A perturbation-theoretic approach to Lagrangian flow networks
Fujiwara, Naoya; Kirchen, Kathrin; Donges, Jonathan F.; Donner, Reik V.
2017-03-01
Complex network approaches have been successfully applied for studying transport processes in complex systems ranging from road, railway, or airline infrastructures over industrial manufacturing to fluid dynamics. Here, we utilize a generic framework for describing the dynamics of geophysical flows such as ocean currents or atmospheric wind fields in terms of Lagrangian flow networks. In this approach, information on the passive advection of particles is transformed into a Markov chain based on transition probabilities of particles between the volume elements of a given partition of space for a fixed time step. We employ perturbation-theoretic methods to investigate the effects of modifications of transport processes in the underlying flow for three different problem classes: efficient absorption (corresponding to particle trapping or leaking), constant input of particles (with additional source terms modeling, e.g., localized contamination), and shifts of the steady state under probability mass conservation (as arising if the background flow is perturbed itself). Our results demonstrate that in all three cases, changes to the steady state solution can be analytically expressed in terms of the eigensystem of the unperturbed flow and the perturbation itself. These results are potentially relevant for developing more efficient strategies for coping with contaminations of fluid or gaseous media such as ocean and atmosphere by oil spills, radioactive substances, non-reactive chemicals, or volcanic aerosols.
Advanced Load Balancing Based on Network Flow Approach in LTE-A Heterogeneous Network
Directory of Open Access Journals (Sweden)
Shucong Jia
2014-01-01
Full Text Available Long-term evolution advanced (LTE-A systems will offer better service to users by applying advanced physical layer transmission techniques and utilizing wider bandwidth. To further improve service quality, low power nodes are overlaid within a macro network, creating what is referred to as a heterogeneous network. However, load imbalance among cells often decreases the network resource utilization ratio and consequently reduces the user experience level. Load balancing (LB is an indispensable function in LTE-A self-organized network (SON to efficiently accommodate the imbalance in traffic. In this paper, we firstly evaluate the negative impact of unbalanced load among cells through Markovian model. Secondly, we formulate LB as an optimization problem which is solved using network flow approach. Furthermore, a novel algorithm named optimal solution-based LB (OSLB is proposed. The proposed OSLB algorithm is shown to be effective in providing up to 20% gain in load distribution index (LDI by a system-level simulation.
A network theory approach for a better understanding of overland flow connectivity
Masselink, Rens; Heckmann, Tobias; Temme, Arnaud; Anders, Niels; Keesstra, Saskia
2016-04-01
Hydrological connectivity describes the physical coupling, or linkages of different elements within a landscape regarding (sub)surface flows. A firm understanding of hydrological connectivity is important for catchment management applications, for e.g. habitat and species protection, and for flood resistance and resilience improvement. Thinking about (geomorphological) systems as networks can lead to new insights, which has been recognised within the scientific community as well, seeing the recent increase in the use of network (graph) theory within the geosciences. Network theory supports the analysis and understanding of complex systems by providing data structures for modelling objects and their linkages, and a versatile toolbox to quantitatively appraise network structure and properties. The objective of this study was to characterise overland flow connectivity dynamics on hillslopes in a humid sub-Mediterranean environment by using a combination of high-resolution digital-terrain models, overland flow sensors and a network approach. Results showed that there are significant differences between overland flow on agricultural areas and semi-natural shrubs areas. Positive correlations between connectivity and precipitation characteristics were found, while negative correlations between connectivity and soil moisture were found, probably due to soil water repellency. The combination of a structural network to determine potential connectivity with dynamic networks to determine the actual connectivity proved a powerful tool in analysing overland flow connectivity.
Garbin, Silvia; Alessi Celegon, Elisa; Fanton, Pietro; Botter, Gianluca
2017-04-01
The temporal variability of river flow regime is a key feature structuring and controlling fluvial ecological communities and ecosystem processes. In particular, streamflow variability induced by climate/landscape heterogeneities or other anthropogenic factors significantly affects the connectivity between streams with notable implication for river fragmentation. Hydrologic connectivity is a fundamental property that guarantees species persistence and ecosystem integrity in riverine systems. In riverine landscapes, most ecological transitions are flow-dependent and the structure of flow regimes may affect ecological functions of endemic biota (i.e., fish spawning or grazing of invertebrate species). Therefore, minimum flow thresholds must be guaranteed to support specific ecosystem services, like fish migration, aquatic biodiversity and habitat suitability. In this contribution, we present a probabilistic approach aiming at a spatially-explicit, quantitative assessment of hydrologic connectivity at the network-scale as derived from river flow variability. Dynamics of daily streamflows are estimated based on catchment-scale climatic and morphological features, integrating a stochastic, physically based approach that accounts for the stochasticity of rainfall with a water balance model and a geomorphic recession flow model. The non-exceedance probability of ecologically meaningful flow thresholds is used to evaluate the fragmentation of individual stream reaches, and the ensuing network-scale connectivity metrics. A multi-dimensional Poisson Process for the stochastic generation of rainfall is used to evaluate the impact of climate signature on reach-scale and catchment-scale connectivity. The analysis shows that streamflow patterns and network-scale connectivity are influenced by the topology of the river network and the spatial variability of climatic properties (rainfall, evapotranspiration). The framework offers a robust basis for the prediction of the impact of
A neural network approach for the blind deconvolution of turbulent flows
Maulik, Romit; San, Omer
2017-11-01
We present a single-layer feedforward artificial neural network architecture trained through a supervised learning approach for the deconvolution of flow variables from their coarse grained computations such as those encountered in large eddy simulations. We stress that the deconvolution procedure proposed in this investigation is blind, i.e. the deconvolved field is computed without any pre-existing information about the filtering procedure or kernel. This may be conceptually contrasted to the celebrated approximate deconvolution approaches where a filter shape is predefined for an iterative deconvolution process. We demonstrate that the proposed blind deconvolution network performs exceptionally well in the a-priori testing of both two-dimensional Kraichnan and three-dimensional Kolmogorov turbulence and shows promise in forming the backbone of a physics-augmented data-driven closure for the Navier-Stokes equations. PhD Student.
A neural network approach for the blind deconvolution of turbulent flows
Maulik, R.; San, O.
2017-11-01
We present a single-layer feedforward artificial neural network architecture trained through a supervised learning approach for the deconvolution of flow variables from their coarse grained computations such as those encountered in large eddy simulations. We stress that the deconvolution procedure proposed in this investigation is blind, i.e. the deconvolved field is computed without any pre-existing information about the filtering procedure or kernel. This may be conceptually contrasted to the celebrated approximate deconvolution approaches where a filter shape is predefined for an iterative deconvolution process. We demonstrate that the proposed blind deconvolution network performs exceptionally well in the a-priori testing of both two-dimensional Kraichnan and three-dimensional Kolmogorov turbulence and shows promise in forming the backbone of a physics-augmented data-driven closure for the Navier-Stokes equations.
Lin, Yi-Kuei; Yeh, Cheng-Ta
2013-03-01
Many real-life systems, such as computer systems, manufacturing systems and logistics systems, are modelled as stochastic-flow networks (SFNs) to evaluate network reliability. Here, network reliability, defined as the probability that the network successfully transmits d units of data/commodity from an origin to a destination, is a performance indicator of the systems. Network reliability maximization is a particular objective, but is costly for many system supervisors. This article solves the multi-objective problem of reliability maximization and cost minimization by finding the optimal component assignment for SFN, in which a set of multi-state components is ready to be assigned to the network. A two-stage approach integrating Non-dominated Sorting Genetic Algorithm II and simple additive weighting are proposed to solve this problem, where network reliability is evaluated in terms of minimal paths and recursive sum of disjoint products. Several practical examples related to computer networks are utilized to demonstrate the proposed approach.
Directory of Open Access Journals (Sweden)
Xiangmin Guan
2015-01-01
Full Text Available Considering reducing the airspace congestion and the flight delay simultaneously, this paper formulates the airway network flow assignment (ANFA problem as a multiobjective optimization model and presents a new multiobjective optimization framework to solve it. Firstly, an effective multi-island parallel evolution algorithm with multiple evolution populations is employed to improve the optimization capability. Secondly, the nondominated sorting genetic algorithm II is applied for each population. In addition, a cooperative coevolution algorithm is adapted to divide the ANFA problem into several low-dimensional biobjective optimization problems which are easier to deal with. Finally, in order to maintain the diversity of solutions and to avoid prematurity, a dynamic adjustment operator based on solution congestion degree is specifically designed for the ANFA problem. Simulation results using the real traffic data from China air route network and daily flight plans demonstrate that the proposed approach can improve the solution quality effectively, showing superiority to the existing approaches such as the multiobjective genetic algorithm, the well-known multiobjective evolutionary algorithm based on decomposition, and a cooperative coevolution multiobjective algorithm as well as other parallel evolution algorithms with different migration topology.
SU-E-T-619: A Network-Flow Solution Approach to VMAT Treatment Plan Optimization.
Salari, E; Craft, D
2012-06-01
To add mathematical rigor to the merging phase of the recently published two-stage VMAT optimization method called VMERGE. Using an exact merging method, we are able to better characterize the tradeoff between delivery efficiency and dose quality. VMERGE begins with an IMRT plan that uses 180 equi-spaced beams and yields the "ideal" dose. Neighboring fluence maps are successively merged, meaning they are added together and delivered as one map. The merging process improves the delivery time at the expense of deviating from the initial high-quality dose distribution. We replace the original heuristic merging method by considering the merging problem as a bi-criteria optimization problem: maximize treatment efficiency and minimize the deviation from the ideal dose. We formulate this using a network-flow model where nodes represent the beam angles along with the starting MLC leaf position and arcs represent the possible merges. Since the problem is non-convex, we employ a customized box algorithm to obtain the Pareto approximation. We also evaluate the performance of several simple heuristics. We test our exact and heuristic solution approaches on a pancreas and a prostate case. For both cases, the shape of the Pareto frontier suggests that starting from a high quality plan, we can obtain efficient VMAT plans through merging neighboring arcs without substantially deviating from the initial dose distribution. The trade-off curves obtained by the various heuristics are contrasted and shown to all be equally capable of initial plan simplifications, but to deviate in quality for more drastic efficiency improvements. This work presents a bi-criteria network-flow solution approach to the merging problem. The obtained Pareto-frontier approximation is used as a benchmark to evaluate the performance of the proposed merging heuristics. The results validate that one of the heuristics in particular can achieve high-quality solutions. © 2012 American Association of Physicists in
DEFF Research Database (Denmark)
Parraguez, Pedro; Eppinger, Steven D.; Maier, Anja
2015-01-01
as those activities are implemented through the network of people executing the project. To address this gap, we develop a dynamic modeling method that integrates both the network of people and the network of activities in the project. We then employ a large dataset collected from an industrial setting...... design process and thus support theory-building toward the evolution of information flows through systems engineering stages. Implications include guidance on how to analyze and predict information flows as well as better planning of information flows in engineering design projects according......, consisting of project-related e-mails and activity records from the design and development of a renewable energy plant over the course of more than three years. Using network metrics for centrality and clustering, we make three important contributions: 1)We demonstrate a novel method for analyzing...
Airport Network Flow Simulator
1978-10-01
The Airport Network Flow Simulator is a FORTRAN IV simulation of the flow of air traffic in the nation's 600 commercial airports. It calculates for any group of selected airports: (a) the landing and take-off (Type A) delays; and (b) the gate departu...
Foodsheds in Virtual Water Flow Networks: A Spectral Graph Theory Approach
Directory of Open Access Journals (Sweden)
Nina Kshetry
2017-06-01
Full Text Available A foodshed is a geographic area from which a population derives its food supply, but a method to determine boundaries of foodsheds has not been formalized. Drawing on the food–water–energy nexus, we propose a formal network science definition of foodsheds by using data from virtual water flows, i.e., water that is virtually embedded in food. In particular, we use spectral graph partitioning for directed graphs. If foodsheds turn out to be geographically compact, it suggests the food system is local and therefore reduces energy and externality costs of food transport. Using our proposed method we compute foodshed boundaries at the global-scale, and at the national-scale in the case of two of the largest agricultural countries: India and the United States. Based on our determination of foodshed boundaries, we are able to better understand commodity flows and whether foodsheds are contiguous and compact, and other factors that impact environmental sustainability. The formal method we propose may be used more broadly to study commodity flows and their impact on environmental sustainability.
Robinson, Julie A.; Tate-Brown, Judy M.
2009-01-01
Using a commercial software CD and minimal up-mass, SNFM monitors the Payload local area network (LAN) to analyze and troubleshoot LAN data traffic. Validating LAN traffic models may allow for faster and more reliable computer networks to sustain systems and science on future space missions. Research Summary: This experiment studies the function of the computer network onboard the ISS. On-orbit packet statistics are captured and used to validate ground based medium rate data link models and enhance the way that the local area network (LAN) is monitored. This information will allow monitoring and improvement in the data transfer capabilities of on-orbit computer networks. The Serial Network Flow Monitor (SNFM) experiment attempts to characterize the network equivalent of traffic jams on board ISS. The SNFM team is able to specifically target historical problem areas including the SAMS (Space Acceleration Measurement System) communication issues, data transmissions from the ISS to the ground teams, and multiple users on the network at the same time. By looking at how various users interact with each other on the network, conflicts can be identified and work can begin on solutions. SNFM is comprised of a commercial off the shelf software package that monitors packet traffic through the payload Ethernet LANs (local area networks) on board ISS.
Liu, Richeng; Li, Bo; Jiang, Yujing; Yu, Liyuan
2018-01-01
Hydro-mechanical properties of rock fractures are core issues for many geoscience and geo-engineering practices. Previous experimental and numerical studies have revealed that shear processes could greatly enhance the permeability of single rock fractures, yet the shear effects on hydraulic properties of fractured rock masses have received little attention. In most previous fracture network models, single fractures are typically presumed to be formed by parallel plates and flow is presumed to obey the cubic law. However, related studies have suggested that the parallel plate model cannot realistically represent the surface characters of natural rock fractures, and the relationship between flow rate and pressure drop will no longer be linear at sufficiently large Reynolds numbers. In the present study, a numerical approach was established to assess the effects of shear on the hydraulic properties of 2-D discrete fracture networks (DFNs) in both linear and nonlinear regimes. DFNs considering fracture surface roughness and variation of aperture in space were generated using an originally developed code DFNGEN. Numerical simulations by solving Navier-Stokes equations were performed to simulate the fluid flow through these DFNs. A fracture that cuts through each model was sheared and by varying the shear and normal displacements, effects of shear on equivalent permeability and nonlinear flow characteristics of DFNs were estimated. The results show that the critical condition of quantifying the transition from a linear flow regime to a nonlinear flow regime is: 10-4 〈 J fluid flow is in a linear regime (i.e., J fluid flow in the nonlinear regime (J 〉 10-3), δ2 is nonlinearly correlated with J. A shear process would reduce the equivalent permeability significantly in the orientation perpendicular to the sheared fracture as much as 53.86% when J = 1, shear displacement Ds = 7 mm, and normal displacement Dn = 1 mm. By fitting the calculated results, the mathematical
Xue, Jie; Gui, Dongwei; Lei, Jiaqiang; Sun, Huaiwei; Zeng, Fanjiang; Feng, Xinlong
2017-12-01
Agriculture and the eco-environment are increasingly competing for water. The extension of intensive farmland for ensuring food security has resulted in excessive water exploitation by agriculture. Consequently, this has led to a lack of water supply in natural ecosystems. This paper proposes a trade-off framework to coordinate the water-use conflict between agriculture and the eco-environment, based on economic compensation for irrigation stakeholders. A hybrid Bayesian network (HBN) is developed to implement the framework, including: (a) agricultural water shortage assessments after meeting environmental flows; (b) water-use tradeoff analysis between agricultural irrigation and environmental flows using the HBN; and (c) quantification of the agricultural economic compensation for different irrigation stakeholders. The constructed HBN is computed by dynamic discretization, which is a more robust and accurate propagation algorithm than general static discretization. A case study of the Qira oasis area in Northwest China demonstrates that the water trade-off based on economic compensation depends on the available water supply and environmental flows at different levels. Agricultural irrigation water extracted for grain crops should be preferentially guaranteed to ensure food security, in spite of higher economic compensation in other cash crops' irrigation for water coordination. Updating water-saving engineering and adopting drip irrigation technology in agricultural facilities after satisfying environmental flows would greatly relieve agricultural water shortage and save the economic compensation for different irrigation stakeholders. The approach in this study can be easily applied in water-stressed areas worldwide for dealing with water competition.
Directory of Open Access Journals (Sweden)
Prima Riza Kadavi
2017-09-01
Full Text Available Volcanic eruptions cause pyroclastic flows, which can destroy plantations and settlements. We used image data from Landsat 7 Bands 7, 4 and 2 and Landsat 8 Bands 7, 5 and 3 to observe and analyze the distribution of pyroclastic flow deposits for two volcanos, Mount Sinabung and Merapi, over a period of 10 years (2001–2017. The satellite data are used in conjunction with an artificial neural network method to produce maps of pyroclastic precipitation for Landsat 7 and 8, then we calculated the pyroclastic precipitation area using an artificial neural network method after dividing the images into four classes based on color. Red, green, blue and yellow were used to indicate pyroclastic deposits, vegetation and forest, water and cloud, and farmland, respectively. The area affected by a volcanic eruption was deduced from the neural network processing, including calculating the area of pyroclastic deposits. The main differences between the pyroclastic flow deposits of Mount Sinabung and Mount Merapi are: the sediment deposits of the pyroclastic flows of Mount Sinabung tend to widen, whereas those of Merapi elongated; the direction of pyroclastic flow differed; and the area affected by an eruption was greater for Mount Merapi than Mount Sinabung because the VEI (Volcanic Explosivity Index during the last 10 years of Mount Merapi was larger than Mount Sinabung.
Flowshop Scheduling Using a Network Approach | Oladeinde ...
African Journals Online (AJOL)
In this paper, a network based formulation of a permutation flow shop problem is presented. Two nuances of flow shop problems with different levels of complexity are solved using different approaches to the linear programming formulation. Key flow shop parameters inclosing makespan of the flow shop problems were ...
Linear Programming and Network Flows
Bazaraa, Mokhtar S; Sherali, Hanif D
2011-01-01
The authoritative guide to modeling and solving complex problems with linear programming-extensively revised, expanded, and updated The only book to treat both linear programming techniques and network flows under one cover, Linear Programming and Network Flows, Fourth Edition has been completely updated with the latest developments on the topic. This new edition continues to successfully emphasize modeling concepts, the design and analysis of algorithms, and implementation strategies for problems in a variety of fields, including industrial engineering, management science, operations research
2012-08-01
is being carried because the packet is always encapsulated within a VPN wrapper. Often, VPNs use a cryptographic protocol such as SSL or IPsec to...Cisco is a popular VPN implementation that can use any pre-defined TCP port for IPSEC over VPN7. The default is 10000. If you know that the network
Queueing networks a fundamental approach
Dijk, Nico
2011-01-01
This handbook aims to highlight fundamental, methodological and computational aspects of networks of queues to provide insights and to unify results that can be applied in a more general manner. The handbook is organized into five parts: Part 1 considers exact analytical results such as of product form type. Topics include characterization of product forms by physical balance concepts and simple traffic flow equations, classes of service and queue disciplines that allow a product form, a unified description of product forms for discrete time queueing networks, insights for insensitivity, and aggregation and decomposition results that allow subnetworks to be aggregated into single nodes to reduce computational burden. Part 2 looks at monotonicity and comparison results such as for computational simplification by either of two approaches: stochastic monotonicity and ordering results based on the ordering of the proces generators, and comparison results and explicit error bounds based on an underlying Markov r...
Stochastic cycle selection in active flow networks
Woodhouse, Francis; Forrow, Aden; Fawcett, Joanna; Dunkel, Jorn
2016-11-01
Active biological flow networks pervade nature and span a wide range of scales, from arterial blood vessels and bronchial mucus transport in humans to bacterial flow through porous media or plasmodial shuttle streaming in slime molds. Despite their ubiquity, little is known about the self-organization principles that govern flow statistics in such non-equilibrium networks. By connecting concepts from lattice field theory, graph theory and transition rate theory, we show how topology controls dynamics in a generic model for actively driven flow on a network. Through theoretical and numerical analysis we identify symmetry-based rules to classify and predict the selection statistics of complex flow cycles from the network topology. Our conceptual framework is applicable to a broad class of biological and non-biological far-from-equilibrium networks, including actively controlled information flows, and establishes a new correspondence between active flow networks and generalized ice-type models.
Power laws and fragility in flow networks.
Shore, Jesse; Chu, Catherine J; Bianchi, Matt T
2013-01-01
What makes economic and ecological networks so unlike other highly skewed networks in their tendency toward turbulence and collapse? Here, we explore the consequences of a defining feature of these networks: their nodes are tied together by flow. We show that flow networks tend to the power law degree distribution (PLDD) due to a self-reinforcing process involving position within the global network structure, and thus present the first random graph model for PLDDs that does not depend on a rich-get-richer function of nodal degree. We also show that in contrast to non-flow networks, PLDD flow networks are dramatically more vulnerable to catastrophic failure than non-PLDD flow networks, a finding with potential explanatory power in our age of resource- and financial-interdependence and turbulence.
Arneson, Heather M.; Dousse, Nicholas; Langbort, Cedric
2014-01-01
We consider control design for positive compartmental systems in which each compartment's outflow rate is described by a concave function of the amount of material in the compartment.We address the problem of determining the routing of material between compartments to satisfy time-varying state constraints while ensuring that material reaches its intended destination over a finite time horizon. We give sufficient conditions for the existence of a time-varying state-dependent routing strategy which ensures that the closed-loop system satisfies basic network properties of positivity, conservation and interconnection while ensuring that capacity constraints are satisfied, when possible, or adjusted if a solution cannot be found. These conditions are formulated as a linear programming problem. Instances of this linear programming problem can be solved iteratively to generate a solution to the finite horizon routing problem. Results are given for the application of this control design method to an example problem. Key words: linear programming; control of networks; positive systems; controller constraints and structure.
Current-flow efficiency of networks
Liu, Kai; Yan, Xiaoyong
2018-02-01
Many real-world networks, from infrastructure networks to social and communication networks, can be formulated as flow networks. How to realistically measure the transport efficiency of these networks is of fundamental importance. The shortest-path-based efficiency measurement has limitations, as it assumes that flow travels only along those shortest paths. Here, we propose a new metric named current-flow efficiency, in which we calculate the average reciprocal effective resistance between all pairs of nodes in the network. This metric takes the multipath effect into consideration and is more suitable for measuring the efficiency of many real-world flow equilibrium networks. Moreover, this metric can handle a disconnected graph and can thus be used to identify critical nodes and edges from the efficiency-loss perspective. We further analyze how the topological structure affects the current-flow efficiency of networks based on some model and real-world networks. Our results enable a better understanding of flow networks and shed light on the design and improvement of such networks with higher transport efficiency.
Computer Networks A Systems Approach
Peterson, Larry L
2011-01-01
This best-selling and classic book teaches you the key principles of computer networks with examples drawn from the real world of network and protocol design. Using the Internet as the primary example, the authors explain various protocols and networking technologies. Their systems-oriented approach encourages you to think about how individual network components fit into a larger, complex system of interactions. Whatever your perspective, whether it be that of an application developer, network administrator, or a designer of network equipment or protocols, you will come away with a "big pictur
Flow whitelisting in SCADA networks
DEFF Research Database (Denmark)
Barbosa, Rafael Ramos Regis; Sadre, Ramin; Pras, Aiko
2013-01-01
Supervisory control and data acquisition (SCADA) networks are commonly deployed in large industrial facilities. Modern SCADA networks are becoming more vulnerable to cyber attacks due to the common use of standard communications protocols and increased interconnections with corporate networks and...
Flow whitelisting in SCADA networks
Barbosa, R.R.R.; Sadre, R.; Pras, Aiko
2013-01-01
Supervisory control and data acquisition (SCADA) networks are commonly deployed in large industrial facilities. Modern SCADA networks are becoming more vulnerable to cyber attacks due to the common use of standard communications protocols and increased interconnections with corporate networks and
Controllability of flow-conservation networks
Zhao, Chen; Zeng, An; Jiang, Rui; Yuan, Zhengzhong; Wang, Wen-Xu
2017-07-01
The ultimate goal of exploring complex networks is to control them. As such, controllability of complex networks has been intensively investigated. Despite recent advances in studying the impact of a network's topology on its controllability, a comprehensive understanding of the synergistic impact of network topology and dynamics on controllability is still lacking. Here, we explore the controllability of flow-conservation networks, trying to identify the minimal number of driver nodes that can guide the network to any desirable state. We develop a method to analyze the controllability on flow-conservation networks based on exact controllability theory, transforming the original analysis on adjacency matrix to Laplacian matrix. With this framework, we systematically investigate the impact of some key factors of networks, including link density, link directionality, and link polarity, on the controllability of these networks. We also obtain the analytical equations by investigating the network's structural properties approximatively and design the efficient tools. Finally, we consider some real networks with flow dynamics, finding that their controllability is significantly different from that predicted by only considering the topology. These findings deepen our understanding of network controllability with flow-conservation dynamics and provide a general framework to incorporate real dynamics in the analysis of network controllability.
Vehicle Scheduling with Network Flow Models
Directory of Open Access Journals (Sweden)
Gustavo P. Silva
2010-04-01
Full Text Available
Este trabalho retrata a primeira fase de uma pesquisa de doutorado voltada para a utilização de modelos de fluxo em redes para programação de veículos (de ônibus, em particular. A utilização de modelos deste tipo ainda e muito pouco explorada na literatura, principalmente pela dificuldade imposta pelo grande numero de variáveis resultante. Neste trabalho são apresentadas formulações para tratamento do problema de programação de veículos associados a um único depósito (ou garagem como problema de fluxo em redes, incluindo duas técnicas para reduzir o numero de arcos na rede criada e, conseqüentemente, o numero de variáveis a tratar. Uma destas técnicas de redução de arcos foi implementada e o problema de fluxo resultante foi direcionado para ser resolvido, nesta fase da pesquisa, por uma versão disponível do algoritmo Simplex para redes. Problemas teste baseados em dados reais da cidade de Reading, UK, foram resolvidos com a utilização da formulação de fluxo em redes adotada, e os resultados comparados com aqueles obtidos pelo método heurístico BOOST, o qual tem sido largamente testado e comercializado pela School of Computer Studies da Universidade de Leeds, UK. Os resultados alcançados demonstram a possibilidade de tratamento de problemas reais com a técnica de redução de arcos.
ABSTRACT
This paper presents the successful results of a first phase of a doctoral research addressed to solving vehicle (bus, in particular scheduling problems through network flow formulations. Network flow modeling for this kind of problem is a promising, but not a well explored approach, mainly because of the large number of variables related to number of arcs of real case networks. The paper presents and discusses some network flow formulations for the single depot bus vehicle scheduling problem, along with two techniques of arc reduction. One of these arc reduction techniques has been implemented and the underlying
Flows in networks under fuzzy conditions
Bozhenyuk, Alexander Vitalievich; Kacprzyk, Janusz; Rozenberg, Igor Naymovich
2017-01-01
This book offers a comprehensive introduction to fuzzy methods for solving flow tasks in both transportation and networks. It analyzes the problems of minimum cost and maximum flow finding with fuzzy nonzero lower flow bounds, and describes solutions to minimum cost flow finding in a network with fuzzy arc capacities and transmission costs. After a concise introduction to flow theory and tasks, the book analyzes two important problems. The first is related to determining the maximum volume for cargo transportation in the presence of uncertain network parameters, such as environmental changes, measurement errors and repair work on the roads. These parameters are represented here as fuzzy triangular, trapezoidal numbers and intervals. The second problem concerns static and dynamic flow finding in networks under fuzzy conditions, and an effective method that takes into account the network’s transit parameters is presented here. All in all, the book provides readers with a practical reference guide to state-of-...
Anomaly Detection Approaches for Communication Networks
Thottan, Marina; Liu, Guanglei; Ji, Chuanyi
In recent years, network anomaly detection has become an important area for both commercial interests as well as academic research. Applications of anomaly detection typically stem from the perspectives of network monitoring and network security. In network monitoring, a service provider is often interested in capturing such network characteristics as heavy flows, flow size distributions, and the number of distinct flows. In network security, the interest lies in characterizing known or unknown anomalous patterns of an attack or a virus.
Polysulfide flow batteries enabled by percolating nanoscale conductor networks.
Fan, Frank Y; Woodford, William H; Li, Zheng; Baram, Nir; Smith, Kyle C; Helal, Ahmed; McKinley, Gareth H; Carter, W Craig; Chiang, Yet-Ming
2014-01-01
A new approach to flow battery design is demonstrated wherein diffusion-limited aggregation of nanoscale conductor particles at ∼1 vol % concentration is used to impart mixed electronic-ionic conductivity to redox solutions, forming flow electrodes with embedded current collector networks that self-heal after shear. Lithium polysulfide flow cathodes of this architecture exhibit electrochemical activity that is distributed throughout the volume of flow electrodes rather than being confined to surfaces of stationary current collectors. The nanoscale network architecture enables cycling of polysulfide solutions deep into precipitation regimes that historically have shown poor capacity utilization and reversibility and may thereby enable new flow battery designs of higher energy density and lower system cost. Lithium polysulfide half-flow cells operating in both continuous and intermittent flow mode are demonstrated for the first time.
Flood estimation: a neural network approach
Energy Technology Data Exchange (ETDEWEB)
Swain, P.C.; Seshachalam, C.; Umamahesh, N.V. [Regional Engineering Coll., Warangal (India). Water and Environment Div.
2000-07-01
The artificial neural network (ANN) approach described in this study aims at predicting the flood flow into a reservoir. This differs from the traditional methods of flow prediction in the sense that it belongs to a class of data driven approaches, where as the traditional methods are model driven. Physical processes influencing the occurrences of streamflow in a river are highly complex, and are very difficult to be modelled by available statistical or deterministic models. ANNs provide model free solutions and hence can be expected to be appropriate in these conditions. Non-linearity, adaptivity, evidential response and fault tolerance are additional properties and capabilities of the neural networks. This paper highlights the applicability of neural networks for predicting daily flood flow taking the Hirakud reservoir on river Mahanadi in Orissa, India as the case study. The correlation between the observed and predicted flows and the relative error are considered to measure the performance of the model. The correlation between the observed and the modelled flows are computed to be 0.9467 in testing phase of the model. (orig.)
Flow Whitelisting in SCADA Networks
Barbosa, R.R.R.; Pras, Aiko; Sadre, R.
2013-01-01
Supervisory Control And Data Acquisition (SCADA) networks are commonly deployed to aid the operation of large industrial facilities. Modern SCADA networks are becoming more vulnerable to network attacks, due to the now common use of standard communication protocols and increased interconnection to
Flow whitelisting in SCADA networks
Barbosa, R.R.R.; Pras, Aiko; Sadre, R.
Supervisory Control And Data Acquisition (SCADA) networks are commonly deployed to aid the operation of large industrial facilities. Modern SCADA networks are becoming more vulnerable to network attacks, due to the now common use of standard communication protocols and increased interconnection to
A Network Traffic Control Enhancement Approach over Bluetooth Networks
DEFF Research Database (Denmark)
Son, L.T.; Schiøler, Henrik; Madsen, Ole Brun
2003-01-01
This paper analyzes network traffic control issues in Bluetooth data networks as convex optimization problem. We formulate the problem of maximizing of total network flows and minimizing the costs of flows. An adaptive distributed network traffic control scheme is proposed as an approximated...... solution of the stated optimization problem that satisfies quality of service requirements and topologically induced constraints in Bluetooth networks, such as link capacity and node resource limitations. The proposed scheme is decentralized and complies with frequent changes of topology as well...... as capacity limitations and flow requirements in the network. Simulation shows that the performance of Bluetooth networks could be improved by applying the adaptive distributed network traffic control scheme...
Employment Growth through Labor Flow Networks
Guerrero, Omar A.; Axtell, Robert L.
2013-01-01
It is conventional in labor economics to treat all workers who are seeking new jobs as belonging to a labor pool, and all firms that have job vacancies as an employer pool, and then match workers to jobs. Here we develop a new approach to study labor and firm dynamics. By combining the emerging science of networks with newly available employment micro-data, comprehensive at the level of whole countries, we are able to broadly characterize the process through which workers move between firms. Specifically, for each firm in an economy as a node in a graph, we draw edges between firms if a worker has migrated between them, possibly with a spell of unemployment in between. An economy's overall graph of firm-worker interactions is an object we call the labor flow network (LFN). This is the first study that characterizes a LFN for an entire economy. We explore the properties of this network, including its topology, its community structure, and its relationship to economic variables. It is shown that LFNs can be useful in identifying firms with high growth potential. We relate LFNs to other notions of high performance firms. Specifically, it is shown that fewer than 10% of firms account for nearly 90% of all employment growth. We conclude with a model in which empirically-salient LFNs emerge from the interaction of heterogeneous adaptive agents in a decentralized labor market. PMID:23658682
Introduction to Focus Issue: Complex network perspectives on flow systems
Donner, Reik V.; Hernández-García, Emilio; Ser-Giacomi, Enrico
2017-03-01
During the last few years, complex network approaches have demonstrated their great potentials as versatile tools for exploring the structural as well as dynamical properties of dynamical systems from a variety of different fields. Among others, recent successful examples include (i) functional (correlation) network approaches to infer hidden statistical interrelationships between macroscopic regions of the human brain or the Earth's climate system, (ii) Lagrangian flow networks allowing to trace dynamically relevant fluid-flow structures in atmosphere, ocean or, more general, the phase space of complex systems, and (iii) time series networks unveiling fundamental organization principles of dynamical systems. In this spirit, complex network approaches have proven useful for data-driven learning of dynamical processes (like those acting within and between sub-components of the Earth's climate system) that are hidden to other analysis techniques. This Focus Issue presents a collection of contributions addressing the description of flows and associated transport processes from the network point of view and its relationship to other approaches which deal with fluid transport and mixing and/or use complex network techniques.
Network Routing Using the Network Tasking Order, a Chron Approach
2015-03-26
iv BATMAN Better Approach to Mobile Ad-hoc Networks...routing in wireless mesh networks. MicroTik has created MME based on the Better Approach to Mobile Ad-hoc Network ( BATMAN ) routing protocol. MME
Brain network clustering with information flow motifs
Märtens, M.; Meier, J.M.; Hillebrand, Arjan; Tewarie, Prejaas; Van Mieghem, P.F.A.
2017-01-01
Recent work has revealed frequency-dependent global patterns of information flow by a network analysis of magnetoencephalography data of the human brain. However, it is unknown which properties on a small subgraph-scale of those functional brain networks are dominant at different frequencies bands.
Network Structure of Inter-Industry Flows
McNerney, J.; Fath, B.D.; Silverberg, G.P.
2015-01-01
We study the structure of inter-industry relationships using networks of money flows between industries in 45 national economies. We find these networks vary around a typical structure characterized by a Weibull link weight distribution, exponential industry size distribution, and a common community
Brocade: Optimal flow placement in SDN networks
CERN. Geneva
2015-01-01
Today' network poses several challanges to network providers. These challanges fall in to a variety of areas ranging from determining efficient utilization of network bandwidth to finding out which user applications consume majority of network resources. Also, how to protect a given network from volumetric and botnet attacks. Optimal placement of flows deal with identifying network issues and addressing them in a real-time. The overall solution helps in building new services where a network is more secure and more efficient. Benefits derived as a result are increased network efficiency due to better capacity and resource planning, better security with real-time threat mitigation, and improved user experience as a result of increased service velocity.
An Innovative Approach for Drainage Network Sizing
Directory of Open Access Journals (Sweden)
Luca Cozzolino
2015-02-01
Full Text Available In this paper, a procedure for the optimal design of rural drainage networks is presented and demonstrated. The suggested approach, exploring the potentialities offered by heuristic methods for the solution of complex optimization problems, is based on the use of a Genetic Algorithm (GA, coupled with a steady and uniform flow hydraulic module. In particular, this work has focused: on one hand, on the problems of a technical nature posed by the correct sizing of a drainage network; on the other hand, on the possibility to use a simple but nevertheless efficient GA to reach the minimal cost solution very quickly. The suitability of the approach is tested with reference to small and large scale drainage networks, already considered in the literature.
Information Flows in Networked Engineering Design Projects
DEFF Research Database (Denmark)
Parraguez, Pedro; Maier, Anja
networks at the project level or in studying the social networks that deliver the “actual information flow”. In this paper we propose and empirically test a model and method that integrates both social and activity networks into one compact representation, allowing to compare actual and required......Complex engineering design projects need to manage simultaneously multiple information flows across design activities associated with different areas of the design process. Previous research on this area has mostly focused on either analysing the “required information flows” through activity...... information flows between design spaces, and to assess the influence that these misalignments could have on the performance of engineering design projects....
Rules Placement Problem in OpenFlow Networks: a Survey
Nguyen, Xuan Nam; Saucez, Damien; Barakat, Chadi; Turletti, Thierry
2016-01-01
International audience; Software-Defined Networking (SDN) abstracts low- level network functionalities to simplify network management and reduce costs. The OpenFlow protocol implements the SDN concept by abstracting network communications as flows to be processed by network elements. In OpenFlow, the high-level policies are translated into network primitives called rules that are distributed over the network. While the abstraction offered by OpenFlow allows to potentially implement any policy...
Mode Selection in Compressible Active Flow Networks
Forrow, Aden; Woodhouse, Francis G.; Dunkel, Jörn
2017-07-01
Coherent, large-scale dynamics in many nonequilibrium physical, biological, or information transport networks are driven by small-scale local energy input. Here, we introduce and explore an analytically tractable nonlinear model for compressible active flow networks. In contrast to thermally driven systems, we find that active friction selects discrete states with a limited number of oscillation modes activated at distinct fixed amplitudes. Using perturbation theory, we systematically predict the stationary states of noisy networks and find good agreement with a Bayesian state estimation based on a hidden Markov model applied to simulated time series data. Our results suggest that the macroscopic response of active network structures, from actomyosin force networks to cytoplasmic flows, can be dominated by a significantly reduced number of modes, in contrast to energy equipartition in thermal equilibrium. The model is also well suited to study topological sound modes and spectral band gaps in active matter.
Software defined networking with OpenFlow
Azodolmolky, Siamak
2013-01-01
A step-by-step, example-based guide which will help you gain hands-on experience with the platforms and debugging tools on OpenFlow.If you are a network engineer, architect, junior researcher or an application developer, this book is ideal for you. You will need to have some level of network experience, knowledge of broad networking concepts, and some familiarity with day- to- day operation of computer networks. Ideally, you should also be familiar with programing scripting/languages (especially Python and Java), and system virtualization.
Rawstron, Andy C.; Orfao, Alberto; Beksac, Meral; Bezdickova, Ludmila; Broolmans, Rik A.; Bumbea, Horia; Dalva, Klara; Fuhler, Gwenny; Gratama, Jan; Hose, Dirk; Kovarova, Lucie; Lioznov, Michael; Mateo, Gema; Morilla, Ricardo; Mylin, Anne K.; Omede, Paola; Pellat-Deceunynck, Catherine; Andres, Martin Perez; Petrucci, Maria; Ruggeri, Marina; Rymkiewicz, Grzegorz; Schmitz, Alexander; Schreder, Martin; Seynaeve, Carine; Spacek, Martin; de Tute, Ruth M.; Van Valckenborgh, Els; Weston-Bell, Nicky; Owen, Roger G.; Miguel, Jesus F. San; Sonneveld, Pieter; Johnsen, Hans E.
The European Myeloma Network (EMN) organized two flow cytometry workshops. The first aimed to identify specific indications for flow cytometry in patients with monoclonal gammopathies, and consensus technical approaches through a questionnaire-based review of current practice in participating
International Trade Modelling Using Open Flow Networks: A Flow-Distance Based Analysis.
Shen, Bin; Zhang, Jiang; Li, Yixiao; Zheng, Qiuhua; Li, Xingsen
2015-01-01
This paper models and analyzes international trade flows using open flow networks (OFNs) with the approaches of flow distances, which provide a novel perspective and effective tools for the study of international trade. We discuss the establishment of OFNs of international trade from two coupled viewpoints: the viewpoint of trading commodity flow and that of money flow. Based on the novel model with flow distance approaches, meaningful insights are gained. First, by introducing the concepts of trade trophic levels and niches, countries' roles and positions in the global supply chains (or value-added chains) can be evaluated quantitatively. We find that the distributions of trading "trophic levels" have the similar clustering pattern for different types of commodities, and summarize some regularities between money flow and commodity flow viewpoints. Second, we find that active and competitive countries trade a wide spectrum of products, while inactive and underdeveloped countries trade a limited variety of products. Besides, some abnormal countries import many types of goods, which the vast majority of countries do not need to import. Third, harmonic node centrality is proposed and we find the phenomenon of centrality stratification. All the results illustrate the usefulness of the model of OFNs with its network approaches for investigating international trade flows.
Flow enforcement algorithms for ATM networks
DEFF Research Database (Denmark)
Dittmann, Lars; Jacobsen, Søren B.; Moth, Klaus
1991-01-01
Four measurement algorithms for flow enforcement in asynchronous transfer mode (ATM) networks are presented. The algorithms are the leaky bucket, the rectangular sliding window, the triangular sliding window, and the exponentially weighted moving average. A comparison, based partly on teletraffic....... Implementations are proposed on the block diagram level, and dimensioning examples are carried out when flow enforcing a renewal-type connection using the four algorithms. The corresponding hardware demands are estimated aid compared......Four measurement algorithms for flow enforcement in asynchronous transfer mode (ATM) networks are presented. The algorithms are the leaky bucket, the rectangular sliding window, the triangular sliding window, and the exponentially weighted moving average. A comparison, based partly on teletraffic...... theory and partly on signal processing theory, is carried out. It is seen that the time constant involved increases with the increasing burstiness of the connection. It is suggested that the RMS measurement bandwidth be used to dimension linear algorithms for equal flow enforcement characteristics...
Network structure of inter-industry flows
McNerney, James; Fath, Brian D.; Silverberg, Gerald
2013-12-01
We study the structure of inter-industry relationships using networks of money flows between industries in 45 national economies. We find these networks vary around a typical structure characterized by a Weibull link weight distribution, exponential industry size distribution, and a common community structure. The community structure is hierarchical, with the top level of the hierarchy comprising five industry communities: food industries, chemical industries, manufacturing industries, service industries, and extraction industries.
Road maintenance planning using network flow modelling
Yang, Chao; Remenyte-Prescott, Rasa; Andrews, John
2015-01-01
This paper presents a road maintenance planning model that can be used to balance out maintenance cost and road user cost, since performing road maintenance at night can be convenient for road users but costly for highway agency. Based on the platform of the network traffic flow modelling, the traffic through the worksite and its adjacent road links is evaluated. Thus, maintenance arrangements at a worksite can be optimized considering the overall network performance. In addition, genetic alg...
Network structure of inter-industry flows
McNerney, James; Silverberg, Gerald
2012-01-01
We study the structure of inter-industry relationships using networks of money flows between industries in 20 national economies. We find these networks vary around a typical structure characterized by a Weibull link weight distribution, exponential industry size distribution, and a common community structure. The community structure is hierarchical, with the top level of the hierarchy comprising five industry communities: food industries, chemical industries, manufacturing industries, service industries, and extraction industries.
Network structure of inter-industry flows
McNerney, J.; Fath, B.D.; G. Silverberg
2012-01-01
We study the structure of inter-industry relationships using networks of money flows between industries in 20 national economies. We find these networks vary around a typical structure characterized by a Weibull link weight distribution, exponential industry size distribution, and a common community structure. The community structure is hierarchical, with the top level of the hierarchy comprising five industry communities: food industries, chemical industries, manufacturing industries, servic...
The maximum flow in dynamic networks
Directory of Open Access Journals (Sweden)
Maria A. Fonoberova
2005-01-01
Full Text Available The dynamic maximum flow problem that generalizes the static maximum flow problem is formulated and studied. We consider the problem on a network with capacities depending on time, fixed transit times on the arcs, and a given time horizon. The corresponding algorithm to solve this problem is proposed and some details concerning its complexity are discussed. Mathematics Subject Classification 2000: 90B10, 90C35, 90C27.
Interest communities and flow roles in directed networks: the Twitter network of the UK riots.
Beguerisse-Díaz, Mariano; Garduño-Hernández, Guillermo; Vangelov, Borislav; Yaliraki, Sophia N; Barahona, Mauricio
2014-12-06
Directionality is a crucial ingredient in many complex networks in which information, energy or influence are transmitted. In such directed networks, analysing flows (and not only the strength of connections) is crucial to reveal important features of the network that might go undetected if the orientation of connections is ignored. We showcase here a flow-based approach for community detection through the study of the network of the most influential Twitter users during the 2011 riots in England. Firstly, we use directed Markov Stability to extract descriptions of the network at different levels of coarseness in terms of interest communities, i.e. groups of nodes within which flows of information are contained and reinforced. Such interest communities reveal user groupings according to location, profession, employer and topic. The study of flows also allows us to generate an interest distance, which affords a personalized view of the attention in the network as viewed from the vantage point of any given user. Secondly, we analyse the profiles of incoming and outgoing long-range flows with a combined approach of role-based similarity and the novel relaxed minimum spanning tree algorithm to reveal that the users in the network can be classified into five roles. These flow roles go beyond the standard leader/follower dichotomy and differ from classifications based on regular/structural equivalence. We then show that the interest communities fall into distinct informational organigrams characterized by a different mix of user roles reflecting the quality of dialogue within them. Our generic framework can be used to provide insight into how flows are generated, distributed, preserved and consumed in directed networks.
Hierarchical social networks and information flow
López, Luis; F. F. Mendes, Jose; Sanjuán, Miguel A. F.
2002-12-01
Using a simple model for the information flow on social networks, we show that the traditional hierarchical topologies frequently used by companies and organizations, are poorly designed in terms of efficiency. Moreover, we prove that this type of structures are the result of the individual aim of monopolizing as much information as possible within the network. As the information is an appropriate measurement of centrality, we conclude that this kind of topology is so attractive for leaders, because the global influence each actor has within the network is completely determined by the hierarchical level occupied.
Cycle flows and multistability in oscillatory networks
Manik, Debsankha; Timme, Marc; Witthaut, Dirk
2017-08-01
We study multistability in phase locked states in networks of phase oscillators under both Kuramoto dynamics and swing equation dynamics—a popular model for studying coarse-scale dynamics of an electrical AC power grid. We first establish the existence of geometrically frustrated states in such systems—where although a steady state flow pattern exists, no fixed point exists in the dynamical variables of phases due to geometrical constraints. We then describe the stable fixed points of the system with phase differences along each edge not exceeding π/2 in terms of cycle flows—constant flows along each simple cycle—as opposed to phase angles or flows. The cycle flow formalism allows us to compute tight upper and lower bounds to the number of fixed points in ring networks. We show that long elementary cycles, strong edge weights, and spatially homogeneous distribution of natural frequencies (for the Kuramoto model) or power injections (for the oscillator model for power grids) cause such networks to have more fixed points. We generalize some of these bounds to arbitrary planar topologies and derive scaling relations in the limit of large capacity and large cycle lengths, which we show to be quite accurate by numerical computation. Finally, we present an algorithm to compute all phase locked states—both stable and unstable—for planar networks.
CUFID-query: accurate network querying through random walk based network flow estimation.
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
Considerations for Software Defined Networking (SDN): Approaches and use cases
Bakshi, K.
Software Defined Networking (SDN) is an evolutionary approach to network design and functionality based on the ability to programmatically modify the behavior of network devices. SDN uses user-customizable and configurable software that's independent of hardware to enable networked systems to expand data flow control. SDN is in large part about understanding and managing a network as a unified abstraction. It will make networks more flexible, dynamic, and cost-efficient, while greatly simplifying operational complexity. And this advanced solution provides several benefits including network and service customizability, configurability, improved operations, and increased performance. There are several approaches to SDN and its practical implementation. Among them, two have risen to prominence with differences in pedigree and implementation. This paper's main focus will be to define, review, and evaluate salient approaches and use cases of the OpenFlow and Virtual Network Overlay approaches to SDN. OpenFlow is a communication protocol that gives access to the forwarding plane of a network's switches and routers. The Virtual Network Overlay relies on a completely virtualized network infrastructure and services to abstract the underlying physical network, which allows the overlay to be mobile to other physical networks. This is an important requirement for cloud computing, where applications and associated network services are migrated to cloud service providers and remote data centers on the fly as resource demands dictate. The paper will discuss how and where SDN can be applied and implemented, including research and academia, virtual multitenant data center, and cloud computing applications. Specific attention will be given to the cloud computing use case, where automated provisioning and programmable overlay for scalable multi-tenancy is leveraged via the SDN approach.
Parameter estimation in channel network flow simulation
Directory of Open Access Journals (Sweden)
Han Longxi
2008-03-01
Full Text Available Simulations of water flow in channel networks require estimated values of roughness for all the individual channel segments that make up a network. When the number of individual channel segments is large, the parameter calibration workload is substantial and a high level of uncertainty in estimated roughness cannot be avoided. In this study, all the individual channel segments are graded according to the factors determining the value of roughness. It is assumed that channel segments with the same grade have the same value of roughness. Based on observed hydrological data, an optimal model for roughness estimation is built. The procedure of solving the optimal problem using the optimal model is described. In a test of its efficacy, this estimation method was applied successfully in the simulation of tidal water flow in a large complicated channel network in the lower reach of the Yangtze River in China.
Weighted complex network analysis of the Beijing subway system: Train and passenger flows
Feng, Jia; Li, Xiamiao; Mao, Baohua; Xu, Qi; Bai, Yun
2017-05-01
In recent years, complex network theory has become an important approach to the study of the structure and dynamics of traffic networks. However, because traffic data is difficult to collect, previous studies have usually focused on the physical topology of subway systems, whereas few studies have considered the characteristics of traffic flows through the network. Therefore, in this paper, we present a multi-layer model to analyze traffic flow patterns in subway networks, based on trip data and an operation timetable obtained from the Beijing Subway System. We characterize the patterns in terms of the spatiotemporal flow size distributions of both the train flow network and the passenger flow network. In addition, we describe the essential interactions between these two networks based on statistical analyses. The results of this study suggest that layered models of transportation systems can elucidate fundamental differences between the coexisting traffic flows and can also clarify the mechanism that causes these differences.
Evolution of karst conduit networks in transition from pressurised flow to free surface flow
Perne, M.; Covington, M. D.; Gabrovšek, F.
2014-06-01
We present a novel modelling approach to study the evolution of conduit networks in soluble rocks. Unlike the models presented so far, the model allows a transition from pressurised (pipe) flow to a free surface (open channel) flow in evolving discrete conduit networks. It calculates flow, solute transport and dissolutional enlargement within each time step and steps through time until a stable flow pattern establishes. The flow in each time step is calculated by calling the EPA Storm Water Management Model (EPA SWMM), which efficiently solves the 1-D Saint Venant equations in a network of conduits. We present several cases with low dip and sub-vertical networks to demonstrate mechanisms of flow pathway selection. In low dip models the inputs were randomly distributed to several junctions. The evolution of pathways progresses upstream: initially pathways linking outlets to the closest inputs evolve fastest because the gradient along these pathways is largest. When a pathway efficiently drains the available recharge, the head drop along the pathway attracts flow from the neighbouring upstream junctions and new connecting pathways evolve. The mechanism progresses from the output boundary inwards until all inputs are connected to the stable flow system. In the pressurised phase, each junction is drained by at least one conduit, but only one conduit remains active in the vadose phase. The selection depends on the initial geometry of a junction, initial distribution of diameters, the evolution in a pressurised regime, and on the dip of the conduits, which plays an important role in vadose entrenchment. In high dip networks, the vadose zone propagates downwards and inwards from the rim of the massif. When a network with randomly distributed initial diameters is supplied with concentrated recharge from the adjacent area, the sink point regresses up upstream along junctions connected to the prominent pathways. Large conductive structures provide deep penetration of high
Coevolution of functional flow processing networks
Kaluza, Pablo
2017-04-01
We present a study about the construction of functional flow processing networks that produce prescribed output patterns (target functions). The constructions are performed with a process of mutations and selections by an annealing-like algorithm. We consider the coevolution of the prescribed target functions during the optimization processes. We propose three different paths for these coevolutions in order to evolve from a simple initial function to a more complex final one. We compute several network properties during the optimizations by using the different path-coevolutions as mean values over network ensembles. As a function of the number of iterations of the optimization we find a similar behavior like a phase transition in the network structures. This result can be seen clearly in the mean motif distributions of the constructed networks. Coevolution allows to identify that feed-forward loops are responsible for the development of the temporal response of these systems. Finally, we observe that with a large number of iterations the optimized networks present similar properties despite the path-coevolution we employed.
Lattice Boltzmann approach for complex nonequilibrium flows.
Montessori, A; Prestininzi, P; La Rocca, M; Succi, S
2015-10-01
We present a lattice Boltzmann realization of Grad's extended hydrodynamic approach to nonequilibrium flows. This is achieved by using higher-order isotropic lattices coupled with a higher-order regularization procedure. The method is assessed for flow across parallel plates and three-dimensional flows in porous media, showing excellent agreement of the mass flow with analytical and numerical solutions of the Boltzmann equation across the full range of Knudsen numbers, from the hydrodynamic regime to ballistic motion.
Neural network system for traffic flow management
Gilmore, John F.; Elibiary, Khalid J.; Petersson, L. E. Rickard
1992-09-01
Atlanta will be the home of several special events during the next five years ranging from the 1996 Olympics to the 1994 Super Bowl. When combined with the existing special events (Braves, Falcons, and Hawks games, concerts, festivals, etc.), the need to effectively manage traffic flow from surface streets to interstate highways is apparent. This paper describes a system for traffic event response and management for intelligent navigation utilizing signals (TERMINUS) developed at Georgia Tech for adaptively managing special event traffic flows in the Atlanta, Georgia area. TERMINUS (the original name given Atlanta, Georgia based upon its role as a rail line terminating center) is an intelligent surface street signal control system designed to manage traffic flow in Metro Atlanta. The system consists of three components. The first is a traffic simulation of the downtown Atlanta area around Fulton County Stadium that models the flow of traffic when a stadium event lets out. Parameters for the surrounding area include modeling for events during various times of day (such as rush hour). The second component is a computer graphics interface with the simulation that shows the traffic flows achieved based upon intelligent control system execution. The final component is the intelligent control system that manages surface street light signals based upon feedback from control sensors that dynamically adapt the intelligent controller's decision making process. The intelligent controller is a neural network model that allows TERMINUS to control the configuration of surface street signals to optimize the flow of traffic away from special events.
A network approach toward literature review
van de Wijngaert, Lidwien; Bouwman, Harry; Contractor, Noshir
2012-01-01
This study introduces a method that uses a network approach towards literature review. To employ this approach, we use hypotheses proposed in scientific publications as building blocks. In network terms, a hypothesis is a directed tie between two concepts or nodes. The network emerges by aggregating
Social network analysis (SNA) is based on a conceptual network representation of social interactions and is an invaluable tool for conservation professionals to increase collaboration, improve information flow, and increase efficiency. We present two approaches to constructing in...
Methodologies and techniques for analysis of network flow data
Energy Technology Data Exchange (ETDEWEB)
Bobyshev, A.; Grigoriev, M.; /Fermilab
2004-12-01
Network flow data gathered at the border routers and core switches is used at Fermilab for statistical analysis of traffic patterns, passive network monitoring, and estimation of network performance characteristics. Flow data is also a critical tool in the investigation of computer security incidents. Development and enhancement of flow based tools is an on-going effort. This paper describes the most recent developments in flow analysis at Fermilab.
Graphs, Ideal Flow, and the Transportation Network
Teknomo, Kardi
2016-01-01
This lecture discusses the mathematical relationship between network structure and network utilization of transportation network. Network structure means the graph itself. Network utilization represent the aggregation of trajectories of agents in using the network graph. I show the similarity and relationship between the structural pattern of the network and network utilization.
A two-stage flow-based intrusion detection model for next-generation networks.
Umer, Muhammad Fahad; Sher, Muhammad; Bi, Yaxin
2018-01-01
The next-generation network provides state-of-the-art access-independent services over converged mobile and fixed networks. Security in the converged network environment is a major challenge. Traditional packet and protocol-based intrusion detection techniques cannot be used in next-generation networks due to slow throughput, low accuracy and their inability to inspect encrypted payload. An alternative solution for protection of next-generation networks is to use network flow records for detection of malicious activity in the network traffic. The network flow records are independent of access networks and user applications. In this paper, we propose a two-stage flow-based intrusion detection system for next-generation networks. The first stage uses an enhanced unsupervised one-class support vector machine which separates malicious flows from normal network traffic. The second stage uses a self-organizing map which automatically groups malicious flows into different alert clusters. We validated the proposed approach on two flow-based datasets and obtained promising results.
Directory of Open Access Journals (Sweden)
Zhang Weiyang
2016-03-01
Full Text Available Previous empirical research on urban networks has used data on infrastructure networks to guesstimate actual inter-city flows. However, with the exception of recent research on airline networks in the context of the world city literature, relatively limited attention has been paid to the degree to which the outline of these infrastructure networks reflects the actual flows they undergird. This study presents a method to improve our estimation of urban interaction in and through infrastructure networks by focusing on the example of passenger railways, which is arguably a key potential data source in research on urban networks in metropolitan regions. We first review common biases when using infrastructure networks to approximate actual inter-city flows, after which we present an alternative approach that draws on research on operational train scheduling. This research has shown that ‘dwell time’ at train stations reflects the length of the alighting and boarding process, and we use this insight to estimate actual interaction through the application of a bimodal network projection function. We apply our method to the high-speed railway (HSR network within the Yangtze River Delta (YRD region, discuss the difference between our modelled network and the original network, and evaluate its validity through a systemic comparison with a benchmark dataset of actual passenger flows.
Mapping information flow in sensorimotor networks.
Directory of Open Access Journals (Sweden)
Max Lungarella
2006-10-01
Full Text Available Biological organisms continuously select and sample information used by their neural structures for perception and action, and for creating coherent cognitive states guiding their autonomous behavior. Information processing, however, is not solely an internal function of the nervous system. Here we show, instead, how sensorimotor interaction and body morphology can induce statistical regularities and information structure in sensory inputs and within the neural control architecture, and how the flow of information between sensors, neural units, and effectors is actively shaped by the interaction with the environment. We analyze sensory and motor data collected from real and simulated robots and reveal the presence of information structure and directed information flow induced by dynamically coupled sensorimotor activity, including effects of motor outputs on sensory inputs. We find that information structure and information flow in sensorimotor networks (a is spatially and temporally specific; (b can be affected by learning, and (c can be affected by changes in body morphology. Our results suggest a fundamental link between physical embeddedness and information, highlighting the effects of embodied interactions on internal (neural information processing, and illuminating the role of various system components on the generation of behavior.
Modelling flow dynamics in water distribution networks using ...
African Journals Online (AJOL)
DR OKE
Keywords: Artificial neural network; Leakage detection technique; Water distribution; Leakages ... techniques, artificial neural networks (ANNs), genetic algorithms (GA), and probabilistic and evidential reasoning. ANNs are mimicry of ..... Implementation of an online artificial intelligence district meter area flow meter data.
Network growth approach to macroevolution
Qin, Shao-Meng; Chen, Yong; Zhang, Pan
2006-01-01
We propose a novel network growth model coupled with the competition interaction to simulate macroevolution. Our work shows that the competition plays an important role in macroevolution and it is more rational to describe the interaction between species by network structures. Our model presents a complete picture of the development of phyla and the splitting process. It is found that periodic mass extinction occurred in our networks without any extraterrestrial factors and the lifetime distr...
OpenFlow Extensions for Programmable Quantum Networks
2017-06-19
systems connected via classical communication channels. These networks differ from standard classical networks by their use of quantum physical phenomena...apply our framework to a physical multinode quantum network after more testing is completed in our emulated environment. Approved for public...ARL-TR-8043 JUN 2017 US Army Research Laboratory OpenFlow Extensions for Programmable Quantum Networks by Venkat Dasari
A network approach to leadership
DEFF Research Database (Denmark)
Lewis, Jenny; Ricard, Lykke Margot
Leaders’ ego-networks within an organization are pivotal as focal points that point to other organizational factors such as innovation capacity and leadership effectiveness. The aim of the paper is to provide a framework for exploring leaders’ ego-networks within the boundary of an organization. We...... redundancy and effective size, and the potential for either divide and conquer or distributed leadership strategies. The empirical testing of this framework adds to our knowledge of the micro level role of individuals within networks. This will be used to examine the relationships between leadership, network...
Network growth approach to macroevolution
Energy Technology Data Exchange (ETDEWEB)
Qin Shaomeng; Chen Yong; Zhang Pan [Institute of Theoretical Physics, Lanzhou University, Lanzhou 730000 (China)
2007-07-15
We propose a novel network growth model coupled with the competition interaction to simulate macroevolution. Our work shows that competition plays an important role in macroevolution and it is more rational to describe the interaction between species by network structures. Our model presents a complete picture of the development of phyla and the splitting process. It is found that periodic mass extinction occurred in our networks without any extraterrestrial factors and the lifetime distribution of species is very close to the fossil record. We also perturb networks with two scenarios of mass extinctions on different hierarchic levels in order to study their recovery.
Network growth approach to macroevolution
Qin, Shao-Meng; Chen, Yong; Zhang, Pan
2007-07-01
We propose a novel network growth model coupled with the competition interaction to simulate macroevolution. Our work shows that competition plays an important role in macroevolution and it is more rational to describe the interaction between species by network structures. Our model presents a complete picture of the development of phyla and the splitting process. It is found that periodic mass extinction occurred in our networks without any extraterrestrial factors and the lifetime distribution of species is very close to the fossil record. We also perturb networks with two scenarios of mass extinctions on different hierarchic levels in order to study their recovery.
Bridging Inter-flow and Intra-flow Network Coding for Video Applications
DEFF Research Database (Denmark)
Hansen, Jonas; Krigslund, Jeppe; Roetter, Daniel Enrique Lucani
2013-01-01
enhance reliability, common of the former, while maintaining an efficient spectrum usage, typical of the latter. This paper uses the intuition provided in [1] to propose a practical implementation of the protocol leveraging Random Linear Network Coding (RLNC) for intra-flow coding, a credit based packet...... transmission approach to decide how much and when to send redundancy in the network, and a minimalistic feedback mechanism to guarantee delivery of generations of the different flows. Given the delay constraints of video applications, we proposed a simple yet effective coding mechanism, Block Coding On The Fly...... (BCFly), that allows a block encoder to be fed on-the-fly, thus reducing the delay to accumulate enough packets that is introduced by typical generation based NC techniques. Our measurements and comparison to forwarding and COPE show that CORE not only outperforms these schemes even for small packet...
Tourism Destinations Network Analysis, Social Network Analysis Approach
Directory of Open Access Journals (Sweden)
2015-09-01
Full Text Available The tourism industry is becoming one of the world's largest economical sources, and is expected to become the world's first industry by 2020. Previous studies have focused on several aspects of this industry including sociology, geography, tourism management and development, but have paid less attention to analytical and quantitative approaches. This study introduces some network analysis techniques and measures aiming at studying the structural characteristics of tourism networks. More specifically, it presents a methodology to analyze tourism destinations network. We apply the methodology to analyze mazandaran’s Tourism destination network, one of the most famous tourism areas of Iran.
Flow Routing for Delineating Supraglacial Meltwater Channel Networks
Directory of Open Access Journals (Sweden)
Leonora King
2016-12-01
Full Text Available Growing interest in supraglacial channels, coupled with the increasing availability of high-resolution remotely sensed imagery of glacier surfaces, motivates the development and testing of new approaches to delineating surface meltwater channels. We utilized a high-resolution (2 m digital elevation model of parts of the western margin of the Greenland Ice Sheet (GrIS and retention of visually identified sinks (i.e., moulins to investigate the ability of a standard D8 flow routing algorithm to delineate supraglacial channels. We compared these delineated channels to manually digitized channels and to channels extracted from multispectral imagery. We delineated GrIS supraglacial channel networks in six high-elevation (above 1000 m and one low-elevation (below 1000 m catchments during and shortly after peak melt (July and August 2012, and investigated the effect of contributing area threshold on flow routing performance. We found that, although flow routing is sensitive to data quality and moulin identification, it can identify 75% to 99% of channels observed with multispectral analysis, as well as low-order, high-density channels (up to 15.7 km/km2 with a 0.01 km2 contributing area threshold in greater detail than multispectral methods. Additionally, we found that flow routing can delineate supraglacial channel networks on rough ice surfaces with widespread crevassing. Our results suggest that supraglacial channel density is sufficiently high during peak melt that low contributing area thresholds can be employed with little risk of overestimating the channel network extent.
A Mixed Approach for Modeling Blood Flow in Brain Microcirculation
Lorthois, Sylvie; Peyrounette, Myriam; Davit, Yohan; Quintard, Michel; Groupe d'Etude sur les Milieux Poreux Team
2015-11-01
Consistent with its distribution and exchange functions, the vascular system of the human brain cortex is a superposition of two components. At small-scale, a homogeneous and space-filling mesh-like capillary network. At large scale, quasi-fractal branched veins and arteries. From a modeling perspective, this is the superposition of: (a) a continuum model resulting from the homogenization of slow transport in the small-scale capillary network; and (b) a discrete network approach describing fast transport in the arteries and veins, which cannot be homogenized because of their fractal nature. This problematic is analogous to fast conducting wells embedded in a reservoir rock in petroleum engineering. An efficient method to reduce the computational cost is to use relatively large grid blocks for the continuum model. This makes it difficult to accurately couple both components. We solve this issue by adapting the ``well model'' concept used in petroleum engineering to brain specific 3D situations. We obtain a unique linear system describing the discrete network, the continuum and the well model. Results are presented for realistic arterial and venous geometries. The mixed approach is compared with full network models including various idealized capillary networks of known permeability. ERC BrainMicroFlow GA615102.
DEFF Research Database (Denmark)
Rawstron, A.C.; Orfao, A.; Beksac, M.
2008-01-01
The European Myeloma Network (EMN) organized two flow cytometry workshops. The first aimed to identify specific indications for flow cytometry in patients with monoclonal gammopathies, and consensus technical approaches through a questionnaire-based review of current practice in participating lab...
A.C. Rawstron; A. Orfao (Alberto); M. Beksaç (Meral); L. Bezdickova (Ludmila); R.A. Brooimans (Rik); H. Bumbea (Horia); K. Dalva (Klara); G.M. Fuhler (Gwenny); J.W. Gratama (Jan-Willem); D. Hose (Dirk); L. Kovarova (Lucie); M. Lioznov (Michael); G. Mateo (Gema); R. Morilla (Ricardo); A.K. Mylin (Anne); P. Omedé (Paola); C. Pellat-Deceunynck (Catherine); M.P. Andres; M. Petrucci (Maria); M. Ruggeri (Marina); G. Rymkiewicz (Grzegorz); A. Schmitz; M. Schreder (Martin); C.M. Seynaeve (Caroline); M. Spacek (Martin); R.M. de Tute (Ruth); E. van Valckenborgh (Els); N. Weston-Bell (Nicky); R.G. Owen (Roger); J.F. San Miguel (Jesús Fernando); P. Sonneveld (Pieter); H.E. Johnsen (Hans)
2008-01-01
textabstractThe European Myeloma Network (EMN) organized two flow cytometry workshops. The first aimed to identify specific indications for flow cytometry in patients with monoclonal gammopathies, and consensus technical approaches through a questionnaire-based review of current practice in
Hierarchicality of trade flow networks reveals complexity of products.
Directory of Open Access Journals (Sweden)
Peiteng Shi
Full Text Available With globalization, countries are more connected than before by trading flows, which amounts to at least 36 trillion dollars today. Interestingly, around 30-60 percents of exports consist of intermediate products in global. Therefore, the trade flow network of particular product with high added values can be regarded as value chains. The problem is weather we can discriminate between these products from their unique flow network structure? This paper applies the flow analysis method developed in ecology to 638 trading flow networks of different products. We claim that the allometric scaling exponent η can be used to characterize the degree of hierarchicality of a flow network, i.e., whether the trading products flow on long hierarchical chains. Then, it is pointed out that the flow networks of products with higher added values and complexity like machinary, transport equipment etc. have larger exponents, meaning that their trade flow networks are more hierarchical. As a result, without the extra data like global input-output table, we can identify the product categories with higher complexity, and the relative importance of a country in the global value chain by the trading network solely.
Network Medicine: A Network-based Approach to Human Diseases
Ghiassian, Susan Dina
With the availability of large-scale data, it is now possible to systematically study the underlying interaction maps of many complex systems in multiple disciplines. Statistical physics has a long and successful history in modeling and characterizing systems with a large number of interacting individuals. Indeed, numerous approaches that were first developed in the context of statistical physics, such as the notion of random walks and diffusion processes, have been applied successfully to study and characterize complex systems in the context of network science. Based on these tools, network science has made important contributions to our understanding of many real-world, self-organizing systems, for example in computer science, sociology and economics. Biological systems are no exception. Indeed, recent studies reflect the necessity of applying statistical and network-based approaches in order to understand complex biological systems, such as cells. In these approaches, a cell is viewed as a complex network consisting of interactions among cellular components, such as genes and proteins. Given the cellular network as a platform, machinery, functionality and failure of a cell can be studied with network-based approaches, a field known as systems biology. Here, we apply network-based approaches to explore human diseases and their associated genes within the cellular network. This dissertation is divided in three parts: (i) A systematic analysis of the connectivity patterns among disease proteins within the cellular network. The quantification of these patterns inspires the design of an algorithm which predicts a disease-specific subnetwork containing yet unknown disease associated proteins. (ii) We apply the introduced algorithm to explore the common underlying mechanism of many complex diseases. We detect a subnetwork from which inflammatory processes initiate and result in many autoimmune diseases. (iii) The last chapter of this dissertation describes the
SIPSON--simulation of interaction between pipe flow and surface overland flow in networks.
Djordjević, S; Prodanović, D; Maksimović, C; Ivetić, M; Savić, D
2005-01-01
The new simulation model, named SIPSON, based on the Preissmann finite difference method and the conjugate gradient method, is presented in the paper. This model simulates conditions when the hydraulic capacity of a sewer system is exceeded, pipe flow is pressurized, the water flows out from the piped system to the streets, and the inlets cannot capture all the runoff. In the mathematical model, buried structures and pipelines, together with surface channels, make a horizontally and vertically looped network involving a complex interaction of flows. In this paper, special internal boundary conditions related to equivalent inlets are discussed. Procedures are described for the simulation of manhole cover loss, basement flooding, the representation of street geometry, and the distribution of runoff hydrographs between surface and underground networks. All these procedures are built into the simulation model. Relevant issues are illustrated on a set of examples, focusing on specific parameters and comparison with field measurements of flooding of the Motilal ki Chal catchment (Indore, India). Satisfactory agreement of observed and simulated hydrographs and maximum surface flooding levels is obtained. It is concluded that the presented approach is an improvement compared to the standard "virtual reservoir" approach commonly applied in most of the models.
A Transdiagnostic Network Approach to Psychosis
Wigman, Johanna T. W.; de Vos, Stijn; Wichers, Marieke; van Os, Jim; Bartels-Velthuis, Agna A.
Our ability to accurately predict development and outcome of early expression of psychosis is limited. To elucidate the mechanisms underlying psychopathology, a broader, transdiagnostic approach that acknowledges the complexity of mental illness is required. The upcoming network paradigm may be
A Bayesian Network Approach to Ontology Mapping
National Research Council Canada - National Science Library
Pan, Rong; Ding, Zhongli; Yu, Yang; Peng, Yun
2005-01-01
.... In this approach, the source and target ontologies are first translated into Bayesian networks (BN); the concept mapping between the two ontologies are treated as evidential reasoning between the two translated BNs...
Modeling Sheet Flow in Oscillatory Flows Using a Mixture Approach
Burdick, G. M.; Slinn, D. N.
2004-12-01
Understanding the transport of sediment is crucial to predicting many coastal engineering processes, such as sedimentation and erosion around structures and beach profile changes. Traditional methods for modeling sediment transport require solving separate equations for fluid and particle motion. For densely-laden flows, this can present challenges in capturing the physics of the system, as fluid-particle and particle-particle interactions must be accounted for, and can present difficulties because of the computational effort required. In this approach, fluid-particle interactions are expressed through the drag and lift forces, while adequate models for particle-particle interactions are currently being developed. We have chosen an alternate approach that assumes a system containing sediment particles can be approximated as a mixture having variable density and viscosity that depend on the local sediment concentration. Here, the interactions are expressed through the mixture viscosity and a stress-induced diffusion term. There are five governing equations that describe the flow field. They are the mixture continuity and momentum equations and a species continuity equation for the sediment. We use the control volume approach on a three-dimensional staggered grid to solve the equations numerically. The turbulent dynamics of an initially stationary densely packed sand layer (60% by volume sand) driven by a sinusoidally oscillating flow are examined and model results are compared with the experimental data of Horikawa, Watanabe, & Katori (1982). The model does a reasonable job of predicting concentration profiles and sheet flow layer thickness. Both the model and the experimental data show that a significant amount of sand is entrained during the acceleration phase of the wave cycle. This entrained sand then falls back to the bed during the deceleration phase of the wave cycle.
Distributed flow optimization and cascading effects in weighted complex networks
Asztalos, Andrea; Sreenivasan, Sameet; Szymanski, Boleslaw K.; Korniss, G.
2011-01-01
We investigate the effect of a specific edge weighting scheme $\\sim (k_i k_j)^{\\beta}$ on distributed flow efficiency and robustness to cascading failures in scale-free networks. In particular, we analyze a simple, yet fundamental distributed flow model: current flow in random resistor networks. By the tuning of control parameter $\\beta$ and by considering two general cases of relative node processing capabilities as well as the effect of bandwidth, we show the dependence of transport efficie...
Directory of Open Access Journals (Sweden)
Jay Krishna Thakur
2015-08-01
Full Text Available The aim of this work is to investigate new approaches using methods based on statistics and geo-statistics for spatio-temporal optimization of groundwater monitoring networks. The formulated and integrated methods were tested with the groundwater quality data set of Bitterfeld/Wolfen, Germany. Spatially, the monitoring network was optimized using geo-statistical methods. Temporal optimization of the monitoring network was carried out using Sen’s method (1968. For geostatistical network optimization, a geostatistical spatio-temporal algorithm was used to identify redundant wells in 2- and 2.5-D Quaternary and Tertiary aquifers. Influences of interpolation block width, dimension, contaminant association, groundwater flow direction and aquifer homogeneity on statistical and geostatistical methods for monitoring network optimization were analysed. The integrated approach shows 37% and 28% redundancies in the monitoring network in Quaternary aquifer and Tertiary aquifer respectively. The geostatistical method also recommends 41 and 22 new monitoring wells in the Quaternary and Tertiary aquifers respectively. In temporal optimization, an overall optimized sampling interval was recommended in terms of lower quartile (238 days, median quartile (317 days and upper quartile (401 days in the research area of Bitterfeld/Wolfen. Demonstrated methods for improving groundwater monitoring network can be used in real monitoring network optimization with due consideration given to influencing factors.
Flow focusing in unsaturated fracture networks: A numerical investigation
Energy Technology Data Exchange (ETDEWEB)
Zhang, Keni; Wu, Yu-Shu; Bodvarsson, G.S.; Liu, Hui-Hai
2003-04-17
A numerical modeling study is presented to investigate flow-focusing phenomena in a large-scale fracture network, constructed using field data collected from the unsaturated zone of Yucca Mountain, Nevada, the proposed repository site for high-level nuclear waste. The two-dimensional fracture network for an area of 100 m x 150 m contains more than 20,000 fractures. Steady-state unsaturated flow in the fracture network is investigated for different boundary conditions and rock properties. Simulation results indicate that flow paths are generally vertical, and that horizontal fractures mainly provide pathways between neighboring vertical paths. In addition to fracture properties, flow-focusing phenomena are also affected by rock-matrix permeability, with lower matrix permeability leading to a high degree of flow focusing. The simulation results further indicate that the average spacing between flow paths in a layered system tends to increase and flow tends to becomes more focused, with depth.
Estimation of blood flow rates in large microvascular networks.
Fry, Brendan C; Lee, Jack; Smith, Nicolas P; Secomb, Timothy W
2012-08-01
Recent methods for imaging microvascular structures provide geometrical data on networks containing thousands of segments. Prediction of functional properties, such as solute transport, requires information on blood flow rates also, but experimental measurement of many individual flows is difficult. Here, a method is presented for estimating flow rates in a microvascular network based on incomplete information on the flows in the boundary segments that feed and drain the network. With incomplete boundary data, the equations governing blood flow form an underdetermined linear system. An algorithm was developed that uses independent information about the distribution of wall shear stresses and pressures in microvessels to resolve this indeterminacy, by minimizing the deviation of pressures and wall shear stresses from target values. The algorithm was tested using previously obtained experimental flow data from four microvascular networks in the rat mesentery. With two or three prescribed boundary conditions, predicted flows showed relatively small errors in most segments and fewer than 10% incorrect flow directions on average. The proposed method can be used to estimate flow rates in microvascular networks, based on incomplete boundary data, and provides a basis for deducing functional properties of microvessel networks. © 2012 John Wiley & Sons Ltd.
Directory of Open Access Journals (Sweden)
Bocewicz Grzegorz
2017-06-01
Full Text Available The problems of designing supply networks and traffic flow routing and scheduling are the subject of intensive research. The problems encompass the management of the supply of a variety of goods using multi-modal transportation. This research also takes into account the various constraints related to route topology, the parameters of the available fleet of vehicles, order values, delivery due dates, etc. Assuming that the structure of a supply network, constrained by a transport network topology that determines its behavior, we develop a declarative model which would enable the analysis of the relationships between the structure of a supply network and its potential behavior resulting in a set of desired delivery-flows. The problem in question can be reduced to determining sufficient conditions that ensure smooth flow in a transport network with a fractal structure. The proposed approach, which assumes a recursive, fractal network structure, enables the assessment of alternative delivery routes and associated schedules in polynomial time. An illustrative example showing the quantitative and qualitative relationships between the morphological characteristics of the investigated supply networks and the functional parameters of the assumed delivery-flows is provided.
Computer networking a top-down approach
Kurose, James
2017-01-01
Unique among computer networking texts, the Seventh Edition of the popular Computer Networking: A Top Down Approach builds on the author’s long tradition of teaching this complex subject through a layered approach in a “top-down manner.” The text works its way from the application layer down toward the physical layer, motivating readers by exposing them to important concepts early in their study of networking. Focusing on the Internet and the fundamentally important issues of networking, this text provides an excellent foundation for readers interested in computer science and electrical engineering, without requiring extensive knowledge of programming or mathematics. The Seventh Edition has been updated to reflect the most important and exciting recent advances in networking.
Approaching human language with complex networks.
Cong, Jin; Liu, Haitao
2014-12-01
The interest in modeling and analyzing human language with complex networks is on the rise in recent years and a considerable body of research in this area has already been accumulated. We survey three major lines of linguistic research from the complex network approach: 1) characterization of human language as a multi-level system with complex network analysis; 2) linguistic typological research with the application of linguistic networks and their quantitative measures; and 3) relationships between the system-level complexity of human language (determined by the topology of linguistic networks) and microscopic linguistic (e.g., syntactic) features (as the traditional concern of linguistics). We show that the models and quantitative tools of complex networks, when exploited properly, can constitute an operational methodology for linguistic inquiry, which contributes to the understanding of human language and the development of linguistics. We conclude our review with suggestions for future linguistic research from the complex network approach: 1) relationships between the system-level complexity of human language and microscopic linguistic features; 2) expansion of research scope from the global properties to other levels of granularity of linguistic networks; and 3) combination of linguistic network analysis with other quantitative studies of language (such as quantitative linguistics). Copyright © 2014 Elsevier B.V. All rights reserved.
Certainty Power Flow Calculation for Distribution Network with Distributed Generation
Directory of Open Access Journals (Sweden)
FU Min
2017-06-01
Full Text Available System nodes need to be renumbered manually when upgrading and reforming distribution network makes network topology change． Because optimization method is inapplicable to the network change，an improved forward and backward sweep algorithm is proposed which is unrelated to node label． In this paper，node type of sorts of distributed generation ( DG in power flow calculation are induced and part of new node type of DG under improved control strategy are provided． The basis of DG as active constant node in certainty power flow calculation is analyzed． Based on improved back － forward sweep algorithm，general programs of power flow calculation in power distribution network of DG are programmed by MATLAB and the impact of DG on flow calculation to distribution network is analyzed quantitatively by plenty of simulation calculation of IEEE33 test system．
Optimal power flow for distribution networks with distributed generation
Directory of Open Access Journals (Sweden)
Radosavljević Jordan
2015-01-01
Full Text Available This paper presents a genetic algorithm (GA based approach for the solution of the optimal power flow (OPF in distribution networks with distributed generation (DG units, including fuel cells, micro turbines, diesel generators, photovoltaic systems and wind turbines. The OPF is formulated as a nonlinear multi-objective optimization problem with equality and inequality constraints. Due to the stochastic nature of energy produced from renewable sources, i.e. wind turbines and photovoltaic systems, as well as load uncertainties, a probabilisticalgorithm is introduced in the OPF analysis. The Weibull and normal distributions are employed to model the input random variables, namely the wind speed, solar irradiance and load power. The 2m+1 point estimate method and the Gram Charlier expansion theory are used to obtain the statistical moments and the probability density functions (PDFs of the OPF results. The proposed approach is examined and tested on a modified IEEE 34 node test feeder with integrated five different DG units. The obtained results prove the efficiency of the proposed approach to solve both deterministic and probabilistic OPF problems for different forms of the multi-objective function. As such, it can serve as a useful decision-making supporting tool for distribution network operators. [Projekat Ministarstva nauke Republike Srbije, br. TR33046
Optimizing dispersal corridors for the Cape Proteaceae using network flow.
Phillips, Steven J; Williams, Paul; Midgley, Guy; Archer, Aaron
2008-07-01
We introduce a new way of measuring and optimizing connectivity in conservation landscapes through time, accounting for both the biological needs of multiple species and the social and financial constraint of minimizing land area requiring additional protection. Our method is based on the concept of network flow; we demonstrate its use by optimizing protected areas in the Western Cape of South Africa to facilitate autogenic species shifts in geographic range under climate change for a family of endemic plants, the Cape Proteaceae. In 2005, P. Williams and colleagues introduced a novel framework for this protected area design task. To ensure population viability, they assumed each species should have a range size of at least 100 km2 of predicted suitable conditions contained in protected areas at all times between 2000 and 2050. The goal was to design multiple dispersal corridors for each species, connecting suitable conditions between time periods, subject to each species' limited dispersal ability, and minimizing the total area requiring additional protection. We show that both minimum range size and limited dispersal abilities can be naturally modeled using the concept of network flow. This allows us to apply well-established tools from operations research and computer science for solving network flow problems. Using the same data and this novel modeling approach, we reduce the area requiring additional protection by a third compared to previous methods, from 4593 km2 to 3062 km , while still achieving the same conservation planning goals. We prove that this is the best solution mathematically possible: the given planning goals cannot be achieved with a smaller area, given our modeling assumptions and data. Our method allows for flexibility and refinement of the underlying climate-change, species-habitat-suitability, and dispersal models. In particular, we propose an alternate formalization of a minimum range size moving through time and use network flow to
Information Flow Between Resting-State Networks.
Diez, Ibai; Erramuzpe, Asier; Escudero, Iñaki; Mateos, Beatriz; Cabrera, Alberto; Marinazzo, Daniele; Sanz-Arigita, Ernesto J; Stramaglia, Sebastiano; Cortes Diaz, Jesus M
2015-11-01
The resting brain dynamics self-organize into a finite number of correlated patterns known as resting-state networks (RSNs). It is well known that techniques such as independent component analysis can separate the brain activity at rest to provide such RSNs, but the specific pattern of interaction between RSNs is not yet fully understood. To this aim, we propose here a novel method to compute the information flow (IF) between different RSNs from resting-state magnetic resonance imaging. After hemodynamic response function blind deconvolution of all voxel signals, and under the hypothesis that RSNs define regions of interest, our method first uses principal component analysis to reduce dimensionality in each RSN to next compute IF (estimated here in terms of transfer entropy) between the different RSNs by systematically increasing k (the number of principal components used in the calculation). When k=1, this method is equivalent to computing IF using the average of all voxel activities in each RSN. For k≥1, our method calculates the k multivariate IF between the different RSNs. We find that the average IF among RSNs is dimension dependent, increasing from k=1 (i.e., the average voxel activity) up to a maximum occurring at k=5 and to finally decay to zero for k≥10. This suggests that a small number of components (close to five) is sufficient to describe the IF pattern between RSNs. Our method--addressing differences in IF between RSNs for any generic data--can be used for group comparison in health or disease. To illustrate this, we have calculated the inter-RSN IF in a data set of Alzheimer's disease (AD) to find that the most significant differences between AD and controls occurred for k=2, in addition to AD showing increased IF w.r.t. The spatial localization of the k=2 component, within RSNs, allows the characterization of IF differences between AD and controls.
A Transfer Learning Approach for Network Modeling
Huang, Shuai; Li, Jing; Chen, Kewei; Wu, Teresa; Ye, Jieping; Wu, Xia; Yao, Li
2012-01-01
Networks models have been widely used in many domains to characterize the interacting relationship between physical entities. A typical problem faced is to identify the networks of multiple related tasks that share some similarities. In this case, a transfer learning approach that can leverage the knowledge gained during the modeling of one task to help better model another task is highly desirable. In this paper, we propose a transfer learning approach, which adopts a Bayesian hierarchical model framework to characterize task relatedness and additionally uses the L1-regularization to ensure robust learning of the networks with limited sample sizes. A method based on the Expectation-Maximization (EM) algorithm is further developed to learn the networks from data. Simulation studies are performed, which demonstrate the superiority of the proposed transfer learning approach over single task learning that learns the network of each task in isolation. The proposed approach is also applied to identification of brain connectivity networks of Alzheimer’s disease (AD) from functional magnetic resonance image (fMRI) data. The findings are consistent with the AD literature. PMID:24526804
The influence of passenger flow on the topology characteristics of urban rail transit networks
Hu, Yingyue; Chen, Feng; Chen, Peiwen; Tan, Yurong
2017-05-01
Current researches on the network characteristics of metro networks are generally carried out on topology networks without passenger flows running on it, thus more complex features of the networks with ridership loaded on it cannot be captured. In this study, we incorporated the load of metro networks, passenger volume, into the exploration of network features. Thus, the network can be examined in the context of operation, which is the ultimate purpose of the existence of a metro network. To this end, section load was selected as an edge weight to demonstrate the influence of ridership on the network, and a weighted calculation method for complex network indicators and robustness were proposed to capture the unique behaviors of a metro network with passengers flowing in it. The proposed method was applied on Beijing Subway. Firstly, the passenger volume in terms of daily origin and destination matrix was extracted from exhausted transit smart card data. Using the established approach and the matrix as weighting, common indicators of complex network including clustering coefficient, betweenness and degree were calculated, and network robustness were evaluated under potential attacks. The results were further compared to that of unweighted networks, and it suggests indicators of the network with consideration of passenger volumes differ from that without ridership to some extent, and networks tend to be more vulnerable than that without load on it. The significance sequence for the stations can be changed. By introducing passenger flow weighting, actual operation status of the network can be reflected more accurately. It is beneficial to determine the crucial stations and make precautionary measures for the entire network’s operation security.
Flow distributions and spatial correlations in human brain capillary networks
Lorthois, Sylvie; Peyrounette, Myriam; Larue, Anne; Le Borgne, Tanguy
2015-11-01
The vascular system of the human brain cortex is composed of a space filling mesh-like capillary network connected upstream and downstream to branched quasi-fractal arterioles and venules. The distribution of blood flow rates in these networks may affect the efficiency of oxygen transfer processes. Here, we investigate the distribution and correlation properties of blood flow velocities from numerical simulations in large 3D human intra-cortical vascular network (10000 segments) obtained from an anatomical database. In each segment, flow is solved from a 1D non-linear model taking account of the complex rheological properties of blood flow in microcirculation to deduce blood pressure, blood flow and red blood cell volume fraction distributions throughout the network. The network structural complexity is found to impart broad and spatially correlated Lagrangian velocity distributions, leading to power law transit time distributions. The origins of this behavior (existence of velocity correlations in capillary networks, influence of the coupling with the feeding arterioles and draining veins, topological disorder, complex blood rheology) are studied by comparison with results obtained in various model capillary networks of controlled disorder. ERC BrainMicroFlow GA615102, ERC ReactiveFronts GA648377.
A general regression artificial neural network for two-phase flow regime identification
Energy Technology Data Exchange (ETDEWEB)
Tambouratzis, Tatiana, E-mail: tatianatambouratzis@gmail.co [Department of Industrial Management and Technology, University of Piraeus, 107 Deligiorgi St., Piraeus 185 34 (Greece); Department of Nuclear Engineering, Chalmers University of Technology, SE-41296 Goeteborg (Sweden); Pazsit, Imre, E-mail: imre@chalmers.s [Department of Nuclear Engineering, Chalmers University of Technology, SE-41296 Goeteborg (Sweden); Department of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, MI 48019 (United States)
2010-05-15
Supplementing the collection of artificial neural network methodologies devised for monitoring energy producing installations, a general regression artificial neural network is proposed for the identification of the two-phase flow that occurs in the coolant channels of boiling water reactors. The utilization of a limited number of image features derived from radiography images affords the proposed approach with efficiency and non-invasiveness. Additionally, the application of counter-clustering to the input patterns prior to training accomplishes an 80% reduction in network size as well as in training and test time. Cross-validation tests confirm accurate on-line flow regime identification.
Cilia driven flow networks in the brain
Wang, Yong; Faubel, Regina; Westendorf, Chrsitian; Eichele, Gregor; Bodenschatz, Eberhard
Neurons exchange soluble substances via the cerebrospinal fluid (CSF) that fills the ventricular system. The walls of the ventricular cavities are covered with motile cilia that constantly beat and thereby induce a directional flow. We recently discovered that cilia in the third ventricle generate a complex flow pattern leading to partitioning of the ventricular volume and site-directed transport paths along the walls. Transient and daily recurrent alterations in the cilia beating direction lead to changes in the flow pattern. This has consequences for delivery of CSF components along the near wall flow. The contribution of this cilia-induced flow to overall CSF flow remains to be investigated. The state-of-art lattice Boltzmann method is adapted for studying the CFS flow. The 3D geometry of the third ventricle at high resolution was reconstructed. Simulation of CSF flow without cilia in this geometry confirmed that the previous idea about unidirectional flow does not explain how different components of CSF can be delivered to their various target sites. We study the contribution of the cilia-induced flow pattern to overall CSF flow and identify target areas for site-specific delivery of CSF-constituents with respect to the temporal changes.
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.
Flow Pattern Identification of Horizontal Two-Phase Refrigerant Flow Using Neural Networks
2015-12-31
making classification difficult. Consequently, Table 5 shows neural net - work classification results for nine flow patterns. The number of runs...AFRL-RQ-WP-TP-2016-0079 FLOW PATTERN IDENTIFICATION OF HORIZONTAL TWO-PHASE REFRIGERANT FLOW USING NEURAL NETWORKS (POSTPRINT) Abdeel J... NEURAL NETWORKS (POSTPRINT) 5a. CONTRACT NUMBER In-house 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 62203F 6. AUTHOR(S) Abdeel J. Roman and
Max-Flow Min-Cut Theorems for Communication Networks Based on Equational Logic
Gadouleau, Maximilien
2010-01-01
Traditionally, communication networks are modeled and analyzed in terms of information flows in graphs. In this paper, we introduce a new symbolic approach to communication networks, where the topology of the underlying network is contained in a set of formal terms. To any choice of coding functions we associate a measure of performance, referred to as the dispersion. We thus show that many communication problems can be recast as dispersion problems in this setup. We state and prove variants of a theorem concerning dispersion of information in communication networks which generalizes the network coding theorem. The dispersion theorem resembles the max-flow min-cut theorem for commodity networks and states that the minimal cut value can be asymptotically achieved by the use of coding functions based on a routing scheme that uses dynamic headers. We then prove that linear coding functions are insufficient in general. More specifically, there exist terms which have an arbitrarily large dispersion for non-linear ...
Clustering: a neural network approach.
Du, K-L
2010-01-01
Clustering is a fundamental data analysis method. It is widely used for pattern recognition, feature extraction, vector quantization (VQ), image segmentation, function approximation, and data mining. As an unsupervised classification technique, clustering identifies some inherent structures present in a set of objects based on a similarity measure. Clustering methods can be based on statistical model identification (McLachlan & Basford, 1988) or competitive learning. In this paper, we give a comprehensive overview of competitive learning based clustering methods. Importance is attached to a number of competitive learning based clustering neural networks such as the self-organizing map (SOM), the learning vector quantization (LVQ), the neural gas, and the ART model, and clustering algorithms such as the C-means, mountain/subtractive clustering, and fuzzy C-means (FCM) algorithms. Associated topics such as the under-utilization problem, fuzzy clustering, robust clustering, clustering based on non-Euclidean distance measures, supervised clustering, hierarchical clustering as well as cluster validity are also described. Two examples are given to demonstrate the use of the clustering methods.
Quantum Phase Transitions: A Network Approach
Vargas, David L.; Larue, David M.; Carr, Lincoln D.
2014-03-01
Understanding the network structure of complex systems has opened up new avenues of research in sociology, biology, technology, and physics. In this talk we present evidence that complex network measures are able to identify the phases in two well known models. We distinguish the ferromagnetic and paramagnetic phases of the transverse Ising Hamiltonian. We also identify the Mott-insulator to superfluid transition of the Bose-Hubbard Hamiltonian. The network approach to the analysis of quantum phase transitions provides us with a new set of tools to explore the many body physics of quantum phase transitions. Supported by NSF and AFOSR.
Intelligent Network Flow Optimization (INFLO) prototype acceptance test summary.
2015-05-01
This report summarizes the results of System Acceptance Testing for the implementation of the Intelligent Network : Flow Optimization (INFLO) Prototype bundle within the Dynamic Mobility Applications (DMA) portion of the Connected : Vehicle Program. ...
Qualitative networks: a symbolic approach to analyze biological signaling networks
Directory of Open Access Journals (Sweden)
Henzinger Thomas A
2007-01-01
Full Text Available Abstract Background A central goal of Systems Biology is to model and analyze biological signaling pathways that interact with one another to form complex networks. Here we introduce Qualitative networks, an extension of Boolean networks. With this framework, we use formal verification methods to check whether a model is consistent with the laboratory experimental observations on which it is based. If the model does not conform to the data, we suggest a revised model and the new hypotheses are tested in-silico. Results We consider networks in which elements range over a small finite domain allowing more flexibility than Boolean values, and add target functions that allow to model a rich set of behaviors. We propose a symbolic algorithm for analyzing the steady state of these networks, allowing us to scale up to a system consisting of 144 elements and state spaces of approximately 1086 states. We illustrate the usefulness of this approach through a model of the interaction between the Notch and the Wnt signaling pathways in mammalian skin, and its extensive analysis. Conclusion We introduce an approach for constructing computational models of biological systems that extends the framework of Boolean networks and uses formal verification methods for the analysis of the model. This approach can scale to multicellular models of complex pathways, and is therefore a useful tool for the analysis of complex biological systems. The hypotheses formulated during in-silico testing suggest new avenues to explore experimentally. Hence, this approach has the potential to efficiently complement experimental studies in biology.
Synchronization in material flow networks with biologically inspired self-organized control
Energy Technology Data Exchange (ETDEWEB)
Donner, Reik; Laemmer, Stefan [TU Dresden (Germany); Helbing, Dirk [ETH Zuerich (Switzerland)
2009-07-01
The efficient operation of material flows in traffic or production networks is a subject of broad economic interest. Traditional centralized as well as decentralized approaches to operating material flow networks are known to have severe disadvantages. As an alternative approach that may help to overcome these problems, we propose a simple self-organization mechanism of conflicting flows that is inspired by oscillatory phenomena of pedestrian or animal counter-flows at bottlenecks. As a result, one may observe a synchronization of the switching dynamics at different intersections in the network. For regular grid topologies, we find different synchronization regimes depending on the inertia of the switching from one service state to the next one. In order to test the robustness of our corresponding observations, we study how the detailed properties of the network as well as dynamic feedbacks between the relevant state variables affect the degree of achievable synchronization and the resulting performance of the network. Our results yield an improved understanding of the conditions that have to be present for efficiently operating material flow networks by a decentralized control, which is of paramount importance for future implementations in real-world traffic or production systems.
Agricultural informational flow in informal communication networks ...
African Journals Online (AJOL)
Despite the rapid growth in the use of modern communication media to improve access to agricultural information, local information networks remain an important means of communication among rural folk. This study examined informal communication networks of rural farmers in the Ahafo Ano south district of Ghana to ...
Performance of Flow-Aware Networking in LTE backbone
DEFF Research Database (Denmark)
Sniady, Aleksander; Soler, José
2012-01-01
technologies, such as Long Term Evolution (LTE). This paper proposes usage of a modified Flow Aware Networking (FAN) technique for enhancing Quality of Service (QoS) in the all-IP transport networks underlying LTE backbone. The results obtained with OPNET Modeler show that FAN, in spite of being relatively...
Water Pipeline Network Analysis Using Simultaneous Loop Flow ...
African Journals Online (AJOL)
2013-03-01
Mar 1, 2013 ... solving for the unknown in water network analysis. It is based on a loop iterative computation. Newton-Raphson method is a better technique for solving the network problems; however, the method adopted here computes simultaneous flow corrections for all loops, hence, the best since the computational.
Analysis and control of flows in pressurized hydraulic networks
Gupta, R.K.
2006-01-01
Analysis, design and flow control problems in pressurized hydraulic networks such as water transmission and distribution systems consisting of pipes and other appurtenant components such as reservoirs, pumps, valves and surge devices are dealt with from the prospective of network synthesis aiming at
Modelling flow dynamics in water distribution networks using ...
African Journals Online (AJOL)
Computational approaches can be used to detect leakages in water distribution networks. One such approach is the Artificial Neural Networks (ANNs) technique. The advantage of ANNs is that they are robust and can be used to model complex linear and non-linear systems without making implicit assumptions. ANNs can ...
Edge anisotropy and the geometric perspective on flow networks
Molkenthin, Nora; Kutza, Hannes; Tupikina, Liubov; Marwan, Norbert; Donges, Jonathan F.; Feudel, Ulrike; Kurths, Jürgen; Donner, Reik V.
2017-03-01
Spatial networks have recently attracted great interest in various fields of research. While the traditional network-theoretic viewpoint is commonly restricted to their topological characteristics (often disregarding the existing spatial constraints), this work takes a geometric perspective, which considers vertices and edges as objects in a metric space and quantifies the corresponding spatial distribution and alignment. For this purpose, we introduce the concept of edge anisotropy and define a class of measures characterizing the spatial directedness of connections. Specifically, we demonstrate that the local anisotropy of edges incident to a given vertex provides useful information about the local geometry of geophysical flows based on networks constructed from spatio-temporal data, which is complementary to topological characteristics of the same flow networks. Taken both structural and geometric viewpoints together can thus assist the identification of underlying flow structures from observations of scalar variables.
CIME course on Modelling and Optimisation of Flows on Networks
Ambrosio, Luigi; Helbing, Dirk; Klar, Axel; Zuazua, Enrique
2013-01-01
In recent years flows in networks have attracted the interest of many researchers from different areas, e.g. applied mathematicians, engineers, physicists, economists. The main reason for this ubiquity is the wide and diverse range of applications, such as vehicular traffic, supply chains, blood flow, irrigation channels, data networks and others. This book presents an extensive set of notes by world leaders on the main mathematical techniques used to address such problems, together with investigations into specific applications. The main focus is on partial differential equations in networks, but ordinary differential equations and optimal transport are also included. Moreover, the modeling is completed by analysis, numerics, control and optimization of flows in networks. The book will be a valuable resource for every researcher or student interested in the subject.
Stabilizing patterns in time: Neural network approach.
Ben-Shushan, Nadav; Tsodyks, Misha
2017-12-01
Recurrent and feedback networks are capable of holding dynamic memories. Nonetheless, training a network for that task is challenging. In order to do so, one should face non-linear propagation of errors in the system. Small deviations from the desired dynamics due to error or inherent noise might have a dramatic effect in the future. A method to cope with these difficulties is thus needed. In this work we focus on recurrent networks with linear activation functions and binary output unit. We characterize its ability to reproduce a temporal sequence of actions over its output unit. We suggest casting the temporal learning problem to a perceptron problem. In the discrete case a finite margin appears, providing the network, to some extent, robustness to noise, for which it performs perfectly (i.e. producing a desired sequence for an arbitrary number of cycles flawlessly). In the continuous case the margin approaches zero when the output unit changes its state, hence the network is only able to reproduce the sequence with slight jitters. Numerical simulation suggest that in the discrete time case, the longest sequence that can be learned scales, at best, as square root of the network size. A dramatic effect occurs when learning several short sequences in parallel, that is, their total length substantially exceeds the length of the longest single sequence the network can learn. This model easily generalizes to an arbitrary number of output units, which boost its performance. This effect is demonstrated by considering two practical examples for sequence learning. This work suggests a way to overcome stability problems for training recurrent networks and further quantifies the performance of a network under the specific learning scheme.
Liu, Lei; Tsuritani, Takehiro; Morita, Itsuro; Guo, Hongxiang; Wu, Jian
2011-12-19
OpenFlow, as an open-source protocol for network virtualization, is also widely regarded as a promising control plane technique for heterogeneous networks. But the utilization of the OpenFlow protocol to control a wavelength switched optical network has not been investigated. In this paper, for the first time, we experimentally present a proof-of-concept demonstration of OpenFlow-based wavelength path control for lightpath provisioning in transparent optical networks. We propose two different approaches (sequential and delayed approaches) for lightpath setup and two different approaches (active and passive approaches) for lightpath release by using the OpenFlow protocol. The overall feasibility of these approaches is experimentally validated and the network performances are quantitatively evaluated. More importantly, all the proposed methodologies are demonstrated and evaluated on a real transparent optical network testbed with both OpenFlow-based control plane and data plane, which allows their feasibility and effectiveness to be verified, and valuable insights of the proposed solutions to be obtained for deploying into real OpenFlow controlled optical networks.
Random fracture networks: percolation, geometry and flow
Adler, P. M.; Thovert, J. F.; Mourzenko, V. V.
2015-12-01
This paper reviews some of the basic properties of fracture networks. Most of the data can only be derived numerically, and to be useful they need to be rationalized, i.e., a large set of numbers should be replaced by a simple formula which is easy to apply for estimating orders of magnitude. Three major tools are found useful in this rationalization effort. First, analytical results can usually be derived for infinite fractures, a limit which corresponds to large densities. Second, the excluded volume and the dimensionless density prove crucial to gather data obtained at intermediate densities. Finally, shape factors can be used to further reduce the influence of fracture shapes. Percolation of fracture networks is of primary importance since this characteristic controls transport properties such as permeability. Recent numerical studies for various types of fracture networks (isotropic, anisotropic, heterogeneous in space, polydisperse, mixture of shapes) are summarized; the percolation threshold rho is made dimensionless by means of the excluded volume. A general correlation for rho is proposed as a function of the gyration radius. The statistical characteristics of the blocks which are cut in the solid matrix by the network are presented, since they control transfers between the porous matrix and the fractures. Results on quantities such as the volume, surface and number of faces are given and semi empirical relations are proposed. The possible intersection of a percolating network and of a cubic cavity is also summarized. This might be of importance for the underground storage of wastes. An approximate reasoning based on the excluded volume of the percolating cluster and of the cubic cavity is proposed. Finally, consequences on the permeability of fracture networks are briefly addressed. An empirical formula which verifies some theoretical properties is proposed.
Smooth information flow in temperature climate network reflects mass transport
Czech Academy of Sciences Publication Activity Database
Hlinka, Jaroslav; Jajcay, Nikola; Hartman, David; Paluš, Milan
2017-01-01
Roč. 27, č. 3 (2017), č. článku 035811. ISSN 1054-1500 R&D Projects: GA ČR GCP103/11/J068; GA MŠk LH14001 Institutional support: RVO:67985807 Keywords : directed network * causal network * Granger causality * climate network * information flow * temperature network Subject RIV: IN - Informatics, Computer Science OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 2.283, year: 2016
Exact Convex Relaxation of Optimal Power Flow in Radial Networks
Energy Technology Data Exchange (ETDEWEB)
Gan, LW; Li, N; Topcu, U; Low, SH
2015-01-01
The optimal power flow (OPF) problem determines a network operating point that minimizes a certain objective such as generation cost or power loss. It is nonconvex. We prove that a global optimum of OPF can be obtained by solving a second-order cone program, under a mild condition after shrinking the OPF feasible set slightly, for radial power networks. The condition can be checked a priori, and holds for the IEEE 13, 34, 37, 123-bus networks and two real-world networks.
Real一time Network Flow Feature Extraction System Design
Directory of Open Access Journals (Sweden)
CHEN Tao
2017-04-01
Full Text Available Aiming at the problem that packet sampling technique has lower flow feature extraction accuracy in high-speed network，a real-time network flow feature extraction system is implemented in NetFPGA. Making full use of NetFPGA high running speed and powerful parallel processing ability，the system can support gigabit data throughput. This real-time extraction system consists of two key elements，including address mapping module and flow table core processing module. The former uses pipeline technique to index flow record quickly through Bob Jenkins hash algorithm. The latter can update flow table rapidly by parallelizing query and match flow record. Online traffic test results show that the system can achieve real-time flow feature extraction in 1 Gbps Internet COTITIeCtI OTI.
A Dynamic Resilience Approach for WDM Optical Networks
Garg, Amit Kumar
2017-12-01
Optical fibres have been developed as a transmission medium to carry traffic in order to provide various services in telecommunications platform. Failure of this fibre caused loss of data which can interrupt communication services. This paper has been focused only on survivable schemes in order to guarantee both protection and restoration in WDM optical networks. In this paper, a dynamic resilience approach has been proposed whose objective is to route the flows in a way which minimizes the total amount of bandwidth used for working and protection paths. In the proposed approach, path-based protection is utilized because it yields lower overhead and is also suitable for global optimization where, in case of a single link failure, all the flows utilizing the failed link are re-routed to a pre-computed set of paths. The simulation results demonstrate that proposed approach is much more efficient as it provides better quality of services (QoS) in terms of network resource utilization, blocking probability etc. as compared to conventional protection and restoration schemes. The proposed approach seems to offer an attractive combination of features, with both ring like speed and mesh-like efficiency.
The Hidden Flow Structure and Metric Space of Network Embedding Algorithms Based on Random Walks.
Gu, Weiwei; Gong, Li; Lou, Xiaodan; Zhang, Jiang
2017-10-13
Network embedding which encodes all vertices in a network as a set of numerical vectors in accordance with it's local and global structures, has drawn widespread attention. Network embedding not only learns significant features of a network, such as the clustering and linking prediction but also learns the latent vector representation of the nodes which provides theoretical support for a variety of applications, such as visualization, link prediction, node classification, and recommendation. As the latest progress of the research, several algorithms based on random walks have been devised. Although those algorithms have drawn much attention for their high scores in learning efficiency and accuracy, there is still a lack of theoretical explanation, and the transparency of those algorithms has been doubted. Here, we propose an approach based on the open-flow network model to reveal the underlying flow structure and its hidden metric space of different random walk strategies on networks. We show that the essence of embedding based on random walks is the latent metric structure defined on the open-flow network. This not only deepens our understanding of random- walk-based embedding algorithms but also helps in finding new potential applications in network embedding.
Lattice Boltzmann simulation of fluid flow in fracture networks with rough, self-affine surfaces.
Madadi, Mahyar; Sahimi, Muhammad
2003-02-01
Using the lattice Boltzmann method, we study fluid flow in a two-dimensional (2D) model of fracture network of rock. Each fracture in a square network is represented by a 2D channel with rough, self-affine internal surfaces. Various parameters of the model, such as the connectivity and the apertures of the fractures, the roughness profile of their surface, as well as the Reynolds number for flow of the fluid, are systematically varied in order to assess their effect on the effective permeability of the fracture network. The distribution of the fractures' apertures is approximated well by a log-normal distribution, which is consistent with experimental data. Due to the roughness of the fractures' surfaces, and the finite size of the networks that can be used in the simulations, the fracture network is anisotropic. The anisotropy increases as the connectivity of the network decreases and approaches the percolation threshold. The effective permeability K of the network follows the power law K approximately (beta), where is the average aperture of the fractures in the network and the exponent beta may depend on the roughness exponent. A crossover from linear to nonlinear flow regime is obtained at a Reynolds number Re approximately O(1), but the precise numerical value of the crossover Re depends on the roughness of the fractures' surfaces.
Neural network approaches for noisy language modeling.
Li, Jun; Ouazzane, Karim; Kazemian, Hassan B; Afzal, Muhammad Sajid
2013-11-01
Text entry from people is not only grammatical and distinct, but also noisy. For example, a user's typing stream contains all the information about the user's interaction with computer using a QWERTY keyboard, which may include the user's typing mistakes as well as specific vocabulary, typing habit, and typing performance. In particular, these features are obvious in disabled users' typing streams. This paper proposes a new concept called noisy language modeling by further developing information theory and applies neural networks to one of its specific application-typing stream. This paper experimentally uses a neural network approach to analyze the disabled users' typing streams both in general and specific ways to identify their typing behaviors and subsequently, to make typing predictions and typing corrections. In this paper, a focused time-delay neural network (FTDNN) language model, a time gap model, a prediction model based on time gap, and a probabilistic neural network model (PNN) are developed. A 38% first hitting rate (HR) and a 53% first three HR in symbol prediction are obtained based on the analysis of a user's typing history through the FTDNN language modeling, while the modeling results using the time gap prediction model and the PNN model demonstrate that the correction rates lie predominantly in between 65% and 90% with the current testing samples, and 70% of all test scores above basic correction rates, respectively. The modeling process demonstrates that a neural network is a suitable and robust language modeling tool to analyze the noisy language stream. The research also paves the way for practical application development in areas such as informational analysis, text prediction, and error correction by providing a theoretical basis of neural network approaches for noisy language modeling.
de la Mata, Tamara; Llano, Carlos
2013-07-01
Recent literature on border effect has fostered research on informal barriers to trade and the role played by network dependencies. In relation to social networks, it has been shown that intensity of trade in goods is positively correlated with migration flows between pairs of countries/regions. In this article, we investigate whether such a relation also holds for interregional trade of services. We also consider whether interregional trade flows in services linked with tourism exhibit spatial and/or social network dependence. Conventional empirical gravity models assume the magnitude of bilateral flows between regions is independent of flows to/from regions located nearby in space, or flows to/from regions related through social/cultural/ethic network connections. With this aim, we provide estimates from a set of gravity models showing evidence of statistically significant spatial and network (demographic) dependence in the bilateral flows of the trade of services considered. The analysis has been applied to the Spanish intra- and interregional monetary flows of services from the accommodation, restaurants and travel agencies for the period 2000-2009, using alternative datasets for the migration stocks and definitions of network effects.
Hybrid Localization Approach for Underwater Sensor Networks
Directory of Open Access Journals (Sweden)
Pei-Hsuan Tsai
2017-01-01
Full Text Available Underwater Wireless Sensor Networks (UWSNs are widely used to collect data in the marine environment. Location and time are essential aspects when sensors collect data, particularly in the case of location-aware data. Many studies on terrestrial sensor networks consider sensor locations as the locations where data is collected and focus on sensor positioning when sensors are fixed. However, underwater sensors are mobile networks and the sensor locations change continuously. Localization schemes designed for static sensor networks need to run periodically to update locations and consume considerable sensor power and increase the communication overhead; hence, they cannot be applied to UWSNs. This paper presents a hybrid localization approach with data-location correction, called Data Localization Correction Approach (DLCA, which positions data without additional communication overhead and power consumption on sensors. Without loss of generality, we simulate the ocean environment based on a kinematic model and meandering current mobility model and conduct extensive simulations. Our results show that DLCA can significantly reduce communication costs, while maintaining relatively high localization accuracy.
Ensemble approach to the analysis of weighted networks
Ahnert, S. E.; Garlaschelli, D.; Fink, T. M. A.; Caldarelli, G.
2007-07-01
We present an approach to the analysis of weighted networks, by providing a straightforward generalization of any network measure defined on unweighted networks, such as the average degree of the nearest neighbors, the clustering coefficient, the “betweenness,” the distance between two nodes, and the diameter of a network. All these measures are well established for unweighted networks but have hitherto proven difficult to define for weighted networks. Our approach is based on the translation of a weighted network into an ensemble of edges. Further introducing this approach we demonstrate its advantages by applying the clustering coefficient constructed in this way to two real-world weighted networks.
The stationary flow in a heterogeneous compliant vessel network
Energy Technology Data Exchange (ETDEWEB)
Filoche, Marcel [Physique de la Matiere Condensee, Ecole Polytechnique, CNRS, 91228 Palaiseau (France); Florens, Magali [CMLA, ENS Cachan, CNRS, UniverSud, 61 av. du President Wilson, Cachan (France)
2011-09-15
We introduce a mathematical model of the hydrodynamic transport into systems consisting in a network of connected flexible pipes. In each pipe of the network, the flow is assumed to be steady and one-dimensional. The fluid-structure interaction is described through tube laws which relate the pipe diameter to the pressure difference across the pipe wall. We show that the resulting one-dimensional differential equation describing the flow in the pipe can be exactly integrated if one is able to estimate averages of the Reynolds number along the pipe. The differential equation is then transformed into a non linear scalar equation relating pressures at both ends of the pipe and the flow rate in the pipe. These equations are coupled throughout the network with mass conservation equations for the flow and zero pressure losses at the branching points of the network. This allows us to derive a general model for the computation of the flow into very large inhomogeneous networks consisting of several thousands of flexible pipes. This model is then applied to perform numerical simulations of the human lung airway system at exhalation. The topology of the system and the tube laws are taken from morphometric and physiological data in the literature. We find good qualitative and quantitative agreement between the simulation results and flow-volume loops measured in real patients. In particular, expiratory flow limitation which is an essential characteristic of forced expiration is found to be well reproduced by our simulations. Finally, a mathematical model of a pathology (Chronic Obstructive Pulmonary Disease) is introduced which allows us to quantitatively assess the influence of a moderate or severe alteration of the airway compliances.
Executable Code Recognition in Network Flows Using Instruction Transition Probabilities
Kim, Ikkyun; Kang, Koohong; Choi, Yangseo; Kim, Daewon; Oh, Jintae; Jang, Jongsoo; Han, Kijun
The ability to recognize quickly inside network flows to be executable is prerequisite for malware detection. For this purpose, we introduce an instruction transition probability matrix (ITPX) which is comprised of the IA-32 instruction sets and reveals the characteristics of executable code's instruction transition patterns. And then, we propose a simple algorithm to detect executable code inside network flows using a reference ITPX which is learned from the known Windows Portable Executable files. We have tested the algorithm with more than thousands of executable and non-executable codes. The results show that it is very promising enough to use in real world.
Optimal Power Flow Solution Using Ant Manners for Electrical Network
Directory of Open Access Journals (Sweden)
ALLAOUA, B.
2009-02-01
Full Text Available This paper presents ant manners and the collective intelligence for electrical network. Solutions for Optimal Power Flow (OPF problem of a power system deliberate via an ant colony optimization metaheuristic method. The objective is to minimize the total fuel cost of thermal generating units and also conserve an acceptable system performance in terms of limits on generator real and reactive power outputs, bus voltages, shunt capacitors/reactors, transformers tap-setting and power flow of transmission lines. Simulation results on the IEEE 30-bus electrical network show that the ant colony optimization method converges quickly to the global optimum.
Sample EP Flow Analysis of Severely Damaged Networks
Energy Technology Data Exchange (ETDEWEB)
Werley, Kenneth Alan [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); McCown, Andrew William [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2016-10-12
These are slides for a presentation at the working group meeting of the WESC SREMP Software Product Integration Team on sample EP flow analysis of severely damaged networks. The following topics are covered: ERCOT EP Transmission Model; Zoomed in to Houston and Overlaying StreetAtlas; EMPACT Solve/Dispatch/Shedding Options; QACS BaseCase Power Flow Solution; 3 Substation Contingency; Gen. & Load/100 Optimal Dispatch; Dispatch Results; Shed Load for Low V; Network Damage Summary; Estimated Service Areas (Potential); Estimated Outage Areas (potential).
Self-control of traffic lights and vehicle flows in urban road networks
Lämmer, Stefan; Helbing, Dirk
2008-04-01
Based on fluid-dynamic and many-particle (car-following) simulations of traffic flows in (urban) networks, we study the problem of coordinating incompatible traffic flows at intersections. Inspired by the observation of self-organized oscillations of pedestrian flows at bottlenecks, we propose a self-organization approach to traffic light control. The problem can be treated as a multi-agent problem with interactions between vehicles and traffic lights. Specifically, our approach assumes a priority-based control of traffic lights by the vehicle flows themselves, taking into account short-sighted anticipation of vehicle flows and platoons. The considered local interactions lead to emergent coordination patterns such as 'green waves' and achieve an efficient, decentralized traffic light control. While the proposed self-control adapts flexibly to local flow conditions and often leads to non-cyclical switching patterns with changing service sequences of different traffic flows, an almost periodic service may evolve under certain conditions and suggests the existence of a spontaneous synchronization of traffic lights despite the varying delays due to variable vehicle queues and travel times. The self-organized traffic light control is based on an optimization and a stabilization rule, each of which performs poorly at high utilizations of the road network, while their proper combination reaches a superior performance. The result is a considerable reduction not only in the average travel times, but also of their variation. Similar control approaches could be applied to the coordination of logistic and production processes.
On Event Detection and Localization in Acyclic Flow Networks
Suresh, Mahima Agumbe
2013-05-01
Acyclic flow networks, present in many infrastructures of national importance (e.g., oil and gas and water distribution systems), have been attracting immense research interest. Existing solutions for detecting and locating attacks against these infrastructures have been proven costly and imprecise, particularly when dealing with large-scale distribution systems. In this article, to the best of our knowledge, for the first time, we investigate how mobile sensor networks can be used for optimal event detection and localization in acyclic flow networks. We propose the idea of using sensors that move along the edges of the network and detect events (i.e., attacks). To localize the events, sensors detect proximity to beacons, which are devices with known placement in the network. We formulate the problem of minimizing the cost of monitoring infrastructure (i.e., minimizing the number of sensors and beacons deployed) in a predetermined zone of interest, while ensuring a degree of coverage by sensors and a required accuracy in locating events using beacons. We propose algorithms for solving the aforementioned problem and demonstrate their effectiveness with results obtained from a realistic flow network simulator.
Interpreting physical flows in networks as a communication system
Indian Academy of Sciences (India)
communication systems and multiple-channel commu- nication systems. Moreover, we are able to interpret directly the information transmission capacity of the network with the network invariants, such as the node degree. 2. Methods and model. The starting point in our approach is to define and to analytically solve the ...
Parametric Identification of Aircraft Loads: An Artificial Neural Network Approach
2016-03-30
Undergraduate Student Paper Postgraduate Student Paper Parametric Identification of Aircraft Loads: An Artificial Neural Network Approach...monitoring, flight parameter, nonlinear modeling, Artificial Neural Network , typical loadcase. Introduction Aircraft load monitoring is an... Neural Networks (ANN), i.e. the BP network and Kohonen Clustering Network , are applied and revised by Kalman Filter and Genetic Algorithm to build
Droplet flows through periodic loop networks
Jeanneret, Raphael; Schindler, Michael; Bartolo, Denis
2010-11-01
Numerous microfluidic experiments have revealed non-trivial traffic dynamics when droplets flow through a channel including a single loop. A complex encoding of the time intervals between the droplets is achieved by the binary choices they make as they enter the loop. Very surprisingly, another set of experiments has demonstrated that the addition of a second loop does not increase the complexity of the droplet pattern. Conversely, the second loop decodes the temporal signal encrypted by the first loop [1]. In this talk we show that no first principle argument based on symmetry or conservation laws can account for this unexpected decoding process. Then, to better understand how a loop maps time intervals between droplets, we consider a simplified model which has proven to describe accurately microfluidic droplet flows. Combining numerical simulations and analytical calculations for the dynamic of three droplets travelling through N loops: (i) We show that three different traffic regimes exist, yet none of them yields exact decoding. (ii) We uncover that for a wide class of loop geometry, the coding process is analogous to a Hamiltonian mapping: regular orbits are destabilized in island chains and separatrix. (iii) Eventually, we propose a simple explanation to solve the apparent paradox with the coding/decoding dynamics observed in experiments. [1] M.J. Fuerstman, P. Garstecki, and G.M. Whitesides, Science, 315:828, 2007.
Decompositions of injection patterns for nodal flow allocation in renewable electricity networks
Schäfer, Mirko; Tranberg, Bo; Hempel, Sabrina; Schramm, Stefan; Greiner, Martin
2017-08-01
The large-scale integration of fluctuating renewable power generation represents a challenge to the technical and economical design of a sustainable future electricity system. In this context, the increasing significance of long-range power transmission calls for innovative methods to understand the emerging complex flow patterns and to integrate price signals about the respective infrastructure needs into the energy market design. We introduce a decomposition method of injection patterns. Contrary to standard flow tracing approaches, it provides nodal allocations of link flows and costs in electricity networks by decomposing the network injection pattern into market-inspired elementary import/export building blocks. We apply the new approach to a simplified data-driven model of a European electricity grid with a high share of renewable wind and solar power generation.
Resistive Network Optimal Power Flow: Uniqueness and Algorithms
Energy Technology Data Exchange (ETDEWEB)
Tan, CW; Cai, DWH; Lou, X
2015-01-01
The optimal power flow (OPF) problem minimizes the power loss in an electrical network by optimizing the voltage and power delivered at the network buses, and is a nonconvex problem that is generally hard to solve. By leveraging a recent development on the zero duality gap of OPF, we propose a second-order cone programming convex relaxation of the resistive network OPF, and study the uniqueness of the optimal solution using differential topology, especially the Poincare-Hopf Index Theorem. We characterize the global uniqueness for different network topologies, e.g., line, radial, and mesh networks. This serves as a starting point to design distributed local algorithms with global behaviors that have low complexity, are computationally fast, and can run under synchronous and asynchronous settings in practical power grids.
Insomnia and Personality—A Network Approach
Directory of Open Access Journals (Sweden)
Kim Dekker
2017-03-01
Full Text Available Studies on personality traits and insomnia have remained inconclusive about which traits show the most direct associations with insomnia severity. It has moreover hardly been explored how traits relate to specific characteristics of insomnia. We here used network analysis in a large sample (N = 2089 to obtain an integrated view on the associations of personality traits with both overall insomnia severity and different insomnia characteristics, while distinguishing direct from indirect associations. We first estimated a network describing the associations among the five factor model personality traits and overall insomnia severity. Overall insomnia severity was associated with neuroticism, agreeableness, and openness. Subsequently, we estimated a separate network describing the associations among the personality traits and each of the seven individual items of the Insomnia Severity Index. This revealed relatively separate clusters of daytime and nocturnal insomnia complaints, that both contributed to dissatisfaction with sleep, and were both most directly associated with neuroticism and conscientiousness. The approach revealed the strongest direct associations between personality traits and the severity of different insomnia characteristics and overall insomnia severity. Differentiating them from indirect associations identified the targets for improving Cognitive Behavioral Therapy for insomnia with the highest probability of effectively changing the network of associated complaints.
DEFF Research Database (Denmark)
Ding, Tao; Li, Cheng; Yang, Yongheng
2017-01-01
The detailed topology of renewable resource bases may have the impact on the optimal power flow of the VSC-HVDC transmission network. To address this issue, this paper develops an optimal power flow with the hybrid VSC-HVDC transmission and active distribution networks to optimally schedule...... the generation output and voltage regulation of both networks, which leads to a non-convex programming model. Furthermore, the non-convex power flow equations are based on the Second Order Cone Programming (SOCP) relaxation approach. Thus, the proposed model can be relaxed to a SOCP that can be tractably solved...
Towards Effective Intra-flow Network Coding in Software Defined Wireless Mesh Networks
Directory of Open Access Journals (Sweden)
Donghai Zhu
2016-01-01
Full Text Available Wireless Mesh Networks (WMNs have potential to provide convenient broadband wireless Internet access to mobile users.With the support of Software-Defined Networking (SDN paradigm that separates control plane and data plane, WMNs can be easily deployed and managed. In addition, by exploiting the broadcast nature of the wireless medium and the spatial diversity of multi-hop wireless networks, intra-flow network coding has shown a greater benefit in comparison with traditional routing paradigms in data transmission for WMNs. In this paper, we develop a novel OpenCoding protocol, which combines the SDN technique with intra-flow network coding for WMNs. Our developed protocol can simplify the deployment and management of the network and improve network performance. In OpenCoding, a controller that works on the control plane makes routing decisions for mesh routers and the hop-by-hop forwarding function is replaced by network coding functions in data plane. We analyze the overhead of OpenCoding. Through a simulation study, we show the effectiveness of the OpenCoding protocol in comparison with existing schemes. Our data shows that OpenCoding outperforms both traditional routing and intra-flow network coding schemes.
A Spatial Clustering Approach for Stochastic Fracture Network Modelling
Seifollahi, S.; Dowd, P. A.; Xu, C.; Fadakar, A. Y.
2014-07-01
Fracture network modelling plays an important role in many application areas in which the behaviour of a rock mass is of interest. These areas include mining, civil, petroleum, water and environmental engineering and geothermal systems modelling. The aim is to model the fractured rock to assess fluid flow or the stability of rock blocks. One important step in fracture network modelling is to estimate the number of fractures and the properties of individual fractures such as their size and orientation. Due to the lack of data and the complexity of the problem, there are significant uncertainties associated with fracture network modelling in practice. Our primary interest is the modelling of fracture networks in geothermal systems and, in this paper, we propose a general stochastic approach to fracture network modelling for this application. We focus on using the seismic point cloud detected during the fracture stimulation of a hot dry rock reservoir to create an enhanced geothermal system; these seismic points are the conditioning data in the modelling process. The seismic points can be used to estimate the geographical extent of the reservoir, the amount of fracturing and the detailed geometries of fractures within the reservoir. The objective is to determine a fracture model from the conditioning data by minimizing the sum of the distances of the points from the fitted fracture model. Fractures are represented as line segments connecting two points in two-dimensional applications or as ellipses in three-dimensional (3D) cases. The novelty of our model is twofold: (1) it comprises a comprehensive fracture modification scheme based on simulated annealing and (2) it introduces new spatial approaches, a goodness-of-fit measure for the fitted fracture model, a measure for fracture similarity and a clustering technique for proposing a locally optimal solution for fracture parameters. We use a simulated dataset to demonstrate the application of the proposed approach
Prediction of Multiphase Flow Properties from Network Models ...
African Journals Online (AJOL)
The prediction of multiphase transport properties of reservoir rocks has been undertaken. This was done by numerical flow simulation of relative permeability and capillary pressure curves from pore network models extracted from Pore Architecture Models (PAMs). These PAMs are three-dimensional images obtained from ...
Exploring the potential of blood flow network data
Poelma, C.
2015-01-01
To gain a better understanding of the role of haemodynamic forces during the development of the cardiovascular system, a series of studies have been reported recently that describe flow fields in the vasculature of model systems. Such data sets, in particular those reporting networks at multiple
Enhancing debris flow modeling parameters integrating Bayesian networks
Graf, C.; Stoffel, M.; Grêt-Regamey, A.
2009-04-01
Applied debris-flow modeling requires suitably constraint input parameter sets. Depending on the used model, there is a series of parameters to define before running the model. Normally, the data base describing the event, the initiation conditions, the flow behavior, the deposition process and mainly the potential range of possible debris flow events in a certain torrent is limited. There are only some scarce places in the world, where we fortunately can find valuable data sets describing event history of debris flow channels delivering information on spatial and temporal distribution of former flow paths and deposition zones. Tree-ring records in combination with detailed geomorphic mapping for instance provide such data sets over a long time span. Considering the significant loss potential associated with debris-flow disasters, it is crucial that decisions made in regard to hazard mitigation are based on a consistent assessment of the risks. This in turn necessitates a proper assessment of the uncertainties involved in the modeling of the debris-flow frequencies and intensities, the possible run out extent, as well as the estimations of the damage potential. In this study, we link a Bayesian network to a Geographic Information System in order to assess debris-flow risk. We identify the major sources of uncertainty and show the potential of Bayesian inference techniques to improve the debris-flow model. We model the flow paths and deposition zones of a highly active debris-flow channel in the Swiss Alps using the numerical 2-D model RAMMS. Because uncertainties in run-out areas cause large changes in risk estimations, we use the data of flow path and deposition zone information of reconstructed debris-flow events derived from dendrogeomorphological analysis covering more than 400 years to update the input parameters of the RAMMS model. The probabilistic model, which consistently incorporates this available information, can serve as a basis for spatial risk
Development of objective flow regime identification method using self-organizing neural network
Energy Technology Data Exchange (ETDEWEB)
Lee, Jae Young; Kim, Nam Seok; Kwak, Nam Yee [Handong Global Univ., Pohang (Korea, Republic of)
2004-07-01
Two-phase flow shows various flow patterns according to the amount of the void and its relative velocity to the liquid flow. This variation directly affect the interfacial transfer which is the key factor for the design or analysis of the phase change systems. Especially the safety analysis of the nuclear power plant has been performed based on the numerical code furnished with the proper constitutive relations depending highly upon the flow regimes. Heavy efforts have been focused to identify the flow regime and at this moment we stand on relative very stable engineering background compare to the other research field. However, the issues related to objectiveness and transient flow regime are still open to study. Lee et al. and Ishii developed the method for the objective and instantaneous flow regime identification based on the neural network and new index of probability distribution of the flow regime which allows just one second observation for the flow regime identification. In the present paper, we developed the self-organized neural network for more objective approach to this problem. Kohonen's Self-Organizing Map (SOM) has been used for clustering, visualization, and abstraction. The SOM is trained through unsupervised competitive learning using a 'winner takes it all' policy. Therefore, its unsupervised training character delete the possible interference of the regime developer to the neural network training. After developing the computer code, we evaluate the performance of the code with the vertically upward two-phase flow in the pipes of 25.4 and 50.4 cmm I.D. Also, the sensitivity of the number of the clusters to the flow regime identification was made.
Power System Structural Vulnerability Assessment based on an Improved Maximum Flow Approach
DEFF Research Database (Denmark)
Fang, Jiakun; Su, Chi; Chen, Zhe
2017-01-01
(PCA) and convex hull. Then the second step is to use an improved maximum flow based complex network approach for topology analysis. Weighted vertices in the network are considered, enabling taking the selected operating conditions into consideration when identifying the vulnerable lines. The proposed......With the increasing complexity of the power grid and the concerns on major blackouts, there is an urgent need for an efficient and effective tool to assess the power system structural vulnerability. To tackle this challenge, this paper proposes a maximum flow based complex network approach...... to identify the critical lines in a system. The proposed method consists of two major steps. First, the power network is modeled as a graph with edges (transmission lines, transformers, etc.) and nodes (buses, substations, etc.). The critical scenarios are identified by using the principal component analysis...
Dye staining and excavation of a lateral preferential flow network
Directory of Open Access Journals (Sweden)
A. E. Anderson
2009-06-01
Full Text Available Preferential flow paths have been found to be important for runoff generation, solute transport, and slope stability in many areas around the world. Although many studies have identified the particular characteristics of individual features and measured the runoff generation and solute transport within hillslopes, very few studies have determined how individual features are hydraulically connected at a hillslope scale. In this study, we used dye staining and excavation to determine the morphology and spatial pattern of a preferential flow network over a large scale (30 m. We explore the feasibility of extending small-scale dye staining techniques to the hillslope scale. We determine the lateral preferential flow paths that are active during the steady-state flow conditions and their interaction with the surrounding soil matrix. We also calculate the velocities of the flow through each cross-section of the hillslope and compare them to hillslope scale applied tracer measurements. Finally, we investigate the relationship between the contributing area and the characteristics of the preferential flow paths. The experiment revealed that larger contributing areas coincided with highly developed and hydraulically connected preferential flow paths that had flow with little interaction with the surrounding soil matrix. We found evidence of subsurface erosion and deposition of soil and organic material laterally and vertically within the soil. These results are important because they add to the understanding of the runoff generation, solute transport, and slope stability of preferential flow-dominated hillslopes.
Information flow in a network of dispersed signalers-receivers
Halupka, Konrad
2017-11-01
I consider a stochastic model of multi-agent communication in regular network. The model describes how dispersed animals exchange information. Each agent can initiate and transfer the signal to its nearest neighbors, who may pass it farther. For an external observer of busy networks, signaling activity may appear random, even though information flow actually thrives. Only when signal initiation and transfer are at low levels do spatiotemporal autocorrelations emerge as clumping signaling activity in space and pink noise time series. Under such conditions, the costs of signaling are moderate, but the signaler can reach a large audience. I propose that real-world networks of dispersed signalers-receivers may self-organize into this state and the flow of information maintains their integrity.
Three-Phase Unbalanced Load Flow Tool for Distribution Networks
DEFF Research Database (Denmark)
Demirok, Erhan; Kjær, Søren Bækhøj; Sera, Dezso
2012-01-01
This work develops a three-phase unbalanced load flow tool tailored for radial distribution networks based on Matlab®. The tool can be used to assess steady-state voltage variations, thermal limits of grid components and power losses in radial MV-LV networks with photovoltaic (PV) generators where...... most of the systems are single phase. New ancillary service such as static reactive power support by PV inverters can be also merged together with the load flow solution tool and thus, the impact of the various reactive power control strategies on the steady-state grid operation can be simply...... investigated. Performance of the load flow solution tool in the sense of resulting bus voltage magnitudes is compared and validated with IEEE 13-bus test feeder....
TCP flow control using link layer information in mobile networks
Koga, Hiroyuki; Kawahara, Kenji; Oie, Yuji
2002-07-01
Mobile Networks have been expanding and IMT-2000 further increases their available bandwidth over wireless links. However, TCP, which is a reliable end-to-end transport protocol, is tuned to perform well in wired networks where bit error rates are very low and packet loss occurs mostly because of congestion. Although a TCP sender can execute flow control to utilize as much available bandwidth as possible in wired networks, it cannot work well in wireless networks characterized by high bit error rates. In the next generation mobile systems, sophisticated error recovery technologies of FEC and ARQ are indeed employed over wireless links, i.e., over Layer 2, to avoid performance degradation of upper layers. However, multiple retransmissions by Layer 2 ARQ can adversely increase transmission delay of TCP segments, which will further make TCP unnecessarily increase RTO (Retransmission TimeOut). Furthermore, a link bandwidth assigned to TCP flows can change in response to changing air conditions to use wireless links efficiently. TCP thus has to adapt its transmission rate according to the changing available bandwidth. The major goal of this study is to develop a receiver-based effective TCP flow control without any modification on TCP senders, which are probably connected with wired networks. For this end, we propose a TCP flow control employing some Layer 2 information on a wireless link at the mobile station. Our performance evaluation of the proposed TCP shows that the receiver-based TCP flow control can moderate the performance degradation very well even if FER on Layer 2 is high.
AC Transmission Network Expansion Planning: A Semidefinite Programming Branch-and-Cut Approach
Ghaddar, Bissan; Jabr, Rabih
2017-01-01
Transmission network expansion planning is a mixed-integer optimization problem, whose solution is used to guide future investment in transmission equipment. An approach is presented to find the global solution of the transmission planning problem using an AC network model. The approach builds on the semidefinite relaxation of the AC optimal power flow problem (ACOPF); its computational engine is a new specialized branch-and-cut algorithm for transmission expansion planning to deal with the u...
Network-induced oscillatory behavior in material flow networks and irregular business cycles.
Helbing, Dirk; Lämmer, Stefen; Witt, Ulrich; Brenner, Thomas
2004-11-01
Network theory is rapidly changing our understanding of complex systems, but the relevance of topological features for the dynamic behavior of metabolic networks, food webs, production systems, information networks, or cascade failures of power grids remains to be explored. Based on a simple model of supply networks, we offer an interpretation of instabilities and oscillations observed in biological, ecological, economic, and engineering systems. We find that most supply networks display damped oscillations, even when their units--and linear chains of these units--behave in a nonoscillatory way. Moreover, networks of damped oscillators tend to produce growing oscillations. This surprising behavior offers, for example, a different interpretation of business cycles and of oscillating or pulsating processes. The network structure of material flows itself turns out to be a source of instability, and cyclical variations are an inherent feature of decentralized adjustments.
Robust Traffic Flow Management: Coevolutionary Approach Project
National Aeronautics and Space Administration — We will develop a Coevolutionary Decision Support Tool (CDST) that explicitly incorporates weather uncertainty (non-probabilistically) into strategic Traffic Flow...
Discrete Network Modeling for Field-Scale Flow and Transport Through Porous Media
National Research Council Canada - National Science Library
Howington, Stacy
1997-01-01
.... Specifically, a stochastic, high-resolution, discrete network model is developed and explored for simulating macroscopic flow and conservative transport through macroscopic porous media Networks...
Identifying Geographic Clusters: A Network Analytic Approach
Catini, Roberto; Penner, Orion; Riccaboni, Massimo
2015-01-01
In recent years there has been a growing interest in the role of networks and clusters in the global economy. Despite being a popular research topic in economics, sociology and urban studies, geographical clustering of human activity has often studied been by means of predetermined geographical units such as administrative divisions and metropolitan areas. This approach is intrinsically time invariant and it does not allow one to differentiate between different activities. Our goal in this paper is to present a new methodology for identifying clusters, that can be applied to different empirical settings. We use a graph approach based on k-shell decomposition to analyze world biomedical research clusters based on PubMed scientific publications. We identify research institutions and locate their activities in geographical clusters. Leading areas of scientific production and their top performing research institutions are consistently identified at different geographic scales.
THE NETWORKS IN TOURISM: A THEORETICAL APPROACH
Directory of Open Access Journals (Sweden)
Maria TĂTĂRUȘANU
2016-12-01
Full Text Available The economic world in which tourism companies act today is in a continuous changing process. The most important factor of these changes is the globalization of their environment, both in economic, social, natural and cultural aspects. The tourism companies can benefit from the opportunities brought by globalization, but also could be menaced by the new context. How could react the companies to these changes in order to create and maintain long term competitive advantage for their business? In the present paper we make a literature review of the new tourism companies´ business approach: the networks - a result and/or a reason for exploiting the opportunities or, on the contrary, for keeping their actual position on the market. It’s a qualitative approach and the research methods used are analyses, synthesis, abstraction, which are considered the most appropriate to achieve the objective of the paper.
Signaling networks: information flow, computation, and decision making.
Azeloglu, Evren U; Iyengar, Ravi
2015-04-01
Signaling pathways come together to form networks that connect receptors to many different cellular machines. Such networks not only receive and transmit signals but also process information. The complexity of these networks requires the use of computational models to understand how information is processed and how input-output relationships are determined. Two major computational approaches used to study signaling networks are graph theory and dynamical modeling. Both approaches are useful; network analysis (application of graph theory) helps us understand how the signaling network is organized and what its information-processing capabilities are, whereas dynamical modeling helps us determine how the system changes in time and space upon receiving stimuli. Computational models have helped us identify a number of emergent properties that signaling networks possess. Such properties include ultrasensitivity, bistability, robustness, and noise-filtering capabilities. These properties endow cell-signaling networks with the ability to ignore small or transient signals and/or amplify signals to drive cellular machines that spawn numerous physiological functions associated with different cell states. Copyright © 2015 Cold Spring Harbor Laboratory Press; all rights reserved.
Neural Network Predictive Control for Vanadium Redox Flow Battery
Directory of Open Access Journals (Sweden)
Hai-Feng Shen
2013-01-01
Full Text Available The vanadium redox flow battery (VRB is a nonlinear system with unknown dynamics and disturbances. The flowrate of the electrolyte is an important control mechanism in the operation of a VRB system. Too low or too high flowrate is unfavorable for the safety and performance of VRB. This paper presents a neural network predictive control scheme to enhance the overall performance of the battery. A radial basis function (RBF network is employed to approximate the dynamics of the VRB system. The genetic algorithm (GA is used to obtain the optimum initial values of the RBF network parameters. The gradient descent algorithm is used to optimize the objective function of the predictive controller. Compared with the constant flowrate, the simulation results show that the flowrate optimized by neural network predictive controller can increase the power delivered by the battery during the discharge and decrease the power consumed during the charge.
Maximum Entropy Approaches to Living Neural Networks
Directory of Open Access Journals (Sweden)
John M. Beggs
2010-01-01
Full Text Available Understanding how ensembles of neurons collectively interact will be a key step in developing a mechanistic theory of cognitive processes. Recent progress in multineuron recording and analysis techniques has generated tremendous excitement over the physiology of living neural networks. One of the key developments driving this interest is a new class of models based on the principle of maximum entropy. Maximum entropy models have been reported to account for spatial correlation structure in ensembles of neurons recorded from several different types of data. Importantly, these models require only information about the firing rates of individual neurons and their pairwise correlations. If this approach is generally applicable, it would drastically simplify the problem of understanding how neural networks behave. Given the interest in this method, several groups now have worked to extend maximum entropy models to account for temporal correlations. Here, we review how maximum entropy models have been applied to neuronal ensemble data to account for spatial and temporal correlations. We also discuss criticisms of the maximum entropy approach that argue that it is not generally applicable to larger ensembles of neurons. We conclude that future maximum entropy models will need to address three issues: temporal correlations, higher-order correlations, and larger ensemble sizes. Finally, we provide a brief list of topics for future research.
PDF approach for compressible turbulent reacting flows
Hsu, A. T.; Tsai, Y.-L. P.; Raju, M. S.
1993-01-01
The objective of the present work is to develop a probability density function (pdf) turbulence model for compressible reacting flows for use with a CFD flow solver. The probability density function of the species mass fraction and enthalpy are obtained by solving a pdf evolution equation using a Monte Carlo scheme. The pdf solution procedure is coupled with a compressible CFD flow solver which provides the velocity and pressure fields. A modeled pdf equation for compressible flows, capable of capturing shock waves and suitable to the present coupling scheme, is proposed and tested. Convergence of the combined finite-volume Monte Carlo solution procedure is discussed, and an averaging procedure is developed to provide smooth Monte-Carlo solutions to ensure convergence. Two supersonic diffusion flames are studied using the proposed pdf model and the results are compared with experimental data; marked improvements over CFD solutions without pdf are observed. Preliminary applications of pdf to 3D flows are also reported.
Non-invasive on-line two-phase flow regime identification employing artificial neural networks
Energy Technology Data Exchange (ETDEWEB)
Tambouratzis, T. [Department of Industrial Management and Technology, University of Piraeus, 107 Deligiorgi St., Piraeus 185 34 (Greece)], E-mail: tatianatambouratzis@gmail.com; Pazsit, I. [Department of Reactor Physics, Chalmers University of Technology, SE-412 Goteborg (Sweden)
2009-05-01
A novel non-invasive approach to the on-line identification of BWR two-phase flow regimes is investigated. The proposed approach receives neutron radiography images of coolant flow recordings as its input and performs feature extraction on each image via simple and directly computable statistical operators. The extracted features are subsequently used as inputs to an ensemble of self-organizing maps whose outputs demonstrate swift and accurate classification of each image into its corresponding flow regime. The novelty of the approach lies in the use of the self-organizing map which generates the different classes by itself, according to feature similarity of the corresponding images; this contrasts traditional artificial neural networks where the user has to define both the number of distinct classes as well as to supply separate training vectors for each class.
Towards Optimal Event Detection and Localization in Acyclic Flow Networks
Agumbe Suresh, Mahima
2012-01-03
Acyclic flow networks, present in many infrastructures of national importance (e.g., oil & gas and water distribution systems), have been attracting immense research interest. Existing solutions for detecting and locating attacks against these infrastructures, have been proven costly and imprecise, especially when dealing with large scale distribution systems. In this paper, to the best of our knowledge for the first time, we investigate how mobile sensor networks can be used for optimal event detection and localization in acyclic flow networks. Sensor nodes move along the edges of the network and detect events (i.e., attacks) and proximity to beacon nodes with known placement in the network. We formulate the problem of minimizing the cost of monitoring infrastructure (i.e., minimizing the number of sensor and beacon nodes deployed), while ensuring a degree of sensing coverage in a zone of interest and a required accuracy in locating events. We propose algorithms for solving these problems and demonstrate their effectiveness with results obtained from a high fidelity simulator.
Evaluation of artificial neural network techniques for flow forecasting in the River Yangtze, China
Directory of Open Access Journals (Sweden)
C. W. Dawson
2002-01-01
Full Text Available While engineers have been quantifying rainfall-runoff processes since the mid-19th century, it is only in the last decade that artificial neural network models have been applied to the same task. This paper evaluates two neural networks in this context: the popular multilayer perceptron (MLP, and the radial basis function network (RBF. Using six-hourly rainfall-runoff data for the River Yangtze at Yichang (upstream of the Three Gorges Dam for the period 1991 to 1993, it is shown that both neural network types can simulate river flows beyond the range of the training set. In addition, an evaluation of alternative RBF transfer functions demonstrates that the popular Gaussian function, often used in RBF networks, is not necessarily the ‘best’ function to use for river flow forecasting. Comparisons are also made between these neural networks and conventional statistical techniques; stepwise multiple linear regression, auto regressive moving average models and a zero order forecasting approach. Keywords: Artificial neural network, multilayer perception, radial basis function, flood forecasting
2014-12-01
The report documents policy considerations for the Intelligent Network Flow Optimization (INFLO) connected vehicle applications : bundle. INFLO aims to optimize network flow on freeways and arterials by informing motorists of existing and impen...
Queueing network model for obstetric patient flow in a hospital.
Takagi, Hideaki; Kanai, Yuta; Misue, Kazuo
2016-03-03
A queueing network is used to model the flow of patients in a hospital using the observed admission rate of patients and the histogram for the length of stay for patients in each ward. A complete log of orders for every movement of all patients from room to room covering two years was provided to us by the Medical Information Department of the University of Tsukuba Hospital in Japan. We focused on obstetric patients, who are generally hospitalized at random times throughout the year, and we analyzed the patient flow probabilistically. On admission, each obstetric patient is assigned to a bed in one of the two wards: one for normal delivery and the other for high-risk delivery. Then, the patient may be transferred between the two wards before discharge. We confirm Little's law of queueing theory for the patient flow in each ward. Next, we propose a new network model of M/G/ ∞ and M/M/ m queues to represent the flow of these patients, which is used to predict the probability distribution for the number of patients staying in each ward at the nightly census time. Although our model is a very rough and simplistic approximation of the real patient flow, the predicted probability distribution shows good agreement with the observed data. The proposed method can be used for capacity planning of hospital wards to predict future patient load in each ward.
Directory of Open Access Journals (Sweden)
Zhengyu Xie
2016-01-01
Full Text Available Passenger flow risk forecasting is a vital task for safety management in high-speed railway transport hub. In this paper, we considered the passenger flow risk forecasting problem in high-speed railway transport hub. Based on the surveillance sensor networks, a passenger flow risk forecasting algorithm was developed based on spatial correlation. Computational results showed that the proposed forecasting approach was effective and significant for the high-speed railway transport hub.
Technology and knowledge flow the power of networks
Trentin, Guglielmo
2011-01-01
This book outlines how network technology can support, foster and enhance the Knowledge Management, Sharing and Development (KMSD) processes in professional environments through the activation of both formal and informal knowledge flows. Understanding how ICT can be made available to such flows in the knowledge society is a factor that cannot be disregarded and is confirmed by the increasing interest of companies in new forms of software-mediated social interaction. The latter factor is in relation both to the possibility of accelerating internal communication and problem solving processes, an
The pdf approach to turbulent flow
Kollmann, W.
1990-01-01
This paper provides a detailed discussion of the theory and application of probability density function (pdf) methods, which provide a complete statistical description of turbulent flow fields at a single point or a finite number of points. The basic laws governing the flow of Newtonian fluids are set up in the Eulerian and the Lagrangian frame, and the exact and linear equations for the characteristic functionals in those frames are discussed. Pdf equations in both frames are derived as Fourier transforms of the equations of the characteristic functions. Possible formulations for the nonclosed terms in the pdf equation are discussed, their properties are assessed, and closure modes for the molecular-transport and the fluctuating pressure-gradient terms are reviewed. The application of pdf methods to turbulent combustion flows, supersonic flows, and the interaction of turbulence with shock waves is discussed.
Outlier Detection Method Use for the Network Flow Anomaly Detection
Directory of Open Access Journals (Sweden)
Rimas Ciplinskas
2016-06-01
Full Text Available New and existing methods of cyber-attack detection are constantly being developed and improved because there is a great number of attacks and the demand to protect from them. In prac-tice, current methods of attack detection operates like antivirus programs, i. e. known attacks signatures are created and attacks are detected by using them. These methods have a drawback – they cannot detect new attacks. As a solution, anomaly detection methods are used. They allow to detect deviations from normal network behaviour that may show a new type of attack. This article introduces a new method that allows to detect network flow anomalies by using local outlier factor algorithm. Accom-plished research allowed to identify groups of features which showed the best results of anomaly flow detection according the highest values of precision, recall and F-measure.
DC Voltage Droop Control Implementation in the AC/DC Power Flow Algorithm: Combinational Approach
DEFF Research Database (Denmark)
Akhter, F.; Macpherson, D.E.; Harrison, G.P.
2015-01-01
In this paper, a combinational AC/DC power flow approach is proposed for the solution of the combined AC/DC network. The unified power flow approach is extended to include DC voltage droop control. In the VSC based MTDC grids, DC droop control is regarded as more advantageous in terms...... of operational flexibility, as more than one VSC station controls the DC link voltage of the MTDC system. This model enables the study of the effects of DC droop control on the power flows of the combined AC/DC system for steady state studies after VSC station outages or transient conditions without needing...... to use its complete dynamic model. Further, the proposed approach can be extended to include multiple AC and DC grids for combined AC/DC power flow analysis. The algorithm is implemented by modifying the MATPOWER based MATACDC program and the results shows that the algorithm works efficiently....
Evolution of weighted complex bus transit networks with flow
Huang, Ailing; Xiong, Jie; Shen, Jinsheng; Guan, Wei
2016-02-01
Study on the intrinsic properties and evolutional mechanism of urban public transit networks (PTNs) has great significance for transit planning and control, particularly considering passengers’ dynamic behaviors. This paper presents an empirical analysis for exploring the complex properties of Beijing’s weighted bus transit network (BTN) based on passenger flow in L-space, and proposes a bi-level evolution model to simulate the development of transit routes from the view of complex network. The model is an iterative process that is driven by passengers’ travel demands and dual-controlled interest mechanism, which is composed of passengers’ spatio-temporal requirements and cost constraint of transit agencies. Also, the flow’s dynamic behaviors, including the evolutions of travel demand, sectional flow attracted by a new link and flow perturbation triggered in nearby routes, are taken into consideration in the evolutional process. We present the numerical experiment to validate the model, where the main parameters are estimated by using distribution functions that are deduced from real-world data. The results obtained have proven that our model can generate a BTN with complex properties, such as the scale-free behavior or small-world phenomenon, which shows an agreement with our empirical results. Our study’s results can be exploited to optimize the real BTN’s structure and improve the network’s robustness.
Parametric and experimental analysis using a power flow approach
Cuschieri, J. M.
1990-01-01
A structural power flow approach for the analysis of structure-borne transmission of vibrations is used to analyze the influence of structural parameters on transmitted power. The parametric analysis is also performed using the Statistical Energy Analysis approach and the results are compared with those obtained using the power flow approach. The advantages of structural power flow analysis are demonstrated by comparing the type of results that are obtained by the two analytical methods. Also, to demonstrate that the power flow results represent a direct physical parameter that can be measured on a typical structure, an experimental study of structural power flow is presented. This experimental study presents results for an L shaped beam for which an available solution was already obtained. Various methods to measure vibrational power flow are compared to study their advantages and disadvantages.
Product flow and price change in an agricultural distribution network
Lee, Daekyung; Yang, Seong-Gyu; Kim, Kibum; Kim, Beom Jun
2018-01-01
We use the structure of a real distribution network of agricultural product in Korea and investigate how the change in the supply may affect the price changes in agents across the distribution network. In particular, we focus on the real network structure of cabbage distribution composed of various types of agents, from farms to consumers, and apply a dynamic model to describe how each participant reacts upon the change of input and output flow of products through the adjustment of price. Our main result implies that the effect of fluctuation of production quantity in the supplying participant can be nontrivial and the consumer price responds to such changes. We believe that our results can be useful to predict what will happen if the agricultural production changes much in the future due to the climate changes.
DEFF Research Database (Denmark)
Rawstron, Andy C; Orfao, Alberto; Beksac, Meral
2008-01-01
The European Myeloma Network (EMN) organized two flow cytometry workshops. The first aimed to identify specific indications for flow cytometry in patients with monoclonal gammopathies, and consensus technical approaches through a questionnaire-based review of current practice in participating...... in patients with MGUS and detecting minimal residual disease. A range of technical recommendations were identified, including: 1) CD38, CD138 and CD45 should all be included in at least one tube for plasma cell identification and enumeration. The primary gate should be based on CD38 vs. CD138 expression; 2...
A Cluster-based Approach Towards Detecting and Modeling Network Dictionary Attacks
Directory of Open Access Journals (Sweden)
A. Tajari Siahmarzkooh
2016-12-01
Full Text Available In this paper, we provide an approach to detect network dictionary attacks using a data set collected as flows based on which a clustered graph is resulted. These flows provide an aggregated view of the network traffic in which the exchanged packets in the network are considered so that more internally connected nodes would be clustered. We show that dictionary attacks could be detected through some parameters namely the number and the weight of clusters in time series and their evolution over the time. Additionally, the Markov model based on the average weight of clusters,will be also created. Finally, by means of our suggested model, we demonstrate that artificial clusters of the flows are created for normal and malicious traffic. The results of the proposed approach on CAIDA 2007 data set suggest a high accuracy for the model and, therefore, it provides a proper method for detecting the dictionary attack.
Statistical and machine learning approaches for network analysis
Dehmer, Matthias
2012-01-01
Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internation
Flow-pattern identification and nonlinear dynamics of gas-liquid two-phase flow in complex networks.
Gao, Zhongke; Jin, Ningde
2009-06-01
The identification of flow pattern is a basic and important issue in multiphase systems. Because of the complexity of phase interaction in gas-liquid two-phase flow, it is difficult to discern its flow pattern objectively. In this paper, we make a systematic study on the vertical upward gas-liquid two-phase flow using complex network. Three unique network construction methods are proposed to build three types of networks, i.e., flow pattern complex network (FPCN), fluid dynamic complex network (FDCN), and fluid structure complex network (FSCN). Through detecting the community structure of FPCN by the community-detection algorithm based on K -mean clustering, useful and interesting results are found which can be used for identifying five vertical upward gas-liquid two-phase flow patterns. To investigate the dynamic characteristics of gas-liquid two-phase flow, we construct 50 FDCNs under different flow conditions, and find that the power-law exponent and the network information entropy, which are sensitive to the flow pattern transition, can both characterize the nonlinear dynamics of gas-liquid two-phase flow. Furthermore, we construct FSCN and demonstrate how network statistic can be used to reveal the fluid structure of gas-liquid two-phase flow. In this paper, from a different perspective, we not only introduce complex network theory to the study of gas-liquid two-phase flow but also indicate that complex network may be a powerful tool for exploring nonlinear time series in practice.
The PDF Approach for Modelling Particle Transport in Turbulent Flows
Reeks, Michael
2004-11-01
The Probabaility Density (PDF) approach for modelling dispersed particle flow is analogous to the classical kinetic theory gases. That is, there exists a master equation (analogous to the Maxwell Boltzmann equation of Kinetic Theory) which can be used in a formal way to derive the two-fluid model equations for both phases of the flow and the associated constitutive relations. In addition the approach deals with the near wall behaviour, incorporating the natural boundary conditions of the flow. There are currently two forms of pdf approach: the first form is similar to kinetic theory in that the pdf P(v,x,t) refers to particle velocity v and position x at time t; a second approach in which the pdf P(v,u,x,t) involves the carrier flow velocity u encountered by a particle based on a generalised Langevin equation. Both approaches deal with both dilute and dense particle flows: the influence of inter-particle collisions is directly analogous to the treatment of molecular collisions in kinetic theory. This presentation will describe how each PDF master equation is derived and the form of the closure approximations for the turbulent fluxes. The form of the continuum equations and constitutive relations derived from these equations will be presented and contrasted and the treatment of near wall behaviour briefly discussed. Validation of these approaches for homogeneous and simple shear flows will be given as well as model predictions for a number of test cases involving transport of particles in non-uniform flows.
Balancing of Network Energy using Observer Approach
Patharlapati, Sai Ram Charan
2016-01-01
Efficient energy use is primarily for any sensor networks to function for a longer time period. There have been many efficient schemes with various progress levels proposed by many researchers. Yet, there still more improvements are needed. This thesis is an attempt to make wireless sensor networks with further efficient on energy usage in the network with respect to rate of delivery of the messages. In sensor network architecture radio, sensing and actuators have influence over the power ...
Scaling of peak flows with constant flow velocity in random self-similar networks
Directory of Open Access Journals (Sweden)
R. Mantilla
2011-07-01
Full Text Available A methodology is presented to understand the role of the statistical self-similar topology of real river networks on scaling, or power law, in peak flows for rainfall-runoff events. We created Monte Carlo generated sets of ensembles of 1000 random self-similar networks (RSNs with geometrically distributed interior and exterior generators having parameters p_{i} and p_{e}, respectively. The parameter values were chosen to replicate the observed topology of real river networks. We calculated flow hydrographs in each of these networks by numerically solving the link-based mass and momentum conservation equation under the assumption of constant flow velocity. From these simulated RSNs and hydrographs, the scaling exponents β and φ characterizing power laws with respect to drainage area, and corresponding to the width functions and flow hydrographs respectively, were estimated. We found that, in general, φ > β, which supports a similar finding first reported for simulations in the river network of the Walnut Gulch basin, Arizona. Theoretical estimation of β and φ in RSNs is a complex open problem. Therefore, using results for a simpler problem associated with the expected width function and expected hydrograph for an ensemble of RSNs, we give heuristic arguments for theoretical derivations of the scaling exponents β^{(E} and φ^{(E} that depend on the Horton ratios for stream lengths and areas. These ratios in turn have a known dependence on the parameters of the geometric distributions of RSN generators. Good agreement was found between the analytically conjectured values of β^{(E} and φ^{(E} and the values estimated by the simulated ensembles of RSNs and hydrographs. The independence of the scaling exponents φ^{(E} and φ with respect to the value of flow velocity and runoff intensity implies an interesting connection between unit
Open home networks: the TEAHA approach
van Dijk, H.W.; Scholten, Johan; Tobalina, Alvaro; García Muñoz, Victor; Milanini, Stephane; Kung, Antonio
2006-01-01
The current trend for home appliances is networking. Although more and more of these appliances are networked, there is not a standard way of interaction, which restrains the development of services for in-home networks. The lack of standardisation is partly due to a legacy of business interests;
Open Home Networks: the TEAHA Approach
van Dijk, H.W.; Scholten, Johan; Tobalina, Alvaro; García Muñoz, Victor; Milanini, Stephane; Kung, Antonio; Dini, C.; Smekal, Z.; Lochin, E.; Verma, P.
2007-01-01
The current trend for home appliances is networking. Although more and more of these appliances are networked, there is not a standard way of interaction, which restrains the development of services for in-home networks. The lack of standardisation is partly due to a legacy of business interests;
Directory of Open Access Journals (Sweden)
Chao-Chih Lin
2017-10-01
Full Text Available A new transient-based hybrid heuristic approach is developed to optimize a transient generation process and to detect leaks in pipe networks. The approach couples the ordinal optimization approach (OOA and the symbiotic organism search (SOS to solve the optimization problem by means of iterations. A pipe network analysis model (PNSOS is first used to determine steady-state head distribution and pipe flow rates. The best transient generation point and its relevant valve operation parameters are optimized by maximizing the objective function of transient energy. The transient event is created at the chosen point, and the method of characteristics (MOC is used to analyze the transient flow. The OOA is applied to sift through the candidate pipes and the initial organisms with leak information. The SOS is employed to determine the leaks by minimizing the sum of differences between simulated and computed head at the observation points. Two synthetic leaking scenarios, a simple pipe network and a water distribution network (WDN, are chosen to test the performance of leak detection ordinal symbiotic organism search (LDOSOS. Leak information can be accurately identified by the proposed approach for both of the scenarios. The presented technique makes a remarkable contribution to the success of leak detection in the pipe networks.
Analyzing the international exergy flow network of ferrous metal ores.
Qi, Hai; An, Haizhong; Hao, Xiaoqing; Zhong, Weiqiong; Zhang, Yanbing
2014-01-01
This paper employs an un-weighted and weighted exergy network to study the properties of ferrous metal ores in countries worldwide and their evolution from 2002 to 2012. We find that there are few countries controlling most of the ferrous metal ore exports in terms of exergy and that the entire exergy flow network is becoming more heterogeneous though the addition of new nodes. The increasing of the average clustering coefficient indicates that the formation of an international exergy flow system and regional integration is improving. When we contrast the average out strength of exergy and the average out strength of currency, we find both similarities and differences. Prices are affected largely by human factors; thus, the growth rate of the average out strength of currency has fluctuated acutely in the eleven years from 2002 to 2012. Exergy is defined as the maximum work that can be extracted from a system and can reflect the true cost in the world, and this parameter fluctuates much less. Performing an analysis based on the two aspects of exergy and currency, we find that the network is becoming uneven.
Heuristic approach to the passive optical network with fibre duct ...
African Journals Online (AJOL)
Integer programming, network flow optimisation, passive optical network, ... algorithm before providing a greedy planning heuristic [11]. The multi- ... A wide range of meta-heuristics have also been employed to solve PONPP, with genetic ... In the case of PONPP, the objective is to find a subset of open facilities F, with every.
Heuristic approach to the passive optical network with fibre duct ...
African Journals Online (AJOL)
PON) plan- ning problem ... This paper uses concepts from network flow optimisation to incorporate fibre duct shar- ing into a ...... 682–686. [31] Yen JY, 1971, Finding the k shortest loopless paths in a network, Management Science, 17(11), pp.
An artificial immune system algorithm approach for reconfiguring distribution network
Syahputra, Ramadoni; Soesanti, Indah
2017-08-01
This paper proposes an artificial immune system (AIS) algorithm approach for reconfiguring distribution network with the presence distributed generators (DG). The distribution network with high-performance is a network that has a low power loss, better voltage profile, and loading balance among feeders. The task for improving the performance of the distribution network is optimization of network configuration. The optimization has become a necessary study with the presence of DG in entire networks. In this work, optimization of network configuration is based on an AIS algorithm. The methodology has been tested in a model of 33 bus IEEE radial distribution networks with and without DG integration. The results have been showed that the optimal configuration of the distribution network is able to reduce power loss and to improve the voltage profile of the distribution network significantly.
Spatial dynamics of ecosystem service flows: a comprehensive approach to quantifying actual services
Bagstad, Kenneth J.; Johnson, Gary W.; Voigt, Brian; Villa, Ferdinando
2013-01-01
Recent ecosystem services research has highlighted the importance of spatial connectivity between ecosystems and their beneficiaries. Despite this need, a systematic approach to ecosystem service flow quantification has not yet emerged. In this article, we present such an approach, which we formalize as a class of agent-based models termed “Service Path Attribution Networks” (SPANs). These models, developed as part of the Artificial Intelligence for Ecosystem Services (ARIES) project, expand on ecosystem services classification terminology introduced by other authors. Conceptual elements needed to support flow modeling include a service's rivalness, its flow routing type (e.g., through hydrologic or transportation networks, lines of sight, or other approaches), and whether the benefit is supplied by an ecosystem's provision of a beneficial flow to people or by absorption of a detrimental flow before it reaches them. We describe our implementation of the SPAN framework for five ecosystem services and discuss how to generalize the approach to additional services. SPAN model outputs include maps of ecosystem service provision, use, depletion, and flows under theoretical, possible, actual, inaccessible, and blocked conditions. We highlight how these different ecosystem service flow maps could be used to support various types of decision making for conservation and resource management planning.
A computational approach to modeling cellular-scale blood flow in complex geometry
Balogh, Peter; Bagchi, Prosenjit
2017-04-01
We present a computational methodology for modeling cellular-scale blood flow in arbitrary and highly complex geometry. Our approach is based on immersed-boundary methods, which allow modeling flows in arbitrary geometry while resolving the large deformation and dynamics of every blood cell with high fidelity. The present methodology seamlessly integrates different modeling components dealing with stationary rigid boundaries of complex shape, moving rigid bodies, and highly deformable interfaces governed by nonlinear elasticity. Thus it enables us to simulate 'whole' blood suspensions flowing through physiologically realistic microvascular networks that are characterized by multiple bifurcating and merging vessels, as well as geometrically complex lab-on-chip devices. The focus of the present work is on the development of a versatile numerical technique that is able to consider deformable cells and rigid bodies flowing in three-dimensional arbitrarily complex geometries over a diverse range of scenarios. After describing the methodology, a series of validation studies are presented against analytical theory, experimental data, and previous numerical results. Then, the capability of the methodology is demonstrated by simulating flows of deformable blood cells and heterogeneous cell suspensions in both physiologically realistic microvascular networks and geometrically intricate microfluidic devices. It is shown that the methodology can predict several complex microhemodynamic phenomena observed in vascular networks and microfluidic devices. The present methodology is robust and versatile, and has the potential to scale up to very large microvascular networks at organ levels.
Computer networks ISE a systems approach
Peterson, Larry L
2007-01-01
Computer Networks, 4E is the only introductory computer networking book written by authors who have had first-hand experience with many of the protocols discussed in the book, who have actually designed some of them as well, and who are still actively designing the computer networks today. This newly revised edition continues to provide an enduring, practical understanding of networks and their building blocks through rich, example-based instruction. The authors' focus is on the why of network design, not just the specifications comprising today's systems but how key technologies and p
A Logical Approach to the Statement of Cash Flows
Petro, Fred; Gean, Farrell
2014-01-01
Of the three financial statements in financial reporting, the Statement of Cash Flows (SCF) is perhaps the most challenging. The most difficult aspect of the SCF is in developing an understanding of how previous transactions are finalized in this document. The purpose of this paper is to logically explain the indirect approach of cash flow whereby…
Flow cytometry approach for studying the interaction between ...
African Journals Online (AJOL)
Flow cytometry approach for studying the interaction between Bacillus mojavensis and Alternaria alternata. Asma Milet, Noreddine Kacem Chaouche, Laid Dehimat, Asma Ait Kaki, Mounira Kara Ali, Philippe Thonart ...
Analysis and Visualization of Discrete Fracture Networks Using a Flow Topology Graph.
Aldrich, Garrett; Hyman, Jeffrey D; Karra, Satish; Gable, Carl W; Makedonska, Nataliia; Viswanathan, Hari; Woodring, Jonathan; Hamann, Bernd
2017-08-01
We present an analysis and visualization prototype using the concept of a flow topology graph (FTG) for characterization of flow in constrained networks, with a focus on discrete fracture networks (DFN), developed collaboratively by geoscientists and visualization scientists. Our method allows users to understand and evaluate flow and transport in DFN simulations by computing statistical distributions, segment paths of interest, and cluster particles based on their paths. The new approach enables domain scientists to evaluate the accuracy of the simulations, visualize features of interest, and compare multiple realizations over a specific domain of interest. Geoscientists can simulate complex transport phenomena modeling large sites for networks consisting of several thousand fractures without compromising the geometry of the network. However, few tools exist for performing higher-level analysis and visualization of simulated DFN data. The prototype system we present addresses this need. We demonstrate its effectiveness for increasingly complex examples of DFNs, covering two distinct use cases - hydrocarbon extraction from unconventional resources and transport of dissolved contaminant from a spent nuclear fuel repository.
RECOVERY ACT - Robust Optimization for Connectivity and Flows in Dynamic Complex Networks
Energy Technology Data Exchange (ETDEWEB)
Balasundaram, Balabhaskar [Oklahoma State Univ., Stillwater, OK (United States); Butenko, Sergiy [Texas A & M Univ., College Station, TX (United States); Boginski, Vladimir [Univ. of Florida, Gainesville, FL (United States); Uryasev, Stan [Univ. of Florida, Gainesville, FL (United States)
2013-12-25
to capture uncertainty and risk using appropriate probabilistic, statistical and optimization concepts. The main difficulty arising in addressing these issues is the dramatic increase in the computational complexity of the resulting optimization problems. This project studied novel models and methodologies for risk-averse network optimization- specifically, network design, network flows and cluster detection problems under uncertainty. The approach taken was to incorporate a quantitative risk measure known as conditional value-at-risk that is widely used in financial applications. This approach presents a viable alternate modeling and optimization framework to chance-constrained optimization and mean-variance optimization, one that also facilitates the detection of risk-averse solutions.
Structural factoring approach for analyzing stochastic networks
Hayhurst, Kelly J.; Shier, Douglas R.
1991-01-01
The problem of finding the distribution of the shortest path length through a stochastic network is investigated. A general algorithm for determining the exact distribution of the shortest path length is developed based on the concept of conditional factoring, in which a directed, stochastic network is decomposed into an equivalent set of smaller, generally less complex subnetworks. Several network constructs are identified and exploited to reduce significantly the computational effort required to solve a network problem relative to complete enumeration. This algorithm can be applied to two important classes of stochastic path problems: determining the critical path distribution for acyclic networks and the exact two-terminal reliability for probabilistic networks. Computational experience with the algorithm was encouraging and allowed the exact solution of networks that have been previously analyzed only by approximation techniques.
Hongying Jin; Linhao Li
2013-01-01
This paper aims at effectively predicting the dynamic network traffic flow based on quantum-behaved particle swarm optimization algorithm. Firstly, the dynamic network traffic flow prediction problem is analyzed through formal description. Secondly, the structure of the network traffic flow prediction model is given. In this structure, Users can used a computer to start the traffic flow prediction process, and data collecting module can collect and return the data through the destination devi...
Catchment organisation, free energy dynamics and network control on critical zone water flows
Zehe, E.; Ehret, U.; Kleidon, A.; Jackisch, C.; Scherer, U.; Blume, T.
2012-04-01
as that these flow structures organize and dominate flows of water, dissolved matter and sediments during rainfall driven conditions at various scales: - Surface connected vertical flow structures of anecic worm burrows or soil cracks organize and dominated vertical flows at the plot scale - this is usually referred to as preferential flow; - Rill networks at the soil surface organise and dominate hillslope scale overland flow response and sediment yields; - Subsurface pipe networks at the bedrock interface organize and dominate hillslope scale lateral subsurface water and tracer flows; - The river net organizes and dominates flows of water, dissolved matter and sediments to the catchment outlet and finally across continental gradients to the sea. Fundamental progress with respect to the parameterization of hydrological models, subscale flow networks and to understand the adaptation of hydro-geo ecosystems to change could be achieved by discovering principles that govern the organization of catchments flow networks in particular at least during steady state conditions. This insight has inspired various scientists to suggest principles for organization of ecosystems, landscapes and flow networks; as Bejans constructural law, Minimum Energy Expenditure , Maximum Entropy Production. In line with these studies we suggest that a thermodynamic/energetic treatment of the catchment is might be a key for understanding the underlying principles that govern organisation of flow and transport. Our approach is to employ a) physically based hydrological model that address at least all the relevant hydrological processes in the critical zone in a coupled way, behavioural representations of the observed organisation of flow structures and textural elements, that are consistent with observations in two well investigated research catchments and have been tested against distributed observations of soil moisture and catchment scale discharge; to simulate the full concert of hydrological
Explosive percolation on directed networks due to monotonic flow of activity
Waagen, Alex; D'Souza, Raissa M.; Lu, Tsai-Ching
2017-07-01
An important class of real-world networks has directed edges, and in addition, some rank ordering on the nodes, for instance the popularity of users in online social networks. Yet, nearly all research related to explosive percolation has been restricted to undirected networks. Furthermore, information on such rank-ordered networks typically flows from higher-ranked to lower-ranked individuals, such as follower relations, replies, and retweets on Twitter. Here we introduce a simple percolation process on an ordered, directed network where edges are added monotonically with respect to the rank ordering. We show with a numerical approach that the emergence of a dominant strongly connected component appears to be discontinuous. Large-scale connectivity occurs at very high density compared with most percolation processes, and this holds not just for the strongly connected component structure but for the weakly connected component structure as well. We present analysis with branching processes, which explains this unusual behavior and gives basic intuition for the underlying mechanisms. We also show that before the emergence of a dominant strongly connected component, multiple giant strongly connected components may exist simultaneously. By adding a competitive percolation rule with a small bias to link uses of similar rank, we show this leads to formation of two distinct components, one of high-ranked users, and one of low-ranked users, with little flow between the two components.
Evaluation of multilayer perceptron algorithms for an analysis of network flow data
Bieniasz, Jedrzej; Rawski, Mariusz; Skowron, Krzysztof; Trzepiński, Mateusz
2016-09-01
The volume of exchanged information through IP networks is larger than ever and still growing. It creates a space for both benign and malicious activities. The second one raises awareness on security network devices, as well as network infrastructure and a system as a whole. One of the basic tools to prevent cyber attacks is Network Instrusion Detection System (NIDS). NIDS could be realized as a signature-based detector or an anomaly-based one. In the last few years the emphasis has been placed on the latter type, because of the possibility of applying smart and intelligent solutions. An ideal NIDS of next generation should be composed of self-learning algorithms that could react on known and unknown malicious network activities respectively. In this paper we evaluated a machine learning approach for detection of anomalies in IP network data represented as NetFlow records. We considered Multilayer Perceptron (MLP) as the classifier and we used two types of learning algorithms - Backpropagation (BP) and Particle Swarm Optimization (PSO). This paper includes a comprehensive survey on determining the most optimal MLP learning algorithm for the classification problem in application to network flow data. The performance, training time and convergence of BP and PSO methods were compared. The results show that PSO algorithm implemented by the authors outperformed other solutions if accuracy of classifications is considered. The major disadvantage of PSO is training time, which could be not acceptable for larger data sets or in real network applications. At the end we compared some key findings with the results from the other papers to show that in all cases results from this study outperformed them.
An Approach for Detecting Attacks in Mobile Adhoc Networks
V. M. Viswanatham; A. A. Chari
2008-01-01
The security of data becomes more important with the increased use of commercial applications over wireless network environments. We presented an approach to handle various attacks for wireless networks. There were several problems of security in wireless networks due to intruders and different type of attacks such as Node Isolation, Route Disruption and Resource Consumption. There were better methods and intruder handling procedures available for fixed networks but it was difficult to analyz...
Implementing the Fussy Baby Network[R] Approach
Gilkerson, Linda; Hofherr, Jennifer; Heffron, Mary Claire; Sims, Jennifer Murphy; Jalowiec, Barbara; Bromberg, Stacey R.; Paul, Jennifer J.
2012-01-01
Erikson Institute Fussy Baby Network[R] (FBN) developed an approach to engaging parents around their urgent concerns about their baby's crying, sleeping, or feeding in a way which builds their longer-term capacities as parents. This approach, called the FAN, is now in place in new Fussy Baby Network programs around the country and is being infused…
A Network Coding Approach to Loss Tomography
DEFF Research Database (Denmark)
Sattari, Pegah; Markopoulou, Athina; Fragouli, Christina
2013-01-01
multicast and/or unicast end-to-end probes. Independently, recent advances in network coding have shown that there are several advantages from allowing intermediate nodes to process and combine, in addition to just forward, packets. In this paper, we pose the problem of loss tomography in networks that have...... network coding capabilities. We design a framework for estimating link loss rates, which leverages network coding capabilities and we show that it improves several aspects of tomography, including the identifiability of links, the tradeoff between estimation accuracy and bandwidth efficiency...... and multiple paths between sources and receivers. This work was the first to make the connection between active network tomography and network coding, and thus opened a new research direction....
Mobile social networking an innovative approach
Zhang, Daqing
2014-01-01
The use of contextually aware, pervasive, distributed computing, and sensor networks to bridge the gap between the physical and online worlds is the basis of mobile social networking. This book shows how applications can be built to provide mobile social networking, the research issues that need to be solved to enable this vision, and how mobile social networking can be used to provide computational intelligence that will improve daily life. With contributions from the fields of sociology, computer science, human-computer interaction and design, this book demonstrates how mobile social networks can be inferred from users' physical interactions both with the environment and with others, as well as how users behave around them and how their behavior differs on mobile vs. traditional online social networks.
An efficient approach to enhance capacity allocation over Bluetooth network
DEFF Research Database (Denmark)
Son, L.T.; Schiøler, Henrik; Madsen, Ole Brun
2002-01-01
With the current development of mobile devices, short range wireless communications have become more and more popular, and research on short range wireless communications, such as Bluetooth, has gained increasing in-terest, in industry as well as in academy. This paper analyzes capacity allocation...... issues in Bluetooth network as convex optimization problem. We formulate the problem of maximizing of total network flows and minimizing the costs of flows. The hybrid distributed capacity allocation scheme HDICA is proposed as an approximated solution of the stated optimization problem that satisfies...... quality of service requirements and topologically induced constraints in the Bluetooth network, such as node and link capacity limitations. The proposed scheme is decentralized and complies with frequent changes of topology as well as capacity limitations and flow requirements in the network. Simulation...
Software defined networks a comprehensive approach
Goransson, Paul
2014-01-01
Software Defined Networks discusses the historical networking environment that gave rise to SDN, as well as the latest advances in SDN technology. The book gives you the state of the art knowledge needed for successful deployment of an SDN, including: How to explain to the non-technical business decision makers in your organization the potential benefits, as well as the risks, in shifting parts of a network to the SDN modelHow to make intelligent decisions about when to integrate SDN technologies in a networkHow to decide if your organization should be developing its own SDN applications or
Unification of theoretical approaches for epidemic spreading on complex networks.
Wang, Wei; Tang, Ming; Eugene Stanley, H; Braunstein, Lidia A
2017-03-01
Models of epidemic spreading on complex networks have attracted great attention among researchers in physics, mathematics, and epidemiology due to their success in predicting and controlling scenarios of epidemic spreading in real-world scenarios. To understand the interplay between epidemic spreading and the topology of a contact network, several outstanding theoretical approaches have been developed. An accurate theoretical approach describing the spreading dynamics must take both the network topology and dynamical correlations into consideration at the expense of increasing the complexity of the equations. In this short survey we unify the most widely used theoretical approaches for epidemic spreading on complex networks in terms of increasing complexity, including the mean-field, the heterogeneous mean-field, the quench mean-field, dynamical message-passing, link percolation, and pairwise approximation. We build connections among these approaches to provide new insights into developing an accurate theoretical approach to spreading dynamics on complex networks.
Neural network approach to parton distributions fitting
Piccione, Andrea; Forte, Stefano; Latorre, Jose I.; Rojo, Joan; Piccione, Andrea; Rojo, Joan
2006-01-01
We will show an application of neural networks to extract information on the structure of hadrons. A Monte Carlo over experimental data is performed to correctly reproduce data errors and correlations. A neural network is then trained on each Monte Carlo replica via a genetic algorithm. Results on the proton and deuteron structure functions, and on the nonsinglet parton distribution will be shown.
Social Networks and Mourning: A Comparative Approach.
Rubin, Nissan
1990-01-01
Suggests using social network theory to explain varieties of mourning behavior in different societies. Compares participation in funeral ceremonies of members of different social circles in American society and Israeli kibbutz. Concludes that results demonstrated validity of concepts deriving from social network analysis in study of bereavement,…
Discovering the Network Topology: An Efficient Approach for SDN
Directory of Open Access Journals (Sweden)
Leonardo OCHOA-ADAY
2016-11-01
Full Text Available Network topology is a physical description of the overall resources in the network. Collecting this information using efficient mechanisms becomes a critical task for important network functions such as routing, network management, quality of service (QoS, among many others. Recent technologies like Software-Defined Networks (SDN have emerged as promising approaches for managing the next generation networks. In order to ensure a proficient topology discovery service in SDN, we propose a simple agents-based mechanism. This mechanism improves the overall efficiency of the topology discovery process. In this paper, an algorithm for a novel Topology Discovery Protocol (SD-TDP is described. This protocol will be implemented in each switch through a software agent. Thus, this approach will provide a distributed solution to solve the problem of network topology discovery in a more simple and efficient way.
Directedness of information flow in mobile phone communication networks
Peruani, Fernando; 10.1371/journal.pone.0028860
2013-01-01
Without having direct access to the information that is being exchanged, traces of information flow can be obtained by looking at temporal sequences of user interactions. These sequences can be represented as causality trees whose statistics result from a complex interplay between the topology of the underlying (social) network and the time correlations among the communications. Here, we study causality trees in mobile-phone data, which can be represented as a dynamical directed network. This representation of the data reveals the existence of super-spreaders and super-receivers. We show that the tree statistics, respectively the information spreading process, are extremely sensitive to the in-out degree correlation exhibited by the users. We also learn that a given information, e.g., a rumor, would require users to retransmit it for more than 30 hours in order to cover a macroscopic fraction of the system. Our analysis indicates that topological node-node correlations of the underlying social network, while ...
Directory of Open Access Journals (Sweden)
M Soltani
Full Text Available Modeling of interstitial fluid flow involves processes such as fluid diffusion, convective transport in extracellular matrix, and extravasation from blood vessels. To date, majority of microvascular flow modeling has been done at different levels and scales mostly on simple tumor shapes with their capillaries. However, with our proposed numerical model, more complex and realistic tumor shapes and capillary networks can be studied. Both blood flow through a capillary network, which is induced by a solid tumor, and fluid flow in tumor's surrounding tissue are formulated. First, governing equations of angiogenesis are implemented to specify the different domains for the network and interstitium. Then, governing equations for flow modeling are introduced for different domains. The conservation laws for mass and momentum (including continuity equation, Darcy's law for tissue, and simplified Navier-Stokes equation for blood flow through capillaries are used for simulating interstitial and intravascular flows and Starling's law is used for closing this system of equations and coupling the intravascular and extravascular flows. This is the first study of flow modeling in solid tumors to naturalistically couple intravascular and extravascular flow through a network. This network is generated by sprouting angiogenesis and consisting of one parent vessel connected to the network while taking into account the non-continuous behavior of blood, adaptability of capillary diameter to hemodynamics and metabolic stimuli, non-Newtonian blood flow, and phase separation of blood flow in capillary bifurcation. The incorporation of the outlined components beyond the previous models provides a more realistic prediction of interstitial fluid flow pattern in solid tumors and surrounding tissues. Results predict higher interstitial pressure, almost two times, for realistic model compared to the simplified model.
Simulation of two-phase flow in horizontal fracture networks with numerical manifold method
Ma, G. W.; Wang, H. D.; Fan, L. F.; Wang, B.
2017-10-01
The paper presents simulation of two-phase flow in discrete fracture networks with numerical manifold method (NMM). Each phase of fluids is considered to be confined within the assumed discrete interfaces in the present method. The homogeneous model is modified to approach the mixed fluids. A new mathematical cover formation for fracture intersection is proposed to satisfy the mass conservation. NMM simulations of two-phase flow in a single fracture, intersection, and fracture network are illustrated graphically and validated by the analytical method or the finite element method. Results show that the motion status of discrete interface significantly depends on the ratio of mobility of two fluids rather than the value of the mobility. The variation of fluid velocity in each fracture segment and the driven fluid content are also influenced by the ratio of mobility. The advantages of NMM in the simulation of two-phase flow in a fracture network are demonstrated in the present study, which can be further developed for practical engineering applications.
Network attacks and defenses a hands-on approach
Trabelsi, Zouheir; Al Braiki, Arwa; Mathew, Sujith Samuel
2012-01-01
The attacks on computers and business networks are growing daily, and the need for security professionals who understand how malfeasants perform attacks and compromise networks is a growing requirement to counter the threat. Network security education generally lacks appropriate textbooks with detailed, hands-on exercises that include both offensive and defensive techniques. Using step-by-step processes to build and generate attacks using offensive techniques, Network Attacks and Defenses: A Hands-on Approach enables students to implement appropriate network security solutions within a laborat
Electrical spectrum & network analyzers a practical approach
Helfrick, Albert D
1991-01-01
This book presents fundamentals and the latest techniques of electrical spectrum analysis. It focuses on instruments and techniques used on spectrum and network analysis, rather than theory. The book covers the use of spectrum analyzers, tracking generators, and network analyzers. Filled with practical examples, the book presents techniques that are widely used in signal processing and communications applications, yet are difficult to find in most literature.Key Features* Presents numerous practical examples, including actual spectrum analyzer circuits* Instruction on how to us
Simulation-Based Dynamic Passenger Flow Assignment Modelling for a Schedule-Based Transit Network
Directory of Open Access Journals (Sweden)
Xiangming Yao
2017-01-01
Full Text Available The online operation management and the offline policy evaluation in complex transit networks require an effective dynamic traffic assignment (DTA method that can capture the temporal-spatial nature of traffic flows. The objective of this work is to propose a simulation-based dynamic passenger assignment framework and models for such applications in the context of schedule-based rail transit systems. In the simulation framework, travellers are regarded as individual agents who are able to obtain complete information on the current traffic conditions. A combined route selection model integrated with pretrip route selection and entrip route switch is established for achieving the dynamic network flow equilibrium status. The train agent is operated strictly with the timetable and its capacity limitation is considered. A continuous time-driven simulator based on the proposed framework and models is developed, whose performance is illustrated through a large-scale network of Beijing subway. The results indicate that more than 0.8 million individual passengers and thousands of trains can be simulated simultaneously at a speed ten times faster than real time. This study provides an efficient approach to analyze the dynamic demand-supply relationship for large schedule-based transit networks.
A study on the forecasting of daily stream flow using the multilayer neural networks model
Energy Technology Data Exchange (ETDEWEB)
Kim, Sung-Won [Colorado State University, Fort Collins, CO(United States)
2000-10-31
In this study, Neural Networks models were used to forecast daily stream flow at Jindong station of the Nakdong River basin. Neural Networks models consist of CASE 1(5-5-1) and CASE 2(5-5-5-1). The criteria which separates two models is the number of hidden layers. Each model has Fletcher-Reeves Conjugate Gradient BackPropagation(FR-CGBP) and Scaled Conjugate Gradient BackPropagation(SCGBP) algorithms, which are better than original BackPropagation(BP) in convergence of global error and training tolerance. The data which are available for model training and validation were composed of wet, average, dry, wet+average, wet+dry, average+dry and wet+average+dry year respectively. During model training, the optimal connection weights and biases were determined using each data set and the daily stream flow was calculated at the same time. Except for wet+dry year, the results of training were good conditions by statistical analysis of forecast errors. And, model validation was carried out using the connection weights and biases which were calculated from model training. The results of validation were satisfactory like those of training. Daily stream flow forecasting using Neural Networks models were compared with those forecasted by Multiple Regression Analysis Model(MRAM). Neural Networks models were displayed slightly better results than MRAM in this study. Thus, Neural Networks models have much advantage to provide a more systematic approach, reduce model parameters, and shorten the time spent in the model development. (author). 22 refs., 9 tabs., 7 figs.
Network Medicine: A Network-based Approach to Human Disease
Barabási, Albert-László; Gulbahce, Natali; Loscalzo, Joseph
2011-01-01
Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular network. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships between apparently distinct (patho)phenotypes. Advances in this direction are essential to identify new diseases genes, to uncover the biological significance of disease-associated mutations identified by genome-wide association studies and full genome sequencing, and to identify drug targets and biomarkers for complex diseases. PMID:21164525
Confidence intervals in Flow Forecasting by using artificial neural networks
Panagoulia, Dionysia; Tsekouras, George
2014-05-01
One of the major inadequacies in implementation of Artificial Neural Networks (ANNs) for flow forecasting is the development of confidence intervals, because the relevant estimation cannot be implemented directly, contrasted to the classical forecasting methods. The variation in the ANN output is a measure of uncertainty in the model predictions based on the training data set. Different methods for uncertainty analysis, such as bootstrap, Bayesian, Monte Carlo, have already proposed for hydrologic and geophysical models, while methods for confidence intervals, such as error output, re-sampling, multi-linear regression adapted to ANN have been used for power load forecasting [1-2]. The aim of this paper is to present the re-sampling method for ANN prediction models and to develop this for flow forecasting of the next day. The re-sampling method is based on the ascending sorting of the errors between real and predicted values for all input vectors. The cumulative sample distribution function of the prediction errors is calculated and the confidence intervals are estimated by keeping the intermediate value, rejecting the extreme values according to the desired confidence levels, and holding the intervals symmetrical in probability. For application of the confidence intervals issue, input vectors are used from the Mesochora catchment in western-central Greece. The ANN's training algorithm is the stochastic training back-propagation process with decreasing functions of learning rate and momentum term, for which an optimization process is conducted regarding the crucial parameters values, such as the number of neurons, the kind of activation functions, the initial values and time parameters of learning rate and momentum term etc. Input variables are historical data of previous days, such as flows, nonlinearly weather related temperatures and nonlinearly weather related rainfalls based on correlation analysis between the under prediction flow and each implicit input
Energetic Variational Approach to Multi-Component Fluid Flows
Kirshtein, Arkadz; Liu, Chun; Brannick, James
2017-11-01
In this talk I will introduce the systematic energetic variational approach for dissipative systems applied to multi-component fluid flows. These variational approaches are motivated by the seminal works of Rayleigh and Onsager. The advantage of this approach is that we have to postulate only energy law and some kinematic relations based on fundamental physical principles. The method gives a clear, quick and consistent way to derive the PDE system. I will compare different approaches to three-component flows using diffusive interface method and discuss their advantages and disadvantages. The diffusive interface method is an approach for modeling interactions among complex substances. The main idea behind this method is to introduce phase field labeling functions in order to model the contact line by smooth change from one type of material to another. The work of Arkadz Kirshtein and Chun Liu is partially supported by NSF Grants DMS-141200 and DMS-1216938.
National Research Council Canada - National Science Library
Wei Li; Ke Zhang; Yuqiao Long; Li Feng
2017-01-01
.... Regarding the correlation between active stream networks and stream recession flow characteristics, we developed a new method to estimate the ASNL, under different wetness conditions, of a catchment...
Library Network Statistics and Performance Measures: Approaches and Issues
Directory of Open Access Journals (Sweden)
John Carlo Bertot
2001-07-01
Full Text Available Library networked statistics and performance measures are important indicators of the use, uses, and users of networked services that libraries offer their patrons. This article focuses on three efforts to develop and standardize library network statistics and performance measures. In particular, the article discusses, compares, and contrasts selected aspects of the International Standards Organization (ISO, U.S. public library network statistics, and Association of Research Library (ARL efforts. The three approaches attempt to capture, describe, and present library networked activities in similar ways through similar approaches – yet they differ in key areas. It is important to note that there are a number of national and international efforts underway that continue to research the library network statistics and performance measure environment.
Sampling of Complex Networks: A Datamining Approach
Loecher, Markus; Dohrmann, Jakob; Bauer, Gernot
2007-03-01
Efficient and accurate sampling of big complex networks is still an unsolved problem. As the degree distribution is one of the most commonly used attributes to characterize a network, there have been many attempts in recent papers to derive the original degree distribution from the data obtained during a traceroute- like sampling process. This talk describes a strategy for predicting the original degree of a node using the data obtained from a network by traceroute-like sampling making use of datamining techniques. Only local quantities (the sampled degree k, the redundancy of node detection r, the time of the first discovery of a node t and the distance to the sampling source d) are used as input for the datamining models. Global properties like the betweenness centrality are ignored. These local quantities are examined theoretically and in simulations to increase their value for the predictions. The accuracy of the models is discussed as a function of the number of sources used in the sampling process and the underlying topology of the network. The purpose of this work is to introduce the techniques of the relatively young field of datamining to the discussion on network sampling.
Network approach to patterns in stratocumulus clouds
Glassmeier, Franziska; Feingold, Graham
2017-10-01
Stratocumulus clouds (Sc) have a significant impact on the amount of sunlight reflected back to space, with important implications for Earth’s climate. Representing Sc and their radiative impact is one of the largest challenges for global climate models. Sc fields self-organize into cellular patterns and thus lend themselves to analysis and quantification in terms of natural cellular networks. Based on large-eddy simulations of Sc fields, we present a first analysis of the geometric structure and self-organization of Sc patterns from this network perspective. Our network analysis shows that the Sc pattern is scale-invariant as a consequence of entropy maximization that is known as Lewis’s Law (scaling parameter: 0.16) and is largely independent of the Sc regime (cloud-free vs. cloudy cell centers). Cells are, on average, hexagonal with a neighbor number variance of about 2, and larger cells tend to be surrounded by smaller cells, as described by an Aboav–Weaire parameter of 0.9. The network structure is neither completely random nor characteristic of natural convection. Instead, it emerges from Sc-specific versions of cell division and cell merging that are shaped by cell expansion. This is shown with a heuristic model of network dynamics that incorporates our physical understanding of cloud processes.
Network approach to patterns in stratocumulus clouds.
Glassmeier, Franziska; Feingold, Graham
2017-10-03
Stratocumulus clouds (Sc) have a significant impact on the amount of sunlight reflected back to space, with important implications for Earth's climate. Representing Sc and their radiative impact is one of the largest challenges for global climate models. Sc fields self-organize into cellular patterns and thus lend themselves to analysis and quantification in terms of natural cellular networks. Based on large-eddy simulations of Sc fields, we present a first analysis of the geometric structure and self-organization of Sc patterns from this network perspective. Our network analysis shows that the Sc pattern is scale-invariant as a consequence of entropy maximization that is known as Lewis's Law (scaling parameter: 0.16) and is largely independent of the Sc regime (cloud-free vs. cloudy cell centers). Cells are, on average, hexagonal with a neighbor number variance of about 2, and larger cells tend to be surrounded by smaller cells, as described by an Aboav-Weaire parameter of 0.9. The network structure is neither completely random nor characteristic of natural convection. Instead, it emerges from Sc-specific versions of cell division and cell merging that are shaped by cell expansion. This is shown with a heuristic model of network dynamics that incorporates our physical understanding of cloud processes.
IPTV inter-destination synchronization: A network-based approach
Stokking, H.M.; Deventer, M.O. van; Niamut, O.A.; Walraven, F.A.; Mekuria, R.N.
2010-01-01
This paper introduces a novel network-based approach to inter-destination media synchronization. The approach meets the need for synchronization in advanced TV concepts like social TV and offers high scalability, unlike conventional end-point based approaches. The solution for interdestination media
Linear Power-Flow Models in Multiphase Distribution Networks: Preprint
Energy Technology Data Exchange (ETDEWEB)
Bernstein, Andrey; Dall' Anese, Emiliano
2017-05-26
This paper considers multiphase unbalanced distribution systems and develops approximate power-flow models where bus-voltages, line-currents, and powers at the point of common coupling are linearly related to the nodal net power injections. The linearization approach is grounded on a fixed-point interpretation of the AC power-flow equations, and it is applicable to distribution systems featuring (i) wye connections; (ii) ungrounded delta connections; (iii) a combination of wye-connected and delta-connected sources/loads; and, (iv) a combination of line-to-line and line-to-grounded-neutral devices at the secondary of distribution transformers. The proposed linear models can facilitate the development of computationally-affordable optimization and control applications -- from advanced distribution management systems settings to online and distributed optimization routines. Performance of the proposed models is evaluated on different test feeders.
Mazvimavi, D.; Meijerink, A.M.J.; Savenije, H.H.G.; Stein, A.
2005-01-01
The feasibility of predicting flow characteristics from basin descriptors using multiple regression and neural networks has been investigated on 52 basins in Zimbabwe. Flow characteristics considered were average annual runoff, base flow index, flow duration curve, and average monthly runoff . Mean
SOCIAL POLARIZATION AND CONFLICT: A NETWORK APPROACH
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Ernesto Cárdenas
2013-12-01
Full Text Available Theoretically, polarization is associated with a higher probability of social conflict. This paper, in a microeconomic model based on the theory of social networks, analyses how changes in the network's structure affect the level of some basic parameters associated with the concept of polarization. This study shows that under upward monotonic preferences, longer sets of affiliations for each individual reduce polarization, whereas under downward monotonic preferences, longer sets of the so-called bad affiliations increase polarization. Finally, in the case of a non-monotonic system of preferences, an expansion of the affiliations set will alter the resulting polarization order in different ways depending on the preferences themselves
Network innovation through OpenFlow and SDN principles and design
Hu, Fei
2014-01-01
FUNDAMENTALSSDN /OpenFlow: Concepts and Applications; Ashley Gerrity and Fei HuAn OpenFlow Network Design Cycle; Pedro A. Aranda Gutiřrez and Diego R. LopezDESIGNIP Source Address Validation Solution with OpenFlow Extension and OpenRouter; Jun BiLanguage and Programming in SDN /OpenFlow; Muhammad Farooq and Fei HuControl and Management Software for SDNs; Natalia Castro Fernandes and Luiz Claudio Schara MagalhêsController Architecture and Performance in Software-Defined Networks; Ting Zhang and Fei HuMobile Applications on Global Clouds Using OpenFlow and Software-Defined Networking; Subharth
Scarsoglio, Stefania; Cazzato, Fabio; Ridolfi, Luca
2017-09-01
A network-based approach is presented to investigate the cerebrovascular flow patterns during atrial fibrillation (AF) with respect to normal sinus rhythm (NSR). AF, the most common cardiac arrhythmia with faster and irregular beating, has been recently and independently associated with the increased risk of dementia. However, the underlying hemodynamic mechanisms relating the two pathologies remain mainly undetermined so far; thus, the contribution of modeling and refined statistical tools is valuable. Pressure and flow rate temporal series in NSR and AF are here evaluated along representative cerebral sites (from carotid arteries to capillary brain circulation), exploiting reliable artificially built signals recently obtained from an in silico approach. The complex network analysis evidences, in a synthetic and original way, a dramatic signal variation towards the distal/capillary cerebral regions during AF, which has no counterpart in NSR conditions. At the large artery level, networks obtained from both AF and NSR hemodynamic signals exhibit elongated and chained features, which are typical of pseudo-periodic series. These aspects are almost completely lost towards the microcirculation during AF, where the networks are topologically more circular and present random-like characteristics. As a consequence, all the physiological phenomena at the microcerebral level ruled by periodicity—such as regular perfusion, mean pressure per beat, and average nutrient supply at the cellular level—can be strongly compromised, since the AF hemodynamic signals assume irregular behaviour and random-like features. Through a powerful approach which is complementary to the classical statistical tools, the present findings further strengthen the potential link between AF hemodynamic and cognitive decline.
Outline of a multilevel approach of the network society
van Dijk, Johannes A.G.M.
2005-01-01
Social and media networks, the Internet in particular, increasingly link interpersonal, organizational and mass communication. It is argued that this gives a cause for an interdisciplinary and multilevel approach of the network society. This will have to link traditional micro- and meso-level
An active, collaborative approach to learning skills in flow cytometry.
Fuller, Kathryn; Linden, Matthew D; Lee-Pullen, Tracey; Fragall, Clayton; Erber, Wendy N; Röhrig, Kimberley J
2016-06-01
Advances in science education research have the potential to improve the way students learn to perform scientific interpretations and understand science concepts. We developed active, collaborative activities to teach skills in manipulating flow cytometry data using FlowJo software. Undergraduate students were given compensated clinical flow cytometry listmode output (FCS) files and asked to design a gating strategy to diagnose patients with different hematological malignancies on the basis of their immunophenotype. A separate cohort of research trainees was given uncompensated data files on which they performed their own compensation, calculated the antibody staining index, designed a sequential gating strategy, and quantified rare immune cell subsets. Student engagement, confidence, and perceptions of flow cytometry were assessed using a survey. Competency against the learning outcomes was assessed by asking students to undertake tasks that required understanding of flow cytometry dot plot data and gating sequences. The active, collaborative approach allowed students to achieve learning outcomes not previously possible with traditional teaching formats, for example, having students design their own gating strategy, without forgoing essential outcomes such as the interpretation of dot plots. In undergraduate students, favorable perceptions of flow cytometry as a field and as a potential career choice were correlated with student confidence but not the ability to perform flow cytometry data analysis. We demonstrate that this new pedagogical approach to teaching flow cytometry is beneficial for student understanding and interpretation of complex concepts. It should be considered as a useful new method for incorporating complex data analysis tasks such as flow cytometry into curricula. Copyright © 2016 The American Physiological Society.
Simulation and evaluation of urban rail transit network based on multi-agent approach
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Xiangming Yao
2013-03-01
Full Text Available Purpose: Urban rail transit is a complex and dynamic system, which is difficult to be described in a global mathematical model for its scale and interaction. In order to analyze the spatial and temporal characteristics of passenger flow distribution and evaluate the effectiveness of transportation strategies, a new and comprehensive method depicted such dynamic system should be given. This study therefore aims at using simulation approach to solve this problem for subway network. Design/methodology/approach: In this thesis a simulation model based on multi-agent approach has been proposed, which is a well suited method to design complex systems. The model includes the specificities of passengers’ travelling behaviors and takes into account of interactions between travelers and trains. Findings: Research limitations/implications: We developed an urban rail transit simulation tool for verification of the validity and accuracy of this model, using real passenger flow data of Beijing subway network to take a case study, results show that our simulation tool can be used to analyze the characteristic of passenger flow distribution and evaluate operation strategies well. Practical implications: The main implications of this work are to provide decision support for traffic management, making train operation plan and dispatching measures in emergency. Originality/value: A new and comprehensive method to analyze and evaluate subway network is presented, accuracy and computational efficiency of the model has been confirmed and meet with the actual needs for large-scale network.
A Gaussian graphical model approach to climate networks
Energy Technology Data Exchange (ETDEWEB)
Zerenner, Tanja, E-mail: tanjaz@uni-bonn.de [Meteorological Institute, University of Bonn, Auf dem Hügel 20, 53121 Bonn (Germany); Friederichs, Petra; Hense, Andreas [Meteorological Institute, University of Bonn, Auf dem Hügel 20, 53121 Bonn (Germany); Interdisciplinary Center for Complex Systems, University of Bonn, Brühler Straße 7, 53119 Bonn (Germany); Lehnertz, Klaus [Department of Epileptology, University of Bonn, Sigmund-Freud-Straße 25, 53105 Bonn (Germany); Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Nussallee 14-16, 53115 Bonn (Germany); Interdisciplinary Center for Complex Systems, University of Bonn, Brühler Straße 7, 53119 Bonn (Germany)
2014-06-15
Distinguishing between direct and indirect connections is essential when interpreting network structures in terms of dynamical interactions and stability. When constructing networks from climate data the nodes are usually defined on a spatial grid. The edges are usually derived from a bivariate dependency measure, such as Pearson correlation coefficients or mutual information. Thus, the edges indistinguishably represent direct and indirect dependencies. Interpreting climate data fields as realizations of Gaussian Random Fields (GRFs), we have constructed networks according to the Gaussian Graphical Model (GGM) approach. In contrast to the widely used method, the edges of GGM networks are based on partial correlations denoting direct dependencies. Furthermore, GRFs can be represented not only on points in space, but also by expansion coefficients of orthogonal basis functions, such as spherical harmonics. This leads to a modified definition of network nodes and edges in spectral space, which is motivated from an atmospheric dynamics perspective. We construct and analyze networks from climate data in grid point space as well as in spectral space, and derive the edges from both Pearson and partial correlations. Network characteristics, such as mean degree, average shortest path length, and clustering coefficient, reveal that the networks posses an ordered and strongly locally interconnected structure rather than small-world properties. Despite this, the network structures differ strongly depending on the construction method. Straightforward approaches to infer networks from climate data while not regarding any physical processes may contain too strong simplifications to describe the dynamics of the climate system appropriately.
ALOHA networks : A game-theoretic approach
Marban, S.; van de Ven, P.; Borm, P.E.M.; Hamers, H.J.M.
2013-01-01
In this paper we consider a wireless network consisting of various nodes, where transmissions are regulated by the slotted ALOHA protocol. Nodes using the protocol behave autonomously, and decide at random whether to transmit in a particular time slot. Simultaneous transmissions by multiple nodes
ALOHA networks: A game-theoretic approach
Marbán, S.; Ven, P. van de; Borm, P.; Hamers, H.
2013-01-01
In this paper we consider a wireless network consisting of various nodes, where transmissions are regulated by the slotted ALOHA protocol. Nodes using the protocol behave autonomously, and decide at random whether to transmit in a particular time slot. Simultaneous transmissions by multiple nodes
Dobrushin's approach to queueing network theory
Directory of Open Access Journals (Sweden)
F. I. Karpelevich
1996-01-01
Full Text Available R.L. Dobrushin (1929-1995 made substantial contributions to Queueing Network Theory (QNT. A review of results from QNT which arose from his ideas or were connected to him in other ways is given. We also comment on various related open problems.
Economic Institutions and Stability : A Network Approach
Gilles, R.P.; Lazarova, E.A.; Ruys, P.H.M.
2011-01-01
We consider a network economy in which economic agents are connected within a structure of value-generating relationships. Agents are assumed to be able to participate in three types of economic activities: autarkic self-provision; binary matching interactions; and multi-person cooperative
Insomnia and Personality-A Network Approach
Dekker, Kim; Blanken, Tessa F; Van Someren, Eus J W
2017-01-01
Studies on personality traits and insomnia have remained inconclusive about which traits show the most direct associations with insomnia severity. It has moreover hardly been explored how traits relate to specific characteristics of insomnia. We here used network analysis in a large sample (N =
Network Flow Simulation of Fluid Transients in Rocket Propulsion Systems
Bandyopadhyay, Alak; Hamill, Brian; Ramachandran, Narayanan; Majumdar, Alok
2011-01-01
Fluid transients, also known as water hammer, can have a significant impact on the design and operation of both spacecraft and launch vehicle propulsion systems. These transients often occur at system activation and shutdown. The pressure rise due to sudden opening and closing of valves of propulsion feed lines can cause serious damage during activation and shutdown of propulsion systems. During activation (valve opening) and shutdown (valve closing), pressure surges must be predicted accurately to ensure structural integrity of the propulsion system fluid network. In the current work, a network flow simulation software (Generalized Fluid System Simulation Program) based on Finite Volume Method has been used to predict the pressure surges in the feed line due to both valve closing and valve opening using two separate geometrical configurations. The valve opening pressure surge results are compared with experimental data available in the literature and the numerical results compared very well within reasonable accuracy (simulation results are compared with the results of Method of Characteristics. Most rocket engines experience a longitudinal acceleration, known as "pogo" during the later stage of engine burn. In the shutdown example problem, an accumulator has been used in the feed system to demonstrate the "pogo" mitigation effects in the feed system of propellant. The simulation results using GFSSP compared very well with the results of Method of Characteristics.
Generalized Flow Tracing for the Analysis of Networked Renewable Electricity Systems
Hörsch, Jonas; Becker, Sarah; Schramm, Stefan; Greiner, Martin
2016-01-01
Flow allocation methods represent a valuable tool set to analyze the power flows in networked electricity systems. Based on this flow allocation, the costs associated with the usage of the underlying network infrastructure can be assigned to the users of the electricity system. This paper presents a generalization of the flow tracing method that is applicable to arbitrary compositions of inflow appearing naturally in aggregated networks. The composition of inflow is followed from net-generating sources through the network and assigns corresponding shares of the total power flow as well as of the outflow to the net-consuming sinks. We showcase the analytical power of this method for a scenario based on the IEEE 118 bus network and emphasize the need of appropriate aggregating measures, which allow to integrate over whole time series of fluctuating flow patterns.
Directory of Open Access Journals (Sweden)
Arjun Verma
2016-07-01
Full Text Available We consider flow systems that have been utilized for small-scale biomolecular computing and digital signal processing in binary-operating biosensors. Signal measurement is optimized by designing a flow-reversal cuvette and analyzing the experimental data to theoretically extract the pulse shape, as well as reveal the level of noise it possesses. Noise reduction is then carried out numerically. We conclude that this can be accomplished physically via the addition of properly designed well-mixing flow-reversal cell(s as an integral part of the flow system. This approach should enable improved networking capabilities and potentially not only digital but analog signal-processing in such systems. Possible applications in complex biocomputing networks and various sense-and-act systems are discussed.
Verma, Arjun; Fratto, Brian E; Privman, Vladimir; Katz, Evgeny
2016-07-05
We consider flow systems that have been utilized for small-scale biomolecular computing and digital signal processing in binary-operating biosensors. Signal measurement is optimized by designing a flow-reversal cuvette and analyzing the experimental data to theoretically extract the pulse shape, as well as reveal the level of noise it possesses. Noise reduction is then carried out numerically. We conclude that this can be accomplished physically via the addition of properly designed well-mixing flow-reversal cell(s) as an integral part of the flow system. This approach should enable improved networking capabilities and potentially not only digital but analog signal-processing in such systems. Possible applications in complex biocomputing networks and various sense-and-act systems are discussed.
Design and Evaluation of a Proxy-Based Monitoring System for OpenFlow Networks.
Taniguchi, Yoshiaki; Tsutsumi, Hiroaki; Iguchi, Nobukazu; Watanabe, Kenzi
2016-01-01
Software-Defined Networking (SDN) has attracted attention along with the popularization of cloud environment and server virtualization. In SDN, the control plane and the data plane are decoupled so that the logical topology and routing control can be configured dynamically depending on network conditions. To obtain network conditions precisely, a network monitoring mechanism is necessary. In this paper, we focus on OpenFlow which is a core technology to realize SDN. We propose, design, implement, and evaluate a network monitoring system for OpenFlow networks. Our proposed system acts as a proxy between an OpenFlow controller and OpenFlow switches. Through experimental evaluations, we confirm that our proposed system can capture packets and monitor traffic information depending on administrator's configuration. In addition, we show that our proposed system does not influence significant performance degradation to overall network performance.
Design and Evaluation of a Proxy-Based Monitoring System for OpenFlow Networks
Directory of Open Access Journals (Sweden)
Yoshiaki Taniguchi
2016-01-01
Full Text Available Software-Defined Networking (SDN has attracted attention along with the popularization of cloud environment and server virtualization. In SDN, the control plane and the data plane are decoupled so that the logical topology and routing control can be configured dynamically depending on network conditions. To obtain network conditions precisely, a network monitoring mechanism is necessary. In this paper, we focus on OpenFlow which is a core technology to realize SDN. We propose, design, implement, and evaluate a network monitoring system for OpenFlow networks. Our proposed system acts as a proxy between an OpenFlow controller and OpenFlow switches. Through experimental evaluations, we confirm that our proposed system can capture packets and monitor traffic information depending on administrator’s configuration. In addition, we show that our proposed system does not influence significant performance degradation to overall network performance.
Approach of Complex Networks for the Determination of Brain Death
Sun, Wei-Gang; Cao, Jian-Ting; Wang, Ru-Bin
2011-06-01
In clinical practice, brain death is the irreversible end of all brain activity. Compared to current statistical methods for the determination of brain death, we focus on the approach of complex networks for real-world electroencephalography in its determination. Brain functional networks constructed by correlation analysis are derived, and statistical network quantities used for distinguishing the patients in coma or brain death state, such as average strength, clustering coefficient and average path length, are calculated. Numerical results show that the values of network quantities of patients in coma state are larger than those of patients in brain death state. Our findings might provide valuable insights on the determination of brain death.
A Dynamic Neural Network Approach to CBM
2011-03-15
Therefore post-processing is needed to extract the time difference between corresponding events from which to calculate the crankshaft rotational speed...potentially already available from existing sensors (such as a crankshaft timing device) and a Neural Network processor to carry out the calculation . As...files are designated with the “_genmod” suffix. These files were the sources for the training and testing sets and made the extraction process easy
An optimization approach for district heating strategic network design
Bordin, Chiara; Gordini, Angelo; Vigo, Daniele
2016-01-01
District heating systems provide the heat generated in a centralized location to a set of users for their residential and commercial heating requirements. Heat distribution is generally obtained by using hot water or steam flowing through a closed network of insulated pipes and heat exchange
Wireless Sensor Networks Formation: Approaches and Techniques
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Miriam Carlos-Mancilla
2016-01-01
Full Text Available Nowadays, wireless sensor networks (WSNs emerge as an active research area in which challenging topics involve energy consumption, routing algorithms, selection of sensors location according to a given premise, robustness, efficiency, and so forth. Despite the open problems in WSNs, there are already a high number of applications available. In all cases for the design of any application, one of the main objectives is to keep the WSN alive and functional as long as possible. A key factor in this is the way the network is formed. This survey presents most recent formation techniques and mechanisms for the WSNs. In this paper, the reviewed works are classified into distributed and centralized techniques. The analysis is focused on whether a single or multiple sinks are employed, nodes are static or mobile, the formation is event detection based or not, and network backbone is formed or not. We focus on recent works and present a discussion of their advantages and drawbacks. Finally, the paper overviews a series of open issues which drive further research in the area.
A Bayesian Networks approach to Operational Risk
Aquaro, V.; Bardoscia, M.; Bellotti, R.; Consiglio, A.; De Carlo, F.; Ferri, G.
2010-04-01
A system for Operational Risk management based on the computational paradigm of Bayesian Networks is presented. The algorithm allows the construction of a Bayesian Network targeted for each bank and takes into account in a simple and realistic way the correlations among different processes of the bank. The internal losses are averaged over a variable time horizon, so that the correlations at different times are removed, while the correlations at the same time are kept: the averaged losses are thus suitable to perform the learning of the network topology and parameters; since the main aim is to understand the role of the correlations among the losses, the assessments of domain experts are not used. The algorithm has been validated on synthetic time series. It should be stressed that the proposed algorithm has been thought for the practical implementation in a mid or small sized bank, since it has a small impact on the organizational structure of a bank and requires an investment in human resources which is limited to the computational area.
Modeling Liner Shipping Service Selection and Container Flows using a Multi-layer Network
DEFF Research Database (Denmark)
Karsten, Christian Vad; Balakrishnan, Anant
We introduce a new formulation for the tactical planning problem facing container shipping companies of selecting the best subset of sailing routes from a given pool of candidate routes so as to maximize profit. Since most containers are sent directly or transshipped at most twice in current liner...... shipping networks, we impose limits on the number of transshipments for each container (which most previous models do not incorporate). Our multi-layer multi-commodity model associates one commodity with each container origin port, and decides the route for each commodity on a logical network layer whose...... arcs represent segments (pairs of ports between which a container can use a single service). This approach, combined with commodity flow variables that are indexed by segment sequence permits us to incorporate the transshipment limits while also tracking the commodity’s outflow from the system...
An efficient neural network approach to dynamic robot motion planning.
Yang, S X; Meng, M
2000-03-01
In this paper, a biologically inspired neural network approach to real-time collision-free motion planning of mobile robots or robot manipulators in a nonstationary environment is proposed. Each neuron in the topologically organized neural network has only local connections, whose neural dynamics is characterized by a shunting equation. Thus the computational complexity linearly depends on the neural network size. The real-time robot motion is planned through the dynamic activity landscape of the neural network without any prior knowledge of the dynamic environment, without explicitly searching over the free workspace or the collision paths, and without any learning procedures. Therefore it is computationally efficient. The global stability of the neural network is guaranteed by qualitative analysis and the Lyapunov stability theory. The effectiveness and efficiency of the proposed approach are demonstrated through simulation studies.
Linking river flow regimes to riparian plant guilds: a community-wide modeling approach.
Lytle, David A; Merritt, David M; Tonkin, Jonathan D; Olden, Julian D; Reynolds, Lindsay V
2017-06-01
Modeling riparian plant dynamics along rivers is complicated by the fact that plants have different edaphic and hydrologic requirements at different life stages. With intensifying human demands for water and continued human alteration of rivers, there is a growing need for predicting responses of vegetation to flow alteration, including responses related to climate change and river flow management. We developed a coupled structured population model that combines stage-specific responses of plant guilds with specific attributes of river hydrologic regime. The model uses information on the vital rates of guilds as they relate to different hydrologic conditions (flood, drought, and baseflow), but deliberately omits biotic interactions from the structure (interaction neutral). Our intent was to (1) consolidate key vital rates concerning plant population dynamics and to incorporate these data into a quantitative framework, (2) determine whether complex plant stand dynamics, including biotic interactions, can be predicted from basic vital rates and river hydrology, and (3) project how altered flow regimes might affect riparian communities. We illustrated the approach using five flow-response guilds that encompass much of the river floodplain community: hydroriparian tree, xeroriparian shrub, hydroriparian shrub, mesoriparian meadow, and desert shrub. We also developed novel network-based tools for predicting community-wide effects of climate-driven shifts and deliberately altered flow regimes. The model recovered known patterns of hydroriparian tree vs. xeroriparian shrub dominance, including the relative proportion of these two guilds as a function of river flow modification. By simulating flow alteration scenarios ranging from increased drought to shifts in flood timing, the model predicted that mature hydroriparian forest should be most abundant near the observed natural flow regime. Multiguild sensitivity analysis identified substantial network connectivity (many
Spray flow-network flow transition of binary Lennard-Jones particle system
Inaoka, Hajime
2010-07-01
We simulate gas-liquid flows caused by rapid depressurization using a molecular dynamics model. The model consists of two types of Lennard-Jones particles, which we call liquid particles and gas particles. These two types of particles are distinguished by their mass and strength of interaction: a liquid particle has heavier mass and stronger interaction than a gas particle. By simulations with various initial number densities of these particles, we found that there is a transition from a spray flow to a network flow with an increase of the number density of the liquid particles. At the transition point, the size of the liquid droplets follows a power-law distribution, while it follows an exponential distribution when the number density of the liquid particles is lower than the critical value. The comparison between the transition of the model and that of models of percolation is discussed. The change of the average droplet size with the initial number density of the gas particles is also presented. © 2010 Elsevier B.V. All rights reserved.
Gong, J.; Rossen, W.R.
2014-01-01
Fracture network connectivity and aperture (or conductivity) distribution are two crucial features controlling the flow behavior of fractured formations. The effect of connectivity on flow properties is well documented. We focus here on the influence of fracture aperture distribution. We model a
Gong, J.; Rossen, W.R.
2017-01-01
Fracture network connectivity and aperture (or conductivity) distribution are two crucial features controlling flow behavior of naturally fractured reservoirs. The effect of connectivity on flow properties is well documented. In this paper, however, we focus here on the influence of fracture
FLOWSHOP SCHEDULING USING A NETWORK APPROACH ...
African Journals Online (AJOL)
eobe
sequencing problem”.Int. J. Manag. Sci., 11, 1983, pp. 91–95. [10] Campbell, H.G., Dudek, R.A. and Smith, M.L,”A heuristic algorithm of then-job,m-machine sequencing problem”, Manag. Sci., 16, 1970, pp. 630–637. [11] Osman, I.H and Potts, C.N “Simulated annealing for permutation flow-shop scheduling”, Omega vol. 17,.
Network-theoretic approach to model vortex interactions
Nair, Aditya; Taira, Kunihiko
2014-11-01
We present a network-theoretic approach to describe a system of point vortices in two-dimensional flow. By considering the point vortices as nodes, a complete graph is constructed with edges connecting each vortex to every other vortex. The interactions between the vortices are captured by the graph edge weights. We employ sparsification techniques on these graph representations based on spectral theory to construct sparsified models of the overall vortical interactions. The edge weights are redistributed through spectral sparsification of the graph such that the sum of the interactions associated with each vortex is maintained constant. In addition, sparse configurations maintain similar spectral properties as the original setup. Through the reduction in the number of interactions, key vortex interactions can be highlighted. Identification of vortex structures based on graph sparsification is demonstrated with an example of clusters of point vortices. We also evaluate the computational performance of sparsification for large collection of point vortices. Work supported by US Army Research Office (W911NF-14-1-0386) and US Air Force Office of Scientific Research (YIP: FA9550-13-1-0183).
A full bayesian approach for boolean genetic network inference.
Directory of Open Access Journals (Sweden)
Shengtong Han
Full Text Available Boolean networks are a simple but efficient model for describing gene regulatory systems. A number of algorithms have been proposed to infer Boolean networks. However, these methods do not take full consideration of the effects of noise and model uncertainty. In this paper, we propose a full Bayesian approach to infer Boolean genetic networks. Markov chain Monte Carlo algorithms are used to obtain the posterior samples of both the network structure and the related parameters. In addition to regular link addition and removal moves, which can guarantee the irreducibility of the Markov chain for traversing the whole network space, carefully constructed mixture proposals are used to improve the Markov chain Monte Carlo convergence. Both simulations and a real application on cell-cycle data show that our method is more powerful than existing methods for the inference of both the topology and logic relations of the Boolean network from observed data.
Approaches to modeling gene regulatory networks: a gentle introduction.
Schlitt, Thomas
2013-01-01
This chapter is split into two main sections; first, I will present an introduction to gene networks. Second, I will discuss various approaches to gene network modeling which will include some examples for using different data sources. Computational modeling has been used for many different biological systems and many approaches have been developed addressing the different needs posed by the different application fields. The modeling approaches presented here are not limited to gene regulatory networks and occasionally I will present other examples. The material covered here is an update based on several previous publications by Thomas Schlitt and Alvis Brazma (FEBS Lett 579(8),1859-1866, 2005; Philos Trans R Soc Lond B Biol Sci 361(1467), 483-494, 2006; BMC Bioinformatics 8(suppl 6), S9, 2007) that formed the foundation for a lecture on gene regulatory networks at the In Silico Systems Biology workshop series at the European Bioinformatics Institute in Hinxton.
Analysis of network-wide transit passenger flows based on principal component analysis
Luo, D.; Cats, O.; van Lint, J.W.C.
2017-01-01
Transit networks are complex systems in which the passenger flow dynamics are difficult to capture and understand. While there is a growing ability to monitor and record travelers' behavior in the past decade, knowledge on network-wide passenger flows, which are essentially high-dimensional
Gender, Friendship Networks, and Delinquency: A Dynamic Network Approach**
Haynie, Dana L.; Doogan, Nathan J.; Soller, Brian
2014-01-01
Researchers have examined selection and influence processes in shaping delinquency similarity among friends, but little is known about the role of gender in moderating these relationships. Our objective is to examine differences between adolescent boys and girls regarding delinquency-based selection and influence processes. Using longitudinal network data from adolescents attending two large schools in AddHealth (N = 1,857) and stochastic actor-oriented models, we evaluate whether girls are influenced to a greater degree by friends' violence or delinquency than boys (influence hypothesis) and whether girls are more likely to select friends based on violent or delinquent behavior than boys (selection hypothesis). The results indicate that girls are more likely than boys to be influenced by their friends' involvement in violence. Although a similar pattern emerges for nonviolent delinquency, the gender differences are not significant. Some evidence shows that boys are influenced toward increasing their violence or delinquency when exposed to more delinquent or violent friends but are immune to reducing their violence or delinquency when associating with less violent or delinquent friends. In terms of selection dynamics, although both boys and girls have a tendency to select friends based on friends' behavior, girls have a stronger tendency to do so, suggesting that among girls, friends' involvement in violence or delinquency is an especially decisive factor for determining friendship ties. PMID:26097241
Gender, Friendship Networks, and Delinquency: A Dynamic Network Approach.
Haynie, Dana L; Doogan, Nathan J; Soller, Brian
2014-11-01
Researchers have examined selection and influence processes in shaping delinquency similarity among friends, but little is known about the role of gender in moderating these relationships. Our objective is to examine differences between adolescent boys and girls regarding delinquency-based selection and influence processes. Using longitudinal network data from adolescents attending two large schools in AddHealth (N = 1,857) and stochastic actor-oriented models, we evaluate whether girls are influenced to a greater degree by friends' violence or delinquency than boys (influence hypothesis) and whether girls are more likely to select friends based on violent or delinquent behavior than boys (selection hypothesis). The results indicate that girls are more likely than boys to be influenced by their friends' involvement in violence. Although a similar pattern emerges for nonviolent delinquency, the gender differences are not significant. Some evidence shows that boys are influenced toward increasing their violence or delinquency when exposed to more delinquent or violent friends but are immune to reducing their violence or delinquency when associating with less violent or delinquent friends. In terms of selection dynamics, although both boys and girls have a tendency to select friends based on friends' behavior, girls have a stronger tendency to do so, suggesting that among girls, friends' involvement in violence or delinquency is an especially decisive factor for determining friendship ties.
Assigning Passenger Flows on a Metro Network Based on Automatic Fare Collection Data and Timetable
Directory of Open Access Journals (Sweden)
Ling Hong
2017-01-01
Full Text Available Assigning passenger flows on a metro network plays an important role in passenger flow analysis that is the foundation of metro operation. Traditional transit assignment models are becoming increasingly complex and inefficient. These models may even not be valid in case of sudden changes in the timetable or disruptions in the metro system. We propose a methodology for assigning passenger flows on a metro network based on automatic fare collection (AFC data and realized timetable. We find that the routes connecting a given origin and destination (O-D pair are related to their observed travel times (OTTs especially their pure travel times (PTTs abstracted from AFC data combined with the realized timetable. A novel clustering algorithm is used to cluster trips between a given O-D pair based on PTTs/OTTs and complete the assignment. An initial application to categorical O-D pairs on the Shanghai metro system, which is one of the largest systems in the world, shows that the proposed methodology works well. Accompanying the initial application, an interesting approach is also provided for determining the theoretical maximum accuracy of the new assignment model.
Combining neural networks and genetic algorithms for hydrological flow forecasting
Neruda, Roman; Srejber, Jan; Neruda, Martin; Pascenko, Petr
2010-05-01
We present a neural network approach to rainfall-runoff modeling for small size river basins based on several time series of hourly measured data. Different neural networks are considered for short time runoff predictions (from one to six hours lead time) based on runoff and rainfall data observed in previous time steps. Correlation analysis shows that runoff data, short time rainfall history, and aggregated API values are the most significant data for the prediction. Neural models of multilayer perceptron and radial basis function networks with different numbers of units are used and compared with more traditional linear time series predictors. Out of possible 48 hours of relevant history of all the input variables, the most important ones are selected by means of input filters created by a genetic algorithm. The genetic algorithm works with population of binary encoded vectors defining input selection patterns. Standard genetic operators of two-point crossover, random bit-flipping mutation, and tournament selection were used. The evaluation of objective function of each individual consists of several rounds of building and testing a particular neural network model. The whole procedure is rather computational exacting (taking hours to days on a desktop PC), thus a high-performance mainframe computer has been used for our experiments. Results based on two years worth data from the Ploucnice river in Northern Bohemia suggest that main problems connected with this approach to modeling are ovetraining that can lead to poor generalization, and relatively small number of extreme events which makes it difficult for a model to predict the amplitude of the event. Thus, experiments with both absolute and relative runoff predictions were carried out. In general it can be concluded that the neural models show about 5 per cent improvement in terms of efficiency coefficient over liner models. Multilayer perceptrons with one hidden layer trained by back propagation algorithm and
Directory of Open Access Journals (Sweden)
Kaisheng Zhang
2016-12-01
Full Text Available Recently, population density has grown quickly with the increasing acceleration of urbanization. At the same time, overcrowded situations are more likely to occur in populous urban areas, increasing the risk of accidents. This paper proposes a synthetic approach to recognize and identify the large pedestrian flow. In particular, a hybrid pedestrian flow detection model was constructed by analyzing real data from major mobile phone operators in China, including information from smartphones and base stations (BS. With the hybrid model, the Log Distance Path Loss (LDPL model was used to estimate the pedestrian density from raw network data, and retrieve information with the Gaussian Progress (GP through supervised learning. Temporal-spatial prediction of the pedestrian data was carried out with Machine Learning (ML approaches. Finally, a case study of a real Central Business District (CBD scenario in Shanghai, China using records of millions of cell phone users was conducted. The results showed that the new approach significantly increases the utility and capacity of the mobile network. A more reasonable overcrowding detection and alert system can be developed to improve safety in subway lines and other hotspot landmark areas, such as the Bundle, People’s Square or Disneyland, where a large passenger flow generally exists.
Xing, Fangyuan; Wang, Honghuan; Yin, Hongxi; Li, Ming; Luo, Shenzi; Wu, Chenguang
2016-02-01
With the extensive application of cloud computing and data centres, as well as the constantly emerging services, the big data with the burst characteristic has brought huge challenges to optical networks. Consequently, the software defined optical network (SDON) that combines optical networks with software defined network (SDN), has attracted much attention. In this paper, an OpenFlow-enabled optical node employed in optical cross-connect (OXC) and reconfigurable optical add/drop multiplexer (ROADM), is proposed. An open source OpenFlow controller is extended on routing strategies. In addition, the experiment platform based on OpenFlow protocol for software defined optical network, is designed. The feasibility and availability of the OpenFlow-enabled optical nodes and the extended OpenFlow controller are validated by the connectivity test, protection switching and load balancing experiments in this test platform.
A Constructive Neural-Network Approach to Modeling Psychological Development
Shultz, Thomas R.
2012-01-01
This article reviews a particular computational modeling approach to the study of psychological development--that of constructive neural networks. This approach is applied to a variety of developmental domains and issues, including Piagetian tasks, shift learning, language acquisition, number comparison, habituation of visual attention, concept…
The Islands Approach to Nearest Neighbor Querying in Spatial Networks
DEFF Research Database (Denmark)
Huang, Xuegang; Jensen, Christian Søndergaard; Saltenis, Simonas
2005-01-01
, and versatile approach to k nearest neighbor computation that obviates the need for using several k nearest neighbor approaches for supporting a single service scenario. The experimental comparison with the existing techniques uses real-world road network data and considers both I/O and CPU performance...
Automatic Distribution Network Reconfiguration: An Event-Driven Approach
Energy Technology Data Exchange (ETDEWEB)
Ding, Fei; Jiang, Huaiguang; Tan, Jin
2016-11-14
This paper proposes an event-driven approach for reconfiguring distribution systems automatically. Specifically, an optimal synchrophasor sensor placement (OSSP) is used to reduce the number of synchrophasor sensors while keeping the whole system observable. Then, a wavelet-based event detection and location approach is used to detect and locate the event, which performs as a trigger for network reconfiguration. With the detected information, the system is then reconfigured using the hierarchical decentralized approach to seek for the new optimal topology. In this manner, whenever an event happens the distribution network can be reconfigured automatically based on the real-time information that is observable and detectable.
A Deep Learning based Approach to Reduced Order Modeling of Fluids using LSTM Neural Networks
Mohan, Arvind; Gaitonde, Datta
2017-11-01
Reduced Order Modeling (ROM) can be used as surrogates to prohibitively expensive simulations to model flow behavior for long time periods. ROM is predicated on extracting dominant spatio-temporal features of the flow from CFD or experimental datasets. We explore ROM development with a deep learning approach, which comprises of learning functional relationships between different variables in large datasets for predictive modeling. Although deep learning and related artificial intelligence based predictive modeling techniques have shown varied success in other fields, such approaches are in their initial stages of application to fluid dynamics. Here, we explore the application of the Long Short Term Memory (LSTM) neural network to sequential data, specifically to predict the time coefficients of Proper Orthogonal Decomposition (POD) modes of the flow for future timesteps, by training it on data at previous timesteps. The approach is demonstrated by constructing ROMs of several canonical flows. Additionally, we show that statistical estimates of stationarity in the training data can indicate a priori how amenable a given flow-field is to this approach. Finally, the potential and limitations of deep learning based ROM approaches will be elucidated and further developments discussed.
A new approach to flow simulation using hybrid models
Solgi, Abazar; Zarei, Heidar; Nourani, Vahid; Bahmani, Ramin
2017-11-01
The necessity of flow prediction in rivers, for proper management of water resource, and the need for determining the inflow to the dam reservoir, designing efficient flood warning systems and so forth, have always led water researchers to think about models with high-speed response and low error. In the recent years, the development of Artificial Neural Networks and Wavelet theory and using the combination of models help researchers to estimate the river flow better and better. In this study, daily and monthly scales were used for simulating the flow of Gamasiyab River, Nahavand, Iran. The first simulation was done using two types of ANN and ANFIS models. Then, using wavelet theory and decomposing input signals of the used parameters, sub-signals were obtained and were fed into the ANN and ANFIS to obtain hybrid models of WANN and WANFIS. In this study, in addition to the parameters of precipitation and flow, parameters of temperature and evaporation were used to analyze their effects on the simulation. The results showed that using wavelet transform improved the performance of the models in both monthly and daily scale. However, it had a better effect on the monthly scale and the WANFIS was the best model.
Research on virtual network load balancing based on OpenFlow
Peng, Rong; Ding, Lei
2017-08-01
The Network based on OpenFlow technology separate the control module and data forwarding module. Global deployment of load balancing strategy through network view of control plane is fast and of high efficiency. This paper proposes a Weighted Round-Robin Scheduling algorithm for virtual network and a load balancing plan for server load based on OpenFlow. Load of service nodes and load balancing tasks distribution algorithm will be taken into account.
A simple network agreement-based approach for combining evidences in a heterogeneous sensor network
Directory of Open Access Journals (Sweden)
Raúl Eusebio-Grande
2015-12-01
Full Text Available In this research we investigate how the evidences provided by both static and mobile nodes that are part of a heterogenous sensor network can be combined to have trustworthy results. A solution relying on a network agreement-based approach was implemented and tested.
Characterization of IP Flows Eligible for Lambda-Connections in Optical Networks
Fioreze, Tiago; Oude Wolbers, Mattijs; van de Meent, R.; Pras, Aiko
2008-01-01
The advance on data transmission in optical networks has allowed data forwarding decisions to be taken at multiple levels in the protocol stack (e.g., at network and optical levels). With such capability, big IP flows can be moved from the network level and switched completely at the optical level
A Network Analysis Approach to fMRI Condition-Specific Functional Connectivity
Shinkareva, Svetlana V; Wang, Jing
2010-01-01
In this work we focus on examination and comparison of whole-brain functional connectivity patterns measured with fMRI across experimental conditions. Direct examination and comparison of condition-specific matrices is challenging due to the large number of elements in a connectivity matrix. We present a framework that uses network analysis to describe condition-specific functional connectivity. Treating the brain as a complex system in terms of a network, we extract the most relevant connectivity information by partitioning each network into clusters representing functionally connected brain regions. Extracted clusters are used as features for predicting experimental condition in a new data set. The approach is illustrated on fMRI data examining functional connectivity patterns during processing of abstract and concrete concepts. Topological (brain regions) and functional (level of connectivity and information flow) systematic differences in the ROI-based functional networks were identified across participan...
Dynamic Network Design Problem under Demand Uncertainty: An Adjustable Robust Optimization Approach
Directory of Open Access Journals (Sweden)
Hua Sun
2014-01-01
Full Text Available This paper develops an adjustable robust optimization approach for a network design problem explicitly incorporating traffic dynamics and demand uncertainty. In particular, a cell transmission model based network design problem of linear programming type is considered to describe dynamic traffic flows, and a polyhedral uncertainty set is used to characterize the demand uncertainty. The major contribution of this paper is to formulate such an adjustable robust network design problem as a tractable linear programming model and justify the model which is less conservative by comparing its solution performance with the robust solution from the usual robust model. The numerical results using one network from the literature demonstrate the modeling advantage of the adjustable robust optimization and provided strategic managerial insights for enacting capacity expansion policies under demand uncertainty.
On the existence of efficient solutions to vector optimization problem of traffic flow on network
Directory of Open Access Journals (Sweden)
T. A. Bozhanova
2009-09-01
Full Text Available We studied traffic flow models in vector-valued optimization statement where the flow is controlled at the nodes of network. We considered the case when an objective mapping possesses a weakened property of upper semicontinuity and made no assumptions on the interior of the ordering cone. The sufficient conditions for the existence of efficient controls of the traffic problems are derived. The existence of efficient solutions of vector optimization problem for traffic flow on network are also proved.
On the existence of efficient solutions to vector optimization problem of traffic flow on network
T. A. Bozhanova
2009-01-01
We studied traffic flow models in vector-valued optimization statement where the flow is controlled at the nodes of network. We considered the case when an objective mapping possesses a weakened property of upper semicontinuity and made no assumptions on the interior of the ordering cone. The sufficient conditions for the existence of efficient controls of the traffic problems are derived. The existence of efficient solutions of vector optimization problem for traffic flow on network are also...
Network Reverse Engineering Approach in Synthetic Biology
Zhang, Haoqian; Liu, Ao; Lu, Yuheng; Sheng, Ying; Wu, Qianzhu; Yin, Zhenzhen; Chen, Yiwei; Liu, Zairan; Pan, Heng; Ouyang, Qi
2013-12-01
Synthetic biology is a new branch of interdisciplinary science that has been developed in recent years. The main purpose of synthetic biology is to apply successful principles that have been developed in electronic and chemical engineering to develop basic biological functional modules, and through rational design, develop man-made biological systems that have predicted useful functions. Here, we discuss an important principle in rational design of functional biological circuits: the reverse engineering design. We will use a research project that was conducted at Peking University for the International Genetic Engineering Machine Competition (iGEM) to illustrate the principle: synthesis a cell which has a semi-log dose-response to the environment. Through this work we try to demonstrate the potential application of network engineering in synthetic biology.
A multi-scale network method for two-phase flow in porous media
Energy Technology Data Exchange (ETDEWEB)
Khayrat, Karim, E-mail: khayratk@ifd.mavt.ethz.ch; Jenny, Patrick
2017-08-01
Pore-network models of porous media are useful in the study of pore-scale flow in porous media. In order to extract macroscopic properties from flow simulations in pore-networks, it is crucial the networks are large enough to be considered representative elementary volumes. However, existing two-phase network flow solvers are limited to relatively small domains. For this purpose, a multi-scale pore-network (MSPN) method, which takes into account flow-rate effects and can simulate larger domains compared to existing methods, was developed. In our solution algorithm, a large pore network is partitioned into several smaller sub-networks. The algorithm to advance the fluid interfaces within each subnetwork consists of three steps. First, a global pressure problem on the network is solved approximately using the multiscale finite volume (MSFV) method. Next, the fluxes across the subnetworks are computed. Lastly, using fluxes as boundary conditions, a dynamic two-phase flow solver is used to advance the solution in time. Simulation results of drainage scenarios at different capillary numbers and unfavourable viscosity ratios are presented and used to validate the MSPN method against solutions obtained by an existing dynamic network flow solver.
An overview of data routing approaches for wireless sensor networks.
Anisi, Mohammad Hossein; Abdullah, Abdul Hanan; Razak, Shukor Abd; Ngadi, Md Asri
2012-03-27
Recent years have witnessed a growing interest in deploying large populations of microsensors that collaborate in a distributed manner to gather and process sensory data and deliver them to a sink node through wireless communications systems. Currently, there is a lot of interest in data routing for Wireless Sensor Networks (WSNs) due to their unique challenges compared to conventional routing in wired networks. In WSNs, each data routing approach follows a specific goal (goals) according to the application. Although the general goal of every data routing approach in WSNs is to extend the network lifetime and every approach should be aware of the energy level of the nodes, data routing approaches may focus on one (or some) specific goal(s) depending on the application. Thus, existing approaches can be categorized according to their routing goals. In this paper, the main goals of data routing approaches in sensor networks are described. Then, the best known and most recent data routing approaches in WSNs are classified and studied according to their specific goals.
The network analysis of urban streets: A dual approach
Porta, Sergio; Crucitti, Paolo; Latora, Vito
2006-09-01
The application of the network approach to the urban case poses several questions in terms of how to deal with metric distances, what kind of graph representation to use, what kind of measures to investigate, how to deepen the correlation between measures of the structure of the network and measures of the dynamics on the network, what are the possible contributions from the GIS community. In this paper, the author considers six cases of urban street networks characterized by different patterns and historical roots. The authors propose a representation of the street networks based firstly on a primal graph, where intersections are turned into nodes and streets into edges. In a second step, a dual graph, where streets are nodes and intersections are edges, is constructed by means of a generalization model named Intersection Continuity Negotiation, which allows to acknowledge the continuity of streets over a plurality of edges. Finally, the authors address a comparative study of some structural properties of the dual graphs, seeking significant similarities among clusters of cases. A wide set of network analysis techniques are implemented over the dual graph: in particular the authors show that the absence of any clue of assortativity differentiates urban street networks from other non-geographic systems and that most of the considered networks have a broad degree distribution typical of scale-free networks and exhibit small-world properties as well.
A Galerkin least squares approach to viscoelastic flow.
Energy Technology Data Exchange (ETDEWEB)
Rao, Rekha R. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Schunk, Peter Randall [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2015-10-01
A Galerkin/least-squares stabilization technique is applied to a discrete Elastic Viscous Stress Splitting formulation of for viscoelastic flow. From this, a possible viscoelastic stabilization method is proposed. This method is tested with the flow of an Oldroyd-B fluid past a rigid cylinder, where it is found to produce inaccurate drag coefficients. Furthermore, it fails for relatively low Weissenberg number indicating it is not suited for use as a general algorithm. In addition, a decoupled approach is used as a way separating the constitutive equation from the rest of the system. A Pressure Poisson equation is used when the velocity and pressure are sought to be decoupled, but this fails to produce a solution when inflow/outflow boundaries are considered. However, a coupled pressure-velocity equation with a decoupled constitutive equation is successful for the flow past a rigid cylinder and seems to be suitable as a general-use algorithm.
Non-Markovian quantum feedback networks II: Controlled flows
Gough, John E.
2017-06-01
The concept of a controlled flow of a dynamical system, especially when the controlling process feeds information back about the system, is of central importance in control engineering. In this paper, we build on the ideas presented by Bouten and van Handel [Quantum Stochastics and Information: Statistics, Filtering and Control (World Scientific, 2008)] and develop a general theory of quantum feedback. We elucidate the relationship between the controlling processes, Z, and the measured processes, Y, and to this end we make a distinction between what we call the input picture and the output picture. We should note that the input-output relations for the noise fields have additional terms not present in the standard theory but that the relationship between the control processes and measured processes themselves is internally consistent—we do this for the two main cases of quadrature measurement and photon-counting measurement. The theory is general enough to include a modulating filter which post-processes the measurement readout Y before returning to the system. This opens up the prospect of applying very general engineering feedback control techniques to open quantum systems in a systematic manner, and we consider a number of specific modulating filter problems. Finally, we give a brief argument as to why most of the rules for making instantaneous feedback connections [J. Gough and M. R. James, Commun. Math. Phys. 287, 1109 (2009)] ought to apply for controlled dynamical networks as well.
Solution of weakly compressible isothermal flow in landfill gas collection networks
Nec, Y.; Huculak, G.
2017-12-01
Pipe networks collecting gas in sanitary landfills operate under the regime of a weakly compressible isothermal flow of ideal gas. The effect of compressibility has been traditionally neglected in this application in favour of simplicity, thereby creating a conceptual incongruity between the flow equations and thermodynamic equation of state. Here the flow is solved by generalisation of the classic Darcy–Weisbach equation for an incompressible steady flow in a pipe to an ordinary differential equation, permitting continuous variation of density, viscosity and related fluid parameters, as well as head loss or gain due to gravity, in isothermal flow. The differential equation is solved analytically in the case of ideal gas for a single edge in the network. Thereafter the solution is used in an algorithm developed to construct the flow equations automatically for a network characterised by an incidence matrix, and determine pressure distribution, flow rates and all associated parameters therein.
Nonlinear analysis of gas-water/oil-water two-phase flow in complex networks
Gao, Zhong-Ke; Wang, Wen-Xu
2014-01-01
Understanding the dynamics of multi-phase flows has been a challenge in the fields of nonlinear dynamics and fluid mechanics. This chapter reviews our work on two-phase flow dynamics in combination with complex network theory. We systematically carried out gas-water/oil-water two-phase flow experiments for measuring the time series of flow signals which is studied in terms of the mapping from time series to complex networks. Three network mapping methods were proposed for the analysis and identification of flow patterns, i.e. Flow Pattern Complex Network (FPCN), Fluid Dynamic Complex Network (FDCN) and Fluid Structure Complex Network (FSCN). Through detecting the community structure of FPCN based on K-means clustering, distinct flow patterns can be successfully distinguished and identified. A number of FDCN’s under different flow conditions were constructed in order to reveal the dynamical characteristics of two-phase flows. The FDCNs exhibit universal power-law degree distributions. The power-law exponent ...
Dynamics of comb-of-comb-network polymers in random layered flows.
Katyal, Divya; Kant, Rama
2016-12-01
We analyze the dynamics of comb-of-comb-network polymers in the presence of external random flows. The dynamics of such structures is evaluated through relevant physical quantities, viz., average square displacement (ASD) and the velocity autocorrelation function (VACF). We focus on comparing the dynamics of the comb-of-comb network with the linear polymer. The present work displays an anomalous diffusive behavior of this flexible network in the random layered flows. The effect of the polymer topology on the dynamics is analyzed by varying the number of generations and branch lengths in these networks. In addition, we investigate the influence of external flow on the dynamics by varying flow parameters, like the flow exponent α and flow strength W_{α}. Our analysis highlights two anomalous power-law regimes, viz., subdiffusive (intermediate-time polymer stretching and flow-induced diffusion) and superdiffusive (long-time flow-induced diffusion). The anomalous long-time dynamics is governed by the temporal exponent ν of ASD, viz., ν=2-α/2. Compared to a linear polymer, the comb-of-comb network shows a shorter crossover time (from the subdiffusive to superdiffusive regime) but a reduced magnitude of ASD. Our theory displays an anomalous VACF in the random layered flows that scales as t^{-α/2}. We show that the network with greater total mass moves faster.
Micro/Nano-pore Network Analysis of Gas Flow in Shale Matrix.
Zhang, Pengwei; Hu, Liming; Meegoda, Jay N; Gao, Shengyan
2015-08-27
The gas flow in shale matrix is of great research interests for optimized shale gas extraction. The gas flow in the nano-scale pore may fall in flow regimes such as viscous flow, slip flow and Knudsen diffusion. A 3-dimensional nano-scale pore network model was developed to simulate dynamic gas flow, and to describe the transient properties of flow regimes. The proposed pore network model accounts for the various size distributions and low connectivity of shale pores. The pore size, pore throat size and coordination number obey normal distribution, and the average values can be obtained from shale reservoir data. The gas flow regimes were simulated using an extracted pore network backbone. The numerical results show that apparent permeability is strongly dependent on pore pressure in the reservoir and pore throat size, which is overestimated by low-pressure laboratory tests. With the decrease of reservoir pressure, viscous flow is weakening, then slip flow and Knudsen diffusion are gradually becoming dominant flow regimes. The fingering phenomenon can be predicted by micro/nano-pore network for gas flow, which provides an effective way to capture heterogeneity of shale gas reservoir.
Energy Technology Data Exchange (ETDEWEB)
Salgado, C.M.; Pereira, Claudio M.N.A.; Brandao, Luis E.B., E-mail: otero@ien.gov.b, E-mail: cmnap@ien.gov.b, E-mail: brandao@ien.gov.b [Instituto de Engenharia Nuclear (DIRA/IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil). Div. de Radiofarmacos
2011-07-01
This work investigates the response of a volume fraction prediction system for water-gas-oil multiphase flows considering variations on water salinity. The approach is based on gamma-ray pulse height distributions pattern recognition by means the artificial neural networks (ANNs). The detection system uses appropriate fan beam geometry, comprised of a dual-energy gamma-ray source and two NaI(Tl) detectors adequately positioned outside the pipe in order measure transmitted and scattered beams. An ideal and static theoretical model for annular flow regime have been developed using MCNP-X code, which was used to provide training, test and validation data for the ANN. More than 500 simulations have been done, in which water salinity have been ranged from 0 to 16% in order to cover a most practical situations. Validation tests have included values of volume fractions and water salinity different from those used in ANN training phase. The results presented here show that the proposed approach may be successfully applied to material volume fraction prediction on watergas- oil multiphase flows considering practical (real) levels of variations in water salinity. (author)
A new approach to estimating Mean Flow in the UK
Directory of Open Access Journals (Sweden)
M. G. R. Holmes
2002-01-01
Full Text Available Traditionally, the estimation of Mean Flow (MF in ungauged catchments has been approached using conceptual water balance models or empirical formulae relating climatic inputs to stream flow. In the UK, these types of models have difficulty in predicting MF in low rainfall areas because the conceptualisation of soil moisture behaviour and its relationship with evaporation rates used is rather simplistic. However, it is in these dry regions where the accurate estimation of flows is most critical to effective management of a scarce resource. A novel approach to estimating MF, specifically designed to improve estimation of runoff in dry catchments, has been developed using a regionalisation of the Penman drying curve theory. The dynamic water balance style Daily Soil Moisture Accounting (DSMA model operates at a daily time step, using inputs of precipitation and potential evaporation and simulates the development of soil moisture deficits explicitly. The model has been calibrated using measured MFs from a large data set of catchments in the United Kingdom. The performance of the DSMA model is superior to existing established steady state and dynamic water-balance models over the entire data set considered and the largest improvement is observed in very low rainfall catchments. It is concluded that the performance of all models in high rainfall areas is likely to be limited by the spatial representation of rainfall. Keywords: hydrological models, regionalisation, water resources, mean flow, runoff, water balance, Penman drying curve, soil moisture model
Xia, Jianguo; Benner, Maia J; Hancock, Robert E W
2014-07-01
Biological network analysis is a powerful approach to gain systems-level understanding of patterns of gene expression in different cell types, disease states and other biological/experimental conditions. Three consecutive steps are required--identification of genes or proteins of interest, network construction and network analysis and visualization. To date, researchers have to learn to use a combination of several tools to accomplish this task. In addition, interactive visualization of large networks has been primarily restricted to locally installed programs. To address these challenges, we have developed NetworkAnalyst, taking advantage of state-of-the-art web technologies, to enable high performance network analysis with rich user experience. NetworkAnalyst integrates all three steps and presents the results via a powerful online network visualization framework. Users can upload gene or protein lists, single or multiple gene expression datasets to perform comprehensive gene annotation and differential expression analysis. Significant genes are mapped to our manually curated protein-protein interaction database to construct relevant networks. The results are presented through standard web browsers for network analysis and interactive exploration. NetworkAnalyst supports common functions for network topology and module analyses. Users can easily search, zoom and highlight nodes or modules, as well as perform functional enrichment analysis on these selections. The networks can be customized with different layouts, colors or node sizes, and exported as PNG, PDF or GraphML files. Comprehensive FAQs, tutorials and context-based tips and instructions are provided. NetworkAnalyst currently supports protein-protein interaction network analysis for human and mouse and is freely available at http://www.networkanalyst.ca. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.
Developing intelligent sensor networks —a technological convergence approach
Vassev, Emil; Hinchey, Mike; Nixon, Paddy
2010-01-01
peer-reviewed We present a technological convergence approach to developing sensor networks capable of self-management. We use ASSL (Autonomic System Specification Language) to formally develop autonomous intelligent sensor nodes and DMF (Demand Migration Framework) to connect these nodes in a sensor network. ASSL provides constructs for modeling special self-management policies that drive the sensor nodes’ behavior and control the communication mechanism provided by DMF.
Virtual networks pluralistic approach for the next generation of Internet
Duarte, Otto Carlos M B
2013-01-01
The first chapter of this title concerns virtualization techniques that allow sharing computational resources basically, slicing a real computational environment into virtual computational environments that are isolated from one another.The Xen and OpenFlow virtualization platforms are then presented in Chapter 2 and a performance analysis of both is provided. This chapter also defines the primitives that the network virtualization infrastructure must provide for allowing the piloting plane to manage virtual network elements.Following this, interfaces for system management of the two platform
A quantitative approach to measure road network information based on edge diversity
Wu, Xun; Zhang, Hong; Lan, Tian; Cao, Weiwei; He, Jing
2015-12-01
The measure of map information has been one of the key issues in assessing cartographic quality and map generalization algorithms. It is also important for developing efficient approaches to transfer geospatial information. Road network is the most common linear object in real world. Approximately describe road network information will benefit road map generalization, navigation map production and urban planning. Most of current approaches focused on node diversities and supposed that all the edges are the same, which is inconsistent to real-life condition, and thus show limitations in measuring network information. As real-life traffic flow are directed and of different quantities, the original undirected vector road map was first converted to a directed topographic connectivity map. Then in consideration of preferential attachment in complex network study and rich-club phenomenon in social network, the from and to weights of each edge are assigned. The from weight of a given edge is defined as the connectivity of its end node to the sum of the connectivities of all the neighbors of the from nodes of the edge. After getting the from and to weights of each edge, edge information, node information and the whole network structure information entropies could be obtained based on information theory. The approach has been applied to several 1 square mile road network samples. Results show that information entropies based on edge diversities could successfully describe the structural differences of road networks. This approach is a complementarity to current map information measurements, and can be extended to measure other kinds of geographical objects.
Energy Technology Data Exchange (ETDEWEB)
Shi, Cindy
2015-07-17
The interactions among different microbial populations in a community could play more important roles in determining ecosystem functioning than species numbers and their abundances, but very little is known about such network interactions at a community level. The goal of this project is to develop novel framework approaches and associated software tools to characterize the network interactions in microbial communities based on high throughput, large scale high-throughput metagenomics data and apply these approaches to understand the impacts of environmental changes (e.g., climate change, contamination) on network interactions among different nitrifying populations and associated microbial communities.
Non-coherent Network Coding: An Arbitrarily Varying Channel Approach
Jafari Siavoshani, Mahdi; Yang, Shenghao; Yeung, Raymond
2012-01-01
In this paper, we propose an “arbitrarily varying channel” (AVC) approach to study the capacity of non-coherent transmission in a network that employs randomized linear network coding. The network operation is modeled by a matrix channel over a ﬁnite ﬁeld where the transfer matrix changes arbitrarily from time-slot to time-slot but up to a known distribution over its rank. By extending the AVC results to this setup, we characterize the capacity of such a non-coherent transmission scheme and s...
Building a glaucoma interaction network using a text mining approach.
Soliman, Maha; Nasraoui, Olfa; Cooper, Nigel G F
2016-01-01
The volume of biomedical literature and its underlying knowledge base is rapidly expanding, making it beyond the ability of a single human being to read through all the literature. Several automated methods have been developed to help make sense of this dilemma. The present study reports on the results of a text mining approach to extract gene interactions from the data warehouse of published experimental results which are then used to benchmark an interaction network associated with glaucoma. To the best of our knowledge, there is, as yet, no glaucoma interaction network derived solely from text mining approaches. The presence of such a network could provide a useful summative knowledge base to complement other forms of clinical information related to this disease. A glaucoma corpus was constructed from PubMed Central and a text mining approach was applied to extract genes and their relations from this corpus. The extracted relations between genes were checked using reference interaction databases and classified generally as known or new relations. The extracted genes and relations were then used to construct a glaucoma interaction network. Analysis of the resulting network indicated that it bears the characteristics of a small world interaction network. Our analysis showed the presence of seven glaucoma linked genes that defined the network modularity. A web-based system for browsing and visualizing the extracted glaucoma related interaction networks is made available at http://neurogene.spd.louisville.edu/GlaucomaINViewer/Form1.aspx. This study has reported the first version of a glaucoma interaction network using a text mining approach. The power of such an approach is in its ability to cover a wide range of glaucoma related studies published over many years. Hence, a bigger picture of the disease can be established. To the best of our knowledge, this is the first glaucoma interaction network to summarize the known literature. The major findings were a set of
Contingent approach to Internet-based supply network integration
Ho, Jessica; Boughton, Nick; Kehoe, Dennis; Michaelides, Zenon
2001-10-01
The Internet is playing an increasingly important role in enhancing the operations of supply networks as many organizations begin to recognize the benefits of Internet- enabled supply arrangements. However, the developments and applications to-date do not extend significantly beyond the dyadic model, whereas the real advantages are to be made with the external and network models to support a coordinated and collaborative based approach. The DOMAIN research group at the University of Liverpool is currently defining new Internet- enabled approaches to enable greater collaboration across supply chains. Different e-business models and tools are focusing on different applications. Using inappropriate e- business models, tools or techniques will bring negative results instead of benefits to all the tiers in the supply network. Thus there are a number of issues to be considered before addressing Internet based supply network integration, in particular an understanding of supply chain management, the emergent business models and evaluating the effects of deploying e-business to the supply network or a particular tier. It is important to utilize a contingent approach to selecting the right e-business model to meet the specific supply chain requirements. This paper addresses the issues and provides a case study on the indirect materials supply networks.
A Shallow Layer Approach for Geo-flow emplacement
Costa, A.; Folch, A.; Mecedonio, G.
2009-04-01
Geophysical flows such as lahars or lava flows severely threat the communities located on or near the volcano flanks. Risks and damages caused by the propagation of this kind of flows require a quantitative description of this phenomenon and reliable tools for forecasting their emplacement. Computational models are a valuable tool for planning risk mitigation countermeasures, such as human intervention to force flow diversion, artificial barriers, and allow for significant economical and social benefits. A FORTRAN 90 code based on a Shallow Layer Approach for Geo-flows (SLAG) for describing transport and emplacement of diluted lahars, water and lava was developed in both serial and parallel version. Three rheological models, such as those describing i) a viscous, ii) a turbulent, and iii) a dilatant flow respectively, were implemented in order to describe transport of lavas, water and diluted lahars. The code was made user-friendly by creating some interfaces that allow the user to easily define the problem, extract and interpolate the topography of the simulation domain. Moreover SLAG outputs can be written in both GRD format (e.g., Surfer), NetCDF format, or visualized directly in GoogleEarth. In SLAG the governing equations were treated using a Godunov splitting method following George (2008) algorithm based on a Riemann solver for the shallow water equations that decomposes an augmented state variable the depth, momentum, momentum flux, and bathymetry into four propagating discontinuities or waves. For our application, the algorithm was generalized for solving the energy equation. For validating the code in simulating real geophysical flows, we performed few simulations the lava flow event of the the 3rd and 4th January 1992 Etna eruption, the July 2001 Etna lava flows, January 2002 Nyragongo lava flows and few test cases for simulating transport of diluted lahars. Ref: George, D.L. (2008), Augmented Riemann Solvers for the Shallow Water Equations over Variable
Directory of Open Access Journals (Sweden)
Juan Reca
2017-11-01
Full Text Available This work presents a new approach to increase the efficiency of the heuristics methods applied to the optimal design of water distribution systems. The approach is based on reducing the search space by bounding the diameters that can be used for every network pipe. To reduce the search space, two opposite extreme flow distribution scenarios are analyzed and velocity restrictions to the pipe flow are then applied. The first scenario produces the most uniform flow distribution in the network. The opposite scenario is represented by the network with the maximum flow accumulation. Both extreme flow distributions are calculated by solving a quadratic programming problem, which is a very robust and efficient procedure. This approach has been coupled to a Genetic Algorithm (GA. The GA has an integer coding scheme and variable number of alleles depending on the number of diameters comprised within the velocity restrictions. The methodology has been applied to several benchmark networks and its performance has been compared to a classic GA formulation with a non-bounded search space. It considerably reduced the search space and provided a much faster and more accurate convergence than the GA formulation. This approach can also be coupled to other metaheuristics.
A dynamical approach to identify vertices' centrality in complex networks
Guo, Long; Zhang, Wen-Yao; Luo, Zhong-Jie; Gao, Fu-Juan; Zhang, Yi-Cheng
2017-12-01
In this paper, we proposed a dynamical approach to assess vertices' centrality according to the synchronization process of the Kuramoto model. In our approach, the vertices' dynamical centrality is calculated based on the Difference of vertices' Synchronization Abilities (DSA), which are different from traditional centrality measurements that are related to the topological properties. Through applying our approach to complex networks with a clear community structure, we have calculated all vertices' dynamical centrality and found that vertices at the end of weak links have higher dynamical centrality. Meanwhile, we analyzed the robustness and efficiency of our dynamical approach through testing the probabilities that some known vital vertices were recognized. Finally, we applied our dynamical approach to identify community due to its satisfactory performance in assessing overlapping vertices. Our present work provides a new perspective and tools to understand the crucial role of heterogeneity in revealing the interplay between the dynamics and structure of complex networks.
Estimation of Leak Flow Rate during Post-LOCA Using Cascaded Fuzzy Neural Networks
Energy Technology Data Exchange (ETDEWEB)
Kim, Dong Yeong [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of); Na, Man Gyun [Chosun University, Gwangju (Korea, Republic of)
2016-10-15
In this study, important parameters such as the break position, size, and leak flow rate of loss of coolant accidents (LOCAs), provide operators with essential information for recovering the cooling capability of the nuclear reactor core, for preventing the reactor core from melting down, and for managing severe accidents effectively. Leak flow rate should consist of break size, differential pressure, temperature, and so on (where differential pressure means difference between internal and external reactor vessel pressure). The leak flow rate is strongly dependent on the break size and the differential pressure, but the break size is not measured and the integrity of pressure sensors is not assured in severe circumstances. In this paper, a cascaded fuzzy neural network (CFNN) model is appropriately proposed to estimate the leak flow rate out of break, which has a direct impact on the important times (time approaching the core exit temperature that exceeds 1200 .deg. F, core uncover time, reactor vessel failure time, etc.). The CFNN is a data-based model, it requires data to develop and verify itself. Because few actual severe accident data exist, it is essential to obtain the data required in the proposed model using numerical simulations. In this study, a CFNN model was developed to predict the leak flow rate before proceeding to severe LOCAs. The simulations showed that the developed CFNN model accurately predicted the leak flow rate with less error than 0.5%. The CFNN model is much better than FNN model under the same conditions, such as the same fuzzy rules. At the result of comparison, the RMS errors of the CFNN model were reduced by approximately 82 ~ 97% of those of the FNN model.
DEFF Research Database (Denmark)
Karsten, Christian Vad; Pisinger, David; Røpke, Stefan
2015-01-01
The multi-commodity network flow problem is an important sub-problem in several heuristics and exact methods for designing route networks for container ships. The sub-problem decides how cargoes should be transported through the network provided by shipping routes. This paper studies the multi......-commodity network flow problem with transit time constraints which puts limits on the duration of the transit of the commodities through the network. It is shown that for the particular application it does not increase the solution time to include the transit time constraints and that including the transit time...... is essential to offer customers a competitive product. © 2015 Elsevier Ltd. All rights reserved....
Real-Time and Resilient Intrusion Detection: A Flow-Based Approach
Hofstede, R.J.; Pras, Aiko
Flow-based intrusion detection will play an important role in high-speed networks, due to the stringent performance requirements of packet-based solutions. Flow monitoring technologies, such as NetFlow or IPFIX, aggregate individual packets into flows, requiring new intrusion detection algorithms to
Social network approaches to leadership: an integrative conceptual review.
Carter, Dorothy R; DeChurch, Leslie A; Braun, Michael T; Contractor, Noshir S
2015-05-01
Contemporary definitions of leadership advance a view of the phenomenon as relational, situated in specific social contexts, involving patterned emergent processes, and encompassing both formal and informal influence. Paralleling these views is a growing interest in leveraging social network approaches to study leadership. Social network approaches provide a set of theories and methods with which to articulate and investigate, with greater precision and rigor, the wide variety of relational perspectives implied by contemporary leadership theories. Our goal is to advance this domain through an integrative conceptual review. We begin by answering the question of why-Why adopt a network approach to study leadership? Then, we offer a framework for organizing prior research. Our review reveals 3 areas of research, which we term: (a) leadership in networks, (b) leadership as networks, and (c) leadership in and as networks. By clarifying the conceptual underpinnings, key findings, and themes within each area, this review serves as a foundation for future inquiry that capitalizes on, and programmatically builds upon, the insights of prior work. Our final contribution is to advance an agenda for future research that harnesses the confluent ideas at the intersection of leadership in and as networks. Leadership in and as networks represents a paradigm shift in leadership research-from an emphasis on the static traits and behaviors of formal leaders whose actions are contingent upon situational constraints, toward an emphasis on the complex and patterned relational processes that interact with the embedding social context to jointly constitute leadership emergence and effectiveness. (c) 2015 APA, all rights reserved.
Neural Network Approach To Sensory Fusion
Pearson, John C.; Gelfand, Jack J.; Sullivan, W. E.; Peterson, Richard M.; Spence, Clay D.
1988-08-01
We present a neural network model for sensory fusion based on the design of the visual/acoustic target localiza-tion system of the barn owl. This system adaptively fuses its separate visual and acoustic representations of object position into a single joint representation used for head orientation. The building block in this system, as in much of the brain, is the neuronal map. Neuronal maps are large arrays of locally interconnected neurons that represent information in a map-like form, that is, parameter values are systematically encoded by the position of neural activation in the array. The computational load is distributed to a hierarchy of maps, and the computation is performed in stages by transforming the representation from map to map via the geometry of the projections between the maps and the local interactions within the maps. For example, azimuthal position is computed from the frequency and binaural phase information encoded in the signals of the acoustic sensors, while elevation is computed in a separate stream using binaural intensity information. These separate streams are merged in their joint projection onto the external nucleus of the inferior colliculus, a two dimensional array of cells which contains a map of acoustic space. This acoustic map, and the visual map of the retina, jointly project onto the optic tectum, creating a fused visual/acoustic representation of position in space that is used for object localization. In this paper we describe our mathematical model of the stage of visual/acoustic fusion in the optic tectum. The model assumes that the acoustic projection from the external nucleus onto the tectum is roughly topographic and one-to-many, while the visual projection from the retina onto the tectum is topographic and one-to-one. A simple process of self-organization alters the strengths of the acoustic connections, effectively forming a focused beam of strong acoustic connections whose inputs are coincident with the visual inputs
Conservation, spillover and gene flow within a network of Northern European marine protected areas.
Directory of Open Access Journals (Sweden)
Mats Brockstedt Olsen Huserbråten
Full Text Available To ensure that marine protected areas (MPAs benefit conservation and fisheries, the effectiveness of MPA designs has to be evaluated in field studies. Using an interdisciplinary approach, we empirically assessed the design of a network of northern MPAs where fishing for European lobster (Homarusgammarus is prohibited. First, we demonstrate a high level of residency and survival (50% for almost a year (363 days within MPAs, despite small MPA sizes (0.5-1 km(2. Second, we demonstrate limited export (4.7% of lobsters tagged within MPAs (N = 1810 to neighbouring fished areas, over a median distance of 1.6 km out to maximum 21 km away from MPA centres. In comparison, median movement distance of lobsters recaptured within MPAs was 164 m, and recapture rate was high (40%. Third, we demonstrate a high level of gene flow within the study region, with an estimated F ST of less than 0.0001 over a ≈ 400 km coastline. Thus, the restricted movement of older life stages, combined with a high level of gene flow suggests that connectivity is primarily driven by larval drift. Larval export from the MPAs can most likely affect areas far beyond their borders. Our findings are of high importance for the design of MPA networks for sedentary species with pelagic early life stages.
Conservation, spillover and gene flow within a network of Northern European marine protected areas.
Huserbråten, Mats Brockstedt Olsen; Moland, Even; Knutsen, Halvor; Olsen, Esben Moland; André, Carl; Stenseth, Nils Chr
2013-01-01
To ensure that marine protected areas (MPAs) benefit conservation and fisheries, the effectiveness of MPA designs has to be evaluated in field studies. Using an interdisciplinary approach, we empirically assessed the design of a network of northern MPAs where fishing for European lobster (Homarusgammarus) is prohibited. First, we demonstrate a high level of residency and survival (50%) for almost a year (363 days) within MPAs, despite small MPA sizes (0.5-1 km(2)). Second, we demonstrate limited export (4.7%) of lobsters tagged within MPAs (N = 1810) to neighbouring fished areas, over a median distance of 1.6 km out to maximum 21 km away from MPA centres. In comparison, median movement distance of lobsters recaptured within MPAs was 164 m, and recapture rate was high (40%). Third, we demonstrate a high level of gene flow within the study region, with an estimated F ST of less than 0.0001 over a ≈ 400 km coastline. Thus, the restricted movement of older life stages, combined with a high level of gene flow suggests that connectivity is primarily driven by larval drift. Larval export from the MPAs can most likely affect areas far beyond their borders. Our findings are of high importance for the design of MPA networks for sedentary species with pelagic early life stages.
Guidelines on CV networking information flow optimization for Texas.
2017-03-01
Recognizing the fundamental role of information flow in future transportation applications, the research team investigated the quality and security of information flow in the connected vehicle (CV) environment. The research team identified key challe...
Hierarchical polynomial network approach to automated target recognition
Kim, Richard Y.; Drake, Keith C.; Kim, Tony Y.
1994-02-01
A hierarchical recognition methodology using abductive networks at several levels of object recognition is presented. Abductive networks--an innovative numeric modeling technology using networks of polynomial nodes--results from nearly three decades of application research and development in areas including statistical modeling, uncertainty management, genetic algorithms, and traditional neural networks. The systems uses pixel-registered multisensor target imagery provided by the Tri-Service Laser Radar sensor. Several levels of recognition are performed using detection, classification, and identification, each providing more detailed object information. Advanced feature extraction algorithms are applied at each recognition level for target characterization. Abductive polynomial networks process feature information and situational data at each recognition level, providing input for the next level of processing. An expert system coordinates the activities of individual recognition modules and enables employment of heuristic knowledge to overcome the limitations provided by a purely numeric processing approach. The approach can potentially overcome limitations of current systems such as catastrophic degradation during unanticipated operating conditions while meeting strict processing requirements. These benefits result from implementation of robust feature extraction algorithms that do not take explicit advantage of peculiar characteristics of the sensor imagery, and the compact, real-time processing capability provided by abductive polynomial networks.
Social Network Analyses and Nutritional Behavior: An Integrated Modeling Approach
Directory of Open Access Journals (Sweden)
Alistair McNair Senior
2016-01-01
Full Text Available Animals have evolved complex foraging strategies to obtain a nutritionally balanced diet and associated fitness benefits. Recent advances in nutrition research, combining state-space models of nutritional geometry with agent-based models of systems biology, show how nutrient targeted foraging behavior can also influence animal social interactions, ultimately affecting collective dynamics and group structures. Here we demonstrate how social network analyses can be integrated into such a modeling framework and provide a tangible and practical analytical tool to compare experimental results with theory. We illustrate our approach by examining the case of nutritionally mediated dominance hierarchies. First we show how nutritionally explicit agent-based models that simulate the emergence of dominance hierarchies can be used to generate social networks. Importantly the structural properties of our simulated networks bear similarities to dominance networks of real animals (where conflicts are not always directly related to nutrition. Finally, we demonstrate how metrics from social network analyses can be used to predict the fitness of agents in these simulated competitive environments. Our results highlight the potential importance of nutritional mechanisms in shaping dominance interactions in a wide range of social and ecological contexts. Nutrition likely influences social interaction in many species, and yet a theoretical framework for exploring these effects is currently lacking. Combining social network analyses with computational models from nutritional ecology may bridge this divide, representing a pragmatic approach for generating theoretical predictions for nutritional experiments.
A service flow model for the liner shipping network design problem
DEFF Research Database (Denmark)
Plum, Christian Edinger Munk; Pisinger, David; Sigurd, Mikkel M.
2014-01-01
Global liner shipping is a competitive industry, requiring liner carriers to carefully deploy their vessels efficiently to construct a cost competitive network. This paper presents a novel compact formulation of the liner shipping network design problem (LSNDP) based on service flows...... of the network and a penalty for not flowed cargo. The model can be used to design liner shipping networks to utilize a container carrier’s assets efficiently and to investigate possible scenarios of changed market conditions. The model is solved as a Mixed Integer Program. Results are presented for the two...
Holland, D J; Blake, A; Tayler, A B; Sederman, A J; Gladden, L F
2011-03-01
Magnetic Resonance (MR) imaging is difficult to apply to multi-phase flows due to both the inherently short T₂* characterising such systems and the relatively long time taken to acquire the data. We develop a Bayesian MR approach for analysing data in k-space that eliminates the need for image acquisition, thereby significantly extending the range of systems that can be studied. We demonstrate the technique by measuring bubble size distributions in gas-liquid flows. The MR approach is compared with an optical technique at a low gas fraction (∼2%), before being applied to a system where the gas fraction is too high for optical measurements (∼15%). Copyright © 2010 Elsevier Inc. All rights reserved.
Stochastic approach to observability analysis in water networks
Directory of Open Access Journals (Sweden)
S. Díaz
2016-07-01
Full Text Available This work presents an alternative technique to the existing methods for observability analysis (OA in water networks, which is a prior essential step for the implementation of state estimation (SE techniques within such systems. The methodology presented here starts from a known hydraulic state and assumes random gaussian distributions for the uncertainty of some hydraulic variables, which is then propagated to the rest of the system. This process is repeated again to analyze the change in the network uncertainty when metering devices considered as error-free are included, based on which the network observability can be evaluated. The method’s potential is presented in an illustrative example, which shows the additional information that this methodology provides with respect to traditional OA approaches. This proposal allows a better understanding of the network and constitutes a practical tool to prioritize the location of additional meters, thus enhancing the transformation of large urban areas into actual smart cities.
A Novel Modulation Classification Approach Using Gabor Filter Network
Ghauri, Sajjad Ahmed; Qureshi, Ijaz Mansoor; Cheema, Tanveer Ahmed; Malik, Aqdas Naveed
2014-01-01
A Gabor filter network based approach is used for feature extraction and classification of digital modulated signals by adaptively tuning the parameters of Gabor filter network. Modulation classification of digitally modulated signals is done under the influence of additive white Gaussian noise (AWGN). The modulations considered for the classification purpose are PSK 2 to 64, FSK 2 to 64, and QAM 4 to 64. The Gabor filter network uses the network structure of two layers; the first layer which is input layer constitutes the adaptive feature extraction part and the second layer constitutes the signal classification part. The Gabor atom parameters are tuned using Delta rule and updating of weights of Gabor filter using least mean square (LMS) algorithm. The simulation results show that proposed novel modulation classification algorithm has high classification accuracy at low signal to noise ratio (SNR) on AWGN channel. PMID:25126603
A Novel Modulation Classification Approach Using Gabor Filter Network
Directory of Open Access Journals (Sweden)
Sajjad Ahmed Ghauri
2014-01-01
Full Text Available A Gabor filter network based approach is used for feature extraction and classification of digital modulated signals by adaptively tuning the parameters of Gabor filter network. Modulation classification of digitally modulated signals is done under the influence of additive white Gaussian noise (AWGN. The modulations considered for the classification purpose are PSK 2 to 64, FSK 2 to 64, and QAM 4 to 64. The Gabor filter network uses the network structure of two layers; the first layer which is input layer constitutes the adaptive feature extraction part and the second layer constitutes the signal classification part. The Gabor atom parameters are tuned using Delta rule and updating of weights of Gabor filter using least mean square (LMS algorithm. The simulation results show that proposed novel modulation classification algorithm has high classification accuracy at low signal to noise ratio (SNR on AWGN channel.
A New Approach for the Stability Analysis of Wave Networks
Directory of Open Access Journals (Sweden)
Ya Xuan Zhang
2014-01-01
Full Text Available We introduce a new approach to investigate the stability of controlled tree-shaped wave networks and subtrees of complex wave networks. It is motivated by regarding the network as branching out from a single edge. We present the recursive relations of the Laplacian transforms of adjacent edges of the system in its branching order, which form the characteristic equation. In the stability analysis, we estimate the infimums of the recursive expressions in the inverse order based on the spectral analysis. It is a feasible way to check whether the system is exponentially stable under any control strategy or parameter choice. As an application we design the control law and study the stability of a 12-edge tree-shaped wave network.
Jamming in Mobile Networks: A Game-Theoretic Approach
2013-03-01
general treatment of multiplayer differential games was presented by Starr and Ho [16], Leitmann [36], Vaisbord and Zhukovskiy [65], Zhukovskiy and...REPORT Jamming in mobile networks: A game -theoretic approach. 14. ABSTRACT 16. SECURITY CLASSIFICATION OF: In this paper, we address the problem of...model the intrusion as a pursuit-evasion game between a mobile jammer and a team of agents. First, we consider a differential game -theoretic approach
Innovation Networks New Approaches in Modelling and Analyzing
Pyka, Andreas
2009-01-01
The science of graphs and networks has become by now a well-established tool for modelling and analyzing a variety of systems with a large number of interacting components. Starting from the physical sciences, applications have spread rapidly to the natural and social sciences, as well as to economics, and are now further extended, in this volume, to the concept of innovations, viewed broadly. In an abstract, systems-theoretical approach, innovation can be understood as a critical event which destabilizes the current state of the system, and results in a new process of self-organization leading to a new stable state. The contributions to this anthology address different aspects of the relationship between innovation and networks. The various chapters incorporate approaches in evolutionary economics, agent-based modeling, social network analysis and econophysics and explore the epistemic tension between insights into economics and society-related processes, and the insights into new forms of complex dynamics.
A Cognitive Approach to Network Monitoring in Heterogeneous Environments
DEFF Research Database (Denmark)
Mihovska, Albena D.
2007-01-01
of information (QoI). QoI means QoS while all the requirements for dependability, security, privacy and trust are satisfied at the highest possible level. This work proposes and describes an approach to network monitoring in a heterogeneous communication environment based on use of cognitive techniques...... for efficient resource allocation, provisioning of network resources or for detection of security violations into the traditional network monitoring approach. The paper describes the cognitive monitoring architecture, the required physical and logical entities, and their functionalities. Further, the paper......Abstract— Introducing intelligence by means of cognition for managing, protecting, processing, and delivering of information in mobile communication systems is the way towards ubiquitous, converged and secure communications. In this context, this paper introduces the concept of quality...
Approaches to the simulation of unconfined flow and perched groundwater flow in MODFLOW
Bedekar, Vivek; Niswonger, Richard G.; Kipp, Kenneth; Panday, Sorab; Tonkin, Matthew
2012-01-01
Various approaches have been proposed to manage the nonlinearities associated with the unconfined flow equation and to simulate perched groundwater conditions using the MODFLOW family of codes. The approaches comprise a variety of numerical techniques to prevent dry cells from becoming inactive and to achieve a stable solution focused on formulations of the unconfined, partially-saturated, groundwater flow equation. Keeping dry cells active avoids a discontinuous head solution which in turn improves the effectiveness of parameter estimation software that relies on continuous derivatives. Most approaches implement an upstream weighting of intercell conductance and Newton-Raphson linearization to obtain robust convergence. In this study, several published approaches were implemented in a stepwise manner into MODFLOW for comparative analysis. First, a comparative analysis of the methods is presented using synthetic examples that create convergence issues or difficulty in handling perched conditions with the more common dry-cell simulation capabilities of MODFLOW. Next, a field-scale three-dimensional simulation is presented to examine the stability and performance of the discussed approaches in larger, practical, simulation settings.
Using artificial neural network approach for modelling rainfall–runoff ...
Indian Academy of Sciences (India)
Home; Journals; Journal of Earth System Science; Volume 122; Issue 2. Using artificial neural network approach for ... In Taiwan, owing to the nonuniform temporal and spatial distribution of rainfall and high mountains all over the country, hydrologic systems are very complex. Therefore, preventing and controlling flood ...
Stochastic approaches for product recovery network design: a case study
O.L. Listes (Ovidiu); R. Dekker (Rommert)
2001-01-01
textabstractIncreased uncertainty is one of the characteristics of product recovery networks. In particular the strategic design of their logistic infrastructure has to take uncertain information into account. In this paper we present stochastic programming based approaches by which a deterministic
Approaches in Anomaly-based Network Intrusion Detection Systems
Bolzoni, D.; Etalle, Sandro
Anomaly-based network intrusion detection systems (NIDSs) can take into consideration packet headers, the payload, or a combination of both. We argue that payload-based approaches are becoming the most effective methods to detect attacks. Nowadays, attacks aim mainly to exploit vulnerabilities at
Network analysis: an integrative approach to the structure of psychopathology
Borsboom, D.; Cramer, A.O.J.
2013-01-01
In network approaches to psychopathology, disorders result from the causal interplay between symptoms (e.g., worry → insomnia → fatigue), possibly involving feedback loops (e.g., a person may engage in substance abuse to forget the problems that arose due to substance abuse). The present review
Evaluating Action Learning: A Critical Realist Complex Network Theory Approach
Burgoyne, John G.
2010-01-01
This largely theoretical paper will argue the case for the usefulness of applying network and complex adaptive systems theory to an understanding of action learning and the challenge it is evaluating. This approach, it will be argued, is particularly helpful in the context of improving capability in dealing with wicked problems spread around…
THE CONTEMPORARY METHODOLOGICAL APPROACHES AND THEORIES EXPLAINING INTERNATIONAL TOURISM FLOWS
Directory of Open Access Journals (Sweden)
Zorina SISCAN
2017-08-01
Full Text Available Proceeding from the fact that international tourism is in permanent growth the last decades, and contributes significantly to the development of contemporary national economies, the authors of the article have focused on methodological and theoretical frame that allows deeper understanding of the touristic flows from various aspects. In doing so, the first author has launched some methodological approaches that allow deepening a perspective research on touristic flows while the second author has concentrated upon grouping and analysis of existing theories used to explain touristic flows. As an outcome, the authors recommend a blend of those approaches and theories to be used for an adequate understanding and managing the existing and perspective touristic flows.ABORDDĂRI METODOLOGICE ȘI TEORII CONTEMPORANE CARE EXPLICĂ FLUXURILE DE TURISM INTERNAȚIONALPornind de la faptul că turismul internațional manifestă permanentă creștere în ultimele decenii, precum și contribuie semnificativ la dezvoltarea economiilor naționale contemporane, autorii articolului s-au axat pe cadrul metodologic și teoretic ce ar permite conceperea aprofundată a fluxurilor de turism internațional. Primul autor a lansat unele abordări metodologice care ar contribui la aprofundarea cercetărilor de perspectivă ale fluxurilor turistice, iar cel de-al doilea autor și-a concentrat atenția asupra grupării și analizei teoriilor existente în care se explică natura fluxurilor de turism. Ca rezultat, autorii recomandă utilizarea îmbinării organice a abordărilor și teoriilor considerate pentru perceperea și gestionarea adecvată a fluxurilor de turism existente și de perspectivă.
Directory of Open Access Journals (Sweden)
Enrique Castillo
2015-01-01
Full Text Available A state-of-the-art review of flow observability, estimation, and prediction problems in traffic networks is performed. Since mathematical optimization provides a general framework for all of them, an integrated approach is used to perform the analysis of these problems and consider them as different optimization problems whose data, variables, constraints, and objective functions are the main elements that characterize the problems proposed by different authors. For example, counted, scanned or “a priori” data are the most common data sources; conservation laws, flow nonnegativity, link capacity, flow definition, observation, flow propagation, and specific model requirements form the most common constraints; and least squares, likelihood, possible relative error, mean absolute relative error, and so forth constitute the bases for the objective functions or metrics. The high number of possible combinations of these elements justifies the existence of a wide collection of methods for analyzing static and dynamic situations.
Event-driven approach of layered multicast to network adaptation in RED-based IP networks
Nahm, Kitae; Li, Qing; Kuo, C.-C. J.
2003-11-01
In this work, we investigate the congestion control problem for layered video multicast in IP networks of active queue management (AQM) using a simple random early detection (RED) queue model. AQM support from networks improves the visual quality of video streaming but makes network adaptation more di+/-cult for existing layered video multicast proticols that use the event-driven timer-based approach. We perform a simplified analysis on the response of the RED algorithm to burst traffic. The analysis shows that the primary problem lies in the weak correlation between the network feedback and the actual network congestion status when the RED queue is driven by burst traffic. Finally, a design guideline of the layered multicast protocol is proposed to overcome this problem.
Olender, M.; Krenczyk, D.
2016-08-01
Modern enterprises have to react quickly to dynamic changes in the market, due to changing customer requirements and expectations. One of the key area of production management, that must continuously evolve by searching for new methods and tools for increasing the efficiency of manufacturing systems is the area of production flow planning and control. These aspects are closely connected with the ability to implement the concept of Virtual Enterprises (VE) and Virtual Manufacturing Network (VMN) in which integrated infrastructure of flexible resources are created. In the proposed approach, the players role perform the objects associated with the objective functions, allowing to solve the multiobjective production flow planning problems based on the game theory, which is based on the theory of the strategic situation. For defined production system and production order models ways of solving the problem of production route planning in VMN on computational examples for different variants of production flow is presented. Possible decision strategy to use together with an analysis of calculation results is shown.
Ontology based approach for video transmission over the network
Rachit Mohan Garg; Yamini Sood; Neha Tyagi
2011-01-01
With the increase in the bandwidth & the transmission speed over the internet, transmission of multimedia objects like video, audio, images has become an easier work. In this paper we provide an approach that can be useful for transmission of video objects over the internet without much fuzz. The approach provides a ontology based framework that is used to establish an automatic deployment of video transmission system. Further the video is compressed using the structural flow mechanism tha...
2015-06-01
This Technical Report on Prototype Intelligent Network Flow Optimization (INFLO) Dynamic Speed Harmonization and : Queue Warning is the final report for the project. It describes the prototyping, acceptance testing and small-scale : demonstration of ...
National Research Council Canada - National Science Library
Cheung, Kit; Schultz, Simon R; Luk, Wayne
2015-01-01
NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs...
Operator Splitting Method for Simulation of Dynamic Flows in Natural Gas Pipeline Networks
Dyachenko, Sergey A; Chertkov, Michael
2016-01-01
We develop an operator splitting method to simulate flows of isothermal compressible natural gas over transmission pipelines. The method solves a system of nonlinear hyperbolic partial differential equations (PDEs) of hydrodynamic type for mass flow and pressure on a metric graph, where turbulent losses of momentum are modeled by phenomenological Darcy-Weisbach friction. Mass flow balance is maintained through the boundary conditions at the network nodes, where natural gas is injected or withdrawn from the system. Gas flow through the network is controlled by compressors boosting pressure at the inlet of the adjoint pipe. Our operator splitting numerical scheme is unconditionally stable and it is second order accurate in space and time. The scheme is explicit, and it is formulated to work with general networks with loops. We test the scheme over range of regimes and network configurations, also comparing its performance with performance of two other state of the art implicit schemes.
Operator splitting method for simulation of dynamic flows in natural gas pipeline networks
Dyachenko, Sergey A.; Zlotnik, Anatoly; Korotkevich, Alexander O.; Chertkov, Michael
2017-12-01
We develop an operator splitting method to simulate flows of isothermal compressible natural gas over transmission pipelines. The method solves a system of nonlinear hyperbolic partial differential equations (PDEs) of hydrodynamic type for mass flow and pressure on a metric graph, where turbulent losses of momentum are modeled by phenomenological Darcy-Weisbach friction. Mass flow balance is maintained through the boundary conditions at the network nodes, where natural gas is injected or withdrawn from the system. Gas flow through the network is controlled by compressors boosting pressure at the inlet of the adjoint pipe. Our operator splitting numerical scheme is unconditionally stable and it is second order accurate in space and time. The scheme is explicit, and it is formulated to work with general networks with loops. We test the scheme over range of regimes and network configurations, also comparing its performance with performance of two other state of the art implicit schemes.
Solving the Bi-Objective Maximum-Flow Network-Interdiction Problem
National Research Council Canada - National Science Library
Royset, Johannes O; Wood, R. K
2006-01-01
...." In this problem, an "interdictor" seeks to interdict (destroy) a set of arcs in a capacitated network that are Pareto-optimal with respect to two objectives, minimizing total interdiction cost and minimizing maximum flow...
DEFF Research Database (Denmark)
Chon, K H; Holstein-Rathlou, N H; Marsh, D J
1998-01-01
kernel estimation method based on Laguerre expansions. The results for the two types of artificial neural networks and the Volterra models are comparable in terms of normalized mean square error (NMSE) of the respective output prediction for independent testing data. However, the Volterra models obtained......In this paper, feedforward neural networks with two types of activation functions (sigmoidal and polynomial) are utilized for modeling the nonlinear dynamic relation between renal blood pressure and flow data, and their performance is compared to Volterra models obtained by use of the leading...... via the Laguerre expansion technique achieve this prediction NMSE with approximately half the number of free parameters relative to either neural-network model. However, both approaches are deemed effective in modeling nonlinear dynamic systems and their cooperative use is recommended in general....
Perrin, Christian L; Tardy, Philippe M J; Sorbie, Ken S; Crawshaw, John C
2006-03-15
The in situ rheology of polymeric solutions has been studied experimentally in etched silicon micromodels which are idealizations of porous media. The rectangular channels in these etched networks have dimensions typical of pore sizes in sandstone rocks. Pressure drop/flow rate relations have been measured for water and non-Newtonian hydrolyzed-polyacrylamide (HPAM) solutions in both individual straight rectangular capillaries and in networks of such capillaries. Results from these experiments have been analyzed using pore-scale network modeling incorporating the non-Newtonian fluid mechanics of a Carreau fluid. Quantitative agreement is seen between the experiments and the network calculations in the Newtonian and shear-thinning flow regions demonstrating that the 'shift factor,'alpha, can be calculated a priori. Shear-thickening behavior was observed at higher flow rates in the micromodel experiments as a result of elastic effects becoming important and this remains to be incorporated in the network model.
Energy Technology Data Exchange (ETDEWEB)
Ababou, R.
1991-08-01
This report develops a broad review and assessment of quantitative modeling approaches and data requirements for large-scale subsurface flow in radioactive waste geologic repository. The data review includes discussions of controlled field experiments, existing contamination sites, and site-specific hydrogeologic conditions at Yucca Mountain. Local-scale constitutive models for the unsaturated hydrodynamic properties of geologic media are analyzed, with particular emphasis on the effect of structural characteristics of the medium. The report further reviews and analyzes large-scale hydrogeologic spatial variability from aquifer data, unsaturated soil data, and fracture network data gathered from the literature. Finally, various modeling strategies toward large-scale flow simulations are assessed, including direct high-resolution simulation, and coarse-scale simulation based on auxiliary hydrodynamic models such as single equivalent continuum and dual-porosity continuum. The roles of anisotropy, fracturing, and broad-band spatial variability are emphasized. 252 refs.
A hybrid least squares support vector machines and GMDH approach for river flow forecasting
Samsudin, R.; Saad, P.; Shabri, A.
2010-06-01
This paper proposes a novel hybrid forecasting model, which combines the group method of data handling (GMDH) and the least squares support vector machine (LSSVM), known as GLSSVM. The GMDH is used to determine the useful input variables for LSSVM model and the LSSVM model which works as time series forecasting. In this study the application of GLSSVM for monthly river flow forecasting of Selangor and Bernam River are investigated. The results of the proposed GLSSVM approach are compared with the conventional artificial neural network (ANN) models, Autoregressive Integrated Moving Average (ARIMA) model, GMDH and LSSVM models using the long term observations of monthly river flow discharge. The standard statistical, the root mean square error (RMSE) and coefficient of correlation (R) are employed to evaluate the performance of various models developed. Experiment result indicates that the hybrid model was powerful tools to model discharge time series and can be applied successfully in complex hydrological modeling.
Application guide for AFINCH (Analysis of Flows in Networks of Channels) described by NHDPlus
Holtschlag, David J.
2009-01-01
AFINCH (Analysis of Flows in Networks of CHannels) is a computer application that can be used to generate a time series of monthly flows at stream segments (flowlines) and water yields for catchments defined in the National Hydrography Dataset Plus (NHDPlus) value-added attribute system. AFINCH provides a basis for integrating monthly flow data from streamgages, water-use data, monthly climatic data, and land-cover characteristics to estimate natural monthly water yields from catchments by user-defined regression equations. Images of monthly water yields for active streamgages are generated in AFINCH and provide a basis for detecting anomalies in water yields, which may be associated with undocumented flow diversions or augmentations. Water yields are multiplied by the drainage areas of the corresponding catchments to estimate monthly flows. Flows from catchments are accumulated downstream through the streamflow network described by the stream segments. For stream segments where streamgages are active, ratios of measured to accumulated flows are computed. These ratios are applied to upstream water yields to proportionally adjust estimated flows to match measured flows. Flow is conserved through the NHDPlus network. A time series of monthly flows can be generated for stream segments that average about 1-mile long, or monthly water yields from catchments that average about 1 square mile. Estimated monthly flows can be displayed within AFINCH, examined for nonstationarity, and tested for monotonic trends. Monthly flows also can be used to estimate flow-duration characteristics at stream segments. AFINCH generates output files of monthly flows and water yields that are compatible with ArcMap, a geographical information system analysis and display environment. Chloropleth maps of monthly water yield and flow can be generated and analyzed within ArcMap by joining NHDPlus data structures with AFINCH output. Matlab code for the AFINCH application is presented.
A network-flow based valve-switching aware binding algorithm for flow-based microfluidic biochips
DEFF Research Database (Denmark)
Tseng, Kai-Han; You, Sheng-Chi; Minhass, Wajid Hassan
2013-01-01
biochip needs more chip-integrated micro-valves, i.e., the basic unit of fluid-handling functionality, to manipulate the fluid flow for biochemical applications. Moreover, frequent switching of micro-valves results in decreased reliability. To minimize the valve-switching activities, we develop a network......Designs of flow-based microfluidic biochips are receiving much attention recently because they replace conventional biological automation paradigm and are able to integrate different biochemical analysis functions on a chip. However, as the design complexity increases, a flow-based microfluidic...
Synthetic Aperture Flow Imaging Using a Dual Stage Beamformer Approach
DEFF Research Database (Denmark)
Li, Ye; Jensen, Jørgen Arendt
2010-01-01
as virtual sources in the second beamformation algorithm. High resolution image lines are generated by the second stage beamformation. Four emissions in each emission sequence repeat over time. The velocity is estimated by cross correlating high resolution lines which is a function of depth. The cross...... respectively. The parameter study showed that larger interspaces gave an increased standard deviation and the bias. Furthermore more emissions in averaging gave better performance. The performance of the simulation indicates that this dual beamformer approach is able to estimate the flow velocity. Interspaces...... between emissions and number of emission sets in averaging have an influence on the estimation....
Early Detection Of Failure Mechanisms In Resilient Biostructures: A Network Flow Study
2017-10-01
the flow network algorithm. This in- house written algorithm extracts the connectivity information from the numerical model ; weights (Von-Mises...cartilage of the rostrum, a model of the rostrum was constructed without the lattice architecture . Figure 23 shows the computational model of the...pressure on the rostrum without the lattice architecture A flow network model of the homogenous rostrum displayed in Figure 23 is constructed from the
Real-Time and Resilient Intrusion Detection: A Flow-Based Approach
Hofstede, R.J.; Pras, Aiko
Due to the demanding performance requirements of packet-based monitoring solutions on network equipment, flow-based intrusion detection systems will play an increasingly important role in current high-speed networks. The required technologies are already available and widely deployed: NetFlow and
Directory of Open Access Journals (Sweden)
Xiaohui Lin
2017-01-01
Full Text Available Connected-vehicles network provides opportunities and conditions for improving traffic signal control, and macroscopic fundamental diagrams (MFD can control the road network at the macrolevel effectively. This paper integrated proposed real-time access to the number of mobile vehicles and the maximum road queuing length in the Connected-vehicles network. Moreover, when implementing a simple control strategy to limit the boundary flow of a road network based on MFD, we determined whether the maximum queuing length of each boundary section exceeds the road-safety queuing length in real-time calculations and timely adjusted the road-network influx rate to avoid the overflow phenomenon in the boundary section. We established a road-network microtraffic simulation model in VISSIM software taking a district as the experimental area, determined MFD of the region based on the number of mobile vehicles, and weighted traffic volume of the road network. When the road network was tending to saturate, we implemented a simple control strategy and our algorithm limits the boundary flow. Finally, we compared the traffic signal control indicators with three strategies: (1 no control strategy, (2 boundary control, and (3 boundary control with limiting queue strategy. The results show that our proposed algorithm is better than the other two.
On a Registration-Based Approach to Sensor Network Localization
Sanyal, Rajat; Jaiswal, Monika; Chaudhury, Kunal Narayan
2017-10-01
We consider a registration-based approach for localizing sensor networks from range measurements. This is based on the assumption that one can find overlapping cliques spanning the network. That is, for each sensor, one can identify geometric neighbors for which all inter-sensor ranges are known. Such cliques can be efficiently localized using multidimensional scaling. However, since each clique is localized in some local coordinate system, we are required to register them in a global coordinate system. In other words, our approach is based on transforming the localization problem into a problem of registration. In this context, the main contributions are as follows. First, we describe an efficient method for partitioning the network into overlapping cliques. Second, we study the problem of registering the localized cliques, and formulate a necessary rigidity condition for uniquely recovering the global sensor coordinates. In particular, we present a method for efficiently testing rigidity, and a proposal for augmenting the partitioned network to enforce rigidity. A recently proposed semidefinite relaxation of global registration is used for registering the cliques. We present simulation results on random and structured sensor networks to demonstrate that the proposed method compares favourably with state-of-the-art methods in terms of run-time, accuracy, and scalability.
Neural network approaches to dynamic collision-free trajectory generation.
Yang, S X; Meng, M
2001-01-01
In this paper, dynamic collision-free trajectory generation in a nonstationary environment is studied using biologically inspired neural network approaches. The proposed neural network is topologically organized, where the dynamics of each neuron is characterized by a shunting equation or an additive equation. The state space of the neural network can be either the Cartesian workspace or the joint space of multi-joint robot manipulators. There are only local lateral connections among neurons. The real-time optimal trajectory is generated through the dynamic activity landscape of the neural network without explicitly searching over the free space nor the collision paths, without explicitly optimizing any global cost functions, without any prior knowledge of the dynamic environment, and without any learning procedures. Therefore the model algorithm is computationally efficient. The stability of the neural network system is guaranteed by the existence of a Lyapunov function candidate. In addition, this model is not very sensitive to the model parameters. Several model variations are presented and the differences are discussed. As examples, the proposed models are applied to generate collision-free trajectories for a mobile robot to solve a maze-type of problem, to avoid concave U-shaped obstacles, to track a moving target and at the same to avoid varying obstacles, and to generate a trajectory for a two-link planar robot with two targets. The effectiveness and efficiency of the proposed approaches are demonstrated through simulation and comparison studies.
Examining Food Risk in the Large using a Complex, Networked System-of-sytems Approach
Energy Technology Data Exchange (ETDEWEB)
Ambrosiano, John [Los Alamos National Laboratory; Newkirk, Ryan [U OF MINNESOTA; Mc Donald, Mark P [VANDERBILT U
2010-12-03
The food production infrastructure is a highly complex system of systems. Characterizing the risks of intentional contamination in multi-ingredient manufactured foods is extremely challenging because the risks depend on the vulnerabilities of food processing facilities and on the intricacies of the supply-distribution networks that link them. A pure engineering approach to modeling the system is impractical because of the overall system complexity and paucity of data. A methodology is needed to assess food contamination risk 'in the large', based on current, high-level information about manufacturing facilities, corrunodities and markets, that will indicate which food categories are most at risk of intentional contamination and warrant deeper analysis. The approach begins by decomposing the system for producing a multi-ingredient food into instances of two subsystem archetypes: (1) the relevant manufacturing and processing facilities, and (2) the networked corrunodity flows that link them to each other and consumers. Ingredient manufacturing subsystems are modeled as generic systems dynamics models with distributions of key parameters that span the configurations of real facilities. Networks representing the distribution systems are synthesized from general information about food corrunodities. This is done in a series of steps. First, probability networks representing the aggregated flows of food from manufacturers to wholesalers, retailers, other manufacturers, and direct consumers are inferred from high-level approximate information. This is followed by disaggregation of the general flows into flows connecting 'large' and 'small' categories of manufacturers, wholesalers, retailers, and consumers. Optimization methods are then used to determine the most likely network flows consistent with given data. Vulnerability can be assessed for a potential contamination point using a modified CARVER + Shock model. Once the facility and
Multiple neural network approaches to clinical expert systems
Stubbs, Derek F.
1990-08-01
We briefly review the concept of computer aided medical diagnosis and more extensively review the the existing literature on neural network applications in the field. Neural networks can function as simple expert systems for diagnosis or prognosis. Using a public database we develop a neural network for the diagnosis of a major presenting symptom while discussing the development process and possible approaches. MEDICAL EXPERTS SYSTEMS COMPUTER AIDED DIAGNOSIS Biomedicine is an incredibly diverse and multidisciplinary field and it is not surprising that neural networks with their many applications are finding more and more applications in the highly non-linear field of biomedicine. I want to concentrate on neural networks as medical expert systems for clinical diagnosis or prognosis. Expert Systems started out as a set of computerized " ifthen" rules. Everything was reduced to boolean logic and the promised land of computer experts was said to be in sight. It never came. Why? First the computer code explodes as the number of " ifs" increases. All the " ifs" have to interact. Second experts are not very good at reducing expertise to language. It turns out that experts recognize patterns and have non-verbal left-brain intuition decision processes. Third learning by example rather than learning by rule is the way natural brains works and making computers work by rule-learning is hideously labor intensive. Neural networks can learn from example. They learn the results
A novel function prediction approach using protein overlap networks.
Liang, Shide; Zheng, Dandan; Standley, Daron M; Guo, Huarong; Zhang, Chi
2013-07-17
Construction of a reliable network remains the bottleneck for network-based protein function prediction. We built an artificial network model called protein overlap network (PON) for the entire genome of yeast, fly, worm, and human, respectively. Each node of the network represents a protein, and two proteins are connected if they share a domain according to InterPro database. The function of a protein can be predicted by counting the occurrence frequency of GO (gene ontology) terms associated with domains of direct neighbors. The average success rate and coverage were 34.3% and 43.9%, respectively, for the test genomes, and were increased to 37.9% and 51.3% when a composite PON of the four species was used for the prediction. As a comparison, the success rate was 7.0% in the random control procedure. We also made predictions with GO term annotations of the second layer nodes using the composite network and obtained an impressive success rate (>30%) and coverage (>30%), even for small genomes. Further improvement was achieved by statistical analysis of manually annotated GO terms for each neighboring protein. The PONs are composed of dense modules accompanied by a few long distance connections. Based on the PONs, we developed multiple approaches effective for protein function prediction.
Numerical approach to plug-flow activated sludge reactor kinetics.
Muslu, Y
2000-07-01
In this study, general relationships applicable to a wide variety of microbial-mediated treatment processes are developed using a number of continuous-flow stirred tank reactors-in-series. Simultaneous equations resulting from mass balance on substrate and biomass are solved numerically taking the longitudinal biomass gradient into account. A relationship between substrate and biomass concentrations is developed as a function of hydraulic residence time. Monod kinetics and mathematical models of reactors-in-series are used to represent the actual conditions resulting from varying degrees of axial dispersion and wastewater quality. Dimensionless quantities are used to reduce the number of parameters to be taken into account. Computer techniques are applied to express the results generally. By means of the numerical approach, the variation of the ratio between the hydraulic residence time of a given reactor and that of an equivalent plug-flow tank with the same inlet and outlet conditions is investigated. Results are expressed in the form of graphs to characterize the plug-flow activated sludge systems and to provide a basis of design. Experimental data reported in the literature are also evaluated to demonstrate the cases where the existing classical solution to the problem differs from the actual results obtained from the computer program and the design graphs given.
Stability and dynamical properties of material flow systems on random networks
Anand, K.; Galla, T.
2009-04-01
The theory of complex networks and of disordered systems is used to study the stability and dynamical properties of a simple model of material flow networks defined on random graphs. In particular we address instabilities that are characteristic of flow networks in economic, ecological and biological systems. Based on results from random matrix theory, we work out the phase diagram of such systems defined on extensively connected random graphs, and study in detail how the choice of control policies and the network structure affects stability. We also present results for more complex topologies of the underlying graph, focussing on finitely connected Erdös-Réyni graphs, Small-World Networks and Barabási-Albert scale-free networks. Results indicate that variability of input-output matrix elements, and random structures of the underlying graph tend to make the system less stable, while fast price dynamics or strong responsiveness to stock accumulation promote stability.
Dynamic aggregation of traffic flows in SDN Applied to backhaul networks
DEFF Research Database (Denmark)
Kentis, Angelos Mimidis; Caba, Cosmin Marius; Soler, José
2016-01-01
A challenge in the adoption of the OpenFlow (OF)-based SDN paradigm is related to the limited number of OF rules supported by the network devices. The technology used to implement the OF rules is TCAM, which is expensive and power demanding. Due to this, the network devices are either very costly...
Dynamic logics of networks: Information flow and the spread of opinion
Christoff, Z.L.
2016-01-01
This thesis uses logical tools to investigate a number of basic features of social networks and their evolution over time, including flow of information and spread of opinions. Part I contains the preliminaries, including an introduction to the basic phenomena in social networks that call for a
Report of the Third Workshop on the Usage of NetFlow/IPFIX in Network Management
Drago, Idilio; Sadre, R.; Pras, Aiko
2011-01-01
The Network Management Research Group (NMRG) organized in 2010 the Third Workshop on the Usage of NetFlow/IPFIX in Network Management, as part of the 78th IETF Meeting in Maastricht. Yearly organized since 2007, the workshop is an opportunity for people from both academia and industry to discuss the
A Potential Approach for Low Flow Selection in Water Resource Supply and Management
Ying Ouyang
2012-01-01
Low flow selections are essential to water resource management, water supply planning, and watershed ecosystem restoration. In this study, a new approach, namely the frequent-low (FL) approach (or frequent-low index), was developed based on the minimum frequent-low flow or level used in minimum flows and/or levels program in northeast Florida, USA. This FL approach was...
Behavioral modeling approach for optical communication network design
Vuorinen, Kimmo; Jacquemod, Gilles; Gaffiot, Frederic; Seassal, Christian
1997-12-01
An optical communication networks can be divided in two levels: communication level, which defines the protocols, the control and the management of the networks and physical level formed by photonic and electronic components in order to transmit and receive the data between different nodes of the network. Traditionally, these two levels are considered separately in the optical communication network design process. This can lead to an erroneous or non-ideal networks implementation, due to the fact that the communication and physical levels are not independent. For example, in WDM communication network the maximum achievable data rate is limited not only by the networks protocol, but depends also on the implementation of the physical level: tuning delay of the optical multiplexers. Also the lack of the possibilities for co-verification of the communication and the physical levels together could lead to misinterpretations between the designers of the different levels and thus induce design faults. Since the prototyping is extremely expensive and time consuming, an integrated simulation of both communication and physical levels is necessary, at least in some extend. In this paper, a behavioral modeling approach that allows a co- simulation of the communication and the physical levels is presented. It is based on the use of a VHDL-AMS-like hardware description language, dedicated to electronic system modeling, but also suitable for modeling and simulation of non- electronic and mixed-domain systems. The behavioral models for photonic and electronic components, as well as the software are integrated in a unique simulator in order to co-simulate the communication (control) and the physical level (data path) of a WDM optical communication network.
Mapping debris flow susceptibility using analytical network process ...
Indian Academy of Sciences (India)
Home; Journals; Journal of Earth System Science; Volume 126; Issue 8. Mapping debris flow ... Rapid debris flows, a mixture of unconsolidated sediments and water travelling at speeds >10 m/s are the most destructive water related mass movements that affect hill and mountain regions. The predisposing factors setting the ...
Field-effect flow control for microfabricated fluidic networks
Schasfoort, Richardus B.M.; Schlautmann, Stefan; Hendrikse, J.; van den Berg, Albert
1999-01-01
The magnitude and direction of the electro-osmotic flow (EOF) inside a microfabricated fluid channel can be controlled by a perpendicular electric field of 1.5 megavolts per centimeter generated by a voltage of only 50 volts. A microdevice called a "flowFET," with functionality comparable to that of
Rate-Based Active Queue Management for TCP Flows over Wired and Wireless Networks
Jun Wang; Min Song
2007-01-01
Current active queue management (AQM) and TCP protocol are designed and tuned to work well on wired networks where packet loss is mainly due to network congestion. In wireless networks, however, communication links suffer from significant transmission bit errors and handoff failures. As a result, the performance of TCP flows is significantly degraded. To mitigate this problem, we analyze existing AQM schemes and propose a rate-based exponential AQM (REAQM) scheme. The proposed REAQM scheme u...
DEFF Research Database (Denmark)
Huang, Shaojun; Wu, Qiuwei; Zhao, Haoran
2016-01-01
Renewable energies are increasingly integrated in electric distribution networks and will cause severe overvoltage issues. Smart grid technologies make it possible to use coordinated control to mitigate the overvoltage issues and the optimal power flow (OPF) method is proven to be efficient...... in the applications such as curtailment management and reactive power control. Nonconvex nature of the OPF makes it difficult to solve and convex relaxation is a promising method to solve the OPF very efficiently. This paper investigates the geometry of the power flows and the convex-relaxed power flows when high...... penetration level of renewables is present in the distribution networks. The geometry study helps understand the fundamental nature of the OPF and its convex-relaxed problem, such as the second-order cone programming (SOCP) problem. A case study based on a three-node system is used to illustrate the geometry...
An improved approach for flow-based cloud point extraction.
Frizzarin, Rejane M; Rocha, Fábio R P
2014-04-11
Novel strategies are proposed to circumvent the main drawbacks of flow-based cloud point extraction (CPE). The surfactant-rich phase (SRP) was directly retained into the optical path of the spectrophotometric cell, thus avoiding its dilution previously to the measurement and yielding higher sensitivity. Solenoid micro-pumps were exploited to improve mixing by the pulsed flow and also to modulate the flow-rate for retention and removal of the SRP, thus avoiding the elution step, often carried out with organic solvents. The heat released and the increase of the salt concentration provided by an on-line neutralization reaction were exploited to induce the cloud point without an external heating device. These innovations were demonstrated by the spectrophotometric determination of iron, yielding a linear response from 10 to 200 μg L(-1) with a coefficient of variation of 2.3% (n=7). Detection limit and sampling rate were estimated at 5 μg L(-1) (95% confidence level) and 26 samples per hour, respectively. The enrichment factor was 8.9 and the procedure consumed only 6 μg of TAN and 390 μg of Triton X-114 per determination. At the 95% confidence level, the results obtained for freshwater samples agreed with the reference procedure and those obtained for digests of bovine muscle, rice flour, brown bread and tort lobster agreed with the certified reference values. The proposed procedure thus shows advantages in relation to previously proposed approaches for flow-based CPE, being a fast and environmental friendly alternative for on-line separation and pre-concentration. Copyright © 2014 Elsevier B.V. All rights reserved.
Flow-Based Network Management: A Report from the IRTF NMRG Workshop
de Oliveira Schmidt, R.; Sadre, R.; Hendriks, Luuk
This is the report on the Workshop on Flow-Based Network Management, held within the 37th IRTF NMRG meeting, during IETF 93, on 24th July 2015, in Prague, Czech Republic. Following the tradition of the IRTF NMRG, the workshop focused on technologies, developments, and challenges of using flow-level
Schallhorn, Paul; Majumdar, Alok
2012-01-01
This paper describes a finite volume based numerical algorithm that allows multi-dimensional computation of fluid flow within a system level network flow analysis. There are several thermo-fluid engineering problems where higher fidelity solutions are needed that are not within the capacity of system level codes. The proposed algorithm will allow NASA's Generalized Fluid System Simulation Program (GFSSP) to perform multi-dimensional flow calculation within the framework of GFSSP s typical system level flow network consisting of fluid nodes and branches. The paper presents several classical two-dimensional fluid dynamics problems that have been solved by GFSSP's multi-dimensional flow solver. The numerical solutions are compared with the analytical and benchmark solution of Poiseulle, Couette and flow in a driven cavity.
Applications of flow-networks to opinion-dynamics
Tupikina, Liubov; Kurths, Jürgen
2015-04-01
Networks were successfully applied to describe complex systems, such as brain, climate, processes in society. Recently a socio-physical problem of opinion-dynamics was studied using network techniques. We present the toy-model of opinion-formation based on the physical model of advection-diffusion. We consider spreading of the opinion on the fixed subject, assuming that opinion on society is binary: if person has opinion then the state of the node in the society-network equals 1, if the person doesn't have opinion state of the node equals 0. Opinion can be spread from one person to another if they know each other, or in the network-terminology, if the nodes are connected. We include into the system governed by advection-diffusion equation the external field to model such effects as for instance influence from media. The assumptions for our model can be formulated as the following: 1.the node-states are influenced by the network structure in such a way, that opinion can be spread only between adjacent nodes (the advective term of the opinion-dynamics), 2.the network evolution can have two scenarios: -network topology is not changing with time; -additional links can appear or disappear each time-step with fixed probability which requires adaptive networks properties. Considering these assumptions for our system we obtain the system of equations describing our model-dynamics which corresponds well to other socio-physics models, for instance, the model of the social cohesion and the famous voter-model. We investigate the behavior of the suggested model studying "waiting time" of the system, time to get to the stable state, stability of the model regimes for different values of model parameters and network topology.
Variations in the Flow Approach to CFCLP-TC for Multiobjective Supply Chain Design
Directory of Open Access Journals (Sweden)
Minor P. Hertwin
2014-01-01
Full Text Available We review the problem for the design of supply chains called Capacitated Fixed Cost Facility Location Problem with Transportation Choices (CFCLP-TC. The problem is based on a production network of two echelons with multiple plants, a set of potential distribution centers, and customers. The problem is formulated as an optimization model with two objective functions based on time and cost. This paper proposes three changes to the original model to compare the sets of efficient solutions and the computational time required to obtain them. The main contribution of this paper is to extend the existing literature by incorporating approaches for the supply of product to customers through multiple sources, the direct flow between plants and customers, without this necessarily implying removing the distribution centers, and the product flow between distribution centers. From these approaches, we generate mathematical programming models and propose to solve through the epsilon-constraint approach for generating Pareto fronts and thus compare each of these approaches with the original model. The models are implemented in GAMS and solved with CPLEX.
Tackling OpenFlow power hog in core networks with KeyFlow
DEFF Research Database (Denmark)
Saldaña Cercos, Silvia; Oliveira, R. E.; Vitoi, R.
2014-01-01
A comprehensive data plane power consumption analysis of an OpenFlow 1.0 switch broken down into its design modules is presented, and KeyFlow as an alternative solution is proposed, since it eliminates a flow table lookup by reducing 53.7% of the overall power consumption.......A comprehensive data plane power consumption analysis of an OpenFlow 1.0 switch broken down into its design modules is presented, and KeyFlow as an alternative solution is proposed, since it eliminates a flow table lookup by reducing 53.7% of the overall power consumption....
Zubek, Julian; Denkiewicz, Michał; Barański, Juliusz; Wróblewski, Przemysław; Rączaszek-Leonardi, Joanna; Plewczynski, Dariusz
2017-01-01
This paper explores how information flow properties of a network affect the formation of categories shared between individuals, who are communicating through that network. Our work is based on the established multi-agent model of the emergence of linguistic categories grounded in external environment. We study how network information propagation efficiency and the direction of information flow affect categorization by performing simulations with idealized network topologies optimizing certain network centrality measures. We measure dynamic social adaptation when either network topology or environment is subject to change during the experiment, and the system has to adapt to new conditions. We find that both decentralized network topology efficient in information propagation and the presence of central authority (information flow from the center to peripheries) are beneficial for the formation of global agreement between agents. Systems with central authority cope well with network topology change, but are less robust in the case of environment change. These findings help to understand which network properties affect processes of social adaptation. They are important to inform the debate on the advantages and disadvantages of centralized systems.
Signal propagation in cortical networks: a digital signal processing approach.
Rodrigues, Francisco Aparecido; da Fontoura Costa, Luciano
2009-01-01
This work reports a digital signal processing approach to representing and modeling transmission and combination of signals in cortical networks. The signal dynamics is modeled in terms of diffusion, which allows the information processing undergone between any pair of nodes to be fully characterized in terms of a finite impulse response (FIR) filter. Diffusion without and with time decay are investigated. All filters underlying the cat and macaque cortical organization are found to be of low-pass nature, allowing the cortical signal processing to be summarized in terms of the respective cutoff frequencies (a high cutoff frequency meaning little alteration of signals through their intermixing). Several findings are reported and discussed, including the fact that the incorporation of temporal activity decay tends to provide more diversified cutoff frequencies. Different filtering intensity is observed for each community in those networks. In addition, the brain regions involved in object recognition tend to present the highest cutoff frequencies for both the cat and macaque networks.
Software-Defined Networking Using OpenFlow: Protocols, Applications and Architectural Design Choices
Directory of Open Access Journals (Sweden)
Wolfgang Braun
2014-05-01
Full Text Available We explain the notion of software-defined networking (SDN, whose southbound interface may be implemented by the OpenFlow protocol. We describe the operation of OpenFlow and summarize the features of specification versions 1.0–1.4. We give an overview of existing SDN-based applications grouped by topic areas. Finally, we point out architectural design choices for SDN using OpenFlow and discuss their performance implications.
Intelligent networks recent approaches and applications in medical systems
Ahamed, Syed V
2013-01-01
This textbook offers an insightful study of the intelligent Internet-driven revolutionary and fundamental forces at work in society. Readers will have access to tools and techniques to mentor and monitor these forces rather than be driven by changes in Internet technology and flow of money. These submerged social and human forces form a powerful synergistic foursome web of (a) processor technology, (b) evolving wireless networks of the next generation, (c) the intelligent Internet, and (d) the motivation that drives individuals and corporations. In unison, the technological forces can tear
Network Intrusion Detection System – A Novel Approach
Directory of Open Access Journals (Sweden)
Krish Pillai
2013-08-01
Full Text Available Network intrusion starts off with a series of unsuccessful breakin attempts and results eventually with the permanent or transient failure of an authentication or authorization system. Due to the current complexity of authentication systems, clandestine attempts at intrusion generally take considerable time before the system gets compromised or damaging change is affected to the system giving administrators a window of opportunity to proactively detect and prevent intrusion. Therefore maintaining a high level of sensitivity to abnormal access patterns is a very effective way of preventing possible break-ins. Under normal circumstances, gross errors on the part of the user can cause authentication and authorization failures on all systems. A normal distribution of failed attempts should be tolerated while abnormal attempts should be recognized as such and flagged. But one cannot manage what one cannot measure. This paper proposes a method that can efficiently quantify the behaviour of users on a network so that transient changes in usage can be detected, categorized based on severity, and closely investigated for possible intrusion. The author proposes the identification of patterns in protocol usage within a network to categorize it for surveillance. Statistical anomaly detection, under which category this approach falls, generally uses simple statistical tests such as mean and standard deviation to detect behavioural changes. The author proposes a novel approach using spectral density as opposed to using time domain data, allowing a clear separation or access patterns based on periodicity. Once a spectral profile has been identified for network, deviations from this profile can be used as an indication of a destabilized or compromised network. Spectral analysis of access patterns is done using the Fast Fourier Transform (FFT, which can be computed in Θ(N log N operations. The paper justifies the use of this approach and presents preliminary
Flow version of statistical neurodynamics for oscillator neural networks
Uchiyama, Satoki
2012-04-01
We consider a neural network of Stuart-Landau oscillators as an associative memory. This oscillator network with N elements is a system of an N-dimensional differential equation, works as an attractor neural network, and is expected to have no Lyapunov functions. Therefore, the technique of equilibrium statistical physics is not applicable to the study of this system in the thermodynamic limit. However, the simplicity of this system allows us to extend statistical neurodynamics [S. Amari, K. Maginu, Neural Netw. 1 (1988) 63-73], which was originally developed to analyse the discrete time evolution of the Hopfield model, into the version for continuous time evolution. We have developed and attempted to apply this method in the analysis of the phase transition of our model network.
A compositional model for confluent dynamic data-flow networks
F.S. de Boer (Frank); M.M. Bonsangue (Marcello)
2000-01-01
textabstractWe introduce a state-based language for programming dynamically changing networks which consist of processes that communicate asynchronously. For this language we introduce an operational semantics and a notion of observable which includes both partial
Coauthorship networks: A directed network approach considering the order and number of coauthors
Kim, Jinseok
2015-01-01
In many scientific fields, the order of coauthors on a paper conveys information about each individual's contribution to a piece of joint work. We argue that in prior network analyses of coauthorship networks, the information on ordering has been insufficiently considered because ties between authors are typically symmetrized. This is basically the same as assuming that each co-author has contributed equally to a paper. We introduce a solution to this problem by adopting a coauthorship credit allocation model proposed by Kim and Diesner (2014), which in its core conceptualizes co-authoring as a directed, weighted, and self-looped network. We test and validate our application of the adopted framework based on a sample data of 861 authors who have published in the journal Psychometrika. Results suggest that this novel sociometric approach can complement traditional measures based on undirected networks and expand insights into coauthoring patterns such as the hierarchy of collaboration among scholars. As anothe...
TCP with source traffic shaping (TCP-STS): an approach for network congestion reduction
Elaywe, Ali H.; Kamal, Ahmed E.
2002-07-01
The Transmission Control Protocol (TCP), provides flow control functions which are based on the window mechanism. Packet losses are detected by various mechanisms, such as timeouts and duplicate acknowledgements, and are then recovered from using different techniques. A problem that arises with the use of window based mechanisms is that the availability of a large number of credits at the source may cause a source to flood the network with back-to-back packets, which may drive the network into congestion, especially if multiple sources become active at the same time. In this paper we propose a new approach for congestion reduction. The approach works by shaping the traffic at the TCP source, such that the basic TCP flow control mechanism is still preserved, but the packet transmissions are spaced in time in order to prevent a sudden surge of traffic from overflowing the routers' buffers. Simulation results show that this technique can result in an improved network performance, in terms of reduced mean delay, delay variance, and packet dropping ratio.
Methodological Approaches to Locating Outlets of the Franchise Retail Network
Directory of Open Access Journals (Sweden)
Grygorenko Tetyana M.
2016-08-01
Full Text Available Methodical approaches to selecting strategic areas of managing the future location of franchise retail network outlets are presented. The main stages in the assessment of strategic areas of managing the future location of franchise retail network outlets have been determined and the evaluation criteria have been suggested. Since such selection requires consideration of a variety of indicators and directions of the assessment, the author proposes a scale of evaluation, which allows generalizing and organizing the research data and calculations of the previous stages of the analysis. The most important criteria and sequence of the selection of the potential franchisees for the franchise retail network have been identified, the technique for their evaluation has been proposed. The use of the suggested methodological approaches will allow the franchiser making sound decisions on the selection of potential target markets, minimizing expenditures of time and efforts on the selection of franchisees and hence optimizing the process of development of the franchise retail network, which will contribute to the formation of its structure.
Yan, Ying; Zhang, Shen; Tang, Jinjun; Wang, Xiaofei
2017-07-01
Discovering dynamic characteristics in traffic flow is the significant step to design effective traffic managing and controlling strategy for relieving traffic congestion in urban cities. A new method based on complex network theory is proposed to study multivariate traffic flow time series. The data were collected from loop detectors on freeway during a year. In order to construct complex network from original traffic flow, a weighted Froenius norm is adopt to estimate similarity between multivariate time series, and Principal Component Analysis is implemented to determine the weights. We discuss how to select optimal critical threshold for networks at different hour in term of cumulative probability distribution of degree. Furthermore, two statistical properties of networks: normalized network structure entropy and cumulative probability of degree, are utilized to explore hourly variation in traffic flow. The results demonstrate these two statistical quantities express similar pattern to traffic flow parameters with morning and evening peak hours. Accordingly, we detect three traffic states: trough, peak and transitional hours, according to the correlation between two aforementioned properties. The classifying results of states can actually represent hourly fluctuation in traffic flow by analyzing annual average hourly values of traffic volume, occupancy and speed in corresponding hours.
Toward a Behavioral Approach to Privacy for Online Social Networks
Banks, Lerone D.; Wu, S. Felix
We examine the correlation between user interactions and self reported information revelation preferences for users of the popular Online Social Network (OSN), Facebook. Our primary goal is to explore the use of indicators of tie strength to inform localized, per-user privacy preferences for users and their ties within OSNs. We examine the limitations of such an approach and discuss future plans to incorporate this approach into the development of an automated system for helping users define privacy policy. As part of future work, we discuss how to define/expand policy to the entire social network. We also present additional collected data similar to other studies such as perceived tie strength and information revelation preferences for OSN users.
MACD-Based Motion Detection Approach in Heterogeneous Networks
Directory of Open Access Journals (Sweden)
Chen Yung-Mu
2008-01-01
Full Text Available Abstract Optimizing the balance between handoff quality and power consumption is a great challenge for seamless mobile communications in wireless networks. Traditional proactive schemes continuously monitor available access networks and exercise handoff. Although such schemes achieve good handoff quality, they consume much power because all interfaces must remain on all the time. To save power, the reactive schemes use fixed RSS thresholds to determine when to search for a new available access network. However, since they do not consider user motion, these approaches require that all interfaces be turned on even when a user is stationary, and they tend initiate excessive unnecessary handoffs. To address this problem, this research presents a novel motion-aware scheme called network discovery with motion detection (NDMD to improve handoff quality and minimize power consumption. The NDMD first applies a moving average convergence divergence (MACD scheme to analyze received signal strength (RSS samples of the current active interface. These results are then used to estimate user's motion. The proposed NDMD scheme adds very little computing overhead to a mobile terminal (MT and can be easily incorporated into existing schemes. The simulation results in this study showed that NDMD can quickly track user motion state without a positioning system and perform network discovery rapidly enough to achieve a much lower handoff-dropping rate with less power consumption.
MACD-Based Motion Detection Approach in Heterogeneous Networks
Directory of Open Access Journals (Sweden)
Chih-Hung Hsu
2008-09-01
Full Text Available Optimizing the balance between handoff quality and power consumption is a great challenge for seamless mobile communications in wireless networks. Traditional proactive schemes continuously monitor available access networks and exercise handoff. Although such schemes achieve good handoff quality, they consume much power because all interfaces must remain on all the time. To save power, the reactive schemes use fixed RSS thresholds to determine when to search for a new available access network. However, since they do not consider user motion, these approaches require that all interfaces be turned on even when a user is stationary, and they tend initiate excessive unnecessary handoffs. To address this problem, this research presents a novel motion-aware scheme called network discovery with motion detection (NDMD to improve handoff quality and minimize power consumption. The NDMD first applies a moving average convergence divergence (MACD scheme to analyze received signal strength (RSS samples of the current active interface. These results are then used to estimate user's motion. The proposed NDMD scheme adds very little computing overhead to a mobile terminal (MT and can be easily incorporated into existing schemes. The simulation results in this study showed that NDMD can quickly track user motion state without a positioning system and perform network discovery rapidly enough to achieve a much lower handoff-dropping rate with less power consumption.
Yang, Hui; Cheng, Lei; Yuan, Jian; Zhang, Jie; Zhao, Yongli; Lee, Young
2015-06-01
With the rapid growth of data center services, the elastic optical network is a very promising networking architecture to interconnect data centers because it can elastically allocate spectrum tailored for various bandwidth requirements. In case of a link failure, to ensure a high-level quality of service (QoS) for user request after the failure becomes a research focus. In light of it, in this paper, we propose and experimentally demonstrate multipath protection for data center services in OpenFlow-based software defined elastic optical network testbed aiming at improving network reliability. We first propose an OpenFlow-based software defined elastic optical network architecture for data center service protection. Then, based on the proposed architecture, multipath protection scheme is figured based on the importance level of the service. To implement the proposed scheme in the architecture, OpenFlow protocol is extended to support multipath protection in elastic optical network. The performance of our proposed multipath protection scheme is evaluated by means of experiment on our OpenFlow-based testbed. The feasibility of our proposed scheme is also demonstrated in software defined elastic optical networks.
Heterogeneity of cerebral blood flow: a fractal approach
Energy Technology Data Exchange (ETDEWEB)
Kuikka, J.T.; Hartikainen, P. [Department of Clinical Physiology, Kuopioo Univ. Hospital (Finland); Department of Neurology, Kuopio Univ. Hosspital (Finland); Niuvanniemi Hospital, Kuopio (Finland)
2000-07-01
Aim: We demonstrate the heterogeneity of regional cerebral blood flow using a fractal approach and single-photon emission computed tomography (SPECT). Method: Tc-99m-labelled ethylcysteine dimer was injected intravenously in 10 healthy controls and in 10 patients with dementia of frontal lobe type. The head was imaged with a gamma camera and transaxial, sagittal and coronal slices were reconstructed. Two hundred fifty-six symmetrical regions of interest (ROIs) were drawn onto each hemisphere of functioning brain matter. Fractal analysis was used to examine the spatial heterogeneity of blood flow as a function of the number of ROIs. Results: Relative dispersion (=coefficient of variation of the regional flows) was fractal-like in healthy subjects and could be characterized by a fractal dimension of 1.17{+-}0.05 (mean{+-}SD) for the left hemisphere and 1.15{+-}0.04 for the right hemisphere, respectively. The fractal dimension of 1.0 reflects completely homogeneous blood flow and 1.5 indicates a random blood flow distribution. Patients with dementia of frontal lobe type had a significantly lower fractal dimension of 1.04{+-}0.03 than in healthy controls. (orig.) [German] Ziel: Unter Einsatz einer fraktalen Annaeherung und SPECT wird die Heterogenitaet der regionalen Hirndruchblutung demonstriert. Methode: Tc-99m-ECD wurde nach intravenoeser Injektion bei zehn Gesunden sowie bei zehn Patienten mit Demenz vom Frontallappen-Typ eingesetzt. Aus dem SPECT-Umlauf wurden transaxiale, sagittale und koronare Schnitte rekonstruiert. 265 symmetrische Regions of Interest wurden im Gebiet der funktionellen grauen Substanz fuer jede Hemisphaere markiert. Die fraktale Analyse wurde eingesetzt zur Bestimmung der raeumlichen Heterogenitaet der Hirndurchblutung als Funktion der ROI-Anzahl. Ergebnisse: Die relative Streuung (Variationskoeffizient der regionalen Durchblutung) war bei Gesunden fraktalaehnlich geordnet und konnte durch eine Fraktaldimension von 1,17{+-}0,05 (Mittelwert
An activities-based approach to network management : An explorative study
Manser, Kristina; Hillebrand, Bas; Klein Woolthuis, R.J.A.; Ziggers, Gerrit Willem; Driessen, Paul H.; Bloemer, Josée
2016-01-01
Over the last few decades, the industrial marketing literature and the business network literature have promoted a holistic approach to marketing and provided a framework for understanding interorganizational networks. However, our understanding of how interorganizational networks govern themselves
An activities-based approach to network management: An explorative study
Manser, K.; Hillebrand, B.; Klein Woolthuis, R.J.A.; Ziggers, G.W.; Driessen, P.H.; Bloemer, J.M.M.; Klein Woolthuis, R.
2016-01-01
Over the last few decades, the industrial marketing literature and the business network literature have promoted a holistic approach to marketing and provided a framework for understanding interorganizational networks. However, our understanding of how interorganizational networks govern themselves
Whitbred, Robert; Fonti, Fabio; Steglich, Christian; Contractor, Noshir
Structuration theory (ST) and network analysis are promising approaches for studying the emergence of communication networks. We offer a model that integrates the conceptual richness of structuration with the precision of relevant concepts and mechanisms offered from communication network research.
Efficient path routing strategy for flows with multiple priorities on scale-free networks.
Zhang, Xi; Zhou, Zhili; Cheng, Dong
2017-01-01
In real networks, traffic flows are different in amount as well as their priorities. However, the latter priority has rarely been examined in routing strategy studies. In this paper, a novel routing algorithm, which is based on the efficient path routing strategy (EP), is proposed to overcome network congestion problem caused by large amount of traffic flows with different priorities. In this scheme, traffic flows with different priorities are transmitted through different routing paths, which are based on EP with different parameters. Simulation results show that the traffic capacity for flows with different priorities can be enhanced by 12% with this method, compared with EP. In addition, the new method contributes to more balanced network traffic load distribution and reduces average transmission jump and delay of packets.
Directory of Open Access Journals (Sweden)
Jeng-Fung Chen
2014-11-01
Full Text Available The accuracy of reservoir flow forecasting has the most significant influence on the assurance of stability and annual operations of hydro-constructions. For instance, accurate forecasting on the ebb and flow of Vietnam’s Hoabinh Reservoir can aid in the preparation and prevention of lowland flooding and drought, as well as regulating electric energy. This raises the need to propose a model that accurately forecasts the incoming flow of the Hoabinh Reservoir. In this study, a solution to this problem based on neural network with the Cuckoo Search (CS algorithm is presented. In particular, we used hydrographic data and predicted total incoming flows of the Hoabinh Reservoir over a period of 10 days. The Cuckoo Search algorithm was utilized to train the feedforward neural network (FNN for prediction. The algorithm optimized the weights between layers and biases of the neuron network. Different forecasting models for the three scenarios were developed. The constructed models have shown high forecasting performance based on the performance indices calculated. These results were also compared with those obtained from the neural networks trained by the particle swarm optimization (PSO and back-propagation (BP, indicating that the proposed approach performed more effectively. Based on the experimental results, the scenario using the rainfall and the flow as input yielded the highest forecasting accuracy when compared with other scenarios. The performance criteria RMSE, MAPE, and R obtained by the CS-FNN in this scenario were calculated as 48.7161, 0.067268 and 0.8965, respectively. These results were highly correlated to actual values. It is expected that this work may be useful for hydrographic forecasting.
Shared Leadership In Work Teams: A Social Network Approach
JUAN CARLOS PASTOR; MARGARITA MAYO
2002-01-01
(WP10/02 Clave pdf) In the past few years, the concept of leadership has shifted from the solitary leader to the team as a potential source of leadership. This shift from a single person to a "shared leadership" model requires new concepts and methods to capture the nature and structure of leadership by teams (Yukl, 1998). In this chapter, we argue that a social network approach helps to provide the conceptual framework and methodological tools to support a shared leadership perspective.
Evolutionary Trends of Developer Coordination: A Network Approach
Joblin, Mitchell; Apel, Sven; Mauerer, Wolfgang
2015-01-01
Software evolution is a fundamental process that transcends the realm of technical artifacts and permeates the entire organizational structure of a software project. By means of a longitudinal empirical study of 18 large open-source projects, we examine and discuss the evolutionary principles that govern the coordination of developers. By applying a network-analytic approach, we found that the implicit and self-organizing structure of developer coordination is ubiquitously described by non-ra...
Analytical approach to continuous and intermittent bottleneck flows
DEFF Research Database (Denmark)
Helbing, Dirk; Johansson, Anders; Mathiesen, Joachim
2006-01-01
Many-particle-inspired theory, Continuous and Intermittent Bottleneck Flows Udgivelsesdato: Oct. 20......Many-particle-inspired theory, Continuous and Intermittent Bottleneck Flows Udgivelsesdato: Oct. 20...
Artificial neural network based approach to EEG signal simulation.
Tomasevic, Nikola M; Neskovic, Aleksandar M; Neskovic, Natasa J
2012-06-01
In this paper a new approach to the electroencephalogram (EEG) signal simulation based on the artificial neural networks (ANN) is proposed. The aim was to simulate the spontaneous human EEG background activity based solely on the experimentally acquired EEG data. Therefore, an EEG measurement campaign was conducted on a healthy awake adult in order to obtain an adequate ANN training data set. As demonstration of the performance of the ANN based approach, comparisons were made against autoregressive moving average (ARMA) filtering based method. Comprehensive quantitative and qualitative statistical analysis showed clearly that the EEG process obtained by the proposed method was in satisfactory agreement with the one obtained by measurements.
Hierarchical brain networks active in approach and avoidance goal pursuit
Directory of Open Access Journals (Sweden)
Jeffrey Martin Spielberg
2013-06-01
Full Text Available Effective approach/avoidance goal pursuit is critical for attaining long-term health and well-being. Research on the neural correlates of key goal pursuit processes (e.g., motivation has long been of interest, with lateralization in prefrontal cortex being a particularly fruitful target of investigation. However, this literature has often been limited by a lack of spatial specificity and has not delineated the precise aspects of approach/avoidance motivation involved. Additionally, the relationships among brain regions (i.e., network connectivity vital to goal pursuit remain largely unexplored. Specificity in location, process, and network relationship is vital for moving beyond gross characterizations of function and identifying the precise cortical mechanisms involved in motivation. The present paper integrates research using more spatially specific methodologies (e.g., functional magnetic resonance imaging with the rich psychological literature on approach/avoidance to propose an integrative network model that takes advantage of the strengths of each of these literatures.
Risk prediction model: Statistical and artificial neural network approach
Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim
2017-04-01
Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.
A neural network approach to dynamic task assignment of multirobots.
Zhu, Anmin; Yang, Simon X
2006-09-01
In this paper, a neural network approach to task assignment, based on a self-organizing map (SOM), is proposed for a multirobot system in dynamic environments subject to uncertainties. It is capable of dynamically controlling a group of mobile robots to achieve multiple tasks at different locations, so that the desired number of robots will arrive at every target location from arbitrary initial locations. In the proposed approach, the robot motion planning is integrated with the task assignment, thus the robots start to move once the overall task is given. The robot navigation can be dynamically adjusted to guarantee that each target location has the desired number of robots, even under uncertainties such as when some robots break down. The proposed approach is capable of dealing with changing environments. The effectiveness and efficiency of the proposed approach are demonstrated by simulation studies.
Predicting commuter flows in spatial networks using a radiation model based on temporal ranges
Ren, Yihui; Ercsey-Ravasz, Mária; Wang, Pu; González, Marta C.; Toroczkai, Zoltán
2014-11-01
Understanding network flows such as commuter traffic in large transportation networks is an ongoing challenge due to the complex nature of the transportation infrastructure and human mobility. Here we show a first-principles based method for traffic prediction using a cost-based generalization of the radiation model for human mobility, coupled with a cost-minimizing algorithm for efficient distribution of the mobility fluxes through the network. Using US census and highway traffic data, we show that traffic can efficiently and accurately be computed from a range-limited, network betweenness type calculation. The model based on travel time costs captures the log-normal distribution of the traffic and attains a high Pearson correlation coefficient (0.75) when compared with real traffic. Because of its principled nature, this method can inform many applications related to human mobility driven flows in spatial networks, ranging from transportation, through urban planning to mitigation of the effects of catastrophic events.
2012-01-24
... Management and Intelligent Network Flow Optimization Operational Concepts; Notice of Public Meeting AGENCY... Traffic and Demand Management (ADTM) and Intelligent Network Flow Optimization (INFLO) operational... transportation network. Issued in Washington, DC, on the 18th day of January 2012. John Augustine, Managing...
A weakly-compressible Cartesian grid approach for hydrodynamic flows
Bigay, P.; Oger, G.; Guilcher, P.-M.; Le Touzé, D.
2017-11-01
The present article aims at proposing an original strategy to solve hydrodynamic flows. In introduction, the motivations for this strategy are developed. It aims at modeling viscous and turbulent flows including complex moving geometries, while avoiding meshing constraints. The proposed approach relies on a weakly-compressible formulation of the Navier-Stokes equations. Unlike most hydrodynamic CFD (Computational Fluid Dynamics) solvers usually based on implicit incompressible formulations, a fully-explicit temporal scheme is used. A purely Cartesian grid is adopted for numerical accuracy and algorithmic simplicity purposes. This characteristic allows an easy use of Adaptive Mesh Refinement (AMR) methods embedded within a massively parallel framework. Geometries are automatically immersed within the Cartesian grid with an AMR compatible treatment. The method proposed uses an Immersed Boundary Method (IBM) adapted to the weakly-compressible formalism and imposed smoothly through a regularization function, which stands as another originality of this work. All these features have been implemented within an in-house solver based on this WCCH (Weakly-Compressible Cartesian Hydrodynamic) method which meets the above requirements whilst allowing the use of high-order (> 3) spatial schemes rarely used in existing hydrodynamic solvers. The details of this WCCH method are presented and validated in this article.
Solving the power flow equations: a monotone operator approach
Energy Technology Data Exchange (ETDEWEB)
Dvijotham, Krishnamurthy [California Inst. of Technology (CalTech), Pasadena, CA (United States); Low, Steven [California Inst. of Technology (CalTech), Pasadena, CA (United States); Chertkov, Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2015-07-21
The AC power flow equations underlie all operational aspects of power systems. They are solved routinely in operational practice using the Newton-Raphson method and its variants. These methods work well given a good initial “guess” for the solution, which is always available in normal system operations. However, with the increase in levels of intermittent generation, the assumption of a good initial guess always being available is no longer valid. In this paper, we solve this problem using the theory of monotone operators. We show that it is possible to compute (using an offline optimization) a “monotonicity domain” in the space of voltage phasors. Given this domain, there is a simple efficient algorithm that will either find a solution in the domain, or provably certify that no solutions exist in it. We validate the approach on several IEEE test cases and demonstrate that the offline optimization can be performed tractably and the computed “monotonicity domain” includes all practically relevant power flow solutions.
A Hybrid Harmony Search Algorithm Approach for Optimal Power Flow
Directory of Open Access Journals (Sweden)
Mimoun YOUNES
2012-08-01
Full Text Available Optimal Power Flow (OPF is one of the main functions of Power system operation. It determines the optimal settings of generating units, bus voltage, transformer tap and shunt elements in Power System with the objective of minimizing total production costs or losses while the system is operating within its security limits. The aim of this paper is to propose a novel methodology (BCGAs-HSA that solves OPF including both active and reactive power dispatch It is based on combining the binary-coded genetic algorithm (BCGAs and the harmony search algorithm (HSA to determine the optimal global solution. This method was tested on the modified IEEE 30 bus test system. The results obtained by this method are compared with those obtained with BCGAs or HSA separately. The results show that the BCGAs-HSA approach can converge to the optimum solution with accuracy compared to those reported recently in the literature.
In silico approaches to study mass and energy flows in microbial consortia: a syntrophic case study
Directory of Open Access Journals (Sweden)
Mallette Natasha
2009-12-01
Full Text Available Abstract Background Three methods were developed for the application of stoichiometry-based network analysis approaches including elementary mode analysis to the study of mass and energy flows in microbial communities. Each has distinct advantages and disadvantages suitable for analyzing systems with different degrees of complexity and a priori knowledge. These approaches were tested and compared using data from the thermophilic, phototrophic mat communities from Octopus and Mushroom Springs in Yellowstone National Park (USA. The models were based on three distinct microbial guilds: oxygenic phototrophs, filamentous anoxygenic phototrophs, and sulfate-reducing bacteria. Two phases, day and night, were modeled to account for differences in the sources of mass and energy and the routes available for their exchange. Results The in silico models were used to explore fundamental questions in ecology including the prediction of and explanation for measured relative abundances of primary producers in the mat, theoretical tradeoffs between overall productivity and the generation of toxic by-products, and the relative robustness of various guild interactions. Conclusion The three modeling approaches represent a flexible toolbox for creating cellular metabolic networks to study microbial communities on scales ranging from cells to ecosystems. A comparison of the three methods highlights considerations for selecting the one most appropriate for a given microbial system. For instance, communities represented only by metagenomic data can be modeled using the pooled method which analyzes a community's total metabolic potential without attempting to partition enzymes to different organisms. Systems with extensive a priori information on microbial guilds can be represented using the compartmentalized technique, employing distinct control volumes to separate guild-appropriate enzymes and metabolites. If the complexity of a compartmentalized network creates an
Global network of embodied water flow by systems input-output simulation
Chen, Zhanming; Chen, Guoqian; Xia, Xiaohua; Xu, Shiyun
2012-09-01
The global water resources network is simulated in the present work for the latest target year with statistical data available and with the most detailed data disaggregation. A top-down approach of systems inputoutput simulation is employed to track the embodied water flows associated with economic flows for the globalized economy in 2004. The numerical simulation provides a database of embodied water intensities for all economic commodities from 4928 producers, based on which the differences between direct and indirect water using efficiencies at the global scale are discussed. The direct and embodied water uses are analyzed at continental level. Besides, the commodity demand in terms of monetary expenditure and the water demand in terms of embodied water use are compared for the world as well as for three major water using regions, i.e., India, China, and the United States. Results show that food product contributes to a significant fraction for water demand, despite the value varies significantly with respect to the economic status of region.
ADHD classification using bag of words approach on network features
Solmaz, Berkan; Dey, Soumyabrata; Rao, A. Ravishankar; Shah, Mubarak
2012-02-01
Attention Deficit Hyperactivity Disorder (ADHD) is receiving lots of attention nowadays mainly because it is one of the common brain disorders among children and not much information is known about the cause of this disorder. In this study, we propose to use a novel approach for automatic classification of ADHD conditioned subjects and control subjects using functional Magnetic Resonance Imaging (fMRI) data of resting state brains. For this purpose, we compute the correlation between every possible voxel pairs within a subject and over the time frame of the experimental protocol. A network of voxels is constructed by representing a high correlation value between any two voxels as an edge. A Bag-of-Words (BoW) approach is used to represent each subject as a histogram of network features; such as the number of degrees per voxel. The classification is done using a Support Vector Machine (SVM). We also investigate the use of raw intensity values in the time series for each voxel. Here, every subject is represented as a combined histogram of network and raw intensity features. Experimental results verified that the classification accuracy improves when the combined histogram is used. We tested our approach on a highly challenging dataset released by NITRC for ADHD-200 competition and obtained promising results. The dataset not only has a large size but also includes subjects from different demography and edge groups. To the best of our knowledge, this is the first paper to propose BoW approach in any functional brain disorder classification and we believe that this approach will be useful in analysis of many brain related conditions.
Analysis of a solar collector field water flow network
Rohde, J. E.; Knoll, R. H.
1976-01-01
A number of methods are presented for minimizing the water flow variation in the solar collector field for the Solar Building Test Facility at the Langley Research Center. The solar collector field investigated consisted of collector panels connected in parallel between inlet and exit collector manifolds to form 12 rows. The rows were in turn connected in parallel between the main inlet and exit field manifolds to complete the field. The various solutions considered included various size manifolds, manifold area change, different locations for the inlets and exits to the manifolds, and orifices or flow control valves. Calculations showed that flow variations of less than 5 percent were obtainable both inside a row between solar collector panels and between various rows.
Trader lead-lag networks and order flow prediction
Challet, Damien; Lallouache, Mehdi; Kassibrakis, Serge
2016-01-01
Using trader-resolved data, we document lead-lag relationships between groups of investors in the foreign exchange market. Because these relationships are systematic and persistent, order flow is predictable from trader-resolved order flow. We thus propose a generic method to exploit trader lead-lag and predict the sign of the total order imbalance over a given time horizon. It first consists in an unsupervised clustering of investors according to their buy/sell/inactivity synchronization. The collective actions of these groups and their lagged values are given as inputs to machine learning methods. When groups of traders and when their lead-lag relationships are sufficiently persistent, highly successful out-of-sample order flow sign predictions are obtained.
Constraints of nonresponding flows based on cross layers in the networks
Zhou, Zhi-Chao; Xiao, Yang; Wang, Dong
2016-02-01
In the active queue management (AQM) scheme, core routers cannot manage and constrain user datagram protocol (UDP) data flows by the sliding window control mechanism in the transport layer due to the nonresponsive nature of such traffic flows. However, the UDP traffics occupy a large part of the network service nowadays which brings a great challenge to the stability of the more and more complex networks. To solve the uncontrollable problem, this paper proposes a cross layers random early detection (CLRED) scheme, which can control the nonresponding UDP-like flows rate effectively when congestion occurs in the access point (AP). The CLRED makes use of the MAC frame acknowledgement (ACK) transmitting congestion information to the sources nodes and utilizes the back-off windows of the MAC layer throttling data rate. Consequently, the UDP-like flows data rate can be restrained timely by the sources nodes in order to alleviate congestion in the complex networks. The proposed CLRED can constrain the nonresponsive flows availably and make the communication expedite, so that the network can sustain stable. The simulation results of network simulator-2 (NS2) verify the proposed CLRED scheme.
Flow-based Networking and Quality of Service
Kleiberg, T.J.
2009-01-01
During the past two decades the Internet has been widely deployed and integrated into the society, radically altering the way people communicate and exchange information. Although the Internet was intended as a research network between few a institutions in the United States, it has grown to take a
Optimization of Gas Flow Network using the Traveling Salesman ...
African Journals Online (AJOL)
The overall goal of this paper is to develop a general formulation for an optimal infrastructure layout design of gas pipeline distribution networks using algorithm developed from the application of two industrial engineering concepts: the traveling salesman problem (TSP) and the nearest neighbor (NN). The focus is on the ...
A Passive Testing Approach for Protocols in Wireless Sensor Networks.
Che, Xiaoping; Maag, Stephane; Tan, Hwee-Xian; Tan, Hwee-Pink; Zhou, Zhangbing
2015-11-19
Smart systems are today increasingly developed with the number of wireless sensor devices drastically increasing. They are implemented within several contexts throughout our environment. Thus, sensed data transported in ubiquitous systems are important, and the way to carry them must be efficient and reliable. For that purpose, several routing protocols have been proposed for wireless sensor networks (WSN). However, one stage that is often neglected before their deployment is the conformance testing process, a crucial and challenging step. Compared to active testing techniques commonly used in wired networks, passive approaches are more suitable to the WSN environment. While some works propose to specify the protocol with state models or to analyze them with simulators and emulators, we here propose a logic-based approach for formally specifying some functional requirements of a novel WSN routing protocol. We provide an algorithm to evaluate these properties on collected protocol execution traces. Further, we demonstrate the efficiency and suitability of our approach by its application into common WSN functional properties, as well as specific ones designed from our own routing protocol. We provide relevant testing verdicts through a real indoor testbed and the implementation of our protocol. Furthermore, the flexibility, genericity and practicability of our approach have been proven by the experimental results.
A Passive Testing Approach for Protocols in Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Xiaoping Che
2015-11-01
Full Text Available Smart systems are today increasingly developed with the number of wireless sensor devices drastically increasing. They are implemented within several contexts throughout our environment. Thus, sensed data transported in ubiquitous systems are important, and the way to carry them must be efficient and reliable. For that purpose, several routing protocols have been proposed for wireless sensor networks (WSN. However, one stage that is often neglected before their deployment is the conformance testing process, a crucial and challenging step. Compared to active testing techniques commonly used in wired networks, passive approaches are more suitable to the WSN environment. While some works propose to specify the protocol with state models or to analyze them with simulators and emulators, we here propose a logic-based approach for formally specifying some functional requirements of a novel WSN routing protocol. We provide an algorithm to evaluate these properties on collected protocol execution traces. Further, we demonstrate the efficiency and suitability of our approach by its application into common WSN functional properties, as well as specific ones designed from our own routing protocol. We provide relevant testing verdicts through a real indoor testbed and the implementation of our protocol. Furthermore, the flexibility, genericity and practicability of our approach have been proven by the experimental results.
Directory of Open Access Journals (Sweden)
Cecilia Maya Ochoa
2004-12-01
Full Text Available There exists an abysm between market prices and traditional valuation approaches such as Discounted Cash Flows (DCF, a fact that neither academics nor practitioners could continue ignoring. Recently, a complementary approach has taken a foothold into the valuation world. Building on the DCF approach yet going further in the sense of incorporating flexibility in management investment decisions, and taking advantage of the advances in option pricing theory, the real options approach (ROA has become the alternative to capital budgeting and, lately, to corporate valuation. Empirical evidence shows that ROA explains actual prices better than DCF approaches and nowadays there is no question that from a theoretical point of view, ROA is a much more appealing concept than passive NPV. However, its acceptance by practitioners has been very slow due to the complexity of real options pricing.Existe un abismo entre precios de Mercado y la valoración estimada por métodos tradicionales tales como Flujos de Caja Descontados, un hecho que ni académicos ni practicantes pueden continuar ignorando. Recientemente, una metodología complementaria ha tomado gran fuerza. Partiendo de los Flujos de Caja Descontados, pero incorporando flexibilidad en las decisiones de inversión y aprovechando los avances en la teoría de valoración de opciones, el enfoque de opciones reales (ROA se ha convertido en la alternativa para presupuestación de capital y valoración de empresas. La evidencia empírica muestra que ROA explica los precios de mercado mejor que los enfoques basados en flujos de caja descontados y, hoy en día, no hay discusión de que es más atractivo desde un punto de vista teórico; sin embargo, su utilización en la práctica ha sido muy limitada debido a las dificultades que presenta la valoración de las opciones reales.
Directory of Open Access Journals (Sweden)
Xiaobing Chen
2014-01-01
Full Text Available In this study, physical experiments and numerical simulations are combined to provide a detailed understanding of flow dynamics in fracture network. Hydraulic parameters such as pressure head, velocity field, Reynolds number on certain monitoring cross points, and total flux rate are examined under various clogging conditions. Applying the COMSOL Multiphysics code to solve the Navier-Stokes equation instead of Reynolds equation and using the measured data to validate the model, the fluid flow in the horizontal 2D cross-sections of the fracture network was simulated. Results show that local clogging leads to a significant reshaping of the flow velocity field and a reduction of the transport capacity of the entire system. The flow rate distribution is highly influenced by the fractures connected to the dominant flow channels, although local disturbances in velocity field are unlikely to spread over the whole network. Also, modeling results indicate that water flow in a fracture network, compared with that in a single fracture, is likely to transit into turbulence earlier under the same hydraulic gradient due to the influence of fracture intersections.
Chemical reaction network approaches to Biochemical Systems Theory.
Arceo, Carlene Perpetua P; Jose, Editha C; Marin-Sanguino, Alberto; Mendoza, Eduardo R
2015-11-01
This paper provides a framework to represent a Biochemical Systems Theory (BST) model (in either GMA or S-system form) as a chemical reaction network with power law kinetics. Using this representation, some basic properties and the application of recent results of Chemical Reaction Network Theory regarding steady states of such systems are shown. In particular, Injectivity Theory, including network concordance [36] and the Jacobian Determinant Criterion [43], a "Lifting Theorem" for steady states [26] and the comprehensive results of Müller and Regensburger [31] on complex balanced equilibria are discussed. A partial extension of a recent Emulation Theorem of Cardelli for mass action systems [3] is derived for a subclass of power law kinetic systems. However, it is also shown that the GMA and S-system models of human purine metabolism [10] do not display the reactant-determined kinetics assumed by Müller and Regensburger and hence only a subset of BST models can be handled with their approach. Moreover, since the reaction networks underlying many BST models are not weakly reversible, results for non-complex balanced equilibria are also needed. Copyright © 2015 Elsevier Inc. All rights reserved.
Network Analysis: A Novel Approach to Understand Suicidal Behaviour
Directory of Open Access Journals (Sweden)
Derek de Beurs
2017-02-01
Full Text Available Although suicide is a major public health issue worldwide, we understand little of the onset and development of suicidal behaviour. Suicidal behaviour is argued to be the end result of the complex interaction between psychological, social and biological factors. Epidemiological studies resulted in a range of risk factors for suicidal behaviour, but we do not yet understand how their interaction increases the risk for suicidal behaviour. A new approach called network analysis can help us better understand this process as it allows us to visualize and quantify the complex association between many different symptoms or risk factors. A network analysis of data containing information on suicidal patients can help us understand how risk factors interact and how their interaction is related to suicidal thoughts and behaviour. A network perspective has been successfully applied to the field of depression and psychosis, but not yet to the field of suicidology. In this theoretical article, I will introduce the concept of network analysis to the field of suicide prevention, and offer directions for future applications and studies.
Domain decomposition approach to extract pore-network models from large 3D porous media images
Timofey, Sizonenko; Marina, Karsanina; Irina, Bayuk; Kirill, Gerke
2017-04-01
Pore-network are very useful and effective method to model porous media structure and properties such as permeability and multi-phase flow. Several methods for pore-network extraction were proposed to date, including median axis, maximal inscribed ball, watershed techniques and their modifications. Input data for pore-network extraction algorithms usually represent 3D binary image. Modern X-ray tomography devices can easily provide scans with dimensions of 4k x 4k x 10k voxels. For such large images extraction algorithms may hit the problem of memory (RAM) consumption or will too time consuming. To overcome such problems or create parallelizable algorithm here we propose to divide the whole volume into sub-volumes with smaller size and extract pore- network sequentially/in parallel manner separately. However, the problem of correct pore-network extraction at the sub-volume connection areas is challenging. In this contribution we address this issue in detail. We propose a method to merge such sub-volumes. Our method explores the slices of porous medium under study at the sub-volumes intersections. Each slice has its own geometric features and associated with a number of pores or throats. Characteristics of pore that associated with slice such as diameter, distance its center to the sub-domain boundary are also taken into account. Based on the pore element properties and also properties of aforementioned slices the algorithm makes decision about how pores from opposite sides of sub-volumes should be connected. There are 3 cases of merging: 1) building a throat between pores, 2) absorption of one pore by the other, 3) breaking connection (no pore or throat are built). We have tested our approach on several different binary 3D images, including soil, sandstones, and carbonates. We also compared this new approach against a conventional one where the extraction is performed using the whole domain without its decomposition into sub-domains. We show that our approach
Kuniansky, Eve L.
2016-09-22
been developed that incorporate the submerged conduits as a one-dimensional pipe network within the aquifer rather than as discrete, extremely transmissive features in a porous-equivalent medium; these submerged conduit models are usually referred to as hybrid models and may include the capability to simulate both laminar and turbulent flow in the one-dimensional pipe network. Comparisons of the application of a porous-equivalent media model with and without turbulence (MODFLOW-Conduit Flow Process mode 2 and basic MODFLOW, respectively) and a hybrid (MODFLOW-Conduit Flow Process mode 1) model to the Woodville Karst Plain near Tallahassee, Florida, indicated that for annual, monthly, or seasonal average hydrologic conditions, all methods met calibration criteria (matched observed groundwater levels and average flows). Thus, the increased effort required, such as the collection of data on conduit location, to develop a hybrid model and its increased computational burden, is not necessary for simulation of average hydrologic conditions (non-laminar flow effects on simulated head and spring discharge were minimal). However, simulation of a large storm event in the Woodville Karst Plain with daily stress periods indicated that turbulence is important for matching daily springflow hydrographs. Thus, if matching streamflow hydrographs over a storm event is required, the simulation of non-laminar flow and the location of conduits are required. The main challenge in application of the methods and approaches for developing hybrid models relates to the difficulty of mapping conduit networks or having high-quality datasets to calibrate these models. Additionally, hybrid models have long simulation times, which can preclude the use of parameter estimation for calibration. Simulation of contaminant transport that does not account for preferential flow through conduits or extremely permeable zones in any approach is ill-advised. Simulation results in other karst aquifers or other
Mapping debris flow susceptibility using analytical network process ...
Indian Academy of Sciences (India)
Evangelin Ramani Sujatha
2017-11-23
Nov 23, 2017 ... roads, power transmission lines, plantations, settle- ments and at rare events disrupts road connectivity between the hill town and the plains. No major injuries or loss of life was reported due to debris flow in this region, but there was considerable disruption to local and tourist traffic as well as cargo carriers.
Unified pipe network method for simulation of water flow in fractured porous rock
Ren, Feng; Ma, Guowei; Wang, Yang; Li, Tuo; Zhu, Hehua
2017-04-01
Rock masses are often conceptualized as dual-permeability media containing fractures or fracture networks with high permeability and porous matrix that is less permeable. In order to overcome the difficulties in simulating fluid flow in a highly discontinuous dual-permeability medium, an effective unified pipe network method is developed, which discretizes the dual-permeability rock mass into a virtual pipe network system. It includes fracture pipe networks and matrix pipe networks. They are constructed separately based on equivalent flow models in a representative area or volume by taking the advantage of the orthogonality of the mesh partition. Numerical examples of fluid flow in 2-D and 3-D domain including porous media and fractured porous media are presented to demonstrate the accuracy, robustness, and effectiveness of the proposed unified pipe network method. Results show that the developed method has good performance even with highly distorted mesh. Water recharge into the fractured rock mass with complex fracture network is studied. It has been found in this case that the effect of aperture change on the water recharge rate is more significant in the early stage compared to the fracture density change.
Directory of Open Access Journals (Sweden)
Garbaa Hela
2016-12-01
Full Text Available A new approach to solve the inverse problem in electrical capacitance tomography is presented. The proposed method is based on an artificial neural network to estimate three different parameters of a circular object present inside a pipeline, i.e. radius and 2D position coordinates. This information allows the estimation of the distribution of material inside a pipe and determination of the characteristic parameters of a range of flows, which are characterised by a circular objects emerging within a cross section such as funnel flow in a silo gravitational discharging process. The main advantages of the proposed approach are explicitly: the desired characteristic flow parameters are estimated directly from the measured capacitances and rapidity, which in turn is crucial for online flow monitoring. In a classic approach in order to obtain these parameters in the first step the image is reconstructed and then the parameters are estimated with the use of image processing methods. The obtained results showed significant reduction of computations time in comparison to the iterative LBP or Levenberg-Marquard algorithms.
A neural network approach to smarter sensor networks for water quality monitoring.
O'Connor, Edel; Smeaton, Alan F; O'Connor, Noel E; Regan, Fiona
2012-01-01
Environmental monitoring is evolving towards large-scale and low-cost sensor networks operating reliability and autonomously over extended periods of time. Sophisticated analytical instrumentation such as chemo-bio sensors present inherent limitations because of the number of samples that they can take. In order to maximize their deployment lifetime, we propose the coordination of multiple heterogeneous information sources. We use rainfall radar images and information from a water depth sensor as input to a neural network (NN) to dictate the sampling frequency of a phosphate analyzer at the River Lee in Cork, Ireland. This approach shows varied performance for different times of the year but overall produces output that is very satisfactory for the application context in question. Our study demonstrates that even with limited training data, a system for controlling the sampling rate of the nutrient sensor can be set up and can improve the efficiency of the more sophisticated nodes of the sensor network.
A power flow mode theory based on a system's damping distribution and power flow design approaches
Xiong, Ye-Ping; Xing, J. T.; Price, W.G.
2005-01-01
A power flow mode theory is developed to describe the natural power flow behaviour of a dynamic system based on its inherent damping distribution. The system's characteristic-damping matrix is constructed and it is shown that the eigenvalues and eigenvectors of this matrix identify natural power flow characteristics. These eigenvectors, or power flow mode vectors, are chosen as a set of base-vectors spanning the power flow space and completely describe the power flow in the system. The genera...
Analysis of HRCT-derived xylem network reveals reverse flow in some vessels.
Lee, Eric F; Matthews, Mark A; McElrone, Andrew J; Phillips, Ronald J; Shackel, Kenneth A; Brodersen, Craig R
2013-09-21
Long distance water and nutrient transport in plants is dependent on the proper functioning of xylem networks, a series of interconnected pipe-like cells that are vulnerable to hydraulic dysfunction as a result of drought-induced embolism and/or xylem-dwelling pathogens. Here, flow in xylem vessels was modeled to determine the role of vessel connectivity by using three dimensional xylem networks derived from High Resolution Computed Tomography (HRCT) images of grapevine (Vitis vinifera cv. 'Chardonnay') stems. Flow in 4-27% of the vessel segments (i.e. any section of vessel elements between connection points associated with intervessel pits) was found to be oriented in the direction opposite to the bulk flow under normal transpiration conditions. In order for the flow in a segment to be in the reverse direction, specific requirements were determined for the location of connections, distribution of vessel endings, diameters of the connected vessels, and the conductivity of the connections. Increasing connectivity and decreasing vessel length yielded increasing numbers of reverse flow segments until a maximum value was reached, after which more interconnected networks and smaller average vessel lengths yielded a decrease in the number of reverse flow segments. Xylem vessel relays also encouraged the formation of reverse flow segments. Based on the calculated flow rates in the xylem network, the downward spread of Xylella fastidiosa bacteria in grape stems was modeled, and reverse flow was shown to be an additional mechanism for the movement of bacteria to the trunk of grapevine. Copyright © 2013 Elsevier Ltd. All rights reserved.
Sport, how people choose it: A network analysis approach.
Ferreri, Luca; Ivaldi, Marco; Daolio, Fabio; Giacobini, Mario; Rainoldi, Alberto; Tomassini, Marco
2015-01-01
In order to investigate the behaviour of athletes in choosing sports, we analyse data from part of the We-Sport database, a vertical social network that links athletes through sports. In particular, we explore connections between people sharing common sports and the role of age and gender by applying "network science" approaches and methods. The results show a disassortative tendency of athletes in choosing sports, a negative correlation between age and number of chosen sports and a positive correlation between age of connected athletes. Some interesting patterns of connection between age classes are depicted. In addition, we propose a method to classify sports, based on the analyses of the behaviour of people practising them. Thanks to this brand new classifications, we highlight the links of class of sports and their unexpected features. We emphasise some gender dependency affinity in choosing sport classes.
An Enhanced Probabilistic Neural Network Approach Applied to Text Classification
Marques Ciarelli, Patrick; Oliveira, Elias
Text classification is still a quite difficult problem to be dealt with both by the academia and by the industrial areas. On the top of that, the importance of aggregating a set of related amount of text documents is steadily growing in importance these days. The presence of multi-labeled texts and great quantity of classes turn this problem even more challenging. In this article we present an enhanced version of Probabilistic Neural Network using centroids to tackle the multi-label classification problem. We carried out some experiments comparing our proposed classifier against the other well known classifiers in the literature which were specially designed to treat this type of problem. By the achieved results, we observed that our novel approach were superior to the other classifiers and faster than the Probabilistic Neural Network without the use of centroids.
Structural Approaches to Sequence Evolution Molecules, Networks, Populations
Bastolla, Ugo; Roman, H. Eduardo; Vendruscolo, Michele
2007-01-01
Structural requirements constrain the evolution of biological entities at all levels, from macromolecules to their networks, right up to populations of biological organisms. Classical models of molecular evolution, however, are focused at the level of the symbols - the biological sequence - rather than that of their resulting structure. Now recent advances in understanding the thermodynamics of macromolecules, the topological properties of gene networks, the organization and mutation capabilities of genomes, and the structure of populations make it possible to incorporate these key elements into a broader and deeply interdisciplinary view of molecular evolution. This book gives an account of such a new approach, through clear tutorial contributions by leading scientists specializing in the different fields involved.
Benchmarking and supplier networking--best practice approaches.
Waixel, B; Laidlaw, J
1996-01-01
This article examines the approach adopted by a health service to benchmark outside the health industry and to network with its own suppliers in its quest for best practice. The Maryborough District Health Service was selected for funding under the Commonwealth Government's Best Practice in the Health Sector Program. This rural health service is setting a fine example of how generic benchmarking can be used to increase efficiency and improve outcomes in an environment of change, increasing demands, and contracting resources. The organisation has networked with its suppliers with a view to ensuring that, as a customer, it has access to the best quality goods and services. The objective is to improve the services and quality of patient care provided by the health service and to minimise its cost structures.
Thermal memristor and neuromorphic networks for manipulating heat flow
Ben-Abdallah, Philippe
2017-06-01
A memristor is one of four fundamental two-terminal solid elements in electronics. In addition with the resistor, the capacitor and the inductor, this passive element relates the electric charges to current in solid state elements. Here we report the existence of a thermal analog for this element made with metal-insulator transition materials. We demonstrate that these memristive systems can be used to create thermal neurons opening so the way to neuromorphic networks for smart thermal management and information treatment.
Local control of traffic flows in networks: Self-organisation of phase synchronised dynamics
Energy Technology Data Exchange (ETDEWEB)
Laemmer, Stefan; Donner, Reik [TU Dresden, Andreas-Schubert-Str. 23, 01062 Dresden (Germany); Helbing, Dirk [ETH Zuerich, Universitaetstr. 41, 8092 Zuerich (Switzerland)
2008-07-01
The effective control of flows in urban traffic networks is a subject of broad economic interest. During the last years, efforts have been made to develop decentralised control strategies that take only the actual state of present traffic conditions into account. In this contribution, we introduce a permeability model for the local control of conflicting material flows on networks, which incorporates a self-organisation of the flows. The dynamics of our model is studied under different situations, with a special emphasis on the development of a phase synchronised switching behaviour at the nodes of the traffic network. In order to improve the potential applicability of our concept, we discuss how a proper demand anticipation and the definition of a priority function can be used to further optimise the performance of the presented strategy.
Sokhal, Abdellah; Bougandoura, Adel; Ouadfeul, Sid-Ali
2015-04-01
In this work, fluid flow paths are discriminated from standard well logs and core data through the utilization of Hydraulic Flow Units Approach (HFU) and an intelligent network. Firstly, the flow zone indicator (FZI), which is a unique parameter for each hydraulic unit, was used to characterize each rock type. The number of hydraulic flow units and mean values of FZI for each HFU were calculated from porosity and permeability measured from core-rocks. Application to data of a borehole located in the Algerian Sahara shows the existence of three HFUs and a correlation coefficient greater than 0.9 in each HFU was observed. Some FZI were attributed for un-cored wells using the Fuzzy Logic system (FL). Well-logs data that are used as an input to train the fuzzy system are the neutron porosity, the bulk density, the slowness of the P wave, the resistivity of the shallow and the deep zones and the natural gamma ray. The calculated FZI associated to these depths interval are used as an output. The presented methodology was successfully applied to a large data set of laboratory and well logging measurements from the Hassi D'zaabat field. Keywords: Fluid flow; FZI, Hydraulic Flow Unit (HFU); Fuzzy logic.
A QCQP Approach for OPF in Multiphase Radial Networks with Wye and Delta Connections: Preprint
Energy Technology Data Exchange (ETDEWEB)
Zamzam, Ahmed, S.; Zhaoy, Changhong; Dall' Anesey, Emiliano; Sidiropoulos, Nicholas D.
2017-06-27
This paper examines the AC Optimal Power Flow (OPF) problem for multiphase distribution networks featuring renewable energy resources (RESs). We start by outlining a power flow model for radial multiphase systems that accommodates wye-connected and delta-connected RESs and non-controllable energy assets. We then formalize an AC OPF problem that accounts for both types of connections. Similar to various AC OPF renditions, the resultant problem is a non convex quadratically-constrained quadratic program. However, the so-called Feasible Point Pursuit-Successive Convex Approximation algorithm is leveraged to obtain a feasible and yet locally-optimal solution. The merits of the proposed solution approach are demonstrated using two unbalanced multiphase distribution feeders with both wye and delta connections.
Zhou, Bao-Rong; Liu, Si-Liang; Zhang, Yong-Jun; Yi, Ying-Qi; Lin, Xiao-Ming
2017-05-01
To mitigate the impact on the distribution networks caused by the stochastic characteristic and high penetration of photovoltaic, a multi-objective optimal power flow model is proposed in this paper. The regulation capability of capacitor, inverter of photovoltaic and energy storage system embedded in active distribution network are considered to minimize the expected value of active power the T loss and probability of voltage violation in this model. Firstly, a probabilistic power flow based on cumulant method is introduced to calculate the value of the objectives. Secondly, NSGA-II algorithm is adopted for optimization to obtain the Pareto optimal solutions. Finally, the best compromise solution can be achieved through fuzzy membership degree method. By the multi-objective optimization calculation of IEEE34-node distribution network, the results show that the model can effectively improve the voltage security and economy of the distribution network on different levels of photovoltaic penetration.
Simulation of unsteady flow and solute transport in a tidal river network
Zhan, X.
2003-01-01
A mathematical model and numerical method for water flow and solute transport in a tidal river network is presented. The tidal river network is defined as a system of open channels of rivers with junctions and cross sections. As an example, the Pearl River in China is represented by a network of 104 channels, 62 nodes, and a total of 330 cross sections with 11 boundary section for one of the applications. The simulations are performed with a supercomputer for seven scenarios of water flow and/or solute transport in the Pearl River, China, with different hydrological and weather conditions. Comparisons with available data are shown. The intention of this study is to summarize previous works and to provide a useful tool for water environmental management in a tidal river network, particularly for the Pearl River, China.
Statistical learning of parts and wholes: A neural network approach.
Plaut, David C; Vande Velde, Anna K
2017-03-01
Statistical learning is often considered to be a means of discovering the units of perception, such as words and objects, and representing them as explicit "chunks." However, entities are not undifferentiated wholes but often contain parts that contribute systematically to their meanings. Studies of incidental auditory or visual statistical learning suggest that, as participants learn about wholes they become insensitive to parts embedded within them, but this seems difficult to reconcile with a broad range of findings in which parts and wholes work together to contribute to behavior. Bayesian approaches provide a principled description of how parts and wholes can contribute simultaneously to performance, but are generally not intended to model the computations that actually give rise to this performance. In the current work, we develop an account based on learning in artificial neural networks in which the representation of parts and wholes is a matter of degree, and the extent to which they cooperate or compete arises naturally through incidental learning. We show that the approach accounts for a wide range of findings concerning the relationship between parts and wholes in auditory and visual statistical learning, including some findings previously thought to be problematic for neural network approaches. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Substrate independent approach for synthesis of graphene platelet networks
Shashurin, A.; Fang, X.; Zemlyanov, D.; Keidar, M.
2017-06-01
Graphene platelet networks (GPNs) comprised of randomly oriented graphene flakes two to three atomic layers thick are synthesized using a novel plasma-based approach. The approach uses a substrate capable of withstanding synthesis temperatures around 800 °C, but is fully independent of the substrate material. The synthesis occurs directly on the substrate surface without the necessity of any additional steps. GPNs were synthesized on various substrate materials including silicon (Si), thermally oxidized Si (SiO2), molybdenum (Mo), nickel (Ni) and copper (Cu), nickel-chromium (NiCr) alloy and alumina ceramics (Al2O3). The mismatch between the atomic structures of sp2 honeycomb carbon networks and the substrate material is fully eliminated shortly after the synthesis initiation, namely when about 100 nm thick deposits are formed on the substrate. GPN structures synthesized on a substrate at a temperature of about 800 °C are significantly more porous in comparison to the much denser packed amorphous carbon deposits synthesized at lower temperatures. The method proposed here can potentially revolutionize the area of electrochemical energy storage by offering a single-step direct approach for the manufacture of graphene-based electrodes for non-Faradaic supercapacitors. Mass production can be achieved using this method if a roll-to-roll system is utilized.
Zhang, Jiawei; Zhang, Jie; Zhao, Yongli; Yang, Hui; Yu, Xiaosong; Wang, Lei; Fu, Xihua
2013-01-28
Due to the prominent performance on networking virtualization and programmability, OpenFlow is widely regarded as a promising control plane technology in packet-switched IP networks as well as wavelength-switched optical networks. For the purpose of applying software programmable feature to future optical networks, we propose an OpenFlow-based control plane in Flexi-Grid optical networks. Experimental results demonstrate its feasibility of dynamic lightpath establishment and adjustment via extended OpenFlow protocol. Wireshark captures of the signaling procedure are printed out. Additionally, the overall latency including signaling and hardware for lightpath setup and adjustment is also reported.
The minimum cost multicommodity flow problem in dynamic networks and an algorithm for its solving
Directory of Open Access Journals (Sweden)
Maria A. Fonoberova
2005-05-01
Full Text Available The dynamic version of the minimum cost multicommodity flow problem that generalizes the static minimum cost multicommodity flow problem is formulated and studied. This dynamic problem is considered on directed networks with a set of commodities, time-varying capacities, fixed transit times on arcs, and a given time horizon. We assume that cost functions, defined on edges, are nonlinear and depend on time and flow and the demand function also depends on time. The corresponding algorithm, based on reducing the dynamic problem to a static problem on a time-expanded network, to solve the minimum cost dynamic multicommodity flow problem is proposed and some details concerning its complexity are discussed. Mathematics Subject Classification 2000: 90B10, 90C35, 90C27.
A Scheme to Optimize Flow Routing and Polling Switch Selection of Software Defined Networks
Chen, Huan; Li, Lemin; Ren, Jing; Wang, Yang; Zhao, Yangming; Wang, Xiong; Wang, Sheng; Xu, Shizhong
2015-01-01
This paper aims at minimizing the communication cost for collecting flow information in Software Defined Networks (SDN). Since flow-based information collecting method requires too much communication cost, and switch-based method proposed recently cannot benefit from controlling flow routing, jointly optimize flow routing and polling switch selection is proposed to reduce the communication cost. To this end, joint optimization problem is formulated as an Integer Linear Programming (ILP) model firstly. Since the ILP model is intractable in large size network, we also design an optimal algorithm for the multi-rooted tree topology and an efficient heuristic algorithm for general topology. According to extensive simulations, it is found that our method can save up to 55.76% communication cost compared with the state-of-the-art switch-based scheme. PMID:26690571
A Scheme to Optimize Flow Routing and Polling Switch Selection of Software Defined Networks.
Directory of Open Access Journals (Sweden)
Huan Chen
Full Text Available This paper aims at minimizing the communication cost for collecting flow information in Software Defined Networks (SDN. Since flow-based information collecting method requires too much communication cost, and switch-based method proposed recently cannot benefit from controlling flow routing, jointly optimize flow routing and polling switch selection is proposed to reduce the communication cost. To this end, joint optimization problem is formulated as an Integer Linear Programming (ILP model firstly. Since the ILP model is intractable in large size network, we also design an optimal algorithm for the multi-rooted tree topology and an efficient heuristic algorithm for general topology. According to extensive simulations, it is found that our method can save up to 55.76% communication cost compared with the state-of-the-art switch-based scheme.
Queueing Models and Stability of Message Flows in Distributed Simulators of Open Queueing Networks
Gupta, Manish; Kumar, Anurag; Shorey, Rajeev
1996-01-01
In this paper we study message flow processes in distributed simulators of open queueing networks. We develop and study queueing models for distributed simulators with maximum lookahead sequencing. We characterize the external arrival process, and the message feedback process in the simulator of a simple queueing network with feedback. We show that a certain natural modelling construct for the arrival process is exactly correct, whereas an obvious model for the feedback process is wrong; we t...
Analysis and modelling of non-steady flow in pipe and channel networks
Jovic, Vinko
2013-01-01
Analysis and Modelling of Non-Steady Flow in Pipe and Channel Networks deals with flows in pipes and channel networks from the standpoints of hydraulics and modelling techniques and methods. These engineering problems occur in the course of the design and construction of hydroenergy plants, water-supply and other systems. In this book, the author presents his experience in solving these problems from the early 1970s to the present day. During this period new methods of solving hydraulic problems have evolved, due to the development of computers and numerical methods. This book
A novel scheme for fast flow classification in GMPLS-based networks
Huang, Qing; Kuo, Geng-Sheng
2005-02-01
In GMPLS-based networks, data is forwarded in manner of label switching on Label Switching Router (LSR). Ingress LSR must classify different IP flows into a set of Forwarding Equivalence Classes (FECs), which is a typical flow classification process. Ingress LSR will be the bottleneck of GMPLS-based networks if it could not provision fast flow classification. In this paper, we propose a novel fast flow classification scheme, coined Hierarchical Dividing Tree Scheme (HDTS), to improve the switching performance of ingress LSR in GMPLS-based networks. Four important advantages can be achieved by the proposed HDTS. First, the preprocess time in HDTS is reasonable. Second, fast FEC update is supported. Third, memory cost of HDTS is very low. Most important, the key factor that affects flow classification speed is not the number of FECs, but the depth of the search trees in HDTS. Theoretical analyses and simulations are conducted to evaluate performance of the proposed HDTS. Based on the analytical and experimental results, we can conclude that our HDTS improves the switching performance of ingress LSR greatly and is very practical for GMPLS-based networks due to its low cost.
Directory of Open Access Journals (Sweden)
Kit eCheung
2016-01-01
Full Text Available NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs. Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimised performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP rule for learning. A 6-FPGA system can simulate a network of up to approximately 600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation.
Cheung, Kit; Schultz, Simon R; Luk, Wayne
2015-01-01
NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation.
Cheung, Kit; Schultz, Simon R.; Luk, Wayne
2016-01-01
NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation. PMID:26834542
Information Extraction from Wireless Sensor Networks: System and Approaches
Directory of Open Access Journals (Sweden)
Tariq ALSBOUI
2012-03-01
Full Text Available Recent advances in wireless communication have made it possible to develop low-cost, and low power Wireless Sensor Networks (WSN. The WSN can be used for several application areas (e.g., habitat monitoring, forest fire detection, and health care. WSN Information Extraction (IE techniques can be classified into four categories depending on the factors that drive data acquisition: event-driven, time-driven, query-based, and hybrid. This paper presents a survey of the state-of-the-art IE techniques in WSNs. The benefits and shortcomings of different IE approaches are presented as motivation for future work into automatic hybridization and adaptation of IE mechanisms.
Thermal memristor and neuromorphic networks for manipulating heat flow
Directory of Open Access Journals (Sweden)
Philippe Ben-Abdallah
2017-06-01
Full Text Available A memristor is one of four fundamental two-terminal solid elements in electronics. In addition with the resistor, the capacitor and the inductor, this passive element relates the electric charges to current in solid state elements. Here we report the existence of a thermal analog for this element made with metal-insulator transition materials. We demonstrate that these memristive systems can be used to create thermal neurons opening so the way to neuromorphic networks for smart thermal management and information treatment.
An Open-Access Modeled Passenger Flow Matrix for the Global Air Network in 2010
Huang, Zhuojie; Wu, Xiao; Garcia, Andres J.; Fik, Timothy J.; Tatem, Andrew J.
2013-01-01
The expanding global air network provides rapid and wide-reaching connections accelerating both domestic and international travel. To understand human movement patterns on the network and their socioeconomic, environmental and epidemiological implications, information on passenger flow is required. However, comprehensive data on global passenger flow remain difficult and expensive to obtain, prompting researchers to rely on scheduled flight seat capacity data or simple models of flow. This study describes the construction of an open-access modeled passenger flow matrix for all airports with a host city-population of more than 100,000 and within two transfers of air travel from various publicly available air travel datasets. Data on network characteristics, city population, and local area GDP amongst others are utilized as covariates in a spatial interaction framework to predict the air transportation flows between airports. Training datasets based on information from various transportation organizations in the United States, Canada and the European Union were assembled. A log-linear model controlling the random effects on origin, destination and the airport hierarchy was then built to predict passenger flows on the network, and compared to the results produced using previously published models. Validation analyses showed that the model presented here produced improved predictive power and accuracy compared to previously published models, yielding the highest successful prediction rate at the global scale. Based on this model, passenger flows between 1,491 airports on 644,406 unique routes were estimated in the prediction dataset. The airport node characteristics and estimated passenger flows are freely available as part of the Vector-Borne Disease Airline Importation Risk (VBD-Air) project at: www.vbd-air.com/data. PMID:23691194
Analisis Unjuk Kerja Flow Control pada Network on Chip dalam Beberapa Kondisi Jaringan
Directory of Open Access Journals (Sweden)
Muhammad Hizrian Hizburrahman
2015-12-01
Full Text Available Network on Chip ialah teknik yang digunakan di System on Chip sebagai pengganti shared bus dan direct point – to – point. Pada Network on Chip terdapat parameter desain dan parameter performansi jaringan. Penentuan parameter desain dan perkembangan dari jaringan dapat menimbulkan permasalahan pada jaringan seperti congestion dan saturasi yang menyebabkan paket hilang. Congestion dapat diatasi dengan menggunakan flow control yang tepat. Pada penelitian ini dilakukan analisis terhadap tiga teknik flow control yaitu Stall / Go , Ack / Nack serta Dynamic Multi Level yang diterapkan pada dua model jaringan. Model jaringan yang pertama untuk mengamati pengaruh flow control terhadap saturasi jaringan dan model kedua untuk mengamati pengaruh flow control terhadap perubahan parameter desain dan mendapatkan teknik flow control yang paling optimal dan pengaruh perubahan parameter desain terhadap parameter performansi jaringan. Dari hasil penelitian, jaringan yang menggunakan flow control tidak mengalami saturasi. Dimana flow control Stall / Go merupakan flow control terbaik dalam meningkatkan throughput sebesar 21.08% , 65.33%, 151% dan 13.37% , menurunkan delay sebesar 407.85, 606.03, 1631.95, 322.59 cycles, menurunkan penggunaan daya sebesar 68.67%, 61.33%, 49.93%, 68.22% untuk masing – masing perubahan ukuran jaringan, perubahan packet injection rate, perubahan ukuran paket dan perubahan ukuran buffer.
A new scripting library for modeling flow and transport in fractured rock with channel networks
Dessirier, Benoît; Tsang, Chin-Fu; Niemi, Auli
2018-02-01
Deep crystalline bedrock formations are targeted to host spent nuclear fuel owing to their overall low permeability. They are however highly heterogeneous and only a few preferential paths pertaining to a small set of dominant rock fractures usually carry most of the flow or mass fluxes, a behavior known as channeling that needs to be accounted for in the performance assessment of repositories. Channel network models have been developed and used to investigate the effect of channeling. They are usually simpler than discrete fracture networks based on rock fracture mappings and rely on idealized full or sparsely populated lattices of channels. This study reexamines the fundamental parameter structure required to describe a channel network in terms of groundwater flow and solute transport, leading to an extended description suitable for unstructured arbitrary networks of channels. An implementation of this formalism in a Python scripting library is presented and released along with this article. A new algebraic multigrid preconditioner delivers a significant speedup in the flow solution step compared to previous channel network codes. 3D visualization is readily available for verification and interpretation of the results by exporting the results to an open and free dedicated software. The new code is applied to three example cases to verify its results on full uncorrelated lattices of channels, sparsely populated percolation lattices and to exemplify the use of unstructured networks to accommodate knowledge on local rock fractures.
Zhao, Shuangming; Zhao, Pengxiang; Cui, Yunfan
2017-07-01
In this paper, we propose an improved network centrality measure framework that takes into account both the topological characteristics and the geometric properties of a road network in order to analyze urban traffic flow in relation to different modes: intersection, road, and community, which correspond to point mode, line mode, and area mode respectively. Degree, betweenness, and PageRank centralities are selected as the analysis measures, and GPS-enabled taxi trajectory data is used to evaluate urban traffic flow. The results show that the mean value of the correlation coefficients between the modified degree, the betweenness, and the PageRank centralities and the traffic flow in all periods are higher than the mean value of the correlation coefficients between the conventional degree, the betweenness, the PageRank centralities and the traffic flow at different modes; this indicates that the modified measurements, for analyzing traffic flow, are superior to conventional centrality measurements. This study helps to shed light into the understanding of urban traffic flow in relation to different modes from the perspective of complex networks.
Unsteady flow phenomena in human undulatory swimming: a numerical approach.
Pacholak, Steffen; Hochstein, Stefan; Rudert, Alexander; Brücker, Christoph
2014-06-01
The undulatory underwater sequence is one of the most important phases in competitive swimming. An understanding of the recurrent vortex dynamics around the human body and their generation could therefore be used to improve swimming techniques. In order to produce a dynamic model, we applied human joint kinematics to three-dimensional (3D) body scans of a female swimmer. The flow around this dynamic model was then calculated using computational fluid dynamics with the aid of moving 3D meshes. Evaluation of the numerical results delivered by the various motion cycles identified characteristic vortex structures for each of the cycles, which exhibited increasing intensity and drag influence. At maximum thrust, drag forces appear to be 12 times higher than those of a passive gliding swimmer. As far as we know, this is the first disclosure of vortex rings merging into vortex tubes in the wake after vortex recapturing. All unsteady structures were visualized using a modified Q-criterion also incorporated into our methods. At the very least, our approach is likely to be suited to further studies examining swimmers engaging in undulatory swimming during training or competition.
Patterns of work attitudes: A neural network approach
Mengov, George D.; Zinovieva, Irina L.; Sotirov, George R.
2000-05-01
In this paper we introduce a neural networks based approach to analyzing empirical data and models from work and organizational psychology (WOP), and suggest possible implications for the practice of managers and business consultants. With this method it becomes possible to have quantitative answers to a bunch of questions like: What are the characteristics of an organization in terms of its employees' motivation? What distinct attitudes towards the work exist? Which pattern is most desirable from the standpoint of productivity and professional achievement? What will be the dynamics of behavior as quantified by our method, during an ongoing organizational change or consultancy intervention? Etc. Our investigation is founded on the theoretical achievements of Maslow (1954, 1970) in human motivation, and of Hackman & Oldham (1975, 1980) in job diagnostics, and applies the mathematical algorithm of the dARTMAP variation (Carpenter et al., 1998) of the Adaptive Resonance Theory (ART) neural networks introduced by Grossberg (1976). We exploit the ART capabilities to visualize the knowledge accumulated in the network's long-term memory in order to interpret the findings in organizational research.
Methodological Approach for Optogenetic Manipulation of Neonatal Neuronal Networks
Directory of Open Access Journals (Sweden)
Sebastian H. Bitzenhofer
2017-08-01
Full Text Available Coordinated patterns of electrical activity are critical for the functional maturation of neuronal networks, yet their interrogation has proven difficult in the developing brain. Optogenetic manipulations strongly contributed to the mechanistic understanding of network activation in the adult brain, but difficulties to specifically and reliably express opsins at neonatal age hampered similar interrogation of developing circuits. Here, we introduce a protocol that enables to control the activity of specific neuronal populations by light, starting from early postnatal development. We show that brain area-, layer- and cell type-specific expression of opsins by in utero electroporation (IUE, as exemplified for the medial prefrontal cortex (PFC and hippocampus (HP, permits the manipulation of neuronal activity in vitro and in vivo. Both individual and population responses to different patterns of light stimulation are monitored by extracellular multi-site recordings in the medial PFC of neonatal mice. The expression of opsins via IUE provides a flexible approach to disentangle the cellular mechanism underlying early rhythmic network activity, and to elucidate the role of early neuronal activity for brain maturation, as well as its contribution to neurodevelopmental disorders.
Methodological Approach for Optogenetic Manipulation of Neonatal Neuronal Networks.
Bitzenhofer, Sebastian H; Ahlbeck, Joachim; Hanganu-Opatz, Ileana L
2017-01-01
Coordinated patterns of electrical activity are critical for the functional maturation of neuronal networks, yet their interrogation has proven difficult in the developing brain. Optogenetic manipulations strongly contributed to the mechanistic understanding of network activation in the adult brain, but difficulties to specifically and reliably express opsins at neonatal age hampered similar interrogation of developing circuits. Here, we introduce a protocol that enables to control the activity of specific neuronal populations by light, starting from early postnatal development. We show that brain area-, layer- and cell type-specific expression of opsins by in utero electroporation (IUE), as exemplified for the medial prefrontal cortex (PFC) and hippocampus (HP), permits the manipulation of neuronal activity in vitro and in vivo. Both individual and population responses to different patterns of light stimulation are monitored by extracellular multi-site recordings in the medial PFC of neonatal mice. The expression of opsins via IUE provides a flexible approach to disentangle the cellular mechanism underlying early rhythmic network activity, and to elucidate the role of early neuronal activity for brain maturation, as well as its contribution to neurodevelopmental disorders.
Crystal Structure Representation for Neural Networks using Topological Approach.
Fedorov, Aleksandr V; Shamanaev, Ivan V
2017-08-01
In the present work we describe a new approach, which uses topology of crystals for physicochemical properties prediction using artificial neural networks (ANN). The topologies of 268 crystal structures were determined using ToposPro software. Quotient graphs were used to identify topological centers and their neighbors. The topological approach was illustrated by training ANN to predict molar heat capacity, standard molar entropy and lattice energy of 268 crystals with different compositions and structures (metals, inorganic salts, oxides, etc.). ANN was trained using Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. Mean absolute percentage error of predicted properties was ≤8 %. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.
Evaluating the impact of interdisciplinary research: a multilayer network approach
Omodei, Elisa; Arenas, Alex
2016-01-01
Nowadays, scientific challenges usually require approaches that cross traditional boundaries between academic disciplines, driving many researchers towards interdisciplinarity. Despite its obvious importance, there is a lack of studies on how to quantify the influence of interdisciplinarity on the research impact, posing uncertainty in a proper evaluation for hiring and funding purposes. Here we propose a method based on the analysis of bipartite interconnected multilayer networks of citations and disciplines, to assess scholars, institutions and countries interdisciplinary importance. Using data about physics publications and US patents, we show that our method allows to reveal, using a quantitative approach, that being more interdisciplinary causes -- in the Granger sense -- benefits in scientific productivity and impact. The proposed method could be used by funding agencies, universities and scientific policy decision makers for hiring and funding purposes, and to complement existing methods to rank univer...
Flow MRI simulation in complex 3D geometries: Application to the cerebral venous network.
Fortin, Alexandre; Salmon, Stéphanie; Baruthio, Joseph; Delbany, Maya; Durand, Emmanuel
2018-02-05
Develop and evaluate a complete tool to include 3D fluid flows in MRI simulation, leveraging from existing software. Simulation of MR spin flow motion is of high interest in the study of flow artifacts and angiography. However, at present, only a few simulators include this option and most are restricted to static tissue imaging. An extension of JEMRIS, one of the most advanced high performance open-source simulation platforms to date, was developed. The implementation of a Lagrangian description of the flow allows simulating any MR experiment, including both static tissues and complex flow data from computational fluid dynamics. Simulations of simple flow models are compared with real experiments on a physical flow phantom. A realistic simulation of 3D flow MRI on the cerebral venous network is also carried out. Simulations and real experiments are in good agreement. The generality of the framework is illustrated in 2D and 3D with some common flow artifacts (misregistration and inflow enhancement) and with the three main angiographic techniques: phase contrast velocimetry (PC), time-of-flight, and contrast-enhanced imaging MRA. The framework provides a versatile and reusable tool for the simulation of any MRI experiment including physiological fluids and arbitrarily complex flow motion. © 2018 International Society for Magnetic Resonance in Medicine.
Bisdom, K.; Nick, H. M.; Bertotti, G.
2017-06-01
Fluid flow in naturally fractured reservoirs is often controlled by subseismic-scale fracture networks. Although the fracture network can be partly sampled in the direct vicinity of wells, the inter-well scale network is poorly constrained in fractured reservoir models. Outcrop analogues can provide data for populating domains of the reservoir model where no direct measurements are available. However, extracting relevant statistics from large outcrops representative of inter-well scale fracture networks remains challenging. Recent advances in outcrop imaging provide high-resolution datasets that can cover areas of several hundred by several hundred meters, i.e. the domain between adjacent wells, but even then, data from the high-resolution models is often upscaled to reservoir flow grids, resulting in loss of accuracy. We present a workflow that uses photorealistic georeferenced outcrop models to construct geomechanical and fluid flow models containing thousands of discrete fractures covering sufficiently large areas, that does not require upscaling to model permeability. This workflow seamlessly integrates geomechanical Finite Element models with flow models that take into account stress-sensitive fracture permeability and matrix flow to determine the full permeability tensor. The applicability of this workflow is illustrated using an outcropping carbonate pavement in the Potiguar basin in Brazil, from which 1082 fractures are digitised. The permeability tensor for a range of matrix permeabilities shows that conventional upscaling to effective grid properties leads to potential underestimation of the true permeability and the orientation of principal permeabilities. The presented workflow yields the full permeability tensor model of discrete fracture networks with stress-induced apertures, instead of relying on effective properties as most conventional flow models do.
Numerical Simulation of Fluid Flow through Fractal-Based Discrete Fractured Network
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Wendong Wang
2018-01-01
Full Text Available Abstract: In recent years, multi-stage hydraulic fracturing technologies have greatly facilitated the development of unconventional oil and gas resources. However, a quantitative description of the “complexity” of the fracture network created by the hydraulic fracturing is confronted with many unsolved challenges. Given the multiple scales and heterogeneity of the fracture system, this study proposes a “bifurcated fractal” model to quantitatively describe the distribution of induced hydraulic fracture networks. The construction theory is employed to generate hierarchical fracture patterns as a scaled numerical model. With the implementation of discrete fractal-fracture network modeling (DFFN, fluid flow characteristics in bifurcated fractal fracture networks are characterized. The effects of bifurcated fracture length, bifurcated tendency, and number of bifurcation stages are examined. A field example of the fractured horizontal well is introduced to calibrate the accuracy of the flow model. The proposed model can provide a more realistic representation of complex fracture networks around a fractured horizontal well, and offer the way to quantify the “complexity” of the fracture network in shale reservoirs. The simulation results indicate that the geometry of the bifurcated fractal fracture network model has a significant impact on production performance in the tight reservoir, and enhancing connectivity of each bifurcate fracture is the key to improve the stimulation performance. In practice, this work provides a novel and efficient workflow for complex fracture characterization and production prediction in naturally-fractured reservoirs of multi-stage fractured horizontal wells.
A network approach to analyzing highly recombinant malaria parasite genes.
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Daniel B Larremore
Full Text Available The var genes of the human malaria parasite Plasmodium falciparum present a challenge to population geneticists due to their extreme diversity, which is generated by high rates of recombination. These genes encode a primary antigen protein called PfEMP1, which is expressed on the surface of infected red blood cells and elicits protective immune responses. Var gene sequences are characterized by pronounced mosaicism, precluding the use of traditional phylogenetic tools that require bifurcating tree-like evolutionary relationships. We present a new method that identifies highly variable regions (HVRs, and then maps each HVR to a complex network in which each sequence is a node and two nodes are linked if they share an exact match of significant length. Here, networks of var genes that recombine freely are expected to have a uniformly random structure, but constraints on recombination will produce network communities that we identify using a stochastic block model. We validate this method on synthetic data, showing that it correctly recovers populations of constrained recombination, before applying it to the Duffy Binding Like-α (DBLα domain of var genes. We find nine HVRs whose network communities map in distinctive ways to known DBLα classifications and clinical phenotypes. We show that the recombinational constraints of some HVRs are correlated, while others are independent. These findings suggest that this micromodular structuring facilitates independent evolutionary trajectories of neighboring mosaic regions, allowing the parasite to retain protein function while generating enormous sequence diversity. Our approach therefore offers a rigorous method for analyzing evolutionary constraints in var genes, and is also flexible enough to be easily applied more generally to any highly recombinant sequences.
Deep convolutional neural network approach for forehead tissue thickness estimation
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Manit Jirapong
2017-09-01
Full Text Available In this paper, we presented a deep convolutional neural network (CNN approach for forehead tissue thickness estimation. We use down sampled NIR laser backscattering images acquired from a novel marker-less near-infrared laser-based head tracking system, combined with the beam’s incident angle parameter. These two-channel augmented images were constructed for the CNN input, while a single node output layer represents the estimated value of the forehead tissue thickness. The models were – separately for each subject – trained and tested on datasets acquired from 30 subjects (high resolution MRI data is used as ground truth. To speed up training, we used a pre-trained network from the first subject to bootstrap training for each of the other subjects. We could show a clear improvement for the tissue thickness estimation (mean RMSE of 0.096 mm. This proposed CNN model outperformed previous support vector regression (mean RMSE of 0.155 mm or Gaussian processes learning approaches (mean RMSE of 0.114 mm and eliminated their restrictions for future research.
Predicting Peer Nominations Among Medical Students: A Social Network Approach.
Michalec, Barret; Grbic, Douglas; Veloski, J Jon; Cuddy, Monica M; Hafferty, Frederic W
2016-06-01
Minimal attention has been paid to what factors may predict peer nomination or how peer nominations might exhibit a clustering effect. Focusing on the homophily principle that "birds of a feather flock together," and using a social network analysis approach, the authors investigated how certain student- and/or school-based factors might predict the likelihood of peer nomination, and the clusters, if any, that occur among those nominations. In 2013, the Jefferson Longitudinal Study of Medical Education included a special instrument to evaluate peer nominations. A total of 211 (81%) of 260 graduating medical students from the Sidney Kimmel Medical College responded to the peer nomination question. Data were analyzed using a relational contingency table and an ANOVA density model. Although peer nominations did not cluster around gender, age, or class rank, those students within an accelerated program, as well as those entering certain specialties, were more likely to nominate each other. The authors suggest that clerkships in certain specialties, as well as the accelerated program, may provide structured opportunities for students to connect and integrate, and that these opportunities may have an impact on peer nomination. The findings suggest that social network analysis is a useful approach to examine various aspects of peer nomination processes. The authors discuss implications regarding harnessing social cohesion within clinical clerkships, the possible development of siloed departmental identity and in-group favoritism, and future research possibilities.
Directory of Open Access Journals (Sweden)
Barbara Martini
2016-06-01
Full Text Available Emerging technologies such as Software-Defined Networks (SDN and Network Function Virtualization (NFV promise to address cost reduction and flexibility in network operation while enabling innovative network service delivery models. However, operational network service delivery solutions still need to be developed that actually exploit these technologies, especially at the multi-provider level. Indeed, the implementation of network functions as software running over a virtualized infrastructure and provisioned on a service basis let one envisage an ecosystem of network services that are dynamically and flexibly assembled by orchestrating Virtual Network Functions even across different provider domains, thereby coping with changeable user and service requirements and context conditions. In this paper we propose an approach that adopts Service-Oriented Architecture (SOA technology-agnostic architectural guidelines in the design of a solution for orchestrating and dynamically chaining Virtual Network Functions. We discuss how SOA, NFV, and SDN may complement each other in realizing dynamic network function chaining through service composition specification, service selection, service delivery, and placement tasks. Then, we describe the architecture of a SOA-inspired NFV orchestrator, which leverages SDN-based network control capabilities to address an effective delivery of elastic chains of Virtual Network Functions. Preliminary results of prototype implementation and testing activities are also presented. The benefits for Network Service Providers are also described that derive from the adaptive network service provisioning in a multi-provider environment through the orchestration of computing and networking services to provide end users with an enhanced service experience.
Flow of Emotional Messages in Artificial Social Networks
Chmiel, Anna; Hołyst, Janusz A.
Models of message flows in an artificial group of users communicating via the Internet are introduced and investigated using numerical simulations. We assumed that messages possess an emotional character with a positive valence and that the willingness to send the next affective message to a given person increases with the number of messages received from this person. As a result, the weights of links between group members evolve over time. Memory effects are introduced, taking into account that the preferential selection of message receivers depends on the communication intensity during the recent period only. We also model the phenomenon of secondary social sharing when the reception of an emotional e-mail triggers the distribution of several emotional e-mails to other people.
A neural network based reputation bootstrapping approach for service selection
Wu, Quanwang; Zhu, Qingsheng; Li, Peng
2015-10-01
With the concept of service-oriented computing becoming widely accepted in enterprise application integration, more and more computing resources are encapsulated as services and published online. Reputation mechanism has been studied to establish trust on prior unknown services. One of the limitations of current reputation mechanisms is that they cannot assess the reputation of newly deployed services as no record of their previous behaviours exists. Most of the current bootstrapping approaches merely assign default reputation values to newcomers. However, by this kind of methods, either newcomers or existing services will be favoured. In this paper, we present a novel reputation bootstrapping approach, where correlations between features and performance of existing services are learned through an artificial neural network (ANN) and they are then generalised to establish a tentative reputation when evaluating new and unknown services. Reputations of services published previously by the same provider are also incorporated for reputation bootstrapping if available. The proposed reputation bootstrapping approach is seamlessly embedded into an existing reputation model and implemented in the extended service-oriented architecture. Empirical studies of the proposed approach are shown at last.
Flow Oriented Channel Assignment for Multi-radio Wireless Mesh Networks
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Niu Zhisheng
2010-01-01
Full Text Available We investigate channel assignment for a multichannel wireless mesh network backbone, where each router is equipped with multiple interfaces. Of particular interest is the development of channel assignment heuristics for multiple flows. We present an optimization formulation and then propose two iterative flow oriented heuristics for the conflict-free and interference-aware cases, respectively. To maximize the aggregate useful end-to-end flow rates, both algorithms identify and resolve congestion at instantaneous bottleneck link in each iteration. Then the link rate is optimally allocated among contending flows that share this link by solving a linear programming (LP problem. A thorough performance evaluation is undertaken as a function of the number of channels and interfaces/node and the number of contending flows. The performance of our algorithm is shown to be significantly superior to best known algorithm in its class in multichannel limited radio scenarios.
CoreFlow: Enriching Bro security events using network traffic monitoring data
Koning, R.; Buraglio, N.; de Laat, C.; Grosso, P.
Attacks against network infrastructures can be detected by Intrusion Detection Systems (IDS). Still reaction to these events are often limited by the lack of larger contextual information in which they occurred. In this paper we present CoreFlow, a framework for the correlation and enrichment of IDS
Impact of trucking network flow on preferred biorefinery locations in the southern United States
Timothy M. Young; Lee D. Han; James H. Perdue; Stephanie R. Hargrove; Frank M. Guess; Xia Huang; Chung-Hao Chen
2017-01-01
The impact of the trucking transportation network flow was modeled for the southern United States. The study addresses a gap in existing research by applying a Bayesian logistic regression and Geographic Information System (GIS) geospatial analysis to predict biorefinery site locations. A one-way trucking cost assuming a 128.8 km (80-mile) haul distance was estimated...
Kim, Jinyoung
2017-12-01
As it becomes common for Internet users to use hashtags when posting and searching information on social media, it is important to understand who builds a hashtag network and how information is circulated within the network. This article focused on unlocking the potential of the #AlphaGo hashtag network by addressing the following questions. First, the current study examined whether traditional opinion leadership (i.e., the influentials hypothesis) or grassroot participation by the public (i.e., the interpersonal hypothesis) drove dissemination of information in the hashtag network. Second, several unique patterns of information distribution by key users were identified. Finally, the association between attributes of key users who exerted great influence on information distribution (i.e., the number of followers and follows) and their central status in the network was tested. To answer the proffered research questions, a social network analysis was conducted using a large-scale hashtag network data set from Twitter (n = 21,870). The results showed that the leading actors in the network were actively receiving information from their followers rather than serving as intermediaries between the original information sources and the public. Moreover, the leading actors played several roles (i.e., conversation starters, influencers, and active engagers) in the network. Furthermore, the number of their follows and followers were significantly associated with their central status in the hashtag network. Based on the results, the current research explained how the information was exchanged in the hashtag network by proposing the reciprocal model of information flow.
Towards a Fair and Efficient Packet Scheduling Scheme in Inter-Flow Network Coding
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Jin Wang
2014-11-01
Full Text Available Network coding techniques are usually applied upon network-layer protocols to improve throughput in wireless networks. In scenarios with multiple unicast sessions, fairness is also an important factor. Therefore, a network coding-aware packet-scheduling algorithm is required. A packet-scheduling algorithm determines which packet to send next from a node’s packet backlog. Existing protocols mostly employ a basic round-robin scheduling algorithm to give “equal” opportunities to different packet flows. In fact, this “equal”-opportunity scheduling is neither fair, nor efficient. This paper intends to accentuate the importance of a coding-aware scheduling scheme. With a good scheduling scheme, we can gain more control over the per-flow throughput and fairness. Specifically, we first formulate a static scheduling problem and propose an algorithm to find the optimal scheduling scheme. We then extend the technique to a dynamic setting and, later, to practical routing protocols. Results show that the algorithm is comparatively scalable, and it can improve the throughput gain when the network is not severely saturated. The fairness among flows is drastically improved as a result of this scheduling scheme.
Network modeling for reverse flows of end-of-life vehicles.
Ene, Seval; Öztürk, Nursel
2015-04-01
Product recovery operations are of critical importance for the automotive industry in complying with environmental regulations concerning end-of-life products management. Manufacturers must take responsibility for their products over the entire life cycle. In this context, there is a need for network design methods for effectively managing recovery operations and waste. The purpose of this study is to develop a mathematical programming model for managing reverse flows in end-of-life vehicles' recovery network. A reverse flow is the collection of used products from consumers and the transportation of these products for the purpose of recycling, reuse or disposal. The proposed model includes all operations in a product recovery and waste management network for used vehicles and reuse for vehicle parts such as collection, disassembly, refurbishing, processing (shredding), recycling, disposal and reuse of vehicle parts. The scope of the network model is to determine the numbers and locations of facilities in the network and the material flows between these facilities. The results show the performance of the model and its applicability for use in the planning of recovery operations in the automotive industry. The main objective of recovery and waste management is to maximize revenue and minimize pollution in end-of-life product operations. This study shows that with an accurate model, these activities may provide economic benefits and incentives in addition to protecting the environment. Copyright © 2015 Elsevier Ltd. All rights reserved.
Modeling the Effect of Fluid Flow on a Growing Network of Fractures in a Porous Medium
Alhashim, Mohammed; Koch, Donald
2015-11-01
The injection of a viscous fluid at high pressure in a geological formation induces the fracturing of pre-existing joints. Assuming a constant solid-matrix stress field, a weak joint saturated with fluid is fractured when the fluid pressure exceeds a critical value that depends on the joint's orientation. In this work, the formation of a network of fractures in a porous medium is modeled. When the average length of the fractures is much smaller than the radius of a cluster of fractured joints, the fluid flow within the network can be described as Darcy flow in a permeable medium consisting of the fracture network. The permeability and porosity of the medium are functions of the number density of activated joints and consequently depend on the fluid pressure. We demonstrate conditions under which these relationships can be derived from percolation theory. Fluid may also be lost from the fracture network by flowing into the permeable rock matrix. The solution of the model shows that the cluster radius grows as a power law with time in two regimes: (1) an intermediate time regime when the network contains many fractures but fluid loss is negligible; and (2) a long time regime when fluid loss dominates. In both regimes, the power law exponent depends on the Euclidean dimension and the injection rate dependence on time.
Hisamatu, Hiroyuki; Ohsaki, Hiroyuki; Murata, Masayuki
2003-08-01
In the current Internet, most of the traffic is transmitted by TCP (Transmission Control Protocol). In our previous work, we have proposed a modeling approach for the entire network, including TCP congestion control mechanisms operating at source hosts and the network seen by TCP connections, as a single feedback system. However, our analytic model is limited to a simple network, where TCP connections have the identical propagation delay. In this paper, we therefore extend our analytic approach to a more generic network, where multiple TCP connections are allowed to have different propagation delays. We derive the packet loss probability in the network, the throughput and the average round-trip time of each TCP connection in steady state. By presenting several numerical examples, we quantitatively investigate how the fairness among TCP connections is degraded when multiple TCP connections with different propagation delays share the single bottleneck link.
A Technical Approach on Large Data Distributed Over a Network
Directory of Open Access Journals (Sweden)
Suhasini G
2011-12-01
Full Text Available Data mining is nontrivial extraction of implicit, previously unknown and potential useful information from the data. For a database with number of records and for a set of classes such that each record belongs to one of the given classes, the problem of classification is to decide the class to which the given record belongs. The classification problem is also to generate a model for each class from given data set. We are going to make use of supervised classification in which we have training dataset of record, and for each record the class to which it belongs is known. There are many approaches to supervised classification. Decision tree is attractive in data mining environment as they represent rules. Rules can readily expressed in natural languages and they can be even mapped o database access languages. Now a days classification based on decision trees is one of the important problems in data mining which has applications in many areas. Now a days database system have become highly distributed, and we are using many paradigms. we consider the problem of inducing decision trees in a large distributed network of highly distributed databases. The classification based on decision tree can be done on the existence of distributed databases in healthcare and in bioinformatics, human computer interaction and by the view that these databases are soon to contain large amounts of data, characterized by its high dimensionality. Current decision tree algorithms would require high communication bandwidth, memory, and they are less efficient and scalability reduces when executed on such large volume of data. So there are some approaches being developed to improve the scalability and even approaches to analyse the data distributed over a network.[keywords: Data mining, Decision tree, decision tree induction, distributed data, classification
Directory of Open Access Journals (Sweden)
Che-Ting Kuo
2015-02-01
Full Text Available This paper introduces a network-based interval type-2 fuzzy inference system (NT2FIS with a dynamic solution agent algorithm water flow like algorithm (WFA, for nonlinear system identification and blind source separation (BSS problem. The NT2FIS consists of interval type-2 asymmetric fuzzy membership functions and TSK-type consequent parts to enhance the performance. The proposed scheme is optimized by a new heuristic learning algorithm, WFA, with dynamic solution agents. The proposed WFA is inspired by the natural behavior of water flow. Splitting, moving, merging, evaporation, and precipitation have all been introduced for optimization. Some modifications, including new moving strategies, such as the application of tabu searching and gradient-descent techniques, are proposed to enhance the performance of the WFA in training the NT2FIS systems. Simulation and comparison results for nonlinear system identification and blind signal separation are presented to illustrate the performance and effectiveness of the proposed approach.
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Ming-Chorng Hwang
2015-01-01
Full Text Available A theoretic formulation on how traffic time information distributed by ITS operations influences the trajectory of network flows is presented in this paper. The interactions between users and ITS operator are decomposed into three parts: (i travel time induced path flow dynamics (PFDTT; (ii demand induced path flow dynamics (PFDD; and (iii predicted travel time dynamics for an origin-destination (OD pair (PTTDOD. PFDTT describes the collective results of user’s daily route selection by pairwise comparison of path travel time provided by ITS services. The other two components, PTTDOD and PFDD, are concentrated on the evolutions of system variables which are predicted and observed, respectively, by ITS operators to act as a benchmark in guiding the target system towards an expected status faster. In addition to the delivered modelings, the stability theorem of the equilibrium solution in the sense of Lyapunov stability is also provided. A Lyapunov function is developed and employed to the proof of stability theorem to show the asymptotic behavior of the aimed system. The information of network flow dynamics plays a key role in traffic control policy-making. The evaluation of ITS-based strategies will not be reasonable without a well-established modeling of network flow evolutions.
A Novel Biobjective Risk-Based Model for Stochastic Air Traffic Network Flow Optimization Problem.
Cai, Kaiquan; Jia, Yaoguang; Zhu, Yanbo; Xiao, Mingming
2015-01-01
Network-wide air traffic flow management (ATFM) is an effective way to alleviate demand-capacity imbalances globally and thereafter reduce airspace congestion and flight delays. The conventional ATFM models assume the capacities of airports or airspace sectors are all predetermined. However, the capacity uncertainties due to the dynamics of convective weather may make the deterministic ATFM measures impractical. This paper investigates the stochastic air traffic network flow optimization (SATNFO) problem, which is formulated as a weighted biobjective 0-1 integer programming model. In order to evaluate the effect of capacity uncertainties on ATFM, the operational risk is modeled via probabilistic risk assessment and introduced as an extra objective in SATNFO problem. Computation experiments using real-world air traffic network data associated with simulated weather data show that presented model has far less constraints compared to stochastic model with nonanticipative constraints, which means our proposed model reduces the computation complexity.
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Tarek Bouktir
2012-06-01
Full Text Available This paper presents solution of optimal power flow (OPF problem of a power system via Differential Evolution (DE algorithm. The purpose of an electric power system is to deliver real power to the greatest number of users at the lowest possible cost all the time. So the objective is to minimize the total fuel cost of the generating units and also maintaining an acceptable system performance in terms of limits on generator reactive power outputs, bus voltages, Static VAR Compensator (SVC parameters and overload in transmission lines. CPU times can be reduced by decomposing the problem in two subproblems, the first subproblem minimize the fuel cost of generation and the second subproblem is a reactive power dispatch so optimum bus voltages can be determined and reduce the losses by controlling tap changes of the transformers and the static Var Compensators (SVC. To verify the proposed approach and for comparison purposes, we perform simulations on the Algerian network with 114 buses, 175 branches (lines and transformers and 15 generators. The obtained results indicate that DE is an easy to use, fast, robust and powerful optimization technique compared to the other global optimization methods such as PSO and GA.
Gong, J.; Rossen, W.R.
2016-01-01
The flow properties of naturally fractured reservoirs are dominated by flow through the fractures. In a previous study we showed that even a well-connected fracture network behaves like a much sparser network when the aperture distribution is broad enough: i.e., most fractures can be eliminated
A new approach to speed-flow curves
DEFF Research Database (Denmark)
Fosgerau, Mogens; Hjorth, Katrine; Jensen, Thomas Christian
We develop a simple model of travel time as a function of travel demand, using loop detector data of travel times and traffic flows on a Danish motorway. Our goal is a model that avoids the potential endogeneity problems related to modelling travel time as a function of observed traffic flow. Ins...
An Active, Collaborative Approach to Learning Skills in Flow Cytometry
Fuller, Kathryn; Linden, Matthew D.; Lee-Pullen, Tracey; Fragall, Clayton; Erber, Wendy N.; Röhrig, Kimberley J.
2016-01-01
Advances in science education research have the potential to improve the way students learn to perform scientific interpretations and understand science concepts. We developed active, collaborative activities to teach skills in manipulating flow cytometry data using FlowJo software. Undergraduate students were given compensated clinical flow…
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Suprayogi Suprayogi
2016-12-01
Full Text Available This paper considers a location problem in a supply chain network. The problem addressed in this paper is motivated by an initiative to develop an efficient supply chain network for supporting the agricultural activities. The supply chain network consists of regions, warehouses, distribution centers, plants, and markets. The products include a set of inbound products and a set of outbound products. In this paper, definitions of the inbound and outbound products are seen from the region’s point of view. The inbound product is the product demanded by regions and produced by plants which flows on a sequence of the following entities: plants, distribution centers, warehouses, and regions. The outbound product is the product demanded by markets and produced by regions and it flows on a sequence of the following entities: regions, warehouses, and markets. The problem deals with determining locations of the warehouses and the distribution centers to be opened and shipment quantities associated with all links on the network that minimizes the total cost. The problem can be considered as a strategic supply chain network problem. A solution approach based on genetic algorithm (GA is proposed. The proposed GA is examined using hypothetical instances and its results are compared to the solution obtained by solving the mixed integer linear programming (MILP model. The comparison shows that there is a small gap (0.23%, on average between the proposed GA and MILP model in terms of the total cost. The proposed GA consistently provides solutions with least total cost. In terms of total cost, based on the experiment, it is demonstrated that coefficients of variation are closed to 0.
Sochi, Taha
2014-01-01
Several deterministic and stochastic multi-variable global optimization algorithms (Conjugate Gradient, Nelder-Mead, Quasi-Newton, and Global) are investigated in conjunction with energy minimization principle to resolve the pressure and volumetric flow rate fields in single ducts and networks of interconnected ducts. The algorithms are tested with seven types of fluid: Newtonian, power law, Bingham, Herschel-Bulkley, Ellis, Ree-Eyring and Casson. The results obtained from all those algorithms for all these types of fluid agree very well with the analytically derived solutions as obtained from the traditional methods which are based on the conservation principles and fluid constitutive relations. The results confirm and generalize the findings of our previous investigations that the energy minimization principle is at the heart of the flow dynamics systems. The investigation also enriches the methods of Computational Fluid Dynamics for solving the flow fields in tubes and networks for various types of Newtoni...
Neuro and Fuzzy Computing Approach for the Flow Sensorless Measurement
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R. Kumar
2009-10-01
Full Text Available An attempt to use differential pressure induced by control valve for flow measurement has been proposed. The flow rate obtained by NFM model is closer to the actual value with the maximum error being ± 3.28 %. In NNM model, the error is 92.2% in the lower flow and 4.39 % in the higher flow rate. The air flow increases more linearly in NFM than NNM with valve position and pressure drops. ARM 7 processor used in this work is a high speed and low power consuming processor and this can be integrated with field bus, CAN bus and internet based system, which is being standardized internationally.
Understanding Networks of Computing Chemical Droplet Neurons Based on Information Flow.
Gruenert, Gerd; Gizynski, Konrad; Escuela, Gabi; Ibrahim, Bashar; Gorecki, Jerzy; Dittrich, Peter
2015-11-01
In this paper, we present general methods that can be used to explore the information processing potential of a medium composed of oscillating (self-exciting) droplets. Networks of Belousov-Zhabotinsky (BZ) droplets seem especially interesting as chemical reaction-diffusion computers because their time evolution is qualitatively similar to neural network activity. Moreover, such networks can be self-generated in microfluidic reactors. However, it is hard to track and to understand the function performed by a medium composed of droplets due to its complex dynamics. Corresponding to recurrent neural networks, the flow of excitations in a network of droplets is not limited to a single direction and spreads throughout the whole medium. In this work, we analyze the operation performed by droplet systems by monitoring the information flow. This is achieved by measuring mutual information and time delayed mutual information of the discretized time evolution of individual droplets. To link the model with reality, we use experimental results to estimate the parameters of droplet interactions. We exemplarily investigate an evolutionary generated droplet structure that operates as a NOR gate. The presented methods can be applied to networks composed of at least hundreds of droplets.
A Novel Topology Link-Controlling Approach for Active Defense of a Node in a Network
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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.
A Novel Topology Link-Controlling Approach for Active Defense of a Node in a Network.
Li, Jun; Hu, HanPing; Ke, Qiao; Xiong, Naixue
2017-03-09
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.
Simulation of Code Spectrum and Code Flow of Cultured Neuronal Networks.
Tamura, Shinichi; Nishitani, Yoshi; Hosokawa, Chie; Miyoshi, Tomomitsu; Sawai, Hajime
2016-01-01
It has been shown that, in cultured neuronal networks on a multielectrode, pseudorandom-like sequences (codes) are detected, and they flow with some spatial decay constant. Each cultured neuronal network is characterized by a specific spectrum curve. That is, we may consider the spectrum curve as a "signature" of its associated neuronal network that is dependent on the characteristics of neurons and network configuration, including the weight distribution. In the present study, we used an integrate-and-fire model of neurons with intrinsic and instantaneous fluctuations of characteristics for performing a simulation of a code spectrum from multielectrodes on a 2D mesh neural network. We showed that it is possible to estimate the characteristics of neurons such as the distribution of number of neurons around each electrode and their refractory periods. Although this process is a reverse problem and theoretically the solutions are not sufficiently guaranteed, the parameters seem to be consistent with those of neurons. That is, the proposed neural network model may adequately reflect the behavior of a cultured neuronal network. Furthermore, such prospect is discussed that code analysis will provide a base of communication within a neural network that will also create a base of natural intelligence.
Energy Technology Data Exchange (ETDEWEB)
Linden, P.; Pazsit, I. [Department of Reactor Physics, Chalmers University of Technology, Goeteborg (Sweden)
1998-08-01
Mass flow of water in a pipe can be measured in a non-intrusive way by the pulsed neutron activation (PNA) technique. From such measurements, mass flow can be estimated by various techniques of time averaging, performed on the time-resolved detector signal(s). However, time averaging methods have a few percent systematic error, which, in addition, is not a constant but varies with flow and measurement parameters. Achieving a precision better than 1% from PNA measurements is a hitherto unsolved task. In this paper a methodology is suggested to solve this task and is tested by simulation methods. The method is based on the use of artificial neural networks to determine mass flow rate from the time resolved detector signal. To achieve this, the network needs to be trained on a large number of real detector data. It is suggested that these data should be obtained by advanced numerical simulation of the PNA measurement. In this paper we use a simplified simulation model for a feasibility study of the methodology. It is shown that a neural network is capable to determine the mass flow rate with a precision of about 0.5%. (orig.) [Deutsch] Das Stroemungsverhalten von Wasser in einer Leitung kann nicht-invasiv durch gepulste Neutronenaktivierungsverfahren (PNA) gemessen werden. Durch solche Messungen kann das Stroemungsverhalten mit Hilfe verschiedener Techniken der Mittelung ueber zeitabhaengige Detektorsignale bestimmt werden. Der systematische Fehler von Zeitmittelungsmethoden liegt jedoch bei ein paar Prozent und ist zusaetzlich nicht konstant, sondern variiert mit den Stroemungs- und Messparametern. Das Erreichen einer Genauigkeit von besser als 1% ist also bei Neutronenaktivierungsverfahren ein bisher ungeloestes Problem. In der vorliegenden Arbeit wird eine Methode zur Loesung dieser Aufgabe vorgeschlagen und mit Hilfe von Simulationsverfahren getestet. Die Methode basiert auf der Verwendung von kuenstlichen, neuralen Netzwerken zur Bestimmung des