WorldWideScience

Sample records for estimating network link

  1. Integration and analysis of neighbor discovery and link quality estimation in wireless sensor networks.

    Science.gov (United States)

    Radi, Marjan; Dezfouli, Behnam; Abu Bakar, Kamalrulnizam; Abd Razak, Shukor

    2014-01-01

    Network connectivity and link quality information are the fundamental requirements of wireless sensor network protocols to perform their desired functionality. Most of the existing discovery protocols have only focused on the neighbor discovery problem, while a few number of them provide an integrated neighbor search and link estimation. As these protocols require a careful parameter adjustment before network deployment, they cannot provide scalable and accurate network initialization in large-scale dense wireless sensor networks with random topology. Furthermore, performance of these protocols has not entirely been evaluated yet. In this paper, we perform a comprehensive simulation study on the efficiency of employing adaptive protocols compared to the existing nonadaptive protocols for initializing sensor networks with random topology. In this regard, we propose adaptive network initialization protocols which integrate the initial neighbor discovery with link quality estimation process to initialize large-scale dense wireless sensor networks without requiring any parameter adjustment before network deployment. To the best of our knowledge, this work is the first attempt to provide a detailed simulation study on the performance of integrated neighbor discovery and link quality estimation protocols for initializing sensor networks. This study can help system designers to determine the most appropriate approach for different applications.

  2. Integration and Analysis of Neighbor Discovery and Link Quality Estimation in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Marjan Radi

    2014-01-01

    Full Text Available Network connectivity and link quality information are the fundamental requirements of wireless sensor network protocols to perform their desired functionality. Most of the existing discovery protocols have only focused on the neighbor discovery problem, while a few number of them provide an integrated neighbor search and link estimation. As these protocols require a careful parameter adjustment before network deployment, they cannot provide scalable and accurate network initialization in large-scale dense wireless sensor networks with random topology. Furthermore, performance of these protocols has not entirely been evaluated yet. In this paper, we perform a comprehensive simulation study on the efficiency of employing adaptive protocols compared to the existing nonadaptive protocols for initializing sensor networks with random topology. In this regard, we propose adaptive network initialization protocols which integrate the initial neighbor discovery with link quality estimation process to initialize large-scale dense wireless sensor networks without requiring any parameter adjustment before network deployment. To the best of our knowledge, this work is the first attempt to provide a detailed simulation study on the performance of integrated neighbor discovery and link quality estimation protocols for initializing sensor networks. This study can help system designers to determine the most appropriate approach for different applications.

  3. Artificial Neural Network Algorithm for Condition Monitoring of DC-link Capacitors Based on Capacitance Estimation

    DEFF Research Database (Denmark)

    Soliman, Hammam Abdelaal Hammam; Wang, Huai; Gadalla, Brwene Salah Abdelkarim

    2015-01-01

    challenges. A capacitance estimation method based on Artificial Neural Network (ANN) algorithm is therefore proposed in this paper. The implemented ANN estimated the capacitance of the DC-link capacitor in a back-toback converter. Analysis of the error of the capacitance estimation is also given......In power electronic converters, reliability of DC-link capacitors is one of the critical issues. The estimation of their health status as an application of condition monitoring have been an attractive subject for industrial field and hence for the academic research filed as well. More reliable...... solutions are required to be adopted by the industry applications in which usage of extra hardware, increased cost, and low estimation accuracy are the main challenges. Therefore, development of new condition monitoring methods based on software solutions could be the new era that covers the aforementioned...

  4. Link-state-estimation-based transmission power control in wireless body area networks.

    Science.gov (United States)

    Kim, Seungku; Eom, Doo-Seop

    2014-07-01

    This paper presents a novel transmission power control protocol to extend the lifetime of sensor nodes and to increase the link reliability in wireless body area networks (WBANs). We first experimentally investigate the properties of the link states using the received signal strength indicator (RSSI). We then propose a practical transmission power control protocol based on both short- and long-term link-state estimations. Both the short- and long-term link-state estimations enable the transceiver to adapt the transmission power level and target the RSSI threshold range, respectively, to simultaneously satisfy the requirements of energy efficiency and link reliability. Finally, the performance of the proposed protocol is experimentally evaluated in two experimental scenarios-body posture change and dynamic body motion-and compared with the typical WBAN transmission power control protocols, a real-time reactive scheme, and a dynamic postural position inference mechanism. From the experimental results, it is found that the proposed protocol increases the lifetime of the sensor nodes by a maximum of 9.86% and enhances the link reliability by reducing the packet loss by a maximum of 3.02%.

  5. Capacitance Estimation for DC-link Capacitors in a Back-to-Back Converter Based on Artificial Neural Network Algorithm

    DEFF Research Database (Denmark)

    Soliman, Hammam Abdelaal Hammam; Wang, Huai; Blaabjerg, Frede

    2016-01-01

    of the aforementioned challenges and shortcomings. In this paper, a pure software condition monitoring method based on Artificial Neural Network (ANN) algorithm is proposed. The implemented ANN estimates the capacitance of the dc-link capacitor in a back-to-back converter. The error analysis of the estimated results......The reliability of dc-link capacitors in power electronic converters is one of the critical aspects to be considered in modern power converter design. The observation of their ageing process and the estimation of their health status have been an attractive subject for the industrial field and hence...

  6. Artificial Neural Network based DC-link Capacitance Estimation in a Diode-bridge Front-end Inverter System

    DEFF Research Database (Denmark)

    Soliman, Hammam Abdelaal Hammam; Abdelsalam, Ibrahim; Wang, Huai

    2017-01-01

    , a proposed software condition monitoring methodology based on Artificial Neural Network (ANN) algorithm is presented. Matlab software is used to train and generate the proposed ANN. The proposed methodology estimates the capacitance of the DC-link capacitor in a three phase front-end diode bridge AC......In modern design of power electronic converters, reliability of DC-link capacitors is an essential aspect to be considered. The industrial field have been attracted to the monitoring of their health condition and the estimation of their ageing process status. The existing condition monitoring...

  7. Capacitance estimation algorithm based on DC-link voltage harmonics using artificial neural network in three-phase motor drive systems

    DEFF Research Database (Denmark)

    Soliman, Hammam Abdelaal Hammam; Davari, Pooya; Wang, Huai

    2017-01-01

    to industry. In this digest, a condition monitoring methodology that estimates the capacitance value of the dc-link capacitor in a three phase Front-End diode bridge motor drive is proposed. The proposed software methodology is based on Artificial Neural Network (ANN) algorithm. The harmonics of the dc......-link voltage are used as training data to the Artificial Neural Network. Fast Fourier Transform (FFT) of the dc-link voltage is analysed in order to study the impact of capacitance variation on the harmonics order. Laboratory experiments are conducted to validate the proposed methodology and the error analysis......In modern design of power electronic converters, reliability of dc-link capacitors is one of the critical considered aspects. The industrial field have been attracted to the monitoring of their health condition and the estimation of their ageing process status. However, the existing condition...

  8. Urban rainfall estimation employing commercial microwave links

    Science.gov (United States)

    Overeem, Aart; Leijnse, Hidde; Uijlenhoet, Remko; ten Veldhuis, Marie-claire

    2015-04-01

    Urban areas often lack rainfall information. To increase the number of rainfall observations in cities, microwave links from operational cellular telecommunication networks may be employed. Although this new potential source of rainfall information has been shown to be promising, its quality needs to be demonstrated more extensively. In the Rain Sense kickstart project of the Amsterdam Institute for Advanced Metropolitan Solutions (AMS), sensors and citizens are preparing Amsterdam for future weather. Part of this project is rainfall estimation using new measurement techniques. Innovative sensing techniques will be utilized such as rainfall estimation from microwave links, umbrellas for weather sensing, low-cost sensors at lamp posts and in drainage pipes for water level observation. These will be combined with information provided by citizens in an active way through smartphone apps and in a passive way through social media posts (Twitter, Flickr etc.). Sensor information will be integrated, visualized and made accessible to citizens to help raise citizen awareness of urban water management challenges and promote resilience by providing information on how citizens can contribute in addressing these. Moreover, citizens and businesses can benefit from reliable weather information in planning their social and commercial activities. In the end city-wide high-resolution rainfall maps will be derived, blending rainfall information from microwave links and weather radars. This information will be used for urban water management. This presentation focuses on rainfall estimation from commercial microwave links. Received signal levels from tens of microwave links within the Amsterdam region (roughly 1 million inhabitants) in the Netherlands are utilized to estimate rainfall with high spatial and temporal resolution. Rainfall maps will be presented and compared to a gauge-adjusted radar rainfall data set. Rainfall time series from gauge(s), radars and links will be compared.

  9. An Accurate Link Correlation Estimator for Improving Wireless Protocol Performance

    Science.gov (United States)

    Zhao, Zhiwei; Xu, Xianghua; Dong, Wei; Bu, Jiajun

    2015-01-01

    Wireless link correlation has shown significant impact on the performance of various sensor network protocols. Many works have been devoted to exploiting link correlation for protocol improvements. However, the effectiveness of these designs heavily relies on the accuracy of link correlation measurement. In this paper, we investigate state-of-the-art link correlation measurement and analyze the limitations of existing works. We then propose a novel lightweight and accurate link correlation estimation (LACE) approach based on the reasoning of link correlation formation. LACE combines both long-term and short-term link behaviors for link correlation estimation. We implement LACE as a stand-alone interface in TinyOS and incorporate it into both routing and flooding protocols. Simulation and testbed results show that LACE: (1) achieves more accurate and lightweight link correlation measurements than the state-of-the-art work; and (2) greatly improves the performance of protocols exploiting link correlation. PMID:25686314

  10. LinkMind: Link Optimization in Swarming Mobile Sensor Networks

    DEFF Research Database (Denmark)

    Ngo, Trung Dung

    2012-01-01

    of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link...... optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm...

  11. LinkMind: link optimization in swarming mobile sensor networks.

    Science.gov (United States)

    Ngo, Trung Dung

    2011-01-01

    A swarming mobile sensor network is comprised of a swarm of wirelessly connected mobile robots equipped with various sensors. Such a network can be applied in an uncertain environment for services such as cooperative navigation and exploration, object identification and information gathering. One of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm is demonstrated and evaluated in simulation.

  12. LinkMind: Link Optimization in Swarming Mobile Sensor Networks

    Directory of Open Access Journals (Sweden)

    Trung Dung Ngo

    2011-08-01

    Full Text Available A swarming mobile sensor network is comprised of a swarm of wirelessly connected mobile robots equipped with various sensors. Such a network can be applied in an uncertain environment for services such as cooperative navigation and exploration, object identification and information gathering. One of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm is demonstrated and evaluated in simulation.

  13. Predicting cryptic links in host-parasite networks.

    Directory of Open Access Journals (Sweden)

    Tad Dallas

    2017-05-01

    Full Text Available Networks are a way to represent interactions among one (e.g., social networks or more (e.g., plant-pollinator networks classes of nodes. The ability to predict likely, but unobserved, interactions has generated a great deal of interest, and is sometimes referred to as the link prediction problem. However, most studies of link prediction have focused on social networks, and have assumed a completely censused network. In biological networks, it is unlikely that all interactions are censused, and ignoring incomplete detection of interactions may lead to biased or incorrect conclusions. Previous attempts to predict network interactions have relied on known properties of network structure, making the approach sensitive to observation errors. This is an obvious shortcoming, as networks are dynamic, and sometimes not well sampled, leading to incomplete detection of links. Here, we develop an algorithm to predict missing links based on conditional probability estimation and associated, node-level features. We validate this algorithm on simulated data, and then apply it to a desert small mammal host-parasite network. Our approach achieves high accuracy on simulated and observed data, providing a simple method to accurately predict missing links in networks without relying on prior knowledge about network structure.

  14. Heuristic Decision Making in Network Linking

    NARCIS (Netherlands)

    M.J.W. Harmsen - Van Hout (Marjolein); B.G.C. Dellaert (Benedict); P.J.J. Herings (Jean-Jacques)

    2015-01-01

    textabstractNetwork formation among individuals constitutes an important part of many OR processes, but relatively little is known about how individuals make their linking decisions in networks. This article provides an investigation of heuristic effects in individual linking decisions for

  15. Link prediction in weighted networks

    DEFF Research Database (Denmark)

    Wind, David Kofoed; Mørup, Morten

    2012-01-01

    Many complex networks feature relations with weight information. Some models utilize this information while other ignore the weight information when inferring the structure. In this paper we investigate if edge-weights when modeling real networks, carry important information about the network...... is to infer presence of edges, but that simpler models are better at inferring the actual weights....

  16. State estimation in networked systems

    NARCIS (Netherlands)

    Sijs, J.

    2012-01-01

    This thesis considers state estimation strategies for networked systems. State estimation refers to a method for computing the unknown state of a dynamic process by combining sensor measurements with predictions from a process model. The most well known method for state estimation is the Kalman

  17. Cascade of links in complex networks

    Energy Technology Data Exchange (ETDEWEB)

    Feng, Yeqian; Sun, Bihui [Department of Management Science, School of Government, Beijing Normal University, 100875 Beijing (China); Zeng, An, E-mail: anzeng@bnu.edu.cn [School of Systems Science, Beijing Normal University, 100875 Beijing (China)

    2017-01-30

    Cascading failure is an important process which has been widely used to model catastrophic events such as blackouts and financial crisis in real systems. However, so far most of the studies in the literature focus on the cascading process on nodes, leaving the possibility of link cascade overlooked. In many real cases, the catastrophic events are actually formed by the successive disappearance of links. Examples exist in the financial systems where the firms and banks (i.e. nodes) still exist but many financial trades (i.e. links) are gone during the crisis, and the air transportation systems where the airports (i.e. nodes) are still functional but many airlines (i.e. links) stop operating during bad weather. In this letter, we develop a link cascade model in complex networks. With this model, we find that both artificial and real networks tend to collapse even if a few links are initially attacked. However, the link cascading process can be effectively terminated by setting a few strong nodes in the network which do not respond to any link reduction. Finally, a simulated annealing algorithm is used to optimize the location of these strong nodes, which significantly improves the robustness of the networks against the link cascade. - Highlights: • We propose a link cascade model in complex networks. • Both artificial and real networks tend to collapse even if a few links are initially attacked. • The link cascading process can be effectively terminated by setting a few strong nodes. • A simulated annealing algorithm is used to optimize the location of these strong nodes.

  18. Cascade of links in complex networks

    International Nuclear Information System (INIS)

    Feng, Yeqian; Sun, Bihui; Zeng, An

    2017-01-01

    Cascading failure is an important process which has been widely used to model catastrophic events such as blackouts and financial crisis in real systems. However, so far most of the studies in the literature focus on the cascading process on nodes, leaving the possibility of link cascade overlooked. In many real cases, the catastrophic events are actually formed by the successive disappearance of links. Examples exist in the financial systems where the firms and banks (i.e. nodes) still exist but many financial trades (i.e. links) are gone during the crisis, and the air transportation systems where the airports (i.e. nodes) are still functional but many airlines (i.e. links) stop operating during bad weather. In this letter, we develop a link cascade model in complex networks. With this model, we find that both artificial and real networks tend to collapse even if a few links are initially attacked. However, the link cascading process can be effectively terminated by setting a few strong nodes in the network which do not respond to any link reduction. Finally, a simulated annealing algorithm is used to optimize the location of these strong nodes, which significantly improves the robustness of the networks against the link cascade. - Highlights: • We propose a link cascade model in complex networks. • Both artificial and real networks tend to collapse even if a few links are initially attacked. • The link cascading process can be effectively terminated by setting a few strong nodes. • A simulated annealing algorithm is used to optimize the location of these strong nodes.

  19. Cross-linked structure of network evolution

    Energy Technology Data Exchange (ETDEWEB)

    Bassett, Danielle S., E-mail: dsb@seas.upenn.edu [Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States); Department of Physics, University of California, Santa Barbara, California 93106 (United States); Sage Center for the Study of the Mind, University of California, Santa Barbara, California 93106 (United States); Wymbs, Nicholas F.; Grafton, Scott T. [Department of Psychology and UCSB Brain Imaging Center, University of California, Santa Barbara, California 93106 (United States); Porter, Mason A. [Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, Oxford OX2 6GG (United Kingdom); CABDyN Complexity Centre, University of Oxford, Oxford, OX1 1HP (United Kingdom); Mucha, Peter J. [Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, North Carolina 27599 (United States); Department of Applied Physical Sciences, University of North Carolina, Chapel Hill, North Carolina 27599 (United States)

    2014-03-15

    We study the temporal co-variation of network co-evolution via the cross-link structure of networks, for which we take advantage of the formalism of hypergraphs to map cross-link structures back to network nodes. We investigate two sets of temporal network data in detail. In a network of coupled nonlinear oscillators, hyperedges that consist of network edges with temporally co-varying weights uncover the driving co-evolution patterns of edge weight dynamics both within and between oscillator communities. In the human brain, networks that represent temporal changes in brain activity during learning exhibit early co-evolution that then settles down with practice. Subsequent decreases in hyperedge size are consistent with emergence of an autonomous subgraph whose dynamics no longer depends on other parts of the network. Our results on real and synthetic networks give a poignant demonstration of the ability of cross-link structure to uncover unexpected co-evolution attributes in both real and synthetic dynamical systems. This, in turn, illustrates the utility of analyzing cross-links for investigating the structure of temporal networks.

  20. Cross-linked structure of network evolution

    International Nuclear Information System (INIS)

    Bassett, Danielle S.; Wymbs, Nicholas F.; Grafton, Scott T.; Porter, Mason A.; Mucha, Peter J.

    2014-01-01

    We study the temporal co-variation of network co-evolution via the cross-link structure of networks, for which we take advantage of the formalism of hypergraphs to map cross-link structures back to network nodes. We investigate two sets of temporal network data in detail. In a network of coupled nonlinear oscillators, hyperedges that consist of network edges with temporally co-varying weights uncover the driving co-evolution patterns of edge weight dynamics both within and between oscillator communities. In the human brain, networks that represent temporal changes in brain activity during learning exhibit early co-evolution that then settles down with practice. Subsequent decreases in hyperedge size are consistent with emergence of an autonomous subgraph whose dynamics no longer depends on other parts of the network. Our results on real and synthetic networks give a poignant demonstration of the ability of cross-link structure to uncover unexpected co-evolution attributes in both real and synthetic dynamical systems. This, in turn, illustrates the utility of analyzing cross-links for investigating the structure of temporal networks

  1. Link prediction in multiplex online social networks

    Science.gov (United States)

    Jalili, Mahdi; Orouskhani, Yasin; Asgari, Milad; Alipourfard, Nazanin; Perc, Matjaž

    2017-02-01

    Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.

  2. Spin networks, quantum automata and link invariants

    International Nuclear Information System (INIS)

    Garnerone, Silvano; Marzuoli, Annalisa; Rasetti, Mario

    2006-01-01

    The spin network simulator model represents a bridge between (generalized) circuit schemes for standard quantum computation and approaches based on notions from Topological Quantum Field Theories (TQFT). More precisely, when working with purely discrete unitary gates, the simulator is naturally modelled as families of quantum automata which in turn represent discrete versions of topological quantum computation models. Such a quantum combinatorial scheme, which essentially encodes SU(2) Racah-Wigner algebra and its braided counterpart, is particularly suitable to address problems in topology and group theory and we discuss here a finite states-quantum automaton able to accept the language of braid group in view of applications to the problem of estimating link polynomials in Chern-Simons field theory

  3. A comprehensive comparison of network similarities for link prediction and spurious link elimination

    Science.gov (United States)

    Zhang, Peng; Qiu, Dan; Zeng, An; Xiao, Jinghua

    2018-06-01

    Identifying missing interactions in complex networks, known as link prediction, is realized by estimating the likelihood of the existence of a link between two nodes according to the observed links and nodes' attributes. Similar approaches have also been employed to identify and remove spurious links in networks which is crucial for improving the reliability of network data. In network science, the likelihood for two nodes having a connection strongly depends on their structural similarity. The key to address these two problems thus becomes how to objectively measure the similarity between nodes in networks. In the literature, numerous network similarity metrics have been proposed and their accuracy has been discussed independently in previous works. In this paper, we systematically compare the accuracy of 18 similarity metrics in both link prediction and spurious link elimination when the observed networks are very sparse or consist of inaccurate linking information. Interestingly, some methods have high prediction accuracy, they tend to perform low accuracy in identification spurious interaction. We further find that methods can be classified into several cluster according to their behaviors. This work is useful for guiding future use of these similarity metrics for different purposes.

  4. Link Prediction in Evolving Networks Based on Popularity of Nodes.

    Science.gov (United States)

    Wang, Tong; He, Xing-Sheng; Zhou, Ming-Yang; Fu, Zhong-Qian

    2017-08-02

    Link prediction aims to uncover the underlying relationship behind networks, which could be utilized to predict missing edges or identify the spurious edges. The key issue of link prediction is to estimate the likelihood of potential links in networks. Most classical static-structure based methods ignore the temporal aspects of networks, limited by the time-varying features, such approaches perform poorly in evolving networks. In this paper, we propose a hypothesis that the ability of each node to attract links depends not only on its structural importance, but also on its current popularity (activeness), since active nodes have much more probability to attract future links. Then a novel approach named popularity based structural perturbation method (PBSPM) and its fast algorithm are proposed to characterize the likelihood of an edge from both existing connectivity structure and current popularity of its two endpoints. Experiments on six evolving networks show that the proposed methods outperform state-of-the-art methods in accuracy and robustness. Besides, visual results and statistical analysis reveal that the proposed methods are inclined to predict future edges between active nodes, rather than edges between inactive nodes.

  5. Time of arrival based location estimation for cooperative relay networks

    KAUST Repository

    Çelebi, Hasari Burak

    2010-09-01

    In this paper, we investigate the performance of a cooperative relay network performing location estimation through time of arrival (TOA). We derive Cramer-Rao lower bound (CRLB) for the location estimates using the relay network. The analysis is extended to obtain average CRLB considering the signal fluctuations in both relay and direct links. The effects of the channel fading of both relay and direct links and amplification factor and location of the relay node on average CRLB are investigated. Simulation results show that the channel fading of both relay and direct links and amplification factor and location of relay node affect the accuracy of TOA based location estimation. ©2010 IEEE.

  6. Time of arrival based location estimation for cooperative relay networks

    KAUST Repository

    Ç elebi, Hasari Burak; Abdallah, Mohamed M.; Hussain, Syed Imtiaz; Qaraqe, Khalid A.; Alouini, Mohamed-Slim

    2010-01-01

    In this paper, we investigate the performance of a cooperative relay network performing location estimation through time of arrival (TOA). We derive Cramer-Rao lower bound (CRLB) for the location estimates using the relay network. The analysis is extended to obtain average CRLB considering the signal fluctuations in both relay and direct links. The effects of the channel fading of both relay and direct links and amplification factor and location of the relay node on average CRLB are investigated. Simulation results show that the channel fading of both relay and direct links and amplification factor and location of relay node affect the accuracy of TOA based location estimation. ©2010 IEEE.

  7. Predicting Hidden Links in Supply Networks

    Directory of Open Access Journals (Sweden)

    A. Brintrup

    2018-01-01

    Full Text Available Manufacturing companies often lack visibility of the procurement interdependencies between the suppliers within their supply network. However, knowledge of these interdependencies is useful to plan for potential operational disruptions. In this paper, we develop the Supply Network Link Predictor (SNLP method to infer supplier interdependencies using the manufacturer’s incomplete knowledge of the network. SNLP uses topological data to extract relational features from the known network to train a classifier for predicting potential links. Using a test case from the automotive industry, four features are extracted: (i number of existing supplier links, (ii overlaps between supplier product portfolios, (iii product outsourcing associations, and (iv likelihood of buyers purchasing from two suppliers together. Naïve Bayes and Logistic Regression are then employed to predict whether these features can help predict interdependencies between two suppliers. Our results show that these features can indeed be used to predict interdependencies in the network and that predictive accuracy is maximised by (i and (iii. The findings give rise to the exciting possibility of using data analytics for improving supply chain visibility. We then proceed to discuss to what extent such approaches can be adopted and their limitations, highlighting next steps for future work in this area.

  8. Earth-Space Link Attenuation Estimation via Ground Radar Kdp

    Science.gov (United States)

    Bolen, Steven M.; Benjamin, Andrew L.; Chandrasekar, V.

    2003-01-01

    A method of predicting attenuation on microwave Earth/spacecraft communication links, over wide areas and under various atmospheric conditions, has been developed. In the area around the ground station locations, a nearly horizontally aimed polarimetric S-band ground radar measures the specific differential phase (Kdp) along the Earth-space path. The specific attenuation along a path of interest is then computed by use of a theoretical model of the relationship between the measured S-band specific differential phase and the specific attenuation at the frequency to be used on the communication link. The model includes effects of rain, wet ice, and other forms of precipitation. The attenuation on the path of interest is then computed by integrating the specific attenuation over the length of the path. This method can be used to determine statistics of signal degradation on Earth/spacecraft communication links. It can also be used to obtain real-time estimates of attenuation along multiple Earth/spacecraft links that are parts of a communication network operating within the radar coverage area, thereby enabling better management of the network through appropriate dynamic routing along the best combination of links.

  9. Link Label Prediction in Signed Citation Network

    KAUST Repository

    Akujuobi, Uchenna

    2016-04-12

    Link label prediction is the problem of predicting the missing labels or signs of all the unlabeled edges in a network. For signed networks, these labels can either be positive or negative. In recent years, different algorithms have been proposed such as using regression, trust propagation and matrix factorization. These approaches have tried to solve the problem of link label prediction by using ideas from social theories, where most of them predict a single missing label given that labels of other edges are known. However, in most real-world social graphs, the number of labeled edges is usually less than that of unlabeled edges. Therefore, predicting a single edge label at a time would require multiple runs and is more computationally demanding. In this thesis, we look at link label prediction problem on a signed citation network with missing edge labels. Our citation network consists of papers from three major machine learning and data mining conferences together with their references, and edges showing the relationship between them. An edge in our network is labeled either positive (dataset relevant) if the reference is based on the dataset used in the paper or negative otherwise. We present three approaches to predict the missing labels. The first approach converts the label prediction problem into a standard classification problem. We then, generate a set of features for each edge and then adopt Support Vector Machines in solving the classification problem. For the second approach, we formalize the graph such that the edges are represented as nodes with links showing similarities between them. We then adopt a label propagation method to propagate the labels on known nodes to those with unknown labels. In the third approach, we adopt a PageRank approach where we rank the nodes according to the number of incoming positive and negative edges, after which we set a threshold. Based on the ranks, we can infer an edge would be positive if it goes a node above the

  10. To trade or not to trade: Link prediction in the virtual water network

    Science.gov (United States)

    Tuninetti, Marta; Tamea, Stefania; Laio, Francesco; Ridolfi, Luca

    2017-12-01

    In the international trade network, links express the (temporary) presence of a commercial exchange of goods between any two countries. Given the dynamical behaviour of the trade network, where links are created and dismissed every year, predicting the link activation/deactivation is an open research question. Through the international trade network of agricultural goods, water resources are 'virtually' transferred from the country of production to the country of consumption. We propose a novel methodology for link prediction applied to the network of virtual water trade. Starting from the assumption of having links between any two countries, we estimate the associated virtual water flows by means of a gravity-law model using country and link characteristics as drivers. We consider the links with estimated flows higher than 1000 m3/year as active links, while the others as non-active links. Flows traded along estimated active links are then re-estimated using a similar but differently-calibrated gravity-law model. We were able to correctly model 84% of the existing links and 93% of the non-existing links in year 2011. It is worth to note that the predicted active links carry 99% of the global virtual water flow; hence, missed links are mainly those where a minimum volume of virtual water is exchanged. Results indicate that, over the period from 1986 to 2011, population, geographical distances between countries, and agricultural efficiency (through fertilizers use) are the major factors driving the link activation and deactivation. As opposed to other (network-based) models for link prediction, the proposed method is able to reconstruct the network architecture without any prior knowledge of the network topology, using only the nodes and links attributes; it thus represents a general method that can be applied to other networks such as food or value trade networks.

  11. Locating inefficient links in a large-scale transportation network

    Science.gov (United States)

    Sun, Li; Liu, Like; Xu, Zhongzhi; Jie, Yang; Wei, Dong; Wang, Pu

    2015-02-01

    Based on data from geographical information system (GIS) and daily commuting origin destination (OD) matrices, we estimated the distribution of traffic flow in the San Francisco road network and studied Braess's paradox in a large-scale transportation network with realistic travel demand. We measured the variation of total travel time Δ T when a road segment is closed, and found that | Δ T | follows a power-law distribution if Δ T 0. This implies that most roads have a negligible effect on the efficiency of the road network, while the failure of a few crucial links would result in severe travel delays, and closure of a few inefficient links would counter-intuitively reduce travel costs considerably. Generating three theoretical networks, we discovered that the heterogeneously distributed travel demand may be the origin of the observed power-law distributions of | Δ T | . Finally, a genetic algorithm was used to pinpoint inefficient link clusters in the road network. We found that closing specific road clusters would further improve the transportation efficiency.

  12. Estimating Rain Attenuation In Satellite Communication Links

    Science.gov (United States)

    Manning, R. M.

    1991-01-01

    Attenuation computed with help of statistical model and meteorological data. NASA Lewis Research Center Satellite Link Attenuation Model (SLAM) program QuickBASIC computer program evaluating static and dynamic statistical assessment of impact of rain attenuation on communication link established between Earth terminal and geosynchronous satellite. Application in specification, design, and assessment of satellite communication links for any terminal location in continental United States. Written in Microsoft QuickBASIC.

  13. Assessing the weather monitoring capabilities of cellular microwave link networks

    Science.gov (United States)

    Fencl, Martin; Vrzba, Miroslav; Rieckermann, Jörg; Bareš, Vojtěch

    2016-04-01

    Using of microwave links for rainfall monitoring was suggested already by (Atlas and Ulbrich, 1977). However, this technique attracted broader attention of scientific community only in the recent decade, with the extensive growth of cellular microwave link (CML) networks, which form the backbone of today's cellular telecommunication infrastructure. Several studies have already shown that CMLs can be conveniently used as weather sensors and have potential to provide near-ground path-integrated observations of rainfall but also humidity or fog. However, although research is still focusing on algorithms to improve the weather sensing capabilities (Fencl et al., 2015), it is not clear how to convince cellular operators to provide the power levels of their network. One step in this direction is to show in which regions or municipalities the networks are sufficiently dense to provide/develop good services. In this contribution we suggest a standardized approach to evaluate CML networks in terms of rainfall observation and to identify suitable regions for CML rainfall monitoring. We estimate precision of single CML based on its sensitivity to rainfall, i.e. as a function of frequency, polarization and path length. Capability of a network to capture rainfall spatial patterns is estimated from the CML coverage and path lengths considering that single CML provides path-integrated rain rates. We also search for suitable predictors for regions where no network topologies are available. We test our approach on several European networks and discuss the results. Our results show that CMLs are very dense in urban areas (> 1 CML/km2), but less in rural areas (online tool. In summary, our results demonstrate that CML represent promising environmental observation network, suitable especially for urban rainfall monitoring. The developed approach integrated into an open source online tool can be conveniently used e.g. by local operators or authorities to evaluate the suitability of

  14. Estimating network effects in China's mobile telecommunications

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    A model is proposed along with empirical investigation to prove the existence of network effects in China's mobile telecommunications market. Futhernore, network effects on China's mobile telecommunications are estimated with a dynamic model. The structural parameters are identified from regression coefficients and the results are analyzed and compared with another literature. Data and estimation issues are also discussed. Conclusions are drawn that network effects are significant in China's mobile telecommunications market, and that ignoring network effects leads to bad policy making.

  15. Estimation of global network statistics from incomplete data.

    Directory of Open Access Journals (Sweden)

    Catherine A Bliss

    Full Text Available Complex networks underlie an enormous variety of social, biological, physical, and virtual systems. A profound complication for the science of complex networks is that in most cases, observing all nodes and all network interactions is impossible. Previous work addressing the impacts of partial network data is surprisingly limited, focuses primarily on missing nodes, and suggests that network statistics derived from subsampled data are not suitable estimators for the same network statistics describing the overall network topology. We generate scaling methods to predict true network statistics, including the degree distribution, from only partial knowledge of nodes, links, or weights. Our methods are transparent and do not assume a known generating process for the network, thus enabling prediction of network statistics for a wide variety of applications. We validate analytical results on four simulated network classes and empirical data sets of various sizes. We perform subsampling experiments by varying proportions of sampled data and demonstrate that our scaling methods can provide very good estimates of true network statistics while acknowledging limits. Lastly, we apply our techniques to a set of rich and evolving large-scale social networks, Twitter reply networks. Based on 100 million tweets, we use our scaling techniques to propose a statistical characterization of the Twitter Interactome from September 2008 to November 2008. Our treatment allows us to find support for Dunbar's hypothesis in detecting an upper threshold for the number of active social contacts that individuals maintain over the course of one week.

  16. Identification of hybrid node and link communities in complex networks.

    Science.gov (United States)

    He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

    2015-03-02

    Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.

  17. Identification of hybrid node and link communities in complex networks

    Science.gov (United States)

    He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

    2015-03-01

    Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.

  18. Dual adjacency matrix : exploring link groups in dense networks

    NARCIS (Netherlands)

    Dinkla, K.; Henry Riche, N.; Westenberg, M.A.

    2015-01-01

    Node grouping is a common way of adding structure and information to networks that aids their interpretation. However, certain networks benefit from the grouping of links instead of nodes. Link communities, for example, are a form of link groups that describe high-quality overlapping node

  19. Utility-Based Link Recommendation in Social Networks

    Science.gov (United States)

    Li, Zhepeng

    2013-01-01

    Link recommendation, which suggests links to connect currently unlinked users, is a key functionality offered by major online social networking platforms. Salient examples of link recommendation include "people you may know"' on Facebook and "who to follow" on Twitter. A social networking platform has two types of stakeholder:…

  20. Lewis Information Network (LINK): Background and overview

    Science.gov (United States)

    Schulte, Roger R.

    1987-01-01

    The NASA Lewis Research Center supports many research facilities with many isolated buildings, including wind tunnels, test cells, and research laboratories. These facilities are all located on a 350 acre campus adjacent to the Cleveland Hopkins Airport. The function of NASA-Lewis is to do basic and applied research in all areas of aeronautics, fluid mechanics, materials and structures, space propulsion, and energy systems. These functions require a great variety of remote high speed, high volume data communications for computing and interactive graphic capabilities. In addition, new requirements for local distribution of intercenter video teleconferencing and data communications via satellite have developed. To address these and future communications requirements for the next 15 yrs, a project team was organized to design and implement a new high speed communication system that would handle both data and video information in a common lab-wide Local Area Network. The project team selected cable television broadband coaxial cable technology as the communications medium and first installation of in-ground cable began in the summer of 1980. The Lewis Information Network (LINK) became operational in August 1982 and has become the backbone of all data communications and video.

  1. 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...

  2. Estimation of Conditional Quantile using Neural Networks

    DEFF Research Database (Denmark)

    Kulczycki, P.; Schiøler, Henrik

    1999-01-01

    The problem of estimating conditional quantiles using neural networks is investigated here. A basic structure is developed using the methodology of kernel estimation, and a theory guaranteeing con-sistency on a mild set of assumptions is provided. The constructed structure constitutes a basis...... for the design of a variety of different neural networks, some of which are considered in detail. The task of estimating conditional quantiles is related to Bayes point estimation whereby a broad range of applications within engineering, economics and management can be suggested. Numerical results illustrating...... the capabilities of the elaborated neural network are also given....

  3. Transmission Delay Based Control over Networks with Wireless Links

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    To achieve the mobility of computers during communication, the TCP connections between fixed host and mobile host may often traverse wired and wireless networks, and the recovery of losses due to wireless transmission error is much different from congestion control. The paper analyzes the side effect of RTT estimation while making the TCP source to handle congestion and wireless error losses properly. Then present a strategy using information feedback by the last hop acknowledgement and monitoring the queuing level of the wired bottleneck link by calculating the changes in transmission delay along the path. With the identification of the early stage of congestion, it can respond to wired congestion quickly while keeping wireless link more reliable, and make TCP react to the different packets losses more appropriately.

  4. Effects of multi-state links in network community detection

    International Nuclear Information System (INIS)

    Rocco, Claudio M.; Moronta, José; Ramirez-Marquez, José E.; Barker, Kash

    2017-01-01

    A community is defined as a group of nodes of a network that are densely interconnected with each other but only sparsely connected with the rest of the network. The set of communities (i.e., the network partition) and their inter-community links could be derived using special algorithms account for the topology of the network and, in certain cases, the possible weights associated to the links. In general, the set of weights represents some characteristic as capacity, flow and reliability, among others. The effects of considering weights could be translated to obtain a different partition. In many real situations, particularly when modeling infrastructure systems, networks must be modeled as multi-state networks (e.g., electric power networks). In such networks, each link is characterized by a vector of known random capacities (i.e., the weight on each link could vary according to a known probability distribution). In this paper a simple Monte Carlo approach is proposed to evaluate the effects of multi-state links on community detection as well as on the performance of the network. The approach is illustrated with the topology of an electric power system. - Highlights: • Identify network communities when considering multi-state links. • Identified how effects of considering weights translate to different partition. • Identified importance of Inter-Community Links and changes with respect to community. • Preamble to performing a resilience assessment able to mimic the evolution of the state of each community.

  5. Measuring urban rainfall using microwave links from commercial cellular communication networks

    NARCIS (Netherlands)

    Overeem, A.; Leijnse, H.; Uijlenhoet, R.

    2011-01-01

    The estimation of rainfall using commercial microwave links is a new and promising measurement technique. Commercial link networks cover large parts of the land surface of the earth and have a high density, particularly in urban areas. Rainfall attenuates the electromagnetic signals transmitted

  6. Voice Quality Estimation in Wireless Networks

    Directory of Open Access Journals (Sweden)

    Petr Zach

    2015-01-01

    Full Text Available This article deals with the impact of Wireless (Wi-Fi networks on the perceived quality of voice services. The Quality of Service (QoS metrics must be monitored in the computer network during the voice data transmission to ensure proper voice service quality the end-user has paid for, especially in the wireless networks. In addition to the QoS, research area called Quality of Experience (QoE provides metrics and methods for quality evaluation from the end-user’s perspective. This article focuses on a QoE estimation of Voice over IP (VoIP calls in the wireless networks using network simulator. Results contribute to voice quality estimation based on characteristics of the wireless network and location of a wireless client.

  7. Visualizing weighted networks: a performance comparison of adjacency matrices versus node-link diagrams

    Science.gov (United States)

    McIntire, John P.; Osesina, O. Isaac; Bartley, Cecilia; Tudoreanu, M. Eduard; Havig, Paul R.; Geiselman, Eric E.

    2012-06-01

    Ensuring the proper and effective ways to visualize network data is important for many areas of academia, applied sciences, the military, and the public. Fields such as social network analysis, genetics, biochemistry, intelligence, cybersecurity, neural network modeling, transit systems, communications, etc. often deal with large, complex network datasets that can be difficult to interact with, study, and use. There have been surprisingly few human factors performance studies on the relative effectiveness of different graph drawings or network diagram techniques to convey information to a viewer. This is particularly true for weighted networks which include the strength of connections between nodes, not just information about which nodes are linked to other nodes. We describe a human factors study in which participants performed four separate network analysis tasks (finding a direct link between given nodes, finding an interconnected node between given nodes, estimating link strengths, and estimating the most densely interconnected nodes) on two different network visualizations: an adjacency matrix with a heat-map versus a node-link diagram. The results should help shed light on effective methods of visualizing network data for some representative analysis tasks, with the ultimate goal of improving usability and performance for viewers of network data displays.

  8. Modeling online social networks based on preferential linking

    International Nuclear Information System (INIS)

    Hu Hai-Bo; Chen Jun; Guo Jin-Li

    2012-01-01

    We study the phenomena of preferential linking in a large-scale evolving online social network and find that the linear preference holds for preferential creation, preferential acceptance, and preferential attachment. Based on the linear preference, we propose an analyzable model, which illustrates the mechanism of network growth and reproduces the process of network evolution. Our simulations demonstrate that the degree distribution of the network produced by the model is in good agreement with that of the real network. This work provides a possible bridge between the micro-mechanisms of network growth and the macrostructures of online social networks

  9. 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

  10. The Algorithm of Link Prediction on Social Network

    Directory of Open Access Journals (Sweden)

    Liyan Dong

    2013-01-01

    Full Text Available At present, most link prediction algorithms are based on the similarity between two entities. Social network topology information is one of the main sources to design the similarity function between entities. But the existing link prediction algorithms do not apply the network topology information sufficiently. For lack of traditional link prediction algorithms, we propose two improved algorithms: CNGF algorithm based on local information and KatzGF algorithm based on global information network. For the defect of the stationary of social network, we also provide the link prediction algorithm based on nodes multiple attributes information. Finally, we verified these algorithms on DBLP data set, and the experimental results show that the performance of the improved algorithm is superior to that of the traditional link prediction algorithm.

  11. Predictors of the peak width for networks with exponential links

    Science.gov (United States)

    Troutman, B.M.; Karlinger, M.R.

    1989-01-01

    We investigate optimal predictors of the peak (S) and distance to peak (T) of the width function of drainage networks under the assumption that the networks are topologically random with independent and exponentially distributed link lengths. Analytical results are derived using the fact that, under these assumptions, the width function is a homogeneous Markov birth-death process. In particular, exact expressions are derived for the asymptotic conditional expectations of S and T given network magnitude N and given mainstream length H. In addition, a simulation study is performed to examine various predictors of S and T, including N, H, and basin morphometric properties; non-asymptotic conditional expectations and variances are estimated. The best single predictor of S is N, of T is H, and of the scaled peak (S divided by the area under the width function) is H. Finally, expressions tested on a set of drainage basins from the state of Wyoming perform reasonably well in predicting S and T despite probable violations of the original assumptions. ?? 1989 Springer-Verlag.

  12. Link overlap, viability, and mutual percolation in multiplex networks

    International Nuclear Information System (INIS)

    Min, Byungjoon; Lee, Sangchul; Lee, Kyu-Min; Goh, K.-I.

    2015-01-01

    Many real-world complex systems are best modeled by multiplex networks. The multiplexity has proved to have broad impact on the system’s structure and function. Most theoretical studies on multiplex networks to date, however, have largely ignored the effect of the link overlap across layers despite strong empirical evidences for its significance. In this article, we investigate the effect of the link overlap in the viability of multiplex networks, both analytically and numerically. After a short recap of the original multiplex viability study, the distinctive role of overlapping links in viability and mutual connectivity is emphasized and exploited for setting up a proper analytic framework. A rich phase diagram for viability is obtained and greatly diversified patterns of hysteretic behavior in viability are observed in the presence of link overlap. Mutual percolation with link overlap is revisited as a limit of multiplex viability problem, and the controversy between existing results is clarified. The distinctive role of overlapping links is further demonstrated by the different responses of networks under random removals of overlapping and non-overlapping links, respectively, as well as under several link-removal strategies. Our results show that the link overlap facilitates the viability and mutual percolation; at the same time, the presence of link overlap poses a challenge in analytical approaches to the problem

  13. Experimental FSO network availability estimation using interactive fog condition monitoring

    Science.gov (United States)

    Turán, Ján.; Ovseník, Łuboš

    2016-12-01

    Free Space Optics (FSO) is a license free Line of Sight (LOS) telecommunication technology which offers full duplex connectivity. FSO uses infrared beams of light to provide optical broadband connection and it can be installed literally in a few hours. Data rates go through from several hundreds of Mb/s to several Gb/s and range is from several 100 m up to several km. FSO link advantages: Easy connection establishment, License free communication, No excavation are needed, Highly secure and safe, Allows through window connectivity and single customer service and Compliments fiber by accelerating the first and last mile. FSO link disadvantages: Transmission media is air, Weather and climate dependence, Attenuation due to rain, snow and fog, Scattering of laser beam, Absorption of laser beam, Building motion and Air pollution. In this paper FSO availability evaluation is based on long term measured data from Fog sensor developed and installed at TUKE experimental FSO network in TUKE campus, Košice, Slovakia. Our FSO experimental network has three links with different physical distances between each FSO heads. Weather conditions have a tremendous impact on FSO operation in terms of FSO availability. FSO link availability is the percentage of time over a year that the FSO link will be operational. It is necessary to evaluate the climate and weather at the actual geographical location where FSO link is going to be mounted. It is important to determine the impact of a light scattering, absorption, turbulence and receiving optical power at the particular FSO link. Visibility has one of the most critical influences on the quality of an FSO optical transmission channel. FSO link availability is usually estimated using visibility information collected from nearby airport weather stations. Raw data from fog sensor (Fog Density, Relative Humidity, Temperature measured at each ms) are collected and processed by FSO Simulator software package developed at our Department. Based

  14. Capacity utilization in resilient wavelength-routed optical networks using link restoration

    DEFF Research Database (Denmark)

    Limal, Emmanuel; Danielsen, Søren Lykke; Stubkjær, Kristian

    1998-01-01

    The construction of resilient wavelength-routed optical networks has attracted much interest. Many network topologies, path and wavelength assignment strategies have been proposed. The assessment of network strategies is very complex and comparison is difficult. Here, we take a novel analytical...... approach in estimating the maximum capacity utilization that is possible in wavelength-division multiplexing (WDM) networks that are resilient against single link failures. The results apply to general network topologies and can therefore be used to evaluate the performance of more specific wavelength...

  15. A new mutually reinforcing network node and link ranking algorithm.

    Science.gov (United States)

    Wang, Zhenghua; Dueñas-Osorio, Leonardo; Padgett, Jamie E

    2015-10-23

    This study proposes a novel Normalized Wide network Ranking algorithm (NWRank) that has the advantage of ranking nodes and links of a network simultaneously. This algorithm combines the mutual reinforcement feature of Hypertext Induced Topic Selection (HITS) and the weight normalization feature of PageRank. Relative weights are assigned to links based on the degree of the adjacent neighbors and the Betweenness Centrality instead of assigning the same weight to every link as assumed in PageRank. Numerical experiment results show that NWRank performs consistently better than HITS, PageRank, eigenvector centrality, and edge betweenness from the perspective of network connectivity and approximate network flow, which is also supported by comparisons with the expensive N-1 benchmark removal criteria based on network efficiency. Furthermore, it can avoid some problems, such as the Tightly Knit Community effect, which exists in HITS. NWRank provides a new inexpensive way to rank nodes and links of a network, which has practical applications, particularly to prioritize resource allocation for upgrade of hierarchical and distributed networks, as well as to support decision making in the design of networks, where node and link importance depend on a balance of local and global integrity.

  16. A new mutually reinforcing network node and link ranking algorithm

    Science.gov (United States)

    Wang, Zhenghua; Dueñas-Osorio, Leonardo; Padgett, Jamie E.

    2015-10-01

    This study proposes a novel Normalized Wide network Ranking algorithm (NWRank) that has the advantage of ranking nodes and links of a network simultaneously. This algorithm combines the mutual reinforcement feature of Hypertext Induced Topic Selection (HITS) and the weight normalization feature of PageRank. Relative weights are assigned to links based on the degree of the adjacent neighbors and the Betweenness Centrality instead of assigning the same weight to every link as assumed in PageRank. Numerical experiment results show that NWRank performs consistently better than HITS, PageRank, eigenvector centrality, and edge betweenness from the perspective of network connectivity and approximate network flow, which is also supported by comparisons with the expensive N-1 benchmark removal criteria based on network efficiency. Furthermore, it can avoid some problems, such as the Tightly Knit Community effect, which exists in HITS. NWRank provides a new inexpensive way to rank nodes and links of a network, which has practical applications, particularly to prioritize resource allocation for upgrade of hierarchical and distributed networks, as well as to support decision making in the design of networks, where node and link importance depend on a balance of local and global integrity.

  17. A new mutually reinforcing network node and link ranking algorithm

    Science.gov (United States)

    Wang, Zhenghua; Dueñas-Osorio, Leonardo; Padgett, Jamie E.

    2015-01-01

    This study proposes a novel Normalized Wide network Ranking algorithm (NWRank) that has the advantage of ranking nodes and links of a network simultaneously. This algorithm combines the mutual reinforcement feature of Hypertext Induced Topic Selection (HITS) and the weight normalization feature of PageRank. Relative weights are assigned to links based on the degree of the adjacent neighbors and the Betweenness Centrality instead of assigning the same weight to every link as assumed in PageRank. Numerical experiment results show that NWRank performs consistently better than HITS, PageRank, eigenvector centrality, and edge betweenness from the perspective of network connectivity and approximate network flow, which is also supported by comparisons with the expensive N-1 benchmark removal criteria based on network efficiency. Furthermore, it can avoid some problems, such as the Tightly Knit Community effect, which exists in HITS. NWRank provides a new inexpensive way to rank nodes and links of a network, which has practical applications, particularly to prioritize resource allocation for upgrade of hierarchical and distributed networks, as well as to support decision making in the design of networks, where node and link importance depend on a balance of local and global integrity. PMID:26492958

  18. Congested Link Inference Algorithms in Dynamic Routing IP Network

    Directory of Open Access Journals (Sweden)

    Yu Chen

    2017-01-01

    Full Text Available The performance descending of current congested link inference algorithms is obviously in dynamic routing IP network, such as the most classical algorithm CLINK. To overcome this problem, based on the assumptions of Markov property and time homogeneity, we build a kind of Variable Structure Discrete Dynamic Bayesian (VSDDB network simplified model of dynamic routing IP network. Under the simplified VSDDB model, based on the Bayesian Maximum A Posteriori (BMAP and Rest Bayesian Network Model (RBNM, we proposed an Improved CLINK (ICLINK algorithm. Considering the concurrent phenomenon of multiple link congestion usually happens, we also proposed algorithm CLILRS (Congested Link Inference algorithm based on Lagrangian Relaxation Subgradient to infer the set of congested links. We validated our results by the experiments of analogy, simulation, and actual Internet.

  19. Mining Heterogeneous Information Networks by Exploring the Power of Links

    Science.gov (United States)

    Han, Jiawei

    Knowledge is power but for interrelated data, knowledge is often hidden in massive links in heterogeneous information networks. We explore the power of links at mining heterogeneous information networks with several interesting tasks, including link-based object distinction, veracity analysis, multidimensional online analytical processing of heterogeneous information networks, and rank-based clustering. Some recent results of our research that explore the crucial information hidden in links will be introduced, including (1) Distinct for object distinction analysis, (2) TruthFinder for veracity analysis, (3) Infonet-OLAP for online analytical processing of information networks, and (4) RankClus for integrated ranking-based clustering. We also discuss some of our on-going studies in this direction.

  20. On the estimation of the volatility-growth link

    DEFF Research Database (Denmark)

    Launov, Andrey; Posch, Olaf; Wälde, Klaus

    It is common practice to estimate the volatility-growth link by specifying a standard growth equation such that the variance of the error term appears as an explanatory variable in this growth equation. The variance in turn is modelled by a second equation. Hardly any of existing applications...... of this framework includes exogenous controls in this second variance equation. Our theoretical …ndings suggest that the absence of relevant explanatory variables in the variance equation leads to a biased and inconsistent estimate of the volatility-growth link. Our simulations show that this effect is large. Once...... the appropriate controls are included in the variance equation consistency is restored. In short, we suggest that the variance equation must include relevant control variables to estimate the volatility-growth link....

  1. Networked Airborne Communications Using Adaptive Multi Beam Directional Links

    Science.gov (United States)

    2016-03-05

    Networked Airborne Communications Using Adaptive Multi-Beam Directional Links R. Bruce MacLeod Member, IEEE, and Adam Margetts Member, IEEE MIT...provide new techniques for increasing throughput in airborne adaptive directional net- works. By adaptive directional linking, we mean systems that can...techniques can dramatically increase the capacity in airborne networks. Advances in digital array technology are beginning to put these gains within reach

  2. Link Label Prediction in Signed Citation Network

    KAUST Repository

    Akujuobi, Uchenna Thankgod

    2016-01-01

    such as using regression, trust propagation and matrix factorization. These approaches have tried to solve the problem of link label prediction by using ideas from social theories, where most of them predict a single missing label given that labels of other

  3. Consistent sensor, relay, and link selection in wireless sensor networks

    NARCIS (Netherlands)

    Arroyo Valles, M.D.R.; Simonetto, A.; Leus, G.J.T.

    2017-01-01

    In wireless sensor networks, where energy is scarce, it is inefficient to have all nodes active because they consume a non-negligible amount of battery. In this paper we consider the problem of jointly selecting sensors, relays and links in a wireless sensor network where the active sensors need

  4. Link Prediction in Social Networks: the State-of-the-Art

    OpenAIRE

    Wang, Peng; Xu, Baowen; Wu, Yurong; Zhou, Xiaoyu

    2014-01-01

    In social networks, link prediction predicts missing links in current networks and new or dissolution links in future networks, is important for mining and analyzing the evolution of social networks. In the past decade, many works have been done about the link prediction in social networks. The goal of this paper is to comprehensively review, analyze and discuss the state-of-the-art of the link prediction in social networks. A systematical category for link prediction techniques and problems ...

  5. An algorithm for link restoration of wavelength routing optical networks

    DEFF Research Database (Denmark)

    Limal, Emmanuel; Stubkjær, Kristian

    1999-01-01

    We present an algorithm for restoration of single link failure in wavelength routing multihop optical networks. The algorithm is based on an innovative study of networks using graph theory. It has the following original features: it (i) assigns working and spare channels simultaneously, (ii......) prevents the search for unacceptable routing paths by pointing out channels required for restoration, (iii) offers a high utilization of the capacity resources and (iv) allows a trivial search for the restoration paths. The algorithm is for link restoration of networks without wavelength translation. Its...

  6. Estimating network effect in geocenter motion: Theory

    Science.gov (United States)

    Zannat, Umma Jamila; Tregoning, Paul

    2017-10-01

    Geophysical models and their interpretations of several processes of interest, such as sea level rise, postseismic relaxation, and glacial isostatic adjustment, are intertwined with the need to realize the International Terrestrial Reference Frame. However, this realization needs to take into account the geocenter motion, that is, the motion of the center of figure of the Earth surface, due to, for example, deformation of the surface by earthquakes or hydrological loading effects. Usually, there is also a discrepancy, known as the network effect, between the theoretically convenient center of figure and the physically accessible center of network frames, because of unavoidable factors such as uneven station distribution, lack of stations in the oceans, disparity in the coverage between the two hemispheres, and the existence of tectonically deforming zones. Here we develop a method to estimate the magnitude of the network effect, that is, the error introduced by the incomplete sampling of the Earth surface, in measuring the geocenter motion, for a network of space geodetic stations of a fixed size N. For this purpose, we use, as our proposed estimate, the standard deviations of the changes in Helmert parameters measured by a random network of the same size N. We show that our estimate scales as 1/√N and give an explicit formula for it in terms of the vector spherical harmonics expansion of the displacement field. In a complementary paper we apply this formalism to coseismic displacements and elastic deformations due to surface water movements.

  7. A novel multilayer model for missing link prediction and future link forecasting in dynamic complex networks

    Science.gov (United States)

    Yasami, Yasser; Safaei, Farshad

    2018-02-01

    The traditional complex network theory is particularly focused on network models in which all network constituents are dealt with equivalently, while fail to consider the supplementary information related to the dynamic properties of the network interactions. This is a main constraint leading to incorrect descriptions of some real-world phenomena or incomplete capturing the details of certain real-life problems. To cope with the problem, this paper addresses the multilayer aspects of dynamic complex networks by analyzing the properties of intrinsically multilayered co-authorship networks, DBLP and Astro Physics, and presenting a novel multilayer model of dynamic complex networks. The model examines the layers evolution (layers birth/death process and lifetime) throughout the network evolution. Particularly, this paper models the evolution of each node's membership in different layers by an Infinite Factorial Hidden Markov Model considering feature cascade, and thereby formulates the link generation process for intra-layer and inter-layer links. Although adjacency matrixes are useful to describe the traditional single-layer networks, such a representation is not sufficient to describe and analyze the multilayer dynamic networks. This paper also extends a generalized mathematical infrastructure to address the problems issued by multilayer complex networks. The model inference is performed using some Markov Chain Monte Carlo sampling strategies, given synthetic and real complex networks data. Experimental results indicate a tremendous improvement in the performance of the proposed multilayer model in terms of sensitivity, specificity, positive and negative predictive values, positive and negative likelihood ratios, F1-score, Matthews correlation coefficient, and accuracy for two important applications of missing link prediction and future link forecasting. The experimental results also indicate the strong predictivepower of the proposed model for the application of

  8. Analysis of Network Vulnerability Under Joint Node and Link Attacks

    Science.gov (United States)

    Li, Yongcheng; Liu, Shumei; Yu, Yao; Cao, Ting

    2018-03-01

    The security problem of computer network system is becoming more and more serious. The fundamental reason is that there are security vulnerabilities in the network system. Therefore, it’s very important to identify and reduce or eliminate these vulnerabilities before they are attacked. In this paper, we are interested in joint node and link attacks and propose a vulnerability evaluation method based on the overall connectivity of the network to defense this attack. Especially, we analyze the attack cost problem from the attackers’ perspective. The purpose is to find the set of least costs for joint links and nodes, and their deletion will lead to serious network connection damage. The simulation results show that the vulnerable elements obtained from the proposed method are more suitable for the attacking idea of the malicious persons in joint node and link attack. It is easy to find that the proposed method has more realistic protection significance.

  9. Meta-path based heterogeneous combat network link prediction

    Science.gov (United States)

    Li, Jichao; Ge, Bingfeng; Yang, Kewei; Chen, Yingwu; Tan, Yuejin

    2017-09-01

    The combat system-of-systems in high-tech informative warfare, composed of many interconnected combat systems of different types, can be regarded as a type of complex heterogeneous network. Link prediction for heterogeneous combat networks (HCNs) is of significant military value, as it facilitates reconfiguring combat networks to represent the complex real-world network topology as appropriate with observed information. This paper proposes a novel integrated methodology framework called HCNMP (HCN link prediction based on meta-path) to predict multiple types of links simultaneously for an HCN. More specifically, the concept of HCN meta-paths is introduced, through which the HCNMP can accumulate information by extracting different features of HCN links for all the six defined types. Next, an HCN link prediction model, based on meta-path features, is built to predict all types of links of the HCN simultaneously. Then, the solution algorithm for the HCN link prediction model is proposed, in which the prediction results are obtained by iteratively updating with the newly predicted results until the results in the HCN converge or reach a certain maximum iteration number. Finally, numerical experiments on the dataset of a real HCN are conducted to demonstrate the feasibility and effectiveness of the proposed HCNMP, in comparison with 30 baseline methods. The results show that the performance of the HCNMP is superior to those of the baseline methods.

  10. Linking Environmental Orientation to Start-ups’ Networking Activities

    DEFF Research Database (Denmark)

    Dickel, Petra; Ritter, Thomas

    Besides for-profit start-ups, an increasing number of firms start their existence with the purpose to “do good” for society – mirrored in an increasing academic discussion of sustainable firms. Yet, there is little research on the networking activities of start-ups that do not have profit...... generation as their primary focus. Addressing this research gap, we develop hypotheses on the different networking activities of environmentally oriented start-ups arguing that their societal focus has a positive impact on the frequency of their networking and the size of their network. For empirically...... investigating such networking differences, we use data from 179 technology-based start-ups and show that start-ups with a strong external environmental orientation have significantly higher networking frequency and build larger networks. On the contrary, strong internal environmental orientation is linked...

  11. Computer simulation of randomly cross-linked polymer networks

    International Nuclear Information System (INIS)

    Williams, Timothy Philip

    2002-01-01

    In this work, Monte Carlo and Stochastic Dynamics computer simulations of mesoscale model randomly cross-linked networks were undertaken. Task parallel implementations of the lattice Monte Carlo Bond Fluctuation model and Kremer-Grest Stochastic Dynamics bead-spring continuum model were designed and used for this purpose. Lattice and continuum precursor melt systems were prepared and then cross-linked to varying degrees. The resultant networks were used to study structural changes during deformation and relaxation dynamics. The effects of a random network topology featuring a polydisperse distribution of strand lengths and an abundance of pendant chain ends, were qualitatively compared to recent published work. A preliminary investigation into the effects of temperature on the structural and dynamical properties was also undertaken. Structural changes during isotropic swelling and uniaxial deformation, revealed a pronounced non-affine deformation dependant on the degree of cross-linking. Fractal heterogeneities were observed in the swollen model networks and were analysed by considering constituent substructures of varying size. The network connectivity determined the length scales at which the majority of the substructure unfolding process occurred. Simulated stress-strain curves and diffraction patterns for uniaxially deformed swollen networks, were found to be consistent with experimental findings. Analysis of the relaxation dynamics of various network components revealed a dramatic slowdown due to the network connectivity. The cross-link junction spatial fluctuations for networks close to the sol-gel threshold, were observed to be at least comparable with the phantom network prediction. The dangling chain ends were found to display the largest characteristic relaxation time. (author)

  12. Retrieval algorithm for rainfall mapping from microwave links in a cellular communication network

    NARCIS (Netherlands)

    Overeem, Aart; Leijnse, Hidde; Uijlenhoet, Remko

    2016-01-01

    Microwave links in commercial cellular communication networks hold a promise for areal rainfall monitoring and could complement rainfall estimates from ground-based weather radars, rain gauges, and satellites. It has been shown that country-wide (≈ 35 500 km2) 15 min rainfall maps can

  13. Position estimation of transceivers in communication networks

    Science.gov (United States)

    Kent, Claudia A [Pleasanton, CA; Dowla, Farid [Castro Valley, CA

    2008-06-03

    This invention provides a system and method using wireless communication interfaces coupled with statistical processing of time-of-flight data to locate by position estimation unknown wireless receivers. Such an invention can be applied in sensor network applications, such as environmental monitoring of water in the soil or chemicals in the air where the position of the network nodes is deemed critical. Moreover, the present invention can be arranged to operate in areas where a Global Positioning System (GPS) is not available, such as inside buildings, caves, and tunnels.

  14. Influence of Personal Preferences on Link Dynamics in Social Networks

    Directory of Open Access Journals (Sweden)

    Ashwin Bahulkar

    2017-01-01

    Full Text Available We study a unique network dataset including periodic surveys and electronic logs of dyadic contacts via smartphones. The participants were a sample of freshmen entering university in the Fall 2011. Their opinions on a variety of political and social issues and lists of activities on campus were regularly recorded at the beginning and end of each semester for the first three years of study. We identify a behavioral network defined by call and text data, and a cognitive network based on friendship nominations in ego-network surveys. Both networks are limited to study participants. Since a wide range of attributes on each node were collected in self-reports, we refer to these networks as attribute-rich networks. We study whether student preferences for certain attributes of friends can predict formation and dissolution of edges in both networks. We introduce a method for computing student preferences for different attributes which we use to predict link formation and dissolution. We then rank these attributes according to their importance for making predictions. We find that personal preferences, in particular political views, and preferences for common activities help predict link formation and dissolution in both the behavioral and cognitive networks.

  15. Estimating cellular network performance during hurricanes

    International Nuclear Information System (INIS)

    Booker, Graham; Torres, Jacob; Guikema, Seth; Sprintson, Alex; Brumbelow, Kelly

    2010-01-01

    Cellular networks serve a critical role during and immediately after a hurricane, allowing citizens to contact emergency services when land-line communication is lost and serving as a backup communication channel for emergency responders. However, due to their ubiquitous deployment and limited design for extreme loading events, basic network elements, such as cellular towers and antennas are prone to failures during adverse weather conditions such as hurricanes. Accordingly, a systematic and computationally feasible approach is required for assessing and improving the reliability of cellular networks during hurricanes. In this paper we develop a new multi-disciplinary approach to efficiently and accurately assess cellular network reliability during hurricanes. We show how the performance of a cellular network during and immediately after future hurricanes can be estimated based on a combination of hurricane wind field models, structural reliability analysis, Monte Carlo simulation, and cellular network models and simulation tools. We then demonstrate the use of this approach for assessing the improvement in system reliability that can be achieved with discrete topological changes in the system. Our results suggest that adding redundancy, particularly through a mesh topology or through the addition of an optical fiber ring around the perimeter of the system can be an effective way to significantly increase the reliability of some cellular systems during hurricanes.

  16. Flood quantile estimation at ungauged sites by Bayesian networks

    Science.gov (United States)

    Mediero, L.; Santillán, D.; Garrote, L.

    2012-04-01

    Estimating flood quantiles at a site for which no observed measurements are available is essential for water resources planning and management. Ungauged sites have no observations about the magnitude of floods, but some site and basin characteristics are known. The most common technique used is the multiple regression analysis, which relates physical and climatic basin characteristic to flood quantiles. Regression equations are fitted from flood frequency data and basin characteristics at gauged sites. Regression equations are a rigid technique that assumes linear relationships between variables and cannot take the measurement errors into account. In addition, the prediction intervals are estimated in a very simplistic way from the variance of the residuals in the estimated model. Bayesian networks are a probabilistic computational structure taken from the field of Artificial Intelligence, which have been widely and successfully applied to many scientific fields like medicine and informatics, but application to the field of hydrology is recent. Bayesian networks infer the joint probability distribution of several related variables from observations through nodes, which represent random variables, and links, which represent causal dependencies between them. A Bayesian network is more flexible than regression equations, as they capture non-linear relationships between variables. In addition, the probabilistic nature of Bayesian networks allows taking the different sources of estimation uncertainty into account, as they give a probability distribution as result. A homogeneous region in the Tagus Basin was selected as case study. A regression equation was fitted taking the basin area, the annual maximum 24-hour rainfall for a given recurrence interval and the mean height as explanatory variables. Flood quantiles at ungauged sites were estimated by Bayesian networks. Bayesian networks need to be learnt from a huge enough data set. As observational data are reduced, a

  17. Porous Cross-Linked Polyimide-Urea Networks

    Science.gov (United States)

    Meador, Mary Ann B. (Inventor); Nguyen, Baochau N. (Inventor)

    2015-01-01

    Porous cross-linked polyimide-urea networks are provided. The networks comprise a subunit comprising two anhydride end-capped polyamic acid oligomers in direct connection via a urea linkage. The oligomers (a) each comprise a repeating unit of a dianhydride and a diamine and a terminal anhydride group and (b) are formulated with 2 to 15 of the repeating units. The subunit was formed by reaction of the diamine and a diisocyanate to form a diamine-urea linkage-diamine group, followed by reaction of the diamine-urea linkage-diamine group with the dianhydride and the diamine to form the subunit. The subunit has been cross-linked via a cross-linking agent, comprising three or more amine groups, at a balanced stoichiometry of the amine groups to the terminal anhydride groups. The subunit has been chemically imidized to yield the porous cross-linked polyimide-urea network. Also provided are wet gels, aerogels, and thin films comprising the networks, and methods of making the networks.

  18. Coarse-grain bandwidth estimation techniques for large-scale network

    Science.gov (United States)

    Cheung, Kar-Ming; Jennings, E.

    In this paper, we describe a top-down analysis and simulation approach to size the bandwidths of a store-and-forward network for a given network topology, a mission traffic scenario, and a set of data types with different latency requirements. We use these techniques to estimate the wide area network (WAN) bandwidths of the ground links for different architecture options of the proposed Integrated Space Communication and Navigation (SCaN) Network.

  19. Coarse-Grain Bandwidth Estimation Techniques for Large-Scale Space Network

    Science.gov (United States)

    Cheung, Kar-Ming; Jennings, Esther

    2013-01-01

    In this paper, we describe a top-down analysis and simulation approach to size the bandwidths of a store-andforward network for a given network topology, a mission traffic scenario, and a set of data types with different latency requirements. We use these techniques to estimate the wide area network (WAN) bandwidths of the ground links for different architecture options of the proposed Integrated Space Communication and Navigation (SCaN) Network.

  20. A link prediction method for heterogeneous networks based on BP neural network

    Science.gov (United States)

    Li, Ji-chao; Zhao, Dan-ling; Ge, Bing-Feng; Yang, Ke-Wei; Chen, Ying-Wu

    2018-04-01

    Most real-world systems, composed of different types of objects connected via many interconnections, can be abstracted as various complex heterogeneous networks. Link prediction for heterogeneous networks is of great significance for mining missing links and reconfiguring networks according to observed information, with considerable applications in, for example, friend and location recommendations and disease-gene candidate detection. In this paper, we put forward a novel integrated framework, called MPBP (Meta-Path feature-based BP neural network model), to predict multiple types of links for heterogeneous networks. More specifically, the concept of meta-path is introduced, followed by the extraction of meta-path features for heterogeneous networks. Next, based on the extracted meta-path features, a supervised link prediction model is built with a three-layer BP neural network. Then, the solution algorithm of the proposed link prediction model is put forward to obtain predicted results by iteratively training the network. Last, numerical experiments on the dataset of examples of a gene-disease network and a combat network are conducted to verify the effectiveness and feasibility of the proposed MPBP. It shows that the MPBP with very good performance is superior to the baseline methods.

  1. Deflating link buffers in a wireless mesh network

    KAUST Repository

    Jamshaid, Kamran; Shihada, Basem; Showail, Ahmad; Levis, Philip

    2014-01-01

    We analyze the problem of buffer sizing for backlogged TCP flows in 802.11-based wireless mesh networks. Our objective is to maintain high network utilization while providing low queueing delays. Unlike wired networks where a single link buffer feeds a bottleneck link, the radio spectral resource in a mesh network is shared among a set of contending mesh routers. We account for this by formulating the buffer size problem as sizing a collective buffer distributed over a set of interfering nodes. In this paper we propose mechanisms for sizing and distributing this collective buffer among the mesh nodes constituting the network bottleneck. Our mechanism factors in the network topology and wireless link rates, improving on pre-set buffer allocations that cannot optimally work across the range of configurations achievable with 802.11 radios. We evaluate our mechanisms using simulations as well as experiments on a testbed. Our results show that we can reduce the RTT of a flow by 6× or more, at the cost of less than 10% drop in end-to-end flow throughput.

  2. Deflating link buffers in a wireless mesh network

    KAUST Repository

    Jamshaid, Kamran

    2014-05-01

    We analyze the problem of buffer sizing for backlogged TCP flows in 802.11-based wireless mesh networks. Our objective is to maintain high network utilization while providing low queueing delays. Unlike wired networks where a single link buffer feeds a bottleneck link, the radio spectral resource in a mesh network is shared among a set of contending mesh routers. We account for this by formulating the buffer size problem as sizing a collective buffer distributed over a set of interfering nodes. In this paper we propose mechanisms for sizing and distributing this collective buffer among the mesh nodes constituting the network bottleneck. Our mechanism factors in the network topology and wireless link rates, improving on pre-set buffer allocations that cannot optimally work across the range of configurations achievable with 802.11 radios. We evaluate our mechanisms using simulations as well as experiments on a testbed. Our results show that we can reduce the RTT of a flow by 6× or more, at the cost of less than 10% drop in end-to-end flow throughput.

  3. Epidemics in Adaptive Social Networks with Temporary Link Deactivation

    Science.gov (United States)

    Tunc, Ilker; Shkarayev, Maxim S.; Shaw, Leah B.

    2013-04-01

    Disease spread in a society depends on the topology of the network of social contacts. Moreover, individuals may respond to the epidemic by adapting their contacts to reduce the risk of infection, thus changing the network structure and affecting future disease spread. We propose an adaptation mechanism where healthy individuals may choose to temporarily deactivate their contacts with sick individuals, allowing reactivation once both individuals are healthy. We develop a mean-field description of this system and find two distinct regimes: slow network dynamics, where the adaptation mechanism simply reduces the effective number of contacts per individual, and fast network dynamics, where more efficient adaptation reduces the spread of disease by targeting dangerous connections. Analysis of the bifurcation structure is supported by numerical simulations of disease spread on an adaptive network. The system displays a single parameter-dependent stable steady state and non-monotonic dependence of connectivity on link deactivation rate.

  4. Home-School Links: Networking the Learning Community.

    Science.gov (United States)

    1996

    The topic of networking the learning community with home-school links is addressed in four papers: "Internet Access via School: Expectations of Students and Parents" (Roy Crotty); "The School Library as Community Information Gateway" (Megan Perry); "Rural Access to the Internet" (Ken Eustace); and "NetDay '96:…

  5. Potential of commercial microwave link network derived rainfall for river runoff simulations

    Science.gov (United States)

    Smiatek, Gerhard; Keis, Felix; Chwala, Christian; Fersch, Benjamin; Kunstmann, Harald

    2017-03-01

    Commercial microwave link networks allow for the quantification of path integrated precipitation because the attenuation by hydrometeors correlates with rainfall between transmitter and receiver stations. The networks, operated and maintained by cellphone companies, thereby provide completely new and country wide precipitation measurements. As the density of traditional precipitation station networks worldwide is significantly decreasing, microwave link derived precipitation estimates receive increasing attention not only by hydrologists but also by meteorological and hydrological services. We investigate the potential of microwave derived precipitation estimates for streamflow prediction and water balance analyses, exemplarily shown for an orographically complex region in the German Alps (River Ammer). We investigate the additional value of link derived rainfall estimations combined with station observations compared to station and weather radar derived values. Our river runoff simulation system employs a distributed hydrological model at 100 × 100 m grid resolution. We analyze the potential of microwave link derived precipitation estimates for two episodes of 30 days with typically moderate river flow and an episode of extreme flooding. The simulation results indicate the potential of this novel precipitation monitoring method: a significant improvement in hydrograph reproduction has been achieved in the extreme flooding period that was characterized by a large number of local strong precipitation events. The present rainfall monitoring gauges alone were not able to correctly capture these events.

  6. Availability and Reliability of FSO Links Estimated from Visibility

    Directory of Open Access Journals (Sweden)

    M. Tatarko

    2012-06-01

    Full Text Available This paper is focused on estimation availability and reliability of FSO systems. Shortcut FSO means Free Space Optics. It is a system which allows optical transmission between two steady points. We can say that it is a last mile communication system. It is an optical communication system, but the propagation media is air. This solution of last mile does not require expensive optical fiber and establishing of connection is very simple. But there are some drawbacks which have a bad influence of quality of services and availability of the link. Number of phenomena in the atmosphere such as scattering, absorption and turbulence cause a large variation of receiving optical power and laser beam attenuation. The influence of absorption and turbulence can be significantly reduced by an appropriate design of FSO link. But the visibility has the main influence on quality of the optical transmission channel. Thus, in typical continental area where rain, snow or fog occurs is important to know their values. This article gives a description of device for measuring weather conditions and information about estimation of availability and reliability of FSO links in Slovakia.

  7. Overlapping community detection in networks with positive and negative links

    International Nuclear Information System (INIS)

    Chen, Y; Wang, X L; Yuan, B; Tang, B Z

    2014-01-01

    Complex networks considering both positive and negative links have gained considerable attention during the past several years. Community detection is one of the main challenges for complex network analysis. Most of the existing algorithms for community detection in a signed network aim at providing a hard-partition of the network where any node should belong to a community or not. However, they cannot detect overlapping communities where a node is allowed to belong to multiple communities. The overlapping communities widely exist in many real-world networks. In this paper, we propose a signed probabilistic mixture (SPM) model for overlapping community detection in signed networks. Compared with the existing models, the advantages of our methodology are (i) providing soft-partition solutions for signed networks; (ii) providing soft memberships of nodes. Experiments on a number of signed networks show that our SPM model: (i) can identify assortative structures or disassortative structures as the same as other state-of-the-art models; (ii) can detect overlapping communities; (iii) outperforms other state-of-the-art models at shedding light on the community detection in synthetic signed networks. (paper)

  8. Network worlds : from link analysis to virtual places.

    Energy Technology Data Exchange (ETDEWEB)

    Joslyn, C. (Cliff)

    2002-01-01

    Significant progress is being made in knowledge systems through recent advances in the science of very large networks. Attention is now turning in many quarters to the potential impact on counter-terrorism methods. After reviewing some of these advances, we will discuss the difference between such 'network analytic' approaches, which focus on large, homogeneous graph strucures, and what we are calling 'link analytic' approaches, which focus on somewhat smaller graphs with heterogeneous link types. We use this venue to begin the process of rigorously defining link analysis methods, especially the concept of chaining of views of multidimensional databases. We conclude with some speculation on potential connections to virtual world architectures.

  9. Resting State Network Estimation in Individual Subjects

    Science.gov (United States)

    Hacker, Carl D.; Laumann, Timothy O.; Szrama, Nicholas P.; Baldassarre, Antonello; Snyder, Abraham Z.

    2014-01-01

    Resting-state functional magnetic resonance imaging (fMRI) has been used to study brain networks associated with both normal and pathological cognitive function. The objective of this work is to reliably compute resting state network (RSN) topography in single participants. We trained a supervised classifier (multi-layer perceptron; MLP) to associate blood oxygen level dependent (BOLD) correlation maps corresponding to pre-defined seeds with specific RSN identities. Hard classification of maps obtained from a priori seeds was highly reliable across new participants. Interestingly, continuous estimates of RSN membership retained substantial residual error. This result is consistent with the view that RSNs are hierarchically organized, and therefore not fully separable into spatially independent components. After training on a priori seed-based maps, we propagated voxel-wise correlation maps through the MLP to produce estimates of RSN membership throughout the brain. The MLP generated RSN topography estimates in individuals consistent with previous studies, even in brain regions not represented in the training data. This method could be used in future studies to relate RSN topography to other measures of functional brain organization (e.g., task-evoked responses, stimulation mapping, and deficits associated with lesions) in individuals. The multi-layer perceptron was directly compared to two alternative voxel classification procedures, specifically, dual regression and linear discriminant analysis; the perceptron generated more spatially specific RSN maps than either alternative. PMID:23735260

  10. Parameter estimation in tree graph metabolic networks

    Directory of Open Access Journals (Sweden)

    Laura Astola

    2016-09-01

    Full Text Available We study the glycosylation processes that convert initially toxic substrates to nutritionally valuable metabolites in the flavonoid biosynthesis pathway of tomato (Solanum lycopersicum seedlings. To estimate the reaction rates we use ordinary differential equations (ODEs to model the enzyme kinetics. A popular choice is to use a system of linear ODEs with constant kinetic rates or to use Michaelis–Menten kinetics. In reality, the catalytic rates, which are affected among other factors by kinetic constants and enzyme concentrations, are changing in time and with the approaches just mentioned, this phenomenon cannot be described. Another problem is that, in general these kinetic coefficients are not always identifiable. A third problem is that, it is not precisely known which enzymes are catalyzing the observed glycosylation processes. With several hundred potential gene candidates, experimental validation using purified target proteins is expensive and time consuming. We aim at reducing this task via mathematical modeling to allow for the pre-selection of most potential gene candidates. In this article we discuss a fast and relatively simple approach to estimate time varying kinetic rates, with three favorable properties: firstly, it allows for identifiable estimation of time dependent parameters in networks with a tree-like structure. Secondly, it is relatively fast compared to usually applied methods that estimate the model derivatives together with the network parameters. Thirdly, by combining the metabolite concentration data with a corresponding microarray data, it can help in detecting the genes related to the enzymatic processes. By comparing the estimated time dynamics of the catalytic rates with time series gene expression data we may assess potential candidate genes behind enzymatic reactions. As an example, we show how to apply this method to select prominent glycosyltransferase genes in tomato seedlings.

  11. Parameter estimation in tree graph metabolic networks.

    Science.gov (United States)

    Astola, Laura; Stigter, Hans; Gomez Roldan, Maria Victoria; van Eeuwijk, Fred; Hall, Robert D; Groenenboom, Marian; Molenaar, Jaap J

    2016-01-01

    We study the glycosylation processes that convert initially toxic substrates to nutritionally valuable metabolites in the flavonoid biosynthesis pathway of tomato (Solanum lycopersicum) seedlings. To estimate the reaction rates we use ordinary differential equations (ODEs) to model the enzyme kinetics. A popular choice is to use a system of linear ODEs with constant kinetic rates or to use Michaelis-Menten kinetics. In reality, the catalytic rates, which are affected among other factors by kinetic constants and enzyme concentrations, are changing in time and with the approaches just mentioned, this phenomenon cannot be described. Another problem is that, in general these kinetic coefficients are not always identifiable. A third problem is that, it is not precisely known which enzymes are catalyzing the observed glycosylation processes. With several hundred potential gene candidates, experimental validation using purified target proteins is expensive and time consuming. We aim at reducing this task via mathematical modeling to allow for the pre-selection of most potential gene candidates. In this article we discuss a fast and relatively simple approach to estimate time varying kinetic rates, with three favorable properties: firstly, it allows for identifiable estimation of time dependent parameters in networks with a tree-like structure. Secondly, it is relatively fast compared to usually applied methods that estimate the model derivatives together with the network parameters. Thirdly, by combining the metabolite concentration data with a corresponding microarray data, it can help in detecting the genes related to the enzymatic processes. By comparing the estimated time dynamics of the catalytic rates with time series gene expression data we may assess potential candidate genes behind enzymatic reactions. As an example, we show how to apply this method to select prominent glycosyltransferase genes in tomato seedlings.

  12. Scaling of Airborne Ad-hoc Network Metrics with Link Range and Satellite Connectivity

    Directory of Open Access Journals (Sweden)

    Kai-Daniel BÜCHTER

    2018-06-01

    Full Text Available In this contribution, large-scale commercial aeronautical ad-hoc networks are evaluated. The investigation is based on a simulation environment with input from 2016 flight schedule and aircraft performance databases for flight movement modelling, along with a defined infrastructure of ground gateways and communication satellites. A cluster-based algorithm is used to build the communication network topology between aircraft. Cloud top pressure data can be considered to estimate cloud height and evaluate the impact of link obscuration on network availability, assuming a free-space optics-based communication network. The effects of communication range, satellite availability, fleet equipage ratio and clouds are discussed. It is shown how network reach and performance can be enhanced by adding taps to the network in the form of high-speed satellite links. The effect of adding these is two-fold: firstly, network reach can be increased by connecting remote aircraft clusters. Secondly, larger clusters can effectively be split into smaller ones in order to increase performance especially with regard to hop count and available overall capacity. In a realistic scenario concerning communication range and with moderate numbers of high-speed satellite terminals, on average, 78% of all widebody aircraft can be reached. With clouds considered (assuming laser links, this number reduces by 10%.

  13. Uncovering the essential links in online commercial networks

    Science.gov (United States)

    Zeng, Wei; Fang, Meiling; Shao, Junming; Shang, Mingsheng

    2016-09-01

    Recommender systems are designed to effectively support individuals' decision-making process on various web sites. It can be naturally represented by a user-object bipartite network, where a link indicates that a user has collected an object. Recently, research on the information backbone has attracted researchers' interests, which is a sub-network with fewer nodes and links but carrying most of the relevant information. With the backbone, a system can generate satisfactory recommenda- tions while saving much computing resource. In this paper, we propose an enhanced topology-aware method to extract the information backbone in the bipartite network mainly based on the information of neighboring users and objects. Our backbone extraction method enables the recommender systems achieve more than 90% of the accuracy of the top-L recommendation, however, consuming only 20% links. The experimental results show that our method outperforms the alternative backbone extraction methods. Moreover, the structure of the information backbone is studied in detail. Finally, we highlight that the information backbone is one of the most important properties of the bipartite network, with which one can significantly improve the efficiency of the recommender system.

  14. Shuttle Planning for Link Closures in Urban Public Transport Networks

    DEFF Research Database (Denmark)

    van der Hurk, Evelien; Koutsopoulos, Haris N.; Wilson, Nigel

    2016-01-01

    Urban public transport systems must periodically close certain links for maintenance, which can have significant effects on the service provided to passengers. In practice, the effects of closures are mitigated by replacing the closed links with a simple shuttle service. However, alternative...... cost, which includes transfers and frequency-dependent waiting time costs. This model is applied to a shuttle design problem based on a real-world case study of the Massachusetts Bay Transportation Authority network of Boston, Massachusetts. The results show that additional shuttle routes can reduce...

  15. Link-quality measurement and reporting in wireless sensor networks.

    Science.gov (United States)

    Chehri, Abdellah; Jeon, Gwanggil; Choi, Byoungjo

    2013-03-04

    Wireless Sensor networks (WSNs) are created by small hardware devices that possess the necessary functionalities to measure and exchange a variety of environmental data in their deployment setting. In this paper, we discuss the experiments in deploying a testbed as a first step towards creating a fully functional heterogeneous wireless network-based underground monitoring system. The system is mainly composed of mobile and static ZigBee nodes, which are deployed on the underground mine galleries for measuring ambient temperature. In addition, we describe the measured results of link characteristics such as received signal strength, latency and throughput for different scenarios.

  16. Link-Quality Measurement and Reporting in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Byoungjo Choi

    2013-03-01

    Full Text Available Wireless Sensor networks (WSNs are created by small hardware devices that possess the necessary functionalities to measure and exchange a variety of environmental data in their deployment setting. In this paper, we discuss the experiments in deploying a testbed as a first step towards creating a fully functional heterogeneous wireless network-based underground monitoring system. The system is mainly composed of mobile and static ZigBee nodes, which are deployed on the underground mine galleries for measuring ambient temperature. In addition, we describe the measured results of link characteristics such as received signal strength, latency and throughput for different scenarios.

  17. Reactor building indoor wireless network channel quality estimation using RSSI measurement of wireless sensor network

    International Nuclear Information System (INIS)

    Merat, S.

    2008-01-01

    Expanding wireless communication network reception inside reactor buildings (RB) and service wings (SW) has always been a technical challenge for operations service team. This is driven by the volume of metal equipment inside the Reactor Buildings (RB) that blocks and somehow shields the signal throughout the link. In this study, to improve wireless reception inside the Reactor Building (RB), an experimental model using indoor localization mesh based on IEEE 802.15 is developed to implement a wireless sensor network. This experimental model estimates the distance between different nodes by measuring the RSSI (Received Signal Strength Indicator). Then by using triangulation and RSSI measurement, the validity of the estimation techniques is verified to simulate the physical environmental obstacles, which block the signal transmission. (author)

  18. Reactor building indoor wireless network channel quality estimation using RSSI measurement of wireless sensor network

    Energy Technology Data Exchange (ETDEWEB)

    Merat, S. [Wardrop Engineering Inc., Toronto, Ontario (Canada)

    2008-07-01

    Expanding wireless communication network reception inside reactor buildings (RB) and service wings (SW) has always been a technical challenge for operations service team. This is driven by the volume of metal equipment inside the Reactor Buildings (RB) that blocks and somehow shields the signal throughout the link. In this study, to improve wireless reception inside the Reactor Building (RB), an experimental model using indoor localization mesh based on IEEE 802.15 is developed to implement a wireless sensor network. This experimental model estimates the distance between different nodes by measuring the RSSI (Received Signal Strength Indicator). Then by using triangulation and RSSI measurement, the validity of the estimation techniques is verified to simulate the physical environmental obstacles, which block the signal transmission. (author)

  19. Infrared and Visible links for medical Body Sensor Networks

    OpenAIRE

    Lebas , C; Sahuguede , S; Julien-Vergonjanne , A; Combeau , P; Aveneau , L

    2018-01-01

    International audience; — Our previous studies focused on channel simulation and performance evaluation of optical wireless links for medical body sensor networks. This allowed us to increase our expertise in this field and to propose here a full optical wireless bidirectional system named as LiFi communication system for medical monitoring applications. The full duplex bidirectional communication is based on an infrared uplink and visible downlink. The studied scenario considers a patient we...

  20. Curing critical links in oscillator networks as power flow models

    International Nuclear Information System (INIS)

    Rohden, Martin; Meyer-Ortmanns, Hildegard; Witthaut, Dirk; Timme, Marc

    2017-01-01

    Modern societies crucially depend on the robust supply with electric energy so that blackouts of power grids can have far reaching consequences. Typically, large scale blackouts take place after a cascade of failures: the failure of a single infrastructure component, such as a critical transmission line, results in several subsequent failures that spread across large parts of the network. Improving the robustness of a network to prevent such secondary failures is thus key for assuring a reliable power supply. In this article we analyze the nonlocal rerouting of power flows after transmission line failures for a simplified AC power grid model and compare different strategies to improve network robustness. We identify critical links in the grid and compute alternative pathways to quantify the grid’s redundant capacity and to find bottlenecks along the pathways. Different strategies are developed and tested to increase transmission capacities to restore stability with respect to transmission line failures. We show that local and nonlocal strategies typically perform alike: one can equally well cure critical links by providing backup capacities locally or by extending the capacities of bottleneck links at remote locations. (paper)

  1. Coevolution of game and network structure with adjustable linking

    Science.gov (United States)

    Qin, Shao-Meng; Zhang, Guo-Yong; Chen, Yong

    2009-12-01

    Most papers about the evolutionary game on graph assume the statistic network structure. However, in the real world, social interaction could change the relationship among people. And the change of social structure will also affect people’s strategies. We build a coevolution model of prisoner’s dilemma game and network structure to study the dynamic interaction in the real world. Differing from other coevolution models, players rewire their network connections according to the density of cooperation and other players’ payoffs. We use a parameter α to control the effect of payoff in the process of rewiring. Based on the asynchronous update rule and Monte Carlo simulation, we find that, when players prefer to rewire their links to those who are richer, the temptation can increase the cooperation density.

  2. Estimation of network path segment delays

    Science.gov (United States)

    Nichols, Kathleen Marie

    2018-05-01

    A method for estimation of a network path segment delay includes determining a scaled time stamp for each packet of a plurality of packets by scaling a time stamp for each respective packet to minimize a difference of at least one of a frequency and a frequency drift between a transport protocol clock of a host and a monitoring point. The time stamp for each packet is provided by the transport protocol clock of the host. A corrected time stamp for each packet is determined by removing from the scaled time stamp for each respective packet, a temporal offset between the transport protocol clock and the monitoring clock by minimizing a temporal delay variation of the plurality of packets traversing a segment between the host and the monitoring point.

  3. Application of radial basis neural network for state estimation of ...

    African Journals Online (AJOL)

    An original application of radial basis function (RBF) neural network for power system state estimation is proposed in this paper. The property of massive parallelism of neural networks is employed for this. The application of RBF neural network for state estimation is investigated by testing its applicability on a IEEE 14 bus ...

  4. Design and Analysis of Underwater Acoustic Networks with Reflected Links

    Science.gov (United States)

    Emokpae, Lloyd

    Underwater acoustic networks (UWANs) have applications in environmental state monitoring, oceanic profile measurements, leak detection in oil fields, distributed surveillance, and navigation. For these applications, sets of nodes are employed to collaboratively monitor an area of interest and track certain events or phenomena. In addition, it is common to find autonomous underwater vehicles (AUVs) acting as mobile sensor nodes that perform search-and-rescue missions, reconnaissance in combat zones, and coastal patrol. These AUVs are to work cooperatively to achieve a desired goal and thus need to be able to, in an ad-hoc manner, establish and sustain communication links in order to ensure some desired level of quality of service. Therefore, each node is required to adapt to environmental changes and be able to overcome broken communication links caused by external noise affecting the communication channel due to node mobility. In addition, since radio waves are quickly absorbed in the water medium, it is common for most underwater applications to rely on acoustic (or sound) rather than radio channels for mid-to-long range communications. However, acoustic channels pose multiple challenging issues, most notably the high transmission delay due to slow signal propagation and the limited channel bandwidth due to high frequency attenuation. Moreover, the inhomogeneous property of the water medium affects the sound speed profile while the signal surface and bottom reflections leads to multipath effects. In this dissertation, we address these networking challenges by developing protocols that take into consideration the underwater physical layer dynamics. We begin by introducing a novel surface-based reflection scheme (SBR), which takes advantage of the multipath effects of the acoustic channel. SBR works by using reflections from the water surface, and bottom, to establish non-line-of-sight (NLOS) communication links. SBR makes it possible to incorporate both line

  5. 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

  6. Community detection, link prediction, and layer interdependence in multilayer networks

    Science.gov (United States)

    De Bacco, Caterina; Power, Eleanor A.; Larremore, Daniel B.; Moore, Cristopher

    2017-04-01

    Complex systems are often characterized by distinct types of interactions between the same entities. These can be described as a multilayer network where each layer represents one type of interaction. These layers may be interdependent in complicated ways, revealing different kinds of structure in the network. In this work we present a generative model, and an efficient expectation-maximization algorithm, which allows us to perform inference tasks such as community detection and link prediction in this setting. Our model assumes overlapping communities that are common between the layers, while allowing these communities to affect each layer in a different way, including arbitrary mixtures of assortative, disassortative, or directed structure. It also gives us a mathematically principled way to define the interdependence between layers, by measuring how much information about one layer helps us predict links in another layer. In particular, this allows us to bundle layers together to compress redundant information and identify small groups of layers which suffice to predict the remaining layers accurately. We illustrate these findings by analyzing synthetic data and two real multilayer networks, one representing social support relationships among villagers in South India and the other representing shared genetic substring material between genes of the malaria parasite.

  7. An RSS based location estimation technique for cognitive relay networks

    KAUST Repository

    Qaraqe, Khalid A.; Hussain, Syed Imtiaz; Ç elebi, Hasari Burak; Abdallah, Mohamed M.; Alouini, Mohamed-Slim

    2010-01-01

    In this paper, a received signal strength (RSS) based location estimation method is proposed for a cooperative wireless relay network where the relay is a cognitive radio. We propose a method for the considered cognitive relay network to determine

  8. Permanent Set of Cross-Linking Networks: Comparison of Theory with Molecular Dynamics Simulations

    DEFF Research Database (Denmark)

    Rottach, Dana R.; Curro, John G.; Budzien, Joanne

    2006-01-01

    The permanent set of cross-linking networks is studied by molecular dynamics. The uniaxial stress for a bead-spring polymer network is investigated as a function of strain and cross-link density history, where cross-links are introduced in unstrained and strained networks. The permanent set...

  9. RAINLINK: Retrieval algorithm for rainfall monitoring employing microwave links from a cellular communication network

    Science.gov (United States)

    Uijlenhoet, R.; Overeem, A.; Leijnse, H.; Rios Gaona, M. F.

    2017-12-01

    The basic principle of rainfall estimation using microwave links is as follows. Rainfall attenuates the electromagnetic signals transmitted from one telephone tower to another. By measuring the received power at one end of a microwave link as a function of time, the path-integrated attenuation due to rainfall can be calculated, which can be converted to average rainfall intensities over the length of a link. Microwave links from cellular communication networks have been proposed as a promising new rainfall measurement technique for one decade. They are particularly interesting for those countries where few surface rainfall observations are available. Yet to date no operational (real-time) link-based rainfall products are available. To advance the process towards operational application and upscaling of this technique, there is a need for freely available, user-friendly computer code for microwave link data processing and rainfall mapping. Such software is now available as R package "RAINLINK" on GitHub (https://github.com/overeem11/RAINLINK). It contains a working example to compute link-based 15-min rainfall maps for the entire surface area of The Netherlands for 40 hours from real microwave link data. This is a working example using actual data from an extensive network of commercial microwave links, for the first time, which will allow users to test their own algorithms and compare their results with ours. The package consists of modular functions, which facilitates running only part of the algorithm. The main processings steps are: 1) Preprocessing of link data (initial quality and consistency checks); 2) Wet-dry classification using link data; 3) Reference signal determination; 4) Removal of outliers ; 5) Correction of received signal powers; 6) Computation of mean path-averaged rainfall intensities; 7) Interpolation of rainfall intensities ; 8) Rainfall map visualisation. Some applications of RAINLINK will be shown based on microwave link data from a

  10. The Linking Probability of Deep Spider-Web Networks

    OpenAIRE

    Pippenger, Nicholas

    2005-01-01

    We consider crossbar switching networks with base $b$ (that is, constructed from $b\\times b$ crossbar switches), scale $k$ (that is, with $b^k$ inputs, $b^k$ outputs and $b^k$ links between each consecutive pair of stages) and depth $l$ (that is, with $l$ stages). We assume that the crossbars are interconnected according to the spider-web pattern, whereby two diverging paths reconverge only after at least $k$ stages. We assume that each vertex is independently idle with probability $q$, the v...

  11. Elasticity in Physically Cross-Linked Amyloid Fibril Networks

    Science.gov (United States)

    Cao, Yiping; Bolisetty, Sreenath; Adamcik, Jozef; Mezzenga, Raffaele

    2018-04-01

    We provide a constitutive model of semiflexible and rigid amyloid fibril networks by combining the affine thermal model of network elasticity with the Derjaguin-Landau-Vervey-Overbeek (DLVO) theory of electrostatically charged colloids. When compared to rheological experiments on β -lactoglobulin and lysozyme amyloid networks, this approach provides the correct scaling of elasticity versus both concentration (G ˜c2.2 and G ˜c2.5 for semiflexible and rigid fibrils, respectively) and ionic strength (G ˜I4.4 and G ˜I3.8 for β -lactoglobulin and lysozyme, independent from fibril flexibility). The pivotal role played by the screening salt is to reduce the electrostatic barrier among amyloid fibrils, converting labile physical entanglements into long-lived cross-links. This gives a power-law behavior of G with I having exponents significantly larger than in other semiflexible polymer networks (e.g., actin) and carrying DLVO traits specific to the individual amyloid fibrils.

  12. Linking structure and activity in nonlinear spiking networks.

    Directory of Open Access Journals (Sweden)

    Gabriel Koch Ocker

    2017-06-01

    Full Text Available Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural activity arises from the structure of neural connectivity and intrinsic neural dynamics. This problem of structure-driven activity has drawn major interest in computational neuroscience. Existing methods for relating activity and architecture in spiking networks rely on linearizing activity around a central operating point and thus fail to capture the nonlinear responses of individual neurons that are the hallmark of neural information processing. Here, we overcome this limitation and present a new relationship between connectivity and activity in networks of nonlinear spiking neurons by developing a diagrammatic fluctuation expansion based on statistical field theory. We explicitly show how recurrent network structure produces pairwise and higher-order correlated activity, and how nonlinearities impact the networks' spiking activity. Our findings open new avenues to investigating how single-neuron nonlinearities-including those of different cell types-combine with connectivity to shape population activity and function.

  13. Linking structure and activity in nonlinear spiking networks.

    Science.gov (United States)

    Ocker, Gabriel Koch; Josić, Krešimir; Shea-Brown, Eric; Buice, Michael A

    2017-06-01

    Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural activity arises from the structure of neural connectivity and intrinsic neural dynamics. This problem of structure-driven activity has drawn major interest in computational neuroscience. Existing methods for relating activity and architecture in spiking networks rely on linearizing activity around a central operating point and thus fail to capture the nonlinear responses of individual neurons that are the hallmark of neural information processing. Here, we overcome this limitation and present a new relationship between connectivity and activity in networks of nonlinear spiking neurons by developing a diagrammatic fluctuation expansion based on statistical field theory. We explicitly show how recurrent network structure produces pairwise and higher-order correlated activity, and how nonlinearities impact the networks' spiking activity. Our findings open new avenues to investigating how single-neuron nonlinearities-including those of different cell types-combine with connectivity to shape population activity and function.

  14. Establishing a Statistical Link between Network Oscillations and Neural Synchrony.

    Directory of Open Access Journals (Sweden)

    Pengcheng Zhou

    2015-10-01

    Full Text Available Pairs of active neurons frequently fire action potentials or "spikes" nearly synchronously (i.e., within 5 ms of each other. This spike synchrony may occur by chance, based solely on the neurons' fluctuating firing patterns, or it may occur too frequently to be explicable by chance alone. When spike synchrony above chances levels is present, it may subserve computation for a specific cognitive process, or it could be an irrelevant byproduct of such computation. Either way, spike synchrony is a feature of neural data that should be explained. A point process regression framework has been developed previously for this purpose, using generalized linear models (GLMs. In this framework, the observed number of synchronous spikes is compared to the number predicted by chance under varying assumptions about the factors that affect each of the individual neuron's firing-rate functions. An important possible source of spike synchrony is network-wide oscillations, which may provide an essential mechanism of network information flow. To establish the statistical link between spike synchrony and network-wide oscillations, we have integrated oscillatory field potentials into our point process regression framework. We first extended a previously-published model of spike-field association and showed that we could recover phase relationships between oscillatory field potentials and firing rates. We then used this new framework to demonstrate the statistical relationship between oscillatory field potentials and spike synchrony in: 1 simulated neurons, 2 in vitro recordings of hippocampal CA1 pyramidal cells, and 3 in vivo recordings of neocortical V4 neurons. Our results provide a rigorous method for establishing a statistical link between network oscillations and neural synchrony.

  15. The vulnerability of the global container shipping network to targeted link disruption

    CSIR Research Space (South Africa)

    Viljoen, NM

    2016-11-01

    Full Text Available transport, complex network theory has had limited application in studying the vulnerability of maritime networks. This study uses targeted link disruption to investigate the strategy specific vulnerability of the network. Although nodal infrastructure...

  16. Distributed estimation based on observations prediction in wireless sensor networks

    KAUST Repository

    Bouchoucha, Taha; Ahmed, Mohammed F A; Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim

    2015-01-01

    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

  17. Link2U: un social network aumentato su dispositivi mobili

    Directory of Open Access Journals (Sweden)

    Monica Sebillo

    2012-04-01

    Full Text Available Negli ultimi anni il connubio mobile entertainment e social network ha attirato un notevole interesse da parte dispecifici settori che, nonostante le criticità del momento, hanno deciso di investire nella realizzazione di ambienti esoluzioni di intrattenimento/divertimento, un fattore chiave alla base del miglioramento della tecnologia.Link2U: augmenting social networks on mobile devicesNowadays, modern mobile devices have become real person-al  computers  that  increase  the  ability  of  building/extending existing applications by combining several technologies such as camera, GPS, 3D graphics and permanent Internet connec-tion. The  resulting  integration  of  such  technologies  allows  to  run complex  applications,  such  as  augmented  reality  and  Social Networks. The goal of our current research is to support mobile users’ daily activities, by developing advanced solutions which take into account principles of human-computer interac-tion and usability. In this paper we propose to exploit the potential of the aug-mented reality and the ability to communicate of social net-works to create a mobile social network, where each commu-nity user may exploit advanced location based services, such as navigation through a two dimensional map, exploration of an area through a camera mode, and identification of points of interest embedded in an augmented reality environment.

  18. Link2U: un social network aumentato su dispositivi mobili

    Directory of Open Access Journals (Sweden)

    Monica Sebillo

    2012-04-01

    Full Text Available Negli ultimi anni il connubio mobile entertainment e social network ha attirato un notevole interesse da parte dispecifici settori che, nonostante le criticità del momento, hanno deciso di investire nella realizzazione di ambienti esoluzioni di intrattenimento/divertimento, un fattore chiave alla base del miglioramento della tecnologia. Link2U: augmenting social networks on mobile devices Nowadays, modern mobile devices have become real person-al  computers  that  increase  the  ability  of  building/extending existing applications by combining several technologies such as camera, GPS, 3D graphics and permanent Internet connec-tion. The  resulting  integration  of  such  technologies  allows  to  run complex  applications,  such  as  augmented  reality  and  Social Networks. The goal of our current research is to support mobile users’ daily activities, by developing advanced solutions which take into account principles of human-computer interac-tion and usability. In this paper we propose to exploit the potential of the aug-mented reality and the ability to communicate of social net-works to create a mobile social network, where each commu-nity user may exploit advanced location based services, such as navigation through a two dimensional map, exploration of an area through a camera mode, and identification of points of interest embedded in an augmented reality environment.

  19. Pinning adaptive synchronization of a class of uncertain complex dynamical networks with multi-link against network deterioration

    International Nuclear Information System (INIS)

    Li, Lixiang; Li, Weiwei; Kurths, Jürgen; Luo, Qun; Yang, Yixian; Li, Shudong

    2015-01-01

    For the reason that the uncertain complex dynamic network with multi-link is quite close to various practical networks, there is superiority in the fields of research and application. In this paper, we focus upon pinning adaptive synchronization for uncertain complex dynamic networks with multi-link against network deterioration. The pinning approach can be applied to adapt uncertain coupling factors of deteriorated networks which can compensate effects of uncertainty. Several new synchronization criterions for networks with multi-link are derived, which ensure the synchronized states to be local or global stable with uncertainty and deterioration. Results of simulation are shown to demonstrate the feasibility and usefulness of our method

  20. The Effect of Error in Item Parameter Estimates on the Test Response Function Method of Linking.

    Science.gov (United States)

    Kaskowitz, Gary S.; De Ayala, R. J.

    2001-01-01

    Studied the effect of item parameter estimation for computation of linking coefficients for the test response function (TRF) linking/equating method. Simulation results showed that linking was more accurate when there was less error in the parameter estimates, and that 15 or 25 common items provided better results than 5 common items under both…

  1. Using Semantic Linking to Understand Persons’ Networks Extracted from Text

    Directory of Open Access Journals (Sweden)

    Alessio Palmero Aprosio

    2017-11-01

    Full Text Available In this work, we describe a methodology to interpret large persons’ networks extracted from text by classifying cliques using the DBpedia ontology. The approach relies on a combination of NLP, Semantic web technologies, and network analysis. The classification methodology that first starts from single nodes and then generalizes to cliques is effective in terms of performance and is able to deal also with nodes that are not linked to Wikipedia. The gold standard manually developed for evaluation shows that groups of co-occurring entities share in most of the cases a category that can be automatically assigned. This holds for both languages considered in this study. The outcome of this work may be of interest to enhance the readability of large networks and to provide an additional semantic layer on top of cliques. This would greatly help humanities scholars when dealing with large amounts of textual data that need to be interpreted or categorized. Furthermore, it represents an unsupervised approach to automatically extend DBpedia starting from a corpus.

  2. Identifying the Critical Links in Road Transportation Networks: Centrality-based approach utilizing structural properties

    Energy Technology Data Exchange (ETDEWEB)

    Chinthavali, Supriya [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

    2016-04-01

    Surface transportation road networks share structural properties similar to other complex networks (e.g., social networks, information networks, biological networks, and so on). This research investigates the structural properties of road networks for any possible correlation with the traffic characteristics such as link flows those determined independently. Additionally, we define a criticality index for the links of the road network that identifies the relative importance in the network. We tested our hypotheses with two sample road networks. Results show that, correlation exists between the link flows and centrality measures of a link of the road (dual graph approach is followed) and the criticality index is found to be effective for one test network to identify the vulnerable nodes.

  3. Link Prediction Methods and Their Accuracy for Different Social Networks and Network Metrics

    Directory of Open Access Journals (Sweden)

    Fei Gao

    2015-01-01

    Full Text Available Currently, we are experiencing a rapid growth of the number of social-based online systems. The availability of the vast amounts of data gathered in those systems brings new challenges that we face when trying to analyse it. One of the intensively researched topics is the prediction of social connections between users. Although a lot of effort has been made to develop new prediction approaches, the existing methods are not comprehensively analysed. In this paper we investigate the correlation between network metrics and accuracy of different prediction methods. We selected six time-stamped real-world social networks and ten most widely used link prediction methods. The results of the experiments show that the performance of some methods has a strong correlation with certain network metrics. We managed to distinguish “prediction friendly” networks, for which most of the prediction methods give good performance, as well as “prediction unfriendly” networks, for which most of the methods result in high prediction error. Correlation analysis between network metrics and prediction accuracy of prediction methods may form the basis of a metalearning system where based on network characteristics it will be able to recommend the right prediction method for a given network.

  4. Teleconnection Paths via Climate Network Direct Link Detection.

    Science.gov (United States)

    Zhou, Dong; Gozolchiani, Avi; Ashkenazy, Yosef; Havlin, Shlomo

    2015-12-31

    Teleconnections describe remote connections (typically thousands of kilometers) of the climate system. These are of great importance in climate dynamics as they reflect the transportation of energy and climate change on global scales (like the El Niño phenomenon). Yet, the path of influence propagation between such remote regions, and weighting associated with different paths, are only partially known. Here we propose a systematic climate network approach to find and quantify the optimal paths between remotely distant interacting locations. Specifically, we separate the correlations between two grid points into direct and indirect components, where the optimal path is found based on a minimal total cost function of the direct links. We demonstrate our method using near surface air temperature reanalysis data, on identifying cross-latitude teleconnections and their corresponding optimal paths. The proposed method may be used to quantify and improve our understanding regarding the emergence of climate patterns on global scales.

  5. Legal Effects of Link Sharing in Social Networks

    Directory of Open Access Journals (Sweden)

    Eugenio Gil

    2015-12-01

    Full Text Available Knowledge sharing among individuals has changed deeply with the advent of social networks in the environment of Web 2.0. Every user has the possibility of publishing what he or she deems of interest for their audience, regardless of the origin or authorship of the piece of knowledge. It is generally accepted that as the user is sharing a link to a document or video, for example, without getting paid for it, there is no point in worrying about the rights of the original author. It seems that the concepts of authorship and originality is about to disappear as promised the structuralists fifty years ago. Nevertheless the legal system has not changed, nor have the economic interests concerned. This paper explores the last developments of the legal system concerning these issues.

  6. Link prediction boosted psychiatry disorder classification for functional connectivity network

    Science.gov (United States)

    Li, Weiwei; Mei, Xue; Wang, Hao; Zhou, Yu; Huang, Jiashuang

    2017-02-01

    Functional connectivity network (FCN) is an effective tool in psychiatry disorders classification, and represents cross-correlation of the regional blood oxygenation level dependent signal. However, FCN is often incomplete for suffering from missing and spurious edges. To accurate classify psychiatry disorders and health control with the incomplete FCN, we first `repair' the FCN with link prediction, and then exact the clustering coefficients as features to build a weak classifier for every FCN. Finally, we apply a boosting algorithm to combine these weak classifiers for improving classification accuracy. Our method tested by three datasets of psychiatry disorder, including Alzheimer's Disease, Schizophrenia and Attention Deficit Hyperactivity Disorder. The experimental results show our method not only significantly improves the classification accuracy, but also efficiently reconstructs the incomplete FCN.

  7. Distributed fusion estimation for sensor networks with communication constraints

    CERN Document Server

    Zhang, Wen-An; Song, Haiyu; Yu, Li

    2016-01-01

    This book systematically presents energy-efficient robust fusion estimation methods to achieve thorough and comprehensive results in the context of network-based fusion estimation. It summarizes recent findings on fusion estimation with communication constraints; several novel energy-efficient and robust design methods for dealing with energy constraints and network-induced uncertainties are presented, such as delays, packet losses, and asynchronous information... All the results are presented as algorithms, which are convenient for practical applications.

  8. Impact of Dual-Link Failures on Impairment-Aware Routed Networks

    DEFF Research Database (Denmark)

    Georgakilas, Konstantinos N; Katrinis, Kostas; Tzanakaki, Anna

    2010-01-01

    This paper evaluates the impact of dual-link failures on single-link failure resilient networks, while physical layer constraints are taken into consideration during demand routing, as dual link failures and equivalent situations appear to be quite probable in core optical networks. In particular...

  9. Heterogeneous Data Fusion Method to Estimate Travel Time Distributions in Congested Road Networks

    Directory of Open Access Journals (Sweden)

    Chaoyang Shi

    2017-12-01

    Full Text Available Travel times in congested urban road networks are highly stochastic. Provision of travel time distribution information, including both mean and variance, can be very useful for travelers to make reliable path choice decisions to ensure higher probability of on-time arrival. To this end, a heterogeneous data fusion method is proposed to estimate travel time distributions by fusing heterogeneous data from point and interval detectors. In the proposed method, link travel time distributions are first estimated from point detector observations. The travel time distributions of links without point detectors are imputed based on their spatial correlations with links that have point detectors. The estimated link travel time distributions are then fused with path travel time distributions obtained from the interval detectors using Dempster-Shafer evidence theory. Based on fused path travel time distribution, an optimization technique is further introduced to update link travel time distributions and their spatial correlations. A case study was performed using real-world data from Hong Kong and showed that the proposed method obtained accurate and robust estimations of link and path travel time distributions in congested road networks.

  10. Heterogeneous Data Fusion Method to Estimate Travel Time Distributions in Congested Road Networks.

    Science.gov (United States)

    Shi, Chaoyang; Chen, Bi Yu; Lam, William H K; Li, Qingquan

    2017-12-06

    Travel times in congested urban road networks are highly stochastic. Provision of travel time distribution information, including both mean and variance, can be very useful for travelers to make reliable path choice decisions to ensure higher probability of on-time arrival. To this end, a heterogeneous data fusion method is proposed to estimate travel time distributions by fusing heterogeneous data from point and interval detectors. In the proposed method, link travel time distributions are first estimated from point detector observations. The travel time distributions of links without point detectors are imputed based on their spatial correlations with links that have point detectors. The estimated link travel time distributions are then fused with path travel time distributions obtained from the interval detectors using Dempster-Shafer evidence theory. Based on fused path travel time distribution, an optimization technique is further introduced to update link travel time distributions and their spatial correlations. A case study was performed using real-world data from Hong Kong and showed that the proposed method obtained accurate and robust estimations of link and path travel time distributions in congested road networks.

  11. Toward Continental-scale Rainfall Monitoring Using Commercial Microwave Links From Cellular Communication Networks

    Science.gov (United States)

    Uijlenhoet, R.; Leijnse, H.; Overeem, A.

    2017-12-01

    Accurate and timely surface precipitation measurements are crucial for water resources management, agriculture, weather prediction, climate research, as well as ground validation of satellite-based precipitation estimates. However, the majority of the land surface of the earth lacks such data, and in many parts of the world the density of surface precipitation gauging networks is even rapidly declining. This development can potentially be counteracted by using received signal level data from the enormous number of microwave links used worldwide in commercial cellular communication networks. Along such links, radio signals propagate from a transmitting antenna at one base station to a receiving antenna at another base station. Rain-induced attenuation and, subsequently, path-averaged rainfall intensity can be retrieved from the signal's attenuation between transmitter and receiver. We have previously shown how one such a network can be used to retrieve the space-time dynamics of rainfall for an entire country (The Netherlands, ˜35,500 km2), based on an unprecedented number of links (˜2,400) and a rainfall retrieval algorithm that can be applied in real time. This demonstrated the potential of such networks for real-time rainfall monitoring, in particular in those parts of the world where networks of dedicated ground-based rainfall sensors are often virtually absent. The presentation will focus on the potential for upscaling this technique to continental-scale rainfall monitoring in Europe. In addition, several examples of recent applications of this technique on other continents (South America, Africa, Asia and Australia) will be given.

  12. A method for identifying hierarchical sub-networks / modules and weighting network links based on their similarity in sub-network / module affiliation

    Directory of Open Access Journals (Sweden)

    WenJun Zhang

    2016-06-01

    Full Text Available Some networks, including biological networks, consist of hierarchical sub-networks / modules. Based on my previous study, in present study a method for both identifying hierarchical sub-networks / modules and weighting network links is proposed. It is based on the cluster analysis in which between-node similarity in sets of adjacency nodes is used. Two matrices, linkWeightMat and linkClusterIDs, are achieved by using the algorithm. Two links with both the same weight in linkWeightMat and the same cluster ID in linkClusterIDs belong to the same sub-network / module. Two links with the same weight in linkWeightMat but different cluster IDs in linkClusterIDs belong to two sub-networks / modules at the same hirarchical level. However, a link with an unique cluster ID in linkClusterIDs does not belong to any sub-networks / modules. A sub-network / module of the greater weight is the more connected sub-network / modules. Matlab codes of the algorithm are presented.

  13. Effectiveness of link prediction for face-to-face behavioral networks.

    Science.gov (United States)

    Tsugawa, Sho; Ohsaki, Hiroyuki

    2013-01-01

    Research on link prediction for social networks has been actively pursued. In link prediction for a given social network obtained from time-windowed observation, new link formation in the network is predicted from the topology of the obtained network. In contrast, recent advances in sensing technology have made it possible to obtain face-to-face behavioral networks, which are social networks representing face-to-face interactions among people. However, the effectiveness of link prediction techniques for face-to-face behavioral networks has not yet been explored in depth. To clarify this point, here we investigate the accuracy of conventional link prediction techniques for networks obtained from the history of face-to-face interactions among participants at an academic conference. Our findings were (1) that conventional link prediction techniques predict new link formation with a precision of 0.30-0.45 and a recall of 0.10-0.20, (2) that prolonged observation of social networks often degrades the prediction accuracy, (3) that the proposed decaying weight method leads to higher prediction accuracy than can be achieved by observing all records of communication and simply using them unmodified, and (4) that the prediction accuracy for face-to-face behavioral networks is relatively high compared to that for non-social networks, but not as high as for other types of social networks.

  14. Network Structure and Biased Variance Estimation in Respondent Driven Sampling.

    Science.gov (United States)

    Verdery, Ashton M; Mouw, Ted; Bauldry, Shawn; Mucha, Peter J

    2015-01-01

    This paper explores bias in the estimation of sampling variance in Respondent Driven Sampling (RDS). Prior methodological work on RDS has focused on its problematic assumptions and the biases and inefficiencies of its estimators of the population mean. Nonetheless, researchers have given only slight attention to the topic of estimating sampling variance in RDS, despite the importance of variance estimation for the construction of confidence intervals and hypothesis tests. In this paper, we show that the estimators of RDS sampling variance rely on a critical assumption that the network is First Order Markov (FOM) with respect to the dependent variable of interest. We demonstrate, through intuitive examples, mathematical generalizations, and computational experiments that current RDS variance estimators will always underestimate the population sampling variance of RDS in empirical networks that do not conform to the FOM assumption. Analysis of 215 observed university and school networks from Facebook and Add Health indicates that the FOM assumption is violated in every empirical network we analyze, and that these violations lead to substantially biased RDS estimators of sampling variance. We propose and test two alternative variance estimators that show some promise for reducing biases, but which also illustrate the limits of estimating sampling variance with only partial information on the underlying population social network.

  15. Parameter estimation using compensatory neural networks

    Indian Academy of Sciences (India)

    of interconnections among neurons but also reduces the total computing time for training. The suggested model has properties of the basic neuron ..... Engelbrecht A P, Cloete I, Geldenhuys J, Zurada J M 1995 Automatic scaling using gamma learning for feedforward neural networks. From natural to artificial computing.

  16. Reducing bias in rainfall estimates from microwave links by considering variable drop size distribution

    Science.gov (United States)

    Fencl, Martin; Jörg, Rieckermann; Vojtěch, Bareš

    2015-04-01

    Commercial microwave links (MWL) are point-to-point radio systems which are used in backhaul networks of cellular operators. For several years, they have been suggested as rainfall sensors complementary to rain gauges and weather radars, because, first, they operate at frequencies where rain drops represent significant source of attenuation and, second, cellular networks almost completely cover urban and rural areas. Usually, path-average rain rates along a MWL are retrieved from the rain-induced attenuation of received MWL signals with a simple model based on a power law relationship. The model is often parameterized based on the characteristics of a particular MWL, such as frequency, polarization and the drop size distribution (DSD) along the MWL. As information on the DSD is usually not available in operational conditions, the model parameters are usually considered constant. Unfortunately, this introduces bias into rainfall estimates from MWL. In this investigation, we propose a generic method to eliminate this bias in MWL rainfall estimates. Specifically, we search for attenuation statistics which makes it possible to classify rain events into distinct groups for which same power-law parameters can be used. The theoretical attenuation used in the analysis is calculated from DSD data using T-Matrix method. We test the validity of our approach on observations from a dedicated field experiment in Dübendorf (CH) with a 1.85-km long commercial dual-polarized microwave link transmitting at a frequency of 38 GHz, an autonomous network of 5 optical distrometers and 3 rain gauges distributed along the path of the MWL. The data is recorded at a high temporal resolution of up to 30s. It is further tested on data from an experimental catchment in Prague (CZ), where 14 MWLs, operating at 26, 32 and 38 GHz frequencies, and reference rainfall from three RGs is recorded every minute. Our results suggest that, for our purpose, rain events can be nicely characterized based on

  17. Link predication based on matrix factorization by fusion of multi class organizations of the network

    OpenAIRE

    Jiao, Pengfei; Cai, Fei; Feng, Yiding; Wang, Wenjun

    2017-01-01

    Link predication aims at forecasting the latent or unobserved edges in the complex networks and has a wide range of applications in reality. Almost existing methods and models only take advantage of one class organization of the networks, which always lose important information hidden in other organizations of the network. In this paper, we propose a link predication framework which makes the best of the structure of networks in different level of organizations based on nonnegative matrix fac...

  18. Multiple leakage localization and leak size estimation in water networks

    NARCIS (Netherlands)

    Abbasi, N.; Habibi, H.; Hurkens, C.A.J.; Klabbers, M.D.; Tijsseling, A.S.; Eijndhoven, van S.J.L.

    2012-01-01

    Water distribution networks experience considerable losses due to leakage, often at multiple locations simultaneously. Leakage detection and localization based on sensor placement and online pressure monitoring could be fast and economical. Using the difference between estimated and measured

  19. Estimating Usage Can Reduce the Stress of Social Networking

    OpenAIRE

    Zhou, Y.; Bird, J.; Cox, A. L.; Brumby, D.

    2013-01-01

    Social networks are increasingly popular and provide benefits such as easy peer group communication. However, there is evidence that they can have negative consequences, such as increased stress levels. For two weeks, we provided participants with an objective measure of their social network usage and also asked them for a daily estimate of their usage over the previous 24 hours. Although their social network usage did not significantly change, participants’ perception of this activity was tr...

  20. Distributed Estimation, Coding, and Scheduling in Wireless Visual Sensor Networks

    Science.gov (United States)

    Yu, Chao

    2013-01-01

    In this thesis, we consider estimation, coding, and sensor scheduling for energy efficient operation of wireless visual sensor networks (VSN), which consist of battery-powered wireless sensors with sensing (imaging), computation, and communication capabilities. The competing requirements for applications of these wireless sensor networks (WSN)…

  1. Reconstruction of financial networks for robust estimation of systemic risk

    International Nuclear Information System (INIS)

    Mastromatteo, Iacopo; Zarinelli, Elia; Marsili, Matteo

    2012-01-01

    In this paper we estimate the propagation of liquidity shocks through interbank markets when the information about the underlying credit network is incomplete. We show that techniques such as maximum entropy currently used to reconstruct credit networks severely underestimate the risk of contagion by assuming a trivial (fully connected) topology, a type of network structure which can be very different from the one empirically observed. We propose an efficient message-passing algorithm to explore the space of possible network structures and show that a correct estimation of the network degree of connectedness leads to more reliable estimations for systemic risk. Such an algorithm is also able to produce maximally fragile structures, providing a practical upper bound for the risk of contagion when the actual network structure is unknown. We test our algorithm on ensembles of synthetic data encoding some features of real financial networks (sparsity and heterogeneity), finding that more accurate estimations of risk can be achieved. Finally we find that this algorithm can be used to control the amount of information that regulators need to require from banks in order to sufficiently constrain the reconstruction of financial networks

  2. Reconstruction of financial networks for robust estimation of systemic risk

    Science.gov (United States)

    Mastromatteo, Iacopo; Zarinelli, Elia; Marsili, Matteo

    2012-03-01

    In this paper we estimate the propagation of liquidity shocks through interbank markets when the information about the underlying credit network is incomplete. We show that techniques such as maximum entropy currently used to reconstruct credit networks severely underestimate the risk of contagion by assuming a trivial (fully connected) topology, a type of network structure which can be very different from the one empirically observed. We propose an efficient message-passing algorithm to explore the space of possible network structures and show that a correct estimation of the network degree of connectedness leads to more reliable estimations for systemic risk. Such an algorithm is also able to produce maximally fragile structures, providing a practical upper bound for the risk of contagion when the actual network structure is unknown. We test our algorithm on ensembles of synthetic data encoding some features of real financial networks (sparsity and heterogeneity), finding that more accurate estimations of risk can be achieved. Finally we find that this algorithm can be used to control the amount of information that regulators need to require from banks in order to sufficiently constrain the reconstruction of financial networks.

  3. The integration of weighted human gene association networks based on link prediction.

    Science.gov (United States)

    Yang, Jian; Yang, Tinghong; Wu, Duzhi; Lin, Limei; Yang, Fan; Zhao, Jing

    2017-01-31

    Physical and functional interplays between genes or proteins have important biological meaning for cellular functions. Some efforts have been made to construct weighted gene association meta-networks by integrating multiple biological resources, where the weight indicates the confidence of the interaction. However, it is found that these existing human gene association networks share only quite limited overlapped interactions, suggesting their incompleteness and noise. Here we proposed a workflow to construct a weighted human gene association network using information of six existing networks, including two weighted specific PPI networks and four gene association meta-networks. We applied link prediction algorithm to predict possible missing links of the networks, cross-validation approach to refine each network and finally integrated the refined networks to get the final integrated network. The common information among the refined networks increases notably, suggesting their higher reliability. Our final integrated network owns much more links than most of the original networks, meanwhile its links still keep high functional relevance. Being used as background network in a case study of disease gene prediction, the final integrated network presents good performance, implying its reliability and application significance. Our workflow could be insightful for integrating and refining existing gene association data.

  4. Constrained Active Learning for Anchor Link Prediction Across Multiple Heterogeneous Social Networks.

    Science.gov (United States)

    Zhu, Junxing; Zhang, Jiawei; Wu, Quanyuan; Jia, Yan; Zhou, Bin; Wei, Xiaokai; Yu, Philip S

    2017-08-03

    Nowadays, people are usually involved in multiple heterogeneous social networks simultaneously. Discovering the anchor links between the accounts owned by the same users across different social networks is crucial for many important inter-network applications, e.g., cross-network link transfer and cross-network recommendation. Many different supervised models have been proposed to predict anchor links so far, but they are effective only when the labeled anchor links are abundant. However, in real scenarios, such a requirement can hardly be met and most anchor links are unlabeled, since manually labeling the inter-network anchor links is quite costly and tedious. To overcome such a problem and utilize the numerous unlabeled anchor links in model building, in this paper, we introduce the active learning based anchor link prediction problem. Different from the traditional active learning problems, due to the one-to-one constraint on anchor links, if an unlabeled anchor link a = ( u , v ) is identified as positive (i.e., existing), all the other unlabeled anchor links incident to account u or account v will be negative (i.e., non-existing) automatically. Viewed in such a perspective, asking for the labels of potential positive anchor links in the unlabeled set will be rewarding in the active anchor link prediction problem. Various novel anchor link information gain measures are defined in this paper, based on which several constraint active anchor link prediction methods are introduced. Extensive experiments have been done on real-world social network datasets to compare the performance of these methods with state-of-art anchor link prediction methods. The experimental results show that the proposed Mean-entropy-based Constrained Active Learning (MC) method can outperform other methods with significant advantages.

  5. Estimation of Anonymous Email Network Characteristics through Statistical Disclosure Attacks

    Directory of Open Access Journals (Sweden)

    Javier Portela

    2016-11-01

    Full Text Available Social network analysis aims to obtain relational data from social systems to identify leaders, roles, and communities in order to model profiles or predict a specific behavior in users’ network. Preserving anonymity in social networks is a subject of major concern. Anonymity can be compromised by disclosing senders’ or receivers’ identity, message content, or sender-receiver relationships. Under strongly incomplete information, a statistical disclosure attack is used to estimate the network and node characteristics such as centrality and clustering measures, degree distribution, and small-world-ness. A database of email networks in 29 university faculties is used to study the method. A research on the small-world-ness and Power law characteristics of these email networks is also developed, helping to understand the behavior of small email networks.

  6. Estimation of Anonymous Email Network Characteristics through Statistical Disclosure Attacks

    Science.gov (United States)

    Portela, Javier; García Villalba, Luis Javier; Silva Trujillo, Alejandra Guadalupe; Sandoval Orozco, Ana Lucila; Kim, Tai-Hoon

    2016-01-01

    Social network analysis aims to obtain relational data from social systems to identify leaders, roles, and communities in order to model profiles or predict a specific behavior in users’ network. Preserving anonymity in social networks is a subject of major concern. Anonymity can be compromised by disclosing senders’ or receivers’ identity, message content, or sender-receiver relationships. Under strongly incomplete information, a statistical disclosure attack is used to estimate the network and node characteristics such as centrality and clustering measures, degree distribution, and small-world-ness. A database of email networks in 29 university faculties is used to study the method. A research on the small-world-ness and Power law characteristics of these email networks is also developed, helping to understand the behavior of small email networks. PMID:27809275

  7. Fiber Orientation Estimation Guided by a Deep Network.

    Science.gov (United States)

    Ye, Chuyang; Prince, Jerry L

    2017-09-01

    Diffusion magnetic resonance imaging (dMRI) is currently the only tool for noninvasively imaging the brain's white matter tracts. The fiber orientation (FO) is a key feature computed from dMRI for tract reconstruction. Because the number of FOs in a voxel is usually small, dictionary-based sparse reconstruction has been used to estimate FOs. However, accurate estimation of complex FO configurations in the presence of noise can still be challenging. In this work we explore the use of a deep network for FO estimation in a dictionary-based framework and propose an algorithm named Fiber Orientation Reconstruction guided by a Deep Network (FORDN). FORDN consists of two steps. First, we use a smaller dictionary encoding coarse basis FOs to represent diffusion signals. To estimate the mixture fractions of the dictionary atoms, a deep network is designed to solve the sparse reconstruction problem. Second, the coarse FOs inform the final FO estimation, where a larger dictionary encoding a dense basis of FOs is used and a weighted ℓ 1 -norm regularized least squares problem is solved to encourage FOs that are consistent with the network output. FORDN was evaluated and compared with state-of-the-art algorithms that estimate FOs using sparse reconstruction on simulated and typical clinical dMRI data. The results demonstrate the benefit of using a deep network for FO estimation.

  8. Parameter estimation in tree graph metabolic networks

    NARCIS (Netherlands)

    Astola, Laura; Stigter, Hans; Gomez Roldan, Maria Victoria; Eeuwijk, van Fred; Hall, Robert D.; Groenenboom, Marian; Molenaar, Jaap J.

    2016-01-01

    We study the glycosylation processes that convert initially toxic substrates to nu- tritionally valuable metabolites in the flavonoid biosynthesis pathway of tomato (Solanum lycopersicum) seedlings. To estimate the reaction rates we use ordinary differential equations (ODEs) to model the enzyme

  9. Graph regularized nonnegative matrix factorization for temporal link prediction in dynamic networks

    Science.gov (United States)

    Ma, Xiaoke; Sun, Penggang; Wang, Yu

    2018-04-01

    Many networks derived from society and nature are temporal and incomplete. The temporal link prediction problem in networks is to predict links at time T + 1 based on a given temporal network from time 1 to T, which is essential to important applications. The current algorithms either predict the temporal links by collapsing the dynamic networks or collapsing features derived from each network, which are criticized for ignoring the connection among slices. to overcome the issue, we propose a novel graph regularized nonnegative matrix factorization algorithm (GrNMF) for the temporal link prediction problem without collapsing the dynamic networks. To obtain the feature for each network from 1 to t, GrNMF factorizes the matrix associated with networks by setting the rest networks as regularization, which provides a better way to characterize the topological information of temporal links. Then, the GrNMF algorithm collapses the feature matrices to predict temporal links. Compared with state-of-the-art methods, the proposed algorithm exhibits significantly improved accuracy by avoiding the collapse of temporal networks. Experimental results of a number of artificial and real temporal networks illustrate that the proposed method is not only more accurate but also more robust than state-of-the-art approaches.

  10. Condition Monitoring for DC-link Capacitors Based on Artificial Neural Network Algorithm

    DEFF Research Database (Denmark)

    Soliman, Hammam Abdelaal Hammam; Wang, Huai; Gadalla, Brwene Salah Abdelkarim

    2015-01-01

    hardware will reduce the cost, and therefore could be more promising for industry applications. A condition monitoring method based on Artificial Neural Network (ANN) algorithm is therefore proposed in this paper. The implementation of the ANN to the DC-link capacitor condition monitoring in a back......In power electronic systems, capacitor is one of the reliability critical components . Recently, the condition monitoring of capacitors to estimate their health status have been attracted by the academic research. Industry applications require more reliable power electronics products...... with preventive maintenance. However, the existing capacitor condition monitoring methods suffer from either increased hardware cost or low estimation accuracy, being the challenges to be adopted in industry applications. New development in condition monitoring technology with software solutions without extra...

  11. Artificial Neural Network Based State Estimators Integrated into Kalmtool

    DEFF Research Database (Denmark)

    Bayramoglu, Enis; Ravn, Ole; Poulsen, Niels Kjølstad

    2012-01-01

    In this paper we present a toolbox enabling easy evaluation and comparison of dierent ltering algorithms. The toolbox is called Kalmtool and is a set of MATLAB tools for state estimation of nonlinear systems. The toolbox now contains functions for Articial Neural Network Based State Estimation as...

  12. Dynamic Reconfiguration and Link Fault Tolerance in a Transputer Network

    Science.gov (United States)

    1989-06-01

    linkO and link3 are connected to the C004s. LinkI and link2 are routed to the P2 edge connector, labelled ConfigUp and ConfiDown for access to...various commands recieved PROC handle.screen (VAL BYTE link.byte, SEQ -place the first byte on screen (source) I F1 linki < 16 -- a link 0 SEQ line.num l...determine characters used on screen for -- display of source & dest IF ((INT(bytel)) < 32) linki : to.slot[INT(bytel)] otherwise linki : 10 IF ((INT(byte2

  13. Observability and Estimation of Distributed Space Systems via Local Information-Exchange Networks

    Science.gov (United States)

    Fathpour, Nanaz; Hadaegh, Fred Y.; Mesbahi, Mehran; Rahmani, Amirreza

    2011-01-01

    Spacecraft formation flying involves the coordination of states among multiple spacecraft through relative sensing, inter-spacecraft communication, and control. Most existing formation-flying estimation algorithms can only be supported via highly centralized, all-to-all, static relative sensing. New algorithms are proposed that are scalable, modular, and robust to variations in the topology and link characteristics of the formation exchange network. These distributed algorithms rely on a local information exchange network, relaxing the assumptions on existing algorithms. Distributed space systems rely on a signal transmission network among multiple spacecraft for their operation. Control and coordination among multiple spacecraft in a formation is facilitated via a network of relative sensing and interspacecraft communications. Guidance, navigation, and control rely on the sensing network. This network becomes more complex the more spacecraft are added, or as mission requirements become more complex. The observability of a formation state was observed by a set of local observations from a particular node in the formation. Formation observability can be parameterized in terms of the matrices appearing in the formation dynamics and observation matrices. An agreement protocol was used as a mechanism for observing formation states from local measurements. An agreement protocol is essentially an unforced dynamic system whose trajectory is governed by the interconnection geometry and initial condition of each node, with a goal of reaching a common value of interest. The observability of the interconnected system depends on the geometry of the network, as well as the position of the observer relative to the topology. For the first time, critical GN&C (guidance, navigation, and control estimation) subsystems are synthesized by bringing the contribution of the spacecraft information-exchange network to the forefront of algorithmic analysis and design. The result is a

  14. Sequential Estimation of Mixtures in Diffusion Networks

    Czech Academy of Sciences Publication Activity Database

    Dedecius, Kamil; Reichl, Jan; Djurić, P. M.

    2015-01-01

    Roč. 22, č. 2 (2015), s. 197-201 ISSN 1070-9908 R&D Projects: GA ČR(CZ) GP14-06678P Institutional support: RVO:67985556 Keywords : distributed estimation * mixture models * bayesian inference Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.661, year: 2015 http://library.utia.cas.cz/separaty/2014/AS/dedecius-0431479.pdf

  15. Congestion estimation technique in the optical network unit registration process.

    Science.gov (United States)

    Kim, Geunyong; Yoo, Hark; Lee, Dongsoo; Kim, Youngsun; Lim, Hyuk

    2016-07-01

    We present a congestion estimation technique (CET) to estimate the optical network unit (ONU) registration success ratio for the ONU registration process in passive optical networks. An optical line terminal (OLT) estimates the number of collided ONUs via the proposed scheme during the serial number state. The OLT can obtain congestion level among ONUs to be registered such that this information may be exploited to change the size of a quiet window to decrease the collision probability. We verified the efficiency of the proposed method through simulation and experimental results.

  16. Efficient network disintegration under incomplete information: the comic effect of link prediction

    Science.gov (United States)

    Tan, Suo-Yi; Wu, Jun; Lü, Linyuan; Li, Meng-Jun; Lu, Xin

    2016-01-01

    The study of network disintegration has attracted much attention due to its wide applications, including suppressing the epidemic spreading, destabilizing terrorist network, preventing financial contagion, controlling the rumor diffusion and perturbing cancer networks. The crux of this matter is to find the critical nodes whose removal will lead to network collapse. This paper studies the disintegration of networks with incomplete link information. An effective method is proposed to find the critical nodes by the assistance of link prediction techniques. Extensive experiments in both synthetic and real networks suggest that, by using link prediction method to recover partial missing links in advance, the method can largely improve the network disintegration performance. Besides, to our surprise, we find that when the size of missing information is relatively small, our method even outperforms than the results based on complete information. We refer to this phenomenon as the “comic effect” of link prediction, which means that the network is reshaped through the addition of some links that identified by link prediction algorithms, and the reshaped network is like an exaggerated but characteristic comic of the original one, where the important parts are emphasized. PMID:26960247

  17. Efficient network disintegration under incomplete information: the comic effect of link prediction

    Science.gov (United States)

    Tan, Suo-Yi; Wu, Jun; Lü, Linyuan; Li, Meng-Jun; Lu, Xin

    2016-03-01

    The study of network disintegration has attracted much attention due to its wide applications, including suppressing the epidemic spreading, destabilizing terrorist network, preventing financial contagion, controlling the rumor diffusion and perturbing cancer networks. The crux of this matter is to find the critical nodes whose removal will lead to network collapse. This paper studies the disintegration of networks with incomplete link information. An effective method is proposed to find the critical nodes by the assistance of link prediction techniques. Extensive experiments in both synthetic and real networks suggest that, by using link prediction method to recover partial missing links in advance, the method can largely improve the network disintegration performance. Besides, to our surprise, we find that when the size of missing information is relatively small, our method even outperforms than the results based on complete information. We refer to this phenomenon as the “comic effect” of link prediction, which means that the network is reshaped through the addition of some links that identified by link prediction algorithms, and the reshaped network is like an exaggerated but characteristic comic of the original one, where the important parts are emphasized.

  18. Probability Density Estimation Using Neural Networks in Monte Carlo Calculations

    International Nuclear Information System (INIS)

    Shim, Hyung Jin; Cho, Jin Young; Song, Jae Seung; Kim, Chang Hyo

    2008-01-01

    The Monte Carlo neutronics analysis requires the capability for a tally distribution estimation like an axial power distribution or a flux gradient in a fuel rod, etc. This problem can be regarded as a probability density function estimation from an observation set. We apply the neural network based density estimation method to an observation and sampling weight set produced by the Monte Carlo calculations. The neural network method is compared with the histogram and the functional expansion tally method for estimating a non-smooth density, a fission source distribution, and an absorption rate's gradient in a burnable absorber rod. The application results shows that the neural network method can approximate a tally distribution quite well. (authors)

  19. Mathematical model of transmission network static state estimation

    Directory of Open Access Journals (Sweden)

    Ivanov Aleksandar

    2012-01-01

    Full Text Available In this paper the characteristics and capabilities of the power transmission network static state estimator are presented. The solving process of the mathematical model containing the measurement errors and their processing is developed. To evaluate difference between the general model of state estimation and the fast decoupled state estimation model, the both models are applied to an example, and so derived results are compared.

  20. Geographical constraints to range-based attacks on links in complex networks

    International Nuclear Information System (INIS)

    Gong Baihua; Liu Jun; Huang Liang; Yang Kongqing; Yang Lei

    2008-01-01

    In this paper, we studied range-based attacks on links in geographically constrained scale-free networks and found that there is a continuous switching of roles of short- and long-range attacks on links when tuning the geographical constraint strength. Our results demonstrate that the geography has a significant impact on the network efficiency and security; thus one can adjust the geographical structure to optimize the robustness and the efficiency of the networks. We introduce a measurement of the impact of links on the efficiency of the network, and an effective attacking strategy is suggested

  1. Estimating Urban Traffic Patterns through Probabilistic Interconnectivity of Road Network Junctions.

    Directory of Open Access Journals (Sweden)

    Ed Manley

    Full Text Available The emergence of large, fine-grained mobility datasets offers significant opportunities for the development and application of new methodologies for transportation analysis. In this paper, the link between routing behaviour and traffic patterns in urban areas is examined, introducing a method to derive estimates of traffic patterns from a large collection of fine-grained routing data. Using this dataset, the interconnectivity between road network junctions is extracted in the form of a Markov chain. This representation encodes the probability of the successive usage of adjacent road junctions, encoding routes as flows between decision points rather than flows along road segments. This network of functional interactions is then integrated within a modified Markov chain Monte Carlo (MCMC framework, adapted for the estimation of urban traffic patterns. As part of this approach, the data-derived links between major junctions influence the movement of directed random walks executed across the network to model origin-destination journeys. The simulation process yields estimates of traffic distribution across the road network. The paper presents an implementation of the modified MCMC approach for London, United Kingdom, building an MCMC model based on a dataset of nearly 700000 minicab routes. Validation of the approach clarifies how each element of the MCMC framework contributes to junction prediction performance, and finds promising results in relation to the estimation of junction choice and minicab traffic distribution. The paper concludes by summarising the potential for the development and extension of this approach to the wider urban modelling domain.

  2. A fuzzy neural network for sensor signal estimation

    International Nuclear Information System (INIS)

    Na, Man Gyun

    2000-01-01

    In this work, a fuzzy neural network is used to estimate the relevant sensor signal using other sensor signals. Noise components in input signals into the fuzzy neural network are removed through the wavelet denoising technique. Principal component analysis (PCA) is used to reduce the dimension of an input space without losing a significant amount of information. A lower dimensional input space will also usually reduce the time necessary to train a fuzzy-neural network. Also, the principal component analysis makes easy the selection of the input signals into the fuzzy neural network. The fuzzy neural network parameters are optimized by two learning methods. A genetic algorithm is used to optimize the antecedent parameters of the fuzzy neural network and a least-squares algorithm is used to solve the consequent parameters. The proposed algorithm was verified through the application to the pressurizer water level and the hot-leg flowrate measurements in pressurized water reactors

  3. PWR system simulation and parameter estimation with neural networks

    International Nuclear Information System (INIS)

    Akkurt, Hatice; Colak, Uener

    2002-01-01

    A detailed nonlinear model for a typical PWR system has been considered for the development of simulation software. Each component in the system has been represented by appropriate differential equations. The SCILAB software was used for solving nonlinear equations to simulate steady-state and transient operational conditions. Overall system has been constructed by connecting individual components to each other. The validity of models for individual components and overall system has been verified. The system response against given transients have been analyzed. A neural network has been utilized to estimate system parameters during transients. Different transients have been imposed in training and prediction stages with neural networks. Reactor power and system reactivity during the transient event have been predicted by the neural network. Results show that neural networks estimations are in good agreement with the calculated response of the reactor system. The maximum errors are within ±0.254% for power and between -0.146 and 0.353% for reactivity prediction cases. Steam generator parameters, pressure and water level, are also successfully predicted by the neural network employed in this study. The noise imposed on the input parameters of the neural network deteriorates the power estimation capability whereas the reactivity estimation capability is not significantly affected

  4. PWR system simulation and parameter estimation with neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Akkurt, Hatice; Colak, Uener E-mail: uc@nuke.hacettepe.edu.tr

    2002-11-01

    A detailed nonlinear model for a typical PWR system has been considered for the development of simulation software. Each component in the system has been represented by appropriate differential equations. The SCILAB software was used for solving nonlinear equations to simulate steady-state and transient operational conditions. Overall system has been constructed by connecting individual components to each other. The validity of models for individual components and overall system has been verified. The system response against given transients have been analyzed. A neural network has been utilized to estimate system parameters during transients. Different transients have been imposed in training and prediction stages with neural networks. Reactor power and system reactivity during the transient event have been predicted by the neural network. Results show that neural networks estimations are in good agreement with the calculated response of the reactor system. The maximum errors are within {+-}0.254% for power and between -0.146 and 0.353% for reactivity prediction cases. Steam generator parameters, pressure and water level, are also successfully predicted by the neural network employed in this study. The noise imposed on the input parameters of the neural network deteriorates the power estimation capability whereas the reactivity estimation capability is not significantly affected.

  5. Control and estimation methods over communication networks

    CERN Document Server

    Mahmoud, Magdi S

    2014-01-01

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

  6. Green IGP Link Weights for Energy-efficiency and Load-balancing in IP Backbone Networks

    OpenAIRE

    Francois, Frederic; Wang, Ning; Moessner, Klaus; Georgoulas, Stylianos; Xu, Ke

    2013-01-01

    The energy consumption of backbone networks has become a primary concern for network operators and regulators due to the pervasive deployment of wired backbone networks to meet the requirements of bandwidth-hungry applications. While traditional optimization of IGP link weights has been used in IP based load-balancing operations, in this paper we introduce a novel link weight setting algorithm, the Green Load-balancing Algorithm (GLA), which is able to jointly optimize both energy efficiency ...

  7. Link and route availability for Inter-working multi-hop wireless networks

    CSIR Research Space (South Africa)

    Salami, O

    2009-09-01

    Full Text Available pairs in inter-working multi-hop wireless networks can be evaluated based on the availability and reliability of radio links that form the communication path linking the nodes. This paper presents an analytical study of the link and route availability...

  8. Small Strain Topological Effects of Biopolymer Networks with Rigid Cross-Links

    NARCIS (Netherlands)

    Zagar, G.; Onck, P. R.; Van der Giessen, E.; Garikipati, K; Arruda, EM

    2010-01-01

    Networks of cross-linked filamentous biopolymers form topological structures characterized by L, T and X cross-link types of connectivity 2, 3 and 4, respectively. The distribution of cross-links over these three types proofs to be very important for the initial elastic shear stiffness of isotropic

  9. Gauge-adjusted rainfall estimates from commercial microwave links

    Directory of Open Access Journals (Sweden)

    M. Fencl

    2017-01-01

    experimental layouts of ground truth from rain gauges (RGs with different spatial and temporal resolutions. The results suggest that CMLs adjusted by RGs with a temporal aggregation of up to 1 h (i provide precise high-resolution QPEs (relative error  < 7 %, Nash–Sutcliffe efficiency coefficient  >  0.75 and (ii that the combination of both sensor types clearly outperforms each individual monitoring system. Unfortunately, adjusting CML observations to RGs with longer aggregation intervals of up to 24 h has drawbacks. Although it substantially reduces bias, it unfavourably smoothes out rainfall peaks of high intensities, which is undesirable for stormwater management. A similar, but less severe, effect occurs due to spatial averaging when CMLs are adjusted to remote RGs. Nevertheless, even here, adjusted CMLs perform better than RGs alone. Furthermore, we provide first evidence that the joint use of multiple CMLs together with RGs also reduces bias in their QPEs. In summary, we believe that our adjustment method has great potential to improve the space–time resolution of current urban rainfall monitoring networks. Nevertheless, future work should aim to better understand the reason for the observed systematic error in QPEs from CMLs.

  10. Centrality Robustness and Link Prediction in Complex Social Networks

    DEFF Research Database (Denmark)

    Davidsen, Søren Atmakuri; Ortiz-Arroyo, Daniel

    2012-01-01

    . Secondly, we present a method to predict edges in dynamic social networks. Our experimental results indicate that the robustness of the centrality measures applied to more realistic social networks follows a predictable pattern and that the use of temporal statistics could improve the accuracy achieved......This chapter addresses two important issues in social network analysis that involve uncertainty. Firstly, we present am analysis on the robustness of centrality measures that extend the work presented in Borgati et al. using three types of complex network structures and one real social network...

  11. Heterogeneous Data Fusion Method to Estimate Travel Time Distributions in Congested Road Networks

    OpenAIRE

    Chaoyang Shi; Bi Yu Chen; William H. K. Lam; Qingquan Li

    2017-01-01

    Travel times in congested urban road networks are highly stochastic. Provision of travel time distribution information, including both mean and variance, can be very useful for travelers to make reliable path choice decisions to ensure higher probability of on-time arrival. To this end, a heterogeneous data fusion method is proposed to estimate travel time distributions by fusing heterogeneous data from point and interval detectors. In the proposed method, link travel time distributions are f...

  12. Modelling, Estimation and Control of Networked Complex Systems

    CERN Document Server

    Chiuso, Alessandro; Frasca, Mattia; Rizzo, Alessandro; Schenato, Luca; Zampieri, Sandro

    2009-01-01

    The paradigm of complexity is pervading both science and engineering, leading to the emergence of novel approaches oriented at the development of a systemic view of the phenomena under study; the definition of powerful tools for modelling, estimation, and control; and the cross-fertilization of different disciplines and approaches. This book is devoted to networked systems which are one of the most promising paradigms of complexity. It is demonstrated that complex, dynamical networks are powerful tools to model, estimate, and control many interesting phenomena, like agent coordination, synchronization, social and economics events, networks of critical infrastructures, resources allocation, information processing, or control over communication networks. Moreover, it is shown how the recent technological advances in wireless communication and decreasing in cost and size of electronic devices are promoting the appearance of large inexpensive interconnected systems, each with computational, sensing and mobile cap...

  13. Foot Plantar Pressure Estimation Using Artificial Neural Networks

    OpenAIRE

    Xidias , Elias; Koutkalaki , Zoi; Papagiannis , Panagiotis; Papanikos , Paraskevas; Azariadis , Philip

    2015-01-01

    Part 1: Smart Products; International audience; In this paper, we present a novel approach to estimate the maximum pressure over the foot plantar surface exerted by a two-layer shoe sole for three distinct phases of the gait cycle. The proposed method is based on Artificial Neural Networks and can be utilized for the determination of the comfort that is related to the sole construction. Input parameters to the proposed neural network are the material properties and the thicknesses of the sole...

  14. Factorized Estimation of Partially Shared Parameters in Diffusion Networks

    Czech Academy of Sciences Publication Activity Database

    Dedecius, Kamil; Sečkárová, Vladimíra

    2017-01-01

    Roč. 65, č. 19 (2017), s. 5153-5163 ISSN 1053-587X R&D Projects: GA ČR(CZ) GP14-06678P; GA ČR GA16-09848S Institutional support: RVO:67985556 Keywords : Diffusion network * Diffusion estimation * Heterogeneous parameters * Multitask networks Subject RIV: BD - Theory of Information OBOR OECD: Applied mathematics Impact factor: 4.300, year: 2016 http://library.utia.cas.cz/separaty/2017/AS/dedecius-0477044.pdf

  15. Exponentially convergent state estimation for delayed switched recurrent neural networks.

    Science.gov (United States)

    Ahn, Choon Ki

    2011-11-01

    This paper deals with the delay-dependent exponentially convergent state estimation problem for delayed switched neural networks. A set of delay-dependent criteria is derived under which the resulting estimation error system is exponentially stable. It is shown that the gain matrix of the proposed state estimator is characterised in terms of the solution to a set of linear matrix inequalities (LMIs), which can be checked readily by using some standard numerical packages. An illustrative example is given to demonstrate the effectiveness of the proposed state estimator.

  16. Enhancement of Localization Accuracy in Cellular Networks via Cooperative AdHoc Links

    DEFF Research Database (Denmark)

    Lhomme, Edouard; Frattasi, Simone; Figueiras, Joao

    2006-01-01

    Positioning information enables new applications for cellular phones, personal communication systems, and specialized mobile radios. The network heterogeneity emerging in the fourth generation (4G) of mobile networks can be utilized for enhancements of the location estimation accuracy...

  17. Topology control algorithm for wireless sensor networks based on Link forwarding

    Science.gov (United States)

    Pucuo, Cairen; Qi, Ai-qin

    2018-03-01

    The research of topology control could effectively save energy and increase the service life of network based on wireless sensor. In this paper, a arithmetic called LTHC (link transmit hybrid clustering) based on link transmit is proposed. It decreases expenditure of energy by changing the way of cluster-node’s communication. The idea is to establish a link between cluster and SINK node when the cluster is formed, and link-node must be non-cluster. Through the link, cluster sends information to SINK nodes. For the sake of achieving the uniform distribution of energy on the network, prolongate the network survival time, and improve the purpose of communication, the communication will cut down much more expenditure of energy for cluster which away from SINK node. In the two aspects of improving the traffic and network survival time, we find that the LTCH is far superior to the traditional LEACH by experiments.

  18. Weed Growth Stage Estimator Using Deep Convolutional Neural Networks

    DEFF Research Database (Denmark)

    Teimouri, Nima; Dyrmann, Mads; Nielsen, Per Rydahl

    2018-01-01

    conditions with regards to soil types, resolution and light settings. Then, 9649 of these images were used for training the computer, which automatically divided the weeds into nine growth classes. The performance of this proposed convolutional neural network approach was evaluated on a further set of 2516...... in estimating the number of leaves and 96% accuracy when accepting a deviation of two leaves. These results show that this new method of using deep convolutional neural networks has a relatively high ability to estimate early growth stages across a wide variety of weed species....

  19. DC Link Current Estimation in Wind-Double Feed Induction Generator Power Conditioning System

    Directory of Open Access Journals (Sweden)

    MARIAN GAICEANU

    2010-12-01

    Full Text Available In this paper the implementation of the DC link current estimator in power conditioning system of the variable speed wind turbine is shown. The wind turbine is connected to double feed induction generator (DFIG. The variable electrical energy parameters delivered by DFIG are fitted with the electrical grid parameters through back-to-back power converter. The bidirectional AC-AC power converter covers a wide speed range from subsynchronous to supersynchronous speeds. The modern control of back-to-back power converter involves power balance concept, therefore its load power should be known in any instant. By using the power balance control, the DC link voltage variation at the load changes can be reduced. In this paper the load power is estimated from the dc link, indirectly, through a second order DC link current estimator. The load current estimator is based on the DC link voltage and on the dc link input current of the rotor side converter. This method presents certain advantages instead of using measured method, which requires a low pass filter: no time delay, the feedforward current component has no ripple, no additional hardware, and more fast control response. Through the numerical simulation the performances of the proposed DC link output current estimator scheme are demonstrated.

  20. The Inverse Contagion Problem (ICP) vs.. Predicting site contagion in real time, when network links are not observable

    Science.gov (United States)

    Mushkin, I.; Solomon, S.

    2017-10-01

    We study the inverse contagion problem (ICP). As opposed to the direct contagion problem, in which the network structure is known and the question is when each node will be contaminated, in the inverse problem the links of the network are unknown but a sequence of contagion histories (the times when each node was contaminated) is observed. We consider two versions of the ICP: The strong problem (SICP), which is the reconstruction of the network and has been studied before, and the weak problem (WICP), which requires "only" the prediction (at each time step) of the nodes that will be contaminated at the next time step (this is often the real life situation in which a contagion is observed and predictions are made in real time). Moreover, our focus is on analyzing the increasing accuracy of the solution, as a function of the number of contagion histories already observed. For simplicity, we discuss the simplest (deterministic and synchronous) contagion dynamics and the simplest solution algorithm, which we have applied to different network types. The main result of this paper is that the complex problem of the convergence of the ICP for a network can be reduced to an individual property of pairs of nodes: the "false link difficulty". By definition, given a pair of unlinked nodes i and j, the difficulty of the false link (i,j) is the probability that in a random contagion history, the nodes i and j are not contaminated at the same time step (or at consecutive time steps). In other words, the "false link difficulty" of a non-existing network link is the probability that the observations during a random contagion history would not rule out that link. This probability is relatively straightforward to calculate, and in most instances relies only on the relative positions of the two nodes (i,j) and not on the entire network structure. We have observed the distribution of false link difficulty for various network types, estimated it theoretically and confronted it

  1. Shuttle Planning for Link Closures in Urban Public Transport Networks

    NARCIS (Netherlands)

    van der Hurk, E.; Koutsopoulos, H.; Wilson, N.H.M.; Kroon, L.G.; Maroti, G.

    2016-01-01

    Urban public transport systems must periodically close certain links for maintenance, which can have significant effects on the service provided to passengers. In practice, the effects of closures are mitigated by replacing the closed links with a simple shuttle service. However, alternative shuttle

  2. Sparse and shrunken estimates of MRI networks in the brain and their influence on network properties

    DEFF Research Database (Denmark)

    Romero-Garcia, Rafael; Clemmensen, Line Katrine Harder

    2014-01-01

    approaches showed more stable results with a relative low variance at the expense of a little bias. Interestingly, topological properties as local and global efficiency estimated in networks constructed from traditional non-regularized correlations also showed higher variability when compared to those from...... regularized networks. Our findings suggest that a population-based connectivity study can achieve a more robust description of cortical topology through regularization of the correlation estimates. Four regularization methods were examined: Two with shrinkage (Ridge and Schäfer’s shrinkage), one with sparsity...... (Lasso) and one with both shrinkage and sparsity (Elastic net). Furthermore, the different regularizations resulted in different correlation estimates as well as network properties. The shrunken estimates resulted in lower variance of the estimates than the sparse estimates....

  3. Estimation of the proteomic cancer co-expression sub networks by using association estimators.

    Directory of Open Access Journals (Sweden)

    Cihat Erdoğan

    Full Text Available In this study, the association estimators, which have significant influences on the gene network inference methods and used for determining the molecular interactions, were examined within the co-expression network inference concept. By using the proteomic data from five different cancer types, the hub genes/proteins within the disease-associated gene-gene/protein-protein interaction sub networks were identified. Proteomic data from various cancer types is collected from The Cancer Proteome Atlas (TCPA. Correlation and mutual information (MI based nine association estimators that are commonly used in the literature, were compared in this study. As the gold standard to measure the association estimators' performance, a multi-layer data integration platform on gene-disease associations (DisGeNET and the Molecular Signatures Database (MSigDB was used. Fisher's exact test was used to evaluate the performance of the association estimators by comparing the created co-expression networks with the disease-associated pathways. It was observed that the MI based estimators provided more successful results than the Pearson and Spearman correlation approaches, which are used in the estimation of biological networks in the weighted correlation network analysis (WGCNA package. In correlation-based methods, the best average success rate for five cancer types was 60%, while in MI-based methods the average success ratio was 71% for James-Stein Shrinkage (Shrink and 64% for Schurmann-Grassberger (SG association estimator, respectively. Moreover, the hub genes and the inferred sub networks are presented for the consideration of researchers and experimentalists.

  4. Linking network usage patterns to traffic Gaussianity fit

    NARCIS (Netherlands)

    de Oliveira Schmidt, R.; Sadre, R.; Melnikov, Nikolay; Schönwälder, Jürgen; Pras, Aiko

    Gaussian traffic models are widely used in the domain of network traffic modeling. The central assumption is that traffic aggregates are Gaussian distributed. Due to its importance, the Gaussian character of network traffic has been extensively assessed by researchers in the past years. In 2001,

  5. A multilayer network analysis of hashtags in twitter via co-occurrence and semantic links

    Science.gov (United States)

    Türker, Ilker; Sulak, Eyüb Ekmel

    2018-02-01

    Complex network studies, as an interdisciplinary framework, span a large variety of subjects including social media. In social networks, several mechanisms generate miscellaneous structures like friendship networks, mention networks, tag networks, etc. Focusing on tag networks (namely, hashtags in twitter), we made a two-layer analysis of tag networks from a massive dataset of Twitter entries. The first layer is constructed by converting the co-occurrences of these tags in a single entry (tweet) into links, while the second layer is constructed converting the semantic relations of the tags into links. We observed that the universal properties of the real networks like small-world property, clustering and power-law distributions in various network parameters are also evident in the multilayer network of hashtags. Moreover, we outlined that co-occurrences of hashtags in tweets are mostly coupled with semantic relations, whereas a small number of semantically unrelated, therefore random links reduce node separation and network diameter in the co-occurrence network layer. Together with the degree distributions, the power-law consistencies of degree difference, edge weight and cosine similarity distributions in both layers are also appealing forms of Zipf’s law evident in nature.

  6. Linking shallow, Linking deep : how scientific intermediaries use the Web for their network of collaborators

    NARCIS (Netherlands)

    Vasileiadou, E.; Besselaar, van den P.

    2006-01-01

    In this paper we explore the possibility of using Web links to study collaborations between organisations, combining the results of qualitative analysis of interviews and quantitative analysis of linking patterns. We use case studies of scientific intermediaries, that is, organisations that mediate

  7. Linking behavior in the physics education research coauthorship network

    Science.gov (United States)

    Anderson, Katharine A.; Crespi, Matthew; Sayre, Eleanor C.

    2017-06-01

    There is considerable long-term interest in understanding the dynamics of collaboration networks, and how these networks form and evolve over time. Most of the work done on the dynamics of social networks focuses on well-established communities. Work examining emerging social networks is rarer, simply because data are difficult to obtain in real time. In this paper, we use thirty years of data from an emerging scientific community to look at that crucial early stage in the development of a social network. We show that when the field was very young, islands of individual researchers labored in relative isolation, and the coauthorship network was disconnected. Thirty years later, rather than a cluster of individuals, we find a true collaborative community, bound together by a robust collaboration network. However, this change did not take place gradually—the network remained a loose assortment of isolated individuals until the mid 2000s, when those smaller parts suddenly knit themselves together into a single whole. In the rest of this paper, we consider the role of three factors in these observed structural changes: growth, changes in social norms, and the introduction of institutions such as field-specific conferences and journals. We have data from the very earliest years of the field, a period which includes the introduction of two different institutions: the first field-specific conference, and the first field-specific journals. We also identify two relevant behavioral shifts: a discrete increase in coauthorship coincident with the first conference, and a shift among established authors away from collaborating with outsiders, towards collaborating with each other. The interaction of these factors gives us insight into the formation of collaboration networks more broadly.

  8. Linking behavior in the physics education research coauthorship network

    Directory of Open Access Journals (Sweden)

    Katharine A. Anderson

    2017-05-01

    Full Text Available There is considerable long-term interest in understanding the dynamics of collaboration networks, and how these networks form and evolve over time. Most of the work done on the dynamics of social networks focuses on well-established communities. Work examining emerging social networks is rarer, simply because data are difficult to obtain in real time. In this paper, we use thirty years of data from an emerging scientific community to look at that crucial early stage in the development of a social network. We show that when the field was very young, islands of individual researchers labored in relative isolation, and the coauthorship network was disconnected. Thirty years later, rather than a cluster of individuals, we find a true collaborative community, bound together by a robust collaboration network. However, this change did not take place gradually—the network remained a loose assortment of isolated individuals until the mid 2000s, when those smaller parts suddenly knit themselves together into a single whole. In the rest of this paper, we consider the role of three factors in these observed structural changes: growth, changes in social norms, and the introduction of institutions such as field-specific conferences and journals. We have data from the very earliest years of the field, a period which includes the introduction of two different institutions: the first field-specific conference, and the first field-specific journals. We also identify two relevant behavioral shifts: a discrete increase in coauthorship coincident with the first conference, and a shift among established authors away from collaborating with outsiders, towards collaborating with each other. The interaction of these factors gives us insight into the formation of collaboration networks more broadly.

  9. Restorability on 3-connected WDM Networks Under Single and Dual Physical Link Failures

    DEFF Research Database (Denmark)

    Gutierrez Lopez, Jose Manuel; Jensen, Michael; Riaz, Tahir

    2013-01-01

    This work studies the influence the network interconnection has over restoration techniques. The way physical links are distributed to interconnect network nodes has a great impact on parameters such as path distances when failures occur and restoration is applied. The work focuses on single and ...... to network planning, the trade-off network length vs. performance of the different topological options is studied. The results show how 3-connected graphs could provide a reasonable trade-off between costs, link failure rates, and restored path parameters....

  10. A new way to improve the robustness of complex communication networks by allocating redundancy links

    International Nuclear Information System (INIS)

    Shi Chunhui; Zhuo Yue; Tang Jieying; Long Keping; Peng Yunfeng

    2012-01-01

    We investigate the robustness of complex communication networks on allocating redundancy links. The protecting key nodes (PKN) strategy is proposed to improve the robustness of complex communication networks against intentional attack. Our numerical simulations show that allocating a few redundant links among key nodes using the PKN strategy will significantly increase the robustness of scale-free complex networks. We have also theoretically proved and demonstrated the effectiveness of the PKN strategy. We expect that our work will help achieve a better understanding of communication networks. (paper)

  11. Parameter estimation in space systems using recurrent neural networks

    Science.gov (United States)

    Parlos, Alexander G.; Atiya, Amir F.; Sunkel, John W.

    1991-01-01

    The identification of time-varying parameters encountered in space systems is addressed, using artificial neural systems. A hybrid feedforward/feedback neural network, namely a recurrent multilayer perception, is used as the model structure in the nonlinear system identification. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard back-propagation-learning algorithm is modified and it is used for both the off-line and on-line supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying parameters of nonlinear dynamic systems is investigated by estimating the mass properties of a representative large spacecraft. The changes in the spacecraft inertia are predicted using a trained neural network, during two configurations corresponding to the early and late stages of the spacecraft on-orbit assembly sequence. The proposed on-line mass properties estimation capability offers encouraging results, though, further research is warranted for training and testing the predictive capabilities of these networks beyond nominal spacecraft operations.

  12. A Gossip-based Churn Estimator for Large Dynamic Networks

    NARCIS (Netherlands)

    Giuffrida, C.; Ortolani, S.

    2010-01-01

    Gossip-based aggregation is an emerging paradigm to perform distributed computations and measurements in a large-scale setting. In this paper we explore the possibility of using gossip-based aggregation to estimate churn in arbitrarily large networks. To this end, we introduce a new model to compute

  13. Iterative Available Bandwidth Estimation for Mobile Transport Networks

    DEFF Research Database (Denmark)

    Ubeda Castellanos, Carlos; López Villa, Dimas; Teyeb, Oumer Mohammed

    2007-01-01

    Available bandwidth estimation has lately been proposed to be used for end-to-end resource management in existing and emerging mobile communication systems, whose transport networks could end up being the bottleneck rather than the air interface. Algorithms for admission control, handover...

  14. 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...

  15. Performance of monitoring networks estimated from a Gaussian plume model

    International Nuclear Information System (INIS)

    Seebregts, A.J.; Hienen, J.F.A.

    1990-10-01

    In support of the ECN study on monitoring strategies after nuclear accidents, the present report describes the analysis of the performance of a monitoring network in a square grid. This network is used to estimate the distribution of the deposition pattern after a release of radioactivity into the atmosphere. The analysis is based upon a single release, a constant wind direction and an atmospheric dispersion according to a simplified Gaussian plume model. A technique is introduced to estimate the parameters in this Gaussian model based upon measurements at specific monitoring locations and linear regression, although this model is intrinsically non-linear. With these estimated parameters and the Gaussian model the distribution of the contamination due to deposition can be estimated. To investigate the relation between the network and the accuracy of the estimates for the deposition, deposition data have been generated by the Gaussian model, including a measurement error by a Monte Carlo simulation and this procedure has been repeated for several grid sizes, dispersion conditions, number of measurements per location, and errors per single measurement. The present technique has also been applied for the mesh sizes of two networks in the Netherlands, viz. the Landelijk Meetnet Radioaciviteit (National Measurement Network on Radioactivity, mesh size approx. 35 km) and the proposed Landelijk Meetnet Nucleaire Incidenten (National Measurement Network on Nuclear Incidents, mesh size approx. 15 km). The results show accuracies of 11 and 7 percent, respectively, if monitoring locations are used more than 10 km away from the postulated accident site. These figures are based upon 3 measurements per location and a dispersion during neutral weather with a wind velocity of 4 m/s. For stable weather conditions and low wind velocities, i.e. a small plume, the calculated accuracies are at least a factor 1.5 worse.The present type of analysis makes a cost-benefit approach to the

  16. An auxiliary optimization method for complex public transit route network based on link prediction

    Science.gov (United States)

    Zhang, Lin; Lu, Jian; Yue, Xianfei; Zhou, Jialin; Li, Yunxuan; Wan, Qian

    2018-02-01

    Inspired by the missing (new) link prediction and the spurious existing link identification in link prediction theory, this paper establishes an auxiliary optimization method for public transit route network (PTRN) based on link prediction. First, link prediction applied to PTRN is described, and based on reviewing the previous studies, the summary indices set and its algorithms set are collected for the link prediction experiment. Second, through analyzing the topological properties of Jinan’s PTRN established by the Space R method, we found that this is a typical small-world network with a relatively large average clustering coefficient. This phenomenon indicates that the structural similarity-based link prediction will show a good performance in this network. Then, based on the link prediction experiment of the summary indices set, three indices with maximum accuracy are selected for auxiliary optimization of Jinan’s PTRN. Furthermore, these link prediction results show that the overall layout of Jinan’s PTRN is stable and orderly, except for a partial area that requires optimization and reconstruction. The above pattern conforms to the general pattern of the optimal development stage of PTRN in China. Finally, based on the missing (new) link prediction and the spurious existing link identification, we propose optimization schemes that can be used not only to optimize current PTRN but also to evaluate PTRN planning.

  17. Energy parameter estimation in solar powered wireless sensor networks

    KAUST Repository

    Mousa, Mustafa

    2014-02-24

    The operation of solar powered wireless sensor networks is associated with numerous challenges. One of the main challenges is the high variability of solar power input and battery capacity, due to factors such as weather, humidity, dust and temperature. In this article, we propose a set of tools that can be implemented onboard high power wireless sensor networks to estimate the battery condition and capacity as well as solar power availability. These parameters are very important to optimize sensing and communications operations and maximize the reliability of the complete system. Experimental results show that the performance of typical Lithium Ion batteries severely degrades outdoors in a matter of weeks or months, and that the availability of solar energy in an urban solar powered wireless sensor network is highly variable, which underlines the need for such power and energy estimation algorithms.

  18. Estimation of scattered photons using a neural network in SPECT

    International Nuclear Information System (INIS)

    Hasegawa, Wataru; Ogawa, Koichi

    1994-01-01

    In single photon emission CT (SPECT), measured projection data involve scattered photons. This causes degradation of spatial resolution and contrast in reconstructed images. The purpose of this study is to estimate the scattered photons, and eliminate them from measured data. To estimate the scattered photons, we used an artificial neural network which consists of five input units, five hidden units, and two output units. The inputs of the network are the ratios of the counts acquired by five narrow energy windows and their sum. The outputs are the ratios of the count of scattered photons and that of primary photons to the total count. The neural network was trained with a back-propagation algorithm using count data obtained by a Monte Carlo simulation. The results of simulation showed improvement of contrast and spatial resolution in reconstructed images. (author)

  19. Energy parameter estimation in solar powered wireless sensor networks

    KAUST Repository

    Mousa, Mustafa; Claudel, Christian G.

    2014-01-01

    The operation of solar powered wireless sensor networks is associated with numerous challenges. One of the main challenges is the high variability of solar power input and battery capacity, due to factors such as weather, humidity, dust and temperature. In this article, we propose a set of tools that can be implemented onboard high power wireless sensor networks to estimate the battery condition and capacity as well as solar power availability. These parameters are very important to optimize sensing and communications operations and maximize the reliability of the complete system. Experimental results show that the performance of typical Lithium Ion batteries severely degrades outdoors in a matter of weeks or months, and that the availability of solar energy in an urban solar powered wireless sensor network is highly variable, which underlines the need for such power and energy estimation algorithms.

  20. Common neighbours and the local-community-paradigm for topological link prediction in bipartite networks

    International Nuclear Information System (INIS)

    Daminelli, Simone; Thomas, Josephine Maria; Durán, Claudio; Vittorio Cannistraci, Carlo

    2015-01-01

    Bipartite networks are powerful descriptions of complex systems characterized by two different classes of nodes and connections allowed only across but not within the two classes. Unveiling physical principles, building theories and suggesting physical models to predict bipartite links such as product-consumer connections in recommendation systems or drug–target interactions in molecular networks can provide priceless information to improve e-commerce or to accelerate pharmaceutical research. The prediction of nonobserved connections starting from those already present in the topology of a network is known as the link-prediction problem. It represents an important subject both in many-body interaction theory in physics and in new algorithms for applied tools in computer science. The rationale is that the existing connectivity structure of a network can suggest where new connections can appear with higher likelihood in an evolving network, or where nonobserved connections are missing in a partially known network. Surprisingly, current complex network theory presents a theoretical bottle-neck: a general framework for local-based link prediction directly in the bipartite domain is missing. Here, we overcome this theoretical obstacle and present a formal definition of common neighbour index and local-community-paradigm (LCP) for bipartite networks. As a consequence, we are able to introduce the first node-neighbourhood-based and LCP-based models for topological link prediction that utilize the bipartite domain. We performed link prediction evaluations in several networks of different size and of disparate origin, including technological, social and biological systems. Our models significantly improve topological prediction in many bipartite networks because they exploit local physical driving-forces that participate in the formation and organization of many real-world bipartite networks. Furthermore, we present a local-based formalism that allows to intuitively

  1. Common neighbours and the local-community-paradigm for topological link prediction in bipartite networks

    Science.gov (United States)

    Daminelli, Simone; Thomas, Josephine Maria; Durán, Claudio; Vittorio Cannistraci, Carlo

    2015-11-01

    Bipartite networks are powerful descriptions of complex systems characterized by two different classes of nodes and connections allowed only across but not within the two classes. Unveiling physical principles, building theories and suggesting physical models to predict bipartite links such as product-consumer connections in recommendation systems or drug-target interactions in molecular networks can provide priceless information to improve e-commerce or to accelerate pharmaceutical research. The prediction of nonobserved connections starting from those already present in the topology of a network is known as the link-prediction problem. It represents an important subject both in many-body interaction theory in physics and in new algorithms for applied tools in computer science. The rationale is that the existing connectivity structure of a network can suggest where new connections can appear with higher likelihood in an evolving network, or where nonobserved connections are missing in a partially known network. Surprisingly, current complex network theory presents a theoretical bottle-neck: a general framework for local-based link prediction directly in the bipartite domain is missing. Here, we overcome this theoretical obstacle and present a formal definition of common neighbour index and local-community-paradigm (LCP) for bipartite networks. As a consequence, we are able to introduce the first node-neighbourhood-based and LCP-based models for topological link prediction that utilize the bipartite domain. We performed link prediction evaluations in several networks of different size and of disparate origin, including technological, social and biological systems. Our models significantly improve topological prediction in many bipartite networks because they exploit local physical driving-forces that participate in the formation and organization of many real-world bipartite networks. Furthermore, we present a local-based formalism that allows to intuitively

  2. 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.

  3. Weed Growth Stage Estimator Using Deep Convolutional Neural Networks

    DEFF Research Database (Denmark)

    Teimouri, Nima; Dyrmann, Mads; Nielsen, Per Rydahl

    2018-01-01

    This study outlines a new method of automatically estimating weed species and growth stages (from cotyledon until eight leaves are visible) of in situ images covering 18 weed species or families. Images of weeds growing within a variety of crops were gathered across variable environmental conditi...... in estimating the number of leaves and 96% accuracy when accepting a deviation of two leaves. These results show that this new method of using deep convolutional neural networks has a relatively high ability to estimate early growth stages across a wide variety of weed species....

  4. Energy Efficient Link Aware Routing with Power Control in Wireless Ad Hoc Networks.

    Science.gov (United States)

    Katiravan, Jeevaa; Sylvia, D; Rao, D Srinivasa

    2015-01-01

    In wireless ad hoc networks, the traditional routing protocols make the route selection based on minimum distance between the nodes and the minimum number of hop counts. Most of the routing decisions do not consider the condition of the network such as link quality and residual energy of the nodes. Also, when a link failure occurs, a route discovery mechanism is initiated which incurs high routing overhead. If the broadcast nature and the spatial diversity of the wireless communication are utilized efficiently it becomes possible to achieve improvement in the performance of the wireless networks. In contrast to the traditional routing scheme which makes use of a predetermined route for packet transmission, such an opportunistic routing scheme defines a predefined forwarding candidate list formed by using single network metrics. In this paper, a protocol is proposed which uses multiple metrics such as residual energy and link quality for route selection and also includes a monitoring mechanism which initiates a route discovery for a poor link, thereby reducing the overhead involved and improving the throughput of the network while maintaining network connectivity. Power control is also implemented not only to save energy but also to improve the network performance. Using simulations, we show the performance improvement attained in the network in terms of packet delivery ratio, routing overhead, and residual energy of the network.

  5. Energy Efficient Link Aware Routing with Power Control in Wireless Ad Hoc Networks

    Directory of Open Access Journals (Sweden)

    Jeevaa Katiravan

    2015-01-01

    Full Text Available In wireless ad hoc networks, the traditional routing protocols make the route selection based on minimum distance between the nodes and the minimum number of hop counts. Most of the routing decisions do not consider the condition of the network such as link quality and residual energy of the nodes. Also, when a link failure occurs, a route discovery mechanism is initiated which incurs high routing overhead. If the broadcast nature and the spatial diversity of the wireless communication are utilized efficiently it becomes possible to achieve improvement in the performance of the wireless networks. In contrast to the traditional routing scheme which makes use of a predetermined route for packet transmission, such an opportunistic routing scheme defines a predefined forwarding candidate list formed by using single network metrics. In this paper, a protocol is proposed which uses multiple metrics such as residual energy and link quality for route selection and also includes a monitoring mechanism which initiates a route discovery for a poor link, thereby reducing the overhead involved and improving the throughput of the network while maintaining network connectivity. Power control is also implemented not only to save energy but also to improve the network performance. Using simulations, we show the performance improvement attained in the network in terms of packet delivery ratio, routing overhead, and residual energy of the network.

  6. Estimating Ads’ Click through Rate with Recurrent Neural Network

    Directory of Open Access Journals (Sweden)

    Chen Qiao-Hong

    2016-01-01

    Full Text Available With the development of the Internet, online advertising spreads across every corner of the world, the ads' click through rate (CTR estimation is an important method to improve the online advertising revenue. Compared with the linear model, the nonlinear models can study much more complex relationships between a large number of nonlinear characteristics, so as to improve the accuracy of the estimation of the ads’ CTR. The recurrent neural network (RNN based on Long-Short Term Memory (LSTM is an improved model of the feedback neural network with ring structure. The model overcomes the problem of the gradient of the general RNN. Experiments show that the RNN based on LSTM exceeds the linear models, and it can effectively improve the estimation effect of the ads’ click through rate.

  7. Artificial Neural Network for Location Estimation in Wireless Communication Systems

    Directory of Open Access Journals (Sweden)

    Chien-Sheng Chen

    2012-03-01

    Full Text Available In a wireless communication system, wireless location is the technique used to estimate the location of a mobile station (MS. To enhance the accuracy of MS location prediction, we propose a novel algorithm that utilizes time of arrival (TOA measurements and the angle of arrival (AOA information to locate MS when three base stations (BSs are available. Artificial neural networks (ANN are widely used techniques in various areas to overcome the problem of exclusive and nonlinear relationships. When the MS is heard by only three BSs, the proposed algorithm utilizes the intersections of three TOA circles (and the AOA line, based on various neural networks, to estimate the MS location in non-line-of-sight (NLOS environments. Simulations were conducted to evaluate the performance of the algorithm for different NLOS error distributions. The numerical analysis and simulation results show that the proposed algorithms can obtain more precise location estimation under different NLOS environments.

  8. Artificial neural network for location estimation in wireless communication systems.

    Science.gov (United States)

    Chen, Chien-Sheng

    2012-01-01

    In a wireless communication system, wireless location is the technique used to estimate the location of a mobile station (MS). To enhance the accuracy of MS location prediction, we propose a novel algorithm that utilizes time of arrival (TOA) measurements and the angle of arrival (AOA) information to locate MS when three base stations (BSs) are available. Artificial neural networks (ANN) are widely used techniques in various areas to overcome the problem of exclusive and nonlinear relationships. When the MS is heard by only three BSs, the proposed algorithm utilizes the intersections of three TOA circles (and the AOA line), based on various neural networks, to estimate the MS location in non-line-of-sight (NLOS) environments. Simulations were conducted to evaluate the performance of the algorithm for different NLOS error distributions. The numerical analysis and simulation results show that the proposed algorithms can obtain more precise location estimation under different NLOS environments.

  9. An RSS based location estimation technique for cognitive relay networks

    KAUST Repository

    Qaraqe, Khalid A.

    2010-11-01

    In this paper, a received signal strength (RSS) based location estimation method is proposed for a cooperative wireless relay network where the relay is a cognitive radio. We propose a method for the considered cognitive relay network to determine the location of the source using the direct and the relayed signal at the destination. We derive the Cramer-Rao lower bound (CRLB) expressions separately for x and y coordinates of the location estimate. We analyze the effects of cognitive behaviour of the relay on the performance of the proposed method. We also discuss and quantify the reliability of the location estimate using the proposed technique if the source is not stationary. The overall performance of the proposed method is presented through simulations. ©2010 IEEE.

  10. Cascaded multiplexed optical link on a telecommunication network for frequency dissemination.

    Science.gov (United States)

    Lopez, Olivier; Haboucha, Adil; Kéfélian, Fabien; Jiang, Haifeng; Chanteau, Bruno; Roncin, Vincent; Chardonnet, Christian; Amy-Klein, Anne; Santarelli, Giorgio

    2010-08-02

    We demonstrate a cascaded optical link for ultrastable frequency dissemination comprised of two compensated links of 150 km and a repeater station. Each link includes 114 km of Internet fiber simultaneously carrying data traffic through a dense wavelength division multiplexing technology, and passes through two routing centers of the telecommunication network. The optical reference signal is inserted in and extracted from the communication network using bidirectional optical add-drop multiplexers. The repeater station operates autonomously ensuring noise compensation on the two links and the ultra-stable signal optical regeneration. The compensated link shows a fractional frequency instability of 3 x 10(-15) at one second measurement time and 5 x 10(-20) at 20 hours. This work paves the way to a wide dissemination of ultra-stable optical clock signals between distant laboratories via the Internet network.

  11. Analysis of the Spatial Variation of Network-Constrained Phenomena Represented by a Link Attribute Using a Hierarchical Bayesian Model

    Directory of Open Access Journals (Sweden)

    Zhensheng Wang

    2017-02-01

    Full Text Available The spatial variation of geographical phenomena is a classical problem in spatial data analysis and can provide insight into underlying processes. Traditional exploratory methods mostly depend on the planar distance assumption, but many spatial phenomena are constrained to a subset of Euclidean space. In this study, we apply a method based on a hierarchical Bayesian model to analyse the spatial variation of network-constrained phenomena represented by a link attribute in conjunction with two experiments based on a simplified hypothetical network and a complex road network in Shenzhen that includes 4212 urban facility points of interest (POIs for leisure activities. Then, the methods named local indicators of network-constrained clusters (LINCS are applied to explore local spatial patterns in the given network space. The proposed method is designed for phenomena that are represented by attribute values of network links and is capable of removing part of random variability resulting from small-sample estimation. The effects of spatial dependence and the base distribution are also considered in the proposed method, which could be applied in the fields of urban planning and safety research.

  12. Small vessel disease is linked to disrupted structural network covariance in Alzheimer's disease.

    Science.gov (United States)

    Nestor, Sean M; Mišić, Bratislav; Ramirez, Joel; Zhao, Jiali; Graham, Simon J; Verhoeff, Nicolaas P L G; Stuss, Donald T; Masellis, Mario; Black, Sandra E

    2017-07-01

    Cerebral small vessel disease (SVD) is thought to contribute to Alzheimer's disease (AD) through abnormalities in white matter networks. Gray matter (GM) hub covariance networks share only partial overlap with white matter connectivity, and their relationship with SVD has not been examined in AD. We developed a multivariate analytical pipeline to elucidate the cortical GM thickness systems that covary with major network hubs and assessed whether SVD and neurodegenerative pathologic markers were associated with attenuated covariance network integrity in mild AD and normal elderly control subjects. SVD burden was associated with reduced posterior cingulate corticocortical GM network integrity and subneocorticocortical hub network integrity in AD. These findings provide evidence that SVD is linked to the selective disruption of cortical hub GM networks in AD brains and point to the need to consider GM hub covariance networks when assessing network disruption in mixed disease. Copyright © 2017 the Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

  13. Application of Functional Link Artificial Neural Network for Prediction of Machinery Noise in Opencast Mines

    Directory of Open Access Journals (Sweden)

    Santosh Kumar Nanda

    2011-01-01

    Full Text Available Functional link-based neural network models were applied to predict opencast mining machineries noise. The paper analyzes the prediction capabilities of functional link neural network based noise prediction models vis-à-vis existing statistical models. In order to find the actual noise status in opencast mines, some of the popular noise prediction models, for example, ISO-9613-2, CONCAWE, VDI, and ENM, have been applied in mining and allied industries to predict the machineries noise by considering various attenuation factors. Functional link artificial neural network (FLANN, polynomial perceptron network (PPN, and Legendre neural network (LeNN were used to predict the machinery noise in opencast mines. The case study is based on data collected from an opencast coal mine of Orissa, India. From the present investigations, it could be concluded that the FLANN model give better noise prediction than the PPN and LeNN model.

  14. Estimating the hydraulic conductivity of two-dimensional fracture networks

    Science.gov (United States)

    Leung, C. T.; Zimmerman, R. W.

    2010-12-01

    Most oil and gas reservoirs, as well as most potential sites for nuclear waste disposal, are naturally fractured. In these sites, the network of fractures will provide the main path for fluid to flow through the rock mass. In many cases, the fracture density is so high as to make it impractical to model it with a discrete fracture network (DFN) approach. For such rock masses, it would be useful to have recourse to analytical, or semi-analytical, methods to estimate the macroscopic hydraulic conductivity of the fracture network. We have investigated single-phase fluid flow through stochastically generated two-dimensional fracture networks. The centres and orientations of the fractures are uniformly distributed, whereas their lengths follow either a lognormal distribution or a power law distribution. We have considered the case where the fractures in the network each have the same aperture, as well as the case where the aperture of each fracture is directly proportional to the fracture length. The discrete fracture network flow and transport simulator NAPSAC, developed by Serco (Didcot, UK), is used to establish the “true” macroscopic hydraulic conductivity of the network. We then attempt to match this conductivity using a simple estimation method that does not require extensive computation. For our calculations, fracture networks are represented as networks composed of conducting segments (bonds) between nodes. Each bond represents the region of a single fracture between two adjacent intersections with other fractures. We assume that the bonds are arranged on a kagome lattice, with some fraction of the bonds randomly missing. The conductance of each bond is then replaced with some effective conductance, Ceff, which we take to be the arithmetic mean of the individual conductances, averaged over each bond, rather than over each fracture. This is in contrast to the usual approximation used in effective medium theories, wherein the geometric mean is used. Our

  15. Force Exertion and Transmission in Cross-Linked Actin Networks

    Science.gov (United States)

    Stam, Samantha

    Cells are responsive to external cues in their environment telling them to proliferate or migrate within their surrounding tissue. Sensing of cues that are mechanical in nature, such stiffness of a tissue or forces transmitted from other cells, is believed to involve the cytoskeleton of a cell. The cytoskeleton is a complex network of proteins consisting of polymers that provide structural support, motor proteins that remodel these structures, and many others. We do not yet have a complete understanding of how cytoskeletal components respond to either internal or external mechanical force and stiffness. Such an understanding should involve mechanisms by which constituent molecules, such as motor proteins, are responsive to mechanics. Additionally, physical models of how forces are transmitted through biopolymer networks are necessary. My research has focused on networks formed by the cytoskeletal filament actin and the molecular motor protein myosin II. Actin filaments form networks and bundles that form a structural framework of the cell, and myosin II slides actin filaments. In this thesis, we show that stiffness of an elastic load that opposes myosin-generated actin sliding has a very sharp effect on the myosin force output in simulations. Secondly, we show that the stiffness and connectivity of cytoskeletal filaments regulates the contractility and anisotropy of network deformations that transmit force on material length scales. Together, these results have implications for predicting and interpreting the deformations and forces in biopolymeric active materials.

  16. Using LTE Networks for UAV Command and Control Link

    DEFF Research Database (Denmark)

    Nguyen, Huan Cong; Amorim, Rafhael Medeiros de; Wigard, Jeroen

    2017-01-01

    In this paper we investigate the ability of Long-Term Evolution (LTE) network to provide coverage for Unmanned Aerial Vehicles (UAVs) in a rural area, in particular for the Command and Control (C2) downlink. The study takes into consideration the dependency of the large-scale path loss on the hei......In this paper we investigate the ability of Long-Term Evolution (LTE) network to provide coverage for Unmanned Aerial Vehicles (UAVs) in a rural area, in particular for the Command and Control (C2) downlink. The study takes into consideration the dependency of the large-scale path loss...... on the height of the UAV, which is derived from actual measurements, and a real-world cellular network layout and configuration. The results indicate that interference is the dominant factor limiting the cellular coverage for UAVs in the downlink: outage level increases from 4.2% at 1.5 m height to 51.7% at 120...

  17. Leveraging Social Links for Trust and Privacy in Networks

    Science.gov (United States)

    Cutillo, Leucio Antonio; Molva, Refik; Strufe, Thorsten

    Existing on-line social networks (OSN) such as Facebook suffer from several weaknesses regarding privacy and security due to their inherent handling of personal data. As pointed out in [4], a preliminary analysis of existing OSNs shows that they are subject to a number of vulnerabilities, ranging from cloning legitimate users to sybil attacks through privacy violations. Starting from these OSN vulnerabilities as the first step of a broader research activity, we came up with a new approach that is very promising in re-visiting security and privacy problems in distributed systems and networks. We suggest a solution that both aims at avoiding any centralized control and leverages on the real life trust between users, that is part of the social network application itself. An anonymization technique based on multi-hop routing among trusted nodes guarantees privacy in data access and, generally speaking, in all the OSN operations.

  18. Models for stiffening in cross-linked biopolymer networks : A comparative study

    NARCIS (Netherlands)

    van Dillen, T.; Onck, P. R.; Van der Giessen, E.

    In a recent publication, we studied the mechanical stiffening behavior in two-dimensional (2D) cross-linked networks of semiflexible biopolymer filaments under simple shear [Onck, P.R., Koeman, T., Van Dillen, T., Van der Giessen, E., 2005. Alternative explanation of stiffening in cross-linked

  19. An Improved Convolutional Neural Network on Crowd Density Estimation

    Directory of Open Access Journals (Sweden)

    Pan Shao-Yun

    2016-01-01

    Full Text Available In this paper, a new method is proposed for crowd density estimation. An improved convolutional neural network is combined with traditional texture feature. The data calculated by the convolutional layer can be treated as a new kind of features.So more useful information of images can be extracted by different features.In the meantime, the size of image has little effect on the result of convolutional neural network. Experimental results indicate that our scheme has adequate performance to allow for its use in real world applications.

  20. Managing coopetition through horizontal supply chain relations : Linking dyadic and network levels of analysis

    NARCIS (Netherlands)

    Wilhelm, Miriam M.

    2011-01-01

    A growing research stream has expanded the level of analysis beyond single buyer-supplier relations to the network, including supplier-supplier relations. These supplier-supplier relations may constitute a missing link between the traditional analysis of the dyadic and the network level of analysis

  1. Managing coopetition through horizontal supply chain relations : Linking dyadic and network levels of analysis

    NARCIS (Netherlands)

    Wilhelm, Miriam M.

    A growing research stream has expanded the level of analysis beyond single buyer-supplier relations to the network, including supplier-supplier relations. These supplier-supplier relations may constitute a missing link between the traditional analysis of the dyadic and the network level of analysis

  2. Building Student Networks with LinkedIn: The Potential for Connections, Internships, and Jobs

    Science.gov (United States)

    Peterson, Robert M.; Dover, Howard F.

    2014-01-01

    Networking is a chance to interact with people, build friendships or business partners, identify opportunities, and create value. Technology has made this process easier, since individuals can readily contact others who were previously unknown. In the professional world, LinkedIn has become the standard way to build virtual and personal networks.…

  3. Processes linked to contact changes in adoptive kinship networks.

    Science.gov (United States)

    Dunbar, Nora; van Dulmen, Manfred H M; Ayers-Lopez, Susan; Berge, Jerica M; Christian, Cinda; Gossman, Ginger; Henney, M Susan M; Mendenhall, Tai J; Grotevant, Harold D; McRoy, Ruth G

    2006-12-01

    The purpose of this study was to reveal underlying processes in adoptive kinship networks that experienced increases or decreases in levels of openness during the child's adolescent years. Intensive case study analyses were conducted for 8 adoptive kinship networks (each including an adoptive mother, adoptive father, adopted adolescent, and birth mother), half of whom had experienced an increase in openness from indirect (mediated) to direct (fully disclosed) contact and half of whom had ceased indirect contact between Waves 1 and 2 of a longitudinal study. Adoptive mothers tended to be more involved in contact with the birth mother than were adoptive fathers or adopted adolescents. Members of adoptive kinship networks in which a decrease in level of contact took place had incongruent perspectives about who initiated the stop in contact and why the stop took place. Birth mothers were less satisfied with their degree of contact than were adoptive parents. Adults' satisfaction with contact was related to feelings of control over type and amount of interactions and permeability of family boundaries. In all adoptive kinship networks, responsibility for contact had shifted toward the adopted adolescent regardless of whether the adolescent was aware of this change in responsibility.

  4. LINKEDIN TRILOGY: Part 1. Top 10 Reasons You Should NOT Join LinkedIn Professional Network!

    Science.gov (United States)

    Berk, Ronald A.

    2013-01-01

    Disclaimer: I have been an active "free" user of LinkedIn for 5.463 years with more than 3000 (1st degree) connections from all over the world. I have no vested interest in LinkedIn other than as a user of the services it provides. Despite the fact that LinkedIn was originally designed as a network for business professionals, not…

  5. Coarse-Grain Bandwidth Estimation Scheme for Large-Scale Network

    Science.gov (United States)

    Cheung, Kar-Ming; Jennings, Esther H.; Sergui, John S.

    2013-01-01

    A large-scale network that supports a large number of users can have an aggregate data rate of hundreds of Mbps at any time. High-fidelity simulation of a large-scale network might be too complicated and memory-intensive for typical commercial-off-the-shelf (COTS) tools. Unlike a large commercial wide-area-network (WAN) that shares diverse network resources among diverse users and has a complex topology that requires routing mechanism and flow control, the ground communication links of a space network operate under the assumption of a guaranteed dedicated bandwidth allocation between specific sparse endpoints in a star-like topology. This work solved the network design problem of estimating the bandwidths of a ground network architecture option that offer different service classes to meet the latency requirements of different user data types. In this work, a top-down analysis and simulation approach was created to size the bandwidths of a store-and-forward network for a given network topology, a mission traffic scenario, and a set of data types with different latency requirements. These techniques were used to estimate the WAN bandwidths of the ground links for different architecture options of the proposed Integrated Space Communication and Navigation (SCaN) Network. A new analytical approach, called the "leveling scheme," was developed to model the store-and-forward mechanism of the network data flow. The term "leveling" refers to the spreading of data across a longer time horizon without violating the corresponding latency requirement of the data type. Two versions of the leveling scheme were developed: 1. A straightforward version that simply spreads the data of each data type across the time horizon and doesn't take into account the interactions among data types within a pass, or between data types across overlapping passes at a network node, and is inherently sub-optimal. 2. Two-state Markov leveling scheme that takes into account the second order behavior of

  6. Link-Prediction Enhanced Consensus Clustering for Complex Networks (Open Access)

    Science.gov (United States)

    2016-05-20

    RESEARCH ARTICLE Link-Prediction Enhanced Consensus Clustering for Complex Networks Matthew Burgess1*, Eytan Adar1,2, Michael Cafarella1 1Computer...consensus clustering algorithm to enhance community detection on incomplete networks. Our framework utilizes existing community detection algorithms that...types of complex networks exhibit community structure: groups of highly connected nodes. Communities or clusters often reflect nodes that share similar

  7. Link Investigation of IEEE 802.15.4 Wireless Sensor Networks in Forests.

    Science.gov (United States)

    Ding, Xingjian; Sun, Guodong; Yang, Gaoxiang; Shang, Xinna

    2016-06-27

    Wireless sensor networks are expected to automatically monitor the ecological evolution and wildlife habits in forests. Low-power links (transceivers) are often adopted in wireless sensor network applications, in order to save the precious sensor energy and then achieve long-term, unattended monitoring. Recent research has presented some performance characteristics of such low-power wireless links under laboratory or outdoor scenarios with less obstacles, and they have found that low-power wireless links are unreliable and prone to be affected by the target environment. However, there is still less understanding about how well the low-power wireless link performs in real-world forests and to what extent the complex in-forest surrounding environments affect the link performances. In this paper, we empirically evaluate the low-power links of wireless sensors in three typical different forest environments. Our experiment investigates the performance of the link layer compatible with the IEEE 802.15.4 standard and analyzes the variation patterns of the packet reception ratio (PRR), the received signal strength indicator (RSSI) and the link quality indicator (LQI) under diverse experimental settings. Some observations of this study are inconsistent with or even contradict prior results that are achieved in open fields or relatively clean environments and thus, provide new insights both into effectively evaluating the low-power wireless links and into efficiently deploying wireless sensor network systems in forest environments.

  8. Modeling the video distribution link in the Next Generation Optical Access Networks

    DEFF Research Database (Denmark)

    Amaya, F.; Cárdenas, A.; Tafur Monroy, Idelfonso

    2011-01-01

    In this work we present a model for the design and optimization of the video distribution link in the next generation optical access network. We analyze the video distribution performance in a SCM-WDM link, including the noise, the distortion and the fiber optic nonlinearities. Additionally, we...... consider in the model the effect of distributed Raman amplification, used to extent the capacity and the reach of the optical link. In the model, we use the nonlinear Schrödinger equation with the purpose to obtain capacity limitations and design constrains of the next generation optical access networks....

  9. Modeling the video distribution link in the Next Generation Optical Access Networks

    International Nuclear Information System (INIS)

    Amaya, F; Cardenas, A; Tafur, I

    2011-01-01

    In this work we present a model for the design and optimization of the video distribution link in the next generation optical access network. We analyze the video distribution performance in a SCM-WDM link, including the noise, the distortion and the fiber optic nonlinearities. Additionally, we consider in the model the effect of distributed Raman amplification, used to extent the capacity and the reach of the optical link. In the model, we use the nonlinear Schroedinger equation with the purpose to obtain capacity limitations and design constrains of the next generation optical access networks.

  10. [Linking: relationships between health professionals in the informal health networks].

    Science.gov (United States)

    Sarradon-Eck, A; Vega, A; Faure, M; Humbert-Gaudart, A; Lustman, M

    2008-07-01

    During the last years, the french health system has been developing formal health networks. So, it was necessary to study informal health networks as networks. More precisely, we studied the nature of relationships between various stakeholders around general practionners wich are commonly considering as the stakeholder of the health system private sector. Fieldwork (ethnography based on direct observations and interviews) was conducted between October 2002 and april 2004, in the South-East of France. Ten monographs of general practioner's offices were achieved in a rural area; then, we achieved fieldwork of the informal health networks identified. There is a cultural frame wich is common to all private professionals. This frame includes a triple ideal (teamwork built up the hospital model, independance, and an relational approach with patients). This frame does not square with the real practices. In fact, regulation mechanisms preserve the balance of relashionships between professionnal groups, by restricting/promoting exchanges and complex alliance strategies. These mecanisms include: (1) a few professionnal's rule as disponibility (to the patients and to the professionnals), as communication about patient, as patient's reference, as obligation to communicate between professionals; (2) some constraints such as territory superposition and competition with other professional groups; (3) some needs for: rileiving (of emotions and worries connected to work), sharing (decisions, responsabilities), of delegation (medical treatment, practices), protection against social and legal risk through the creation of trust relationships. These trust relationships are based on several logics (affinity, solidarity, similarity). The study shows the major place of the patient who is often the main organizer of his network, and even though he makes an important structuring work between medical staff, and an information transfer (on his diagnosis, on his treatment, and professionals

  11. Buckshot Routing with Distance Vectors in Three Application Scenarios for Wireless Sensor Networks with Unstable Network Topologies and Unidirectional Links

    Directory of Open Access Journals (Sweden)

    Reinhardt Karnapke

    2015-02-01

    Full Text Available Experiments have shown that the number of asymmetric and unidirectional links often exceeds the number of bidirectional ones, especially in the transitional area of the communication range of wireless sensor nodes. Still, most of today’s routing protocols ignore their existence or try to remove their implications. Also, links are not stable over time, and routes become unusable often, resulting in a need for new routing protocols that can handle highly dynamic links and use unidirectional links to their advantage. At SENSORCOMM' 2014, we presented BuckshotDV, a routing protocol which is resilient against link fluctuations and uses the longer reach of unidirectional links to increase its performance. Furthermore, its distance vector nature makes it scalable for large sensor networks. This paper is an extended version which adds some implementation details and the evaluation of BuckshotDV in two more application scenarios.

  12. High speed serial link for UA1 microprocessor network

    CERN Document Server

    Cittolin, S; Zurfluh, E

    1981-01-01

    The UA1 data acquisition system consists of a set of distributed microprocessor units. An interprocessor link, independent of the CAMAC data readout, has been developed in order to have continuous remote control and run-time data handling, e.g. transmission of calibration programs/parameters, equipment test/status and histogram accumulation. The data transmission system is designed to be used in a loop configuration equipped with transceivers for twisted pair cables (RS-422). As an economical system, it is running as an ancillary serial loop-link between microprocessors, like Data Acquisition Crate Controllers and systems with distributed intelligence. The software driver consists of a loop-controller package, which may run in a BAMBI Computer Language environment and a fully interrupt controlled program for all other secondary stations. A special single-character mode provides a handy link for remote debugging in a pseudo-full-duplex mode. The format is based on the HDLC protocol without sequence numbering. ...

  13. High speed serial link for UA1 microprocessor network

    International Nuclear Information System (INIS)

    Cittolin, S.; Loefstedt, B.; Zurfluh, E.

    1981-01-01

    The UA1 data acquisition system consists of a set of distributed microprocessor units. An interprocessor link, independent of the CAMAC data readout, has been developed in order to have continuous remote control and run-time data handling, e.g. transmission of calibration programs/parameters, equipment rest/status and histogram accumulation. The data transmission system is designed to be used in a loop configuration equipped with transceivers for twisted pair cables (RS-422). As an economical system it is running as an ancillary serial loop-link between microprocessors Like Data Acquisition Crate Controllers and systems with distributed intelligence. The software driver consists of a loop-controller package, which may run in a BAMBI Computer Language environment and a fully interrupt controlled program for all other secondary stations. A special single-character mode provides a handy link for remote debugging in a pseudo-full-duplex mode. The format is based on the HDLC protocol without sequence numbering. The Chip MC-6854 from Motorola, Inc. enables an implementation with few components. (orig.)

  14. High speed serial link for UA1 microprocessor network

    CERN Document Server

    Cittolin, Sergio; Zurfluh, E

    1981-01-01

    The UA1 data acquisition system consists of a set of distributed microprocessor units. An interprocessor link, independent of the CAMAC data readout, has been developed in order to have continuous remote control and run-time data handling, e.g. transmission of calibration programs/parameters, equipment test/status and histogram accumulation. The data transmission system is designed to be used in a loop configuration equipped with transceivers for twisted pair cables (RS-422). As an economical system, it is running as an ancillary serial loop-link between microprocessors, like data acquisition crate controllers and systems with distributed intelligence. The software driver consists of a loop-controller package, which may run in a BAMBI computer language environment and a fully interrupt controlled program for all other secondary stations. A special single-character mode provides a handy link for remote debugging in a pseudo-full-duplex mode. The format is based on the HDLC protocol without sequence numbering. ...

  15. High-speed packet switching network to link computers

    CERN Document Server

    Gerard, F M

    1980-01-01

    Virtually all of the experiments conducted at CERN use minicomputers today; some simply acquire data and store results on magnetic tape while others actually control experiments and help to process the resulting data. Currently there are more than two hundred minicomputers being used in the laboratory. In order to provide the minicomputer users with access to facilities available on mainframes and also to provide intercommunication between various experimental minicomputers, CERN opted for a packet switching network back in 1975. It was decided to use Modcomp II computers as switching nodes. The only software to be taken was a communications-oriented operating system called Maxcom. Today eight Modcomp II 16-bit computers plus six newer Classic minicomputers from Modular Computer Services have been purchased for the CERNET data communications networks. The current configuration comprises 11 nodes connecting more than 40 user machines to one another and to the laboratory's central computing facility. (0 refs).

  16. Novel links in the plant TOR kinase signaling network.

    Science.gov (United States)

    Xiong, Yan; Sheen, Jen

    2015-12-01

    Nutrient and energy sensing and signaling mechanisms constitute the most ancient and fundamental regulatory networks to control growth and development in all life forms. The target of rapamycin (TOR) protein kinase is modulated by diverse nutrient, energy, hormone and stress inputs and plays a central role in regulating cell proliferation, growth, metabolism and stress responses from yeasts to plants and animals. Recent chemical, genetic, genomic and metabolomic analyses have enabled significant progress toward molecular understanding of the TOR signaling network in multicellular plants. This review discusses the applications of new chemical tools to probe plant TOR functions and highlights recent findings and predictions on TOR-mediate biological processes. Special focus is placed on novel and evolutionarily conserved TOR kinase effectors as positive and negative signaling regulators that control transcription, translation and metabolism to support cell proliferation, growth and maintenance from embryogenesis to senescence in the plant system. Copyright © 2015 Elsevier Ltd. All rights reserved.

  17. Real time data acquisition of a countrywide commercial microwave link network

    Science.gov (United States)

    Chwala, Christian; Keis, Felix; Kunstmann, Harald

    2015-04-01

    Research in recent years has shown that data from commercial microwave link networks can provide very valuable precipitation information. Since these networks comprise the backbone of the cell phone network, they provide countrywide coverage. However acquiring the necessary data from the network operators is still difficult. Data is usually made available for researchers with a large time delay and often at irregular basis. This of course hinders the exploitation of commercial microwave link data in operational applications like QPE forecasts running at national meteorological services. To overcome this, we have developed a custom software in joint cooperation with our industry partner Ericsson. The software is installed on a dedicated server at Ericsson and is capable of acquiring data from the countrywide microwave link network in Germany. In its current first operational testing phase, data from several hundred microwave links in southern Germany is recorded. All data is instantaneously sent to our server where it is stored and organized in an emerging database. Time resolution for the Ericsson data is one minute. The custom acquisition software, however, is capable of processing higher sampling rates. Additionally we acquire and manage 1 Hz data from four microwave links operated by the skiing resort in Garmisch-Partenkirchen. We will present the concept of the data acquisition and show details of the custom-built software. Additionally we will showcase the accessibility and basic processing of real time microwave link data via our database web frontend.

  18. Distributed and Cooperative Link Scheduling for Large-Scale Multihop Wireless Networks

    Directory of Open Access Journals (Sweden)

    Swami Ananthram

    2007-01-01

    Full Text Available A distributed and cooperative link-scheduling (DCLS algorithm is introduced for large-scale multihop wireless networks. With this algorithm, each and every active link in the network cooperatively calibrates its environment and converges to a desired link schedule for data transmissions within a time frame of multiple slots. This schedule is such that the entire network is partitioned into a set of interleaved subnetworks, where each subnetwork consists of concurrent cochannel links that are properly separated from each other. The desired spacing in each subnetwork can be controlled by a tuning parameter and the number of time slots specified for each frame. Following the DCLS algorithm, a distributed and cooperative power control (DCPC algorithm can be applied to each subnetwork to ensure a desired data rate for each link with minimum network transmission power. As shown consistently by simulations, the DCLS algorithm along with a DCPC algorithm yields significant power savings. The power savings also imply an increased feasible region of averaged link data rates for the entire network.

  19. Distributed and Cooperative Link Scheduling for Large-Scale Multihop Wireless Networks

    Directory of Open Access Journals (Sweden)

    Ananthram Swami

    2007-12-01

    Full Text Available A distributed and cooperative link-scheduling (DCLS algorithm is introduced for large-scale multihop wireless networks. With this algorithm, each and every active link in the network cooperatively calibrates its environment and converges to a desired link schedule for data transmissions within a time frame of multiple slots. This schedule is such that the entire network is partitioned into a set of interleaved subnetworks, where each subnetwork consists of concurrent cochannel links that are properly separated from each other. The desired spacing in each subnetwork can be controlled by a tuning parameter and the number of time slots specified for each frame. Following the DCLS algorithm, a distributed and cooperative power control (DCPC algorithm can be applied to each subnetwork to ensure a desired data rate for each link with minimum network transmission power. As shown consistently by simulations, the DCLS algorithm along with a DCPC algorithm yields significant power savings. The power savings also imply an increased feasible region of averaged link data rates for the entire network.

  20. Paradoxes in networks with zero emission links: implications for telecommunications versus transportation

    Energy Technology Data Exchange (ETDEWEB)

    Nagurney, A. [University of Massachusetts, Amherst, MA (United States). Isenberg School of Management; June Dong [State University of New York, Oswego, NY (United States). School of Business

    2001-07-01

    In this paper, we consider networks in which a link is characterized by zero emissions as is typical of networks in which certain links correspond to telecommunication links. We identify three new and distinct paradoxical phenomena that can occur in such networks, which demonstrate that so-called improvements to the network may result in increases in total emissions generated. In particular, we illustrate, through specific examples, the following: (1) the addition of a link with zero emissions may result in an increase in total emissions with no change in travel demand; (2) the total emissions on a network with a zero emission link may increase with a decrease in travel demand; and (3) the addition of a path connecting an origin/destination (O/D) pair and consisting solely of a zero emission link may result in an increase in total emissions. We then propose an emission pricing policy which guarantees that such paradoxes do not occur. The pricing policy is shown to be equivalent to a particular weighting mechanism associated with the criterion of emission generation provided that the users are now multicriteria decision-makers who seek to minimize both the cost of their route choices as well as the emissions that they generate. (author)

  1. Application of Artificial Neural Networks for estimating index floods

    Science.gov (United States)

    Šimor, Viliam; Hlavčová, Kamila; Kohnová, Silvia; Szolgay, Ján

    2012-12-01

    This article presents an application of Artificial Neural Networks (ANNs) and multiple regression models for estimating mean annual maximum discharge (index flood) at ungauged sites. Both approaches were tested for 145 small basins in Slovakia in areas ranging from 20 to 300 km2. Using the objective clustering method, the catchments were divided into ten homogeneous pooling groups; for each pooling group, mutually independent predictors (catchment characteristics) were selected for both models. The neural network was applied as a simple multilayer perceptron with one hidden layer and with a back propagation learning algorithm. Hyperbolic tangents were used as an activation function in the hidden layer. Estimating index floods by the multiple regression models were based on deriving relationships between the index floods and catchment predictors. The efficiencies of both approaches were tested by the Nash-Sutcliffe and a correlation coefficients. The results showed the comparative applicability of both models with slightly better results for the index floods achieved using the ANNs methodology.

  2. Channel estimation for physical layer network coding systems

    CERN Document Server

    Gao, Feifei; Wang, Gongpu

    2014-01-01

    This SpringerBrief presents channel estimation strategies for the physical later network coding (PLNC) systems. Along with a review of PLNC architectures, this brief examines new challenges brought by the special structure of bi-directional two-hop transmissions that are different from the traditional point-to-point systems and unidirectional relay systems. The authors discuss the channel estimation strategies over typical fading scenarios, including frequency flat fading, frequency selective fading and time selective fading, as well as future research directions. Chapters explore the performa

  3. Effectiveness evaluation of double-layered satellite network with laser and microwave hybrid links based on fuzzy analytic hierarchy process

    Science.gov (United States)

    Zhang, Wei; Rao, Qiaomeng

    2018-01-01

    In order to solve the problem of high speed, large capacity and limited spectrum resources of satellite communication network, a double-layered satellite network with global seamless coverage based on laser and microwave hybrid links is proposed in this paper. By analyzing the characteristics of the double-layered satellite network with laser and microwave hybrid links, an effectiveness evaluation index system for the network is established. And then, the fuzzy analytic hierarchy process, which combines the analytic hierarchy process and the fuzzy comprehensive evaluation theory, is used to evaluate the effectiveness of the double-layered satellite network with laser and microwave hybrid links. Furthermore, the evaluation result of the proposed hybrid link network is obtained by simulation. The effectiveness evaluation process of the proposed double-layered satellite network with laser and microwave hybrid links can help to optimize the design of hybrid link double-layered satellite network and improve the operating efficiency of the satellite system.

  4. Looplessness in networks is linked to trophic coherence.

    Science.gov (United States)

    Johnson, Samuel; Jones, Nick S

    2017-05-30

    Many natural, complex systems are remarkably stable thanks to an absence of feedback acting on their elements. When described as networks these exhibit few or no cycles, and associated matrices have small leading eigenvalues. It has been suggested that this architecture can confer advantages to the system as a whole, such as "qualitative stability," but this observation does not in itself explain how a loopless structure might arise. We show here that the number of feedback loops in a network, as well as the eigenvalues of associated matrices, is determined by a structural property called trophic coherence, a measure of how neatly nodes fall into distinct levels. Our theory correctly classifies a variety of networks-including those derived from genes, metabolites, species, neurons, words, computers, and trading nations-into two distinct regimes of high and low feedback and provides a null model to gauge the significance of related magnitudes. Because trophic coherence suppresses feedback, whereas an absence of feedback alone does not lead to coherence, our work suggests that the reasons for "looplessness" in nature should be sought in coherence-inducing mechanisms.

  5. Neuronal network disintegration: common pathways linking neurodegenerative diseases.

    Science.gov (United States)

    Ahmed, Rebekah M; Devenney, Emma M; Irish, Muireann; Ittner, Arne; Naismith, Sharon; Ittner, Lars M; Rohrer, Jonathan D; Halliday, Glenda M; Eisen, Andrew; Hodges, John R; Kiernan, Matthew C

    2016-11-01

    Neurodegeneration refers to a heterogeneous group of brain disorders that progressively evolve. It has been increasingly appreciated that many neurodegenerative conditions overlap at multiple levels and therefore traditional clinicopathological correlation approaches to better classify a disease have met with limited success. Neuronal network disintegration is fundamental to neurodegeneration, and concepts based around such a concept may better explain the overlap between their clinical and pathological phenotypes. In this Review, promoters of overlap in neurodegeneration incorporating behavioural, cognitive, metabolic, motor, and extrapyramidal presentations will be critically appraised. In addition, evidence that may support the existence of large-scale networks that might be contributing to phenotypic differentiation will be considered across a neurodegenerative spectrum. Disintegration of neuronal networks through different pathological processes, such as prion-like spread, may provide a better paradigm of disease and thereby facilitate the identification of novel therapies for neurodegeneration. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

  6. The structural role of weak and strong links in a financial market network

    Science.gov (United States)

    Garas, A.; Argyrakis, P.; Havlin, S.

    2008-05-01

    We investigate the properties of correlation based networks originating from economic complex systems, such as the network of stocks traded at the New York Stock Exchange (NYSE). The weaker links (low correlation) of the system are found to contribute to the overall connectivity of the network significantly more than the strong links (high correlation). We find that nodes connected through strong links form well defined communities. These communities are clustered together in more complex ways compared to the widely used classification according to the economic activity. We find that some companies, such as General Electric (GE), Coca Cola (KO), and others, can be involved in different communities. The communities are found to be quite stable over time. Similar results were obtained by investigating markets completely different in size and properties, such as the Athens Stock Exchange (ASE). The present method may be also useful for other networks generated through correlations.

  7. Space Network Time Distribution and Synchronization Protocol Development for Mars Proximity Link

    Science.gov (United States)

    Woo, Simon S.; Gao, Jay L.; Mills, David

    2010-01-01

    Time distribution and synchronization in deep space network are challenging due to long propagation delays, spacecraft movements, and relativistic effects. Further, the Network Time Protocol (NTP) designed for terrestrial networks may not work properly in space. In this work, we consider the time distribution protocol based on time message exchanges similar to Network Time Protocol (NTP). We present the Proximity-1 Space Link Interleaved Time Synchronization (PITS) algorithm that can work with the CCSDS Proximity-1 Space Data Link Protocol. The PITS algorithm provides faster time synchronization via two-way time transfer over proximity links, improves scalability as the number of spacecraft increase, lowers storage space requirement for collecting time samples, and is robust against packet loss and duplication which underlying protocol mechanisms provide.

  8. Vision-based stress estimation model for steel frame structures with rigid links

    Science.gov (United States)

    Park, Hyo Seon; Park, Jun Su; Oh, Byung Kwan

    2017-07-01

    This paper presents a stress estimation model for the safety evaluation of steel frame structures with rigid links using a vision-based monitoring system. In this model, the deformed shape of a structure under external loads is estimated via displacements measured by a motion capture system (MCS), which is a non-contact displacement measurement device. During the estimation of the deformed shape, the effective lengths of the rigid link ranges in the frame structure are identified. The radius of the curvature of the structural member to be monitored is calculated using the estimated deformed shape and is employed to estimate stress. Using MCS in the presented model, the safety of a structure can be assessed gauge-freely. In addition, because the stress is directly extracted from the radius of the curvature obtained from the measured deformed shape, information on the loadings and boundary conditions of the structure are not required. Furthermore, the model, which includes the identification of the effective lengths of the rigid links, can consider the influences of the stiffness of the connection and support on the deformation in the stress estimation. To verify the applicability of the presented model, static loading tests for a steel frame specimen were conducted. By comparing the stress estimated by the model with the measured stress, the validity of the model was confirmed.

  9. Data Transmission Over Networks for Estimation and Control

    OpenAIRE

    Gupta, Vijay; Dana, Amir F.; Hespanha, Joao P.; Murray, Richard M.; Hassibi, Babak

    2009-01-01

    We consider the problem of controlling a linear time invariant process when the controller is located at a location remote from where the sensor measurements are being generated. The communication from the sensor to the controller is supported by a communication network with arbitrary topology composed of analog erasure channels. Using a separation principle, we prove that the optimal linear-quadratic-Gaussian (LQG) controller consists of an LQ optimal regulator along with an estimator that e...

  10. Artificial neural networks for stiffness estimation in magnetic resonance elastography.

    Science.gov (United States)

    Murphy, Matthew C; Manduca, Armando; Trzasko, Joshua D; Glaser, Kevin J; Huston, John; Ehman, Richard L

    2018-07-01

    To investigate the feasibility of using artificial neural networks to estimate stiffness from MR elastography (MRE) data. Artificial neural networks were fit using model-based training patterns to estimate stiffness from images of displacement using a patch size of ∼1 cm in each dimension. These neural network inversions (NNIs) were then evaluated in a set of simulation experiments designed to investigate the effects of wave interference and noise on NNI accuracy. NNI was also tested in vivo, comparing NNI results against currently used methods. In 4 simulation experiments, NNI performed as well or better than direct inversion (DI) for predicting the known stiffness of the data. Summary NNI results were also shown to be significantly correlated with DI results in the liver (R 2  = 0.974) and in the brain (R 2  = 0.915), and also correlated with established biological effects including fibrosis stage in the liver and age in the brain. Finally, repeatability error was lower in the brain using NNI compared to DI, and voxel-wise modeling using NNI stiffness maps detected larger effects than using DI maps with similar levels of smoothing. Artificial neural networks represent a new approach to inversion of MRE data. Summary results from NNI and DI are highly correlated and both are capable of detecting biologically relevant signals. Preliminary evidence suggests that NNI stiffness estimates may be more resistant to noise than an algebraic DI approach. Taken together, these results merit future investigation into NNIs to improve the estimation of stiffness in small regions. Magn Reson Med 80:351-360, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

  11. Using Of Learning Vector Quantization Network for Pan Evaporation Estimation

    Directory of Open Access Journals (Sweden)

    Kamil7 A. Abdulmohsen

    2013-05-01

    Full Text Available A modern technique is presented to study the evaporation process which is considered as an important component of the hydrological cycle. The Pan Evaporation depth is estimated depending upon four metrological factors viz. (temperature, relative humidity, sunshine, and wind speed. Unsupervised Artificial Neural Network has been proposed to accomplish the study goal, specifically, a type called Linear Vector Quantitization, (LVQ.  A step by step method is used to cope with difficulties that usually associated with computation procedures inherent in these kind of networks. Such systematic approach may close the gap between the hesitation of the user to make use of the capabilities of these type of neural networks and the relative complexity involving the computations procedures. The results reveal the possibility of using LVQ for of Pan Evaporation depth estimation where a good agreement has been noticed between the outputs of the proposed network and the observed values of the Pan Evaporation depth with a correlation coefficient of 0.986. 

  12. Estimation of parameter sensitivities for stochastic reaction networks

    KAUST Repository

    Gupta, Ankit

    2016-01-07

    Quantification of the effects of parameter uncertainty is an important and challenging problem in Systems Biology. We consider this problem in the context of stochastic models of biochemical reaction networks where the dynamics is described as a continuous-time Markov chain whose states represent the molecular counts of various species. For such models, effects of parameter uncertainty are often quantified by estimating the infinitesimal sensitivities of some observables with respect to model parameters. The aim of this talk is to present a holistic approach towards this problem of estimating parameter sensitivities for stochastic reaction networks. Our approach is based on a generic formula which allows us to construct efficient estimators for parameter sensitivity using simulations of the underlying model. We will discuss how novel simulation techniques, such as tau-leaping approximations, multi-level methods etc. can be easily integrated with our approach and how one can deal with stiff reaction networks where reactions span multiple time-scales. We will demonstrate the efficiency and applicability of our approach using many examples from the biological literature.

  13. The Jodrell Bank radio-linked interferometer network

    International Nuclear Information System (INIS)

    Davies, J.G.; Anderson, B.; Morison, I.

    1980-01-01

    The capabilities of the Multi Telescope Radio Linked Interferometer (MTRLI) situated at Jodrell Bank, are described and some of the first maps to be made with it are presented. MTRLI produces high quality maps of radio sources with resolutions varying from approximately 1 arc s to approximately 0.02 arc s depending on the frequency of operation. The maps presented here were made at 408 MHz and are all of extragalactic sources. They illustrate the ability of MTRLI to map at low frequencies the steep spectrum emission which tends to be overlooked with existing synthesis instruments which have to work at much higher frequencies to obtain the same resolution. (U.K.)

  14. A Glider-Assisted Link Disruption Restoration Mechanism in Underwater Acoustic Sensor Networks.

    Science.gov (United States)

    Jin, Zhigang; Wang, Ning; Su, Yishan; Yang, Qiuling

    2018-02-07

    Underwater acoustic sensor networks (UASNs) have become a hot research topic. In UASNs, nodes can be affected by ocean currents and external forces, which could result in sudden link disruption. Therefore, designing a flexible and efficient link disruption restoration mechanism to ensure the network connectivity is a challenge. In the paper, we propose a glider-assisted restoration mechanism which includes link disruption recognition and related link restoring mechanism. In the link disruption recognition mechanism, the cluster heads collect the link disruption information and then schedule gliders acting as relay nodes to restore the disrupted link. Considering the glider's sawtooth motion, we design a relay location optimization algorithm with a consideration of both the glider's trajectory and acoustic channel attenuation model. The utility function is established by minimizing the channel attenuation and the optimal location of glider is solved by a multiplier method. The glider-assisted restoration mechanism can greatly improve the packet delivery rate and reduce the communication energy consumption and it is more general for the restoration of different link disruption scenarios. The simulation results show that glider-assisted restoration mechanism can improve the delivery rate of data packets by 15-33% compared with cooperative opportunistic routing (OVAR), the hop-by-hop vector-based forwarding (HH-VBF) and the vector based forward (VBF) methods, and reduce communication energy consumption by 20-58% for a typical network's setting.

  15. Social networks and links to isolation and loneliness among elderly HCBS clients.

    Science.gov (United States)

    Medvene, Louis J; Nilsen, Kari M; Smith, Rachel; Ofei-Dodoo, Samuel; DiLollo, Anthony; Webster, Noah; Graham, Annette; Nance, Anita

    2016-01-01

    The purpose of this study was to explore the network types of HCBS clients based on the structural characteristics of their social networks. We also examined how the network types were associated with social isolation, relationship quality and loneliness. Forty personal interviews were carried out with HCBS clients to assess the structure of their social networks as indicated by frequency of contact with children, friends, family and participation in religious and community organizations. Hierarchical cluster analysis was conducted to identify network types. Four network types were found including: family (n = 16), diverse (n = 8), restricted (n = 8) and religious (n = 7). Family members comprised almost half of participants' social networks, and friends comprised less than one-third. Clients embedded in family, diverse and religious networks had significantly more positive relationships than clients embedded in restricted networks. Clients embedded in restricted networks had significantly higher social isolation scores and were lonelier than clients in diverse and family networks. The findings suggest that HCBS clients' isolation and loneliness are linked to the types of social networks in which they are embedded. The findings also suggest that clients embedded in restricted networks are at high risk for negative outcomes.

  16. Criteria for Evaluating Alternative Network and Link Layer Protocols for the NASA Constellation Program Communication Architecture

    Science.gov (United States)

    Benbenek, Daniel; Soloff, Jason; Lieb, Erica

    2010-01-01

    Selecting a communications and network architecture for future manned space flight requires an evaluation of the varying goals and objectives of the program, development of communications and network architecture evaluation criteria, and assessment of critical architecture trades. This paper uses Cx Program proposed exploration activities as a guideline; lunar sortie, outpost, Mars, and flexible path options are described. A set of proposed communications network architecture criteria are proposed and described. They include: interoperability, security, reliability, and ease of automating topology changes. Finally a key set of architecture options are traded including (1) multiplexing data at a common network layer vs. at the data link layer, (2) implementing multiple network layers vs. a single network layer, and (3) the use of a particular network layer protocol, primarily IPv6 vs. Delay Tolerant Networking (DTN). In summary, the protocol options are evaluated against the proposed exploration activities and their relative performance with respect to the criteria are assessed. An architectural approach which includes (a) the capability of multiplexing at both the network layer and the data link layer and (b) a single network layer for operations at each program phase, as these solutions are best suited to respond to the widest array of program needs and meet each of the evaluation criteria.

  17. Link predication based on matrix factorization by fusion of multi class organizations of the network.

    Science.gov (United States)

    Jiao, Pengfei; Cai, Fei; Feng, Yiding; Wang, Wenjun

    2017-08-21

    Link predication aims at forecasting the latent or unobserved edges in the complex networks and has a wide range of applications in reality. Almost existing methods and models only take advantage of one class organization of the networks, which always lose important information hidden in other organizations of the network. In this paper, we propose a link predication framework which makes the best of the structure of networks in different level of organizations based on nonnegative matrix factorization, which is called NMF 3 here. We first map the observed network into another space by kernel functions, which could get the different order organizations. Then we combine the adjacency matrix of the network with one of other organizations, which makes us obtain the objective function of our framework for link predication based on the nonnegative matrix factorization. Third, we derive an iterative algorithm to optimize the objective function, which converges to a local optimum, and we propose a fast optimization strategy for large networks. Lastly, we test the proposed framework based on two kernel functions on a series of real world networks under different sizes of training set, and the experimental results show the feasibility, effectiveness, and competitiveness of the proposed framework.

  18. Link removal for the control of stochastically evolving epidemics over networks: a comparison of approaches.

    Science.gov (United States)

    Enns, Eva A; Brandeau, Margaret L

    2015-04-21

    For many communicable diseases, knowledge of the underlying contact network through which the disease spreads is essential to determining appropriate control measures. When behavior change is the primary intervention for disease prevention, it is important to understand how to best modify network connectivity using the limited resources available to control disease spread. We describe and compare four algorithms for selecting a limited number of links to remove from a network: two "preventive" approaches (edge centrality, R0 minimization), where the decision of which links to remove is made prior to any disease outbreak and depends only on the network structure; and two "reactive" approaches (S-I edge centrality, optimal quarantining), where information about the initial disease states of the nodes is incorporated into the decision of which links to remove. We evaluate the performance of these algorithms in minimizing the total number of infections that occur over the course of an acute outbreak of disease. We consider different network structures, including both static and dynamic Erdös-Rényi random networks with varying levels of connectivity, a real-world network of residential hotels connected through injection drug use, and a network exhibiting community structure. We show that reactive approaches outperform preventive approaches in averting infections. Among reactive approaches, removing links in order of S-I edge centrality is favored when the link removal budget is small, while optimal quarantining performs best when the link removal budget is sufficiently large. The budget threshold above which optimal quarantining outperforms the S-I edge centrality algorithm is a function of both network structure (higher for unstructured Erdös-Rényi random networks compared to networks with community structure or the real-world network) and disease infectiousness (lower for highly infectious diseases). We conduct a value-of-information analysis of knowing which

  19. Value Co-creation and Co-innovation: Linking Networked Organisations and Customer Communities

    Science.gov (United States)

    Romero, David; Molina, Arturo

    Strategic networks such as Collaborative Networked Organisations (CNOs) and Virtual Customer Communities (VCCs) show a high potential as drivers of value co-creation and collaborative innovation in today’s Networking Era. Both look at the network structures as a source of jointly value creation and open innovation through access to new skills, knowledge, markets and technologies by sharing risk and integrating complementary competencies. This collaborative endeavour has proven to be able to enhance the adaptability and flexibility of CNOs and VCCs value creating systems in order to react in response to external drivers such as collaborative (business) opportunities. This paper presents a reference framework for creating interface networks, also known as ‘experience-centric networks’, as enablers for linking networked organisations and customer communities in order to support the establishment of user-driven and collaborative innovation networks.

  20. Guidance system monitors regional link network; Leitsystem ueberwacht regionales Verbundleitungsnetz

    Energy Technology Data Exchange (ETDEWEB)

    Gorgas, O. [IDS GmbH, Ettlingen (Germany)

    2002-07-01

    The Harzwasserwerke GmbH, as a regional supplier in Lower Saxony, treat drinking water in dam and ground water works. An approximately 500 km long supply network supplies water to around 70 towns and communities in Northern Germany. This means around one and a half million people are supplied with the life elixir in renowned towns such as Hannover, Wolfsburg (with the Volkswagen AG), Braunschweig, Hildesheim, Goettingen and Bremen. A new network guidance system has been installed to monitor the pipeline system and the feed to water works and feed in and out of high-level reservoirs. (orig.) [German] Die Harzwasserwerke GmbH als Regionalversorger in Niedersachsen bereiten Trinkwasser in Talsperren- und Grundwasserwerken auf. Ueber ein rund 500 km langes Leitungsnetz werden im Norden Deutschlands etwa 70 Staedte und Gemeinden versorgt. Damit werden rund eineinhalb Millionen Menschen mit dem Lebensmittel Nummer Eins, unter anderem in namhaften Staedten wie Hannover, Wolfsburg mit der Volkswagen AG, Braunschweig, Hildesheim, Goettingen und Bremen beliefert. Zur Ueberwachung des Leitungssystems und der Einspeisung an Wasserwerken bzw. Ein- und Ausspeisungen an Hochbehaeltern wurde ein neues Netzleitsystem installiert. (orig.)

  1. Adjustment of positional geodetic networks by unconventional estimations

    Directory of Open Access Journals (Sweden)

    Silvia Gašincová

    2010-06-01

    Full Text Available The content of this paper is the adjustment of positional geodetic networks by robust estimations. The techniques (basedon the unconventional estimations of repeated least-square method which have turned out to be suitable and applicable in the practisehave been demonstrated on the example of the local geodetic network, which was founded to compose this thesis. In the thesisthe following techniques have been chosen to compare the Method of least-squares with those many published in foreign literature:M-estimation of Biweight,M-estimation of Welsch and Danish method. All presented methods are based on the repeated least-squaremethod principle with gradual changing of weight of individual measurements. In the first stage a standard least-square method wascarried out in the following steps – iterations we gradually change individual weights according to the relevant instructions/ regulation(so-called weight function. Iteration process will be stopped when no deviated measurements are found in the file of measured data.MatLab programme version 5.2 T was used to implement mathematical adjustment.

  2. Estimation of local rainfall erosivity using artificial neural network

    Directory of Open Access Journals (Sweden)

    Paulo Tarso Sanches Oliveira

    2011-08-01

    Full Text Available The information retrieval of local values of rainfall erosivity is essential for soil loss estimation with the Universal Soil Loss Equation (USLE, and thus is very useful in soil and water conservation planning. In this manner, the objective of this study was to develop an Artificial Neural Network (ANN with the capacity of estimating, with satisfactory accuracy, the rainfall erosivity in any location of the Mato Grosso do Sul state. We used data from rain erosivity, latitude, longitude, altitude of pluviometric and pluviographic stations located in the state to train and test an ANN. After training with various network configurations, we selected the best performance and higher coefficient of determination calculated on the basis of data erosivity of the sample test and the values estimated by ANN. In evaluating the results, the confidence and the agreement indices were used in addition to the coefficient of determination. It was found that it is possible to estimate the rainfall erosivity for any location in the state of Mato Grosso do Sul, in a reliable way, using only data of geographical coordinates and altitude.

  3. Time development in the early history of social networks: link stabilization, group dynamics, and segregation.

    Science.gov (United States)

    Bruun, Jesper; Bearden, Ian G

    2014-01-01

    Studies of the time development of empirical networks usually investigate late stages where lasting connections have already stabilized. Empirical data on early network history are rare but needed for a better understanding of how social network topology develops in real life. Studying students who are beginning their studies at a university with no or few prior connections to each other offers a unique opportunity to investigate the formation and early development of link patterns and community structure in social networks. During a nine week introductory physics course, first year physics students were asked to identify those with whom they communicated about problem solving in physics during the preceding week. We use these students' self reports to produce time dependent student interaction networks. We investigate these networks to elucidate possible effects of different student attributes in early network formation. Changes in the weekly number of links show that while roughly half of all links change from week to week, students also reestablish a growing number of links as they progress through their first weeks of study. Using the Infomap community detection algorithm, we show that the networks exhibit community structure, and we use non-network student attributes, such as gender and end-of-course grade to characterize communities during their formation. Specifically, we develop a segregation measure and show that students structure themselves according to gender and pre-organized sections (in which students engage in problem solving and laboratory work), but not according to end-of-coure grade. Alluvial diagrams of consecutive weeks' communities show that while student movement between groups are erratic in the beginning of their studies, they stabilize somewhat towards the end of the course. Taken together, the analyses imply that student interaction networks stabilize quickly and that students establish collaborations based on who is immediately

  4. Estimating wheat and maize daily evapotranspiration using artificial neural network

    Science.gov (United States)

    Abrishami, Nazanin; Sepaskhah, Ali Reza; Shahrokhnia, Mohammad Hossein

    2018-02-01

    In this research, artificial neural network (ANN) is used for estimating wheat and maize daily standard evapotranspiration. Ten ANN models with different structures were designed for each crop. Daily climatic data [maximum temperature (T max), minimum temperature (T min), average temperature (T ave), maximum relative humidity (RHmax), minimum relative humidity (RHmin), average relative humidity (RHave), wind speed (U 2), sunshine hours (n), net radiation (Rn)], leaf area index (LAI), and plant height (h) were used as inputs. For five structures of ten, the evapotranspiration (ETC) values calculated by ETC = ET0 × K C equation (ET0 from Penman-Monteith equation and K C from FAO-56, ANNC) were used as outputs, and for the other five structures, the ETC values measured by weighing lysimeter (ANNM) were used as outputs. In all structures, a feed forward multiple-layer network with one or two hidden layers and sigmoid transfer function and BR or LM training algorithm was used. Favorite network was selected based on various statistical criteria. The results showed the suitable capability and acceptable accuracy of ANNs, particularly those having two hidden layers in their structure in estimating the daily evapotranspiration. Best model for estimation of maize daily evapotranspiration is «M»ANN1 C (8-4-2-1), with T max, T min, RHmax, RHmin, U 2, n, LAI, and h as input data and LM training rule and its statistical parameters (NRMSE, d, and R2) are 0.178, 0.980, and 0.982, respectively. Best model for estimation of wheat daily evapotranspiration is «W»ANN5 C (5-2-3-1), with T max, T min, Rn, LAI, and h as input data and LM training rule, its statistical parameters (NRMSE, d, and R 2) are 0.108, 0.987, and 0.981 respectively. In addition, if the calculated ETC used as the output of the network for both wheat and maize, higher accurate estimation was obtained. Therefore, ANN is suitable method for estimating evapotranspiration of wheat and maize.

  5. The Weakest Link: The Risks Associated with Social Networking Website

    Directory of Open Access Journals (Sweden)

    Yosef Lehrman

    2010-01-01

    Full Text Available The relatively rapid rise in popularity of social networking services is now well known. MySpace, Twitter, and Facebook have become well known sites and terms. According to the Web traffic tracking site Alexa.com, as of December 2009, Facebook had 350 million registered users, MySpace just under 475 million, and Twitter 44.5 million. Many people think very little of posting prodigious amounts of personal information on social networking sites, not realizing that this information puts them at risk. Specifically, those in the law enforcement and military communities may not realize that information posted on these sites can compromise operational security and potentially endanger lives. In July 2009, the Associated Press ran a story which was picked up by most major news outlets in the USA, in which it was reported that the wife of the incoming head of Britain's MI6 intelligence agency had posted pictures and family details on her Facebook page. Astonishingly, there were those that argued that this was not a security breach! Although it is true that, in general, photos of a vacationing family would not be considered sensitive, when you consider that the family taking the vacation includes the head of the British foreign intelligence service, it is easy to see how this kind of exposure could open the door to potential blackmail.We are all too aware of the possibility of terrorist "sleeper cells" living among typical American families under false identities. It is vital to understand how these individuals melt into the crowd, hiding their true identities while they hatch their nefarious plots. Recent events in Denver and New York City only serve to underscore the urgency of this need. This article will examine social networking in the context of social engineering. There are no easy or fast solutions to this problem, and this paper does not pretend to propose any. Rather, it is the purpose of this paper to enhance understanding of this very critical

  6. Error Control for Network-on-Chip Links

    CERN Document Server

    Fu, Bo

    2012-01-01

    As technology scales into nanoscale regime, it is impossible to guarantee the perfect hardware design. Moreover, if the requirement of 100% correctness in hardware can be relaxed, the cost of manufacturing, verification, and testing will be significantly reduced. Many approaches have been proposed to address the reliability problem of on-chip communications. This book focuses on the use of error control codes (ECCs) to improve on-chip interconnect reliability. Coverage includes detailed description of key issues in NOC error control faced by circuit and system designers, as well as practical error control techniques to minimize the impact of these errors on system performance. Provides a detailed background on the state of error control methods for on-chip interconnects; Describes the use of more complex concatenated codes such as Hamming Product Codes with Type-II HARQ, while emphasizing integration techniques for on-chip interconnect links; Examines energy-efficient techniques for integrating multiple error...

  7. Reef-fish larval dispersal patterns validate no-take marine reserve network connectivity that links human communities

    Science.gov (United States)

    Abesamis, Rene A.; Saenz-Agudelo, Pablo; Berumen, Michael L.; Bode, Michael; Jadloc, Claro Renato L.; Solera, Leilani A.; Villanoy, Cesar L.; Bernardo, Lawrence Patrick C.; Alcala, Angel C.; Russ, Garry R.

    2017-09-01

    Networks of no-take marine reserves (NTMRs) are a widely advocated strategy for managing coral reefs. However, uncertainty about the strength of population connectivity between individual reefs and NTMRs through larval dispersal remains a major obstacle to effective network design. In this study, larval dispersal among NTMRs and fishing grounds in the Philippines was inferred by conducting genetic parentage analysis on a coral-reef fish ( Chaetodon vagabundus). Adult and juvenile fish were sampled intensively in an area encompassing approximately 90 km of coastline. Thirty-seven true parent-offspring pairs were accepted after screening 1978 juveniles against 1387 adults. The data showed all types of dispersal connections that may occur in NTMR networks, with assignments suggesting connectivity among NTMRs and fishing grounds ( n = 35) far outnumbering those indicating self-recruitment ( n = 2). Critically, half (51%) of the inferred occurrences of larval dispersal linked reefs managed by separate, independent municipalities and constituent villages, emphasising the need for nested collaborative management arrangements across management units to sustain NTMR networks. Larval dispersal appeared to be influenced by wind-driven seasonal reversals in the direction of surface currents. The best-fit larval dispersal kernel estimated from the parentage data predicted that 50% of larvae originating from a population would attempt to settle within 33 km, and 95% within 83 km. Mean larval dispersal distance was estimated to be 36.5 km. These results suggest that creating a network of closely spaced (less than a few tens of km apart) NTMRs can enhance recruitment for protected and fished populations throughout the NTMR network. The findings underscore major challenges for regional coral-reef management initiatives that must be addressed with priority: (1) strengthening management of NTMR networks across political or customary boundaries; and (2) achieving adequate population

  8. Reef-fish larval dispersal patterns validate no-take marine reserve network connectivity that links human communities

    KAUST Repository

    Abesamis, Rene A.

    2017-03-24

    Networks of no-take marine reserves (NTMRs) are a widely advocated strategy for managing coral reefs. However, uncertainty about the strength of population connectivity between individual reefs and NTMRs through larval dispersal remains a major obstacle to effective network design. In this study, larval dispersal among NTMRs and fishing grounds in the Philippines was inferred by conducting genetic parentage analysis on a coral-reef fish (Chaetodon vagabundus). Adult and juvenile fish were sampled intensively in an area encompassing approximately 90 km of coastline. Thirty-seven true parent-offspring pairs were accepted after screening 1978 juveniles against 1387 adults. The data showed all types of dispersal connections that may occur in NTMR networks, with assignments suggesting connectivity among NTMRs and fishing grounds (n = 35) far outnumbering those indicating self-recruitment (n = 2). Critically, half (51%) of the inferred occurrences of larval dispersal linked reefs managed by separate, independent municipalities and constituent villages, emphasising the need for nested collaborative management arrangements across management units to sustain NTMR networks. Larval dispersal appeared to be influenced by wind-driven seasonal reversals in the direction of surface currents. The best-fit larval dispersal kernel estimated from the parentage data predicted that 50% of larvae originating from a population would attempt to settle within 33 km, and 95% within 83 km. Mean larval dispersal distance was estimated to be 36.5 km. These results suggest that creating a network of closely spaced (less than a few tens of km apart) NTMRs can enhance recruitment for protected and fished populations throughout the NTMR network. The findings underscore major challenges for regional coral-reef management initiatives that must be addressed with priority: (1) strengthening management of NTMR networks across political or customary boundaries; and (2) achieving adequate population

  9. Evaluation of mobile ad hoc network reliability using propagation-based link reliability model

    International Nuclear Information System (INIS)

    Padmavathy, N.; Chaturvedi, Sanjay K.

    2013-01-01

    A wireless mobile ad hoc network (MANET) is a collection of solely independent nodes (that can move randomly around the area of deployment) making the topology highly dynamic; nodes communicate with each other by forming a single hop/multi-hop network and maintain connectivity in decentralized manner. MANET is modelled using geometric random graphs rather than random graphs because the link existence in MANET is a function of the geometric distance between the nodes and the transmission range of the nodes. Among many factors that contribute to the MANET reliability, the reliability of these networks also depends on the robustness of the link between the mobile nodes of the network. Recently, the reliability of such networks has been evaluated for imperfect nodes (transceivers) with binary model of communication links based on the transmission range of the mobile nodes and the distance between them. However, in reality, the probability of successful communication decreases as the signal strength deteriorates due to noise, fading or interference effects even up to the nodes' transmission range. Hence, in this paper, using a propagation-based link reliability model rather than a binary-model with nodes following a known failure distribution to evaluate the network reliability (2TR m , ATR m and AoTR m ) of MANET through Monte Carlo Simulation is proposed. The method is illustrated with an application and some imperative results are also presented

  10. Sensor Data Security Level Estimation Scheme for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Alex Ramos

    2015-01-01

    Full Text Available Due to their increasing dissemination, wireless sensor networks (WSNs have become the target of more and more sophisticated attacks, even capable of circumventing both attack detection and prevention mechanisms. This may cause WSN users, who totally trust these security mechanisms, to think that a sensor reading is secure, even when an adversary has corrupted it. For that reason, a scheme capable of estimating the security level (SL that these mechanisms provide to sensor data is needed, so that users can be aware of the actual security state of this data and can make better decisions on its use. However, existing security estimation schemes proposed for WSNs fully ignore detection mechanisms and analyze solely the security provided by prevention mechanisms. In this context, this work presents the sensor data security estimator (SDSE, a new comprehensive security estimation scheme for WSNs. SDSE is designed for estimating the sensor data security level based on security metrics that analyze both attack prevention and detection mechanisms. In order to validate our proposed scheme, we have carried out extensive simulations that show the high accuracy of SDSE estimates.

  11. Sensor Data Security Level Estimation Scheme for Wireless Sensor Networks

    Science.gov (United States)

    Ramos, Alex; Filho, Raimir Holanda

    2015-01-01

    Due to their increasing dissemination, wireless sensor networks (WSNs) have become the target of more and more sophisticated attacks, even capable of circumventing both attack detection and prevention mechanisms. This may cause WSN users, who totally trust these security mechanisms, to think that a sensor reading is secure, even when an adversary has corrupted it. For that reason, a scheme capable of estimating the security level (SL) that these mechanisms provide to sensor data is needed, so that users can be aware of the actual security state of this data and can make better decisions on its use. However, existing security estimation schemes proposed for WSNs fully ignore detection mechanisms and analyze solely the security provided by prevention mechanisms. In this context, this work presents the sensor data security estimator (SDSE), a new comprehensive security estimation scheme for WSNs. SDSE is designed for estimating the sensor data security level based on security metrics that analyze both attack prevention and detection mechanisms. In order to validate our proposed scheme, we have carried out extensive simulations that show the high accuracy of SDSE estimates. PMID:25608215

  12. Sensor data security level estimation scheme for wireless sensor networks.

    Science.gov (United States)

    Ramos, Alex; Filho, Raimir Holanda

    2015-01-19

    Due to their increasing dissemination, wireless sensor networks (WSNs) have become the target of more and more sophisticated attacks, even capable of circumventing both attack detection and prevention mechanisms. This may cause WSN users, who totally trust these security mechanisms, to think that a sensor reading is secure, even when an adversary has corrupted it. For that reason, a scheme capable of estimating the security level (SL) that these mechanisms provide to sensor data is needed, so that users can be aware of the actual security state of this data and can make better decisions on its use. However, existing security estimation schemes proposed for WSNs fully ignore detection mechanisms and analyze solely the security provided by prevention mechanisms. In this context, this work presents the sensor data security estimator (SDSE), a new comprehensive security estimation scheme for WSNs. SDSE is designed for estimating the sensor data security level based on security metrics that analyze both attack prevention and detection mechanisms. In order to validate our proposed scheme, we have carried out extensive simulations that show the high accuracy of SDSE estimates.

  13. Distributed parameter estimation in unreliable sensor networks via broadcast gossip algorithms.

    Science.gov (United States)

    Wang, Huiwei; Liao, Xiaofeng; Wang, Zidong; Huang, Tingwen; Chen, Guo

    2016-01-01

    In this paper, we present an asynchronous algorithm to estimate the unknown parameter under an unreliable network which allows new sensors to join and old sensors to leave, and can tolerate link failures. Each sensor has access to partially informative measurements when it is awakened. In addition, the proposed algorithm can avoid the interference among messages and effectively reduce the accumulated measurement and quantization errors. Based on the theory of stochastic approximation, we prove that our proposed algorithm almost surely converges to the unknown parameter. Finally, we present a numerical example to assess the performance and the communication cost of the algorithm. Copyright © 2015 Elsevier Ltd. All rights reserved.

  14. Multi-link faults localization and restoration based on fuzzy fault set for dynamic optical networks.

    Science.gov (United States)

    Zhao, Yongli; Li, Xin; Li, Huadong; Wang, Xinbo; Zhang, Jie; Huang, Shanguo

    2013-01-28

    Based on a distributed method of bit-error-rate (BER) monitoring, a novel multi-link faults restoration algorithm is proposed for dynamic optical networks. The concept of fuzzy fault set (FFS) is first introduced for multi-link faults localization, which includes all possible optical equipment or fiber links with a membership describing the possibility of faults. Such a set is characterized by a membership function which assigns each object a grade of membership ranging from zero to one. OSPF protocol extension is designed for the BER information flooding in the network. The BER information can be correlated to link faults through FFS. Based on the BER information and FFS, multi-link faults localization mechanism and restoration algorithm are implemented and experimentally demonstrated on a GMPLS enabled optical network testbed with 40 wavelengths in each fiber link. Experimental results show that the novel localization mechanism has better performance compared with the extended limited perimeter vector matching (LVM) protocol and the restoration algorithm can improve the restoration success rate under multi-link faults scenario.

  15. Detection of the dominant direction of information flow and feedback links in densely interconnected regulatory networks

    Directory of Open Access Journals (Sweden)

    Ispolatov Iaroslav

    2008-10-01

    Full Text Available Abstract Background Finding the dominant direction of flow of information in densely interconnected regulatory or signaling networks is required in many applications in computational biology and neuroscience. This is achieved by first identifying and removing links which close up feedback loops in the original network and hierarchically arranging nodes in the remaining network. In mathematical language this corresponds to a problem of making a graph acyclic by removing as few links as possible and thus altering the original graph in the least possible way. The exact solution of this problem requires enumeration of all cycles and combinations of removed links, which, as an NP-hard problem, is computationally prohibitive even for modest-size networks. Results We introduce and compare two approximate numerical algorithms for solving this problem: the probabilistic one based on a simulated annealing of the hierarchical layout of the network which minimizes the number of "backward" links going from lower to higher hierarchical levels, and the deterministic, "greedy" algorithm that sequentially cuts the links that participate in the largest number of feedback cycles. We find that the annealing algorithm outperforms the deterministic one in terms of speed, memory requirement, and the actual number of removed links. To further improve a visual perception of the layout produced by the annealing algorithm, we perform an additional minimization of the length of hierarchical links while keeping the number of anti-hierarchical links at their minimum. The annealing algorithm is then tested on several examples of regulatory and signaling networks/pathways operating in human cells. Conclusion The proposed annealing algorithm is powerful enough to performs often optimal layouts of protein networks in whole organisms, consisting of around ~104 nodes and ~105 links, while the applicability of the greedy algorithm is limited to individual pathways with ~100

  16. Space Link Extension Protocol Emulation for High-Throughput, High-Latency Network Connections

    Science.gov (United States)

    Tchorowski, Nicole; Murawski, Robert

    2014-01-01

    New space missions require higher data rates and new protocols to meet these requirements. These high data rate space communication links push the limitations of not only the space communication links, but of the ground communication networks and protocols which forward user data to remote ground stations (GS) for transmission. The Consultative Committee for Space Data Systems, (CCSDS) Space Link Extension (SLE) standard protocol is one protocol that has been proposed for use by the NASA Space Network (SN) Ground Segment Sustainment (SGSS) program. New protocol implementations must be carefully tested to ensure that they provide the required functionality, especially because of the remote nature of spacecraft. The SLE protocol standard has been tested in the NASA Glenn Research Center's SCENIC Emulation Lab in order to observe its operation under realistic network delay conditions. More specifically, the delay between then NASA Integrated Services Network (NISN) and spacecraft has been emulated. The round trip time (RTT) delay for the continental NISN network has been shown to be up to 120ms; as such the SLE protocol was tested with network delays ranging from 0ms to 200ms. Both a base network condition and an SLE connection were tested with these RTT delays, and the reaction of both network tests to the delay conditions were recorded. Throughput for both of these links was set at 1.2Gbps. The results will show that, in the presence of realistic network delay, the SLE link throughput is significantly reduced while the base network throughput however remained at the 1.2Gbps specification. The decrease in SLE throughput has been attributed to the implementation's use of blocking calls. The decrease in throughput is not acceptable for high data rate links, as the link requires constant data a flow in order for spacecraft and ground radios to stay synchronized, unless significant data is queued a the ground station. In cases where queuing the data is not an option

  17. Weed Growth Stage Estimator Using Deep Convolutional Neural Networks.

    Science.gov (United States)

    Teimouri, Nima; Dyrmann, Mads; Nielsen, Per Rydahl; Mathiassen, Solvejg Kopp; Somerville, Gayle J; Jørgensen, Rasmus Nyholm

    2018-05-16

    This study outlines a new method of automatically estimating weed species and growth stages (from cotyledon until eight leaves are visible) of in situ images covering 18 weed species or families. Images of weeds growing within a variety of crops were gathered across variable environmental conditions with regards to soil types, resolution and light settings. Then, 9649 of these images were used for training the computer, which automatically divided the weeds into nine growth classes. The performance of this proposed convolutional neural network approach was evaluated on a further set of 2516 images, which also varied in term of crop, soil type, image resolution and light conditions. The overall performance of this approach achieved a maximum accuracy of 78% for identifying Polygonum spp. and a minimum accuracy of 46% for blackgrass. In addition, it achieved an average 70% accuracy rate in estimating the number of leaves and 96% accuracy when accepting a deviation of two leaves. These results show that this new method of using deep convolutional neural networks has a relatively high ability to estimate early growth stages across a wide variety of weed species.

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

    Science.gov (United States)

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

    2014-03-01

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

  19. Energy-Aware Topology Evolution Model with Link and Node Deletion in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Xiaojuan Luo

    2012-01-01

    Full Text Available Based on the complex network theory, a new topological evolving model is proposed. In the evolution of the topology of sensor networks, the energy-aware mechanism is taken into account, and the phenomenon of change of the link and node in the network is discussed. Theoretical analysis and numerical simulation are conducted to explore the topology characteristics and network performance with different node energy distribution. We find that node energy distribution has the weak effect on the degree distribution P(k that evolves into the scale-free state, nodes with more energy carry more connections, and degree correlation is nontrivial disassortative. Moreover, the results show that, when nodes energy is more heterogeneous, the network is better clustered and enjoys higher performance in terms of the network efficiency and the average path length for transmitting data.

  20. A Review Paper On Exploring Text Link And Spacial-Temporal Information In Social Media Networks

    Directory of Open Access Journals (Sweden)

    Dr. Mamta Madan

    2015-03-01

    Full Text Available ABSTRACT The objective of this paper is to have a literature review on the various methods to mine the knowledge from the social media by taking advantage of embedded heterogeneous information. Specifically we are trying to review different types of mining framework which provides us useful information from these networks that have heterogeneous data types including text spacial-temporal and data association LINK information. Firstly we will discuss the link mining to study the link structure with respect to Social Media SM. Secondly we summarize the various text mining models thirdly we shall review spacial as well the temporal models to extract or detect the frequent related topics from SM. Fourthly we will try to figure out few improvised models that take advantage of the link textual temporal and spacial information which motivates to discover progressive principles and fresh methodologies for DM Data Mining in social media networks SMNs.

  1. Superimposed Training-Based Channel Estimation for MIMO Relay Networks

    Directory of Open Access Journals (Sweden)

    Xiaoyan Xu

    2012-01-01

    Full Text Available We introduce the superimposed training strategy into the multiple-input multiple-output (MIMO amplify-and-forward (AF one-way relay network (OWRN to perform the individual channel estimation at the destination. Through the superposition of a group of additional training vectors at the relay subject to power allocation, the separated estimates of the source-relay and relay-destination channels can be obtained directly at the destination, and the accordance with the two-hop AF strategy can be guaranteed at the same time. The closed-form Bayesian Cramér-Rao lower bound (CRLB is derived for the estimation of two sets of flat-fading MIMO channel under random channel parameters and further exploited to design the optimal training vectors. A specific suboptimal channel estimation algorithm is applied in the MIMO AF OWRN using the optimal training sequences, and the normalized mean square error performance for the estimation is provided to verify the Bayesian CRLB results.

  2. Linked versus unlinked hospital discharge data on hip fractures for estimating incidence and comorbidity profiles.

    Science.gov (United States)

    Vu, Trang; Day, Lesley; Finch, Caroline F

    2012-08-01

    Studies comparing internally linked (person-identifying) and unlinked (episodes of care) hospital discharge data (HDD) on hip fractures have mainly focused on incidence overestimation by unlinked HDD, but little is known about the impact of overestimation on patient profiles such as comorbidity estimates. In view of the continuing use of unlinked HDD in hip fracture research and the desire to apply research results to hip fracture prevention, we concurrently assessed the accuracy of both incidence and comorbidity estimates derived from unlinked HDD compared to those estimated from internally linked HDD. We analysed unlinked and internally linked HDD between 01 July 2005 and 30 June 2008, inclusive, from Victoria, Australia to estimate the incidence of hospital admission for fall-related hip fracture in community-dwelling older people aged 65+ years and determine the prevalence of comorbidity in patients. Community-dwelling status was defined as living in private residence, supported residential facilities or special accommodation but not in nursing homes. We defined internally linked HDD as the reference standard and calculated measures of accuracy of fall-related hip fracture incidence by unlinked HDD using standard definitions. The extent to which comorbidity prevalence estimates by unlinked HDD differed from those by the reference standard was assessed in absolute terms. The sensitivity and specificity of a standard approach for estimating fall-related hip fracture incidence using unlinked HDD (i.e. omitting records of in-hospital deaths, inter-hospital transfers and readmissions within 30 days of discharge) were 94.4% and 97.5%, respectively. The standard approach and its variants underestimated the prevalence of some comorbidities and altered their ranking. The use of more stringent selection criteria led to major improvements in all measures of accuracy as well as overall and specific comorbidity estimates. This study strongly supports the use of linked

  3. A Glider-Assisted Link Disruption Restoration Mechanism in Underwater Acoustic Sensor Networks

    Directory of Open Access Journals (Sweden)

    Zhigang Jin

    2018-02-01

    Full Text Available Underwater acoustic sensor networks (UASNs have become a hot research topic. In UASNs, nodes can be affected by ocean currents and external forces, which could result in sudden link disruption. Therefore, designing a flexible and efficient link disruption restoration mechanism to ensure the network connectivity is a challenge. In the paper, we propose a glider-assisted restoration mechanism which includes link disruption recognition and related link restoring mechanism. In the link disruption recognition mechanism, the cluster heads collect the link disruption information and then schedule gliders acting as relay nodes to restore the disrupted link. Considering the glider’s sawtooth motion, we design a relay location optimization algorithm with a consideration of both the glider’s trajectory and acoustic channel attenuation model. The utility function is established by minimizing the channel attenuation and the optimal location of glider is solved by a multiplier method. The glider-assisted restoration mechanism can greatly improve the packet delivery rate and reduce the communication energy consumption and it is more general for the restoration of different link disruption scenarios. The simulation results show that glider-assisted restoration mechanism can improve the delivery rate of data packets by 15–33% compared with cooperative opportunistic routing (OVAR, the hop-by-hop vector-based forwarding (HH-VBF and the vector based forward (VBF methods, and reduce communication energy consumption by 20–58% for a typical network’s setting.

  4. Tropical Cyclone Intensity Estimation Using Deep Convolutional Neural Networks

    Science.gov (United States)

    Maskey, Manil; Cecil, Dan; Ramachandran, Rahul; Miller, Jeffrey J.

    2018-01-01

    Estimating tropical cyclone intensity by just using satellite image is a challenging problem. With successful application of the Dvorak technique for more than 30 years along with some modifications and improvements, it is still used worldwide for tropical cyclone intensity estimation. A number of semi-automated techniques have been derived using the original Dvorak technique. However, these techniques suffer from subjective bias as evident from the most recent estimations on October 10, 2017 at 1500 UTC for Tropical Storm Ophelia: The Dvorak intensity estimates ranged from T2.3/33 kt (Tropical Cyclone Number 2.3/33 knots) from UW-CIMSS (University of Wisconsin-Madison - Cooperative Institute for Meteorological Satellite Studies) to T3.0/45 kt from TAFB (the National Hurricane Center's Tropical Analysis and Forecast Branch) to T4.0/65 kt from SAB (NOAA/NESDIS Satellite Analysis Branch). In this particular case, two human experts at TAFB and SAB differed by 20 knots in their Dvorak analyses, and the automated version at the University of Wisconsin was 12 knots lower than either of them. The National Hurricane Center (NHC) estimates about 10-20 percent uncertainty in its post analysis when only satellite based estimates are available. The success of the Dvorak technique proves that spatial patterns in infrared (IR) imagery strongly relate to tropical cyclone intensity. This study aims to utilize deep learning, the current state of the art in pattern recognition and image recognition, to address the need for an automated and objective tropical cyclone intensity estimation. Deep learning is a multi-layer neural network consisting of several layers of simple computational units. It learns discriminative features without relying on a human expert to identify which features are important. Our study mainly focuses on convolutional neural network (CNN), a deep learning algorithm, to develop an objective tropical cyclone intensity estimation. CNN is a supervised learning

  5. Enhancing network performance under single link failure with AS-disjoint BGP extension

    DEFF Research Database (Denmark)

    Manolova, Anna Vasileva; Romeral, S.; Ruepp, Sarah Renée

    2009-01-01

    In this paper we propose an enhancement of the BGP protocol for obtaining AS-disjoint paths in GMPLS multi-domain networks. We evaluate the benefits of having AS-disjoint paths under single inter-domain link failure for two main applications: routing of future connection requests during routing...... protocol re-convergence and applying multi-domain restoration as survivability mechanism in case of a single link failure. The proposed BGP modification is a simple and effective solution for disjoint path selection in connection-oriented multi-domain networks. Our results show that applying the proper...

  6. A Mobility-Aware Link Enhancement Mechanism for Vehicular Ad Hoc Networks

    Directory of Open Access Journals (Sweden)

    Kai-Wen Hu

    2008-04-01

    Full Text Available With the growth up of internet in mobile commerce, researchers have reproduced various mobile applications that vary from entertainment and commercial services to diagnostic and safety tools. Mobility management has widely been recognized as one of the most challenging problems for seamless access to wireless networks. In this paper, a novel link enhancement mechanism is proposed to deal with mobility management problem in vehicular ad hoc networks. Two machine learning techniques, namely, particle swarm optimization and fuzzy logic systems, are incorporated into the proposed schemes to enhance the accuracy of prediction of link break and congestion occurrence. The experimental results verify the effectiveness and feasibility of the proposed schemes.

  7. A Mobility-Aware Link Enhancement Mechanism for Vehicular Ad Hoc Networks

    Directory of Open Access Journals (Sweden)

    Huang Chenn-Jung

    2008-01-01

    Full Text Available Abstract With the growth up of internet in mobile commerce, researchers have reproduced various mobile applications that vary from entertainment and commercial services to diagnostic and safety tools. Mobility management has widely been recognized as one of the most challenging problems for seamless access to wireless networks. In this paper, a novel link enhancement mechanism is proposed to deal with mobility management problem in vehicular ad hoc networks. Two machine learning techniques, namely, particle swarm optimization and fuzzy logic systems, are incorporated into the proposed schemes to enhance the accuracy of prediction of link break and congestion occurrence. The experimental results verify the effectiveness and feasibility of the proposed schemes.

  8. Space Link Extension (SLE) Emulation for High-Throughput Network Communication

    Science.gov (United States)

    Murawski, Robert W.; Tchorowski, Nicole; Golden, Bert

    2014-01-01

    As the data rate requirements for space communications increases, significant stress is placed not only on the wireless satellite communication links, but also on the ground networks which forward data from end-users to remote ground stations. These wide area network (WAN) connections add delay and jitter to the end-to-end satellite communication link, effects which can have significant impacts on the wireless communication link. It is imperative that any ground communication protocol can react to these effects such that the ground network does not become a bottleneck in the communication path to the satellite. In this paper, we present our SCENIC Emulation Lab testbed which was developed to test the CCSDS SLE protocol implementations proposed for use on future NASA communication networks. Our results show that in the presence of realistic levels of network delay, high-throughput SLE communication links can experience significant data rate throttling. Based on our observations, we present some insight into why this data throttling happens, and trace the probable issue back to non-optimal blocking communication which is sup-ported by the CCSDS SLE API recommended practices. These issues were presented as well to the SLE implementation developers which, based on our reports, developed a new release for SLE which we show fixes the SLE blocking issue and greatly improves the protocol throughput. In this paper, we also discuss future developments for our end-to-end emulation lab and how these improvements can be used to develop and test future space communication technologies.

  9. Architecture and design of optical path networks utilizing waveband virtual links

    Science.gov (United States)

    Ito, Yusaku; Mori, Yojiro; Hasegawa, Hiroshi; Sato, Ken-ichi

    2016-02-01

    We propose a novel optical network architecture that uses waveband virtual links, each of which can carry several optical paths, to directly bridge distant node pairs. Future photonic networks should not only transparently cover extended areas but also expand fiber capacity. However, the traversal of many ROADM nodes impairs the optical signal due to spectrum narrowing. To suppress the degradation, the bandwidth of guard bands needs to be increased, which degrades fiber frequency utilization. Waveband granular switching allows us to apply broader pass-band filtering at ROADMs and to insert sufficient guard bands between wavebands with minimum frequency utilization offset. The scheme resolves the severe spectrum narrowing effect. Moreover, the guard band between optical channels in a waveband can be minimized, which increases the number of paths that can be accommodated per fiber. In the network, wavelength path granular routing is done without utilizing waveband virtual links, and it still suffers from spectrum narrowing. A novel network design algorithm that can bound the spectrum narrowing effect by limiting the number of hops (traversed nodes that need wavelength path level routing) is proposed in this paper. This algorithm dynamically changes the waveband virtual link configuration according to the traffic distribution variation, where optical paths that need many node hops are effectively carried by virtual links. Numerical experiments demonstrate that the number of necessary fibers is reduced by 23% compared with conventional optical path networks.

  10. Design and implementation of a fiber optic link for a token ring local area network

    OpenAIRE

    Doran, Thomas J.

    1992-01-01

    Approved for public release; distribution is unlimited This thesis described the design and implementation of a fiber optic link for a token ring local area network (LAN). It features the use of fiber optic channels as the transmission medium between a computer system and a wiring concentrator to convert a physical ring design into a star-wired configuration. The LAN was controlled by the TMS380 LAN Adapter chipset, which provided all diagnostic and network management features to include...

  11. Joint Hybrid Backhaul and Access Links Design in Cloud-Radio Access Networks

    KAUST Repository

    Dhifallah, Oussama Najeeb

    2015-09-06

    The cloud-radio access network (CRAN) is expected to be the core network architecture for next generation mobile radio systems. In this paper, we consider the downlink of a CRAN formed of one central processor (the cloud) and several base station (BS), where each BS is connected to the cloud via either a wireless or capacity-limited wireline backhaul link. The paper addresses the joint design of the hybrid backhaul links (i.e., designing the wireline and wireless backhaul connections from the cloud to the BSs) and the access links (i.e., determining the sparse beamforming solution from the BSs to the users). The paper formulates the hybrid backhaul and access link design problem by minimizing the total network power consumption. The paper solves the problem using a two-stage heuristic algorithm. At one stage, the sparse beamforming solution is found using a weighted mixed 11/12 norm minimization approach; the correlation matrix of the quantization noise of the wireline backhaul links is computed using the classical rate-distortion theory. At the second stage, the transmit powers of the wireless backhaul links are found by solving a power minimization problem subject to quality-of-service constraints, based on the principle of conservation of rate by utilizing the rates found in the first stage. Simulation results suggest that the performance of the proposed algorithm approaches the global optimum solution, especially at high signal-to-interference-plus-noise ratio (SINR).

  12. Link Power Budget and Traffict QoS Performance Analysis of Gygabit Passive Optical Network

    Science.gov (United States)

    Ubaidillah, A.; Alfita, R.; Toyyibah

    2018-01-01

    Data service of telecommunication network is needed widely in the world; therefore extra wide bandwidth must be provided. For this case, PT. Telekomunikasi Tbk. applies GPON (Gigabit Passive Optical Network) as optical fibre based on telecommunication network system. GPON is a point to a multipoint technology of FTTx (Fiber to The x) that transmits information signals to the subscriber over optical fibre. In GPON trunking system, from OLT (Optical Line Terminal), the network is split to many ONT (Optical Network Terminal) of the subscribers, so it causes path loss and attenuation. In this research, the GPON performance is measured from the link power budget system and the Quality of Service (QoS) of the traffic. And the observation result shows that the link power budget system of this GPON is in good condition. The link power budget values from the mathematical calculation and direct measurement are satisfy the ITU-T G984 Class B standard, that the power level must be between -8 dBm to -27 dBm. While from the traffic performance, the observation result shows that the network resource utility of the subscribers of the observed area is not optimum. The mean of subscriber utility rate is 27.985 bps for upstream and 79.687 bps for downstream. While maximally, It should be 60.800 bps for upstream and 486.400 bps for downstream.

  13. Non-linear multivariable predictive control of an alcoholic fermentation process using functional link networks

    Directory of Open Access Journals (Sweden)

    Luiz Augusto da Cruz Meleiro

    2005-06-01

    Full Text Available In this work a MIMO non-linear predictive controller was developed for an extractive alcoholic fermentation process. The internal model of the controller was represented by two MISO Functional Link Networks (FLNs, identified using simulated data generated from a deterministic mathematical model whose kinetic parameters were determined experimentally. The FLN structure presents as advantages fast training and guaranteed convergence, since the estimation of the weights is a linear optimization problem. Besides, the elimination of non-significant weights generates parsimonious models, which allows for fast execution in an MPC-based algorithm. The proposed algorithm showed good potential in identification and control of non-linear processes.Neste trabalho um controlador preditivo não linear multivariável foi desenvolvido para um processo de fermentação alcoólica extrativa. O modelo interno do controlador foi representado por duas redes do tipo Functional Link (FLN, identificadas usando dados de simulação gerados a partir de um modelo validado experimentalmente. A estrutura FLN apresenta como vantagem o treinamento rápido e convergência garantida, já que a estimação dos seus pesos é um problema de otimização linear. Além disso, a eliminação de pesos não significativos gera modelos parsimoniosos, o que permite a rápida execução em algoritmos de controle preditivo baseado em modelo. Os resultados mostram que o algoritmo proposto tem grande potencial para identificação e controle de processos não lineares.

  14. Linking macroecology and community ecology: refining predictions of species distributions using biotic interaction networks.

    Science.gov (United States)

    Staniczenko, Phillip P A; Sivasubramaniam, Prabu; Suttle, K Blake; Pearson, Richard G

    2017-06-01

    Macroecological models for predicting species distributions usually only include abiotic environmental conditions as explanatory variables, despite knowledge from community ecology that all species are linked to other species through biotic interactions. This disconnect is largely due to the different spatial scales considered by the two sub-disciplines: macroecologists study patterns at large extents and coarse resolutions, while community ecologists focus on small extents and fine resolutions. A general framework for including biotic interactions in macroecological models would help bridge this divide, as it would allow for rigorous testing of the role that biotic interactions play in determining species ranges. Here, we present an approach that combines species distribution models with Bayesian networks, which enables the direct and indirect effects of biotic interactions to be modelled as propagating conditional dependencies among species' presences. We show that including biotic interactions in distribution models for species from a California grassland community results in better range predictions across the western USA. This new approach will be important for improving estimates of species distributions and their dynamics under environmental change. © 2017 The Authors. Ecology Letters published by CNRS and John Wiley & Sons Ltd.

  15. Application of Bayesian Networks for Estimation of Individual Psychological Characteristics

    KAUST Repository

    Litvinenko, Alexander

    2017-07-19

    In this paper we apply Bayesian networks for developing more accurate final overall estimations of psychological characteristics of an individual, based on psychological test results. Psychological tests which identify how much an individual possesses a certain factor are very popular and quite common in the modern world. We call this value for a given factor -- the final overall estimation. Examples of factors could be stress resistance, the readiness to take a risk, the ability to concentrate on certain complicated work and many others. An accurate qualitative and comprehensive assessment of human potential is one of the most important challenges in any company or collective. The most common way of studying psychological characteristics of each single person is testing. Psychologists and sociologists are constantly working on improvement of the quality of their tests. Despite serious work, done by psychologists, the questions in tests often do not produce enough feedback due to the use of relatively poor estimation systems. The overall estimation is usually based on personal experiences and the subjective perception of a psychologist or a group of psychologists about the investigated psychological personality factors.

  16. Estimation of reservoir parameter using a hybrid neural network

    Energy Technology Data Exchange (ETDEWEB)

    Aminzadeh, F. [FACT, Suite 201-225, 1401 S.W. FWY Sugarland, TX (United States); Barhen, J.; Glover, C.W. [Center for Engineering Systems Advanced Research, Oak Ridge National Laboratory, Oak Ridge, TN (United States); Toomarian, N.B. [Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA (United States)

    1999-11-01

    Estimation of an oil field's reservoir properties using seismic data is a crucial issue. The accuracy of those estimates and the associated uncertainty are also important information. This paper demonstrates the use of the k-fold cross validation technique to obtain confidence bound on an Artificial Neural Network's (ANN) accuracy statistic from a finite sample set. In addition, we also show that an ANN's classification accuracy is dramatically improved by transforming the ANN's input feature space to a dimensionally smaller, new input space. The new input space represents a feature space that maximizes the linear separation between classes. Thus, the ANN's convergence time and accuracy are improved because the ANN must merely find nonlinear perturbations to the starting linear decision boundaries. These technique for estimating ANN accuracy bounds and feature space transformations are demonstrated on the problem of estimating the sand thickness in an oil field reservoir based only on remotely sensed seismic data.

  17. Application of Bayesian Networks for Estimation of Individual Psychological Characteristics

    KAUST Repository

    Litvinenko, Alexander; Litvinenko, Natalya

    2017-01-01

    In this paper we apply Bayesian networks for developing more accurate final overall estimations of psychological characteristics of an individual, based on psychological test results. Psychological tests which identify how much an individual possesses a certain factor are very popular and quite common in the modern world. We call this value for a given factor -- the final overall estimation. Examples of factors could be stress resistance, the readiness to take a risk, the ability to concentrate on certain complicated work and many others. An accurate qualitative and comprehensive assessment of human potential is one of the most important challenges in any company or collective. The most common way of studying psychological characteristics of each single person is testing. Psychologists and sociologists are constantly working on improvement of the quality of their tests. Despite serious work, done by psychologists, the questions in tests often do not produce enough feedback due to the use of relatively poor estimation systems. The overall estimation is usually based on personal experiences and the subjective perception of a psychologist or a group of psychologists about the investigated psychological personality factors.

  18. Cellular neural networks for motion estimation and obstacle detection

    Directory of Open Access Journals (Sweden)

    D. Feiden

    2003-01-01

    Full Text Available Obstacle detection is an important part of Video Processing because it is indispensable for a collision prevention of autonomously navigating moving objects. For example, vehicles driving without human guidance need a robust prediction of potential obstacles, like other vehicles or pedestrians. Most of the common approaches of obstacle detection so far use analytical and statistical methods like motion estimation or generation of maps. In the first part of this contribution a statistical algorithm for obstacle detection in monocular video sequences is presented. The proposed procedure is based on a motion estimation and a planar world model which is appropriate to traffic scenes. The different processing steps of the statistical procedure are a feature extraction, a subsequent displacement vector estimation and a robust estimation of the motion parameters. Since the proposed procedure is composed of several processing steps, the error propagation of the successive steps often leads to inaccurate results. In the second part of this contribution it is demonstrated, that the above mentioned problems can be efficiently overcome by using Cellular Neural Networks (CNN. It will be shown, that a direct obstacle detection algorithm can be easily performed, based only on CNN processing of the input images. Beside the enormous computing power of programmable CNN based devices, the proposed method is also very robust in comparison to the statistical method, because is shows much less sensibility to noisy inputs. Using the proposed approach of obstacle detection in planar worlds, a real time processing of large input images has been made possible.

  19. On the Impacts and Benefits of Implementing Full-Duplex Communications Links in an Underwater Acoustic Network

    National Research Council Canada - National Science Library

    Gibson, J; Larraza, A; Rice, J; Smith, K; Xie, G

    2002-01-01

    .... These networks may provide command and control for autonomous underwater vehicles, forward reporting by arrays of sensor grids, ad hoc communications links to covert forces, or positive control...

  20. Cascaded optical fiber link using the internet network for remote clocks comparison.

    Science.gov (United States)

    Chiodo, Nicola; Quintin, Nicolas; Stefani, Fabio; Wiotte, Fabrice; Camisard, Emilie; Chardonnet, Christian; Santarelli, Giorgio; Amy-Klein, Anne; Pottie, Paul-Eric; Lopez, Olivier

    2015-12-28

    We report a cascaded optical link of 1100 km for ultra-stable frequency distribution over an Internet fiber network. The link is composed of four spans for which the propagation noise is actively compensated. The robustness and the performance of the link are ensured by five fully automated optoelectronic stations, two of them at the link ends, and three deployed on the field and connecting the spans. This device coherently regenerates the optical signal with the heterodyne optical phase locking of a low-noise laser diode. Optical detection of the beat-note signals for the laser lock and the link noise compensation are obtained with stable and low-noise fibered optical interferometer. We show 3.5 days of continuous operation of the noise-compensated 4-span cascaded link leading to fractional frequency instability of 4x10(-16) at 1-s measurement time and 1x10(-19) at 2000 s. This cascaded link was extended to 1480-km with the same performance. This work is a significant step towards a sustainable wide area ultra-stable optical frequency distribution and comparison network at a very high level of performance.

  1. Accurate location estimation of moving object In Wireless Sensor network

    Directory of Open Access Journals (Sweden)

    Vinay Bhaskar Semwal

    2011-12-01

    Full Text Available One of the central issues in wirless sensor networks is track the location, of moving object which have overhead of saving data, an accurate estimation of the target location of object with energy constraint .We do not have any mechanism which control and maintain data .The wireless communication bandwidth is also very limited. Some field which is using this technique are flood and typhoon detection, forest fire detection, temperature and humidity and ones we have these information use these information back to a central air conditioning and ventilation.In this research paper, we propose protocol based on the prediction and adaptive based algorithm which is using less sensor node reduced by an accurate estimation of the target location. We had shown that our tracking method performs well in terms of energy saving regardless of mobility pattern of the mobile target. We extends the life time of network with less sensor node. Once a new object is detected, a mobile agent will be initiated to track the roaming path of the object.

  2. Estimating the size of the solution space of metabolic networks

    Directory of Open Access Journals (Sweden)

    Mulet Roberto

    2008-05-01

    Full Text Available Abstract Background Cellular metabolism is one of the most investigated system of biological interactions. While the topological nature of individual reactions and pathways in the network is quite well understood there is still a lack of comprehension regarding the global functional behavior of the system. In the last few years flux-balance analysis (FBA has been the most successful and widely used technique for studying metabolism at system level. This method strongly relies on the hypothesis that the organism maximizes an objective function. However only under very specific biological conditions (e.g. maximization of biomass for E. coli in reach nutrient medium the cell seems to obey such optimization law. A more refined analysis not assuming extremization remains an elusive task for large metabolic systems due to algorithmic limitations. Results In this work we propose a novel algorithmic strategy that provides an efficient characterization of the whole set of stable fluxes compatible with the metabolic constraints. Using a technique derived from the fields of statistical physics and information theory we designed a message-passing algorithm to estimate the size of the affine space containing all possible steady-state flux distributions of metabolic networks. The algorithm, based on the well known Bethe approximation, can be used to approximately compute the volume of a non full-dimensional convex polytope in high dimensions. We first compare the accuracy of the predictions with an exact algorithm on small random metabolic networks. We also verify that the predictions of the algorithm match closely those of Monte Carlo based methods in the case of the Red Blood Cell metabolic network. Then we test the effect of gene knock-outs on the size of the solution space in the case of E. coli central metabolism. Finally we analyze the statistical properties of the average fluxes of the reactions in the E. coli metabolic network. Conclusion We propose a

  3. Selective removal of heavy metal ions by disulfide linked polymer networks

    Energy Technology Data Exchange (ETDEWEB)

    Ko, Dongah [Department of Environmental Engineering, Technical University of Denmark, Miljøvej 113, 2800 Kgs. Lyngby (Denmark); Lee, Joo Sung [Graduate School of EEWS, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141 (Korea, Republic of); Patel, Hasmukh A. [Department of Chemistry, Northwestern University, Evanston, IL 60208 (United States); Jakobsen, Mogens H. [Department of Micro and Nano technology, Technical University of Denmark, Ørsteds Plads, 345B, 2800 Kgs. Lyngby (Denmark); Hwang, Yuhoon [Department of Environmental Engineering, Seoul National University of Science and Technology, 232 Gongreung-ro, Nowon-gu, Seoul 01811 (Korea, Republic of); Yavuz, Cafer T. [Graduate School of EEWS, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141 (Korea, Republic of); Hansen, Hans Chr. Bruun [Department of Plant and Environmental Sciences, University of Copenhagen, Frederiksberg, Thorvaldsensvej 40, 1871 Frederiksberg C (Denmark); Andersen, Henrik R., E-mail: henrik@ndersen.net [Department of Environmental Engineering, Technical University of Denmark, Miljøvej 113, 2800 Kgs. Lyngby (Denmark)

    2017-06-15

    Highlights: • Disulfide/thiol polymer networks are promising as sorbent for heavy metals. • Rapid sorption and high Langmuir affinity constant (a{sub L}) for stormwater treatment. • Selective sorption for copper, cadmium, and zinc in the presence of calcium. • Reusability likely due to structure stability of disulfide linked polymer networks. - Abstract: Heavy metal contaminated surface water is one of the oldest pollution problems, which is critical to ecosystems and human health. We devised disulfide linked polymer networks and employed as a sorbent for removing heavy metal ions from contaminated water. Although the polymer network material has a moderate surface area, it demonstrated cadmium removal efficiency equivalent to highly porous activated carbon while it showed 16 times faster sorption kinetics compared to activated carbon, owing to the high affinity of cadmium towards disulfide and thiol functionality in the polymer network. The metal sorption mechanism on polymer network was studied by sorption kinetics, effect of pH, and metal complexation. We observed that the metal ions–copper, cadmium, and zinc showed high binding affinity in polymer network, even in the presence of competing cations like calcium in water.

  4. Selective removal of heavy metal ions by disulfide linked polymer networks

    DEFF Research Database (Denmark)

    Ko, Dongah; Sung Lee, Joo; Patel, Hasmukh A.

    2017-01-01

    Heavy metal contaminated surface water is one of the oldest pollution problems, which is critical to ecosystems and human health. We devised disulfide linked polymer networks and employed as a sorbent for removing heavy metal ions from contaminated water. Although the polymer network material has...... a moderate surface area, it demonstrated cadmium removal efficiency equivalent to highly porous activated carbon while it showed 16 times faster sorption kinetics compared to activated carbon, owing to the high affinity of cadmium towards disulfide and thiol functionality in the polymer network. The metal...... sorption mechanism on polymer network was studied by sorption kinetics, effect of pH, and metal complexation. We observed that the metal ions―copper, cadmium, and zinc showed high binding affinity in polymer network, even in the presence of competing cations like calcium in water....

  5. Efficient Algorithm for Computing Link-based Similarity in Real World Networks

    DEFF Research Database (Denmark)

    Cai, Yuanzhe; Cong, Gao; Xu, Jia

    2009-01-01

    Similarity calculation has many applications, such as information retrieval, and collaborative filtering, among many others. It has been shown that link-based similarity measure, such as SimRank, is very effective in characterizing the object similarities in networks, such as the Web, by exploiti...

  6. Kinetic limitation in the formation of end-linked elastomer networks

    Czech Academy of Sciences Publication Activity Database

    Meissner, Bohumil; Matějka, Libor

    2005-01-01

    Roč. 46, č. 24 (2005), s. 10618-10625 ISSN 0032-3861 R&D Projects: GA ČR GA203/05/2252 Keywords : end-linked elastomer networks * effect of stoichiometry * kinetic limitations Subject RIV: CD - Macromolecular Chemistry Impact factor: 2.849, year: 2005

  7. Estimating Memory Deterioration Rates Following Neurodegeneration and Traumatic Brain Injuries in a Hopfield Network Model

    Directory of Open Access Journals (Sweden)

    Melanie Weber

    2017-11-01

    Full Text Available Neurodegenerative diseases and traumatic brain injuries (TBI are among the main causes of cognitive dysfunction in humans. At a neuronal network level, they both extensively exhibit focal axonal swellings (FAS, which in turn, compromise the information encoded in spike trains and lead to potentially severe functional deficits. There are currently no satisfactory quantitative predictors of decline in memory-encoding neuronal networks based on the impact and statistics of FAS. Some of the challenges of this translational approach include our inability to access small scale injuries with non-invasive methods, the overall complexity of neuronal pathologies, and our limited knowledge of how networks process biological signals. The purpose of this computational study is three-fold: (i to extend Hopfield's model for associative memory to account for the effects of FAS, (ii to calibrate FAS parameters from biophysical observations of their statistical distribution and size, and (iii to systematically evaluate deterioration rates for different memory-recall tasks as a function of FAS injury. We calculate deterioration rates for a face-recognition task to account for highly correlated memories and also for a discrimination task of random, uncorrelated memories with a size at the capacity limit of the Hopfield network. While it is expected that the performance of any injured network should decrease with injury, our results link, for the first time, the memory recall ability to observed FAS statistics. This allows for plausible estimates of cognitive decline for different stages of brain disorders within neuronal networks, bridging experimental observations following neurodegeneration and TBI with compromised memory recall. The work lends new insights to help close the gap between theory and experiment on how biological signals are processed in damaged, high-dimensional functional networks, and towards positing new diagnostic tools to measure cognitive

  8. Estimating Memory Deterioration Rates Following Neurodegeneration and Traumatic Brain Injuries in a Hopfield Network Model

    Science.gov (United States)

    Weber, Melanie; Maia, Pedro D.; Kutz, J. Nathan

    2017-01-01

    Neurodegenerative diseases and traumatic brain injuries (TBI) are among the main causes of cognitive dysfunction in humans. At a neuronal network level, they both extensively exhibit focal axonal swellings (FAS), which in turn, compromise the information encoded in spike trains and lead to potentially severe functional deficits. There are currently no satisfactory quantitative predictors of decline in memory-encoding neuronal networks based on the impact and statistics of FAS. Some of the challenges of this translational approach include our inability to access small scale injuries with non-invasive methods, the overall complexity of neuronal pathologies, and our limited knowledge of how networks process biological signals. The purpose of this computational study is three-fold: (i) to extend Hopfield's model for associative memory to account for the effects of FAS, (ii) to calibrate FAS parameters from biophysical observations of their statistical distribution and size, and (iii) to systematically evaluate deterioration rates for different memory-recall tasks as a function of FAS injury. We calculate deterioration rates for a face-recognition task to account for highly correlated memories and also for a discrimination task of random, uncorrelated memories with a size at the capacity limit of the Hopfield network. While it is expected that the performance of any injured network should decrease with injury, our results link, for the first time, the memory recall ability to observed FAS statistics. This allows for plausible estimates of cognitive decline for different stages of brain disorders within neuronal networks, bridging experimental observations following neurodegeneration and TBI with compromised memory recall. The work lends new insights to help close the gap between theory and experiment on how biological signals are processed in damaged, high-dimensional functional networks, and towards positing new diagnostic tools to measure cognitive deficits. PMID

  9. A generic method for estimating system reliability using Bayesian networks

    International Nuclear Information System (INIS)

    Doguc, Ozge; Ramirez-Marquez, Jose Emmanuel

    2009-01-01

    This study presents a holistic method for constructing a Bayesian network (BN) model for estimating system reliability. BN is a probabilistic approach that is used to model and predict the behavior of a system based on observed stochastic events. The BN model is a directed acyclic graph (DAG) where the nodes represent system components and arcs represent relationships among them. Although recent studies on using BN for estimating system reliability have been proposed, they are based on the assumption that a pre-built BN has been designed to represent the system. In these studies, the task of building the BN is typically left to a group of specialists who are BN and domain experts. The BN experts should learn about the domain before building the BN, which is generally very time consuming and may lead to incorrect deductions. As there are no existing studies to eliminate the need for a human expert in the process of system reliability estimation, this paper introduces a method that uses historical data about the system to be modeled as a BN and provides efficient techniques for automated construction of the BN model, and hence estimation of the system reliability. In this respect K2, a data mining algorithm, is used for finding associations between system components, and thus building the BN model. This algorithm uses a heuristic to provide efficient and accurate results while searching for associations. Moreover, no human intervention is necessary during the process of BN construction and reliability estimation. The paper provides a step-by-step illustration of the method and evaluation of the approach with literature case examples

  10. A generic method for estimating system reliability using Bayesian networks

    Energy Technology Data Exchange (ETDEWEB)

    Doguc, Ozge [Stevens Institute of Technology, Hoboken, NJ 07030 (United States); Ramirez-Marquez, Jose Emmanuel [Stevens Institute of Technology, Hoboken, NJ 07030 (United States)], E-mail: jmarquez@stevens.edu

    2009-02-15

    This study presents a holistic method for constructing a Bayesian network (BN) model for estimating system reliability. BN is a probabilistic approach that is used to model and predict the behavior of a system based on observed stochastic events. The BN model is a directed acyclic graph (DAG) where the nodes represent system components and arcs represent relationships among them. Although recent studies on using BN for estimating system reliability have been proposed, they are based on the assumption that a pre-built BN has been designed to represent the system. In these studies, the task of building the BN is typically left to a group of specialists who are BN and domain experts. The BN experts should learn about the domain before building the BN, which is generally very time consuming and may lead to incorrect deductions. As there are no existing studies to eliminate the need for a human expert in the process of system reliability estimation, this paper introduces a method that uses historical data about the system to be modeled as a BN and provides efficient techniques for automated construction of the BN model, and hence estimation of the system reliability. In this respect K2, a data mining algorithm, is used for finding associations between system components, and thus building the BN model. This algorithm uses a heuristic to provide efficient and accurate results while searching for associations. Moreover, no human intervention is necessary during the process of BN construction and reliability estimation. The paper provides a step-by-step illustration of the method and evaluation of the approach with literature case examples.

  11. Estimation of the number of extreme pathways for metabolic networks

    Directory of Open Access Journals (Sweden)

    Thiele Ines

    2007-09-01

    Full Text Available Abstract Background The set of extreme pathways (ExPa, {pi}, defines the convex basis vectors used for the mathematical characterization of the null space of the stoichiometric matrix for biochemical reaction networks. ExPa analysis has been used for a number of studies to determine properties of metabolic networks as well as to obtain insight into their physiological and functional states in silico. However, the number of ExPas, p = |{pi}|, grows with the size and complexity of the network being studied, and this poses a computational challenge. For this study, we investigated the relationship between the number of extreme pathways and simple network properties. Results We established an estimating function for the number of ExPas using these easily obtainable network measurements. In particular, it was found that log [p] had an exponential relationship with log⁡[∑i=1Rd−id+ici] MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaacyGGSbaBcqGGVbWBcqGGNbWzdaWadaqaamaaqadabaGaemizaq2aaSbaaSqaaiabgkHiTmaaBaaameaacqWGPbqAaeqaaaWcbeaakiabdsgaKnaaBaaaleaacqGHRaWkdaWgaaadbaGaemyAaKgabeaaaSqabaGccqWGJbWydaWgaaWcbaGaemyAaKgabeaaaeaacqWGPbqAcqGH9aqpcqaIXaqmaeaacqWGsbGua0GaeyyeIuoaaOGaay5waiaaw2faaaaa@4414@, where R = |Reff| is the number of active reactions in a network, d−i MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaacqWGKbazdaWgaaWcbaGaeyOeI0YaaSbaaWqaaiabdMgaPbqabaaaleqaaaaa@30A9@ and d+i MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb

  12. An Integrated Approach for Reliable Facility Location/Network Design Problem with Link Disruption

    Directory of Open Access Journals (Sweden)

    Davood Shishebori

    2015-05-01

    Full Text Available Proposing a robust designed facility location is one of the most effective ways to hedge against unexpected disruptions and failures in a transportation network system. This paper considers the combined facility location/network design problem with regard to transportation link disruptions and develops a mixed integer linear programming formulation to model it. With respect to the probability of link disruptions, the objective function of the model minimizes the total costs, including location costs, link construction costs and also the expected transportation costs. An efficient hybrid algorithm based on LP relaxation and variable neighbourhood search metaheuristic is developed in order to solve the mathematical model. Numerical results demonstrate that the proposed hybrid algorithm has suitable efficiency in terms of duration of solution time and determining excellent solution quality.

  13. High-Capacity Hybrid Optical Fiber-Wireless Communications Links in Access Networks

    DEFF Research Database (Denmark)

    Pang, Xiaodan

    of broadband services access. To realize the seamless convergence between the two network segments, the lower capacity of wireless systems need to be increased to match the continuously increasing bandwidth of fiber-optic systems. The research works included in this thesis are devoted to experimental...... investigations of photonic-wireless links with record high capacities to fulfill the requirements of next generation hybrid optical fiber-wireless access networks. The main contributions of this thesis have expanded the state-of-the-art in two main areas: high speed millimeter-wave (mm-wave) communication links......Integration between fiber-optic and wireless communications systems in the "last mile" access networks is currently considered as a promising solution for both service providers and users, in terms of minimizing deployment cost, shortening upgrading period and increasing mobility and flexibility...

  14. A Study of an Optical Lunar Surface Communications Network with High Bandwidth Direct to Earth Link

    Science.gov (United States)

    Wilson, K.; Biswas, A.; Schoolcraft, J.

    2011-01-01

    Analyzed optical DTE (direct to earth) and lunar relay satellite link analyses, greater than 200 Mbps downlink to 1-m Earth receiver and greater than 1 Mbps uplink achieved with mobile 5-cm lunar transceiver, greater than 1Gbps downlink and greater than 10 Mpbs uplink achieved with 10-cm stationary lunar transceiver, MITLL (MIT Lincoln Laboratory) 2013 LLCD (Lunar Laser Communications Demonstration) plans to demonstrate 622 Mbps downlink with 20 Mbps uplink between lunar orbiter and ground station; Identified top five technology challenges to deploying lunar optical network, Performed preliminary experiments on two of challenges: (i) lunar dust removal and (ii)DTN over optical carrier, Exploring opportunities to evaluate DTN (delay-tolerant networking) over optical link in a multi-node network e.g. Desert RATS.

  15. Wireless Power Transfer for Distributed Estimation in Sensor Networks

    Science.gov (United States)

    Mai, Vien V.; Shin, Won-Yong; Ishibashi, Koji

    2017-04-01

    This paper studies power allocation for distributed estimation of an unknown scalar random source in sensor networks with a multiple-antenna fusion center (FC), where wireless sensors are equipped with radio-frequency based energy harvesting technology. The sensors' observation is locally processed by using an uncoded amplify-and-forward scheme. The processed signals are then sent to the FC, and are coherently combined at the FC, at which the best linear unbiased estimator (BLUE) is adopted for reliable estimation. We aim to solve the following two power allocation problems: 1) minimizing distortion under various power constraints; and 2) minimizing total transmit power under distortion constraints, where the distortion is measured in terms of mean-squared error of the BLUE. Two iterative algorithms are developed to solve the non-convex problems, which converge at least to a local optimum. In particular, the above algorithms are designed to jointly optimize the amplification coefficients, energy beamforming, and receive filtering. For each problem, a suboptimal design, a single-antenna FC scenario, and a common harvester deployment for colocated sensors, are also studied. Using the powerful semidefinite relaxation framework, our result is shown to be valid for any number of sensors, each with different noise power, and for an arbitrarily number of antennas at the FC.

  16. Reservoir parameter estimation using a hybrid neural network

    Energy Technology Data Exchange (ETDEWEB)

    Aminzadeh, F. [DGB USA and FACT Inc., Sugarland, TX (United States); Barhen, J.; Glover, C.W. [Oak Ridge National Laboratory (United States). Center for Engineering Systems Advanced Resesarch; Toomarian, N.B. [California Institute of Technology (United States). Jet Propulsion Laboratory

    2000-10-01

    The accuracy of an artificial neural network (ANN) algorithm is a crucial issue in the estimation of an oil field's reservoir properties from the log and seismic data. This paper demonstrates the use of the k-fold cross validation technique to obtain confidence bounds on an ANN's accuracy statistic from a finite sample set. In addition, we also show that an ANN's classification accuracy is dramatically improved by transforming the ANN's input feature space to a dimensionally smaller new input space. The new input space represents a feature space that maximizes the linear separation between classes. Thus, the ANN's convergence time and accuracy are improved because the ANN must merely find nonlinear perturbations to the starting linear decision boundaries. These techniques for estimating ANN accuracy bounds and feature space transformations are demonstrated on the problem of estimating the sand thickness in an oil field reservoir based only on remotely sensed seismic data. (author)

  17. 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.

  18. Mobile Stride Length Estimation With Deep Convolutional Neural Networks.

    Science.gov (United States)

    Hannink, Julius; Kautz, Thomas; Pasluosta, Cristian F; Barth, Jens; Schulein, Samuel; GaBmann, Karl-Gunter; Klucken, Jochen; Eskofier, Bjoern M

    2018-03-01

    Accurate estimation of spatial gait characteristics is critical to assess motor impairments resulting from neurological or musculoskeletal disease. Currently, however, methodological constraints limit clinical applicability of state-of-the-art double integration approaches to gait patterns with a clear zero-velocity phase. We describe a novel approach to stride length estimation that uses deep convolutional neural networks to map stride-specific inertial sensor data to the resulting stride length. The model is trained on a publicly available and clinically relevant benchmark dataset consisting of 1220 strides from 101 geriatric patients. Evaluation is done in a tenfold cross validation and for three different stride definitions. Even though best results are achieved with strides defined from midstance to midstance with average accuracy and precision of , performance does not strongly depend on stride definition. The achieved precision outperforms state-of-the-art methods evaluated on the same benchmark dataset by . Due to the independence of stride definition, the proposed method is not subject to the methodological constrains that limit applicability of state-of-the-art double integration methods. Furthermore, it was possible to improve precision on the benchmark dataset. With more precise mobile stride length estimation, new insights to the progression of neurological disease or early indications might be gained. Due to the independence of stride definition, previously uncharted diseases in terms of mobile gait analysis can now be investigated by retraining and applying the proposed method.

  19. Virtual Sensor for Kinematic Estimation of Flexible Links in Parallel Robots.

    Science.gov (United States)

    Bengoa, Pablo; Zubizarreta, Asier; Cabanes, Itziar; Mancisidor, Aitziber; Pinto, Charles; Mata, Sara

    2017-08-23

    The control of flexible link parallel manipulators is still an open area of research, endpoint trajectory tracking being one of the main challenges in this type of robot. The flexibility and deformations of the limbs make the estimation of the Tool Centre Point (TCP) position a challenging one. Authors have proposed different approaches to estimate this deformation and deduce the location of the TCP. However, most of these approaches require expensive measurement systems or the use of high computational cost integration methods. This work presents a novel approach based on a virtual sensor which can not only precisely estimate the deformation of the flexible links in control applications (less than 2% error), but also its derivatives (less than 6% error in velocity and 13% error in acceleration) according to simulation results. The validity of the proposed Virtual Sensor is tested in a Delta Robot, where the position of the TCP is estimated based on the Virtual Sensor measurements with less than a 0.03% of error in comparison with the flexible approach developed in ADAMS Multibody Software.

  20. Power-Aware Routing and Network Design with Bundled Links: Solutions and Analysis

    Directory of Open Access Journals (Sweden)

    Rosario G. Garroppo

    2013-01-01

    Full Text Available The paper deeply analyzes a novel network-wide power management problem, called Power-Aware Routing and Network Design with Bundled Links (PARND-BL, which is able to take into account both the relationship between the power consumption and the traffic throughput of the nodes and to power off both the chassis and even the single Physical Interface Card (PIC composing each link. The solutions of the PARND-BL model have been analyzed by taking into account different aspects associated with the actual applicability in real network scenarios: (i the time for obtaining the solution, (ii the deployed network topology and the resulting topology provided by the solution, (iii the power behavior of the network elements, (iv the traffic load, (v the QoS requirement, and (vi the number of paths to route each traffic demand. Among the most interesting and novel results, our analysis shows that the strategy of minimizing the number of powered-on network elements through the traffic consolidation does not always produce power savings, and the solution of this kind of problems, in some cases, can lead to spliting a single traffic demand into a high number of paths.

  1. Probing Rubber Cross-Linking Generation of Industrial Polymer Networks at Nanometer Scale.

    Science.gov (United States)

    Gabrielle, Brice; Gomez, Emmanuel; Korb, Jean-Pierre

    2016-06-23

    We present improved analyses of rheometric torque measurements as well as (1)H double-quantum (DQ) nuclear magnetic resonance (NMR) buildup data on polymer networks of industrial compounds. This latter DQ NMR analysis allows finding the distribution of an orientation order parameter (Dres) resulting from the noncomplete averaging of proton dipole-dipole couplings within the cross-linked polymer chains. We investigate the influence of the formulation (filler and vulcanization systems) as well as the process (curing temperature) ending to the final polymer network. We show that DQ NMR follows the generation of the polymer network during the vulcanization process from a heterogeneous network to a very homogeneous one. The time variations of microscopic Dres and macroscopic rheometric torques present power-law behaviors above a threshold time scale with characteristic exponents of the percolation theory. We observe also a very good linear correlation between the kinetics of Dres and rheometric data routinely performed in industry. All these observations confirm the description of the polymer network generation as a critical phenomenon. On the basis of all these results, we believe that DQ NMR could become a valuable tool for investigating in situ the cross-linking of industrial polymer networks at the nanometer scale.

  2. Capacity planning of link restorable optical networks under dynamic change of traffic

    Science.gov (United States)

    Ho, Kwok Shing; Cheung, Kwok Wai

    2005-11-01

    Future backbone networks shall require full-survivability and support dynamic changes of traffic demands. The Generalized Survivable Networks (GSN) was proposed to meet these challenges. GSN is fully-survivable under dynamic traffic demand changes, so it offers a practical and guaranteed characterization framework for ASTN / ASON survivable network planning and bandwidth-on-demand resource allocation 4. The basic idea of GSN is to incorporate the non-blocking network concept into the survivable network models. In GSN, each network node must specify its I/O capacity bound which is taken as constraints for any allowable traffic demand matrix. In this paper, we consider the following generic GSN network design problem: Given the I/O bounds of each network node, find a routing scheme (and the corresponding rerouting scheme under failure) and the link capacity assignment (both working and spare) which minimize the cost, such that any traffic matrix consistent with the given I/O bounds can be feasibly routed and it is single-fault tolerant under the link restoration scheme. We first show how the initial, infeasible formal mixed integer programming formulation can be transformed into a more feasible problem using the duality transformation of the linear program. Then we show how the problem can be simplified using the Lagrangian Relaxation approach. Previous work has outlined a two-phase approach for solving this problem where the first phase optimizes the working capacity assignment and the second phase optimizes the spare capacity assignment. In this paper, we present a jointly optimized framework for dimensioning the survivable optical network with the GSN model. Experiment results show that the jointly optimized GSN can bring about on average of 3.8% cost savings when compared with the separate, two-phase approach. Finally, we perform a cost comparison and show that GSN can be deployed with a reasonable cost.

  3. Automated mammographic breast density estimation using a fully convolutional network.

    Science.gov (United States)

    Lee, Juhun; Nishikawa, Robert M

    2018-03-01

    The purpose of this study was to develop a fully automated algorithm for mammographic breast density estimation using deep learning. Our algorithm used a fully convolutional network, which is a deep learning framework for image segmentation, to segment both the breast and the dense fibroglandular areas on mammographic images. Using the segmented breast and dense areas, our algorithm computed the breast percent density (PD), which is the faction of dense area in a breast. Our dataset included full-field digital screening mammograms of 604 women, which included 1208 mediolateral oblique (MLO) and 1208 craniocaudal (CC) views. We allocated 455, 58, and 91 of 604 women and their exams into training, testing, and validation datasets, respectively. We established ground truth for the breast and the dense fibroglandular areas via manual segmentation and segmentation using a simple thresholding based on BI-RADS density assessments by radiologists, respectively. Using the mammograms and ground truth, we fine-tuned a pretrained deep learning network to train the network to segment both the breast and the fibroglandular areas. Using the validation dataset, we evaluated the performance of the proposed algorithm against radiologists' BI-RADS density assessments. Specifically, we conducted a correlation analysis between a BI-RADS density assessment of a given breast and its corresponding PD estimate by the proposed algorithm. In addition, we evaluated our algorithm in terms of its ability to classify the BI-RADS density using PD estimates, and its ability to provide consistent PD estimates for the left and the right breast and the MLO and CC views of the same women. To show the effectiveness of our algorithm, we compared the performance of our algorithm against a state of the art algorithm, laboratory for individualized breast radiodensity assessment (LIBRA). The PD estimated by our algorithm correlated well with BI-RADS density ratings by radiologists. Pearson's rho values of

  4. SDN control of optical nodes in metro networks for high capacity inter-datacentre links

    Science.gov (United States)

    Magalhães, Eduardo; Perry, Philip; Barry, Liam

    2017-11-01

    Worldwide demand for bandwidth has been growing fast for some years and continues to do so. To cover this, mega datacentres need scalable connectivity to provide rich connectivity to handle the heavy traffic across them. Therefore, hardware infrastructures must be able to play different roles according to service and traffic requirements. In this context, software defined networking (SDN) decouples the network control and forwarding functions enabling the network control to become directly programmable and the underlying infrastructure to be abstracted for applications and network services. In addition, elastic optical networking (EON) technologies enable efficient spectrum utilization by allocating variable bandwidth to each user according to their actual needs. In particular, flexible transponders and reconfigurable optical add/drop multiplexers (ROADMs) are key elements since they can offer degrees of freedom to self adapt accordingly. Thus, it is crucial to design control methods in order to optimize the hardware utilization and offer high reconfigurability, flexibility and adaptability. In this paper, we propose and analyze, using a simulation framework, a method of capacity maximization through optical power profile manipulation for inter datacentre links that use existing metropolitan optical networks by exploiting the global network view afforded by SDN. Results show that manipulating the loss profiles of the ROADMs in the metro-network can yield optical signal-to-noise ratio (OSNR) improvements up to 10 dB leading to an increase in 112% in total capacity.

  5. Estimating plant root water uptake using a neural network approach

    DEFF Research Database (Denmark)

    Qiao, D M; Shi, H B; Pang, H B

    2010-01-01

    but has not yet been addressed. This paper presents and tests such an approach. The method is based on a neural network model, estimating the water uptake using different types of data that are easy to measure in the field. Sunflower grown in a sandy loam subjected to water stress and salinity was taken......Water uptake by plant roots is an important process in the hydrological cycle, not only for plant growth but also for the role it plays in shaping microbial community and bringing in physical and biochemical changes to soils. The ability of roots to extract water is determined by combined soil...... and plant characteristics, and how to model it has been of interest for many years. Most macroscopic models for water uptake operate at soil profile scale under the assumption that the uptake rate depends on root density and soil moisture. Whilst proved appropriate, these models need spatio-temporal root...

  6. Estimation of LOCA break size using cascaded Fuzzy neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Choi, Geon Pil; Yoo, Kwae Hwan; Back, Ju Hyun; Na, Man Gyun [Dept. of Nuclear Engineering, Chosun University, Gwangju (Korea, Republic of)

    2017-04-15

    Operators of nuclear power plants may not be equipped with sufficient information during a loss-of-coolant accident (LOCA), which can be fatal, or they may not have sufficient time to analyze the information they do have, even if this information is adequate. It is not easy to predict the progression of LOCAs in nuclear power plants. Therefore, accurate information on the LOCA break position and size should be provided to efficiently manage the accident. In this paper, the LOCA break size is predicted using a cascaded fuzzy neural network (CFNN) model. The input data of the CFNN model are the time-integrated values of each measurement signal for an initial short-time interval after a reactor scram. The training of the CFNN model is accomplished by a hybrid method combined with a genetic algorithm and a least squares method. As a result, LOCA break size is estimated exactly by the proposed CFNN model.

  7. Estimating under-reporting of road crash injuries to police using multiple linked data collections.

    Science.gov (United States)

    Watson, Angela; Watson, Barry; Vallmuur, Kirsten

    2015-10-01

    The reliance on police data for the counting of road crash injuries can be problematic, as it is well known that not all road crash injuries are reported to police which under-estimates the overall burden of road crash injuries. The aim of this study was to use multiple linked data sources to estimate the extent of under-reporting of road crash injuries to police in the Australian state of Queensland. Data from the Queensland Road Crash Database (QRCD), the Queensland Hospital Admitted Patients Data Collection (QHAPDC), Emergency Department Information System (EDIS), and the Queensland Injury Surveillance Unit (QISU) for the year 2009 were linked. The completeness of road crash cases reported to police was examined via discordance rates between the police data (QRCD) and the hospital data collections. In addition, the potential bias of this discordance (under-reporting) was assessed based on gender, age, road user group, and regional location. Results showed that the level of under-reporting varied depending on the data set with which the police data was compared. When all hospital data collections are examined together the estimated population of road crash injuries was approximately 28,000, with around two-thirds not linking to any record in the police data. The results also showed that the under-reporting was more likely for motorcyclists, cyclists, males, young people, and injuries occurring in Remote and Inner Regional areas. These results have important implications for road safety research and policy in terms of: prioritising funding and resources; targeting road safety interventions into areas of higher risk; and estimating the burden of road crash injuries. Copyright © 2015 Elsevier Ltd. All rights reserved.

  8. Estimating extreme river discharges in Europe through a Bayesian network

    Science.gov (United States)

    Paprotny, Dominik; Morales-Nápoles, Oswaldo

    2017-06-01

    Large-scale hydrological modelling of flood hazards requires adequate extreme discharge data. In practise, models based on physics are applied alongside those utilizing only statistical analysis. The former require enormous computational power, while the latter are mostly limited in accuracy and spatial coverage. In this paper we introduce an alternate, statistical approach based on Bayesian networks (BNs), a graphical model for dependent random variables. We use a non-parametric BN to describe the joint distribution of extreme discharges in European rivers and variables representing the geographical characteristics of their catchments. Annual maxima of daily discharges from more than 1800 river gauges (stations with catchment areas ranging from 1.4 to 807 000 km2) were collected, together with information on terrain, land use and local climate. The (conditional) correlations between the variables are modelled through copulas, with the dependency structure defined in the network. The results show that using this method, mean annual maxima and return periods of discharges could be estimated with an accuracy similar to existing studies using physical models for Europe and better than a comparable global statistical model. Performance of the model varies slightly between regions of Europe, but is consistent between different time periods, and remains the same in a split-sample validation. Though discharge prediction under climate change is not the main scope of this paper, the BN was applied to a large domain covering all sizes of rivers in the continent both for present and future climate, as an example. Results show substantial variation in the influence of climate change on river discharges. The model can be used to provide quick estimates of extreme discharges at any location for the purpose of obtaining input information for hydraulic modelling.

  9. Estimation of Minimum DNBR Using Cascaded Fuzzy Neural Networks

    International Nuclear Information System (INIS)

    Kim, Dong Yeong; Yoo, Kwae Hwan; Na, Man Gyun

    2015-01-01

    This phenomenon of boiling crisis is called a departure from nucleate boiling (DNB). The DNB phenomena can influence the fuel cladding and fuel pellets. The DNB ratio (DNBR) is defined as the ratio of the expected DNB heat flux to the actual fuel rod heat flux. Since it is very important to monitor and predict the minimum DNBR in a reactor core to prevent the boiling crisis and clad melting, a number of researches have been conducted to predict DNBR values. The aim of this study is to estimate the minimum DNBR in a reactor core using the measured signals of the reactor coolant system (RCS) by applying cascaded fuzzy neural networks (CFNN) according to operating conditions. Reactor core monitoring and protection systems require minimum DNBR prediction. The CFNN can be used to optimize the minimum DNBR value through the process of adding fuzzy neural networks (FNN) repeatedly. The proposed algorithm is trained by using the data set prepared for training (development data) and verified by using another data set different (independent) from the development data. The developed CFNN models were applied to the first fuel cycle of OPR1000. The RMS errors are 0.23% and 0.12% for the positive and negative ASI, respectively

  10. Projection-based circular constrained state estimation and fusion over long-haul links

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Qiang [ORNL; Rao, Nageswara S. [ORNL

    2017-07-01

    In this paper, we consider a scenario where sensors are deployed over a large geographical area for tracking a target with circular nonlinear constraints on its motion dynamics. The sensor state estimates are sent over long-haul networks to a remote fusion center for fusion. We are interested in different ways to incorporate the constraints into the estimation and fusion process in the presence of communication loss. In particular, we consider closed-form projection-based solutions, including rules for fusing the estimates and for incorporating the constraints, which jointly can guarantee timely fusion often required in realtime systems. We test the performance of these methods in the long-haul tracking environment using a simple example.

  11. MUSIC algorithm DoA estimation for cooperative node location in mobile ad hoc networks

    Science.gov (United States)

    Warty, Chirag; Yu, Richard Wai; ElMahgoub, Khaled; Spinsante, Susanna

    In recent years the technological development has encouraged several applications based on distributed communications network without any fixed infrastructure. The problem of providing a collaborative early warning system for multiple mobile nodes against a fast moving object. The solution is provided subject to system level constraints: motion of nodes, antenna sensitivity and Doppler effect at 2.4 GHz and 5.8 GHz. This approach consists of three stages. The first phase consists of detecting the incoming object using a highly directive two element antenna at 5.0 GHz band. The second phase consists of broadcasting the warning message using a low directivity broad antenna beam using 2× 2 antenna array which then in third phase will be detected by receiving nodes by using direction of arrival (DOA) estimation technique. The DOA estimation technique is used to estimate the range and bearing of the incoming nodes. The position of fast arriving object can be estimated using the MUSIC algorithm for warning beam DOA estimation. This paper is mainly intended to demonstrate the feasibility of early detection and warning system using a collaborative node to node communication links. The simulation is performed to show the behavior of detecting and broadcasting antennas as well as performance of the detection algorithm. The idea can be further expanded to implement commercial grade detection and warning system

  12. Down-link power distributions for 2G and 3G mobile communication networks

    International Nuclear Information System (INIS)

    Colombi, D.; Thors, B.; Persson, T.; Wiren, N.; Larsson, L. E.; Jonsson, M.; Toernevik, C.

    2013-01-01

    Knowledge of realistic power levels is key when conducting accurate EMF exposure assessments. In this study, down-link output power distributions for radio base stations in 2G and 3G mobile communication networks have been assessed. The distributions were obtained from network measurement data collected from the Operations Support System, which normally is used for network monitoring and management. Significant amounts of data were gathered simultaneously for large sets of radio base stations covering wide geographical areas and different environments. The method was validated with in situ measurements. For the 3G network, the 90. percentile of the averaged output power during high traffic hours was found to be 43 % of the maximum available power. The corresponding number for 2G, with two or more transceivers installed, was 65 % or below. (authors)

  13. Linked Heritage: a collaborative terminology management platform for a network of multilingual thesauri and controlled vocabularies

    Directory of Open Access Journals (Sweden)

    Marie-Veronique Leroi

    2013-01-01

    Full Text Available Terminology and multilingualism have been one of the main focuses of the Athena Project. Linked Heritage as a legacy of this project also deals with terminology and bring theory to practice applying the recommendations given in the Athena Project. Linked Heritage as a direct follow-up of these recommendations on terminology and multilingualism is currently working on the development of a Terminology Management Platform (TMP. This platform will allow any cultural institution to register, SKOSify and manage its terminology in a collaborative way. This Terminology Management Platform will provide a network of multilingual and cross-domain terminologies.

  14. An efficient strategy for enhancing traffic capacity by removing links in scale-free networks

    International Nuclear Information System (INIS)

    Huang, Wei; Chow, Tommy W S

    2010-01-01

    An efficient link-removal strategy, called the variance-of-neighbor-degree-reduction (VNDR) strategy, for enhancing the traffic capacity of scale-free networks is proposed in this paper. The VNDR strategy, which considers the important role of hub nodes, balances the amounts of packets routed from each node to the node's neighbors. Compared against the outcomes of strategies that remove links among hub nodes, our results show that the traffic capacity can be greatly enhanced, especially under the shortest path routing strategy. It is also found that the average transport time is effectively reduced by using the VNDR strategy only under the shortest path routing strategy

  15. Broadband nanophotonic wireless links and networks using on-chip integrated plasmonic antennas.

    Science.gov (United States)

    Yang, Yuanqing; Li, Qiang; Qiu, Min

    2016-01-19

    Owing to their high capacity and flexibility, broadband wireless communications have been widely employed in radio and microwave regimes, playing indispensable roles in our daily life. Their optical analogs, however, have not been demonstrated at the nanoscale. In this paper, by exploiting plasmonic nanoantennas, we demonstrate the complete design of broadband wireless links and networks in the realm of nanophotonics. With a 100-fold enhancement in power transfer superior to previous designs as well as an ultrawide bandwidth that covers the entire telecommunication wavelength range, such broadband nanolinks and networks are expected to pave the way for future optical integrated nanocircuits.

  16. The role of detachment of in-links in scale-free networks

    International Nuclear Information System (INIS)

    Lansky, P; Polito, F; Sacerdote, L

    2014-01-01

    Real-world networks may exhibit a detachment phenomenon determined by the canceling of previously existing connections. We discuss a tractable extension of the Yule model to account for this feature. Analytical results are derived and discussed both asymptotically and for a finite number of links. Comparison with the original model is performed in the supercritical case. The first-order asymptotic tail behavior of the two models is similar but differences arise in the second-order term. We explicitly refer to world wide web modeling and we show the agreement of the proposed model on very recent data. However, other possible network applications are also mentioned. (paper)

  17. On the plausibility of socioeconomic mortality estimates derived from linked data: a demographic approach.

    Science.gov (United States)

    Lerch, Mathias; Spoerri, Adrian; Jasilionis, Domantas; Viciana Fernandèz, Francisco

    2017-07-14

    Reliable estimates of mortality according to socioeconomic status play a crucial role in informing the policy debate about social inequality, social cohesion, and exclusion as well as about the reform of pension systems. Linked mortality data have become a gold standard for monitoring socioeconomic differentials in survival. Several approaches have been proposed to assess the quality of the linkage, in order to avoid the misclassification of deaths according to socioeconomic status. However, the plausibility of mortality estimates has never been scrutinized from a demographic perspective, and the potential problems with the quality of the data on the at-risk populations have been overlooked. Using indirect demographic estimation (i.e., the synthetic extinct generation method), we analyze the plausibility of old-age mortality estimates according to educational attainment in four European data contexts with different quality issues: deterministic and probabilistic linkage of deaths, as well as differences in the methodology of the collection of educational data. We evaluate whether the at-risk population according to educational attainment is misclassified and/or misestimated, correct these biases, and estimate the education-specific linkage rates of deaths. The results confirm a good linkage of death records within different educational strata, even when probabilistic matching is used. The main biases in mortality estimates concern the classification and estimation of the person-years of exposure according to educational attainment. Changes in the census questions about educational attainment led to inconsistent information over time, which misclassified the at-risk population. Sample censuses also misestimated the at-risk populations according to educational attainment. The synthetic extinct generation method can be recommended for quality assessments of linked data because it is capable not only of quantifying linkage precision, but also of tracking problems in

  18. Multi-stimulus-responsive shape-memory polymer nanocomposite network cross-linked by cellulose nanocrystals.

    Science.gov (United States)

    Liu, Ye; Li, Ying; Yang, Guang; Zheng, Xiaotong; Zhou, Shaobing

    2015-02-25

    In this study, we developed a thermoresponsive and water-responsive shape-memory polymer nanocomposite network by chemically cross-linking cellulose nanocrystals (CNCs) with polycaprolactone (PCL) and polyethylene glycol (PEG). The nanocomposite network was fully characterized, including the microstructure, cross-link density, water contact angle, water uptake, crystallinity, thermal properties, and static and dynamic mechanical properties. We found that the PEG[60]-PCL[40]-CNC[10] nanocomposite exhibited excellent thermo-induced and water-induced shape-memory effects in water at 37 °C (close to body temperature), and the introduction of CNC clearly improved the mechanical properties of the mixture of both PEG and PCL polymers with low molecular weights. In addition, Alamar blue assays based on osteoblasts indicated that the nanocomposites possessed good cytocompatibility. Therefore, this thermoresponsive and water-responsive shape-memory nanocomposite could be potentially developed into a new smart biomaterial.

  19. Enhancing the reliability of AC transmission networks by using a long distance HVDC-link

    Energy Technology Data Exchange (ETDEWEB)

    Fleckenstein, Marco; Balzer, Gerd; Wasserrab, Andreas; Neumann, Claus [Technische Univ. Darmstadt (Germany). Inst. of Electrical Power and Energy

    2012-07-01

    The absorption of energy from offshore wind farms is a difficult task for Transmission system operators (TSO) in Germany, even if no offshore wind farm is connected directly. The role of the TSOs in the north it is to transport this energy to the south of Germany. The transmission capacities of overhead lines are limited. With the expected rapid expansion of wind farms, this is no longer enough. Thus, the transmission system must be enhanced. One possibility is the installation of an HVDC link from north to south between two main substations in the 380 kV network. Based on a realistic case study, the impact of such a link on the reliability of the entire extra high voltage network is shown. (orig.)

  20. Deep mining heterogeneous networks of biomedical linked data to predict novel drug-target associations.

    Science.gov (United States)

    Zong, Nansu; Kim, Hyeoneui; Ngo, Victoria; Harismendy, Olivier

    2017-08-01

    A heterogeneous network topology possessing abundant interactions between biomedical entities has yet to be utilized in similarity-based methods for predicting drug-target associations based on the array of varying features of drugs and their targets. Deep learning reveals features of vertices of a large network that can be adapted in accommodating the similarity-based solutions to provide a flexible method of drug-target prediction. We propose a similarity-based drug-target prediction method that enhances existing association discovery methods by using a topology-based similarity measure. DeepWalk, a deep learning method, is adopted in this study to calculate the similarities within Linked Tripartite Network (LTN), a heterogeneous network generated from biomedical linked datasets. This proposed method shows promising results for drug-target association prediction: 98.96% AUC ROC score with a 10-fold cross-validation and 99.25% AUC ROC score with a Monte Carlo cross-validation with LTN. By utilizing DeepWalk, we demonstrate that: (i) this method outperforms other existing topology-based similarity computation methods, (ii) the performance is better for tripartite than with bipartite networks and (iii) the measure of similarity using network topology outperforms the ones derived from chemical structure (drugs) or genomic sequence (targets). Our proposed methodology proves to be capable of providing a promising solution for drug-target prediction based on topological similarity with a heterogeneous network, and may be readily re-purposed and adapted in the existing of similarity-based methodologies. The proposed method has been developed in JAVA and it is available, along with the data at the following URL: https://github.com/zongnansu1982/drug-target-prediction . nazong@ucsd.edu. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  1. Using LinkedIn in the Marketing Classroom: Exploratory Insights and Recommendations for Teaching Social Media/Networking

    Science.gov (United States)

    McCorkle, Denny E.; McCorkle, Yuhua Li

    2012-01-01

    With the rapid growth of social networking and media comes their consideration for use in the marketing classroom. Social networking skills are becoming essential for personal branding (e.g., networking, self-marketing) and corporate/product branding (e.g., marketing communication). This paper addresses the use of LinkedIn (i.e., an online…

  2. Symmetric Link Key Management for Secure Neighbor Discovery in a Decentralized Wireless Sensor Network

    Science.gov (United States)

    2017-09-01

    KEY MANAGEMENT FOR SECURE NEIGHBOR DISCOVERY IN A DECENTRALIZED WIRELESS SENSOR NETWORK by Kelvin T. Chew September 2017 Thesis Advisor...and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188) Washington, DC 20503. 1. AGENCY USE ONLY (Leave blank) 2. REPORT...DATE September 2017 3. REPORT TYPE AND DATES COVERED Master’s thesis 4. TITLE AND SUBTITLE SYMMETRIC LINK KEY MANAGEMENT FOR SECURE NEIGHBOR

  3. An Energy-Efficient Link with Adaptive Transmit Power Control for Long Range Networks

    DEFF Research Database (Denmark)

    Lynggaard, P.; Blaszczyk, Tomasz

    2016-01-01

    A considerable amount of research is carried out to develop a reliable smart sensor system with high energy efficiency for battery operated wireless IoT devices in the agriculture sector. However, only a limited amount of research has covered automatic transmission power adjustment schemes...... and algorithms which are essential for deployment of wireless IoT nodes. This paper presents an adaptive link algorithm for farm applications with emphasis on power adjustment for long range communication networks....

  4. A Survey of Congestion Control Techniques and Data Link Protocols in Satellite Networks

    OpenAIRE

    Fahmy, Sonia; Jain, Raj; Lu, Fang; Kalyanaraman, Shivkumar

    1998-01-01

    Satellite communication systems are the means of realizing a global broadband integrated services digital network. Due to the statistical nature of the integrated services traffic, the resulting rate fluctuations and burstiness render congestion control a complicated, yet indispensable function. The long propagation delay of the earth-satellite link further imposes severe demands and constraints on the congestion control schemes, as well as the media access control techniques and retransmissi...

  5. Impact of Radio Link Unreliability on the Connectivity of Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Gorce Jean-Marie

    2007-01-01

    Full Text Available Many works have been devoted to connectivity of ad hoc networks. This is an important feature for wireless sensor networks (WSNs to provide the nodes with the capability of communicating with one or several sinks. In most of these works, radio links are assumed ideal, that is, with no transmission errors. To fulfil this assumption, the reception threshold should be high enough to guarantee that radio links have a low transmission error probability. As a consequence, all unreliable links are dismissed. This approach is suboptimal concerning energy consumption because unreliable links should permit to reduce either the transmission power or the number of active nodes. The aim of this paper is to quantify the contribution of unreliable long hops to an increase of the connectivity of WSNs. In our model, each node is assumed to be connected to each other node in a probabilistic manner. Such a network is modeled as a complete random graph, that is, all edges exist. The instantaneous node degree is then defined as the number of simultaneous valid single-hop receptions of the same message, and finally the mean node degree is computed analytically in both AWGN and block-fading channels. We show the impact on connectivity of two MACs and routing parameters. The first one is the energy detection level such as the one used in carrier sense mechanisms. The second one is the reliability threshold used by the routing layer to select stable links only. Both analytic and simulation results show that using opportunistic protocols is challenging.

  6. Impact of Radio Link Unreliability on the Connectivity of Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Jean-Marie Gorce

    2007-06-01

    Full Text Available Many works have been devoted to connectivity of ad hoc networks. This is an important feature for wireless sensor networks (WSNs to provide the nodes with the capability of communicating with one or several sinks. In most of these works, radio links are assumed ideal, that is, with no transmission errors. To fulfil this assumption, the reception threshold should be high enough to guarantee that radio links have a low transmission error probability. As a consequence, all unreliable links are dismissed. This approach is suboptimal concerning energy consumption because unreliable links should permit to reduce either the transmission power or the number of active nodes. The aim of this paper is to quantify the contribution of unreliable long hops to an increase of the connectivity of WSNs. In our model, each node is assumed to be connected to each other node in a probabilistic manner. Such a network is modeled as a complete random graph, that is, all edges exist. The instantaneous node degree is then defined as the number of simultaneous valid single-hop receptions of the same message, and finally the mean node degree is computed analytically in both AWGN and block-fading channels. We show the impact on connectivity of two MACs and routing parameters. The first one is the energy detection level such as the one used in carrier sense mechanisms. The second one is the reliability threshold used by the routing layer to select stable links only. Both analytic and simulation results show that using opportunistic protocols is challenging.

  7. Link importance incorporated failure probability measuring solution for multicast light-trees in elastic optical networks

    Science.gov (United States)

    Li, Xin; Zhang, Lu; Tang, Ying; Huang, Shanguo

    2018-03-01

    The light-tree-based optical multicasting (LT-OM) scheme provides a spectrum- and energy-efficient method to accommodate emerging multicast services. Some studies focus on the survivability technologies for LTs against a fixed number of link failures, such as single-link failure. However, a few studies involve failure probability constraints when building LTs. It is worth noting that each link of an LT plays different important roles under failure scenarios. When calculating the failure probability of an LT, the importance of its every link should be considered. We design a link importance incorporated failure probability measuring solution (LIFPMS) for multicast LTs under independent failure model and shared risk link group failure model. Based on the LIFPMS, we put forward the minimum failure probability (MFP) problem for the LT-OM scheme. Heuristic approaches are developed to address the MFP problem in elastic optical networks. Numerical results show that the LIFPMS provides an accurate metric for calculating the failure probability of multicast LTs and enhances the reliability of the LT-OM scheme while accommodating multicast services.

  8. Link and Network Layers Design for Ultra-High-Speed Terahertz-Band Communications Networks

    Science.gov (United States)

    2017-01-01

    rod in position 13. . . . . . . . . . . 28 14 Measured and approximated received pulse energy as functions of the transmission distance...rates. For example, in Long-Term Evolution Advanced ( LTE -A) networks, peak data rates on the order of 1 Gigabit-per-second (Gbps) are feasible over 100...The antennas are meshed with a resolution of spp/5. To simulate the array performance measures of gain and directivity, the same parameters are

  9. Inference of directed climate networks: role of instability of causality estimation methods

    Science.gov (United States)

    Hlinka, Jaroslav; Hartman, David; Vejmelka, Martin; Paluš, Milan

    2013-04-01

    Climate data are increasingly analyzed by complex network analysis methods, including graph-theoretical approaches [1]. For such analysis, links between localized nodes of climate network are typically quantified by some statistical measures of dependence (connectivity) between measured variables of interest. To obtain information on the directionality of the interactions in the networks, a wide range of methods exists. These can be broadly divided into linear and nonlinear methods, with some of the latter having the theoretical advantage of being model-free, and principally a generalization of the former [2]. However, as a trade-off, this generality comes together with lower accuracy - in particular if the system was close to linear. In an overall stationary system, this may potentially lead to higher variability in the nonlinear network estimates. Therefore, with the same control of false alarms, this may lead to lower sensitivity for detection of real changes in the network structure. These problems are discussed on the example of daily SAT and SLP data from the NCEP/NCAR reanalysis dataset. We first reduce the dimensionality of data using PCA with VARIMAX rotation to detect several dozens of components that together explain most of the data variability. We further construct directed climate networks applying a selection of most widely used methods - variants of linear Granger causality and conditional mutual information. Finally, we assess the stability of the detected directed climate networks by computing them in sliding time windows. To understand the origin of the observed instabilities and their range, we also apply the same procedure to two types of surrogate data: either with non-stationarity in network structure removed, or imposed in a controlled way. In general, the linear methods show stable results in terms of overall similarity of directed climate networks inferred. For instance, for different decades of SAT data, the Spearman correlation of edge

  10. On Efficient Link Recommendation in Social Networks Using Actor-Fact Matrices

    Directory of Open Access Journals (Sweden)

    Michał Ciesielczyk

    2015-01-01

    Full Text Available Link recommendation is a popular research subject in the field of social network analysis and mining. Often, the main emphasis is put on the development of new recommendation algorithms, semantic enhancements to existing solutions, design of new similarity measures, and so forth. However, relatively little scientific attention has been paid to the impact that various data representation models have on the performance of recommendation algorithms. And by performance we do not mean the time or memory efficiency of algorithms, but the precision and recall of recommender systems. Our recent findings unanimously show that the choice of network representation model has an important and measurable impact on the quality of recommendations. In this paper we argue that the computation quality of link recommendation algorithms depends significantly on the social network representation and we advocate the use of actor-fact matrix as the best alternative. We verify our findings using several state-of-the-art link recommendation algorithms, such as SVD, RSVD, and RRI using both single-relation and multirelation dataset.

  11. Ultrasensitivity in signaling cascades revisited: Linking local and global ultrasensitivity estimations.

    Directory of Open Access Journals (Sweden)

    Edgar Altszyler

    Full Text Available Ultrasensitive response motifs, capable of converting graded stimuli into binary responses, are well-conserved in signal transduction networks. Although it has been shown that a cascade arrangement of multiple ultrasensitive modules can enhance the system's ultrasensitivity, how a given combination of layers affects a cascade's ultrasensitivity remains an open question for the general case. Here, we introduce a methodology that allows us to determine the presence of sequestration effects and to quantify the relative contribution of each module to the overall cascade's ultrasensitivity. The proposed analysis framework provides a natural link between global and local ultrasensitivity descriptors and it is particularly well-suited to characterize and understand mathematical models used to study real biological systems. As a case study, we have considered three mathematical models introduced by O'Shaughnessy et al. to study a tunable synthetic MAPK cascade, and we show how our methodology can help modelers better understand alternative models.

  12. Fast and Accurate Video PQoS Estimation over Wireless Networks

    Directory of Open Access Journals (Sweden)

    Emanuele Viterbo

    2008-06-01

    Full Text Available This paper proposes a curve fitting technique for fast and accurate estimation of the perceived quality of streaming media contents, delivered within a wireless network. The model accounts for the effects of various network parameters such as congestion, radio link power, and video transmission bit rate. The evaluation of the perceived quality of service (PQoS is based on the well-known VQM objective metric, a powerful technique which is highly correlated to the more expensive and time consuming subjective metrics. Currently, PQoS is used only for offline analysis after delivery of the entire video content. Thanks to the proposed simple model, we can estimate in real time the video PQoS and we can rapidly adapt the content transmission through scalable video coding and bit rates in order to offer the best perceived quality to the end users. The designed model has been validated through many different measurements in realistic wireless environments using an ad hoc WiFi test bed.

  13. Unavailability of critical SCADA communication links interconnecting a power grid and a Telco network

    International Nuclear Information System (INIS)

    Bobbio, A.; Bonanni, G.; Ciancamerla, E.; Clemente, R.; Iacomini, A.; Minichino, M.; Scarlatti, A.; Terruggia, R.; Zendri, E.

    2010-01-01

    The availability of power supply to power grid customers depends upon the availability of services of supervision, control and data acquisition (SCADA) system, which constitutes the nervous system of a power grid. In turn, SCADA services depend on the availability of the interconnected networks supporting such services. We propose a service oriented stochastic modelling methodology to investigate the availability of large interconnected networks, based on the hierarchical application of different modelling formalisms to different parts of the networks. Interconnected networks are decomposed according to the specific services delivered until the failure and repair mechanisms of the decomposed elementary blocks can be identified. We represent each network by a convenient stochastic modelling formalism, able to capture the main technological issues and to cope with realistic assumptions about failure and recovery mechanisms. This procedure confines the application of the more intensive computational techniques to those subsystems that actually require it. The paper concentrates on an actual failure scenario, occurred in Rome in January 2004 that involved the outage of critical SCADA communication links, interconnecting a power grid and a Telco network.

  14. Unavailability of critical SCADA communication links interconnecting a power grid and a Telco network

    Energy Technology Data Exchange (ETDEWEB)

    Bobbio, A. [Dipartimento di Informatica, Universita del Piemonte Orientale, Viale Michel 11, 15121 Alessandria (Italy); Bonanni, G.; Ciancamerla, E. [ENEA - CRE Casaccia, Via Anguillarese 301, 00060 Roma (Italy); Clemente, R. [Telecom Italia Mobile, Via Isonzo112, 10141 Torino (Italy); Iacomini, A. [ACEA, Pl. Ostiense 2, 00154 Roma (Italy); Minichino, M., E-mail: minichino@casaccia.enea.i [ENEA - CRE Casaccia, Via Anguillarese 301, 00060 Roma (Italy); Scarlatti, A. [ACEA, Pl. Ostiense 2, 00154 Roma (Italy); Terruggia, R. [Dipartimento di Informatica, Universita del Piemonte Orientale, Viale Michel 11, 15121 Alessandria (Italy); Zendri, E. [ACEA, Pl. Ostiense 2, 00154 Roma (Italy)

    2010-12-15

    The availability of power supply to power grid customers depends upon the availability of services of supervision, control and data acquisition (SCADA) system, which constitutes the nervous system of a power grid. In turn, SCADA services depend on the availability of the interconnected networks supporting such services. We propose a service oriented stochastic modelling methodology to investigate the availability of large interconnected networks, based on the hierarchical application of different modelling formalisms to different parts of the networks. Interconnected networks are decomposed according to the specific services delivered until the failure and repair mechanisms of the decomposed elementary blocks can be identified. We represent each network by a convenient stochastic modelling formalism, able to capture the main technological issues and to cope with realistic assumptions about failure and recovery mechanisms. This procedure confines the application of the more intensive computational techniques to those subsystems that actually require it. The paper concentrates on an actual failure scenario, occurred in Rome in January 2004 that involved the outage of critical SCADA communication links, interconnecting a power grid and a Telco network.

  15. Transport link scanner: simulating geographic transport network expansion through individual investments

    Science.gov (United States)

    Jacobs-Crisioni, C.; Koopmans, C. C.

    2016-07-01

    This paper introduces a GIS-based model that simulates the geographic expansion of transport networks by several decision-makers with varying objectives. The model progressively adds extensions to a growing network by choosing the most attractive investments from a limited choice set. Attractiveness is defined as a function of variables in which revenue and broader societal benefits may play a role and can be based on empirically underpinned parameters that may differ according to private or public interests. The choice set is selected from an exhaustive set of links and presumably contains those investment options that best meet private operator's objectives by balancing the revenues of additional fare against construction costs. The investment options consist of geographically plausible routes with potential detours. These routes are generated using a fine-meshed regularly latticed network and shortest path finding methods. Additionally, two indicators of the geographic accuracy of the simulated networks are introduced. A historical case study is presented to demonstrate the model's first results. These results show that the modelled networks reproduce relevant results of the historically built network with reasonable accuracy.

  16. A Network Analysis Perspective to Implementation: The Example of Health Links to Promote Coordinated Care.

    Science.gov (United States)

    Yousefi Nooraie, Reza; Khan, Sobia; Gutberg, Jennifer; Baker, G Ross

    2018-01-01

    Although implementation models broadly recognize the importance of social relationships, our knowledge about applying social network analysis (SNA) to formative, process, and outcome evaluations of health system interventions is limited. We explored applications of adopting an SNA lens to inform implementation planning, engagement and execution, and evaluation. We used Health Links, a province-wide program in Canada aiming to improve care coordination among multiple providers of high-needs patients, as an example of a health system intervention. At the planning phase, an SNA can depict the structure, network influencers, and composition of clusters at various levels. It can inform the engagement and execution by identifying potential targets (e.g., opinion leaders) and by revealing structural gaps and clusters. It can also be used to assess the outcomes of the intervention, such as its success in increasing network connectivity; changing the position of certain actors; and bridging across specialties, organizations, and sectors. We provided an overview of how an SNA lens can shed light on the complexity of implementation along the entire implementation pathway, by revealing the relational barriers and facilitators, the application of network-informed and network-altering interventions, and testing hypotheses on network consequences of the implementation.

  17. Use of artificial neural networks as estimators and controllers

    Science.gov (United States)

    Concilio, Antonio; Sorrentino, A.

    1996-04-01

    Active noise control is one among the most promising applications of the so-called Smart Structures, because it ensures, or promises, lower weight, lower cost, more effectiveness and all what is desirable in a vehicle design process, with respect to the current solutions. More and more attention in the research world has been devoting to this argument, pushed by both political, economical and environmental reasons, the one connected to the others. Piezoceramic actuators, integrated into the structure, seem to offer the most fashionable and practical solutions among all the proposed architectures, [1-2]. As sensors, microphones demonstrated to be the most performing, above all because they give the most suitable representation of the field that has to be cancelled, [3-4]. This approach is known as Acousto-Structural Active Control, ASAC, [5]. However, according to Fuller's definition, [6] , an intelligent controller is needed to ensure the development of an "Intelligent Structure" . Its main characteristic should be represented by the capability of learning by examples, of following the structure during its evolution, of being the system "brain" . This peculiarity may be offered by Artificial Neural Networks (ANN's), [7-8]. They present other important features, like the capability, in principle, of treating non-linear as well as linear problems, [9], of identifying dynamic systems, [10], of properly acting as a controller. Then, such a net could integrate in itself the function of "system estimator" or "observer" ,and of interpolator - extrapolator and controller, contemporarily. The authors have been working on such subjects for a long time, proposing for instance ANN's as time-domain structural parameters estimators on a simple 2D element ( a framed plate), [11], as noise and vibration controllers in a FF system, [12-13], as materials damping parameters extractors from experimental data, [14]. All these applications were aimed at noise reduction problems. The

  18. Linking Climate Risk, Policy Networks and Adaptation Planning in Public Lands

    Science.gov (United States)

    Lubell, M.; Schwartz, M.; Peters, C.

    2014-12-01

    Federal public land management agencies in the United States have engaged a variety of planning efforts to address climate adaptation. A major goal of these efforts is to build policy networks that enable land managers to access information and expertise needed for responding to local climate risks. This paper investigates whether the perceived and modeled climate risk faced by different land managers is leading to larger networks or more participating in climate adaptation. In theory, the benefits of climate planning networks are larger when land managers are facing more potential changes. The basic hypothesis is tested with a survey of public land managers from hundreds of local and regional public lands management units in the Southwestern United States, as well as other stakeholders involved with climate adaptation planning. All survey respondents report their perceptions of climate risk along a variety of dimensions, as well as their participation in climate adaptation planning and information sharing networks. For a subset of respondents, we have spatially explicity GIS data about their location, which will be linked with downscaled climate model data. With the focus on climate change, the analysis is a subset of the overall idea of linking social and ecological systems.

  19. A common brain network links development, aging, and vulnerability to disease.

    Science.gov (United States)

    Douaud, Gwenaëlle; Groves, Adrian R; Tamnes, Christian K; Westlye, Lars Tjelta; Duff, Eugene P; Engvig, Andreas; Walhovd, Kristine B; James, Anthony; Gass, Achim; Monsch, Andreas U; Matthews, Paul M; Fjell, Anders M; Smith, Stephen M; Johansen-Berg, Heidi

    2014-12-09

    Several theories link processes of development and aging in humans. In neuroscience, one model posits for instance that healthy age-related brain degeneration mirrors development, with the areas of the brain thought to develop later also degenerating earlier. However, intrinsic evidence for such a link between healthy aging and development in brain structure remains elusive. Here, we show that a data-driven analysis of brain structural variation across 484 healthy participants (8-85 y) reveals a largely--but not only--transmodal network whose lifespan pattern of age-related change intrinsically supports this model of mirroring development and aging. We further demonstrate that this network of brain regions, which develops relatively late during adolescence and shows accelerated degeneration in old age compared with the rest of the brain, characterizes areas of heightened vulnerability to unhealthy developmental and aging processes, as exemplified by schizophrenia and Alzheimer's disease, respectively. Specifically, this network, while derived solely from healthy subjects, spatially recapitulates the pattern of brain abnormalities observed in both schizophrenia and Alzheimer's disease. This network is further associated in our large-scale healthy population with intellectual ability and episodic memory, whose impairment contributes to key symptoms of schizophrenia and Alzheimer's disease. Taken together, our results suggest that the common spatial pattern of abnormalities observed in these two disorders, which emerge at opposite ends of the life spectrum, might be influenced by the timing of their separate and distinct pathological processes in disrupting healthy cerebral development and aging, respectively.

  20. Social Networking Sites: Guidelines For Creating New Business Opportunities Through Facebook, Twitter And LinkedIn

    Directory of Open Access Journals (Sweden)

    Rodica Maria Savulescu

    2013-05-01

    Full Text Available The world is swiftly evolving. We now face the challenge of adapting the business sector to the increasingly dynamic transformation brought about by Web 2.0 technologies and social networks in particular. The extensive use of social networking sites (SNSs such as Facebook, Twitter and LinkedIn has spawned questions regarding the possibility of using such new platforms in order to generate more business revenue.While it is demonstrated that social networking can be profitable for companies and their brands in terms of exposure, brand awareness and actual sales, it can also prove detrimental if not managed correctly. At the same time, SNSs can affect every aspects of the business environment, like product development, marketing communication or the process of recruiting. This article explores the characteristics of social media and their impact on business and proposes several guidelines for companies that decide to employ SNSs in their activity.

  1. Link reliability based hybrid routing for tactical mobile ad hoc network

    Institute of Scientific and Technical Information of China (English)

    Xie Xiaochuan; Wei Gang; Wu Keping; Wang Gang; Jia Shilou

    2008-01-01

    Tactical mobile ad hoc network (MANET) is a collection of mobile nodes forming a temporary network,without the aid of pre-established network infrastructure. The routing protocol has a crucial impact on the networkperformance in battlefields. Link reliability based hybrid routing (LRHR) is proposed, which is a novel hybrid routing protocol, for tactical MANET. Contrary to the traditional single path routing strategy, multiple paths are established between a pair of source-destination nodes. In the hybrid routing strategy, the rate of topological change provides a natural mechanism for switching dynamically between table-driven and on-demand routing. The simulation results indicate that the performances of the protocol in packet delivery ratio, routing overhead, and average end-to-end delay are better than the conventional routing protocol.

  2. High voltage direct current (HVDC) link between the power networks of Italy and Greece

    International Nuclear Information System (INIS)

    Carcano, C.; Oliva, P.; Voyatzakis, J.

    1996-01-01

    Interconnection between the power networks of Italy and Greece has long been declared of European interest. The link, which will directly connect Greece with the power network of UCPTE, is perfectly in line with the targets of the European Union in terms of trans-European power networks. The interconnection, which benefits of a financial contribution of the EU, will rely on a 400 kV d.c. transmission system with one submarine cable between the Italian and Greek coasts, overhead lines on land, d.c./a.c. conversion stations, return of current to sea via marine electrodes. The main technical features of the project are described, highlighting its most significant design concepts. (author)

  3. The Trust-Committment-Flexibility Link in Transnational Buyer-Supplier Relationships: A Network Perspective

    Directory of Open Access Journals (Sweden)

    Matevž Rašković

    2015-06-01

    Full Text Available The purpose of this paper is to analyze the manner in which trust and commitment impact relationship flexibility in a transnational buyer-supplier network context. There is an abundance of research on trust and commitment related to buyer-supplier relationships in the marketing literature; however, their link to relationship flexibility in particular has not attracted much attention within the marketing field to date. Whereas the marketing literature tends to focus on traditional performance outcomes in buyer-supplier relationships (i.e. financial performance, satisfaction, loyalty, the supply chain management literature emphasizes the importance of flexibility as fundamental characteristics of well-performing supply networks. In this paper, a novel network analysis approach is employed for the marketing literature to analyze the link between trust, commitment and relationship flexibility. The analyzed network is a two-mode, egocentric and valued network, consisting of 11 purchasing managers and 53 suppliers connected to a transnational company in the steel construction industry with headquarters in Slovenia. To analyze the impact of trust and commitment on buyer-supplier relationship flexibility, a Multiple Regression Quadratic Assignment Procedure (MRQAP approach was used. Results show that trust and commitment are not just important determinants of buyer-supplier relationship flexibility in a network context, but also how their impact on relationship flexibility changes depending on the importance of the buyer-supplier relationship. In high importance relationships trust is the overwhelming determinant of relationship flexibility, while in low importance relationships commitment is a more important determinant of relationship flexibility.

  4. Promoting Wired Links in Wireless Mesh Networks: An Efficient Engineering Solution

    Science.gov (United States)

    Barekatain, Behrang; Raahemifar, Kaamran; Ariza Quintana, Alfonso; Triviño Cabrera, Alicia

    2015-01-01

    Wireless Mesh Networks (WMNs) cannot completely guarantee good performance of traffic sources such as video streaming. To improve the network performance, this study proposes an efficient engineering solution named Wireless-to-Ethernet-Mesh-Portal-Passageway (WEMPP) that allows effective use of wired communication in WMNs. WEMPP permits transmitting data through wired and stable paths even when the destination is in the same network as the source (Intra-traffic). Tested with four popular routing protocols (Optimized Link State Routing or OLSR as a proactive protocol, Dynamic MANET On-demand or DYMO as a reactive protocol, DYMO with spanning tree ability and HWMP), WEMPP considerably decreases the end-to-end delay, jitter, contentions and interferences on nodes, even when the network size or density varies. WEMPP is also cost-effective and increases the network throughput. Moreover, in contrast to solutions proposed by previous studies, WEMPP is easily implemented by modifying the firmware of the actual Ethernet hardware without altering the routing protocols and/or the functionality of the IP/MAC/Upper layers. In fact, there is no need for modifying the functionalities of other mesh components in order to work with WEMPPs. The results of this study show that WEMPP significantly increases the performance of all routing protocols, thus leading to better video quality on nodes. PMID:25793516

  5. A systems approach identifies networks and genes linking sleep and stress: implications for neuropsychiatric disorders.

    Science.gov (United States)

    Jiang, Peng; Scarpa, Joseph R; Fitzpatrick, Karrie; Losic, Bojan; Gao, Vance D; Hao, Ke; Summa, Keith C; Yang, He S; Zhang, Bin; Allada, Ravi; Vitaterna, Martha H; Turek, Fred W; Kasarskis, Andrew

    2015-05-05

    Sleep dysfunction and stress susceptibility are comorbid complex traits that often precede and predispose patients to a variety of neuropsychiatric diseases. Here, we demonstrate multilevel organizations of genetic landscape, candidate genes, and molecular networks associated with 328 stress and sleep traits in a chronically stressed population of 338 (C57BL/6J × A/J) F2 mice. We constructed striatal gene co-expression networks, revealing functionally and cell-type-specific gene co-regulations important for stress and sleep. Using a composite ranking system, we identified network modules most relevant for 15 independent phenotypic categories, highlighting a mitochondria/synaptic module that links sleep and stress. The key network regulators of this module are overrepresented with genes implicated in neuropsychiatric diseases. Our work suggests that the interplay among sleep, stress, and neuropathology emerges from genetic influences on gene expression and their collective organization through complex molecular networks, providing a framework for interrogating the mechanisms underlying sleep, stress susceptibility, and related neuropsychiatric disorders. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

  6. Ophthalmology on social networking sites: an observational study of Facebook, Twitter, and LinkedIn

    Directory of Open Access Journals (Sweden)

    Micieli JA

    2015-02-01

    Full Text Available Jonathan A Micieli,1 Edmund Tsui2 1Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, ON, Canada; 2Department of Surgery, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA Background: The use of social media in ophthalmology remains largely unknown. Our aim was to evaluate the extent and involvement of ophthalmology journals, professional associations, trade publications, and patient advocacy and fundraising groups on social networking sites. Methods: An archived list of 107 ophthalmology journals from SCImago, trade publications, professional ophthalmology associations, and patient advocacy organizations were searched for their presence on Facebook, Twitter, and LinkedIn. Activity and popularity of each account was quantified by using the number of “likes” on Facebook, the number of followers on Twitter, and members on LinkedIn. Results: Of the 107 journals ranked by SCImago, 21.5% were present on Facebook and 18.7% were present on Twitter. Journal of Community Eye Health was the most popular on Facebook and JAMA Ophthalmology was most popular on Twitter. Among the 133 members of the International Council of Ophthalmology, 17.3% were present on Facebook, 12.8% were present on Twitter, and 7.5% were present on LinkedIn. The most popular on Facebook was the International Council of Ophthalmology, and the American Academy of Ophthalmology was most popular on Twitter and LinkedIn. Patient advocacy organizations were more popular on all sites compared with journals, professional association, and trade publications. Among the top ten most popular pages in each category, patient advocacy groups were most active followed by trade publications, professional associations, and journals. Conclusion: Patient advocacy groups lead the way in social networking followed by professional organizations and journals. Although some journals use social media, most have yet to engage its full potential and maximize the number of

  7. Multiscale Bayesian neural networks for soil water content estimation

    Science.gov (United States)

    Jana, Raghavendra B.; Mohanty, Binayak P.; Springer, Everett P.

    2008-08-01

    Artificial neural networks (ANN) have been used for some time now to estimate soil hydraulic parameters from other available or more easily measurable soil properties. However, most such uses of ANNs as pedotransfer functions (PTFs) have been at matching spatial scales (1:1) of inputs and outputs. This approach assumes that the outputs are only required at the same scale as the input data. Unfortunately, this is rarely true. Different hydrologic, hydroclimatic, and contaminant transport models require soil hydraulic parameter data at different spatial scales, depending upon their grid sizes. While conventional (deterministic) ANNs have been traditionally used in these studies, the use of Bayesian training of ANNs is a more recent development. In this paper, we develop a Bayesian framework to derive soil water retention function including its uncertainty at the point or local scale using PTFs trained with coarser-scale Soil Survey Geographic (SSURGO)-based soil data. The approach includes an ANN trained with Bayesian techniques as a PTF tool with training and validation data collected across spatial extents (scales) in two different regions in the United States. The two study areas include the Las Cruces Trench site in the Rio Grande basin of New Mexico, and the Southern Great Plains 1997 (SGP97) hydrology experimental region in Oklahoma. Each region-specific Bayesian ANN is trained using soil texture and bulk density data from the SSURGO database (scale 1:24,000), and predictions of the soil water contents at different pressure heads with point scale data (1:1) inputs are made. The resulting outputs are corrected for bias using both linear and nonlinear correction techniques. The results show good agreement between the soil water content values measured at the point scale and those predicted by the Bayesian ANN-based PTFs for both the study sites. Overall, Bayesian ANNs coupled with nonlinear bias correction are found to be very suitable tools for deriving soil

  8. A Novel Dedicated Route Protection Scheme for Survivability of Link Failure in Elastic Optical Networks

    Directory of Open Access Journals (Sweden)

    Sridhar Iyer

    2017-11-01

    Full Text Available The spectrally efficient transportation of the high bit rate(s data is achievable by the Elastic optical networks (EONs. However, in the EONs, owing to the failure occurrence even in an individual simple element, different service(s maybe interrupted. Hence, it is imperative that the schemes for survivability be developed so that the issues due to the possible failure(s can be overcome. In the current work, in view of survivability of the link failure(s in the EONs, we propose the Spectrum Continuity and Contiguity Established DRP (SCC-E-DRP algorithm which is a novel dedicated route protection (DRP scheme that attempts to avoid the problem of trap topology during its exploration for a pair of link disjoint path. Further, to evaluate the link disjoint paths, we resort to the use of the SCC Established Shortest Route (SCC-E-SR algorithm which is a modified Dijkstra’s algorithm based scheme that selects the path(s pair(s based on the end-toend SCC. We conduct extensive simulations considering realistic network topologies, and compare the performance of the SCCE-DRP scheme with the existing techniques. The obtained results show that, compared to the existing schemes, the SCC-E-DRP scheme achieves better results in terms of blocking probability.

  9. Internal measuring models in trained neural networks for parameter estimation from images

    NARCIS (Netherlands)

    Feng, Tian-Jin; Feng, T.J.; Houkes, Z.; Korsten, Maarten J.; Spreeuwers, Lieuwe Jan

    1992-01-01

    The internal representations of 'learned' knowledge in neural networks are still poorly understood, even for backpropagation networks. The paper discusses a possible interpretation of learned knowledge of a network trained for parameter estimation from images. The outputs of the hidden layer are the

  10. Using Linked Electronic Health Records to Estimate Healthcare Costs: Key Challenges and Opportunities.

    Science.gov (United States)

    Asaria, Miqdad; Grasic, Katja; Walker, Simon

    2016-02-01

    This paper discusses key challenges and opportunities that arise when using linked electronic health records (EHR) in health economics and outcomes research (HEOR), with a particular focus on estimating healthcare costs. These challenges and opportunities are framed in the context of a case study modelling the costs of stable coronary artery disease in England. The challenges and opportunities discussed fall broadly into the categories of (1) handling and organising data of this size and sensitivity; (2) extracting clinical endpoints from datasets that have not been designed and collected with such endpoints in mind; and (3) the principles and practice of costing resource use from routinely collected data. We find that there are a number of new challenges and opportunities that arise when working with EHR compared with more traditional sources of data for HEOR. These call for greater clinician involvement and intelligent use of sensitivity analysis.

  11. Distributed state estimation for multi-agent based active distribution networks

    NARCIS (Netherlands)

    Nguyen, H.P.; Kling, W.L.

    2010-01-01

    Along with the large-scale implementation of distributed generators, the current distribution networks have changed gradually from passive to active operation. State estimation plays a vital role to facilitate this transition. In this paper, a suitable state estimation method for the active network

  12. The effect of tracking network configuration on GPS baseline estimates for the CASA Uno experiment

    Science.gov (United States)

    Wolf, S. Kornreich; Dixon, T. H.; Freymueller, J. T.

    1990-01-01

    The effect of the tracking network on long (greater than 100 km) GPS baseline estimates was estimated using various subsets of the global tracking network initiated by the first Central and South America (CASA Uno) experiment. It was found that best results could be obtained with a global tacking network consisting of three U.S. stations, two sites in the southwestern Pacific, and two sites in Europe. In comparison with smaller subsets, this global network improved the baseline repeatability, the resolution of carrier phase cycle ambiguities, and formal errors of the orbit estimates.

  13. Effective network inference through multivariate information transfer estimation

    Science.gov (United States)

    Dahlqvist, Carl-Henrik; Gnabo, Jean-Yves

    2018-06-01

    Network representation has steadily gained in popularity over the past decades. In many disciplines such as finance, genetics, neuroscience or human travel to cite a few, the network may not directly be observable and needs to be inferred from time-series data, leading to the issue of separating direct interactions between two entities forming the network from indirect interactions coming through its remaining part. Drawing on recent contributions proposing strategies to deal with this problem such as the so-called "global silencing" approach of Barzel and Barabasi or "network deconvolution" of Feizi et al. (2013), we propose a novel methodology to infer an effective network structure from multivariate conditional information transfers. Its core principal is to test the information transfer between two nodes through a step-wise approach by conditioning the transfer for each pair on a specific set of relevant nodes as identified by our algorithm from the rest of the network. The methodology is model free and can be applied to high-dimensional networks with both inter-lag and intra-lag relationships. It outperforms state-of-the-art approaches for eliminating the redundancies and more generally retrieving simulated artificial networks in our Monte-Carlo experiments. We apply the method to stock market data at different frequencies (15 min, 1 h, 1 day) to retrieve the network of US largest financial institutions and then document how bank's centrality measurements relate to bank's systemic vulnerability.

  14. Estimating marine aerosol particle volume and number from Maritime Aerosol Network data

    Directory of Open Access Journals (Sweden)

    A. M. Sayer

    2012-09-01

    Full Text Available As well as spectral aerosol optical depth (AOD, aerosol composition and concentration (number, volume, or mass are of interest for a variety of applications. However, remote sensing of these quantities is more difficult than for AOD, as it is more sensitive to assumptions relating to aerosol composition. This study uses spectral AOD measured on Maritime Aerosol Network (MAN cruises, with the additional constraint of a microphysical model for unpolluted maritime aerosol based on analysis of Aerosol Robotic Network (AERONET inversions, to estimate these quantities over open ocean. When the MAN data are subset to those likely to be comprised of maritime aerosol, number and volume concentrations obtained are physically reasonable. Attempts to estimate surface concentration from columnar abundance, however, are shown to be limited by uncertainties in vertical distribution. Columnar AOD at 550 nm and aerosol number for unpolluted maritime cases are also compared with Moderate Resolution Imaging Spectroradiometer (MODIS data, for both the present Collection 5.1 and forthcoming Collection 6. MODIS provides a best-fitting retrieval solution, as well as the average for several different solutions, with different aerosol microphysical models. The "average solution" MODIS dataset agrees more closely with MAN than the "best solution" dataset. Terra tends to retrieve lower aerosol number than MAN, and Aqua higher, linked with differences in the aerosol models commonly chosen. Collection 6 AOD is likely to agree more closely with MAN over open ocean than Collection 5.1. In situations where spectral AOD is measured accurately, and aerosol microphysical properties are reasonably well-constrained, estimates of aerosol number and volume using MAN or similar data would provide for a greater variety of potential comparisons with aerosol properties derived from satellite or chemistry transport model data. However, without accurate AOD data and prior knowledge of

  15. Single Tree Vegetation Depth Estimation Tool for Satellite Services Link Design

    Directory of Open Access Journals (Sweden)

    Z. Hasirci

    2016-04-01

    Full Text Available Attenuation caused by tree shadowing is an important factor for describing the propagation channel of satellite services. Thus, vegetation effects should be determined by experimental studies or empirical formulations. In this study, tree types in the Black Sea Region of Turkey are classified based on their geometrical shapes into four groups such as conic, ellipsoid, spherical and hemispherical. The variations of the vegetation depth according to different tree shapes are calculated with ray tracing method. It is showed that different geometrical shapes have different vegetation depths even if they have same foliage volume for different elevation angles. The proposed method is validated with the related literature in terms of average single tree attenuation. On the other hand, due to decrease system requirements (speed, memory usage etc. of ray tracing method, an artificial neural network is proposed as an alternative. A graphical user interface is created for the above processes in MATLAB environment named vegetation depth estimation tool (VdET.

  16. From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks.

    KAUST Repository

    Cannistraci, C.V.

    2013-04-08

    Growth and remodelling impact the network topology of complex systems, yet a general theory explaining how new links arise between existing nodes has been lacking, and little is known about the topological properties that facilitate link-prediction. Here we investigate the extent to which the connectivity evolution of a network might be predicted by mere topological features. We show how a link/community-based strategy triggers substantial prediction improvements because it accounts for the singular topology of several real networks organised in multiple local communities - a tendency here named local-community-paradigm (LCP). We observe that LCP networks are mainly formed by weak interactions and characterise heterogeneous and dynamic systems that use self-organisation as a major adaptation strategy. These systems seem designed for global delivery of information and processing via multiple local modules. Conversely, non-LCP networks have steady architectures formed by strong interactions, and seem designed for systems in which information/energy storage is crucial.

  17. Fine-granularity inference and estimations to network traffic for SDN.

    Directory of Open Access Journals (Sweden)

    Dingde Jiang

    Full Text Available An end-to-end network traffic matrix is significantly helpful for network management and for Software Defined Networks (SDN. However, the end-to-end network traffic matrix's inferences and estimations are a challenging problem. Moreover, attaining the traffic matrix in high-speed networks for SDN is a prohibitive challenge. This paper investigates how to estimate and recover the end-to-end network traffic matrix in fine time granularity from the sampled traffic traces, which is a hard inverse problem. Different from previous methods, the fractal interpolation is used to reconstruct the finer-granularity network traffic. Then, the cubic spline interpolation method is used to obtain the smooth reconstruction values. To attain an accurate the end-to-end network traffic in fine time granularity, we perform a weighted-geometric-average process for two interpolation results that are obtained. The simulation results show that our approaches are feasible and effective.

  18. Fine-granularity inference and estimations to network traffic for SDN.

    Science.gov (United States)

    Jiang, Dingde; Huo, Liuwei; Li, Ya

    2018-01-01

    An end-to-end network traffic matrix is significantly helpful for network management and for Software Defined Networks (SDN). However, the end-to-end network traffic matrix's inferences and estimations are a challenging problem. Moreover, attaining the traffic matrix in high-speed networks for SDN is a prohibitive challenge. This paper investigates how to estimate and recover the end-to-end network traffic matrix in fine time granularity from the sampled traffic traces, which is a hard inverse problem. Different from previous methods, the fractal interpolation is used to reconstruct the finer-granularity network traffic. Then, the cubic spline interpolation method is used to obtain the smooth reconstruction values. To attain an accurate the end-to-end network traffic in fine time granularity, we perform a weighted-geometric-average process for two interpolation results that are obtained. The simulation results show that our approaches are feasible and effective.

  19. Estimating mental states of a depressed person with bayesian networks

    NARCIS (Netherlands)

    Klein, Michel C.A.; Modena, Gabriele

    2013-01-01

    In this work in progress paper we present an approach based on Bayesian Networks to model the relationship between mental states and empirical observations in a depressed person. We encode relationships and domain expertise as a Hierarchical Bayesian Network. Mental states are represented as latent

  20. Multiple Time Series Forecasting Using Quasi-Randomized Functional Link Neural Networks

    Directory of Open Access Journals (Sweden)

    Thierry Moudiki

    2018-03-01

    Full Text Available We are interested in obtaining forecasts for multiple time series, by taking into account the potential nonlinear relationships between their observations. For this purpose, we use a specific type of regression model on an augmented dataset of lagged time series. Our model is inspired by dynamic regression models (Pankratz 2012, with the response variable’s lags included as predictors, and is known as Random Vector Functional Link (RVFL neural networks. The RVFL neural networks have been successfully applied in the past, to solving regression and classification problems. The novelty of our approach is to apply an RVFL model to multivariate time series, under two separate regularization constraints on the regression parameters.

  1. Cross-Linked Liquid Crystalline Systems From Rigid Polymer Networks to Elastomers

    CERN Document Server

    Broer, Dirk

    2011-01-01

    With rapidly expanding interest in liquid crystalline polymers and elastomers among the liquid crystal community, researchers are currently exploring the wide range of possible application areas for these unique materials, including optical elements on displays, tunable lasers, strain gauges, micro-structures, and artificial muscles. Written by respected scientists from academia and industry around the world, who are not only active in the field but also well-known in more traditional areas of research, "Cross-Linked Liquid Crystalline Systems: From Rigid Polymer Networks to Elastomers&qu

  2. Escape routes, weak links, and desynchronization in fluctuation-driven networks

    DEFF Research Database (Denmark)

    Schäfer, Benjamin; Matthiae, Moritz; Zhang, Xiaozhu

    2017-01-01

    Shifting our electricity generation from fossil fuel to renewable energy sources introduces large fluctuations to the power system. Here, we demonstrate how increased fluctuations, reduced damping, and reduced intertia may undermine the dynamical robustness of power grid networks. Focusing...... on fundamental noise models, we derive analytic insights into which factors limit the dynamic robustness and how fluctuations may induce a system escape from an operating state. Moreover, we identify weak links in the grid that make it particularly vulnerable to fluctuations. These results thereby not only...

  3. Weak Links: Stabilizers of Complex Systems from Proteins to Social Networks

    Science.gov (United States)

    Csermely, Peter

    Why do women stabilize our societies? Why can we enjoy and understand Shakespeare? Why are fruitflies uniform? Why do omnivorous eating habits aid our survival? Why is Mona Lisa's smile beautiful? -- Is there any answer to these questions? This book shows that the statement: "weak links stabilize complex systems" holds the answers to all of the surprising questions above. The author (recipientof several distinguished science communication prizes) uses weak (low affinity, low probability) interactions as a thread to introduce a vast varietyof networks from proteins to ecosystems.

  4. Estimation of transfused red cell survival using an enzyme-linked antiglobulin test

    International Nuclear Information System (INIS)

    Kickler, T.S.; Smith, B.; Bell, W.; Drew, H.; Baldwin, M.; Ness, P.M.

    1985-01-01

    An enzyme-linked antiglobulin test (ELAT) method was developed to estimate survival of transfused red cells. This procedure is based on a principle analogous to that of the Ashby technique were antigenically distinct red cells are transfused and their survival studied. The authors compared the ELAT survival to the 51 Chromium method ( 51 Cr) in four patients. Three patients with hypoproliferative anemias showed T 1/2 by ELAT of 17.5, 18, and 17 days versus 18.5, 20, and 19 days by the 51 Cr method. A fourth patient with traumatic cardiac hemolysis had two studies performed. In this case, the ELAT showed a T 1/2 of 10 and 8.1 days while 51 Cr T 1/2 values were 11 and 10.5 days. The ELAT method for measuring red cell survival yielded data which agreed closely with the results of the 51 Cr method. Although 51 Cr is the accepted method for red cell survival, the ELAT method can be used to estimate transfused red cell survival

  5. MyGeneFriends: A Social Network Linking Genes, Genetic Diseases, and Researchers.

    Science.gov (United States)

    Allot, Alexis; Chennen, Kirsley; Nevers, Yannis; Poidevin, Laetitia; Kress, Arnaud; Ripp, Raymond; Thompson, Julie Dawn; Poch, Olivier; Lecompte, Odile

    2017-06-16

    The constant and massive increase of biological data offers unprecedented opportunities to decipher the function and evolution of genes and their roles in human diseases. However, the multiplicity of sources and flow of data mean that efficient access to useful information and knowledge production has become a major challenge. This challenge can be addressed by taking inspiration from Web 2.0 and particularly social networks, which are at the forefront of big data exploration and human-data interaction. MyGeneFriends is a Web platform inspired by social networks, devoted to genetic disease analysis, and organized around three types of proactive agents: genes, humans, and genetic diseases. The aim of this study was to improve exploration and exploitation of biological, postgenomic era big data. MyGeneFriends leverages conventions popularized by top social networks (Facebook, LinkedIn, etc), such as networks of friends, profile pages, friendship recommendations, affinity scores, news feeds, content recommendation, and data visualization. MyGeneFriends provides simple and intuitive interactions with data through evaluation and visualization of connections (friendships) between genes, humans, and diseases. The platform suggests new friends and publications and allows agents to follow the activity of their friends. It dynamically personalizes information depending on the user's specific interests and provides an efficient way to share information with collaborators. Furthermore, the user's behavior itself generates new information that constitutes an added value integrated in the network, which can be used to discover new connections between biological agents. We have developed MyGeneFriends, a Web platform leveraging conventions from popular social networks to redefine the relationship between humans and biological big data and improve human processing of biomedical data. MyGeneFriends is available at lbgi.fr/mygenefriends. ©Alexis Allot, Kirsley Chennen, Yannis

  6. Ophthalmology on social networking sites: an observational study of Facebook, Twitter, and LinkedIn.

    Science.gov (United States)

    Micieli, Jonathan A; Tsui, Edmund

    2015-01-01

    The use of social media in ophthalmology remains largely unknown. Our aim was to evaluate the extent and involvement of ophthalmology journals, professional associations, trade publications, and patient advocacy and fundraising groups on social networking sites. An archived list of 107 ophthalmology journals from SCImago, trade publications, professional ophthalmology associations, and patient advocacy organizations were searched for their presence on Facebook, Twitter, and LinkedIn. Activity and popularity of each account was quantified by using the number of "likes" on Facebook, the number of followers on Twitter, and members on LinkedIn. Of the 107 journals ranked by SCImago, 21.5% were present on Facebook and 18.7% were present on Twitter. Journal of Community Eye Health was the most popular on Facebook and JAMA Ophthalmology was most popular on Twitter. Among the 133 members of the International Council of Ophthalmology, 17.3% were present on Facebook, 12.8% were present on Twitter, and 7.5% were present on LinkedIn. The most popular on Facebook was the International Council of Ophthalmology, and the American Academy of Ophthalmology was most popular on Twitter and LinkedIn. Patient advocacy organizations were more popular on all sites compared with journals, professional association, and trade publications. Among the top ten most popular pages in each category, patient advocacy groups were most active followed by trade publications, professional associations, and journals. Patient advocacy groups lead the way in social networking followed by professional organizations and journals. Although some journals use social media, most have yet to engage its full potential and maximize the number of potential interested individuals.

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

    OpenAIRE

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

    2015-01-01

    An output-feedback control strategy for pollution mitigation in combined sewer networks is presented. The proposed strategy provides means to apply model-based predictive control to large-scale sewer networks, in-spite of the lack of measurements at most of the network sewers. In previous works, the authors presented a hybrid linear control-oriented model for sewer networks together with the formulation of Optimal Control Problems (OCP) and State Estimation Problems (SEP). By iteratively solv...

  8. Hermite Functional Link Neural Network for Solving the Van der Pol-Duffing Oscillator Equation.

    Science.gov (United States)

    Mall, Susmita; Chakraverty, S

    2016-08-01

    Hermite polynomial-based functional link artificial neural network (FLANN) is proposed here to solve the Van der Pol-Duffing oscillator equation. A single-layer hermite neural network (HeNN) model is used, where a hidden layer is replaced by expansion block of input pattern using Hermite orthogonal polynomials. A feedforward neural network model with the unsupervised error backpropagation principle is used for modifying the network parameters and minimizing the computed error function. The Van der Pol-Duffing and Duffing oscillator equations may not be solved exactly. Here, approximate solutions of these types of equations have been obtained by applying the HeNN model for the first time. Three mathematical example problems and two real-life application problems of Van der Pol-Duffing oscillator equation, extracting the features of early mechanical failure signal and weak signal detection problems, are solved using the proposed HeNN method. HeNN approximate solutions have been compared with results obtained by the well known Runge-Kutta method. Computed results are depicted in term of graphs. After training the HeNN model, we may use it as a black box to get numerical results at any arbitrary point in the domain. Thus, the proposed HeNN method is efficient. The results reveal that this method is reliable and can be applied to other nonlinear problems too.

  9. Linking Supply Chain Network Complexity to Interdependence and Risk-Assessment: Scale Development and Empirical Investigation

    Directory of Open Access Journals (Sweden)

    Samyadip Chakraborty

    2015-12-01

    Full Text Available Concepts like supply chain network complexity, interdependence and risk assessment have been prominently discussed directly and indirectly in management literature over past decades and plenty of frameworks and conceptual prescriptive research works have been published contributing towards building the body of knowledge. However previous studies often lacked quantification of the findings. Consequently, the need for suitable scales becomes prominent for measuring those constructs to empirically support the conceptualized relationships. This paper expands the understanding of supply chain network complexity (SCNC and also highlights its implications on interdependence (ID between the actors and risk assessment (RAS in transaction relationships. In doing so, SCNC and RAS are operationalized to understand how SCNC affects interdependence and risk assessment between the actors in the supply chain network. The contribution of this study lies in developing and validating multi-item scales for these constructs and empirically establishing the hypothesized relationships in the Indian context based on firm data collected using survey–based questionnaire. The methodology followed included structural equation modeling. The study findings indicate that SCNC had significant relationship with interdependence, which in turn significantly affected risk assessment. This study carries both academic and managerial implications and provides an empirically supported framework linking network complexity with the two key variables (ID and RAS, playing crucial roles in managerial decision making. This study contributes to the body of knowledge and aims at guiding managers in better understanding transaction relationships.

  10. Listening to Whispers of Ripple: Linking Wallets and Deanonymizing Transactions in the Ripple Network

    Directory of Open Access Journals (Sweden)

    Moreno-Sanchez Pedro

    2016-10-01

    Full Text Available The decentralized I owe you (IOU transaction network Ripple is gaining prominence as a fast, low-cost and efficient method for performing same and cross-currency payments. Ripple keeps track of IOU credit its users have granted to their business partners or friends, and settles transactions between two connected Ripple wallets by appropriately changing credit values on the connecting paths. Similar to cryptocurrencies such as Bitcoin, while the ownership of the wallets is implicitly pseudonymous in Ripple, IOU credit links and transaction flows between wallets are publicly available in an online ledger. In this paper, we present the first thorough study that analyzes this globally visible log and characterizes the privacy issues with the current Ripple network. In particular, we define two novel heuristics and perform heuristic clustering to group wallets based on observations on the Ripple network graph. We then propose reidentification mechanisms to deanonymize the operators of those clusters and show how to reconstruct the financial activities of deanonymized Ripple wallets. Our analysis motivates the need for better privacy-preserving payment mechanisms for Ripple and characterizes the privacy challenges faced by the emerging credit networks.

  11. MSD-MAP: A Network-Based Systems Biology Platform for Predicting Disease-Metabolite Links.

    Science.gov (United States)

    Wathieu, Henri; Issa, Naiem T; Mohandoss, Manisha; Byers, Stephen W; Dakshanamurthy, Sivanesan

    2017-01-01

    Cancer-associated metabolites result from cell-wide mechanisms of dysregulation. The field of metabolomics has sought to identify these aberrant metabolites as disease biomarkers, clues to understanding disease mechanisms, or even as therapeutic agents. This study was undertaken to reliably predict metabolites associated with colorectal, esophageal, and prostate cancers. Metabolite and disease biological action networks were compared in a computational platform called MSD-MAP (Multi Scale Disease-Metabolite Association Platform). Using differential gene expression analysis with patient-based RNAseq data from The Cancer Genome Atlas, genes up- or down-regulated in cancer compared to normal tissue were identified. Relational databases were used to map biological entities including pathways, functions, and interacting proteins, to those differential disease genes. Similar relational maps were built for metabolites, stemming from known and in silico predicted metabolite-protein associations. The hypergeometric test was used to find statistically significant relationships between disease and metabolite biological signatures at each tier, and metabolites were assessed for multi-scale association with each cancer. Metabolite networks were also directly associated with various other diseases using a disease functional perturbation database. Our platform recapitulated metabolite-disease links that have been empirically verified in the scientific literature, with network-based mapping of jointly-associated biological activity also matching known disease mechanisms. This was true for colorectal, esophageal, and prostate cancers, using metabolite action networks stemming from both predicted and known functional protein associations. By employing systems biology concepts, MSD-MAP reliably predicted known cancermetabolite links, and may serve as a predictive tool to streamline conventional metabolomic profiling methodologies. Copyright© Bentham Science Publishers; For any

  12. Robust nonlinear autoregressive moving average model parameter estimation using stochastic recurrent artificial neural networks

    DEFF Research Database (Denmark)

    Chon, K H; Hoyer, D; Armoundas, A A

    1999-01-01

    In this study, we introduce a new approach for estimating linear and nonlinear stochastic autoregressive moving average (ARMA) model parameters, given a corrupt signal, using artificial recurrent neural networks. This new approach is a two-step approach in which the parameters of the deterministic...... part of the stochastic ARMA model are first estimated via a three-layer artificial neural network (deterministic estimation step) and then reestimated using the prediction error as one of the inputs to the artificial neural networks in an iterative algorithm (stochastic estimation step). The prediction...... error is obtained by subtracting the corrupt signal of the estimated ARMA model obtained via the deterministic estimation step from the system output response. We present computer simulation examples to show the efficacy of the proposed stochastic recurrent neural network approach in obtaining accurate...

  13. Energy flow models for the estimation of technical losses in distribution network

    International Nuclear Information System (INIS)

    Au, Mau Teng; Tan, Chin Hooi

    2013-01-01

    This paper presents energy flow models developed to estimate technical losses in distribution network. Energy flow models applied in this paper is based on input energy and peak demand of distribution network, feeder length and peak demand, transformer loading capacity, and load factor. Two case studies, an urban distribution network and a rural distribution network are used to illustrate application of the energy flow models. Results on technical losses obtained for the two distribution networks are consistent and comparable to network of similar types and characteristics. Hence, the energy flow models are suitable for practical application.

  14. Usage of link-level performance indicators for HSDPA network-level simulations in E-UMTS

    NARCIS (Netherlands)

    Brouwer, Frank; de Bruin, I.C.C.; Silva, João Carlos; Souto, Nuno; Cercas, Francisco; Correia, Américo

    2004-01-01

    The paper describes integration of HSDPA (high-speed downlink packet access) link-level simulation results into network-level simulations for enhanced UMTS. The link-level simulations model all physical layer features depicted in the 3GPP standards. These include: generation of transport blocks;

  15. Improving the Network Scale-Up Estimator: Incorporating Means of Sums, Recursive Back Estimation, and Sampling Weights.

    Directory of Open Access Journals (Sweden)

    Patrick Habecker

    Full Text Available Researchers interested in studying populations that are difficult to reach through traditional survey methods can now draw on a range of methods to access these populations. Yet many of these methods are more expensive and difficult to implement than studies using conventional sampling frames and trusted sampling methods. The network scale-up method (NSUM provides a middle ground for researchers who wish to estimate the size of a hidden population, but lack the resources to conduct a more specialized hidden population study. Through this method it is possible to generate population estimates for a wide variety of groups that are perhaps unwilling to self-identify as such (for example, users of illegal drugs or other stigmatized populations via traditional survey tools such as telephone or mail surveys--by asking a representative sample to estimate the number of people they know who are members of such a "hidden" subpopulation. The original estimator is formulated to minimize the weight a single scaling variable can exert upon the estimates. We argue that this introduces hidden and difficult to predict biases, and instead propose a series of methodological advances on the traditional scale-up estimation procedure, including a new estimator. Additionally, we formalize the incorporation of sample weights into the network scale-up estimation process, and propose a recursive process of back estimation "trimming" to identify and remove poorly performing predictors from the estimation process. To demonstrate these suggestions we use data from a network scale-up mail survey conducted in Nebraska during 2014. We find that using the new estimator and recursive trimming process provides more accurate estimates, especially when used in conjunction with sampling weights.

  16. Passivity-based control and estimation in networked robotics

    CERN Document Server

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

    2015-01-01

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

  17. Probability Grid: A Location Estimation Scheme for Wireless Sensor Networks

    National Research Council Canada - National Science Library

    Stoleru, Radu; Stankovic, John A

    2004-01-01

    Location information is of paramount importance for Wireless Sensor Networks (WSN). The accuracy of collected data can significantly be affected by an imprecise positioning of the event of interest...

  18. Artificial neural network approach for estimation of surface specific ...

    Indian Academy of Sciences (India)

    R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22

    Meteorology and Oceanography Group, Space Applications Centre (ISRO), Ahmedabad 380 015, India. Microwave .... Total number of Qa observations in the sample-I dataset. techniques ... class of networks consists of multiple layers of com-.

  19. Estimation of parameter sensitivities for stochastic reaction networks

    KAUST Repository

    Gupta, Ankit

    2016-01-01

    Quantification of the effects of parameter uncertainty is an important and challenging problem in Systems Biology. We consider this problem in the context of stochastic models of biochemical reaction networks where the dynamics is described as a

  20. Models and synchronization of time-delayed complex dynamical networks with multi-links based on adaptive control

    International Nuclear Information System (INIS)

    Peng Haipeng; Wei Nan; Li Lixiang; Xie Weisheng; Yang Yixian

    2010-01-01

    In this Letter, time-delay has been introduced in to split the networks, upon which a model of complex dynamical networks with multi-links has been constructed. Moreover, based on Lyapunov stability theory and some hypotheses, we achieve synchronization between two complex networks with different structures by designing effective controllers. The validity of the results was proved through numerical simulations of this Letter.

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

    Science.gov (United States)

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

    2015-03-17

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

  2. An automated method for estimating reliability of grid systems using Bayesian networks

    International Nuclear Information System (INIS)

    Doguc, Ozge; Emmanuel Ramirez-Marquez, Jose

    2012-01-01

    Grid computing has become relevant due to its applications to large-scale resource sharing, wide-area information transfer, and multi-institutional collaborating. In general, in grid computing a service requests the use of a set of resources, available in a grid, to complete certain tasks. Although analysis tools and techniques for these types of systems have been studied, grid reliability analysis is generally computation-intensive to obtain due to the complexity of the system. Moreover, conventional reliability models have some common assumptions that cannot be applied to the grid systems. Therefore, new analytical methods are needed for effective and accurate assessment of grid reliability. This study presents a new method for estimating grid service reliability, which does not require prior knowledge about the grid system structure unlike the previous studies. Moreover, the proposed method does not rely on any assumptions about the link and node failure rates. This approach is based on a data-mining algorithm, the K2, to discover the grid system structure from raw historical system data, that allows to find minimum resource spanning trees (MRST) within the grid then, uses Bayesian networks (BN) to model the MRST and estimate grid service reliability.

  3. Dynamic behavior of dual cross-linked nanoparticle networks under oscillatory shear

    International Nuclear Information System (INIS)

    Iyer, Balaji V S; Yashin, Victor V; Balazs, Anna C

    2014-01-01

    Via computer simulations, we investigate the linear and nonlinear viscoelastic response of polymer grafted nanoparticle networks subject to oscillatory shear at different amplitudes and frequencies. The individual nanoparticles are composed of a rigid spherical core and a corona of grafted polymers that encompass reactive end groups. With the overlap of the coronas on adjacent particles, the reactive end groups form permanent or labile bonds, and thus form a ‘dual cross-linked’ network. The existing labile bonds between particles can break and reform depending on the bond rupture rate, extent of deformation and the frequency of oscillation. We study how the viscoelastic behavior of the material depends on the energy of the labile bonds and identify the network characteristics that give rise to the observed viscoelastic response. We observe that with an increase in labile bond energy, the storage modulus increases while the loss modulus shows a more complex response depending on the labile bond energy. Specifically, in the case of the samples with the weaker labile bonds, the loss modulus increases monotonically, while for the samples with the stronger labile bonds, the loss modulus exhibits a minimum with an increase in frequency. We show that an increase in the storage modulus corresponds to an enhancement in the average number of bonds in the samples and the characteristics of the loss modulus depend on both the bond kinetics and the mobility of the particles in the network. Furthermore, we determine that the effective contribution of the bonds to the storage modulus decreases with increase in strain amplitude. In particular, while bond formation at small amplitude drives an increase in storage modulus, at large amplitudes it promotes clustering and formation of voids leading to strain softening. Our simulations provide a mesoscopic picture of how the nature of labile bonds affects the performance of cross-linked polymer-grafted nanoparticle networks. (paper)

  4. Transnational city carbon footprint networks – Exploring carbon links between Australian and Chinese cities

    International Nuclear Information System (INIS)

    Chen, Guangwu; Wiedmann, Thomas; Wang, Yafei; Hadjikakou, Michalis

    2016-01-01

    Highlights: • A trans-national, multi-region input-output analysis for cities is presented. • We examine the carbon footprint network of ten cities. • The balance of emissions embodied in trade discloses a hierarchy of responsibility. • We model how emissions reductions spread through the city carbon networks. • Implications on the Chinese and Australian carbon trading schemes are discussed. - Abstract: Cities are leading actions against climate change through global networks. More than 360 global cities announced during the 2015 Paris Climate Conference that the collective impact of their commitments will deliver over half of the world’s urban greenhouse gas emissions reductions by 2020. Previous studies on multi-city carbon footprint networks using sub-national, multi-region input-output (MRIO) modelling have identified additional opportunities for addressing the negative impacts of climate change through joint actions between cities within a country. However, similar links between city carbon footprints have not yet been studied across countries. In this study we focus on inter-city and inter-country carbon flows between two trading partners in a first attempt to address this gap. We construct a multi-scale, global MRIO model to describe a transnational city carbon footprint network among five Chinese megacities and the five largest Australian capital cities. First, we quantify city carbon footprints by sectors and regions. Based on the carbon map concept we show how local emissions reductions influence other regions’ carbon footprints. We then present a city emissions ’outsourcing hierarchy’ based on the balance of emissions embodied in intercity and international trade. The differences between cities and their position in the hierarchy emphasize the need for a bespoke treatment of their responsibilities towards climate change mitigation. Finally, we evaluate and discuss the potentially significant benefits of harmonising and aligning China

  5. CUFID-query: accurate network querying through random walk based network flow estimation.

    Science.gov (United States)

    Jeong, Hyundoo; Qian, Xiaoning; Yoon, Byung-Jun

    2017-12-28

    Functional modules in biological networks consist of numerous biomolecules and their complicated interactions. Recent studies have shown that biomolecules in a functional module tend to have similar interaction patterns and that such modules are often conserved across biological networks of different species. As a result, such conserved functional modules can be identified through comparative analysis of biological networks. In this work, we propose a novel network querying algorithm based on the CUFID (Comparative network analysis Using the steady-state network Flow to IDentify orthologous proteins) framework combined with an efficient seed-and-extension approach. The proposed algorithm, CUFID-query, can accurately detect conserved functional modules as small subnetworks in the target network that are expected to perform similar functions to the given query functional module. The CUFID framework was recently developed for probabilistic pairwise global comparison of biological networks, and it has been applied to pairwise global network alignment, where the framework was shown to yield accurate network alignment results. In the proposed CUFID-query algorithm, we adopt the CUFID framework and extend it for local network alignment, specifically to solve network querying problems. First, in the seed selection phase, the proposed method utilizes the CUFID framework to compare the query and the target networks and to predict the probabilistic node-to-node correspondence between the networks. Next, the algorithm selects and greedily extends the seed in the target network by iteratively adding nodes that have frequent interactions with other nodes in the seed network, in a way that the conductance of the extended network is maximally reduced. Finally, CUFID-query removes irrelevant nodes from the querying results based on the personalized PageRank vector for the induced network that includes the fully extended network and its neighboring nodes. Through extensive

  6. Constraint Network Analysis (CNA): a Python software package for efficiently linking biomacromolecular structure, flexibility, (thermo-)stability, and function.

    Science.gov (United States)

    Pfleger, Christopher; Rathi, Prakash Chandra; Klein, Doris L; Radestock, Sebastian; Gohlke, Holger

    2013-04-22

    For deriving maximal advantage from information on biomacromolecular flexibility and rigidity, results from rigidity analyses must be linked to biologically relevant characteristics of a structure. Here, we describe the Python-based software package Constraint Network Analysis (CNA) developed for this task. CNA functions as a front- and backend to the graph-based rigidity analysis software FIRST. CNA goes beyond the mere identification of flexible and rigid regions in a biomacromolecule in that it (I) provides a refined modeling of thermal unfolding simulations that also considers the temperature-dependence of hydrophobic tethers, (II) allows performing rigidity analyses on ensembles of network topologies, either generated from structural ensembles or by using the concept of fuzzy noncovalent constraints, and (III) computes a set of global and local indices for quantifying biomacromolecular stability. This leads to more robust results from rigidity analyses and extends the application domain of rigidity analyses in that phase transition points ("melting points") and unfolding nuclei ("structural weak spots") are determined automatically. Furthermore, CNA robustly handles small-molecule ligands in general. Such advancements are important for applying rigidity analysis to data-driven protein engineering and for estimating the influence of ligand molecules on biomacromolecular stability. CNA maintains the efficiency of FIRST such that the analysis of a single protein structure takes a few seconds for systems of several hundred residues on a single core. These features make CNA an interesting tool for linking biomacromolecular structure, flexibility, (thermo-)stability, and function. CNA is available from http://cpclab.uni-duesseldorf.de/software for nonprofit organizations.

  7. Linking plant specialization to dependence in interactions for seed set in pollination networks.

    Science.gov (United States)

    Tur, Cristina; Castro-Urgal, Rocío; Traveset, Anna

    2013-01-01

    Studies on pollination networks have provided valuable information on the number, frequency, distribution and identity of interactions between plants and pollinators. However, little is still known on the functional effect of these interactions on plant reproductive success. Information on the extent to which plants depend on such interactions will help to make more realistic predictions of the potential impacts of disturbances on plant-pollinator networks. Plant functional dependence on pollinators (all interactions pooled) can be estimated by comparing seed set with and without pollinators (i.e. bagging flowers to exclude them). Our main goal in this study was thus to determine whether plant dependence on current insect interactions is related to plant specialization in a pollination network. We studied two networks from different communities, one in a coastal dune and one in a mountain. For ca. 30% of plant species in each community, we obtained the following specialization measures: (i) linkage level (number of interactions), (ii) diversity of interactions, and (iii) closeness centrality (a measure of how much a species is connected to other plants via shared pollinators). Phylogenetically controlled regression analyses revealed that, for the largest and most diverse coastal community, plants highly dependent on pollinators were the most generalists showing the highest number and diversity of interactions as well as occupying central positions in the network. The mountain community, by contrast, did not show such functional relationship, what might be attributable to their lower flower-resource heterogeneity and diversity of interactions. We conclude that plants with a wide array of pollinator interactions tend to be those that are more strongly dependent upon them for seed production and thus might be those more functionally vulnerable to the loss of network interaction, although these outcomes might be context-dependent.

  8. Linking plant specialization to dependence in interactions for seed set in pollination networks.

    Directory of Open Access Journals (Sweden)

    Cristina Tur

    Full Text Available Studies on pollination networks have provided valuable information on the number, frequency, distribution and identity of interactions between plants and pollinators. However, little is still known on the functional effect of these interactions on plant reproductive success. Information on the extent to which plants depend on such interactions will help to make more realistic predictions of the potential impacts of disturbances on plant-pollinator networks. Plant functional dependence on pollinators (all interactions pooled can be estimated by comparing seed set with and without pollinators (i.e. bagging flowers to exclude them. Our main goal in this study was thus to determine whether plant dependence on current insect interactions is related to plant specialization in a pollination network. We studied two networks from different communities, one in a coastal dune and one in a mountain. For ca. 30% of plant species in each community, we obtained the following specialization measures: (i linkage level (number of interactions, (ii diversity of interactions, and (iii closeness centrality (a measure of how much a species is connected to other plants via shared pollinators. Phylogenetically controlled regression analyses revealed that, for the largest and most diverse coastal community, plants highly dependent on pollinators were the most generalists showing the highest number and diversity of interactions as well as occupying central positions in the network. The mountain community, by contrast, did not show such functional relationship, what might be attributable to their lower flower-resource heterogeneity and diversity of interactions. We conclude that plants with a wide array of pollinator interactions tend to be those that are more strongly dependent upon them for seed production and thus might be those more functionally vulnerable to the loss of network interaction, although these outcomes might be context-dependent.

  9. Charge transport in nanoscale "all-inorganic" networks of semiconductor nanorods linked by metal domains.

    Science.gov (United States)

    Lavieville, Romain; Zhang, Yang; Casu, Alberto; Genovese, Alessandro; Manna, Liberato; Di Fabrizio, Enzo; Krahne, Roman

    2012-04-24

    Charge transport across metal-semiconductor interfaces at the nanoscale is a crucial issue in nanoelectronics. Chains of semiconductor nanorods linked by Au particles represent an ideal model system in this respect, because the metal-semiconductor interface is an intrinsic feature of the nanosystem and does not manifest solely as the contact to the macroscopic external electrodes. Here we investigate charge transport mechanisms in all-inorganic hybrid metal-semiconductor networks fabricated via self-assembly in solution, in which CdSe nanorods were linked to each other by Au nanoparticles. Thermal annealing of our devices changed the morphology of the networks and resulted in the removal of small Au domains that were present on the lateral nanorod facets, and in ripening of the Au nanoparticles in the nanorod junctions with more homogeneous metal-semiconductor interfaces. In such thermally annealed devices the voltage dependence of the current at room temperature can be well described by a Schottky barrier lowering at a metal semiconductor contact under reverse bias, if the spherical shape of the gold nanoparticles is considered. In this case the natural logarithm of the current does not follow the square-root dependence of the voltage as in the bulk, but that of V(2/3). From our fitting with this model we extract the effective permittivity that agrees well with theoretical predictions for the permittivity near the surface of CdSe nanorods. Furthermore, the annealing improved the network conductance at cryogenic temperatures, which could be related to the reduction of the number of trap states.

  10. Equipment Management for Sensor Networks: Linking Physical Infrastructure and Actions to Observational Data

    Science.gov (United States)

    Jones, A. S.; Horsburgh, J. S.; Matos, M.; Caraballo, J.

    2015-12-01

    Networks conducting long term monitoring using in situ sensors need the functionality to track physical equipment as well as deployments, calibrations, and other actions related to site and equipment maintenance. The observational data being generated by sensors are enhanced if direct linkages to equipment details and actions can be made. This type of information is typically recorded in field notebooks or in static files, which are rarely linked to observations in a way that could be used to interpret results. However, the record of field activities is often relevant to analysis or post-processing of the observational data. We have developed an underlying database schema and deployed a web interface for recording and retrieving information on physical infrastructure and related actions for observational networks. The database schema for equipment was designed as an extension to the Observations Data Model 2 (ODM2), a community-developed information model for spatially discrete, feature based earth observations. The core entities of ODM2 describe location, observed variable, and timing of observations, and the equipment extension contains entities to provide additional metadata specific to the inventory of physical infrastructure and associated actions. The schema is implemented in a relational database system for storage and management with an associated web interface. We designed the web-based tools for technicians to enter and query information on the physical equipment and actions such as site visits, equipment deployments, maintenance, and calibrations. These tools were implemented for the iUTAH (innovative Urban Transitions and Aridregion Hydrosustainability) ecohydrologic observatory, and we anticipate that they will be useful for similar large-scale monitoring networks desiring to link observing infrastructure to observational data to increase the quality of sensor-based data products.

  11. A Joint Link and Channel Assignment Routing Scheme for Cognitive Radio Networks

    Directory of Open Access Journals (Sweden)

    H.S.Zhao

    2013-12-01

    Full Text Available Cognitive radio (CR is a promising technology that enables opportunistic utilization of the temporarily vacant spectrum to mitigate the spectrum scarcity in wireless communications. Since secondary users (SUs should vacate the channel in use immediately after detecting the reappearances of primary users (PUs in cognitive radio networks (CRNs, the route reliability is a distinctive challenge for routing in CRNs. Furthermore, the throughput requirement of an SU session should be satisfied and it is always preferable to select a route with less negative influence on other current or latish sessions. To account for the route reliability challenge, we study the joint link and channel assignment routing problem for CRNs. It is formulated in a form of integer nonlinear programming (INLP, which is NP-hard, with the objective of minimizing the interference of a new route to other routes while providing route reliability and throughput guarantee. An on-demand route discovery algorithm is proposed to find reliable candidate paths, while a joint link and channel assignment routing algorithm with sequentially-connected-link coordination is proposed to choose the near-optimal route for improving the route reliability and minimizing negative influence. Simulation results demonstrate that the proposed algorithm achieves considerable improvement over existing schemes in both route reliability and throughput.

  12. Linking in with LinkedIn[R]: Three Exercises That Enhance Professional Social Networking and Career Building

    Science.gov (United States)

    Gerard, Joseph G.

    2012-01-01

    Getting students to network with one another can be one of the biggest challenges in college courses, despite being a highly important function of higher education. Networking can, in fact, lead to that first job or to professional advancement, and technology can improve the success of individual and institutional efforts. This article describes…

  13. Deep convolutional neural networks for estimating porous material parameters with ultrasound tomography

    Science.gov (United States)

    Lähivaara, Timo; Kärkkäinen, Leo; Huttunen, Janne M. J.; Hesthaven, Jan S.

    2018-02-01

    We study the feasibility of data based machine learning applied to ultrasound tomography to estimate water-saturated porous material parameters. In this work, the data to train the neural networks is simulated by solving wave propagation in coupled poroviscoelastic-viscoelastic-acoustic media. As the forward model, we consider a high-order discontinuous Galerkin method while deep convolutional neural networks are used to solve the parameter estimation problem. In the numerical experiment, we estimate the material porosity and tortuosity while the remaining parameters which are of less interest are successfully marginalized in the neural networks-based inversion. Computational examples confirms the feasibility and accuracy of this approach.

  14. Estimation of Heavy Metals Contamination in the Soil of Zaafaraniya City Using the Neural Network

    Science.gov (United States)

    Ghazi, Farah F.

    2018-05-01

    The aim of this paper is to estimate the heavy metals Contamination in soils which can be used to determine the rate of environmental contamination by using new technique depend on design feedback neural network as an alternative accurate technique. The network simulates to estimate the concentration of Cadmium (Cd), Nickel (Ni), Lead (Pb), Zinc (Zn) and Copper (Cu). Then to show the accuracy and efficiency of suggested design we applied the technique in Al- Zafaraniyah in Baghdad city. The results of this paper show that the suggested networks can be successfully applied to the rapid and accuracy estimation of concentration of heavy metals.

  15. Automated and comprehensive link engineering supporting branched, ring, and mesh network topologies

    Science.gov (United States)

    Farina, J.; Khomchenko, D.; Yevseyenko, D.; Meester, J.; Richter, A.

    2016-02-01

    Link design, while relatively easy in the past, can become quite cumbersome with complex channel plans and equipment configurations. The task of designing optical transport systems and selecting equipment is often performed by an applications or sales engineer using simple tools, such as custom Excel spreadsheets. Eventually, every individual has their own version of the spreadsheet as well as their own methodology for building the network. This approach becomes unmanageable very quickly and leads to mistakes, bending of the engineering rules and installations that do not perform as expected. We demonstrate a comprehensive planning environment, which offers an efficient approach to unify, control and expedite the design process by controlling libraries of equipment and engineering methodologies, automating the process and providing the analysis tools necessary to predict system performance throughout the system and for all channels. In addition to the placement of EDFAs and DCEs, performance analysis metrics are provided at every step of the way. Metrics that can be tracked include power, CD and OSNR, SPM, XPM, FWM and SBS. Automated routine steps assist in design aspects such as equalization, padding and gain setting for EDFAs, the placement of ROADMs and transceivers, and creating regeneration points. DWDM networks consisting of a large number of nodes and repeater huts, interconnected in linear, branched, mesh and ring network topologies, can be designed much faster when compared with conventional design methods. Using flexible templates for all major optical components, our technology-agnostic planning approach supports the constant advances in optical communications.

  16. Eliminating bias in rainfall estimates from microwave links due to antenna wetting

    Science.gov (United States)

    Fencl, Martin; Rieckermann, Jörg; Bareš, Vojtěch

    2014-05-01

    Commercial microwave links (MWLs) are point-to-point radio systems which are widely used in telecommunication systems. They operate at frequencies where the transmitted power is mainly disturbed by precipitation. Thus, signal attenuation from MWLs can be used to estimate path-averaged rain rates, which is conceptually very promising, since MWLs cover about 20 % of surface area. Unfortunately, MWL rainfall estimates are often positively biased due to additional attenuation caused by antenna wetting. To correct MWL observations a posteriori to reduce the wet antenna effect (WAE), both empirically and physically based models have been suggested. However, it is challenging to calibrate these models, because the wet antenna attenuation depends both on the MWL properties (frequency, type of antennas, shielding etc.) and different climatic factors (temperature, due point, wind velocity and direction, etc.). Instead, it seems straight forward to keep antennas dry by shielding them. In this investigation we compare the effectiveness of antenna shielding to model-based corrections to reduce the WAE. The experimental setup, located in Dübendorf-Switzerland, consisted of 1.85-km long commercial dual-polarization microwave link at 38 GHz and 5 optical disdrometers. The MWL was operated without shielding in the period from March to October 2011 and with shielding from October 2011 to July 2012. This unique experimental design made it possible to identify the attenuation due to antenna wetting, which can be computed as the difference between the measured and theoretical attenuation. The theoretical path-averaged attenuation was calculated from the path-averaged drop size distribution. During the unshielded periods, the total bias caused by WAE was 0.74 dB, which was reduced by shielding to 0.39 dB for the horizontal polarization (vertical: reduction from 0.96 dB to 0.44 dB). Interestingly, the model-based correction (Schleiss et al. 2013) was more effective because it reduced

  17. Rate-based congestion control in networks with smart links, revision. B.S. Thesis - May 1988

    Science.gov (United States)

    Heybey, Andrew Tyrrell

    1990-01-01

    The author uses a network simulator to explore rate-based congestion control in networks with smart links that can feed back information to tell senders to adjust their transmission rates. This method differs in a very important way from congestion control in which a congested network component just drops packets - the most commonly used method. It is clearly advantageous for the links in the network to communicate with the end users about the network capacity, rather than the users unilaterally picking a transmission rate. The components in the middle of the network, not the end users, have information about the capacity and traffic in the network. The author experiments with three different algorithms for calculating the control rate to feed back to the users. All of the algorithms exhibit problems in the form of large queues when simulated with a configuration modeling the dynamics of a packet-voice system. However, the problems are not with the algorithms themselves, but with the fact that feedback takes time. If the network steady-state utilization is low enough that it can absorb transients in the traffic through it, then the large queues disappear. If the users are modified to start sending slowly, to allow the network to adapt to a new flow without causing congestion, a greater portion of the network's bandwidth can be used.

  18. Dependency links can hinder the evolution of cooperation in the prisoner's dilemma game on lattices and networks.

    Directory of Open Access Journals (Sweden)

    Xuwen Wang

    Full Text Available Networks with dependency links are more vulnerable when facing the attacks. Recent research also has demonstrated that the interdependent groups support the spreading of cooperation. We study the prisoner's dilemma games on spatial networks with dependency links, in which a fraction of individual pairs is selected to depend on each other. The dependency individuals can gain an extra payoff whose value is between the payoff of mutual cooperation and the value of temptation to defect. Thus, this mechanism reflects that the dependency relation is stronger than the relation of ordinary mutual cooperation, but it is not large enough to cause the defection of the dependency pair. We show that the dependence of individuals hinders, promotes and never affects the cooperation on regular ring networks, square lattice, random and scale-free networks, respectively. The results for the square lattice and regular ring networks are demonstrated by the pair approximation.

  19. Ocean wave parameters estimation using backpropagation neural networks

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; SubbaRao; Raju, D.H.

    : the RPROP algorithm. San Francisco: ICNN; 1993. p. 586–591. [15] Demuth H, Beale M. Neural network toolbox for use with MATLAB, user guide. USA: The Math Works Inc.; 2000 (http://www.mathworks.com). [16] Baba M, Dattatri J. Ocean wave spectra off cochin...

  20. Network Inference and Maximum Entropy Estimation on Information Diagrams

    Czech Academy of Sciences Publication Activity Database

    Martin, E.A.; Hlinka, J.; Meinke, A.; Děchtěrenko, Filip; Tintěra, J.; Oliver, I.; Davidsen, J.

    2017-01-01

    Roč. 7, č. 1 (2017), s. 1-15, č. článku 7062. ISSN 2045-2322 R&D Projects: GA ČR GA13-23940S Institutional support: RVO:68081740 Keywords : complex networks * mutual information * entropy maximization * fMRI Subject RIV: AN - Psychology OBOR OECD: Cognitive sciences Impact factor: 4.259, year: 2016

  1. Bernstein approximations in glasso-based estimation of biological networks

    NARCIS (Netherlands)

    Purutcuoglu, Vilda; Agraz, Melih; Wit, Ernst

    The Gaussian graphical model (GGM) is one of the common dynamic modelling approaches in the construction of gene networks. In inference of this modelling the interaction between genes can be detected mainly via graphical lasso (glasso) or coordinate descent-based approaches. Although these methods

  2. Dynamic state estimation for distribution networks with renewable energy integration

    NARCIS (Netherlands)

    Nguyen, P.H.; Venayagamoorthy, G.K.; Kling, W.L.; Ribeiro, P.F.

    2013-01-01

    The massive integration of variable and unpredictable Renewable Energy Sources (RES) and new types of load consumptions increases the dynamic and uncertain nature of the electricity grid. Emerging interests have focused on improving the monitoring capabilities of network operators so that they can

  3. Nonlinear neural network for hemodynamic model state and input estimation using fMRI data

    KAUST Repository

    Karam, Ayman M.

    2014-11-01

    Originally inspired by biological neural networks, artificial neural networks (ANNs) are powerful mathematical tools that can solve complex nonlinear problems such as filtering, classification, prediction and more. This paper demonstrates the first successful implementation of ANN, specifically nonlinear autoregressive with exogenous input (NARX) networks, to estimate the hemodynamic states and neural activity from simulated and measured real blood oxygenation level dependent (BOLD) signals. Blocked and event-related BOLD data are used to test the algorithm on real experiments. The proposed method is accurate and robust even in the presence of signal noise and it does not depend on sampling interval. Moreover, the structure of the NARX networks is optimized to yield the best estimate with minimal network architecture. The results of the estimated neural activity are also discussed in terms of their potential use.

  4. Estimation of mean grain size of seafloor sediments using neural network

    Digital Repository Service at National Institute of Oceanography (India)

    De, C.; Chakraborty, B.

    The feasibility of an artificial neural network based approach is investigated to estimate the values of mean grain size of seafloor sediments using four dominant echo features, extracted from acoustic backscatter data. The acoustic backscatter data...

  5. Instrumental variables estimates of peer effects in social networks.

    Science.gov (United States)

    An, Weihua

    2015-03-01

    Estimating peer effects with observational data is very difficult because of contextual confounding, peer selection, simultaneity bias, and measurement error, etc. In this paper, I show that instrumental variables (IVs) can help to address these problems in order to provide causal estimates of peer effects. Based on data collected from over 4000 students in six middle schools in China, I use the IV methods to estimate peer effects on smoking. My design-based IV approach differs from previous ones in that it helps to construct potentially strong IVs and to directly test possible violation of exogeneity of the IVs. I show that measurement error in smoking can lead to both under- and imprecise estimations of peer effects. Based on a refined measure of smoking, I find consistent evidence for peer effects on smoking. If a student's best friend smoked within the past 30 days, the student was about one fifth (as indicated by the OLS estimate) or 40 percentage points (as indicated by the IV estimate) more likely to smoke in the same time period. The findings are robust to a variety of robustness checks. I also show that sharing cigarettes may be a mechanism for peer effects on smoking. A 10% increase in the number of cigarettes smoked by a student's best friend is associated with about 4% increase in the number of cigarettes smoked by the student in the same time period. Copyright © 2014 Elsevier Inc. All rights reserved.

  6. The Kernel Mixture Network: A Nonparametric Method for Conditional Density Estimation of Continuous Random Variables

    OpenAIRE

    Ambrogioni, Luca; Güçlü, Umut; van Gerven, Marcel A. J.; Maris, Eric

    2017-01-01

    This paper introduces the kernel mixture network, a new method for nonparametric estimation of conditional probability densities using neural networks. We model arbitrarily complex conditional densities as linear combinations of a family of kernel functions centered at a subset of training points. The weights are determined by the outer layer of a deep neural network, trained by minimizing the negative log likelihood. This generalizes the popular quantized softmax approach, which can be seen ...

  7. An estimation of the domain of attraction and convergence rate for Hopfield continuous feedback neural networks

    International Nuclear Information System (INIS)

    Cao Jinde

    2004-01-01

    In this Letter, the domain of attraction of memory patterns and exponential convergence rate of the network trajectories to memory patterns for Hopfield continuous associative memory are estimated by means of matrix measure and comparison principle. A new estimation is given for the domain of attraction of memory patterns and exponential convergence rate. These results can be used for the evaluation of fault-tolerance capability and the synthesis procedures for Hopfield continuous feedback associative memory neural networks

  8. Branch current state estimation of three phase distribution networks suitable for paralellization

    NARCIS (Netherlands)

    Blaauwbroek, N.; Nguyen, H.P.; Gibescu, M.; Slootweg, J.G.

    2017-01-01

    The evolution of distribution networks from passive to active distribution systems puts new requirements on the monitoring and control capabilities of these systems. The development of state estimation algorithms to gain insight in the actual system state of a distribution network has resulted in a

  9. Networks systems and operations. [wideband communication techniques for data links with satellites

    Science.gov (United States)

    1975-01-01

    The application of wideband communication techniques for data links with satellites is discussed. A diagram of the demand assigned voice communications system is provided. The development of prototype integrated spacecraft paramps at S- and C-bands is described and the performance of space-qualified paramps is tabulated. The characteristics of a dual parabolic cylinder monopulse zoom antenna for use with the tracking and data relay satellite system (TDRSS) are analyzed. The development of a universally applicable transponder at S-band is reported. A block diagram of the major subassemblies of the S-band transponder is included. The technology aspects of network timing and synchronization of communication systems are to show the use of the Omega navigation system. The telemetry data compression system used during the Skylab program is evaluated.

  10. Effects of contact network structure on epidemic transmission trees: implications for data required to estimate network structure.

    Science.gov (United States)

    Carnegie, Nicole Bohme

    2018-01-30

    Understanding the dynamics of disease spread is key to developing effective interventions to control or prevent an epidemic. The structure of the network of contacts over which the disease spreads has been shown to have a strong influence on the outcome of the epidemic, but an open question remains as to whether it is possible to estimate contact network features from data collected in an epidemic. The approach taken in this paper is to examine the distributions of epidemic outcomes arising from epidemics on networks with particular structural features to assess whether that structure could be measured from epidemic data and what other constraints might be needed to make the problem identifiable. To this end, we vary the network size, mean degree, and transmissibility of the pathogen, as well as the network feature of interest: clustering, degree assortativity, or attribute-based preferential mixing. We record several standard measures of the size and spread of the epidemic, as well as measures that describe the shape of the transmission tree in order to ascertain whether there are detectable signals in the final data from the outbreak. The results suggest that there is potential to estimate contact network features from transmission trees or pure epidemic data, particularly for diseases with high transmissibility or for which the relevant contact network is of low mean degree. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  11. Linking field observations, Landsat and MODIS data to estimate agricultural change in European Russia.

    Science.gov (United States)

    de Beurs, K. M.; Ioffe, G.

    2011-12-01

    Agricultural reform has been one of the most important anthropogenic change processes in European Russia that has been unfolding since the formal collapse of the Soviet Union at the end of 1991. Widespread land abandonment is perhaps the most vivid side effect of the reform, even visible in synoptic imagery. Currently, Russia is transitioning into a country with an internal "archipelago" of islands of productive agriculture around cities embedded in a matrix of unproductive, abandoned lands. This heterogeneous spatial pattern is mainly driven by depopulation of the least favorable parts of the countryside, where "least favorable" is a function of fertility, remoteness, and their interaction. In this work we provide a satellite, GIS and field based overview of the current agricultural developments in Russia and look beyond the unstable period immediately following the collapse of the Soviet Union. We apply Landsat images in one of Russia's oblasts to create a detailed land cover map. We then use a logistic model to link the Landsat land cover map with the inter-annual variability in key phenological parameters calculated from MODIS to derive the percent of cropland per 500m MODIS pixel. By evaluating the phenological characteristics of the MODIS curves for each year we determine whether a pixel was actually cropped or left fallow. A comparison of satellite-estimated cropped areas with regional statistics (by rayon) revealed that the satellite estimates are highly correlated with the regional statistics for both arable lands and successfully cropped areas. We use the crop maps to determine the number of times a particular area was cropped between 2002 and 2009 by summing all the years with crops per pixel. This variable provides a good indication about the intensification and de-intensification of the Russian croplands over the last decade. We have visited several rural areas in Russia and we link the satellite data with information acquired through field interviews

  12. Core reactivity estimation in space reactors using recurrent dynamic networks

    Science.gov (United States)

    Parlos, Alexander G.; Tsai, Wei K.

    1991-01-01

    A recurrent multilayer perceptron network topology is used in the identification of nonlinear dynamic systems from only the input/output measurements. The identification is performed in the discrete time domain, with the learning algorithm being a modified form of the back propagation (BP) rule. The recurrent dynamic network (RDN) developed is applied for the total core reactivity prediction of a spacecraft reactor from only neutronic power level measurements. Results indicate that the RDN can reproduce the nonlinear response of the reactor while keeping the number of nodes roughly equal to the relative order of the system. As accuracy requirements are increased, the number of required nodes also increases, however, the order of the RDN necessary to obtain such results is still in the same order of magnitude as the order of the mathematical model of the system. It is believed that use of the recurrent MLP structure with a variety of different learning algorithms may prove useful in utilizing artificial neural networks for recognition, classification, and prediction of dynamic systems.

  13. Evolution of semilocal string networks. II. Velocity estimators

    Science.gov (United States)

    Lopez-Eiguren, A.; Urrestilla, J.; Achúcarro, A.; Avgoustidis, A.; Martins, C. J. A. P.

    2017-07-01

    We continue a comprehensive numerical study of semilocal string networks and their cosmological evolution. These can be thought of as hybrid networks comprised of (nontopological) string segments, whose core structure is similar to that of Abelian Higgs vortices, and whose ends have long-range interactions and behavior similar to that of global monopoles. Our study provides further evidence of a linear scaling regime, already reported in previous studies, for the typical length scale and velocity of the network. We introduce a new algorithm to identify the position of the segment cores. This allows us to determine the length and velocity of each individual segment and follow their evolution in time. We study the statistical distribution of segment lengths and velocities for radiation- and matter-dominated evolution in the regime where the strings are stable. Our segment detection algorithm gives higher length values than previous studies based on indirect detection methods. The statistical distribution shows no evidence of (anti)correlation between the speed and the length of the segments.

  14. A neural network model of semantic memory linking feature-based object representation and words.

    Science.gov (United States)

    Cuppini, C; Magosso, E; Ursino, M

    2009-06-01

    Recent theories in cognitive neuroscience suggest that semantic memory is a distributed process, which involves many cortical areas and is based on a multimodal representation of objects. The aim of this work is to extend a previous model of object representation to realize a semantic memory, in which sensory-motor representations of objects are linked with words. The model assumes that each object is described as a collection of features, coded in different cortical areas via a topological organization. Features in different objects are segmented via gamma-band synchronization of neural oscillators. The feature areas are further connected with a lexical area, devoted to the representation of words. Synapses among the feature areas, and among the lexical area and the feature areas are trained via a time-dependent Hebbian rule, during a period in which individual objects are presented together with the corresponding words. Simulation results demonstrate that, during the retrieval phase, the network can deal with the simultaneous presence of objects (from sensory-motor inputs) and words (from acoustic inputs), can correctly associate objects with words and segment objects even in the presence of incomplete information. Moreover, the network can realize some semantic links among words representing objects with shared features. These results support the idea that semantic memory can be described as an integrated process, whose content is retrieved by the co-activation of different multimodal regions. In perspective, extended versions of this model may be used to test conceptual theories, and to provide a quantitative assessment of existing data (for instance concerning patients with neural deficits).

  15. A new method to estimate parameters of linear compartmental models using artificial neural networks

    International Nuclear Information System (INIS)

    Gambhir, Sanjiv S.; Keppenne, Christian L.; Phelps, Michael E.; Banerjee, Pranab K.

    1998-01-01

    At present, the preferred tool for parameter estimation in compartmental analysis is an iterative procedure; weighted nonlinear regression. For a large number of applications, observed data can be fitted to sums of exponentials whose parameters are directly related to the rate constants/coefficients of the compartmental models. Since weighted nonlinear regression often has to be repeated for many different data sets, the process of fitting data from compartmental systems can be very time consuming. Furthermore the minimization routine often converges to a local (as opposed to global) minimum. In this paper, we examine the possibility of using artificial neural networks instead of weighted nonlinear regression in order to estimate model parameters. We train simple feed-forward neural networks to produce as outputs the parameter values of a given model when kinetic data are fed to the networks' input layer. The artificial neural networks produce unbiased estimates and are orders of magnitude faster than regression algorithms. At noise levels typical of many real applications, the neural networks are found to produce lower variance estimates than weighted nonlinear regression in the estimation of parameters from mono- and biexponential models. These results are primarily due to the inability of weighted nonlinear regression to converge. These results establish that artificial neural networks are powerful tools for estimating parameters for simple compartmental models. (author)

  16. A neural network-based estimator for the mixture ratio of the Space Shuttle Main Engine

    Science.gov (United States)

    Guo, T. H.; Musgrave, J.

    1992-11-01

    In order to properly utilize the available fuel and oxidizer of a liquid propellant rocket engine, the mixture ratio is closed loop controlled during main stage (65 percent - 109 percent power) operation. However, because of the lack of flight-capable instrumentation for measuring mixture ratio, the value of mixture ratio in the control loop is estimated using available sensor measurements such as the combustion chamber pressure and the volumetric flow, and the temperature and pressure at the exit duct on the low pressure fuel pump. This estimation scheme has two limitations. First, the estimation formula is based on an empirical curve fitting which is accurate only within a narrow operating range. Second, the mixture ratio estimate relies on a few sensor measurements and loss of any of these measurements will make the estimate invalid. In this paper, we propose a neural network-based estimator for the mixture ratio of the Space Shuttle Main Engine. The estimator is an extension of a previously developed neural network based sensor failure detection and recovery algorithm (sensor validation). This neural network uses an auto associative structure which utilizes the redundant information of dissimilar sensors to detect inconsistent measurements. Two approaches have been identified for synthesizing mixture ratio from measurement data using a neural network. The first approach uses an auto associative neural network for sensor validation which is modified to include the mixture ratio as an additional output. The second uses a new network for the mixture ratio estimation in addition to the sensor validation network. Although mixture ratio is not directly measured in flight, it is generally available in simulation and in test bed firing data from facility measurements of fuel and oxidizer volumetric flows. The pros and cons of these two approaches will be discussed in terms of robustness to sensor failures and accuracy of the estimate during typical transients using

  17. A rapid reliability estimation method for directed acyclic lifeline networks with statistically dependent components

    International Nuclear Information System (INIS)

    Kang, Won-Hee; Kliese, Alyce

    2014-01-01

    Lifeline networks, such as transportation, water supply, sewers, telecommunications, and electrical and gas networks, are essential elements for the economic and societal functions of urban areas, but their components are highly susceptible to natural or man-made hazards. In this context, it is essential to provide effective pre-disaster hazard mitigation strategies and prompt post-disaster risk management efforts based on rapid system reliability assessment. This paper proposes a rapid reliability estimation method for node-pair connectivity analysis of lifeline networks especially when the network components are statistically correlated. Recursive procedures are proposed to compound all network nodes until they become a single super node representing the connectivity between the origin and destination nodes. The proposed method is applied to numerical network examples and benchmark interconnected power and water networks in Memphis, Shelby County. The connectivity analysis results show the proposed method's reasonable accuracy and remarkable efficiency as compared to the Monte Carlo simulations

  18. Unravelling Protein-Protein Interaction Networks Linked to Aliphatic and Indole Glucosinolate Biosynthetic Pathways in Arabidopsis

    Directory of Open Access Journals (Sweden)

    Sebastian J. Nintemann

    2017-11-01

    Full Text Available Within the cell, biosynthetic pathways are embedded in protein-protein interaction networks. In Arabidopsis, the biosynthetic pathways of aliphatic and indole glucosinolate defense compounds are well-characterized. However, little is known about the spatial orchestration of these enzymes and their interplay with the cellular environment. To address these aspects, we applied two complementary, untargeted approaches—split-ubiquitin yeast 2-hybrid and co-immunoprecipitation screens—to identify proteins interacting with CYP83A1 and CYP83B1, two homologous enzymes specific for aliphatic and indole glucosinolate biosynthesis, respectively. Our analyses reveal distinct functional networks with substantial interconnection among the identified interactors for both pathway-specific markers, and add to our knowledge about how biochemical pathways are connected to cellular processes. Specifically, a group of protein interactors involved in cell death and the hypersensitive response provides a potential link between the glucosinolate defense compounds and defense against biotrophic pathogens, mediated by protein-protein interactions.

  19. Can shoulder joint reaction forces be estimated by neural networks?

    NARCIS (Netherlands)

    de Vries, W.H.K.; Veeger, H.E.J.; Baten, C.T.M.; van der Helm, F.C.T.

    2016-01-01

    To facilitate the development of future shoulder endoprostheses, a long term load profile of the shoulder joint is desired. A musculoskeletal model using 3D kinematics and external forces as input can estimate the mechanical load on the glenohumeral joint, in terms of joint reaction forces. For long

  20. Temperature estimation of induction machines based on wireless sensor networks

    Directory of Open Access Journals (Sweden)

    Y. Huang

    2018-04-01

    Full Text Available In this paper, a fourth-order Kalman filter (KF algorithm is implemented in the wireless sensor node to estimate the temperatures of the stator winding, the rotor cage and the stator core in the induction machine. Three separate wireless sensor nodes are used as the data acquisition systems for different input signals. Six Hall sensors are used to acquire the three-phase stator currents and voltages of the induction machine. All of them are processed to root mean square (rms in ampere and volt. A rotary encoder is mounted for the rotor speed and Pt-1000 is used for the temperature of the coolant air. The processed signals in the physical unit are transmitted wirelessly to the host wireless sensor node, where the KF is implemented with fixed-point arithmetic in Contiki OS. Time-division multiple access (TDMA is used to make the wireless transmission more stable. Compared to the floating-point implementation, the fixed-point implementation has the same estimation accuracy at only about one-fifth of the computation time. The temperature estimation system can work under any work condition as long as there are currents through the machine. It can also be rebooted for estimation even when wireless transmission has collapsed or packages are missing.

  1. Network Inference and Maximum Entropy Estimation on Information Diagrams

    Czech Academy of Sciences Publication Activity Database

    Martin, E.A.; Hlinka, Jaroslav; Meinke, A.; Děchtěrenko, Filip; Tintěra, J.; Oliver, I.; Davidsen, J.

    2017-01-01

    Roč. 7, č. 1 (2017), č. článku 7062. ISSN 2045-2322 R&D Projects: GA ČR GA13-23940S; GA MZd(CZ) NV15-29835A Grant - others:GA MŠk(CZ) LO1611 Institutional support: RVO:67985807 Keywords : complex networks * mutual information * entropy maximization * fMRI Subject RIV: BD - Theory of Information OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 4.259, year: 2016

  2. Estimation of Solar Radiation using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Slamet Suprayogi

    2004-01-01

    Full Text Available The solar radiation is the most important fator affeccting evapotranspiration, the mechanism of transporting the vapor from the water surface has also a great effect. The main objectives of this study were to investigate the potential of using Artificial Neural Network (ANN to predict solar radiation related to temperature. The three-layer backpropagation were developed, trained, and tested to forecast solar radiation for Ciriung sub Cachment. Result revealed that the ANN were able to well learn the events they were trained to recognize. Moreover, they were capable of effecctively generalize their training by predicting solar radiation for sets unseen cases.

  3. Individual Channel Estimation in a Diamond Relay Network Using Relay-Assisted Training

    Directory of Open Access Journals (Sweden)

    Xianwen He

    2017-01-01

    Full Text Available We consider the training design and channel estimation in the amplify-and-forward (AF diamond relay network. Our strategy is to transmit the source training in time-multiplexing (TM mode while each relay node superimposes its own relay training over the amplified received data signal without bandwidth expansion. The principal challenge is to obtain accurate channel state information (CSI of second-hop link due to the multiaccess interference (MAI and cooperative data interference (CDI. To maintain the orthogonality between data and training, a modified relay-assisted training scheme is proposed to migrate the CDI, where some of the cooperative data at the relay are discarded to accommodate relay training. Meanwhile, a couple of optimal zero-correlation zone (ZCZ relay-assisted sequences are designed to avoid MAI. At the destination node, the received signals from the two relay nodes are combined to achieve spatial diversity and enhanced data reliability. The simulation results are presented to validate the performance of the proposed schemes.

  4. Dually cross-linked single network poly(acrylic acid) hydrogels with superior mechanical properties and water absorbency.

    Science.gov (United States)

    Zhong, Ming; Liu, Yi-Tao; Liu, Xiao-Ying; Shi, Fu-Kuan; Zhang, Li-Qin; Zhu, Mei-Fang; Xie, Xu-Ming

    2016-06-28

    Poly(acrylic acid) (PAA) hydrogels with superior mechanical properties, based on a single network structure with dual cross-linking, are prepared by one-pot free radical polymerization. The network structure of the PAA hydrogels is composed of dual cross-linking: a dynamic and reversible ionic cross-linking among the PAA chains enabled by Fe(3+) ions, and a sparse covalent cross-linking enabled by a covalent cross-linker (Bis). Under deformation, the covalently cross-linked PAA chains remain intact to maintain their original configuration, while the Fe(3+)-enabled ionic cross-linking among the PAA chains is broken to dissipate energy and then recombined. It is found that the mechanical properties of the PAA hydrogels are significantly influenced by the contents of covalent cross-linkers, Fe(3+) ions and water, which can be adjusted within a substantial range and thus broaden the applications of the hydrogels. Meanwhile, the PAA hydrogels have excellent recoverability based on the dynamic and reversible ionic cross-linking enabled by Fe(3+) ions. Moreover, the swelling capacity of the PAA hydrogels is as high as 1800 times in deionized water due to the synergistic effects of ionic and covalent cross-linkings. The combination of balanced mechanical properties, efficient recoverability, high swelling capacity and facile preparation provides a new method to obtain high-performance hydrogels.

  5. Distributed estimation of sensors position in underwater wireless sensor network

    Science.gov (United States)

    Zandi, Rahman; Kamarei, Mahmoud; Amiri, Hadi

    2016-05-01

    In this paper, a localisation method for determining the position of fixed sensor nodes in an underwater wireless sensor network (UWSN) is introduced. In this simple and range-free scheme, the node localisation is achieved by utilising an autonomous underwater vehicle (AUV) that transverses through the network deployment area, and that periodically emits a message block via four directional acoustic beams. A message block contains the actual known AUV position as well as a directional dependent marker that allows a node to identify the respective transmit beam. The beams form a fixed angle with the AUV body. If a node passively receives message blocks, it could calculate the arithmetic mean of the coordinates existing in each messages sequence, to find coordinates at two different time instants via two different successive beams. The node position can be derived from the two computed positions of the AUV. The major advantage of the proposed localisation algorithm is that it is silent, which leads to energy efficiency for sensor nodes. The proposed method does not require any synchronisation among the nodes owing to being silent. Simulation results, using MATLAB, demonstrated that the proposed method had better performance than other similar AUV-based localisation methods in terms of the rates of well-localised sensor nodes and positional root mean square error.

  6. Neural networks for link prediction in realistic biomedical graphs: a multi-dimensional evaluation of graph embedding-based approaches.

    Science.gov (United States)

    Crichton, Gamal; Guo, Yufan; Pyysalo, Sampo; Korhonen, Anna

    2018-05-21

    Link prediction in biomedical graphs has several important applications including predicting Drug-Target Interactions (DTI), Protein-Protein Interaction (PPI) prediction and Literature-Based Discovery (LBD). It can be done using a classifier to output the probability of link formation between nodes. Recently several works have used neural networks to create node representations which allow rich inputs to neural classifiers. Preliminary works were done on this and report promising results. However they did not use realistic settings like time-slicing, evaluate performances with comprehensive metrics or explain when or why neural network methods outperform. We investigated how inputs from four node representation algorithms affect performance of a neural link predictor on random- and time-sliced biomedical graphs of real-world sizes (∼ 6 million edges) containing information relevant to DTI, PPI and LBD. We compared the performance of the neural link predictor to those of established baselines and report performance across five metrics. In random- and time-sliced experiments when the neural network methods were able to learn good node representations and there was a negligible amount of disconnected nodes, those approaches outperformed the baselines. In the smallest graph (∼ 15,000 edges) and in larger graphs with approximately 14% disconnected nodes, baselines such as Common Neighbours proved a justifiable choice for link prediction. At low recall levels (∼ 0.3) the approaches were mostly equal, but at higher recall levels across all nodes and average performance at individual nodes, neural network approaches were superior. Analysis showed that neural network methods performed well on links between nodes with no previous common neighbours; potentially the most interesting links. Additionally, while neural network methods benefit from large amounts of data, they require considerable amounts of computational resources to utilise them. Our results indicate

  7. Application of Artificial Neural Networks for Efficient High-Resolution 2D DOA Estimation

    Directory of Open Access Journals (Sweden)

    M. Agatonović

    2012-12-01

    Full Text Available A novel method to provide high-resolution Two-Dimensional Direction of Arrival (2D DOA estimation employing Artificial Neural Networks (ANNs is presented in this paper. The observed space is divided into azimuth and elevation sectors. Multilayer Perceptron (MLP neural networks are employed to detect the presence of a source in a sector while Radial Basis Function (RBF neural networks are utilized for DOA estimation. It is shown that a number of appropriately trained neural networks can be successfully used for the high-resolution DOA estimation of narrowband sources in both azimuth and elevation. The training time of each smaller network is significantly re¬duced as different training sets are used for networks in detection and estimation stage. By avoiding the spectral search, the proposed method is suitable for real-time ap¬plications as it provides DOA estimates in a matter of seconds. At the same time, it demonstrates the accuracy comparable to that of the super-resolution 2D MUSIC algorithm.

  8. Equal Graph Partitioning on Estimated Infection Network as an Effective Epidemic Mitigation Measure

    Science.gov (United States)

    Hadidjojo, Jeremy; Cheong, Siew Ann

    2011-01-01

    Controlling severe outbreaks remains the most important problem in infectious disease area. With time, this problem will only become more severe as population density in urban centers grows. Social interactions play a very important role in determining how infectious diseases spread, and organization of people along social lines gives rise to non-spatial networks in which the infections spread. Infection networks are different for diseases with different transmission modes, but are likely to be identical or highly similar for diseases that spread the same way. Hence, infection networks estimated from common infections can be useful to contain epidemics of a more severe disease with the same transmission mode. Here we present a proof-of-concept study demonstrating the effectiveness of epidemic mitigation based on such estimated infection networks. We first generate artificial social networks of different sizes and average degrees, but with roughly the same clustering characteristic. We then start SIR epidemics on these networks, censor the simulated incidences, and use them to reconstruct the infection network. We then efficiently fragment the estimated network by removing the smallest number of nodes identified by a graph partitioning algorithm. Finally, we demonstrate the effectiveness of this targeted strategy, by comparing it against traditional untargeted strategies, in slowing down and reducing the size of advancing epidemics. PMID:21799777

  9. A case study to estimate costs using Neural Networks and regression based models

    Directory of Open Access Journals (Sweden)

    Nadia Bhuiyan

    2012-07-01

    Full Text Available Bombardier Aerospace’s high performance aircrafts and services set the utmost standard for the Aerospace industry. A case study in collaboration with Bombardier Aerospace is conducted in order to estimate the target cost of a landing gear. More precisely, the study uses both parametric model and neural network models to estimate the cost of main landing gears, a major aircraft commodity. A comparative analysis between the parametric based model and those upon neural networks model will be considered in order to determine the most accurate method to predict the cost of a main landing gear. Several trials are presented for the design and use of the neural network model. The analysis for the case under study shows the flexibility in the design of the neural network model. Furthermore, the performance of the neural network model is deemed superior to the parametric models for this case study.

  10. [Cost estimation of an epidemiological surveillance network for animal diseases in Central Africa: a case study of the Chad network].

    Science.gov (United States)

    Ouagal, M; Berkvens, D; Hendrikx, P; Fecher-Bourgeois, F; Saegerman, C

    2012-12-01

    In sub-Saharan Africa, most epidemiological surveillance networks for animal diseases were temporarily funded by foreign aid. It should be possible for national public funds to ensure the sustainability of such decision support tools. Taking the epidemiological surveillance network for animal diseases in Chad (REPIMAT) as an example, this study aims to estimate the network's cost by identifying the various costs and expenditures for each level of intervention. The network cost was estimated on the basis of an analysis of the operational organisation of REPIMAT, additional data collected in surveys and interviews with network field workers and a market price listing for Chad. These costs were then compared with those of other epidemiological surveillance networks in West Africa. The study results indicate that REPIMAT costs account for 3% of the State budget allocated to the Ministry of Livestock. In Chad in general, as in other West African countries, fixed costs outweigh variable costs at every level of intervention. The cost of surveillance principally depends on what is needed for surveillance at the local level (monitoring stations) and at the intermediate level (official livestock sectors and regional livestock delegations) and on the cost of the necessary equipment. In African countries, the cost of surveillance per square kilometre depends on livestock density.

  11. 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.

  12. State estimation for networked control systems using fixed data rates

    Science.gov (United States)

    Liu, Qing-Quan; Jin, Fang

    2017-07-01

    This paper investigates state estimation for linear time-invariant systems where sensors and controllers are geographically separated and connected via a bandwidth-limited and errorless communication channel with the fixed data rate. All plant states are quantised, coded and converted together into a codeword in our quantisation and coding scheme. We present necessary and sufficient conditions on the fixed data rate for observability of such systems, and further develop the data-rate theorem. It is shown in our results that there exists a quantisation and coding scheme to ensure observability of the system if the fixed data rate is larger than the lower bound given, which is less conservative than the one in the literature. Furthermore, we also examine the role that the disturbances have on the state estimation problem in the case with data-rate limitations. Illustrative examples are given to demonstrate the effectiveness of the proposed method.

  13. Estimation of effective connectivity using multi-layer perceptron artificial neural network.

    Science.gov (United States)

    Talebi, Nasibeh; Nasrabadi, Ali Motie; Mohammad-Rezazadeh, Iman

    2018-02-01

    Studies on interactions between brain regions estimate effective connectivity, (usually) based on the causality inferences made on the basis of temporal precedence. In this study, the causal relationship is modeled by a multi-layer perceptron feed-forward artificial neural network, because of the ANN's ability to generate appropriate input-output mapping and to learn from training examples without the need of detailed knowledge of the underlying system. At any time instant, the past samples of data are placed in the network input, and the subsequent values are predicted at its output. To estimate the strength of interactions, the measure of " Causality coefficient " is defined based on the network structure, the connecting weights and the parameters of hidden layer activation function. Simulation analysis demonstrates that the method, called "CREANN" (Causal Relationship Estimation by Artificial Neural Network), can estimate time-invariant and time-varying effective connectivity in terms of MVAR coefficients. The method shows robustness with respect to noise level of data. Furthermore, the estimations are not significantly influenced by the model order (considered time-lag), and the different initial conditions (initial random weights and parameters of the network). CREANN is also applied to EEG data collected during a memory recognition task. The results implicate that it can show changes in the information flow between brain regions, involving in the episodic memory retrieval process. These convincing results emphasize that CREANN can be used as an appropriate method to estimate the causal relationship among brain signals.

  14. On the estimation variance for the specific Euler-Poincaré characteristic of random networks.

    Science.gov (United States)

    Tscheschel, A; Stoyan, D

    2003-07-01

    The specific Euler number is an important topological characteristic in many applications. It is considered here for the case of random networks, which may appear in microscopy either as primary objects of investigation or as secondary objects describing in an approximate way other structures such as, for example, porous media. For random networks there is a simple and natural estimator of the specific Euler number. For its estimation variance, a simple Poisson approximation is given. It is based on the general exact formula for the estimation variance. In two examples of quite different nature and topology application of the formulas is demonstrated.

  15. H∞ state estimation of stochastic memristor-based neural networks with time-varying delays.

    Science.gov (United States)

    Bao, Haibo; Cao, Jinde; Kurths, Jürgen; Alsaedi, Ahmed; Ahmad, Bashir

    2018-03-01

    This paper addresses the problem of H ∞ state estimation for a class of stochastic memristor-based neural networks with time-varying delays. Under the framework of Filippov solution, the stochastic memristor-based neural networks are transformed into systems with interval parameters. The present paper is the first to investigate the H ∞ state estimation problem for continuous-time Itô-type stochastic memristor-based neural networks. By means of Lyapunov functionals and some stochastic technique, sufficient conditions are derived to ensure that the estimation error system is asymptotically stable in the mean square with a prescribed H ∞ performance. An explicit expression of the state estimator gain is given in terms of linear matrix inequalities (LMIs). Compared with other results, our results reduce control gain and control cost effectively. Finally, numerical simulations are provided to demonstrate the efficiency of the theoretical results. Copyright © 2018 Elsevier Ltd. All rights reserved.

  16. Forward Link Power Control Strategy and its Optimum Issue in CDMA Cellular Networks

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    In this paper, we propose a theoretical method in order to estimate the forward link outage probability and user capacity of a cellular system which are based on IS-95 CDMA standard, especialy impact of power control strategy and voice activity monitoring in the system under long-term fading effects, in which the light and heavy fadings are considered. According to the numerical results obtained in this paper, the power control strategy leads to approximately the threefold user capacity in contrast to the situation without power control strategy. The reults are compared with Interference-to-Signal Ratio (ISR) driven power control scheme[6][9] which can be used only for simulation of the system. The power control strategy not only improves the desired signal to the interference ratio in the reference user's receiver, but also offers uniform service to the user wherever it is located in the cell.

  17. Cardinality Estimation Algorithm in Large-Scale Anonymous Wireless Sensor Networks

    KAUST Repository

    Douik, Ahmed

    2017-08-30

    Consider a large-scale anonymous wireless sensor network with unknown cardinality. In such graphs, each node has no information about the network topology and only possesses a unique identifier. This paper introduces a novel distributed algorithm for cardinality estimation and topology discovery, i.e., estimating the number of node and structure of the graph, by querying a small number of nodes and performing statistical inference methods. While the cardinality estimation allows the design of more efficient coding schemes for the network, the topology discovery provides a reliable way for routing packets. The proposed algorithm is shown to produce a cardinality estimate proportional to the best linear unbiased estimator for dense graphs and specific running times. Simulation results attest the theoretical results and reveal that, for a reasonable running time, querying a small group of nodes is sufficient to perform an estimation of 95% of the whole network. Applications of this work include estimating the number of Internet of Things (IoT) sensor devices, online social users, active protein cells, etc.

  18. Arterial link travel time estimation using loop detector data : phase 1

    Science.gov (United States)

    1997-11-01

    The envisioned operational tests of Advanced Traveler Information Systems (ATIS) and Advanced Traffic Management Systems (ATMS) in the Minneapolis/St. Paul area call for the provision of timely and reliable travel times over an entire rod network. Un...

  19. Automated mode shape estimation in agent-based wireless sensor networks

    Science.gov (United States)

    Zimmerman, Andrew T.; Lynch, Jerome P.

    2010-04-01

    Recent advances in wireless sensing technology have made it possible to deploy dense networks of sensing transducers within large structural systems. Because these networks leverage the embedded computing power and agent-based abilities integral to many wireless sensing devices, it is possible to analyze sensor data autonomously and in-network. In this study, market-based techniques are used to autonomously estimate mode shapes within a network of agent-based wireless sensors. Specifically, recent work in both decentralized Frequency Domain Decomposition and market-based resource allocation is leveraged to create a mode shape estimation algorithm derived from free-market principles. This algorithm allows an agent-based wireless sensor network to autonomously shift emphasis between improving mode shape accuracy and limiting the consumption of certain scarce network resources: processing time, storage capacity, and power consumption. The developed algorithm is validated by successfully estimating mode shapes using a network of wireless sensor prototypes deployed on the mezzanine balcony of Hill Auditorium, located on the University of Michigan campus.

  20. A network-based multi-target computational estimation scheme for anticoagulant activities of compounds.

    Directory of Open Access Journals (Sweden)

    Qian Li

    Full Text Available BACKGROUND: Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target. METHODOLOGY: We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671 between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery. CONCLUSIONS: This article proposes a network-based multi-target computational estimation

  1. A network-based multi-target computational estimation scheme for anticoagulant activities of compounds.

    Science.gov (United States)

    Li, Qian; Li, Xudong; Li, Canghai; Chen, Lirong; Song, Jun; Tang, Yalin; Xu, Xiaojie

    2011-03-22

    Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target. We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671) between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery. This article proposes a network-based multi-target computational estimation method for anticoagulant activities of compounds by

  2. The effect of destination linked feature selection in real-time network intrusion detection

    CSIR Research Space (South Africa)

    Mzila, P

    2013-07-01

    Full Text Available techniques in the network intrusion detection system (NIDS) is the feature selection technique. The ability of NIDS to accurately identify intrusion from the network traffic relies heavily on feature selection, which describes the pattern of the network...

  3. Coupled dynamics of node and link states in complex networks: a model for language competition

    International Nuclear Information System (INIS)

    Carro, Adrián; Toral, Raúl; Miguel, Maxi San

    2016-01-01

    Inspired by language competition processes, we present a model of coupled evolution of node and link states. In particular, we focus on the interplay between the use of a language and the preference or attitude of the speakers towards it, which we model, respectively, as a property of the interactions between speakers (a link state) and as a property of the speakers themselves (a node state). Furthermore, we restrict our attention to the case of two socially equivalent languages and to socially inspired network topologies based on a mechanism of triadic closure. As opposed to most of the previous literature, where language extinction is an inevitable outcome of the dynamics, we find a broad range of possible asymptotic configurations, which we classify as: frozen extinction states, frozen coexistence states, and dynamically trapped coexistence states. Moreover, metastable coexistence states with very long survival times and displaying a non-trivial dynamics are found to be abundant. Interestingly, a system size scaling analysis shows, on the one hand, that the probability of language extinction vanishes exponentially for increasing system sizes and, on the other hand, that the time scale of survival of the non-trivial dynamical metastable states increases linearly with the size of the system. Thus, non-trivial dynamical coexistence is the only possible outcome for large enough systems. Finally, we show how this coexistence is characterized by one of the languages becoming clearly predominant while the other one becomes increasingly confined to ‘ghetto-like’ structures: small groups of bilingual speakers arranged in triangles, with a strong preference for the minority language, and using it for their intra-group interactions while they switch to the predominant language for communications with the rest of the population. (paper)

  4. Beyond neutral and forbidden links: morphological matches and the assembly of mutualistic hawkmoth-plant networks.

    Science.gov (United States)

    Sazatornil, Federico D; Moré, Marcela; Benitez-Vieyra, Santiago; Cocucci, Andrea A; Kitching, Ian J; Schlumpberger, Boris O; Oliveira, Paulo E; Sazima, Marlies; Amorim, Felipe W

    2016-11-01

    A major challenge in evolutionary ecology is to understand how co-evolutionary processes shape patterns of interactions between species at community level. Pollination of flowers with long corolla tubes by long-tongued hawkmoths has been invoked as a showcase model of co-evolution. Recently, optimal foraging models have predicted that there might be a close association between mouthparts' length and the corolla depth of the visited flowers, thus favouring trait convergence and specialization at community level. Here, we assessed whether hawkmoths more frequently pollinate plants with floral tube lengths similar to their proboscis lengths (morphological match hypothesis) against abundance-based processes (neutral hypothesis) and ecological trait mismatches constraints (forbidden links hypothesis), and how these processes structure hawkmoth-plant mutualistic networks from five communities in four biogeographical regions of South America. We found convergence in morphological traits across the five communities and that the distribution of morphological differences between hawkmoths and plants is consistent with expectations under the morphological match hypothesis in three of the five communities. In the two remaining communities, which are ecotones between two distinct biogeographical areas, interactions are better predicted by the neutral hypothesis. Our findings are consistent with the idea that diffuse co-evolution drives the evolution of extremely long proboscises and flower tubes, and highlight the importance of morphological traits, beyond the forbidden links hypothesis, in structuring interactions between mutualistic partners, revealing that the role of niche-based processes can be much more complex than previously known. © 2016 The Authors. Journal of Animal Ecology © 2016 British Ecological Society.

  5. 3D position estimation using an artificial neural network for a continuous scintillator PET detector

    International Nuclear Information System (INIS)

    Wang, Y; Zhu, W; Cheng, X; Li, D

    2013-01-01

    Continuous crystal based PET detectors have features of simple design, low cost, good energy resolution and high detection efficiency. Through single-end readout of scintillation light, direct three-dimensional (3D) position estimation could be another advantage that the continuous crystal detector would have. In this paper, we propose to use artificial neural networks to simultaneously estimate the plane coordinate and DOI coordinate of incident γ photons with detected scintillation light. Using our experimental setup with an ‘8 + 8’ simplified signal readout scheme, the training data of perpendicular irradiation on the front surface and one side surface are obtained, and the plane (x, y) networks and DOI networks are trained and evaluated. The test results show that the artificial neural network for DOI estimation is as effective as for plane estimation. The performance of both estimators is presented by resolution and bias. Without bias correction, the resolution of the plane estimator is on average better than 2 mm and that of the DOI estimator is about 2 mm over the whole area of the detector. With bias correction, the resolution at the edge area for plane estimation or at the end of the block away from the readout PMT for DOI estimation becomes worse, as we expect. The comprehensive performance of the 3D positioning by a neural network is accessed by the experimental test data of oblique irradiations. To show the combined effect of the 3D positioning over the whole area of the detector, the 2D flood images of oblique irradiation are presented with and without bias correction. (paper)

  6. Distinct regimes of elastic response and deformation modes of cross-linked cytoskeletal and semiflexible polymer networks

    NARCIS (Netherlands)

    Head, D.A.; Levine, A.M.; Mac Kintosh, F.C.

    2003-01-01

    Semiflexible polymers such as filamentous actin (F-actin) play a vital role in the mechanical behavior of cells, yet the basic properties of cross-linked F-actin networks remain poorly understood. To address this issue, we have performed numerical studies of the linear response of homogeneous and

  7. Reprocessing and Recycling of Highly Cross-Linked Ion-Conducting Networks through Transalkylation Exchanges of C-N Bonds.

    Science.gov (United States)

    Obadia, Mona M; Mudraboyina, Bhanu P; Serghei, Anatoli; Montarnal, Damien; Drockenmuller, Eric

    2015-05-13

    Exploiting exchangeable covalent bonds as dynamic cross-links recently afforded a new class of polymer materials coined as vitrimers. These permanent networks are insoluble and infusible, but the network topology can be reshuffled at high temperatures, thus enabling glasslike plastic deformation and reprocessing without depolymerization. We disclose herein the development of functional and high-value ion-conducting vitrimers that take inspiration from poly(ionic liquid)s. Tunable networks with high ionic content are obtained by the solvent- and catalyst-free polyaddition of an α-azide-ω-alkyne monomer and simultaneous alkylation of the resulting poly(1,2,3-triazole)s with a series of difunctional cross-linking agents. Temperature-induced transalkylation exchanges of C-N bonds between 1,2,3-triazolium cross-links and halide-functionalized dangling chains enable recycling and reprocessing of these highly cross-linked permanent networks. They can also be recycled by depolymerization with specific solvents able to displace the transalkylation equilibrium, and they display a great potential for applications that require solid electrolytes with excellent mechanical performances and facile processing such as supercapacitors, batteries, fuel cells, and separation membranes.

  8. S-curve networks and an approximate method for estimating degree distributions of complex networks

    OpenAIRE

    Guo, Jin-Li

    2010-01-01

    In the study of complex networks almost all theoretical models have the property of infinite growth, but the size of actual networks is finite. According to statistics from the China Internet IPv4 (Internet Protocol version 4) addresses, this paper proposes a forecasting model by using S curve (Logistic curve). The growing trend of IPv4 addresses in China is forecasted. There are some reference value for optimizing the distribution of IPv4 address resource and the development of IPv6. Based o...

  9. Estimation of Branch Topology Errors in Power Networks by WLAN State Estimation

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Hong Rae [Soonchunhyang University(Korea); Song, Kyung Bin [Kei Myoung University(Korea)

    2000-06-01

    The purpose of this paper is to detect and identify topological errors in order to maintain a reliable database for the state estimator. In this paper, a two stage estimation procedure is used to identify the topology errors. At the first stage, the WLAV state estimator which has characteristics to remove bad data during the estimation procedure is run for finding out the suspected branches at which topology errors take place. The resulting residuals are normalized and the measurements with significant normalized residuals are selected. A set of suspected branches is formed based on these selected measurements; if the selected measurement if a line flow, the corresponding branch is suspected; if it is an injection, then all the branches connecting the injection bus to its immediate neighbors are suspected. A new WLAV state estimator adding the branch flow errors in the state vector is developed to identify the branch topology errors. Sample cases of single topology error and topology error with a measurement error are applied to IEEE 14 bus test system. (author). 24 refs., 1 fig., 9 tabs.

  10. S-curve networks and an approximate method for estimating degree distributions of complex networks

    International Nuclear Information System (INIS)

    Guo Jin-Li

    2010-01-01

    In the study of complex networks almost all theoretical models have the property of infinite growth, but the size of actual networks is finite. According to statistics from the China Internet IPv4 (Internet Protocol version 4) addresses, this paper proposes a forecasting model by using S curve (logistic curve). The growing trend of IPv4 addresses in China is forecasted. There are some reference values for optimizing the distribution of IPv4 address resource and the development of IPv6. Based on the laws of IPv4 growth, that is, the bulk growth and the finitely growing limit, it proposes a finite network model with a bulk growth. The model is said to be an S-curve network. Analysis demonstrates that the analytic method based on uniform distributions (i.e., Barabási-Albert method) is not suitable for the network. It develops an approximate method to predict the growth dynamics of the individual nodes, and uses this to calculate analytically the degree distribution and the scaling exponents. The analytical result agrees with the simulation well, obeying an approximately power-law form. This method can overcome a shortcoming of Barabási-Albert method commonly used in current network research. (general)

  11. S-curve networks and an approximate method for estimating degree distributions of complex networks

    Science.gov (United States)

    Guo, Jin-Li

    2010-12-01

    In the study of complex networks almost all theoretical models have the property of infinite growth, but the size of actual networks is finite. According to statistics from the China Internet IPv4 (Internet Protocol version 4) addresses, this paper proposes a forecasting model by using S curve (logistic curve). The growing trend of IPv4 addresses in China is forecasted. There are some reference values for optimizing the distribution of IPv4 address resource and the development of IPv6. Based on the laws of IPv4 growth, that is, the bulk growth and the finitely growing limit, it proposes a finite network model with a bulk growth. The model is said to be an S-curve network. Analysis demonstrates that the analytic method based on uniform distributions (i.e., Barabási-Albert method) is not suitable for the network. It develops an approximate method to predict the growth dynamics of the individual nodes, and uses this to calculate analytically the degree distribution and the scaling exponents. The analytical result agrees with the simulation well, obeying an approximately power-law form. This method can overcome a shortcoming of Barabási-Albert method commonly used in current network research.

  12. Data Reduction with Quantization Constraints for Decentralized Estimation in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yang Weng

    2014-01-01

    Full Text Available The unknown vector estimation problem with bandwidth constrained wireless sensor network is considered. In such networks, sensor nodes make distributed observations on the unknown vector and collaborate with a fusion center to generate a final estimate. Due to power and communication bandwidth limitations, each sensor node must compress its data and transmit to the fusion center. In this paper, both centralized and decentralized estimation frameworks are developed. The closed-form solution for the centralized estimation framework is proposed. The computational complexity of decentralized estimation problem is proven to be NP-hard and a Gauss-Seidel algorithm to search for an optimal solution is also proposed. Simulation results show the good performance of the proposed algorithms.

  13. State estimation for Markov-type genetic regulatory networks with delays and uncertain mode transition rates

    International Nuclear Information System (INIS)

    Liang Jinling; Lam, James; Wang Zidong

    2009-01-01

    This Letter is concerned with the robust state estimation problem for uncertain time-delay Markovian jumping genetic regulatory networks (GRNs) with SUM logic, where the uncertainties enter into both the network parameters and the mode transition rate. The nonlinear functions describing the feedback regulation are assumed to satisfy the sector-like conditions. The main purpose of the problem addressed is to design a linear estimator to approximate the true concentrations of the mRNA and protein through available measurement outputs. By resorting to the Lyapunov functional method and some stochastic analysis tools, it is shown that if a set of linear matrix inequalities (LMIs) is feasible, the desired state estimator, that can ensure the estimation error dynamics to be globally robustly asymptotically stable in the mean square, exists. The obtained LMI conditions are dependent on both the lower and the upper bounds of the delays. An illustrative example is presented to demonstrate the feasibility of the proposed estimation schemes.

  14. An Interference-Aware Traffic-Priority-Based Link Scheduling Algorithm for Interference Mitigation in Multiple Wireless Body Area Networks

    Directory of Open Access Journals (Sweden)

    Thien T. T. Le

    2016-12-01

    Full Text Available Currently, wireless body area networks (WBANs are effectively used for health monitoring services. However, in cases where WBANs are densely deployed, interference among WBANs can cause serious degradation of network performance and reliability. Inter-WBAN interference can be reduced by scheduling the communication links of interfering WBANs. In this paper, we propose an interference-aware traffic-priority-based link scheduling (ITLS algorithm to overcome inter-WBAN interference in densely deployed WBANs. First, we model a network with multiple WBANs as an interference graph where node-level interference and traffic priority are taken into account. Second, we formulate link scheduling for multiple WBANs as an optimization model where the objective is to maximize the throughput of the entire network while ensuring the traffic priority of sensor nodes. Finally, we propose the ITLS algorithm for multiple WBANs on the basis of the optimization model. High spatial reuse is also achieved in the proposed ITLS algorithm. The proposed ITLS achieves high spatial reuse while considering traffic priority, packet length, and the number of interfered sensor nodes. Our simulation results show that the proposed ITLS significantly increases spatial reuse and network throughput with lower delay by mitigating inter-WBAN interference.

  15. On H∞ Fault Estimator Design for Linear Discrete Time-Varying Systems under Unreliable Communication Link

    Directory of Open Access Journals (Sweden)

    Yueyang Li

    2014-01-01

    Full Text Available This paper investigates the H∞ fixed-lag fault estimator design for linear discrete time-varying (LDTV systems with intermittent measurements, which is described by a Bernoulli distributed random variable. Through constructing a novel partially equivalent dynamic system, the fault estimator design is converted into a deterministic quadratic minimization problem. By applying the innovation reorganization technique and the projection formula in Krein space, a necessary and sufficient condition is obtained for the existence of the estimator. The parameter matrices of the estimator are derived by recursively solving two standard Riccati equations. An illustrative example is provided to show the effectiveness and applicability of the proposed algorithm.

  16. Error estimation of deformable image registration of pulmonary CT scans using convolutional neural networks.

    Science.gov (United States)

    Eppenhof, Koen A J; Pluim, Josien P W

    2018-04-01

    Error estimation in nonlinear medical image registration is a nontrivial problem that is important for validation of registration methods. We propose a supervised method for estimation of registration errors in nonlinear registration of three-dimensional (3-D) images. The method is based on a 3-D convolutional neural network that learns to estimate registration errors from a pair of image patches. By applying the network to patches centered around every voxel, we construct registration error maps. The network is trained using a set of representative images that have been synthetically transformed to construct a set of image pairs with known deformations. The method is evaluated on deformable registrations of inhale-exhale pairs of thoracic CT scans. Using ground truth target registration errors on manually annotated landmarks, we evaluate the method's ability to estimate local registration errors. Estimation of full domain error maps is evaluated using a gold standard approach. The two evaluation approaches show that we can train the network to robustly estimate registration errors in a predetermined range, with subvoxel accuracy. We achieved a root-mean-square deviation of 0.51 mm from gold standard registration errors and of 0.66 mm from ground truth landmark registration errors.

  17. Neural feedback linearization adaptive control for affine nonlinear systems based on neural network estimator

    Directory of Open Access Journals (Sweden)

    Bahita Mohamed

    2011-01-01

    Full Text Available In this work, we introduce an adaptive neural network controller for a class of nonlinear systems. The approach uses two Radial Basis Functions, RBF networks. The first RBF network is used to approximate the ideal control law which cannot be implemented since the dynamics of the system are unknown. The second RBF network is used for on-line estimating the control gain which is a nonlinear and unknown function of the states. The updating laws for the combined estimator and controller are derived through Lyapunov analysis. Asymptotic stability is established with the tracking errors converging to a neighborhood of the origin. Finally, the proposed method is applied to control and stabilize the inverted pendulum system.

  18. A simple structure wavelet transform circuit employing function link neural networks and SI filters

    Science.gov (United States)

    Mu, Li; Yigang, He

    2016-12-01

    Signal processing by means of analog circuits offers advantages from a power consumption viewpoint. Implementing wavelet transform (WT) using analog circuits is of great interest when low-power consumption becomes an important issue. In this article, a novel simple structure WT circuit in analog domain is presented by employing functional link neural network (FLNN) and switched-current (SI) filters. First, the wavelet base is approximated using FLNN algorithms for giving a filter transfer function that is suitable for simple structure WT circuit implementation. Next, the WT circuit is constructed with the wavelet filter bank, whose impulse response is the approximated wavelet and its dilations. The filter design that follows is based on a follow-the-leader feedback (FLF) structure with multiple output bilinear SI integrators and current mirrors as the main building blocks. SI filter is well suited for this application since the dilation constant across different scales of the transform can be precisely implemented and controlled by the clock frequency of the circuit with the same system architecture. Finally, to illustrate the design procedure, a seventh-order FLNN-approximated Gaussian wavelet is implemented as an example. Simulations have successfully verified that the designed simple structure WT circuit has low sensitivity, low-power consumption and litter effect to the imperfections.

  19. Weak Links The Universal Key to the Stability of Networks and Complex Systems

    CERN Document Server

    Csermely, Peter

    2009-01-01

    How can our societies be stabilized in a crisis? Why can we enjoy and understand Shakespeare? Why are fruitflies uniform? How do omnivorous eating habits aid our survival? What makes the Mona Lisa’s smile beautiful? How do women keep our social structures intact? – Could there possibly be a single answer to all these questions? This book shows that the statement: "weak links stabilize complex systems" provides the key to understanding each of these intriguing puzzles, and many others too. The author (recipient of several distinguished science communication prizes) uses weak (low affinity, low probability) interactions as a thread to introduce a vast variety of networks from proteins to economics and ecosystems. Many people, from Nobel Laureates to high-school students have helped to make the book understandable to all interested readers. This unique book and the ideas it develops will have a significant impact on many, seemingly diverse, fields of study. A very personal, engaging, and unique book that wil...

  20. A Novel Topology Link-Controlling Approach for Active Defense of a Node in a Network

    Directory of Open Access Journals (Sweden)

    Jun Li

    2017-03-01

    Full Text Available With the rapid development of virtual machine technology and cloud computing, distributed denial of service (DDoS attacks, or some peak traffic, poses a great threat to the security of the network. In this paper, a novel topology link control technique and mitigation attacks in real-time environments is proposed. Firstly, a non-invasive method of deploying virtual sensors in the nodes is built, which uses the resource manager of each monitored node as a sensor. Secondly, a general topology-controlling approach of resisting the tolerant invasion is proposed. In the proposed approach, a prediction model is constructed by using copula functions for predicting the peak of a resource through another resource. The result of prediction determines whether or not to initiate the active defense. Finally, a minority game with incomplete strategy is employed to suppress attack flows and improve the permeability of the normal flows. The simulation results show that the proposed approach is very effective in protecting nodes.

  1. Linking Neurons to Network Function and Behavior by Two-Photon Holographic Optogenetics and Volumetric Imaging.

    Science.gov (United States)

    Dal Maschio, Marco; Donovan, Joseph C; Helmbrecht, Thomas O; Baier, Herwig

    2017-05-17

    We introduce a flexible method for high-resolution interrogation of circuit function, which combines simultaneous 3D two-photon stimulation of multiple targeted neurons, volumetric functional imaging, and quantitative behavioral tracking. This integrated approach was applied to dissect how an ensemble of premotor neurons in the larval zebrafish brain drives a basic motor program, the bending of the tail. We developed an iterative photostimulation strategy to identify minimal subsets of channelrhodopsin (ChR2)-expressing neurons that are sufficient to initiate tail movements. At the same time, the induced network activity was recorded by multiplane GCaMP6 imaging across the brain. From this dataset, we computationally identified activity patterns associated with distinct components of the elicited behavior and characterized the contributions of individual neurons. Using photoactivatable GFP (paGFP), we extended our protocol to visualize single functionally identified neurons and reconstruct their morphologies. Together, this toolkit enables linking behavior to circuit activity with unprecedented resolution. Copyright © 2017 Elsevier Inc. All rights reserved.

  2. Novel enzymatically cross-linked hyaluronan hydrogels support the formation of 3D neuronal networks.

    Science.gov (United States)

    Broguiere, Nicolas; Isenmann, Luca; Zenobi-Wong, Marcy

    2016-08-01

    Hyaluronan (HA) is an essential component of the central nervous system's extracellular matrix and its high molecular weight (MW) form has anti-inflammatory and anti-fibrotic properties relevant for regenerative medicine. Here, we introduce a new hydrogel based on high MW HA which is cross-linked using the transglutaminase (TG) activity of the activated blood coagulation factor XIII (FXIIIa). These HA-TG gels have significant advantages for neural tissue engineering compared to previous HA gels. Due to their chemical inertness in the absence of FXIIIa, the material can be stored long-term, is stable in solution, and shows no cytotoxicity. The gelation is completely cell-friendly due to the specificity of the enzyme and the gelation rate can be tuned from seconds to hours at physiological pH and independently of stiffness. The gels are injectable, and attach covalently to fibrinogen and fibrin, two common bioactive components in in vitro tissue engineering, as well as proteins present in vivo, allowing the gels to covalently bind to brain or spinal cord defects. These optimal chemical and bioactive properties of HA-TG gels enabled the formation of 3D neuronal cultures of unprecedented performance, showing fast neurite outgrowth, axonal and dendritic speciation, strong synaptic connectivity in 3D networks, and rapidly-occurring and long-lasting coordinated electrical activity. Copyright © 2016 Elsevier Ltd. All rights reserved.

  3. Weather Radar Estimations Feeding an Artificial Neural Network Model Weather Radar Estimations Feeding an Artificial Neural Network Model

    Directory of Open Access Journals (Sweden)

    Dawei Han

    2012-02-01

    Full Text Available The application of ANNs (Artifi cial Neural Networks has been studied by many researchers in modelling rainfall runoff processes. However, the work so far has been focused on the rainfall data from traditional raingauges. Weather radar is a modern technology which could provide high resolution rainfall in time and space. In this study, a comparison in rainfall runoff modelling between the raingauge and weather radar has been carried out. The data were collected from Brue catchment in Southwest of England, with 49 raingauges covering 136 km2 and two C-band weather radars. This raingauge network is extremely dense (for research purposes and does not represent the usual raingauge density in operational flood forecasting systems. The ANN models were set up with both lumped and spatial rainfall input. The results showed that raingauge data outperformed radar data in all the events tested, regardless of the lumped and spatial input. La aplicación de Redes Neuronales Artificiales (RNA en el modelado de lluvia-flujo ha sido estudiada ampliamente. Sin embargo, hasta ahora se han utilizado datos provenientes de pluviómetros tradicionales. Los radares meteorológicos son una tecnología moderna que puede proveer datos de lluvia de alta resolución en tiempo y espacio. Este es un trabajo de comparación en el modelado lluvia-flujo entre pluviómetros y radares meteorológicos. Los datos provienen de la cuenca del río Brue en el suroeste de Inglaterra, con 49 pluviómetros cubriendo 136 km2 y dos radares meteorológicos en la banda C. Esta red de pluviómetros es extremadamente densa (para investigación y no representa la densidad usual en sistemas de predicción de inundaciones. Los modelos de RNA fueron implementados con datos de entrada de lluvia tanto espaciados como no distribuidos. Los resultados muestran que los datos de los pluviómetros fueron mejores que los datos de los radares en todos los eventos probados.

  4. Rock property estimates using multiple seismic attributes and neural networks; Pegasus Field, West Texas

    Energy Technology Data Exchange (ETDEWEB)

    Schuelke, J.S.; Quirein, J.A.; Sarg, J.F.

    1998-12-31

    This case study shows the benefit of using multiple seismic trace attributes and the pattern recognition capabilities of neural networks to predict reservoir architecture and porosity distribution in the Pegasus Field, West Texas. The study used the power of neural networks to integrate geologic, borehole and seismic data. Illustrated are the improvements between the new neural network approach and the more traditional method of seismic trace inversion for porosity estimation. Comprehensive statistical methods and interpretational/subjective measures are used in the prediction of porosity from seismic attributes. A 3-D volume of seismic derived porosity estimates for the Devonian reservoir provide a very detailed estimate of porosity, both spatially and vertically, for the field. The additional reservoir porosity detail provided, between the well control, allows for optimal placement of horizontal wells and improved field development. 6 refs., 2 figs.

  5. The Everglades Depth Estimation Network (EDEN) surface-water model, version 2

    Science.gov (United States)

    Telis, Pamela A.; Xie, Zhixiao; Liu, Zhongwei; Li, Yingru; Conrads, Paul

    2015-01-01

    The Everglades Depth Estimation Network (EDEN) is an integrated network of water-level gages, interpolation models that generate daily water-level and water-depth data, and applications that compute derived hydrologic data across the freshwater part of the greater Everglades landscape. The U.S. Geological Survey Greater Everglades Priority Ecosystems Science provides support for EDEN in order for EDEN to provide quality-assured monitoring data for the U.S. Army Corps of Engineers Comprehensive Everglades Restoration Plan.

  6. Antibiofouling hybrid dendritic Boltorn/star PEG thiol-ene cross-linked networks.

    Science.gov (United States)

    Bartels, Jeremy W; Imbesi, Philip M; Finlay, John A; Fidge, Christopher; Ma, Jun; Seppala, Jonathan E; Nystrom, Andreas M; Mackay, Michael E; Callow, James A; Callow, Maureen E; Wooley, Karen L

    2011-06-01

    A series of thiol-ene generated amphiphilic cross-linked networks was prepared by reaction of alkene-modified Boltorn polyesters (Boltorn-ene) with varying weight percent of 4-armed poly(ethylene glycol) (PEG) tetrathiol (0-25 wt%) and varying equivalents of pentaerythritol tetrakis(3-mercaptopropionate) (PETMP) (0-64 wt%). These materials were designed to present complex surface topographies and morphologies, with heterogeneity of surface composition and properties and robust mechanical properties, to serve as nontoxic antibiofouling coatings that are amenable to large-scale production for application in the marine environment. Therefore, a two-dimensional matrix of materials compositions was prepared to study the physical and mechanical properties, over which the compositions spanned from 0 to 25 wt% PEG tetrathiol and 0-64 wt% PETMP (the overall thiol/alkene (SH/ene) ratios ranged from 0.00 to 1.00 equiv), with both cross-linker weight percentages calculated with respect to the weight of Boltorn-ene. The Boltorn-ene components were prepared through the esterification of commercially available Boltorn H30 with 3-butenoic acid. The subsequent cross-linking of the Boltorn-PEG-PETMP films was monitored using IR spectroscopy, where it was found that near-complete consumption of both thiol and alkene groups occurred when the stoichiometry was ca. 48 wt% PETMP (0.75 equiv SH/ene, independent of PEG amount). The thermal properties of the films showed an increase in T(g) with an increase in 4-armed PEG-tetrathiol wt%, regardless of the PETMP concentration. Investigation of the bulk mechanical properties in dry and wet states found that the Young's modulus was the greatest at 48 wt% PETMP (0.75 equiv of SH/ene). The ultimate tensile strength increased when PETMP was constant and the PEG concentration was increased. The Young's modulus was slightly lower for wet films at constant PEG or constant PETMP amounts, than for the dry samples. The nanoscopic surface features were

  7. Performance emulation and parameter estimation for nonlinear fibre-optic links

    DEFF Research Database (Denmark)

    Piels, Molly; Porto da Silva, Edson; Zibar, Darko

    2016-01-01

    Fibre-optic communication systems, especially when operating in the nonlinear regime, generally do not perform exactly as theory would predict. A number of methods for data-based evaluation of nonlinear fibre-optic link parameters, both for accurate performance emulation and optimization...

  8. Exploring Alternative Characteristic Curve Approaches to Linking Parameter Estimates from the Generalized Partial Credit Model.

    Science.gov (United States)

    Roberts, James S.; Bao, Han; Huang, Chun-Wei; Gagne, Phill

    Characteristic curve approaches for linking parameters from the generalized partial credit model were examined for cases in which common (anchor) items are calibrated separately in two groups. Three of these approaches are simple extensions of the test characteristic curve (TCC), item characteristic curve (ICC), and operating characteristic curve…

  9. Linear and nonlinear ARMA model parameter estimation using an artificial neural network

    Science.gov (United States)

    Chon, K. H.; Cohen, R. J.

    1997-01-01

    This paper addresses parametric system identification of linear and nonlinear dynamic systems by analysis of the input and output signals. Specifically, we investigate the relationship between estimation of the system using a feedforward neural network model and estimation of the system by use of linear and nonlinear autoregressive moving-average (ARMA) models. By utilizing a neural network model incorporating a polynomial activation function, we show the equivalence of the artificial neural network to the linear and nonlinear ARMA models. We compare the parameterization of the estimated system using the neural network and ARMA approaches by utilizing data generated by means of computer simulations. Specifically, we show that the parameters of a simulated ARMA system can be obtained from the neural network analysis of the simulated data or by conventional least squares ARMA analysis. The feasibility of applying neural networks with polynomial activation functions to the analysis of experimental data is explored by application to measurements of heart rate (HR) and instantaneous lung volume (ILV) fluctuations.

  10. Estimating tree bole volume using artificial neural network models for four species in Turkey.

    Science.gov (United States)

    Ozçelik, Ramazan; Diamantopoulou, Maria J; Brooks, John R; Wiant, Harry V

    2010-01-01

    Tree bole volumes of 89 Scots pine (Pinus sylvestris L.), 96 Brutian pine (Pinus brutia Ten.), 107 Cilicica fir (Abies cilicica Carr.) and 67 Cedar of Lebanon (Cedrus libani A. Rich.) trees were estimated using Artificial Neural Network (ANN) models. Neural networks offer a number of advantages including the ability to implicitly detect complex nonlinear relationships between input and output variables, which is very helpful in tree volume modeling. Two different neural network architectures were used and produced the Back propagation (BPANN) and the Cascade Correlation (CCANN) Artificial Neural Network models. In addition, tree bole volume estimates were compared to other established tree bole volume estimation techniques including the centroid method, taper equations, and existing standard volume tables. An overview of the features of ANNs and traditional methods is presented and the advantages and limitations of each one of them are discussed. For validation purposes, actual volumes were determined by aggregating the volumes of measured short sections (average 1 meter) of the tree bole using Smalian's formula. The results reported in this research suggest that the selected cascade correlation artificial neural network (CCANN) models are reliable for estimating the tree bole volume of the four examined tree species since they gave unbiased results and were superior to almost all methods in terms of error (%) expressed as the mean of the percentage errors. 2009 Elsevier Ltd. All rights reserved.

  11. Facing a Problem of Electrical Energy Quality in Ship Networks-measurements, Estimation, Control

    Institute of Scientific and Technical Information of China (English)

    Tomasz Tarasiuk; Janusz Mindykowski; Xiaoyan Xu

    2003-01-01

    In this paper, electrical energy quality and its indices in ship electric networks are introduced, especially the meaning of electrical energy quality terms in voltage and active and reactive power distribution indices. Then methods of measurement of marine electrical energy indices are introduced in details and a microprocessor measurement-diagnosis system with the function of measurement and control is designed. Afterwards, estimation and control of electrical power quality of marine electrical power networks are introduced. And finally, according to the existing method of measurement and control of electrical power quality in ship power networks, the improvement of relative method is proposed.

  12. Manifold absolute pressure estimation using neural network with hybrid training algorithm.

    Directory of Open Access Journals (Sweden)

    Mohd Taufiq Muslim

    Full Text Available In a modern small gasoline engine fuel injection system, the load of the engine is estimated based on the measurement of the manifold absolute pressure (MAP sensor, which took place in the intake manifold. This paper present a more economical approach on estimating the MAP by using only the measurements of the throttle position and engine speed, resulting in lower implementation cost. The estimation was done via two-stage multilayer feed-forward neural network by combining Levenberg-Marquardt (LM algorithm, Bayesian Regularization (BR algorithm and Particle Swarm Optimization (PSO algorithm. Based on the results found in 20 runs, the second variant of the hybrid algorithm yields a better network performance than the first variant of hybrid algorithm, LM, LM with BR and PSO by estimating the MAP closely to the simulated MAP values. By using a valid experimental training data, the estimator network that trained with the second variant of the hybrid algorithm showed the best performance among other algorithms when used in an actual retrofit fuel injection system (RFIS. The performance of the estimator was also validated in steady-state and transient condition by showing a closer MAP estimation to the actual value.

  13. Manifold absolute pressure estimation using neural network with hybrid training algorithm.

    Science.gov (United States)

    Muslim, Mohd Taufiq; Selamat, Hazlina; Alimin, Ahmad Jais; Haniff, Mohamad Fadzli

    2017-01-01

    In a modern small gasoline engine fuel injection system, the load of the engine is estimated based on the measurement of the manifold absolute pressure (MAP) sensor, which took place in the intake manifold. This paper present a more economical approach on estimating the MAP by using only the measurements of the throttle position and engine speed, resulting in lower implementation cost. The estimation was done via two-stage multilayer feed-forward neural network by combining Levenberg-Marquardt (LM) algorithm, Bayesian Regularization (BR) algorithm and Particle Swarm Optimization (PSO) algorithm. Based on the results found in 20 runs, the second variant of the hybrid algorithm yields a better network performance than the first variant of hybrid algorithm, LM, LM with BR and PSO by estimating the MAP closely to the simulated MAP values. By using a valid experimental training data, the estimator network that trained with the second variant of the hybrid algorithm showed the best performance among other algorithms when used in an actual retrofit fuel injection system (RFIS). The performance of the estimator was also validated in steady-state and transient condition by showing a closer MAP estimation to the actual value.

  14. Sequential optimization of methotrexate encapsulation in micellar nano-networks of polyethyleneimine ionomer containing redox-sensitive cross-links.

    Science.gov (United States)

    Abolmaali, Samira Sadat; Tamaddon, Ali; Yousefi, Gholamhossein; Javidnia, Katayoun; Dinarvand, Rasoul

    2014-01-01

    A functional polycation nanonetwork was developed for delivery of water soluble chemotherapeutic agents. The complexes of polyethyleneimine grafted methoxy polyethylene glycol (PEI-g-mPEG) and Zn(2+) were utilized as the micellar template for cross-linking with dithiodipropionic acid, followed by an acidic pH dialysis to remove the metal ion from the micellar template. The synthesis method was optimized according to pH, the molar ratio of Zn(2+), and the cross-link ratio. The atomic force microscopy showed soft, discrete, and uniform nano-networks. They were sensitive to the simulated reductive environment as determined by Ellman's assay. They showed few positive ζ potential and an average hydrodynamic diameter of 162±10 nm, which decreased to 49±11 nm upon dehydration. The ionic character of the nano-networks allowed the achievement of a higher-loading capacity of methotrexate (MTX), approximately 57% weight per weight, depending on the cross-link and the drug feed ratios. The nano-networks actively loaded with MTX presented some suitable properties, such as the hydrodynamic size of 117±16 nm, polydispersity index of 0.22, and a prolonged swelling-controlled release profile over 24 hours that boosted following reductive activation of the nanonetwork biodegradation. Unlike the PEI ionomer, the nano-networks provided an acceptable cytotoxicity profile. The drug-loaded nano-networks exhibited more specific cytotoxicity against human hepatocellular carcinoma cells if compared to free MTX at concentrations above 1 μM. The enhanced antitumor activity in vitro might be attributed to endocytic entry of MTX-loaded nano-networks that was found in the epifluorescence microscopy experiment for the fluorophore-labeled nano-networks.

  15. Gross domestic product estimation based on electricity utilization by artificial neural network

    Science.gov (United States)

    Stevanović, Mirjana; Vujičić, Slađana; Gajić, Aleksandar M.

    2018-01-01

    The main goal of the paper was to estimate gross domestic product (GDP) based on electricity estimation by artificial neural network (ANN). The electricity utilization was analyzed based on different sources like renewable, coal and nuclear sources. The ANN network was trained with two training algorithms namely extreme learning method and back-propagation algorithm in order to produce the best prediction results of the GDP. According to the results it can be concluded that the ANN model with extreme learning method could produce the acceptable prediction of the GDP based on the electricity utilization.

  16. Analysis of wireless sensor network topology and estimation of optimal network deployment by deterministic radio channel characterization.

    Science.gov (United States)

    Aguirre, Erik; Lopez-Iturri, Peio; Azpilicueta, Leire; Astrain, José Javier; Villadangos, Jesús; Falcone, Francisco

    2015-02-05

    One of the main challenges in the implementation and design of context-aware scenarios is the adequate deployment strategy for Wireless Sensor Networks (WSNs), mainly due to the strong dependence of the radiofrequency physical layer with the surrounding media, which can lead to non-optimal network designs. In this work, radioplanning analysis for WSN deployment is proposed by employing a deterministic 3D ray launching technique in order to provide insight into complex wireless channel behavior in context-aware indoor scenarios. The proposed radioplanning procedure is validated with a testbed implemented with a Mobile Ad Hoc Network WSN following a chain configuration, enabling the analysis and assessment of a rich variety of parameters, such as received signal level, signal quality and estimation of power consumption. The adoption of deterministic radio channel techniques allows the design and further deployment of WSNs in heterogeneous wireless scenarios with optimized behavior in terms of coverage, capacity, quality of service and energy consumption.

  17. Permeability Estimation of Rock Reservoir Based on PCA and Elman Neural Networks

    Science.gov (United States)

    Shi, Ying; Jian, Shaoyong

    2018-03-01

    an intelligent method which based on fuzzy neural networks with PCA algorithm, is proposed to estimate the permeability of rock reservoir. First, the dimensionality reduction process is utilized for these parameters by principal component analysis method. Further, the mapping relationship between rock slice characteristic parameters and permeability had been found through fuzzy neural networks. The estimation validity and reliability for this method were tested with practical data from Yan’an region in Ordos Basin. The result showed that the average relative errors of permeability estimation for this method is 6.25%, and this method had the better convergence speed and more accuracy than other. Therefore, by using the cheap rock slice related information, the permeability of rock reservoir can be estimated efficiently and accurately, and it is of high reliability, practicability and application prospect.

  18. A Sentiment Delivering Estimate Scheme Based on Trust Chain in Mobile Social Network

    Directory of Open Access Journals (Sweden)

    Meizi Li

    2015-01-01

    Full Text Available User sentiment analysis has become a flourishing frontier in data mining mobile social network platform since the mobile social network plays a significant role in users’ daily communication and sentiment interaction. This study studies the scheme of sentiment estimate by using the users’ trustworthy relationships for evaluating sentiment delivering. First, we address an overview of sentiment delivering estimate scheme and propose its related definitions, that is, trust chain among users, sentiment semantics, and sentiment ontology. Second, this study proposes the trust chain model and its evaluation method, which is composed of evaluation of atomic, serial, parallel, and combined trust chains. Then, we propose sentiment modeling method by presenting its modeling rules. Further, we propose the sentiment delivering estimate scheme from two aspects: explicit and implicit sentiment delivering estimate schemes, based on trust chain and sentiment modeling method. Finally, examinations and results are given to further explain effectiveness and feasibility of our scheme.

  19. Networked traffic state estimation involving mixed fixed-mobile sensor data using Hamilton-Jacobi equations

    KAUST Repository

    Canepa, Edward S.; Claudel, Christian G.

    2017-01-01

    Nowadays, traffic management has become a challenge for urban areas, which are covering larger geographic spaces and facing the generation of different kinds of traffic data. This article presents a robust traffic estimation framework for highways modeled by a system of Lighthill Whitham Richards equations that is able to assimilate different sensor data available. We first present an equivalent formulation of the problem using a Hamilton–Jacobi equation. Then, using a semi-analytic formula, we show that the model constraints resulting from the Hamilton–Jacobi equation are linear ones. We then pose the problem of estimating the traffic density given incomplete and inaccurate traffic data as a Mixed Integer Program. We then extend the density estimation framework to highway networks with any available data constraint and modeling junctions. Finally, we present a travel estimation application for a small network using real traffic measurements obtained obtained during Mobile Century traffic experiment, and comparing the results with ground truth data.

  20. Networked traffic state estimation involving mixed fixed-mobile sensor data using Hamilton-Jacobi equations

    KAUST Repository

    Canepa, Edward S.

    2017-06-19

    Nowadays, traffic management has become a challenge for urban areas, which are covering larger geographic spaces and facing the generation of different kinds of traffic data. This article presents a robust traffic estimation framework for highways modeled by a system of Lighthill Whitham Richards equations that is able to assimilate different sensor data available. We first present an equivalent formulation of the problem using a Hamilton–Jacobi equation. Then, using a semi-analytic formula, we show that the model constraints resulting from the Hamilton–Jacobi equation are linear ones. We then pose the problem of estimating the traffic density given incomplete and inaccurate traffic data as a Mixed Integer Program. We then extend the density estimation framework to highway networks with any available data constraint and modeling junctions. Finally, we present a travel estimation application for a small network using real traffic measurements obtained obtained during Mobile Century traffic experiment, and comparing the results with ground truth data.