Fujimoto, Richard
2006-01-01
"Network Simulation" presents a detailed introduction to the design, implementation, and use of network simulation tools. Discussion topics include the requirements and issues faced for simulator design and use in wired networks, wireless networks, distributed simulation environments, and fluid model abstractions. Several existing simulations are given as examples, with details regarding design decisions and why those decisions were made. Issues regarding performance and scalability are discussed in detail, describing how one can utilize distributed simulation methods to increase the
Modified network simulation model with token method of bus access
Directory of Open Access Journals (Sweden)
L.V. Stribulevich
2013-08-01
Full Text Available Purpose. To study the characteristics of the local network with the marker method of access to the bus its modified simulation model was developed. Methodology. Defining characteristics of the network is carried out on the developed simulation model, which is based on the state diagram-layer network station with the mechanism of processing priorities, both in steady state and in the performance of control procedures: the initiation of a logical ring, the entrance and exit of the station network with a logical ring. Findings. A simulation model, on the basis of which can be obtained the dependencies of the application the maximum waiting time in the queue for different classes of access, and the reaction time usable bandwidth on the data rate, the number of network stations, the generation rate applications, the number of frames transmitted per token holding time, frame length was developed. Originality. The technique of network simulation reflecting its work in the steady condition and during the control procedures, the mechanism of priority ranking and handling was proposed. Practical value. Defining network characteristics in the real-time systems on railway transport based on the developed simulation model.
Sánchez-Pérez, J F; Marín, F; Morales, J L; Cánovas, M; Alhama, F
2018-01-01
Mathematical models simulating different and representative engineering problem, atomic dry friction, the moving front problems and elastic and solid mechanics are presented in the form of a set of non-linear, coupled or not coupled differential equations. For different parameters values that influence the solution, the problem is numerically solved by the network method, which provides all the variables of the problems. Although the model is extremely sensitive to the above parameters, no assumptions are considered as regards the linearization of the variables. The design of the models, which are run on standard electrical circuit simulation software, is explained in detail. The network model results are compared with common numerical methods or experimental data, published in the scientific literature, to show the reliability of the model.
2018-01-01
Mathematical models simulating different and representative engineering problem, atomic dry friction, the moving front problems and elastic and solid mechanics are presented in the form of a set of non-linear, coupled or not coupled differential equations. For different parameters values that influence the solution, the problem is numerically solved by the network method, which provides all the variables of the problems. Although the model is extremely sensitive to the above parameters, no assumptions are considered as regards the linearization of the variables. The design of the models, which are run on standard electrical circuit simulation software, is explained in detail. The network model results are compared with common numerical methods or experimental data, published in the scientific literature, to show the reliability of the model. PMID:29518121
Simulating Social Networks of Online Communities: Simulation as a Method for Sociability Design
Ang, Chee Siang; Zaphiris, Panayiotis
We propose the use of social simulations to study and support the design of online communities. In this paper, we developed an Agent-Based Model (ABM) to simulate and study the formation of social networks in a Massively Multiplayer Online Role Playing Game (MMORPG) guild community. We first analyzed the activities and the social network (who-interacts-with-whom) of an existing guild community to identify its interaction patterns and characteristics. Then, based on the empirical results, we derived and formalized the interaction rules, which were implemented in our simulation. Using the simulation, we reproduced the observed social network of the guild community as a means of validation. The simulation was then used to examine how various parameters of the community (e.g. the level of activity, the number of neighbors of each agent, etc) could potentially influence the characteristic of the social networks.
Network reliability analysis of complex systems using a non-simulation-based method
International Nuclear Information System (INIS)
Kim, Youngsuk; Kang, Won-Hee
2013-01-01
Civil infrastructures such as transportation, water supply, sewers, telecommunications, and electrical and gas networks often establish highly complex networks, due to their multiple source and distribution nodes, complex topology, and functional interdependence between network components. To understand the reliability of such complex network system under catastrophic events such as earthquakes and to provide proper emergency management actions under such situation, efficient and accurate reliability analysis methods are necessary. In this paper, a non-simulation-based network reliability analysis method is developed based on the Recursive Decomposition Algorithm (RDA) for risk assessment of generic networks whose operation is defined by the connections of multiple initial and terminal node pairs. The proposed method has two separate decomposition processes for two logical functions, intersection and union, and combinations of these processes are used for the decomposition of any general system event with multiple node pairs. The proposed method is illustrated through numerical network examples with a variety of system definitions, and is applied to a benchmark gas transmission pipe network in Memphis TN to estimate the seismic performance and functional degradation of the network under a set of earthquake scenarios.
First-order design of geodetic networks using the simulated annealing method
Berné, J. L.; Baselga, S.
2004-09-01
The general problem of the optimal design for a geodetic network subject to any extrinsic factors, namely the first-order design problem, can be dealt with as a numeric optimization problem. The classic theory of this problem and the optimization methods are revised. Then the innovative use of the simulated annealing method, which has been successfully applied in other fields, is presented for this classical geodetic problem. This method, belonging to iterative heuristic techniques in operational research, uses a thermodynamical analogy to crystalline networks to offer a solution that converges probabilistically to the global optimum. Basic formulation and some examples are studied.
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.
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.
A simulation training evaluation method for distribution network fault based on radar chart
Directory of Open Access Journals (Sweden)
Yuhang Xu
2018-01-01
Full Text Available In order to solve the problem of automatic evaluation of dispatcher fault simulation training in distribution network, a simulation training evaluation method based on radar chart for distribution network fault is proposed. The fault handling information matrix is established to record the dispatcher fault handling operation sequence and operation information. The four situations of the dispatcher fault isolation operation are analyzed. The fault handling anti-misoperation rule set is established to describe the rules prohibiting dispatcher operation. Based on the idea of artificial intelligence reasoning, the feasibility of dispatcher fault handling is described by the feasibility index. The relevant factors and evaluation methods are discussed from the three aspects of the fault handling result feasibility, the anti-misoperation correctness and the operation process conciseness. The detailed calculation formula is given. Combining the independence and correlation between the three evaluation angles, a comprehensive evaluation method of distribution network fault simulation training based on radar chart is proposed. The method can comprehensively reflect the fault handling process of dispatchers, and comprehensively evaluate the fault handling process from various angles, which has good practical value.
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.
Bernal, Javier; Torres-Jimenez, Jose
2015-01-01
SAGRAD (Simulated Annealing GRADient), a Fortran 77 program for computing neural networks for classification using batch learning, is discussed. Neural network training in SAGRAD is based on a combination of simulated annealing and Møller's scaled conjugate gradient algorithm, the latter a variation of the traditional conjugate gradient method, better suited for the nonquadratic nature of neural networks. Different aspects of the implementation of the training process in SAGRAD are discussed, such as the efficient computation of gradients and multiplication of vectors by Hessian matrices that are required by Møller's algorithm; the (re)initialization of weights with simulated annealing required to (re)start Møller's algorithm the first time and each time thereafter that it shows insufficient progress in reaching a possibly local minimum; and the use of simulated annealing when Møller's algorithm, after possibly making considerable progress, becomes stuck at a local minimum or flat area of weight space. Outlines of the scaled conjugate gradient algorithm, the simulated annealing procedure and the training process used in SAGRAD are presented together with results from running SAGRAD on two examples of training data.
2014-01-01
Background Motif mining has always been a hot research topic in bioinformatics. Most of current research on biological networks focuses on exact motif mining. However, due to the inevitable experimental error and noisy data, biological network data represented as the probability model could better reflect the authenticity and biological significance, therefore, it is more biological meaningful to discover probability motif in uncertain biological networks. One of the key steps in probability motif mining is frequent pattern discovery which is usually based on the possible world model having a relatively high computational complexity. Methods In this paper, we present a novel method for detecting frequent probability patterns based on circuit simulation in the uncertain biological networks. First, the partition based efficient search is applied to the non-tree like subgraph mining where the probability of occurrence in random networks is small. Then, an algorithm of probability isomorphic based on circuit simulation is proposed. The probability isomorphic combines the analysis of circuit topology structure with related physical properties of voltage in order to evaluate the probability isomorphism between probability subgraphs. The circuit simulation based probability isomorphic can avoid using traditional possible world model. Finally, based on the algorithm of probability subgraph isomorphism, two-step hierarchical clustering method is used to cluster subgraphs, and discover frequent probability patterns from the clusters. Results The experiment results on data sets of the Protein-Protein Interaction (PPI) networks and the transcriptional regulatory networks of E. coli and S. cerevisiae show that the proposed method can efficiently discover the frequent probability subgraphs. The discovered subgraphs in our study contain all probability motifs reported in the experiments published in other related papers. Conclusions The algorithm of probability graph isomorphism
IMPROVEMENT OF RECOGNITION QUALITY IN DEEP LEARNING NETWORKS BY SIMULATED ANNEALING METHOD
Directory of Open Access Journals (Sweden)
A. S. Potapov
2014-09-01
Full Text Available The subject of this research is deep learning methods, in which automatic construction of feature transforms is taken place in tasks of pattern recognition. Multilayer autoencoders have been taken as the considered type of deep learning networks. Autoencoders perform nonlinear feature transform with logistic regression as an upper classification layer. In order to verify the hypothesis of possibility to improve recognition rate by global optimization of parameters for deep learning networks, which are traditionally trained layer-by-layer by gradient descent, a new method has been designed and implemented. The method applies simulated annealing for tuning connection weights of autoencoders while regression layer is simultaneously trained by stochastic gradient descent. Experiments held by means of standard MNIST handwritten digit database have shown the decrease of recognition error rate from 1.1 to 1.5 times in case of the modified method comparing to the traditional method, which is based on local optimization. Thus, overfitting effect doesn’t appear and the possibility to improve learning rate is confirmed in deep learning networks by global optimization methods (in terms of increasing recognition probability. Research results can be applied for improving the probability of pattern recognition in the fields, which require automatic construction of nonlinear feature transforms, in particular, in the image recognition. Keywords: pattern recognition, deep learning, autoencoder, logistic regression, simulated annealing.
Random vs. Combinatorial Methods for Discrete Event Simulation of a Grid Computer Network
Kuhn, D. Richard; Kacker, Raghu; Lei, Yu
2010-01-01
This study compared random and t-way combinatorial inputs of a network simulator, to determine if these two approaches produce significantly different deadlock detection for varying network configurations. Modeling deadlock detection is important for analyzing configuration changes that could inadvertently degrade network operations, or to determine modifications that could be made by attackers to deliberately induce deadlock. Discrete event simulation of a network may be conducted using random generation, of inputs. In this study, we compare random with combinatorial generation of inputs. Combinatorial (or t-way) testing requires every combination of any t parameter values to be covered by at least one test. Combinatorial methods can be highly effective because empirical data suggest that nearly all failures involve the interaction of a small number of parameters (1 to 6). Thus, for example, if all deadlocks involve at most 5-way interactions between n parameters, then exhaustive testing of all n-way interactions adds no additional information that would not be obtained by testing all 5-way interactions. While the maximum degree of interaction between parameters involved in the deadlocks clearly cannot be known in advance, covering all t-way interactions may be more efficient than using random generation of inputs. In this study we tested this hypothesis for t = 2, 3, and 4 for deadlock detection in a network simulation. Achieving the same degree of coverage provided by 4-way tests would have required approximately 3.2 times as many random tests; thus combinatorial methods were more efficient for detecting deadlocks involving a higher degree of interactions. The paper reviews explanations for these results and implications for modeling and simulation.
International Nuclear Information System (INIS)
Alliston, W.H.; Johnson, S.J.; Mutafelija, B.A.
1975-01-01
A description is given of a training simulator for the real-time dynamic operation of a nuclear power plant which utilizes apparatus that includes control consoles having manual and automatic devices corresponding to simulated plant components and indicating devices for monitoring physical values in the simulated plant. A digital computer configuration is connected to the control consoles to calculate the dynamic real-time simulated operation of the plant in accordance with the simulated plant components to provide output data including data for operating the control console indicating devices. In the method and system for simulating a fluid distribution network of the power plant, such as that which includes, for example, a main steam system which distributes steam from steam generators to high pressure turbine steam reheaters, steam dump valves, and feedwater heaters, the simultaneous solution of linearized non-linear algebraic equations is used to calculate all the flows throughout the simulated system. A plurality of parallel connected steam generators that supply steam to the system are simulated individually, and include the simulation of shrink-swell characteristics
Packet Tracer network simulator
Jesin, A
2014-01-01
A practical, fast-paced guide that gives you all the information you need to successfully create networks and simulate them using Packet Tracer.Packet Tracer Network Simulator is aimed at students, instructors, and network administrators who wish to use this simulator to learn how to perform networking instead of investing in expensive, specialized hardware. This book assumes that you have a good amount of Cisco networking knowledge, and it will focus more on Packet Tracer rather than networking.
Wang, Ting; Plecháč, Petr
2017-12-01
Stochastic reaction networks that exhibit bistable behavior are common in systems biology, materials science, and catalysis. Sampling of stationary distributions is crucial for understanding and characterizing the long-time dynamics of bistable stochastic dynamical systems. However, simulations are often hindered by the insufficient sampling of rare transitions between the two metastable regions. In this paper, we apply the parallel replica method for a continuous time Markov chain in order to improve sampling of the stationary distribution in bistable stochastic reaction networks. The proposed method uses parallel computing to accelerate the sampling of rare transitions. Furthermore, it can be combined with the path-space information bounds for parametric sensitivity analysis. With the proposed methodology, we study three bistable biological networks: the Schlögl model, the genetic switch network, and the enzymatic futile cycle network. We demonstrate the algorithmic speedup achieved in these numerical benchmarks. More significant acceleration is expected when multi-core or graphics processing unit computer architectures and programming tools such as CUDA are employed.
Wang, Ting; Plecháč, Petr
2017-12-21
Stochastic reaction networks that exhibit bistable behavior are common in systems biology, materials science, and catalysis. Sampling of stationary distributions is crucial for understanding and characterizing the long-time dynamics of bistable stochastic dynamical systems. However, simulations are often hindered by the insufficient sampling of rare transitions between the two metastable regions. In this paper, we apply the parallel replica method for a continuous time Markov chain in order to improve sampling of the stationary distribution in bistable stochastic reaction networks. The proposed method uses parallel computing to accelerate the sampling of rare transitions. Furthermore, it can be combined with the path-space information bounds for parametric sensitivity analysis. With the proposed methodology, we study three bistable biological networks: the Schlögl model, the genetic switch network, and the enzymatic futile cycle network. We demonstrate the algorithmic speedup achieved in these numerical benchmarks. More significant acceleration is expected when multi-core or graphics processing unit computer architectures and programming tools such as CUDA are employed.
A Multilevel Adaptive Reaction-splitting Simulation Method for Stochastic Reaction Networks
Moraes, Alvaro; Tempone, Raul; Vilanova, Pedro
2016-01-01
In this work, we present a novel multilevel Monte Carlo method for kinetic simulation of stochastic reaction networks characterized by having simultaneously fast and slow reaction channels. To produce efficient simulations, our method adaptively classifies the reactions channels into fast and slow channels. To this end, we first introduce a state-dependent quantity named level of activity of a reaction channel. Then, we propose a low-cost heuristic that allows us to adaptively split the set of reaction channels into two subsets characterized by either a high or a low level of activity. Based on a time-splitting technique, the increments associated with high-activity channels are simulated using the tau-leap method, while those associated with low-activity channels are simulated using an exact method. This path simulation technique is amenable for coupled path generation and a corresponding multilevel Monte Carlo algorithm. To estimate expected values of observables of the system at a prescribed final time, our method bounds the global computational error to be below a prescribed tolerance, TOL, within a given confidence level. This goal is achieved with a computational complexity of order O(TOL-2), the same as with a pathwise-exact method, but with a smaller constant. We also present a novel low-cost control variate technique based on the stochastic time change representation by Kurtz, showing its performance on a numerical example. We present two numerical examples extracted from the literature that show how the reaction-splitting method obtains substantial gains with respect to the standard stochastic simulation algorithm and the multilevel Monte Carlo approach by Anderson and Higham. © 2016 Society for Industrial and Applied Mathematics.
A Multilevel Adaptive Reaction-splitting Simulation Method for Stochastic Reaction Networks
Moraes, Alvaro
2016-07-07
In this work, we present a novel multilevel Monte Carlo method for kinetic simulation of stochastic reaction networks characterized by having simultaneously fast and slow reaction channels. To produce efficient simulations, our method adaptively classifies the reactions channels into fast and slow channels. To this end, we first introduce a state-dependent quantity named level of activity of a reaction channel. Then, we propose a low-cost heuristic that allows us to adaptively split the set of reaction channels into two subsets characterized by either a high or a low level of activity. Based on a time-splitting technique, the increments associated with high-activity channels are simulated using the tau-leap method, while those associated with low-activity channels are simulated using an exact method. This path simulation technique is amenable for coupled path generation and a corresponding multilevel Monte Carlo algorithm. To estimate expected values of observables of the system at a prescribed final time, our method bounds the global computational error to be below a prescribed tolerance, TOL, within a given confidence level. This goal is achieved with a computational complexity of order O(TOL-2), the same as with a pathwise-exact method, but with a smaller constant. We also present a novel low-cost control variate technique based on the stochastic time change representation by Kurtz, showing its performance on a numerical example. We present two numerical examples extracted from the literature that show how the reaction-splitting method obtains substantial gains with respect to the standard stochastic simulation algorithm and the multilevel Monte Carlo approach by Anderson and Higham. © 2016 Society for Industrial and Applied Mathematics.
Timetable-based simulation method for choice set generation in large-scale public transport networks
DEFF Research Database (Denmark)
Rasmussen, Thomas Kjær; Anderson, Marie Karen; Nielsen, Otto Anker
2016-01-01
The composition and size of the choice sets are a key for the correct estimation of and prediction by route choice models. While existing literature has posed a great deal of attention towards the generation of path choice sets for private transport problems, the same does not apply to public...... transport problems. This study proposes a timetable-based simulation method for generating path choice sets in a multimodal public transport network. Moreover, this study illustrates the feasibility of its implementation by applying the method to reproduce 5131 real-life trips in the Greater Copenhagen Area...... and to assess the choice set quality in a complex multimodal transport network. Results illustrate the applicability of the algorithm and the relevance of the utility specification chosen for the reproduction of real-life path choices. Moreover, results show that the level of stochasticity used in choice set...
A Control Simulation Method of High-Speed Trains on Railway Network with Irregular Influence
International Nuclear Information System (INIS)
Yang Lixing; Li Xiang; Li Keping
2011-01-01
Based on the discrete time method, an effective movement control model is designed for a group of highspeed trains on a rail network. The purpose of the model is to investigate the specific traffic characteristics of high-speed trains under the interruption of stochastic irregular events. In the model, the high-speed rail traffic system is supposed to be equipped with the moving-block signalling system to guarantee maximum traversing capacity of the railway. To keep the safety of trains' movements, some operational strategies are proposed to control the movements of trains in the model, including traction operation, braking operation, and entering-station operation. The numerical simulations show that the designed model can well describe the movements of high-speed trains on the rail network. The research results can provide the useful information not only for investigating the propagation features of relevant delays under the irregular disturbance but also for rerouting and rescheduling trains on the rail network. (general)
CAISSON: Interconnect Network Simulator
Springer, Paul L.
2006-01-01
Cray response to HPCS initiative. Model future petaflop computer interconnect. Parallel discrete event simulation techniques for large scale network simulation. Built on WarpIV engine. Run on laptop and Altix 3000. Can be sized up to 1000 simulated nodes per host node. Good parallel scaling characteristics. Flexible: multiple injectors, arbitration strategies, queue iterators, network topologies.
Data flow methods for dynamic system simulation - A CSSL-IV microcomputer network interface
Makoui, A.; Karplus, W. J.
1983-01-01
A major problem in employing networks of microcomputers for the real-time simulation of complex systems is to allocate computational tasks to the various microcomputers in such a way that idle time and time lost in interprocess communication is minimized. The research reported in this paper is directed to the development of a software interface between a higher-level simulation language and a network of microcomputers. A CSSL-IV source program is translated to a data flow graph. This graph is then analyzed automatically so as to allocate computing tasks to the various processors.
Method of construction of rational corporate network using the simulation model
Directory of Open Access Journals (Sweden)
V.N. Pakhomovа
2013-06-01
Full Text Available Purpose. Search for new options of the transition from Ethernet technology. Methodology. Physical structuring of the Fast Ethernet network based on hubs and logical structuring of Fast Ethernet network using commutators. Organization of VLAN based on ports grouping and in accordance with the standard IEEE 802 .1Q. Findings. The options for improving of the Ethernet network are proposed. According to the Fast Ethernet and VLAN technologies on the simulation models in packages NetCraker and Cisco Packet Traker respectively. Origiality. The technique of designing of local area network using the VLAN technology is proposed. Practical value.Each of the options of "Dniprozaliznychproekt" network improving has its advantages. Transition from the Ethernet to Fast Ethernet technology is simple and economical, it requires only one commutator, when the VLAN organization requires at least two. VLAN technology, however, has the following advantages: reducing the load on the network, isolation of the broadcast traffic, change of the logical network structure without changing its physical structure, improving the network security. The transition from Ethernet to the VLAN technology allows you to separate the physical topology from the logical one, and the format of the ÌEEE 802.1Q standard frames allows you to simplify the process of virtual networks implementation to enterprises.
Shih, Kuo-Tung
1990-01-01
Approved for public release, distribution is unlimited This thesis presents a computer simulation of a multinode data communication network using a virtual network model to determine the effects of various system parameters on overall network performance. Lieutenant Commander, Republic of China (Taiwan) Navy
Kannan, R. M.; Pullepu, Bapuji; Immanuel, Y.
2018-04-01
A two dimensional mathematical model is formulated for the transient laminar free convective flow with heat transfer over an incompressible viscous fluid past a vertical cone with uniform surface heat flux with combined effects of viscous dissipation and radiation. The dimensionless boundary layer equations of the flow which are transient, coupled and nonlinear Partial differential equations are solved using the Network Simulation Method (NSM), a powerful numerical technique which demonstrates high efficiency and accuracy by employing the network simulator computer code Pspice. The velocity and temperature profiles have been investigated for various factors, namely viscous dissipation parameter ε, Prandtl number Pr and radiation Rd are analyzed graphically.
Directory of Open Access Journals (Sweden)
Fernando Gimeno Bellver
Full Text Available In this paper, we explore the chaotic behavior of resistively and capacitively shunted Josephson junctions via the so-called Network Simulation Method. Such a numerical approach establishes a formal equivalence among physical transport processes and electrical networks, and hence, it can be applied to efficiently deal with a wide range of differential systems.The generality underlying that electrical equivalence allows to apply the circuit theory to several scientific and technological problems. In this work, the Fast Fourier Transform has been applied for chaos detection purposes and the calculations have been carried out in PSpice, an electrical circuit software.Overall, it holds that such a numerical approach leads to quickly computationally solve Josephson differential models. An empirical application regarding the study of the Josephson model completes the paper. Keywords: Electrical analogy, Network Simulation Method, Josephson junction, Chaos indicator, Fast Fourier Transform
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...
Lvovich, I. Ya; Preobrazhenskiy, A. P.; Choporov, O. N.
2018-05-01
The paper deals with the issue of electromagnetic scattering on a perfectly conducting diffractive body of a complex shape. Performance calculation of the body scattering is carried out through the integral equation method. Fredholm equation of the second time was used for calculating electric current density. While solving the integral equation through the moments method, the authors have properly described the core singularity. The authors determined piecewise constant functions as basic functions. The chosen equation was solved through the moments method. Within the Kirchhoff integral approach it is possible to define the scattered electromagnetic field, in some way related to obtained electrical currents. The observation angles sector belongs to the area of the front hemisphere of the diffractive body. To improve characteristics of the diffractive body, the authors used a neural network. All the neurons contained a logsigmoid activation function and weighted sums as discriminant functions. The paper presents the matrix of weighting factors of the connectionist model, as well as the results of the optimized dimensions of the diffractive body. The paper also presents some basic steps in calculation technique of the diffractive bodies, based on the combination of integral equation and neural networks methods.
Simulation Methods for Design of Networked Power Electronics and Information Systems
2014-07-01
advanced using a fixed time step. The mathematical formulation of the method was completed and then implemented by using the code generating capability...environment - extending the capability for automatic generation of C code from the system definition to permit automafic generation of VHDL code. [California... implemented allows a design space model to be exported to external simulation tools via application program interfaces (APIs) in those tools, so that a human
International Nuclear Information System (INIS)
Huseby, Arne B.; Natvig, Bent
2013-01-01
Discrete event models are frequently used in simulation studies to model and analyze pure jump processes. A discrete event model can be viewed as a system consisting of a collection of stochastic processes, where the states of the individual processes change as results of various kinds of events occurring at random points of time. We always assume that each event only affects one of the processes. Between these events the states of the processes are considered to be constant. In the present paper we use discrete event simulation in order to analyze a multistate network flow system of repairable components. In order to study how the different components contribute to the system, it is necessary to describe the often complicated interaction between component processes and processes at the system level. While analytical considerations may throw some light on this, a simulation study often allows the analyst to explore more details. By producing stable curve estimates for the development of the various processes, one gets a much better insight in how such systems develop over time. These methods are particulary useful in the study of advanced importancez measures of repairable components. Such measures can be very complicated, and thus impossible to calculate analytically. By using discrete event simulations, however, this can be done in a very natural and intuitive way. In particular significant differences between the Barlow–Proschan measure and the Natvig measure in multistate network flow systems can be explored
Simulations of biopolymer networks under shear
Huisman, Elisabeth Margaretha
2011-01-01
In this thesis we present a new method to simulate realistic three-dimensional networks of biopolymers under shear. These biopolymer networks are important for the structural functions of cells and tissues. We use the method to analyze these networks under shear, and consider the elastic modulus,
Directory of Open Access Journals (Sweden)
Brenton J Prettejohn
2011-03-01
Full Text Available Many simulations of networks in computational neuroscience assume completely homogenous random networks of the Erd"{o}s-R'{e}nyi type, or regular networks, despite it being recognized for some time that anatomical brain networks are more complex in their connectivity and can, for example, exhibit the `scale-free' and `small-world' properties. We review the most well known algorithms for constructing networks with given non-homogeneous statistical properties and provide simple pseudo-code for reproducing such networks in software simulations. We also review some useful mathematical results and approximations associated with the statistics that describe these network models, including degree distribution, average path length and clustering coefficient. We demonstrate how such results can be used as partial verification and validation of implementations. Finally, we discuss a sometimes overlooked modeling choice that can be crucially important for the properties of simulated networks: that of network directedness. The most well known network algorithms produce undirected networks, and we emphasize this point by highlighting how simple adaptations can instead produce directed networks.
Directory of Open Access Journals (Sweden)
Manuel Cánovas
2017-09-01
Full Text Available Density-driven flow and heat transport processes in 2-D porous media scenarios are governed by coupled, non-linear, partial differential equations that normally have to be solved numerically. In the present work, a model based on the network method simulation is designed and applied to simulate these processes, providing steady state patterns that demonstrate its computational power and reliability. The design is relatively simple and needs very few rules. Two applications in which heat is transported by natural convection in confined and saturated media are studied: slender boxes heated from below (a kind of Bénard problem and partially heated horizontal plates in rectangular domains (the Elder problem. The streamfunction and temperature patterns show that the results are coherent with those of other authors: steady state patterns and heat transfer depend both on the Rayleigh number and on the characteristic Darcy velocity derived from the values of the hydrological, thermal and geometrical parameters of the problems.
Tama, I. P.; Akbar, Z.; Eunike, A.
2018-04-01
Vegetables are categorized as a perishable product, which is a product with short lifespan thus requires proper handling and planning to reduce losses caused by the short lifespan. In order to reduce the losses, coordination among the players in the supply chain is required. On the other hand, the decision in the supply chain of vegetables and other farming products in the traditional market of developing country is independent among the players. This research is conducted by using System Dynamic Simulation method to develop model and scenario by coordinating the supply quantity amongst players in the supply chain. The scenarios are developed based on newsboy inventory model. This study aims to compare scenarios combining tiers involved in coordination program. The result shows that coordination in supply chain increases total supply chain profit, although there will always be players who experienced decrements in profit. The scenario of coordination among the farmer, the distributor, and the wholesaler resulted in the highest increase in total supply chain profit compared to other coordination scenarios, with an increased value of 10.49%.
Splitting Strategy for Simulating Genetic Regulatory Networks
Directory of Open Access Journals (Sweden)
Xiong You
2014-01-01
Full Text Available The splitting approach is developed for the numerical simulation of genetic regulatory networks with a stable steady-state structure. The numerical results of the simulation of a one-gene network, a two-gene network, and a p53-mdm2 network show that the new splitting methods constructed in this paper are remarkably more effective and more suitable for long-term computation with large steps than the traditional general-purpose Runge-Kutta methods. The new methods have no restriction on the choice of stepsize due to their infinitely large stability regions.
Welsh, Chris
2013-01-01
GNS3 Network Simulation Guide is an easy-to-follow yet comprehensive guide which is written in a tutorial format helping you grasp all the things you need for accomplishing your certification or simulation goal. If you are a networking professional who wants to learn how to simulate networks using GNS3, this book is ideal for you. The introductory examples within the book only require minimal networking knowledge, but as the book progresses onto more advanced topics, users will require knowledge of TCP/IP and routing.
Simulating synchronization in neuronal networks
Fink, Christian G.
2016-06-01
We discuss several techniques used in simulating neuronal networks by exploring how a network's connectivity structure affects its propensity for synchronous spiking. Network connectivity is generated using the Watts-Strogatz small-world algorithm, and two key measures of network structure are described. These measures quantify structural characteristics that influence collective neuronal spiking, which is simulated using the leaky integrate-and-fire model. Simulations show that adding a small number of random connections to an otherwise lattice-like connectivity structure leads to a dramatic increase in neuronal synchronization.
Bitcoin network simulator data explotation
Berini Sarrias, Martí
2015-01-01
This project starts with a brief introduction to the concepts of Bitcoin and blockchain, followed by the description of the di erent known attacks to the Bitcoin network. Once reached this point, the basic structure of the Bitcoin network simulator is presented. The main objective of this project is to help in the security assessment of the Bitcoin network. To accomplish that, we try to identify useful metrics, explain them and implement them in the corresponding simulator modules, aiming to ...
Wireless network simulation - Your window on future network performance
Fledderus, E.
2005-01-01
The paper describes three relevant perspectives on current wireless simulation practices. In order to obtain the key challenges for future network simulations, the characteristics of "beyond 3G" networks are described, including their impact on simulation.
Han, Zong-wei; Huang, Wei; Luo, Yun; Zhang, Chun-di; Qi, Da-cheng
2015-03-01
Taking the soil organic matter in eastern Zhongxiang County, Hubei Province, as a research object, thirteen sample sets from different regions were arranged surrounding the road network, the spatial configuration of which was optimized by the simulated annealing approach. The topographic factors of these thirteen sample sets, including slope, plane curvature, profile curvature, topographic wetness index, stream power index and sediment transport index, were extracted by the terrain analysis. Based on the results of optimization, a multiple linear regression model with topographic factors as independent variables was built. At the same time, a multilayer perception model on the basis of neural network approach was implemented. The comparison between these two models was carried out then. The results revealed that the proposed approach was practicable in optimizing soil sampling scheme. The optimal configuration was capable of gaining soil-landscape knowledge exactly, and the accuracy of optimal configuration was better than that of original samples. This study designed a sampling configuration to study the soil attribute distribution by referring to the spatial layout of road network, historical samples, and digital elevation data, which provided an effective means as well as a theoretical basis for determining the sampling configuration and displaying spatial distribution of soil organic matter with low cost and high efficiency.
Stochastic simulation of karst conduit networks
Pardo-Igúzquiza, Eulogio; Dowd, Peter A.; Xu, Chaoshui; Durán-Valsero, Juan José
2012-01-01
Karst aquifers have very high spatial heterogeneity. Essentially, they comprise a system of pipes (i.e., the network of conduits) superimposed on rock porosity and on a network of stratigraphic surfaces and fractures. This heterogeneity strongly influences the hydraulic behavior of the karst and it must be reproduced in any realistic numerical model of the karst system that is used as input to flow and transport modeling. However, the directly observed karst conduits are only a small part of the complete karst conduit system and knowledge of the complete conduit geometry and topology remains spatially limited and uncertain. Thus, there is a special interest in the stochastic simulation of networks of conduits that can be combined with fracture and rock porosity models to provide a realistic numerical model of the karst system. Furthermore, the simulated model may be of interest per se and other uses could be envisaged. The purpose of this paper is to present an efficient method for conditional and non-conditional stochastic simulation of karst conduit networks. The method comprises two stages: generation of conduit geometry and generation of topology. The approach adopted is a combination of a resampling method for generating conduit geometries from templates and a modified diffusion-limited aggregation method for generating the network topology. The authors show that the 3D karst conduit networks generated by the proposed method are statistically similar to observed karst conduit networks or to a hypothesized network model. The statistical similarity is in the sense of reproducing the tortuosity index of conduits, the fractal dimension of the network, the direction rose of directions, the Z-histogram and Ripley's K-function of the bifurcation points (which differs from a random allocation of those bifurcation points). The proposed method (1) is very flexible, (2) incorporates any experimental data (conditioning information) and (3) can easily be modified when
Network modelling methods for FMRI.
Smith, Stephen M; Miller, Karla L; Salimi-Khorshidi, Gholamreza; Webster, Matthew; Beckmann, Christian F; Nichols, Thomas E; Ramsey, Joseph D; Woolrich, Mark W
2011-01-15
There is great interest in estimating brain "networks" from FMRI data. This is often attempted by identifying a set of functional "nodes" (e.g., spatial ROIs or ICA maps) and then conducting a connectivity analysis between the nodes, based on the FMRI timeseries associated with the nodes. Analysis methods range from very simple measures that consider just two nodes at a time (e.g., correlation between two nodes' timeseries) to sophisticated approaches that consider all nodes simultaneously and estimate one global network model (e.g., Bayes net models). Many different methods are being used in the literature, but almost none has been carefully validated or compared for use on FMRI timeseries data. In this work we generate rich, realistic simulated FMRI data for a wide range of underlying networks, experimental protocols and problematic confounds in the data, in order to compare different connectivity estimation approaches. Our results show that in general correlation-based approaches can be quite successful, methods based on higher-order statistics are less sensitive, and lag-based approaches perform very poorly. More specifically: there are several methods that can give high sensitivity to network connection detection on good quality FMRI data, in particular, partial correlation, regularised inverse covariance estimation and several Bayes net methods; however, accurate estimation of connection directionality is more difficult to achieve, though Patel's τ can be reasonably successful. With respect to the various confounds added to the data, the most striking result was that the use of functionally inaccurate ROIs (when defining the network nodes and extracting their associated timeseries) is extremely damaging to network estimation; hence, results derived from inappropriate ROI definition (such as via structural atlases) should be regarded with great caution. Copyright © 2010 Elsevier Inc. All rights reserved.
Speeding Up Network Simulations Using Discrete Time
Lucas, Aaron; Armbruster, Benjamin
2013-01-01
We develop a way of simulating disease spread in networks faster at the cost of some accuracy. Instead of a discrete event simulation (DES) we use a discrete time simulation. This aggregates events into time periods. We prove a bound on the accuracy attained. We also discuss the choice of step size and do an analytical comparison of the computational costs. Our error bound concept comes from the theory of numerical methods for SDEs and the basic proof structure comes from the theory of numeri...
Sharudin, R. W.; AbdulBari Ali, S.; Zulkarnain, M.; Shukri, M. A.
2018-05-01
This study reports on the integration of Artificial Neural Network (ANNs) with experimental data in predicting the solubility of carbon dioxide (CO2) blowing agent in SEBS by generating highest possible value for Regression coefficient (R2). Basically, foaming of thermoplastic elastomer with CO2 is highly affected by the CO2 solubility. The ability of ANN in predicting interpolated data of CO2 solubility was investigated by comparing training results via different method of network training. Regards to the final prediction result for CO2 solubility by ANN, the prediction trend (output generate) was corroborated with the experimental results. The obtained result of different method of training showed the trend of output generated by Gradient Descent with Momentum & Adaptive LR (traingdx) required longer training time and required more accurate input to produce better output with final Regression Value of 0.88. However, it goes vice versa with Levenberg-Marquardt (trainlm) technique as it produced better output in quick detention time with final Regression Value of 0.91.
GEM simulation methods development
International Nuclear Information System (INIS)
Tikhonov, V.; Veenhof, R.
2002-01-01
A review of methods used in the simulation of processes in gas electron multipliers (GEMs) and in the accurate calculation of detector characteristics is presented. Such detector characteristics as effective gas gain, transparency, charge collection and losses have been calculated and optimized for a number of GEM geometries and compared with experiment. A method and a new special program for calculations of detector macro-characteristics such as signal response in a real detector readout structure, and spatial and time resolution of detectors have been developed and used for detector optimization. A detailed development of signal induction on readout electrodes and electronics characteristics are included in the new program. A method for the simulation of charging-up effects in GEM detectors is described. All methods show good agreement with experiment
Hybrid simulation models of production networks
Kouikoglou, Vassilis S
2001-01-01
This book is concerned with a most important area of industrial production, that of analysis and optimization of production lines and networks using discrete-event models and simulation. The book introduces a novel approach that combines analytic models and discrete-event simulation. Unlike conventional piece-by-piece simulation, this method observes a reduced number of events between which the evolution of the system is tracked analytically. Using this hybrid approach, several models are developed for the analysis of production lines and networks. The hybrid approach combines speed and accuracy for exceptional analysis of most practical situations. A number of optimization problems, involving buffer design, workforce planning, and production control, are solved through the use of hybrid models.
Simulation of Stimuli-Responsive Polymer Networks
Directory of Open Access Journals (Sweden)
Thomas Gruhn
2013-11-01
Full Text Available The structure and material properties of polymer networks can depend sensitively on changes in the environment. There is a great deal of progress in the development of stimuli-responsive hydrogels for applications like sensors, self-repairing materials or actuators. Biocompatible, smart hydrogels can be used for applications, such as controlled drug delivery and release, or for artificial muscles. Numerical studies have been performed on different length scales and levels of details. Macroscopic theories that describe the network systems with the help of continuous fields are suited to study effects like the stimuli-induced deformation of hydrogels on large scales. In this article, we discuss various macroscopic approaches and describe, in more detail, our phase field model, which allows the calculation of the hydrogel dynamics with the help of a free energy that considers physical and chemical impacts. On a mesoscopic level, polymer systems can be modeled with the help of the self-consistent field theory, which includes the interactions, connectivity, and the entropy of the polymer chains, and does not depend on constitutive equations. We present our recent extension of the method that allows the study of the formation of nano domains in reversibly crosslinked block copolymer networks. Molecular simulations of polymer networks allow the investigation of the behavior of specific systems on a microscopic scale. As an example for microscopic modeling of stimuli sensitive polymer networks, we present our Monte Carlo simulations of a filament network system with crosslinkers.
Li, Mingchao; Han, Shuai; Zhou, Sibao; Zhang, Ye
2018-06-01
Based on a 3D model of a discrete fracture network (DFN) in a rock mass, an improved projective method for computing the 3D mechanical connectivity rate was proposed. The Monte Carlo simulation method, 2D Poisson process and 3D geological modeling technique were integrated into a polyhedral DFN modeling approach, and the simulation results were verified by numerical tests and graphical inspection. Next, the traditional projective approach for calculating the rock mass connectivity rate was improved using the 3D DFN models by (1) using the polyhedral model to replace the Baecher disk model; (2) taking the real cross section of the rock mass, rather than a part of the cross section, as the test plane; and (3) dynamically searching the joint connectivity rates using different dip directions and dip angles at different elevations to calculate the maximum, minimum and average values of the joint connectivity at each elevation. In a case study, the improved method and traditional method were used to compute the mechanical connectivity rate of the slope of a dam abutment. The results of the two methods were further used to compute the cohesive force of the rock masses. Finally, a comparison showed that the cohesive force derived from the traditional method had a higher error, whereas the cohesive force derived from the improved method was consistent with the suggested values. According to the comparison, the effectivity and validity of the improved method were verified indirectly.
LANES - LOCAL AREA NETWORK EXTENSIBLE SIMULATOR
Gibson, J.
1994-01-01
The Local Area Network Extensible Simulator (LANES) provides a method for simulating the performance of high speed local area network (LAN) technology. LANES was developed as a design and analysis tool for networking on board the Space Station. The load, network, link and physical layers of a layered network architecture are all modeled. LANES models to different lower-layer protocols, the Fiber Distributed Data Interface (FDDI) and the Star*Bus. The load and network layers are included in the model as a means of introducing upper-layer processing delays associated with message transmission; they do not model any particular protocols. FDDI is an American National Standard and an International Organization for Standardization (ISO) draft standard for a 100 megabit-per-second fiber-optic token ring. Specifications for the LANES model of FDDI are taken from the Draft Proposed American National Standard FDDI Token Ring Media Access Control (MAC), document number X3T9.5/83-16 Rev. 10, February 28, 1986. This is a mature document describing the FDDI media-access-control protocol. Star*Bus, also known as the Fiber Optic Demonstration System, is a protocol for a 100 megabit-per-second fiber-optic star-topology LAN. This protocol, along with a hardware prototype, was developed by Sperry Corporation under contract to NASA Goddard Space Flight Center as a candidate LAN protocol for the Space Station. LANES can be used to analyze performance of a networking system based on either FDDI or Star*Bus under a variety of loading conditions. Delays due to upper-layer processing can easily be nullified, allowing analysis of FDDI or Star*Bus as stand-alone protocols. LANES is a parameter-driven simulation; it provides considerable flexibility in specifying both protocol an run-time parameters. Code has been optimized for fast execution and detailed tracing facilities have been included. LANES was written in FORTRAN 77 for implementation on a DEC VAX under VMS 4.6. It consists of two
Hybrid Method Simulation of Slender Marine Structures
DEFF Research Database (Denmark)
Christiansen, Niels Hørbye
This present thesis consists of an extended summary and five appended papers concerning various aspects of the implementation of a hybrid method which combines classical simulation methods and artificial neural networks. The thesis covers three main topics. Common for all these topics...... only recognize patterns similar to those comprised in the data used to train the network. Fatigue life evaluation of marine structures often considers simulations of more than a hundred different sea states. Hence, in order for this method to be useful, the training data must be arranged so...... that a single neural network can cover all relevant sea states. The applicability and performance of the present hybrid method is demonstrated on a numerical model of a mooring line attached to a floating offshore platform. The second part of the thesis demonstrates how sequential neural networks can be used...
Communication Network Analysis Methods.
Farace, Richard V.; Mabee, Timothy
This paper reviews a variety of analytic procedures that can be applied to network data, discussing the assumptions and usefulness of each procedure when applied to the complexity of human communication. Special attention is paid to the network properties measured or implied by each procedure. Factor analysis and multidimensional scaling are among…
A Method for Upper Bounding on Network Access Speed
DEFF Research Database (Denmark)
Knudsen, Thomas Phillip; Patel, A.; Pedersen, Jens Myrup
2004-01-01
This paper presents a method for calculating an upper bound on network access speed growth and gives guidelines for further research experiments and simulations. The method is aimed at providing a basis for simulation of long term network development and resource management.......This paper presents a method for calculating an upper bound on network access speed growth and gives guidelines for further research experiments and simulations. The method is aimed at providing a basis for simulation of long term network development and resource management....
Introduction to Network Simulator NS2
Issariyakul, Teerawat
2012-01-01
"Introduction to Network Simulator NS2" is a primer providing materials for NS2 beginners, whether students, professors, or researchers for understanding the architecture of Network Simulator 2 (NS2) and for incorporating simulation modules into NS2. The authors discuss the simulation architecture and the key components of NS2 including simulation-related objects, network objects, packet-related objects, and helper objects. The NS2 modules included within are nodes, links, SimpleLink objects, packets, agents, and applications. Further, the book covers three helper modules: timers, ra
Methods of channeling simulation
International Nuclear Information System (INIS)
Barrett, J.H.
1989-06-01
Many computer simulation programs have been used to interpret experiments almost since the first channeling measurements were made. Certain aspects of these programs are important in how accurately they simulate ions in crystals; among these are the manner in which the structure of the crystal is incorporated, how any quantity of interest is computed, what ion-atom potential is used, how deflections are computed from the potential, incorporation of thermal vibrations of the lattice atoms, correlations of thermal vibrations, and form of stopping power. Other aspects of the programs are included to improve the speed; among these are table lookup, importance sampling, and the multiparameter method. It is desirable for programs to facilitate incorporation of special features of interest in special situations; examples are relaxations and enhanced vibrations of surface atoms, easy substitution of an alternate potential for comparison, change of row directions from layer to layer in strained-layer lattices, and different vibration amplitudes for substitutional solute or impurity atoms. Ways of implementing all of these aspects and features and the consequences of them will be discussed. 30 refs., 3 figs
Chin, Alex
Singlet fission (SF) is an ultrafast process in which a singlet exciton spontaneously converts into a pair of entangled triplet excitons on neighbouring organic molecules. As a mechanism of multiple exciton generation, it has been suggested as a way to increase the efficiency of organic photovoltaic devices, and its underlying photophysics across a wide range of molecules and materials has attracted significant theoretical attention. Recently, a number of studies using ultrafast nonlinear optics have underscored the importance of intramolecular vibrational dynamics in efficient SF systems, prompting a need for methods capable of simulating open quantum dynamics in the presence of highly structured and strongly coupled environments. Here, a combination of ab initio electronic structure techniques and a new tensor-network methodology for simulating open vibronic dynamics is presented and applied to a recently synthesised dimer of pentacene (DP-Mes). We show that ultrafast (300 fs) SF in this system is driven entirely by symmetry breaking vibrations, and our many-body approach enables the real-time identification and tracking of the ''functional' vibrational dynamics and the role of the ''bath''-like parts of the environment. Deeper analysis of the emerging wave functions points to interesting links between the time at which parts of the environment become relevant to the SF process and the optimal topology of the tensor networks, highlighting the additional insight provided by moving the problem into the natural language of correlated quantum states and how this could lead to simulations of much larger multichromophore systems Supported by The Winton Programme for the Physics of Sustainability.
Biological transportation networks: Modeling and simulation
Albi, Giacomo
2015-09-15
We present a model for biological network formation originally introduced by Cai and Hu [Adaptation and optimization of biological transport networks, Phys. Rev. Lett. 111 (2013) 138701]. The modeling of fluid transportation (e.g., leaf venation and angiogenesis) and ion transportation networks (e.g., neural networks) is explained in detail and basic analytical features like the gradient flow structure of the fluid transportation network model and the impact of the model parameters on the geometry and topology of network formation are analyzed. We also present a numerical finite-element based discretization scheme and discuss sample cases of network formation simulations.
Program Helps Simulate Neural Networks
Villarreal, James; Mcintire, Gary
1993-01-01
Neural Network Environment on Transputer System (NNETS) computer program provides users high degree of flexibility in creating and manipulating wide variety of neural-network topologies at processing speeds not found in conventional computing environments. Supports back-propagation and back-propagation-related algorithms. Back-propagation algorithm used is implementation of Rumelhart's generalized delta rule. NNETS developed on INMOS Transputer(R). Predefines back-propagation network, Jordan network, and reinforcement network to assist users in learning and defining own networks. Also enables users to configure other neural-network paradigms from NNETS basic architecture. Small portion of software written in OCCAM(R) language.
Developed hydraulic simulation model for water pipeline networks
Directory of Open Access Journals (Sweden)
A. Ayad
2013-03-01
Full Text Available A numerical method that uses linear graph theory is presented for both steady state, and extended period simulation in a pipe network including its hydraulic components (pumps, valves, junctions, etc.. The developed model is based on the Extended Linear Graph Theory (ELGT technique. This technique is modified to include new network components such as flow control valves and tanks. The technique also expanded for extended period simulation (EPS. A newly modified method for the calculation of updated flows improving the convergence rate is being introduced. Both benchmarks, ad Actual networks are analyzed to check the reliability of the proposed method. The results reveal the finer performance of the proposed method.
Parallel discrete-event simulation of FCFS stochastic queueing networks
Nicol, David M.
1988-01-01
Physical systems are inherently parallel. Intuition suggests that simulations of these systems may be amenable to parallel execution. The parallel execution of a discrete-event simulation requires careful synchronization of processes in order to ensure the execution's correctness; this synchronization can degrade performance. Largely negative results were recently reported in a study which used a well-known synchronization method on queueing network simulations. Discussed here is a synchronization method (appointments), which has proven itself to be effective on simulations of FCFS queueing networks. The key concept behind appointments is the provision of lookahead. Lookahead is a prediction on a processor's future behavior, based on an analysis of the processor's simulation state. It is shown how lookahead can be computed for FCFS queueing network simulations, give performance data that demonstrates the method's effectiveness under moderate to heavy loads, and discuss performance tradeoffs between the quality of lookahead, and the cost of computing lookahead.
Ranking important nodes in complex networks by simulated annealing
International Nuclear Information System (INIS)
Sun Yu; Yao Pei-Yang; Shen Jian; Zhong Yun; Wan Lu-Jun
2017-01-01
In this paper, based on simulated annealing a new method to rank important nodes in complex networks is presented. First, the concept of an importance sequence (IS) to describe the relative importance of nodes in complex networks is defined. Then, a measure used to evaluate the reasonability of an IS is designed. By comparing an IS and the measure of its reasonability to a state of complex networks and the energy of the state, respectively, the method finds the ground state of complex networks by simulated annealing. In other words, the method can construct a most reasonable IS. The results of experiments on real and artificial networks show that this ranking method not only is effective but also can be applied to different kinds of complex networks. (paper)
Signal Processing and Neural Network Simulator
Tebbe, Dennis L.; Billhartz, Thomas J.; Doner, John R.; Kraft, Timothy T.
1995-04-01
The signal processing and neural network simulator (SPANNS) is a digital signal processing simulator with the capability to invoke neural networks into signal processing chains. This is a generic tool which will greatly facilitate the design and simulation of systems with embedded neural networks. The SPANNS is based on the Signal Processing WorkSystemTM (SPWTM), a commercial-off-the-shelf signal processing simulator. SPW provides a block diagram approach to constructing signal processing simulations. Neural network paradigms implemented in the SPANNS include Backpropagation, Kohonen Feature Map, Outstar, Fully Recurrent, Adaptive Resonance Theory 1, 2, & 3, and Brain State in a Box. The SPANNS was developed by integrating SAIC's Industrial Strength Neural Networks (ISNN) Software into SPW.
Sampling of temporal networks: Methods and biases
Rocha, Luis E. C.; Masuda, Naoki; Holme, Petter
2017-11-01
Temporal networks have been increasingly used to model a diversity of systems that evolve in time; for example, human contact structures over which dynamic processes such as epidemics take place. A fundamental aspect of real-life networks is that they are sampled within temporal and spatial frames. Furthermore, one might wish to subsample networks to reduce their size for better visualization or to perform computationally intensive simulations. The sampling method may affect the network structure and thus caution is necessary to generalize results based on samples. In this paper, we study four sampling strategies applied to a variety of real-life temporal networks. We quantify the biases generated by each sampling strategy on a number of relevant statistics such as link activity, temporal paths and epidemic spread. We find that some biases are common in a variety of networks and statistics, but one strategy, uniform sampling of nodes, shows improved performance in most scenarios. Given the particularities of temporal network data and the variety of network structures, we recommend that the choice of sampling methods be problem oriented to minimize the potential biases for the specific research questions on hand. Our results help researchers to better design network data collection protocols and to understand the limitations of sampled temporal network data.
Network Modeling and Simulation A Practical Perspective
Guizani, Mohsen; Khan, Bilal
2010-01-01
Network Modeling and Simulation is a practical guide to using modeling and simulation to solve real-life problems. The authors give a comprehensive exposition of the core concepts in modeling and simulation, and then systematically address the many practical considerations faced by developers in modeling complex large-scale systems. The authors provide examples from computer and telecommunication networks and use these to illustrate the process of mapping generic simulation concepts to domain-specific problems in different industries and disciplines. Key features: Provides the tools and strate
Simulating individual-based models of epidemics in hierarchical networks
Quax, R.; Bader, D.A.; Sloot, P.M.A.
2009-01-01
Current mathematical modeling methods for the spreading of infectious diseases are too simplified and do not scale well. We present the Simulator of Epidemic Evolution in Complex Networks (SEECN), an efficient simulator of detailed individual-based models by parameterizing separate dynamics
Interfacing Network Simulations and Empirical Data
2009-05-01
contraceptive innovations in the Cameroon. He found that real-world adoption rates did not follow simulation models when the network relationships were...Analysis of the Coevolution of Adolescents ’ Friendship Networks, Taste in Music, and Alcohol Consumption. Methodology, 2: 48-56. Tichy, N.M., Tushman
National Research Council Canada - National Science Library
Frazier, John; Chusak, Yaroslav; Foy, Brent
2008-01-01
.... The software uses either exact or approximate stochastic simulation algorithms for generating Monte Carlo trajectories that describe the time evolution of the behavior of biomolecular reaction networks...
Network simulations of optical illusions
Shinbrot, Troy; Lazo, Miguel Vivar; Siu, Theo
We examine a dynamical network model of visual processing that reproduces several aspects of a well-known optical illusion, including subtle dependencies on curvature and scale. The model uses a genetic algorithm to construct the percept of an image, and we show that this percept evolves dynamically so as to produce the illusions reported. We find that the perceived illusions are hardwired into the model architecture and we propose that this approach may serve as an archetype to distinguish behaviors that are due to nature (i.e. a fixed network architecture) from those subject to nurture (that can be plastically altered through learning).
Optimization of blanking process using neural network simulation
International Nuclear Information System (INIS)
Hambli, R.
2005-01-01
The present work describes a methodology using the finite element method and neural network simulation in order to predict the optimum punch-die clearance during sheet metal blanking processes. A damage model is used in order to describe crack initiation and propagation into the sheet. The proposed approach combines predictive finite element and neural network modeling of the leading blanking parameters. Numerical results obtained by finite element computation including damage and fracture modeling were utilized to train the developed simulation environment based on back propagation neural network modeling. The comparative study between the numerical results and the experimental ones shows the good agreement. (author)
Multiple network interface core apparatus and method
Underwood, Keith D [Albuquerque, NM; Hemmert, Karl Scott [Albuquerque, NM
2011-04-26
A network interface controller and network interface control method comprising providing a single integrated circuit as a network interface controller and employing a plurality of network interface cores on the single integrated circuit.
Hierarchical Network Design Using Simulated Annealing
DEFF Research Database (Denmark)
Thomadsen, Tommy; Clausen, Jens
2002-01-01
networks are described and a mathematical model is proposed for a two level version of the hierarchical network problem. The problem is to determine which edges should connect nodes, and how demand is routed in the network. The problem is solved heuristically using simulated annealing which as a sub......-algorithm uses a construction algorithm to determine edges and route the demand. Performance for different versions of the algorithm are reported in terms of runtime and quality of the solutions. The algorithm is able to find solutions of reasonable quality in approximately 1 hour for networks with 100 nodes....
Network Simulation of Technical Architecture
National Research Council Canada - National Science Library
Cave, William
1998-01-01
..., and development of the Army Battle Command System (ABCS). PSI delivered a hierarchical iconic modeling facility that can be used to structure and restructure both models and scenarios, interactively, while simulations are running...
Reprocessing process simulation network; PRONET
International Nuclear Information System (INIS)
Mitsui, T.; Takada, H.; Kamishima, N.; Tsukamoto, T.; Harada, N.; Fujita, N.; Gonda, K.
1991-01-01
The effectiveness of simulation technology and its wide application to nuclear fuel reprocessing plants has been recognized recently. The principal aim of applying simulation is to predict the process behavior accurately based on the quantitative relations among substances in physical and chemical phenomena. Mitsubishi Heavy Industries Ltd. has engaged positively in the development and the application study of this technology. All the software products of its recent activities were summarized in the integrated form named 'PRONET'. The PRONET is classified into two independent software groups from the viewpoint of computer system. One is off-line Process Simulation Group, and the other is Dynamic Real-time Simulator Group. The former is called 'PRONET System', and the latter is called 'PRONET Simulator'. These have several subsystems with the prefix 'MR' meaning Mitsubishi Reprocessing Plant. Each MR subsystem is explained in this report. The technical background, the objective of the PRONET, the system and the function of the PRONET, and the future application to an on-line real-time simulator and the development of MR EXPERT are described. (K.I.)
EVALUATING AUSTRALIAN FOOTBALL LEAGUE PLAYER CONTRIBUTIONS USING INTERACTIVE NETWORK SIMULATION
Directory of Open Access Journals (Sweden)
Jonathan Sargent
2013-03-01
Full Text Available This paper focuses on the contribution of Australian Football League (AFL players to their team's on-field network by simulating player interactions within a chosen team list and estimating the net effect on final score margin. A Visual Basic computer program was written, firstly, to isolate the effective interactions between players from a particular team in all 2011 season matches and, secondly, to generate a symmetric interaction matrix for each match. Negative binomial distributions were fitted to each player pairing in the Geelong Football Club for the 2011 season, enabling an interactive match simulation model given the 22 chosen players. Dynamic player ratings were calculated from the simulated network using eigenvector centrality, a method that recognises and rewards interactions with more prominent players in the team network. The centrality ratings were recorded after every network simulation and then applied in final score margin predictions so that each player's match contribution-and, hence, an optimal team-could be estimated. The paper ultimately demonstrates that the presence of highly rated players, such as Geelong's Jimmy Bartel, provides the most utility within a simulated team network. It is anticipated that these findings will facilitate optimal AFL team selection and player substitutions, which are key areas of interest to coaches. Network simulations are also attractive for use within betting markets, specifically to provide information on the likelihood of a chosen AFL team list "covering the line".
Implementation of quantum key distribution network simulation module in the network simulator NS-3
Mehic, Miralem; Maurhart, Oliver; Rass, Stefan; Voznak, Miroslav
2017-10-01
As the research in quantum key distribution (QKD) technology grows larger and becomes more complex, the need for highly accurate and scalable simulation technologies becomes important to assess the practical feasibility and foresee difficulties in the practical implementation of theoretical achievements. Due to the specificity of the QKD link which requires optical and Internet connection between the network nodes, to deploy a complete testbed containing multiple network hosts and links to validate and verify a certain network algorithm or protocol would be very costly. Network simulators in these circumstances save vast amounts of money and time in accomplishing such a task. The simulation environment offers the creation of complex network topologies, a high degree of control and repeatable experiments, which in turn allows researchers to conduct experiments and confirm their results. In this paper, we described the design of the QKD network simulation module which was developed in the network simulator of version 3 (NS-3). The module supports simulation of the QKD network in an overlay mode or in a single TCP/IP mode. Therefore, it can be used to simulate other network technologies regardless of QKD.
The Airport Network Flow Simulator.
1976-05-01
The impact of investment at an individual airport is felt through-out the National Airport System by reduction of delays at other airports in the the system. A GPSS model was constructed to simulate the propagation of delays through a nine-airport sy...
Quantitative Efficiency Evaluation Method for Transportation Networks
Directory of Open Access Journals (Sweden)
Jin Qin
2014-11-01
Full Text Available An effective evaluation of transportation network efficiency/performance is essential to the establishment of sustainable development in any transportation system. Based on a redefinition of transportation network efficiency, a quantitative efficiency evaluation method for transportation network is proposed, which could reflect the effects of network structure, traffic demands, travel choice, and travel costs on network efficiency. Furthermore, the efficiency-oriented importance measure for network components is presented, which can be used to help engineers identify the critical nodes and links in the network. The numerical examples show that, compared with existing efficiency evaluation methods, the network efficiency value calculated by the method proposed in this paper can portray the real operation situation of the transportation network as well as the effects of main factors on network efficiency. We also find that the network efficiency and the importance values of the network components both are functions of demands and network structure in the transportation network.
Simulating activation propagation in social networks using the graph theory
Directory of Open Access Journals (Sweden)
František Dařena
2010-01-01
Full Text Available The social-network formation and analysis is nowadays one of objects that are in a focus of intensive research. The objective of the paper is to suggest the perspective of representing social networks as graphs, with the application of the graph theory to problems connected with studying the network-like structures and to study spreading activation algorithm for reasons of analyzing these structures. The paper presents the process of modeling multidimensional networks by means of directed graphs with several characteristics. The paper also demonstrates using Spreading Activation algorithm as a good method for analyzing multidimensional network with the main focus on recommender systems. The experiments showed that the choice of parameters of the algorithm is crucial, that some kind of constraint should be included and that the algorithm is able to provide a stable environment for simulations with networks.
System Identification, Prediction, Simulation and Control with Neural Networks
DEFF Research Database (Denmark)
Sørensen, O.
1997-01-01
a Gauss-Newton search direction is applied. 3) Amongst numerous model types, often met in control applications, only the Non-linear ARMAX (NARMAX) model, representing input/output description, is examined. A simulated example confirms that a neural network has the potential to perform excellent System......The intention of this paper is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: 1) Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. 2) Amongst numerous training algorithms, only the Recursive Prediction Error Method using...
New methods in plasma simulation
International Nuclear Information System (INIS)
Mason, R.J.
1990-01-01
The development of implicit methods of particle-in-cell (PIC) computer simulation in recent years, and their merger with older hybrid methods have created a new arsenal of simulation techniques for the treatment of complex practical problems in plasma physics. The new implicit hybrid codes are aimed at transitional problems that lie somewhere between the long time scale, high density regime associated with MHD modeling, and the short time scale, low density regime appropriate to PIC particle-in-cell techniques. This transitional regime arises in ICF coronal plasmas, in pulsed power plasma switches, in Z-pinches, and in foil implosions. Here, we outline how such a merger of implicit and hybrid methods has been carried out, specifically in the ANTHEM computer code, and demonstrate the utility of implicit hybrid simulation in applications. 25 refs., 5 figs
SELANSI: a toolbox for simulation of stochastic gene regulatory networks.
Pájaro, Manuel; Otero-Muras, Irene; Vázquez, Carlos; Alonso, Antonio A
2018-03-01
Gene regulation is inherently stochastic. In many applications concerning Systems and Synthetic Biology such as the reverse engineering and the de novo design of genetic circuits, stochastic effects (yet potentially crucial) are often neglected due to the high computational cost of stochastic simulations. With advances in these fields there is an increasing need of tools providing accurate approximations of the stochastic dynamics of gene regulatory networks (GRNs) with reduced computational effort. This work presents SELANSI (SEmi-LAgrangian SImulation of GRNs), a software toolbox for the simulation of stochastic multidimensional gene regulatory networks. SELANSI exploits intrinsic structural properties of gene regulatory networks to accurately approximate the corresponding Chemical Master Equation with a partial integral differential equation that is solved by a semi-lagrangian method with high efficiency. Networks under consideration might involve multiple genes with self and cross regulations, in which genes can be regulated by different transcription factors. Moreover, the validity of the method is not restricted to a particular type of kinetics. The tool offers total flexibility regarding network topology, kinetics and parameterization, as well as simulation options. SELANSI runs under the MATLAB environment, and is available under GPLv3 license at https://sites.google.com/view/selansi. antonio@iim.csic.es. © The Author(s) 2017. Published by Oxford University Press.
Stochastic sensitivity analysis and Langevin simulation for neural network learning
International Nuclear Information System (INIS)
Koda, Masato
1997-01-01
A comprehensive theoretical framework is proposed for the learning of a class of gradient-type neural networks with an additive Gaussian white noise process. The study is based on stochastic sensitivity analysis techniques, and formal expressions are obtained for stochastic learning laws in terms of functional derivative sensitivity coefficients. The present method, based on Langevin simulation techniques, uses only the internal states of the network and ubiquitous noise to compute the learning information inherent in the stochastic correlation between noise signals and the performance functional. In particular, the method does not require the solution of adjoint equations of the back-propagation type. Thus, the present algorithm has the potential for efficiently learning network weights with significantly fewer computations. Application to an unfolded multi-layered network is described, and the results are compared with those obtained by using a back-propagation method
Prototyping and Simulation of Robot Group Intelligence using Kohonen Networks.
Wang, Zhijun; Mirdamadi, Reza; Wang, Qing
2016-01-01
Intelligent agents such as robots can form ad hoc networks and replace human being in many dangerous scenarios such as a complicated disaster relief site. This project prototypes and builds a computer simulator to simulate robot kinetics, unsupervised learning using Kohonen networks, as well as group intelligence when an ad hoc network is formed. Each robot is modeled using an object with a simple set of attributes and methods that define its internal states and possible actions it may take under certain circumstances. As the result, simple, reliable, and affordable robots can be deployed to form the network. The simulator simulates a group of robots as an unsupervised learning unit and tests the learning results under scenarios with different complexities. The simulation results show that a group of robots could demonstrate highly collaborative behavior on a complex terrain. This study could potentially provide a software simulation platform for testing individual and group capability of robots before the design process and manufacturing of robots. Therefore, results of the project have the potential to reduce the cost and improve the efficiency of robot design and building.
Adaptive Importance Sampling Simulation of Queueing Networks
de Boer, Pieter-Tjerk; Nicola, V.F.; Rubinstein, N.; Rubinstein, Reuven Y.
2000-01-01
In this paper, a method is presented for the efficient estimation of rare-event (overflow) probabilities in Jackson queueing networks using importance sampling. The method differs in two ways from methods discussed in most earlier literature: the change of measure is state-dependent, i.e., it is a
A new fault detection method for computer networks
International Nuclear Information System (INIS)
Lu, Lu; Xu, Zhengguo; Wang, Wenhai; Sun, Youxian
2013-01-01
Over the past few years, fault detection for computer networks has attracted extensive attentions for its importance in network management. Most existing fault detection methods are based on active probing techniques which can detect the occurrence of faults fast and precisely. But these methods suffer from the limitation of traffic overhead, especially in large scale networks. To relieve traffic overhead induced by active probing based methods, a new fault detection method, whose key is to divide the detection process into multiple stages, is proposed in this paper. During each stage, only a small region of the network is detected by using a small set of probes. Meanwhile, it also ensures that the entire network can be covered after multiple detection stages. This method can guarantee that the traffic used by probes during each detection stage is small sufficiently so that the network can operate without severe disturbance from probes. Several simulation results verify the effectiveness of the proposed method
Multiscale Quantum Mechanics/Molecular Mechanics Simulations with Neural Networks.
Shen, Lin; Wu, Jingheng; Yang, Weitao
2016-10-11
Molecular dynamics simulation with multiscale quantum mechanics/molecular mechanics (QM/MM) methods is a very powerful tool for understanding the mechanism of chemical and biological processes in solution or enzymes. However, its computational cost can be too high for many biochemical systems because of the large number of ab initio QM calculations. Semiempirical QM/MM simulations have much higher efficiency. Its accuracy can be improved with a correction to reach the ab initio QM/MM level. The computational cost on the ab initio calculation for the correction determines the efficiency. In this paper we developed a neural network method for QM/MM calculation as an extension of the neural-network representation reported by Behler and Parrinello. With this approach, the potential energy of any configuration along the reaction path for a given QM/MM system can be predicted at the ab initio QM/MM level based on the semiempirical QM/MM simulations. We further applied this method to three reactions in water to calculate the free energy changes. The free-energy profile obtained from the semiempirical QM/MM simulation is corrected to the ab initio QM/MM level with the potential energies predicted with the constructed neural network. The results are in excellent accordance with the reference data that are obtained from the ab initio QM/MM molecular dynamics simulation or corrected with direct ab initio QM/MM potential energies. Compared with the correction using direct ab initio QM/MM potential energies, our method shows a speed-up of 1 or 2 orders of magnitude. It demonstrates that the neural network method combined with the semiempirical QM/MM calculation can be an efficient and reliable strategy for chemical reaction simulations.
Simulated, Emulated, and Physical Investigative Analysis (SEPIA) of networked systems.
Energy Technology Data Exchange (ETDEWEB)
Burton, David P.; Van Leeuwen, Brian P.; McDonald, Michael James; Onunkwo, Uzoma A.; Tarman, Thomas David; Urias, Vincent E.
2009-09-01
This report describes recent progress made in developing and utilizing hybrid Simulated, Emulated, and Physical Investigative Analysis (SEPIA) environments. Many organizations require advanced tools to analyze their information system's security, reliability, and resilience against cyber attack. Today's security analysis utilize real systems such as computers, network routers and other network equipment, computer emulations (e.g., virtual machines) and simulation models separately to analyze interplay between threats and safeguards. In contrast, this work developed new methods to combine these three approaches to provide integrated hybrid SEPIA environments. Our SEPIA environments enable an analyst to rapidly configure hybrid environments to pass network traffic and perform, from the outside, like real networks. This provides higher fidelity representations of key network nodes while still leveraging the scalability and cost advantages of simulation tools. The result is to rapidly produce large yet relatively low-cost multi-fidelity SEPIA networks of computers and routers that let analysts quickly investigate threats and test protection approaches.
Neural Network Emulation of Reionization Simulations
Schmit, Claude J.; Pritchard, Jonathan R.
2018-05-01
Next generation radio experiments such as LOFAR, HERA and SKA are expected to probe the Epoch of Reionization and claim a first direct detection of the cosmic 21cm signal within the next decade. One of the major challenges for these experiments will be dealing with enormous incoming data volumes. Machine learning is key to increasing our data analysis efficiency. We consider the use of an artificial neural network to emulate 21cmFAST simulations and use it in a Bayesian parameter inference study. We then compare the network predictions to a direct evaluation of the EoR simulations and analyse the dependence of the results on the training set size. We find that the use of a training set of size 100 samples can recover the error contours of a full scale MCMC analysis which evaluates the model at each step.
Enabling parallel simulation of large-scale HPC network systems
International Nuclear Information System (INIS)
Mubarak, Misbah; Carothers, Christopher D.; Ross, Robert B.; Carns, Philip
2016-01-01
Here, with the increasing complexity of today’s high-performance computing (HPC) architectures, simulation has become an indispensable tool for exploring the design space of HPC systems—in particular, networks. In order to make effective design decisions, simulations of these systems must possess the following properties: (1) have high accuracy and fidelity, (2) produce results in a timely manner, and (3) be able to analyze a broad range of network workloads. Most state-of-the-art HPC network simulation frameworks, however, are constrained in one or more of these areas. In this work, we present a simulation framework for modeling two important classes of networks used in today’s IBM and Cray supercomputers: torus and dragonfly networks. We use the Co-Design of Multi-layer Exascale Storage Architecture (CODES) simulation framework to simulate these network topologies at a flit-level detail using the Rensselaer Optimistic Simulation System (ROSS) for parallel discrete-event simulation. Our simulation framework meets all the requirements of a practical network simulation and can assist network designers in design space exploration. First, it uses validated and detailed flit-level network models to provide an accurate and high-fidelity network simulation. Second, instead of relying on serial time-stepped or traditional conservative discrete-event simulations that limit simulation scalability and efficiency, we use the optimistic event-scheduling capability of ROSS to achieve efficient and scalable HPC network simulations on today’s high-performance cluster systems. Third, our models give network designers a choice in simulating a broad range of network workloads, including HPC application workloads using detailed network traces, an ability that is rarely offered in parallel with high-fidelity network simulations
Mesoscopic Simulations of Crosslinked Polymer Networks
Megariotis, Grigorios; Vogiatzis, Georgios G.; Schneider, Ludwig; Müller, Marcus; Theodorou, Doros N.
2016-08-01
A new methodology and the corresponding C++ code for mesoscopic simulations of elastomers are presented. The test system, crosslinked ds-1’4-polyisoprene’ is simulated with a Brownian Dynamics/kinetic Monte Carlo algorithm as a dense liquid of soft, coarse-grained beads, each representing 5-10 Kuhn segments. From the thermodynamic point of view, the system is described by a Helmholtz free-energy containing contributions from entropic springs between successive beads along a chain, slip-springs representing entanglements between beads on different chains, and non-bonded interactions. The methodology is employed for the calculation of the stress relaxation function from simulations of several microseconds at equilibrium, as well as for the prediction of stress-strain curves of crosslinked polymer networks under deformation.
Diagnosis method utilizing neural networks
International Nuclear Information System (INIS)
Watanabe, K.; Tamayama, K.
1990-01-01
Studies have been made on the technique of neural networks, which will be used to identify a cause of a small anomalous state in the reactor coolant system of the ATR (Advance Thermal Reactor). Three phases of analyses were carried out in this study. First, simulation for 100 seconds was made to determine how the plant parameters respond after the occurence of a transient decrease in reactivity, flow rate and temperature of feed water and increase in the steam flow rate and steam pressure, which would produce a decrease of water level in a steam drum of the ATR. Next, the simulation data was analysed utilizing an autoregressive model. From this analysis, a total of 36 coherency functions up to 0.5 Hz in each transient were computed among nine important and detectable plant parameters: neutron flux, flow rate of coolant, steam or feed water, water level in the steam drum, pressure and opening area of control valve in a steam pipe, feed water temperature and electrical power. Last, learning of neural networks composed of 96 input, 4-9 hidden and 5 output layer units was done by use of the generalized delta rule, namely a back-propagation algorithm. These convergent computations were continued as far as the difference between the desired outputs, 1 for direct cause or 0 for four other ones and actual outputs reached less than 10%. (1) Coherency functions were not governed by decreasing rate of reactivity in the range of 0.41x10 -2 dollar/s to 1.62x10 -2 dollar /s or by decreasing depth of the feed water temperature in the range of 3 deg C to 10 deg C or by a change of 10% or less in the three other causes. Change in coherency functions only depended on the type of cause. (2) The direct cause from the other four ones could be discriminated with 0.94+-0.01 of output level. A maximum of 0.06 output height was found among the other four causes. (3) Calculation load which is represented as products of learning times and numbers of the hidden units did not depend on the
SIMULATION OF NEGATIVE PRESSURE WAVE PROPAGATION IN WATER PIPE NETWORK
Directory of Open Access Journals (Sweden)
Tang Van Lam
2017-11-01
Full Text Available Subject: factors such as pipe wall roughness, mechanical properties of pipe materials, physical properties of water affect the pressure surge in the water supply pipes. These factors make it difficult to analyze the transient problem of pressure evolution using simple programming language, especially in the studies that consider only the magnitude of the positive pressure surge with the negative pressure phase being neglected. Research objectives: determine the magnitude of the negative pressure in the pipes on the experimental model. The propagation distance of the negative pressure wave will be simulated by the valve closure scenarios with the help of the HAMMER software and it is compared with an experimental model to verify the quality the results. Materials and methods: academic version of the Bentley HAMMER software is used to simulate the pressure surge wave propagation due to closure of the valve in water supply pipe network. The method of characteristics is used to solve the governing equations of transient process of pressure change in the pipeline. This method is implemented in the HAMMER software to calculate the pressure surge value in the pipes. Results: the method has been applied for water pipe networks of experimental model, the results show the affected area of negative pressure wave from valve closure and thereby we assess the largest negative pressure that may appear in water supply pipes. Conclusions: the experiment simulates the water pipe network with a consumption node for various valve closure scenarios to determine possibility of appearance of maximum negative pressure value in the pipes. Determination of these values in real-life network is relatively costly and time-consuming but nevertheless necessary for identification of the risk of pipe failure, and therefore, this paper proposes using the simulation model by the HAMMER software. Initial calibration of the model combined with the software simulation results and
A Short-Circuit Method for Networks.
Ong, P. P.
1983-01-01
Describes a method of network analysis that allows avoidance of Kirchoff's Laws (providing the network is symmetrical) by reduction to simple series/parallel resistances. The method can be extended to symmetrical alternating current, capacitance or inductance if corresponding theorems are used. Symmetric cubic network serves as an example. (JM)
Dynamic simulation of a steam generator by neural networks
International Nuclear Information System (INIS)
Masini, R.; Padovani, E.; Ricotti, M.E.; Zio, E.
1999-01-01
Numerical simulation by computers of the dynamic evolution of complex systems and components is a fundamental phase of any modern engineering design activity. This is of particular importance for risk-based design projects which require that the system behavior be analyzed under several and often extreme conditions. The traditional methods of simulation typically entail long, iterative, processes which lead to large simulation times, often exceeding the transients real time. Artificial neural networks (ANNs) may be exploited in this context, their advantages residing mainly in the speed of computation, in the capability of generalizing from few examples, in the robustness to noisy and partially incomplete data and in the capability of performing empirical input-output mapping without complete knowledge of the underlying physics. In this paper we present a novel approach to dynamic simulation by ANNs based on a superposition scheme in which a set of networks are individually trained, each one to respond to a different input forcing function. The dynamic simulation of a steam generator is considered as an example to show the potentialities of this tool and to point out the difficulties and crucial issues which typically arise when attempting to establish an efficient neural network simulator. The structure of the networks system is such to feedback, at each time step, a portion of the past evolution of the transient and this allows a good reproduction of also non-linear dynamic behaviors. A nice characteristic of the approach is that the modularization of the training reduces substantially its burden and gives this neural simulation tool a nice feature of transportability. (orig.)
Simulation of lung motions using an artificial neural network
International Nuclear Information System (INIS)
Laurent, R.; Henriet, J.; Sauget, M.; Gschwind, R.; Makovicka, L.; Salomon, M.; Nguyen, F.
2011-01-01
Purpose. A way to improve the accuracy of lung radiotherapy for a patient is to get a better understanding of its lung motion. Indeed, thanks to this knowledge it becomes possible to follow the displacements of the clinical target volume (CTV) induced by the lung breathing. This paper presents a feasibility study of an original method to simulate the positions of points in patient's lung at all breathing phases. Patients and methods. This method, based on an artificial neural network, allowed learning the lung motion on real cases and then to simulate it for new patients for which only the beginning and the end breathing data are known. The neural network learning set is made up of more than 600 points. These points, shared out on three patients and gathered on a specific lung area, were plotted by a MD. Results. - The first results are promising: an average accuracy of 1 mm is obtained for a spatial resolution of 1 x 1 x 2.5 mm 3 . Conclusion. We have demonstrated that it is possible to simulate lung motion with accuracy using an artificial neural network. As future work we plan to improve the accuracy of our method with the addition of new patient data and a coverage of the whole lungs. (authors)
Neural network stochastic simulation applied for quantifying uncertainties
Directory of Open Access Journals (Sweden)
N Foudil-Bey
2016-09-01
Full Text Available Generally the geostatistical simulation methods are used to generate several realizations of physical properties in the sub-surface, these methods are based on the variogram analysis and limited to measures correlation between variables at two locations only. In this paper, we propose a simulation of properties based on supervised Neural network training at the existing drilling data set. The major advantage is that this method does not require a preliminary geostatistical study and takes into account several points. As a result, the geological information and the diverse geophysical data can be combined easily. To do this, we used a neural network with multi-layer perceptron architecture like feed-forward, then we used the back-propagation algorithm with conjugate gradient technique to minimize the error of the network output. The learning process can create links between different variables, this relationship can be used for interpolation of the properties on the one hand, or to generate several possible distribution of physical properties on the other hand, changing at each time and a random value of the input neurons, which was kept constant until the period of learning. This method was tested on real data to simulate multiple realizations of the density and the magnetic susceptibility in three-dimensions at the mining camp of Val d'Or, Québec (Canada.
Analysis of Time Delay Simulation in Networked Control System
Nyan Phyo Aung; Zaw Min Naing; Hla Myo Tun
2016-01-01
The paper presents a PD controller for the Networked Control Systems (NCS) with delay. The major challenges in this networked control system (NCS) are the delay of the data transmission throughout the communication network. The comparative performance analysis is carried out for different delays network medium. In this paper, simulation is carried out on Ac servo motor control system using CAN Bus as communication network medium. The True Time toolbox of MATLAB is used for simulation to analy...
Learning in innovation networks: Some simulation experiments
Gilbert, Nigel; Ahrweiler, Petra; Pyka, Andreas
2007-05-01
According to the organizational learning literature, the greatest competitive advantage a firm has is its ability to learn. In this paper, a framework for modeling learning competence in firms is presented to improve the understanding of managing innovation. Firms with different knowledge stocks attempt to improve their economic performance by engaging in radical or incremental innovation activities and through partnerships and networking with other firms. In trying to vary and/or to stabilize their knowledge stocks by organizational learning, they attempt to adapt to environmental requirements while the market strongly selects on the results. The simulation experiments show the impact of different learning activities, underlining the importance of innovation and learning.
Mobile-ip Aeronautical Network Simulation Study
Ivancic, William D.; Tran, Diepchi T.
2001-01-01
NASA is interested in applying mobile Internet protocol (mobile-ip) technologies to its space and aeronautics programs. In particular, mobile-ip will play a major role in the Advanced Aeronautic Transportation Technology (AATT), the Weather Information Communication (WINCOMM), and the Small Aircraft Transportation System (SATS) aeronautics programs. This report presents the results of a simulation study of mobile-ip for an aeronautical network. The study was performed to determine the performance of the transmission control protocol (TCP) in a mobile-ip environment and to gain an understanding of how long delays, handoffs, and noisy channels affect mobile-ip performance.
A low complexity method for the optimization of network path length in spatially embedded networks
International Nuclear Information System (INIS)
Chen, Guang; Yang, Xu-Hua; Xu, Xin-Li; Ming, Yong; Chen, Sheng-Yong; Wang, Wan-Liang
2014-01-01
The average path length of a network is an important index reflecting the network transmission efficiency. In this paper, we propose a new method of decreasing the average path length by adding edges. A new indicator is presented, incorporating traffic flow demand, to assess the decrease in the average path length when a new edge is added during the optimization process. With the help of the indicator, edges are selected and added into the network one by one. The new method has a relatively small time computational complexity in comparison with some traditional methods. In numerical simulations, the new method is applied to some synthetic spatially embedded networks. The result shows that the method can perform competitively in decreasing the average path length. Then, as an example of an application of this new method, it is applied to the road network of Hangzhou, China. (paper)
Primitive chain network simulations of probe rheology.
Masubuchi, Yuichi; Amamoto, Yoshifumi; Pandey, Ankita; Liu, Cheng-Yang
2017-09-27
Probe rheology experiments, in which the dynamics of a small amount of probe chains dissolved in immobile matrix chains is discussed, have been performed for the development of molecular theories for entangled polymer dynamics. Although probe chain dynamics in probe rheology is considered hypothetically as single chain dynamics in fixed tube-shaped confinement, it has not been fully elucidated. For instance, the end-to-end relaxation of probe chains is slower than that for monodisperse melts, unlike the conventional molecular theories. In this study, the viscoelastic and dielectric relaxations of probe chains were calculated by primitive chain network simulations. The simulations semi-quantitatively reproduced the dielectric relaxation, which reflects the effect of constraint release on the end-to-end relaxation. Fair agreement was also obtained for the viscoelastic relaxation time. However, the viscoelastic relaxation intensity was underestimated, possibly due to some flaws in the model for the inter-chain cross-correlations between probe and matrix chains.
Chain networking revealed by molecular dynamics simulation
Zheng, Yexin; Tsige, Mesfin; Wang, Shi-Qing
Based on Kremer-Grest model for entangled polymer melts, we demonstrate how the response of a polymer glass depends critically on the chain length. After quenching two melts of very different chain lengths (350 beads per chain and 30 beads per chain) into deeply glassy states, we subject them to uniaxial extension. Our MD simulations show that the glass of long chains undergoes stable necking after yielding whereas the system of short chains is unable to neck and breaks up after strain localization. During ductile extension of the polymer glass made of long chain significant chain tension builds up in the load-bearing strands (LBSs). Further analysis is expected to reveal evidence of activation of the primary structure during post-yield extension. These results lend support to the recent molecular model 1 and are the simulations to demonstrate the role of chain networking. This work is supported, in part, by a NSF Grant (DMR-EAGER-1444859)
Modeling And Simulation Of Multimedia Communication Networks
Vallee, Richard; Orozco-Barbosa, Luis; Georganas, Nicolas D.
1989-05-01
In this paper, we present a simulation study of a browsing system involving radiological image servers. The proposed IEEE 802.6 DQDB MAN standard is designated as the computer network to transfer radiological images from file servers to medical workstations, and to simultaneously support real time voice communications. Storage and transmission of original raster scanned images and images compressed according to pyramid data structures are considered. Different types of browsing as well as various image sizes and bit rates in the DQDB MAN are also compared. The elapsed time, measured from the time an image request is issued until the image is displayed on the monitor, is the parameter considered to evaluate the system performance. Simulation results show that image browsing can be supported by the DQDB MAN.
Image reconstruction using Monte Carlo simulation and artificial neural networks
International Nuclear Information System (INIS)
Emert, F.; Missimner, J.; Blass, W.; Rodriguez, A.
1997-01-01
PET data sets are subject to two types of distortions during acquisition: the imperfect response of the scanner and attenuation and scattering in the active distribution. In addition, the reconstruction of voxel images from the line projections composing a data set can introduce artifacts. Monte Carlo simulation provides a means for modeling the distortions and artificial neural networks a method for correcting for them as well as minimizing artifacts. (author) figs., tab., refs
Li, Haibin; He, Yun; Nie, Xiaobo
2018-01-01
Structural reliability analysis under uncertainty is paid wide attention by engineers and scholars due to reflecting the structural characteristics and the bearing actual situation. The direct integration method, started from the definition of reliability theory, is easy to be understood, but there are still mathematics difficulties in the calculation of multiple integrals. Therefore, a dual neural network method is proposed for calculating multiple integrals in this paper. Dual neural network consists of two neural networks. The neural network A is used to learn the integrand function, and the neural network B is used to simulate the original function. According to the derivative relationships between the network output and the network input, the neural network B is derived from the neural network A. On this basis, the performance function of normalization is employed in the proposed method to overcome the difficulty of multiple integrations and to improve the accuracy for reliability calculations. The comparisons between the proposed method and Monte Carlo simulation method, Hasofer-Lind method, the mean value first-order second moment method have demonstrated that the proposed method is an efficient and accurate reliability method for structural reliability problems.
[Simulation of lung motions using an artificial neural network].
Laurent, R; Henriet, J; Salomon, M; Sauget, M; Nguyen, F; Gschwind, R; Makovicka, L
2011-04-01
A way to improve the accuracy of lung radiotherapy for a patient is to get a better understanding of its lung motion. Indeed, thanks to this knowledge it becomes possible to follow the displacements of the clinical target volume (CTV) induced by the lung breathing. This paper presents a feasibility study of an original method to simulate the positions of points in patient's lung at all breathing phases. This method, based on an artificial neural network, allowed learning the lung motion on real cases and then to simulate it for new patients for which only the beginning and the end breathing data are known. The neural network learning set is made up of more than 600 points. These points, shared out on three patients and gathered on a specific lung area, were plotted by a MD. The first results are promising: an average accuracy of 1mm is obtained for a spatial resolution of 1 × 1 × 2.5mm(3). We have demonstrated that it is possible to simulate lung motion with accuracy using an artificial neural network. As future work we plan to improve the accuracy of our method with the addition of new patient data and a coverage of the whole lungs. Copyright © 2010 Société française de radiothérapie oncologique (SFRO). Published by Elsevier SAS. All rights reserved.
Event-based simulation of networks with pulse delayed coupling
Klinshov, Vladimir; Nekorkin, Vladimir
2017-10-01
Pulse-mediated interactions are common in networks of different nature. Here we develop a general framework for simulation of networks with pulse delayed coupling. We introduce the discrete map governing the dynamics of such networks and describe the computation algorithm for its numerical simulation.
Control and estimation methods over communication networks
Mahmoud, Magdi S
2014-01-01
This book provides a rigorous framework in which to study problems in the analysis, stability and design of networked control systems. Four dominant sources of difficulty are considered: packet dropouts, communication bandwidth constraints, parametric uncertainty, and time delays. Past methods and results are reviewed from a contemporary perspective, present trends are examined, and future possibilities proposed. Emphasis is placed on robust and reliable design methods. New control strategies for improving the efficiency of sensor data processing and reducing associated time delay are presented. The coverage provided features: · an overall assessment of recent and current fault-tolerant control algorithms; · treatment of several issues arising at the junction of control and communications; · key concepts followed by their proofs and efficient computational methods for their implementation; and · simulation examples (including TrueTime simulations) to...
Fracture network modeling and GoldSim simulation support
International Nuclear Information System (INIS)
Sugita, Kenichirou; Dershowitz, W.
2005-01-01
During Heisei-16, Golder Associates provided support for JNC Tokai through discrete fracture network data analysis and simulation of the Mizunami Underground Research Laboratory (MIU), participation in Task 6 of the AEspoe Task Force on Modeling of Groundwater Flow and Transport, and development of methodologies for analysis of repository site characterization strategies and safety assessment. MIU support during H-16 involved updating the H-15 FracMan discrete fracture network (DFN) models for the MIU shaft region, and developing improved simulation procedures. Updates to the conceptual model included incorporation of 'Step2' (2004) versions of the deterministic structures, and revision of background fractures to be consistent with conductive structure data from the DH-2 borehole. Golder developed improved simulation procedures for these models through the use of hybrid discrete fracture network (DFN), equivalent porous medium (EPM), and nested DFN/EPM approaches. For each of these models, procedures were documented for the entire modeling process including model implementation, MMP simulation, and shaft grouting simulation. Golder supported JNC participation in Task 6AB, 6D and 6E of the AEspoe Task Force on Modeling of Groundwater Flow and Transport during H-16. For Task 6AB, Golder developed a new technique to evaluate the role of grout in performance assessment time-scale transport. For Task 6D, Golder submitted a report of H-15 simulations to SKB. For Task 6E, Golder carried out safety assessment time-scale simulations at the block scale, using the Laplace Transform Galerkin method. During H-16, Golder supported JNC's Total System Performance Assessment (TSPA) strategy by developing technologies for the analysis of the use site characterization data in safety assessment. This approach will aid in the understanding of the use of site characterization to progressively reduce site characterization uncertainty. (author)
The design of a network emulation and simulation laboratory
CSIR Research Space (South Africa)
Von Solms, S
2015-07-01
Full Text Available The development of the Network Emulation and Simulation Laboratory is motivated by the drive to contribute to the enhancement of the security and resilience of South Africa's critical information infrastructure. The goal of the Network Emulation...
A simulated annealing approach for redesigning a warehouse network problem
Khairuddin, Rozieana; Marlizawati Zainuddin, Zaitul; Jiun, Gan Jia
2017-09-01
Now a day, several companies consider downsizing their distribution networks in ways that involve consolidation or phase-out of some of their current warehousing facilities due to the increasing competition, mounting cost pressure and taking advantage on the economies of scale. Consequently, the changes on economic situation after a certain period of time require an adjustment on the network model in order to get the optimal cost under the current economic conditions. This paper aimed to develop a mixed-integer linear programming model for a two-echelon warehouse network redesign problem with capacitated plant and uncapacitated warehouses. The main contribution of this study is considering capacity constraint for existing warehouses. A Simulated Annealing algorithm is proposed to tackle with the proposed model. The numerical solution showed the model and method of solution proposed was practical.
Systems and methods for modeling and analyzing networks
Hill, Colin C; Church, Bruce W; McDonagh, Paul D; Khalil, Iya G; Neyarapally, Thomas A; Pitluk, Zachary W
2013-10-29
The systems and methods described herein utilize a probabilistic modeling framework for reverse engineering an ensemble of causal models, from data and then forward simulating the ensemble of models to analyze and predict the behavior of the network. In certain embodiments, the systems and methods described herein include data-driven techniques for developing causal models for biological networks. Causal network models include computational representations of the causal relationships between independent variables such as a compound of interest and dependent variables such as measured DNA alterations, changes in mRNA, protein, and metabolites to phenotypic readouts of efficacy and toxicity.
Programmable multi-node quantum network design and simulation
Dasari, Venkat R.; Sadlier, Ronald J.; Prout, Ryan; Williams, Brian P.; Humble, Travis S.
2016-05-01
Software-defined networking offers a device-agnostic programmable framework to encode new network functions. Externally centralized control plane intelligence allows programmers to write network applications and to build functional network designs. OpenFlow is a key protocol widely adopted to build programmable networks because of its programmability, flexibility and ability to interconnect heterogeneous network devices. We simulate the functional topology of a multi-node quantum network that uses programmable network principles to manage quantum metadata for protocols such as teleportation, superdense coding, and quantum key distribution. We first show how the OpenFlow protocol can manage the quantum metadata needed to control the quantum channel. We then use numerical simulation to demonstrate robust programmability of a quantum switch via the OpenFlow network controller while executing an application of superdense coding. We describe the software framework implemented to carry out these simulations and we discuss near-term efforts to realize these applications.
Integrated workflows for spiking neuronal network simulations
Directory of Open Access Journals (Sweden)
Ján eAntolík
2013-12-01
Full Text Available The increasing availability of computational resources is enabling more detailed, realistic modelling in computational neuroscience, resulting in a shift towards more heterogeneous models of neuronal circuits, and employment of complex experimental protocols. This poses a challenge for existing tool chains, as the set of tools involved in a typical modeller's workflow is expanding concomitantly, with growing complexity in the metadata flowing between them. For many parts of the workflow, a range of tools is available; however, numerous areas lack dedicated tools, while integration of existing tools is limited. This forces modellers to either handle the workflow manually, leading to errors, or to write substantial amounts of code to automate parts of the workflow, in both cases reducing their productivity.To address these issues, we have developed Mozaik: a workflow system for spiking neuronal network simulations written in Python. Mozaik integrates model, experiment and stimulation specification, simulation execution, data storage, data analysis and visualisation into a single automated workflow, ensuring that all relevant metadata are available to all workflow components. It is based on several existing tools, including PyNN, Neo and Matplotlib. It offers a declarative way to specify models and recording configurations using hierarchically organised configuration files. Mozaik automatically records all data together with all relevant metadata about the experimental context, allowing automation of the analysis and visualisation stages. Mozaik has a modular architecture, and the existing modules are designed to be extensible with minimal programming effort. Mozaik increases the productivity of running virtual experiments on highly structured neuronal networks by automating the entire experimental cycle, while increasing the reliability of modelling studies by relieving the user from manual handling of the flow of metadata between the individual
Cut Based Method for Comparing Complex Networks.
Liu, Qun; Dong, Zhishan; Wang, En
2018-03-23
Revealing the underlying similarity of various complex networks has become both a popular and interdisciplinary topic, with a plethora of relevant application domains. The essence of the similarity here is that network features of the same network type are highly similar, while the features of different kinds of networks present low similarity. In this paper, we introduce and explore a new method for comparing various complex networks based on the cut distance. We show correspondence between the cut distance and the similarity of two networks. This correspondence allows us to consider a broad range of complex networks and explicitly compare various networks with high accuracy. Various machine learning technologies such as genetic algorithms, nearest neighbor classification, and model selection are employed during the comparison process. Our cut method is shown to be suited for comparisons of undirected networks and directed networks, as well as weighted networks. In the model selection process, the results demonstrate that our approach outperforms other state-of-the-art methods with respect to accuracy.
Characterization of Background Traffic in Hybrid Network Simulation
National Research Council Canada - National Science Library
Lauwens, Ben; Scheers, Bart; Van de Capelle, Antoine
2006-01-01
.... Two approaches are common: discrete event simulation and fluid approximation. A discrete event simulation generates a huge amount of events for a full-blown battlefield communication network resulting in a very long runtime...
A random network based, node attraction facilitated network evolution method
Directory of Open Access Journals (Sweden)
WenJun Zhang
2016-03-01
Full Text Available In present study, I present a method of network evolution that based on random network, and facilitated by node attraction. In this method, I assume that the initial network is a random network, or a given initial network. When a node is ready to connect, it tends to link to the node already owning the most connections, which coincides with the general rule (Barabasi and Albert, 1999 of node connecting. In addition, a node may randomly disconnect a connection i.e., the addition of connections in the network is accompanied by the pruning of some connections. The dynamics of network evolution is determined of the attraction factor Lamda of nodes, the probability of node connection, the probability of node disconnection, and the expected initial connectance. The attraction factor of nodes, the probability of node connection, and the probability of node disconnection are time and node varying. Various dynamics can be achieved by adjusting these parameters. Effects of simplified parameters on network evolution are analyzed. The changes of attraction factor Lamda can reflect various effects of the node degree on connection mechanism. Even the changes of Lamda only will generate various networks from the random to the complex. Therefore, the present algorithm can be treated as a general model for network evolution. Modeling results show that to generate a power-law type of network, the likelihood of a node attracting connections is dependent upon the power function of the node's degree with a higher-order power. Matlab codes for simplified version of the method are provided.
Directory of Open Access Journals (Sweden)
Elston Timothy C
2004-03-01
Full Text Available Abstract Background Intrinsic fluctuations due to the stochastic nature of biochemical reactions can have large effects on the response of biochemical networks. This is particularly true for pathways that involve transcriptional regulation, where generally there are two copies of each gene and the number of messenger RNA (mRNA molecules can be small. Therefore, there is a need for computational tools for developing and investigating stochastic models of biochemical networks. Results We have developed the software package Biochemical Network Stochastic Simulator (BioNetS for efficientlyand accurately simulating stochastic models of biochemical networks. BioNetS has a graphical user interface that allows models to be entered in a straightforward manner, and allows the user to specify the type of random variable (discrete or continuous for each chemical species in the network. The discrete variables are simulated using an efficient implementation of the Gillespie algorithm. For the continuous random variables, BioNetS constructs and numerically solvesthe appropriate chemical Langevin equations. The software package has been developed to scale efficiently with network size, thereby allowing large systems to be studied. BioNetS runs as a BioSpice agent and can be downloaded from http://www.biospice.org. BioNetS also can be run as a stand alone package. All the required files are accessible from http://x.amath.unc.edu/BioNetS. Conclusions We have developed BioNetS to be a reliable tool for studying the stochastic dynamics of large biochemical networks. Important features of BioNetS are its ability to handle hybrid models that consist of both continuous and discrete random variables and its ability to model cell growth and division. We have verified the accuracy and efficiency of the numerical methods by considering several test systems.
BioNessie - a grid enabled biochemical networks simulation environment
Liu, X.; Jiang, J.; Ajayi, O.; Gu, X.; Gilbert, D.; Sinnott, R.O.
2008-01-01
The simulation of biochemical networks provides insight and understanding about the underlying biochemical processes and pathways used by cells and organisms. BioNessie is a biochemical network simulator which has been developed at the University of Glasgow. This paper describes the simulator and focuses in particular on how it has been extended to benefit from a wide variety of high performance compute resources across the UK through Grid technologies to support larger scale simulations.
Ekofisk chalk: core measurements, stochastic reconstruction, network modeling and simulation
Energy Technology Data Exchange (ETDEWEB)
Talukdar, Saifullah
2002-07-01
porosity in chalk in the form of foraminifer shells. A hybrid reconstruction technique that initializes the simulated annealing reconstruction with input generated using the Gaussian random field method has also been introduced. The technique was found to accelerate significantly the rate of convergence of the simulated annealing method. This finding is important because the main advantage of the simulated annealing method, namely its ability to impose a variety of reconstruction constraints, is usually compromised by its very slow rate of convergence. Absolute permeability, formation factor and mercury-air capillary pressure are computed from simple network models. The input parameters for the network models were extracted from a reconstructed chalk sample. The computed permeability, formation factor and mercury-air capillary pressure correspond well with the experimental data. The predictive power of a network model for chalk is further extended through incorporating important pore-level displacement phenomena and realistic description of pore space geometry and topology. Limited results show that the model may be used to compute absolute and relative permeabilities, capillary pressure, formation factor, resistivity index and saturation exponent. The above findings suggest that the network modeling technique may be used for prediction of petrophysical and reservoir engineering properties of chalk. Further works are necessary and an outline is given with considerable details. Two 2D, one 3D and a dual-porosity fractured reservoir models have been developed and an imbibition process involving water displacing oil is simulated at various injection rates and with different oil-to-water viscosity ratios using four widely used conventional up scaling techniques. The up scaling techniques are the Kyte and Berry, Pore Volume Weighted, Weighted Relative Permeability, and Stone. The results suggest that up scaling of fractured reservoirs may be possible using the conventional
Prior-knowledge-based feedforward network simulation of true boiling point curve of crude oil.
Chen, C W; Chen, D Z
2001-11-01
Theoretical results and practical experience indicate that feedforward networks can approximate a wide class of functional relationships very well. This property is exploited in modeling chemical processes. Given finite and noisy training data, it is important to encode the prior knowledge in neural networks to improve the fit precision and the prediction ability of the model. In this paper, as to the three-layer feedforward networks and the monotonic constraint, the unconstrained method, Joerding's penalty function method, the interpolation method, and the constrained optimization method are analyzed first. Then two novel methods, the exponential weight method and the adaptive method, are proposed. These methods are applied in simulating the true boiling point curve of a crude oil with the condition of increasing monotonicity. The simulation experimental results show that the network models trained by the novel methods are good at approximating the actual process. Finally, all these methods are discussed and compared with each other.
Method and system for mesh network embedded devices
Wang, Ray (Inventor)
2009-01-01
A method and system for managing mesh network devices. A mesh network device with integrated features creates an N-way mesh network with a full mesh network topology or a partial mesh network topology.
The design and implementation of a network simulation platform
CSIR Research Space (South Africa)
Von Solms, S
2013-11-01
Full Text Available these events and their effects can enable researchers to identify these threats and find ways to counter them. In this paper we present the design of a network simulation platform which can enable researchers to study dynamic behaviour of networks, network...
An artifical neural network for detection of simulated dental caries
Energy Technology Data Exchange (ETDEWEB)
Kositbowornchai, S. [Khon Kaen Univ. (Thailand). Dept. of Oral Diagnosis; Siriteptawee, S.; Plermkamon, S.; Bureerat, S. [Khon Kaen Univ. (Thailand). Dept. of Mechanical Engineering; Chetchotsak, D. [Khon Kaen Univ. (Thailand). Dept. of Industrial Engineering
2006-08-15
Objects: A neural network was developed to diagnose artificial dental caries using images from a charged-coupled device (CCD)camera and intra-oral digital radiography. The diagnostic performance of this neural network was evaluated against a gold standard. Materials and methods: The neural network design was the Learning Vector Quantization (LVQ) used to classify a tooth surface as sound or as having dental caries. The depth of the dental caries was indicated on a graphic user interface (GUI) screen developed by Matlab programming. Forty-nine images of both sound and simulated dental caries, derived from a CCD camera and by digital radiography, were used to 'train' an artificial neural network. After the 'training' process, a separate test-set comprising 322 unseen images was evaluated. Tooth sections and microscopic examinations were used to confirm the actual dental caries status.The performance of neural network was evaluated using diagnostic test. Results: The sensitivity (95%CI)/specificity (95%CI) of dental caries detection by the CCD camera and digital radiography were 0.77(0.68-0.85)/0.85(0.75-0.92) and 0.81(0.72-0.88)/0.93(0.84-0.97), respectively. The accuracy of caries depth-detection by the CCD camera and digital radiography was 58 and 40%, respectively. Conclusions: The model neural network used in this study could be a prototype for caries detection but should be improved for classifying caries depth. Our study suggests an artificial neural network can be trained to make the correct interpretations of dental caries. (orig.)
An artifical neural network for detection of simulated dental caries
International Nuclear Information System (INIS)
Kositbowornchai, S.; Siriteptawee, S.; Plermkamon, S.; Bureerat, S.; Chetchotsak, D.
2006-01-01
Objects: A neural network was developed to diagnose artificial dental caries using images from a charged-coupled device (CCD)camera and intra-oral digital radiography. The diagnostic performance of this neural network was evaluated against a gold standard. Materials and methods: The neural network design was the Learning Vector Quantization (LVQ) used to classify a tooth surface as sound or as having dental caries. The depth of the dental caries was indicated on a graphic user interface (GUI) screen developed by Matlab programming. Forty-nine images of both sound and simulated dental caries, derived from a CCD camera and by digital radiography, were used to 'train' an artificial neural network. After the 'training' process, a separate test-set comprising 322 unseen images was evaluated. Tooth sections and microscopic examinations were used to confirm the actual dental caries status.The performance of neural network was evaluated using diagnostic test. Results: The sensitivity (95%CI)/specificity (95%CI) of dental caries detection by the CCD camera and digital radiography were 0.77(0.68-0.85)/0.85(0.75-0.92) and 0.81(0.72-0.88)/0.93(0.84-0.97), respectively. The accuracy of caries depth-detection by the CCD camera and digital radiography was 58 and 40%, respectively. Conclusions: The model neural network used in this study could be a prototype for caries detection but should be improved for classifying caries depth. Our study suggests an artificial neural network can be trained to make the correct interpretations of dental caries. (orig.)
Accelerator and feedback control simulation using neural networks
International Nuclear Information System (INIS)
Nguyen, D.; Lee, M.; Sass, R.; Shoaee, H.
1991-05-01
Unlike present constant model feedback system, neural networks can adapt as the dynamics of the process changes with time. Using a process model, the ''Accelerator'' network is first trained to simulate the dynamics of the beam for a given beam line. This ''Accelerator'' network is then used to train a second ''Controller'' network which performs the control function. In simulation, the networks are used to adjust corrector magnetics to control the launch angle and position of the beam to keep it on the desired trajectory when the incoming beam is perturbed. 4 refs., 3 figs
Simulation and Evaluation of Ethernet Passive Optical Network
Directory of Open Access Journals (Sweden)
Salah A. Jaro Alabady
2013-05-01
Full Text Available This paper studies simulation and evaluation of Ethernet Passive Optical Network (EPON system, IEEE802.3ah based OPTISM 3.6 simulation program. The simulation program is used in this paper to build a typical ethernet passive optical network, and to evaluate the network performance when using the (1580, 1625 nm wavelength instead of (1310, 1490 nm that used in Optical Line Terminal (OLT and Optical Network Units (ONU's in system architecture of Ethernet passive optical network at different bit rate and different fiber optic length. The results showed enhancement in network performance by increase the number of nodes (subscribers connected to the network, increase the transmission distance, reduces the received power and reduces the Bit Error Rate (BER.
Rare event simulation using Monte Carlo methods
Rubino, Gerardo
2009-01-01
In a probabilistic model, a rare event is an event with a very small probability of occurrence. The forecasting of rare events is a formidable task but is important in many areas. For instance a catastrophic failure in a transport system or in a nuclear power plant, the failure of an information processing system in a bank, or in the communication network of a group of banks, leading to financial losses. Being able to evaluate the probability of rare events is therefore a critical issue. Monte Carlo Methods, the simulation of corresponding models, are used to analyze rare events. This book sets out to present the mathematical tools available for the efficient simulation of rare events. Importance sampling and splitting are presented along with an exposition of how to apply these tools to a variety of fields ranging from performance and dependability evaluation of complex systems, typically in computer science or in telecommunications, to chemical reaction analysis in biology or particle transport in physics. ...
A Systematic, Automated Network Planning Method
DEFF Research Database (Denmark)
Holm, Jens Åge; Pedersen, Jens Myrup
2006-01-01
This paper describes a case study conducted to evaluate the viability of a systematic, automated network planning method. The motivation for developing the network planning method was that many data networks are planned in an adhoc manner with no assurance of quality of the solution with respect...... structures, that are ready to implement in a real world scenario, are discussed in the end of the paper. These are in the area of ensuring line independence and complexity of the design rules for the planning method....
An improved sampling method of complex network
Gao, Qi; Ding, Xintong; Pan, Feng; Li, Weixing
2014-12-01
Sampling subnet is an important topic of complex network research. Sampling methods influence the structure and characteristics of subnet. Random multiple snowball with Cohen (RMSC) process sampling which combines the advantages of random sampling and snowball sampling is proposed in this paper. It has the ability to explore global information and discover the local structure at the same time. The experiments indicate that this novel sampling method could keep the similarity between sampling subnet and original network on degree distribution, connectivity rate and average shortest path. This method is applicable to the situation where the prior knowledge about degree distribution of original network is not sufficient.
Network simulation of nonstationary ionic transport through liquid junctions
International Nuclear Information System (INIS)
Castilla, J.; Horno, J.
1993-01-01
Nonstationary ionic transport across the liquid junctions has been studied using Network Thermodynamics. A network model for the time-dependent Nernst-Plack-Poisson system of equation is proposed. With this network model and the electrical circuit simulation program PSPICE, the concentrations, charge density, and electrical potentials, at short times, have been simulated for the binary system NaCl/NaCl. (Author) 13 refs
A study of reactor monitoring method with neural network
Energy Technology Data Exchange (ETDEWEB)
Nabeshima, Kunihiko [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment
2001-03-01
The purpose of this study is to investigate the methodology of Nuclear Power Plant (NPP) monitoring with neural networks, which create the plant models by the learning of the past normal operation patterns. The concept of this method is to detect the symptom of small anomalies by monitoring the deviations between the process signals measured from an actual plant and corresponding output signals from the neural network model, which might not be equal if the abnormal operational patterns are presented to the input of the neural network. Auto-associative network, which has same output as inputs, can detect an kind of anomaly condition by using normal operation data only. The monitoring tests of the feedforward neural network with adaptive learning were performed using the PWR plant simulator by which many kinds of anomaly conditions can be easily simulated. The adaptively trained feedforward network could follow the actual plant dynamics and the changes of plant condition, and then find most of the anomalies much earlier than the conventional alarm system during steady state and transient operations. Then the off-line and on-line test results during one year operation at the actual NPP (PWR) showed that the neural network could detect several small anomalies which the operators or the conventional alarm system didn't noticed. Furthermore, the sensitivity analysis suggests that the plant models by neural networks are appropriate. Finally, the simulation results show that the recurrent neural network with feedback connections could successfully model the slow behavior of the reactor dynamics without adaptive learning. Therefore, the recurrent neural network with adaptive learning will be the best choice for the actual reactor monitoring system. (author)
Approximation methods for efficient learning of Bayesian networks
Riggelsen, C
2008-01-01
This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. The topics discussed are: basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and, the concept of incomplete data. In order to provide a coherent treatment of matters, thereby helping the reader to gain a thorough understanding of the whole concept of learning Bayesian networks from (in)complete data, this publication combines in a clarifying way all the issues presented in the papers with previously unpublished work.
Constructing an Intelligent Patent Network Analysis Method
Directory of Open Access Journals (Sweden)
Chao-Chan Wu
2012-11-01
Full Text Available Patent network analysis, an advanced method of patent analysis, is a useful tool for technology management. This method visually displays all the relationships among the patents and enables the analysts to intuitively comprehend the overview of a set of patents in the field of the technology being studied. Although patent network analysis possesses relative advantages different from traditional methods of patent analysis, it is subject to several crucial limitations. To overcome the drawbacks of the current method, this study proposes a novel patent analysis method, called the intelligent patent network analysis method, to make a visual network with great precision. Based on artificial intelligence techniques, the proposed method provides an automated procedure for searching patent documents, extracting patent keywords, and determining the weight of each patent keyword in order to generate a sophisticated visualization of the patent network. This study proposes a detailed procedure for generating an intelligent patent network that is helpful for improving the efficiency and quality of patent analysis. Furthermore, patents in the field of Carbon Nanotube Backlight Unit (CNT-BLU were analyzed to verify the utility of the proposed method.
Complex networks principles, methods and applications
Latora, Vito; Russo, Giovanni
2017-01-01
Networks constitute the backbone of complex systems, from the human brain to computer communications, transport infrastructures to online social systems and metabolic reactions to financial markets. Characterising their structure improves our understanding of the physical, biological, economic and social phenomena that shape our world. Rigorous and thorough, this textbook presents a detailed overview of the new theory and methods of network science. Covering algorithms for graph exploration, node ranking and network generation, among the others, the book allows students to experiment with network models and real-world data sets, providing them with a deep understanding of the basics of network theory and its practical applications. Systems of growing complexity are examined in detail, challenging students to increase their level of skill. An engaging presentation of the important principles of network science makes this the perfect reference for researchers and undergraduate and graduate students in physics, ...
Biological transportation networks: Modeling and simulation
Albi, Giacomo; Artina, Marco; Foransier, Massimo; Markowich, Peter A.
2015-01-01
We present a model for biological network formation originally introduced by Cai and Hu [Adaptation and optimization of biological transport networks, Phys. Rev. Lett. 111 (2013) 138701]. The modeling of fluid transportation (e.g., leaf venation
Toward Designing a Quantum Key Distribution Network Simulation Model
Miralem Mehic; Peppino Fazio; Miroslav Voznak; Erik Chromy
2016-01-01
As research in quantum key distribution network technologies grows larger and more complex, the need for highly accurate and scalable simulation technologies becomes important to assess the practical feasibility and foresee difficulties in the practical implementation of theoretical achievements. In this paper, we described the design of simplified simulation environment of the quantum key distribution network with multiple links and nodes. In such simulation environment, we analyzed several ...
Binary Classification Method of Social Network Users
Directory of Open Access Journals (Sweden)
I. A. Poryadin
2017-01-01
Full Text Available The subject of research is a binary classification method of social network users based on the data analysis they have placed. Relevance of the task to gain information about a person by examining the content of his/her pages in social networks is exemplified. The most common approach to its solution is a visual browsing. The order of the regional authority in our country illustrates that its using in school education is needed. The article shows restrictions on the visual browsing of pupil’s pages in social networks as a tool for the teacher and the school psychologist and justifies that a process of social network users’ data analysis should be automated. Explores publications, which describe such data acquisition, processing, and analysis methods and considers their advantages and disadvantages. The article also gives arguments to support a proposal to study the classification method of social network users. One such method is credit scoring, which is used in banks and credit institutions to assess the solvency of clients. Based on the high efficiency of the method there is a proposal for significant expansion of its using in other areas of society. The possibility to use logistic regression as the mathematical apparatus of the proposed method of binary classification has been justified. Such an approach enables taking into account the different types of data extracted from social networks. Among them: the personal user data, information about hobbies, friends, graphic and text information, behaviour characteristics. The article describes a number of existing methods of data transformation that can be applied to solve the problem. An experiment of binary gender-based classification of social network users is described. A logistic model obtained for this example includes multiple logical variables obtained by transforming the user surnames. This experiment confirms the feasibility of the proposed method. Further work is to define a system
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 (< 5%) for a wide range of inlet-to-initial pressure ratios. A Fast Fourier Transform is preformed on the pressure oscillations to predict the various modal frequencies of the pressure wave. The shutdown problem, i.e. valve closing problem, the 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.
Methods for Analyzing Pipe Networks
DEFF Research Database (Denmark)
Nielsen, Hans Bruun
1989-01-01
to formulate the flow equations in terms of pipe discharges than in terms of energy heads. The behavior of some iterative methods is compared in the initial phase with large errors. It is explained why the linear theory method oscillates when the iteration gets close to the solution, and it is further...... demonstrated that this method offers good starting values for a Newton-Raphson iteration....
BioNSi: A Discrete Biological Network Simulator Tool.
Rubinstein, Amir; Bracha, Noga; Rudner, Liat; Zucker, Noga; Sloin, Hadas E; Chor, Benny
2016-08-05
Modeling and simulation of biological networks is an effective and widely used research methodology. The Biological Network Simulator (BioNSi) is a tool for modeling biological networks and simulating their discrete-time dynamics, implemented as a Cytoscape App. BioNSi includes a visual representation of the network that enables researchers to construct, set the parameters, and observe network behavior under various conditions. To construct a network instance in BioNSi, only partial, qualitative biological data suffices. The tool is aimed for use by experimental biologists and requires no prior computational or mathematical expertise. BioNSi is freely available at http://bionsi.wix.com/bionsi , where a complete user guide and a step-by-step manual can also be found.
Graphical user interface for wireless sensor networks simulator
Paczesny, Tomasz; Paczesny, Daniel; Weremczuk, Jerzy
2008-01-01
Wireless Sensor Networks (WSN) are currently very popular area of development. It can be suited in many applications form military through environment monitoring, healthcare, home automation and others. Those networks, when working in dynamic, ad-hoc model, need effective protocols which must differ from common computer networks algorithms. Research on those protocols would be difficult without simulation tool, because real applications often use many nodes and tests on such a big networks take much effort and costs. The paper presents Graphical User Interface (GUI) for simulator which is dedicated for WSN studies, especially in routing and data link protocols evaluation.
A Flexible System for Simulating Aeronautical Telecommunication Network
Maly, Kurt; Overstreet, C. M.; Andey, R.
1998-01-01
At Old Dominion University, we have built Aeronautical Telecommunication Network (ATN) Simulator with NASA being the fund provider. It provides a means to evaluate the impact of modified router scheduling algorithms on the network efficiency, to perform capacity studies on various network topologies and to monitor and study various aspects of ATN through graphical user interface (GUI). In this paper we describe briefly about the proposed ATN model and our abstraction of this model. Later we describe our simulator architecture highlighting some of the design specifications, scheduling algorithms and user interface. At the end, we have provided the results of performance studies on this simulator.
Artificial neural network intelligent method for prediction
Trifonov, Roumen; Yoshinov, Radoslav; Pavlova, Galya; Tsochev, Georgi
2017-09-01
Accounting and financial classification and prediction problems are high challenge and researchers use different methods to solve them. Methods and instruments for short time prediction of financial operations using artificial neural network are considered. The methods, used for prediction of financial data as well as the developed forecasting system with neural network are described in the paper. The architecture of a neural network used four different technical indicators, which are based on the raw data and the current day of the week is presented. The network developed is used for forecasting movement of stock prices one day ahead and consists of an input layer, one hidden layer and an output layer. The training method is algorithm with back propagation of the error. The main advantage of the developed system is self-determination of the optimal topology of neural network, due to which it becomes flexible and more precise The proposed system with neural network is universal and can be applied to various financial instruments using only basic technical indicators as input data.
DEFF Research Database (Denmark)
Andersen, Lars Nonboe; Au, Whitlow; Larsen, Jan
1999-01-01
This paper describes a method integrating neural networks into a system for recognizing underwater objects. The system is based on a combination of simulated dolphin sonar signals, simulated auditory filters and artificial neural networks. The system is tested on a cylinder wall thickness...... difference experiment and demonstrates high accuracy for small wall thickness differences. Results from the experiment are compared with results obtained by a false killer whale (pseudorca crassidens)....
Reduction Method for Active Distribution Networks
DEFF Research Database (Denmark)
Raboni, Pietro; Chen, Zhe
2013-01-01
On-line security assessment is traditionally performed by Transmission System Operators at the transmission level, ignoring the effective response of distributed generators and small loads. On the other hand the required computation time and amount of real time data for including Distribution...... Networks also would be too large. In this paper an adaptive aggregation method for subsystems with power electronic interfaced generators and voltage dependant loads is proposed. With this tool may be relatively easier including distribution networks into security assessment. The method is validated...... by comparing the results obtained in PSCAD® with the detailed network model and with the reduced one. Moreover the control schemes of a wind turbine and a photovoltaic plant included in the detailed network model are described....
Vulnerability analysis methods for road networks
Bíl, Michal; Vodák, Rostislav; Kubeček, Jan; Rebok, Tomáš; Svoboda, Tomáš
2014-05-01
Road networks rank among the most important lifelines of modern society. They can be damaged by either random or intentional events. Roads are also often affected by natural hazards, the impacts of which are both direct and indirect. Whereas direct impacts (e.g. roads damaged by a landslide or due to flooding) are localized in close proximity to the natural hazard occurrence, the indirect impacts can entail widespread service disabilities and considerable travel delays. The change in flows in the network may affect the population living far from the places originally impacted by the natural disaster. These effects are primarily possible due to the intrinsic nature of this system. The consequences and extent of the indirect costs also depend on the set of road links which were damaged, because the road links differ in terms of their importance. The more robust (interconnected) the road network is, the less time is usually needed to secure the serviceability of an area hit by a disaster. These kinds of networks also demonstrate a higher degree of resilience. Evaluating road network structures is therefore essential in any type of vulnerability and resilience analysis. There are a range of approaches used for evaluation of the vulnerability of a network and for identification of the weakest road links. Only few of them are, however, capable of simulating the impacts of the simultaneous closure of numerous links, which often occurs during a disaster. The primary problem is that in the case of a disaster, which usually has a large regional extent, the road network may remain disconnected. The majority of the commonly used indices use direct computation of the shortest paths or time between OD (origin - destination) pairs and therefore cannot be applied when the network breaks up into two or more components. Since extensive break-ups often occur in cases of major disasters, it is important to study the network vulnerability in these cases as well, so that appropriate
Methods for Monte Carlo simulations of biomacromolecules.
Vitalis, Andreas; Pappu, Rohit V
2009-01-01
The state-of-the-art for Monte Carlo (MC) simulations of biomacromolecules is reviewed. Available methodologies for sampling conformational equilibria and associations of biomacromolecules in the canonical ensemble, given a continuum description of the solvent environment, are reviewed. Detailed sections are provided dealing with the choice of degrees of freedom, the efficiencies of MC algorithms and algorithmic peculiarities, as well as the optimization of simple movesets. The issue of introducing correlations into elementary MC moves, and the applicability of such methods to simulations of biomacromolecules is discussed. A brief discussion of multicanonical methods and an overview of recent simulation work highlighting the potential of MC methods are also provided. It is argued that MC simulations, while underutilized biomacromolecular simulation community, hold promise for simulations of complex systems and phenomena that span multiple length scales, especially when used in conjunction with implicit solvation models or other coarse graining strategies.
Numerical methods used in simulation
International Nuclear Information System (INIS)
Caseau, Paul; Perrin, Michel; Planchard, Jacques
1978-01-01
The fundamental numerical problem posed by simulation problems is the stability of the resolution diagram. The system of the most used equations is defined, since there is a family of models of increasing complexity with 3, 4 or 5 equations although only models with 3 and 4 equations have been used extensively. After defining what is meant by explicit or implicit, the best established stability results is given for one-dimension problems and then for two-dimension problems. It is shown that two types of discretisation may be defined: four and eight point diagrams (in one or two dimensions) and six and ten point diagrams (in one or two dimensions). To end, some results are given on problems that are not usually treated very much, i.e. non-asymptotic stability and the stability of diagrams based on finite elements [fr
Methods for simulating turbulent phase screen
International Nuclear Information System (INIS)
Zhang Jianzhu; Zhang Feizhou; Wu Yi
2012-01-01
Some methods for simulating turbulent phase screen are summarized, and their characteristics are analyzed by calculating the phase structure function, decomposing phase screens into Zernike polynomials, and simulating laser propagation in the atmosphere. Through analyzing, it is found that, the turbulent high-frequency components are well contained by those phase screens simulated by the FFT method, but the low-frequency components are little contained. The low-frequency components are well contained by screens simulated by Zernike method, but the high-frequency components are not contained enough. The high frequency components contained will be improved by increasing the order of the Zernike polynomial, but they mainly lie in the edge-area. Compared with the two methods above, the fractal method is a better method to simulate turbulent phase screens. According to the radius of the focal spot and the variance of the focal spot jitter, there are limitations in the methods except the fractal method. Combining the FFT and Zernike method or combining the FFT method and self-similar theory to simulate turbulent phase screens is an effective and appropriate way. In general, the fractal method is probably the best way. (authors)
Toward Designing a Quantum Key Distribution Network Simulation Model
Directory of Open Access Journals (Sweden)
Miralem Mehic
2016-01-01
Full Text Available As research in quantum key distribution network technologies grows larger and more complex, the need for highly accurate and scalable simulation technologies becomes important to assess the practical feasibility and foresee difficulties in the practical implementation of theoretical achievements. In this paper, we described the design of simplified simulation environment of the quantum key distribution network with multiple links and nodes. In such simulation environment, we analyzed several routing protocols in terms of the number of sent routing packets, goodput and Packet Delivery Ratio of data traffic flow using NS-3 simulator.
A Network Contention Model for the Extreme-scale Simulator
Energy Technology Data Exchange (ETDEWEB)
Engelmann, Christian [ORNL; Naughton III, Thomas J [ORNL
2015-01-01
The Extreme-scale Simulator (xSim) is a performance investigation toolkit for high-performance computing (HPC) hardware/software co-design. It permits running a HPC application with millions of concurrent execution threads, while observing its performance in a simulated extreme-scale system. This paper details a newly developed network modeling feature for xSim, eliminating the shortcomings of the existing network modeling capabilities. The approach takes a different path for implementing network contention and bandwidth capacity modeling using a less synchronous and accurate enough model design. With the new network modeling feature, xSim is able to simulate on-chip and on-node networks with reasonable accuracy and overheads.
S-curve networks and an approximate method for estimating degree distributions of complex networks
International Nuclear Information System (INIS)
Guo Jin-Li
2010-01-01
In the study of complex networks almost all theoretical models have the property of infinite growth, but the size of actual networks is finite. According to statistics from the China Internet IPv4 (Internet Protocol version 4) addresses, this paper proposes a forecasting model by using S curve (logistic curve). The growing trend of IPv4 addresses in China is forecasted. There are some reference values for optimizing the distribution of IPv4 address resource and the development of IPv6. Based on the laws of IPv4 growth, that is, the bulk growth and the finitely growing limit, it proposes a finite network model with a bulk growth. The model is said to be an S-curve network. Analysis demonstrates that the analytic method based on uniform distributions (i.e., Barabási-Albert method) is not suitable for the network. It develops an approximate method to predict the growth dynamics of the individual nodes, and uses this to calculate analytically the degree distribution and the scaling exponents. The analytical result agrees with the simulation well, obeying an approximately power-law form. This method can overcome a shortcoming of Barabási-Albert method commonly used in current network research. (general)
S-curve networks and an approximate method for estimating degree distributions of complex networks
Guo, Jin-Li
2010-12-01
In the study of complex networks almost all theoretical models have the property of infinite growth, but the size of actual networks is finite. According to statistics from the China Internet IPv4 (Internet Protocol version 4) addresses, this paper proposes a forecasting model by using S curve (logistic curve). The growing trend of IPv4 addresses in China is forecasted. There are some reference values for optimizing the distribution of IPv4 address resource and the development of IPv6. Based on the laws of IPv4 growth, that is, the bulk growth and the finitely growing limit, it proposes a finite network model with a bulk growth. The model is said to be an S-curve network. Analysis demonstrates that the analytic method based on uniform distributions (i.e., Barabási-Albert method) is not suitable for the network. It develops an approximate method to predict the growth dynamics of the individual nodes, and uses this to calculate analytically the degree distribution and the scaling exponents. The analytical result agrees with the simulation well, obeying an approximately power-law form. This method can overcome a shortcoming of Barabási-Albert method commonly used in current network research.
Research on the method of measuring space information network capacity in communication service
Directory of Open Access Journals (Sweden)
Zhu Shichao
2017-02-01
Full Text Available Because of the large scale characteristic of space information network in terms of space and time and the increasing of its complexity,existing measuring methods of information transmission capacity have been unable to measure the existing and future space information networkeffectively.In this study,we firstly established a complex model of space information network,and measured the whole space information network capacity by means of analyzing data access capability to the network and data transmission capability within the network.At last,we verified the rationality of the proposed measuring method by using STK and Matlab simulation software for collaborative simulation.
WDM Systems and Networks Modeling, Simulation, Design and Engineering
Ellinas, Georgios; Roudas, Ioannis
2012-01-01
WDM Systems and Networks: Modeling, Simulation, Design and Engineering provides readers with the basic skills, concepts, and design techniques used to begin design and engineering of optical communication systems and networks at various layers. The latest semi-analytical system simulation techniques are applied to optical WDM systems and networks, and a review of the various current areas of optical communications is presented. Simulation is mixed with experimental verification and engineering to present the industry as well as state-of-the-art research. This contributed volume is divided into three parts, accommodating different readers interested in various types of networks and applications. The first part of the book presents modeling approaches and simulation tools mainly for the physical layer including transmission effects, devices, subsystems, and systems), whereas the second part features more engineering/design issues for various types of optical systems including ULH, access, and in-building system...
Wang, Wei; Huang, Li; Liang, Xuedong
2018-01-06
This paper investigates the reliability of complex emergency logistics networks, as reliability is crucial to reducing environmental and public health losses in post-accident emergency rescues. Such networks' statistical characteristics are analyzed first. After the connected reliability and evaluation indices for complex emergency logistics networks are effectively defined, simulation analyses of network reliability are conducted under two different attack modes using a particular emergency logistics network as an example. The simulation analyses obtain the varying trends in emergency supply times and the ratio of effective nodes and validates the effects of network characteristics and different types of attacks on network reliability. The results demonstrate that this emergency logistics network is both a small-world and a scale-free network. When facing random attacks, the emergency logistics network steadily changes, whereas it is very fragile when facing selective attacks. Therefore, special attention should be paid to the protection of supply nodes and nodes with high connectivity. The simulation method provides a new tool for studying emergency logistics networks and a reference for similar studies.
An algebra-based method for inferring gene regulatory networks.
Vera-Licona, Paola; Jarrah, Abdul; Garcia-Puente, Luis David; McGee, John; Laubenbacher, Reinhard
2014-03-26
The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used. This paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also predicting several of the
Liu, Jinkun
2013-01-01
Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MATLAB simulation of stable adaptive RBF neural control strategies. In this book, a broad range of implementable neural network control design methods for mechanical systems are presented, such as robot manipulators, inverted pendulums, single link flexible joint robots, motors, etc. Advanced neural network controller design methods and their stability analysis are explored. The book provides readers with the fundamentals of neural network control system design. This book is intended for the researchers in the fields of neural adaptive control, mechanical systems, Matlab simulation, engineering design, robotics and automation. Jinkun Liu is a professor at Beijing University of Aeronautics and Astronauti...
An introduction to network modeling and simulation for the practicing engineer
Burbank, Jack; Ward, Jon
2011-01-01
This book provides the practicing engineer with a concise listing of commercial and open-source modeling and simulation tools currently available including examples of implementing those tools for solving specific Modeling and Simulation examples. Instead of focusing on the underlying theory of Modeling and Simulation and fundamental building blocks for custom simulations, this book compares platforms used in practice, and gives rules enabling the practicing engineer to utilize available Modeling and Simulation tools. This book will contain insights regarding common pitfalls in network Modeling and Simulation and practical methods for working engineers.
Detector Simulation: Data Treatment and Analysis Methods
Apostolakis, J
2011-01-01
Detector Simulation in 'Data Treatment and Analysis Methods', part of 'Landolt-Börnstein - Group I Elementary Particles, Nuclei and Atoms: Numerical Data and Functional Relationships in Science and Technology, Volume 21B1: Detectors for Particles and Radiation. Part 1: Principles and Methods'. This document is part of Part 1 'Principles and Methods' of Subvolume B 'Detectors for Particles and Radiation' of Volume 21 'Elementary Particles' of Landolt-Börnstein - Group I 'Elementary Particles, Nuclei and Atoms'. It contains the Section '4.1 Detector Simulation' of Chapter '4 Data Treatment and Analysis Methods' with the content: 4.1 Detector Simulation 4.1.1 Overview of simulation 4.1.1.1 Uses of detector simulation 4.1.2 Stages and types of simulation 4.1.2.1 Tools for event generation and detector simulation 4.1.2.2 Level of simulation and computation time 4.1.2.3 Radiation effects and background studies 4.1.3 Components of detector simulation 4.1.3.1 Geometry modeling 4.1.3.2 External fields 4.1.3.3 Intro...
Isogeometric methods for numerical simulation
Bordas, Stéphane
2015-01-01
The book presents the state of the art in isogeometric modeling and shows how the method has advantaged. First an introduction to geometric modeling with NURBS and T-splines is given followed by the implementation into computer software. The implementation in both the FEM and BEM is discussed.
Wireless Power Transfer Protocols in Sensor Networks: Experiments and Simulations
Directory of Open Access Journals (Sweden)
Sotiris Nikoletseas
2017-04-01
Full Text Available Rapid technological advances in the domain of Wireless Power Transfer pave the way for novel methods for power management in systems of wireless devices, and recent research works have already started considering algorithmic solutions for tackling emerging problems. In this paper, we investigate the problem of efficient and balanced Wireless Power Transfer in Wireless Sensor Networks. We employ wireless chargers that replenish the energy of network nodes. We propose two protocols that configure the activity of the chargers. One protocol performs wireless charging focused on the charging efficiency, while the other aims at proper balance of the chargers’ residual energy. We conduct detailed experiments using real devices and we validate the experimental results via larger scale simulations. We observe that, in both the experimental evaluation and the evaluation through detailed simulations, both protocols achieve their main goals. The Charging Oriented protocol achieves good charging efficiency throughout the experiment, while the Energy Balancing protocol achieves a uniform distribution of energy within the chargers.
Geometrical methods for power network analysis
Energy Technology Data Exchange (ETDEWEB)
Bellucci, Stefano; Tiwari, Bhupendra Nath [Istituto Nazioneale di Fisica Nucleare, Frascati, Rome (Italy). Lab. Nazionali di Frascati; Gupta, Neeraj [Indian Institute of Technology, Kanpur (India). Dept. of Electrical Engineering
2013-02-01
Uses advanced geometrical methods to analyse power networks. Provides a self-contained and tutorial introduction. Includes a fully worked-out example for the IEEE 5 bus system. This book is a short introduction to power system planning and operation using advanced geometrical methods. The approach is based on well-known insights and techniques developed in theoretical physics in the context of Riemannian manifolds. The proof of principle and robustness of this approach is examined in the context of the IEEE 5 bus system. This work addresses applied mathematicians, theoretical physicists and power engineers interested in novel mathematical approaches to power network theory.
Development of neural network simulating power distribution of a BWR fuel bundle
International Nuclear Information System (INIS)
Tanabe, A.; Yamamoto, T.; Shinfuku, K.; Nakamae, T.
1992-01-01
A neural network model is developed to simulate the precise nuclear physics analysis program code for quick scoping survey calculations. The relation between enrichment and local power distribution of BWR fuel bundles was learned using two layers neural network (ENET). A new model is to introduce burnable neutron absorber (Gadolinia), added to several fuel rods to decrease initial reactivity of fresh bundle. The 2nd stages three layers neural network (GNET) is added on the 1st stage network ENET. GNET studies the local distribution difference caused by Gadolinia. Using this method, it becomes possible to survey of the gradients of sigmoid functions and back propagation constants with reasonable time. Using 99 learning patterns of zero burnup, good error convergence curve is obtained after many trials. This neural network model is able to simulate no learned cases fairly as well as the learned cases. Computer time of this neural network model is about 100 times faster than a precise analysis model. (author)
Mesoscopic simulations of crosslinked polymer networks
Megariotis, G.; Vogiatzis, G.G.; Schneider, L.; Müller, M.; Theodorou, D.N.
2016-01-01
A new methodology and the corresponding C++ code for mesoscopic simulations of elastomers are presented. The test system, crosslinked ds-1'4-polyisoprene' is simulated with a Brownian Dynamics/kinetic Monte Carlo algorithm as a dense liquid of soft, coarse-grained beads, each representing 5-10 Kuhn
On the Simulation-Based Reliability of Complex Emergency Logistics Networks in Post-Accident Rescues
Wang, Wei; Huang, Li; Liang, Xuedong
2018-01-01
This paper investigates the reliability of complex emergency logistics networks, as reliability is crucial to reducing environmental and public health losses in post-accident emergency rescues. Such networks’ statistical characteristics are analyzed first. After the connected reliability and evaluation indices for complex emergency logistics networks are effectively defined, simulation analyses of network reliability are conducted under two different attack modes using a particular emergency logistics network as an example. The simulation analyses obtain the varying trends in emergency supply times and the ratio of effective nodes and validates the effects of network characteristics and different types of attacks on network reliability. The results demonstrate that this emergency logistics network is both a small-world and a scale-free network. When facing random attacks, the emergency logistics network steadily changes, whereas it is very fragile when facing selective attacks. Therefore, special attention should be paid to the protection of supply nodes and nodes with high connectivity. The simulation method provides a new tool for studying emergency logistics networks and a reference for similar studies. PMID:29316614
Simulation studies of a wide area health care network.
McDaniel, J. G.
1994-01-01
There is an increasing number of efforts to install wide area health care networks. Some of these networks are being built to support several applications over a wide user base consisting primarily of medical practices, hospitals, pharmacies, medical laboratories, payors, and suppliers. Although on-line, multi-media telecommunication is desirable for some purposes such as cardiac monitoring, store-and-forward messaging is adequate for many common, high-volume applications. Laboratory test results and payment claims, for example, can be distributed using electronic messaging networks. Several network prototypes have been constructed to determine the technical problems and to assess the effectiveness of electronic messaging in wide area health care networks. Our project, Health Link, developed prototype software that was able to use the public switched telephone network to exchange messages automatically, reliably and securely. The network could be configured to accommodate the many different traffic patterns and cost constraints of its users. Discrete event simulations were performed on several network models. Canonical star and mesh networks, that were composed of nodes operating at steady state under equal loads, were modeled. Both topologies were found to support the throughput of a generic wide area health care network. The mean message delivery time of the mesh network was found to be less than that of the star network. Further simulations were conducted for a realistic large-scale health care network consisting of 1,553 doctors, 26 hospitals, four medical labs, one provincial lab and one insurer. Two network topologies were investigated: one using predominantly peer-to-peer communication, the other using client-server communication.(ABSTRACT TRUNCATED AT 250 WORDS) PMID:7949966
Validating module network learning algorithms using simulated data.
Michoel, Tom; Maere, Steven; Bonnet, Eric; Joshi, Anagha; Saeys, Yvan; Van den Bulcke, Tim; Van Leemput, Koenraad; van Remortel, Piet; Kuiper, Martin; Marchal, Kathleen; Van de Peer, Yves
2007-05-03
In recent years, several authors have used probabilistic graphical models to learn expression modules and their regulatory programs from gene expression data. Despite the demonstrated success of such algorithms in uncovering biologically relevant regulatory relations, further developments in the area are hampered by a lack of tools to compare the performance of alternative module network learning strategies. Here, we demonstrate the use of the synthetic data generator SynTReN for the purpose of testing and comparing module network learning algorithms. We introduce a software package for learning module networks, called LeMoNe, which incorporates a novel strategy for learning regulatory programs. Novelties include the use of a bottom-up Bayesian hierarchical clustering to construct the regulatory programs, and the use of a conditional entropy measure to assign regulators to the regulation program nodes. Using SynTReN data, we test the performance of LeMoNe in a completely controlled situation and assess the effect of the methodological changes we made with respect to an existing software package, namely Genomica. Additionally, we assess the effect of various parameters, such as the size of the data set and the amount of noise, on the inference performance. Overall, application of Genomica and LeMoNe to simulated data sets gave comparable results. However, LeMoNe offers some advantages, one of them being that the learning process is considerably faster for larger data sets. Additionally, we show that the location of the regulators in the LeMoNe regulation programs and their conditional entropy may be used to prioritize regulators for functional validation, and that the combination of the bottom-up clustering strategy with the conditional entropy-based assignment of regulators improves the handling of missing or hidden regulators. We show that data simulators such as SynTReN are very well suited for the purpose of developing, testing and improving module network
A gene network simulator to assess reverse engineering algorithms.
Di Camillo, Barbara; Toffolo, Gianna; Cobelli, Claudio
2009-03-01
In the context of reverse engineering of biological networks, simulators are helpful to test and compare the accuracy of different reverse-engineering approaches in a variety of experimental conditions. A novel gene-network simulator is presented that resembles some of the main features of transcriptional regulatory networks related to topology, interaction among regulators of transcription, and expression dynamics. The simulator generates network topology according to the current knowledge of biological network organization, including scale-free distribution of the connectivity and clustering coefficient independent of the number of nodes in the network. It uses fuzzy logic to represent interactions among the regulators of each gene, integrated with differential equations to generate continuous data, comparable to real data for variety and dynamic complexity. Finally, the simulator accounts for saturation in the response to regulation and transcription activation thresholds and shows robustness to perturbations. It therefore provides a reliable and versatile test bed for reverse engineering algorithms applied to microarray data. Since the simulator describes regulatory interactions and expression dynamics as two distinct, although interconnected aspects of regulation, it can also be used to test reverse engineering approaches that use both microarray and protein-protein interaction data in the process of learning. A first software release is available at http://www.dei.unipd.it/~dicamill/software/netsim as an R programming language package.
International Nuclear Information System (INIS)
Ding, Yi; Wang, Peng; Goel, Lalit; Billinton, Roy; Karki, Rajesh
2007-01-01
This paper presents a technique to evaluate reliability of a restructured power system with a bilateral market. The proposed technique is based on the combination of the reliability network equivalent and pseudo-sequential simulation approaches. The reliability network equivalent techniques have been implemented in the Monte Carlo simulation procedure to reduce the computational burden of the analysis. Pseudo-sequential simulation has been used to increase the computational efficiency of the non-sequential simulation method and to model the chronological aspects of market trading and system operation. Multi-state Markov models for generation and transmission systems are proposed and implemented in the simulation. A new load shedding scheme is proposed during generation inadequacy and network congestion to minimize the load curtailment. The IEEE reliability test system (RTS) is used to illustrate the technique. (author)
Temporal Gillespie Algorithm: Fast Simulation of Contagion Processes on Time-Varying Networks.
Vestergaard, Christian L; Génois, Mathieu
2015-10-01
Stochastic simulations are one of the cornerstones of the analysis of dynamical processes on complex networks, and are often the only accessible way to explore their behavior. The development of fast algorithms is paramount to allow large-scale simulations. The Gillespie algorithm can be used for fast simulation of stochastic processes, and variants of it have been applied to simulate dynamical processes on static networks. However, its adaptation to temporal networks remains non-trivial. We here present a temporal Gillespie algorithm that solves this problem. Our method is applicable to general Poisson (constant-rate) processes on temporal networks, stochastically exact, and up to multiple orders of magnitude faster than traditional simulation schemes based on rejection sampling. We also show how it can be extended to simulate non-Markovian processes. The algorithm is easily applicable in practice, and as an illustration we detail how to simulate both Poissonian and non-Markovian models of epidemic spreading. Namely, we provide pseudocode and its implementation in C++ for simulating the paradigmatic Susceptible-Infected-Susceptible and Susceptible-Infected-Recovered models and a Susceptible-Infected-Recovered model with non-constant recovery rates. For empirical networks, the temporal Gillespie algorithm is here typically from 10 to 100 times faster than rejection sampling.
Computational Aspects of Sensor Network Protocols (Distributed Sensor Network Simulator
Directory of Open Access Journals (Sweden)
Vasanth Iyer
2009-08-01
Full Text Available In this work, we model the sensor networks as an unsupervised learning and clustering process. We classify nodes according to its static distribution to form known class densities (CCPD. These densities are chosen from specific cross-layer features which maximizes lifetime of power-aware routing algorithms. To circumvent computational complexities of a power-ware communication STACK we introduce path-loss models at the nodes only for high density deployments. We study the cluster heads and formulate the data handling capacity for an expected deployment and use localized probability models to fuse the data with its side information before transmission. So each cluster head has a unique Pmax but not all cluster heads have the same measured value. In a lossless mode if there are no faults in the sensor network then we can show that the highest probability given by Pmax is ambiguous if its frequency is ≤ n/2 otherwise it can be determined by a local function. We further show that the event detection at the cluster heads can be modelled with a pattern 2m and m, the number of bits can be a correlated pattern of 2 bits and for a tight lower bound we use 3-bit Huffman codes which have entropy < 1. These local algorithms are further studied to optimize on power, fault detection and to maximize on the distributed routing algorithm used at the higher layers. From these bounds in large network, it is observed that the power dissipation is network size invariant. The performance of the routing algorithms solely based on success of finding healthy nodes in a large distribution. It is also observed that if the network size is kept constant and the density of the nodes is kept closer then the local pathloss model effects the performance of the routing algorithms. We also obtain the maximum intensity of transmitting nodes for a given category of routing algorithms for an outage constraint, i.e., the lifetime of sensor network.
Fomekong-Nanfack, Y.; Postma, M.; Kaandorp, J.A.
2009-01-01
Abstract Background Inference of gene regulatory networks (GRNs) requires accurate data, a method to simulate the expression patterns and an efficient optimization algorithm to estimate the unknown parameters. Using this approach it is possible to obtain alternative circuits without making any a priori assumptions about the interactions, which all simulate the observed patterns. It is important to analyze the properties of the circuits. Findings We have analyzed the simulated gene expression ...
Directory of Open Access Journals (Sweden)
Ufa Ruslan A.
2015-01-01
Full Text Available The motivation of the presented research is based on the needs for development of new methods and tools for adequate simulation of intelligent electric power systems with active-adaptive electric networks (IES including Flexible Alternating Current Transmission System (FACTS devices. The key requirements for the simulation were formed. The presented analysis of simulation results of IES confirms the need to use a hybrid modelling approach.
Meeting the memory challenges of brain-scale network simulation
Directory of Open Access Journals (Sweden)
Susanne eKunkel
2012-01-01
Full Text Available The development of high-performance simulation software is crucial for studying the brain connectome. Using connectome data to generate neurocomputational models requires software capable of coping with models on a variety of scales: from the microscale, investigating plasticity and dynamics of circuits in local networks, to the macroscale, investigating the interactions between distinct brain regions. Prior to any serious dynamical investigation, the first task of network simulations is to check the consistency of data integrated in the connectome and constrain ranges for yet unknown parameters. Thanks to distributed computing techniques, it is possible today to routinely simulate local cortical networks of around 10^5 neurons with up to 10^9 synapses on clusters and multi-processor shared-memory machines. However, brain-scale networks are one or two orders of magnitude larger than such local networks, in terms of numbers of neurons and synapses as well as in terms of computational load. Such networks have been studied in individual studies, but the underlying simulation technologies have neither been described in sufficient detail to be reproducible nor made publicly available. Here, we discover that as the network model sizes approach the regime of meso- and macroscale simulations, memory consumption on individual compute nodes becomes a critical bottleneck. This is especially relevant on modern supercomputers such as the Bluegene/P architecture where the available working memory per CPU core is rather limited. We develop a simple linear model to analyze the memory consumption of the constituent components of a neuronal simulator as a function of network size and the number of cores used. This approach has multiple benefits. The model enables identification of key contributing components to memory saturation and prediction of the effects of potential improvements to code before any implementation takes place.
Method Accelerates Training Of Some Neural Networks
Shelton, Robert O.
1992-01-01
Three-layer networks trained faster provided two conditions are satisfied: numbers of neurons in layers are such that majority of work done in synaptic connections between input and hidden layers, and number of neurons in input layer at least as great as number of training pairs of input and output vectors. Based on modified version of back-propagation method.
Distributed dynamic simulations of networked control and building performance applications.
Yahiaoui, Azzedine
2018-02-01
The use of computer-based automation and control systems for smart sustainable buildings, often so-called Automated Buildings (ABs), has become an effective way to automatically control, optimize, and supervise a wide range of building performance applications over a network while achieving the minimum energy consumption possible, and in doing so generally refers to Building Automation and Control Systems (BACS) architecture. Instead of costly and time-consuming experiments, this paper focuses on using distributed dynamic simulations to analyze the real-time performance of network-based building control systems in ABs and improve the functions of the BACS technology. The paper also presents the development and design of a distributed dynamic simulation environment with the capability of representing the BACS architecture in simulation by run-time coupling two or more different software tools over a network. The application and capability of this new dynamic simulation environment are demonstrated by an experimental design in this paper.
Modeling and Simulation Network Data Standards
2011-09-30
approaches . 2.3. JNAT. JNAT is a Web application that provides connectivity and network analysis capability. JNAT uses propagation models and low-fidelity...COMBATXXI Movement Logger Data Output Dictionary. Field # Geocentric Coordinates (GCC) Heading Geodetic Coordinates (GDC) Heading Universal...B-8 Field # Geocentric Coordinates (GCC) Heading Geodetic Coordinates (GDC) Heading Universal Transverse Mercator (UTM) Heading
Hybrid recommendation methods in complex networks.
Fiasconaro, A; Tumminello, M; Nicosia, V; Latora, V; Mantegna, R N
2015-07-01
We propose two recommendation methods, based on the appropriate normalization of already existing similarity measures, and on the convex combination of the recommendation scores derived from similarity between users and between objects. We validate the proposed measures on three data sets, and we compare the performance of our methods to other recommendation systems recently proposed in the literature. We show that the proposed similarity measures allow us to attain an improvement of performances of up to 20% with respect to existing nonparametric methods, and that the accuracy of a recommendation can vary widely from one specific bipartite network to another, which suggests that a careful choice of the most suitable method is highly relevant for an effective recommendation on a given system. Finally, we study how an increasing presence of random links in the network affects the recommendation scores, finding that one of the two recommendation algorithms introduced here can systematically outperform the others in noisy data sets.
Power Aware Simulation Framework for Wireless Sensor Networks and Nodes
Directory of Open Access Journals (Sweden)
Daniel Weber
2008-07-01
Full Text Available The constrained resources of sensor nodes limit analytical techniques and cost-time factors limit test beds to study wireless sensor networks (WSNs. Consequently, simulation becomes an essential tool to evaluate such systems.We present the power aware wireless sensors (PAWiS simulation framework that supports design and simulation of wireless sensor networks and nodes. The framework emphasizes power consumption capturing and hence the identification of inefficiencies in various hardware and software modules of the systems. These modules include all layers of the communication system, the targeted class of application itself, the power supply and energy management, the central processing unit (CPU, and the sensor-actuator interface. The modular design makes it possible to simulate heterogeneous systems. PAWiS is an OMNeT++ based discrete event simulator written in C++. It captures the node internals (modules as well as the node surroundings (network, environment and provides specific features critical to WSNs like capturing power consumption at various levels of granularity, support for mobility, and environmental dynamics as well as the simulation of timing effects. A module library with standardized interfaces and a power analysis tool have been developed to support the design and analysis of simulation models. The performance of the PAWiS simulator is comparable with other simulation environments.
A method of network topology optimization design considering application process characteristic
Wang, Chunlin; Huang, Ning; Bai, Yanan; Zhang, Shuo
2018-03-01
Communication networks are designed to meet the usage requirements of users for various network applications. The current studies of network topology optimization design mainly considered network traffic, which is the result of network application operation, but not a design element of communication networks. A network application is a procedure of the usage of services by users with some demanded performance requirements, and has obvious process characteristic. In this paper, we first propose a method to optimize the design of communication network topology considering the application process characteristic. Taking the minimum network delay as objective, and the cost of network design and network connective reliability as constraints, an optimization model of network topology design is formulated, and the optimal solution of network topology design is searched by Genetic Algorithm (GA). Furthermore, we investigate the influence of network topology parameter on network delay under the background of multiple process-oriented applications, which can guide the generation of initial population and then improve the efficiency of GA. Numerical simulations show the effectiveness and validity of our proposed method. Network topology optimization design considering applications can improve the reliability of applications, and provide guidance for network builders in the early stage of network design, which is of great significance in engineering practices.
Artificial neural network simulation of battery performance
Energy Technology Data Exchange (ETDEWEB)
O`Gorman, C.C.; Ingersoll, D.; Jungst, R.G.; Paez, T.L.
1998-12-31
Although they appear deceptively simple, batteries embody a complex set of interacting physical and chemical processes. While the discrete engineering characteristics of a battery such as the physical dimensions of the individual components, are relatively straightforward to define explicitly, their myriad chemical and physical processes, including interactions, are much more difficult to accurately represent. Within this category are the diffusive and solubility characteristics of individual species, reaction kinetics and mechanisms of primary chemical species as well as intermediates, and growth and morphology characteristics of reaction products as influenced by environmental and operational use profiles. For this reason, development of analytical models that can consistently predict the performance of a battery has only been partially successful, even though significant resources have been applied to this problem. As an alternative approach, the authors have begun development of a non-phenomenological model for battery systems based on artificial neural networks. Both recurrent and non-recurrent forms of these networks have been successfully used to develop accurate representations of battery behavior. The connectionist normalized linear spline (CMLS) network has been implemented with a self-organizing layer to model a battery system with the generalized radial basis function net. Concurrently, efforts are under way to use the feedforward back propagation network to map the {open_quotes}state{close_quotes} of a battery system. Because of the complexity of battery systems, accurate representation of the input and output parameters has proven to be very important. This paper describes these initial feasibility studies as well as the current models and makes comparisons between predicted and actual performance.
Allag , Hicham; Kedous-Lebouc , Afef; Latreche , Mohamed E. H.
2008-01-01
International audience; In this work, an implementation of static magnetic hysteresis in the reluctance network method is presented and its effectiveness is demonstrated. This implementation is achieved by a succession of iterative steps in the form of algorithm explained and developed for simple examples. However it remains valid for any magnetic circuit. The results obtained are compared to those given by finite element method simulation and essentially the effect of relaxation is discussed...
Utilization of Selected Data Mining Methods for Communication Network Analysis
Directory of Open Access Journals (Sweden)
V. Ondryhal
2011-06-01
Full Text Available The aim of the project was to analyze the behavior of military communication networks based on work with real data collected continuously since 2005. With regard to the nature and amount of the data, data mining methods were selected for the purpose of analyses and experiments. The quality of real data is often insufficient for an immediate analysis. The article presents the data cleaning operations which have been carried out with the aim to improve the input data sample to obtain reliable models. Gradually, by means of properly chosen SW, network models were developed to verify generally valid patterns of network behavior as a bulk service. Furthermore, unlike the commercially available communication networks simulators, the models designed allowed us to capture nonstandard models of network behavior under an increased load, verify the correct sizing of the network to the increased load, and thus test its reliability. Finally, based on previous experience, the models enabled us to predict emergency situations with a reasonable accuracy.
Spectral Methods in Numerical Plasma Simulation
DEFF Research Database (Denmark)
Coutsias, E.A.; Hansen, F.R.; Huld, T.
1989-01-01
An introduction is given to the use of spectral methods in numerical plasma simulation. As examples of the use of spectral methods, solutions to the two-dimensional Euler equations in both a simple, doubly periodic region, and on an annulus will be shown. In the first case, the solution is expanded...
Evaluation of structural reliability using simulation methods
Directory of Open Access Journals (Sweden)
Baballëku Markel
2015-01-01
Full Text Available Eurocode describes the 'index of reliability' as a measure of structural reliability, related to the 'probability of failure'. This paper is focused on the assessment of this index for a reinforced concrete bridge pier. It is rare to explicitly use reliability concepts for design of structures, but the problems of structural engineering are better known through them. Some of the main methods for the estimation of the probability of failure are the exact analytical integration, numerical integration, approximate analytical methods and simulation methods. Monte Carlo Simulation is used in this paper, because it offers a very good tool for the estimation of probability in multivariate functions. Complicated probability and statistics problems are solved through computer aided simulations of a large number of tests. The procedures of structural reliability assessment for the bridge pier and the comparison with the partial factor method of the Eurocodes have been demonstrated in this paper.
Dynamic Interactions for Network Visualization and Simulation
2009-03-01
projects.htm, Site accessed January 5, 2009. 12. John S. Weir, Major, USAF, Mediated User-Simulator Interactive Command with Visualization ( MUSIC -V). Master’s...Computing Sciences in Colleges, December 2005). 14. Enrique Campos -Nanez, “nscript user manual,” Department of System Engineer- ing University of
2-d Simulations of Test Methods
DEFF Research Database (Denmark)
Thrane, Lars Nyholm
2004-01-01
One of the main obstacles for the further development of self-compacting concrete is to relate the fresh concrete properties to the form filling ability. Therefore, simulation of the form filling ability will provide a powerful tool in obtaining this goal. In this paper, a continuum mechanical...... approach is presented by showing initial results from 2-d simulations of the empirical test methods slump flow and L-box. This method assumes a homogeneous material, which is expected to correspond to particle suspensions e.g. concrete, when it remains stable. The simulations have been carried out when...... using both a Newton and Bingham model for characterisation of the rheological properties of the concrete. From the results, it is expected that both the slump flow and L-box can be simulated quite accurately when the model is extended to 3-d and the concrete is characterised according to the Bingham...
Multiple Linear Regression Model Based on Neural Network and Its Application in the MBR Simulation
Directory of Open Access Journals (Sweden)
Chunqing Li
2012-01-01
Full Text Available The computer simulation of the membrane bioreactor MBR has become the research focus of the MBR simulation. In order to compensate for the defects, for example, long test period, high cost, invisible equipment seal, and so forth, on the basis of conducting in-depth study of the mathematical model of the MBR, combining with neural network theory, this paper proposed a three-dimensional simulation system for MBR wastewater treatment, with fast speed, high efficiency, and good visualization. The system is researched and developed with the hybrid programming of VC++ programming language and OpenGL, with a multifactor linear regression model of affecting MBR membrane fluxes based on neural network, applying modeling method of integer instead of float and quad tree recursion. The experiments show that the three-dimensional simulation system, using the above models and methods, has the inspiration and reference for the future research and application of the MBR simulation technology.
Novel Methods for Electromagnetic Simulation and Design
2016-08-03
modeling software that can handle complicated, electrically large objects in a manner that is sufficiently fast to allow design by simulation. 15. SUBJECT...electrically large objects in a manner that is sufficiently fast to allow design by simulation. We also developed new methods for scattering from cavities in a...basis for high fidelity modeling software that can handle complicated, electrically large objects in a manner that is sufficiently fast to allow
Directory of Open Access Journals (Sweden)
Željko Gavrić
2018-01-01
Full Text Available Wireless sensor networks are now used in various fields. The information transmitted in the wireless sensor networks is very sensitive, so the security issue is very important. DOS (denial of service attacks are a fundamental threat to the functioning of wireless sensor networks. This paper describes some of the most common DOS attacks and potential methods of protection against them. The case study shows one of the most frequent attacks on wireless sensor networks – the interference attack. In the introduction of this paper authors assume that the attack interference can cause significant obstruction of wireless sensor networks. This assumption has been proved in the case study through simulation scenario and simulation results.
Promoting Simulation Globally: Networking with Nursing Colleagues Across Five Continents.
Alfes, Celeste M; Madigan, Elizabeth A
Simulation education is gaining momentum internationally and may provide the opportunity to enhance clinical education while disseminating evidence-based practice standards for clinical simulation and learning. There is a need to develop a cohesive leadership group that fosters support, networking, and sharing of simulation resources globally. The Frances Payne Bolton School of Nursing at Case Western Reserve University has had the unique opportunity to establish academic exchange programs with schools of nursing across five continents. Although the joint and mutual simulation activities have been extensive, each international collaboration has also provided insight into the innovations developed by global partners.
HSimulator: Hybrid Stochastic/Deterministic Simulation of Biochemical Reaction Networks
Directory of Open Access Journals (Sweden)
Luca Marchetti
2017-01-01
Full Text Available HSimulator is a multithread simulator for mass-action biochemical reaction systems placed in a well-mixed environment. HSimulator provides optimized implementation of a set of widespread state-of-the-art stochastic, deterministic, and hybrid simulation strategies including the first publicly available implementation of the Hybrid Rejection-based Stochastic Simulation Algorithm (HRSSA. HRSSA, the fastest hybrid algorithm to date, allows for an efficient simulation of the models while ensuring the exact simulation of a subset of the reaction network modeling slow reactions. Benchmarks show that HSimulator is often considerably faster than the other considered simulators. The software, running on Java v6.0 or higher, offers a simulation GUI for modeling and visually exploring biological processes and a Javadoc-documented Java library to support the development of custom applications. HSimulator is released under the COSBI Shared Source license agreement (COSBI-SSLA.
Gritsun, T.; Stegenga, J.; le Feber, Jakob; Rutten, Wim
2009-01-01
In this paper we address the issue of spontaneous bursting activity in cortical neuronal cultures and explain what might cause this collective behavior using computer simulations of two different neural network models. While the common approach to acivate a passive network is done by introducing
Matrix method for acoustic levitation simulation.
Andrade, Marco A B; Perez, Nicolas; Buiochi, Flavio; Adamowski, Julio C
2011-08-01
A matrix method is presented for simulating acoustic levitators. A typical acoustic levitator consists of an ultrasonic transducer and a reflector. The matrix method is used to determine the potential for acoustic radiation force that acts on a small sphere in the standing wave field produced by the levitator. The method is based on the Rayleigh integral and it takes into account the multiple reflections that occur between the transducer and the reflector. The potential for acoustic radiation force obtained by the matrix method is validated by comparing the matrix method results with those obtained by the finite element method when using an axisymmetric model of a single-axis acoustic levitator. After validation, the method is applied in the simulation of a noncontact manipulation system consisting of two 37.9-kHz Langevin-type transducers and a plane reflector. The manipulation system allows control of the horizontal position of a small levitated sphere from -6 mm to 6 mm, which is done by changing the phase difference between the two transducers. The horizontal position of the sphere predicted by the matrix method agrees with the horizontal positions measured experimentally with a charge-coupled device camera. The main advantage of the matrix method is that it allows simulation of non-symmetric acoustic levitators without requiring much computational effort.
Visibility Network Patterns and Methods for Studying Visual Relational Phenomena in Archeology
Directory of Open Access Journals (Sweden)
Tom Brughmans
2017-08-01
Full Text Available A review of the archeological and non-archeological use of visibility networks reveals the use of a limited range of formal techniques, in particular for representing visibility theories. This paper aims to contribute to the study of complex visual relational phenomena in landscape archeology by proposing a range of visibility network patterns and methods. We propose first- and second-order visibility graph representations of total and cumulative viewsheds, and two-mode representations of cumulative viewsheds. We present network patterns that can be used to represent aspects of visibility theories and that can be used in statistical simulation models to compare theorized networks with observed networks. We argue for the need to incorporate observed visibility network density in these simulation models, by illustrating strong differences in visibility network density in three example landscapes. The approach is illustrated through a brief case study of visibility networks of long barrows in Cranborne Chase.
Simulation Of Wireless Networked Control System Using TRUETIME And MATLAB
Directory of Open Access Journals (Sweden)
Nyan Phyo Aung
2015-08-01
Full Text Available Wireless networked control systems WNCS are attracting an increasing research interests in the past decade. Wireless networked control system WNCS is composed of a group of distributed sensors and actuators that communicate through wireless link which achieves distributed sensing and executing tasks. This is particularly relevant for the areas of communication control and computing where successful design of WNCS brings about new challenges to the researchers. The primary motivation of this survey paper is to examine the design issues and to provide directions for successful simulation and implementation of WNCS. The paper also as well reviews some simulation tools for such systems.
Simulation of nonlinear random vibrations using artificial neural networks
Energy Technology Data Exchange (ETDEWEB)
Paez, T.L.; Tucker, S.; O`Gorman, C.
1997-02-01
The simulation of mechanical system random vibrations is important in structural dynamics, but it is particularly difficult when the system under consideration is nonlinear. Artificial neural networks provide a useful tool for the modeling of nonlinear systems, however, such modeling may be inefficient or insufficiently accurate when the system under consideration is complex. This paper shows that there are several transformations that can be used to uncouple and simplify the components of motion of a complex nonlinear system, thereby making its modeling and random vibration simulation, via component modeling with artificial neural networks, a much simpler problem. A numerical example is presented.
Quartet-based methods to reconstruct phylogenetic networks.
Yang, Jialiang; Grünewald, Stefan; Xu, Yifei; Wan, Xiu-Feng
2014-02-20
Phylogenetic networks are employed to visualize evolutionary relationships among a group of nucleotide sequences, genes or species when reticulate events like hybridization, recombination, reassortant and horizontal gene transfer are believed to be involved. In comparison to traditional distance-based methods, quartet-based methods consider more information in the reconstruction process and thus have the potential to be more accurate. We introduce QuartetSuite, which includes a set of new quartet-based methods, namely QuartetS, QuartetA, and QuartetM, to reconstruct phylogenetic networks from nucleotide sequences. We tested their performances and compared them with other popular methods on two simulated nucleotide sequence data sets: one generated from a tree topology and the other from a complicated evolutionary history containing three reticulate events. We further validated these methods to two real data sets: a bacterial data set consisting of seven concatenated genes of 36 bacterial species and an influenza data set related to recently emerging H7N9 low pathogenic avian influenza viruses in China. QuartetS, QuartetA, and QuartetM have the potential to accurately reconstruct evolutionary scenarios from simple branching trees to complicated networks containing many reticulate events. These methods could provide insights into the understanding of complicated biological evolutionary processes such as bacterial taxonomy and reassortant of influenza viruses.
Web Page Classification Method Using Neural Networks
Selamat, Ali; Omatu, Sigeru; Yanagimoto, Hidekazu; Fujinaka, Toru; Yoshioka, Michifumi
Automatic categorization is the only viable method to deal with the scaling problem of the World Wide Web (WWW). In this paper, we propose a news web page classification method (WPCM). The WPCM uses a neural network with inputs obtained by both the principal components and class profile-based features (CPBF). Each news web page is represented by the term-weighting scheme. As the number of unique words in the collection set is big, the principal component analysis (PCA) has been used to select the most relevant features for the classification. Then the final output of the PCA is combined with the feature vectors from the class-profile which contains the most regular words in each class before feeding them to the neural networks. We have manually selected the most regular words that exist in each class and weighted them using an entropy weighting scheme. The fixed number of regular words from each class will be used as a feature vectors together with the reduced principal components from the PCA. These feature vectors are then used as the input to the neural networks for classification. The experimental evaluation demonstrates that the WPCM method provides acceptable classification accuracy with the sports news datasets.
Spectral Analysis Methods of Social Networks
Directory of Open Access Journals (Sweden)
P. G. Klyucharev
2017-01-01
Full Text Available Online social networks (such as Facebook, Twitter, VKontakte, etc. being an important channel for disseminating information are often used to arrange an impact on the social consciousness for various purposes - from advertising products or services to the full-scale information war thereby making them to be a very relevant object of research. The paper reviewed the analysis methods of social networks (primarily, online, based on the spectral theory of graphs. Such methods use the spectrum of the social graph, i.e. a set of eigenvalues of its adjacency matrix, and also the eigenvectors of the adjacency matrix.Described measures of centrality (in particular, centrality based on the eigenvector and PageRank, which reflect a degree of impact one or another user of the social network has. A very popular PageRank measure uses, as a measure of centrality, the graph vertices, the final probabilities of the Markov chain, whose matrix of transition probabilities is calculated on the basis of the adjacency matrix of the social graph. The vector of final probabilities is an eigenvector of the matrix of transition probabilities.Presented a method of dividing the graph vertices into two groups. It is based on maximizing the network modularity by computing the eigenvector of the modularity matrix.Considered a method for detecting bots based on the non-randomness measure of a graph to be computed using the spectral coordinates of vertices - sets of eigenvector components of the adjacency matrix of a social graph.In general, there are a number of algorithms to analyse social networks based on the spectral theory of graphs. These algorithms show very good results, but their disadvantage is the relatively high (albeit polynomial computational complexity for large graphs.At the same time it is obvious that the practical application capacity of the spectral graph theory methods is still underestimated, and it may be used as a basis to develop new methods.The work
Synthesis of recurrent neural networks for dynamical system simulation.
Trischler, Adam P; D'Eleuterio, Gabriele M T
2016-08-01
We review several of the most widely used techniques for training recurrent neural networks to approximate dynamical systems, then describe a novel algorithm for this task. The algorithm is based on an earlier theoretical result that guarantees the quality of the network approximation. We show that a feedforward neural network can be trained on the vector-field representation of a given dynamical system using backpropagation, then recast it as a recurrent network that replicates the original system's dynamics. After detailing this algorithm and its relation to earlier approaches, we present numerical examples that demonstrate its capabilities. One of the distinguishing features of our approach is that both the original dynamical systems and the recurrent networks that simulate them operate in continuous time. Copyright © 2016 Elsevier Ltd. All rights reserved.
Efficient Neural Network Modeling for Flight and Space Dynamics Simulation
Directory of Open Access Journals (Sweden)
Ayman Hamdy Kassem
2011-01-01
Full Text Available This paper represents an efficient technique for neural network modeling of flight and space dynamics simulation. The technique will free the neural network designer from guessing the size and structure for the required neural network model and will help to minimize the number of neurons. For linear flight/space dynamics systems, the technique can find the network weights and biases directly by solving a system of linear equations without the need for training. Nonlinear flight dynamic systems can be easily modeled by training its linearized models keeping the same network structure. The training is fast, as it uses the linear system knowledge to speed up the training process. The technique is tested on different flight/space dynamic models and showed promising results.
Social Network Mixing Patterns In Mergers & Acquisitions - A Simulation Experiment
Directory of Open Access Journals (Sweden)
Robert Fabac
2011-01-01
Full Text Available In the contemporary world of global business and continuously growing competition, organizations tend to use mergers and acquisitions to enforce their position on the market. The future organization’s design is a critical success factor in such undertakings. The field of social network analysis can enhance our uderstanding of these processes as it lets us reason about the development of networks, regardless of their origin. The analysis of mixing patterns is particularly useful as it provides an insight into how nodes in a network connect with each other. We hypothesize that organizational networks with compatible mixing patterns will be integrated more successfully. After conducting a simulation experiment, we suggest an integration model based on the analysis of network assortativity. The model can be a guideline for organizational integration, such as occurs in mergers and acquisitions.
Simulated annealing for tensor network states
International Nuclear Information System (INIS)
Iblisdir, S
2014-01-01
Markov chains for probability distributions related to matrix product states and one-dimensional Hamiltonians are introduced. With appropriate ‘inverse temperature’ schedules, these chains can be combined into a simulated annealing scheme for ground states of such Hamiltonians. Numerical experiments suggest that a linear, i.e., fast, schedule is possible in non-trivial cases. A natural extension of these chains to two-dimensional settings is next presented and tested. The obtained results compare well with Euclidean evolution. The proposed Markov chains are easy to implement and are inherently sign problem free (even for fermionic degrees of freedom). (paper)
Transforming network simulation data to semantic data for network attack planning
CSIR Research Space (South Africa)
Chan, Ke Fai Peter
2017-03-01
Full Text Available study was performed, using the Common Open Research Emulator (CORE), to generate the necessary network simulation data. The simulation data was analysed, and then transformed into linked data. The result of the transformation is a data file that adheres...
Computer simulation of randomly cross-linked polymer networks
International Nuclear Information System (INIS)
Williams, Timothy Philip
2002-01-01
In this work, Monte Carlo and Stochastic Dynamics computer simulations of mesoscale model randomly cross-linked networks were undertaken. Task parallel implementations of the lattice Monte Carlo Bond Fluctuation model and Kremer-Grest Stochastic Dynamics bead-spring continuum model were designed and used for this purpose. Lattice and continuum precursor melt systems were prepared and then cross-linked to varying degrees. The resultant networks were used to study structural changes during deformation and relaxation dynamics. The effects of a random network topology featuring a polydisperse distribution of strand lengths and an abundance of pendant chain ends, were qualitatively compared to recent published work. A preliminary investigation into the effects of temperature on the structural and dynamical properties was also undertaken. Structural changes during isotropic swelling and uniaxial deformation, revealed a pronounced non-affine deformation dependant on the degree of cross-linking. Fractal heterogeneities were observed in the swollen model networks and were analysed by considering constituent substructures of varying size. The network connectivity determined the length scales at which the majority of the substructure unfolding process occurred. Simulated stress-strain curves and diffraction patterns for uniaxially deformed swollen networks, were found to be consistent with experimental findings. Analysis of the relaxation dynamics of various network components revealed a dramatic slowdown due to the network connectivity. The cross-link junction spatial fluctuations for networks close to the sol-gel threshold, were observed to be at least comparable with the phantom network prediction. The dangling chain ends were found to display the largest characteristic relaxation time. (author)
Relabeling exchange method (REM) for learning in neural networks
Wu, Wen; Mammone, Richard J.
1994-02-01
The supervised training of neural networks require the use of output labels which are usually arbitrarily assigned. In this paper it is shown that there is a significant difference in the rms error of learning when `optimal' label assignment schemes are used. We have investigated two efficient random search algorithms to solve the relabeling problem: the simulated annealing and the genetic algorithm. However, we found them to be computationally expensive. Therefore we shall introduce a new heuristic algorithm called the Relabeling Exchange Method (REM) which is computationally more attractive and produces optimal performance. REM has been used to organize the optimal structure for multi-layered perceptrons and neural tree networks. The method is a general one and can be implemented as a modification to standard training algorithms. The motivation of the new relabeling strategy is based on the present interpretation of dyslexia as an encoding problem.
Modeling a secular trend by Monte Carlo simulation of height biased migration in a spatial network.
Groth, Detlef
2017-04-01
Background: In a recent Monte Carlo simulation, the clustering of body height of Swiss military conscripts within a spatial network with characteristic features of the natural Swiss geography was investigated. In this study I examined the effect of migration of tall individuals into network hubs on the dynamics of body height within the whole spatial network. The aim of this study was to simulate height trends. Material and methods: Three networks were used for modeling, a regular rectangular fishing net like network, a real world example based on the geographic map of Switzerland, and a random network. All networks contained between 144 and 148 districts and between 265-307 road connections. Around 100,000 agents were initially released with average height of 170 cm, and height standard deviation of 6.5 cm. The simulation was started with the a priori assumption that height variation within a district is limited and also depends on height of neighboring districts (community effect on height). In addition to a neighborhood influence factor, which simulates a community effect, body height dependent migration of conscripts between adjacent districts in each Monte Carlo simulation was used to re-calculate next generation body heights. In order to determine the direction of migration for taller individuals, various centrality measures for the evaluation of district importance within the spatial network were applied. Taller individuals were favored to migrate more into network hubs, backward migration using the same number of individuals was random, not biased towards body height. Network hubs were defined by the importance of a district within the spatial network. The importance of a district was evaluated by various centrality measures. In the null model there were no road connections, height information could not be delivered between the districts. Results: Due to the favored migration of tall individuals into network hubs, average body height of the hubs, and later
Simulation teaching method in Engineering Optics
Lu, Qieni; Wang, Yi; Li, Hongbin
2017-08-01
We here introduce a pedagogical method of theoretical simulation as one major means of the teaching process of "Engineering Optics" in course quality improvement action plan (Qc) in our school. Students, in groups of three to five, complete simulations of interference, diffraction, electromagnetism and polarization of light; each student is evaluated and scored in light of his performance in the interviews between the teacher and the student, and each student can opt to be interviewed many times until he is satisfied with his score and learning. After three years of Qc practice, the remarkable teaching and learning effect is obatined. Such theoretical simulation experiment is a very valuable teaching method worthwhile for physical optics which is highly theoretical and abstruse. This teaching methodology works well in training students as to how to ask questions and how to solve problems, which can also stimulate their interest in research learning and their initiative to develop their self-confidence and sense of innovation.
Aggregated Representation of Distribution Networks for Large-Scale Transmission Network Simulations
DEFF Research Database (Denmark)
Göksu, Ömer; Altin, Müfit; Sørensen, Poul Ejnar
2014-01-01
As a common practice of large-scale transmission network analysis the distribution networks have been represented as aggregated loads. However, with increasing share of distributed generation, especially wind and solar power, in the distribution networks, it became necessary to include...... the distributed generation within those analysis. In this paper a practical methodology to obtain aggregated behaviour of the distributed generation is proposed. The methodology, which is based on the use of the IEC standard wind turbine models, is applied on a benchmark distribution network via simulations....
A Simulation Method Measuring Psychomotor Nursing Skills.
McBride, Helena; And Others
1981-01-01
The development of a simulation technique to evaluate performance of psychomotor skills in an undergraduate nursing program is described. This method is used as one admission requirement to an alternate route nursing program. With modifications, any health profession could use this technique where psychomotor skills performance is important.…
Numerical simulation for gas-liquid two-phase flow in pipe networks
International Nuclear Information System (INIS)
Li Xiaoyan; Kuang Bo; Zhou Guoliang; Xu Jijun
1998-01-01
The complex pipe network characters can not directly presented in single phase flow, gas-liquid two phase flow pressure drop and void rate change model. Apply fluid network theory and computer numerical simulation technology to phase flow pipe networks carried out simulate and compute. Simulate result shows that flow resistance distribution is non-linear in two phase pipe network
Improving a Computer Networks Course Using the Partov Simulation Engine
Momeni, B.; Kharrazi, M.
2012-01-01
Computer networks courses are hard to teach as there are many details in the protocols and techniques involved that are difficult to grasp. Employing programming assignments as part of the course helps students to obtain a better understanding and gain further insight into the theoretical lectures. In this paper, the Partov simulation engine and…
Fracture Network Modeling and GoldSim Simulation Support
杉田 健一郎; Dershowiz, W.
2003-01-01
During Heisei-14, Golder Associates provided support for JNC Tokai through data analysis and simulation of the MIU Underground Rock Laboratory, participation in Task 6 of the Aspo Task Force on Modelling of Groundwater Flow and Transport, and analysis of repository safety assessment technologies including cell networks for evaluation of the disturbed rock zone (DRZ) and total systems performance assessment (TSPA).
An Efficient Simulation Method for Rare Events
Rached, Nadhir B.
2015-01-07
Estimating the probability that a sum of random variables (RVs) exceeds a given threshold is a well-known challenging problem. Closed-form expressions for the sum distribution do not generally exist, which has led to an increasing interest in simulation approaches. A crude Monte Carlo (MC) simulation is the standard technique for the estimation of this type of probability. However, this approach is computationally expensive, especially when dealing with rare events. Variance reduction techniques are alternative approaches that can improve the computational efficiency of naive MC simulations. We propose an Importance Sampling (IS) simulation technique based on the well-known hazard rate twisting approach, that presents the advantage of being asymptotically optimal for any arbitrary RVs. The wide scope of applicability of the proposed method is mainly due to our particular way of selecting the twisting parameter. It is worth observing that this interesting feature is rarely satisfied by variance reduction algorithms whose performances were only proven under some restrictive assumptions. It comes along with a good efficiency, illustrated by some selected simulation results comparing the performance of our method with that of an algorithm based on a conditional MC technique.
An Efficient Simulation Method for Rare Events
Rached, Nadhir B.; Benkhelifa, Fatma; Kammoun, Abla; Alouini, Mohamed-Slim; Tempone, Raul
2015-01-01
Estimating the probability that a sum of random variables (RVs) exceeds a given threshold is a well-known challenging problem. Closed-form expressions for the sum distribution do not generally exist, which has led to an increasing interest in simulation approaches. A crude Monte Carlo (MC) simulation is the standard technique for the estimation of this type of probability. However, this approach is computationally expensive, especially when dealing with rare events. Variance reduction techniques are alternative approaches that can improve the computational efficiency of naive MC simulations. We propose an Importance Sampling (IS) simulation technique based on the well-known hazard rate twisting approach, that presents the advantage of being asymptotically optimal for any arbitrary RVs. The wide scope of applicability of the proposed method is mainly due to our particular way of selecting the twisting parameter. It is worth observing that this interesting feature is rarely satisfied by variance reduction algorithms whose performances were only proven under some restrictive assumptions. It comes along with a good efficiency, illustrated by some selected simulation results comparing the performance of our method with that of an algorithm based on a conditional MC technique.
Distributed Sensor Network Software Development Testing through Simulation
Energy Technology Data Exchange (ETDEWEB)
Brennan, Sean M. [Univ. of New Mexico, Albuquerque, NM (United States)
2003-12-01
The distributed sensor network (DSN) presents a novel and highly complex computing platform with dif culties and opportunities that are just beginning to be explored. The potential of sensor networks extends from monitoring for threat reduction, to conducting instant and remote inventories, to ecological surveys. Developing and testing for robust and scalable applications is currently practiced almost exclusively in hardware. The Distributed Sensors Simulator (DSS) is an infrastructure that allows the user to debug and test software for DSNs independent of hardware constraints. The exibility of DSS allows developers and researchers to investigate topological, phenomenological, networking, robustness and scaling issues, to explore arbitrary algorithms for distributed sensors, and to defeat those algorithms through simulated failure. The user speci es the topology, the environment, the application, and any number of arbitrary failures; DSS provides the virtual environmental embedding.
PWR system simulation and parameter estimation with neural networks
International Nuclear Information System (INIS)
Akkurt, Hatice; Colak, Uener
2002-01-01
A detailed nonlinear model for a typical PWR system has been considered for the development of simulation software. Each component in the system has been represented by appropriate differential equations. The SCILAB software was used for solving nonlinear equations to simulate steady-state and transient operational conditions. Overall system has been constructed by connecting individual components to each other. The validity of models for individual components and overall system has been verified. The system response against given transients have been analyzed. A neural network has been utilized to estimate system parameters during transients. Different transients have been imposed in training and prediction stages with neural networks. Reactor power and system reactivity during the transient event have been predicted by the neural network. Results show that neural networks estimations are in good agreement with the calculated response of the reactor system. The maximum errors are within ±0.254% for power and between -0.146 and 0.353% for reactivity prediction cases. Steam generator parameters, pressure and water level, are also successfully predicted by the neural network employed in this study. The noise imposed on the input parameters of the neural network deteriorates the power estimation capability whereas the reactivity estimation capability is not significantly affected
PWR system simulation and parameter estimation with neural networks
Energy Technology Data Exchange (ETDEWEB)
Akkurt, Hatice; Colak, Uener E-mail: uc@nuke.hacettepe.edu.tr
2002-11-01
A detailed nonlinear model for a typical PWR system has been considered for the development of simulation software. Each component in the system has been represented by appropriate differential equations. The SCILAB software was used for solving nonlinear equations to simulate steady-state and transient operational conditions. Overall system has been constructed by connecting individual components to each other. The validity of models for individual components and overall system has been verified. The system response against given transients have been analyzed. A neural network has been utilized to estimate system parameters during transients. Different transients have been imposed in training and prediction stages with neural networks. Reactor power and system reactivity during the transient event have been predicted by the neural network. Results show that neural networks estimations are in good agreement with the calculated response of the reactor system. The maximum errors are within {+-}0.254% for power and between -0.146 and 0.353% for reactivity prediction cases. Steam generator parameters, pressure and water level, are also successfully predicted by the neural network employed in this study. The noise imposed on the input parameters of the neural network deteriorates the power estimation capability whereas the reactivity estimation capability is not significantly affected.
Hybrid Multilevel Monte Carlo Simulation of Stochastic Reaction Networks
Moraes, Alvaro
2015-01-07
Stochastic reaction networks (SRNs) is a class of continuous-time Markov chains intended to describe, from the kinetic point of view, the time-evolution of chemical systems in which molecules of different chemical species undergo a finite set of reaction channels. This talk is based on articles [4, 5, 6], where we are interested in the following problem: given a SRN, X, defined though its set of reaction channels, and its initial state, x0, estimate E (g(X(T))); that is, the expected value of a scalar observable, g, of the process, X, at a fixed time, T. This problem lead us to define a series of Monte Carlo estimators, M, such that, with high probability can produce values close to the quantity of interest, E (g(X(T))). More specifically, given a user-selected tolerance, TOL, and a small confidence level, η, find an estimator, M, based on approximate sampled paths of X, such that, P (|E (g(X(T))) − M| ≤ TOL) ≥ 1 − η; even more, we want to achieve this objective with near optimal computational work. We first introduce a hybrid path-simulation scheme based on the well-known stochastic simulation algorithm (SSA)[3] and the tau-leap method [2]. Then, we introduce a Multilevel Monte Carlo strategy that allows us to achieve a computational complexity of order O(T OL−2), this is the same computational complexity as in an exact method but with a smaller constant. We provide numerical examples to show our results.
Research on Fault Diagnosis Method Based on Rule Base Neural Network
Directory of Open Access Journals (Sweden)
Zheng Ni
2017-01-01
Full Text Available The relationship between fault phenomenon and fault cause is always nonlinear, which influences the accuracy of fault location. And neural network is effective in dealing with nonlinear problem. In order to improve the efficiency of uncertain fault diagnosis based on neural network, a neural network fault diagnosis method based on rule base is put forward. At first, the structure of BP neural network is built and the learning rule is given. Then, the rule base is built by fuzzy theory. An improved fuzzy neural construction model is designed, in which the calculated methods of node function and membership function are also given. Simulation results confirm the effectiveness of this method.
Simulation of Attacks for Security in Wireless Sensor Network.
Diaz, Alvaro; Sanchez, Pablo
2016-11-18
The increasing complexity and low-power constraints of current Wireless Sensor Networks (WSN) require efficient methodologies for network simulation and embedded software performance analysis of nodes. In addition, security is also a very important feature that has to be addressed in most WSNs, since they may work with sensitive data and operate in hostile unattended environments. In this paper, a methodology for security analysis of Wireless Sensor Networks is presented. The methodology allows designing attack-aware embedded software/firmware or attack countermeasures to provide security in WSNs. The proposed methodology includes attacker modeling and attack simulation with performance analysis (node's software execution time and power consumption estimation). After an analysis of different WSN attack types, an attacker model is proposed. This model defines three different types of attackers that can emulate most WSN attacks. In addition, this paper presents a virtual platform that is able to model the node hardware, embedded software and basic wireless channel features. This virtual simulation analyzes the embedded software behavior and node power consumption while it takes into account the network deployment and topology. Additionally, this simulator integrates the previously mentioned attacker model. Thus, the impact of attacks on power consumption and software behavior/execution-time can be analyzed. This provides developers with essential information about the effects that one or multiple attacks could have on the network, helping them to develop more secure WSN systems. This WSN attack simulator is an essential element of the attack-aware embedded software development methodology that is also introduced in this work.
Simulation of Attacks for Security in Wireless Sensor Network
Diaz, Alvaro; Sanchez, Pablo
2016-01-01
The increasing complexity and low-power constraints of current Wireless Sensor Networks (WSN) require efficient methodologies for network simulation and embedded software performance analysis of nodes. In addition, security is also a very important feature that has to be addressed in most WSNs, since they may work with sensitive data and operate in hostile unattended environments. In this paper, a methodology for security analysis of Wireless Sensor Networks is presented. The methodology allows designing attack-aware embedded software/firmware or attack countermeasures to provide security in WSNs. The proposed methodology includes attacker modeling and attack simulation with performance analysis (node’s software execution time and power consumption estimation). After an analysis of different WSN attack types, an attacker model is proposed. This model defines three different types of attackers that can emulate most WSN attacks. In addition, this paper presents a virtual platform that is able to model the node hardware, embedded software and basic wireless channel features. This virtual simulation analyzes the embedded software behavior and node power consumption while it takes into account the network deployment and topology. Additionally, this simulator integrates the previously mentioned attacker model. Thus, the impact of attacks on power consumption and software behavior/execution-time can be analyzed. This provides developers with essential information about the effects that one or multiple attacks could have on the network, helping them to develop more secure WSN systems. This WSN attack simulator is an essential element of the attack-aware embedded software development methodology that is also introduced in this work. PMID:27869710
Methods and procedures for the verification and validation of artificial neural networks
Taylor, Brian J
2006-01-01
Neural networks are members of a class of software that have the potential to enable intelligent computational systems capable of simulating characteristics of biological thinking and learning. This volume introduces some of the methods and techniques used for the verification and validation of neural networks and adaptive systems.
Brian: a simulator for spiking neural networks in Python
Directory of Open Access Journals (Sweden)
Dan F M Goodman
2008-11-01
Full Text Available Brian is a new simulator for spiking neural networks, written in Python (http://brian.di.ens.fr. It is an intuitive and highly flexible tool for rapidly developing new models, especially networks of single-compartment neurons. In addition to using standard types of neuron models, users can define models by writing arbitrary differential equations in ordinary mathematical notation. Python scientific libraries can also be used for defining models and analysing data. Vectorisation techniques allow efficient simulations despite the overheads of an interpreted language. Brian will be especially valuable for working on non-standard neuron models not easily covered by existing software, and as an alternative to using Matlab or C for simulations. With its easy and intuitive syntax, Brian is also very well suited for teaching computational neuroscience.
Brian: a simulator for spiking neural networks in python.
Goodman, Dan; Brette, Romain
2008-01-01
"Brian" is a new simulator for spiking neural networks, written in Python (http://brian. di.ens.fr). It is an intuitive and highly flexible tool for rapidly developing new models, especially networks of single-compartment neurons. In addition to using standard types of neuron models, users can define models by writing arbitrary differential equations in ordinary mathematical notation. Python scientific libraries can also be used for defining models and analysing data. Vectorisation techniques allow efficient simulations despite the overheads of an interpreted language. Brian will be especially valuable for working on non-standard neuron models not easily covered by existing software, and as an alternative to using Matlab or C for simulations. With its easy and intuitive syntax, Brian is also very well suited for teaching computational neuroscience.
Meer, van der A.A.; Hendriks, R.L.; Gibescu, M.; Ferreira, J.A.; Kling, W.L.
2011-01-01
This paper deals with the inclusion of VSC-HVdc transmission schemes into stability-type simulations by hybrid methods. These methods allow selected parts of the network to be simulated in detail by including electro-magnetic behaviour of devices and network elements whereas the remainder of the
ESIM_DSN Web-Enabled Distributed Simulation Network
Bedrossian, Nazareth; Novotny, John
2002-01-01
In this paper, the eSim(sup DSN) approach to achieve distributed simulation capability using the Internet is presented. With this approach a complete simulation can be assembled from component subsystems that run on different computers. The subsystems interact with each other via the Internet The distributed simulation uses a hub-and-spoke type network topology. It provides the ability to dynamically link simulation subsystem models to different computers as well as the ability to assign a particular model to each computer. A proof-of-concept demonstrator is also presented. The eSim(sup DSN) demonstrator can be accessed at http://www.jsc.draper.com/esim which hosts various examples of Web enabled simulations.
Directory of Open Access Journals (Sweden)
Andrey Shorov
2014-01-01
Full Text Available The paper outlines a bioinspired approach named “network nervous system" and methods of simulation of infrastructure attacks and protection mechanisms based on this approach. The protection mechanisms based on this approach consist of distributed prosedures of information collection and processing, which coordinate the activities of the main devices of a computer network, identify attacks, and determine nessesary countermeasures. Attacks and protection mechanisms are specified as structural models using a set-theoretic approach. An environment for simulation of protection mechanisms based on the biological metaphor is considered; the experiments demonstrating the effectiveness of the protection mechanisms are described.
Shorov, Andrey; Kotenko, Igor
2014-01-01
The paper outlines a bioinspired approach named "network nervous system" and methods of simulation of infrastructure attacks and protection mechanisms based on this approach. The protection mechanisms based on this approach consist of distributed procedures of information collection and processing, which coordinate the activities of the main devices of a computer network, identify attacks, and determine necessary countermeasures. Attacks and protection mechanisms are specified as structural models using a set-theoretic approach. An environment for simulation of protection mechanisms based on the biological metaphor is considered; the experiments demonstrating the effectiveness of the protection mechanisms are described.
Fomekong-Nanfack, Y.; Postma, M.; Kaandorp, J.A.
2009-01-01
Background: Inference of gene regulatory networks (GRNs) requires accurate data, a method to simulate the expression patterns and an efficient optimization algorithm to estimate the unknown parameters. Using this approach it is possible to obtain alternative circuits without making any a priori
Simulation methods for nuclear production scheduling
International Nuclear Information System (INIS)
Miles, W.T.; Markel, L.C.
1975-01-01
Recent developments and applications of simulation methods for use in nuclear production scheduling and fuel management are reviewed. The unique characteristics of the nuclear fuel cycle as they relate to the overall optimization of a mixed nuclear-fossil system in both the short-and mid-range time frame are described. Emphasis is placed on the various formulations and approaches to the mid-range planning problem, whose objective is the determination of an optimal (least cost) system operation strategy over a multi-year planning horizon. The decomposition of the mid-range problem into power system simulation, reactor core simulation and nuclear fuel management optimization, and system integration models is discussed. Present utility practices, requirements, and research trends are described. 37 references
Application and Simulation of Fuzzy Neural Network PID Controller in the Aircraft Cabin Temperature
Directory of Open Access Journals (Sweden)
Ding Fang
2013-06-01
Full Text Available Considering complex factors of affecting ambient temperature in Aircraft cabin, and some shortages of traditional PID control like the parameters difficult to be tuned and control ineffective, this paper puts forward the intelligent PID algorithm that makes fuzzy logic method and neural network together, scheming out the fuzzy neural net PID controller. After the correction of the fuzzy inference and dynamic learning of neural network, PID parameters of the controller get the optimal parameters. MATLAB simulation results of the cabin temperature control model show that the performance of the fuzzy neural network PID controller has been greatly improved, with faster response, smaller overshoot and better adaptability.
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.
A multi-scale network method for two-phase flow in porous media
International Nuclear Information System (INIS)
Khayrat, Karim; Jenny, Patrick
2017-01-01
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.
SIMULATION OF WIRELESS SENSOR NETWORK WITH HYBRID TOPOLOGY
Directory of Open Access Journals (Sweden)
J. Jaslin Deva Gifty
2016-03-01
Full Text Available The design of low rate Wireless Personal Area Network (WPAN by IEEE 802.15.4 standard has been developed to support lower data rates and low power consuming application. Zigbee Wireless Sensor Network (WSN works on the network and application layer in IEEE 802.15.4. Zigbee network can be configured in star, tree or mesh topology. The performance varies from topology to topology. The performance parameters such as network lifetime, energy consumption, throughput, delay in data delivery and sensor field coverage area varies depending on the network topology. In this paper, designing of hybrid topology by using two possible combinations such as star-tree and star-mesh is simulated to verify the communication reliability. This approach is to combine all the benefits of two network model. The parameters such as jitter, delay and throughput are measured for these scenarios. Further, MAC parameters impact such as beacon order (BO and super frame order (SO for low power consumption and high channel utilization, has been analysed for star, tree and mesh topology in beacon disable mode and beacon enable mode by varying CBR traffic loads.
Hybrid neural network bushing model for vehicle dynamics simulation
International Nuclear Information System (INIS)
Sohn, Jeong Hyun; Lee, Seung Kyu; Yoo, Wan Suk
2008-01-01
Although the linear model was widely used for the bushing model in vehicle suspension systems, it could not express the nonlinear characteristics of bushing in terms of the amplitude and the frequency. An artificial neural network model was suggested to consider the hysteretic responses of bushings. This model, however, often diverges due to the uncertainties of the neural network under the unexpected excitation inputs. In this paper, a hybrid neural network bushing model combining linear and neural network is suggested. A linear model was employed to represent linear stiffness and damping effects, and the artificial neural network algorithm was adopted to take into account the hysteretic responses. A rubber test was performed to capture bushing characteristics, where sine excitation with different frequencies and amplitudes is applied. Random test results were used to update the weighting factors of the neural network model. It is proven that the proposed model has more robust characteristics than a simple neural network model under step excitation input. A full car simulation was carried out to verify the proposed bushing models. It was shown that the hybrid model results are almost identical to the linear model under several maneuvers
Distance Based Method for Outlier Detection of Body Sensor Networks
Directory of Open Access Journals (Sweden)
Haibin Zhang
2016-01-01
Full Text Available We propose a distance based method for the outlier detection of body sensor networks. Firstly, we use a Kernel Density Estimation (KDE to calculate the probability of the distance to k nearest neighbors for diagnosed data. If the probability is less than a threshold, and the distance of this data to its left and right neighbors is greater than a pre-defined value, the diagnosed data is decided as an outlier. Further, we formalize a sliding window based method to improve the outlier detection performance. Finally, to estimate the KDE by training sensor readings with errors, we introduce a Hidden Markov Model (HMM based method to estimate the most probable ground truth values which have the maximum probability to produce the training data. Simulation results show that the proposed method possesses a good detection accuracy with a low false alarm rate.
International Nuclear Information System (INIS)
Kang, Won-Hee; Kliese, Alyce
2014-01-01
Lifeline networks, such as transportation, water supply, sewers, telecommunications, and electrical and gas networks, are essential elements for the economic and societal functions of urban areas, but their components are highly susceptible to natural or man-made hazards. In this context, it is essential to provide effective pre-disaster hazard mitigation strategies and prompt post-disaster risk management efforts based on rapid system reliability assessment. This paper proposes a rapid reliability estimation method for node-pair connectivity analysis of lifeline networks especially when the network components are statistically correlated. Recursive procedures are proposed to compound all network nodes until they become a single super node representing the connectivity between the origin and destination nodes. The proposed method is applied to numerical network examples and benchmark interconnected power and water networks in Memphis, Shelby County. The connectivity analysis results show the proposed method's reasonable accuracy and remarkable efficiency as compared to the Monte Carlo simulations
Directory of Open Access Journals (Sweden)
Yang Sun
2018-01-01
Full Text Available Using Pareto optimization in Multi-Objective Reinforcement Learning (MORL leads to better learning results for network defense games. This is particularly useful for network security agents, who must often balance several goals when choosing what action to take in defense of a network. If the defender knows his preferred reward distribution, the advantages of Pareto optimization can be retained by using a scalarization algorithm prior to the implementation of the MORL. In this paper, we simulate a network defense scenario by creating a multi-objective zero-sum game and using Pareto optimization and MORL to determine optimal solutions and compare those solutions to different scalarization approaches. We build a Pareto Defense Strategy Selection Simulator (PDSSS system for assisting network administrators on decision-making, specifically, on defense strategy selection, and the experiment results show that the Satisficing Trade-Off Method (STOM scalarization approach performs better than linear scalarization or GUESS method. The results of this paper can aid network security agents attempting to find an optimal defense policy for network security games.
Lagrangian numerical methods for ocean biogeochemical simulations
Paparella, Francesco; Popolizio, Marina
2018-05-01
We propose two closely-related Lagrangian numerical methods for the simulation of physical processes involving advection, reaction and diffusion. The methods are intended to be used in settings where the flow is nearly incompressible and the Péclet numbers are so high that resolving all the scales of motion is unfeasible. This is commonplace in ocean flows. Our methods consist in augmenting the method of characteristics, which is suitable for advection-reaction problems, with couplings among nearby particles, producing fluxes that mimic diffusion, or unresolved small-scale transport. The methods conserve mass, obey the maximum principle, and allow to tune the strength of the diffusive terms down to zero, while avoiding unwanted numerical dissipation effects.
Simulation and the Monte Carlo method
Rubinstein, Reuven Y
2016-01-01
Simulation and the Monte Carlo Method, Third Edition reflects the latest developments in the field and presents a fully updated and comprehensive account of the major topics that have emerged in Monte Carlo simulation since the publication of the classic First Edition over more than a quarter of a century ago. While maintaining its accessible and intuitive approach, this revised edition features a wealth of up-to-date information that facilitates a deeper understanding of problem solving across a wide array of subject areas, such as engineering, statistics, computer science, mathematics, and the physical and life sciences. The book begins with a modernized introduction that addresses the basic concepts of probability, Markov processes, and convex optimization. Subsequent chapters discuss the dramatic changes that have occurred in the field of the Monte Carlo method, with coverage of many modern topics including: Markov Chain Monte Carlo, variance reduction techniques such as the transform likelihood ratio...
Heuristic urban transportation network design method, a multilayer coevolution approach
Ding, Rui; Ujang, Norsidah; Hamid, Hussain bin; Manan, Mohd Shahrudin Abd; Li, Rong; Wu, Jianjun
2017-08-01
The design of urban transportation networks plays a key role in the urban planning process, and the coevolution of urban networks has recently garnered significant attention in literature. However, most of these recent articles are based on networks that are essentially planar. In this research, we propose a heuristic multilayer urban network coevolution model with lower layer network and upper layer network that are associated with growth and stimulate one another. We first use the relative neighbourhood graph and the Gabriel graph to simulate the structure of rail and road networks, respectively. With simulation we find that when a specific number of nodes are added, the total travel cost ratio between an expanded network and the initial lower layer network has the lowest value. The cooperation strength Λ and the changeable parameter average operation speed ratio Θ show that transit users' route choices change dramatically through the coevolution process and that their decisions, in turn, affect the multilayer network structure. We also note that the simulated relation between the Gini coefficient of the betweenness centrality, Θ and Λ have an optimal point for network design. This research could inspire the analysis of urban network topology features and the assessment of urban growth trends.
A comparative analysis on computational methods for fitting an ERGM to biological network data
Directory of Open Access Journals (Sweden)
Sudipta Saha
2015-03-01
Full Text Available Exponential random graph models (ERGM based on graph theory are useful in studying global biological network structure using its local properties. However, computational methods for fitting such models are sensitive to the type, structure and the number of the local features of a network under study. In this paper, we compared computational methods for fitting an ERGM with local features of different types and structures. Two commonly used methods, such as the Markov Chain Monte Carlo Maximum Likelihood Estimation and the Maximum Pseudo Likelihood Estimation are considered for estimating the coefficients of network attributes. We compared the estimates of observed network to our random simulated network using both methods under ERGM. The motivation was to ascertain the extent to which an observed network would deviate from a randomly simulated network if the physical numbers of attributes were approximately same. Cut-off points of some common attributes of interest for different order of nodes were determined through simulations. We implemented our method to a known regulatory network database of Escherichia coli (E. coli.
Simulating colloid hydrodynamics with lattice Boltzmann methods
International Nuclear Information System (INIS)
Cates, M E; Stratford, K; Adhikari, R; Stansell, P; Desplat, J-C; Pagonabarraga, I; Wagner, A J
2004-01-01
We present a progress report on our work on lattice Boltzmann methods for colloidal suspensions. We focus on the treatment of colloidal particles in binary solvents and on the inclusion of thermal noise. For a benchmark problem of colloids sedimenting and becoming trapped by capillary forces at a horizontal interface between two fluids, we discuss the criteria for parameter selection, and address the inevitable compromise between computational resources and simulation accuracy
An improved method for simulating radiographs
International Nuclear Information System (INIS)
Laguna, G.W.
1986-01-01
The parameters involved in generating actual radiographs and what can and cannot be modeled are examined in this report. Using the spectral distribution of the radiation source and the mass absorption curve for the material comprising the part to be modeled, the actual amount of radiation that would pass through the part and reach the film is determined. This method increases confidence in the results of the simulation and enables the modeling of parts made of multiple materials
OPNET simulation Signaling System No.7 (SS7) network interfaces
Ow, Kong Chung.
2000-01-01
This thesis presents an OPNET model and simulation of the Signaling System No.7 (SS7) network, which is dubbed the world's largest data communications network. The main focus of the study is to model one of its levels, the Message Transfer Part Level 3, in accordance with the ITU.T recommendation Q.704. An overview of SS7 that includes the evolution and basics of SS7 architecture is provided to familarize the reader with the topic. This includes the protocol stack, signaling points, signaling...
Simulation and evaluation of urban rail transit network based on multi-agent approach
Directory of Open Access Journals (Sweden)
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 simulation model for aligning smart home networks and deploying smart objects
DEFF Research Database (Denmark)
Lynggaard, Per
Smart homes use sensor based networks to capture activities and offer learned services to the user. These smart home networks are challenging because they mainly use wireless communication at frequencies that are shared with other services and equipments. One of the major challenges...... is the interferences produced by WiFi access points in smart home networks which are expensive to overcome in terms of battery energy. Currently, different method exists to handle this. However, they use complex mechanisms such as sharing frequencies, sharing time slots, and spatial reuse of frequencies. This paper...... introduces a unique concept which saves battery energy and lowers the interference level by simulating the network alignment and assign the necessary amount of transmit power to each individual network node and finally, deploy the smart objects. The needed transmit powers are calculated by the presented...
Spectral methods in numerical plasma simulation
International Nuclear Information System (INIS)
Coutsias, E.A.; Hansen, F.R.; Huld, T.; Knorr, G.; Lynov, J.P.
1989-01-01
An introduction is given to the use of spectral methods in numerical plasma simulation. As examples of the use of spectral methods, solutions to the two-dimensional Euler equations in both a simple, doubly periodic region, and on an annulus will be shown. In the first case, the solution is expanded in a two-dimensional Fourier series, while a Chebyshev-Fourier expansion is employed in the second case. A new, efficient algorithm for the solution of Poisson's equation on an annulus is introduced. Problems connected to aliasing and to short wavelength noise generated by gradient steepening are discussed. (orig.)
Electromagnetic simulation using the FDTD method
Sullivan, Dennis M
2013-01-01
A straightforward, easy-to-read introduction to the finite-difference time-domain (FDTD) method Finite-difference time-domain (FDTD) is one of the primary computational electrodynamics modeling techniques available. Since it is a time-domain method, FDTD solutions can cover a wide frequency range with a single simulation run and treat nonlinear material properties in a natural way. Written in a tutorial fashion, starting with the simplest programs and guiding the reader up from one-dimensional to the more complex, three-dimensional programs, this book provides a simple, yet comp
Dual Arm Work Package performance estimates and telerobot task network simulation
International Nuclear Information System (INIS)
Draper, J.V.
1997-01-01
This paper describes the methodology and results of a network simulation study of the Dual Arm Work Package (DAWP), to be employed for dismantling the Argonne National Laboratory CP-5 reactor. The development of the simulation model was based upon the results of a task analysis for the same system. This study was performed by the Oak Ridge National Laboratory (ORNL), in the Robotics and Process Systems Division. Funding was provided the US Department of Energy's Office of Technology Development, Robotics Technology Development Program (RTDP). The RTDP is developing methods of computer simulation to estimate telerobotic system performance. Data were collected to provide point estimates to be used in a task network simulation model. Three skilled operators performed six repetitions of a pipe cutting task representative of typical teleoperation cutting operations
Simulating market dynamics: interactions between consumer psychology and social networks.
Janssen, Marco A; Jager, Wander
2003-01-01
Markets can show different types of dynamics, from quiet markets dominated by one or a few products, to markets with continual penetration of new and reintroduced products. In a previous article we explored the dynamics of markets from a psychological perspective using a multi-agent simulation model. The main results indicated that the behavioral rules dominating the artificial consumer's decision making determine the resulting market dynamics, such as fashions, lock-in, and unstable renewal. Results also show the importance of psychological variables like social networks, preferences, and the need for identity to explain the dynamics of markets. In this article we extend this work in two directions. First, we will focus on a more systematic investigation of the effects of different network structures. The previous article was based on Watts and Strogatz's approach, which describes the small-world and clustering characteristics in networks. More recent research demonstrated that many large networks display a scale-free power-law distribution for node connectivity. In terms of market dynamics this may imply that a small proportion of consumers may have an exceptional influence on the consumptive behavior of others (hubs, or early adapters). We show that market dynamics is a self-organized property depending on the interaction between the agents' decision-making process (heuristics), the product characteristics (degree of satisfaction of unit of consumption, visibility), and the structure of interactions between agents (size of network and hubs in a social network).
Measurement methods on the complexity of network
Institute of Scientific and Technical Information of China (English)
LIN Lin; DING Gang; CHEN Guo-song
2010-01-01
Based on the size of network and the number of paths in the network,we proposed a model of topology complexity of a network to measure the topology complexity of the network.Based on the analyses of the effects of the number of the equipment,the types of equipment and the processing time of the node on the complexity of the network with the equipment-constrained,a complexity model of equipment-constrained network was constructed to measure the integrated complexity of the equipment-constrained network.The algorithms for the two models were also developed.An automatic generator of the random single label network was developed to test the models.The results show that the models can correctly evaluate the topology complexity and the integrated complexity of the networks.
Directory of Open Access Journals (Sweden)
Ling Yongfa
2016-01-01
Full Text Available The paper proposes a mobile control sink node data collection method in the wireless sensor network based on compressive sensing. This method, with regular track, selects the optimal data collection points in the monitoring area via the disc method, calcu-lates the shortest path by using the quantum genetic algorithm, and hence determines the data collection route. Simulation results show that this method has higher network throughput and better energy efficiency, capable of collecting a huge amount of data with balanced energy consumption in the network.
Meshless Method for Simulation of Compressible Flow
Nabizadeh Shahrebabak, Ebrahim
In the present age, rapid development in computing technology and high speed supercomputers has made numerical analysis and computational simulation more practical than ever before for large and complex cases. Numerical simulations have also become an essential means for analyzing the engineering problems and the cases that experimental analysis is not practical. There are so many sophisticated and accurate numerical schemes, which do these simulations. The finite difference method (FDM) has been used to solve differential equation systems for decades. Additional numerical methods based on finite volume and finite element techniques are widely used in solving problems with complex geometry. All of these methods are mesh-based techniques. Mesh generation is an essential preprocessing part to discretize the computation domain for these conventional methods. However, when dealing with mesh-based complex geometries these conventional mesh-based techniques can become troublesome, difficult to implement, and prone to inaccuracies. In this study, a more robust, yet simple numerical approach is used to simulate problems in an easier manner for even complex problem. The meshless, or meshfree, method is one such development that is becoming the focus of much research in the recent years. The biggest advantage of meshfree methods is to circumvent mesh generation. Many algorithms have now been developed to help make this method more popular and understandable for everyone. These algorithms have been employed over a wide range of problems in computational analysis with various levels of success. Since there is no connectivity between the nodes in this method, the challenge was considerable. The most fundamental issue is lack of conservation, which can be a source of unpredictable errors in the solution process. This problem is particularly evident in the presence of steep gradient regions and discontinuities, such as shocks that frequently occur in high speed compressible flow
The analysis of transient noise of PCB P/G network based on PI/SI co-simulation
Haohang, Su
2018-02-01
With the frequency of the space camera become higher than before, the power noise of the imaging electronic system become the important factor. Much more power noise would disturb the transmissions signal, and even influence the image sharpness and system noise. "Target impedance method" is one of the traditional design method of P/G network (power and ground network), which is shorted of transient power noise analysis and often made "over design". In this paper, a new design method of P/G network is provided which simulated by PI/SI co-simulation. The transient power noise can be simulated and then applied in the design of noise reduction, thus effectively controlling the change of the noise in the P/G network. The method can efficiently control the number of adding decoupling capacitor, and is very efficient and feasible to keep the power integrity.
Analyzing, Modeling, and Simulation for Human Dynamics in Social Network
Directory of Open Access Journals (Sweden)
Yunpeng Xiao
2012-01-01
Full Text Available This paper studies the human behavior in the top-one social network system in China (Sina Microblog system. By analyzing real-life data at a large scale, we find that the message releasing interval (intermessage time obeys power law distribution both at individual level and at group level. Statistical analysis also reveals that human behavior in social network is mainly driven by four basic elements: social pressure, social identity, social participation, and social relation between individuals. Empirical results present the four elements' impact on the human behavior and the relation between these elements. To further understand the mechanism of such dynamic phenomena, a hybrid human dynamic model which combines “interest” of individual and “interaction” among people is introduced, incorporating the four elements simultaneously. To provide a solid evaluation, we simulate both two-agent and multiagent interactions with real-life social network topology. We achieve the consistent results between empirical studies and the simulations. The model can provide a good understanding of human dynamics in social network.
Method and tool for network vulnerability analysis
Swiler, Laura Painton [Albuquerque, NM; Phillips, Cynthia A [Albuquerque, NM
2006-03-14
A computer system analysis tool and method that will allow for qualitative and quantitative assessment of security attributes and vulnerabilities in systems including computer networks. The invention is based on generation of attack graphs wherein each node represents a possible attack state and each edge represents a change in state caused by a single action taken by an attacker or unwitting assistant. Edges are weighted using metrics such as attacker effort, likelihood of attack success, or time to succeed. Generation of an attack graph is accomplished by matching information about attack requirements (specified in "attack templates") to information about computer system configuration (contained in a configuration file that can be updated to reflect system changes occurring during the course of an attack) and assumed attacker capabilities (reflected in "attacker profiles"). High risk attack paths, which correspond to those considered suited to application of attack countermeasures given limited resources for applying countermeasures, are identified by finding "epsilon optimal paths."
A new hierarchical method to find community structure in networks
Saoud, Bilal; Moussaoui, Abdelouahab
2018-04-01
Community structure is very important to understand a network which represents a context. Many community detection methods have been proposed like hierarchical methods. In our study, we propose a new hierarchical method for community detection in networks based on genetic algorithm. In this method we use genetic algorithm to split a network into two networks which maximize the modularity. Each new network represents a cluster (community). Then we repeat the splitting process until we get one node at each cluster. We use the modularity function to measure the strength of the community structure found by our method, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our method are highly effective at discovering community structure in both computer-generated and real-world network data.
Social network analysis: Presenting an underused method for nursing research.
Parnell, James Michael; Robinson, Jennifer C
2018-06-01
This paper introduces social network analysis as a versatile method with many applications in nursing research. Social networks have been studied for years in many social science fields. The methods continue to advance but remain unknown to most nursing scholars. Discussion paper. English language and interpreted literature was searched from Ovid Healthstar, CINAHL, PubMed Central, Scopus and hard copy texts from 1965 - 2017. Social network analysis first emerged in nursing literature in 1995 and appears minimally through present day. To convey the versatility and applicability of social network analysis in nursing, hypothetical scenarios are presented. The scenarios are illustrative of three approaches to social network analysis and include key elements of social network research design. The methods of social network analysis are underused in nursing research, primarily because they are unknown to most scholars. However, there is methodological flexibility and epistemological versatility capable of supporting quantitative and qualitative research. The analytic techniques of social network analysis can add new insight into many areas of nursing inquiry, especially those influenced by cultural norms. Furthermore, visualization techniques associated with social network analysis can be used to generate new hypotheses. Social network analysis can potentially uncover findings not accessible through methods commonly used in nursing research. Social networks can be analysed based on individual-level attributes, whole networks and subgroups within networks. Computations derived from social network analysis may stand alone to answer a research question or incorporated as variables into robust statistical models. © 2018 John Wiley & Sons Ltd.
International Nuclear Information System (INIS)
Reifman, J.; Vitela, E.J.; Lee, J.C.
1993-01-01
Two complementary methods, statistical feature selection and nonlinear optimization through conjugate gradients, are used to expedite feedforward neural network training. Statistical feature selection techniques in the form of linear correlation coefficients and information-theoretic entropy are used to eliminate redundant and non-informative plant parameters to reduce the size of the network. The method of conjugate gradients is used to accelerate the network training convergence and to systematically calculate the Teaming and momentum constants at each iteration. The proposed techniques are compared with the backpropagation algorithm using the entire set of plant parameters in the training of neural networks to identify transients simulated with the Midland Nuclear Power Plant Unit 2 simulator. By using 25% of the plant parameters and the conjugate gradients, a 30-fold reduction in CPU time was obtained without degrading the diagnostic ability of the network
Simulation model for centrifugal pump in flow networks based on internal characteristics
International Nuclear Information System (INIS)
Sun, Ji-Lin; Xue, Ruo-Jun; Peng, Min-Jun
2018-01-01
For the simulation of centrifugal pump in flow network system, in general three approaches can be used, the fitting model, the numerical method and the internal characteristics model. The fitting model is simple and rapid thus widely used. The numerical method can provide more detailed information in comparison with the fitting model, but increases implementation complexity and computational cost. In real-time simulations of flow networks, to simulate the condition out of the rated condition, especially for the volume flow rate, which the accuracy of fitting model is incredible, a new method for simulating centrifugal pumps was proposed in this research. The method based on the theory head and hydraulic loss in centrifugal pumps, and cavitation is also to be considered. The simulation results are verified with experimental benchmark data from an actual pump. The comparison confirms that the proposed method could fit the flow-head curves well, and the responses of main parameters in dynamic-state operations are consistent with theoretical analyses.
A new method for simulating human emotions
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
How to make machines express emotions would be instrumental in establishing a completely new paradigm for man ma-chine interaction. A new method for simulating and assessing artificial psychology has been developed for the research of the emo-tion robot. The human psychology activity is regarded as a Markov process. An emotion space and psychology model is constructedbased on Markov process. The conception of emotion entropy is presented to assess the artificial emotion complexity. The simulatingresults play up to human psychology activity. This model can also be applied to consumer-friendly human-computer interfaces, andinteractive video etc.
Comparative Analysis of Disruption Tolerant Network Routing Simulations in the One and NS-3
2017-12-01
The added levels of simulation increase the processing required by a simulation . ns-3’s simulation of other layers of the network stack permits...NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS COMPARATIVE ANALYSIS OF DISRUPTION TOLERANT NETWORK ROUTING SIMULATIONS IN THE ONE AND NS-3...Thesis 03-23-2016 to 12-15-2017 4. TITLE AND SUBTITLE COMPARATIVE ANALYSIS OF DISRUPTION TOLERANT NETWORK ROUTING SIMULATIONS IN THE ONE AND NS-3 5
Comparison of validation methods for forming simulations
Schug, Alexander; Kapphan, Gabriel; Bardl, Georg; Hinterhölzl, Roland; Drechsler, Klaus
2018-05-01
The forming simulation of fibre reinforced thermoplastics could reduce the development time and improve the forming results. But to take advantage of the full potential of the simulations it has to be ensured that the predictions for material behaviour are correct. For that reason, a thorough validation of the material model has to be conducted after characterising the material. Relevant aspects for the validation of the simulation are for example the outer contour, the occurrence of defects and the fibre paths. To measure these features various methods are available. Most relevant and also most difficult to measure are the emerging fibre orientations. For that reason, the focus of this study was on measuring this feature. The aim was to give an overview of the properties of different measuring systems and select the most promising systems for a comparison survey. Selected were an optical, an eddy current and a computer-assisted tomography system with the focus on measuring the fibre orientations. Different formed 3D parts made of unidirectional glass fibre and carbon fibre reinforced thermoplastics were measured. Advantages and disadvantages of the tested systems were revealed. Optical measurement systems are easy to use, but are limited to the surface plies. With an eddy current system also lower plies can be measured, but it is only suitable for carbon fibres. Using a computer-assisted tomography system all plies can be measured, but the system is limited to small parts and challenging to evaluate.
Personalizes lung motion simulation fore external radiotherapy using an artificial neural network
International Nuclear Information System (INIS)
Laurent, R.
2011-01-01
The development of new techniques in the field of external radiotherapy opens new ways of gaining accuracy in dose distribution, in particular through the knowledge of individual lung motion. The numeric simulation NEMOSIS (Neural Network Motion Simulation System) we describe is based on artificial neural networks (ANN) and allows, in addition to determining motion in a personalized way, to reduce the necessary initial doses to determine it. In the first part, we will present current treatment options, lung motion as well as existing simulation or estimation methods. The second part describes the artificial neural network used and the steps for defining its parameters. An accurate evaluation of our approach was carried out on original patient data. The obtained results are compared with an existing motion estimated method. The extremely short computing time, in the range of milliseconds for the generation of one respiratory phase, would allow its use in clinical routine. Modifications to NEMOSIS in order to meet the requirements for its use in external radiotherapy are described, and a study of the motion of tumor outlines is carried out. This work lays the basis for lung motion simulation with ANNs and validates our approach. Its real time implementation coupled to its predication accuracy makes NEMOSIS promising tool for the simulation of motion synchronized with breathing. (author)
COMPLEX NETWORK SIMULATION OF FOREST NETWORK SPATIAL PATTERN IN PEARL RIVER DELTA
Directory of Open Access Journals (Sweden)
Y. Zeng
2017-09-01
Full Text Available Forest network-construction uses for the method and model with the scale-free features of complex network theory based on random graph theory and dynamic network nodes which show a power-law distribution phenomenon. The model is suitable for ecological disturbance by larger ecological landscape Pearl River Delta consistent recovery. Remote sensing and GIS spatial data are available through the latest forest patches. A standard scale-free network node distribution model calculates the area of forest network’s power-law distribution parameter value size; The recent existing forest polygons which are defined as nodes can compute the network nodes decaying index value of the network’s degree distribution. The parameters of forest network are picked up then make a spatial transition to GIS real world models. Hence the connection is automatically generated by minimizing the ecological corridor by the least cost rule between the near nodes. Based on scale-free network node distribution requirements, select the number compared with less, a huge point of aggregation as a future forest planning network’s main node, and put them with the existing node sequence comparison. By this theory, the forest ecological projects in the past avoid being fragmented, scattered disorderly phenomena. The previous regular forest networks can be reduced the required forest planting costs by this method. For ecological restoration of tropical and subtropical in south China areas, it will provide an effective method for the forest entering city project guidance and demonstration with other ecological networks (water, climate network, etc. for networking a standard and base datum.
Hybrid numerical methods for multiscale simulations of subsurface biogeochemical processes
International Nuclear Information System (INIS)
Scheibe, T D; Tartakovsky, A M; Tartakovsky, D M; Redden, G D; Meakin, P
2007-01-01
Many subsurface flow and transport problems of importance today involve coupled non-linear flow, transport, and reaction in media exhibiting complex heterogeneity. In particular, problems involving biological mediation of reactions fall into this class of problems. Recent experimental research has revealed important details about the physical, chemical, and biological mechanisms involved in these processes at a variety of scales ranging from molecular to laboratory scales. However, it has not been practical or possible to translate detailed knowledge at small scales into reliable predictions of field-scale phenomena important for environmental management applications. A large assortment of numerical simulation tools have been developed, each with its own characteristic scale. Important examples include 1. molecular simulations (e.g., molecular dynamics); 2. simulation of microbial processes at the cell level (e.g., cellular automata or particle individual-based models); 3. pore-scale simulations (e.g., lattice-Boltzmann, pore network models, and discrete particle methods such as smoothed particle hydrodynamics); and 4. macroscopic continuum-scale simulations (e.g., traditional partial differential equations solved by finite difference or finite element methods). While many problems can be effectively addressed by one of these models at a single scale, some problems may require explicit integration of models across multiple scales. We are developing a hybrid multi-scale subsurface reactive transport modeling framework that integrates models with diverse representations of physics, chemistry and biology at different scales (sub-pore, pore and continuum). The modeling framework is being designed to take advantage of advanced computational technologies including parallel code components using the Common Component Architecture, parallel solvers, gridding, data and workflow management, and visualization. This paper describes the specific methods/codes being used at each
Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS
Directory of Open Access Journals (Sweden)
Christopher Bergmeir
2012-01-01
Full Text Available Neural networks are important standard machine learning procedures for classification and regression. We describe the R package RSNNS that provides a convenient interface to the popular Stuttgart Neural Network Simulator SNNS. The main features are (a encapsulation of the relevant SNNS parts in a C++ class, for sequential and parallel usage of different networks, (b accessibility of all of the SNNSalgorithmic functionality from R using a low-level interface, and (c a high-level interface for convenient, R-style usage of many standard neural network procedures. The package also includes functions for visualization and analysis of the models and the training procedures, as well as functions for data input/output from/to the original SNNSfile formats.
Real-Time-Simulation of IEEE-5-Bus Network on OPAL-RT-OP4510 Simulator
Atul Bhandakkar, Anjali; Mathew, Lini, Dr.
2018-03-01
The Real-Time Simulator tools have high computing technologies, improved performance. They are widely used for design and improvement of electrical systems. The advancement of the software tools like MATLAB/SIMULINK with its Real-Time Workshop (RTW) and Real-Time Windows Target (RTWT), real-time simulators are used extensively in many engineering fields, such as industry, education, and research institutions. OPAL-RT-OP4510 is a Real-Time Simulator which is used in both industry and academia. In this paper, the real-time simulation of IEEE-5-Bus network is carried out by means of OPAL-RT-OP4510 with CRO and other hardware. The performance of the network is observed with the introduction of fault at various locations. The waveforms of voltage, current, active and reactive power are observed in the MATLAB simulation environment and on the CRO. Also, Load Flow Analysis (LFA) of IEEE-5-Bus network is computed using MATLAB/Simulink power-gui load flow tool.
A Biologically Inspired Energy-Efficient Duty Cycle Design Method for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Jie Zhou
2017-01-01
Full Text Available The recent success of emerging wireless sensor networks technology has encouraged researchers to develop new energy-efficient duty cycle design algorithm in this field. The energy-efficient duty cycle design problem is a typical NP-hard combinatorial optimization problem. In this paper, we investigate an improved elite immune evolutionary algorithm (IEIEA strategy to optimize energy-efficient duty cycle design scheme and monitored area jointly to enhance the network lifetimes. Simulation results show that the network lifetime of the proposed IEIEA method increased compared to the other two methods, which means that the proposed method improves the full coverage constraints.
SCM: A method to improve network service layout efficiency with network evolution
Zhao, Qi; Zhang, Chuanhao
2017-01-01
Network services are an important component of the Internet, which are used to expand network functions for third-party developers. Network function virtualization (NFV) can improve the speed and flexibility of network service deployment. However, with the evolution of the network, network service layout may become inefficient. Regarding this problem, this paper proposes a service chain migration (SCM) method with the framework of “software defined network + network function virtualization” (SDN+NFV), which migrates service chains to adapt to network evolution and improves the efficiency of the network service layout. SCM is modeled as an integer linear programming problem and resolved via particle swarm optimization. An SCM prototype system is designed based on an SDN controller. Experiments demonstrate that SCM could reduce the network traffic cost and energy consumption efficiently. PMID:29267299
SCM: A method to improve network service layout efficiency with network evolution.
Zhao, Qi; Zhang, Chuanhao; Zhao, Zheng
2017-01-01
Network services are an important component of the Internet, which are used to expand network functions for third-party developers. Network function virtualization (NFV) can improve the speed and flexibility of network service deployment. However, with the evolution of the network, network service layout may become inefficient. Regarding this problem, this paper proposes a service chain migration (SCM) method with the framework of "software defined network + network function virtualization" (SDN+NFV), which migrates service chains to adapt to network evolution and improves the efficiency of the network service layout. SCM is modeled as an integer linear programming problem and resolved via particle swarm optimization. An SCM prototype system is designed based on an SDN controller. Experiments demonstrate that SCM could reduce the network traffic cost and energy consumption efficiently.
Leon, Barbara D.; Heller, Paul R.
1987-05-01
A surveillance network is a group of multiplatform sensors cooperating to improve network performance. Network control is distributed as a measure to decrease vulnerability to enemy threat. The network may contain diverse sensor types such as radar, ESM (Electronic Support Measures), IRST (Infrared search and track) and E-0 (Electro-Optical). Each platform may contain a single sensor or suite of sensors. In a surveillance network it is desirable to control sensors to make the overall system more effective. This problem has come to be known as sensor management and control (SM&C). Two major facets of network performance are surveillance and survivability. In a netted environment, surveillance can be enhanced if information from all sensors is combined and sensor operating conditions are controlled to provide a synergistic effect. In contrast, when survivability is the main concern for the network, the best operating status for all sensors would be passive or off. Of course, improving survivability tends to degrade surveillance. Hence, the objective of SM&C is to optimize surveillance and survivability of the network. Too voluminous data of various formats and the quick response time are two characteristics of this problem which make it an ideal application for Artificial Intelligence. A solution to the SM&C problem, presented as a computer simulation, will be presented in this paper. The simulation is a hybrid production written in LISP and FORTRAN. It combines the latest conventional computer programming methods with Artificial Intelligence techniques to produce a flexible state-of-the-art tool to evaluate network performance. The event-driven simulation contains environment models coupled with an expert system. These environment models include sensor (track-while-scan and agile beam) and target models, local tracking, and system tracking. These models are used to generate the environment for the sensor management and control expert system. The expert system
IP2P K-means: an efficient method for data clustering on sensor networks
Directory of Open Access Journals (Sweden)
Peyman Mirhadi
2013-03-01
Full Text Available Many wireless sensor network applications require data gathering as the most important parts of their operations. There are increasing demands for innovative methods to improve energy efficiency and to prolong the network lifetime. Clustering is considered as an efficient topology control methods in wireless sensor networks, which can increase network scalability and lifetime. This paper presents a method, IP2P K-means – Improved P2P K-means, which uses efficient leveling in clustering approach, reduces false labeling and restricts the necessary communication among various sensors, which obviously saves more energy. The proposed method is examined in Network Simulator Ver.2 (NS2 and the preliminary results show that the algorithm works effectively and relatively more precisely.
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.
Pavlogiannis, Andreas; Mozhayskiy, Vadim; Tagkopoulos, Ilias
2013-04-24
Biological networks tend to have high interconnectivity, complex topologies and multiple types of interactions. This renders difficult the identification of sub-networks that are involved in condition- specific responses. In addition, we generally lack scalable methods that can reveal the information flow in gene regulatory and biochemical pathways. Doing so will help us to identify key participants and paths under specific environmental and cellular context. This paper introduces the theory of network flooding, which aims to address the problem of network minimization and regulatory information flow in gene regulatory networks. Given a regulatory biological network, a set of source (input) nodes and optionally a set of sink (output) nodes, our task is to find (a) the minimal sub-network that encodes the regulatory program involving all input and output nodes and (b) the information flow from the source to the sink nodes of the network. Here, we describe a novel, scalable, network traversal algorithm and we assess its potential to achieve significant network size reduction in both synthetic and E. coli networks. Scalability and sensitivity analysis show that the proposed method scales well with the size of the network, and is robust to noise and missing data. The method of network flooding proves to be a useful, practical approach towards information flow analysis in gene regulatory networks. Further extension of the proposed theory has the potential to lead in a unifying framework for the simultaneous network minimization and information flow analysis across various "omics" levels.
Social network extraction based on Web: 1. Related superficial methods
Khairuddin Matyuso Nasution, Mahyuddin
2018-01-01
Often the nature of something affects methods to resolve the related issues about it. Likewise, methods to extract social networks from the Web, but involve the structured data types differently. This paper reveals several methods of social network extraction from the same sources that is Web: the basic superficial method, the underlying superficial method, the description superficial method, and the related superficial methods. In complexity we derive the inequalities between methods and so are their computations. In this case, we find that different results from the same tools make the difference from the more complex to the simpler: Extraction of social network by involving co-occurrence is more complex than using occurrences.
Jacobs-Crisioni, C.; Koopmans, C. C.
2016-07-01
This paper introduces a GIS-based model that simulates the geographic expansion of transport networks by several decision-makers with varying objectives. The model progressively adds extensions to a growing network by choosing the most attractive investments from a limited choice set. Attractiveness is defined as a function of variables in which revenue and broader societal benefits may play a role and can be based on empirically underpinned parameters that may differ according to private or public interests. The choice set is selected from an exhaustive set of links and presumably contains those investment options that best meet private operator's objectives by balancing the revenues of additional fare against construction costs. The investment options consist of geographically plausible routes with potential detours. These routes are generated using a fine-meshed regularly latticed network and shortest path finding methods. Additionally, two indicators of the geographic accuracy of the simulated networks are introduced. A historical case study is presented to demonstrate the model's first results. These results show that the modelled networks reproduce relevant results of the historically built network with reasonable accuracy.
COEL: A Cloud-based Reaction Network Simulator
Directory of Open Access Journals (Sweden)
Peter eBanda
2016-04-01
Full Text Available Chemical Reaction Networks (CRNs are a formalism to describe the macroscopic behavior of chemical systems. We introduce COEL, a web- and cloud-based CRN simulation framework that does not require a local installation, runs simulations on a large computational grid, provides reliable database storage, and offers a visually pleasing and intuitive user interface. We present an overview of the underlying software, the technologies, and the main architectural approaches employed. Some of COEL's key features include ODE-based simulations of CRNs and multicompartment reaction networks with rich interaction options, a built-in plotting engine, automatic DNA-strand displacement transformation and visualization, SBML/Octave/Matlab export, and a built-in genetic-algorithm-based optimization toolbox for rate constants.COEL is an open-source project hosted on GitHub (http://dx.doi.org/10.5281/zenodo.46544, which allows interested research groups to deploy it on their own sever. Regular users can simply use the web instance at no cost at http://coel-sim.org. The framework is ideally suited for a collaborative use in both research and education.
Fracture network modeling and GoldSim simulation support
International Nuclear Information System (INIS)
Sugita, Kenichiro; Dershowitz, William
2003-01-01
During Heisei-14, Golder Associates provided support for JNC Tokai through data analysis and simulation of the MIU Underground Rock Laboratory, participation in Task 6 of the Aespoe Task Force on Modelling of Groundwater Flow and Transport, and analysis of repository safety assessment technologies including cell networks for evaluation of the disturbed rock zone (DRZ) and total systems performance assessment (TSPA). MIU Underground Rock Laboratory support during H-14 involved discrete fracture network (DFN) modelling in support of the Multiple Modelling Project (MMP) and the Long Term Pumping Test (LPT). Golder developed updated DFN models for the MIU site, reflecting updated analyses of fracture data. Golder also developed scripts to support JNC simulations of flow and transport pathways within the MMP. Golder supported JNC participation in Task 6 of the Aespoe Task Force on Modelling of Groundwater Flow and Transport during H-14. Task 6A and 6B compared safety assessment (PA) and experimental time scale simulations along a pipe transport pathway. Task 6B2 extended Task 6B simulations from 1-D to 2-D. For Task 6B2, Golder carried out single fracture transport simulations on a wide variety of generic heterogeneous 2D fractures using both experimental and safety assessment boundary conditions. The heterogeneous 2D fractures were implemented according to a variety of in plane heterogeneity patterns. Multiple immobile zones were considered including stagnant zones, infillings, altered wall rock, and intact rock. During H-14, JNC carried out extensive studies of the distributed rock zone (DRZ) surrounding repository tunnels and drifts. Golder supported this activity be evaluating the calculation time necessary for simulating a reference heterogeneous DRZ cell network for a range of computational strategies. To support the development of JNC's total system performance assessment (TSPA) strategy, Golder carried out a review of the US DOE Yucca Mountain Project TSPA. This
Digitalization and networking of analog simulators and portal images
Energy Technology Data Exchange (ETDEWEB)
Pesznyak, C.; Zarand, P.; Mayer, A. [Uzsoki Hospital, Budapest (Hungary). Inst. of Oncoradiology
2007-03-15
Background: Many departments have analog simulators and irradiation facilities (especially cobalt units) without electronic portal imaging. Import of the images into the R and V (Record and Verify) system is required. Material and Methods: Simulator images are grabbed while portal films scanned by using a laser scanner and both converted into DICOM RT (Digital Imaging and Communications in Medicine Radiotherapy) images. Results: Image intensifier output of a simulator and portal films are converted to DICOM RT images and used in clinical practice. The simulator software was developed in cooperation at the authors' hospital. Conclusion: The digitalization of analog simulators is a valuable updating in clinical use replacing screen-film technique. Film scanning and digitalization permit the electronic archiving of films. Conversion into DICOM RT images is a precondition of importing to the R and V system. (orig.)
Coarse-grained simulation of a real-time process control network under peak load
International Nuclear Information System (INIS)
George, A.D.; Clapp, N.E. Jr.
1992-01-01
This paper presents a simulation study on the real-time process control network proposed for the new ANS reactor system at ORNL. A background discussion is provided on networks, modeling, and simulation, followed by an overview of the ANS process control network, its three peak-load models, and the results of a series of coarse-grained simulation studies carried out on these models using implementations of 802.3, 802.4, and 802.5 standard local area networks
Combined Simulated Annealing and Genetic Algorithm Approach to Bus Network Design
Liu, Li; Olszewski, Piotr; Goh, Pong-Chai
A new method - combined simulated annealing (SA) and genetic algorithm (GA) approach is proposed to solve the problem of bus route design and frequency setting for a given road network with fixed bus stop locations and fixed travel demand. The method involves two steps: a set of candidate routes is generated first and then the best subset of these routes is selected by the combined SA and GA procedure. SA is the main process to search for a better solution to minimize the total system cost, comprising user and operator costs. GA is used as a sub-process to generate new solutions. Bus demand assignment on two alternative paths is performed at the solution evaluation stage. The method was implemented on four theoretical grid networks of different size and a benchmark network. Several GA operators (crossover and mutation) were utilized and tested for their effectiveness. The results show that the proposed method can efficiently converge to the optimal solution on a small network but computation time increases significantly with network size. The method can also be used for other transport operation management problems.
Lin, Yen Ting; Chylek, Lily A; Lemons, Nathan W; Hlavacek, William S
2018-06-21
The chemical kinetics of many complex systems can be concisely represented by reaction rules, which can be used to generate reaction events via a kinetic Monte Carlo method that has been termed network-free simulation. Here, we demonstrate accelerated network-free simulation through a novel approach to equation-free computation. In this process, variables are introduced that approximately capture system state. Derivatives of these variables are estimated using short bursts of exact stochastic simulation and finite differencing. The variables are then projected forward in time via a numerical integration scheme, after which a new exact stochastic simulation is initialized and the whole process repeats. The projection step increases efficiency by bypassing the firing of numerous individual reaction events. As we show, the projected variables may be defined as populations of building blocks of chemical species. The maximal number of connected molecules included in these building blocks determines the degree of approximation. Equation-free acceleration of network-free simulation is found to be both accurate and efficient.
Can surgical simulation be used to train detection and classification of neural networks?
Zisimopoulos, Odysseas; Flouty, Evangello; Stacey, Mark; Muscroft, Sam; Giataganas, Petros; Nehme, Jean; Chow, Andre; Stoyanov, Danail
2017-10-01
Computer-assisted interventions (CAI) aim to increase the effectiveness, precision and repeatability of procedures to improve surgical outcomes. The presence and motion of surgical tools is a key information input for CAI surgical phase recognition algorithms. Vision-based tool detection and recognition approaches are an attractive solution and can be designed to take advantage of the powerful deep learning paradigm that is rapidly advancing image recognition and classification. The challenge for such algorithms is the availability and quality of labelled data used for training. In this Letter, surgical simulation is used to train tool detection and segmentation based on deep convolutional neural networks and generative adversarial networks. The authors experiment with two network architectures for image segmentation in tool classes commonly encountered during cataract surgery. A commercially-available simulator is used to create a simulated cataract dataset for training models prior to performing transfer learning on real surgical data. To the best of authors' knowledge, this is the first attempt to train deep learning models for surgical instrument detection on simulated data while demonstrating promising results to generalise on real data. Results indicate that simulated data does have some potential for training advanced classification methods for CAI systems.
Computational Simulations and the Scientific Method
Kleb, Bil; Wood, Bill
2005-01-01
As scientific simulation software becomes more complicated, the scientific-software implementor's need for component tests from new model developers becomes more crucial. The community's ability to follow the basic premise of the Scientific Method requires independently repeatable experiments, and model innovators are in the best position to create these test fixtures. Scientific software developers also need to quickly judge the value of the new model, i.e., its cost-to-benefit ratio in terms of gains provided by the new model and implementation risks such as cost, time, and quality. This paper asks two questions. The first is whether other scientific software developers would find published component tests useful, and the second is whether model innovators think publishing test fixtures is a feasible approach.
Fracture network modeling and GoldSim simulation support
International Nuclear Information System (INIS)
Sugita, Kenichiro; Dershowitz, William
2004-01-01
During Heisei-15, Golder Associates provided support for JNC Tokai through discrete fracture network data analysis and simulation of the MIU Underground Rock Laboratory, participation in Task 6 of the Aespoe Task Force on Modelling of Groundwater Flow and Transport, and development of methodologies for analysis of repository site characterization strategies and safety assessment. MIU Underground Rock Laboratory support during H-15 involved development of new discrete fracture network (DFN) models for the MIU Shoba-sama Site, in the region of shaft development. Golder developed three DFN models for the site using discrete fracture network, equivalent porous medium (EPM), and nested DFN/EPM approaches. Each of these models were compared based upon criteria established for the multiple modeling project (MMP). Golder supported JNC participation in Task 6AB, 6D and 6E of the Aespoe Task Force on Modelling of Groundwater Flow and Transport during H-15. For Task 6AB, Golder implemented an updated microstructural model in GoldSim, and used this updated model to simulate the propagation of uncertainty from experimental to safety assessment time scales, for 5 m scale transport path lengths. Task 6D and 6E compared safety assessment (PA) and experimental time scale simulations in a 200 m scale discrete fracture network. For Task 6D, Golder implemented a DFN model using FracMan/PA Works, and determined the sensitivity of solute transport to a range of material property and geometric assumptions. For Task 6E, Golder carried out demonstration FracMan/PA Works transport calculations at a 1 million year time scale, to ensure that task specifications are realistic. The majority of work for Task 6E will be carried out during H-16. During H-15, Golder supported JNC's Total System Performance Assessment (TSPO) strategy by developing technologies for the analysis of precipitant concentration. These approaches were based on the GoldSim precipitant data management features, and were
DC Collection Network Simulation for Offshore Wind Farms
DEFF Research Database (Denmark)
Vogel, Stephan; Rasmussen, Tonny Wederberg; El-Khatib, Walid Ziad
2015-01-01
The possibility to connect offshore wind turbines with a collection network based on Direct Current (DC), instead of Alternating Current (AC), gained attention in the scientific and industrial environment. There are many promising properties of DC components that could be beneficial such as......: smaller dimensions, less weight, fewer conductors, no reactive power considerations, and less overall losses due to the absence of proximity and skin effects. This work describes a study about the simulation of a Medium Voltage DC (MVDC) grid in an offshore wind farm. Suitable converter concepts...
A divisive spectral method for network community detection
International Nuclear Information System (INIS)
Cheng, Jianjun; Li, Longjie; Yao, Yukai; Chen, Xiaoyun; Leng, Mingwei; Lu, Weiguo
2016-01-01
Community detection is a fundamental problem in the domain of complex network analysis. It has received great attention, and many community detection methods have been proposed in the last decade. In this paper, we propose a divisive spectral method for identifying community structures from networks which utilizes a sparsification operation to pre-process the networks first, and then uses a repeated bisection spectral algorithm to partition the networks into communities. The sparsification operation makes the community boundaries clearer and sharper, so that the repeated spectral bisection algorithm extract high-quality community structures accurately from the sparsified networks. Experiments show that the combination of network sparsification and a spectral bisection algorithm is highly successful, the proposed method is more effective in detecting community structures from networks than the others. (paper: interdisciplinary statistical mechanics)
Neural Networks Simulation of the Transport of Contaminants in Groundwater
Directory of Open Access Journals (Sweden)
Enrico Zio
2009-12-01
Full Text Available The performance assessment of an engineered solution for the disposal of radioactive wastes is based on mathematical models of the disposal system response to predefined accidental scenarios, within a probabilistic approach to account for the involved uncertainties. As the most significant potential pathway for the return of radionuclides to the biosphere is groundwater flow, intensive computational efforts are devoted to simulating the behaviour of the groundwater system surrounding the waste deposit, for different values of its hydrogeological parameters and for different evolution scenarios. In this paper, multilayered neural networks are trained to simulate the transport of contaminants in monodimensional and bidimensional aquifers. The results obtained in two case studies indicate that the approximation errors are within the uncertainties which characterize the input data.
Accurate lithography simulation model based on convolutional neural networks
Watanabe, Yuki; Kimura, Taiki; Matsunawa, Tetsuaki; Nojima, Shigeki
2017-07-01
Lithography simulation is an essential technique for today's semiconductor manufacturing process. In order to calculate an entire chip in realistic time, compact resist model is commonly used. The model is established for faster calculation. To have accurate compact resist model, it is necessary to fix a complicated non-linear model function. However, it is difficult to decide an appropriate function manually because there are many options. This paper proposes a new compact resist model using CNN (Convolutional Neural Networks) which is one of deep learning techniques. CNN model makes it possible to determine an appropriate model function and achieve accurate simulation. Experimental results show CNN model can reduce CD prediction errors by 70% compared with the conventional model.
Railway optimal network simulation for the development of regional transport-logistics system
Directory of Open Access Journals (Sweden)
Mikhail Borisovich Petrov
2013-12-01
Full Text Available The dependence of logistics on mineral fuel is a stable tendency of regions development, though when making strategic plans of logistics in the regions, it is necessary to provide the alternative possibilities of power-supply sources change together with population density, transport infrastructure peculiarities, and demographic changes forecast. On the example of timber processing complex of the Sverdlovsk region, the authors suggest the algorithm of decision of the optimal logistics infrastructure allocation. The problem of regional railway network organization at the stage of slow transition from the prolonged stagnation to the new development is carried out. The transport networks’ configurations of countries on the Pacific Rim, which successfully developed nowadays, are analyzed. The authors offer some results of regional transport network simulation on the basis of artificial intelligence method. These methods let to solve the task with incomplete data. The ways of the transport network improvement in the Sverdlovsk region are offered.
A novel community detection method in bipartite networks
Zhou, Cangqi; Feng, Liang; Zhao, Qianchuan
2018-02-01
Community structure is a common and important feature in many complex networks, including bipartite networks, which are used as a standard model for many empirical networks comprised of two types of nodes. In this paper, we propose a two-stage method for detecting community structure in bipartite networks. Firstly, we extend the widely-used Louvain algorithm to bipartite networks. The effectiveness and efficiency of the Louvain algorithm have been proved by many applications. However, there lacks a Louvain-like algorithm specially modified for bipartite networks. Based on bipartite modularity, a measure that extends unipartite modularity and that quantifies the strength of partitions in bipartite networks, we fill the gap by developing the Bi-Louvain algorithm that iteratively groups the nodes in each part by turns. This algorithm in bipartite networks often produces a balanced network structure with equal numbers of two types of nodes. Secondly, for the balanced network yielded by the first algorithm, we use an agglomerative clustering method to further cluster the network. We demonstrate that the calculation of the gain of modularity of each aggregation, and the operation of joining two communities can be compactly calculated by matrix operations for all pairs of communities simultaneously. At last, a complete hierarchical community structure is unfolded. We apply our method to two benchmark data sets and a large-scale data set from an e-commerce company, showing that it effectively identifies community structure in bipartite networks.
Input data preprocessing method for exchange rate forecasting via neural network
Directory of Open Access Journals (Sweden)
Antić Dragan S.
2014-01-01
Full Text Available The aim of this paper is to present a method for neural network input parameters selection and preprocessing. The purpose of this network is to forecast foreign exchange rates using artificial intelligence. Two data sets are formed for two different economic systems. Each system is represented by six categories with 70 economic parameters which are used in the analysis. Reduction of these parameters within each category was performed by using the principal component analysis method. Component interdependencies are established and relations between them are formed. Newly formed relations were used to create input vectors of a neural network. The multilayer feed forward neural network is formed and trained using batch training. Finally, simulation results are presented and it is concluded that input data preparation method is an effective way for preprocessing neural network data. [Projekat Ministarstva nauke Republike Srbije, br.TR 35005, br. III 43007 i br. III 44006
Fast Detection Method in Cooperative Cognitive Radio Networks
Directory of Open Access Journals (Sweden)
Zhengyi Li
2010-01-01
Full Text Available Cognitive Radio (CR technology improves the utilization of spectrum highly via opportunistic spectrum sharing, which requests fast detection as the spectrum utilization is dynamic. Taking into consideration the characteristic of wireless channels, we propose a fast detection scheme for a cooperative cognitive radio network, which consists of multiple CRs and a central control office. Specifically, each CR makes individual detection decision using the sequential probability ratio test combined with Neyman Pearson detection with respect to a specific observation window length. The proposed method upper bounds the detection delay. In addition, a weighted K out of N fusion rule is also proposed for the central control office to reach fast global decision based on the information collected from CRs, with more weights assigned for CRs with good channel conditions. Simulation results show that the proposed scheme can achieve fast detection while maintaining the detection accuracy.
Directory of Open Access Journals (Sweden)
Kaushikbhai C. Parmar
2017-04-01
Full Text Available Simulation gives different results when using different methods for the same simulation. Autodesk Moldflow Simulation software provide two different facilities for creating mold for the simulation of injection molding process. Mold can be created inside the Moldflow or it can be imported as CAD file. The aim of this paper is to study the difference in the simulation results like mold temperature part temperature deflection in different direction time for the simulation and coolant temperature for this two different methods.
Coarse-graining stochastic biochemical networks: adiabaticity and fast simulations
Energy Technology Data Exchange (ETDEWEB)
Nemenman, Ilya [Los Alamos National Laboratory; Sinitsyn, Nikolai [Los Alamos National Laboratory; Hengartner, Nick [Los Alamos National Laboratory
2008-01-01
We propose a universal approach for analysis and fast simulations of stiff stochastic biochemical kinetics networks, which rests on elimination of fast chemical species without a loss of information about mesoscoplc, non-Poissonian fluctuations of the slow ones. Our approach, which is similar to the Born-Oppenhelmer approximation in quantum mechanics, follows from the stochastic path Integral representation of the cumulant generating function of reaction events. In applications with a small number of chemIcal reactions, It produces analytical expressions for cumulants of chemical fluxes between the slow variables. This allows for a low-dimensional, Interpretable representation and can be used for coarse-grained numerical simulation schemes with a small computational complexity and yet high accuracy. As an example, we derive the coarse-grained description for a chain of biochemical reactions, and show that the coarse-grained and the microscopic simulations are in an agreement, but the coarse-gralned simulations are three orders of magnitude faster.
Simulating Real-Time Aspects of Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Christian Nastasi
2010-01-01
Full Text Available Wireless Sensor Networks (WSNs technology has been mainly used in the applications with low-frequency sampling and little computational complexity. Recently, new classes of WSN-based applications with different characteristics are being considered, including process control, industrial automation and visual surveillance. Such new applications usually involve relatively heavy computations and also present real-time requirements as bounded end-to- end delay and guaranteed Quality of Service. It becomes then necessary to employ proper resource management policies, not only for communication resources but also jointly for computing resources, in the design and development of such WSN-based applications. In this context, simulation can play a critical role, together with analytical models, for validating a system design against the parameters of Quality of Service demanded for. In this paper, we present RTNS, a publicly available free simulation tool which includes Operating System aspects in wireless distributed applications. RTNS extends the well-known NS-2 simulator with models of the CPU, the Real-Time Operating System and the application tasks, to take into account delays due to the computation in addition to the communication. We demonstrate the benefits of RTNS by presenting our simulation study for a complex WSN-based multi-view vision system for real-time event detection.
STEADY-STATE modeling and simulation of pipeline networks for compressible fluids
Directory of Open Access Journals (Sweden)
A.L.H. Costa
1998-12-01
Full Text Available This paper presents a model and an algorithm for the simulation of pipeline networks with compressible fluids. The model can predict pressures, flow rates, temperatures and gas compositions at any point of the network. Any network configuration can be simulated; the existence of cycles is not an obstacle. Numerical results from simulated data on a proposed network are shown for illustration. The potential of the simulator is explored by the analysis of a pressure relief network, using a stochastic procedure for the evaluation of system performance.
Experimental method to predict avalanches based on neural networks
Directory of Open Access Journals (Sweden)
V. V. Zhdanov
2016-01-01
Full Text Available The article presents results of experimental use of currently available statistical methods to classify the avalanche‑dangerous precipitations and snowfalls in the Kishi Almaty river basin. The avalanche service of Kazakhstan uses graphical methods for prediction of avalanches developed by I.V. Kondrashov and E.I. Kolesnikov. The main objective of this work was to develop a modern model that could be used directly at the avalanche stations. Classification of winter precipitations into dangerous snowfalls and non‑dangerous ones was performed by two following ways: the linear discriminant function (canonical analysis and artificial neural networks. Observational data on weather and avalanches in the gorge Kishi Almaty in the gorge Kishi Almaty were used as a training sample. Coefficients for the canonical variables were calculated by the software «Statistica» (Russian version 6.0, and then the necessary formula had been constructed. The accuracy of the above classification was 96%. Simulator by the authors L.N. Yasnitsky and F.М. Cherepanov was used to learn the neural networks. The trained neural network demonstrated 98% accuracy of the classification. Prepared statistical models are recommended to be tested at the snow‑avalanche stations. Results of the tests will be used for estimation of the model quality and its readiness for the operational work. In future, we plan to apply these models for classification of the avalanche danger by the five‑point international scale.
Hybrid Multilevel Monte Carlo Simulation of Stochastic Reaction Networks
Moraes, Alvaro
2015-01-01
even more, we want to achieve this objective with near optimal computational work. We first introduce a hybrid path-simulation scheme based on the well-known stochastic simulation algorithm (SSA)[3] and the tau-leap method [2]. Then, we introduce a Multilevel Monte Carlo strategy that allows us to achieve a computational complexity of order O(T OL−2), this is the same computational complexity as in an exact method but with a smaller constant. We provide numerical examples to show our results.
Data delivery method based on neighbor nodes' information in a mobile ad hoc network.
Kashihara, Shigeru; Hayashi, Takuma; Taenaka, Yuzo; Okuda, Takeshi; Yamaguchi, Suguru
2014-01-01
This paper proposes a data delivery method based on neighbor nodes' information to achieve reliable communication in a mobile ad hoc network (MANET). In a MANET, it is difficult to deliver data reliably due to instabilities in network topology and wireless network condition which result from node movement. To overcome such unstable communication, opportunistic routing and network coding schemes have lately attracted considerable attention. Although an existing method that employs such schemes, MAC-independent opportunistic routing and encoding (MORE), Chachulski et al. (2007), improves the efficiency of data delivery in an unstable wireless mesh network, it does not address node movement. To efficiently deliver data in a MANET, the method proposed in this paper thus first employs the same opportunistic routing and network coding used in MORE and also uses the location information and transmission probabilities of neighbor nodes to adapt to changeable network topology and wireless network condition. The simulation experiments showed that the proposed method can achieve efficient data delivery with low network load when the movement speed is relatively slow.
Data Delivery Method Based on Neighbor Nodes’ Information in a Mobile Ad Hoc Network
Directory of Open Access Journals (Sweden)
Shigeru Kashihara
2014-01-01
Full Text Available This paper proposes a data delivery method based on neighbor nodes’ information to achieve reliable communication in a mobile ad hoc network (MANET. In a MANET, it is difficult to deliver data reliably due to instabilities in network topology and wireless network condition which result from node movement. To overcome such unstable communication, opportunistic routing and network coding schemes have lately attracted considerable attention. Although an existing method that employs such schemes, MAC-independent opportunistic routing and encoding (MORE, Chachulski et al. (2007, improves the efficiency of data delivery in an unstable wireless mesh network, it does not address node movement. To efficiently deliver data in a MANET, the method proposed in this paper thus first employs the same opportunistic routing and network coding used in MORE and also uses the location information and transmission probabilities of neighbor nodes to adapt to changeable network topology and wireless network condition. The simulation experiments showed that the proposed method can achieve efficient data delivery with low network load when the movement speed is relatively slow.
Development of a general method for obtaining the geometry of microfluidic networks
International Nuclear Information System (INIS)
Razavi, Mohammad Sayed; Salimpour, M. R.; Shirani, Ebrahim
2014-01-01
In the present study, a general method for geometry of fluidic networks is developed with emphasis on pressure-driven flows in the microfluidic applications. The design method is based on general features of network's geometry such as cross-sectional area and length of channels. Also, the method is applicable to various cross-sectional shapes such as circular, rectangular, triangular, and trapezoidal cross sections. Using constructal theory, the flow resistance, energy loss and performance of the network are optimized. Also, by this method, practical design strategies for the fabrication of microfluidic networks can be improved. The design method enables rapid prediction of fluid flow in the complex network of channels and is very useful for improving proper miniaturization and integration of microfluidic networks. Minimization of flow resistance of the network of channels leads to universal constants for consecutive cross-sectional areas and lengths. For a Y-shaped network, the optimal ratios of consecutive cross-section areas (A i+1 /A i ) and lengths (L i+1 /L i ) are obtained as A i+1 /A i = 2 −2/3 and L i+1 /L i = 2 −1/3 , respectively. It is shown that energy loss in the network is proportional to the volume of network. It is also seen when the number of channels is increased both the hydraulic resistance and the volume occupied by the network are increased in a similar manner. Furthermore, the method offers that fabrication of multi-depth and multi-width microchannels should be considered as an integral part of designing procedures. Finally, numerical simulations for the fluid flow in the network have been performed and results show very good agreement with analytic results
Simulating Quantitative Cellular Responses Using Asynchronous Threshold Boolean Network Ensembles
Directory of Open Access Journals (Sweden)
Shah Imran
2011-07-01
Full Text Available Abstract Background With increasing knowledge about the potential mechanisms underlying cellular functions, it is becoming feasible to predict the response of biological systems to genetic and environmental perturbations. Due to the lack of homogeneity in living tissues it is difficult to estimate the physiological effect of chemicals, including potential toxicity. Here we investigate a biologically motivated model for estimating tissue level responses by aggregating the behavior of a cell population. We assume that the molecular state of individual cells is independently governed by discrete non-deterministic signaling mechanisms. This results in noisy but highly reproducible aggregate level responses that are consistent with experimental data. Results We developed an asynchronous threshold Boolean network simulation algorithm to model signal transduction in a single cell, and then used an ensemble of these models to estimate the aggregate response across a cell population. Using published data, we derived a putative crosstalk network involving growth factors and cytokines - i.e., Epidermal Growth Factor, Insulin, Insulin like Growth Factor Type 1, and Tumor Necrosis Factor α - to describe early signaling events in cell proliferation signal transduction. Reproducibility of the modeling technique across ensembles of Boolean networks representing cell populations is investigated. Furthermore, we compare our simulation results to experimental observations of hepatocytes reported in the literature. Conclusion A systematic analysis of the results following differential stimulation of this model by growth factors and cytokines suggests that: (a using Boolean network ensembles with asynchronous updating provides biologically plausible noisy individual cellular responses with reproducible mean behavior for large cell populations, and (b with sufficient data our model can estimate the response to different concentrations of extracellular ligands. Our
New approach for simulating groundwater flow in discrete fracture network
Fang, H.; Zhu, J.
2017-12-01
In this study, we develop a new approach to calculate groundwater flowrate and hydraulic head distribution in two-dimensional discrete fracture network (DFN) where both laminar and turbulent flows co-exist in individual fractures. The cubic law is used to calculate hydraulic head distribution and flow behaviors in fractures where flow is laminar, while the Forchheimer's law is used to quantify turbulent flow behaviors. Reynolds number is used to distinguish flow characteristics in individual fractures. The combination of linear and non-linear equations is solved iteratively to determine flowrates in all fractures and hydraulic heads at all intersections. We examine potential errors in both flowrate and hydraulic head from the approach of uniform flow assumption. Applying the cubic law in all fractures regardless of actual flow conditions overestimates the flowrate when turbulent flow may exist while applying the Forchheimer's law indiscriminately underestimate the flowrate when laminar flows exist in the network. The contrast of apertures of large and small fractures in the DFN has significant impact on the potential errors of using only the cubic law or the Forchheimer's law. Both the cubic law and Forchheimer's law simulate similar hydraulic head distributions as the main difference between these two approaches lies in predicting different flowrates. Fracture irregularity does not significantly affect the potential errors from using only the cubic law or the Forchheimer's law if network configuration remains similar. Relative density of fractures does not significantly affect the relative performance of the cubic law and Forchheimer's law.
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.
DEFF Research Database (Denmark)
Papaleo, Elena
2015-01-01
that we observe and the functional properties of these important cellular machines. To make progresses in this direction, we need to improve the physical models used to describe proteins and solvent in molecular dynamics, as well as to strengthen the integration of experiments and simulations to overcome...... with the possibility to validate simulation methods and physical models against a broad range of experimental observables. On the other side, it also allows a complementary and comprehensive view on protein structure and dynamics. What is needed now is a better understanding of the link between the dynamic properties...... simulations with attention to the effects that can be propagated over long distances and are often associated to important biological functions. In this context, approaches inspired by network analysis can make an important contribution to the analysis of molecular dynamics simulations....
Multilevel method for modeling large-scale networks.
Energy Technology Data Exchange (ETDEWEB)
Safro, I. M. (Mathematics and Computer Science)
2012-02-24
Understanding the behavior of real complex networks is of great theoretical and practical significance. It includes developing accurate artificial models whose topological properties are similar to the real networks, generating the artificial networks at different scales under special conditions, investigating a network dynamics, reconstructing missing data, predicting network response, detecting anomalies and other tasks. Network generation, reconstruction, and prediction of its future topology are central issues of this field. In this project, we address the questions related to the understanding of the network modeling, investigating its structure and properties, and generating artificial networks. Most of the modern network generation methods are based either on various random graph models (reinforced by a set of properties such as power law distribution of node degrees, graph diameter, and number of triangles) or on the principle of replicating an existing model with elements of randomization such as R-MAT generator and Kronecker product modeling. Hierarchical models operate at different levels of network hierarchy but with the same finest elements of the network. However, in many cases the methods that include randomization and replication elements on the finest relationships between network nodes and modeling that addresses the problem of preserving a set of simplified properties do not fit accurately enough the real networks. Among the unsatisfactory features are numerically inadequate results, non-stability of algorithms on real (artificial) data, that have been tested on artificial (real) data, and incorrect behavior at different scales. One reason is that randomization and replication of existing structures can create conflicts between fine and coarse scales of the real network geometry. Moreover, the randomization and satisfying of some attribute at the same time can abolish those topological attributes that have been undefined or hidden from
Do-it-yourself networks: a novel method of generating weighted networks.
Shanafelt, D W; Salau, K R; Baggio, J A
2017-11-01
Network theory is finding applications in the life and social sciences for ecology, epidemiology, finance and social-ecological systems. While there are methods to generate specific types of networks, the broad literature is focused on generating unweighted networks. In this paper, we present a framework for generating weighted networks that satisfy user-defined criteria. Each criterion hierarchically defines a feature of the network and, in doing so, complements existing algorithms in the literature. We use a general example of ecological species dispersal to illustrate the method and provide open-source code for academic purposes.
Anomaly-based Network Intrusion Detection Methods
Directory of Open Access Journals (Sweden)
Pavel Nevlud
2013-01-01
Full Text Available The article deals with detection of network anomalies. Network anomalies include everything that is quite different from the normal operation. For detection of anomalies were used machine learning systems. Machine learning can be considered as a support or a limited type of artificial intelligence. A machine learning system usually starts with some knowledge and a corresponding knowledge organization so that it can interpret, analyse, and test the knowledge acquired. There are several machine learning techniques available. We tested Decision tree learning and Bayesian networks. The open source data-mining framework WEKA was the tool we used for testing the classify, cluster, association algorithms and for visualization of our results. The WEKA is a collection of machine learning algorithms for data mining tasks.
An effective method to improve the robustness of small-world networks under attack
International Nuclear Information System (INIS)
Zhang Zheng-Zhen; Xu Wen-Jun; Lin Jia-Ru; Zeng Shang-You
2014-01-01
In this study, the robustness of small-world networks to three types of attack is investigated. Global efficiency is introduced as the network coefficient to measure the robustness of a small-world network. The simulation results prove that an increase in rewiring probability or average degree can enhance the robustness of the small-world network under all three types of attack. The effectiveness of simultaneously increasing both rewiring probability and average degree is also studied, and the combined increase is found to significantly improve the robustness of the small-world network. Furthermore, the combined effect of rewiring probability and average degree on network robustness is shown to be several times greater than that of rewiring probability or average degree individually. This means that small-world networks with a relatively high rewiring probability and average degree have advantages both in network communications and in good robustness to attacks. Therefore, simultaneously increasing rewiring probability and average degree is an effective method of constructing realistic networks. Consequently, the proposed method is useful to construct efficient and robust networks in a realistic scenario. (interdisciplinary physics and related areas of science and technology)
A new metric method-improved structural holes researches on software networks
Li, Bo; Zhao, Hai; Cai, Wei; Li, Dazhou; Li, Hui
2013-03-01
The scale software systems quickly increase with the rapid development of software technologies. Hence, how to understand, measure, manage and control software structure is a great challenge for software engineering. there are also many researches on software networks metrics: C&K, MOOD, McCabe and etc, the aim of this paper is to propose a new and better method to metric software networks. The metric method structural holes are firstly introduced to in this paper, which can not directly be applied as a result of modular characteristics on software network. Hence, structural holes is redefined in this paper and improved, calculation process and results are described in detail. The results shows that the new method can better reflect bridge role of vertexes on software network and there is a significant correlation between degree and improved structural holes. At last, a hydropower simulation system is taken as an example to show validity of the new metric method.
Mean field methods for cortical network dynamics
DEFF Research Database (Denmark)
Hertz, J.; Lerchner, Alexander; Ahmadi, M.
2004-01-01
We review the use of mean field theory for describing the dynamics of dense, randomly connected cortical circuits. For a simple network of excitatory and inhibitory leaky integrate- and-fire neurons, we can show how the firing irregularity, as measured by the Fano factor, increases...... with the strength of the synapses in the network and with the value to which the membrane potential is reset after a spike. Generalizing the model to include conductance-based synapses gives insight into the connection between the firing statistics and the high- conductance state observed experimentally in visual...
Narasimha Rao, Gudikandhula; Jagadeeswara Rao, Peddada; Duvvuru, Rajesh
2016-09-01
Wild fires have significant impact on atmosphere and lives. The demand of predicting exact fire area in forest may help fire management team by using drone as a robot. These are flexible, inexpensive and elevated-motion remote sensing systems that use drones as platforms are important for substantial data gaps and supplementing the capabilities of manned aircraft and satellite remote sensing systems. In addition, powerful computational tools are essential for predicting certain burned area in the duration of a forest fire. The reason of this study is to built up a smart system based on semantic neural networking for the forecast of burned areas. The usage of virtual reality simulator is used to support the instruction process of fire fighters and all users for saving of surrounded wild lives by using a naive method Semantic Neural Network System (SNNS). Semantics are valuable initially to have a enhanced representation of the burned area prediction and better alteration of simulation situation to the users. In meticulous, consequences obtained with geometric semantic neural networking is extensively superior to other methods. This learning suggests that deeper investigation of neural networking in the field of forest fires prediction could be productive.
A Method for Automated Planning of FTTH Access Network Infrastructures
DEFF Research Database (Denmark)
Riaz, Muhammad Tahir; Pedersen, Jens Myrup; Madsen, Ole Brun
2005-01-01
In this paper a method for automated planning of Fiber to the Home (FTTH) access networks is proposed. We introduced a systematic approach for planning access network infrastructure. The GIS data and a set of algorithms were employed to make the planning process more automatic. The method explains...... method. The method, however, does not fully automate the planning but make the planning process significantly fast. The results and discussion are presented and conclusion is given in the end....
Modelling and Simulation of National Electronic Product Code Network Demonstrator Project
Mo, John P. T.
The National Electronic Product Code (EPC) Network Demonstrator Project (NDP) was the first large scale consumer goods track and trace investigation in the world using full EPC protocol system for applying RFID technology in supply chains. The NDP demonstrated the methods of sharing information securely using EPC Network, providing authentication to interacting parties, and enhancing the ability to track and trace movement of goods within the entire supply chain involving transactions among multiple enterprise. Due to project constraints, the actual run of the NDP was 3 months only and was unable to consolidate with quantitative results. This paper discusses the modelling and simulation of activities in the NDP in a discrete event simulation environment and provides an estimation of the potential benefits that can be derived from the NDP if it was continued for one whole year.
Mixed Methods Analysis of Enterprise Social Networks
DEFF Research Database (Denmark)
Behrendt, Sebastian; Richter, Alexander; Trier, Matthias
2014-01-01
The increasing use of enterprise social networks (ESN) generates vast amounts of data, giving researchers and managerial decision makers unprecedented opportunities for analysis. However, more transparency about the available data dimensions and how these can be combined is needed to yield accurate...
Redesigning rain gauges network in Johor using geostatistics and simulated annealing
Energy Technology Data Exchange (ETDEWEB)
Aziz, Mohd Khairul Bazli Mohd, E-mail: mkbazli@yahoo.com [Centre of Preparatory and General Studies, TATI University College, 24000 Kemaman, Terengganu, Malaysia and Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor (Malaysia); Yusof, Fadhilah, E-mail: fadhilahy@utm.my [Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor (Malaysia); Daud, Zalina Mohd, E-mail: zalina@ic.utm.my [UTM Razak School of Engineering and Advanced Technology, Universiti Teknologi Malaysia, UTM KL, 54100 Kuala Lumpur (Malaysia); Yusop, Zulkifli, E-mail: zulyusop@utm.my [Institute of Environmental and Water Resource Management (IPASA), Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor (Malaysia); Kasno, Mohammad Afif, E-mail: mafifkasno@gmail.com [Malaysia - Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, UTM KL, 54100 Kuala Lumpur (Malaysia)
2015-02-03
Recently, many rainfall network design techniques have been developed, discussed and compared by many researchers. Present day hydrological studies require higher levels of accuracy from collected data. In numerous basins, the rain gauge stations are located without clear scientific understanding. In this study, an attempt is made to redesign rain gauge network for Johor, Malaysia in order to meet the required level of accuracy preset by rainfall data users. The existing network of 84 rain gauges in Johor is optimized and redesigned into a new locations by using rainfall, humidity, solar radiation, temperature and wind speed data collected during the monsoon season (November - February) of 1975 until 2008. This study used the combination of geostatistics method (variance-reduction method) and simulated annealing as the algorithm of optimization during the redesigned proses. The result shows that the new rain gauge location provides minimum value of estimated variance. This shows that the combination of geostatistics method (variance-reduction method) and simulated annealing is successful in the development of the new optimum rain gauge system.
Redesigning rain gauges network in Johor using geostatistics and simulated annealing
International Nuclear Information System (INIS)
Aziz, Mohd Khairul Bazli Mohd; Yusof, Fadhilah; Daud, Zalina Mohd; Yusop, Zulkifli; Kasno, Mohammad Afif
2015-01-01
Recently, many rainfall network design techniques have been developed, discussed and compared by many researchers. Present day hydrological studies require higher levels of accuracy from collected data. In numerous basins, the rain gauge stations are located without clear scientific understanding. In this study, an attempt is made to redesign rain gauge network for Johor, Malaysia in order to meet the required level of accuracy preset by rainfall data users. The existing network of 84 rain gauges in Johor is optimized and redesigned into a new locations by using rainfall, humidity, solar radiation, temperature and wind speed data collected during the monsoon season (November - February) of 1975 until 2008. This study used the combination of geostatistics method (variance-reduction method) and simulated annealing as the algorithm of optimization during the redesigned proses. The result shows that the new rain gauge location provides minimum value of estimated variance. This shows that the combination of geostatistics method (variance-reduction method) and simulated annealing is successful in the development of the new optimum rain gauge system
Redesigning rain gauges network in Johor using geostatistics and simulated annealing
Aziz, Mohd Khairul Bazli Mohd; Yusof, Fadhilah; Daud, Zalina Mohd; Yusop, Zulkifli; Kasno, Mohammad Afif
2015-02-01
Recently, many rainfall network design techniques have been developed, discussed and compared by many researchers. Present day hydrological studies require higher levels of accuracy from collected data. In numerous basins, the rain gauge stations are located without clear scientific understanding. In this study, an attempt is made to redesign rain gauge network for Johor, Malaysia in order to meet the required level of accuracy preset by rainfall data users. The existing network of 84 rain gauges in Johor is optimized and redesigned into a new locations by using rainfall, humidity, solar radiation, temperature and wind speed data collected during the monsoon season (November - February) of 1975 until 2008. This study used the combination of geostatistics method (variance-reduction method) and simulated annealing as the algorithm of optimization during the redesigned proses. The result shows that the new rain gauge location provides minimum value of estimated variance. This shows that the combination of geostatistics method (variance-reduction method) and simulated annealing is successful in the development of the new optimum rain gauge system.
Laurito, Andres; The ATLAS collaboration
2017-01-01
Simulation is an important tool to validate the performance impact of control decisions in Software Defined Networks (SDN). Yet, the manual modeling of complex topologies that may change often during a design process can be a tedious error-prone task. We present TopoGen, a general purpose architecture and tool for systematic translation and generation of network topologies. TopoGen can be used to generate network simulation models automatically by querying information available at diverse sources, notably SDN controllers. The DEVS modeling and simulation framework facilitates a systematic translation of structured knowledge about a network topology into a formal modular and hierarchical coupling of preexisting or new models of network entities (physical or logical). TopoGen can be flexibly extended with new parsers and generators to grow its scope of applicability. This permits to design arbitrary workflows of topology transformations. We tested TopoGen in a network engineering project for the ATLAS detector ...
Laurito, Andres; The ATLAS collaboration
2018-01-01
Simulation is an important tool to validate the performance impact of control decisions in Software Defined Networks (SDN). Yet, the manual modeling of complex topologies that may change often during a design process can be a tedious error-prone task. We present TopoGen, a general purpose architecture and tool for systematic translation and generation of network topologies. TopoGen can be used to generate network simulation models automatically by querying information available at diverse sources, notably SDN controllers. The DEVS modeling and simulation framework facilitates a systematic translation of structured knowledge about a network topology into a formal modular and hierarchical coupling of preexisting or new models of network entities (physical or logical). TopoGen can be flexibly extended with new parsers and generators to grow its scope of applicability. This permits to design arbitrary workflows of topology transformations. We tested TopoGen in a network engineering project for the ATLAS detector ...
Dynamic baseline detection method for power data network service
Chen, Wei
2017-08-01
This paper proposes a dynamic baseline Traffic detection Method which is based on the historical traffic data for the Power data network. The method uses Cisco's NetFlow acquisition tool to collect the original historical traffic data from network element at fixed intervals. This method uses three dimensions information including the communication port, time, traffic (number of bytes or number of packets) t. By filtering, removing the deviation value, calculating the dynamic baseline value, comparing the actual value with the baseline value, the method can detect whether the current network traffic is abnormal.
Application of Integrated Neural Network Method to Fault Diagnosis of Nuclear Steam Generator
International Nuclear Information System (INIS)
Zhou Gang; Yang Li
2009-01-01
A new fault diagnosis method based on integrated neural networks for nuclear steam generator (SG) was proposed in view of the shortcoming of the conventional fault monitoring and diagnosis method. In the method, two neural networks (ANNs) were employed for the fault diagnosis of steam generator. A neural network, which was used for predicting the values of steam generator operation parameters, was taken as the dynamics model of steam generator. The principle of fault monitoring method using the neural network model is to detect the deviations between process signals measured from an operating steam generator and corresponding output signals from the neural network model of steam generator. When the deviation exceeds the limit set in advance, the abnormal event is thought to occur. The other neural network as a fault classifier conducts the fault classification of steam generator. So, the fault types of steam generator are given by the fault classifier. The clear information on steam generator faults was obtained by fusing the monitoring and diagnosis results of two neural networks. The simulation results indicate that employing integrated neural networks can improve the capacity of fault monitoring and diagnosis for the steam generator. (authors)
Imaging Simulations for the Korean VLBI Network (KVN
Directory of Open Access Journals (Sweden)
Tae-Hyun Jung
2005-03-01
Full Text Available The Korean VLBI Network (KVN will open a new field of research in astronomy, geodesy and earth science using the newest three 21m radio telescopes. This will expand our ability to look at the Universe in the millimeter regime. Imaging capability of radio interferometry is highly dependent upon the antenna configuration, source size, declination and the shape of target. In this paper, imaging simulations are carried out with the KVN system configuration. Five test images were used which were a point source, multi-point sources, a uniform sphere with two different sizes compared to the synthesis beam of the KVN and a Very Large Array (VLA image of Cygnus A. The declination for the full time simulation was set as +60 degrees and the observation time range was --6 to +6 hours around transit. Simulations have been done at 22GHz, one of the KVN observation frequency. All these simulations and data reductions have been run with the Astronomical Image Processing System (AIPS software package. As the KVN array has a resolution of about 6 mas (milli arcsecond at 22GHz, in case of model source being approximately the beam size or smaller, the ratio of peak intensity over RMS shows about 10000:1 and 5000:1. The other case in which model source is larger than the beam size, this ratio shows very low range of about 115:1 and 34:1. This is due to the lack of short baselines and the small number of antenna. We compare the coordinates of the model images with those of the cleaned images. The result shows mostly perfect correspondence except in the case of the 12mas uniform sphere. Therefore, the main astronomical targets for the KVN will be the compact sources and the KVN will have an excellent performance in the astrometry for these sources.
An introduction to neural network methods for differential equations
Yadav, Neha; Kumar, Manoj
2015-01-01
This book introduces a variety of neural network methods for solving differential equations arising in science and engineering. The emphasis is placed on a deep understanding of the neural network techniques, which has been presented in a mostly heuristic and intuitive manner. This approach will enable the reader to understand the working, efficiency and shortcomings of each neural network technique for solving differential equations. The objective of this book is to provide the reader with a sound understanding of the foundations of neural networks, and a comprehensive introduction to neural network methods for solving differential equations together with recent developments in the techniques and their applications. The book comprises four major sections. Section I consists of a brief overview of differential equations and the relevant physical problems arising in science and engineering. Section II illustrates the history of neural networks starting from their beginnings in the 1940s through to the renewed...
A Novel Reliability Enhanced Handoff Method in Future Wireless Heterogeneous Networks
Directory of Open Access Journals (Sweden)
Wang YuPeng
2016-01-01
Full Text Available As the demand increases, future networks will follow the trends of network variety and service flexibility, which requires heterogeneous type of network deployment and reliable communication method. In practice, most communication failure happens due to the bad radio link quality, i.e., high-speed users suffers a lot on the problem of radio link failure, which causes the problem of communication interrupt and radio link recovery. To make the communication more reliable, especially for the high mobility users, we propose a novel communication handoff mechanism to reduce the occurrence of service interrupt. Based on computer simulation, we find that the reliability on the service is greatly improved.
Link Prediction Methods and Their Accuracy for Different Social Networks and Network Metrics
Directory of Open Access Journals (Sweden)
Fei Gao
2015-01-01
Full Text Available Currently, we are experiencing a rapid growth of the number of social-based online systems. The availability of the vast amounts of data gathered in those systems brings new challenges that we face when trying to analyse it. One of the intensively researched topics is the prediction of social connections between users. Although a lot of effort has been made to develop new prediction approaches, the existing methods are not comprehensively analysed. In this paper we investigate the correlation between network metrics and accuracy of different prediction methods. We selected six time-stamped real-world social networks and ten most widely used link prediction methods. The results of the experiments show that the performance of some methods has a strong correlation with certain network metrics. We managed to distinguish “prediction friendly” networks, for which most of the prediction methods give good performance, as well as “prediction unfriendly” networks, for which most of the methods result in high prediction error. Correlation analysis between network metrics and prediction accuracy of prediction methods may form the basis of a metalearning system where based on network characteristics it will be able to recommend the right prediction method for a given network.
Numerical methods in simulation of resistance welding
DEFF Research Database (Denmark)
Nielsen, Chris Valentin; Martins, Paulo A.F.; Zhang, Wenqi
2015-01-01
Finite element simulation of resistance welding requires coupling betweenmechanical, thermal and electrical models. This paper presents the numerical models and theircouplings that are utilized in the computer program SORPAS. A mechanical model based onthe irreducible flow formulation is utilized...... a resistance welding point of view, the most essential coupling between the above mentioned models is the heat generation by electrical current due to Joule heating. The interaction between multiple objects is anothercritical feature of the numerical simulation of resistance welding because it influences...... thecontact area and the distribution of contact pressure. The numerical simulation of resistancewelding is illustrated by a spot welding example that includes subsequent tensile shear testing...
Virtual Crowds Methods, Simulation, and Control
Pelechano, Nuria; Allbeck, Jan
2008-01-01
There are many applications of computer animation and simulation where it is necessary to model virtual crowds of autonomous agents. Some of these applications include site planning, education, entertainment, training, and human factors analysis for building evacuation. Other applications include simulations of scenarios where masses of people gather, flow, and disperse, such as transportation centers, sporting events, and concerts. Most crowd simulations include only basic locomotive behaviors possibly coupled with a few stochastic actions. Our goal in this survey is to establish a baseline o
Computer simulation of the Blumlein pulse forming network
International Nuclear Information System (INIS)
Edwards, C.B.
1981-03-01
A computer simulation of the Blumlein pulse-forming network is described. The model is able to treat the case of time varying loads, non-zero conductor resistance, and switch closure effects as exhibited by real systems employing non-ohmic loads such as field-emission vacuum diodes in which the impedance is strongly time and voltage dependent. The application of the code to various experimental arrangements is discussed, with particular reference to the prediction of the behaviour of the output circuit of 'ELF', the electron beam generator in operation at the Rutherford Laboratory. The output from the code is compared directly with experimentally obtained voltage waveforms applied to the 'ELF' diode. (author)
Learning Methods for Radial Basis Functions Networks
Czech Academy of Sciences Publication Activity Database
Neruda, Roman; Kudová, Petra
2005-01-01
Roč. 21, - (2005), s. 1131-1142 ISSN 0167-739X R&D Projects: GA ČR GP201/03/P163; GA ČR GA201/02/0428 Institutional research plan: CEZ:AV0Z10300504 Keywords : radial basis function networks * hybrid supervised learning * genetic algorithms * benchmarking Subject RIV: BA - General Mathematics Impact factor: 0.555, year: 2005
Classification Method in Integrated Information Network Using Vector Image Comparison
Directory of Open Access Journals (Sweden)
Zhou Yuan
2014-05-01
Full Text Available Wireless Integrated Information Network (WMN consists of integrated information that can get data from its surrounding, such as image, voice. To transmit information, large resource is required which decreases the service time of the network. In this paper we present a Classification Approach based on Vector Image Comparison (VIC for WMN that improve the service time of the network. The available methods for sub-region selection and conversion are also proposed.
Spectral Methods for Immunization of Large Networks
Directory of Open Access Journals (Sweden)
Muhammad Ahmad
2017-11-01
Full Text Available Given a network of nodes, minimizing the spread of a contagion using a limited budget is a well-studied problem with applications in network security, viral marketing, social networks, and public health. In real graphs, virus may infect a node which in turn infects its neighbour nodes and this may trigger an epidemic in the whole graph. The goal thus is to select the best k nodes (budget constraint that are immunized (vaccinated, screened, filtered so as the remaining graph is less prone to the epidemic. It is known that the problem is, in all practical models, computationally intractable even for moderate sized graphs. In this paper we employ ideas from spectral graph theory to define relevance and importance of nodes. Using novel graph theoretic techniques, we then design an efficient approximation algorithm to immunize the graph. Theoretical guarantees on the running time of our algorithm show that it is more efficient than any other known solution in the literature. We test the performance of our algorithm on several real world graphs. Experiments show that our algorithm scales well for large graphs and outperforms state of the art algorithms both in quality (containment of epidemic and efficiency (runtime and space complexity.
Tensor network method for reversible classical computation
Yang, Zhi-Cheng; Kourtis, Stefanos; Chamon, Claudio; Mucciolo, Eduardo R.; Ruckenstein, Andrei E.
2018-03-01
We develop a tensor network technique that can solve universal reversible classical computational problems, formulated as vertex models on a square lattice [Nat. Commun. 8, 15303 (2017), 10.1038/ncomms15303]. By encoding the truth table of each vertex constraint in a tensor, the total number of solutions compatible with partial inputs and outputs at the boundary can be represented as the full contraction of a tensor network. We introduce an iterative compression-decimation (ICD) scheme that performs this contraction efficiently. The ICD algorithm first propagates local constraints to longer ranges via repeated contraction-decomposition sweeps over all lattice bonds, thus achieving compression on a given length scale. It then decimates the lattice via coarse-graining tensor contractions. Repeated iterations of these two steps gradually collapse the tensor network and ultimately yield the exact tensor trace for large systems, without the need for manual control of tensor dimensions. Our protocol allows us to obtain the exact number of solutions for computations where a naive enumeration would take astronomically long times.
Semigroup methods for evolution equations on networks
Mugnolo, Delio
2014-01-01
This concise text is based on a series of lectures held only a few years ago and originally intended as an introduction to known results on linear hyperbolic and parabolic equations. Yet the topic of differential equations on graphs, ramified spaces, and more general network-like objects has recently gained significant momentum and, well beyond the confines of mathematics, there is a lively interdisciplinary discourse on all aspects of so-called complex networks. Such network-like structures can be found in virtually all branches of science, engineering and the humanities, and future research thus calls for solid theoretical foundations. This book is specifically devoted to the study of evolution equations – i.e., of time-dependent differential equations such as the heat equation, the wave equation, or the Schrödinger equation (quantum graphs) – bearing in mind that the majority of the literature in the last ten years on the subject of differential equations of graphs has been devoted to ellip...
Model and simulation of Krause model in dynamic open network
Zhu, Meixia; Xie, Guangqiang
2017-08-01
The construction of the concept of evolution is an effective way to reveal the formation of group consensus. This study is based on the modeling paradigm of the HK model (Hegsekmann-Krause). This paper analyzes the evolution of multi - agent opinion in dynamic open networks with member mobility. The results of the simulation show that when the number of agents is constant, the interval distribution of the initial distribution will affect the number of the final view, The greater the distribution of opinions, the more the number of views formed eventually; The trust threshold has a decisive effect on the number of views, and there is a negative correlation between the trust threshold and the number of opinions clusters. The higher the connectivity of the initial activity group, the more easily the subjective opinion in the evolution of opinion to achieve rapid convergence. The more open the network is more conducive to the unity of view, increase and reduce the number of agents will not affect the consistency of the group effect, but not conducive to stability.
Optimization-based Method for Automated Road Network Extraction
International Nuclear Information System (INIS)
Xiong, D
2001-01-01
Automated road information extraction has significant applicability in transportation. It provides a means for creating, maintaining, and updating transportation network databases that are needed for purposes ranging from traffic management to automated vehicle navigation and guidance. This paper is to review literature on the subject of road extraction and to describe a study of an optimization-based method for automated road network extraction
Diagrammatic perturbation methods in networks and sports ranking combinatorics
International Nuclear Information System (INIS)
Park, Juyong
2010-01-01
Analytic and computational tools developed in statistical physics are being increasingly applied to the study of complex networks. Here we present recent developments in the diagrammatic perturbation methods for the exponential random graph models, and apply them to the combinatoric problem of determining the ranking of nodes in directed networks that represent pairwise competitions
Simulation technologies in networking and communications selecting the best tool for the test
Pathan, Al-Sakib Khan; Khan, Shafiullah
2014-01-01
Simulation is a widely used mechanism for validating the theoretical models of networking and communication systems. Although the claims made based on simulations are considered to be reliable, how reliable they really are is best determined with real-world implementation trials.Simulation Technologies in Networking and Communications: Selecting the Best Tool for the Test addresses the spectrum of issues regarding the different mechanisms related to simulation technologies in networking and communications fields. Focusing on the practice of simulation testing instead of the theory, it presents
Simulation of complex fracture networks influenced by natural fractures in shale gas reservoir
Directory of Open Access Journals (Sweden)
Zhao Jinzhou
2014-10-01
Full Text Available When hydraulic fractures intersect with natural fractures, the geometry and complexity of a fracture network are determined by the initiation and propagation pattern which is affected by a number of factors. Based on the fracture mechanics, the criterion for initiation and propagation of a fracture was introduced to analyze the tendency of a propagating angle and factors affecting propagating pressure. On this basis, a mathematic model with a complex fracture network was established to investigate how the fracture network form changes with different parameters, including rock mechanics, in-situ stress distribution, fracture properties, and frac treatment parameters. The solving process of this model was accelerated by classifying the calculation nodes on the extending direction of the fracture by equal pressure gradients, and solving the geometrical parameters prior to the iteration fitting flow distribution. With the initiation and propagation criterion as the bases for the propagation of branch fractures, this method decreased the iteration times through eliminating the fitting of the fracture length in conventional 3D fracture simulation. The simulation results indicated that the formation with abundant natural fractures and smaller in-situ stress difference is sufficient conditions for fracture network development. If the pressure in the hydraulic fractures can be kept at a high level by temporary sealing or diversion, the branch fractures will propagate further with minor curvature radius, thus enlarging the reservoir stimulation area. The simulated shape of fracture network can be well matched with the field microseismic mapping in data point range and distribution density, validating the accuracy of this model.
OpenFlow Switching Performance using Network Simulator - 3
Sriram Prashanth, Naguru
2016-01-01
Context. In the present network inventive world, there is a quick expansion of switches and protocols, which are used to cope up with the increase in customer requirement in the networking. With increasing demand for higher bandwidths and lower latency and to meet these requirements new network paths are introduced. To reduce network load in present switching network, development of new innovative switching is required. These required results can be achieved by Software Define Network or Trad...
Method of optimization onboard communication network
Platoshin, G. A.; Selvesuk, N. I.; Semenov, M. E.; Novikov, V. M.
2018-02-01
In this article the optimization levels of onboard communication network (OCN) are proposed. We defined the basic parameters, which are necessary for the evaluation and comparison of modern OCN, we identified also a set of initial data for possible modeling of the OCN. We also proposed a mathematical technique for implementing the OCN optimization procedure. This technique is based on the principles and ideas of binary programming. It is shown that the binary programming technique allows to obtain an inherently optimal solution for the avionics tasks. An example of the proposed approach implementation to the problem of devices assignment in OCN is considered.
The Virtual Brain: a simulator of primate brain network dynamics.
Sanz Leon, Paula; Knock, Stuart A; Woodman, M Marmaduke; Domide, Lia; Mersmann, Jochen; McIntosh, Anthony R; Jirsa, Viktor
2013-01-01
We present The Virtual Brain (TVB), a neuroinformatics platform for full brain network simulations using biologically realistic connectivity. This simulation environment enables the model-based inference of neurophysiological mechanisms across different brain scales that underlie the generation of macroscopic neuroimaging signals including functional MRI (fMRI), EEG and MEG. Researchers from different backgrounds can benefit from an integrative software platform including a supporting framework for data management (generation, organization, storage, integration and sharing) and a simulation core written in Python. TVB allows the reproduction and evaluation of personalized configurations of the brain by using individual subject data. This personalization facilitates an exploration of the consequences of pathological changes in the system, permitting to investigate potential ways to counteract such unfavorable processes. The architecture of TVB supports interaction with MATLAB packages, for example, the well known Brain Connectivity Toolbox. TVB can be used in a client-server configuration, such that it can be remotely accessed through the Internet thanks to its web-based HTML5, JS, and WebGL graphical user interface. TVB is also accessible as a standalone cross-platform Python library and application, and users can interact with the scientific core through the scripting interface IDLE, enabling easy modeling, development and debugging of the scientific kernel. This second interface makes TVB extensible by combining it with other libraries and modules developed by the Python scientific community. In this article, we describe the theoretical background and foundations that led to the development of TVB, the architecture and features of its major software components as well as potential neuroscience applications.
The Virtual Brain: a simulator of primate brain network dynamics
Sanz Leon, Paula; Knock, Stuart A.; Woodman, M. Marmaduke; Domide, Lia; Mersmann, Jochen; McIntosh, Anthony R.; Jirsa, Viktor
2013-01-01
We present The Virtual Brain (TVB), a neuroinformatics platform for full brain network simulations using biologically realistic connectivity. This simulation environment enables the model-based inference of neurophysiological mechanisms across different brain scales that underlie the generation of macroscopic neuroimaging signals including functional MRI (fMRI), EEG and MEG. Researchers from different backgrounds can benefit from an integrative software platform including a supporting framework for data management (generation, organization, storage, integration and sharing) and a simulation core written in Python. TVB allows the reproduction and evaluation of personalized configurations of the brain by using individual subject data. This personalization facilitates an exploration of the consequences of pathological changes in the system, permitting to investigate potential ways to counteract such unfavorable processes. The architecture of TVB supports interaction with MATLAB packages, for example, the well known Brain Connectivity Toolbox. TVB can be used in a client-server configuration, such that it can be remotely accessed through the Internet thanks to its web-based HTML5, JS, and WebGL graphical user interface. TVB is also accessible as a standalone cross-platform Python library and application, and users can interact with the scientific core through the scripting interface IDLE, enabling easy modeling, development and debugging of the scientific kernel. This second interface makes TVB extensible by combining it with other libraries and modules developed by the Python scientific community. In this article, we describe the theoretical background and foundations that led to the development of TVB, the architecture and features of its major software components as well as potential neuroscience applications. PMID:23781198
International Nuclear Information System (INIS)
Li, Ming; Kang, Zhan; Huang, Xiaobo
2015-01-01
Hydrogen is clean, sustainable, and renewable, thus is viewed as promising energy carrier. However, its industrial utilization is greatly hampered by the lack of effective hydrogen storage and release method. Carbon nanotubes (CNTs) were viewed as one of the potential hydrogen containers, but it has been proved that pure CNTs cannot attain the desired target capacity of hydrogen storage. In this paper, we present a numerical study on the material-driven and structure-driven hydrogen adsorption of 3D silicon networks and propose a deformation-driven hydrogen desorption approach based on molecular simulations. Two types of 3D nanostructures, silicon nanotube-network (Si-NN) and silicon film-network (Si-FN), are first investigated in terms of hydrogen adsorption and desorption capacity with grand canonical Monte Carlo simulations. It is revealed that the hydrogen storage capacity is determined by the lithium doping ratio and geometrical parameters, and the maximum hydrogen uptake can be achieved by a 3D nanostructure with optimal configuration and doping ratio obtained through design optimization technique. For hydrogen desorption, a mechanical-deformation-driven-hydrogen-release approach is proposed. Compared with temperature/pressure change-induced hydrogen desorption method, the proposed approach is so effective that nearly complete hydrogen desorption can be achieved by Si-FN nanostructures under sufficient compression but without structural failure observed. The approach is also reversible since the mechanical deformation in Si-FN nanostructures can be elastically recovered, which suggests a good reusability. This study may shed light on the mechanism of hydrogen adsorption and desorption and thus provide useful guidance toward engineering design of microstructural hydrogen (or other gas) adsorption materials
Collaborative simulation method with spatiotemporal synchronization process control
Zou, Yisheng; Ding, Guofu; Zhang, Weihua; Zhang, Jian; Qin, Shengfeng; Tan, John Kian
2016-10-01
When designing a complex mechatronics system, such as high speed trains, it is relatively difficult to effectively simulate the entire system's dynamic behaviors because it involves multi-disciplinary subsystems. Currently,a most practical approach for multi-disciplinary simulation is interface based coupling simulation method, but it faces a twofold challenge: spatial and time unsynchronizations among multi-directional coupling simulation of subsystems. A new collaborative simulation method with spatiotemporal synchronization process control is proposed for coupling simulating a given complex mechatronics system across multiple subsystems on different platforms. The method consists of 1) a coupler-based coupling mechanisms to define the interfacing and interaction mechanisms among subsystems, and 2) a simulation process control algorithm to realize the coupling simulation in a spatiotemporal synchronized manner. The test results from a case study show that the proposed method 1) can certainly be used to simulate the sub-systems interactions under different simulation conditions in an engineering system, and 2) effectively supports multi-directional coupling simulation among multi-disciplinary subsystems. This method has been successfully applied in China high speed train design and development processes, demonstrating that it can be applied in a wide range of engineering systems design and simulation with improved efficiency and effectiveness.
Application of Network Analysis Method to VHTR core
International Nuclear Information System (INIS)
Lee, Jeong Hun; Yoon, Su Jong; Park, Goon Cherl
2012-01-01
A Very High Temperature Reactor (VHTR) is currently envisioned as a promising future reactor concept because of its high-efficiency and capability of generating hydrogen. Prismatic Modular Reactor (PMR) is one of the main VHTR concepts, which consists of hexagonal prismatic fuel blocks and reflector blocks made of nuclear grade graphite. However their shape could be changed by neutron damage during the reactor operation and the shape change can makes the gaps between the blocks inducing bypass flow. Most of reactor coolant flows through the coolant channel within the fuel block, but some portion of the reactor coolant bypasses to the interstitial gaps. The vertical gap and horizontal gap are called bypass gap and cross gap, respectively. CFD simulation for the full core of VHTR might be possible but it requires vast computational cost and time. Therefore, fast, flexible and reliable code is required to predict the flow distribution corresponding to the various bypass gap distribution. Consequently in this study, the flow network analysis method is applied to analyze the core flow of VHTR. The applied method was validated by comparing with SNU VHTR multiblock experiment. As a result, the calculated results show good agreements with experimental data although computational time and cost of the developed code was very small
Math-Based Simulation Tools and Methods
National Research Council Canada - National Science Library
Arepally, Sudhakar
2007-01-01
.... The following methods are reviewed: matrix operations, ordinary and partial differential system of equations, Lagrangian operations, Fourier transforms, Taylor Series, Finite Difference Methods, implicit and explicit finite element...
Comparing methods of targeting obesity interventions in populations: An agent-based simulation.
Beheshti, Rahmatollah; Jalalpour, Mehdi; Glass, Thomas A
2017-12-01
Social networks as well as neighborhood environments have been shown to effect obesity-related behaviors including energy intake and physical activity. Accordingly, harnessing social networks to improve targeting of obesity interventions may be promising to the extent this leads to social multiplier effects and wider diffusion of intervention impact on populations. However, the literature evaluating network-based interventions has been inconsistent. Computational methods like agent-based models (ABM) provide researchers with tools to experiment in a simulated environment. We develop an ABM to compare conventional targeting methods (random selection, based on individual obesity risk, and vulnerable areas) with network-based targeting methods. We adapt a previously published and validated model of network diffusion of obesity-related behavior. We then build social networks among agents using a more realistic approach. We calibrate our model first against national-level data. Our results show that network-based targeting may lead to greater population impact. We also present a new targeting method that outperforms other methods in terms of intervention effectiveness at the population level.
Network Forensics Method Based on Evidence Graph and Vulnerability Reasoning
Directory of Open Access Journals (Sweden)
Jingsha He
2016-11-01
Full Text Available As the Internet becomes larger in scale, more complex in structure and more diversified in traffic, the number of crimes that utilize computer technologies is also increasing at a phenomenal rate. To react to the increasing number of computer crimes, the field of computer and network forensics has emerged. The general purpose of network forensics is to find malicious users or activities by gathering and dissecting firm evidences about computer crimes, e.g., hacking. However, due to the large volume of Internet traffic, not all the traffic captured and analyzed is valuable for investigation or confirmation. After analyzing some existing network forensics methods to identify common shortcomings, we propose in this paper a new network forensics method that uses a combination of network vulnerability and network evidence graph. In our proposed method, we use vulnerability evidence and reasoning algorithm to reconstruct attack scenarios and then backtrack the network packets to find the original evidences. Our proposed method can reconstruct attack scenarios effectively and then identify multi-staged attacks through evidential reasoning. Results of experiments show that the evidence graph constructed using our method is more complete and credible while possessing the reasoning capability.
High-resolution method for evolving complex interface networks
Pan, Shucheng; Hu, Xiangyu Y.; Adams, Nikolaus A.
2018-04-01
In this paper we describe a high-resolution transport formulation of the regional level-set approach for an improved prediction of the evolution of complex interface networks. The novelty of this method is twofold: (i) construction of local level sets and reconstruction of a global level set, (ii) local transport of the interface network by employing high-order spatial discretization schemes for improved representation of complex topologies. Various numerical test cases of multi-region flow problems, including triple-point advection, single vortex flow, mean curvature flow, normal driven flow, dry foam dynamics and shock-bubble interaction show that the method is accurate and suitable for a wide range of complex interface-network evolutions. Its overall computational cost is comparable to the Semi-Lagrangian regional level-set method while the prediction accuracy is significantly improved. The approach thus offers a viable alternative to previous interface-network level-set method.
Quartet-net: a quartet-based method to reconstruct phylogenetic networks.
Yang, Jialiang; Grünewald, Stefan; Wan, Xiu-Feng
2013-05-01
Phylogenetic networks can model reticulate evolutionary events such as hybridization, recombination, and horizontal gene transfer. However, reconstructing such networks is not trivial. Popular character-based methods are computationally inefficient, whereas distance-based methods cannot guarantee reconstruction accuracy because pairwise genetic distances only reflect partial information about a reticulate phylogeny. To balance accuracy and computational efficiency, here we introduce a quartet-based method to construct a phylogenetic network from a multiple sequence alignment. Unlike distances that only reflect the relationship between a pair of taxa, quartets contain information on the relationships among four taxa; these quartets provide adequate capacity to infer a more accurate phylogenetic network. In applications to simulated and biological data sets, we demonstrate that this novel method is robust and effective in reconstructing reticulate evolutionary events and it has the potential to infer more accurate phylogenetic distances than other conventional phylogenetic network construction methods such as Neighbor-Joining, Neighbor-Net, and Split Decomposition. This method can be used in constructing phylogenetic networks from simple evolutionary events involving a few reticulate events to complex evolutionary histories involving a large number of reticulate events. A software called "Quartet-Net" is implemented and available at http://sysbio.cvm.msstate.edu/QuartetNet/.
A method for ensemble wildland fire simulation
Mark A. Finney; Isaac C. Grenfell; Charles W. McHugh; Robert C. Seli; Diane Trethewey; Richard D. Stratton; Stuart Brittain
2011-01-01
An ensemble simulation system that accounts for uncertainty in long-range weather conditions and two-dimensional wildland fire spread is described. Fuel moisture is expressed based on the energy release component, a US fire danger rating index, and its variation throughout the fire season is modeled using time series analysis of historical weather data. This analysis...
A novel method for energy harvesting simulation based on scenario generation
Wang, Zhe; Li, Taoshen; Xiao, Nan; Ye, Jin; Wu, Min
2018-06-01
Energy harvesting network (EHN) is a new form of computer networks. It converts ambient energy into usable electric energy and supply the electrical energy as a primary or secondary power source to the communication devices. However, most of the EHN uses the analytical probability distribution function to describe the energy harvesting process, which cannot accurately identify the actual situation for the lack of authenticity. We propose an EHN simulation method based on scenario generation in this paper. Firstly, instead of setting a probability distribution in advance, it uses optimal scenario reduction technology to generate representative scenarios in single period based on the historical data of the harvested energy. Secondly, it uses homogeneous simulated annealing algorithm to generate optimal daily energy harvesting scenario sequences to get a more accurate simulation of the random characteristics of the energy harvesting network. Then taking the actual wind power data as an example, the accuracy and stability of the method are verified by comparing with the real data. Finally, we cite an instance to optimize the network throughput, which indicate the feasibility and effectiveness of the method we proposed from the optimal solution and data analysis in energy harvesting simulation.
Supervisory control of mobile sensor networks: math formulation, simulation, and implementation.
Giordano, Vincenzo; Ballal, Prasanna; Lewis, Frank; Turchiano, Biagio; Zhang, Jing Bing
2006-08-01
This paper uses a novel discrete-event controller (DEC) for the coordination of cooperating heterogeneous wireless sensor networks (WSNs) containing both unattended ground sensors (UGSs) and mobile sensor robots. The DEC sequences the most suitable tasks for each agent and assigns sensor resources according to the current perception of the environment. A matrix formulation makes this DEC particularly useful for WSN, where missions change and sensor agents may be added or may fail. WSN have peculiarities that complicate their supervisory control. Therefore, this paper introduces several new tools for DEC design and operation, including methods for generating the required supervisory matrices based on mission planning, methods for modifying the matrices in the event of failed nodes, or nodes entering the network, and a novel dynamic priority assignment weighting approach for selecting the most appropriate and useful sensors for a given mission task. The resulting DEC represents a complete dynamical description of the WSN system, which allows a fast programming of deployable WSN, a computer simulation analysis, and an efficient implementation. The DEC is actually implemented on an experimental wireless-sensor-network prototyping system. Both simulation and experimental results are presented to show the effectiveness and versatility of the developed control architecture.
Gur, M.; Zomot, E.; Bahar, I.
2013-09-01
The Anton supercomputing technology recently developed for efficient molecular dynamics simulations permits us to examine micro- to milli-second events at full atomic resolution for proteins in explicit water and lipid bilayer. It also permits us to investigate to what extent the collective motions predicted by network models (that have found broad use in molecular biophysics) agree with those exhibited by full-atomic long simulations. The present study focuses on Anton trajectories generated for two systems: the bovine pancreatic trypsin inhibitor, and an archaeal aspartate transporter, GltPh. The former, a thoroughly studied system, helps benchmark the method of comparative analysis, and the latter provides new insights into the mechanism of function of glutamate transporters. The principal modes of motion derived from both simulations closely overlap with those predicted for each system by the anisotropic network model (ANM). Notably, the ANM modes define the collective mechanisms, or the pathways on conformational energy landscape, that underlie the passage between the crystal structure and substates visited in simulations. In particular, the lowest frequency ANM modes facilitate the conversion between the most probable substates, lending support to the view that easy access to functional substates is a robust determinant of evolutionarily selected native contact topology.
Gerlach, Kathy D; Spreng, R Nathan; Madore, Kevin P; Schacter, Daniel L
2014-12-01
We spend much of our daily lives imagining how we can reach future goals and what will happen when we attain them. Despite the prevalence of such goal-directed simulations, neuroimaging studies on planning have mainly focused on executive processes in the frontal lobe. This experiment examined the neural basis of process simulations, during which participants imagined themselves going through steps toward attaining a goal, and outcome simulations, during which participants imagined events they associated with achieving a goal. In the scanner, participants engaged in these simulation tasks and an odd/even control task. We hypothesized that process simulations would recruit default and frontoparietal control network regions, and that outcome simulations, which allow us to anticipate the affective consequences of achieving goals, would recruit default and reward-processing regions. Our analysis of brain activity that covaried with process and outcome simulations confirmed these hypotheses. A functional connectivity analysis with posterior cingulate, dorsolateral prefrontal cortex and anterior inferior parietal lobule seeds showed that their activity was correlated during process simulations and associated with a distributed network of default and frontoparietal control network regions. During outcome simulations, medial prefrontal cortex and amygdala seeds covaried together and formed a functional network with default and reward-processing regions. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.
Yu, Bin; Xu, Jia-Meng; Li, Shan; Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Zhang, Yan; Wang, Ming-Hui
2017-10-06
Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli , and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs.
Directory of Open Access Journals (Sweden)
О.С. Якушенко
2010-01-01
Full Text Available The article is devoted to the problem of gas turbine engine (GTE technical state class automatic recognition with operation parameters by neuron networks. The one of main problems for creation the neuron networks is determination of their optimal structures size (amount of layers in network and count of neurons in each layer.The method of neuron network size optimization intended for classification of GTE technical state is considered in the article. Optimization is cared out with taking into account of overlearning effect possibility when a learning network loses property of generalization and begins strictly describing educational data set. To determinate a moment when overlearning effect is appeared in learning neuron network the method of three data sets is used. The method is based on the comparison of recognition quality parameters changes which were calculated during recognition of educational and control data sets. As the moment when network overlearning effect is appeared the moment when control data set recognition quality begins deteriorating but educational data set recognition quality continues still improving is used. To determinate this moment learning process periodically is terminated and simulation of network with education and control data sets is fulfilled. The optimization of two-, three- and four-layer networks is conducted and some results of optimization are shown. Also the extended educational set is created and shown. The set describes 16 GTE technical state classes and each class is represented with 200 points (200 possible technical state class realizations instead of 20 points using in the former articles. It was done to increase representativeness of data set.In the article the algorithm of optimization is considered and some results which were obtained with it are shown. The results of experiments were analyzed to determinate most optimal neuron network structure. This structure provides most high-quality GTE
The computer simulation of the resonant network for the B-factory model power supply
International Nuclear Information System (INIS)
Zhou, W.; Endo, K.
1993-07-01
A high repetition model power supply and the resonant magnet network are simulated with the computer in order to check and improve the design of the power supply for the B-factory booster. We put our key point on a transient behavior of the power supply and the resonant magnet network. The results of the simulation are given. (author)
ns-2 extension to simulate localization system in wireless sensor networks
CSIR Research Space (South Africa)
Abu-Mahfouz, Adnan M
2011-09-01
Full Text Available The ns-2 network simulator is one of the most widely used tools by researchers to investigate the characteristics of wireless sensor networks. Academic papers focus on results and rarely include details of how ns-2 simulations are implemented...
A Grooming Nodes Optimal Allocation Method for Multicast in WDM Networks
Directory of Open Access Journals (Sweden)
Chengying Wei
2016-01-01
Full Text Available The grooming node has the capability of grooming multicast traffic with the small granularity into established light at high cost of complexity and node architecture. In the paper, a grooming nodes optimal allocation (GNOA method is proposed to optimize the allocation of the grooming nodes constraint by the blocking probability for multicast traffic in sparse WDM networks. In the proposed GNOA method, the location of each grooming node is determined by the SCLD strategy. The improved smallest cost largest degree (SCLD strategy is designed to select the nongrooming nodes in the proposed GNOA method. The simulation results show that the proposed GNOA method can reduce the required number of grooming nodes and decrease the cost of constructing a network to guarantee a certain request blocking probability when the wavelengths per fiber and transmitter/receiver ports per node are sufficient for the optical multicast in WDM networks.
Wang, Lei; Fan, Youping; Zhang, Dai; Ge, Mengxin; Zou, Xianbin; Li, Jingjiao
2017-09-01
This paper proposes a method to simulate a back-to-back modular multilevel converter (MMC) HVDC transmission system. In this paper we utilize an equivalent networks to simulate the dynamic power system. Moreover, to account for the performance of converter station, core components of model of the converter station gives a basic model of simulation. The proposed method is applied to an equivalent real power system.
Interactive methods for exploring particle simulation data
Energy Technology Data Exchange (ETDEWEB)
Co, Christopher S.; Friedman, Alex; Grote, David P.; Vay, Jean-Luc; Bethel, E. Wes; Joy, Kenneth I.
2004-05-01
In this work, we visualize high-dimensional particle simulation data using a suite of scatter plot-based visualizations coupled with interactive selection tools. We use traditional 2D and 3D projection scatter plots as well as a novel oriented disk rendering style to convey various information about the data. Interactive selection tools allow physicists to manually classify ''interesting'' sets of particles that are highlighted across multiple, linked views of the data. The power of our application is the ability to correspond new visual representations of the simulation data with traditional, well understood visualizations. This approach supports the interactive exploration of the high-dimensional space while promoting discovery of new particle behavior.
Hospital Registration Process Reengineering Using Simulation Method
Directory of Open Access Journals (Sweden)
Qiang Su
2010-01-01
Full Text Available With increasing competition, many healthcare organizations have undergone tremendous reform in the last decade aiming to increase efficiency, decrease waste, and reshape the way that care is delivered. This study focuses on the operational efficiency improvement of hospital’s registration process. The operational efficiency related factors including the service process, queue strategy, and queue parameters were explored systematically and illustrated with a case study. Guided by the principle of business process reengineering (BPR, a simulation approach was employed for process redesign and performance optimization. As a result, the queue strategy is changed from multiple queues and multiple servers to single queue and multiple servers with a prepare queue. Furthermore, through a series of simulation experiments, the length of the prepare queue and the corresponding registration process efficiency was quantitatively evaluated and optimized.
Numerical simulation methods for electron and ion optics
International Nuclear Information System (INIS)
Munro, Eric
2011-01-01
This paper summarizes currently used techniques for simulation and computer-aided design in electron and ion beam optics. Topics covered include: field computation, methods for computing optical properties (including Paraxial Rays and Aberration Integrals, Differential Algebra and Direct Ray Tracing), simulation of Coulomb interactions, space charge effects in electron and ion sources, tolerancing, wave optical simulations and optimization. Simulation examples are presented for multipole aberration correctors, Wien filter monochromators, imaging energy filters, magnetic prisms, general curved axis systems and electron mirrors.
Direct numerical simulation of cellular-scale blood flow in microvascular networks
Balogh, Peter; Bagchi, Prosenjit
2017-11-01
A direct numerical simulation method is developed to study cellular-scale blood flow in physiologically realistic microvascular networks that are constructed in silico following published in vivo images and data, and are comprised of bifurcating, merging, and winding vessels. The model resolves large deformation of individual red blood cells (RBC) flowing in such complex networks. The vascular walls and deformable interfaces of the RBCs are modeled using the immersed-boundary methods. Time-averaged hemodynamic quantities obtained from the simulations agree quite well with published in vivo data. Our simulations reveal that in several vessels the flow rates and pressure drops could be negatively correlated. The flow resistance and hematocrit are also found to be negatively correlated in some vessels. These observations suggest a deviation from the classical Poiseuille's law in such vessels. The cells are observed to frequently jam at vascular bifurcations resulting in reductions in hematocrit and flow rate in the daughter and mother vessels. We find that RBC jamming results in several orders of magnitude increase in hemodynamic resistance, and thus provides an additional mechanism of increased in vivo blood viscosity as compared to that determined in vitro. Funded by NSF CBET 1604308.
Unified Approach to Modeling and Simulation of Space Communication Networks and Systems
Barritt, Brian; Bhasin, Kul; Eddy, Wesley; Matthews, Seth
2010-01-01
Network simulator software tools are often used to model the behaviors and interactions of applications, protocols, packets, and data links in terrestrial communication networks. Other software tools that model the physics, orbital dynamics, and RF characteristics of space systems have matured to allow for rapid, detailed analysis of space communication links. However, the absence of a unified toolset that integrates the two modeling approaches has encumbered the systems engineers tasked with the design, architecture, and analysis of complex space communication networks and systems. This paper presents the unified approach and describes the motivation, challenges, and our solution - the customization of the network simulator to integrate with astronautical analysis software tools for high-fidelity end-to-end simulation. Keywords space; communication; systems; networking; simulation; modeling; QualNet; STK; integration; space networks
Modelisation et simulation d'un PON (Passive Optical Network) base ...
African Journals Online (AJOL)
English Title: Modeling and simulation of a PON (Passive Optical Network) Based on hybrid technology WDM/TDM. English Abstract. This development is part of dynamism of design for a model combining WDM and TDM multiplexing in the optical network of PON (Passive Optical Network) type, in order to satisfy the high bit ...
Protocol independent transmission method in software defined optical network
Liu, Yuze; Li, Hui; Hou, Yanfang; Qiu, Yajun; Ji, Yuefeng
2016-10-01
With the development of big data and cloud computing technology, the traditional software-defined network is facing new challenges (e.i., ubiquitous accessibility, higher bandwidth, more flexible management and greater security). Using a proprietary protocol or encoding format is a way to improve information security. However, the flow, which carried by proprietary protocol or code, cannot go through the traditional IP network. In addition, ultra- high-definition video transmission service once again become a hot spot. Traditionally, in the IP network, the Serial Digital Interface (SDI) signal must be compressed. This approach offers additional advantages but also bring some disadvantages such as signal degradation and high latency. To some extent, HD-SDI can also be regard as a proprietary protocol, which need transparent transmission such as optical channel. However, traditional optical networks cannot support flexible traffics . In response to aforementioned challenges for future network, one immediate solution would be to use NFV technology to abstract the network infrastructure and provide an all-optical switching topology graph for the SDN control plane. This paper proposes a new service-based software defined optical network architecture, including an infrastructure layer, a virtualization layer, a service abstract layer and an application layer. We then dwell on the corresponding service providing method in order to implement the protocol-independent transport. Finally, we experimentally evaluate that proposed service providing method can be applied to transmit the HD-SDI signal in the software-defined optical network.
Battery Performance Modelling ad Simulation: a Neural Network Based Approach
Ottavianelli, Giuseppe; Donati, Alessandro
2002-01-01
This project has developed on the background of ongoing researches within the Control Technology Unit (TOS-OSC) of the Special Projects Division at the European Space Operations Centre (ESOC) of the European Space Agency. The purpose of this research is to develop and validate an Artificial Neural Network tool (ANN) able to model, simulate and predict the Cluster II battery system's performance degradation. (Cluster II mission is made of four spacecraft flying in tetrahedral formation and aimed to observe and study the interaction between sun and earth by passing in and out of our planet's magnetic field). This prototype tool, named BAPER and developed with a commercial neural network toolbox, could be used to support short and medium term mission planning in order to improve and maximise the batteries lifetime, determining which are the future best charge/discharge cycles for the batteries given their present states, in view of a Cluster II mission extension. This study focuses on the five Silver-Cadmium batteries onboard of Tango, the fourth Cluster II satellite, but time restrains have allowed so far to perform an assessment only on the first battery. In their most basic form, ANNs are hyper-dimensional curve fits for non-linear data. With their remarkable ability to derive meaning from complicated or imprecise history data, ANN can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. ANNs learn by example, and this is why they can be described as an inductive, or data-based models for the simulation of input/target mappings. A trained ANN can be thought of as an "expert" in the category of information it has been given to analyse, and this expert can then be used, as in this project, to provide projections given new situations of interest and answer "what if" questions. The most appropriate algorithm, in terms of training speed and memory storage requirements, is clearly the Levenberg
Electromagnetic field computation by network methods
Felsen, Leopold B; Russer, Peter
2009-01-01
This monograph proposes a systematic and rigorous treatment of electromagnetic field representations in complex structures. The book presents new strong models by combining important computational methods. This is the last book of the late Leopold Felsen.
Distribution network planning method considering distributed generation for peak cutting
International Nuclear Information System (INIS)
Ouyang Wu; Cheng Haozhong; Zhang Xiubin; Yao Liangzhong
2010-01-01
Conventional distribution planning method based on peak load brings about large investment, high risk and low utilization efficiency. A distribution network planning method considering distributed generation (DG) for peak cutting is proposed in this paper. The new integrated distribution network planning method with DG implementation aims to minimize the sum of feeder investments, DG investments, energy loss cost and the additional cost of DG for peak cutting. Using the solution techniques combining genetic algorithm (GA) with the heuristic approach, the proposed model determines the optimal planning scheme including the feeder network and the siting and sizing of DG. The strategy for the site and size of DG, which is based on the radial structure characteristics of distribution network, reduces the complexity degree of solving the optimization model and eases the computational burden substantially. Furthermore, the operation schedule of DG at the different load level is also provided.
Information loss method to measure node similarity in networks
Li, Yongli; Luo, Peng; Wu, Chong
2014-09-01
Similarity measurement for the network node has been paid increasing attention in the field of statistical physics. In this paper, we propose an entropy-based information loss method to measure the node similarity. The whole model is established based on this idea that less information loss is caused by seeing two more similar nodes as the same. The proposed new method has relatively low algorithm complexity, making it less time-consuming and more efficient to deal with the large scale real-world network. In order to clarify its availability and accuracy, this new approach was compared with some other selected approaches on two artificial examples and synthetic networks. Furthermore, the proposed method is also successfully applied to predict the network evolution and predict the unknown nodes' attributions in the two application examples.
Modelling Altitude Information in Two-Dimensional Traffic Networks for Electric Mobility Simulation
Directory of Open Access Journals (Sweden)
Diogo Santos
2016-06-01
Full Text Available Elevation data is important for electric vehicle simulation. However, traffic simulators are often two-dimensional and do not offer the capability of modelling urban networks taking elevation into account. Specifically, SUMO - Simulation of Urban Mobility, a popular microscopic traffic simulator, relies on networks previously modelled with elevation data as to provide this information during simulations. This work tackles the problem of adding elevation data to urban network models - particularly for the case of the Porto urban network, in Portugal. With this goal in mind, a comparison between different altitude information retrieval approaches is made and a simple tool to annotate network models with altitude data is proposed. The work starts by describing the methodological approach followed during research and development, then describing and analysing its main findings. This description includes an in-depth explanation of the proposed tool. Lastly, this work reviews some related work to the subject.
Application of Looped Network Analysis Method to Core of Prismatic VHTR
International Nuclear Information System (INIS)
Lee, Jeong-Hun; Cho, Hyoung-Kyu; Park, Goon-Cherl
2016-01-01
Most of reactor coolant flows through the coolant channel within the fuel block, but some portion of the reactor coolant bypasses to the interstitial gaps. The vertical gap and horizontal gap are called bypass gap and cross gap, respectively as shown in Fig. 1. CFD simulation for the full core of VHTR might be possible but it requires vast computational cost and time. Moreover, it is hard to cover whole cases corresponding to the various bypass gap distribution in the whole VHTR core. In order to solve this problem, in this study, the flow network analysis code, FastNet (Flow Analysis for Steady-state Network), was developed using the Looped Network Analysis Method. The applied method was validated by comparing with SNU VHTR multi-block experiment. A 3-demensional network modeling was conducted representing flow paths as flow resistances. Flow network analysis code, FastNet, was developed to evaluate the core bypass flow distribution by using looped network analysis method. Complex flow network could be solved simply by converting the non-linear momentum equation to the linearized equation. The FastNet code predicted the flow distribution of the SNU multi-block experiment accurately
Messina, Luca; Castin, Nicolas; Domain, Christophe; Olsson, Pär
2017-02-01
The quality of kinetic Monte Carlo (KMC) simulations of microstructure evolution in alloys relies on the parametrization of point-defect migration rates, which are complex functions of the local chemical composition and can be calculated accurately with ab initio methods. However, constructing reliable models that ensure the best possible transfer of physical information from ab initio to KMC is a challenging task. This work presents an innovative approach, where the transition rates are predicted by artificial neural networks trained on a database of 2000 migration barriers, obtained with density functional theory (DFT) in place of interatomic potentials. The method is tested on copper precipitation in thermally aged iron alloys, by means of a hybrid atomistic-object KMC model. For the object part of the model, the stability and mobility properties of copper-vacancy clusters are analyzed by means of independent atomistic KMC simulations, driven by the same neural networks. The cluster diffusion coefficients and mean free paths are found to increase with size, confirming the dominant role of coarsening of medium- and large-sized clusters in the precipitation kinetics. The evolution under thermal aging is in better agreement with experiments with respect to a previous interatomic-potential model, especially concerning the experiment time scales. However, the model underestimates the solubility of copper in iron due to the excessively high solution energy predicted by the chosen DFT method. Nevertheless, this work proves the capability of neural networks to transfer complex ab initio physical properties to higher-scale models, and facilitates the extension to systems with increasing chemical complexity, setting the ground for reliable microstructure evolution simulations in a wide range of alloys and applications.
Momentum integral network method for thermal-hydraulic transient analysis
International Nuclear Information System (INIS)
Van Tuyle, G.J.
1983-01-01
A new momentum integral network method has been developed, and tested in the MINET computer code. The method was developed in order to facilitate the transient analysis of complex fluid flow and heat transfer networks, such as those found in the balance of plant of power generating facilities. The method employed in the MINET code is a major extension of a momentum integral method reported by Meyer. Meyer integrated the momentum equation over several linked nodes, called a segment, and used a segment average pressure, evaluated from the pressures at both ends. Nodal mass and energy conservation determined nodal flows and enthalpies, accounting for fluid compression and thermal expansion
A link prediction method for heterogeneous networks based on BP neural network
Li, Ji-chao; Zhao, Dan-ling; Ge, Bing-Feng; Yang, Ke-Wei; Chen, Ying-Wu
2018-04-01
Most real-world systems, composed of different types of objects connected via many interconnections, can be abstracted as various complex heterogeneous networks. Link prediction for heterogeneous networks is of great significance for mining missing links and reconfiguring networks according to observed information, with considerable applications in, for example, friend and location recommendations and disease-gene candidate detection. In this paper, we put forward a novel integrated framework, called MPBP (Meta-Path feature-based BP neural network model), to predict multiple types of links for heterogeneous networks. More specifically, the concept of meta-path is introduced, followed by the extraction of meta-path features for heterogeneous networks. Next, based on the extracted meta-path features, a supervised link prediction model is built with a three-layer BP neural network. Then, the solution algorithm of the proposed link prediction model is put forward to obtain predicted results by iteratively training the network. Last, numerical experiments on the dataset of examples of a gene-disease network and a combat network are conducted to verify the effectiveness and feasibility of the proposed MPBP. It shows that the MPBP with very good performance is superior to the baseline methods.
Benchmarking HRA methods against different NPP simulator data
International Nuclear Information System (INIS)
Petkov, Gueorgui; Filipov, Kalin; Velev, Vladimir; Grigorov, Alexander; Popov, Dimiter; Lazarov, Lazar; Stoichev, Kosta
2008-01-01
The paper presents both international and Bulgarian experience in assessing HRA methods, underlying models approaches for their validation and verification by benchmarking HRA methods against different NPP simulator data. The organization, status, methodology and outlooks of the studies are described
Math-Based Simulation Tools and Methods
National Research Council Canada - National Science Library
Arepally, Sudhakar
2007-01-01
...: HMMWV 30-mph Rollover Test, Soldier Gear Effects, Occupant Performance in Blast Effects, Anthropomorphic Test Device, Human Models, Rigid Body Modeling, Finite Element Methods, Injury Criteria...
Particle-transport simulation with the Monte Carlo method
International Nuclear Information System (INIS)
Carter, L.L.; Cashwell, E.D.
1975-01-01
Attention is focused on the application of the Monte Carlo method to particle transport problems, with emphasis on neutron and photon transport. Topics covered include sampling methods, mathematical prescriptions for simulating particle transport, mechanics of simulating particle transport, neutron transport, and photon transport. A literature survey of 204 references is included. (GMT)
Massobrio, Carlo; Bernasconi, Marco; Salmon, Philip S
2015-01-01
This book is a unique reference work in the area of atomic-scale simulation of glasses. For the first time, a highly selected panel of about 20 researchers provides, in a single book, their views, methodologies and applications on the use of molecular dynamics as a tool to describe glassy materials. The book covers a wide range of systems covering ""traditional"" network glasses, such as chalcogenides and oxides, as well as glasses for applications in the area of phase change materials. The novelty of this work is the interplay between molecular dynamics methods (both at the classical and firs
International Nuclear Information System (INIS)
Otero, F
1998-01-01
This article presents the current status of the use of Artificial Neural Networks (ANNs) in process engineering applications where common mathematical methods do not completely represent the behavior shown by experimental observations, results, and plant operating data. Three examples of the use of ANNs in typical process engineering applications such as prediction of activity in solvent-polymer binary systems, prediction of a surfactant self-diffusion coefficient of micellar systems, and process control and simulation are shown. These examples are important for polymerization applications, enhanced-oil recovery, and automatic process control
Real-time hybrid simulation using the convolution integral method
International Nuclear Information System (INIS)
Kim, Sung Jig; Christenson, Richard E; Wojtkiewicz, Steven F; Johnson, Erik A
2011-01-01
This paper proposes a real-time hybrid simulation method that will allow complex systems to be tested within the hybrid test framework by employing the convolution integral (CI) method. The proposed CI method is potentially transformative for real-time hybrid simulation. The CI method can allow real-time hybrid simulation to be conducted regardless of the size and complexity of the numerical model and for numerical stability to be ensured in the presence of high frequency responses in the simulation. This paper presents the general theory behind the proposed CI method and provides experimental verification of the proposed method by comparing the CI method to the current integration time-stepping (ITS) method. Real-time hybrid simulation is conducted in the Advanced Hazard Mitigation Laboratory at the University of Connecticut. A seismically excited two-story shear frame building with a magneto-rheological (MR) fluid damper is selected as the test structure to experimentally validate the proposed method. The building structure is numerically modeled and simulated, while the MR damper is physically tested. Real-time hybrid simulation using the proposed CI method is shown to provide accurate results
Environmental regulation in a network of simulated microbial ecosystems.
Williams, Hywel T P; Lenton, Timothy M
2008-07-29
The Earth possesses a number of regulatory feedback mechanisms involving life. In the absence of a population of competing biospheres, it has proved hard to find a robust evolutionary mechanism that would generate environmental regulation. It has been suggested that regulation must require altruistic environmental alterations by organisms and, therefore, would be evolutionarily unstable. This need not be the case if organisms alter the environment as a selectively neutral by-product of their metabolism, as in the majority of biogeochemical reactions, but a question then arises: Why should the combined by-product effects of the biota have a stabilizing, rather than destabilizing, influence on the environment? Under certain conditions, selection acting above the level of the individual can be an effective adaptive force. Here we present an evolutionary simulation model in which environmental regulation involving higher-level selection robustly emerges in a network of interconnected microbial ecosystems. Spatial structure creates conditions for a limited form of higher-level selection to act on the collective environment-altering properties of local communities. Local communities that improve their environmental conditions achieve larger populations and are better colonizers of available space, whereas local communities that degrade their environment shrink and become susceptible to invasion. The spread of environment-improving communities alters the global environment toward the optimal conditions for growth and tends to regulate against external perturbations. This work suggests a mechanism for environmental regulation that is consistent with evolutionary theory.
Simulation of tunneling construction methods of the Cisumdawu toll road
Abduh, Muhamad; Sukardi, Sapto Nugroho; Ola, Muhammad Rusdian La; Ariesty, Anita; Wirahadikusumah, Reini D.
2017-11-01
Simulation can be used as a tool for planning and analysis of a construction method. Using simulation technique, a contractor could design optimally resources associated with a construction method and compare to other methods based on several criteria, such as productivity, waste, and cost. This paper discusses the use of simulation using Norwegian Method of Tunneling (NMT) for a 472-meter tunneling work in the Cisumdawu Toll Road project. Primary and secondary data were collected to provide useful information for simulation as well as problems that may be faced by the contractor. The method was modelled using the CYCLONE and then simulated using the WebCYCLONE. The simulation could show the duration of the project from the duration model of each work tasks which based on literature review, machine productivity, and several assumptions. The results of simulation could also show the total cost of the project that was modeled based on journal construction & building unit cost and online websites of local and international suppliers. The analysis of the advantages and disadvantages of the method was conducted based on its, wastes, and cost. The simulation concluded the total cost of this operation is about Rp. 900,437,004,599 and the total duration of the tunneling operation is 653 days. The results of the simulation will be used for a recommendation to the contractor before the implementation of the already selected tunneling operation.
BWR-plant simulator and its neural network companion with programming under mat lab environment
International Nuclear Information System (INIS)
Ghenniwa, Fatma Suleiman
2008-01-01
Stand alone nuclear power plant simulators, as well as building blocks based nuclear power simulator are available from different companies throughout the world. In this work, a review of such simulators has been explored for both types. Also a survey of the possible authoring tools for such simulators development has been performed. It is decided, in this research, to develop prototype simulator based on components building blocks. Further more, the authoring tool (Mat lab software) has been selected for programming. It has all the basic tools required for the simulator development similar to that developed by specialized companies for simulator like MMS, APROS and others. Components simulations, as well as integrated components for power plant simulation have been demonstrated. Preliminary neural network reactor model as part of a prepared neural network modules library has been used to demonstrate module order shuffling during simulation. The developed components library can be refined and extended for further development. (author)
Interval sampling methods and measurement error: a computer simulation.
Wirth, Oliver; Slaven, James; Taylor, Matthew A
2014-01-01
A simulation study was conducted to provide a more thorough account of measurement error associated with interval sampling methods. A computer program simulated the application of momentary time sampling, partial-interval recording, and whole-interval recording methods on target events randomly distributed across an observation period. The simulation yielded measures of error for multiple combinations of observation period, interval duration, event duration, and cumulative event duration. The simulations were conducted up to 100 times to yield measures of error variability. Although the present simulation confirmed some previously reported characteristics of interval sampling methods, it also revealed many new findings that pertain to each method's inherent strengths and weaknesses. The analysis and resulting error tables can help guide the selection of the most appropriate sampling method for observation-based behavioral assessments. © Society for the Experimental Analysis of Behavior.
An algebraic topological method for multimodal brain networks comparison
Directory of Open Access Journals (Sweden)
Tiago eSimas
2015-07-01
Full Text Available Understanding brain connectivity is one of the most important issues in neuroscience. Nonetheless, connectivity data can reflect either functional relationships of brain activities or anatomical connections between brain areas. Although both representations should be related, this relationship is not straightforward. We have devised a powerful method that allows different operations between networks that share the same set of nodes, by embedding them in a common metric space, enforcing transitivity to the graph topology. Here, we apply this method to construct an aggregated network from a set of functional graphs, each one from a different subject. Once this aggregated functional network is constructed, we use again our method to compare it with the structural connectivity to identify particular brain regions that differ in both modalities (anatomical and functional. Remarkably, these brain regions include functional areas that form part of the classical resting state networks. We conclude that our method -based on the comparison of the aggregated functional network- reveals some emerging features that could not be observed when the comparison is performed with the classical averaged functional network.
Buttles, John W
2013-04-23
Wireless communication devices include a software-defined radio coupled to processing circuitry. The system controller is configured to execute computer programming code. Storage media is coupled to the system controller and includes computer programming code configured to cause the system controller to configure and reconfigure the software-defined radio to operate on each of a plurality of communication networks according to a selected sequence. Methods for communicating with a wireless device and methods of wireless network-hopping are also disclosed.
Combining neural networks and signed particles to simulate quantum systems more efficiently
Sellier, Jean Michel
2018-04-01
Recently a new formulation of quantum mechanics has been suggested which describes systems by means of ensembles of classical particles provided with a sign. This novel approach mainly consists of two steps: the computation of the Wigner kernel, a multi-dimensional function describing the effects of the potential over the system, and the field-less evolution of the particles which eventually create new signed particles in the process. Although this method has proved to be extremely advantageous in terms of computational resources - as a matter of fact it is able to simulate in a time-dependent fashion many-body systems on relatively small machines - the Wigner kernel can represent the bottleneck of simulations of certain systems. Moreover, storing the kernel can be another issue as the amount of memory needed is cursed by the dimensionality of the system. In this work, we introduce a new technique which drastically reduces the computation time and memory requirement to simulate time-dependent quantum systems which is based on the use of an appropriately tailored neural network combined with the signed particle formalism. In particular, the suggested neural network is able to compute efficiently and reliably the Wigner kernel without any training as its entire set of weights and biases is specified by analytical formulas. As a consequence, the amount of memory for quantum simulations radically drops since the kernel does not need to be stored anymore as it is now computed by the neural network itself, only on the cells of the (discretized) phase-space which are occupied by particles. As its is clearly shown in the final part of this paper, not only this novel approach drastically reduces the computational time, it also remains accurate. The author believes this work opens the way towards effective design of quantum devices, with incredible practical implications.
Decoherence and Entanglement Simulation in a Model of Quantum Neural Network Based on Quantum Dots
Directory of Open Access Journals (Sweden)
Altaisky Mikhail V.
2016-01-01
Full Text Available We present the results of the simulation of a quantum neural network based on quantum dots using numerical method of path integral calculation. In the proposed implementation of the quantum neural network using an array of single-electron quantum dots with dipole-dipole interaction, the coherence is shown to survive up to 0.1 nanosecond in time and up to the liquid nitrogen temperature of 77K.We study the quantum correlations between the quantum dots by means of calculation of the entanglement of formation in a pair of quantum dots on the GaAs based substrate with dot size of 100 ÷ 101 nanometer and interdot distance of 101 ÷ 102 nanometers order.
A Hybrid Communications Network Simulation-Independent Toolkit
National Research Council Canada - National Science Library
Dines, David M
2008-01-01
.... Evolving a grand design of the enabling network will require a flexible evaluation platform to try and select the right combination of network strategies and protocols in the realms of topology control and routing...
MULTI-SENSOR NETWORK FOR LANDSLIDES SIMULATION AND HAZARD MONITORING - DESIGN AND DEPLOYMENT
Directory of Open Access Journals (Sweden)
H. Wu
2012-08-01
Full Text Available This paper describes a newly developed multi-sensor network system for landslide and hazard monitoring. Landslide hazard is one of the most destructive natural disasters, which has severely affected human safety, properties and infrastructures. We report the results of designing and deploying the multi-sensor network, based on the simulated landslide model, to monitor typical landslide areas and with a goal to predict landslide hazard and mitigate damages. The integration and deployment of the prototype sensor network were carried out in an experiment area at Tongji University in Shanghai. In order to simulate a real landslide, a contractible landslide body is constructed in the experiment area by 7m*1.5m. Then, some different kind of sensors, such as camera, GPS, crackmeter, accelerometer, laser scanning system, inclinometer, etc., are installed near or in the landslide body. After the sensors are powered, continuous sampling data will be generated. With the help of communication method, such as GPRS, and certain transport devices, such as iMesh and 3G router, all the sensor data will be transported to the server and stored in Oracle. These are the current results of an ongoing project of the center. Further research results will be updated and presented in the near future.
Potential of commercial microwave link network derived rainfall for river runoff simulations
Smiatek, Gerhard; Keis, Felix; Chwala, Christian; Fersch, Benjamin; Kunstmann, Harald
2017-03-01
Commercial microwave link networks allow for the quantification of path integrated precipitation because the attenuation by hydrometeors correlates with rainfall between transmitter and receiver stations. The networks, operated and maintained by cellphone companies, thereby provide completely new and country wide precipitation measurements. As the density of traditional precipitation station networks worldwide is significantly decreasing, microwave link derived precipitation estimates receive increasing attention not only by hydrologists but also by meteorological and hydrological services. We investigate the potential of microwave derived precipitation estimates for streamflow prediction and water balance analyses, exemplarily shown for an orographically complex region in the German Alps (River Ammer). We investigate the additional value of link derived rainfall estimations combined with station observations compared to station and weather radar derived values. Our river runoff simulation system employs a distributed hydrological model at 100 × 100 m grid resolution. We analyze the potential of microwave link derived precipitation estimates for two episodes of 30 days with typically moderate river flow and an episode of extreme flooding. The simulation results indicate the potential of this novel precipitation monitoring method: a significant improvement in hydrograph reproduction has been achieved in the extreme flooding period that was characterized by a large number of local strong precipitation events. The present rainfall monitoring gauges alone were not able to correctly capture these events.
'BioNessie(G) - a grid enabled biochemical networks simulation environment
Liu, X; Jiang, J; Ajayi, O; Gu, X; Gilbert, D; Sinnott, R
2008-01-01
The simulation of biochemical networks provides insight and understanding about the underlying biochemical processes and pathways used by cells and organisms. BioNessie is a biochemical network simulator which has been developed at the University of Glasgow. This paper describes the simulator and focuses in particular on how it has been extended to benefit from a wide variety of high performance compute resources across the UK through Grid technologies to support larger scal...
The frontal method in hydrodynamics simulations
Walters, R.A.
1980-01-01
The frontal solution method has proven to be an effective means of solving the matrix equations resulting from the application of the finite element method to a variety of problems. In this study, several versions of the frontal method were compared in efficiency for several hydrodynamics problems. Three basic modifications were shown to be of value: 1. Elimination of equations with boundary conditions beforehand, 2. Modification of the pivoting procedures to allow dynamic management of the equation size, and 3. Storage of the eliminated equations in a vector. These modifications are sufficiently general to be applied to other classes of problems. ?? 1980.
A new method to construct co-author networks
Liu, Jie; Li, Yunpeng; Ruan, Zichan; Fu, Guangyuan; Chen, Xiaowu; Sadiq, Rehan; Deng, Yong
2015-02-01
In this paper, we propose a new method to evaluate the importance of nodes in a given network. The proposed method is based on the PageRank algorithm. However, we have made necessary improvements to combine the importance of the node itself and that of its community status. First, we propose an improved method to better evaluate the real impact of a paper. The proposed method calibrates the real influence of a paper over time. Then we propose a scheme of evaluating the contribution of each author in a paper. We later develop a new method to combine the information of the author itself and the structure of the co-author network. We use the number of co-authorship to calculate the effective distance between two authors, and evaluate the strength of their influence to each other with the law of gravity. The strength of influence is used to build a new network of authors, which is a comprehensive topological representation of both the quality of the node and its role in network. Finally, we apply our method to the Erdos co-author community and AMiner Citation Network to identify the most influential authors.
A DDoS Attack Detection Method Based on SVM in Software Defined Network
Directory of Open Access Journals (Sweden)
Jin Ye
2018-01-01
Full Text Available The detection of DDoS attacks is an important topic in the field of network security. The occurrence of software defined network (SDN (Zhang et al., 2018 brings up some novel methods to this topic in which some deep learning algorithm is adopted to model the attack behavior based on collecting from the SDN controller. However, the existing methods such as neural network algorithm are not practical enough to be applied. In this paper, the SDN environment by mininet and floodlight (Ning et al., 2014 simulation platform is constructed, 6-tuple characteristic values of the switch flow table is extracted, and then DDoS attack model is built by combining the SVM classification algorithms. The experiments show that average accuracy rate of our method is 95.24% with a small amount of flow collecting. Our work is of good value for the detection of DDoS attack in SDN.
Method and Apparatus for Predicting Unsteady Pressure and Flow Rate Distribution in a Fluid Network
Majumdar, Alok K. (Inventor)
2009-01-01
A method and apparatus for analyzing steady state and transient flow in a complex fluid network, modeling phase changes, compressibility, mixture thermodynamics, external body forces such as gravity and centrifugal force and conjugate heat transfer. In some embodiments, a graphical user interface provides for the interactive development of a fluid network simulation having nodes and branches. In some embodiments, mass, energy, and specific conservation equations are solved at the nodes, and momentum conservation equations are solved in the branches. In some embodiments, contained herein are data objects for computing thermodynamic and thermophysical properties for fluids. In some embodiments, the systems of equations describing the fluid network are solved by a hybrid numerical method that is a combination of the Newton-Raphson and successive substitution methods.
Handwritten Javanese Character Recognition Using Several Artificial Neural Network Methods
Directory of Open Access Journals (Sweden)
Gregorius Satia Budhi
2015-07-01
Full Text Available Javanese characters are traditional characters that are used to write the Javanese language. The Javanese language is a language used by many people on the island of Java, Indonesia. The use of Javanese characters is diminishing more and more because of the difficulty of studying the Javanese characters themselves. The Javanese character set consists of basic characters, numbers, complementary characters, and so on. In this research we have developed a system to recognize Javanese characters. Input for the system is a digital image containing several handwritten Javanese characters. Preprocessing and segmentation are performed on the input image to get each character. For each character, feature extraction is done using the ICZ-ZCZ method. The output from feature extraction will become input for an artificial neural network. We used several artificial neural networks, namely a bidirectional associative memory network, a counterpropagation network, an evolutionary network, a backpropagation network, and a backpropagation network combined with chi2. From the experimental results it can be seen that the combination of chi2 and backpropagation achieved better recognition accuracy than the other methods.
The dynamical modeling and simulation analysis of the recommendation on the user-movie network
Zhang, Shujuan; Jin, Zhen; Zhang, Juan
2016-12-01
At present, most research about the recommender system is based on graph theory and algebraic methods, but these methods cannot predict the evolution of the system with time under the recommendation method, and cannot dynamically analyze the long-term utility of the recommendation method. However, these two aspects can be studied by the dynamical method, which essentially investigates the intrinsic evolution mechanism of things, and is widely used to study a variety of actual problems. So, in this paper, network dynamics is used to study the recommendation on the user-movie network, which consists of users and movies, and the movies are watched either by the personal search or through the recommendation. Firstly, dynamical models are established to characterize the personal search and the system recommendation mechanism: the personal search model, the random recommendation model, the preference recommendation model, the degree recommendation model and the hybrid recommendation model. The rationality of the models established is verified by comparing the stochastic simulation with the numerical simulation. Moreover, the validity of the recommendation methods is evaluated by studying the movie degree, which is defined as the number of the movie that has been watched. Finally, we combine the personal search and the recommendation to establish a more general model. The change of the average degree of all the movies is given with the strength of the recommendation. Results show that for each recommendation method, the change of the movie degree is different, and is related to the initial degree of movies, the adjacency matrix A representing the relation between users and movies, the time t. Additionally, we find that in a long time, the degree recommendation is not as good as that in a short time, which fully demonstrates the advantage of the dynamical method. For the whole user-movie system, the preference recommendation is the best.
Modeling and Simulation of Handover Scheme in Integrated EPON-WiMAX Networks
DEFF Research Database (Denmark)
Yan, Ying; Dittmann, Lars
2011-01-01
In this paper, we tackle the seamless handover problem in integrated optical wireless networks. Our model applies for the convergence network of EPON and WiMAX and a mobilityaware signaling protocol is proposed. The proposed handover scheme, Integrated Mobility Management Scheme (IMMS), is assisted...... by enhancing the traditional MPCP signaling protocol, which cooperatively collects mobility information from the front-end wireless network and makes centralized bandwidth allocation decisions in the backhaul optical network. The integrated network architecture and the joint handover scheme are simulated using...... OPNET modeler. Results show validation of the protocol, i.e., integrated handover scheme gains better network performances....
Multiple time-scale methods in particle simulations of plasmas
International Nuclear Information System (INIS)
Cohen, B.I.
1985-01-01
This paper surveys recent advances in the application of multiple time-scale methods to particle simulation of collective phenomena in plasmas. These methods dramatically improve the efficiency of simulating low-frequency kinetic behavior by allowing the use of a large timestep, while retaining accuracy. The numerical schemes surveyed provide selective damping of unwanted high-frequency waves and preserve numerical stability in a variety of physics models: electrostatic, magneto-inductive, Darwin and fully electromagnetic. The paper reviews hybrid simulation models, the implicitmoment-equation method, the direct implicit method, orbit averaging, and subcycling
Directory of Open Access Journals (Sweden)
Shan Yang
2016-01-01
Full Text Available Power flow calculation and short circuit calculation are the basis of theoretical research for distribution network with inverter based distributed generation. The similarity of equivalent model for inverter based distributed generation during normal and fault conditions of distribution network and the differences between power flow and short circuit calculation are analyzed in this paper. Then an integrated power flow and short circuit calculation method for distribution network with inverter based distributed generation is proposed. The proposed method let the inverter based distributed generation be equivalent to Iθ bus, which makes it suitable to calculate the power flow of distribution network with a current limited inverter based distributed generation. And the low voltage ride through capability of inverter based distributed generation can be considered as well in this paper. Finally, some tests of power flow and short circuit current calculation are performed on a 33-bus distribution network. The calculated results from the proposed method in this paper are contrasted with those by the traditional method and the simulation method, whose results have verified the effectiveness of the integrated method suggested in this paper.
An Entropy-Based Network Anomaly Detection Method
Directory of Open Access Journals (Sweden)
Przemysław Bereziński
2015-04-01
Full Text Available Data mining is an interdisciplinary subfield of computer science involving methods at the intersection of artificial intelligence, machine learning and statistics. One of the data mining tasks is anomaly detection which is the analysis of large quantities of data to identify items, events or observations which do not conform to an expected pattern. Anomaly detection is applicable in a variety of domains, e.g., fraud detection, fault detection, system health monitoring but this article focuses on application of anomaly detection in the field of network intrusion detection.The main goal of the article is to prove that an entropy-based approach is suitable to detect modern botnet-like malware based on anomalous patterns in network. This aim is achieved by realization of the following points: (i preparation of a concept of original entropy-based network anomaly detection method, (ii implementation of the method, (iii preparation of original dataset, (iv evaluation of the method.
Dynamic Subsidy Method for Congestion Management in Distribution Networks
DEFF Research Database (Denmark)
Huang, Shaojun; Wu, Qiuwei
2016-01-01
Dynamic subsidy (DS) is a locational price paid by the distribution system operator (DSO) to its customers in order to shift energy consumption to designated hours and nodes. It is promising for demand side management and congestion management. This paper proposes a new DS method for congestion...... management in distribution networks, including the market mechanism, the mathematical formulation through a two-level optimization, and the method solving the optimization by tightening the constraints and linearization. Case studies were conducted with a one node system and the Bus 4 distribution network...... of the Roy Billinton Test System (RBTS) with high penetration of electric vehicles (EVs) and heat pumps (HPs). The case studies demonstrate the efficacy of the DS method for congestion management in distribution networks. Studies in this paper show that the DS method offers the customers a fair opportunity...
Decomposition method for analysis of closed queuing networks
Directory of Open Access Journals (Sweden)
Yu. G. Nesterov
2014-01-01
Full Text Available This article deals with the method to estimate the average residence time in nodes of closed queuing networks with priorities and a wide range of conservative disciplines to be served. The method is based on a decomposition of entire closed queuing network into a set of simple basic queuing systems such as M|GI|m|N for each node. The unknown average residence times in the network nodes are interrelated through a system of nonlinear equations. The fact that there is a solution of this system has been proved. An iterative procedure based on Newton-Kantorovich method is proposed for finding the solution of such system. This procedure provides fast convergence to solution. Today possibilities of proposed method are limited by known analytical solutions for simple basic queuing systems of M|GI|m|N type.
Shen, Lin; Yang, Weitao
2018-03-13
Direct molecular dynamics (MD) simulation with ab initio quantum mechanical and molecular mechanical (QM/MM) methods is very powerful for studying the mechanism of chemical reactions in a complex environment but also very time-consuming. The computational cost of QM/MM calculations during MD simulations can be reduced significantly using semiempirical QM/MM methods with lower accuracy. To achieve higher accuracy at the ab initio QM/MM level, a correction on the existing semiempirical QM/MM model is an attractive idea. Recently, we reported a neural network (NN) method as QM/MM-NN to predict the potential energy difference between semiempirical and ab initio QM/MM approaches. The high-level results can be obtained using neural network based on semiempirical QM/MM MD simulations, but the lack of direct MD samplings at the ab initio QM/MM level is still a deficiency that limits the applications of QM/MM-NN. In the present paper, we developed a dynamic scheme of QM/MM-NN for direct MD simulations on the NN-predicted potential energy surface to approximate ab initio QM/MM MD. Since some configurations excluded from the database for NN training were encountered during simulations, which may cause some difficulties on MD samplings, an adaptive procedure inspired by the selection scheme reported by Behler [ Behler Int. J. Quantum Chem. 2015 , 115 , 1032 ; Behler Angew. Chem., Int. Ed. 2017 , 56 , 12828 ] was employed with some adaptions to update NN and carry out MD iteratively. We further applied the adaptive QM/MM-NN MD method to the free energy calculation and transition path optimization on chemical reactions in water. The results at the ab initio QM/MM level can be well reproduced using this method after 2-4 iteration cycles. The saving in computational cost is about 2 orders of magnitude. It demonstrates that the QM/MM-NN with direct MD simulations has great potentials not only for the calculation of thermodynamic properties but also for the characterization of
Uncertainty assessment in geodetic network adjustment by combining GUM and Monte-Carlo-simulations
Niemeier, Wolfgang; Tengen, Dieter
2017-06-01
In this article first ideas are presented to extend the classical concept of geodetic network adjustment by introducing a new method for uncertainty assessment as two-step analysis. In the first step the raw data and possible influencing factors are analyzed using uncertainty modeling according to GUM (Guidelines to the Expression of Uncertainty in Measurements). This approach is well established in metrology, but rarely adapted within Geodesy. The second step consists of Monte-Carlo-Simulations (MC-simulations) for the complete processing chain from raw input data and pre-processing to adjustment computations and quality assessment. To perform these simulations, possible realizations of raw data and the influencing factors are generated, using probability distributions for all variables and the established concept of pseudo-random number generators. Final result is a point cloud which represents the uncertainty of the estimated coordinates; a confidence region can be assigned to these point clouds, as well. This concept may replace the common concept of variance propagation and the quality assessment of adjustment parameters by using their covariance matrix. It allows a new way for uncertainty assessment in accordance with the GUM concept for uncertainty modelling and propagation. As practical example the local tie network in "Metsähovi Fundamental Station", Finland is used, where classical geodetic observations are combined with GNSS data.
Network 'small-world-ness': a quantitative method for determining canonical network equivalence.
Directory of Open Access Journals (Sweden)
Mark D Humphries
Full Text Available BACKGROUND: Many technological, biological, social, and information networks fall into the broad class of 'small-world' networks: they have tightly interconnected clusters of nodes, and a shortest mean path length that is similar to a matched random graph (same number of nodes and edges. This semi-quantitative definition leads to a categorical distinction ('small/not-small' rather than a quantitative, continuous grading of networks, and can lead to uncertainty about a network's small-world status. Moreover, systems described by small-world networks are often studied using an equivalent canonical network model--the Watts-Strogatz (WS model. However, the process of establishing an equivalent WS model is imprecise and there is a pressing need to discover ways in which this equivalence may be quantified. METHODOLOGY/PRINCIPAL FINDINGS: We defined a precise measure of 'small-world-ness' S based on the trade off between high local clustering and short path length. A network is now deemed a 'small-world' if S>1--an assertion which may be tested statistically. We then examined the behavior of S on a large data-set of real-world systems. We found that all these systems were linked by a linear relationship between their S values and the network size n. Moreover, we show a method for assigning a unique Watts-Strogatz (WS model to any real-world network, and show analytically that the WS models associated with our sample of networks also show linearity between S and n. Linearity between S and n is not, however, inevitable, and neither is S maximal for an arbitrary network of given size. Linearity may, however, be explained by a common limiting growth process. CONCLUSIONS/SIGNIFICANCE: We have shown how the notion of a small-world network may be quantified. Several key properties of the metric are described and the use of WS canonical models is placed on a more secure footing.
Explicit integration of extremely stiff reaction networks: partial equilibrium methods
International Nuclear Information System (INIS)
Guidry, M W; Hix, W R; Billings, J J
2013-01-01
In two preceding papers (Guidry et al 2013 Comput. Sci. Disc. 6 015001 and Guidry and Harris 2013 Comput. Sci. Disc. 6 015002), we have shown that when reaction networks are well removed from equilibrium, explicit asymptotic and quasi-steady-state approximations can give algebraically stabilized integration schemes that rival standard implicit methods in accuracy and speed for extremely stiff systems. However, we also showed that these explicit methods remain accurate but are no longer competitive in speed as the network approaches equilibrium. In this paper, we analyze this failure and show that it is associated with the presence of fast equilibration timescales that neither asymptotic nor quasi-steady-state approximations are able to remove efficiently from the numerical integration. Based on this understanding, we develop a partial equilibrium method to deal effectively with the approach to equilibrium and show that explicit asymptotic methods, combined with the new partial equilibrium methods, give an integration scheme that can plausibly deal with the stiffest networks, even in the approach to equilibrium, with accuracy and speed competitive with that of implicit methods. Thus we demonstrate that such explicit methods may offer alternatives to implicit integration of even extremely stiff systems and that these methods may permit integration of much larger networks than have been possible before in a number of fields. (paper)
A Global Network Alignment Method Using Discrete Particle Swarm Optimization.
Huang, Jiaxiang; Gong, Maoguo; Ma, Lijia
2016-10-19
Molecular interactions data increase exponentially with the advance of biotechnology. This makes it possible and necessary to comparatively analyse the different data at a network level. Global network alignment is an important network comparison approach to identify conserved subnetworks and get insight into evolutionary relationship across species. Network alignment which is analogous to subgraph isomorphism is known to be an NP-hard problem. In this paper, we introduce a novel heuristic Particle-Swarm-Optimization based Network Aligner (PSONA), which optimizes a weighted global alignment model considering both protein sequence similarity and interaction conservations. The particle statuses and status updating rules are redefined in a discrete form by using permutation. A seed-and-extend strategy is employed to guide the searching for the superior alignment. The proposed initialization method "seeds" matches with high sequence similarity into the alignment, which guarantees the functional coherence of the mapping nodes. A greedy local search method is designed as the "extension" procedure to iteratively optimize the edge conservations. PSONA is compared with several state-of-art methods on ten network pairs combined by five species. The experimental results demonstrate that the proposed aligner can map the proteins with high functional coherence and can be used as a booster to effectively refine the well-studied aligners.
Natural tracer test simulation by stochastic particle tracking method
International Nuclear Information System (INIS)
Ackerer, P.; Mose, R.; Semra, K.
1990-01-01
Stochastic particle tracking methods are well adapted to 3D transport simulations where discretization requirements of other methods usually cannot be satisfied. They do need a very accurate approximation of the velocity field. The described code is based on the mixed hybrid finite element method (MHFEM) to calculated the piezometric and velocity field. The random-walk method is used to simulate mass transport. The main advantages of the MHFEM over FD or FE are the simultaneous calculation of pressure and velocity, which are considered as unknowns; the possibility of interpolating velocities everywhere; and the continuity of the normal component of the velocity vector from one element to another. For these reasons, the MHFEM is well adapted for particle tracking methods. After a general description of the numerical methods, the model is used to simulate the observations made during the Twin Lake Tracer Test in 1983. A good match is found between observed and simulated heads and concentrations. (Author) (12 refs., 4 figs.)
Directory of Open Access Journals (Sweden)
Ondrej Vondrous
2017-01-01
Full Text Available In this article, we would like to deal with challenges and analysis approaches in the area of narrow band communication networks. Especially those networks which use TCP/IP protocol family. We also present a new universal data generator for OMNeT++ simulation environment. We created this generator to satisfy the evaluation, stress testing and benchmarking demands of more and more complex industrial and the Internet of Things networks. We also present the methods for evaluation and comparison of results obtained from simulated and real TCP/IP based networks in this article.
International Nuclear Information System (INIS)
Hamilton, S.; Veselka, T.D.; Cirillo, R.R.
1991-01-01
Global warming control strategies which mandate stringent caps on emissions of greenhouse forcing gases can substantially alter a country's demand, production, and imports of energy products. Although there is a large degree of uncertainty when attempting to estimate the potential impact of these strategies, insights into the problem can be acquired through computer model simulations. This paper presents one method of structuring a general equilibrium model, the ENergy and Power Evaluation Program/Global Climate Change (ENPEP/GCC), to simulate changes in a country's energy supply and demand balance in response to global warming control strategies. The equilibrium model presented in this study is based on the principle of decomposition, whereby a large complex problem is divided into a number of smaller submodules. Submodules simulate energy activities and conversion processes such as electricity production. These submodules are linked together to form an energy supply and demand network. Linkages identify energy and fuel flows among various activities. Since global warming control strategies can have wide reaching effects, a complex network was constructed. The network represents all energy production, conversion, transportation, distribution, and utilization activities. The structure of the network depicts interdependencies within and across economic sectors and was constructed such that energy prices and demand responses can be simulated. Global warming control alternatives represented in the network include: (1) conservation measures through increased efficiency; and (2) substitution of fuels that have high greenhouse gas emission rates with fuels that have lower emission rates. 6 refs., 4 figs., 4 tabs
A semi-automatic method for extracting thin line structures in images as rooted tree network
Energy Technology Data Exchange (ETDEWEB)
Brazzini, Jacopo [Los Alamos National Laboratory; Dillard, Scott [Los Alamos National Laboratory; Soille, Pierre [EC - JRC
2010-01-01
This paper addresses the problem of semi-automatic extraction of line networks in digital images - e.g., road or hydrographic networks in satellite images, blood vessels in medical images, robust. For that purpose, we improve a generic method derived from morphological and hydrological concepts and consisting in minimum cost path estimation and flow simulation. While this approach fully exploits the local contrast and shape of the network, as well as its arborescent nature, we further incorporate local directional information about the structures in the image. Namely, an appropriate anisotropic metric is designed by using both the characteristic features of the target network and the eigen-decomposition of the gradient structure tensor of the image. Following, the geodesic propagation from a given seed with this metric is combined with hydrological operators for overland flow simulation to extract the line network. The algorithm is demonstrated for the extraction of blood vessels in a retina image and of a river network in a satellite image.
Experimental Evaluation of Simulation Abstractions for Wireless Sensor Network MAC Protocols
Halkes, G.P.; Langendoen, K.G.
2010-01-01
The evaluation ofMAC protocols forWireless Sensor Networks (WSNs) is often performed through simulation. These simulations necessarily abstract away from reality inmany ways. However, the impact of these abstractions on the results of the simulations has received only limited attention. Moreover,
How Crime Spreads Through Imitation in Social Networks: A Simulation Model
Punzo, Valentina
In this chapter an agent-based model for investigating how crime spreads through social networks is presented. Some theoretical issues related to the sociological explanation of crime are tested through simulation. The agent-based simulation allows us to investigate the relative impact of some mechanisms of social influence on crime, within a set of controlled simulated experiments.
Limits to high-speed simulations of spiking neural networks using general-purpose computers.
Zenke, Friedemann; Gerstner, Wulfram
2014-01-01
To understand how the central nervous system performs computations using recurrent neuronal circuitry, simulations have become an indispensable tool for theoretical neuroscience. To study neuronal circuits and their ability to self-organize, increasing attention has been directed toward synaptic plasticity. In particular spike-timing-dependent plasticity (STDP) creates specific demands for simulations of spiking neural networks. On the one hand a high temporal resolution is required to capture the millisecond timescale of typical STDP windows. On the other hand network simulations have to evolve over hours up to days, to capture the timescale of long-term plasticity. To do this efficiently, fast simulation speed is the crucial ingredient rather than large neuron numbers. Using different medium-sized network models consisting of several thousands of neurons and off-the-shelf hardware, we compare the simulation speed of the simulators: Brian, NEST and Neuron as well as our own simulator Auryn. Our results show that real-time simulations of different plastic network models are possible in parallel simulations in which numerical precision is not a primary concern. Even so, the speed-up margin of parallelism is limited and boosting simulation speeds beyond one tenth of real-time is difficult. By profiling simulation code we show that the run times of typical plastic network simulations encounter a hard boundary. This limit is partly due to latencies in the inter-process communications and thus cannot be overcome by increased parallelism. Overall, these results show that to study plasticity in medium-sized spiking neural networks, adequate simulation tools are readily available which run efficiently on small clusters. However, to run simulations substantially faster than real-time, special hardware is a prerequisite.
Factorization method for simulating QCD at finite density
International Nuclear Information System (INIS)
Nishimura, Jun
2003-01-01
We propose a new method for simulating QCD at finite density. The method is based on a general factorization property of distribution functions of observables, and it is therefore applicable to any system with a complex action. The so-called overlap problem is completely eliminated by the use of constrained simulations. We test this method in a Random Matrix Theory for finite density QCD, where we are able to reproduce the exact results for the quark number density. (author)
Hybrid Network Simulation for the ATLAS Trigger and Data Acquisition (TDAQ) System
Bonaventura, Matias Alejandro; The ATLAS collaboration; Castro, Rodrigo Daniel; Foguelman, Daniel Jacob
2015-01-01
The poster shows the ongoing research in the ATLAS TDAQ group in collaboration with the University of Buenos Aires in the area of hybrid data network simulations. he Data Network and Processing Cluster filters data in real-time, achieving a rejection factor in the order of 40000x and has real-time latency constrains. The dataflow between the processing units (TPUs) and Readout System (ROS) presents a “TCP Incast”-type network pathology which TCP cannot handle it efficiently. A credits system is in place which limits rate of queries and reduces latency. This large computer network, and the complex dataflow has been modelled and simulated using a PowerDEVS, a DEVS-based simulator. The simulation has been validated and used to produce what-if scenarios in the real network. Network Simulation with Hybrid Flows: Speedups and accuracy, combined • For intensive network traffic, Discrete Event simulation models (packet-level granularity) soon becomes prohibitive: Too high computing demands. • Fluid Flow simul...
A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran
International Nuclear Information System (INIS)
Azadeh, A.; Ghaderi, S.F.; Sohrabkhani, S.
2008-01-01
This study presents an integrated algorithm for forecasting monthly electrical energy consumption based on artificial neural network (ANN), computer simulation and design of experiments using stochastic procedures. First, an ANN approach is illustrated based on supervised multi-layer perceptron (MLP) network for the electrical consumption forecasting. The chosen model, therefore, can be compared to that of estimated by time series model. Computer simulation is developed to generate random variables for monthly electricity consumption. This is achieved to foresee the effects of probabilistic distribution on monthly electricity consumption. The simulated-based ANN model is then developed. Therefore, there are four treatments to be considered in analysis of variance (ANOVA), which are actual data, time series, ANN and simulated-based ANN. Furthermore, ANOVA is used to test the null hypothesis of the above four alternatives being statistically equal. If the null hypothesis is accepted, then the lowest mean absolute percentage error (MAPE) value is used to select the best model, otherwise the Duncan method (DMRT) of paired comparison is used to select the optimum model which could be time series, ANN or simulated-based ANN. In case of ties the lowest MAPE value is considered as the benchmark. The integrated algorithm has several unique features. First, it is flexible and identifies the best model based on the results of ANOVA and MAPE, whereas previous studies consider the best fitted ANN model based on MAPE or relative error results. Second, the proposed algorithm may identify conventional time series as the best model for future electricity consumption forecasting because of its dynamic structure, whereas previous studies assume that ANN always provide the best solutions and estimation. To show the applicability and superiority of the proposed algorithm, the monthly electricity consumption in Iran from March 1994 to February 2005 (131 months) is used and applied to
Evaluation of full-scope simulator testing methods
Energy Technology Data Exchange (ETDEWEB)
Feher, M P; Moray, N; Senders, J W; Biron, K [Human Factors North Inc., Toronto, ON (Canada)
1995-03-01
This report discusses the use of full scope nuclear power plant simulators in licensing examinations for Unit First Operators of CANDU reactors. The existing literature is reviewed, and an annotated bibliography of the more important sources provided. Since existing methods are judged inadequate, conceptual bases for designing a system for licensing are discussed, and a method proposed which would make use of objective scoring methods based on data collection in full-scope simulators. A field trial of such a method is described. The practicality of such a method is critically discussed and possible advantages of subjective methods of evaluation considered. (author). 32 refs., 1 tab., 4 figs.
Evaluation of full-scope simulator testing methods
International Nuclear Information System (INIS)
Feher, M.P.; Moray, N.; Senders, J.W.; Biron, K.
1995-03-01
This report discusses the use of full scope nuclear power plant simulators in licensing examinations for Unit First Operators of CANDU reactors. The existing literature is reviewed, and an annotated bibliography of the more important sources provided. Since existing methods are judged inadequate, conceptual bases for designing a system for licensing are discussed, and a method proposed which would make use of objective scoring methods based on data collection in full-scope simulators. A field trial of such a method is described. The practicality of such a method is critically discussed and possible advantages of subjective methods of evaluation considered. (author). 32 refs., 1 tab., 4 figs
Benjamin, Norman M.; Gill, Tepper; Charles, Mary
1994-01-01
The network control center (NCC) provides scheduling, monitoring, and control of services to the NASA space network. The space network provides tracking and data acquisition services to many low-earth orbiting spacecraft. This report describes the second phase in the development of simulation models for the FCC. Phase one concentrated on the computer systems and interconnecting network.Phase two focuses on the implementation of the network message dialogs and the resources controlled by the NCC. Performance measures were developed along with selected indicators of the NCC's operational effectiveness.The NCC performance indicators were defined in terms of the following: (1) transfer rate, (2) network delay, (3) channel establishment time, (4) line turn around time, (5) availability, (6) reliability, (7) accuracy, (8) maintainability, and (9) security. An NCC internal and external message manual is appended to this report.
Constraint methods that accelerate free-energy simulations of biomolecules.
Perez, Alberto; MacCallum, Justin L; Coutsias, Evangelos A; Dill, Ken A
2015-12-28
Atomistic molecular dynamics simulations of biomolecules are critical for generating narratives about biological mechanisms. The power of atomistic simulations is that these are physics-based methods that satisfy Boltzmann's law, so they can be used to compute populations, dynamics, and mechanisms. But physical simulations are computationally intensive and do not scale well to the sizes of many important biomolecules. One way to speed up physical simulations is by coarse-graining the potential function. Another way is to harness structural knowledge, often by imposing spring-like restraints. But harnessing external knowledge in physical simulations is problematic because knowledge, data, or hunches have errors, noise, and combinatoric uncertainties. Here, we review recent principled methods for imposing restraints to speed up physics-based molecular simulations that promise to scale to larger biomolecules and motions.
PAFit: A Statistical Method for Measuring Preferential Attachment in Temporal Complex Networks.
Directory of Open Access Journals (Sweden)
Thong Pham
Full Text Available Preferential attachment is a stochastic process that has been proposed to explain certain topological features characteristic of complex networks from diverse domains. The systematic investigation of preferential attachment is an important area of research in network science, not only for the theoretical matter of verifying whether this hypothesized process is operative in real-world networks, but also for the practical insights that follow from knowledge of its functional form. Here we describe a maximum likelihood based estimation method for the measurement of preferential attachment in temporal complex networks. We call the method PAFit, and implement it in an R package of the same name. PAFit constitutes an advance over previous methods primarily because we based it on a nonparametric statistical framework that enables attachment kernel estimation free of any assumptions about its functional form. We show this results in PAFit outperforming the popular methods of Jeong and Newman in Monte Carlo simulations. What is more, we found that the application of PAFit to a publically available Flickr social network dataset yielded clear evidence for a deviation of the attachment kernel from the popularly assumed log-linear form. Independent of our main work, we provide a correction to a consequential error in Newman's original method which had evidently gone unnoticed since its publication over a decade ago.
Integration of Continuous-Time Dynamics in a Spiking Neural Network Simulator
Directory of Open Access Journals (Sweden)
Jan Hahne
2017-05-01
Full Text Available Contemporary modeling approaches to the dynamics of neural networks include two important classes of models: biologically grounded spiking neuron models and functionally inspired rate-based units. We present a unified simulation framework that supports the combination of the two for multi-scale modeling, enables the quantitative validation of mean-field approaches by spiking network simulations, and provides an increase in reliability by usage of the same simulation code and the same network model specifications for both model classes. While most spiking simulations rely on the communication of discrete events, rate models require time-continuous interactions between neurons. Exploiting the conceptual similarity to the inclusion of gap junctions in spiking network simulations, we arrive at a reference implementation of instantaneous and delayed interactions between rate-based models in a spiking network simulator. The separation of rate dynamics from the general connection and communication infrastructure ensures flexibility of the framework. In addition to the standard implementation we present an iterative approach based on waveform-relaxation techniques to reduce communication and increase performance for large-scale simulations of rate-based models with instantaneous interactions. Finally we demonstrate the broad applicability of the framework by considering various examples from the literature, ranging from random networks to neural-field models. The study provides the prerequisite for interactions between rate-based and spiking models in a joint simulation.
Integration of Continuous-Time Dynamics in a Spiking Neural Network Simulator.
Hahne, Jan; Dahmen, David; Schuecker, Jannis; Frommer, Andreas; Bolten, Matthias; Helias, Moritz; Diesmann, Markus
2017-01-01
Contemporary modeling approaches to the dynamics of neural networks include two important classes of models: biologically grounded spiking neuron models and functionally inspired rate-based units. We present a unified simulation framework that supports the combination of the two for multi-scale modeling, enables the quantitative validation of mean-field approaches by spiking network simulations, and provides an increase in reliability by usage of the same simulation code and the same network model specifications for both model classes. While most spiking simulations rely on the communication of discrete events, rate models require time-continuous interactions between neurons. Exploiting the conceptual similarity to the inclusion of gap junctions in spiking network simulations, we arrive at a reference implementation of instantaneous and delayed interactions between rate-based models in a spiking network simulator. The separation of rate dynamics from the general connection and communication infrastructure ensures flexibility of the framework. In addition to the standard implementation we present an iterative approach based on waveform-relaxation techniques to reduce communication and increase performance for large-scale simulations of rate-based models with instantaneous interactions. Finally we demonstrate the broad applicability of the framework by considering various examples from the literature, ranging from random networks to neural-field models. The study provides the prerequisite for interactions between rate-based and spiking models in a joint simulation.
New Method for Leakage Detection by Using Artificial Neural Networks
Directory of Open Access Journals (Sweden)
Mohammad Attari
2018-03-01
Full Text Available Nowadays water loss has been turned into a global concern and on the other hand the demand for water is increasing. This problem has made the demand management and consumption pattern reform necessary. One of the most important methods for managing water consumption is to decrease the water loss. In this study by using neural networks, a new method is presented to specify the location and quantity of leakages in water distribution networks. In this method, by producing the training data and applying it to neural network, the network is able to determine approximate location and quantity of nodal leakage with receiving the nodal pressure. Production of training data is carried out by applying assumed leakage to specific nodes in the network and calculating the new nodal pressures. The results show that by minimum use of hydraulic data taken from pressures, not only this method can determine the location of nodal leakages, but also it can specify the amount of leakage on each node with reasonable accuracy.
New method of fast simulation for a hadron calorimeter response
International Nuclear Information System (INIS)
Kul'chitskij, Yu.; Sutiak, J.; Tokar, S.; Zenis, T.
2003-01-01
In this work we present the new method of a fast Monte-Carlo simulation of a hadron calorimeter response. It is based on the three-dimensional parameterization of the hadronic shower obtained from the ATLAS TILECAL test beam data and GEANT simulations. A new approach of including the longitudinal fluctuations of hadronic shower is described. The obtained results of the fast simulation are in good agreement with the TILECAL experimental data
Energy Technology Data Exchange (ETDEWEB)
Laurent, R.; Henriet, J.; Sauget, M.; Gschwind, R.; Makovicka, L. [IRMA/ENISYS/FEMTO-ST, UMR 6174 CNRS, pole universitaire des Portes du Jura, BP 71427, 25211 Montbeliard cedex (France); Salomon, M. [AND/LIFC, universite de Franche-Comte, BP 527, rue Engel-Gros, 90016 Belfort cedex (France); Nguyen, F. [Service de radiotherapie, CHU Jean-Minjoz, 3, boulevard Fleming, 25030 Besancon cedex (France)
2011-04-15
Purpose. A way to improve the accuracy of lung radiotherapy for a patient is to get a better understanding of its lung motion. Indeed, thanks to this knowledge it becomes possible to follow the displacements of the clinical target volume (CTV) induced by the lung breathing. This paper presents a feasibility study of an original method to simulate the positions of points in patient's lung at all breathing phases. Patients and methods. This method, based on an artificial neural network, allowed learning the lung motion on real cases and then to simulate it for new patients for which only the beginning and the end breathing data are known. The neural network learning set is made up of more than 600 points. These points, shared out on three patients and gathered on a specific lung area, were plotted by a MD. Results. - The first results are promising: an average accuracy of 1 mm is obtained for a spatial resolution of 1 x 1 x 2.5 mm{sup 3}. Conclusion. We have demonstrated that it is possible to simulate lung motion with accuracy using an artificial neural network. As future work we plan to improve the accuracy of our method with the addition of new patient data and a coverage of the whole lungs. (authors)
Safranski, David L.; Crabtree, Jacob C.; Huq, Yameen R.; Gall, Ken
2011-01-01
Poly(β-amino ester) networks are being explored for biomedical applications, but they may lack the mechanical properties necessary for long term implantation. The objective of this study is to evaluate the effect of adding methyl methacrylate on networks' mechanical properties under simulated physiological conditions. The networks were synthesized in two parts: (1) a biodegradable crosslinker was formed from a diacrylate and amine, (2) and then varying concentrations of methyl methacrylate were added prior to photopolymerizing the network. Degradation rate, mechanical properties, and glass transition temperature were studied as a function of methyl methacrylate composition. The crosslinking density played a limited role on mechanical properties for these networks, but increasing methyl methacrylate concentration improved the toughness by several orders of magnitude. Under simulated physiological conditions, networks showed increasing toughness or sustained toughness as degradation occurred. This work establishes a method of creating degradable networks with tailorable toughness while undergoing partial degradation. PMID:21966028
Modeling and Simulation of DC Power Electronics Systems Using Harmonic State Space (HSS) Method
DEFF Research Database (Denmark)
Kwon, Jun Bum; Wang, Xiongfei; Bak, Claus Leth
2015-01-01
based on the state-space averaging and generalized averaging, these also have limitations to show the same results as with the non-linear time domain simulations. This paper presents a modeling and simulation method for a large dc power electronic system by using Harmonic State Space (HSS) modeling......For the efficiency and simplicity of electric systems, the dc based power electronics systems are widely used in variety applications such as electric vehicles, ships, aircrafts and also in homes. In these systems, there could be a number of dynamic interactions between loads and other dc-dc....... Through this method, the required computation time and CPU memory for large dc power electronics systems can be reduced. Besides, the achieved results show the same results as with the non-linear time domain simulation, but with the faster simulation time which is beneficial in a large network....
Daylighting simulation: methods, algorithms, and resources
Energy Technology Data Exchange (ETDEWEB)
Carroll, William L.
1999-12-01
This document presents work conducted as part of Subtask C, ''Daylighting Design Tools'', Subgroup C2, ''New Daylight Algorithms'', of the IEA SHC Task 21 and the ECBCS Program Annex 29 ''Daylight in Buildings''. The search for and collection of daylighting analysis methods and algorithms led to two important observations. First, there is a wide range of needs for different types of methods to produce a complete analysis tool. These include: Geometry; Light modeling; Characterization of the natural illumination resource; Materials and components properties, representations; and Usability issues (interfaces, interoperability, representation of analysis results, etc). Second, very advantageously, there have been rapid advances in many basic methods in these areas, due to other forces. They are in part driven by: The commercial computer graphics community (commerce, entertainment); The lighting industry; Architectural rendering and visualization for projects; and Academia: Course materials, research. This has led to a very rich set of information resources that have direct applicability to the small daylighting analysis community. Furthermore, much of this information is in fact available online. Because much of the information about methods and algorithms is now online, an innovative reporting strategy was used: the core formats are electronic, and used to produce a printed form only secondarily. The electronic forms include both online WWW pages and a downloadable .PDF file with the same appearance and content. Both electronic forms include live primary and indirect links to actual information sources on the WWW. In most cases, little additional commentary is provided regarding the information links or citations that are provided. This in turn allows the report to be very concise. The links are expected speak for themselves. The report consists of only about 10+ pages, with about 100+ primary links, but
CoSimulating Communication Networks and Electrical System for Performance Evaluation in Smart Grid
Directory of Open Access Journals (Sweden)
Hwantae Kim
2018-01-01
Full Text Available In smart grid research domain, simulation study is the first choice, since the analytic complexity is too high and constructing a testbed is very expensive. However, since communication infrastructure and the power grid are tightly coupled with each other in the smart grid, a well-defined combination of simulation tools for the systems is required for the simulation study. Therefore, in this paper, we propose a cosimulation work called OOCoSim, which consists of OPNET (network simulation tool and OpenDSS (power system simulation tool. By employing the simulation tool, an organic and dynamic cosimulation can be realized since both simulators operate on the same computing platform and provide external interfaces through which the simulation can be managed dynamically. In this paper, we provide OOCoSim design principles including a synchronization scheme and detailed descriptions of its implementation. To present the effectiveness of OOCoSim, we define a smart grid application model and conduct a simulation study to see the impact of the defined application and the underlying network system on the distribution system. The simulation results show that the proposed OOCoSim can successfully simulate the integrated scenario of the power and network systems and produce the accurate effects of the networked control in the smart grid.
A Type of Low-Latency Data Gathering Method with Multi-Sink for Sensor Networks
Sha, Chao; Qiu, Jian-mei; Li, Shu-yan; Qiang, Meng-ye; Wang, Ru-chuan
2016-01-01
To balance energy consumption and reduce latency on data transmission in Wireless Sensor Networks (WSNs), a type of low-latency data gathering method with multi-Sink (LDGM for short) is proposed in this paper. The network is divided into several virtual regions consisting of three or less data gathering units and the leader of each region is selected according to its residual energy as well as distance to all of the other nodes. Only the leaders in each region need to communicate with the mobile Sinks which have effectively reduced energy consumption and the end-to-end delay. Moreover, with the help of the sleep scheduling and the sensing radius adjustment strategies, redundancy in network coverage could also be effectively reduced. Simulation results show that LDGM is energy efficient in comparison with MST as well as MWST and its time efficiency on data collection is higher than one Sink based data gathering methods. PMID:27338401
A Type of Low-Latency Data Gathering Method with Multi-Sink for Sensor Networks
Directory of Open Access Journals (Sweden)
Chao Sha
2016-06-01
Full Text Available To balance energy consumption and reduce latency on data transmission in Wireless Sensor Networks (WSNs, a type of low-latency data gathering method with multi-Sink (LDGM for short is proposed in this paper. The network is divided into several virtual regions consisting of three or less data gathering units and the leader of each region is selected according to its residual energy as well as distance to all of the other nodes. Only the leaders in each region need to communicate with the mobile Sinks which have effectively reduced energy consumption and the end-to-end delay. Moreover, with the help of the sleep scheduling and the sensing radius adjustment strategies, redundancy in network coverage could also be effectively reduced. Simulation results show that LDGM is energy efficient in comparison with MST as well as MWST and its time efficiency on data collection is higher than one Sink based data gathering methods.
Simulation of heat exchanger network (HEN) and planning the optimum cleaning schedule
International Nuclear Information System (INIS)
Sanaye, Sepehr; Niroomand, Behzad
2007-01-01
Modeling and simulation of heat exchanger networks for estimating the amount of fouling, variations in overall heat transfer coefficient, and variations in outlet temperatures of hot and cold streams has a significant effect on production analysis. In this analysis, parameters such as the exchangers' types and arrangements, their heat transfer surface areas, mass flow rates of hot and cold streams, heat transfer coefficients and variations of fouling with time are required input data. The main goal is to find the variations of the outlet temperatures of the hot and cold streams with time to plan the optimum cleaning schedule of heat exchangers that provides the minimum operational cost or maximum amount of savings. In this paper, the simulation of heat exchanger networks is performed by choosing an asymptotic fouling function. Two main parameters in the asymptotic fouling formation model, i.e. the decay time of fouling formation (τ) and the asymptotic fouling resistance (R f ∼ ) were obtained from empirical data as input parameters to the simulation relations. These data were extracted from the technical history sheets of the Khorasan Petrochemical Plant to guaranty the consistency between our model outputs and the real operating conditions. The output results of the software program developed, including the variations with time of the outlet temperatures of the hot and cold streams, the heat transfer coefficient and the heat transfer rate in the exchangers, are presented for two case studies. Then, an objective function (operational cost) was defined, and the optimal cleaning schedule of the HEN (heat exchanger network) in the Urea and Ammonia units were found by minimizing the objective function using a numerical search method. Based on this minimization procedure, the decision was made whether a heat exchanger should be cleaned or continue to operate. The final result was the most cost effective plan for the HEN cleaning schedule. The corresponding savings by
Simulation of heat exchanger network (HEN) and planning the optimum cleaning schedule
Energy Technology Data Exchange (ETDEWEB)
Sanaye, Sepehr [Energy Systems Improvement Laboratory, Mechanical Engineering Department, Iran University of Science and Technology (IUST), Narmak, Tehran 16488 (Iran, Islamic Republic of)]. E-mail: sepehr@iust.ac.ir; Niroomand, Behzad [Energy Systems Improvement Laboratory, Mechanical Engineering Department, Iran University of Science and Technology (IUST), Narmak, Tehran 16488 (Iran, Islamic Republic of)
2007-05-15
Modeling and simulation of heat exchanger networks for estimating the amount of fouling, variations in overall heat transfer coefficient, and variations in outlet temperatures of hot and cold streams has a significant effect on production analysis. In this analysis, parameters such as the exchangers' types and arrangements, their heat transfer surface areas, mass flow rates of hot and cold streams, heat transfer coefficients and variations of fouling with time are required input data. The main goal is to find the variations of the outlet temperatures of the hot and cold streams with time to plan the optimum cleaning schedule of heat exchangers that provides the minimum operational cost or maximum amount of savings. In this paper, the simulation of heat exchanger networks is performed by choosing an asymptotic fouling function. Two main parameters in the asymptotic fouling formation model, i.e. the decay time of fouling formation ({tau}) and the asymptotic fouling resistance (R{sub f}{sup {approx}}) were obtained from empirical data as input parameters to the simulation relations. These data were extracted from the technical history sheets of the Khorasan Petrochemical Plant to guaranty the consistency between our model outputs and the real operating conditions. The output results of the software program developed, including the variations with time of the outlet temperatures of the hot and cold streams, the heat transfer coefficient and the heat transfer rate in the exchangers, are presented for two case studies. Then, an objective function (operational cost) was defined, and the optimal cleaning schedule of the HEN (heat exchanger network) in the Urea and Ammonia units were found by minimizing the objective function using a numerical search method. Based on this minimization procedure, the decision was made whether a heat exchanger should be cleaned or continue to operate. The final result was the most cost effective plan for the HEN cleaning schedule. The
Yucel, Abdulkadir C.
2015-05-05
An efficient method for statistically characterizing multiconductor transmission line (MTL) networks subject to a large number of manufacturing uncertainties is presented. The proposed method achieves its efficiency by leveraging a high-dimensional model representation (HDMR) technique that approximates observables (quantities of interest in MTL networks, such as voltages/currents on mission-critical circuits) in terms of iteratively constructed component functions of only the most significant random variables (parameters that characterize the uncertainties in MTL networks, such as conductor locations and widths, and lumped element values). The efficiency of the proposed scheme is further increased using a multielement probabilistic collocation (ME-PC) method to compute the component functions of the HDMR. The ME-PC method makes use of generalized polynomial chaos (gPC) expansions to approximate the component functions, where the expansion coefficients are expressed in terms of integrals of the observable over the random domain. These integrals are numerically evaluated and the observable values at the quadrature/collocation points are computed using a fast deterministic simulator. The proposed method is capable of producing accurate statistical information pertinent to an observable that is rapidly varying across a high-dimensional random domain at a computational cost that is significantly lower than that of gPC or Monte Carlo methods. The applicability, efficiency, and accuracy of the method are demonstrated via statistical characterization of frequency-domain voltages in parallel wire, interconnect, and antenna corporate feed networks.
Zhang, Shuhua; Sun, Shuyu; Yang, Hongtao
2014-01-01
A discontinuous Galerkin method is considered to simulate materials flow in a supply chain network problem which is governed by a system of conservation laws. By means of a novel interpolation and superclose analysis technique, the optimal and superconvergence error estimates are established under two physically meaningful assumptions on the connectivity matrix. Numerical examples are presented to validate the theoretical results. © 2014 Elsevier Ltd. All rights reserved.
Zhang, Shuhua
2014-09-01
A discontinuous Galerkin method is considered to simulate materials flow in a supply chain network problem which is governed by a system of conservation laws. By means of a novel interpolation and superclose analysis technique, the optimal and superconvergence error estimates are established under two physically meaningful assumptions on the connectivity matrix. Numerical examples are presented to validate the theoretical results. © 2014 Elsevier Ltd. All rights reserved.
Simulation of thermal-hydraulic process in reactor of HTR-PM based on flow and heat transfer network
International Nuclear Information System (INIS)
Zhou Kefeng; Zhou Yangping; Sui Zhe; Ma Yuanle
2012-01-01
The development of HTR-PM full scale simulator (FSS) is an important part in the project. The simulation of thermal-hydraulic process in reactor is one of the key technologies in the development of FSS. The simulation of thermal-hydraulic process in reactor was studied. According to the geometry structures and the characteristics of thermal-hydraulic process in reactor, the model was setup in components construction way. Based on the established simulation method of flow and heat transfer network, a Fortran code was developed and the simulation of thermal-hydraulic process was achieved. The simulation results of 50% FP steady state, 100% FP steady state and control rod mistakenly ascension accidents were given. The verification of simulation results was carried out by comparing with the design and analysis code THERMIX. The results show that the method and model based on flow and heat transfer network can meet the requirements of FSS and reflect the features of thermal-hydraulic process in HTR-PM. (authors)
Directory of Open Access Journals (Sweden)
Chander Prabha
2016-09-01
Full Text Available The data loss and disconnection of nodes are frequent in the opportunistic networks. The social information plays an important role in reducing the data loss because it depends on the connectivity of nodes. The appropriate selection of next hop based on social information is critical for improving the performance of routing in opportunistic networks. The frequent disconnection problem is overcome by optimising the social information with Ant Colony Optimization method which depends on the topology of opportunistic network. The proposed protocol is examined thoroughly via analysis and simulation in order to assess their performance in comparison with other social based routing protocols in opportunistic network under various parameters settings.
WMAXC: a weighted maximum clique method for identifying condition-specific sub-network.
Directory of Open Access Journals (Sweden)
Bayarbaatar Amgalan
Full Text Available Sub-networks can expose complex patterns in an entire bio-molecular network by extracting interactions that depend on temporal or condition-specific contexts. When genes interact with each other during cellular processes, they may form differential co-expression patterns with other genes across different cell states. The identification of condition-specific sub-networks is of great importance in investigating how a living cell adapts to environmental changes. In this work, we propose the weighted MAXimum clique (WMAXC method to identify a condition-specific sub-network. WMAXC first proposes scoring functions that jointly measure condition-specific changes to both individual genes and gene-gene co-expressions. It then employs a weaker formula of a general maximum clique problem and relates the maximum scored clique of a weighted graph to the optimization of a quadratic objective function under sparsity constraints. We combine a continuous genetic algorithm and a projection procedure to obtain a single optimal sub-network that maximizes the objective function (scoring function over the standard simplex (sparsity constraints. We applied the WMAXC method to both simulated data and real data sets of ovarian and prostate cancer. Compared with previous methods, WMAXC selected a large fraction of cancer-related genes, which were enriched in cancer-related pathways. The results demonstrated that our method efficiently captured a subset of genes relevant under the investigated condition.
Baker-Doyle, Kira J.
2015-01-01
Social network research on teachers and schools has risen exponentially in recent years as an innovative method to reveal the role of social networks in education. However, scholars are still exploring ways to incorporate traditional quantitative methods of Social Network Analysis (SNA) with qualitative approaches to social network research. This…
A particle-based method for granular flow simulation
Chang, Yuanzhang; Bao, Kai; Zhu, Jian; Wu, Enhua
2012-01-01
We present a new particle-based method for granular flow simulation. In the method, a new elastic stress term, which is derived from a modified form of the Hooke's law, is included in the momentum governing equation to handle the friction of granular materials. Viscosity force is also added to simulate the dynamic friction for the purpose of smoothing the velocity field and further maintaining the simulation stability. Benefiting from the Lagrangian nature of the SPH method, large flow deformation can be well handled easily and naturally. In addition, a signed distance field is also employed to enforce the solid boundary condition. The experimental results show that the proposed method is effective and efficient for handling the flow of granular materials, and different kinds of granular behaviors can be well simulated by adjusting just one parameter. © 2012 Science China Press and Springer-Verlag Berlin Heidelberg.
Research on Monte Carlo simulation method of industry CT system
International Nuclear Information System (INIS)
Li Junli; Zeng Zhi; Qui Rui; Wu Zhen; Li Chunyan
2010-01-01
There are a series of radiation physical problems in the design and production of industry CT system (ICTS), including limit quality index analysis; the effect of scattering, efficiency of detectors and crosstalk to the system. Usually the Monte Carlo (MC) Method is applied to resolve these problems. Most of them are of little probability, so direct simulation is very difficult, and existing MC methods and programs can't meet the needs. To resolve these difficulties, particle flux point auto-important sampling (PFPAIS) is given on the basis of auto-important sampling. Then, on the basis of PFPAIS, a particular ICTS simulation method: MCCT is realized. Compared with existing MC methods, MCCT is proved to be able to simulate the ICTS more exactly and effectively. Furthermore, the effects of all kinds of disturbances of ICTS are simulated and analyzed by MCCT. To some extent, MCCT can guide the research of the radiation physical problems in ICTS. (author)
A simple method for potential flow simulation of cascades
Indian Academy of Sciences (India)
vortex panel method to simulate potential flow in cascades is presented. The cascade ... The fluid loading on the blades, such as the normal force and pitching moment, may ... of such discrete infinite array singularities along the blade surface.
A particle-based method for granular flow simulation
Chang, Yuanzhang
2012-03-16
We present a new particle-based method for granular flow simulation. In the method, a new elastic stress term, which is derived from a modified form of the Hooke\\'s law, is included in the momentum governing equation to handle the friction of granular materials. Viscosity force is also added to simulate the dynamic friction for the purpose of smoothing the velocity field and further maintaining the simulation stability. Benefiting from the Lagrangian nature of the SPH method, large flow deformation can be well handled easily and naturally. In addition, a signed distance field is also employed to enforce the solid boundary condition. The experimental results show that the proposed method is effective and efficient for handling the flow of granular materials, and different kinds of granular behaviors can be well simulated by adjusting just one parameter. © 2012 Science China Press and Springer-Verlag Berlin Heidelberg.
Simulating large-scale spiking neuronal networks with NEST
Schücker, Jannis; Eppler, Jochen Martin
2014-01-01
The Neural Simulation Tool NEST [1, www.nest-simulator.org] is the simulator for spiking neural networkmodels of the HBP that focuses on the dynamics, size and structure of neural systems rather than on theexact morphology of individual neurons. Its simulation kernel is written in C++ and it runs on computinghardware ranging from simple laptops to clusters and supercomputers with thousands of processor cores.The development of NEST is coordinated by the NEST Initiative [www.nest-initiative.or...
Comparing three methods for participatory simulation of hospital work systems
DEFF Research Database (Denmark)
Broberg, Ole; Andersen, Simone Nyholm
Summative Statement: This study compared three participatory simulation methods using different simulation objects: Low resolution table-top setup using Lego figures, full scale mock-ups, and blueprints using Lego figures. It was concluded the three objects by differences in fidelity and affordance...... scenarios using the objects. Results: Full scale mock-ups significantly addressed the local space and technology/tool elements of a work system. In contrast, the table-top simulation object addressed the organizational issues of the future work system. The blueprint based simulation addressed...
Adaptive and dynamic meshing methods for numerical simulations
Acikgoz, Nazmiye
-hoc application of the simulated annealing technique, which improves the likelihood of removing poor elements from the grid. Moreover, a local implementation of the simulated annealing is proposed to reduce the computational cost. Many challenging multi-physics and multi-field problems that are unsteady in nature are characterized by moving boundaries and/or interfaces. When the boundary displacements are large, which typically occurs when implicit time marching procedures are used, degenerate elements are easily formed in the grid such that frequent remeshing is required. To deal with this problem, in the second part of this work, we propose a new r-adaptation methodology. The new technique is valid for both simplicial (e.g., triangular, tet) and non-simplicial (e.g., quadrilateral, hex) deforming grids that undergo large imposed displacements at their boundaries. A two- or three-dimensional grid is deformed using a network of linear springs composed of edge springs and a set of virtual springs. The virtual springs are constructed in such a way as to oppose element collapsing. This is accomplished by confining each vertex to its ball through springs that are attached to the vertex and its projection on the ball entities. The resulting linear problem is solved using a preconditioned conjugate gradient method. The new method is compared with the classical spring analogy technique in two- and three-dimensional examples, highlighting the performance improvements achieved by the new method. Meshes are an important part of numerical simulations. Depending on the geometry and flow conditions, the most suitable mesh for each particular problem is different. Meshes are usually generated by either using a suitable software package or solving a PDE. In both cases, engineering intuition plays a significant role in deciding where clusterings should take place. In addition, for unsteady problems, the gradients vary for each time step, which requires frequent remeshing during simulations
Forest canopy BRDF simulation using Monte Carlo method
Huang, J.; Wu, B.; Zeng, Y.; Tian, Y.
2006-01-01
Monte Carlo method is a random statistic method, which has been widely used to simulate the Bidirectional Reflectance Distribution Function (BRDF) of vegetation canopy in the field of visible remote sensing. The random process between photons and forest canopy was designed using Monte Carlo method.
International Nuclear Information System (INIS)
Nabeshima Kunihiko; Suzuki Katsuo; Nose, Shoichi; Kudo, Kazuhiko
1996-01-01
The purpose of this paper is to demonstrate a nuclear power plant monitoring system using artificial neural network (ANN). The major advantages of the monitoring system are that a multi-output process system can be modelled using measurement information without establishing any mathematical expressions. The dynamics model of reactor plant was constructed by using three layered auto-associative neural network with backpropagation learning algorithm. The basic idea of anomaly detection method is to monitor the deviation between process signals measured from actual plant and corresponding output signals from the ANN plant model. The simulator used is a self contained system designed for training. Four kinds of simulated malfunction caused by equipment failure during steady state operation were used to evaluate the capability of the neural network monitoring system. The results showed that this monitoring system detected the symptom of small anomaly earlier than the prevailing alarm system. (author). 7 refs, 7 figs, 2 tabs
Energy Technology Data Exchange (ETDEWEB)
Kunihiko, Nabeshima; Katsuo, Suzuki [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan); Nose, Shoichi; Kudo, Kazuhiko [Kyushu Univ., Fukuoka (Japan). Faculty of Engineering
1997-12-31
The purpose of this paper is to demonstrate a nuclear power plant monitoring system using artificial neural network (ANN). The major advantages of the monitoring system are that a multi-output process system can be modelled using measurement information without establishing any mathematical expressions. The dynamics model of reactor plant was constructed by using three layered auto-associative neural network with backpropagation learning algorithm. The basic idea of anomaly detection method is to monitor the deviation between process signals measured from actual plant and corresponding output signals from the ANN plant model. The simulator used is a self contained system designed for training. Four kinds of simulated malfunction caused by equipment failure during steady state operation were used to evaluate the capability of the neural network monitoring system. The results showed that this monitoring system detected the symptom of small anomaly earlier than the prevailing alarm system. (author). 7 refs, 7 figs, 2 tabs.
Enterprise Networks for Competences Exchange: A Simulation Model
Remondino, Marco; Pironti, Marco; Pisano, Paola
A business process is a set of logically related tasks performed to achieve a defined business and related to improving organizational processes. Process innovation can happen at various levels: incrementally, redesign of existing processes, new processes. The knowledge behind process innovation can be shared, acquired, changed and increased by the enterprises inside a network. An enterprise can decide to exploit innovative processes it owns, thus potentially gaining competitive advantage, but risking, in turn, that other players could reach the same technological levels. Or it could decide to share it, in exchange for other competencies or money. These activities could be the basis for a network formation and/or impact the topology of an existing network. In this work an agent based model is introduced (E3), aiming to explore how a process innovation can facilitate network formation, affect its topology, induce new players to enter the market and spread onto the network by being shared or developed by new players.
Simulated Annealing Technique for Routing in a Rectangular Mesh Network
Directory of Open Access Journals (Sweden)
Noraziah Adzhar
2014-01-01
Full Text Available In the process of automatic design for printed circuit boards (PCBs, the phase following cell placement is routing. On the other hand, routing process is a notoriously difficult problem, and even the simplest routing problem which consists of a set of two-pin nets is known to be NP-complete. In this research, our routing region is first tessellated into a uniform Nx×Ny array of square cells. The ultimate goal for a routing problem is to achieve complete automatic routing with minimal need for any manual intervention. Therefore, shortest path for all connections needs to be established. While classical Dijkstra’s algorithm guarantees to find shortest path for a single net, each routed net will form obstacles for later paths. This will add complexities to route later nets and make its routing longer than the optimal path or sometimes impossible to complete. Today’s sequential routing often applies heuristic method to further refine the solution. Through this process, all nets will be rerouted in different order to improve the quality of routing. Because of this, we are motivated to apply simulated annealing, one of the metaheuristic methods to our routing model to produce better candidates of sequence.
Evaluation and Simulation of Common Video Conference Traffics in Communication Networks
Directory of Open Access Journals (Sweden)
Farhad faghani
2014-01-01
Full Text Available Multimedia traffics are the basic traffics in data communication networks. Especially Video conferences are the most desirable traffics in huge networks(wired, wireless, …. Traffic modeling can help us to evaluate the real networks. So, in order to have good services in data communication networks which provide multimedia services, QoS will be very important .In this research we tried to have an exact traffic model design and simulation to overcome QoS challenges. Also, we predict bandwidth by Kalman filter in Ethernet networks.
Simulation methods of nuclear electromagnetic pulse effects in integrated circuits
International Nuclear Information System (INIS)
Cheng Jili; Liu Yuan; En Yunfei; Fang Wenxiao; Wei Aixiang; Yang Yuanzhen
2013-01-01
In the paper the ways to compute the response of transmission line (TL) illuminated by electromagnetic pulse (EMP) were introduced firstly, which include finite-difference time-domain (FDTD) and trans-mission line matrix (TLM); then the feasibility of electromagnetic topology (EMT) in ICs nuclear electromagnetic pulse (NEMP) effect simulation was discussed; in the end, combined with the methods computing the response of TL, a new method of simulate the transmission line in IC illuminated by NEMP was put forward. (authors)
Computer Networks E-learning Based on Interactive Simulations and SCORM
Directory of Open Access Journals (Sweden)
Francisco Andrés Candelas
2011-05-01
Full Text Available This paper introduces a new set of compact interactive simulations developed for the constructive learning of computer networks concepts. These simulations, which compose a virtual laboratory implemented as portable Java applets, have been created by combining EJS (Easy Java Simulations with the KivaNS API. Furthermore, in this work, the skills and motivation level acquired by the students are evaluated and measured when these simulations are combined with Moodle and SCORM (Sharable Content Object Reference Model documents. This study has been developed to improve and stimulate the autonomous constructive learning in addition to provide timetable flexibility for a Computer Networks subject.
Dynamic Simulation of Water Networks to Control and Reduce Physical Unaccounted-for Water
Directory of Open Access Journals (Sweden)
Nima Zorriasateyn
2005-09-01
Full Text Available A significant percentage of unaccounted-for water consists of leakage in water distribution networks in Iran. To detect leakage area with less costs and time spending and then identify the exact place of it with special instruments, would be economical and a better water resource management. In this research, a real case has been selected and examined with dynamic simulation using MIKE NET. The method that has been carried out in this research based on maximizing the correlation coefficient and minimizing the sum of error squares between pressure measured inputs (observed data and calculated pressure (by model. According to the results, dynamic simulation of municipal water distribution system can be used as a guide to determine the place and the amount of leakage.Thereby the area of large leakage can be simulated with appropriate accuracy through measured pressure. Therefor from management aspect, dynamic simulation can be used to decrease time consumption and to save costs for detecting leakage.
Directory of Open Access Journals (Sweden)
Elena ePapaleo
2015-05-01
Full Text Available In the last years, we have been observing remarkable improvements in the field of protein dynamics. Indeed, we can now study protein dynamics in atomistic details over several timescales with a rich portfolio of experimental and computational techniques. On one side, this provides us with the possibility to validate simulation methods and physical models against a broad range of experimental observables. On the other side, it also allows a complementary and comprehensive view on protein structure and dynamics. What is needed now is a better understanding of the link between the dynamic properties that we observe and the functional properties of these important cellular machines. To make progresses in this direction, we need to improve the physical models used to describe proteins and solvent in molecular dynamics, as well as to strengthen the integration of experiments and simulations to overcome their own limitations. Moreover, now that we have the means to study protein dynamics in great details, we need new tools to understand the information embedded in the protein ensembles and in their dynamic signature. With this aim in mind, we should enrich the current tools for analysis of biomolecular simulations with attention to the effects that can be propagated over long distances and are often associated to important biological functions. In this context, approaches inspired by network analysis can make an important contribution to the analysis of molecular dynamics simulations.
Impact of Loss Synchronization on Reliable High Speed Networks: A Model Based Simulation
Directory of Open Access Journals (Sweden)
Suman Kumar
2014-01-01
Full Text Available Contemporary nature of network evolution demands for simulation models which are flexible, scalable, and easily implementable. In this paper, we propose a fluid based model for performance analysis of reliable high speed networks. In particular, this paper aims to study the dynamic relationship between congestion control algorithms and queue management schemes, in order to develop a better understanding of the causal linkages between the two. We propose a loss synchronization module which is user configurable. We validate our model through simulations under controlled settings. Also, we present a performance analysis to provide insights into two important issues concerning 10 Gbps high speed networks: (i impact of bottleneck buffer size on the performance of 10 Gbps high speed network and (ii impact of level of loss synchronization on link utilization-fairness tradeoffs. The practical impact of the proposed work is to provide design guidelines along with a powerful simulation tool to protocol designers and network developers.
Directory of Open Access Journals (Sweden)
Wen Zhou
Full Text Available As computer science and complex network theory develop, non-cooperative games and their formation and application on complex networks have been important research topics. In the inter-firm innovation network, it is a typical game behavior for firms to invest in their alliance partners. Accounting for the possibility that firms can be resource constrained, this paper analyzes a coordination game using the Nash bargaining solution as allocation rules between firms in an inter-firm innovation network. We build an extended inter-firm n-player game based on nonidealized conditions, describe four investment strategies and simulate the strategies on an inter-firm innovation network in order to compare their performance. By analyzing the results of our experiments, we find that our proposed greedy strategy is the best-performing in most situations. We hope this study provides a theoretical insight into how firms make investment decisions.
Zhou, Wen; Koptyug, Nikita; Ye, Shutao; Jia, Yifan; Lu, Xiaolong
2016-01-01
As computer science and complex network theory develop, non-cooperative games and their formation and application on complex networks have been important research topics. In the inter-firm innovation network, it is a typical game behavior for firms to invest in their alliance partners. Accounting for the possibility that firms can be resource constrained, this paper analyzes a coordination game using the Nash bargaining solution as allocation rules between firms in an inter-firm innovation network. We build an extended inter-firm n-player game based on nonidealized conditions, describe four investment strategies and simulate the strategies on an inter-firm innovation network in order to compare their performance. By analyzing the results of our experiments, we find that our proposed greedy strategy is the best-performing in most situations. We hope this study provides a theoretical insight into how firms make investment decisions.
An introduction to computer simulation methods applications to physical systems
Gould, Harvey; Christian, Wolfgang
2007-01-01
Now in its third edition, this book teaches physical concepts using computer simulations. The text incorporates object-oriented programming techniques and encourages readers to develop good programming habits in the context of doing physics. Designed for readers at all levels , An Introduction to Computer Simulation Methods uses Java, currently the most popular programming language. Introduction, Tools for Doing Simulations, Simulating Particle Motion, Oscillatory Systems, Few-Body Problems: The Motion of the Planets, The Chaotic Motion of Dynamical Systems, Random Processes, The Dynamics of Many Particle Systems, Normal Modes and Waves, Electrodynamics, Numerical and Monte Carlo Methods, Percolation, Fractals and Kinetic Growth Models, Complex Systems, Monte Carlo Simulations of Thermal Systems, Quantum Systems, Visualization and Rigid Body Dynamics, Seeing in Special and General Relativity, Epilogue: The Unity of Physics For all readers interested in developing programming habits in the context of doing phy...
Motion simulation of hydraulic driven safety rod using FSI method
International Nuclear Information System (INIS)
Jung, Jaeho; Kim, Sanghaun; Yoo, Yeonsik; Cho, Yeonggarp; Kim, Jong In
2013-01-01
Hydraulic driven safety rod which is one of them is being developed by Division for Reactor Mechanical Engineering, KAERI. In this paper the motion of this rod is simulated by fluid structure interaction (FSI) method before manufacturing for design verification and pump sizing. A newly designed hydraulic driven safety rod which is one of reactivity control mechanism is simulated using FSI method for design verification and pump sizing. The simulation is done in CFD domain with UDF. The pressure drop is changed slightly by flow rates. It means that the pressure drop is mainly determined by weight of moving part. The simulated velocity of piston is linearly proportional to flow rates so the pump can be sized easily according to the rising and drop time requirement of the safety rod using the simulation results
DEFF Research Database (Denmark)
Brochado, Ana Rita; Andrejev, Sergej; Maranas, Costas D.
2012-01-01
the formulation of the desired objective functions, by casting objective functions using metabolite turnovers rather than fluxes. By simulating perturbed metabolic networks, we demonstrate that the use of stoichiometry representation independent algorithms is fundamental for unambiguously linking modeling results...
A Network Reconfiguration Method Considering Data Uncertainties in Smart Distribution Networks
Directory of Open Access Journals (Sweden)
Ke-yan Liu
2017-05-01
Full Text Available This work presents a method for distribution network reconfiguration with the simultaneous consideration of distributed generation (DG allocation. The uncertainties of load fluctuation before the network reconfiguration are also considered. Three optimal objectives, including minimal line loss cost, minimum Expected Energy Not Supplied, and minimum switch operation cost, are investigated. The multi-objective optimization problem is further transformed into a single-objective optimization problem by utilizing weighting factors. The proposed network reconfiguration method includes two periods. The first period is to create a feasible topology network by using binary particle swarm optimization (BPSO. Then the DG allocation problem is solved by utilizing sensitivity analysis and a Harmony Search algorithm (HSA. In the meanwhile, interval analysis is applied to deal with the uncertainties of load and devices parameters. Test cases are studied using the standard IEEE 33-bus and PG&E 69-bus systems. Different scenarios and comparisons are analyzed in the experiments. The results show the applicability of the proposed method. The performance analysis of the proposed method is also investigated. The computational results indicate that the proposed network reconfiguration algorithm is feasible.
FERN - a Java framework for stochastic simulation and evaluation of reaction networks.
Erhard, Florian; Friedel, Caroline C; Zimmer, Ralf
2008-08-29
Stochastic simulation can be used to illustrate the development of biological systems over time and the stochastic nature of these processes. Currently available programs for stochastic simulation, however, are limited in that they either a) do not provide the most efficient simulation algorithms and are difficult to extend, b) cannot be easily integrated into other applications or c) do not allow to monitor and intervene during the simulation process in an easy and intuitive way. Thus, in order to use stochastic simulation in innovative high-level modeling and analysis approaches more flexible tools are necessary. In this article, we present FERN (Framework for Evaluation of Reaction Networks), a Java framework for the efficient simulation of chemical reaction networks. FERN is subdivided into three layers for network representation, simulation and visualization of the simulation results each of which can be easily extended. It provides efficient and accurate state-of-the-art stochastic simulation algorithms for well-mixed chemical systems and a powerful observer system, which makes it possible to track and control the simulation progress on every level. To illustrate how FERN can be easily integrated into other systems biology applications, plugins to Cytoscape and CellDesigner are included. These plugins make it possible to run simulations and to observe the simulation progress in a reaction network in real-time from within the Cytoscape or CellDesigner environment. FERN addresses shortcomings of currently available stochastic simulation programs in several ways. First, it provides a broad range of efficient and accurate algorithms both for exact and approximate stochastic simulation and a simple interface for extending to new algorithms. FERN's implementations are considerably faster than the C implementations of gillespie2 or the Java implementations of ISBJava. Second, it can be used in a straightforward way both as a stand-alone program and within new
A hierarchical network modeling method for railway tunnels safety assessment
Zhou, Jin; Xu, Weixiang; Guo, Xin; Liu, Xumin
2017-02-01
Using network theory to model risk-related knowledge on accidents is regarded as potential very helpful in risk management. A large amount of defects detection data for railway tunnels is collected in autumn every year in China. It is extremely important to discover the regularities knowledge in database. In this paper, based on network theories and by using data mining techniques, a new method is proposed for mining risk-related regularities to support risk management in railway tunnel projects. A hierarchical network (HN) model which takes into account the tunnel structures, tunnel defects, potential failures and accidents is established. An improved Apriori algorithm is designed to rapidly and effectively mine correlations between tunnel structures and tunnel defects. Then an algorithm is presented in order to mine the risk-related regularities table (RRT) from the frequent patterns. At last, a safety assessment method is proposed by consideration of actual defects and possible risks of defects gained from the RRT. This method cannot only generate the quantitative risk results but also reveal the key defects and critical risks of defects. This paper is further development on accident causation network modeling methods which can provide guidance for specific maintenance measure.
Method and device for monitoring distortion in an optical network
2012-01-01
A method and a device for monitoring of distortion in an optical network are provided, wherein at least one reference signal and at least one data signal are conveyed via an optical link and wherein a distortion of the at least one data signal is determined based on the at least one reference
Improvement on the Performance of Canal Network and Method of ...
African Journals Online (AJOL)
This paper presents the required improvement on the performance of canal network and method of on-farm water application systems at Tunga-Kawo irrigation scheme, Wushishi, Niger state. The problems of poor delivery of water to the farmland were identified to include erosion of canal embarkment, lack of water ...
A simulation method for lightning surge response of switching power
International Nuclear Information System (INIS)
Wei, Ming; Chen, Xiang
2013-01-01
In order to meet the need of protection design for lighting surge, a prediction method of lightning electromagnetic pulse (LEMP) response which is based on system identification is presented. Experiments of switching power's surge injection were conducted, and the input and output data were sampled, de-noised and de-trended. In addition, the model of energy coupling transfer function was obtained by system identification method. Simulation results show that the system identification method can predict the surge response of linear circuit well. The method proposed in the paper provided a convenient and effective technology for simulation of lightning effect.
Energy Technology Data Exchange (ETDEWEB)
Chen, Y W [Surface Physics Laboratory and Department of Physics, Fudan University, Shanghai 200433 (China); Zhang, L F [Surface Physics Laboratory and Department of Physics, Fudan University, Shanghai 200433 (China); Huang, J P [Surface Physics Laboratory and Department of Physics, Fudan University, Shanghai 200433 (China)
2007-07-20
By using theoretical analysis and computer simulations, we develop the Watts-Strogatz network model by including degree distribution, in an attempt to improve the comparison between characteristic path lengths and clustering coefficients predicted by the original Watts-Strogatz network model and those of the real networks with the small-world property. Good agreement between the predictions of the theoretical analysis and those of the computer simulations has been shown. It is found that the developed Watts-Strogatz network model can fit the real small-world networks more satisfactorily. Some other interesting results are also reported by adjusting the parameters in a model degree-distribution function. The developed Watts-Strogatz network model is expected to help in the future analysis of various social problems as well as financial markets with the small-world property.
International Nuclear Information System (INIS)
Chen, Y W; Zhang, L F; Huang, J P
2007-01-01
By using theoretical analysis and computer simulations, we develop the Watts-Strogatz network model by including degree distribution, in an attempt to improve the comparison between characteristic path lengths and clustering coefficients predicted by the original Watts-Strogatz network model and those of the real networks with the small-world property. Good agreement between the predictions of the theoretical analysis and those of the computer simulations has been shown. It is found that the developed Watts-Strogatz network model can fit the real small-world networks more satisfactorily. Some other interesting results are also reported by adjusting the parameters in a model degree-distribution function. The developed Watts-Strogatz network model is expected to help in the future analysis of various social problems as well as financial markets with the small-world property
Agent-based model of angiogenesis simulates capillary sprout initiation in multicellular networks.
Walpole, J; Chappell, J C; Cluceru, J G; Mac Gabhann, F; Bautch, V L; Peirce, S M
2015-09-01
Many biological processes are controlled by both deterministic and stochastic influences. However, efforts to model these systems often rely on either purely stochastic or purely rule-based methods. To better understand the balance between stochasticity and determinism in biological processes a computational approach that incorporates both influences may afford additional insight into underlying biological mechanisms that give rise to emergent system properties. We apply a combined approach to the simulation and study of angiogenesis, the growth of new blood vessels from existing networks. This complex multicellular process begins with selection of an initiating endothelial cell, or tip cell, which sprouts from the parent vessels in response to stimulation by exogenous cues. We have constructed an agent-based model of sprouting angiogenesis to evaluate endothelial cell sprout initiation frequency and location, and we have experimentally validated it using high-resolution time-lapse confocal microscopy. ABM simulations were then compared to a Monte Carlo model, revealing that purely stochastic simulations could not generate sprout locations as accurately as the rule-informed agent-based model. These findings support the use of rule-based approaches for modeling the complex mechanisms underlying sprouting angiogenesis over purely stochastic methods.
Energy Technology Data Exchange (ETDEWEB)
Aid, R.
1998-01-07
This work comes from an industrial problem of validating numerical solutions of ordinary differential equations modeling power systems. This problem is solved using asymptotic estimators of the global error. Four techniques are studied: Richardson estimator (RS), Zadunaisky's techniques (ZD), integration of the variational equation (EV), and Solving for the correction (SC). We give some precisions on the relative order of SC w.r.t. the order of the numerical method. A new variant of ZD is proposed that uses the Modified Equation. In the case of variable step-size, it is shown that under suitable restriction, on the hypothesis of the step-size selection, ZD and SC are still valid. Moreover, some Runge-Kutta methods are shown to need less hypothesis on the step-sizes to exhibit a valid order of convergence for ZD and SC. Numerical tests conclude this analysis. Industrial cases are given. Finally, an algorithm to avoid the a priori specification of the integration path for complex time differential equations is proposed. (author)
Simulation of noise-assisted transport via optical cavity networks
International Nuclear Information System (INIS)
Caruso, Filippo; Plenio, Martin B.; Spagnolo, Nicolo; Vitelli, Chiara; Sciarrino, Fabio
2011-01-01
Recently, the presence of noise has been found to play a key role in assisting the transport of energy and information in complex quantum networks and even in biomolecular systems. Here we propose an experimentally realizable optical network scheme for the demonstration of the basic mechanisms underlying noise-assisted transport. The proposed system consists of a network of coupled quantum-optical cavities, injected with a single photon, whose transmission efficiency can be measured. Introducing dephasing in the photon path, this system exhibits a characteristic enhancement of the transport efficiency that can be observed with presently available technology.
Smart Grid: Network simulator for smart grid test-bed
International Nuclear Information System (INIS)
Lai, L C; Ong, H S; Che, Y X; Do, N Q; Ong, X J
2013-01-01
Smart Grid become more popular, a smaller scale of smart grid test-bed is set up at UNITEN to investigate the performance and to find out future enhancement of smart grid in Malaysia. The fundamental requirement in this project is design a network with low delay, no packet drop and with high data rate. Different type of traffic has its own characteristic and is suitable for different type of network and requirement. However no one understands the natural of traffic in smart grid. This paper presents the comparison between different types of traffic to find out the most suitable traffic for the optimal network performance.
Real time simulation method for fast breeder reactors dynamics
International Nuclear Information System (INIS)
Miki, Tetsushi; Mineo, Yoshiyuki; Ogino, Takamichi; Kishida, Koji; Furuichi, Kenji.
1985-01-01
The development of multi-purpose real time simulator models with suitable plant dynamics was made; these models can be used not only in training operators but also in designing control systems, operation sequences and many other items which must be studied for the development of new type reactors. The prototype fast breeder reactor ''Monju'' is taken as an example. Analysis is made on various factors affecting the accuracy and computer load of its dynamic simulation. A method is presented which determines the optimum number of nodes in distributed systems and time steps. The oscillations due to the numerical instability are observed in the dynamic simulation of evaporators with a small number of nodes, and a method to cancel these oscillations is proposed. It has been verified through the development of plant dynamics simulation codes that these methods can provide efficient real time dynamics models of fast breeder reactors. (author)
Simulation of plume dynamics by the Lattice Boltzmann Method
Mora, Peter; Yuen, David A.
2017-09-01
The Lattice Boltzmann Method (LBM) is a semi-microscopic method to simulate fluid mechanics by modelling distributions of particles moving and colliding on a lattice. We present 2-D simulations using the LBM of a fluid in a rectangular box being heated from below, and cooled from above, with a Rayleigh of Ra = 108, similar to current estimates of the Earth's mantle, and a Prandtl number of 5000. At this Prandtl number, the flow is found to be in the non-inertial regime where the inertial terms denoted I ≪ 1. Hence, the simulations presented lie within the regime of relevance for geodynamical problems. We obtain narrow upwelling plumes with mushroom heads and chutes of downwelling fluid as expected of a flow in the non-inertial regime. The method developed demonstrates that the LBM has great potential for simulating thermal convection and plume dynamics relevant to geodynamics, albeit with some limitations.
Importance Sampling Simulation of Population Overflow in Two-node Tandem Networks
Nicola, V.F.; Zaburnenko, T.S.; Baier, C; Chiola, G.; Smirni, E.
2005-01-01
In this paper we consider the application of importance sampling in simulations of Markovian tandem networks in order to estimate the probability of rare events, such as network population overflow. We propose a heuristic methodology to obtain a good approximation to the 'optimal' state-dependent
Simulated epidemics in an empirical spatiotemporal network of 50,185 sexual contacts.
Directory of Open Access Journals (Sweden)
Luis E C Rocha
2011-03-01
Full Text Available Sexual contact patterns, both in their temporal and network structure, can influence the spread of sexually transmitted infections (STI. Most previous literature has focused on effects of network topology; few studies have addressed the role of temporal structure. We simulate disease spread using SI and SIR models on an empirical temporal network of sexual contacts in high-end prostitution. We compare these results with several other approaches, including randomization of the data, classic mean-field approaches, and static network simulations. We observe that epidemic dynamics in this contact structure have well-defined, rather high epidemic thresholds. Temporal effects create a broad distribution of outbreak sizes, even if the per-contact transmission probability is taken to its hypothetical maximum of 100%. In general, we conclude that the temporal correlations of our network accelerate outbreaks, especially in the early phase of the epidemics, while the network topology (apart from the contact-rate distribution slows them down. We find that the temporal correlations of sexual contacts can significantly change simulated outbreaks in a large empirical sexual network. Thus, temporal structures are needed alongside network topology to fully understand the spread of STIs. On a side note, our simulations further suggest that the specific type of commercial sex we investigate is not a reservoir of major importance for HIV.
Koopmans, C.C.; Jacobs, C.G.W.
2016-01-01
This paper introduces a GIS-based model that simulates the geographic expansion of transport networks by several decision-makers with varying objectives. The model progressively adds extensions to a growing network by choosing the most attractive investments from a limited choice set. Attractiveness
Method of simulating dose reduction for digital radiographic systems
International Nuclear Information System (INIS)
Baath, M.; Haakansson, M.; Tingberg, A.; Maansson, L. G.
2005-01-01
The optimisation of image quality vs. radiation dose is an important task in medical imaging. To obtain maximum validity of the optimisation, it must be based on clinical images. Images at different dose levels can then either be obtained by collecting patient images at the different dose levels sought to investigate - including additional exposures and permission from an ethical committee - or by manipulating images to simulate different dose levels. The aim of the present work was to develop a method of simulating dose reduction for digital radiographic systems. The method uses information about the detective quantum efficiency and noise power spectrum at the original and simulated dose levels to create an image containing filtered noise. When added to the original image this results in an image with noise which, in terms of frequency content, agrees with the noise present in an image collected at the simulated dose level. To increase the validity, the method takes local dose variations in the original image into account. The method was tested on a computed radiography system and was shown to produce images with noise behaviour similar to that of images actually collected at the simulated dose levels. The method can, therefore, be used to modify an image collected at one dose level so that it simulates an image of the same object collected at any lower dose level. (authors)
A tool for simulating parallel branch-and-bound methods
Golubeva, Yana; Orlov, Yury; Posypkin, Mikhail
2016-01-01
The Branch-and-Bound method is known as one of the most powerful but very resource consuming global optimization methods. Parallel and distributed computing can efficiently cope with this issue. The major difficulty in parallel B&B method is the need for dynamic load redistribution. Therefore design and study of load balancing algorithms is a separate and very important research topic. This paper presents a tool for simulating parallel Branchand-Bound method. The simulator allows one to run load balancing algorithms with various numbers of processors, sizes of the search tree, the characteristics of the supercomputer's interconnect thereby fostering deep study of load distribution strategies. The process of resolution of the optimization problem by B&B method is replaced by a stochastic branching process. Data exchanges are modeled using the concept of logical time. The user friendly graphical interface to the simulator provides efficient visualization and convenient performance analysis.
A tool for simulating parallel branch-and-bound methods
Directory of Open Access Journals (Sweden)
Golubeva Yana
2016-01-01
Full Text Available The Branch-and-Bound method is known as one of the most powerful but very resource consuming global optimization methods. Parallel and distributed computing can efficiently cope with this issue. The major difficulty in parallel B&B method is the need for dynamic load redistribution. Therefore design and study of load balancing algorithms is a separate and very important research topic. This paper presents a tool for simulating parallel Branchand-Bound method. The simulator allows one to run load balancing algorithms with various numbers of processors, sizes of the search tree, the characteristics of the supercomputer’s interconnect thereby fostering deep study of load distribution strategies. The process of resolution of the optimization problem by B&B method is replaced by a stochastic branching process. Data exchanges are modeled using the concept of logical time. The user friendly graphical interface to the simulator provides efficient visualization and convenient performance analysis.