposed algorithm are then provided. Further, for nonconvex DRO problem, the corresponding approach and algorithm framework are also provided.

  10. Dictionary Networking in an LSP Learning Context

    DEFF Research Database (Denmark)

    Nielsen, Sandro

    2007-01-01

    and usage of a subject-field, particularly when they have to read, write or translate domain-specific texts. The modern theory of dictionary functions presented in Bergenholtz and Tarp (2002) opens up exciting new possibilities for theoretical and practical lexicography and encourages lexicographers...... text production, but discusses an individual dictionary for a particular function. It is shown that in a general context of learning accounting and its relevant LSP with a view to writing or translating financial reporting texts, the modern theory of dictionary functions provides a good theoretical...... and practical basis. This paper describes how the study of communication-oriented and cognitive-oriented functions may lead to the creation of a network of four on-line accounting dictionaries for learning accounting and its LSP. The dictionary network described consists of two monolingual and two bilingual...

  11. Learning of N-layers neural network

    Directory of Open Access Journals (Sweden)

    Vladimír Konečný

    2005-01-01

    Full Text Available In the last decade we can observe increasing number of applications based on the Artificial Intelligence that are designed to solve problems from different areas of human activity. The reason why there is so much interest in these technologies is that the classical way of solutions does not exist or these technologies are not suitable because of their robustness. They are often used in applications like Business Intelligence that enable to obtain useful information for high-quality decision-making and to increase competitive advantage.One of the most widespread tools for the Artificial Intelligence are the artificial neural networks. Their high advantage is relative simplicity and the possibility of self-learning based on set of pattern situations.For the learning phase is the most commonly used algorithm back-propagation error (BPE. The base of BPE is the method minima of error function representing the sum of squared errors on outputs of neural net, for all patterns of the learning set. However, while performing BPE and in the first usage, we can find out that it is necessary to complete the handling of the learning factor by suitable method. The stability of the learning process and the rate of convergence depend on the selected method. In the article there are derived two functions: one function for the learning process management by the relative great error function value and the second function when the value of error function approximates to global minimum.The aim of the article is to introduce the BPE algorithm in compact matrix form for multilayer neural networks, the derivation of the learning factor handling method and the presentation of the results.

  12. Learning in Neural Networks: VLSI Implementation Strategies

    Science.gov (United States)

    Duong, Tuan Anh

    1995-01-01

    Fully-parallel hardware neural network implementations may be applied to high-speed recognition, classification, and mapping tasks in areas such as vision, or can be used as low-cost self-contained units for tasks such as error detection in mechanical systems (e.g. autos). Learning is required not only to satisfy application requirements, but also to overcome hardware-imposed limitations such as reduced dynamic range of connections.

  13. Learning Affinity via Spatial Propagation Networks

    OpenAIRE

    Liu, Sifei; De Mello, Shalini; Gu, Jinwei; Zhong, Guangyu; Yang, Ming-Hsuan; Kautz, Jan

    2017-01-01

    In this paper, we propose spatial propagation networks for learning the affinity matrix for vision tasks. We show that by constructing a row/column linear propagation model, the spatially varying transformation matrix exactly constitutes an affinity matrix that models dense, global pairwise relationships of an image. Specifically, we develop a three-way connection for the linear propagation model, which (a) formulates a sparse transformation matrix, where all elements can be the output from a...

  14. Designing Distributed Learning Environments with Intelligent Software Agents

    Science.gov (United States)

    Lin, Fuhua, Ed.

    2005-01-01

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

  15. Solutions to operate transmission and distribution gas networks

    Directory of Open Access Journals (Sweden)

    Neacsu Sorin

    2017-01-01

    Full Text Available In order to respect the actual and future regulations, besides SCADA, there is a need for further modern operating solutions for the transmission and distribution gas network. The paper presents the newest operating principles and modern software solutions that represent a considerable help to operate the transmission and distribution gas networks.

  16. Delivering Sustainability Through Supply Chain Distribution Network Redesign

    Directory of Open Access Journals (Sweden)

    Denise Ravet

    2013-09-01

    Full Text Available Purpose - Companies could gain (cost, service, green/sustainable competitive advantage through the supply chain network. The goal of this article is to study how to deliver sustainability through the supply chain distribution network redesign.Design/methodology/approach - A literature review is conducted to examine research relating to sustainable supply chain strategies and supply chain distribution network redesign.Findings - A study of the supply chain literature reveals the importance to rethink the supply chain distribution network design and to treat sustainability as integral to operations.

  17. THE IMPACTS OF SOCIAL NETWORKING SITES IN HIGHER LEARNING

    OpenAIRE

    Mohd Ishak Bin Ismail; Ruzaini Bin Abdullah Arshah

    2016-01-01

    Social networking sites, a web-based application have permeated the boundary between personal lives and student lives. Nowadays, students in higher learning used social networking site such as Facebook to facilitate their learning through the academic collaboration which it further enhances students’ social capital. Social networking site has many advantages to improve students’ learning. To date, Facebook is the leading social networking sites at this time which it being widely used by stude...

  18. The Design, Experience and Practice of Networked Learning

    DEFF Research Database (Denmark)

    Gleerup, Janne; Heilesen, Simon; Helms, Niels Henrik

    2014-01-01

    . The Design, Experience and Practice of Networked Learning will prove indispensable reading for researchers, teachers, consultants, and instructional designers in higher and continuing education; for those involved in staff and educational development, and for those studying post graduate qualifications...... in learning and teaching. This, the second volume in the Springer Book Series on Researching Networked Learning, is based on a selection of papers presented at the 2012 Networked Learning Conference held in Maastricht, The Netherlands....

  19. Network Capacity Assessment of CHP-based Distributed Generation on Urban Energy Distribution Networks

    Science.gov (United States)

    Zhang, Xianjun

    The combined heat and power (CHP)-based distributed generation (DG) or dis-tributed energy resources (DERs) are mature options available in the present energy market, considered to be an effective solution to promote energy efficiency. In the urban environment, the electricity, water and natural gas distribution networks are becoming increasingly interconnected with the growing penetration of the CHP-based DG. Subsequently, this emerging interdependence leads to new topics meriting serious consideration: how much of the CHP-based DG can be accommodated and where to locate these DERs, and given preexisting constraints, how to quantify the mutual impacts on operation performances between these urban energy distribution networks and the CHP-based DG. The early research work was conducted to investigate the feasibility and design methods for one residential microgrid system based on existing electricity, water and gas infrastructures of a residential community, mainly focusing on the economic planning. However, this proposed design method cannot determine the optimal DG sizing and siting for a larger test bed with the given information of energy infrastructures. In this context, a more systematic as well as generalized approach should be developed to solve these problems. In the later study, the model architecture that integrates urban electricity, water and gas distribution networks, and the CHP-based DG system was developed. The proposed approach addressed the challenge of identifying the optimal sizing and siting of the CHP-based DG on these urban energy networks and the mutual impacts on operation performances were also quantified. For this study, the overall objective is to maximize the electrical output and recovered thermal output of the CHP-based DG units. The electricity, gas, and water system models were developed individually and coupled by the developed CHP-based DG system model. The resultant integrated system model is used to constrain the DG's electrical

  20. Greening radio access networks using distributed base station architectures

    DEFF Research Database (Denmark)

    Kardaras, Georgios; Soler, José; Dittmann, Lars

    2010-01-01

    . However besides this, increasing energy efficiency represents a key factor for reducing operating expenses and deploying cost effective mobile networks. This paper presents how distributed base station architectures can contribute in greening radio access networks. More specifically, the advantages...... energy saving. Different subsystems have to be coordinated real-time and intelligent network nodes supporting complicated functionalities are necessary. Distributed base station architectures are ideal for this purpose mainly because of their high degree of configurability and self...

  1. Delivering Sustainability Through Supply Chain Distribution Network Redesign

    OpenAIRE

    Denise Ravet

    2013-01-01

    Purpose - Companies could gain (cost, service, green/sustainable) competitive advantage through the supply chain network. The goal of this article is to study how to deliver sustainability through the supply chain distribution network redesign.Design/methodology/approach - A literature review is conducted to examine research relating to sustainable supply chain strategies and supply chain distribution network redesign.Findings - A study of the supply chain literature reveals the importance to...

  2. Machine learning for identifying botnet network traffic

    DEFF Research Database (Denmark)

    Stevanovic, Matija; Pedersen, Jens Myrup

    2013-01-01

    . Due to promise of non-invasive and resilient detection, botnet detection based on network traffic analysis has drawn a special attention of the research community. Furthermore, many authors have turned their attention to the use of machine learning algorithms as the mean of inferring botnet......-related knowledge from the monitored traffic. This paper presents a review of contemporary botnet detection methods that use machine learning as a tool of identifying botnet-related traffic. The main goal of the paper is to provide a comprehensive overview on the field by summarizing current scientific efforts....... The contribution of the paper is three-fold. First, the paper provides a detailed insight on the existing detection methods by investigating which bot-related heuristic were assumed by the detection systems and how different machine learning techniques were adapted in order to capture botnet-related knowledge...

  3. Robust quantum network architectures and topologies for entanglement distribution

    Science.gov (United States)

    Das, Siddhartha; Khatri, Sumeet; Dowling, Jonathan P.

    2018-01-01

    Entanglement distribution is a prerequisite for several important quantum information processing and computing tasks, such as quantum teleportation, quantum key distribution, and distributed quantum computing. In this work, we focus on two-dimensional quantum networks based on optical quantum technologies using dual-rail photonic qubits for the building of a fail-safe quantum internet. We lay out a quantum network architecture for entanglement distribution between distant parties using a Bravais lattice topology, with the technological constraint that quantum repeaters equipped with quantum memories are not easily accessible. We provide a robust protocol for simultaneous entanglement distribution between two distant groups of parties on this network. We also discuss a memory-based quantum network architecture that can be implemented on networks with an arbitrary topology. We examine networks with bow-tie lattice and Archimedean lattice topologies and use percolation theory to quantify the robustness of the networks. In particular, we provide figures of merit on the loss parameter of the optical medium that depend only on the topology of the network and quantify the robustness of the network against intermittent photon loss and intermittent failure of nodes. These figures of merit can be used to compare the robustness of different network topologies in order to determine the best topology in a given real-world scenario, which is critical in the realization of the quantum internet.

  4. Optimizing Cellular Networks Enabled with Renewal Energy via Strategic Learning.

    Science.gov (United States)

    Sohn, Insoo; Liu, Huaping; Ansari, Nirwan

    2015-01-01

    An important issue in the cellular industry is the rising energy cost and carbon footprint due to the rapid expansion of the cellular infrastructure. Greening cellular networks has thus attracted attention. Among the promising green cellular network techniques, the renewable energy-powered cellular network has drawn increasing attention as a critical element towards reducing carbon emissions due to massive energy consumption in the base stations deployed in cellular networks. Game theory is a branch of mathematics that is used to evaluate and optimize systems with multiple players with conflicting objectives and has been successfully used to solve various problems in cellular networks. In this paper, we model the green energy utilization and power consumption optimization problem of a green cellular network as a pilot power selection strategic game and propose a novel distributed algorithm based on a strategic learning method. The simulation results indicate that the proposed algorithm achieves correlated equilibrium of the pilot power selection game, resulting in optimum green energy utilization and power consumption reduction.

  5. Implementation of a Practical Distributed Calculation System with Browsers and JavaScript, and Application to Distributed Deep Learning

    OpenAIRE

    Miura, Ken; Harada, Tatsuya

    2015-01-01

    Deep learning can achieve outstanding results in various fields. However, it requires so significant computational power that graphics processing units (GPUs) and/or numerous computers are often required for the practical application. We have developed a new distributed calculation framework called "Sashimi" that allows any computer to be used as a distribution node only by accessing a website. We have also developed a new JavaScript neural network framework called "Sukiyaki" that uses genera...

  6. The Relationships Between Policy, Boundaries and Research in Networked Learning

    DEFF Research Database (Denmark)

    Ryberg, Thomas; Sinclair, Christine

    2016-01-01

    The biennial Networked Learning Conference is an established locus for work on practice, research and epistemology in the field of networked learning. That work continues between the conferences through the researchers’ own networks, ‘hot seat’ debates, and through publications, especially...... conferences, such as the inclusion of sociomaterial perspectives and recognition of informal networked learning. The chapters here each bring a particular perspective to the themes of Policy, Boundaries and Research in Networked Learning which we have chosen as the focus of the book. The selection...

  7. Monitoring water distribution systems: understanding and managing sensor networks

    OpenAIRE

    Ediriweera, D. D.; Marshall, I. W.

    2010-01-01

    Sensor networks are currently being trialed by the water distribution industry for monitoring complex distribution infrastructure. The paper presents an investigation in to the architecture and performance of a sensor system deployed for monitoring such a distribution network. The study reveals lapses in systems design and management, resulting in a fifth of the data being either missing or erroneous. Findings identify the importance of undertaking in-depth consideration of all aspects of a l...

  8. Monitoring of Students' Interaction in Online Learning Settings by Structural Network Analysis and Indicators.

    Science.gov (United States)

    Ammenwerth, Elske; Hackl, Werner O

    2017-01-01

    Learning as a constructive process works best in interaction with other learners. Support of social interaction processes is a particular challenge within online learning settings due to the spatial and temporal distribution of participants. It should thus be carefully monitored. We present structural network analysis and related indicators to analyse and visualize interaction patterns of participants in online learning settings. We validate this approach in two online courses and show how the visualization helps to monitor interaction and to identify activity profiles of learners. Structural network analysis is a feasible approach for an analysis of the intensity and direction of interaction in online learning settings.

  9. How and What Do Academics Learn through Their Personal Networks

    Science.gov (United States)

    Pataraia, Nino; Margaryan, Anoush; Falconer, Isobel; Littlejohn, Allison

    2015-01-01

    This paper investigates the role of personal networks in academics' learning in relation to teaching. Drawing on in-depth interviews with 11 academics, this study examines, first, how and what academics learn through their personal networks; second, the perceived value of networks in relation to academics' professional development; and, third,…

  10. 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.

  11. Estimating Conditional Distributions by Neural Networks

    DEFF Research Database (Denmark)

    Kulczycki, P.; Schiøler, Henrik

    1998-01-01

    Neural Networks for estimating conditionaldistributions and their associated quantiles are investigated in this paper. A basic network structure is developed on the basis of kernel estimation theory, and consistency property is considered from a mild set of assumptions. A number of applications...

  12. Learning as Issue Framing in Agricultural Innovation Networks

    Science.gov (United States)

    Tisenkopfs, Talis; Kunda, Ilona; Šumane, Sandra

    2014-01-01

    Purpose: Networks are increasingly viewed as entities of learning and innovation in agriculture. In this article we explore learning as issue framing in two agricultural innovation networks. Design/methodology/approach: We combine frame analysis and social learning theories to analyse the processes and factors contributing to frame convergence and…

  13. Leading to learn in networks of practice: Two leadership strategies

    NARCIS (Netherlands)

    Soekijad, M.; van den Hooff, B.J.; Agterberg, L.C.M.; Huysman, M.H.

    2011-01-01

    This paper outlines two leadership strategies to support organizational learning through networks of practice (NOPs). An in-depth case study in a development organization reveals that network leaders cope with a learning tension between management involvement and emergent learning processes by

  14. An Improved Harmony Search Algorithm for Power Distribution Network Planning

    Directory of Open Access Journals (Sweden)

    Wei Sun

    2015-01-01

    Full Text Available Distribution network planning because of involving many variables and constraints is a multiobjective, discrete, nonlinear, and large-scale optimization problem. Harmony search (HS algorithm is a metaheuristic algorithm inspired by the improvisation process of music players. HS algorithm has several impressive advantages, such as easy implementation, less adjustable parameters, and quick convergence. But HS algorithm still has some defects such as premature convergence and slow convergence speed. According to the defects of the standard algorithm and characteristics of distribution network planning, an improved harmony search (IHS algorithm is proposed in this paper. We set up a mathematical model of distribution network structure planning, whose optimal objective function is to get the minimum annual cost and constraint conditions are overload and radial network. IHS algorithm is applied to solve the complex optimization mathematical model. The empirical results strongly indicate that IHS algorithm can effectively provide better results for solving the distribution network planning problem compared to other optimization algorithms.

  15. Reliability based rehabilitation of water distribution networks by means of Bayesian networks

    OpenAIRE

    Lakehal Abdelaziz; Laouacheria Fares

    2017-01-01

    Water plays an essential role in the everyday lives of the people. To supply subscribers with good quality of water and to ensure continuity of service, the operators use water distribution networks (WDN). The main elements of water distribution network (WDN) are: pipes and valves. The work developed in this paper focuses on a water distribution network rehabilitation in the short and long term. Priorities for rehabilitation actions were defined and the information system consolidated, as wel...

  16. Network structure classification and features of water distribution systems

    Science.gov (United States)

    Giustolisi, Orazio; Simone, Antonietta; Ridolfi, Luca

    2017-04-01

    The network connectivity structure of water distribution systems (WDSs) represents the domain where hydraulic processes occur, driving the emerging behavior of such systems, for example with respect to robustness and vulnerability. In complex network theory (CNT), a common way of classifying the network structure and connectivity is the association of the nodal degree distribution to specific probability distribution models, and during the last decades, researchers classified many real networks using the Poisson or Pareto distributions. In spite of the fact that degree-based network classification could play a crucial role to assess WDS vulnerability, this task is not easy because the network structure of WDSs is strongly constrained by spatial characteristics of the environment where they are constructed. The consequence of these spatial constraints is that the nodal degree spans very small ranges in WDSs hindering a reliable classification by the standard approach based on the nodal degree distribution. This work investigates the classification of the network structure of 22 real WDSs, built in different environments, demonstrating that the Poisson distribution generally models the degree distributions very well. In order to overcome the problem of the reliable classification based on the standard nodal degree, we define the "neighborhood" degree, equal to the sum of the nodal degrees of the nearest topological neighbors (i.e., the adjacent nodes). This definition of "neighborhood" degree is consistent with the fact that the degree of a single node is not significant for analysis of WDSs.

  17. Online network applications for municipal distribution networks; Online-Sicherheitsrechnungen fuer staedtische Stromverteilungsnetze

    Energy Technology Data Exchange (ETDEWEB)

    Kuessel, Reinhard; Hoffmann, Johannes [BBC/ABB, Mannheim (Germany). Geschaeftsbereich Netzleittechnik; Kaiser, Ulrich [BBC/ABB, Mannheim (Germany). Bereich Netzberechnungsfunktionen

    2009-07-01

    The operational management of large municipal distribution networks puts high demands on the operational staff. Availability, cost effectiveness and sustainability are important target goals for the power supply, especially in the area of power distribution. The operators have a need for efficient tools to evaluate the current state of the network, to analyse historical states and to simulate future states of the network. This information gives important hints for the mains operation and for the planning of network expansions. This article gives an overview on experiences with online network applications gained in the network control system of a megacity in Asia. (orig.)

  18. Degree Distribution in Quantum Walks on Complex Networks

    Science.gov (United States)

    Faccin, Mauro; Johnson, Tomi; Biamonte, Jacob; Kais, Sabre; Migdał, Piotr

    2013-10-01

    In this theoretical study, we analyze quantum walks on complex networks, which model network-based processes ranging from quantum computing to biology and even sociology. Specifically, we analytically relate the average long-time probability distribution for the location of a unitary quantum walker to that of a corresponding classical walker. The distribution of the classical walker is proportional to the distribution of degrees, which measures the connectivity of the network nodes and underlies many methods for analyzing classical networks, including website ranking. The quantum distribution becomes exactly equal to the classical distribution when the walk has zero energy, and at higher energies, the difference, the so-called quantumness, is bounded by the energy of the initial state. We give an example for which the quantumness equals a Rényi entropy of the normalized weighted degrees, guiding us to regimes for which the classical degree-dependent result is recovered and others for which quantum effects dominate.

  19. Pain: A Distributed Brain Information Network?

    Science.gov (United States)

    Mano, Hiroaki; Seymour, Ben

    2015-01-01

    Understanding how pain is processed in the brain has been an enduring puzzle, because there doesn't appear to be a single “pain cortex” that directly codes the subjective perception of pain. An emerging concept is that, instead, pain might emerge from the coordinated activity of an integrated brain network. In support of this view, Woo and colleagues present evidence that distinct brain networks support the subjective changes in pain that result from nociceptive input and self-directed cognitive modulation. This evidence for the sensitivity of distinct neural subsystems to different aspects of pain opens up the way to more formal computational network theories of pain. PMID:25562782

  20. A Unified Network Security Architecture for Large, Distributed Networks Project

    Data.gov (United States)

    National Aeronautics and Space Administration — In typical, multi-organizational networking environments, it is difficult to define and maintain a uniform authentication scheme that provides users with easy access...

  1. Maximum penetration of microgeneration photovoltaic panels in distribution network

    Energy Technology Data Exchange (ETDEWEB)

    Morais, Hugo; Faria, Pedro; Vale, Zita [Polytechnic of Porto (Portugal). GECAD - Knowledge Engineering and Decision Support Research Center

    2012-07-01

    In smart grids context, the distributed generation units based in renewable resources, play an important rule. The photovoltaic solar units are a technology in evolution and their prices decrease significantly in recent years due to the high penetration of this technology in the low voltage and medium voltage networks supported by governmental policies and incentives. This paper proposes a methodology to determine the maximum penetration of photovoltaic units in a distribution network. The paper presents a case study, with four different scenarios, that considers a 32-bus medium voltage distribution network and the inclusion storage units. (orig.)

  2. Learning network theory : its contribution to our understanding of work-based learning projects and learning climate

    OpenAIRE

    Poell, R.F.; Moorsel, M.A.A.H. van

    1996-01-01

    This paper discusses the relevance of Van der Krogt's learning network theory (1995) for our understanding of the concepts of work-related learning projects and learning climate in organisations. The main assumptions of the learning network theory are presented and transferred to the level of learning groups in organisations. Four theoretical types of learning projects are distinguished. Four different approaches to the learning climate of work groups are compared to the approach offered by t...

  3. Optimization of Power Distribution Networks in Megacities

    Science.gov (United States)

    Manusov, V. Z.; Matrenin, P. V.; Ahyoev, J. S.; Atabaeva, L. Sh

    2017-06-01

    The study deals with the problem of city electrical networks optimization in big towns and megacities to increase electrical energy quality and decrease real and active power losses in the networks as well as in domestic consumers. The optimization is carried out according to the location selection and separate reactive power source in 10 kW networks of Swarm Intelligence algorithms, in particular, of Particle Swarm one. The problem solution based on Particle Swarm algorithm is determined by variables being discrete quantities and, in addition, there are several local minimums (troughs) to be available for a global minimum to be found. It is proved that the city power supply system optimization is carried out by the additional reactive power source to be installed at consumers location reducing reactive power flow, thereby, ensuring increase of power supply system quality and decrease of power losses in city networks.

  4. Secure data networks for electrical distribution applications

    OpenAIRE

    Laverty, David M.; O'Raw, John B.; Li, Kang; Morrow, D. John

    2015-01-01

    Smart Grids are characterized by the application of information communication technology (ICT) to solve electrical energy challenges. Electric power networks span large geographical areas, thus a necessary component of many Smart Grid applications is a wide area network (WAN). For the Smart Grid to be successful, utilities must be confident that the communications infrastructure is secure. This paper describes how a WAN can be deployed using WiMAX radio technology to provide high bandwidth co...

  5. A Team Formation and Project-based Learning Support Service for Social Learning Networks

    NARCIS (Netherlands)

    Spoelstra, Howard; Van Rosmalen, Peter; Van de Vrie, Evert; Obreza, Matija; Sloep, Peter

    2014-01-01

    The Internet affords new approaches to learning. Geographically dispersed self-directed learners can learn in computer-supported communities, forming social learning networks. However, self-directed learners can suffer from a lack of continuous motivation. And surprisingly, social learning networks

  6. ERT Conditions for Productive Learning in Networked Learning Environments: Leadership Report

    DEFF Research Database (Denmark)

    Dirckinck-Holmfeld, Lone

    This report provides a concluding account of the activities within the European Research Team: Conditions for Productive Learning in Networked Learning Environmentments......This report provides a concluding account of the activities within the European Research Team: Conditions for Productive Learning in Networked Learning Environmentments...

  7. 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.

  8. Learning comunication strategies for distributed artificial intelligence

    Science.gov (United States)

    Kinney, Michael; Tsatsoulis, Costas

    1992-08-01

    We present a methodology that allows collections of intelligent system to automatically learn communication strategies, so that they can exchange information and coordinate their problem solving activity. In our methodology communication between agents is determined by the agents themselves, which consider the progress of their individual problem solving activities compared to the communication needs of their surrounding agents. Through learning, communication lines between agents might be established or disconnected, communication frequencies modified, and the system can also react to dynamic changes in the environment that might force agents to cease to exist or to be added. We have established dynamic, quantitative measures of the usefulness of a fact, the cost of a fact, the work load of an agent, and the selfishness of an agent (a measure indicating an agent's preference between transmitting information versus performing individual problem solving), and use these values to adapt the communication between intelligent agents. In this paper we present the theoretical foundations of our work together with experimental results and performance statistics of networks of agents involved in cooperative problem solving activities.

  9. Reliability based rehabilitation of water distribution networks by means of Bayesian networks

    Directory of Open Access Journals (Sweden)

    Lakehal Abdelaziz

    2017-09-01

    Full Text Available Water plays an essential role in the everyday lives of the people. To supply subscribers with good quality of water and to ensure continuity of service, the operators use water distribution networks (WDN. The main elements of water distribution network (WDN are: pipes and valves. The work developed in this paper focuses on a water distribution network rehabilitation in the short and long term. Priorities for rehabilitation actions were defined and the information system consolidated, as well as decision-making. The reliability data were conjugated in decision making tools on water distribution network rehabilitation in a forecasting context. As the pipes are static elements and the valves are dynamic elements, a Bayesian network (static-dynamic has been developed, which can help to predict the failure scenario regarding water distribution. A relationship between reliability and prioritization of rehabilitation actions has been investigated. Modelling based on a Static Bayesian Network (SBN is implemented to analyse qualitatively and quantitatively the availability of water in the different segments of the network. Dynamic Bayesian networks (DBN are then used to assess the valves reliability as function of time, which allows management of water distribution based on water availability assessment in different segments. Before finishing the paper by giving some conclusions, a case study of a network supplying a city was presented. The results show the importance and effectiveness of the proposed Bayesian approach in the anticipatory management and for prioritizing rehabilitation of water distribution networks.

  10. WEB BASED LEARNING OF COMPUTER NETWORK COURSE

    Directory of Open Access Journals (Sweden)

    Hakan KAPTAN

    2004-04-01

    Full Text Available As a result of developing on Internet and computer fields, web based education becomes one of the area that many improving and research studies are done. In this study, web based education materials have been explained for multimedia animation and simulation aided Computer Networks course in Technical Education Faculties. Course content is formed by use of university course books, web based education materials and technology web pages of companies. Course content is formed by texts, pictures and figures to increase motivation of students and facilities of learning some topics are supported by animations. Furthermore to help working principles of routing algorithms and congestion control algorithms simulators are constructed in order to interactive learning

  11. On local optima in learning bayesian networks

    DEFF Research Database (Denmark)

    Dalgaard, Jens; Kocka, Tomas; Pena, Jose

    2003-01-01

    This paper proposes and evaluates the k-greedy equivalence search algorithm (KES) for learning Bayesian networks (BNs) from complete data. The main characteristic of KES is that it allows a trade-off between greediness and randomness, thus exploring different good local optima. When greediness...... is set at maximum, KES corresponds to the greedy equivalence search algorithm (GES). When greediness is kept at minimum, we prove that under mild assumptions KES asymptotically returns any inclusion optimal BN with nonzero probability. Experimental results for both synthetic and real data are reported...

  12. Learning Reproducibility with a Yearly Networking Contest

    KAUST Repository

    Canini, Marco

    2017-08-10

    Better reproducibility of networking research results is currently a major goal that the academic community is striving towards. This position paper makes the case that improving the extent and pervasiveness of reproducible research can be greatly fostered by organizing a yearly international contest. We argue that holding a contest undertaken by a plurality of students will have benefits that are two-fold. First, it will promote hands-on learning of skills that are helpful in producing artifacts at the replicable-research level. Second, it will advance the best practices regarding environments, testbeds, and tools that will aid the tasks of reproducibility evaluation committees by and large.

  13. Statistical and machine learning approaches for network analysis

    CERN Document Server

    Dehmer, Matthias

    2012-01-01

    Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internation

  14. Stochastic Online Learning in Dynamic Networks under Unknown Models

    Science.gov (United States)

    2016-08-02

    Stochastic Online Learning in Dynamic Networks under Unknown Models This research aims to develop fundamental theories and practical algorithms for...12211 Research Triangle Park, NC 27709-2211 Online learning , multi-armed bandit, dynamic networks REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S... Online Learning in Dynamic Networks under Unknown Models Report Title This research aims to develop fundamental theories and practical algorithms for

  15. Sequential Nonlinear Learning for Distributed Multiagent Systems via Extreme Learning Machines.

    Science.gov (United States)

    Vanli, Nuri Denizcan; Sayin, Muhammed O; Delibalta, Ibrahim; Kozat, Suleyman Serdar

    2017-03-01

    We study online nonlinear learning over distributed multiagent systems, where each agent employs a single hidden layer feedforward neural network (SLFN) structure to sequentially minimize arbitrary loss functions. In particular, each agent trains its own SLFN using only the data that is revealed to itself. On the other hand, the aim of the multiagent system is to train the SLFN at each agent as well as the optimal centralized batch SLFN that has access to all the data, by exchanging information between neighboring agents. We address this problem by introducing a distributed subgradient-based extreme learning machine algorithm. The proposed algorithm provides guaranteed upper bounds on the performance of the SLFN at each agent and shows that each of these individual SLFNs asymptotically achieves the performance of the optimal centralized batch SLFN. Our performance guarantees explicitly distinguish the effects of data- and network-dependent parameters on the convergence rate of the proposed algorithm. The experimental results illustrate that the proposed algorithm achieves the oracle performance significantly faster than the state-of-the-art methods in the machine learning and signal processing literature. Hence, the proposed method is highly appealing for the applications involving big data.

  16. Graduate Employability: The Perspective of Social Network Learning

    Science.gov (United States)

    Chen, Yong

    2017-01-01

    This study provides a conceptual framework for understanding how the graduate acquire employability through the social network in the Chinese context, using insights from the social network theory. This paper builds a conceptual model of the relationship among social network, social network learning and the graduate employability, and uses…

  17. Playing distributed two-party quantum games on quantum networks

    Science.gov (United States)

    Liu, Bo-Yang; Dai, Hong-Yi; Zhang, Ming

    2017-12-01

    This paper investigates quantum games between two remote players on quantum networks. We propose two schemes for distributed remote quantum games: the client-server scheme based on states transmission between nodes of the network and the peer-to-peer scheme devised upon remote quantum operations. Following these schemes, we construct two designs of the distributed prisoners' dilemma game on quantum entangling networks, where concrete methods are employed for teleportation and nonlocal two-qubits unitary gates, respectively. It seems to us that the requirement for playing distributed quantum games on networks is still an open problem. We explore this problem by comparing and characterizing the two schemes from the viewpoints of network structures, quantum and classical operations, experimental realization and simplification.

  18. Adaptive relaying for ground fault protection of a distribution network

    Energy Technology Data Exchange (ETDEWEB)

    Sachdev, M.S.; Sidhu, T.S.; Talukdar, B.K.

    1995-12-31

    With the advent of digital technology and microprocessor-based relays, it is possible to continuously monitor a power network, analyze it in real time, and change the relay settings to those most suitable at that time, thereby achieving improved protection of the network. This approach, known as adaptive relaying, was applied to the Saskatoon distribution network. This paper describes the software modules developed for setting ground fault overcurrent relays in the adaptive relay protection system. The major task in this system was the on-line coordination of relays, as most faults in a distribution system are of the single-phase to ground type and current unbalance due to single-phase loading contributes to the complexity of relay coordination. The modules served for network topology detection, state estimation, fault analysis, and relay setting and coordination. The paper also presents results of a study of the proposed adaptive ground fault protection scheme using a model distribution network.

  19. Designing a Rational Distribution Network for Trading Companies

    Directory of Open Access Journals (Sweden)

    Dybskaya V. V.

    2017-09-01

    Full Text Available This article considers the modern methods and approaches to design the company’s distribution network. The authors point out the relevance of this problem for the modern trading and manufacturing companies, give examples of the strategic goal setting of the company in the logistics network reorganization, and the benchmarks of possible economic effects of its conduction. The work reviews the scientific articles of contemporary American, European and Russian authors devoted to the approaches, concerning the implementation of projects for designing a distribution network, methods and models for its optimization. The article concludes that there is no single “language” and an approach to design the logistics networks, with a proper level of detail that takes into account the strategic features and industry specificity of the certain company. The authors propose an algorithm for designing a rational distribution network.

  20. Researching Design, Experience and Practice of Networked Learning

    DEFF Research Database (Denmark)

    Hodgson, Vivien; de Laat, Maarten; McConnell, David

    2014-01-01

    and final section draws attention to a growing topic of interest within networked learning: that of networked learning in informal practices. In addition, we provide a reflection on the theories, methods and settings featured in the networked learning research of the chapters. We conclude the introduction......In the introductory chapter, we explore how networked learning has developed in recent years by summarising and discussing the research presented in the chapters of the book. The chapters are structured in three sections, each highlighting a particular aspect of practice. The first section focuses...

  1. Measuring Team Collaboration in a Distributed Coalition Network

    National Research Council Canada - National Science Library

    Bowman, Elizabeth K

    2007-01-01

    ...). Multinational Experiment 4 (MNE 4) provided researchers an opportunity to evaluate how distributed teams interact in a collaborative, networked environment to conduct the Effects Based Approach to Operations (EBAO...

  2. Water distribution network modelling of a small community using ...

    African Journals Online (AJOL)

    Water distribution network modelling of a small community using watercad simulator. ... Global Journal of Engineering Research ... Pipes P-6, P-12, P-15 and P-19 expectedly have relatively low flow velocities due to the low average day ...

  3. Kinetics of distribution of infections in networks

    Science.gov (United States)

    Avramov, I.

    2007-06-01

    SummaryWe develop a model for disease spreading in networks in a manner similar to the kinetics of crystallization of undercooled melts. The same kind of equations can be used in ecology and in sociology studies. For instance, they control the spread of gossip among the population. The time t dependence of the overall fraction α( t) of an infected network mass (individuals) affected by the disease is represented by an S-shaped curve. The derivative, i.e. the time dependence of intensity W( t) with which the epidemic evolves, is a bell-shaped curve. In essence, an analytical solution is offered describing the kinetics of spread of information along a ( d-dimensional) network.

  4. Distributional learning of vowel categories in infants and adults

    NARCIS (Netherlands)

    Wanrooij, K.E.

    2015-01-01

    Distributional learning is learning from simple exposure to the environment, without receiving explicit instruction or feedback. This thesis examines to what extent this basic form of learning contributes to learning the vowels of a language, both in infancy, when the mother tongue must be acquired,

  5. Boltzmann learning of parameters in cellular neural networks

    DEFF Research Database (Denmark)

    Hansen, Lars Kai

    1992-01-01

    The use of Bayesian methods to design cellular neural networks for signal processing tasks and the Boltzmann machine learning rule for parameter estimation is discussed. The learning rule can be used for models with hidden units, or for completely unsupervised learning. The latter is exemplified...... by unsupervised adaptation of an image segmentation cellular network. The learning rule is applied to adaptive segmentation of satellite imagery...

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

    CERN Document Server

    Hu, Fei

    2012-01-01

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

  7. A Distributed Algorithm for Energy Optimization in Hydraulic Networks

    DEFF Research Database (Denmark)

    Kallesøe, Carsten; Wisniewski, Rafal; Jensen, Tom Nørgaard

    2014-01-01

    An industrial case study in the form of a large-scale hydraulic network underlying a district heating system is considered. A distributed control is developed that minimizes the aggregated electrical energy consumption of the pumps in the network without violating the control demands. The algorithm...... a Plug & Play control system as most commissioning can be done during the manufacture of the pumps. Only information on the graph-structure of the hydraulic network is needed during installation....

  8. A Novel Frequency Communication Technology in Power Distribution Communication Network

    OpenAIRE

    Li Ying-Jun

    2017-01-01

    With the expansion of the power terminal access network scale, the main road corridor resources, branch line cable Laying difficulties has become an important factor restricting the construction of the network. In this paper, we focus on the frequency communication technology in power distribution communication network, and design a novel technology in communication mode, error correcting coding and data transfer frame format. We also discuss the influence of voltage phase difference on power...

  9. Distributed flow optimization and cascading effects in weighted complex networks

    OpenAIRE

    Asztalos, Andrea; Sreenivasan, Sameet; Szymanski, Boleslaw K.; Korniss, G.

    2011-01-01

    We investigate the effect of a specific edge weighting scheme $\\sim (k_i k_j)^{\\beta}$ on distributed flow efficiency and robustness to cascading failures in scale-free networks. In particular, we analyze a simple, yet fundamental distributed flow model: current flow in random resistor networks. By the tuning of control parameter $\\beta$ and by considering two general cases of relative node processing capabilities as well as the effect of bandwidth, we show the dependence of transport efficie...

  10. Modelling flow dynamics in water distribution networks using ...

    African Journals Online (AJOL)

    Computational approaches can be used to detect leakages in water distribution networks. One such approach is the Artificial Neural Networks (ANNs) technique. The advantage of ANNs is that they are robust and can be used to model complex linear and non-linear systems without making implicit assumptions. ANNs can ...

  11. The redesign of a warranty distribution network with recovery processes

    NARCIS (Netherlands)

    Ashayeri, J.; Ma, N.; Sotirov, R.

    A warranty distribution network provides aftersales warranty services to customers and resembles a closed-loop supply chain network with specific challenges for reverse flows management like recovery, repair, and reflow of refurbished products. We present here a nonlinear and nonconvex mixed integer

  12. Water distribution network modelling of a small community using ...

    African Journals Online (AJOL)

    In this study a network model was constructed for the hydraulic analysis and design of a small community (Sakwa) water distribution network in North Eastern geopolitical region of Nigeria using WaterCAD simulator. The analysis included a review of pressures, velocities and head loss gradients under steady state average ...

  13. THE OPERATION MODES OPTIMIZATION OF THE NEUTRAL DISTRIBUTION NETWORKS

    Directory of Open Access Journals (Sweden)

    F. P. Shkarbets

    2009-03-01

    Full Text Available The variants of grounding the neutral wire of electric networks are considered and the recommendations are presented on increasing the level of operational reliability and electric safety of distribution networks with 6 kV voltage on the basis of limitation and suppression of transitional processes at asymmetrical damages.

  14. Water Distribution Network Modelling of a Small Community using ...

    African Journals Online (AJOL)

    Water Distribution Network Modelling of a Small Community using Watercad Simulator. ... Global Journal of Engineering Research ... with respect to pressure or available fire flow for the proposed service area and also that flow velocities are not excessive while head loss gradients in the network are within acceptable limits.

  15. Multimedia distribution using network coding on the iphone platform

    DEFF Research Database (Denmark)

    Vingelmann, Peter; Pedersen, Morten Videbæk; Fitzek, Frank

    2010-01-01

    This paper looks into the implementation details of random linear network coding on the Apple iPhone and iPod Touch mobile platforms for multimedia distribution. Previous implementations of network coding on this platform failed to achieve a throughput which is sufficient to saturate the WLAN...

  16. Pattern Recognition for Reliability Assessment of Water Distribution Networks

    NARCIS (Netherlands)

    Trifunović, N.

    2012-01-01

    The study presented in this manuscript investigates the patterns that describe reliability of water distribution networks focusing to the node connectivity, energy balance, and economics of construction, operation and maintenance. A number of measures to evaluate the network resilience has been

  17. 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

  18. Resilience-based optimal design of water distribution network

    Science.gov (United States)

    Suribabu, C. R.

    2017-11-01

    Optimal design of water distribution network is generally aimed to minimize the capital cost of the investments on tanks, pipes, pumps, and other appurtenances. Minimizing the cost of pipes is usually considered as a prime objective as its proportion in capital cost of the water distribution system project is very high. However, minimizing the capital cost of the pipeline alone may result in economical network configuration, but it may not be a promising solution in terms of resilience point of view. Resilience of the water distribution network has been considered as one of the popular surrogate measures to address ability of network to withstand failure scenarios. To improve the resiliency of the network, the pipe network optimization can be performed with two objectives, namely minimizing the capital cost as first objective and maximizing resilience measure of the configuration as secondary objective. In the present work, these two objectives are combined as single objective and optimization problem is solved by differential evolution technique. The paper illustrates the procedure for normalizing the objective functions having distinct metrics. Two of the existing resilience indices and power efficiency are considered for optimal design of water distribution network. The proposed normalized objective function is found to be efficient under weighted method of handling multi-objective water distribution design problem. The numerical results of the design indicate the importance of sizing pipe telescopically along shortest path of flow to have enhanced resiliency indices.

  19. Resilience-based optimal design of water distribution network

    Science.gov (United States)

    Suribabu, C. R.

    2017-04-01

    Optimal design of water distribution network is generally aimed to minimize the capital cost of the investments on tanks, pipes, pumps, and other appurtenances. Minimizing the cost of pipes is usually considered as a prime objective as its proportion in capital cost of the water distribution system project is very high. However, minimizing the capital cost of the pipeline alone may result in economical network configuration, but it may not be a promising solution in terms of resilience point of view. Resilience of the water distribution network has been considered as one of the popular surrogate measures to address ability of network to withstand failure scenarios. To improve the resiliency of the network, the pipe network optimization can be performed with two objectives, namely minimizing the capital cost as first objective and maximizing resilience measure of the configuration as secondary objective. In the present work, these two objectives are combined as single objective and optimization problem is solved by differential evolution technique. The paper illustrates the procedure for normalizing the objective functions having distinct metrics. Two of the existing resilience indices and power efficiency are considered for optimal design of water distribution network. The proposed normalized objective function is found to be efficient under weighted method of handling multi-objective water distribution design problem. The numerical results of the design indicate the importance of sizing pipe telescopically along shortest path of flow to have enhanced resiliency indices.

  20. Flow distributions and spatial correlations in human brain capillary networks

    Science.gov (United States)

    Lorthois, Sylvie; Peyrounette, Myriam; Larue, Anne; Le Borgne, Tanguy

    2015-11-01

    The vascular system of the human brain cortex is composed of a space filling mesh-like capillary network connected upstream and downstream to branched quasi-fractal arterioles and venules. The distribution of blood flow rates in these networks may affect the efficiency of oxygen transfer processes. Here, we investigate the distribution and correlation properties of blood flow velocities from numerical simulations in large 3D human intra-cortical vascular network (10000 segments) obtained from an anatomical database. In each segment, flow is solved from a 1D non-linear model taking account of the complex rheological properties of blood flow in microcirculation to deduce blood pressure, blood flow and red blood cell volume fraction distributions throughout the network. The network structural complexity is found to impart broad and spatially correlated Lagrangian velocity distributions, leading to power law transit time distributions. The origins of this behavior (existence of velocity correlations in capillary networks, influence of the coupling with the feeding arterioles and draining veins, topological disorder, complex blood rheology) are studied by comparison with results obtained in various model capillary networks of controlled disorder. ERC BrainMicroFlow GA615102, ERC ReactiveFronts GA648377.

  1. Learning network theory : its contribution to our understanding of work-based learning projects and learning climate

    NARCIS (Netherlands)

    Poell, R.F.; Moorsel, M.A.A.H. van

    1996-01-01

    This paper discusses the relevance of Van der Krogt's learning network theory (1995) for our understanding of the concepts of work-related learning projects and learning climate in organisations. The main assumptions of the learning network theory are presented and transferred to the level of

  2. Ambient Learning Displays - Distributed Mixed Reality Information Mash-ups to support Ubiquitous Learning

    NARCIS (Netherlands)

    Börner, Dirk

    2010-01-01

    Börner, D. (2010, 19-21 March). Ambient Learning Displays Distributed Mixed Reality Information Mash-ups to support Ubiquitous Learning. Presented at the IADIS International Conference Mobile Learning 2010, Porto, Portugal.

  3. Scalable infrastructure for distributed sensor networks

    National Research Council Canada - National Science Library

    Chakrabarty, Krishnendu; Iyengar, S. S

    2005-01-01

    ... network application is inventory tracking in factory warehouses. A single sensor node can be attached to each item in the warehouse. These sensor nodes can then be used for tracking the location of the items as they are moved within the warehouse. They can also provide information on the location of nearby items as well as the history of movement...

  4. Stability of drinking water distribution network

    DEFF Research Database (Denmark)

    Leth, Tobias; Kallesøe, Carsten Skovmose; Sloth, Christoffer

    2016-01-01

    We strive to prove stability of a hydraulic network, where the pressure at the end user is controlled with PI control. The non-polynomial model is represented by numerous polynomial systems defined on sub-sets of Rn. The sub-sets are defined by compact basic semi-algebraic sets. The stability...

  5. A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors

    Science.gov (United States)

    Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

    2017-01-01

    In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors. PMID:28934163

  6. A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors

    Directory of Open Access Journals (Sweden)

    Jilin Zhang

    2017-09-01

    Full Text Available In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT. Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP, which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS. This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors.

  7. A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors.

    Science.gov (United States)

    Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

    2017-09-21

    In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors.

  8. Impacts of distributed generation on low-voltage distribution network protection

    OpenAIRE

    Ogden, Richard; Yang, Jin

    2015-01-01

    This paper reports work of a MEng student final year project, which looks in detail at the impacts that distributed generation can have on existing low-voltage distribution network protection systems. After a review of up-to-date protection issues, this paper will investigate several key issues that face distributed generation connections when it comes to network protection systems. These issues include, the blinding of protection systems, failure to automatically reclose, unintentional islan...

  9. Optimal placement of distributed generation in distribution networks

    African Journals Online (AJOL)

    user

    This paper proposes the application of Particle Swarm Optimization (PSO) technique to find the optimal size and optimum ... DG also has several benefits like energy costs through combined ... and high capacity of DG can be explained by the fact that the distribution system was initially designed such that power flows.

  10. Costs of Residential Solar PV Plants in Distribution Grid Networks

    DEFF Research Database (Denmark)

    Kjær, Søren Bækhøj; Yang, Guangya; Ipsen, Hans Henrik

    2015-01-01

    In this article we investigate the impact of residential solar PV plants on energy losses in distribution networks and their impact on distribution transformers lifetime. Current guidelines in Denmark states that distribution transformers should not be loaded with more than 67% solar PV power...... to avoid accelerated loss of life. If a solar PV plant causes this limit to be exceeded, the particular owner has to pay for upgrading the transformer. Distribution Network Operators also charge an annual tariff from the solar PV plants to cover the expenses to keep the grid capacity available, the so...... called “Availability Tariff”. According to the Danish Energy Regulatory Authority, the Availability Tariff must cover the exact expenses, with energy savings etc. from the solar PV plants taken into consideration. Our conclusion is that a distribution network, which represents a typical residential...

  11. Architectural transformations in network services and distributed systems

    CERN Document Server

    Luntovskyy, Andriy

    2017-01-01

    With the given work we decided to help not only the readers but ourselves, as the professionals who actively involved in the networking branch, with understanding the trends that have developed in recent two decades in distributed systems and networks. Important architecture transformations of distributed systems have been examined. The examples of new architectural solutions are discussed. Content Periodization of service development Energy efficiency Architectural transformations in Distributed Systems Clustering and Parallel Computing, performance models Cloud Computing, RAICs, Virtualization, SDN Smart Grid, Internet of Things, Fog Computing Mobile Communication from LTE to 5G, DIDO, SAT-based systems Data Security Guaranteeing Distributed Systems Target Groups Students in EE and IT of universities and (dual) technical high schools Graduated engineers as well as teaching staff About the Authors Andriy Luntovskyy provides classes on networks, mobile communication, software technology, distributed systems, ...

  12. Adaptive control for active distribution networks

    OpenAIRE

    Sansawatt, Thipnatee Punim

    2012-01-01

    Rise of the global environmental awareness and climate change impacts caused by greenhouse gases emissions brings about a revolution in the power and energy industries to reduce fossil fuels and promote low-carbon and renewable distributed generation (DG). The new dimensions, mainly encouraged by the governments’ legislative targets and incentives, have allowed the development of DG worldwide. In the U.K., renewable DG especially wind is being connected on distribution netwo...

  13. Hybrid Collaborative Learning for Classification and Clustering in Sensor Networks

    Science.gov (United States)

    Wagstaff, Kiri L.; Sosnowski, Scott; Lane, Terran

    2012-01-01

    Traditionally, nodes in a sensor network simply collect data and then pass it on to a centralized node that archives, distributes, and possibly analyzes the data. However, analysis at the individual nodes could enable faster detection of anomalies or other interesting events as well as faster responses, such as sending out alerts or increasing the data collection rate. There is an additional opportunity for increased performance if learners at individual nodes can communicate with their neighbors. In previous work, methods were developed by which classification algorithms deployed at sensor nodes can communicate information about event labels to each other, building on prior work with co-training, self-training, and active learning. The idea of collaborative learning was extended to function for clustering algorithms as well, similar to ideas from penta-training and consensus clustering. However, collaboration between these learner types had not been explored. A new protocol was developed by which classifiers and clusterers can share key information about their observations and conclusions as they learn. This is an active collaboration in which learners of either type can query their neighbors for information that they then use to re-train or re-learn the concept they are studying. The protocol also supports broadcasts from the classifiers and clusterers to the rest of the network to announce new discoveries. Classifiers observe an event and assign it a label (type). Clusterers instead group observations into clusters without assigning them a label, and they collaborate in terms of pairwise constraints between two events [same-cluster (mustlink) or different-cluster (cannot-link)]. Fundamentally, these two learner types speak different languages. To bridge this gap, the new communication protocol provides four types of exchanges: hybrid queries for information, hybrid "broadcasts" of learned information, each specified for classifiers-to-clusterers, and clusterers

  14. Fast, Distributed Algorithms in Deep Networks

    Science.gov (United States)

    2016-05-11

    dataset consists of images of house numbers taken from the Google Streetview car . Each data point consisted of a cropped image of a single digit which...1989. [9] Navneet Dalal and Bill Triggs. Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, volume 1...Bengio. Understanding the difficult of training deep feed- forward neural networks. In International Conference on Artificial Intelligence and Statistics

  15. Directed networks' different link formation mechanisms causing degree distribution distinction

    Science.gov (United States)

    Behfar, Stefan Kambiz; Turkina, Ekaterina; Cohendet, Patrick; Burger-Helmchen, Thierry

    2016-11-01

    Within undirected networks, scientists have shown much interest in presenting power-law features. For instance, Barabási and Albert (1999) claimed that a common property of many large networks is that vertex connectivity follows scale-free power-law distribution, and in another study Barabási et al. (2002) showed power law evolution in the social network of scientific collaboration. At the same time, Jiang et al. (2011) discussed deviation from power-law distribution; others indicated that size effect (Bagrow et al., 2008), information filtering mechanism (Mossa et al., 2002), and birth and death process (Shi et al., 2005) could account for this deviation. Within directed networks, many authors have considered that outlinks follow a similar mechanism of creation as inlinks' (Faloutsos et al., 1999; Krapivsky et al., 2001; Tanimoto, 2009) with link creation rate being the linear function of node degree, resulting in a power-law shape for both indegree and outdegree distribution. Some other authors have made an assumption that directed networks, such as scientific collaboration or citation, behave as undirected, resulting in a power-law degree distribution accordingly (Barabási et al., 2002). At the same time, we claim (1) Outlinks feature different degree distributions than inlinks; where different link formation mechanisms cause the distribution distinctions, (2) in/outdegree distribution distinction holds for different levels of system decomposition; therefore this distribution distinction is a property of directed networks. First, we emphasize in/outlink formation mechanisms as causal factors for distinction between indegree and outdegree distributions (where this distinction has already been noticed in Barker et al. (2010) and Baxter et al. (2006)) within a sample network of OSS projects as well as Java software corpus as a network. Second, we analyze whether this distribution distinction holds for different levels of system decomposition: open

  16. Dense distributed processing in a hindlimb scratch motor network

    DEFF Research Database (Denmark)

    Guzulaitis, Robertas; Hounsgaard, Jørn Dybkjær

    2014-01-01

    In reduced preparations, hindlimb movements can be generated by a minimal network of neurons in the limb innervating spinal segments. The network of neurons that generates real movements is less well delineated. In an ex vivo carapace-spinal cord preparation from adult turtles (Trachemys scripta...... elegans), we show that ventral horn interneurons in mid-thoracic spinal segments are functionally integrated in the hindlimb scratch network. First, mid-thoracic interneurons receive intense synaptic input during scratching and behave like neurons in the hindlimb enlargement. Second, some mid...... of a distributed motor network that secures motor coherence....

  17. On Distributed Computation in Noisy Random Planar Networks

    OpenAIRE

    Kanoria, Y.; Manjunath, D.

    2007-01-01

    We consider distributed computation of functions of distributed data in random planar networks with noisy wireless links. We present a new algorithm for computation of the maximum value which is order optimal in the number of transmissions and computation time.We also adapt the histogram computation algorithm of Ying et al to make the histogram computation time optimal.

  18. Development of Tools for DER Components in a Distribution Network

    DEFF Research Database (Denmark)

    Mihet-Popa, Lucian; Koch-Ciobotaru, C; Isleifsson, Fridrik Rafn

    2012-01-01

    The increasing amount of Distributed Energy Resources (DER) components into distribution networks involves the development of accurate simulation models that take into account an increasing number of factors that influence the output power from the DG systems. This paper presents two simulation m...

  19. 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

  20. An Optimal Design Model for New Water Distribution Networks in ...

    African Journals Online (AJOL)

    This paper is concerned with the problem of optimizing the distribution of water in Kigali City at a minimum cost. The mathematical formulation is a Linear Programming Problem (LPP) which involves the design of a new network of water distribution considering the cost in the form of unit price of pipes, the hydraulic gradient ...

  1. On the distribution of signal phase in body area networks

    NARCIS (Netherlands)

    Wilson, S.K.; Cotton, Simon L.; Dias, Ugo S.; Scanlon, W.G.; Yacoub, Michel D.

    2010-01-01

    In this letter, we investigate the distribution of the phase component of the complex received signal observed in practical experiments using body area networks. Two phase distributions, the recently proposed κ-μ and η-μ probability densities, which together encompass the most widely used fading

  2. Incentive-Based Voltage Regulation in Distribution Networks

    Energy Technology Data Exchange (ETDEWEB)

    Dall-Anese, Emiliano [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Baker, Kyri A [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhou, Xinyang [University of Colorado; Chen, Lijun [University of Colorado

    2017-07-03

    This paper considers distribution networks fea- turing distributed energy resources, and designs incentive-based mechanisms that allow the network operator and end-customers to pursue given operational and economic objectives, while concurrently ensuring that voltages are within prescribed limits. Two different network-customer coordination mechanisms that require different amounts of information shared between the network operator and end-customers are developed to identify a solution of a well-defined social-welfare maximization prob- lem. Notably, the signals broadcast by the network operator assume the connotation of prices/incentives that induce the end- customers to adjust the generated/consumed powers in order to avoid the violation of the voltage constraints. Stability of the proposed schemes is analytically established and numerically corroborated.

  3. Mitigation of Voltage Sags in CIGRE Low Voltage Distribution Network

    DEFF Research Database (Denmark)

    Mustafa, Ghullam; Bak-Jensen, Birgitte; Mahat, Pukar

    2013-01-01

    problems in the distribution system. The voltage problems dealt with in this paper are to show how to mitigate voltage sags in the CIGRE Low Voltage (LV) test network and networks like this. The voltage sags, for the tested cases in the CIGRE LV test network are mainly due to three phase faults....... The compensation of voltage sags in the different parts of CIGRE distribution network is done by using the four STATCOM compensators already existing in the test grid. The simulations are carried out in DIgSILENT power factory software version 15.0.......Any problem in voltage in a power network is undesirable as it aggravates the quality of the power. Power electronic devices such as Voltage Source Converter (VSC) based Static Synchronous Compensator (STATCOM), Dynamic Voltage Restorer (DVR) etc. are commonly used for the mitigation of voltage...

  4. Distributed applications monitoring at system and network level

    CERN Document Server

    Aderholz, Michael; Augé, E; Bagliesi, G; Banistoni, G; Barone, L; Boschini, M; Brunengo, A; Bunn, J J; Butler, J; Campanella, M; Capiluppi, P; D'Amato, M; Darneri, M; Di Mattia, A; Dorokhov, A E; Gagliardi, F; Gaines, I; Gasparini, U; Ghiselli, A; Gordon, J; Grandi, C; Gálvez, P; Harris, F; Holtman, K; Karimäki, V; Karita, Y; Klem, J T; Legrand, I; Leltchouk, M; Linglin, D; Lubrano, P; Luminari, L; McArthur, I C; Michelotto, M; Morita, Y; Nazarenko, A; Newman, H; O'Dell, Vivian; O'Neale, S W; Osculati, B; Pepé, M; Perini, L; Pinfold, James L; Pordes, R; Prelz, F; Putzer, A; Resconi, S; Robertson, L; Rolli, S; Sasaki, T; Sato, H; Schaffer, R D; Schalk, T L; Servoli, L; Sgaravatto, M; Shiers, J; Silvestris, L; Siroli, G P; Sliwa, K; Smith, T; Somigliana, R; Stanescu, C; Stockinger, H E; Ugolotti, D; Valente, E; Vistoli, C; Wilkinson, R P; Willers, Ian Malcolm; Williams, D O

    2001-01-01

    Most distributed applications are based on architectural models that do not involve real-time knowledge of network status and of their network usage. Moreover the new "network aware" architectures are still under development and their design is not yet complete. We considered, as a use case, an application using ODBMS (Objectivity /DB) for the distributed analysis of experimental data. The dynamic usage of system and network resources at host and application levels has been measured in different client/server configurations, and on several LAN and WAN layouts. The aim was to study the application efficiency and behavior versus the network characteristics and conditions. The most interesting results of the LAN and WAN tests are described. System bottlenecks and limitations have been identified, and efficient working conditions in the different scenarios have been defined. The behavior observed when moving away from the optimal working conditions is also described.

  5. Incentive-Based Voltage Regulation in Distribution Networks: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, Xinyang; Chen, Lijun; Dall' Anese, Emiliano; Baker, Kyri

    2017-03-03

    This paper considers distribution networks fea- turing distributed energy resources, and designs incentive-based mechanisms that allow the network operator and end-customers to pursue given operational and economic objectives, while concurrently ensuring that voltages are within prescribed limits. Two different network-customer coordination mechanisms that require different amounts of information shared between the network operator and end-customers are developed to identify a solution of a well-defined social-welfare maximization prob- lem. Notably, the signals broadcast by the network operator assume the connotation of prices/incentives that induce the end- customers to adjust the generated/consumed powers in order to avoid the violation of the voltage constraints. Stability of the proposed schemes is analytically established and numerically corroborated.

  6. Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

    Science.gov (United States)

    Chernoded, Andrey; Dudko, Lev; Myagkov, Igor; Volkov, Petr

    2017-10-01

    Most of the modern analyses in high energy physics use signal-versus-background classification techniques of machine learning methods and neural networks in particular. Deep learning neural network is the most promising modern technique to separate signal and background and now days can be widely and successfully implemented as a part of physical analysis. In this article we compare Deep learning and Bayesian neural networks application as a classifiers in an instance of top quark analysis.

  7. Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

    Directory of Open Access Journals (Sweden)

    Chernoded Andrey

    2017-01-01

    Full Text Available Most of the modern analyses in high energy physics use signal-versus-background classification techniques of machine learning methods and neural networks in particular. Deep learning neural network is the most promising modern technique to separate signal and background and now days can be widely and successfully implemented as a part of physical analysis. In this article we compare Deep learning and Bayesian neural networks application as a classifiers in an instance of top quark analysis.

  8. Towards a Better Distributed Framework for Learning Big Data

    Science.gov (United States)

    2017-06-14

    Second, they focused on a client-server learning scenario in which an online , semi-supervised learning approach is designed to reduce the...distributed data. Second, we focus on a client-server learning scenario in which an online , semi-supervised learning approach is designed to reduce the...3) showing the effectiveness of our methods on several datasets. Communication-Efficient Online Semi-Supervised Learning in Client-Server

  9. Learning about learning: Mining human brain sub-network biomarkers from fMRI data.

    Science.gov (United States)

    Bogdanov, Petko; Dereli, Nazli; Dang, Xuan-Hong; Bassett, Danielle S; Wymbs, Nicholas F; Grafton, Scott T; Singh, Ambuj K

    2017-01-01

    Modeling the brain as a functional network can reveal the relationship between distributed neurophysiological processes and functional interactions between brain structures. Existing literature on functional brain networks focuses mainly on a battery of network properties in "resting state" employing, for example, modularity, clustering, or path length among regions. In contrast, we seek to uncover functionally connected subnetworks that predict or correlate with cohort differences and are conserved within the subjects within a cohort. We focus on differences in both the rate of learning as well as overall performance in a sensorimotor task across subjects and develop a principled approach for the discovery of discriminative subgraphs of functional connectivity based on imaging acquired during practice. We discover two statistically significant subgraph regions: one involving multiple regions in the visual cortex and another involving the parietal operculum and planum temporale. High functional coherence in the former characterizes sessions in which subjects take longer to perform the task, while high coherence in the latter is associated with high learning rate (performance improvement across trials). Our proposed methodology is general, in that it can be applied to other cognitive tasks, to study learning or to differentiate between healthy patients and patients with neurological disorders, by revealing the salient interactions among brain regions associated with the observed global state. The discovery of such significant discriminative subgraphs promises a better data-driven understanding of the dynamic brain processes associated with high-level cognitive functions.

  10. Towards a Social Networks Model for Online Learning & Performance

    Science.gov (United States)

    Chung, Kon Shing Kenneth; Paredes, Walter Christian

    2015-01-01

    In this study, we develop a theoretical model to investigate the association between social network properties, "content richness" (CR) in academic learning discourse, and performance. CR is the extent to which one contributes content that is meaningful, insightful and constructive to aid learning and by social network properties we…

  11. The Fire Learning Network: A promising conservation strategy for forestry

    Science.gov (United States)

    Bruce E. Goldstein; William H. Butler; R. Bruce. Hull

    2010-01-01

    Conservation Learning Networks (CLN) are an emerging conservation strategy for addressing complex resource management challenges that face the forestry profession. The US Fire Learning Network (FLN) is a successful example of a CLN that operates on a national scale. Developed in 2001 as a partnership between The Nature Conservancy, the US Forest Service, and land-...

  12. Social networks as ICT collaborative and supportive learning media ...

    African Journals Online (AJOL)

    ... ICT collaborative and supportive learning media utilisation within the Nigerian educational system. The concept of ICT was concisely explained vis-à-vis the social network concept, theory and collaborative and supportive learning media utilisation. Different types of social network are highlighted among which Facebook, ...

  13. Problems in the Deployment of Learning Networks In Small Organizations

    NARCIS (Netherlands)

    Shankle, Dean E.; Shankle, Jeremy P.

    2006-01-01

    Please, cite this publication as: Shankle, D.E., & Shankle, J.P. (2006). Problems in the Deployment of Learning Networks In Small Organizations. Proceedings of International Workshop in Learning Networks for Lifelong Competence Development, TENCompetence Conference. March 30th-31st, Sofia, Bulgaria:

  14. The Practices of Student Network as Cooperative Learning in Ethiopia

    Science.gov (United States)

    Reda, Weldemariam Nigusse; Hagos, Girmay Tsegay

    2015-01-01

    Student network is a teaching strategy introduced as cooperative learning to all educational levels above the upper primary schools (grade 5 and above) in Ethiopia. The study was, therefore, aimed at investigating to what extent the student network in Ethiopia is actually practiced in line with the principles of cooperative learning. Consequently,…

  15. Learning Networks--Enabling Change through Community Action Research

    Science.gov (United States)

    Bleach, Josephine

    2016-01-01

    Learning networks are a critical element of ethos of the community action research approach taken by the Early Learning Initiative at the National College of Ireland, a community-based educational initiative in the Dublin Docklands. Key criteria for networking, whether at local, national or international level, are the individual's and…

  16. Network characteristics emerging from agent interactions in balanced distributed system.

    Science.gov (United States)

    Salman, Mahdi Abed; Bertelle, Cyrille; Sanlaville, Eric

    2015-01-01

    A distributed computing system behaves like a complex network, the interactions between nodes being essential information exchanges and migrations of jobs or services to execute. These actions are performed by software agents, which behave like the members of social networks, cooperating and competing to obtain knowledge and services. The load balancing consists in distributing the load evenly between system nodes. It aims at enhancing the resource usage. A load balancing strategy specifies scenarios for the cooperation. Its efficiency depends on quantity, accuracy, and distribution of available information. Nevertheless, the distribution of information on the nodes, together with the initial network structure, may create different logical network structures. In this paper, different load balancing strategies are tested on different network structures using a simulation. The four tested strategies are able to distribute evenly the load so that the system reaches a steady state (the mean response time of the jobs is constant), but it is shown that a given strategy indeed behaves differently according to structural parameters and information spreading. Such a study, devoted to distributed computing systems (DCSs), can be useful to understand and drive the behavior of other complex systems.

  17. Life cycle assessment of the Danish electricity distribution network

    DEFF Research Database (Denmark)

    Turconi, Roberto; Simonsen, Christian G.; Byriel, Inger P.

    2014-01-01

    and overhead) and 0.4 kV (copper and aluminum) were modeled. Results and discussion Electricity transmission and distribution provided nonnegligible impacts, related mainly to power losses. Impacts from electricity distribution were larger than those from transmission because of higher losses and higher......Purpose This article provides life cycle inventory data for electricity distribution networks and a life cycle assessment (LCA) of the Danish transmission and distribution networks. The aim of the study was to evaluate the potential importance of environmental impacts associated with distribution......, in current and future electricity systems. Methods The functional unit was delivery of 1 kWh of electricity in Denmark. The focus of the assessment was distribution of electricity, and the related impacts were compared to the generation and transmission of electricity, in order to evaluate the importance...

  18. Improved deep reinforcement learning for robotics through distribution-based experience retention

    NARCIS (Netherlands)

    de Bruin, T.D.; Kober, J.; Tuyls, K.P.; Babuska, R.; Kwon, Dong-Soo; Kang, Chul-Goo; Suh, Il Hong

    2016-01-01

    Recent years have seen a growing interest in the use of deep neural networks as function approximators in reinforcement learning. In this paper, an experience replay method is proposed that ensures that the distribution of the experiences used for training is between that of the policy and a uniform

  19. Distributional Language Learning: Mechanisms and Models of ategory Formation.

    Science.gov (United States)

    Aslin, Richard N; Newport, Elissa L

    2014-09-01

    In the past 15 years, a substantial body of evidence has confirmed that a powerful distributional learning mechanism is present in infants, children, adults and (at least to some degree) in nonhuman animals as well. The present article briefly reviews this literature and then examines some of the fundamental questions that must be addressed for any distributional learning mechanism to operate effectively within the linguistic domain. In particular, how does a naive learner determine the number of categories that are present in a corpus of linguistic input and what distributional cues enable the learner to assign individual lexical items to those categories? Contrary to the hypothesis that distributional learning and category (or rule) learning are separate mechanisms, the present article argues that these two seemingly different processes---acquiring specific structure from linguistic input and generalizing beyond that input to novel exemplars---actually represent a single mechanism. Evidence in support of this single-mechanism hypothesis comes from a series of artificial grammar-learning studies that not only demonstrate that adults can learn grammatical categories from distributional information alone, but that the specific patterning of distributional information among attested utterances in the learning corpus enables adults to generalize to novel utterances or to restrict generalization when unattested utterances are consistently absent from the learning corpus. Finally, a computational model of distributional learning that accounts for the presence or absence of generalization is reviewed and the implications of this model for linguistic-category learning are summarized.

  20. Department Networks and Distributed Leadership in Schools

    Science.gov (United States)

    de Lima, Jorge Avila

    2008-01-01

    Many schools are organised into departments which function as contexts that frame teachers' professional experiences in important ways. Some educational systems have adopted distributed forms of leadership within schools that rely strongly on the departmental structure and on the role of the department coordinator as teacher leader. This paper…

  1. STAR Network Distributed Computer Systems Evaluation Results.

    Science.gov (United States)

    1981-02-12

    image processing systems. Further, because of the small data require- ments a segment of TOTT is a good candidate for VLSI. It can attain the...broadcast capabilities of the distributed architecture to isolate the overhead of accounting and enhacing of fault isolation (see Figure B-1). B-1 The

  2. Determination of size distribution using neural networks

    NARCIS (Netherlands)

    Stevens, JH; Nijhuis, JAG; Spaanenburg, L; Mohammadian, M

    1999-01-01

    In this paper we present a novel approach to the estimation of size distributions of grains in water from images. External conditions such as the concentrations of grains in water cannot be controlled. This poses problems for local image analysis which tries to identify and measure single grains.

  3. Adaptive relaying for ground fault protection of distribution network

    Energy Technology Data Exchange (ETDEWEB)

    Sachdev, M. S.; Sidhu, T. S.; Talukdar, B. K.

    1995-06-01

    In consequence of the increasing complexity of power distribution networks frequent changes in relay settings to achieve effective protection against ground faults is essential. The principal focus of this paper was adaptive relaying which makes use of digital technology and microprocessors to design systems which can provide protection of complex distribution networks under all operating conditions. Specifically, the paper described software modules that were developed to achieve this capability, developed for the City of Saskatoon`s distribution network. The system provides reliable, fast and selective protection of all components of the distribution system by constantly monitoring all the buses and currents in the circuit by substation computers, which are under the control of a central control computer. In addition to adaptive protection, the system can also provide optimal control of feeder loads, transformers, reactors, and capacitors, cold load pick up and reclosing of circuit breakers and reclosers. 2 refs., 8 figs.

  4. Peer-Assisted Content Distribution with Random Linear Network Coding

    DEFF Research Database (Denmark)

    Hundebøll, Martin; Ledet-Pedersen, Jeppe; Sluyterman, Georg

    2014-01-01

    Peer-to-peer networks constitute a widely used, cost-effective and scalable technology to distribute bandwidth-intensive content. The technology forms a great platform to build distributed cloud storage without the need of a central provider. However, the majority of todays peer-to-peer systems...... require complex algorithms to schedule what parts of obtained content to forward to other peers. Random Linear Network Coding can greatly simplify these algorithm by removing the need for coordination between the distributing nodes. In this paper we propose and evaluate the structure of the BRONCO peer-to-peer....... Furthermore, we evaluate the performance of different parameters and suggest a suitable trade-off between CPU utilization and network overhead. Within the limitations of the used test environment, we have shown that networkc coding is usable in peer-assisted content distribution and we suggest further...

  5. EduCamp Colombia: Social Networked Learning for Teacher Training

    OpenAIRE

    Diego Ernesto Leal Fonseca

    2011-01-01

    This paper describes a learning experience called EduCamp, which was launched by the Ministry of Education of Colombia in 2007, based on emerging concepts such as e-Learning 2.0, connectivism, and personal learning environments. An EduCamp proposes an unstructured collective learning experience, which intends to make palpable the possibilities of social software tools in learning and interaction processes while demonstrating face-to-face organizational forms that reflect social networked lear...

  6. On Learning Cluster Coefficient of Private Networks.

    Science.gov (United States)

    Wang, Yue; Wu, Xintao; Zhu, Jun; Xiang, Yang

    2012-01-01

    Enabling accurate analysis of social network data while preserving differential privacy has been challenging since graph features such as clustering coefficient or modularity often have high sensitivity, which is different from traditional aggregate functions (e.g., count and sum) on tabular data. In this paper, we treat a graph statistics as a function f and develop a divide and conquer approach to enforce differential privacy. The basic procedure of this approach is to first decompose the target computation f into several less complex unit computations f1, …, fm connected by basic mathematical operations (e.g., addition, subtraction, multiplication, division), then perturb the output of each fi with Laplace noise derived from its own sensitivity value and the distributed privacy threshold ε i , and finally combine those perturbed fi as the perturbed output of computation f. We examine how various operations affect the accuracy of complex computations. When unit computations have large global sensitivity values, we enforce the differential privacy by calibrating noise based on the smooth sensitivity, rather than the global sensitivity. By doing this, we achieve the strict differential privacy guarantee with smaller magnitude noise. We illustrate our approach by using clustering coefficient, which is a popular statistics used in social network analysis. Empirical evaluations on five real social networks and various synthetic graphs generated from three random graph models show the developed divide and conquer approach outperforms the direct approach.

  7. Enabling content distribution in vehicular ad hoc networks

    CERN Document Server

    Luan, Tom H; Bai, Fan

    2014-01-01

    This SpringerBrief presents key enabling technologies and state-of-the-art research on delivering efficient content distribution services to fast moving vehicles. It describes recent research developments and proposals towards the efficient, resilient and scalable content distribution to vehicles through both infrastructure-based and infrastructure-less vehicular networks. The authors focus on the rich multimedia services provided by vehicular environment content distribution including vehicular communications and media playback, giving passengers many infotainment applications. Common problem

  8. Biogas in the natural gas distribution network; Biogas til nettet

    Energy Technology Data Exchange (ETDEWEB)

    Kvist Jensen, T.

    2009-05-15

    With the Danish 'Thorsoe Biogas Plant' as reference case, an assessment of the possibility of using the existing natural gas distribution network for distributing biogas was carried out. Technologies for and cost of upgrading biogas to natural gas quality are presented. Furthermore, a socio-economic analysis has been performed, including the Danish financial conditions, the market models, and the role of the natural gas distribution companies.

  9. Learning Networks for Lifelong Learning: An Exploratory Survey on Distance Learners’ preferences

    NARCIS (Netherlands)

    Berlanga, Adriana; Rusman, Ellen; Eshuis, Jannes; Hermans, Henry; Sloep, Peter

    2010-01-01

    Berlanga, A. J., Rusman, E., Eshuis, J., Hermans, H., & Sloep, P. B. (2010, 3 May). Learning Networks for Lifelong Learning: An Exploratory Survey on Distance Learners’ preferences. Presentation at the 7th International Conference on Networked Learning (NLC-2010), Aalborg, Denmark.

  10. Globally Decoupled Reputations for Large Distributed Networks

    Directory of Open Access Journals (Sweden)

    Gayatri Swamynathan

    2007-01-01

    Full Text Available Reputation systems help establish social control in peer-to-peer networks. To be truly effective, however, a reputation system should counter attacks that compromise the reliability of user ratings. Existing reputation approaches either average a peer's lifetime ratings or account for rating credibility by weighing each piece of feedback by the reputation of its source. While these systems improve cooperation in a P2P network, they are extremely vulnerable to unfair ratings attacks. In this paper, we recommend that reputation systems decouple a peer's service provider reputation from its service recommender reputation, thereby, making reputations more resistant to tampering. We propose a scalable approach to system-wide decoupled service and feedback reputations and demonstrate the effectiveness of our model against previous nondecoupled reputation approaches. Our results indicate that decoupled approache significantly improves reputation accuracy, resulting in more successful transactions. Furthermore, we demonstrate the effectiveness and scalability of our decoupled approach as compared to PeerTrust, an alternative mechanism proposed for decoupled reputations. Our results are compiled from comprehensive logs collected from Maze, a large file-sharing system with over 1.4 million users supporting searches on 226TB of data.

  11. Definition of Distribution Network Tariffs Considering Distribution Generation and Demand Response

    DEFF Research Database (Denmark)

    Soares, Tiago; Faria, Pedro; Vale, Zita

    2014-01-01

    The use of distribution networks in the current scenario of high penetration of Distributed Generation (DG) is a problem of great importance. In the competitive environment of electricity markets and smart grids, Demand Response (DR) is also gaining notable impact with several benefits for the wh......The use of distribution networks in the current scenario of high penetration of Distributed Generation (DG) is a problem of great importance. In the competitive environment of electricity markets and smart grids, Demand Response (DR) is also gaining notable impact with several benefits...... the determination of topological distribution factors, and consequent application of the MW-mile method. The application of the proposed tariffs definition methodology is illustrated in a distribution network with 33 buses, 66 DG units, and 32 consumers with DR capacity...

  12. Supervised Learning with Complex-valued Neural Networks

    CERN Document Server

    Suresh, Sundaram; Savitha, Ramasamy

    2013-01-01

    Recent advancements in the field of telecommunications, medical imaging and signal processing deal with signals that are inherently time varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial neural networks.  Furthermore, to efficiently preserve the physical characteristics of these complex-valued signals, it is important to develop complex-valued neural networks and derive their learning algorithms to represent these signals at every step of the learning process. This monograph comprises a collection of new supervised learning algorithms along with novel architectures for complex-valued neural networks. The concepts of meta-cognition equipped with a self-regulated learning have been known to be the best human learning strategy. In this monograph, the principles of meta-cognition have been introduced for complex-valued neural networks in both the batch and sequential learning modes. For applications where the computati...

  13. Distributed FDI of a networked embedded microdrone

    OpenAIRE

    Tanwani, Aneel; Gentil, Sylviane; Lesecq, Suzanne; Thiriet, Jean-Marc

    2006-01-01

    International audience; Embedded systems constitute a category whose safety is critical and where FDI real time constraints are particularly important. Embedded algorithms must be the simplest possible and computations may be distributed between the embedded system and a more powerful distant computer. This paper proposes a bank of observers to diagnose faults of a small helicopter controlled in closed loop. The studied prototype is a 4 rotors mini drone equipped with an attitude central for ...

  14. ANOMALY NETWORK INTRUSION DETECTION SYSTEM BASED ON DISTRIBUTED TIME-DELAY NEURAL NETWORK (DTDNN

    Directory of Open Access Journals (Sweden)

    LAHEEB MOHAMMAD IBRAHIM

    2010-12-01

    Full Text Available In this research, a hierarchical off-line anomaly network intrusion detection system based on Distributed Time-Delay Artificial Neural Network is introduced. This research aims to solve a hierarchical multi class problem in which the type of attack (DoS, U2R, R2L and Probe attack detected by dynamic neural network. The results indicate that dynamic neural nets (Distributed Time-Delay Artificial Neural Network can achieve a high detection rate, where the overall accuracy classification rate average is equal to 97.24%.

  15. Online experimentation and interactive learning resources for teaching network engineering

    OpenAIRE

    Mikroyannidis, Alexander; Gomez-Goiri, Aitor; Smith, Andrew; Domingue, John

    2017-01-01

    This paper presents a case study on teaching network engineering in conjunction with interactive learning resources. This case study has been developed in collaboration with the Cisco Networking Academy in the context of the FORGE project, which promotes online learning and experimentation by offering access to virtual and remote labs. The main goal of this work is allowing learners and educators to perform network simulations within a web browser or an interactive eBook by using any type of ...

  16. Reconstructing cancer drug response networks using multitask learning.

    Science.gov (United States)

    Ruffalo, Matthew; Stojanov, Petar; Pillutla, Venkata Krishna; Varma, Rohan; Bar-Joseph, Ziv

    2017-10-10

    Translating in vitro results to clinical tests is a major challenge in systems biology. Here we present a new Multi-Task learning framework which integrates thousands of cell line expression experiments to reconstruct drug specific response networks in cancer. The reconstructed networks correctly identify several shared key proteins and pathways while simultaneously highlighting many cell type specific proteins. We used top proteins from each drug network to predict survival for patients prescribed the drug. Predictions based on proteins from the in-vitro derived networks significantly outperformed predictions based on known cancer genes indicating that Multi-Task learning can indeed identify accurate drug response networks.

  17. Flexibility and Balancing in Active Distribution Networks

    DEFF Research Database (Denmark)

    Kordheili, Reza Ahmadi

    , and causes higher fluctuations in the demand. In countries such as Denmark, different incentives have been proposed and applied to encourage customers for investing on solar photovoltaic (PV) panels. These policies have increased the number of household PV panels. However, presence of such small energy...... in these batteries. A detailed modeling of Li-ion battery is presented in chapter 2 as well. PV panels are modelled as a function of solar irradiation and ambient temperature. In the next step, the impact of PV panels and electric vehicles on LV network was quantified separately. For PV panels, different placement......Environmental concerns, together with the fast-pacing changes in the renewable energy technologies, have led to significant growth of renewable energy sources (RESs) in energy systems. Among different sources of renewable energy, wind and solar energy are the most progressed sources so far. However...

  18. Novel methodology for optimal reconfiguration of distribution networks with distributed energy resources

    DEFF Research Database (Denmark)

    Chittur Ramaswamy, Parvathy; Tant, Jeroen; Pillai, Jayakrishnan Radhakrishna

    2015-01-01

    This paper develops three novel methodologies for optimal reconfiguration of distribution networks in the presence of distributed energy resources (DERs). The novelty is in achieving a non-conservative and robust solution to grid reconfiguration. The optimal solution is the minimum......-loss-configuration of the distribution network taking into account the cost of switching and the grid operational constraints. The methods are based on the concepts of receding horizon control (RHC) and scenario analysis (SA) which inherently optimize switching costs and losses. The salient feature of incorporating RHC and SA...... of the distribution network since they indicate the most frequently open switches in the network and the time at which they are to be operated....

  19. Distributed Constrained Stochastic Subgradient Algorithms Based on Random Projection and Asynchronous Broadcast over Networks

    Directory of Open Access Journals (Sweden)

    Junlong Zhu

    2017-01-01

    Full Text Available We consider a distributed constrained optimization problem over a time-varying network, where each agent only knows its own cost functions and its constraint set. However, the local constraint set may not be known in advance or consists of huge number of components in some applications. To deal with such cases, we propose a distributed stochastic subgradient algorithm over time-varying networks, where the estimate of each agent projects onto its constraint set by using random projection technique and the implement of information exchange between agents by employing asynchronous broadcast communication protocol. We show that our proposed algorithm is convergent with probability 1 by choosing suitable learning rate. For constant learning rate, we obtain an error bound, which is defined as the expected distance between the estimates of agent and the optimal solution. We also establish an asymptotic upper bound between the global objective function value at the average of the estimates and the optimal value.

  20. Distribution Learning in Evolutionary Strategies and Restricted Boltzmann Machines

    DEFF Research Database (Denmark)

    Krause, Oswin

    The thesis is concerned with learning distributions in the two settings of Evolutionary Strategies (ESs) and Restricted Boltzmann Machines (RBMs). In both cases, the distributions are learned from samples, albeit with different goals. Evolutionary Strategies are concerned with finding an optimum...

  1. Distributed learning process: principles of design and implementation

    Directory of Open Access Journals (Sweden)

    G. N. Boychenko

    2016-01-01

    Full Text Available At the present stage, broad information and communication technologies (ICT usage in educational practices is one of the leading trends of global education system development. This trend has led to the instructional interaction models transformation. Scientists have developed the theory of distributed cognition (Salomon, G., Hutchins, E., and distributed education and training (Fiore, S. M., Salas, E., Oblinger, D. G., Barone, C. A., Hawkins, B. L.. Educational process is based on two separated in time and space sub-processes of learning and teaching which are aimed at the organization of fl exible interactions between learners, teachers and educational content located in different non-centralized places.The purpose of this design research is to fi nd a solution for the problem of formalizing distributed learning process design and realization that is signifi cant in instructional design. The solution to this problem should take into account specifi cs of distributed interactions between team members, which becomes collective subject of distributed cognition in distributed learning process. This makes it necessary to design roles and functions of the individual team members performing distributed educational activities. Personal educational objectives should be determined by decomposition of team objectives into functional roles of its members with considering personal and learning needs and interests of students.Theoretical and empirical methods used in the study: theoretical analysis of philosophical, psychological, and pedagogical literature on the issue, analysis of international standards in the e-learning domain; exploration on practical usage of distributed learning in academic and corporate sectors; generalization, abstraction, cognitive modelling, ontology engineering methods.Result of the research is methodology for design and implementation of distributed learning process based on the competency approach. Methodology proposed by

  2. Fusion of deep learning architectures, multilayer feedforward networks and learning vector quantizers for deep classification learning

    NARCIS (Netherlands)

    Villmann, T.; Biehl, M.; Villmann, A.; Saralajew, S.

    2017-01-01

    The advantage of prototype based learning vector quantizers are the intuitive and simple model adaptation as well as the easy interpretability of the prototypes as class representatives for the class distribution to be learned. Although they frequently yield competitive performance and show robust

  3. Enhancing Formal E-Learning with Edutainment on Social Networks

    Science.gov (United States)

    Labus, A.; Despotovic-Zrakic, M.; Radenkovic, B.; Bogdanovic, Z.; Radenkovic, M.

    2015-01-01

    This paper reports on the investigation of the possibilities of enhancing the formal e-learning process by harnessing the potential of informal game-based learning on social networks. The goal of the research is to improve the outcomes of the formal learning process through the design and implementation of an educational game on a social network…

  4. An ART neural network model of discrimination shift learning

    NARCIS (Netherlands)

    Raijmakers, M.E.J.; Coffey, E.; Stevenson, C.; Winkel, J.; Berkeljon, A.; Taatgen, N.; van Rijn, H.

    2009-01-01

    We present an ART-based neural network model (adapted from [2]) of the development of discrimination-shift learning that models the trial-by-trial learning process in great detail. In agreement with the results of human participants (4-20 years of age) in [1] the model revealed two distinct learning

  5. Distributed Learning over Massive XML Documents in ELM Feature Space

    Directory of Open Access Journals (Sweden)

    Xin Bi

    2015-01-01

    Full Text Available With the exponentially increasing volume of XML data, centralized learning solutions are unable to meet the requirements of mining applications with massive training samples. In this paper, a solution to distributed learning over massive XML documents is proposed, which provides distributed conversion of XML documents into representation model in parallel based on MapReduce and a distributed learning component based on Extreme Learning Machine for mining tasks of classification or clustering. Within this framework, training samples are converted from raw XML datasets with better efficiency and information representation ability and taken to distributed learning algorithms in Extreme Learning Machine (ELM feature space. Extensive experiments are conducted on massive XML documents datasets to verify the effectiveness and efficiency for both classification and clustering applications.

  6. EduCamp Colombia: Social Networked Learning for Teacher Training

    Directory of Open Access Journals (Sweden)

    Diego Ernesto Leal Fonseca

    2011-03-01

    Full Text Available This paper describes a learning experience called EduCamp, which was launched by the Ministry of Education of Colombia in 2007, based on emerging concepts such as e-Learning 2.0, connectivism, and personal learning environments. An EduCamp proposes an unstructured collective learning experience, which intends to make palpable the possibilities of social software tools in learning and interaction processes while demonstrating face-to-face organizational forms that reflect social networked learning ideas. The experience opens new perspectives for the design of technology training workshops and for the development of lifelong learning experiences.

  7. Optimum allocation of the maximum possible distributed generation penetration in a distribution network

    Energy Technology Data Exchange (ETDEWEB)

    Koutroumpezis, G.N.; Safigianni, A.S. [Electrical and Computer Engineering Department, Democritus University of Thrace, University Campus (Kimmeria), GR 67100 Xanthi (Greece)

    2010-12-15

    In this paper a method is proposed to determine the optimum allocation of the maximum distributed generation penetration in medium voltage power distribution networks. The method is based on an already-known but suitably modified and optimized method. Technical constraints, such as thermal rating, transformer capacity, voltage profile and short-circuit level are considered. A real network with already installed distributed generation resources is examined as a case study. The type, locations and ratings of these resources are predetermined. The satisfaction of the aforementioned technical constraints is examined initially in the framework of the existing network situation. The problems observed are solved by applying the required modifications in the network structure. Next, the proposed method is used to determine the optimum allocation of the maximum distributed generation penetration either in the predetermined network buses or in other random buses, in order to overcome the technical problems, without changing the network structure. Finally, the results are suitably estimated and extended in order to allow for more general conclusions concerning real power distribution networks. (author)

  8. Distributed Generation Management in Distribution Networks; Gestion de la production decentralisee dans les reseaux de distribution

    Energy Technology Data Exchange (ETDEWEB)

    Caire, R.

    2004-04-15

    Deregulations of the energy market, followed by many privatizations, and vertical disintegrations brought a complete reorganization of the electric sector. The opening of the energy markets as well as the technological developments of the means of production of small and average power strongly encourage this evolution. A systematic methodology to study the transmission of impacts between the Low and Medium Voltage is initially proposed, after a quick state of the art of the various possible impacts. The voltage deviation is then identified as the most critical impact. This criticality is supported by quantitative studies on French typical networks, and is confirmed by the related literature. In order to solve this impact, a research of the means of action within tension of the distribution network and their modeling is carried out. As the manipulated variables of the means of adjustment available are discrete or continuous, specific tools are then developed to coordinate them. This coordination is pressed on optimization algorithms developed by holding account of inherent specificity with the manipulated variables. A methodology for the choice or optimal location of the adjustment means associated with a management of the voltage deviation is presented. Lastly, 'decentralized' strategies of coordination for the means of adjustment and a proposal for an experimental validation are presented, thanks to a real time simulator, making it possible to test the strategies of coordination and the necessary means of communication. (author)

  9. Optimal Sensor Networks Scheduling in Identification of Distributed Parameter Systems

    CERN Document Server

    Patan, Maciej

    2012-01-01

    Sensor networks have recently come into prominence because they hold the potential to revolutionize a wide spectrum of both civilian and military applications. An ingenious characteristic of sensor networks is the distributed nature of data acquisition. Therefore they seem to be ideally prepared for the task of monitoring processes with spatio-temporal dynamics which constitute one of most general and important classes of systems in modelling of the real-world phenomena. It is clear that careful deployment and activation of sensor nodes are critical for collecting the most valuable information from the observed environment. Optimal Sensor Network Scheduling in Identification of Distributed Parameter Systems discusses the characteristic features of the sensor scheduling problem, analyzes classical and recent approaches, and proposes a wide range of original solutions, especially dedicated for networks with mobile and scanning nodes. Both researchers and practitioners will find the case studies, the proposed al...

  10. Real-world experimentation of distributed DSA network algorithms

    DEFF Research Database (Denmark)

    Tonelli, Oscar; Berardinelli, Gilberto; Tavares, Fernando Menezes Leitão

    2013-01-01

    of the available spectrum by nodes in a network, without centralized coordination. While proof-of-concept and statistical validation of such algorithms is typically achieved by using system level simulations, experimental activities are valuable contributions for the investigation of particular aspects......The problem of spectrum scarcity in uncoordinated and/or heterogeneous wireless networks is the key aspect driving the research in the field of flexible management of frequency resources. In particular, distributed dynamic spectrum access (DSA) algorithms enable an efficient sharing...... such as a dynamic propagation environment, human presence impact and terminals mobility. This chapter focuses on the practical aspects related to the real world-experimentation with distributed DSA network algorithms over a testbed network. Challenges and solutions are extensively discussed, from the testbed design...

  11. Hybrid Distributed Iterative Capacity Allocation over Bluetooth Network

    DEFF Research Database (Denmark)

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

    2002-01-01

    With the current development of mobile devices, short range wireless communications have become more and more popular, and many researches on short range wireless communications, such as Bluetooth, have gained special interests, in industry as well as in academy. This paper analyzes capacity...... allocation issues in Bluetooth network as convex optimization problem. We formulate the problem of maximizing of total network flows and minimizing the costs of flows. The hybrid distributed capacity allocation scheme is proposed as an approximated solution of the formulated problem that satisfies quality...... of service requirements and constraints in Bluetooth network, such as limited capacity, decentralized, frequent changes of topology and of capacities assigned to nodes in the network. The simulation shows that the performance of Bluetooth could be improved by applying the hybrid distributed iterative...

  12. Hybrid Distributed Iterative Capacity Allocation over Bluetooth Network

    DEFF Research Database (Denmark)

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

    With the current development of mobile devices, short range wireless communications have become more and more popular, and many researches on short range wireless communications, such as Bluetooth, have gained special interests, in industry as well as in academy. This paper analyzes capacity...... allocation issues in Bluetooth network as convex optimization problem. We formulate the problem of maximizing of total network flows and minimizing the costs of flows. The hybrid distributed capacity allocation scheme is proposed as an approximated solution of the formulated problem that satisfies quality...... of service requirements and constraints in Bluetooth network, such as limited capacity, decentralized, frequent changes of topology and of capacities assigned to nodes in the network. The simulation shows that the performance of Bluetooth could be improved by applying the hybrid distributed iterative...

  13. Analysis and Comparison of Typical Models within Distribution Network Design

    DEFF Research Database (Denmark)

    Jørgensen, Hans Jacob; Larsen, Allan; Madsen, Oli B.G.

    This paper investigates the characteristics of typical optimisation models within Distribution Network Design. During the paper fourteen models known from the literature will be thoroughly analysed. Through this analysis a schematic approach to categorisation of distribution network design models...... for educational purposes. Furthermore, the paper can be seen as a practical introduction to network design modelling as well as a being an art manual or recipe when constructing such a model....... are covered in the categorisation include fixed vs. general networks, specialised vs. general nodes, linear vs. nonlinear costs, single vs. multi commodity, uncapacitated vs. capacitated activities, single vs. multi modal and static vs. dynamic. The models examined address both strategic and tactical planning...

  14. Exploring empowerment in settings: mapping distributions of network power.

    Science.gov (United States)

    Neal, Jennifer Watling

    2014-06-01

    This paper brings together two trends in the empowerment literature-understanding empowerment in settings and understanding empowerment as relational-by examining what makes settings empowering from a social network perspective. Specifically, extending Neal and Neal's (Am J Community Psychol 48(3/4):157-167, 2011) conception of network power, an empowering setting is defined as one in which (1) actors have existing relationships that allow for the exchange of resources and (2) the distribution of network power among actors in the setting is roughly equal. The paper includes a description of how researchers can examine distributions of network power in settings. Next, this process is illustrated in both an abstract example and using empirical data on early adolescents' peer relationships in urban classrooms. Finally, implications for theory, methods, and intervention related to understanding empowering settings are explored.

  15. The high-order Boltzmann machine: learned distribution and topology.

    Science.gov (United States)

    Albizuri, F X; Danjou, A; Grana, M; Torrealdea, J; Hernandez, M C

    1995-01-01

    In this paper we give a formal definition of the high-order Boltzmann machine (BM), and extend the well-known results on the convergence of the learning algorithm of the two-order BM. From the Bahadur-Lazarsfeld expansion we characterize the probability distribution learned by the high order BM. Likewise a criterion is given to establish the topology of the BM depending on the significant correlations of the particular probability distribution to be learned.

  16. Distributed Sensor Network Software Development Testing through Simulation

    Energy Technology Data Exchange (ETDEWEB)

    Brennan, Sean M. [Univ. of New Mexico, Albuquerque, NM (United States)

    2003-12-01

    The distributed sensor network (DSN) presents a novel and highly complex computing platform with dif culties and opportunities that are just beginning to be explored. The potential of sensor networks extends from monitoring for threat reduction, to conducting instant and remote inventories, to ecological surveys. Developing and testing for robust and scalable applications is currently practiced almost exclusively in hardware. The Distributed Sensors Simulator (DSS) is an infrastructure that allows the user to debug and test software for DSNs independent of hardware constraints. The exibility of DSS allows developers and researchers to investigate topological, phenomenological, networking, robustness and scaling issues, to explore arbitrary algorithms for distributed sensors, and to defeat those algorithms through simulated failure. The user speci es the topology, the environment, the application, and any number of arbitrary failures; DSS provides the virtual environmental embedding.

  17. Adaptive relaying for ground fault protection of a distribution network

    Energy Technology Data Exchange (ETDEWEB)

    Sachdev, M.S.; Sidhu, T.S.; Talukdar, B.K. [Saskatchewan Univ., Saskatoon, SK (Canada)

    1995-12-31

    Adaptive protection was used for designing a protection system for the City of Saskatoon`s distribution network. The software and hardware were developed and the protection system was implemented in the laboratory at the University of Saskatchewan. In the first phase of the project, phase overcurrent relays were coordinated on the basis of three-phase faults. Most faults in distribution networks were single-phase to ground faults. Ground fault currents varied due to different grounding practices, changes in operating conditions and system topology. In the second phase of the project, adaptive capabilities for ground overcurrent and directional ground overcurrent protection were added. Software modules developed for achieving adaptive ground fault protection were described. Results from system studies carried out using the City of Saskatoon`s distribution network were also analyzed. 7 refs., 8 figs.

  18. Voltage Sags Matching to Locate Faults for Underground Distribution Networks

    Directory of Open Access Journals (Sweden)

    BAKAR, A. H. A.

    2011-05-01

    Full Text Available A voltage sags matching to locate a fault for underground distribution network is presented in this paper. Firstly the method identifies the faulted section by matching a voltage sags measured at the primary substation during a fault with pre-developed voltage sag database. From the identified faulted section, the distance of a fault from sending-end is calculated. The problem of multiple sections is addressed by ranking approach. Test results on an underground distribution network shows most faults can be located by the first attempt within high accuracy distance. Only few faulted sections found by the second attempt. Since the method is using only voltage sag data, monitored at the primary substation, the method is considers economical to be implemented for a rural distribution network.

  19. Effectiveness of frequency relays on networks with multiple distributed generation

    Directory of Open Access Journals (Sweden)

    A.A.M. Hassan

    2015-05-01

    Full Text Available Distributed generation (DG has gained a vital role in distribution utilities. So, it is important to correctly detect islanding of DG units. Frequency relays are one of the most commonly used loss of mains detection method. However, distribution utilities may be faced by concern related to false operation of these frequency relays. The commercially available frequency relays reported considering standard tight setting. This paper investigates some factors related to relays internal algorithm that contribute to their different operating responses. The factors that will be investigated are frequency measuring techniques, measuring windows, time delays and under voltage interlock function. With the increasing penetration of DG into the network, it is becoming common to have multiple DG units connected at the same network location. Two generators connected at the same location and employing frequency relays with the same setting but different characteristics were simulated. When subjected to the same network disturbances the possible interference between the two relays is analyzed.

  20. Value Assessment of Distribution Network Reconfiguration: A Danish Case Study

    DEFF Research Database (Denmark)

    Vaskantiras, Georgios; You, Shi

    2016-01-01

    Distribution network reconfiguration is a mechanism that can improve the distribution system performance from multiple perspectives. In the context of smart grid wherein the degrees of automation and intelligence are high, the potential value of network reconfiguration can be significant....... This paper presents a case study-based analysis to explore the potential value of reconfiguration in detail. The study is performed using a 10kV distribution grid of Denmark, while reconfiguration is applied to minimize the energy losses under both normal and post-fault conditions. The results show...... that although the reconfiguration is performed to achieve a single objective, the overall network performance is improved. In addition, the value achieved by reconfiguration can be very sensitive to the reconfiguration frequency and the associated cost....

  1. Product flow and price change in an agricultural distribution network

    Science.gov (United States)

    Lee, Daekyung; Yang, Seong-Gyu; Kim, Kibum; Kim, Beom Jun

    2018-01-01

    We use the structure of a real distribution network of agricultural product in Korea and investigate how the change in the supply may affect the price changes in agents across the distribution network. In particular, we focus on the real network structure of cabbage distribution composed of various types of agents, from farms to consumers, and apply a dynamic model to describe how each participant reacts upon the change of input and output flow of products through the adjustment of price. Our main result implies that the effect of fluctuation of production quantity in the supplying participant can be nontrivial and the consumer price responds to such changes. We believe that our results can be useful to predict what will happen if the agricultural production changes much in the future due to the climate changes.

  2. FACTORS OF REFUSALS IN ELECTRICAL DISTRIBUTION NETWORKS 10 KV

    Directory of Open Access Journals (Sweden)

    V. POPESCU

    2017-03-01

    Full Text Available At present, in electrical distribution networks with tension 10 kV take place a significant number of refusals, which affect the reliability of electricity supply to all consumers. The behavior of these interruptions permit the development of the mechanism for ensuring continuity of electricity supply to consumers. Ensurance of continuity of quality power supply of consumer can be achieved only on the bases of profound knowledge of the phenomena that accompany this process, which permits a justified planning from technical and economic point of view, measures and activities of exploitation services of electrical distribution networks with tension 10 kV, in view to ensure the normal indicators of reliability. The paper is devoted to assessing the distributions of refusals in electrical networks with tension 10 kV, for developing the forecasting mechanism and to ensure the reliability of power supply.

  3. Energy Efficient Content Distribution in an ISP Network

    OpenAIRE

    Modrzejewski, Remigiusz; Chiaraviglio, Luca; Tahiri, Issam; Giroire, Frédéric; Rouzic, Esther Le; Bonetto, Edoardo; Masumeci, Francesco; Gonzalez, Roberto; Guerro, Carmen

    2013-01-01

    International audience; We study the problem of reducing power consump- tion in an Internet Service Provider (ISP) network by designing the content distribution infrastructure managed by the operator. We propose an algorithm to optimally decide where to cache the content inside the ISP network. We evaluate our solution over two case studies driven by operators feedback. Results show that the energy-efficient design of the content infrastructure brings substantial savings, both in terms of ene...

  4. Flexible Transmission Network Planning Considering the Impacts of Distributed Generation

    OpenAIRE

    Junhua Zhao; John Foster

    2010-01-01

    The restructuring of global power industries has introduced a number of challenges, such as conflicting planning objectives and increasing uncertainties,to transmission network planners. During the recent past, a number of distributed generation technologies also reached a stage allowing large scale implementation, which will profoundly influence the power industry, as well as the practice of transmission network expansion. In the new market environment, new approaches are needed to meet the ...

  5. A Network Neuroscience of Human Learning: Potential to Inform Quantitative Theories of Brain and Behavior.

    Science.gov (United States)

    Bassett, Danielle S; Mattar, Marcelo G

    2017-04-01

    Humans adapt their behavior to their external environment in a process often facilitated by learning. Efforts to describe learning empirically can be complemented by quantitative theories that map changes in neurophysiology to changes in behavior. In this review we highlight recent advances in network science that offer a sets of tools and a general perspective that may be particularly useful in understanding types of learning that are supported by distributed neural circuits. We describe recent applications of these tools to neuroimaging data that provide unique insights into adaptive neural processes, the attainment of knowledge, and the acquisition of new skills, forming a network neuroscience of human learning. While promising, the tools have yet to be linked to the well-formulated models of behavior that are commonly utilized in cognitive psychology. We argue that continued progress will require the explicit marriage of network approaches to neuroimaging data and quantitative models of behavior. Copyright © 2017 Elsevier Ltd. All rights reserved.

  6. Learning OpenStack networking (Neutron)

    CERN Document Server

    Denton, James

    2014-01-01

    If you are an OpenStack-based cloud operator with experience in OpenStack Compute and nova-network but are new to Neutron networking, then this book is for you. Some networking experience is recommended, and a physical network infrastructure is required to provide connectivity to instances and other network resources configured in the book.

  7. Detecting Distributed Network Traffic Anomaly with Network-Wide Correlation Analysis

    Science.gov (United States)

    Zonglin, Li; Guangmin, Hu; Xingmiao, Yao; Dan, Yang

    2008-12-01

    Distributed network traffic anomaly refers to a traffic abnormal behavior involving many links of a network and caused by the same source (e.g., DDoS attack, worm propagation). The anomaly transiting in a single link might be unnoticeable and hard to detect, while the anomalous aggregation from many links can be prevailing, and does more harm to the networks. Aiming at the similar features of distributed traffic anomaly on many links, this paper proposes a network-wide detection method by performing anomalous correlation analysis of traffic signals' instantaneous parameters. In our method, traffic signals' instantaneous parameters are firstly computed, and their network-wide anomalous space is then extracted via traffic prediction. Finally, an anomaly is detected by a global correlation coefficient of anomalous space. Our evaluation using Abilene traffic traces demonstrates the excellent performance of this approach for distributed traffic anomaly detection.

  8. Detecting Distributed Network Traffic Anomaly with Network-Wide Correlation Analysis

    Directory of Open Access Journals (Sweden)

    Yang Dan

    2008-12-01

    Full Text Available Distributed network traffic anomaly refers to a traffic abnormal behavior involving many links of a network and caused by the same source (e.g., DDoS attack, worm propagation. The anomaly transiting in a single link might be unnoticeable and hard to detect, while the anomalous aggregation from many links can be prevailing, and does more harm to the networks. Aiming at the similar features of distributed traffic anomaly on many links, this paper proposes a network-wide detection method by performing anomalous correlation analysis of traffic signals' instantaneous parameters. In our method, traffic signals' instantaneous parameters are firstly computed, and their network-wide anomalous space is then extracted via traffic prediction. Finally, an anomaly is detected by a global correlation coefficient of anomalous space. Our evaluation using Abilene traffic traces demonstrates the excellent performance of this approach for distributed traffic anomaly detection.

  9. Theoretical framework on selected core issues on conditions for productive learning in networked learning environments

    DEFF Research Database (Denmark)

    Dirckinck-Holmfeld, Lone; Svendsen, Brian Møller; Ponti, Marisa

    The report documents and summarises the elements and dimensions that have been identified to describe and analyse the case studies collected in the Kaleidoscope Jointly Executed Integrating Research Project (JEIRP) on Conditions for productive learning in network learning environments....

  10. Distributed data networks: a blueprint for Big Data sharing and healthcare analytics.

    Science.gov (United States)

    Popovic, Jennifer R

    2017-01-01

    This paper defines the attributes of distributed data networks and outlines the data and analytic infrastructure needed to build and maintain a successful network. We use examples from one successful implementation of a large-scale, multisite, healthcare-related distributed data network, the U.S. Food and Drug Administration-sponsored Sentinel Initiative. Analytic infrastructure-development concepts are discussed from the perspective of promoting six pillars of analytic infrastructure: consistency, reusability, flexibility, scalability, transparency, and reproducibility. This paper also introduces one use case for machine learning algorithm development to fully utilize and advance the portfolio of population health analytics, particularly those using multisite administrative data sources. © 2016 New York Academy of Sciences.

  11. Optimal Planning of Communication System of CPS for Distribution Network

    Directory of Open Access Journals (Sweden)

    Ting Yang

    2017-01-01

    Full Text Available IoT is the technical basis to realize the CPS (Cyber Physical System for distribution networks, with which the complex system becomes more intelligent and controllable. Because of the multihop and self-organization characteristics, the large-scale heterogeneous CPS network becomes more difficult to plan. Using topological potential theory, one of typical big data analysis technologies, this paper proposed a novel optimal CPS planning model. Topological potential equalization is considered as the optimization objective function in heterogeneous CPS network with the constraints of communication requirements, physical infrastructures, and network reliability. An improved binary particle swarm optimization algorithm is proposed to solve this complex optimal problem. Two IEEE classic examples are adopted in the simulation, and the results show that, compared with benchmark algorithms, our proposed method can provide an effective topology optimization scheme to improve the network reliability and transmitting performance.

  12. Digital associative memory neural network with optical learning capability

    Science.gov (United States)

    Watanabe, Minoru; Ohtsubo, Junji

    1994-12-01

    A digital associative memory neural network system with optical learning and recalling capabilities is proposed by using liquid crystal television spatial light modulators and an Optic RAM detector. In spite of the drawback of the limited memory capacity compared with optical analogue associative memory neural network, the proposed optical digital neural network has the advantage of all optical learning and recalling capabilities, thus an all optics network system is easily realized. Some experimental results of the learning and the recalling for character recognitions are presented. This new optical architecture offers compactness of the system and the fast learning and recalling properties. Based on the results, the practical system for the implementation of a faster optical digital associative memory neural network system with ferro-electric liquid crystal SLMs is also proposed.

  13. Reliability based rehabilitation of water distribution networks by means of Bayesian networks

    National Research Council Canada - National Science Library

    Abdelaziz Lakehal; Fares Laouacheria

    2017-01-01

    Water plays an essential role in the everyday lives of the people. To supply subscribers with good quality of water and to ensure continuity of service, the operators use water distribution networks (WDN...

  14. High-precision multi-node clock network distribution

    Science.gov (United States)

    Chen, Xing; Cui, Yifan; Lu, Xing; Ci, Cheng; Zhang, Xuesong; Liu, Bo; Wu, Hong; Tang, Tingsong; Shi, Kebin; Zhang, Zhigang

    2017-10-01

    A high precision multi-node clock network for multiple users was built following the precise frequency transmission and time synchronization of 120 km fiber. The network topology adopts a simple star-shaped network structure. The clock signal of a hydrogen maser (synchronized with UTC) was recovered from a 120 km telecommunication fiber link and then was distributed to 4 sub-stations. The fractional frequency instability of all substations is in the level of 10-15 in a second and the clock offset instability is in sub-ps in root-mean-square average.

  15. Intelligent Fault Diagnosis in a Power Distribution Network

    Directory of Open Access Journals (Sweden)

    Oluleke O. Babayomi

    2016-01-01

    Full Text Available This paper presents a novel method of fault diagnosis by the use of fuzzy logic and neural network-based techniques for electric power fault detection, classification, and location in a power distribution network. A real network was used as a case study. The ten different types of line faults including single line-to-ground, line-to-line, double line-to-ground, and three-phase faults were investigated. The designed system has 89% accuracy for fault type identification. It also has 93% accuracy for fault location. The results indicate that the proposed technique is effective in detecting, classifying, and locating low impedance faults.

  16. Automatic Distribution Network Reconfiguration: An Event-Driven Approach

    Energy Technology Data Exchange (ETDEWEB)

    Ding, Fei; Jiang, Huaiguang; Tan, Jin

    2016-11-14

    This paper proposes an event-driven approach for reconfiguring distribution systems automatically. Specifically, an optimal synchrophasor sensor placement (OSSP) is used to reduce the number of synchrophasor sensors while keeping the whole system observable. Then, a wavelet-based event detection and location approach is used to detect and locate the event, which performs as a trigger for network reconfiguration. With the detected information, the system is then reconfigured using the hierarchical decentralized approach to seek for the new optimal topology. In this manner, whenever an event happens the distribution network can be reconfigured automatically based on the real-time information that is observable and detectable.

  17. Load Balancing of Large Distribution Network Model Calculations

    Directory of Open Access Journals (Sweden)

    MARTINOVIC, L.

    2017-11-01

    Full Text Available Performance measurement and evaluation study of calculations based on load flow analysis in power distribution network is presented. The focus is on the choice of load index as it is the basic input for efficient dynamic load balancing. The basic description of problem along with the proposed architecture is given. Different server resources are inspected and analyzed while running calculations, and based on this investigation, recommendations regarding the choice of load index are made. Short description of used static and dynamic load balancing algorithms is given and the proposition of load index choice is supported by tests run on large real-world power distribution network models.

  18. Distributed Velocity-Dependent Protocol for Multihop Cellular Sensor Networks

    OpenAIRE

    Deepthi Chander; Bhushan Jagyasi; Desai, U. B.; Merchant, S N

    2009-01-01

    Abstract Cell phones are embedded with sensors form a Cellular Sensor Network which can be used to localize a moving event. The inherent mobility of the application and of the cell phone users warrants distributed structure-free data aggregation and on-the-fly routing. We propose a Distributed Velocity-Dependent (DVD) protocol to localize a moving event using a Multihop Cellular Sensor Network (MCSN). DVD is based on a novel form of connectivity determined by the waiting time of nodes for a R...

  19. Toward Designing a Quantum Key Distribution Network Simulation Model

    Directory of Open Access Journals (Sweden)

    Miralem Mehic

    2016-01-01

    Full Text Available As research in quantum key distribution network technologies grows larger and more complex, the need for highly accurate and scalable simulation technologies becomes important to assess the practical feasibility and foresee difficulties in the practical implementation of theoretical achievements. In this paper, we described the design of simplified simulation environment of the quantum key distribution network with multiple links and nodes. In such simulation environment, we analyzed several routing protocols in terms of the number of sent routing packets, goodput and Packet Delivery Ratio of data traffic flow using NS-3 simulator.

  20. THE IMPACTS OF SOCIAL NETWORKING SITES IN HIGHER LEARNING

    Directory of Open Access Journals (Sweden)

    Mohd Ishak Bin Ismail

    2016-02-01

    Full Text Available Social networking sites, a web-based application have permeated the boundary between personal lives and student lives. Nowadays, students in higher learning used social networking site such as Facebook to facilitate their learning through the academic collaboration which it further enhances students’ social capital. Social networking site has many advantages to improve students’ learning. To date, Facebook is the leading social networking sites at this time which it being widely used by students in higher learning to communicate to each other, to carry out academic collaboration and sharing resources. Learning through social networking sites is based on the social interaction which learning are emphasizing on students, real world resources, active students` participation, diversity of learning resources and the use of digital tools to deliver meaningful learning. Many studies found the positive, neutral and negative impact of social networking sites on academic performance. Thus, this study will determine the relationship between Facebook usage and academic achievement. Also, it will investigate the association of social capital and academic collaboration to Facebook usage.

  1. Multimedia Cross–Platform Content Distribution for Mobile Peer–to–Peer Networks using Network Coding

    DEFF Research Database (Denmark)

    Pedersen, Morten Videbæk; Heide, Janus; Vingelmann, Peter

    2010-01-01

    This paper is looking into the possibility of multimedia content distribution over multiple mobile platforms forming wireless peer–to–peer networks. State of the art mobile networks are centralized and base station or access point oriented. Current developments break ground for device to device...

  2. Moving beyond Blackboard: Using a Social Network as a Learning Management System

    Science.gov (United States)

    Thacker, Christopher

    2012-01-01

    Web 2.0 is a paradigm of a participatory Internet, which has implications for the delivery of online courses. Instructors and students can now develop, distribute, and aggregate content through the use of third-party web applications, particularly social networking platforms, which combine to form a user-created learning management system (LMS).…

  3. Protein interaction network constructing based on text mining and reinforcement learning with application to prostate cancer.

    Science.gov (United States)

    Zhu, Fei; Liu, Quan; Zhang, Xiaofang; Shen, Bairong

    2015-08-01

    Constructing interaction network from biomedical texts is a very important and interesting work. The authors take advantage of text mining and reinforcement learning approaches to establish protein interaction network. Considering the high computational efficiency of co-occurrence-based interaction extraction approaches and high precision of linguistic patterns approaches, the authors propose an interaction extracting algorithm where they utilise frequently used linguistic patterns to extract the interactions from texts and then find out interactions from extended unprocessed texts under the basic idea of co-occurrence approach, meanwhile they discount the interaction extracted from extended texts. They put forward a reinforcement learning-based algorithm to establish a protein interaction network, where nodes represent proteins and edges denote interactions. During the evolutionary process, a node selects another node and the attained reward determines which predicted interaction should be reinforced. The topology of the network is updated by the agent until an optimal network is formed. They used texts downloaded from PubMed to construct a prostate cancer protein interaction network by the proposed methods. The results show that their method brought out pretty good matching rate. Network topology analysis results also demonstrate that the curves of node degree distribution, node degree probability and probability distribution of constructed network accord with those of the scale-free network well.

  4. Distributed Velocity-Dependent Protocol for Multihop Cellular Sensor Networks

    Directory of Open Access Journals (Sweden)

    Deepthi Chander

    2009-01-01

    Full Text Available Cell phones are embedded with sensors form a Cellular Sensor Network which can be used to localize a moving event. The inherent mobility of the application and of the cell phone users warrants distributed structure-free data aggregation and on-the-fly routing. We propose a Distributed Velocity-Dependent (DVD protocol to localize a moving event using a Multihop Cellular Sensor Network (MCSN. DVD is based on a novel form of connectivity determined by the waiting time of nodes for a Random Waypoint (RWP distribution of cell phone users. This paper analyzes the time-stationary and spatial distribution of the proposed waiting time to explain the superior event localization and delay performances of DVD over the existing Randomized Waiting (RW protocol. A sensitivity analysis is also performed to compare the performance of DVD with RW and the existing Centralized approach.

  5. Distributed Velocity-Dependent Protocol for Multihop Cellular Sensor Networks

    Directory of Open Access Journals (Sweden)

    Jagyasi Bhushan

    2009-01-01

    Full Text Available Abstract Cell phones are embedded with sensors form a Cellular Sensor Network which can be used to localize a moving event. The inherent mobility of the application and of the cell phone users warrants distributed structure-free data aggregation and on-the-fly routing. We propose a Distributed Velocity-Dependent (DVD protocol to localize a moving event using a Multihop Cellular Sensor Network (MCSN. DVD is based on a novel form of connectivity determined by the waiting time of nodes for a Random Waypoint (RWP distribution of cell phone users. This paper analyzes the time-stationary and spatial distribution of the proposed waiting time to explain the superior event localization and delay performances of DVD over the existing Randomized Waiting (RW protocol. A sensitivity analysis is also performed to compare the performance of DVD with RW and the existing Centralized approach.

  6. Learning oncogenetic networks by reducing to mixed integer linear programming.

    Science.gov (United States)

    Shahrabi Farahani, Hossein; Lagergren, Jens

    2013-01-01

    Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events. Finding the order in which the genetic events have occurred and progression pathways are of vital importance in understanding the disease. In order to model cancer progression, we propose Progression Networks, a special case of Bayesian networks, that are tailored to model disease progression. Progression networks have similarities with Conjunctive Bayesian Networks (CBNs) [1],a variation of Bayesian networks also proposed for modeling disease progression. We also describe a learning algorithm for learning Bayesian networks in general and progression networks in particular. We reduce the hard problem of learning the Bayesian and progression networks to Mixed Integer Linear Programming (MILP). MILP is a Non-deterministic Polynomial-time complete (NP-complete) problem for which very good heuristics exists. We tested our algorithm on synthetic and real cytogenetic data from renal cell carcinoma. We also compared our learned progression networks with the networks proposed in earlier publications. The software is available on the website https://bitbucket.org/farahani/diprog.

  7. Dynamic Subsidy Method for Congestion Management in Distribution Networks

    OpenAIRE

    Huang, Shaojun; Wu, Qiuwei

    2016-01-01

    Dynamic subsidy (DS) is a locational price paid by the distribution system operator (DSO) to its customers in order to shift energy consumption to designated hours and nodes. It is promising for demand side management and congestion management. This paper proposes a new DS method for congestion management in distribution networks, including the market mechanism, the mathematical formulation through a two-level optimization, and the method solving the optimization by tightening the constraints...

  8. A neural network applied to estimate Burr XII distribution parameters

    Energy Technology Data Exchange (ETDEWEB)

    Abbasi, B., E-mail: b.abbasi@gmail.co [Department of Industrial Engineering, Sharif University of Technology, Tehran (Iran, Islamic Republic of); Hosseinifard, S.Z. [Department of Statistics and Operations Research, RMIT University, Melbourne (Australia); Coit, D.W. [Department of Industrial and System Engineering, Rutgers University, Piscataway, NJ (United States)

    2010-06-15

    The Burr XII distribution can closely approximate many other well-known probability density functions such as the normal, gamma, lognormal, exponential distributions as well as Pearson type I, II, V, VII, IX, X, XII families of distributions. Considering a wide range of shape and scale parameters of the Burr XII distribution, it can have an important role in reliability modeling, risk analysis and process capability estimation. However, estimating parameters of the Burr XII distribution can be a complicated task and the use of conventional methods such as maximum likelihood estimation (MLE) and moment method (MM) is not straightforward. Some tables to estimate Burr XII parameters have been provided by Burr (1942) but they are not adequate for many purposes or data sets. Burr tables contain specific values of skewness and kurtosis and their corresponding Burr XII parameters. Using interpolation or extrapolation to estimate other values may provide inappropriate estimations. In this paper, we present a neural network to estimate Burr XII parameters for different values of skewness and kurtosis as inputs. A trained network is presented, and one can use it without previous knowledge about neural networks to estimate Burr XII distribution parameters. Accurate estimation of the Burr parameters is an extension of simulation studies.

  9. Analyzing Distributed Generation Impact on the Reliability of Electric Distribution Network

    OpenAIRE

    Sanaullah Ahmad; Sana Sardar; Babar Noor; Azzam ul Asar

    2016-01-01

    With proliferation of Distribution Generation (DG) and renewable energy technologies the power system is becoming more complex, with passage of time the development of distributed generation technologies is becoming diverse and broad. Power system reliability is one of most vital area in electric power system which deals with continuous supply of power and customer satisfaction. Distribution network in power system contributed up to 80% of reliability problems. This paper analyzes the impact ...

  10. Hydraulic Analysis of Water Distribution Network Using Shuffled Complex Evolution

    Directory of Open Access Journals (Sweden)

    Naser Moosavian

    2014-01-01

    Full Text Available Hydraulic analysis of water distribution networks is an important problem in civil engineering. A widely used approach in steady-state analysis of water distribution networks is the global gradient algorithm (GGA. However, when the GGA is applied to solve these networks, zero flows cause a computation failure. On the other hand, there are different mathematical formulations for hydraulic analysis under pressure-driven demand and leakage simulation. This paper introduces an optimization model for the hydraulic analysis of water distribution networks using a metaheuristic method called shuffled complex evolution (SCE algorithm. In this method, applying if-then rules in the optimization model is a simple way in handling pressure-driven demand and leakage simulation, and there is no need for an initial solution vector which must be chosen carefully in many other procedures if numerical convergence is to be achieved. The overall results indicate that the proposed method has the capability of handling various pipe networks problems without changing in model or mathematical formulation. Application of SCE in optimization model can lead to accurate solutions in pipes with zero flows. Finally, it can be concluded that the proposed method is a suitable alternative optimizer challenging other methods especially in terms of accuracy.

  11. The degree distribution of fixed act-size collaboration networks

    Indian Academy of Sciences (India)

    There are a large number of fixed act-size collaboration networks [11]. For ex- ample, each football team has eleven players. In athletic sports or other items, the number of players is fixed, etc. In this paper, we propose a new approach to provide a rigorous proof for the existence of the degree distribution of this model, and ...

  12. Optimal operation of water distribution networks by predictive control ...

    African Journals Online (AJOL)

    This paper presents an approach for the operational optimisation of potable water distribution networks. The maximisation of the use of low-cost power (e.g. overnight pumping) and the maintenance of a target chlorine concentration at final delivery points were defined as important optimisation objectives. The first objective ...

  13. Hydraulic Network Modelling of Small Community Water Distribution ...

    African Journals Online (AJOL)

    Prof Anyata

    A hydraulic analysis of a water distribution network is required to determine the pressure contours and flow pattern of the system (Sincero and Sincero, ... sum of the head loss of all the elements along any route between the points and the total head loss is the same by all routes. The energy or loop equations are of the form.

  14. Optimum reliable operation of water distribution networks by ...

    African Journals Online (AJOL)

    In recent decades much attention has been paid to optimal operation of water distribution networks (WDNs). In this regard, the system operation costs, including energy and disinfection chemicals, as well as system reliability should be simultaneously considered in system performance optimisation, to provide the minimum ...

  15. Impact of Electric Vehicle Charging Station Load on Distribution Network

    Directory of Open Access Journals (Sweden)

    Sanchari Deb

    2018-01-01

    Full Text Available Recent concerns about environmental pollution and escalating energy consumption accompanied by the advancements in battery technology have initiated the electrification of the transportation sector. With the universal resurgence of Electric Vehicles (EVs the adverse impact of the EV charging loads on the operating parameters of the power system has been noticed. The detrimental impact of EV charging station loads on the electricity distribution network cannot be neglected. The high charging loads of the fast charging stations results in increased peak load demand, reduced reserve margins, voltage instability, and reliability problems. Further, the penalty paid by the utility for the degrading performance of the power system cannot be neglected. This work aims to investigate the impact of the EV charging station loads on the voltage stability, power losses, reliability indices, as well as economic losses of the distribution network. The entire analysis is performed on the IEEE 33 bus test system representing a standard radial distribution network for six different cases of EV charging station placement. It is observed that the system can withstand placement of fast charging stations at the strong buses up to a certain level, but the placement of fast charging stations at the weak buses of the system hampers the smooth operation of the power system. Further, a strategy for the placement of the EV charging stations on the distribution network is proposed based on a novel Voltage stability, Reliability, and Power loss (VRP index. The results obtained indicate the efficacy of the VRP index.

  16. an improved voltage regulation of a distribution network using facts

    African Journals Online (AJOL)

    OMEJE CO

    2013-07-02

    Jul 2, 2013 ... trouble free, since the TCR achieves its fundamental frequency steady-state operating point at the .... injected by the generator at bus K. PLK and. QLK represent the active and reactive powers drawn by ..... energy efficiency of the power distribution networks. Figure 10: Comparative plot of the bus voltage ...

  17. Fast demand response in support of the active distribution network

    NARCIS (Netherlands)

    MacDougall, P.; Heskes, P.; Crolla, P.; Burt, G.; Warmer, C.

    2013-01-01

    Demand side management has traditionally been investigated for "normal" operation services such as balancing and congestion management. However they potentially could be utilized for Distributed Network Operator (DNO) services. This paper investigates and validates the use of a supply and demand

  18. Exploration of Heterogeneity in Distributed Research Network Drug Safety Analyses

    Science.gov (United States)

    Hansen, Richard A.; Zeng, Peng; Ryan, Patrick; Gao, Juan; Sonawane, Kalyani; Teeter, Benjamin; Westrich, Kimberly; Dubois, Robert W.

    2014-01-01

    Distributed data networks representing large diverse populations are an expanding focus of drug safety research. However, interpreting results is difficult when treatment effect estimates vary across datasets (i.e., heterogeneity). In a previous study, risk estimates were generated for selected drugs and potential adverse outcomes. Analyses were…

  19. The effect of the earthquake on the water distribution network ...

    African Journals Online (AJOL)

    ... and in some areas they pass necessarily from areas with fault lines. Thus studying the pipelines in earthquake-prone areas is of utmost importance. In this paper, the effect of the earthquake on the water distribution network has been discussed. Keywords: Water Foundations, Earthquake, Vibrations, Connections, Pipes ...

  20. Degree distribution of a new model for evolving networks

    Indian Academy of Sciences (India)

    ution, P(k) ∼ exp(−k/m), where m is a constant. Empirical results demonstrate that many networks in nature appear to exhibit ... There are many examples where the distribution is neither power-law nor exponential, such ..... [13] Z Hou, X Kong, D Shi, G Chen and Q Zhao, cond-mat/09011418. [14] O Stolz, Vorlesungen uber ...

  1. Voltage Estimation in Active Distribution Grids Using Neural Networks

    DEFF Research Database (Denmark)

    Pertl, Michael; Heussen, Kai; Gehrke, Oliver

    2016-01-01

    the observability of distribution systems has to be improved. To increase the situational awareness of the power system operator data driven methods can be employed. These methods benefit from newly available data sources such as smart meters. This paper presents a voltage estimation method based on neural networks...

  2. Games as Distributed Teaching and Learning Systems

    Science.gov (United States)

    Gee, Elisabeth; Gee, James Paul

    2017-01-01

    Background: Videogames and virtual worlds have frequently been studied as learning environments in isolation; that is, scholars have focused on understanding the features of games or virtual worlds as separate from or different than "real world" environments for learning. Although more recently, scholars have explored the teaching and…

  3. A Distributed Intelligent E-Learning System

    Science.gov (United States)

    Kristensen, Terje

    2016-01-01

    An E-learning system based on a multi-agent (MAS) architecture combined with the Dynamic Content Manager (DCM) model of E-learning, is presented. We discuss the benefits of using such a multi-agent architecture. Finally, the MAS architecture is compared with a pure service-oriented architecture (SOA). This MAS architecture may also be used within…

  4. Reliable distribution networks design with nonlinear fortification function

    Science.gov (United States)

    Li, Qingwei; Savachkin, Alex

    2016-03-01

    Distribution networks have been facing an increased exposure to the risk of unpredicted disruptions causing significant economic losses. The current literature features a limited number of studies considering fortification of network facilities. In this paper, we develop a reliable uncapacitated fixed-charge location model with fortification to support the design of distribution networks. The model considers heterogeneous facility failure probabilities, one layer of supplier backup, and facility fortification within a finite budget. Facility reliability improvement is modelled as a nonlinear function of fortification investment. The problem is formulated as a nonlinear mixed integer programming model proven to be ?-hard. A Lagrangian relaxation-based heuristic algorithm is developed and its computational efficiency for solving large-scale problems is demonstrated.

  5. Analysis and Comparison of Typical Models within Distribution Network Design

    DEFF Research Database (Denmark)

    Jørgensen, Hans Jacob; Larsen, Allan; Madsen, Oli B.G.

    Efficient and cost effective transportation and logistics plays a vital role in the supply chains of the modern world’s manufacturers. Global distribution of goods is a very complicated matter as it involves many different distinct planning problems. The focus of this presentation is to demonstrate...... a number of important issues which have been identified when addressing the Distribution Network Design problem from a modelling angle. More specifically, we present an analysis of the research which has been performed in utilizing operational research in developing and optimising distribution systems....

  6. 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 ...

  7. Joint physical and numerical modeling of water distribution networks.

    Energy Technology Data Exchange (ETDEWEB)

    Zimmerman, Adam; O' Hern, Timothy John; Orear, Leslie Jr.; Kajder, Karen C.; Webb, Stephen Walter; Cappelle, Malynda A.; Khalsa, Siri Sahib; Wright, Jerome L.; Sun, Amy Cha-Tien; Chwirka, J. Benjamin; Hartenberger, Joel David; McKenna, Sean Andrew; van Bloemen Waanders, Bart Gustaaf; McGrath, Lucas K.; Ho, Clifford Kuofei

    2009-01-01

    This report summarizes the experimental and modeling effort undertaken to understand solute mixing in a water distribution network conducted during the last year of a 3-year project. The experimental effort involves measurement of extent of mixing within different configurations of pipe networks, measurement of dynamic mixing in a single mixing tank, and measurement of dynamic solute mixing in a combined network-tank configuration. High resolution analysis of turbulence mixing is carried out via high speed photography as well as 3D finite-volume based Large Eddy Simulation turbulence models. Macroscopic mixing rules based on flow momentum balance are also explored, and in some cases, implemented in EPANET. A new version EPANET code was developed to yield better mixing predictions. The impact of a storage tank on pipe mixing in a combined pipe-tank network during diurnal fill-and-drain cycles is assessed. Preliminary comparison between dynamic pilot data and EPANET-BAM is also reported.

  8. Network learning: a methodological propose to shareholders and executives education

    Directory of Open Access Journals (Sweden)

    Daniel Jardim Pardini

    2012-07-01

    Full Text Available DOI: http://dx.doi.org/10.5007/2175-8077.2012v14n33p25 This article aims to analyze the dynamics of the learning networks operation of business and the differential of this methodological practice to other conventional models of teaching. The review of the epistemological theories of learning and educational psychology identified constructivism collective (LAROCHELLE et al., 1998 as the approach that most closely resembles conceptions of teaching in network format. To understand the way they are planned and organized networks and their distinctions for other types of courses targeted at executives use the methods of case study and thematic analysis. The study highlight the differences in entrepreneurial learning networks, still little diffusined in Brazil, for the traditional teaching methods taught in the open courses postgraduate to the public and in-company to complement and update the learning people in executive business function.

  9. Tweetstorming PLNs: Using Twitter to Brainstorm about Personal Learning Networks

    NARCIS (Netherlands)

    Sie, Rory; Boursinou, Eleni; Rajagopal, Kamakshi; Pataraia, Nino

    2012-01-01

    Sie, R., Boursinou, E., Rajagopal, K., & Pataraia, N. (2011). Tweetstorming PLNs: Using Twitter to Brainstorm about Personal Learning Networks. In Proceedings of The PLE Conference 2011. July, 10-12, 2011, Southampton, UK.

  10. Using machine learning, neural networks and statistics to predict bankruptcy

    NARCIS (Netherlands)

    Pompe, P.P.M.; Feelders, A.J.; Feelders, A.J.

    1997-01-01

    Recent literature strongly suggests that machine learning approaches to classification outperform "classical" statistical methods. We make a comparison between the performance of linear discriminant analysis, classification trees, and neural networks in predicting corporate bankruptcy. Linear

  11. Personal Profiles: Enhancing Social Interaction in Learning Networks

    NARCIS (Netherlands)

    Berlanga, Adriana; Bitter-Rijpkema, Marlies; Brouns, Francis; Sloep, Peter; Fetter, Sibren

    2009-01-01

    Berlanga, A. J., Bitter-Rijpkema, M., Brouns, F., Sloep, P. B., & Fetter, S. (2011). Personal Profiles: Enhancing Social Interaction in Learning Networks. International Journal of Web Based Communities, 7(1), 66-82.

  12. Communal learning within a distributed robotic control system

    Science.gov (United States)

    Digney, Bruce L.

    2001-09-01

    It is accepted that the ability to learn and adapt is key to prosperity and survival in both individuals and societies. The same is true of populations of robots. Those robots within a population that are able to learn will outperform, survive longer and perhaps exploit their non-learning co- workers. This paper describes the ongoing results of Communal Learning in the Cognitive Colonies Project (CMU/Robotics and DRES), funded jointly by DARPA ITO- Software for Distributed Robotics and DRDC-DRES. Discussed will be how communal learning fits into the free market architecture for distributed control. Techniques for representing experiences, learned behaviors, maps and computational resources as commodities within the market economy will be presented. Once in a commodity structure, the cycle of speculate, act, receive profits or sustain losses and then learn of the market economy. This allows successful control strategies to emerge and the individuals who discovered them to become established as successful. This paper will discuss: learning to predict costs and make better deals, learning transition confidences, learning causes of death, learning with robot sacrifice and learning model parameters.

  13. Thermodynamic efficiency of learning a rule in neural networks

    Science.gov (United States)

    Goldt, Sebastian; Seifert, Udo

    2017-11-01

    Biological systems have to build models from their sensory input data that allow them to efficiently process previously unseen inputs. Here, we study a neural network learning a binary classification rule for these inputs from examples provided by a teacher. We analyse the ability of the network to apply the rule to new inputs, that is to generalise from past experience. Using stochastic thermodynamics, we show that the thermodynamic costs of the learning process provide an upper bound on the amount of information that the network is able to learn from its teacher for both batch and online learning. This allows us to introduce a thermodynamic efficiency of learning. We analytically compute the dynamics and the efficiency of a noisy neural network performing online learning in the thermodynamic limit. In particular, we analyse three popular learning algorithms, namely Hebbian, Perceptron and AdaTron learning. Our work extends the methods of stochastic thermodynamics to a new type of learning problem and might form a suitable basis for investigating the thermodynamics of decision-making.

  14. Stochastic margin-based structure learning of Bayesian network classifiers.

    Science.gov (United States)

    Pernkopf, Franz; Wohlmayr, Michael

    2013-02-01

    The margin criterion for parameter learning in graphical models gained significant impact over the last years. We use the maximum margin score for discriminatively optimizing the structure of Bayesian network classifiers. Furthermore, greedy hill-climbing and simulated annealing search heuristics are applied to determine the classifier structures. In the experiments, we demonstrate the advantages of maximum margin optimized Bayesian network structures in terms of classification performance compared to traditionally used discriminative structure learning methods. Stochastic simulated annealing requires less score evaluations than greedy heuristics. Additionally, we compare generative and discriminative parameter learning on both generatively and discriminatively structured Bayesian network classifiers. Margin-optimized Bayesian network classifiers achieve similar classification performance as support vector machines. Moreover, missing feature values during classification can be handled by discriminatively optimized Bayesian network classifiers, a case where purely discriminative classifiers usually require mechanisms to complete unknown feature values in the data first.

  15. Power Quality Investigation of Distribution Networks Embedded Wind Turbines

    Directory of Open Access Journals (Sweden)

    A. Elsherif

    2016-01-01

    Full Text Available In recent years a multitude of events have created a new environment for the electric power infrastructure. The presence of small-scale generation near load spots is becoming common especially with the advent of renewable energy sources such as wind power energy. This type of generation is known as distributed generation (DG. The expansion of the distributed generators- (DGs- based wind energy raises constraints on the distribution networks operation and power quality issues: voltage sag, voltage swell, voltage interruption, harmonic contents, flickering, frequency deviation, unbalance, and so forth. Consequently, the public distribution network conception and connection studies evolve in order to keep the distribution system operating in optimal conditions. In this paper, a comprehensive power quality investigation of a distribution system with embedded wind turbines has been carried out. This investigation is carried out in a comparison aspect between the conventional synchronous generators, as DGs are widely in use at present, and the different wind turbines technologies, which represent the foresightedness of the DGs. The obtained results are discussed with the IEC 61400-21 standard for testing and assessing power quality characteristics of grid-connected wind energy and the IEEE 1547-2003 standard for interconnecting distributed resources with electric power systems.

  16. MACHINE LEARNING FOR THE SELF-ORGANIZATION OF DISTRIBUTED SYSTEMS IN ECONOMIC APPLICATIONS

    Directory of Open Access Journals (Sweden)

    Jerzy Balicki

    2017-03-01

    Full Text Available In this paper, an application of machine learning to the problem of self-organization of distributed systems has been discussed with regard to economic applications, with particular emphasis on supervised neural network learning to predict stock investments and some ratings of companies. In addition, genetic programming can play an important role in the preparation and testing of several financial information systems. For this reason, machine learning applications have been discussed because some software applications can be automatically constructed by genetic programming. To obtain a competitive advantage, machine learning can be used for the management of self-organizing cloud computing systems performing calculations for business. Also the use of selected economic self-organizing distributed systems has been described, including some testing methods of predicting borrower reliability. Finally, some conclusions and directions for further research have been proposed.

  17. Compensatory Motor Network Connectivity is Associated with Motor Sequence Learning after Subcortical Stroke

    Science.gov (United States)

    Wadden, Katie P.; Woodward, Todd S.; Metzak, Paul D.; Lavigne, Katie M.; Lakhani, Bimal; Auriat, Angela M.; Boyd, Lara A.

    2015-01-01

    Following stroke, functional networks reorganize and the brain demonstrates widespread alterations in cortical activity. Implicit motor learning is preserved after stroke. However the manner in which brain reorganization occurs, and how it supports behaviour within the damaged brain remains unclear. In this functional magnetic resonance imaging (fMRI) study, we evaluated whole brain patterns of functional connectivity during the performance of an implicit tracking task at baseline and retention, following 5 days of practice. Following motor practice, a significant difference in connectivity within a motor network, consisting of bihemispheric activation of the sensory and motor cortices, parietal lobules, cerebellar and occipital lobules, was observed at retention. Healthy subjects demonstrated greater activity within this motor network during sequence learning compared to random practice. The stroke group did not show the same level of functional network integration, presumably due to the heterogeneity of functional reorganization following stroke. In a secondary analysis, a binary mask of the functional network activated from the aforementioned whole brain analyses was created to assess within-network connectivity, decreasing the spatial distribution and large variability of activation that exists within the lesioned brain. The stroke group demonstrated reduced clusters of connectivity within the masked brain regions as compared to the whole brain approach. Connectivity within this smaller motor network correlated with repeated sequence performance on the retention test. Increased functional integration within the motor network may be an important neurophysiological predictor of motor learning-related change in individuals with stroke. PMID:25757996

  18. Monitoring water distribution systems: understanding and managing sensor networks

    Directory of Open Access Journals (Sweden)

    D. D. Ediriweera

    2010-09-01

    Full Text Available Sensor networks are currently being trialed by the water distribution industry for monitoring complex distribution infrastructure. The paper presents an investigation in to the architecture and performance of a sensor system deployed for monitoring such a distribution network. The study reveals lapses in systems design and management, resulting in a fifth of the data being either missing or erroneous. Findings identify the importance of undertaking in-depth consideration of all aspects of a large sensor system with access to either expertise on every detail, or to reference manuals capable of transferring the knowledge to non-specialists. First steps towards defining a set of such guidelines are presented here, with supporting evidence.

  19. Influences of Wind Energy Integration into the Distribution Network

    Directory of Open Access Journals (Sweden)

    G. M. Shafiullah

    2013-01-01

    Full Text Available Wind energy is one of the most promising renewable energy sources due to its availability and climate-friendly attributes. Large-scale integration of wind energy sources creates potential technical challenges due to the intermittent nature that needs to be investigated and mitigated as part of developing a sustainable power system for the future. Therefore, this study developed simulation models to investigate the potential challenges, in particular voltage fluctuations, zone substation, and distribution transformer loading, power flow characteristics, and harmonic emissions with the integration of wind energy into both the high voltage (HV and low voltage (LV distribution network (DN. From model analysis, it has been clearly indicated that influences of these problems increase with the increased integration of wind energy into both the high voltage and low voltage distribution network; however, the level of adverse impacts is higher in the LV DN compared to the HV DN.

  20. Wide area network performance study of a distribution management system

    Energy Technology Data Exchange (ETDEWEB)

    Su, C.-L.; Lu, C.-N. [National Sun Yat-Sen University, Kaohsiung (Taiwan). Dept. of Electrical Engineering; Lin, M.-C. [Taiwan Power Company, Kaohsiung (Taiwan). Kaoping Regional Office

    2000-01-01

    Distribution automation is considered a necessity for providing better power service in a more competitive environment. When new automatic functions are included in a distribution management system (DMS), loadings of the data links in the supervisory control and data acquisition (SCADA) system will become very heavy. In order to maintain a proper performance, system upgrade or migration will need to be considered. Two wide area network (WAN) architectures for a Taiwan Power Company's regional DMS are investigated. The WAN modeling presented in this paper is aimed to verify whether the hardware design could accommodate the communications load and to avoid overpaying for network equipment. Simulation results indicate that, to cover feeder automation functions, a WAN with distributed processing capability would provide better SCADA performance than an extension of the old centralized system. (author)

  1. Design of a Distribution Network Using Primal-Dual Decomposition

    Directory of Open Access Journals (Sweden)

    J. A. Marmolejo

    2016-01-01

    Full Text Available A method to solve the design of a distribution network for bottled drinks company is introduced. The distribution network proposed includes three stages: manufacturing centers, consolidation centers using cross-docking, and distribution centers. The problem is formulated using a mixed-integer programming model in the deterministic and single period contexts. Because the problem considers several elements in each stage, a direct solution is very complicated. For medium-to-large instances the problem falls into large scale. Based on that, a primal-dual decomposition known as cross decomposition is proposed in this paper. This approach allows exploring simultaneously the primal and dual subproblems of the original problem. A comparison of the direct solution with a mixed-integer lineal programming solver versus the cross decomposition is shown for several randomly generated instances. Results show the good performance of the method proposed.

  2. Embedded generation connection incentives for distribution network operators

    Energy Technology Data Exchange (ETDEWEB)

    Williams, P.; Andrews, S.

    2002-07-01

    This is the final report with respect to work commissioned by the Department of Trade and Industry (DTI) as part of the New and Renewable Energy Programme into incentives for distribution network operators (DNOs) for the connection of embedded generation. This report, which incorporates the contents of the interim report submitted in February 2002, considers the implications of changes in the structure and regulation in the UK electricity industry on the successful technical and commercial integrated of embedded generation into distribution networks. The report examines: the obligations of public electricity suppliers (PESs); current DNO practices regarding the connection of embedded generation; the changes introduced by the Utilities Act 2000, including the impact of new obligations placed on DNOs on the connection of embedded generation and the requirements of the new Electricity Distribution Standard Licence conditions; and problems and prospects for DNO incentives.

  3. A constructive algorithm for unsupervised learning with incremental neural network

    OpenAIRE

    Wang, Jenq-Haur; Wang, Hsin-Yang; Chen, Yen-Lin; Liu, Chuan-Ming

    2015-01-01

    Artificial neural network (ANN) has wide applications such as data processing and classification. However, comparing with other classification methods, ANN needs enormous memory space and training time to build the model. This makes ANN infeasible in practical applications. In this paper, we try to integrate the ideas of human learning mechanism with the existing models of ANN. We propose an incremental neural network construction framework for unsupervised learning. In this framework, a neur...

  4. Dynamic Network Centrality Summarizes Learning in the Human Brain

    OpenAIRE

    Mantzaris, Alexander V.; Bassett, Danielle S.; Wymbs, Nicholas F.; Estrada, Ernesto; Porter, Mason A.; Mucha, Peter J; Grafton, Scott T.; Higham, Desmond J.

    2012-01-01

    We study functional activity in the human brain using functional Magnetic Resonance Imaging and recently developed tools from network science. The data arise from the performance of a simple behavioural motor learning task. Unsupervised clustering of subjects with respect to similarity of network activity measured over three days of practice produces significant evidence of `learning', in the sense that subjects typically move between clusters (of subjects whose dynamics are similar) as time ...

  5. Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data

    Science.gov (United States)

    2015-07-01

    Bayesian networks. In IJCNN, pp. 2391– 2396. Ghahramani, Z., & Jordan, M. I. (1997). Factorial hidden markov models. Machine Learning, 29(2-3), 245–273...algorithms like EM (which require inference). 1 INTRODUCTION When learning the parameters of a Bayesian network from data with missing values, the...missing at random assumption (MAR), but also for a broad class of data that is not MAR. Their analysis is based on a graphical representation for

  6. Approximation Methods for Efficient Learning of Bayesian Networks

    NARCIS (Netherlands)

    Riggelsen, C.

    2006-01-01

    Learning from data ranges between extracting essentials from the data, to the more fundamental and very challenging task of learning the underlying data generating process in terms of a probability distribution. In particular, in this thesis we assume that this distribution can be modelled as a

  7. The Contribution of Social Networks to Individual Learning in Service Organizations

    Science.gov (United States)

    Poell, Rob F.; Van der Krogt, Ferd J.

    2007-01-01

    This study investigates how social networks in service organizations contribute to employee learning. Two specific types of social network seem especially relevant to individual learning: first, the service network, where employees carry out and improve their work, which may lead to learning; and second, the learning network, where employees…

  8. Simulation of Two High Pressure Distribution Network Operation in one-Network Connection

    Directory of Open Access Journals (Sweden)

    Perju Sorin

    2014-09-01

    Full Text Available The programs developed by the water supply system operators in view of metering the branches and reducing the potable water losses from the distribution network pipes lead to the performance reassessment of these networks. As a result the energetic consumption of the pumping stations should meet the accepted limits. An essential role in the evaluation of the operation parameters of the network performance is played by hydraulic modeling, by means of which the network performance simulation can be done in different scenarios. The present article describes the concept of two high-pressure network coupling. These networks are supplied by two repumping stations, in which the water flows were drastically reduced due to the present situation

  9. Modeling isotopomer distributions in biochemical networks using isotopomer mapping matrices

    DEFF Research Database (Denmark)

    Schmidt, Karsten; Carlsen, Morten; Nielsen, Jens Bredal

    1997-01-01

    Within the last decades NMR spectroscopy has undergone tremendous development and has become a powerful analytical tool for the investigation of intracellular flux distributions in biochemical networks using C-13-labeled substrates. Not only are the experiments much easier to conduct than...... of the isotopomer distribution in metabolite pools can be obtained. The isotopomer distribution is the maximum amount of information that in theory can be obtained from C-13-tracer studies. The wealth of information contained in NMR spectra frequently leads to overdetermined algebraic systems. Consequently, fluxes...... must be estimated by nonlinear least squares analysis, in which experimental labeling data is compared with simulated steady state isotopomer distributions. Hence, mathematical models are required to compute the steady state isotopomer distribution as a function of a given set of steady state fluxes...

  10. Bipartite producer consumer networks and the size distribution of firms

    Science.gov (United States)

    Dahui, Wang; Li, Zhou; Zengru, Di

    2006-05-01

    A bipartite producer-consumer network is constructed to describe the industrial structure. The edges from consumer to producer represent the choices of the consumer for the final products and the degree of producer can represent its market share. So the size distribution of firms can be characterized by producer's degree distribution. The probability for a producer receiving a new consumption is determined by its competency described by initial attractiveness and the self-reinforcing mechanism in the competition described by preferential attachment. The cases with constant total consumption and with growing market are studied. The following results are obtained: (1) Without market growth and a uniform initial attractiveness a, the final distribution of firm sizes is Gamma distribution for a>1 and is exponential for a=1. If amarket, the size distribution of firms obeys the power-law. The exponent is affected by the market growth and the initial attractiveness of the firms.

  11. A distributed data base management system. [for Deep Space Network

    Science.gov (United States)

    Bryan, A. I.

    1975-01-01

    Major system design features of a distributed data management system for the NASA Deep Space Network (DSN) designed for continuous two-way deep space communications are described. The reasons for which the distributed data base utilizing third-generation minicomputers is selected as the optimum approach for the DSN are threefold: (1) with a distributed master data base, valid data is available in real-time to support DSN management activities at each location; (2) data base integrity is the responsibility of local management; and (3) the data acquisition/distribution and processing power of a third-generation computer enables the computer to function successfully as a data handler or as an on-line process controller. The concept of the distributed data base is discussed along with the software, data base integrity, and hardware used. The data analysis/update constraint is examined.

  12. Distributed Optimization based Dynamic Tariff for Congestion Management in Distribution Networks

    DEFF Research Database (Denmark)

    Huang, Shaojun; Wu, Qiuwei; Zhao, Haoran

    2017-01-01

    This paper proposes a distributed optimization based dynamic tariff (DDT) method for congestion management in distribution networks with high penetration of electric vehicles (EVs) and heat pumps (HPs). The DDT method employs a decomposition based optimization method to have aggregators explicitly...... participate in congestion management, which gives more certainty and transparency compared to the normal DT method. With the DDT method, aggregators reveal their final aggregated plan and respect the plan during operation. By establishing an equivalent overall optimization, it is proven that the DDT method...... where challenges arise due to multiple congestion points, multiple types of flexible demands and network constraints. The case studies demonstrate the efficacy of the DDT method for congestion management in distribution networks....

  13. Distributed Optimal Dispatch of Distributed Energy Resources Over Lossy Communication Networks

    Energy Technology Data Exchange (ETDEWEB)

    Wu, Junfeng; Yang, Tao; Wu, Di; Kalsi, Karanjit; Johansson, Karl Henrik

    2017-11-01

    In this paper, we consider the economic dispatch problem (EDP), where a cost function that is assumed to be strictly convex is assigned to each of distributed energy resources (DERs), over packet dropping networks. The goal of a standard EDP is to minimize the total generation cost while meeting total demand and satisfying individual generator output limit. We propose a distributed algorithm for solving the EDP over networks. The proposed algorithm is resilient against packet drops over communication links. Under the assumption that the underlying communication network is strongly connected with a positive probability and the packet drops are independent and identically distributed (i.i.d.), we show that the proposed algorithm is able to solve the EDP. Numerical simulation results are used to validate and illustrate the main results of the paper.

  14. Development of Pseudo Autonomous Wireless Sensor Monitoring System for Water Distribution Network

    OpenAIRE

    Kondratjevs, K; Zabašta, A; Kuņicina, N; Ribickis, L

    2014-01-01

    Water distribution networks require long term autonomous monitoring solutions, integrated, reliable and cost effective data transfer methods. This paper investigates the data delivery infrastructure of water distribution network sensor equipment used for network monitoring and billing of the subscribers. Water distribution network usually apply sensors to measure water flow, pressure and temperature. The main goal is to offer a wireless sensor system architecture comprisi...

  15. Hadoop neural network for parallel and distributed feature selection.

    Science.gov (United States)

    Hodge, Victoria J; O'Keefe, Simon; Austin, Jim

    2016-06-01

    In this paper, we introduce a theoretical basis for a Hadoop-based neural network for parallel and distributed feature selection in Big Data sets. It is underpinned by an associative memory (binary) neural network which is highly amenable to parallel and distributed processing and fits with the Hadoop paradigm. There are many feature selectors described in the literature which all have various strengths and weaknesses. We present the implementation details of five feature selection algorithms constructed using our artificial neural network framework embedded in Hadoop YARN. Hadoop allows parallel and distributed processing. Each feature selector can be divided into subtasks and the subtasks can then be processed in parallel. Multiple feature selectors can also be processed simultaneously (in parallel) allowing multiple feature selectors to be compared. We identify commonalities among the five features selectors. All can be processed in the framework using a single representation and the overall processing can also be greatly reduced by only processing the common aspects of the feature selectors once and propagating these aspects across all five feature selectors as necessary. This allows the best feature selector and the actual features to select to be identified for large and high dimensional data sets through exploiting the efficiency and flexibility of embedding the binary associative-memory neural network in Hadoop. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

  16. Towards the distribution network of time and frequency

    Science.gov (United States)

    Lipiński, M.; Krehlik, P.; Śliwczyński, Ł.; Buczek, Ł.; Kołodziej, J.; Nawrocki, J.; Nogaś, P.; Dunst, P.; Lemański, D.; Czubla, A.; Pieczerak, J.; Adamowicz, W.; Pawszak, T.; Igalson, J.; Binczewski, A.; Bogacki, W.; Ostapowicz, P.; Stroiński, M.; Turza, K.

    2014-05-01

    In the paper the genesis, current stage and perspectives of the OPTIME project are described. The main goal of the project is to demonstrate that the newdeveloped at AGH technology of fiber optic transfer of the atomic clocks reference signals is ready to be used in building the domestic Time and Frequency distribution network. In the first part we summarize the two-year continuous operation of 420 kmlong link connecting the Laboratory of Time and Frequency at Central Office of Measures GUM in Warsaw and Time Service Laboratory at Astrogeodynamic Obserwatory AOS in Borowiec near Poznan. For the first time, we are reporting the two year comparison of UTC(PL) and UTC(AOS) atomic timescales with this link, and we refer it to the results of comparisons performed by GPS-based methods. We also address some practical aspects of maintaining time and frequency dissemination over fiber optical network. In the second part of the paper the concept of the general architecture of the distribution network with two Reference Time and Frequency Laboratories and local repositories is proposed. Moreover the brief project of the second branch connecting repositories in Poznan Polish Supercomputing and Networking Center and Torun Nicolaus Copernicus University with the first end-users in Torun such as National Laboratory of Atomic, Molecular and Optical Physics and Nicolaus Copernicus Astronomical Center is described. In the final part the perspective of developing the network both in the domestic range as far as extention with the international connections possibilities are presented.

  17. Exploring dynamic mechanisms of learning networks for resource conservation

    Directory of Open Access Journals (Sweden)

    Petr Matous

    2015-06-01

    Full Text Available The importance of networks for social-ecological processes has been recognized in the literature; however, existing studies have not sufficiently addressed the dynamic nature of networks. Using data on the social learning networks of 265 farmers in Ethiopia for 2011 and 2012 and stochastic actor-oriented modeling, we explain the mechanisms of network evolution and soil conservation. The farmers' preferences for information exchange within the same social groups support the creation of interactive, clustered, nonhierarchical structures within the evolving learning networks, which contributed to the diffusion of the practice of composting. The introduced methods can be applied to determine whether and how social networks can be used to facilitate environmental interventions in various contexts.

  18. Learning algorithms for feedforward networks based on finite samples

    Energy Technology Data Exchange (ETDEWEB)

    Rao, N.S.V.; Protopopescu, V.; Mann, R.C.; Oblow, E.M.; Iyengar, S.S.

    1994-09-01

    Two classes of convergent algorithms for learning continuous functions (and also regression functions) that are represented by feedforward networks, are discussed. The first class of algorithms, applicable to networks with unknown weights located only in the output layer, is obtained by utilizing the potential function methods of Aizerman et al. The second class, applicable to general feedforward networks, is obtained by utilizing the classical Robbins-Monro style stochastic approximation methods. Conditions relating the sample sizes to the error bounds are derived for both classes of algorithms using martingale-type inequalities. For concreteness, the discussion is presented in terms of neural networks, but the results are applicable to general feedforward networks, in particular to wavelet networks. The algorithms can be directly adapted to concept learning problems.

  19. Social Software: Participants' Experience Using Social Networking for Learning

    Science.gov (United States)

    Batchelder, Cecil W.

    2010-01-01

    Social networking tools used in learning provides instructional design with tools for transformative change in education. This study focused on defining the meanings and essences of social networking through the lived common experiences of 7 college students. The problem of the study was a lack of learner voice in understanding the value of social…

  20. Social Media and Social Networking Applications for Teaching and Learning

    Science.gov (United States)

    Yeo, Michelle Mei Ling

    2014-01-01

    This paper aims to better understand the experiences of the youth and the educators with the tapping of social media like YouTube videos and the social networking application of Facebook for teaching and learning. This paper is interested in appropriating the benefits of leveraging of social media and networking applications like YouTube and…

  1. Modelling Framework and the Quantitative Analysis of Distributed Energy Resources in Future Distribution Networks

    DEFF Research Database (Denmark)

    Han, Xue; Sandels, Claes; Zhu, Kun

    2013-01-01

    , comprising distributed generation, active demand and electric vehicles. Subsequently, quantitative analysis was made on the basis of the current and envisioned DER deployment scenarios proposed for Sweden. Simulations are performed in two typical distribution network models for four seasons. The simulation......There has been a large body of statements claiming that the large-scale deployment of Distributed Energy Resources (DERs) could eventually reshape the future distribution grid operation in numerous ways. Thus, it is necessary to introduce a framework to measure to what extent the power system...

  2. Distributed Scaffolding in a Service-Learning Course

    Science.gov (United States)

    Smagorinsky, Peter; Clayton, Christopher M.; Johnson, Lindy L.

    2015-01-01

    This article argues that the instructional scaffolding metaphor may be reconceived as distributed scaffolding when multiple means of influence are provided in a service-learning setting. In the service-learning course described here, the professor's role is largely as designer of activity settings for preservice teacher candidates, through…

  3. Distributed Emotions in the Design of Learning Technologies

    Science.gov (United States)

    Kim, Beaumie; Kim, Mi Song

    2010-01-01

    Learning is a social activity, which requires interactions with the environment, tools, people, and also ourselves (e.g., our previous experiences). Each interaction provides different meanings to learners, and the associated emotion affects their learning and performance. With the premise that emotion and cognition are distributed, the authors…

  4. A Survey of Application Distribution in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Kuorilehto Mauri

    2005-01-01

    Full Text Available Wireless sensor networks (WSNs are deployed to an area of interest to sense phenomena, process sensed data, and take actions accordingly. Due to the limited WSN node resources, distributed processing is required for completing application tasks. Proposals implementing distribution services for WSNs are evolving on different levels of generality. In this paper, these solutions are reviewed in order to determine the current status. According to the review, existing distribution technologies for computer networks are not applicable for WSNs. Operating systems (OSs and middleware architectures for WSNs implement separate services for distribution within the existing constraints but an approach providing a complete distributed environment for applications is absent. In order to implement an efficient and adaptive environment, a middleware should be tightly integrated in the underlying OS. We recommend a framework in which a middleware distributes the application processing to a WSN so that the application lifetime is maximized. OS implements services for application tasks and information gathering as well as control interfaces for the middleware.

  5. Optimizing Knowledge Sharing In Learning Networks Through Peer Tutoring

    OpenAIRE

    Hsiao, Amy; Brouns, Francis; Kester, Liesbeth; Sloep, Peter

    2009-01-01

    Hsiao, Y. P., Brouns, F., Kester, L., & Sloep, P. B. (2009). Optimizing Knowledge Sharing In Learning Networks Through Peer Tutoring. In D. Kinshuk, J. Sampson, J. Spector, P. Isaías, P. Barbosa & D. Ifenthaler (Eds.). Proceedings of IADIS International Conference Cognition and Exploratory Learning in Digital Age (CELDA 2009) (pp. 550-551). November, 20-22, 2009, Rome, Italy: Springer.

  6. Optimizing Knowledge Sharing In Learning Networks Through Peer Tutoring

    NARCIS (Netherlands)

    Hsiao, Amy; Brouns, Francis; Kester, Liesbeth; Sloep, Peter

    2009-01-01

    Hsiao, Y. P., Brouns, F., Kester, L., & Sloep, P. B. (2009). Optimizing Knowledge Sharing In Learning Networks Through Peer Tutoring. In D. Kinshuk, J. Sampson, J. Spector, P. Isaías, P. Barbosa & D. Ifenthaler (Eds.). Proceedings of IADIS International Conference Cognition and Exploratory Learning

  7. Student Learning Networks on Residential Field Courses: Does Size Matter?

    Science.gov (United States)

    Langan, A. Mark; Cullen, W. Rod; Shuker, David M.

    2008-01-01

    This article describes learner and tutor reports of a learning network that formed during the completion of investigative projects on a residential field course. Staff and students recorded project-related interactions, who they were with and how long they lasted over four phases during the field course. An enquiry based learning format challenged…

  8. Using overlay network architectures for scalable video distribution

    Science.gov (United States)

    Patrikakis, Charalampos Z.; Despotopoulos, Yannis; Fafali, Paraskevi; Cha, Jihun; Kim, Kyuheon

    2004-11-01

    Within the last years, the enormous growth of Internet based communication as well as the rapid increase of available processing power has lead to the widespread use of multimedia streaming as a means to convey information. This work aims at providing an open architecture designed to support scalable streaming to a large number of clients using application layer multicast. The architecture is based on media relay nodes that can be deployed transparently to any existing media distribution scheme, which can support media streamed using the RTP and RTSP protocols. The architecture is based on overlay networks at application level, featuring rate adaptation mechanisms for responding to network congestion.

  9. Fast Distributed Dynamics of Semantic Networks via Social Media

    Directory of Open Access Journals (Sweden)

    Facundo Carrillo

    2015-01-01

    Full Text Available We investigate the dynamics of semantic organization using social media, a collective expression of human thought. We propose a novel, time-dependent semantic similarity measure (TSS, based on the social network Twitter. We show that TSS is consistent with static measures of similarity but provides high temporal resolution for the identification of real-world events and induced changes in the distributed structure of semantic relationships across the entire lexicon. Using TSS, we measured the evolution of a concept and its movement along the semantic neighborhood, driven by specific news/events. Finally, we showed that particular events may trigger a temporary reorganization of elements in the semantic network.

  10. Distribution Networks Management with High Penetration of Photovoltaic Panels

    DEFF Research Database (Denmark)

    Morais, Hugo

    2013-01-01

    The photovoltaic solar panels penetration increases significantly in recent years in several European countries, mainly in the low voltage and medium voltage networks supported by governmental policies and incentives. Consequently, the acquisition and installation costs of PV panels decrease...... and the know–how increase significantly. Presently is important the use of new management methodologies in distribution networks to support the growing penetration of PV panels. In some countries, like in Germany and in Italy, the solar generation based in photovoltaic panels supply 40% of the demand in some...

  11. Distributing Workflows over a Ubiquitous P2P Network

    Directory of Open Access Journals (Sweden)

    Eddie Al-Shakarchi

    2007-01-01

    Full Text Available This paper discusses issues in the distribution of bundled workflows across ubiquitous peer-to-peer networks for the application of music information retrieval. The underlying motivation for this work is provided by the DART project, which aims to develop a novel music recommendation system by gathering statistical data using collaborative filtering techniques and the analysis of the audio itsel, in order to create a reliable and comprehensive database of the music that people own and which they listen to. To achieve this, the DART scientists creating the algorithms need the ability to distribute the Triana workflows they create, representing the analysis to be performed, across the network on a regular basis (perhaps even daily in order to update the network as a whole with new workflows to be executed for the analysis. DART uses a similar approach to BOINC but differs in that the workers receive input data in the form of a bundled Triana workflow, which is executed in order to process any MP3 files that they own on their machine. Once analysed, the results are returned to DART's distributed database that collects and aggregates the resulting information. DART employs the use of package repositories to decentralise the distribution of such workflow bundles and this approach is validated in this paper through simulations that show that suitable scalability is maintained through the system as the number of participants increases. The results clearly illustrate the effectiveness of the approach.

  12. Distributed Global Function Model Finding for Wireless Sensor Network Data

    Directory of Open Access Journals (Sweden)

    Song Deng

    2016-01-01

    Full Text Available Function model finding has become an important tool for analysis of data collected from wireless sensor networks (WSNs. With the development of WSNs, a large number of sensors have been widely deployed so that the collected data show the characteristics of distribution and mass. For distributed and massive sensor data, traditional centralized function model finding algorithms would lead to a significant decrease in performance. To solve this problem, this paper proposes a distributed global function model finding algorithm for wireless sensor network data (DGFMF-WSND. In DGFMF-WSND, on the basis of gene expression programming (GEP, an adaptive population generation strategy based on sub-population associated evolution is applied to improve the convergence speed of GEP. Secondly, to solve the generation of global function model in distributed wireless sensor networks data, this paper provides a global model generation algorithm based on unconstrained nonlinear least squares. Four representative datasets are used to evaluate the performance of the proposed algorithm. The comparative results show that the improved GEP with adaptive population generation strategy outperforms all other algorithms on the average convergence speed, time-consumption, value of R-square, and prediction accuracy. Meanwhile, experimental results also show that DGFMF-WSND has a clear advantage in terms of time-consumption and error of fitting. Moreover, with increasing of dataset size, DGFMF-WSND also demonstrates good speed-up ratio and scale-up ratio.

  13. Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons.

    Directory of Open Access Journals (Sweden)

    Kendra S Burbank

    2015-12-01

    Full Text Available The autoencoder algorithm is a simple but powerful unsupervised method for training neural networks. Autoencoder networks can learn sparse distributed codes similar to those seen in cortical sensory areas such as visual area V1, but they can also be stacked to learn increasingly abstract representations. Several computational neuroscience models of sensory areas, including Olshausen & Field's Sparse Coding algorithm, can be seen as autoencoder variants, and autoencoders have seen extensive use in the machine learning community. Despite their power and versatility, autoencoders have been difficult to implement in a biologically realistic fashion. The challenges include their need to calculate differences between two neuronal activities and their requirement for learning rules which lead to identical changes at feedforward and feedback connections. Here, we study a biologically realistic network of integrate-and-fire neurons with anatomical connectivity and synaptic plasticity that closely matches that observed in cortical sensory areas. Our choice of synaptic plasticity rules is inspired by recent experimental and theoretical results suggesting that learning at feedback connections may have a different form from learning at feedforward connections, and our results depend critically on this novel choice of plasticity rules. Specifically, we propose that plasticity rules at feedforward versus feedback connections are temporally opposed versions of spike-timing dependent plasticity (STDP, leading to a symmetric combined rule we call Mirrored STDP (mSTDP. We show that with mSTDP, our network follows a learning rule that approximately minimizes an autoencoder loss function. When trained with whitened natural image patches, the learned synaptic weights resemble the receptive fields seen in V1. Our results use realistic synaptic plasticity rules to show that the powerful autoencoder learning algorithm could be within the reach of real biological

  14. 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.

  15. Managed Networks of Competence in Distributed Organizations - The role of ICT and Identity Construction in Knowledge Sharing

    OpenAIRE

    Hermanrud, Inge

    2014-01-01

    Knowledge is seen as a main driving force for current public organizations to fulfill their mission in changing environments, and for some organizations the response is to design managed networks for knowledge sharing and learning. Distributed organizations, which this study examines, are particularly challenged to develop knowledge sharing and learning across distance to strengthen their operative units. Communities of practice have become a central notion for the management o...

  16. Learning Convex Bodies under uniform distribution

    NARCIS (Netherlands)

    Kern, Walter

    1992-01-01

    We prove that the class of convex bodies contained in a fixed (prescribed) bounded region R c lQd is PAC-learnable, if the positive examples are drawn according to the uniform distribution Df on the target concept (the distribution Dm on the negative examples may be arbitrary). Our results extend

  17. Security analysis of quantum key distribution on passive optical networks.

    Science.gov (United States)

    Lim, Kyongchun; Ko, Heasin; Suh, Changho; Rhee, June-Koo Kevin

    2017-05-15

    Needs for providing security to end users have brought installation of quantum key distribution (QKD) in one-to-many access networks such as passive optical networks. In the networks, a presence of optical power splitters makes issues for secure key rate more important. However, researches for QKD in access networks have mainly focused on implementation issues rather than protocol development for key rate enhancement. Since secure key rate is theoretically limited by a protocol, researches without protocol development cannot overcome the limit of secure key rate given by a protocol. This brings need of researches for protocol development. In this paper, we provide a new approach which provides secure key rate enhancement over the conventional protocol. Specifically, we propose the secure key rate formula in a passive optical network by extending the secure key rate formula based on the decoy-state BB84 protocol. For a passive optical network, we provide a way that incorporates cooperation across end users. Then, we show that the way can mitigate a photon number splitting (PNS) attack which is crucial in an well known decoy BB84 protocol. Especially, the proposed scheme enables multi-photon states to serve as secure keys unlike the conventional decoy BB84 protocol. Numerical simulations demonstrate that our proposed scheme outperforms the decoy BB84 protocol in secure key rate.

  18. FeUdal Networks for Hierarchical Reinforcement Learning

    OpenAIRE

    Vezhnevets, Alexander Sasha; Osindero, Simon; Schaul, Tom; Heess, Nicolas; Jaderberg, Max; Silver, David; Kavukcuoglu, Koray

    2017-01-01

    We introduce FeUdal Networks (FuNs): a novel architecture for hierarchical reinforcement learning. Our approach is inspired by the feudal reinforcement learning proposal of Dayan and Hinton, and gains power and efficacy by decoupling end-to-end learning across multiple levels -- allowing it to utilise different resolutions of time. Our framework employs a Manager module and a Worker module. The Manager operates at a lower temporal resolution and sets abstract goals which are conveyed to and e...

  19. A Framework for Distributed Deep Learning Layer Design in Python

    OpenAIRE

    McLeod, Clay

    2015-01-01

    In this paper, a framework for testing Deep Neural Network (DNN) design in Python is presented. First, big data, machine learning (ML), and Artificial Neural Networks (ANNs) are discussed to familiarize the reader with the importance of such a system. Next, the benefits and detriments of implementing such a system in Python are presented. Lastly, the specifics of the system are explained, and some experimental results are presented to prove the effectiveness of the system.

  20. A Contextualised Multi-Platform Framework to Support Blended Learning Scenarios in Learning Networks

    NARCIS (Netherlands)

    De Jong, Tim; Fuertes, Alba; Schmeits, Tally; Specht, Marcus; Koper, Rob

    2008-01-01

    De Jong, T., Fuertes, A., Schmeits, T., Specht, M., & Koper, R. (2009). A Contextualised Multi-Platform Framework to Support Blended Learning Scenarios in Learning Networks. In D. Goh (Ed.), Multiplatform E-Learning Systems and Technologies: Mobile Devices for Ubiquitous ICT-Based Education (pp.

  1. The teacher as designer? What is the role of ‘learning design’ in networked learning?

    DEFF Research Database (Denmark)

    Konnerup, Ulla; Ryberg, Thomas; Sørensen, Mia Thyrre

    2018-01-01

    (TEL), networked learning, designs for learning and draw out their development and branching to understand potentially different ontological or epistemological roots they draw on. Further, we wish to inquire into how the area of ‘Learning Design’ relate to or distances itself from the philosophy...

  2. QSAR modelling using combined simple competitive learning networks and RBF neural networks.

    Science.gov (United States)

    Sheikhpour, R; Sarram, M A; Rezaeian, M; Sheikhpour, E

    2018-04-01

    The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.

  3. Learning Local Components to Understand Large Bayesian Networks

    DEFF Research Database (Denmark)

    Zeng, Yifeng; Xiang, Yanping; Cordero, Jorge

    2009-01-01

    (domain experts) to extract accurate information from a large Bayesian network due to dimensional difficulty. We define a formulation of local components and propose a clustering algorithm to learn such local components given complete data. The algorithm groups together most inter-relevant attributes...... in a domain. We evaluate its performance on three benchmark Bayesian networks and provide results in support. We further show that the learned components may represent local knowledge more precisely in comparison to the full Bayesian networks when working with a small amount of data....

  4. Understanding the Context of Learning in an Online Social Network for Health Professionals' Informal Learning.

    Science.gov (United States)

    Li, Xin; Gray, Kathleen; Verspoor, Karin; Barnett, Stephen

    2017-01-01

    Online social networks (OSN) enable health professionals to learn informally, for example by sharing medical knowledge, or discussing practice management challenges and clinical issues. Understanding the learning context in OSN is necessary to get a complete picture of the learning process, in order to better support this type of learning. This study proposes critical contextual factors for understanding the learning context in OSN for health professionals, and demonstrates how these contextual factors can be used to analyse the learning context in a designated online learning environment for health professionals.

  5. Evolution of individual versus social learning on social networks.

    Science.gov (United States)

    Tamura, Kohei; Kobayashi, Yutaka; Ihara, Yasuo

    2015-03-06

    A number of studies have investigated the roles played by individual and social learning in cultural phenomena and the relative advantages of the two learning strategies in variable environments. Because social learning involves the acquisition of behaviours from others, its utility depends on the availability of 'cultural models' exhibiting adaptive behaviours. This indicates that social networks play an essential role in the evolution of learning. However, possible effects of social structure on the evolution of learning have not been fully explored. Here, we develop a mathematical model to explore the evolutionary dynamics of learning strategies on social networks. We first derive the condition under which social learners (SLs) are selectively favoured over individual learners in a broad range of social network. We then obtain an analytical approximation of the long-term average frequency of SLs in homogeneous networks, from which we specify the condition, in terms of three relatedness measures, for social structure to facilitate the long-term evolution of social learning. Finally, we evaluate our approximation by Monte Carlo simulations in complete graphs, regular random graphs and scale-free networks. We formally show that whether social structure favours the evolution of social learning is determined by the relative magnitudes of two effects of social structure: localization in competition, by which competition between learning strategies is evaded, and localization in cultural transmission, which slows down the spread of adaptive traits. In addition, our estimates of the relatedness measures suggest that social structure disfavours the evolution of social learning when selection is weak. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

  6. SOCIAL NETWORKS AS A MEANS OF LEARNING PROCESS

    Directory of Open Access Journals (Sweden)

    T. Arhipova

    2015-02-01

    Full Text Available This paper presents an analysis of social networks in terms of their possible use in the education system. The integration of new information and communication technologies with the technologies of learning is gradually changing the concept of modern education and promotes educational environment focused on the interests and personal development, achievement of her current levels of education, internationalization and increasing access to educational resources, creating conditions for mobility of students and teachers improving the quality of education and the formation of a single educational space. The peculiarity of such an environment is to provide creative research activity of the teacher and students in the learning process. Network services provide the means by which students can act as active creators of media content. The paper presents the results of a study of the advantages and disadvantages of using web communities in the educational process. Articulated pedagogical conditions of the effective organization of educational process in the virtual learning environment using social networks. The experience of the use of social networks in the learning process of the university. Such networking technologies, such as forums, blogs, wikis, educational portals and automated systems for distance learning, having undoubted didactic and methodological advantages, inferior social networks in terms of involving users in their communication space, as well as compliance with the intellectual, creative and social needs.

  7. Situation awareness of active distribution network: roadmap, technologies, and bottlenecks

    DEFF Research Database (Denmark)

    Lin, Jin; Wan, Can; Song, Yonghua

    2016-01-01

    With the rapid development of local generation and demand response, the active distribution network (ADN), which aggregates and manages miscellaneous distributed resources, has moved from theory to practice. Secure and optimal operations now require an advanced situation awareness (SA) system so...... in the project of developing an SA system as the basic component of a practical active distribution management system (ADMS) deployed in Beijing, China, is presented. This paper reviews the ADN’s development roadmap by illustrating the changes that are made in elements, topology, structure, and control scheme....... Taking into consideration these hardware changes, a systematic framework is proposed for the main components and the functional hierarchy of an SA system for the ADN. The SA system’s implementation bottlenecks are also presented, including, but not limited to issues in big data platform, distribution...

  8. 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.

  9. Teachers' Self-Initiated Professional Learning through Personal Learning Networks

    Science.gov (United States)

    Tour, Ekaterina

    2017-01-01

    It is widely acknowledged that to be able to teach language and literacy with digital technologies, teachers need to engage in relevant professional learning. Existing formal models of professional learning are often criticised for being ineffective. In contrast, informal and self-initiated forms of learning have been recently recognised as…

  10. Learning Networks using Learning Design. A firt collection of papers

    NARCIS (Netherlands)

    Koper, Rob; Spoelstra, Howard; Burgos, Daniel

    2004-01-01

    CONTENT
    THE LEARNING DESIGN SPECIFICATION. INTRODUCTION
    1. Modeling units of study from a pedagogical perspective. The pedagogical meta-model behind EML 2. Representing the Learning Design of Units of Learning 3. Educational Modelling Language: Modelling reusable, interoperable, rich and

  11. Optimizing intermittent water supply in urban pipe distribution networks

    CERN Document Server

    Lieb, Anna M; Wilkening, Jon

    2015-01-01

    In many urban areas of the developing world, piped water is supplied only intermittently, as valves direct water to different parts of the water distribution system at different times. The flow is transient, and may transition between free-surface and pressurized, resulting in complex dynamical features with important consequences for water suppliers and users. Here, we develop a computational model of transition, transient pipe flow in a network, accounting for a wide variety of realistic boundary conditions. We validate the model against several published data sets, and demonstrate its use on a real pipe network. The model is extended to consider several optimization problems motivated by realistic scenarios. We demonstrate how to infer water flow in a small pipe network from a single pressure sensor, and show how to control water inflow to minimize damaging pressure gradients.

  12. A cognitive fault diagnosis system for distributed sensor networks.

    Science.gov (United States)

    Alippi, Cesare; Ntalampiras, Stavros; Roveri, Manuel

    2013-08-01

    This paper introduces a novel cognitive fault diagnosis system (FDS) for distributed sensor networks that takes advantage of spatial and temporal relationships among sensors. The proposed FDS relies on a suitable functional graph representation of the network and a two-layer hierarchical architecture designed to promptly detect and isolate faults. The lower processing layer exploits a novel change detection test (CDT) based on hidden Markov models (HMMs) configured to detect variations in the relationships between couples of sensors. HMMs work in the parameter space of linear time-invariant dynamic systems, approximating, over time, the relationship between two sensors; changes in the approximating model are detected by inspecting the HMM likelihood. Information provided by the CDT layer is then passed to the cognitive one, which, by exploiting the graph representation of the network, aggregates information to discriminate among faults, changes in the environment, and false positives induced by the model bias of the HMMs.

  13. Research on three-phase unbalanced distribution network reconfiguration strategy

    Science.gov (United States)

    Hu, Shuang; Li, Ke-Jun; Xu, Yanshun; Liu, Zhijie; Guo, Jing; Wang, Zhuodi

    2017-01-01

    With the development of social economy, the loads installed in the distribution network become more and more complex which may cause the three-phase unbalance problems. This paper proposes an optimal reconfiguration approach based on mixed integer quadric programming (MIQP) method to address the three-phase unbalance problem. It aims to minimize the total network losses of the system. By using several square constraints to substitute the circular constraint, the original optimization problem is linearized and converted into a mixed-integer linear programming (MILP) model. Then this MILP problem is solved in general algebraic model system (GAMS) software using CPLEX solver. The additional losses caused by three-phase unbalanced are also considered. An IEEE 34 nodes test system is used to demonstrate the feasibility and effectiveness of the proposed method. The results show that the losses and the voltage violation mitigation in the network can be reduced significantly.

  14. Distributed cloud association in downlink multicloud radio access networks

    KAUST Repository

    Dahrouj, Hayssam

    2015-03-01

    This paper considers a multicloud radio access network (M-CRAN), wherein each cloud serves a cluster of base-stations (BS\\'s) which are connected to the clouds through high capacity digital links. The network comprises several remote users, where each user can be connected to one (and only one) cloud. This paper studies the user-to-cloud-assignment problem by maximizing a network-wide utility subject to practical cloud connectivity constraints. The paper solves the problem by using an auction-based iterative algorithm, which can be implemented in a distributed fashion through a reasonable exchange of information between the clouds. The paper further proposes a centralized heuristic algorithm, with low computational complexity. Simulations results show that the proposed algorithms provide appreciable performance improvements as compared to the conventional cloud-less assignment solutions. © 2015 IEEE.

  15. A Distributed Network Mobility Management Scheme for Hierarchical Mobile IPv6 Networks

    Science.gov (United States)

    Kawano, Keita; Kinoshita, Kazuhiko; Yamai, Nariyoshi

    Route optimization for network mobility is a key technique for providing a node in a mobile network (Mobile Network Node or MNN) with high quality broadband communications. Many schemes adding route optimization function to Network Mobility (NEMO) Basic Support protocol, the standardized network mobility management protocol from the IETF nemo working group, have already been proposed in recent years. One such scheme, a scheme using Hierarchical Mobile IPv6 (HMIPv6) aims to overcome micromobility management issues as well by applying a mechanism based on HMIPv6. The traditional scheme, however, suffers from a significant number of signaling messages as the number of MNNs and/or the number of their Correspondent Nodes (CNs) increase, because many messages notifying the MNNs' Home Agents (HAMNNs) and the CNs of the mobile network's movement are generated simultaneously each time the mobile network moves to the domain of another micromobility management router (Mobility Anchor Point or MAP). This paper proposes a scheme to overcome this problem. Our scheme reduces the number of signaling messages generated at the same time by managing the mobility of MNNs using multiple MAPs distributed within a network for load sharing. The results of simulation experiments show that our scheme works efficiently compared to the traditional scheme when a mobile network has many MNNs and/or these MNNs communicate with many CNs.

  16. Implementation of quantum key distribution network simulation module in the network simulator NS-3

    Science.gov (United States)

    Mehic, Miralem; Maurhart, Oliver; Rass, Stefan; Voznak, Miroslav

    2017-10-01

    As the research in quantum key distribution (QKD) technology grows larger and becomes more complex, the need for highly accurate and scalable simulation technologies becomes important to assess the practical feasibility and foresee difficulties in the practical implementation of theoretical achievements. Due to the specificity of the QKD link which requires optical and Internet connection between the network nodes, to deploy a complete testbed containing multiple network hosts and links to validate and verify a certain network algorithm or protocol would be very costly. Network simulators in these circumstances save vast amounts of money and time in accomplishing such a task. The simulation environment offers the creation of complex network topologies, a high degree of control and repeatable experiments, which in turn allows researchers to conduct experiments and confirm their results. In this paper, we described the design of the QKD network simulation module which was developed in the network simulator of version 3 (NS-3). The module supports simulation of the QKD network in an overlay mode or in a single TCP/IP mode. Therefore, it can be used to simulate other network technologies regardless of QKD.

  17. Approximation methods for efficient learning of Bayesian networks

    CERN Document Server

    Riggelsen, C

    2008-01-01

    This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. The topics discussed are: basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and, the concept of incomplete data. In order to provide a coherent treatment of matters, thereby helping the reader to gain a thorough understanding of the whole concept of learning Bayesian networks from (in)complete data, this publication combines in a clarifying way all the issues presented in the papers with previously unpublished work.

  18. Using ELECTRE TRI to support maintenance of water distribution networks

    Directory of Open Access Journals (Sweden)

    Flavio Trojan

    2012-08-01

    Full Text Available Problems encountered in the context of the maintenance management of water supply are evidenced by the lack of decision support models which gives a manager overview of the system. This paper, therefore, develops a model that uses, in its framework, the multicriteria outranking method ELECTRE TRI. The objective is to sort the areas of water flow measurement of a water distribution network, by priority of maintenance, with data collected from an automated system of abnormalities detection. This sorting is designed to support maintenance decisions in terms of the measure more appropriate to be applied per region. To illustrate the proposed model, an application was performed in a city with 100 thousand water connections. With this model it becomes possible to improve the allocation of maintenance measures for regions and mainly to improve the operation of the distribution network.

  19. Mapping distributed brain function and networks with diffuse optical tomography

    Science.gov (United States)

    Eggebrecht, Adam T.; Ferradal, Silvina L.; Robichaux-Viehoever, Amy; Hassanpour, Mahlega S.; Dehghani, Hamid; Snyder, Abraham Z.; Hershey, Tamara; Culver, Joseph P.

    2014-06-01

    Mapping of human brain function has revolutionized systems neuroscience. However, traditional functional neuroimaging by positron emission tomography or functional magnetic resonance imaging cannot be used when applications require portability, or are contraindicated because of ionizing radiation (positron emission tomography) or implanted metal (functional magnetic resonance imaging). Optical neuroimaging offers a non-invasive alternative that is radiation free and compatible with implanted metal and electronic devices (for example, pacemakers). However, optical imaging technology has heretofore lacked the combination of spatial resolution and wide field of view sufficient to map distributed brain functions. Here, we present a high-density diffuse optical tomography imaging array that can map higher-order, distributed brain function. The system was tested by imaging four hierarchical language tasks and multiple resting-state networks including the dorsal attention and default mode networks. Finally, we imaged brain function in patients with Parkinson's disease and implanted deep brain stimulators that preclude functional magnetic resonance imaging.

  20. Distributed Signal Processing for Wireless EEG Sensor Networks.

    Science.gov (United States)

    Bertrand, Alexander

    2015-11-01

    Inspired by ongoing evolutions in the field of wireless body area networks (WBANs), this tutorial paper presents a conceptual and exploratory study of wireless electroencephalography (EEG) sensor networks (WESNs), with an emphasis on distributed signal processing aspects. A WESN is conceived as a modular neuromonitoring platform for high-density EEG recordings, in which each node is equipped with an electrode array, a signal processing unit, and facilities for wireless communication. We first address the advantages of such a modular approach, and we explain how distributed signal processing algorithms make WESNs more power-efficient, in particular by avoiding data centralization. We provide an overview of distributed signal processing algorithms that are potentially applicable in WESNs, and for illustration purposes, we also provide a more detailed case study of a distributed eye blink artifact removal algorithm. Finally, we study the power efficiency of these distributed algorithms in comparison to their centralized counterparts in which all the raw sensor signals are centralized in a near-end or far-end fusion center.

  1. Distributed estimation based on observations prediction in wireless sensor networks

    KAUST Repository

    Bouchoucha, Taha

    2015-03-19

    We consider wireless sensor networks (WSNs) used for distributed estimation of unknown parameters. Due to the limited bandwidth, sensor nodes quantize their noisy observations before transmission to a fusion center (FC) for the estimation process. In this letter, the correlation between observations is exploited to reduce the mean-square error (MSE) of the distributed estimation. Specifically, sensor nodes generate local predictions of their observations and then transmit the quantized prediction errors (innovations) to the FC rather than the quantized observations. The analytic and numerical results show that transmitting the innovations rather than the observations mitigates the effect of quantization noise and hence reduces the MSE. © 2015 IEEE.

  2. Dynamic Subsidy Method for Congestion Management in Distribution Networks

    DEFF Research Database (Denmark)

    Huang, Shaojun; Wu, Qiuwei

    2016-01-01

    Dynamic subsidy (DS) is a locational price paid by the distribution system operator (DSO) to its customers in order to shift energy consumption to designated hours and nodes. It is promising for demand side management and congestion management. This paper proposes a new DS method for congestion...... of the Roy Billinton Test System (RBTS) with high penetration of electric vehicles (EVs) and heat pumps (HPs). The case studies demonstrate the efficacy of the DS method for congestion management in distribution networks. Studies in this paper show that the DS method offers the customers a fair opportunity...

  3. Distributed Leadership and Digital Collaborative Learning: A Synergistic Relationship?

    Science.gov (United States)

    Harris, Alma; Jones, Michelle; Baba, Suria

    2013-01-01

    This paper explores the synergy between distributed leadership and digital collaborative learning. It argues that distributed leadership offers an important theoretical lens for understanding and explaining how digital collaboration is best supported and led. Drawing upon evidence from two online educational platforms, the paper explores the…

  4. Distance metric learning for complex networks: Towards size-independent comparison of network structures

    Science.gov (United States)

    Aliakbary, Sadegh; Motallebi, Sadegh; Rashidian, Sina; Habibi, Jafar; Movaghar, Ali

    2015-02-01

    Real networks show nontrivial topological properties such as community structure and long-tail degree distribution. Moreover, many network analysis applications are based on topological comparison of complex networks. Classification and clustering of networks, model selection, and anomaly detection are just some applications of network comparison. In these applications, an effective similarity metric is needed which, given two complex networks of possibly different sizes, evaluates the amount of similarity between the structural features of the two networks. Traditional graph comparison approaches, such as isomorphism-based methods, are not only too time consuming but also inappropriate to compare networks with different sizes. In this paper, we propose an intelligent method based on the genetic algorithms for integrating, selecting, and weighting the network features in order to develop an effective similarity measure for complex networks. The proposed similarity metric outperforms state of the art methods with respect to different evaluation criteria.

  5. Prespeech motor learning in a neural network using reinforcement.

    Science.gov (United States)

    Warlaumont, Anne S; Westermann, Gert; Buder, Eugene H; Oller, D Kimbrough

    2013-02-01

    Vocal motor development in infancy provides a crucial foundation for language development. Some significant early accomplishments include learning to control the process of phonation (the production of sound at the larynx) and learning to produce the sounds of one's language. Previous work has shown that social reinforcement shapes the kinds of vocalizations infants produce. We present a neural network model that provides an account of how vocal learning may be guided by reinforcement. The model consists of a self-organizing map that outputs to muscles of a realistic vocalization synthesizer. Vocalizations are spontaneously produced by the network. If a vocalization meets certain acoustic criteria, it is reinforced, and the weights are updated to make similar muscle activations increasingly likely to recur. We ran simulations of the model under various reinforcement criteria and tested the types of vocalizations it produced after learning in the different conditions. When reinforcement was contingent on the production of phonated (i.e. voiced) sounds, the network's post-learning productions were almost always phonated, whereas when reinforcement was not contingent on phonation, the network's post-learning productions were almost always not phonated. When reinforcement was contingent on both phonation and proximity to English vowels as opposed to Korean vowels, the model's post-learning productions were more likely to resemble the English vowels and vice versa. Copyright © 2012 Elsevier Ltd. All rights reserved.

  6. Functional brain networks develop from a "local to distributed" organization.

    Directory of Open Access Journals (Sweden)

    Damien A Fair

    2009-05-01

    Full Text Available The mature human brain is organized into a collection of specialized functional networks that flexibly interact to support various cognitive functions. Studies of development often attempt to identify the organizing principles that guide the maturation of these functional networks. In this report, we combine resting state functional connectivity MRI (rs-fcMRI, graph analysis, community detection, and spring-embedding visualization techniques to analyze four separate networks defined in earlier studies. As we have previously reported, we find, across development, a trend toward 'segregation' (a general decrease in correlation strength between regions close in anatomical space and 'integration' (an increased correlation strength between selected regions distant in space. The generalization of these earlier trends across multiple networks suggests that this is a general developmental principle for changes in functional connectivity that would extend to large-scale graph theoretic analyses of large-scale brain networks. Communities in children are predominantly arranged by anatomical proximity, while communities in adults predominantly reflect functional relationships, as defined from adult fMRI studies. In sum, over development, the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more "distributed" architecture in young adults. We argue that this "local to distributed" developmental characterization has important implications for understanding the development of neural systems underlying cognition. Further, graph metrics (e.g., clustering coefficients and average path lengths are similar in child and adult graphs, with both showing "small-world"-like properties, while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults. These observations suggest that early school age children and adults

  7. Functional brain networks develop from a "local to distributed" organization.

    Science.gov (United States)

    Fair, Damien A; Cohen, Alexander L; Power, Jonathan D; Dosenbach, Nico U F; Church, Jessica A; Miezin, Francis M; Schlaggar, Bradley L; Petersen, Steven E

    2009-05-01

    The mature human brain is organized into a collection of specialized functional networks that flexibly interact to support various cognitive functions. Studies of development often attempt to identify the organizing principles that guide the maturation of these functional networks. In this report, we combine resting state functional connectivity MRI (rs-fcMRI), graph analysis, community detection, and spring-embedding visualization techniques to analyze four separate networks defined in earlier studies. As we have previously reported, we find, across development, a trend toward 'segregation' (a general decrease in correlation strength) between regions close in anatomical space and 'integration' (an increased correlation strength) between selected regions distant in space. The generalization of these earlier trends across multiple networks suggests that this is a general developmental principle for changes in functional connectivity that would extend to large-scale graph theoretic analyses of large-scale brain networks. Communities in children are predominantly arranged by anatomical proximity, while communities in adults predominantly reflect functional relationships, as defined from adult fMRI studies. In sum, over development, the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more "distributed" architecture in young adults. We argue that this "local to distributed" developmental characterization has important implications for understanding the development of neural systems underlying cognition. Further, graph metrics (e.g., clustering coefficients and average path lengths) are similar in child and adult graphs, with both showing "small-world"-like properties, while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults. These observations suggest that early school age children and adults both have

  8. Anti-correlations in the degree distribution increase stimulus detection performance in noisy spiking neural networks.

    Science.gov (United States)

    Martens, Marijn B; Houweling, Arthur R; E Tiesinga, Paul H

    2017-02-01

    Neuronal circuits in the rodent barrel cortex are characterized by stable low firing rates. However, recent experiments show that short spike trains elicited by electrical stimulation in single neurons can induce behavioral responses. Hence, the underlying neural networks provide stability against internal fluctuations in the firing rate, while simultaneously making the circuits sensitive to small external perturbations. Here we studied whether stability and sensitivity are affected by the connectivity structure in recurrently connected spiking networks. We found that anti-correlation between the number of afferent (in-degree) and efferent (out-degree) synaptic connections of neurons increases stability against pathological bursting, relative to networks where the degrees were either positively correlated or uncorrelated. In the stable network state, stimulation of a few cells could lead to a detectable change in the firing rate. To quantify the ability of networks to detect the stimulation, we used a receiver operating characteristic (ROC) analysis. For a given level of background noise, networks with anti-correlated degrees displayed the lowest false positive rates, and consequently had the highest stimulus detection performance. We propose that anti-correlation in the degree distribution may be a computational strategy employed by sensory cortices to increase the detectability of external stimuli. We show that networks with anti-correlated degrees can in principle be formed by applying learning rules comprised of a combination of spike-timing dependent plasticity, homeostatic plasticity and pruning to networks with uncorrelated degrees. To test our prediction we suggest a novel experimental method to estimate correlations in the degree distribution.

  9. Application of geographic information system in distribution power network automation

    Science.gov (United States)

    Wei, Xianmin

    2011-02-01

    Geographic information system (GIS) is the computer system in support of computer software with collection, storage, management, retrieval and comprehensive analysis of a variety of geospatial information, with various forms output data and graphics products. This paper introduced GIS data organization and its main applications in distribution power network automation, including both offline and online, and proposed component-based system development model and the need to establish WEBGIS and reliability.

  10. Distributed Optimization of Multi Beam Directional Communication Networks

    Science.gov (United States)

    2017-06-30

    spatial processing strategies for multi-beam transmission with a simple MAC layer in a simulation study. Full-duplex communications ease the burden on...Distributed Optimization of Multi-Beam Directional Communication Networks Theodoros Tsiligkaridis MIT Lincoln Laboratory Lexington, MA 02141, USA...based routing. I. INTRODUCTION Missions where multiple communication goals are of in- terest are becoming more prevalent in military applications

  11. Impact of Demand Side Management in Active Distribution Networks

    DEFF Research Database (Denmark)

    Ponnaganti, Pavani; Bak-Jensen, Birgitte; Pillai, Jayakrishnan Radhakrishna

    2017-01-01

    Demand Side Management (DSM) is an efficient flexible program which helps distribution network operators to meet the future critical peak demand. It is executed in cases of not only technical issues like voltage sag or swell, transformer burdening, cable congestions, but also to increase the degree...... vehicle, electric heating etc. are present. Simulations are carried out in Danish low voltage grid for summer and winter cases....

  12. Optimizing intermittent water supply in urban pipe distribution networks

    OpenAIRE

    Lieb, Anna M.; Rycroft, Chris H.; Wilkening, Jon

    2015-01-01

    In many urban areas of the developing world, piped water is supplied only intermittently, as valves direct water to different parts of the water distribution system at different times. The flow is transient, and may transition between free-surface and pressurized, resulting in complex dynamical features with important consequences for water suppliers and users. Here, we develop a computational model of transition, transient pipe flow in a network, accounting for a wide variety of realistic bo...

  13. DS+: Reliable Distributed Snapshot Algorithm for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Gamze Uslu

    2013-01-01

    Full Text Available Acquiring the snapshot of a distributed system helps gathering system related global state. In wireless sensor networks (WSNs, global state shows if a node is terminated or deadlock occurs along with many other situations which prevents a WSN from fully functioning. In this paper, we present a fully distributed snapshot acquisition algorithm adapted to tree topology wireless sensor networks (WSNs. Since snapshot acquisition is through control messages sent over highly lossy wireless channels and congested nodes, we enhanced the snapshot algorithm with a sink based reliability suit to achieve robustness. We analyzed the performance of the algorithm in terms of snapshot success ratio and response time in simulation and experimental small test bed environment. The results reveal that the proposed tailor made reliability model increases snapshot acquisition performance by a factor of seven and response time by a factor of two in a 30-node network. We have also shown that the proposed algorithm outperforms its counterparts in the specified network setting.

  14. 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.

  15. Optimization model for the design of distributed wastewater treatment networks

    Directory of Open Access Journals (Sweden)

    Ibrić Nidret

    2012-01-01

    Full Text Available In this paper we address the synthesis problem of distributed wastewater networks using mathematical programming approach based on the superstructure optimization. We present a generalized superstructure and optimization model for the design of the distributed wastewater treatment networks. The superstructure includes splitters, treatment units, mixers, with all feasible interconnections including water recirculation. Based on the superstructure the optimization model is presented. The optimization model is given as a nonlinear programming (NLP problem where the objective function can be defined to minimize the total amount of wastewater treated in treatment operations or to minimize the total treatment costs. The NLP model is extended to a mixed integer nonlinear programming (MINLP problem where binary variables are used for the selection of the wastewater treatment technologies. The bounds for all flowrates and concentrations in the wastewater network are specified as general equations. The proposed models are solved using the global optimization solvers (BARON and LINDOGlobal. The application of the proposed models is illustrated on the two wastewater network problems of different complexity. First one is formulated as the NLP and the second one as the MINLP. For the second one the parametric and structural optimization is performed at the same time where optimal flowrates, concentrations as well as optimal technologies for the wastewater treatment are selected. Using the proposed model both problems are solved to global optimality.

  16. Uses and benefits of information and communication technologies in distributed learning systems

    OpenAIRE

    Einan, Sveinung

    1999-01-01

    Masteroppgave i informasjons- og kommunikasjonsteknologi 1999 - Høgskolen i Agder, Grimstad Purpose and Methods. The paper is a presentation of my post graduate thesis at HiA. The focus of this study is Akademiet, a distributed learning system developed by Telenor Kompetanse in Grimstad, Norway. Akademiet is a web application developed for use by Telenor employees and clients who wish to take courses facilitated by the Internet. The increasing dissemination of networked computers, espec...

  17. Software Comparison for Renewable Energy Deployment in a Distribution Network

    Energy Technology Data Exchange (ETDEWEB)

    Gao, David Wenzhong [Alternative Power Innovations, LLC, Sharonville, OH (United States); Muljadi, Eduard [National Renewable Energy Lab. (NREL), Golden, CO (United States); Tian, Tian [National Renewable Energy Lab. (NREL), Golden, CO (United States); Miller, Mackay [National Renewable Energy Lab. (NREL), Golden, CO (United States)

    2017-02-22

    The main objective of this report is to evaluate different software options for performing robust distributed generation (DG) power system modeling. The features and capabilities of four simulation tools, OpenDSS, GridLAB-D, CYMDIST, and PowerWorld Simulator, are compared to analyze their effectiveness in analyzing distribution networks with DG. OpenDSS and GridLAB-D, two open source software, have the capability to simulate networks with fluctuating data values. These packages allow the running of a simulation each time instant by iterating only the main script file. CYMDIST, a commercial software, allows for time-series simulation to study variations on network controls. PowerWorld Simulator, another commercial tool, has a batch mode simulation function through the 'Time Step Simulation' tool, which obtains solutions for a list of specified time points. PowerWorld Simulator is intended for analysis of transmission-level systems, while the other three are designed for distribution systems. CYMDIST and PowerWorld Simulator feature easy-to-use graphical user interfaces (GUIs). OpenDSS and GridLAB-D, on the other hand, are based on command-line programs, which increase the time necessary to become familiar with the software packages.

  18. Determining Vision Graphs for Distributed Camera Networks Using Feature Digests

    Directory of Open Access Journals (Sweden)

    Richard J. Radke

    2007-01-01

    Full Text Available We propose a decentralized method for obtaining the vision graph for a distributed, ad-hoc camera network, in which each edge of the graph represents two cameras that image a sufficiently large part of the same environment. Each camera encodes a spatially well-distributed set of distinctive, approximately viewpoint-invariant feature points into a fixed-length “feature digest” that is broadcast throughout the network. Each receiver camera robustly matches its own features with the decompressed digest and decides whether sufficient evidence exists to form a vision graph edge. We also show how a camera calibration algorithm that passes messages only along vision graph edges can recover accurate 3D structure and camera positions in a distributed manner. We analyze the performance of different message formation schemes, and show that high detection rates (>0.8 can be achieved while maintaining low false alarm rates (<0.05 using a simulated 60-node outdoor camera network.

  19. Natural gas distribution network of Lima and Callao, Peru

    Energy Technology Data Exchange (ETDEWEB)

    Maroye, Stephane; Aerssens, Andre [Tractebel Engineering, Lima (Peru)

    2005-07-01

    In May 2002, Suez-Tractebel was awarded by the government of Peru a 30-year concession for the construction and operation of the gas distribution network in Lima, Peru. On 10 July, 2004, first gas was delivered to Lima, 1 month ahead of the official date. This gas distribution network, operated by GNLC (Gas Natural de Lima y Callao), delivers gas to some of the largest industries and power generators in and around Lima and the harbour area of Callao. Gas delivered in Lima comes through a 700 km HP gas pipeline from Camisea fields. This pipeline is operated by TGP (Transportadora de Gas del Peru). A City Gate is located at Lurin, on the southern side of the city. The gas distribution network is made of a 62 km main pipeline (20') with 25 km laterals. The main pipeline is operated at 50 bar, as the main customer, the Etevensa power plant, is located on the northern side of the city. Due to this high operating pressure combined to the surroundings, specific design philosophies were adopted to meet the extreme safety requirements. This paper highlights the specific measures taken during construction phase and the experience of the first months of operation of this challenging project. (author)

  20. Automatic analysis of attack data from distributed honeypot network

    Science.gov (United States)

    Safarik, Jakub; Voznak, MIroslav; Rezac, Filip; Partila, Pavol; Tomala, Karel

    2013-05-01

    There are many ways of getting real data about malicious activity in a network. One of them relies on masquerading monitoring servers as a production one. These servers are called honeypots and data about attacks on them brings us valuable information about actual attacks and techniques used by hackers. The article describes distributed topology of honeypots, which was developed with a strong orientation on monitoring of IP telephony traffic. IP telephony servers can be easily exposed to various types of attacks, and without protection, this situation can lead to loss of money and other unpleasant consequences. Using a distributed topology with honeypots placed in different geological locations and networks provides more valuable and independent results. With automatic system of gathering information from all honeypots, it is possible to work with all information on one centralized point. Communication between honeypots and centralized data store use secure SSH tunnels and server communicates only with authorized honeypots. The centralized server also automatically analyses data from each honeypot. Results of this analysis and also other statistical data about malicious activity are simply accessible through a built-in web server. All statistical and analysis reports serve as information basis for an algorithm which classifies different types of used VoIP attacks. The web interface then brings a tool for quick comparison and evaluation of actual attacks in all monitored networks. The article describes both, the honeypots nodes in distributed architecture, which monitor suspicious activity, and also methods and algorithms used on the server side for analysis of gathered data.