Introducing Synchronisation in Deterministic Network Models
DEFF Research Database (Denmark)
Schiøler, Henrik; Jessen, Jan Jakob; Nielsen, Jens Frederik D.
2006-01-01
The paper addresses performance analysis for distributed real time systems through deterministic network modelling. Its main contribution is the introduction and analysis of models for synchronisation between tasks and/or network elements. Typical patterns of synchronisation are presented leading...
Suppression of phase synchronisation in network based on cat's brain
Lameu, Ewandson L.; Borges, Fernando S.; Borges, Rafael R.; Iarosz, Kelly C.; Caldas, Iberê L.; Batista, Antonio M.; Viana, Ricardo L.; Kurths, Jürgen
2016-04-01
We have studied the effects of perturbations on the cat's cerebral cortex. According to the literature, this cortex structure can be described by a clustered network. This way, we construct a clustered network with the same number of areas as in the cat matrix, where each area is described as a sub-network with a small-world property. We focus on the suppression of neuronal phase synchronisation considering different kinds of perturbations. Among the various controlling interventions, we choose three methods: delayed feedback control, external time-periodic driving, and activation of selected neurons. We simulate these interventions to provide a procedure to suppress undesired and pathological abnormal rhythms that can be associated with many forms of synchronisation. In our simulations, we have verified that the efficiency of synchronisation suppression by delayed feedback control is higher than external time-periodic driving and activation of selected neurons of the cat's cerebral cortex with the same coupling strengths.
DEFF Research Database (Denmark)
Fjelde, Tina; Hansen, Peter Bukhave; Kloch, Allan
1999-01-01
We show that complex packet synchronisation may be avoided in optical packetswitched networks. Detailed traffic analysis demonstrates that packet lossratios of 1e-10 are feasible under bursty traffic conditions for a highcapacity network consisting of asynchronously operated add-drop switch nodes...
Augmenting a TV Broadcast with Synchronised User Generated Video and Relevant Social Network Content
Stokking, H.M.; Veenhuizen, A.T.; Kaptein, A.M.; Niamut, O.A.
2014-01-01
As TNO, we have developed an Augmented Live Broadcast use case, using components from the FP7 STEER project. In this use case, a television broadcast of a live event is augmented with user generated content. This user generated content consists of videos made by users at the event, and also of relevant social network content. The current implementation uses timestamps inserted in the media streams to synchronise related media streams. Social networks are searched using EPG information as a st...
Synaptic Plasticity and Spike Synchronisation in Neuronal Networks
Borges, Rafael R.; Borges, Fernando S.; Lameu, Ewandson L.; Protachevicz, Paulo R.; Iarosz, Kelly C.; Caldas, Iberê L.; Viana, Ricardo L.; Macau, Elbert E. N.; Baptista, Murilo S.; Grebogi, Celso; Batista, Antonio M.
2017-09-01
Brain plasticity, also known as neuroplasticity, is a fundamental mechanism of neuronal adaptation in response to changes in the environment or due to brain injury. In this review, we show our results about the effects of synaptic plasticity on neuronal networks composed by Hodgkin-Huxley neurons. We show that the final topology of the evolved network depends crucially on the ratio between the strengths of the inhibitory and excitatory synapses. Excitation of the same order of inhibition revels an evolved network that presents the rich-club phenomenon, well known to exist in the brain. For initial networks with considerably larger inhibitory strengths, we observe the emergence of a complex evolved topology, where neurons sparsely connected to other neurons, also a typical topology of the brain. The presence of noise enhances the strength of both types of synapses, but if the initial network has synapses of both natures with similar strengths. Finally, we show how the synchronous behaviour of the evolved network will reflect its evolved topology.
Synaptic Plasticity and Spike Synchronisation in Neuronal Networks
Borges, Rafael R.; Borges, Fernando S.; Lameu, Ewandson L.; Protachevicz, Paulo R.; Iarosz, Kelly C.; Caldas, Iberê L.; Viana, Ricardo L.; Macau, Elbert E. N.; Baptista, Murilo S.; Grebogi, Celso; Batista, Antonio M.
2017-12-01
Brain plasticity, also known as neuroplasticity, is a fundamental mechanism of neuronal adaptation in response to changes in the environment or due to brain injury. In this review, we show our results about the effects of synaptic plasticity on neuronal networks composed by Hodgkin-Huxley neurons. We show that the final topology of the evolved network depends crucially on the ratio between the strengths of the inhibitory and excitatory synapses. Excitation of the same order of inhibition revels an evolved network that presents the rich-club phenomenon, well known to exist in the brain. For initial networks with considerably larger inhibitory strengths, we observe the emergence of a complex evolved topology, where neurons sparsely connected to other neurons, also a typical topology of the brain. The presence of noise enhances the strength of both types of synapses, but if the initial network has synapses of both natures with similar strengths. Finally, we show how the synchronous behaviour of the evolved network will reflect its evolved topology.
Vertical and oblique HF sounding with a network of synchronised ionosondes
Verhulst, Tobias; Altadill, David; Mielich, Jens; Reinisch, Bodo; Galkin, Ivan; Mouzakis, Angelos; Belehaki, Anna; Burešová, Dalia; Stankov, Stanimir; Blanch, Estefania; Kouba, Daniel
2017-10-01
A network of ionosondes in Europe has been established to monitor travelling ionospheric disturbances (TIDs) by simultaneously making vertical and oblique incidence HF sounding measurements. This network is the outcome of the Net-TIDE project, a collaboration between European Digisonde operators that have synchronised the sounding schedules of the Digisondes in order to record vertical and oblique ionogram traces simultaneously, and have added Digisonde-to-Digisonde (D2D) fixed frequency oblique-incidence measurements to the measurement schedule. The distances between the observatories involved in the project range from 500 km to over 2000 km. The technical feasibility of this network approach is explored. The challenge for the fixed-frequency D2D skymap measurements is the automatic selection of the sounding frequencies depending on the geometry of the sounding paths, the diurnal and seasonal ionospheric changes, and space weather induced events.
Local control of traffic flows in networks: Self-organisation of phase synchronised dynamics
Energy Technology Data Exchange (ETDEWEB)
Laemmer, Stefan; Donner, Reik [TU Dresden, Andreas-Schubert-Str. 23, 01062 Dresden (Germany); Helbing, Dirk [ETH Zuerich, Universitaetstr. 41, 8092 Zuerich (Switzerland)
2008-07-01
The effective control of flows in urban traffic networks is a subject of broad economic interest. During the last years, efforts have been made to develop decentralised control strategies that take only the actual state of present traffic conditions into account. In this contribution, we introduce a permeability model for the local control of conflicting material flows on networks, which incorporates a self-organisation of the flows. The dynamics of our model is studied under different situations, with a special emphasis on the development of a phase synchronised switching behaviour at the nodes of the traffic network. In order to improve the potential applicability of our concept, we discuss how a proper demand anticipation and the definition of a priority function can be used to further optimise the performance of the presented strategy.
Modelling of synchronisation and energy performance of FBE- and LBE-based standalone LTE-U networks
Directory of Open Access Journals (Sweden)
Jiamin Li
2017-06-01
Full Text Available Without the aid of licensed channel, deploying long-term evolution (LTE networks over unlicensed spectrum (named standalone LTE-U networks faces the difficulty of establishing and maintaining synchronisation between user equipments and base stations. In this work, considering the two modes of listen-before-talk-based channel access scheme, frame-based equipment (FBE and load-based equipment (LBE, the authors propose analytical frameworks to study the successful probability of synchronisation and the energy consumption of synchronisation in a standalone LTE-U network. Specifically, for the LBE mode, the authors also propose a Lattice-Poisson algorithm-based approach to derive the distribution of the channel non-occupancy period of a standalone LTE-U network. Furthermore, the authors explore the impact of diverse protocol parameters of both FBE and LBE modes on the two studied performance metrics. Simulation results demonstrate the accuracy of the analysis, and shed some light on the selection of FBE and LBE for standalone LTE-U networks, in terms of synchronisation, energy consumption, and throughput of standalone LTE-U and Wi-Fi networks.
van Kleunen, W.A.P.; Meratnia, Nirvana; Havinga, Paul J.M.
2012-01-01
In this paper we describe a design for an underwater MAC protocol which combines localization, time-synchronisation and communication. This protocol is designed for small-scale clustered networks in which all nodes are able to ommunicate with each other. We consider an integrated design of
Modelling cell cycle synchronisation in networks of coupled radial glial cells.
Barrack, Duncan S; Thul, Rüdiger; Owen, Markus R
2015-07-21
Radial glial cells play a crucial role in the embryonic mammalian brain. Their proliferation is thought to be controlled, in part, by ATP mediated calcium signals. It has been hypothesised that these signals act to locally synchronise cell cycles, so that clusters of cells proliferate together, shedding daughter cells in uniform sheets. In this paper we investigate this cell cycle synchronisation by taking an ordinary differential equation model that couples the dynamics of intracellular calcium and the cell cycle and extend it to populations of cells coupled via extracellular ATP signals. Through bifurcation analysis we show that although ATP mediated calcium release can lead to cell cycle synchronisation, a number of other asynchronous oscillatory solutions including torus solutions dominate the parameter space and cell cycle synchronisation is far from guaranteed. Despite this, numerical results indicate that the transient and not the asymptotic behaviour of the system is important in accounting for cell cycle synchronisation. In particular, quiescent cells can be entrained on to the cell cycle via ATP mediated calcium signals initiated by a driving cell and crucially will cycle in near synchrony with the driving cell for the duration of neurogenesis. This behaviour is highly sensitive to the timing of ATP release, with release at the G1/S phase transition of the cell cycle far more likely to lead to near synchrony than release during mid G1 phase. This result, which suggests that ATP release timing is critical to radial glia cell cycle synchronisation, may help us to understand normal and pathological brain development. Copyright © 2015 Elsevier Ltd. All rights reserved.
Towards Optimal Transport Networks
Directory of Open Access Journals (Sweden)
Erik P. Vargo
2010-08-01
Full Text Available Our ultimate goal is to design transportation net- works whose dynamic performance metrics (e.g. pas- senger throughput, passenger delay, and insensitivity to weather disturbances are optimized. Here the fo- cus is on optimizing static features of the network that are known to directly aﬀect the network dynamics. First, we present simulation results which support a connection between maximizing the ﬁrst non-trivial eigenvalue of a network's Laplacian and superior air- port network performance. Then, we explore the ef- fectiveness of a tabu search heuristic for optimizing this metric by comparing experimental results to the- oretical upper bounds. We also consider generating upper bounds on a network's algebraic connectivity via the solution of semideﬁnite programming (SDP relaxations. A modiﬁcation of an existing subgraph extraction algorithm is implemented to explore the underlying regional structures in the U.S. airport net- work, with the hope that the resulting localized struc- tures can be optimized independently and reconnected via a "backbone" network to achieve superior network performance.
Synchronisation of fractional-order time delayed chaotic systems with ring connection
He, S.; Sun, K.; Wang, H.
2016-02-01
In this paper, synchronisation of fractional-order time delayed chaotic systems in ring networks is investigated. Based on Lyapunov stability theory, a new generic synchronisation criterion for N-coupled chaotic systems with time delay is proposed. The synchronisation scheme is applied to N-coupled fractional-order time delayed simplified Lorenz systems, and the Adomian decomposition method (ADM) is developed for solving these chaotic systems. Performance analysis of the synchronisation network is carried out. Numerical experiments demonstrate that synchronisation realises in both state variables and intermediate variables, which verifies the effectiveness of the proposed method.
DEFF Research Database (Denmark)
Hansen, C. T.; Madsen, Kaj; Nielsen, Hans Bruun
1991-01-01
algorithm using successive linear programming is presented. The performance of the algorithm is illustrated by optimizing a network with 201 pipes and 172 nodes. It is concluded that the new algorithm seems to be very efficient and stable, and that it always finds a solution with a cost near the best...
OPTIMAL NETWORK TOPOLOGY DESIGN
Yuen, J. H.
1994-01-01
This program was developed as part of a research study on the topology design and performance analysis for the Space Station Information System (SSIS) network. It uses an efficient algorithm to generate candidate network designs (consisting of subsets of the set of all network components) in increasing order of their total costs, and checks each design to see if it forms an acceptable network. This technique gives the true cost-optimal network, and is particularly useful when the network has many constraints and not too many components. It is intended that this new design technique consider all important performance measures explicitly and take into account the constraints due to various technical feasibilities. In the current program, technical constraints are taken care of by the user properly forming the starting set of candidate components (e.g. nonfeasible links are not included). As subsets are generated, they are tested to see if they form an acceptable network by checking that all requirements are satisfied. Thus the first acceptable subset encountered gives the cost-optimal topology satisfying all given constraints. The user must sort the set of "feasible" link elements in increasing order of their costs. The program prompts the user for the following information for each link: 1) cost, 2) connectivity (number of stations connected by the link), and 3) the stations connected by that link. Unless instructed to stop, the program generates all possible acceptable networks in increasing order of their total costs. The program is written only to generate topologies that are simply connected. Tests on reliability, delay, and other performance measures are discussed in the documentation, but have not been incorporated into the program. This program is written in PASCAL for interactive execution and has been implemented on an IBM PC series computer operating under PC DOS. The disk contains source code only. This program was developed in 1985.
Strategic production line synchronisation
Directory of Open Access Journals (Sweden)
Hattingh, Teresa
2016-08-01
Full Text Available A study was conducted at the sole global producer of suspension struts for a particular vehicle manufacturer. This supplier is currently able to meet customer demand. However, it does so because of a large finished goods and work-in-progress (WIP inventory. The plant operates two production processes that are separated by a large buffer of WIP, which essentially decouples the production processes. This study aimed to reduce this WIP buffer; this would require the processes to become synchronised, bearing in mind that the reliability of delivery should not decrease. A tool that considers time, quality, and machine capacity was developed to assess the impact of line synchronisation on company performance figures. It was found that line synchronisation produced several benefits for the supplier, including batch size reduction, lower inventory levels, and associated shorter lead times. This further allowed the supplier to improve flow in the plant by introducing a pull system. Improved visual oversight could lead to further improved problem-solving and innovation.
Optimal Fragile Financial Networks
Castiglionesi, F.; Navarro, N.
2007-01-01
We study a financial network characterized by the presence of depositors, banks and their shareholders. Belonging to a financial network is beneficial for both the depositors and banks' shareholders since the return to investment increases with the number of banks connected. However, the network is
Combinatorial optimization networks and matroids
Lawler, Eugene
2011-01-01
Perceptively written text examines optimization problems that can be formulated in terms of networks and algebraic structures called matroids. Chapters cover shortest paths, network flows, bipartite matching, nonbipartite matching, matroids and the greedy algorithm, matroid intersections, and the matroid parity problems. A suitable text or reference for courses in combinatorial computing and concrete computational complexity in departments of computer science and mathematics.
Cluster-modified function projective synchronisation of complex ...
Indian Academy of Sciences (India)
Permanent link: http://www.ias.ac.in/article/fulltext/pram/090/02/0025 ... We use adaptive control method to derive CMFPS criteria based on Lyapunov stability theory. Each cluster of networks is synchronised with target system by state transformation with scaling function matrix. Numerical simulation results are presented ...
Optimizations in Heterogeneous Mobile Networks
DEFF Research Database (Denmark)
Popovska Avramova, Andrijana
Heterogeneous Mobile Networks bring advantages over homogeneous deployments in achieving the demand for mobile network capacity and coverage not just outdoor rural and urban areas, but also to homes and enterprises where the large portion of the mobile traffic is generated. However......, the heterogeneity in the mobile networks bring many challenges that are discusses with this dissertation. More focus is placed on specific issues with indifferent areas of heterogeneity by proposing optimizations in order to overcome the considered problems.The heterogeneity of mobile networks, together...... with the densification of the base stations, bring into a very complex network management and operation control for the mobile operators. Furthermore, the need to provide always best connection and service with high quality demands for a joint overall network resource management. This thesis addresses this challenge...
Optimization of spatial complex networks
Guillier, S.; Muñoz, V.; Rogan, J.; Zarama, R.; Valdivia, J. A.
2017-02-01
First, we estimate the connectivity properties of a predefined (fixed node locations) spatial network which optimizes a connectivity functional that balances construction and transportation costs. In this case we obtain a Gaussian distribution for the connectivity. However, when we consider these spatial networks in a growing process, we obtain a power law distribution for the connectivity. If the transportation costs in the functional involve the shortest geometrical path, we obtain a scaling exponent γ = 2.5. However, if the transportation costs in the functional involve just the shortest path, we obtain γ = 2.2. Both cases may be useful to analyze in some real networks.
Synchronised Swimming of Two Fish
Novati, Guido; Alexeev, Dmitry; Rossinelli, Diego; van Rees, Wim M; Koumoutsakos, Petros
2016-01-01
We study the fluid dynamics of two fish-like bodies with synchronised swimming patterns. Our studies are based on two-dimensional simulations of viscous incompressible flows. We distinguish between motion patterns that are externally imposed on the swimmers and self-propelled swimmers that learn manoeuvres to achieve certain goals. Simulations of two rigid bodies executing pre-specified motion indicate that flow-mediated interactions can lead to substantial drag reduction and may even generate thrust intermittently. In turn we examine two self-propelled swimmers arranged in a leader-follower configuration, with a-priori specified body-deformations. We find that the swimming of the leader remains largely unaffected, while the follower experiences either an increase or decrease in swimming speed, depending on the initial conditions. Finally, we consider a follower that synchronises its motion so as to minimise its lateral deviations from the leader's path. The leader employs a steady gait while the follower use...
Optimization in a Networked Economy
Directory of Open Access Journals (Sweden)
Ahmet Sekreter
2017-10-01
Full Text Available An age of network has been living for the last decades. The information technologies have been used by hundreds of millions of users. These technologies are enabling to connect businesses and economic activities. One of the characteristics of the networked economy is the amount of data that produced due to the interlinking of firms, individuals, processes by businesses, and economic activities. Another issue with the networked economy is the complexity of the data. Extraction of the knowledge from the networked economy has challenges by the traditional approach since data is large scale, second decentralized, and third they connect many heterogeneous agents. The challenges can be overcome by the new optimization methods including human element or the social interactions with technological infrastructure.
Optimal monitoring of computer networks
Energy Technology Data Exchange (ETDEWEB)
Fedorov, V.V.; Flanagan, D.
1997-08-01
The authors apply the ideas from optimal design theory to the very specific area of monitoring large computer networks. The behavior of these networks is so complex and uncertain that it is quite natural to use the statistical methods of experimental design which were originated in such areas as biology, behavioral sciences and agriculture, where the random character of phenomena is a crucial component and systems are too complicated to be described by some sophisticated deterministic models. They want to emphasize that only the first steps have been completed, and relatively simple underlying concepts about network functions have been used. Their immediate goal is to initiate studies focused on developing efficient experimental design techniques which can be used by practitioners working with large networks operating and evolving in a random environment.
Optimization with Potts Neural Networks
Söderberg, Bo
The Potts Neural Network approach to non-binary discrete optimization problems is described. It applies to problems that can be described as a set of elementary `multiple choice' options. Instead of the conventional binary (Ising) neurons, mean field Potts neurons, having several available states, are used to describe the elementary degrees of freedom of such problems. The dynamics consists of iterating the mean field equations with annealing until convergence. Due to its deterministic character, the method is quite fast. When applied to problems of Graph Partition and scheduling types, it produces very good solutions also for problems of considerable size.
THE SYNCHRONISATION OF OESTRUS IN SHEEP. 4 ...
African Journals Online (AJOL)
synchronisation of ovulation, since the degree of synchronisation of oestrus in sheep is much higher following the ... However, considering that ovulation takes place approximately 30 hours after the commencement of oestrus (Van ... inject i on ( Accurttu la t ive ). ^1\\. - Fixed Time A.t.. GROUP I INTRAVAGINAL SPONGES.
Airborne Network Optimization with Dynamic Network Update
2015-03-26
Faculty Department of Electrical and Computer Engineering Graduate School of Engineering and Management Air Force Institute of Technology Air University...require small amounts of network bandwidth to perform routing. This thesis advocates the use of Kalman filters to predict network congestion in...airborne networks. Intelligent agents can make use of Kalman filter predictions to make informed decisions to manage communication in airborne networks. The
2016 Network Games, Control, and Optimization Conference
Jimenez, Tania; Solan, Eilon
2017-01-01
This contributed volume offers a collection of papers presented at the 2016 Network Games, Control, and Optimization conference (NETGCOOP), held at the University of Avignon in France, November 23-25, 2016. These papers highlight the increasing importance of network control and optimization in many networking application domains, such as mobile and fixed access networks, computer networks, social networks, transportation networks, and, more recently, electricity grids and biological networks. Covering a wide variety of both theoretical and applied topics in the areas listed above, the authors explore several conceptual and algorithmic tools that are needed for efficient and robust control operation, performance optimization, and better understanding the relationships between entities that may be acting cooperatively or selfishly in uncertain and possibly adversarial environments. As such, this volume will be of interest to applied mathematicians, computer scientists, engineers, and researchers in other relate...
Optimization for communications and networks
Saengudomlert, Poompat
2011-01-01
PrefaceList of FiguresIntroductionComponents of Optimization ProblemsClasses of Optimization ProblemsConvex OptimizationConvex Sets and Convex FunctionsProperties of Convex OptimizationFurther Properties of Convex SetsDual ProblemsLagrange MultipliersPrimal-Dual Optimality ConditionsSensitivity AnalysisNotes on Maximization ProblemsNumerical Algorithms for Unconstrained OptimizationNumerical Algorithms for Constrained OptimizationApplication: Transmit Power AllocationApplication: Minimum Delay RoutingExercise ProblemsLinear OptimizationIllustrative ExampleProperties of Linear Optimization Prob
Optimizing the next generation optical access networks
DEFF Research Database (Denmark)
Amaya Fernández, Ferney Orlando; Soto, Ana Cardenas; Tafur Monroy, Idelfonso
2009-01-01
Several issues in the design and optimization of the next generation optical access network (NG-OAN) are presented. The noise, the distortion and the fiber optic nonlinearities are considered to optimize the video distribution link in a passive optical network (PON). A discussion of the effect of...
Mathematical model of highways network optimization
Sakhapov, R. L.; Nikolaeva, R. V.; Gatiyatullin, M. H.; Makhmutov, M. M.
2017-12-01
The article deals with the issue of highways network design. Studies show that the main requirement from road transport for the road network is to ensure the realization of all the transport links served by it, with the least possible cost. The goal of optimizing the network of highways is to increase the efficiency of transport. It is necessary to take into account a large number of factors that make it difficult to quantify and qualify their impact on the road network. In this paper, we propose building an optimal variant for locating the road network on the basis of a mathematical model. The article defines the criteria for optimality and objective functions that reflect the requirements for the road network. The most fully satisfying condition for optimality is the minimization of road and transport costs. We adopted this indicator as a criterion of optimality in the economic-mathematical model of a network of highways. Studies have shown that each offset point in the optimal binding road network is associated with all other corresponding points in the directions providing the least financial costs necessary to move passengers and cargo from this point to the other corresponding points. The article presents general principles for constructing an optimal network of roads.
Optimization of temporal networks under uncertainty
Wiesemann, Wolfram
2012-01-01
Many decision problems in Operations Research are defined on temporal networks, that is, workflows of time-consuming tasks whose processing order is constrained by precedence relations. For example, temporal networks are used to model projects, computer applications, digital circuits and production processes. Optimization problems arise in temporal networks when a decision maker wishes to determine a temporal arrangement of the tasks and/or a resource assignment that optimizes some network characteristic (e.g. the time required to complete all tasks). The parameters of these optimization probl
Adaptive optimization and control using neural networks
Energy Technology Data Exchange (ETDEWEB)
Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.
1993-10-22
Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.
Sensitive Dependence of Optimal Network Dynamics on Network Structure
Directory of Open Access Journals (Sweden)
Takashi Nishikawa
2017-11-01
Full Text Available The relation between network structure and dynamics is determinant for the behavior of complex systems in numerous domains. An important long-standing problem concerns the properties of the networks that optimize the dynamics with respect to a given performance measure. Here, we show that such optimization can lead to sensitive dependence of the dynamics on the structure of the network. Specifically, using diffusively coupled systems as examples, we demonstrate that the stability of a dynamical state can exhibit sensitivity to unweighted structural perturbations (i.e., link removals and node additions for undirected optimal networks and to weighted perturbations (i.e., small changes in link weights for directed optimal networks. As mechanisms underlying this sensitivity, we identify discontinuous transitions occurring in the complement of undirected optimal networks and the prevalence of eigenvector degeneracy in directed optimal networks. These findings establish a unified characterization of networks optimized for dynamical stability, which we illustrate using Turing instability in activator-inhibitor systems, synchronization in power-grid networks, network diffusion, and several other network processes. Our results suggest that the network structure of a complex system operating near an optimum can potentially be fine-tuned for a significantly enhanced stability compared to what one might expect from simple extrapolation. On the other hand, they also suggest constraints on how close to the optimum the system can be in practice. Finally, the results have potential implications for biophysical networks, which have evolved under the competing pressures of optimizing fitness while remaining robust against perturbations.
Mathematical Aspects of Network Routing Optimization
Oliveira, Carlos AS
2011-01-01
Before the appearance of broadband links and wireless systems, networks have been used to connect people in new ways. Now, the modern world is connected through large-scale, computational networked systems such as the Internet. Because of the ever-advancing technology of networking, efficient algorithms have become increasingly necessary to solve some of the problems developing in this area. "Mathematical Aspects of Network Routing Optimization" focuses on computational issues arising from the process of optimizing network routes, such as quality of the resulting links and their reli
Neural network optimization, components, and design selection
Weller, Scott W.
1991-01-01
Neural Networks are part of a revived technology which has received a lot of hype in recent years. As is apt to happen in any hyped technology, jargon and predictions make its assimilation and application difficult. Nevertheless, Neural Networks have found use in a number of areas, working on non-trivial and non-contrived problems. For example, one net has been trained to "read", translating English text into phoneme sequences. Other applications of Neural Networks include data base manipulation and the solving of routing and classification types of optimization problems. It was their use in optimization that got me involved with Neural Networks. As it turned out, "optimization" used in this context was somewhat misleading, because while some network configurations could indeed solve certain kinds of optimization problems, the configuring or "training" of a Neural Network itself is an optimization problem, and most of the literature which talked about Neural Nets and optimization in the same breath did not speak to my goal of using Neural Nets to help solve lens optimization problems. I did eventually apply Neural Network to lens optimization, and I will touch on those results. The application of Neural Nets to the problem of lens selection was much more successful, and those results will dominate this paper.
Speed Optimization in Liner Shipping Network Design
DEFF Research Database (Denmark)
Brouer, Berit Dangaard; Karsten, Christian Vad; Pisinger, David
for the bunker consumption in the network as well as the transit time of cargo. Speed optimization has been considered for tramp shipping showing significant reductions in fuel consumption. However, variable speeds has not been considered for post optimization of the LSNDP, where speed optimization could result...
Location based Network Optimizations for Mobile Wireless Networks
DEFF Research Database (Denmark)
Nielsen, Jimmy Jessen
The availability of location information in mobile devices, e.g., through built-in GPS receivers in smart phones, has motivated the investigation of the usefulness of location based network optimizations. Since the quality of input information is important for network optimizations, a main focus...... of this work is to evaluate how location based network optimizations are affected by varying quality of input information such as location information and user movements. The first contribution in this thesis concerns cooperative network-based localization systems. The investigations focus on assessing...... the achievable accuracy of future localization system in mobile settings, as well as quantifying the impact of having a realistic model of the required measurement exchanges. Secondly, this work has considered different large scale and small scale location based network optimizations, namely centralized relay...
Power consumption optimization strategy for wireless networks
DEFF Research Database (Denmark)
Cornean, Horia; Kumar, Sanjay; Marchetti, Nicola
2011-01-01
In this paper, we focus on the optimization of the total power utilization in a communication network. The utility function used in this paper aims for the maximization of joint network capacity in an interference limited environment. This paper outlines the various approaches currently being used...... in order to reduce the total power consumption in a multi cellular network. We present an algorithm for power optimization under no interference and in presence of interference conditions, targeting to maximize the network capacity. The convergence of the algorithm is guaranteed if the interference...
Optimal design of network distribution systems
Directory of Open Access Journals (Sweden)
U. Passy
2003-12-01
Full Text Available The problem of finding the optimal distribution of pressure drop over a network is solved via an unconstrained gradient type algorithm. The developed algorithm is computationally attractive. Problems with several hundred variables and constraints were solved.
Genetic Algorithm Optimized Neural Networks Ensemble as ...
African Journals Online (AJOL)
NJD
Genetic Algorithm Optimized Neural Networks Ensemble as. Calibration Model for Simultaneous Spectrophotometric. Estimation of Atenolol and Losartan Potassium in Tablets. Dondeti Satyanarayana*, Kamarajan Kannan and Rajappan Manavalan. Department of Pharmacy, Annamalai University, Annamalainagar, Tamil ...
Neural Networks for Optimal Control
DEFF Research Database (Denmark)
Sørensen, O.
1995-01-01
Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process.......Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process....
Epidemiologically optimal static networks from temporal network data
Holme, Petter
2013-01-01
Network epidemiology's most important assumption is that the contact structure over which infectious diseases propagate can be represented as a static network. However, contacts are highly dynamic, changing at many time scales. In this paper, we investigate conceptually simple methods to construct static graphs for network epidemiology from temporal contact data. We evaluate these methods on empirical and synthetic model data. For almost all our cases, the network representation that captures most relevant information is a so-called exponential-threshold network. In these, each contact contributes with a weight decreasing exponentially with time, and there is an edge between a pair of vertices if the weight between them exceeds a threshold. Networks of aggregated contacts over an optimally chosen time window perform almost as good as the exponential-threshold networks. On the other hand, networks of accumulated contacts over the entire sampling time, and networks of concurrent partnerships, perform worse. We ...
WiMAX network performance monitoring & optimization
DEFF Research Database (Denmark)
Zhang, Qi; Dam, H
2008-01-01
In this paper we present our WiMAX (worldwide interoperability for microwave access) network performance monitoring and optimization solution. As a new and small WiMAX network operator, there are many demanding issues that we have to deal with, such as limited available frequency resource, tight ...
Brocade: Optimal flow placement in SDN networks
CERN. Geneva
2015-01-01
Today' network poses several challanges to network providers. These challanges fall in to a variety of areas ranging from determining efficient utilization of network bandwidth to finding out which user applications consume majority of network resources. Also, how to protect a given network from volumetric and botnet attacks. Optimal placement of flows deal with identifying network issues and addressing them in a real-time. The overall solution helps in building new services where a network is more secure and more efficient. Benefits derived as a result are increased network efficiency due to better capacity and resource planning, better security with real-time threat mitigation, and improved user experience as a result of increased service velocity.
Optimizing sentinel surveillance in temporal network epidemiology
Bai, Yuan; Yang, Bo; LIN, LIJUAN; Jose L Herrera; Du, Zhanwei; Holme, Petter
2017-01-01
To help health policy makers gain response time to mitigate infectious disease threats, it is essential to have an efficient epidemic surveillance. One common method of disease surveillance is to carefully select nodes (sentinels, or sensors) in the network to report outbreaks. One would like to choose sentinels so that they discover the outbreak as early as possible. The optimal choice of sentinels depends on the network structure. Studies have addressed this problem for static networks, but...
Optimal hub location in pipeline networks
Energy Technology Data Exchange (ETDEWEB)
Dott, D.R.; Wirasinghe, S.C.; Chakma, A. [Univ. of Calgary, Alberta (Canada)
1996-12-31
This paper discusses optimization strategies and techniques for the location of natural gas marketing hubs in the North American gas pipeline network. A hub is a facility at which inbound and outbound network links meet and freight is redirected towards their destinations. Common examples of hubs used in the gas pipeline industry include gas plants, interconnects and market centers. Characteristics of the gas pipeline industry which are relevant to the optimization of transportation costs using hubs are presented. Allocation techniques for solving location-allocation problems are discussed. An outline of the research in process by the authors in the field of optimal gas hub location concludes the paper.
Optimization of Power Distribution Networks in Megacities
Manusov, V. Z.; Matrenin, P. V.; Ahyoev, J. S.; Atabaeva, L. Sh
2017-06-01
The study deals with the problem of city electrical networks optimization in big towns and megacities to increase electrical energy quality and decrease real and active power losses in the networks as well as in domestic consumers. The optimization is carried out according to the location selection and separate reactive power source in 10 kW networks of Swarm Intelligence algorithms, in particular, of Particle Swarm one. The problem solution based on Particle Swarm algorithm is determined by variables being discrete quantities and, in addition, there are several local minimums (troughs) to be available for a global minimum to be found. It is proved that the city power supply system optimization is carried out by the additional reactive power source to be installed at consumers location reducing reactive power flow, thereby, ensuring increase of power supply system quality and decrease of power losses in city networks.
Small cell networks deployment, management, and optimization
Claussen, Holger; Ho, Lester; Razavi, Rouzbeh; Kucera, Stepan
2018-01-01
Small Cell Networks: Deployment, Management, and Optimization addresses key problems of the cellular network evolution towards HetNets. It focuses on the latest developments in heterogeneous and small cell networks, as well as their deployment, operation, and maintenance. It also covers the full spectrum of the topic, from academic, research, and business to the practice of HetNets in a coherent manner. Additionally, it provides complete and practical guidelines to vendors and operators interested in deploying small cells. The first comprehensive book written by well-known researchers and engineers from Nokia Bell Labs, Small Cell Networks begins with an introduction to the subject--offering chapters on capacity scaling and key requirements of future networks. It then moves on to sections on coverage and capacity optimization, and interference management. From there, the book covers mobility management, energy efficiency, and small cell deployment, ending with a section devoted to future trends and applicat...
Improving the ultra-low-power performance of IEEE 802.15.6 by adaptive synchronisation
Timmons, N.F.; Scanlon, W.G.
2011-01-01
In ultra-low data rate wireless sensor networks (WSNs) waking up just to listen to a beacon every superframe can be a major waste of energy. This study introduces MedMAC, a medium access protocol for ultra-low data rate WSNs that achieves significant energy efficiency through a novel synchronisation
Optimal configuration of digital communication network
Hwang, Yong G.
1990-12-01
As the costs for maintaining computer communication networks are rapidly rising, it is particularly important to design the network efficiently. The objective of this thesis is to model the minimum cost design of digital communications networks and propose a heuristical solution approach to the formulated model. The minimum cost design has been modeled as a zero-one integer programming problem. The Lagrangian relaxation method and subgradient optimization procedure have been used to find reasonably good feasible solutions. Although the reliability for computer communication networks is as important as the cost factor, only the cost factor is considered in the context of this thesis.
MEKELLE TRANSMISSION NETWORK USING OPTIMAL ...
African Journals Online (AJOL)
lab
In this paper, constraint dependent mutation function is implemented. The above mentioned operations of selection, crossover and mutation are repeated until the best solution is obtained. Genetic Algorithm Tool of MATLAB. The Optimization Tool of MATLAB is a graphical user interface (GUI) that can be used to solve an.
Overlap Synchronisation in Multipartite Random Energy Models
Genovese, Giuseppe; Tantari, Daniele
2017-12-01
In a multipartite random energy model, made of a number of coupled generalised random energy models (GREMs), we determine the joint law of the overlaps in terms of the ones of the single GREMs. This provides the simplest example of the so-called overlap synchronisation.
Memory-optimal neural network approximation
Bölcskei, Helmut; Grohs, Philipp; Kutyniok, Gitta; Petersen, Philipp
2017-08-01
We summarize the main results of a recent theory-developed by the authors-establishing fundamental lower bounds on the connectivity and memory requirements of deep neural networks as a function of the complexity of the function class to be approximated by the network. These bounds are shown to be achievable. Specifically, all function classes that are optimally approximated by a general class of representation systems-so-called affine systems-can be approximated by deep neural networks with minimal connectivity and memory requirements. Affine systems encompass a wealth of representation systems from applied harmonic analysis such as wavelets, shearlets, ridgelets, α-shearlets, and more generally α-molecules. This result elucidates a remarkable universality property of deep neural networks and shows that they achieve the optimum approximation properties of all affine systems combined. Finally, we present numerical experiments demonstrating that the standard stochastic gradient descent algorithm generates deep neural networks which provide close-to-optimal approximation rates at minimal connectivity. Moreover, stochastic gradient descent is found to actually learn approximations that are sparse in the representation system optimally sparsifying the function class the network is trained on.
On sparsely connected optimal neural networks
Energy Technology Data Exchange (ETDEWEB)
Beiu, V. [Los Alamos National Lab., NM (United States); Draghici, S. [Wayne State Univ., Detroit, MI (United States)
1997-10-01
This paper uses two different approaches to show that VLSI- and size-optimal discrete neural networks are obtained for small fan-in values. These have applications to hardware implementations of neural networks, but also reveal an intrinsic limitation of digital VLSI technology: its inability to cope with highly connected structures. The first approach is based on implementing F{sub n,m} functions. The authors show that this class of functions can be implemented in VLSI-optimal (i.e., minimizing AT{sup 2}) neural networks of small constant fan-ins. In order to estimate the area (A) and the delay (T) of such networks, the following cost functions will be used: (i) the connectivity and the number-of-bits for representing the weights and thresholds--for good estimates of the area; and (ii) the fan-ins and the length of the wires--for good approximates of the delay. The second approach is based on implementing Boolean functions for which the classical Shannon`s decomposition can be used. Such a solution has already been used to prove bounds on the size of fan-in 2 neural networks. They will generalize the result presented there to arbitrary fan-in, and prove that the size is minimized by small fan-in values. Finally, a size-optimal neural network of small constant fan-ins will be suggested for F{sub n,m} functions.
WiMax network planning and optimization
Zhang, Yan
2009-01-01
This book offers a comprehensive explanation on how to dimension, plan, and optimize WiMAX networks. The first part of the text introduces WiMAX networks architecture, physical layer, standard, protocols, security mechanisms, and highly related radio access technologies. It covers system framework, topology, capacity, mobility management, handoff management, congestion control, medium access control (MAC), scheduling, Quality of Service (QoS), and WiMAX mesh networks and security. Enabling easy understanding of key concepts and technologies, the second part presents practical examples and illu
Modeling, Optimization & Control of Hydraulic Networks
DEFF Research Database (Denmark)
Tahavori, Maryamsadat
2014-01-01
in water network is pressure management. By reducing the pressure in the water network, the leakage can be reduced significantly. Also it reduces the amount of energy consumption in water networks. The primary purpose of this work is to develop control algorithms for pressure control in water supply....... The nonlinear network model is derived based on the circuit theory. A suitable projection is used to reduce the state vector and to express the model in standard state-space form. Then, the controllability of nonlinear nonaffine hydraulic networks is studied. The Lie algebra-based controllability matrix is used...... to solve nonlinear optimal control problems. In the water supply system model, the hydraulic resistance of the valve is estimated by real data and it is considered to be a disturbance. The disturbance in our system is updated every 24 hours based on the amount of water usage by consumers every day. Model...
Optimal Sales Schemes for Network Goods
DEFF Research Database (Denmark)
Parakhonyak, Alexei; Vikander, Nick
This paper examines the optimal sequencing of sales in the presence of network externalities. A firm sells a good to a group of consumers whose payoff from buying is increasing in total quantity sold. The firm selects the order to serve consumers so as to maximize expected sales. It can serve all...
Optimizing Key Updates in Sensor Networks
DEFF Research Database (Denmark)
Yuksel, Ender; Nielson, Hanne Riis; Nielson, Flemming
2011-01-01
restrict the amount of data that may be exposed when a key is compromised. In this paper, we propose novel key update methods, and benefiting from stochastic model checking we propose a novel method for determining optimal key update strategies for custom network scenarios. We also present a case study...... where an application in commercial building automation is considered....
Generalized networking engineering: optimal pricing and routing in multiservice networks
Mitra, Debasis; Wang, Qiong
2002-07-01
One of the functions of network engineering is to allocate resources optimally to forecasted demand. We generalize the mechanism by incorporating price-demand relationships into the problem formulation, and optimizing pricing and routing jointly to maximize total revenue. We consider a network, with fixed topology and link bandwidths, that offers multiple services, such as voice and data, each having characteristic price elasticity of demand, and quality of service and policy requirements on routing. Prices, which depend on service type and origin-destination, determine demands, that are routed, subject to their constraints, so as to maximize revenue. We study the basic properties of the optimal solution and prove that link shadow costs provide the basis for both optimal prices and optimal routing policies. We investigate the impact of input parameters, such as link capacities and price elasticities, on prices, demand growth, and routing policies. Asymptotic analyses, in which network bandwidth is scaled to grow, give results that are noteworthy for their qualitative insights. Several numerical examples illustrate the analyses.
Phase transitions in Pareto optimal complex networks.
Seoane, Luís F; Solé, Ricard
2015-09-01
The organization of interactions in complex systems can be described by networks connecting different units. These graphs are useful representations of the local and global complexity of the underlying systems. The origin of their topological structure can be diverse, resulting from different mechanisms including multiplicative processes and optimization. In spatial networks or in graphs where cost constraints are at work, as it occurs in a plethora of situations from power grids to the wiring of neurons in the brain, optimization plays an important part in shaping their organization. In this paper we study network designs resulting from a Pareto optimization process, where different simultaneous constraints are the targets of selection. We analyze three variations on a problem, finding phase transitions of different kinds. Distinct phases are associated with different arrangements of the connections, but the need of drastic topological changes does not determine the presence or the nature of the phase transitions encountered. Instead, the functions under optimization do play a determinant role. This reinforces the view that phase transitions do not arise from intrinsic properties of a system alone, but from the interplay of that system with its external constraints.
Neural network optimization, components, and design selection
Weller, Scott W.
1990-07-01
Neural Networks are part of a revived technology which has received a lot of hype in recent years. As is apt to happen in any hyped technology, jargon and predictions make its assimilation and application difficult. Nevertheless, Neural Networks have found use in a number of areas, working on non-trivial and noncontrived problems. For example, one net has been trained to "read", translating English text into phoneme sequences. Other applications of Neural Networks include data base manipulation and the solving of muting and classification types of optimization problems. Neural Networks are constructed from neurons, which in electronics or software attempt to model but are not constrained by the real thing, i.e., neurons in our gray matter. Neurons are simple processing units connected to many other neurons over pathways which modify the incoming signals. A single synthetic neuron typically sums its weighted inputs, runs this sum through a non-linear function, and produces an output. In the brain, neurons are connected in a complex topology: in hardware/software the topology is typically much simpler, with neurons lying side by side, forming layers of neurons which connect to the layer of neurons which receive their outputs. This simplistic model is much easier to construct than the real thing, and yet can solve real problems. The information in a network, or its "memory", is completely contained in the weights on the connections from one neuron to another. Establishing these weights is called "training" the network. Some networks are trained by design -- once constructed no further learning takes place. Other types of networks require iterative training once wired up, but are not trainable once taught Still other types of networks can continue to learn after initial construction. The main benefit to using Neural Networks is their ability to work with conflicting or incomplete ("fuzzy") data sets. This ability and its usefulness will become evident in the following
Optimized null model for protein structure networks.
Milenković, Tijana; Filippis, Ioannis; Lappe, Michael; Przulj, Natasa
2009-06-26
Much attention has recently been given to the statistical significance of topological features observed in biological networks. Here, we consider residue interaction graphs (RIGs) as network representations of protein structures with residues as nodes and inter-residue interactions as edges. Degree-preserving randomized models have been widely used for this purpose in biomolecular networks. However, such a single summary statistic of a network may not be detailed enough to capture the complex topological characteristics of protein structures and their network counterparts. Here, we investigate a variety of topological properties of RIGs to find a well fitting network null model for them. The RIGs are derived from a structurally diverse protein data set at various distance cut-offs and for different groups of interacting atoms. We compare the network structure of RIGs to several random graph models. We show that 3-dimensional geometric random graphs, that model spatial relationships between objects, provide the best fit to RIGs. We investigate the relationship between the strength of the fit and various protein structural features. We show that the fit depends on protein size, structural class, and thermostability, but not on quaternary structure. We apply our model to the identification of significantly over-represented structural building blocks, i.e., network motifs, in protein structure networks. As expected, choosing geometric graphs as a null model results in the most specific identification of motifs. Our geometric random graph model may facilitate further graph-based studies of protein conformation space and have important implications for protein structure comparison and prediction. The choice of a well-fitting null model is crucial for finding structural motifs that play an important role in protein folding, stability and function. To our knowledge, this is the first study that addresses the challenge of finding an optimized null model for RIGs, by
Optimized null model for protein structure networks.
Directory of Open Access Journals (Sweden)
Tijana Milenković
Full Text Available Much attention has recently been given to the statistical significance of topological features observed in biological networks. Here, we consider residue interaction graphs (RIGs as network representations of protein structures with residues as nodes and inter-residue interactions as edges. Degree-preserving randomized models have been widely used for this purpose in biomolecular networks. However, such a single summary statistic of a network may not be detailed enough to capture the complex topological characteristics of protein structures and their network counterparts. Here, we investigate a variety of topological properties of RIGs to find a well fitting network null model for them. The RIGs are derived from a structurally diverse protein data set at various distance cut-offs and for different groups of interacting atoms. We compare the network structure of RIGs to several random graph models. We show that 3-dimensional geometric random graphs, that model spatial relationships between objects, provide the best fit to RIGs. We investigate the relationship between the strength of the fit and various protein structural features. We show that the fit depends on protein size, structural class, and thermostability, but not on quaternary structure. We apply our model to the identification of significantly over-represented structural building blocks, i.e., network motifs, in protein structure networks. As expected, choosing geometric graphs as a null model results in the most specific identification of motifs. Our geometric random graph model may facilitate further graph-based studies of protein conformation space and have important implications for protein structure comparison and prediction. The choice of a well-fitting null model is crucial for finding structural motifs that play an important role in protein folding, stability and function. To our knowledge, this is the first study that addresses the challenge of finding an optimized null model
Optimal content delivery with network coding
Leong, Derek; Ho, Tracey; Cathey, Rebecca
2009-01-01
We present a unified linear program formulation for optimal content delivery in content delivery networks (CDNs), taking into account various costs and constraints associated with content dissemination from the origin server to storage nodes, data storage, and the eventual fetching of content from storage nodes by end users. Our formulation can be used to achieve a variety of performance goals and system behavior, including the bounding of fetch delay, load balancing, and robustness against...
Optimal network modularity for information diffusion.
Nematzadeh, Azadeh; Ferrara, Emilio; Flammini, Alessandro; Ahn, Yong-Yeol
2014-08-22
We investigate the impact of community structure on information diffusion with the linear threshold model. Our results demonstrate that modular structure may have counterintuitive effects on information diffusion when social reinforcement is present. We show that strong communities can facilitate global diffusion by enhancing local, intracommunity spreading. Using both analytic approaches and numerical simulations, we demonstrate the existence of an optimal network modularity, where global diffusion requires the minimal number of early adopters.
Optimal transportation networks models and theory
Bernot, Marc; Morel, Jean-Michel
2009-01-01
The transportation problem can be formalized as the problem of finding the optimal way to transport a given measure into another with the same mass. In contrast to the Monge-Kantorovitch problem, recent approaches model the branched structure of such supply networks as minima of an energy functional whose essential feature is to favour wide roads. Such a branched structure is observable in ground transportation networks, in draining and irrigation systems, in electrical power supply systems and in natural counterparts such as blood vessels or the branches of trees. These lectures provide mathematical proof of several existence, structure and regularity properties empirically observed in transportation networks. The link with previous discrete physical models of irrigation and erosion models in geomorphology and with discrete telecommunication and transportation models is discussed. It will be mathematically proven that the majority fit in the simple model sketched in this volume.
Perception of string quartet synchronisation
Directory of Open Access Journals (Sweden)
Alan M Wing
2014-10-01
Full Text Available Timing variation in small group musical performance results from intentional, expressive and unintentional, error components in individual player timing. These timing fluctuations produce variability in between-player note asynchrony and require timing adjustments to keep the ensemble together. The size of the adjustments relative to the asynchrony (correction gain affects the amount and nature of asynchrony variability. We present new listening tests to estimate thresholds for perception of between-player asynchrony variability and to determine whether listeners use differences in the nature of the variability, as well as in its magnitude, to judge asynchrony.In two experiments, computer-simulated ensemble performances of a 48-note excerpt from Haydn Op. 74 No. 1 were generated. Between-player note asynchrony was systematically manipulated in terms of level of within-player timing variability (Exp.1 and correction gain (Exp. 2. On each trial, participants listened to two samples, one (target with more between-player asynchrony variability than the other (test, and reported which was less together. In both experiments, the test sample correction gain was fixed at the statistically optimal vlaue of 0.25 and the within-player timing variability was minimal (zero except for random variability in the initial note. In Exp. 1 the target correction gain was fixed at 0.25 and the timing variability was adjusted over trials by a staircase algorithm designed to converge on the level of asynchrony variability giving 75% correct identification. In Exp. 2 the timing variability in the target was set at half that in Exp. 1 and the correction gain was varied to converge on 75% correct identification.Our results show that the between-player asynchrony variability giving 75% correct identification in Exp. 2 was significantly lower than in Exp. 1. This finding indicates that people are sensitive to both the degree of variance and the micro-structure of the time-series
Functional optimization of the arterial network
Mauroy, Benjamin
2014-01-01
We build an evolutionary scenario that explains how some crucial physiological constraints in the arterial network of mammals - i.e. hematocrit, vessels diameters and arterial pressure drops - could have been selected by evolution. We propose that the arterial network evolved while being constrained by its function as an organ. To support this hypothesis, we focus our study on one of the main function of blood network: oxygen supply to the organs. We consider an idealized organ with a given oxygen need and we optimize blood network geometry and hematocrit with the constraint that it must fulfill the organ oxygen need. Our model accounts for the non-Newtonian behavior of blood, its maintenance cost and F\\aa hr\\ae us effects (decrease in average concentration of red blood cells as the vessel diameters decrease). We show that the mean shear rates (relative velocities of fluid layers) in the tree vessels follow a scaling law related to the multi-scale property of the tree network, and we show that this scaling la...
Value Stream Management zur Synchronisation im Unternehmensverbund
Plapper, Peter
2016-01-01
Das Thema „Wertstrom Management“ wird in Luxemburg bereits in der Bachelor Ausbildung vermittelt und für die Master vertieft. In der Forschung erweist dieses Werkzeug großes Potential zur Synchronisation und Optimierung der Wertschöpfung im Unternehmensverbund, das wir hier vorstellen möchten: Mit der Methode VSM lässt sich Verschwendung erkennen und Wertschöpfungspotentiale heben. Die Verschwendung wird damit nicht nur in den produktiven Unternehmensbereichen, sondern auch in indirekten ...
Contrast research of CDMA and GSM network optimization
Wu, Yanwen; Liu, Zehong; Zhou, Guangyue
2004-03-01
With the development of mobile telecommunication network, users of CDMA advanced their request of network service quality. While the operators also change their network management object from signal coverage to performance improvement. In that case, reasonably layout & optimization of mobile telecommunication network, reasonably configuration of network resource, improvement of the service quality, and increase the enterprise's core competition ability, all those have been concerned by the operator companies. This paper firstly looked into the flow of CDMA network optimization. Then it dissertated to some keystones in the CDMA network optimization, like PN code assignment, calculation of soft handover, etc. As GSM is also the similar cellular mobile telecommunication system like CDMA, so this paper also made a contrast research of CDMA and GSM network optimization in details, including the similarity and the different. In conclusion, network optimization is a long time job; it will run through the whole process of network construct. By the adjustment of network hardware (like BTS equipments, RF systems, etc.) and network software (like parameter optimized, configuration optimized, capacity optimized, etc.), network optimization work can improve the performance and service quality of the network.
Optimal flux patterns in cellular metabolic networks
Energy Technology Data Exchange (ETDEWEB)
Almaas, E
2007-01-20
The availability of whole-cell level metabolic networks of high quality has made it possible to develop a predictive understanding of bacterial metabolism. Using the optimization framework of flux balance analysis, I investigate metabolic response and activity patterns to variations in the availability of nutrient and chemical factors such as oxygen and ammonia by simulating 30,000 random cellular environments. The distribution of reaction fluxes is heavy-tailed for the bacteria H. pylori and E. coli, and the eukaryote S. cerevisiae. While the majority of flux balance investigations have relied on implementations of the simplex method, it is necessary to use interior-point optimization algorithms to adequately characterize the full range of activity patterns on metabolic networks. The interior-point activity pattern is bimodal for E. coli and S. cerevisiae, suggesting that most metabolic reaction are either in frequent use or are rarely active. The trimodal activity pattern of H. pylori indicates that a group of its metabolic reactions (20%) are active in approximately half of the simulated environments. Constructing the high-flux backbone of the network for every environment, there is a clear trend that the more frequently a reaction is active, the more likely it is a part of the backbone. Finally, I briefly discuss the predicted activity patterns of the central-carbon metabolic pathways for the sample of random environments.
The synchronisation of fractional-order hyperchaos compound system
Indian Academy of Sciences (India)
Naeimadeen Noghredani
2018-01-24
Jan 24, 2018 ... security of secure communication. The corresponding theoretical analysis and results of simulation validated the effectiveness of the proposed synchronisation scheme using MATLAB. Keywords. Compound systems; memristor chaotic system; fractional-order chaotic systems; synchronisation. PACS Nos ...
The synchronisation of fractional-order hyperchaos compound system
Indian Academy of Sciences (India)
According to the suitability of compound synchronisation as a reliable solution for secure communication, we then examined the application of the proposed adaptive compound synchronisation scheme in the presence of noise for secure communication. In addition, the unpredictability and complexity of the drive systems ...
Optimal channel efficiency in a sensory network
Mosqueiro, Thiago S.; Maia, Leonardo P.
2013-07-01
Spontaneous neural activity has been increasingly recognized as a subject of key relevance in neuroscience. It exhibits nontrivial spatiotemporal structure reflecting the organization of the underlying neural network and has proved to be closely intertwined with stimulus-induced activity patterns. As an additional contribution in this regard, we report computational studies that strongly suggest that a stimulus-free feature rules the behavior of an important psychophysical measure of the sensibility of a sensory system to a stimulus, the so-called dynamic range. Indeed in this paper we show that the entropy of the distribution of avalanche lifetimes (information efficiency, since it can be interpreted as the efficiency of the network seen as a communication channel) always accompanies the dynamic range in the benchmark model for sensory systems. Specifically, by simulating the Kinouchi-Copelli (KC) model on two broad families of model networks, we generically observed that both quantities always increase or decrease together as functions of the average branching ratio (the control parameter of the KC model) and that the information efficiency typically exhibits critical optimization jointly with the dynamic range (i.e., both quantities are optimized at the same value of that control parameter, that turns out to be the critical point of a nonequilibrium phase transition). In contrast with the practice of taking power laws to identify critical points in most studies describing measured neuronal avalanches, we rely on data collapses as more robust signatures of criticality to claim that critical optimization may happen even when the distribution of avalanche lifetimes is not a power law, as suggested by a recent experiment. Finally, we note that the entropy of the size distribution of avalanches (information capacity) does not always follow the dynamic range and the information efficiency when they are critically optimized, despite being more widely used than the
Distributed Robust Optimization in Networked System.
Wang, Shengnan; Li, Chunguang
2016-10-11
In this paper, we consider a distributed robust optimization (DRO) problem, where multiple agents in a networked system cooperatively minimize a global convex objective function with respect to a global variable under the global constraints. The objective function can be represented by a sum of local objective functions. The global constraints contain some uncertain parameters which are partially known, and can be characterized by some inequality constraints. After problem transformation, we adopt the Lagrangian primal-dual method to solve this problem. We prove that the primal and dual optimal solutions of the problem are restricted in some specific sets, and we give a method to construct these sets. Then, we propose a DRO algorithm to find the primal-dual optimal solutions of the Lagrangian function, which consists of a subgradient step, a projection step, and a diffusion step, and in the projection step of the algorithm, the optimized variables are projected onto the specific sets to guarantee the boundedness of the subgradients. Convergence analysis and numerical simulations verifying the performance of the proposed algorithm are then provided. Further, for nonconvex DRO problem, the corresponding approach and algorithm framework are also provided.
Optimization of OSPF Routing in IP Networks
Bley, Andreas; Fortz, Bernard; Gourdin, Eric; Holmberg, Kaj; Klopfenstein, Olivier; Pióro, Michał; Tomaszewski, Artur; Ümit, Hakan
The Internet is a huge world-wide packet switching network comprised of more than 13,000 distinct subnetworks, referred to as Autonomous Systems (ASs) autonomous system AS . They all rely on the Internet Protocol (IP) internet protocol IP for transport of packets across the network. And most of them use shortest path routing protocols shortest path routing!protocols , such as OSPF or IS-IS, to control the routing of IP packets routing!of IP packets within an AS. The idea of the routing is extremely simple — every packet is forwarded on IP links along the shortest route between its source and destination nodes of the AS. The AS network administrator can manage the routing of packets in the AS by supplying the so-called administrative weights of IP links, which specify the link lengths that are used by the routing protocols for their shortest path computations. The main advantage of the shortest path routing policy is its simplicity, allowing for little administrative overhead. From the network engineering perspective, however, shortest path routing can pose problems in achieving satisfactory traffic handling efficiency. As all routing paths depend on the same routing metric routing!metric , it is not possible to configure the routing paths for the communication demands between different pairs of nodes explicitly or individually; the routing can be controlled only indirectly and only as a whole by modifying the routing metric. Thus, one of the main tasks when planning such networks is to find administrative link weights that induce a globally efficient traffic routing traffic!routing configuration of an AS. It turns out that this task leads to very difficult mathematical optimization problems. In this chapter, we discuss and describe exact integer programming models and solution approaches as well as practically efficient smart heuristics for such shortest path routing problems shortest path routing!problems .
Optimal Brain Surgeon on Artificial Neural Networks in
DEFF Research Database (Denmark)
Christiansen, Niels Hørbye; Job, Jonas Hultmann; Klyver, Katrine
2012-01-01
It is shown how the procedure know as optimal brain surgeon can be used to trim and optimize artificial neural networks in nonlinear structural dynamics. Beside optimizing the neural network, and thereby minimizing computational cost in simulation, the surgery procedure can also serve as a quick...
Optimizing the spatial pattern of networks for monitoring radioactive releases
Melles, S.J.; Heuvelink, G.B.M.; Twenhofel, C.J.W.; Dijk, van A.; Hiemstra, P.H.; Baume, O.P.; Stohlker, U.
2011-01-01
This study presents a method to optimize the sampling design of environmental monitoring networks in a multi-objective setting. We optimize the permanent network of radiation monitoring stations in the Netherlands and parts of Germany as an example. The optimization method proposed combines
Transmission network expansion planning with simulation optimization
Energy Technology Data Exchange (ETDEWEB)
Bent, Russell W [Los Alamos National Laboratory; Berscheid, Alan [Los Alamos National Laboratory; Toole, G. Loren [Los Alamos National Laboratory
2010-01-01
Within the electric power literatW''e the transmi ssion expansion planning problem (TNEP) refers to the problem of how to upgrade an electric power network to meet future demands. As this problem is a complex, non-linear, and non-convex optimization problem, researchers have traditionally focused on approximate models. Often, their approaches are tightly coupled to the approximation choice. Until recently, these approximations have produced results that are straight-forward to adapt to the more complex (real) problem. However, the power grid is evolving towards a state where the adaptations are no longer easy (i.e. large amounts of limited control, renewable generation) that necessitates new optimization techniques. In this paper, we propose a generalization of the powerful Limited Discrepancy Search (LDS) that encapsulates the complexity in a black box that may be queJied for information about the quality of a proposed expansion. This allows the development of a new optimization algOlitlun that is independent of the underlying power model.
Broadband Access Network Planning Optimization Considering Real Copper Cable Lengths
Peternel, Blaž Kos, Andrej
Broadband access network planning strategies with techno-economic calculations are important topics, when optimal broadband network deployments are considered. This paper analyzes optimal deployment combination of digital subscriber line technologies (xDSL) and fiber to the home technologies (FTTx), following different user bandwidth demand scenarios. For this reason, optimal placement of remote digital subscriber line multiplexer (RDSLAM) is examined. Furthermore, the article also discusses the economy of investments, depending on certain investment threshold and the reach of different xDSL technologies. Finally, the difference between broadband network deployment in a characteristic urban and rural area in Republic of Slovenia, in terms of required optical cable dig length per household is shown. A tree structure network model of a traditional copper access network is introduced. A dynamic programming logic, with recursion as a basis of a tree structure examination and evaluation of optimal network elements placement is used. The tree structure network model considers several real network parameters (e. g.: copper cable lengths, user coordinates, node coordinates). The main input for the optimization is a local loop distance between each user and a candidate node for RDSLAM placement. Modelling of copper access networks with a tree structure makes new extensions in planning optimization of broadband access networks. Optimization of network elements placement has direct influence on efficiency and profitability of broadband access telecommunication networks.
Optimization Models for Flexible and Adaptive SDN Network Virtualization Layers
Zerwas, Johannes; Blenk, Andreas; Kellerer, Wolfgang
2016-01-01
Network hypervisors provide the network virtualization layer for Software Defined Networking (SDN). They enable virtual network (VN) tenants to bring their SDN controllers to program their logical networks individually according to their demands. In order to make use of the high flexibility of virtual SDN networks and to provide high performance, the deployment of the virtualization layer needs to adapt to changing VN demands. This paper initializes the study of the optimization of dynamic SD...
Optimal Channel Efficiency in a Sensory Network
Mosqueiro, Thiago S
2012-01-01
We show that the entropy of the distribution of avalanche lifetimes in the Kinouchi-Copelli model always achieves a maximum jointly with the dynamic range. This is noteworthy and nontrivial because while the dynamic range is an equilibrium average measure of the sensibility of a sensory system to a stimulus, the entropy of relaxation times is a purely dynamical quantity, independent of the stimulus rate, that can be interpreted as the efficiency of the network seen as a communication channel. The newly found optimization occurs for all topologies we tested, even when the distribution of avalanche lifetimes itself is not a power-law and when the entropy of the size distribution of avalanches is not concomitantly maximized, strongly suggesting that dynamical rules allowing a proper temporal matching of the states of the interacting neurons is the key for achieving good performance in information processing, rather than increasing the number of available units.
Parallel Evolutionary Optimization for Neuromorphic Network Training
Energy Technology Data Exchange (ETDEWEB)
Schuman, Catherine D [ORNL; Disney, Adam [University of Tennessee (UT); Singh, Susheela [North Carolina State University (NCSU), Raleigh; Bruer, Grant [University of Tennessee (UT); Mitchell, John Parker [University of Tennessee (UT); Klibisz, Aleksander [University of Tennessee (UT); Plank, James [University of Tennessee (UT)
2016-01-01
One of the key impediments to the success of current neuromorphic computing architectures is the issue of how best to program them. Evolutionary optimization (EO) is one promising programming technique; in particular, its wide applicability makes it especially attractive for neuromorphic architectures, which can have many different characteristics. In this paper, we explore different facets of EO on a spiking neuromorphic computing model called DANNA. We focus on the performance of EO in the design of our DANNA simulator, and on how to structure EO on both multicore and massively parallel computing systems. We evaluate how our parallel methods impact the performance of EO on Titan, the U.S.'s largest open science supercomputer, and BOB, a Beowulf-style cluster of Raspberry Pi's. We also focus on how to improve the EO by evaluating commonality in higher performing neural networks, and present the result of a study that evaluates the EO performed by Titan.
Topology Optimization for Energy Management in Underwater Sensor Networks
2015-02-01
and communication. This multi-objective cost functional leads to non-dominant optimization. Such a problem is formulated as a Pareto -optimal trade...K. Jha1 Thomas A. Wettergren2 Asok Ray1 Kushal Mukherjee3 Keywords: Underwater Sensor Network, Energy Management, Pareto Optimization, Adaptation...communication models for under- water environment. The approximate Pareto -optimal surface is obtained as a trade-off between network lifetime and probability
Determining optimal speed limits in traffic networks
Directory of Open Access Journals (Sweden)
Mansour Hadji Hosseinlou
2015-07-01
Full Text Available Determining the speed limit of road transport systems has a significant role in the speed management of vehicles. In most cases, setting a speed limit is considered as a trade-off between reducing travel time on one hand and reducing road accidents on the other, and the two factors of vehicle fuel consumption and emission rate of air pollutants have been neglected. This paper aims to evaluate optimal speed limits in traffic networks in a way that economized societal costs are incurred. In this study, experimental and field data as well as data from simulations are used to determine how speed is related to the emission of pollutants, fuel consumption, travel time, and the number of accidents. This paper also proposes a simple model to calculate the societal costs of travel and relate them to speed. As a case study, using emission test results on cars manufactured domestically and by simulating the suburban traffic flow by Aimsun software, the total societal costs of the Shiraz-Marvdasht motorway, which is one of the most traversed routes in Iran, have been estimated. The results of the study show that from a societal perspective, the optimal speed would be 73 km/h, and from a road user perspective, it would be 82 km/h (in 2011, the average speed of the passing vehicles on that motorway was 82 km/h. The experiments in this paper were run on three different vehicles with different types of fuel. In a comparative study, the results show that the calculated speed limit is lower than the optimal speed limits in Sweden, Norway, and Australia.
District Heating Network Design and Configuration Optimization with Genetic Algorithm
Directory of Open Access Journals (Sweden)
Hongwei Li
2013-12-01
Full Text Available In this paper, the configuration of a district heating network which connects from the heating plant to the end users is optimized. Each end user in the network represents a building block. The connections between the heat generation plant and the end users are represented with mixed integer and the pipe friction and heat loss formulations are non-linear. In order to find the optimal district heating network configuration, genetic algorithm which handles the mixed integer nonlinear programming problem is chosen. The network configuration is represented with binary and integer encoding and is optimized in terms of the net present cost. The optimization results indicates that the optimal DH network configuration is determined by multiple factors such as the consumer heating load, the distance between the heating plant to the consumer, the design criteria regarding the pressure and temperature limitation, as well as the corresponding network heat loss.
Neural network for constrained nonsmooth optimization using Tikhonov regularization.
Qin, Sitian; Fan, Dejun; Wu, Guangxi; Zhao, Lijun
2015-03-01
This paper presents a one-layer neural network to solve nonsmooth convex optimization problems based on the Tikhonov regularization method. Firstly, it is shown that the optimal solution of the original problem can be approximated by the optimal solution of a strongly convex optimization problems. Then, it is proved that for any initial point, the state of the proposed neural network enters the equality feasible region in finite time, and is globally convergent to the unique optimal solution of the related strongly convex optimization problems. Compared with the existing neural networks, the proposed neural network has lower model complexity and does not need penalty parameters. In the end, some numerical examples and application are given to illustrate the effectiveness and improvement of the proposed neural network. Copyright © 2014 Elsevier Ltd. All rights reserved.
Synchronisation of ovulation for management of reproduction in dairy cows
National Research Council Canada - National Science Library
Bisinotto, R S; Ribeiro, E S; Santos, J E P
2014-01-01
Important developments have occurred in the last two decades, since the advent of the Ovsynch protocol, on the understanding and use of synchronisation programmes for management of reproduction in dairy herds...
Neural Network for Optimization of Existing Control Systems
DEFF Research Database (Denmark)
Madsen, Per Printz
1995-01-01
The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems.......The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems....
Directory of Open Access Journals (Sweden)
Jay Krishna Thakur
2015-08-01
Full Text Available The aim of this work is to investigate new approaches using methods based on statistics and geo-statistics for spatio-temporal optimization of groundwater monitoring networks. The formulated and integrated methods were tested with the groundwater quality data set of Bitterfeld/Wolfen, Germany. Spatially, the monitoring network was optimized using geo-statistical methods. Temporal optimization of the monitoring network was carried out using Sen’s method (1968. For geostatistical network optimization, a geostatistical spatio-temporal algorithm was used to identify redundant wells in 2- and 2.5-D Quaternary and Tertiary aquifers. Influences of interpolation block width, dimension, contaminant association, groundwater flow direction and aquifer homogeneity on statistical and geostatistical methods for monitoring network optimization were analysed. The integrated approach shows 37% and 28% redundancies in the monitoring network in Quaternary aquifer and Tertiary aquifer respectively. The geostatistical method also recommends 41 and 22 new monitoring wells in the Quaternary and Tertiary aquifers respectively. In temporal optimization, an overall optimized sampling interval was recommended in terms of lower quartile (238 days, median quartile (317 days and upper quartile (401 days in the research area of Bitterfeld/Wolfen. Demonstrated methods for improving groundwater monitoring network can be used in real monitoring network optimization with due consideration given to influencing factors.
GENETIC ALGORITHM BASED CONCEPT DESIGN TO OPTIMIZE NETWORK LOAD BALANCE
Directory of Open Access Journals (Sweden)
Ashish Jain
2012-07-01
Full Text Available Multiconstraints optimal network load balancing is an NP-hard problem and it is an important part of traffic engineering. In this research we balance the network load using classical method (brute force approach and dynamic programming is used but result shows the limitation of this method but at a certain level we recognized that the optimization of balanced network load with increased number of nodes and demands is intractable using the classical method because the solution set increases exponentially. In such case the optimization techniques like evolutionary techniques can employ for optimizing network load balance. In this paper we analyzed proposed classical algorithm and evolutionary based genetic approach is devise as well as proposed in this paper for optimizing the balance network load.
Optimal Planning of Communication System of CPS for Distribution Network
Directory of Open Access Journals (Sweden)
Ting Yang
2017-01-01
Full Text Available IoT is the technical basis to realize the CPS (Cyber Physical System for distribution networks, with which the complex system becomes more intelligent and controllable. Because of the multihop and self-organization characteristics, the large-scale heterogeneous CPS network becomes more difficult to plan. Using topological potential theory, one of typical big data analysis technologies, this paper proposed a novel optimal CPS planning model. Topological potential equalization is considered as the optimization objective function in heterogeneous CPS network with the constraints of communication requirements, physical infrastructures, and network reliability. An improved binary particle swarm optimization algorithm is proposed to solve this complex optimal problem. Two IEEE classic examples are adopted in the simulation, and the results show that, compared with benchmark algorithms, our proposed method can provide an effective topology optimization scheme to improve the network reliability and transmitting performance.
Self-Optimization of LTE Networks Utilizing Celnet Xplorer
Buvaneswari, A; Polakos, Paul; Buvaneswari, Arumugam
2010-01-01
In order to meet demanding performance objectives in Long Term Evolution (LTE) networks, it is mandatory to implement highly efficient, autonomic self-optimization and configuration processes. Self-optimization processes have already been studied in second generation (2G) and third generation (3G) networks, typically with the objective of improving radio coverage and channel capacity. The 3rd Generation Partnership Project (3GPP) standard for LTE self-organization of networks (SON) provides guidelines on self-configuration of physical cell ID and neighbor relation function and self-optimization for mobility robustness, load balancing, and inter-cell interference reduction. While these are very important from an optimization perspective of local phenomenon (i.e., the eNodeB's interaction with its neighbors), it is also essential to architect control algorithms to optimize the network as a whole. In this paper, we propose a Celnet Xplorer-based SON architecture that allows detailed analysis of network performan...
Neural Networks for Synthesis and Optimization of Antenna Arrays
Directory of Open Access Journals (Sweden)
S. A. Djennas
2007-04-01
Full Text Available This paper describes a usual application of back-propagation neural networks for synthesis and optimization of antenna array. The neural network is able to model and to optimize the antennas arrays, by acting on radioelectric or geometric parameters and by taking into account predetermined general criteria. The neural network allows not only establishing important analytical equations for the optimization step, but also a great flexibility between the system parameters in input and output. This step of optimization becomes then possible due to the explicit relation given by the neural network. According to different formulations of the synthesis problem such as acting on the feed law (amplitude and/or phase and/or space position of the radiating sources, results on antennas arrays synthesis and optimization by neural networks are presented and discussed. However ANN is able to generate very fast the results of synthesis comparing to other approaches.
UMTS network planning, optimization, and inter-operation with GSM
Rahnema, Moe
2008-01-01
UMTS Network Planning, Optimization, and Inter-Operation with GSM is an accessible, one-stop reference to help engineers effectively reduce the time and costs involved in UMTS deployment and optimization. Rahnema includes detailed coverage from both a theoretical and practical perspective on the planning and optimization aspects of UMTS, and a number of other new techniques to help operators get the most out of their networks. Provides an end-to-end perspective, from network design to optimizationIncorporates the hands-on experiences of numerous researchersSingle
Context-Aware Local Optimization of Sensor Network Deployment
Directory of Open Access Journals (Sweden)
Meysam Argany
2015-07-01
Full Text Available Wireless sensor networks are increasingly used for tracking and monitoring dynamic phenomena in urban and natural areas. Spatial coverage is an important issue in sensor networks in order to fulfill the needs of sensing applications. Optimization methods are widely used to efficiently distribute sensor nodes in the network to achieve a desired level of coverage. Most of the existing algorithms do not consider the characteristics of the real environment in the optimization process. In this paper, we propose the integration of contextual information in optimization algorithms to improve sensor network coverage. First, we investigate the implication of contextual information in sensor networks. Then, a conceptual framework for local context-aware sensor network deployment optimization method is introduced and related algorithms are presented in detail. Finally, several experiments are carried out to evaluate the validity of the proposed method. The results obtained from these experiments show the effectiveness of our approach in different contextual situations.
International Synchronisation of the Pork Cycle
Directory of Open Access Journals (Sweden)
HOLST
2012-02-01
Full Text Available The development of pork prices has been analysed since the 1920s. Well known economic concepts such as Hanaus pork cycle or Ezekiels cobweb theorem are based on the empirical analysis of pork markets. We analyze whether pork price developments in different countries have become more synchronised over time. In a first stage of our analysis, annual pork price data collected by the FAO reveals much heterogeneity of pork price developments across countries. However, for some groups of countries the observed price patterns are very similar or even identical. This is especially the case for neighbouring countries with integrated pork markets, such as the members of the European Union (EU. We then compare pork price developments in Germany and the USA based on 36 years of monthly producer prices for slaughter pigs. Since the middle of the 1990s cyclical pork price movements in the USA and Germany have become increasingly synchronous. We attribute this to two developments: the fact that the USA has become a large net exporter of pork over this period, and policy reform in the EU that has strengthened the link between international and EU feed prices.
Optimal resource allocation for efficient transport on complex networks
Gong, Xiaofeng; Kun, Li; Lai, C.-H.
2008-07-01
The problem of efficient transport on a complex network is studied in this paper. We find that there exists an optimal way to allocate resources for information processing on each node to achieve the best transport capacity of the network, or the largest input information rate which does not cause jamming in network traffic, provided that the network structure and routing strategy are given. More interestingly, this achievable network capacity limit is closely related to the topological structure of the network, and is actually inversely proportional to the average distance of the network, measured according to the same routing rule.
Optimizing online social networks for information propagation.
Directory of Open Access Journals (Sweden)
Duan-Bing Chen
Full Text Available Online users nowadays are facing serious information overload problem. In recent years, recommender systems have been widely studied to help people find relevant information. Adaptive social recommendation is one of these systems in which the connections in the online social networks are optimized for the information propagation so that users can receive interesting news or stories from their leaders. Validation of such adaptive social recommendation methods in the literature assumes uniform distribution of users' activity frequency. In this paper, our empirical analysis shows that the distribution of online users' activity is actually heterogenous. Accordingly, we propose a more realistic multi-agent model in which users' activity frequency are drawn from a power-law distribution. We find that previous social recommendation methods lead to serious delay of information propagation since many users are connected to inactive leaders. To solve this problem, we design a new similarity measure which takes into account users' activity frequencies. With this similarity measure, the average delay is significantly shortened and the recommendation accuracy is largely improved.
Optimizing online social networks for information propagation.
Chen, Duan-Bing; Wang, Guan-Nan; Zeng, An; Fu, Yan; Zhang, Yi-Cheng
2014-01-01
Online users nowadays are facing serious information overload problem. In recent years, recommender systems have been widely studied to help people find relevant information. Adaptive social recommendation is one of these systems in which the connections in the online social networks are optimized for the information propagation so that users can receive interesting news or stories from their leaders. Validation of such adaptive social recommendation methods in the literature assumes uniform distribution of users' activity frequency. In this paper, our empirical analysis shows that the distribution of online users' activity is actually heterogenous. Accordingly, we propose a more realistic multi-agent model in which users' activity frequency are drawn from a power-law distribution. We find that previous social recommendation methods lead to serious delay of information propagation since many users are connected to inactive leaders. To solve this problem, we design a new similarity measure which takes into account users' activity frequencies. With this similarity measure, the average delay is significantly shortened and the recommendation accuracy is largely improved.
A Projection Neural Network for Constrained Quadratic Minimax Optimization.
Liu, Qingshan; Wang, Jun
2015-11-01
This paper presents a projection neural network described by a dynamic system for solving constrained quadratic minimax programming problems. Sufficient conditions based on a linear matrix inequality are provided for global convergence of the proposed neural network. Compared with some of the existing neural networks for quadratic minimax optimization, the proposed neural network in this paper is capable of solving more general constrained quadratic minimax optimization problems, and the designed neural network does not include any parameter. Moreover, the neural network has lower model complexities, the number of state variables of which is equal to that of the dimension of the optimization problems. The simulation results on numerical examples are discussed to demonstrate the effectiveness and characteristics of the proposed neural network.
LinkMind: Link Optimization in Swarming Mobile Sensor Networks
Directory of Open Access Journals (Sweden)
Trung Dung Ngo
2011-08-01
Full Text Available A swarming mobile sensor network is comprised of a swarm of wirelessly connected mobile robots equipped with various sensors. Such a network can be applied in an uncertain environment for services such as cooperative navigation and exploration, object identification and information gathering. One of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm is demonstrated and evaluated in simulation.
LinkMind: Link Optimization in Swarming Mobile Sensor Networks
DEFF Research Database (Denmark)
Ngo, Trung Dung
2012-01-01
optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm......A swarming mobile sensor network is comprised of a swarm of wirelessly connected mobile robots equipped with various sensors. Such a network can be applied in an uncertain environment for services such as cooperative navigation and exploration, object identification and information gathering. One...... of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link...
Network anomaly detection system with optimized DS evidence theory.
Liu, Yuan; Wang, Xiaofeng; Liu, Kaiyu
2014-01-01
Network anomaly detection has been focused on by more people with the fast development of computer network. Some researchers utilized fusion method and DS evidence theory to do network anomaly detection but with low performance, and they did not consider features of network-complicated and varied. To achieve high detection rate, we present a novel network anomaly detection system with optimized Dempster-Shafer evidence theory (ODS) and regression basic probability assignment (RBPA) function. In this model, we add weights for each sensor to optimize DS evidence theory according to its previous predict accuracy. And RBPA employs sensor's regression ability to address complex network. By four kinds of experiments, we find that our novel network anomaly detection model has a better detection rate, and RBPA as well as ODS optimization methods can improve system performance significantly.
Particle swarm optimization of a neural network model in a ...
Indian Academy of Sciences (India)
This paper presents a particle swarm optimization (PSO) technique to train an artificial neural network (ANN) for prediction of flank wear in drilling, and compares the network performance with that of the back propagation neural network (BPNN). This analysis is carried out following a series of experiments employing high ...
Perspective Application of Passive Optical Network with Optimized Bus Topology
Directory of Open Access Journals (Sweden)
P. Lafata
2012-06-01
Full Text Available Passive optical networks (PONs represent a promising solution for modern access telecommunication networks.These networks are able to meet the increasing demands on transmission rate for demanding multimedia services,while they can offer typical shared transmission speed of 1.25 or 2.5 Gbps. The major role in deploying opticaldistribution networks ODNs plays the maximum attenuable loss, which is caused mainly by passive optical splitters.This paper proposes an innovative application of passive optical networks with optimized bus topology especially forlocal backbone data networks. Due to using only passive components, it is necessary to optimize certain parameters,especially an overall attenuation balance. Considering the possibility of such optimization, the passive optical networkwith optimized bus topology provides several interesting opportunities for specific applications. This paper will presentselected aspects of passive optical networks and splitters with asymmetric splitting ratio. The essential part is focusedon the practical demonstration of their use to optimize the passive optical network with bus topology, which acts as alocal backbone network for structured cabling systems, and for local data networks in large buildings.
Global Optimization for Transport Network Expansion and Signal Setting
Directory of Open Access Journals (Sweden)
Haoxiang Liu
2015-01-01
Full Text Available This paper proposes a model to address an urban transport planning problem involving combined network design and signal setting in a saturated network. Conventional transport planning models usually deal with the network design problem and signal setting problem separately. However, the fact that network capacity design and capacity allocation determined by network signal setting combine to govern the transport network performance requires the optimal transport planning to consider the two problems simultaneously. In this study, a combined network capacity expansion and signal setting model with consideration of vehicle queuing on approaching legs of intersection is developed to consider their mutual interactions so that best transport network performance can be guaranteed. We formulate the model as a bilevel program and design an approximated global optimization solution method based on mixed-integer linearization approach to solve the problem, which is inherently nnonlinear and nonconvex. Numerical experiments are conducted to demonstrate the model application and the efficiency of solution algorithm.
Inferring biomolecular interaction networks based on convex optimization.
Han, Soohee; Yoon, Yeoin; Cho, Kwang-Hyun
2007-10-01
We present an optimization-based inference scheme to unravel the functional interaction structure of biomolecular components within a cell. The regulatory network of a cell is inferred from the data obtained by perturbation of adjustable parameters or initial concentrations of specific components. It turns out that the identification procedure leads to a convex optimization problem with regularization as we have to achieve the sparsity of a network and also reflect any a priori information on the network structure. Since the convex optimization has been well studied for a long time, a variety of efficient algorithms were developed and many numerical solvers are freely available. In order to estimate time derivatives from discrete-time samples, a cubic spline fitting is incorporated into the proposed optimization procedure. Throughout simulation studies on several examples, it is shown that the proposed convex optimization scheme can effectively uncover the functional interaction structure of a biomolecular regulatory network with reasonable accuracy.
Genetical Swarm Optimization of Multihop Routes in Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Davide Caputo
2010-01-01
Full Text Available In recent years, wireless sensor networks have been attracting considerable research attention for a wide range of applications, but they still present significant network communication challenges, involving essentially the use of large numbers of resource-constrained nodes operating unattended and exposed to potential local failures. In order to maximize the network lifespan, in this paper, genetical swarm optimization (GSO is applied, a class of hybrid evolutionary techniques developed in order to exploit in the most effective way the uniqueness and peculiarities of two classical optimization approaches; particle swarm optimization (PSO and genetic algorithms (GA. This procedure is here implemented to optimize the communication energy consumption in a wireless network by selecting the optimal multihop routing schemes, with a suitable hybridization of different routing criteria, confirming itself as a flexible and useful tool for engineering applications.
Optimizing information processing in neuronal networks beyond critical states.
Ferraz, Mariana Sacrini Ayres; Melo-Silva, Hiago Lucas Cardeal; Kihara, Alexandre Hiroaki
2017-01-01
Critical dynamics have been postulated as an ideal regime for neuronal networks in the brain, considering optimal dynamic range and information processing. Herein, we focused on how information entropy encoded in spatiotemporal activity patterns may vary in critical networks. We employed branching process based models to investigate how entropy can be embedded in spatiotemporal patterns. We determined that the information capacity of critical networks may vary depending on the manipulation of microscopic parameters. Specifically, the mean number of connections governed the number of spatiotemporal patterns in the networks. These findings are compatible with those of the real neuronal networks observed in specific brain circuitries, where critical behavior is necessary for the optimal dynamic range response but the uncertainty provided by high entropy as coded by spatiotemporal patterns is not required. With this, we were able to reveal that information processing can be optimized in neuronal networks beyond critical states.
Optimizing information processing in neuronal networks beyond critical states.
Directory of Open Access Journals (Sweden)
Mariana Sacrini Ayres Ferraz
Full Text Available Critical dynamics have been postulated as an ideal regime for neuronal networks in the brain, considering optimal dynamic range and information processing. Herein, we focused on how information entropy encoded in spatiotemporal activity patterns may vary in critical networks. We employed branching process based models to investigate how entropy can be embedded in spatiotemporal patterns. We determined that the information capacity of critical networks may vary depending on the manipulation of microscopic parameters. Specifically, the mean number of connections governed the number of spatiotemporal patterns in the networks. These findings are compatible with those of the real neuronal networks observed in specific brain circuitries, where critical behavior is necessary for the optimal dynamic range response but the uncertainty provided by high entropy as coded by spatiotemporal patterns is not required. With this, we were able to reveal that information processing can be optimized in neuronal networks beyond critical states.
Wireless Sensor Network Optimization: Multi-Objective Paradigm
Iqbal, Muhammad; Naeem, Muhammad; Anpalagan, Alagan; Ahmed, Ashfaq; Azam, Muhammad
2015-01-01
Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks. PMID:26205271
Wireless Sensor Network Optimization: Multi-Objective Paradigm.
Iqbal, Muhammad; Naeem, Muhammad; Anpalagan, Alagan; Ahmed, Ashfaq; Azam, Muhammad
2015-07-20
Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks.
Wireless Sensor Network Optimization: Multi-Objective Paradigm
Directory of Open Access Journals (Sweden)
Muhammad Iqbal
2015-07-01
Full Text Available Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks.
Simultaneous optimization of water and heat exchange networks
Energy Technology Data Exchange (ETDEWEB)
Chen, Zhiyou; Hou, Yanlong; Li, Xiaoduan; Wang, Jingtao [Tianjin University, Tianjin (China)
2014-04-15
This paper focuses on the simultaneous optimization of the heat-integrated water allocation networks. A mathematic model is established to illustrate the modified state-space representation of this problem. An easy logical method is employed to help identify the streams of hot or cold ones. In this model, the water exchange networks (WEN), heat exchange networks (HEN), and the interactions between the WEN and HEN combine together as one unity. Thus, the whole network can be solved at one time, which enhances the possibility to get a global optimal result. Examples from the literature and a PVC plant are analyzed to illustrate the accuracy and applicability of this method.
Time and Frequency Synchronisation in 4G OFDM Systems
Directory of Open Access Journals (Sweden)
2009-03-01
Full Text Available This paper presents a complete synchronisation scheme of a baseband OFDM receiver for the currently designed 4G mobile communication system. Since the OFDM transmission is vulnerable to time and frequency offsets, accurate estimation of these parameters is one of the most important tasks of the OFDM receiver. In this paper, the design of a single OFDM synchronisation pilot symbol is introduced. The pilot is used for coarse timing offset and fractional frequency offset estimation. However, it can be applied for fine timing synchronisation and integer frequency offset estimation algorithms as well. A new timing metric that improves the performance of the coarse timing synchronisation is presented. Time domain synchronisation is completed after receiving this single OFDM pilot symbol. During the tracking phase, carrier frequency and sampling frequency offsets are tracked and corrected by means of the nondata-aided algorithm developed by the author. The proposed concept was tested by means of computer simulations, where the OFDM signal was transmitted over a multipath Rayleigh fading channel characterised by the WINNER channel models with Doppler shift and additive white Gaussian noise.
Time and Frequency Synchronisation in 4G OFDM Systems
Directory of Open Access Journals (Sweden)
Langowski Adrian
2009-01-01
Full Text Available This paper presents a complete synchronisation scheme of a baseband OFDM receiver for the currently designed 4G mobile communication system. Since the OFDM transmission is vulnerable to time and frequency offsets, accurate estimation of these parameters is one of the most important tasks of the OFDM receiver. In this paper, the design of a single OFDM synchronisation pilot symbol is introduced. The pilot is used for coarse timing offset and fractional frequency offset estimation. However, it can be applied for fine timing synchronisation and integer frequency offset estimation algorithms as well. A new timing metric that improves the performance of the coarse timing synchronisation is presented. Time domain synchronisation is completed after receiving this single OFDM pilot symbol. During the tracking phase, carrier frequency and sampling frequency offsets are tracked and corrected by means of the nondata-aided algorithm developed by the author. The proposed concept was tested by means of computer simulations, where the OFDM signal was transmitted over a multipath Rayleigh fading channel characterised by the WINNER channel models with Doppler shift and additive white Gaussian noise.
ENERGY OPTIMIZATION IN CLUSTER BASED WIRELESS SENSOR NETWORKS
Directory of Open Access Journals (Sweden)
T. SHANKAR
2014-04-01
Full Text Available Wireless sensor networks (WSN are made up of sensor nodes which are usually battery-operated devices, and hence energy saving of sensor nodes is a major design issue. To prolong the networks lifetime, minimization of energy consumption should be implemented at all layers of the network protocol stack starting from the physical to the application layer including cross-layer optimization. Optimizing energy consumption is the main concern for designing and planning the operation of the WSN. Clustering technique is one of the methods utilized to extend lifetime of the network by applying data aggregation and balancing energy consumption among sensor nodes of the network. This paper proposed new version of Low Energy Adaptive Clustering Hierarchy (LEACH, protocols called Advanced Optimized Low Energy Adaptive Clustering Hierarchy (AOLEACH, Optimal Deterministic Low Energy Adaptive Clustering Hierarchy (ODLEACH, and Varying Probability Distance Low Energy Adaptive Clustering Hierarchy (VPDL combination with Shuffled Frog Leap Algorithm (SFLA that enables selecting best optimal adaptive cluster heads using improved threshold energy distribution compared to LEACH protocol and rotating cluster head position for uniform energy dissipation based on energy levels. The proposed algorithm optimizing the life time of the network by increasing the first node death (FND time and number of alive nodes, thereby increasing the life time of the network.
District Heating Network Design and Configuration Optimization with Genetic Algorithm
DEFF Research Database (Denmark)
Li, Hongwei; Svendsen, Svend
2011-01-01
the heating plant location is allowed to vary. The connection between the heat generation plant and the end users can be represented with mixed integer and the pipe friction and heat loss formulations are non-linear. In order to find the optimal DH distribution pipeline configuration, the genetic algorithm...... which handles the mixed integer nonlinear programming problem was chosen. The network configuration was represented through binary and integer encoding and was optimized in terms of the net present cost (NPC). The optimization results indicated that the optimal DH network configuration is determined...
CFD Optimization on Network-Based Parallel Computer System
Cheung, Samson H.; VanDalsem, William (Technical Monitor)
1994-01-01
Combining multiple engineering workstations into a network-based heterogeneous parallel computer allows application of aerodynamic optimization with advance computational fluid dynamics codes, which is computationally expensive in mainframe supercomputer. This paper introduces a nonlinear quasi-Newton optimizer designed for this network-based heterogeneous parallel computer on a software called Parallel Virtual Machine. This paper will introduce the methodology behind coupling a Parabolized Navier-Stokes flow solver to the nonlinear optimizer. This parallel optimization package has been applied to reduce the wave drag of a body of revolution and a wing/body configuration with results of 5% to 6% drag reduction.
Stochastic network optimization with application to communication and queueing systems
Neely, Michael
2010-01-01
This text presents a modern theory of analysis, control, and optimization for dynamic networks. Mathematical techniques of Lyapunov drift and Lyapunov optimization are developed and shown to enable constrained optimization of time averages in general stochastic systems. The focus is on communication and queueing systems, including wireless networks with time-varying channels, mobility, and randomly arriving traffic. A simple drift-plus-penalty framework is used to optimize time averages such as throughput, throughput-utility, power, and distortion. Explicit performance-delay tradeoffs are prov
Designing optimal greenhouse gas monitoring networks for Australia
Ziehn, T.; Law, R. M.; Rayner, P. J.; Roff, G.
2016-01-01
Atmospheric transport inversion is commonly used to infer greenhouse gas (GHG) flux estimates from concentration measurements. The optimal location of ground-based observing stations that supply these measurements can be determined by network design. Here, we use a Lagrangian particle dispersion model (LPDM) in reverse mode together with a Bayesian inverse modelling framework to derive optimal GHG observing networks for Australia. This extends the network design for carbon dioxide (CO2) performed by Ziehn et al. (2014) to also minimise the uncertainty on the flux estimates for methane (CH4) and nitrous oxide (N2O), both individually and in a combined network using multiple objectives. Optimal networks are generated by adding up to five new stations to the base network, which is defined as two existing stations, Cape Grim and Gunn Point, in southern and northern Australia respectively. The individual networks for CO2, CH4 and N2O and the combined observing network show large similarities because the flux uncertainties for each GHG are dominated by regions of biologically productive land. There is little penalty, in terms of flux uncertainty reduction, for the combined network compared to individually designed networks. The location of the stations in the combined network is sensitive to variations in the assumed data uncertainty across locations. A simple assessment of economic costs has been included in our network design approach, considering both establishment and maintenance costs. Our results suggest that, while site logistics change the optimal network, there is only a small impact on the flux uncertainty reductions achieved with increasing network size.
Optimization of demand assigned SCPC satellite networks
Laborde, E.
1985-09-01
This paper investigates various system aspects and price tradeoffs involved in providing cost-effective Demand Assignment (DA) satellite channel service. Those network characteristics which significantly affect the ultimate cost-based decision are discussed. The number of stations participating in the DA or PA system, the number of satellite channels, and the traffic are kept parametric within expected limits, covering most of the present and future applications. In particular, the interrelationships between the network requirements (e.g., grade of service) and network elements, and the impacts of different blocking assignment allocations on the number of modems in the network is examined. A cost model is then derived that allows the evaluation and comparison of both DA and PA networks. Absolute and differential costing of PA and DA networks is permitted using economic quantities available to the system planner. These include modem cost, satellite channel cost, network size, and defined efficiency factors. Based on the differential cost comparisons for several DA and PA network strategies, tradeoffs have been derived to aid the system designer in configuring the most cost-effective DA network.
Medical Optimization Network for Space Telemedicine Resources
Shah, R. V.; Mulcahy, R.; Rubin, D.; Antonsen, E. L.; Kerstman, E. L.; Reyes, D.
2017-01-01
INTRODUCTION: Long-duration missions beyond low Earth orbit introduce new constraints to the space medical system such as the inability to evacuate to Earth, communication delays, and limitations in clinical skillsets. NASA recognizes the need to improve capabilities for autonomous care on such missions. As the medical system is developed, it is important to have an ability to evaluate the trade space of what resources will be most important. The Medical Optimization Network for Space Telemedicine Resources was developed for this reason, and is now a system to gauge the relative importance of medical resources in addressing medical conditions. METHODS: A list of medical conditions of potential concern for an exploration mission was referenced from the Integrated Medical Model, a probabilistic model designed to quantify in-flight medical risk. The diagnostic and treatment modalities required to address best and worst-case scenarios of each medical condition, at the terrestrial standard of care, were entered into a database. This list included tangible assets (e.g. medications) and intangible assets (e.g. clinical skills to perform a procedure). A team of physicians working within the Exploration Medical Capability Element of NASA's Human Research Program ranked each of the items listed according to its criticality. Data was then obtained from the IMM for the probability of occurrence of the medical conditions, including a breakdown of best case and worst case, during a Mars reference mission. The probability of occurrence information and criticality for each resource were taken into account during analytics performed using Tableau software. RESULTS: A database and weighting system to evaluate all the diagnostic and treatment modalities was created by combining the probability of condition occurrence data with the criticalities assigned by the physician team. DISCUSSION: Exploration Medical Capabilities research at NASA is focused on providing a medical system to
Online Algorithms for Adaptive Optimization in Heterogeneous Delay Tolerant Networks
Directory of Open Access Journals (Sweden)
Wissam Chahin
2013-12-01
Full Text Available Delay Tolerant Networks (DTNs are an emerging type of networks which do not need a predefined infrastructure. In fact, data forwarding in DTNs relies on the contacts among nodes which may possess different features, radio range, battery consumption and radio interfaces. On the other hand, efficient message delivery under limited resources, e.g., battery or storage, requires to optimize forwarding policies. We tackle optimal forwarding control for a DTN composed of nodes of different types, forming a so-called heterogeneous network. Using our model, we characterize the optimal policies and provide a suitable framework to design a new class of multi-dimensional stochastic approximation algorithms working for heterogeneous DTNs. Crucially, our proposed algorithms drive online the source node to the optimal operating point without requiring explicit estimation of network parameters. A thorough analysis of the convergence properties and stability of our algorithms is presented.
Intelligent Network Flow Optimization (INFLO) prototype acceptance test summary.
2015-05-01
This report summarizes the results of System Acceptance Testing for the implementation of the Intelligent Network : Flow Optimization (INFLO) Prototype bundle within the Dynamic Mobility Applications (DMA) portion of the Connected : Vehicle Program. ...
Optimal traffic control in highway transportation networks using linear programming
Li, Yanning
2014-06-01
This article presents a framework for the optimal control of boundary flows on transportation networks. The state of the system is modeled by a first order scalar conservation law (Lighthill-Whitham-Richards PDE). Based on an equivalent formulation of the Hamilton-Jacobi PDE, the problem of controlling the state of the system on a network link in a finite horizon can be posed as a Linear Program. Assuming all intersections in the network are controllable, we show that the optimization approach can be extended to an arbitrary transportation network, preserving linear constraints. Unlike previously investigated transportation network control schemes, this framework leverages the intrinsic properties of the Halmilton-Jacobi equation, and does not require any discretization or boolean variables on the link. Hence this framework is very computational efficient and provides the globally optimal solution. The feasibility of this framework is illustrated by an on-ramp metering control example.
Resistive Network Optimal Power Flow: Uniqueness and Algorithms
Energy Technology Data Exchange (ETDEWEB)
Tan, CW; Cai, DWH; Lou, X
2015-01-01
The optimal power flow (OPF) problem minimizes the power loss in an electrical network by optimizing the voltage and power delivered at the network buses, and is a nonconvex problem that is generally hard to solve. By leveraging a recent development on the zero duality gap of OPF, we propose a second-order cone programming convex relaxation of the resistive network OPF, and study the uniqueness of the optimal solution using differential topology, especially the Poincare-Hopf Index Theorem. We characterize the global uniqueness for different network topologies, e.g., line, radial, and mesh networks. This serves as a starting point to design distributed local algorithms with global behaviors that have low complexity, are computationally fast, and can run under synchronous and asynchronous settings in practical power grids.
Optimization model for the design of distributed wastewater treatment networks
Directory of Open Access Journals (Sweden)
Ibrić Nidret
2012-01-01
Full Text Available In this paper we address the synthesis problem of distributed wastewater networks using mathematical programming approach based on the superstructure optimization. We present a generalized superstructure and optimization model for the design of the distributed wastewater treatment networks. The superstructure includes splitters, treatment units, mixers, with all feasible interconnections including water recirculation. Based on the superstructure the optimization model is presented. The optimization model is given as a nonlinear programming (NLP problem where the objective function can be defined to minimize the total amount of wastewater treated in treatment operations or to minimize the total treatment costs. The NLP model is extended to a mixed integer nonlinear programming (MINLP problem where binary variables are used for the selection of the wastewater treatment technologies. The bounds for all flowrates and concentrations in the wastewater network are specified as general equations. The proposed models are solved using the global optimization solvers (BARON and LINDOGlobal. The application of the proposed models is illustrated on the two wastewater network problems of different complexity. First one is formulated as the NLP and the second one as the MINLP. For the second one the parametric and structural optimization is performed at the same time where optimal flowrates, concentrations as well as optimal technologies for the wastewater treatment are selected. Using the proposed model both problems are solved to global optimality.
Genetic Algorithm Optimized Neural Networks Ensemble as ...
African Journals Online (AJOL)
Improvements in neural network calibration models by a novel approach using neural network ensemble (NNE) for the simultaneous spectrophotometric multicomponent analysis are suggested, with a study on the estimation of the components of an antihypertensive combination, namely, atenolol and losartan potassium.
Optimal Redesign of the Dutch Road Network
Snelder, M.; Wagelmans, A.P.M.; Schrijver, J.M.; Van Zuylen, H.J.; Immers, L.H.
2007-01-01
The Dutch national road network has been developed over several decades. In the past, roads were constructed according to the then current spatial and transportation planning philosophies. Because the existing road network is a result of a long process of successive developments, the question can be
A Medical Center Network for Optimized Lung Cancer Biospecimen Banking
2015-10-01
1 Award Number: W81XWH-10-1-0818 TITLE: “A Medical Center Network for Optimized Lung Cancer Biospecimen Banking ” PRINCIPAL INVESTIGATOR: Christopher...To) 20Sep2014 - 19Sep2015 4. TITLE AND SUBTITLE “A Medical Center Network for Optimized Lung Cancer Biospecimen Banking ” 5a. CONTRACT NUMBER 5b...Although new subject enrollments and specimen collection have ceased, the LCBRN is committed to the outcome of this project, which is a bank of
Oxytocin improves synchronisation in leader-follower interaction
DEFF Research Database (Denmark)
Gebauer, L.; Witek, M. A. G.; Hansen, N. C.
2016-01-01
The neuropeptide oxytocin has been shown to affect social interaction. Meanwhile, the underlying mechanism remains highly debated. Using an interpersonal finger-tapping paradigm, we investigated whether oxytocin affects the ability to synchronise with and adapt to the behaviour of others. Dyads...... received either oxytocin or a non-active placebo, intranasally. We show that in conditions where one dyad-member was tapping to another unresponsive dyad-member - i.e. one was following another who was leading/self-pacing - dyads given oxytocin were more synchronised than dyads given placebo. However......, there was no effect when following a regular metronome or when both tappers were mutually adapting to each other. Furthermore, relative to their self-paced tapping partners, oxytocin followers were less variable than placebo followers. Our data suggests that oxytocin improves synchronisation to an unresponsive...
High-resolution optimal quantization for stochastic pooling networks
McDonnell, Mark D.; Amblard, Pierre-Olivier; Stocks, Nigel G.; Zozor, Steeve; Abbott, Derek
2007-01-01
Pooling networks of noisy threshold devices are good models for natural networks (e.g. neural networks in some parts of sensory pathways in vertebrates, networks of mossy fibers in the hippothalamus, . . . ) as well as for artificial networks (e.g. digital beamformers for sonar arrays, flash analog-to-digital converters, rate-constrained distributed sensor networks, . . . ). Such pooling networks exhibit the curious effect of suprathreshold stochastic resonance, which means that an optimal stochastic control of the network exists. Recently, some progress has been made in understanding pooling networks of identical, but independently noisy, threshold devices. One aspect concerns the behavior of information processing in the asymptotic limit of large networks, which is a limit of high relevance for neuroscience applications. The mutual information between the input and the output of the network has been evaluated, and its extremization has been performed. The aim of the present work is to extend these asymptotic results to study the more general case when the threshold values are no longer identical. In this situation, the values of thresholds can be described by a density, rather than by exact locations. We present a derivation of Shannon's mutual information between the input and output of these networks. The result is an approximation that relies a weak version of the law of large numbers, and a version of the central limit theorem. Optimization of the mutual information is then discussed.
Genetic algorithm for neural networks optimization
Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta
2004-11-01
This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB«.
District Heating Network Design and Configuration Optimization with Genetic Algorithm
DEFF Research Database (Denmark)
Li, Hongwei; Svendsen, Svend
2013-01-01
and the pipe friction and heat loss formulations are non-linear. In order to find the optimal district heating network configuration, genetic algorithm which handles the mixed integer nonlinear programming problem is chosen. The network configuration is represented with binary and integer encoding...
Secure digital communication using controlled projective synchronisation of chaos
Energy Technology Data Exchange (ETDEWEB)
Chee, C.Y. [School of Mechanical and Production Engineering, Nanyang Technological University, Singapore 639798 (Singapore)]. E-mail: chinyi_chee@pmail.ntu.edu.sg; Xu Daolin [School of Mechanical and Production Engineering, Nanyang Technological University, Singapore 639798 (Singapore)
2005-02-01
A new approach to chaos communication is proposed to encrypt digital information using controlled projective synchronisation. The scheme encrypts a binary sequence by manipulating the scaling feature of synchronisation from the coupled system. The transmitted signal therefore embeds only a single set of statistical properties. This prevents cryptanalysts from breaking the chaotic encryption scheme by using characteristic cryptanalysis that aims to detect switching of statistical properties in the intercepted information carrier signal. Pseudo-random switching key is incorporated into the scheme to masked out the deterministic nature of the underlying coupled system.
Optimization with PDE constraints ESF networking program 'OPTPDE'
2014-01-01
This book on PDE Constrained Optimization contains contributions on the mathematical analysis and numerical solution of constrained optimal control and optimization problems where a partial differential equation (PDE) or a system of PDEs appears as an essential part of the constraints. The appropriate treatment of such problems requires a fundamental understanding of the subtle interplay between optimization in function spaces and numerical discretization techniques and relies on advanced methodologies from the theory of PDEs and numerical analysis as well as scientific computing. The contributions reflect the work of the European Science Foundation Networking Programme ’Optimization with PDEs’ (OPTPDE).
Optimal scope of supply chain network & operations design
Ma, N.
2014-01-01
The increasingly complex supply chain networks and operations call for the development of decision support systems and optimization techniques that take a holistic view of supply chain issues and provide support for integrated decision-making. The economic impacts of optimized supply chain are
Optimizing and Understanding Network Structure for Diffusion
Zhang, Yao
2017-01-01
Given a population contact network and electronic medical records of patients, how to distribute vaccines to individuals to effectively control a flu epidemic? Similarly, given the Twitter following network and tweets, how to choose the best communities/groups to stop rumors from spreading? How to find the best accounts that bridge celebrities and ordinary users? These questions are related to diffusion (aka propagation) phenomena. Diffusion can be treated as a behavior of spreading contagion...
Trajectory Based Optimal Segment Computation in Road Network Databases
DEFF Research Database (Denmark)
Li, Xiaohui; Ceikute, Vaida; Jensen, Christian S.
2013-01-01
that are shown empirically to be scalable. Given a road network, a set of existing facilities, and a collection of customer route traversals, an optimal segment query returns the optimal road network segment(s) for a new facility. We propose a practical framework for computing this query, where each route...... that adopt different approaches to computing the query. Algorithm AUG uses graph augmentation, and ITE uses iterative road-network partitioning. Empirical studies with real data sets demonstrate that the algorithms are capable of offering high performance in realistic settings....
Chaotic Hopfield Neural Network Swarm Optimization and Its Application
Directory of Open Access Journals (Sweden)
Yanxia Sun
2013-01-01
Full Text Available A new neural network based optimization algorithm is proposed. The presented model is a discrete-time, continuous-state Hopfield neural network and the states of the model are updated synchronously. The proposed algorithm combines the advantages of traditional PSO, chaos and Hopfield neural networks: particles learn from their own experience and the experiences of surrounding particles, their search behavior is ergodic, and convergence of the swarm is guaranteed. The effectiveness of the proposed approach is demonstrated using simulations and typical optimization problems.
Control and Optimization of Network in Networked Control System
Directory of Open Access Journals (Sweden)
Wang Zhiwen
2014-01-01
Full Text Available In order to avoid quality of performance (QoP degradation resulting from quality of service (QoS, the solution to network congestion from the point of control theory, which marks departure of our results from the existing methods, is proposed in this paper. The congestion and bandwidth are regarded as state and control variables, respectively; then, the linear time-invariant (LTI model between congestion state and bandwidth of network is established. Consequently, linear quadratic method is used to eliminate the network congestion by allocating bandwidth dynamically. At last, numerical simulation results are given to illustrate the effectiveness of this modeling approach.
A Generic Methodology for Superstructure Optimization of Different Processing Networks
DEFF Research Database (Denmark)
Bertran, Maria-Ona; Frauzem, Rebecca; Zhang, Lei
2016-01-01
In this paper, we propose a generic computer-aided methodology for synthesis of different processing networks using superstructure optimization. The methodology can handle different network optimization problems of various application fields. It integrates databases with a common data architecture......, a generic model to represent the processing steps, and appropriate optimization tools. A special software interface has been created to automate the steps in the methodology workflow, allow the transfer of data between tools and obtain the mathematical representation of the problem as required...
Quasi-Lagrangian neural network for convex quadratic optimization.
Costantini, Giovanni; Perfetti, Renzo; Todisco, Massimiliano
2008-10-01
A new neural network for convex quadratic optimization is presented in this brief. The proposed network can handle both equality and inequality constraints, as well as bound constraints on the optimization variables. It is based on the Lagrangian approach, but exploits a partial dual method in order to keep the number of variables at minimum. The dynamic evolution is globally convergent and the steady-state solutions satisfy the necessary and sufficient conditions of optimality. The circuit implementation is simpler with respect to existing solutions for the same class of problems. The validity of the proposed approach is verified through some simulation examples.
Optimal Power Flow Solution Using Ant Manners for Electrical Network
Directory of Open Access Journals (Sweden)
ALLAOUA, B.
2009-02-01
Full Text Available This paper presents ant manners and the collective intelligence for electrical network. Solutions for Optimal Power Flow (OPF problem of a power system deliberate via an ant colony optimization metaheuristic method. The objective is to minimize the total fuel cost of thermal generating units and also conserve an acceptable system performance in terms of limits on generator real and reactive power outputs, bus voltages, shunt capacitors/reactors, transformers tap-setting and power flow of transmission lines. Simulation results on the IEEE 30-bus electrical network show that the ant colony optimization method converges quickly to the global optimum.
Resource Optimization Scheme for Multimedia-Enabled Wireless Mesh Networks
Ali, Amjad; Ahmed, Muhammad Ejaz; Piran, Md. Jalil; Suh, Doug Young
2014-01-01
Wireless mesh networking is a promising technology that can support numerous multimedia applications. Multimedia applications have stringent quality of service (QoS) requirements, i.e., bandwidth, delay, jitter, and packet loss ratio. Enabling such QoS-demanding applications over wireless mesh networks (WMNs) require QoS provisioning routing protocols that lead to the network resource underutilization problem. Moreover, random topology deployment leads to have some unused network resources. Therefore, resource optimization is one of the most critical design issues in multi-hop, multi-radio WMNs enabled with multimedia applications. Resource optimization has been studied extensively in the literature for wireless Ad Hoc and sensor networks, but existing studies have not considered resource underutilization issues caused by QoS provisioning routing and random topology deployment. Finding a QoS-provisioned path in wireless mesh networks is an NP complete problem. In this paper, we propose a novel Integer Linear Programming (ILP) optimization model to reconstruct the optimal connected mesh backbone topology with a minimum number of links and relay nodes which satisfies the given end-to-end QoS demands for multimedia traffic and identification of extra resources, while maintaining redundancy. We further propose a polynomial time heuristic algorithm called Link and Node Removal Considering Residual Capacity and Traffic Demands (LNR-RCTD). Simulation studies prove that our heuristic algorithm provides near-optimal results and saves about 20% of resources from being wasted by QoS provisioning routing and random topology deployment. PMID:25111241
Resource optimization scheme for multimedia-enabled wireless mesh networks.
Ali, Amjad; Ahmed, Muhammad Ejaz; Piran, Md Jalil; Suh, Doug Young
2014-08-08
Wireless mesh networking is a promising technology that can support numerous multimedia applications. Multimedia applications have stringent quality of service (QoS) requirements, i.e., bandwidth, delay, jitter, and packet loss ratio. Enabling such QoS-demanding applications over wireless mesh networks (WMNs) require QoS provisioning routing protocols that lead to the network resource underutilization problem. Moreover, random topology deployment leads to have some unused network resources. Therefore, resource optimization is one of the most critical design issues in multi-hop, multi-radio WMNs enabled with multimedia applications. Resource optimization has been studied extensively in the literature for wireless Ad Hoc and sensor networks, but existing studies have not considered resource underutilization issues caused by QoS provisioning routing and random topology deployment. Finding a QoS-provisioned path in wireless mesh networks is an NP complete problem. In this paper, we propose a novel Integer Linear Programming (ILP) optimization model to reconstruct the optimal connected mesh backbone topology with a minimum number of links and relay nodes which satisfies the given end-to-end QoS demands for multimedia traffic and identification of extra resources, while maintaining redundancy. We further propose a polynomial time heuristic algorithm called Link and Node Removal Considering Residual Capacity and Traffic Demands (LNR-RCTD). Simulation studies prove that our heuristic algorithm provides near-optimal results and saves about 20% of resources from being wasted by QoS provisioning routing and random topology deployment.
Selecting Optimal Parameters of Random Linear Network Coding for Wireless Sensor Networks
DEFF Research Database (Denmark)
Heide, Janus; Zhang, Qi; Fitzek, Frank
2013-01-01
This work studies how to select optimal code parameters of Random Linear Network Coding (RLNC) in Wireless Sensor Networks (WSNs). With Rateless Deluge [1] the authors proposed to apply Network Coding (NC) for Over-the-Air Programming (OAP) in WSNs, and demonstrated that with NC a significant...
Wavelength Converters Placement in Optical Networks Using Bee Colony Optimization
Directory of Open Access Journals (Sweden)
MARKOVIC, G. Z.
2016-02-01
Full Text Available Wavelength converters placement (WCP in all-optical WDM networks belongs to the class of hard combinatorial optimization problems. So far, this problem has been solved by various heuristic strategies or by application of metaheuristic approaches such as genetic algorithms (GA, particle swarm optimization (PSO, differential evolution (DE, etc. In this paper, we introduce the application of Bee Colony Optimization (BCO metaheuristic to solve the WCP problem in all-optical WDM networks. Numerous studies prove that BCO is a fast, robust and computationally efficient tool in tackling complex optimization problems. The objective of the proposed BCO-WCP algorithm is to find the best placement of limited number of wavelength converters in given optical network such that the overall network blocking probability is minimized. To evaluate the performances of the BCO-WCP algorithm, numerous simulation experiments have been performed over some realistic optical network examples. The blocking probability performance and computational complexity are compared with optimal solution obtained by exhaustive search (ES approach as well as with DE and PSO metaheuristics. It will be shown that the BCO-WCP algorithm is not only be able to produce high quality (optimal solution, but significantly outperforms the computational efficiency of other considered approaches.
Resilience-based optimal design of water distribution network
Suribabu, C. R.
2017-11-01
Optimal design of water distribution network is generally aimed to minimize the capital cost of the investments on tanks, pipes, pumps, and other appurtenances. Minimizing the cost of pipes is usually considered as a prime objective as its proportion in capital cost of the water distribution system project is very high. However, minimizing the capital cost of the pipeline alone may result in economical network configuration, but it may not be a promising solution in terms of resilience point of view. Resilience of the water distribution network has been considered as one of the popular surrogate measures to address ability of network to withstand failure scenarios. To improve the resiliency of the network, the pipe network optimization can be performed with two objectives, namely minimizing the capital cost as first objective and maximizing resilience measure of the configuration as secondary objective. In the present work, these two objectives are combined as single objective and optimization problem is solved by differential evolution technique. The paper illustrates the procedure for normalizing the objective functions having distinct metrics. Two of the existing resilience indices and power efficiency are considered for optimal design of water distribution network. The proposed normalized objective function is found to be efficient under weighted method of handling multi-objective water distribution design problem. The numerical results of the design indicate the importance of sizing pipe telescopically along shortest path of flow to have enhanced resiliency indices.
Resilience-based optimal design of water distribution network
Suribabu, C. R.
2017-04-01
Optimal design of water distribution network is generally aimed to minimize the capital cost of the investments on tanks, pipes, pumps, and other appurtenances. Minimizing the cost of pipes is usually considered as a prime objective as its proportion in capital cost of the water distribution system project is very high. However, minimizing the capital cost of the pipeline alone may result in economical network configuration, but it may not be a promising solution in terms of resilience point of view. Resilience of the water distribution network has been considered as one of the popular surrogate measures to address ability of network to withstand failure scenarios. To improve the resiliency of the network, the pipe network optimization can be performed with two objectives, namely minimizing the capital cost as first objective and maximizing resilience measure of the configuration as secondary objective. In the present work, these two objectives are combined as single objective and optimization problem is solved by differential evolution technique. The paper illustrates the procedure for normalizing the objective functions having distinct metrics. Two of the existing resilience indices and power efficiency are considered for optimal design of water distribution network. The proposed normalized objective function is found to be efficient under weighted method of handling multi-objective water distribution design problem. The numerical results of the design indicate the importance of sizing pipe telescopically along shortest path of flow to have enhanced resiliency indices.
Optimal satisfaction degree in energy harvesting cognitive radio networks
Li, Zan; Liu, Bo-Yang; Si, Jiang-Bo; Zhou, Fu-Hui
2015-12-01
A cognitive radio (CR) network with energy harvesting (EH) is considered to improve both spectrum efficiency and energy efficiency. A hidden Markov model (HMM) is used to characterize the imperfect spectrum sensing process. In order to maximize the whole satisfaction degree (WSD) of the cognitive radio network, a tradeoff between the average throughput of the secondary user (SU) and the interference to the primary user (PU) is analyzed. We formulate the satisfaction degree optimization problem as a mixed integer nonlinear programming (MINLP) problem. The satisfaction degree optimization problem is solved by using differential evolution (DE) algorithm. The proposed optimization problem allows the network to adaptively achieve the optimal solution based on its required quality of service (Qos). Numerical results are given to verify our analysis. Project supported by the National Natural Science Foundation of China (Grant No. 61301179), the Doctorial Programs Foundation of the Ministry of Education of China (Grant No. 20110203110011), and the 111 Project (Grant No. B08038).
Autonomous Optimization of Targeted Stimulation of Neuronal Networks.
Kumar, Sreedhar S; Wülfing, Jan; Okujeni, Samora; Boedecker, Joschka; Riedmiller, Martin; Egert, Ulrich
2016-08-01
Driven by clinical needs and progress in neurotechnology, targeted interaction with neuronal networks is of increasing importance. Yet, the dynamics of interaction between intrinsic ongoing activity in neuronal networks and their response to stimulation is unknown. Nonetheless, electrical stimulation of the brain is increasingly explored as a therapeutic strategy and as a means to artificially inject information into neural circuits. Strategies using regular or event-triggered fixed stimuli discount the influence of ongoing neuronal activity on the stimulation outcome and are therefore not optimal to induce specific responses reliably. Yet, without suitable mechanistic models, it is hardly possible to optimize such interactions, in particular when desired response features are network-dependent and are initially unknown. In this proof-of-principle study, we present an experimental paradigm using reinforcement-learning (RL) to optimize stimulus settings autonomously and evaluate the learned control strategy using phenomenological models. We asked how to (1) capture the interaction of ongoing network activity, electrical stimulation and evoked responses in a quantifiable 'state' to formulate a well-posed control problem, (2) find the optimal state for stimulation, and (3) evaluate the quality of the solution found. Electrical stimulation of generic neuronal networks grown from rat cortical tissue in vitro evoked bursts of action potentials (responses). We show that the dynamic interplay of their magnitudes and the probability to be intercepted by spontaneous events defines a trade-off scenario with a network-specific unique optimal latency maximizing stimulus efficacy. An RL controller was set to find this optimum autonomously. Across networks, stimulation efficacy increased in 90% of the sessions after learning and learned latencies strongly agreed with those predicted from open-loop experiments. Our results show that autonomous techniques can exploit quantitative
Topological Effects and Performance Optimization in Transportation Continuous Network Design
Directory of Open Access Journals (Sweden)
Jianjun Wu
2014-01-01
Full Text Available Because of the limitation of budget, in the planning of road works, increased efforts should be made on links that are more critical to the whole traffic system. Therefore, it would be helpful to model and evaluate the vulnerability and reliability of the transportation network when the network design is processing. This paper proposes a bilevel transportation network design model, in which the upper level is to minimize the performance of the network under the given budgets, while the lower level is a typical user equilibrium assignment problem. A new solution approach based on particle swarm optimization (PSO method is presented. The topological effects on the performance of transportation networks are studied with the consideration of three typical networks, regular lattice, random graph, and small-world network. Numerical examples and simulations are presented to demonstrate the proposed model.
Optimization of recurrent neural networks for time series modeling
DEFF Research Database (Denmark)
Pedersen, Morten With
1997-01-01
series. The overall objective s are to improve training by application of second-order methods and to improve generalization ability by architecture optimization accomplished by pruning. The major topics covered in the thesis are: 1. The problem of training recurrent networks is analyzed from a numerical...... of solution obtained as well as computation time required. 3. A theoretical definition of the generalization error for recurrent networks is provided. This definition justifies a commonly adopted approach for estimating generalization ability. 4. The viability of pruning recurrent networks by the Optimal...... networks is proposed. The tool allows for assessment of the length of the effe ctive memory of previous inputs built up in the recurrent network during application. Time series modeling is also treated from a more general point of view, namely modeling of the joint probability distribution function...
Optimization of wireless sensor networks based on chicken swarm optimization algorithm
Wang, Qingxi; Zhu, Lihua
2017-05-01
In order to reduce the energy consumption of wireless sensor network and improve the survival time of network, the clustering routing protocol of wireless sensor networks based on chicken swarm optimization algorithm was proposed. On the basis of LEACH agreement, it was improved and perfected that the points on the cluster and the selection of cluster head using the chicken group optimization algorithm, and update the location of chicken which fall into the local optimum by Levy flight, enhance population diversity, ensure the global search capability of the algorithm. The new protocol avoided the die of partial node of intensive using by making balanced use of the network nodes, improved the survival time of wireless sensor network. The simulation experiments proved that the protocol is better than LEACH protocol on energy consumption, also is better than that of clustering routing protocol based on particle swarm optimization algorithm.
Neural networks, penalty logic and optimality theory
Blutner, R.; Benz, A.; Blutner, R.
2009-01-01
Ever since the discovery of neural networks, there has been a controversy between two modes of information processing. On the one hand, symbolic systems have proven indispensable for our understanding of higher intelligence, especially when cognitive domains like language and reasoning are examined.
FERTILTTY IN COWS AF-TER SYNCHRONISATION OF OESTRUS ...
African Journals Online (AJOL)
n cornparison one insemination at a fixed time after PG F2o treat- ment results in significiantly lower fertility (Cooper and Jackson, 1975 ; Lamnring et al., 1975). Regimes for synchronisation of oestrus would be simplified if normal fertility resulted fr:t-rnr r:ne insenrina- tion given at a pre-determined tirne after Pf-i f"ro treat-.
design and implementation of conveyor line speed synchroniser
African Journals Online (AJOL)
user
2016-07-03
Jul 3, 2016 ... Keywords: Conveyor belt, DC motor, PID Controller, Speed Synchroniser, Laplace transform. 1. INTRODUCTION. A conveyor is a mechanical system that is often spread out over a considerable distance. These are used to move either bulk materials or unit items through the manufacturing process, and are ...
SYNCHRONISATION OF OESTRUS IN SHEEP EARLY IN THE ...
African Journals Online (AJOL)
SYNCHRONISATION OF OESTRUS IN SHEEP EARLY IN THE BREEDING SEASON: THE VAGINAL ENVIRONN1ENT AND FERTILITY. J.l\\I. van der Westhuvsen* and C.H. van Niekerk. Department of Animal Physiologlt, University of Stellenbosch, Stellenbosch. OPSOMMING: SINKRONISASIE VAN OESTRUS BY SKAPE ...
Globalisation, the Challenge of Educational Synchronisation and Teacher Education
Papastephanou, Marianna; Christou, Miranda; Gregoriou, Zelia
2013-01-01
In this article, we set out from the challenge that globalising synchronisation--usually exemplified by Organization for Economic Cooperation and Development and World Bank initiatives--presents for education to argue that the time-space compression effected by globalisation must educationally be dealt with with caution, critical vigilance and a…
Synchronisation of glycolytic oscillations in a suspension of human neutrophils
DEFF Research Database (Denmark)
Brasen, Jens Christian; Poulsen, Allan K.; Olsen, Lars Folke
Neutrophils are known to be able to synchronize their production of superoxide. We show that glycolysis is also synchronized in human neutrophils being in suspension and suggest that oscillations in glycolysis are driving the pulsatile production of superoxide. The synchronising agent remains so...... far unknown, however, much evident points to that it might be hydrogen peroxide or an intermediate in glycolysis....
FERTILTTY IN COWS AF-TER SYNCHRONISATION OF OESTRUS ...
African Journals Online (AJOL)
Dunn, l9-/4; Rodriquez" Fields, Burns. Franke, llentges,. Thatcher and Warnick, 1975). However, injections of oestrogen (oestradiol benzoate) in cows synchronised with PG F2o have been shown to reduce the variation in the time of LH release (Nancarrow and Radford, 1915;. Welch et al., 1975). Moreover, fertility of such ...
Design of optimised backstepping controller for the synchronisation ...
Indian Academy of Sciences (India)
In this paper, an adaptive backstepping controller has been tuned to synchronise two chaotic Colpitts oscillators in a master–slave configuration. The parameters of the controller are determined using shark smell optimisation (SSO) algorithm. Numerical results are presented and compared with those of particle swarm ...
Design of optimised backstepping controller for the synchronisation ...
Indian Academy of Sciences (India)
Ehsan Fouladi
2017-12-18
Dec 18, 2017 ... Abstract. In this paper, an adaptive backstepping controller has been tuned to synchronise two chaotic Colpitts oscillators in a master–slave configuration. The parameters of the controller are determined using shark smell optimisation (SSO) algorithm. Numerical results are presented and compared with ...
Optimizing of Passive Optical Network Deployment Using Algorithm with Metrics
Directory of Open Access Journals (Sweden)
Tomas Pehnelt
2017-01-01
Full Text Available Various approaches and methods are used for designing of optimum deployment of Passive Optical Networks (PON according to selected optimization criteria, such as optimal trenching distance, endpoint attenuation and overall installed fibre length. This article describes the ideas and possibilities for an algorithm with the application of graph algorithms for finding the shortest path from Optical Line Termination to Optical Network Terminal unit. This algorithm uses a combination of different methods for generating of an optimal metric, thus creating the optimized tree topology mainly focused on summary trenching distance. Furthermore, it deals with algorithms for finding an optimal placement of optical splitter with the help of K-Means clustering method and hierarchical clustering technique. The results of the proposed algorithm are compared with existing methods.
Optimization of TTEthernet Networks to Support Best-Effort Traffic
DEFF Research Database (Denmark)
Tamas-Selicean, Domitian; Pop, Paul
2014-01-01
This paper focuses on the optimization of the TTEthernet communication protocol, which offers three traffic classes: time-triggered (TT), sent according to static schedules, rate-constrained (RC) that has bounded end-to-end latency, and best-effort (BE), the classic Ethernet traffic, with no timing...... guarantees. In our earlier work we have proposed an optimization approach named DOTTS that performs the routing, scheduling and packing / fragmenting of TT and RC messages, such that the TT and RC traffic is schedulable. Although backwards compatibility with classic Ethernet networks is one of TTEthernet......’s strong points, there is little research on this topic. However, in this paper, we extend our DOTTS optimization approach to optimize TTEthernet networks, such that not only the TT and RC messages are schedulable, but we also maximize the available bandwidth for BE messages. The proposed optimization has...
Optimal vaccination and treatment of an epidemic network model
Energy Technology Data Exchange (ETDEWEB)
Chen, Lijuan [Department of Mathematics, Tongji University, Shanghai 200092 (China); College of Mathematics and Computer Science, Fuzhou University, Fuzhou, Fujian 350002 (China); Sun, Jitao, E-mail: sunjt@sh163.net [Department of Mathematics, Tongji University, Shanghai 200092 (China)
2014-08-22
In this Letter, we firstly propose an epidemic network model incorporating two controls which are vaccination and treatment. For the constant controls, by using Lyapunov function, global stability of the disease-free equilibrium and the endemic equilibrium of the model is investigated. For the non-constant controls, by using the optimal control strategy, we discuss an optimal strategy to minimize the total number of the infected and the cost associated with vaccination and treatment. Table 1 and Figs. 1–5 are presented to show the global stability and the efficiency of this optimal control. - Highlights: • Propose an optimally controlled SIRS epidemic model on heterogeneous networks. • Obtain criteria of global stability of the disease-free equilibrium and the endemic equilibrium. • Investigate existence of optimal control for the control problem. • The results be illustrated by some numerical simulations.
Exact Convex Relaxation of Optimal Power Flow in Radial Networks
Energy Technology Data Exchange (ETDEWEB)
Gan, LW; Li, N; Topcu, U; Low, SH
2015-01-01
The optimal power flow (OPF) problem determines a network operating point that minimizes a certain objective such as generation cost or power loss. It is nonconvex. We prove that a global optimum of OPF can be obtained by solving a second-order cone program, under a mild condition after shrinking the OPF feasible set slightly, for radial power networks. The condition can be checked a priori, and holds for the IEEE 13, 34, 37, 123-bus networks and two real-world networks.
An Optimal Routing Algorithm in Service Customized 5G Networks
Directory of Open Access Journals (Sweden)
Haipeng Yao
2016-01-01
Full Text Available With the widespread use of Internet, the scale of mobile data traffic grows explosively, which makes 5G networks in cellular networks become a growing concern. Recently, the ideas related to future network, for example, Software Defined Networking (SDN, Content-Centric Networking (CCN, and Big Data, have drawn more and more attention. In this paper, we propose a service-customized 5G network architecture by introducing the ideas of separation between control plane and data plane, in-network caching, and Big Data processing and analysis to resolve the problems traditional cellular radio networks face. Moreover, we design an optimal routing algorithm for this architecture, which can minimize average response hops in the network. Simulation results reveal that, by introducing the cache, the network performance can be obviously improved in different network conditions compared to the scenario without a cache. In addition, we explore the change of cache hit rate and average response hops under different cache replacement policies, cache sizes, content popularity, and network topologies, respectively.
A Stochastic Multiobjective Optimization Framework for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Shibo He
2010-01-01
Full Text Available In wireless sensor networks (WSNs, there generally exist many different objective functions to be optimized. In this paper, we propose a stochastic multiobjective optimization approach to solve such kind of problem. We first formulate a general multiobjective optimization problem. We then decompose the optimization formulation through Lagrange dual decomposition and adopt the stochastic quasigradient algorithm to solve the primal-dual problem in a distributed way. We show theoretically that our algorithm converges to the optimal solution of the primal problem by using the knowledge of stochastic programming. Furthermore, the formulation provides a general stochastic multiobjective optimization framework for WSNs. We illustrate how the general framework works by considering an example of the optimal rate allocation problem in multipath WSNs with time-varying channel. Extensive simulation results are given to demonstrate the effectiveness of our algorithm.
Fluid Limits of Optimally Controlled Queueing Networks
Guodong Pang; Day, Martin V.
2007-01-01
We consider a class of queueing processes represented by a Skorokhod problem coupled with a controlled point process. Posing a discounted control problem for such processes, we show that the optimal value functions converge, in the fluid limit, to the value of an analogous deterministic control problem for fluid processes. Peer Reviewed
Fluid Limits of Optimally Controlled Queueing Networks
Directory of Open Access Journals (Sweden)
Guodong Pang
2007-01-01
Full Text Available We consider a class of queueing processes represented by a Skorokhod problem coupled with a controlled point process. Posing a discounted control problem for such processes, we show that the optimal value functions converge, in the fluid limit, to the value of an analogous deterministic control problem for fluid processes.
LTE-Advanced Radio and Network Optimization
DEFF Research Database (Denmark)
Velez, Fernando J.; Sousa, Sofia; Flores, Jessica Acevedo
2015-01-01
In cellular optimization, the UL and DL the values from carrier-to-noise-plus-interference ratio (CNIR) from/at the mobile station are very important parameters. From a detailed analysis of its variation with the coverage and reuse distances for different values of the Channel Quality Indicator (...
Complex brain networks: From topological communities to clustered ...
Indian Academy of Sciences (India)
We investigate synchronisation dynamics on the corticocortical network of the cat by modelling each node of the network (cortical area) with a subnetwork of interacting excitable neurons. We find that this network of networks displays clustered synchronisation behaviour and the dynamical clusters closely coincide with the ...
Directory of Open Access Journals (Sweden)
Krishan Kumar
2017-08-01
Full Text Available It is envisaged that in future Cognitive Radio (CR networks deployment, multiple radio access networks may coexist. The networks may have different characteristics in terms of multiple attributes. CRs will have choices of selecting the optimal network out of the available networks. Optimal network selection is a challenging task that can be performed by spectrum handoff with Multiple Attribute Decision Making (MADM. The spectrum handoff decision with MADM provides wider and optimal choice with quality of service. This motivates the devolopment of a spectrum handoff scheme with MADM methods such as simple additive weighting, a technique for order preference by similarity to the ideal solution, a grey relational analysis and a cost function based method, which is the objective of this study. The CR preferences are based on voice, video and data services, called triple play services. The numerical results show that all MADM methods are effective for selecting the optimal network for spectrum handoff with a reduced complexity for the spectrum handoff decision. The paper shows that the proposed spectrum handoff scheme can be effectively implemented to select the optimal network according to triple play services in CR networks.
Optimal Band Allocation for Cognitive Cellular Networks
Liu,Tingting; Jiang, Chengling
2011-01-01
FCC new regulation for cognitive use of the TV white space spectrum provides a new means for improving traditional cellular network performance. But it also introduces a number of technical challenges. This letter studies one of the challenges, that is, given the significant differences in the propagation property and the transmit power limitations between the cellular band and the TV white space, how to jointly utilize both bands such that the benefit from the TV white space for improving ce...
Optimized evolution of networks for principal eigenvector localization
Pradhan, Priodyuti; Yadav, Alok; Dwivedi, Sanjiv K.; Jalan, Sarika
2017-08-01
Network science is increasingly being developed to get new insights about behavior and properties of complex systems represented in terms of nodes and interactions. One useful approach is investigating the localization properties of eigenvectors having diverse applications including disease-spreading phenomena in underlying networks. In this work, we evolve an initial random network with an edge rewiring optimization technique considering the inverse participation ratio as a fitness function. The evolution process yields a network having a localized principal eigenvector. We analyze various properties of the optimized networks and those obtained at the intermediate stage. Our investigations reveal the existence of a few special structural features of such optimized networks, for instance, the presence of a set of edges which are necessary for localization, and rewiring only one of them leads to complete delocalization of the principal eigenvector. Furthermore, we report that principal eigenvector localization is not a consequence of changes in a single network property and, preferably, requires the collective influence of various distinct structural as well as spectral features.
Optimal localization of diffusion sources in complex networks
Hu, Zhao-Long; Han, Xiao; Lai, Ying-Cheng
2017-01-01
Locating sources of diffusion and spreading from minimum data is a significant problem in network science with great applied values to the society. However, a general theoretical framework dealing with optimal source localization is lacking. Combining the controllability theory for complex networks and compressive sensing, we develop a framework with high efficiency and robustness for optimal source localization in arbitrary weighted networks with arbitrary distribution of sources. We offer a minimum output analysis to quantify the source locatability through a minimal number of messenger nodes that produce sufficient measurement for fully locating the sources. When the minimum messenger nodes are discerned, the problem of optimal source localization becomes one of sparse signal reconstruction, which can be solved using compressive sensing. Application of our framework to model and empirical networks demonstrates that sources in homogeneous and denser networks are more readily to be located. A surprising finding is that, for a connected undirected network with random link weights and weak noise, a single messenger node is sufficient for locating any number of sources. The framework deepens our understanding of the network source localization problem and offers efficient tools with broad applications. PMID:28484635
Robust Optimization of Fourth Party Logistics Network Design under Disruptions
Directory of Open Access Journals (Sweden)
Jia Li
2015-01-01
Full Text Available The Fourth Party Logistics (4PL network faces disruptions of various sorts under the dynamic and complex environment. In order to explore the robustness of the network, the 4PL network design with consideration of random disruptions is studied. The purpose of the research is to construct a 4PL network that can provide satisfactory service to customers at a lower cost when disruptions strike. Based on the definition of β-robustness, a robust optimization model of 4PL network design under disruptions is established. Based on the NP-hard characteristic of the problem, the artificial fish swarm algorithm (AFSA and the genetic algorithm (GA are developed. The effectiveness of the algorithms is tested and compared by simulation examples. By comparing the optimal solutions of the 4PL network for different robustness level, it is indicated that the robust optimization model can evade the market risks effectively and save the cost in the maximum limit when it is applied to 4PL network design.
Optimization of stochastic discrete systems and control on complex networks computational networks
Lozovanu, Dmitrii
2014-01-01
This book presents the latest findings on stochastic dynamic programming models and on solving optimal control problems in networks. It includes the authors' new findings on determining the optimal solution of discrete optimal control problems in networks and on solving game variants of Markov decision problems in the context of computational networks. First, the book studies the finite state space of Markov processes and reviews the existing methods and algorithms for determining the main characteristics in Markov chains, before proposing new approaches based on dynamic programming and combinatorial methods. Chapter two is dedicated to infinite horizon stochastic discrete optimal control models and Markov decision problems with average and expected total discounted optimization criteria, while Chapter three develops a special game-theoretical approach to Markov decision processes and stochastic discrete optimal control problems. In closing, the book's final chapter is devoted to finite horizon stochastic con...
Singularities in minimax optimization of networks
DEFF Research Database (Denmark)
Madsen, Kaj; Schjær-Jacobsen, Hans
1976-01-01
A theoretical treatment of singularities in nonlinear minimax optimization problems, which allows for a classification in regular and singular problems, is presented. A theorem for determining a singularity that is present in a given problem is formulated. A group of problems often used...... in the literature to test nonlinear minimax algorithms, i.e., minimax design of multisection quarter-wave transformers, is shown to exhibit singularities and the reason for this is pointed out. Based on the theoretical results presented an algorithm for nonlinear minimax optimization is developed. The new algorithm...... maintains the quadratic convergence property of a recent algorithm by Madsen et al. when applied to regular problems and it is demonstrated to significantly improve the final convergence on singular problems....
Online Advertisement, Optimization and Stochastic Networks
Tan, Bo; Srikant, R.
2010-01-01
In this paper, we propose a stochastic model to describe how search service providers charge client companies based on users' queries for the keywords related to these companies' ads by using certain advertisement assignment strategies. We formulate an optimization problem to maximize the long-term average revenue for the service provider under each client's long-term average budget constraint, and design an online algorithm which captures the stochastic properties of users' queries and click...
Optimal paths on the road network as directed polymers
Solon, A. P.; Bunin, G.; Chu, S.; Kardar, M.
2017-11-01
We analyze the statistics of the shortest and fastest paths on the road network between randomly sampled end points. We find that, to a good approximation, the optimal paths can be described as directed polymers in a disordered medium, which belong to the Kardar-Parisi-Zhang universality class of interface roughening. Comparing the scaling behavior of our data with simulations of directed polymers and previous theoretical results, we are able to point out the few characteristics of the road network that are relevant to the large-scale statistics of optimal paths. Indeed, we show that the local structure is akin to a disordered environment with a power-law distribution which become less important at large scales where long-ranged correlations in the network control the scaling behavior of the optimal paths.
On the Optimality of Trust Network Analysis with Subjective Logic
Directory of Open Access Journals (Sweden)
PARK, Y.
2014-08-01
Full Text Available Building and measuring trust is one of crucial aspects in e-commerce, social networking and computer security. Trust networks are widely used to formalize trust relationships and to conduct formal reasoning of trust values. Diverse trust network analysis methods have been developed so far and one of the most widely used schemes is TNA-SL (Trust Network Analysis with Subjective Logic. Recent papers claimed that TNA-SL always finds the optimal solution by producing the least uncertainty. In this paper, we present some counter-examples, which imply that TNA-SL is not an optimal algorithm. Furthermore, we present a probabilistic algorithm in edge splitting to minimize uncertainty.
Multi-objective optimization in computer networks using metaheuristics
Donoso, Yezid
2007-01-01
Metaheuristics are widely used to solve important practical combinatorial optimization problems. Many new multicast applications emerging from the Internet-such as TV over the Internet, radio over the Internet, and multipoint video streaming-require reduced bandwidth consumption, end-to-end delay, and packet loss ratio. It is necessary to design and to provide for these kinds of applications as well as for those resources necessary for functionality. Multi-Objective Optimization in Computer Networks Using Metaheuristics provides a solution to the multi-objective problem in routing computer networks. It analyzes layer 3 (IP), layer 2 (MPLS), and layer 1 (GMPLS and wireless functions). In particular, it assesses basic optimization concepts, as well as several techniques and algorithms for the search of minimals; examines the basic multi-objective optimization concepts and the way to solve them through traditional techniques and through several metaheuristics; and demonstrates how to analytically model the compu...
Operational predictive optimal control of Barcelona water transport network
Pascual, J.; Romera, J.; Puig, V.; Cembrano, G.; Creus, R.; Minoves, M.
2013-01-01
This paper describes the application of model-based predictive control (MPC) techniques to the supervisory flow management in large-scale drinking water networks including a telemetry/telecontrol system. MPC is used to generate flow control strategies (set-points for the regulatory controllers) from the sources to the consumer areas to meet future demands, optimizing performance indexes associated to operational goals such as economic cost, safety storage volumes in the network and smoothness...
SEWER NETWORK DISCHARGE OPTIMIZATION USING THE DYNAMIC PROGRAMMING
Directory of Open Access Journals (Sweden)
Viorel MINZU
2015-12-01
Full Text Available It is necessary to adopt an optimal control that allows an efficient usage of the existing sewer networks, in order to avoid the building of new retention facilities. The main objective of the control action is to minimize the overflow volume of a sewer network. This paper proposes a method to apply a solution obtained by discrete dynamic programming through a realistic closed loop system.
On limited fan-in optimal neural networks
Energy Technology Data Exchange (ETDEWEB)
Beiu, V.; Makaruk, H.E. [Los Alamos National Lab., NM (United States); Draghici, S. [Wayne State Univ., Detroit, MI (United States). Vision and Neural Networks Lab.
1998-03-01
Because VLSI implementations do not cope well with highly interconnected nets the area of a chip growing as the cube of the fan-in--this paper analyses the influence of limited fan in on the size and VLSI optimality of such nets. Two different approaches will show that VLSI- and size-optimal discrete neural networks can be obtained for small (i.e. lower than linear) fan-in values. They have applications to hardware implementations of neural networks. The first approach is based on implementing a certain sub class of Boolean functions, IF{sub n,m} functions. The authors will show that this class of functions can be implemented in VLSI optimal (i.e., minimizing AT{sup 2}) neural networks of small constant fan ins. The second approach is based on implementing Boolean functions for which the classical Shannon`s decomposition can be used. Such a solution has already been used to prove bounds on neural networks with fan-ins limited to 2. They generalize the result presented there to arbitrary fan-in, and prove that the size is minimized by small fan in values, while relative minimum size solutions can be obtained for fan-ins strictly lower than linear. Finally, a size-optimal neural network having small constant fan-ins will be suggested for IF{sub n,m} functions.
Influence maximization in complex networks through optimal percolation
Morone, Flaviano; Makse, Hernan; CUNY Collaboration; CUNY Collaboration
The whole frame of interconnections in complex networks hinges on a specific set of structural nodes, much smaller than the total size, which, if activated, would cause the spread of information to the whole network, or, if immunized, would prevent the diffusion of a large scale epidemic. Localizing this optimal, that is, minimal, set of structural nodes, called influencers, is one of the most important problems in network science. Here we map the problem onto optimal percolation in random networks to identify the minimal set of influencers, which arises by minimizing the energy of a many-body system, where the form of the interactions is fixed by the non-backtracking matrix of the network. Big data analyses reveal that the set of optimal influencers is much smaller than the one predicted by previous heuristic centralities. Remarkably, a large number of previously neglected weakly connected nodes emerges among the optimal influencers. Reference: F. Morone, H. A. Makse, Nature 524,65-68 (2015)
Improved Differential Evolution Algorithm for Wireless Sensor Network Coverage Optimization
Directory of Open Access Journals (Sweden)
Xing Xu
2014-04-01
Full Text Available In order to serve for the ecological monitoring efficiency of Poyang Lake, an improved hybrid algorithm, mixed with differential evolution and particle swarm optimization, is proposed and applied to optimize the coverage problem of wireless sensor network. And then, the affect of the population size and the number of iterations on the coverage performance are both discussed and analyzed. The four kinds of statistical results about the coverage rate are obtained through lots of simulation experiments.
An Evolutionary Optimization Framework for Neural Networks and Neuromorphic Architectures
Energy Technology Data Exchange (ETDEWEB)
Schuman, Catherine D [ORNL; Plank, James [University of Tennessee (UT); Disney, Adam [University of Tennessee (UT); Reynolds, John [University of Tennessee (UT)
2016-01-01
As new neural network and neuromorphic architectures are being developed, new training methods that operate within the constraints of the new architectures are required. Evolutionary optimization (EO) is a convenient training method for new architectures. In this work, we review a spiking neural network architecture and a neuromorphic architecture, and we describe an EO training framework for these architectures. We present the results of this training framework on four classification data sets and compare those results to other neural network and neuromorphic implementations. We also discuss how this EO framework may be extended to other architectures.
5G heterogeneous networks self-organizing and optimization
Rong, Bo; Kadoch, Michel; Sun, Songlin; Li, Wenjing
2016-01-01
This SpringerBrief provides state-of-the-art technical reviews on self-organizing and optimization in 5G systems. It covers the latest research results from physical-layer channel modeling to software defined network (SDN) architecture. This book focuses on the cutting-edge wireless technologies such as heterogeneous networks (HetNets), self-organizing network (SON), smart low power node (LPN), 3D-MIMO, and more. It will help researchers from both the academic and industrial worlds to better understand the technical momentum of 5G key technologies.
Service network design of bike sharing systems analysis and optimization
Vogel, Patrick
2016-01-01
This monograph presents a tactical planning approach for service network design in metropolitan areas. Designing the service network requires the suitable aggregation of demand data as well as the anticipation of operational relocation decisions. To this end, an integrated approach of data analysis and mathematical optimization is introduced. The book also includes a case study based on real-world data to demonstrate the benefit of the proposed service network design approach. The target audience comprises primarily research experts in the field of traffic engineering, but the book may also be beneficial for graduate students.
Directory of Open Access Journals (Sweden)
Zhengyu Xie
2015-01-01
Full Text Available We consider the sensor networks hierarchical optimization problem in high-speed railway transport hub (HRTH. The sensor networks are optimized from three hierarchies which are key area sensors optimization, passenger line sensors optimization, and whole area sensors optimization. Case study on a specific HRTH in China showed that the hierarchical optimization method is effective to optimize the sensor networks for security monitoring in HRTH.
A multiobjective optimization framework for multicontaminant industrial water network design.
Boix, Marianne; Montastruc, Ludovic; Pibouleau, Luc; Azzaro-Pantel, Catherine; Domenech, Serge
2011-07-01
The optimal design of multicontaminant industrial water networks according to several objectives is carried out in this paper. The general formulation of the water allocation problem (WAP) is given as a set of nonlinear equations with binary variables representing the presence of interconnections in the network. For optimization purposes, three antagonist objectives are considered: F(1), the freshwater flow-rate at the network entrance, F(2), the water flow-rate at inlet of regeneration units, and F(3), the number of interconnections in the network. The multiobjective problem is solved via a lexicographic strategy, where a mixed-integer nonlinear programming (MINLP) procedure is used at each step. The approach is illustrated by a numerical example taken from the literature involving five processes, one regeneration unit and three contaminants. The set of potential network solutions is provided in the form of a Pareto front. Finally, the strategy for choosing the best network solution among those given by Pareto fronts is presented. This Multiple Criteria Decision Making (MCDM) problem is tackled by means of two approaches: a classical TOPSIS analysis is first implemented and then an innovative strategy based on the global equivalent cost (GEC) in freshwater that turns out to be more efficient for choosing a good network according to a practical point of view. Copyright © 2011 Elsevier Ltd. All rights reserved.
Self-organization in neural networks - Applications in structural optimization
Hajela, Prabhat; Fu, B.; Berke, Laszlo
1993-01-01
The present paper discusses the applicability of ART (Adaptive Resonance Theory) networks, and the Hopfield and Elastic networks, in problems of structural analysis and design. A characteristic of these network architectures is the ability to classify patterns presented as inputs into specific categories. The categories may themselves represent distinct procedural solution strategies. The paper shows how this property can be adapted in the structural analysis and design problem. A second application is the use of Hopfield and Elastic networks in optimization problems. Of particular interest are problems characterized by the presence of discrete and integer design variables. The parallel computing architecture that is typical of neural networks is shown to be effective in such problems. Results of preliminary implementations in structural design problems are also included in the paper.
Optimizing Signal Behavior of Femtocells for Improved Network
Directory of Open Access Journals (Sweden)
Meera Joseph
2016-10-01
Full Text Available The high demand for network coverage in an indoor setting brought about the acceptance of femtocell technology as a solution using the backhaul connectivity in the existing network. The quality of signal, voice calling, Internet, security and data are improved through the use femtocell at the indoor environment. Here the service provider attempts to reduce their operation cost by presenting self-organizing mechanisms for optimization of the network. The remarkable part is that, femtocells improves coverage, enhances the data rate at the indoor environment. Therefore, the challenges of the femtocell also known as interference deteriorates the capacity and quality performance of the whole cellular network. In this paper we simulate the bit error rate against signal behaviour at the indoor environment and we also simulate the transmitting power over signal for both macrocells and femtocells. We focus on the transmitting power that might cause interference within the cellular network.
Hasegawa, Mikio; Tran, Ha Nguyen; Miyamoto, Goh; Murata, Yoshitoshi; Harada, Hiroshi; Kato, Shuzo
We propose a neurodynamical approach to a large-scale optimization problem in Cognitive Wireless Clouds, in which a huge number of mobile terminals with multiple different air interfaces autonomously utilize the most appropriate infrastructure wireless networks, by sensing available wireless networks, selecting the most appropriate one, and reconfiguring themselves with seamless handover to the target networks. To deal with such a cognitive radio network, game theory has been applied in order to analyze the stability of the dynamical systems consisting of the mobile terminals' distributed behaviors, but it is not a tool for globally optimizing the state of the network. As a natural optimization dynamical system model suitable for large-scale complex systems, we introduce the neural network dynamics which converges to an optimal state since its property is to continually decrease its energy function. In this paper, we apply such neurodynamics to the optimization problem of radio access technology selection. We compose a neural network that solves the problem, and we show that it is possible to improve total average throughput simply by using distributed and autonomous neuron updates on the terminal side.
Towards probabilistic synchronisation of local controllers
Czech Academy of Sciences Publication Activity Database
Herzallah, R.; Kárný, Miroslav
2017-01-01
Roč. 48, č. 3 (2017), s. 604-615 ISSN 0020-7721 R&D Projects: GA ČR GA13-13502S Institutional support: RVO:67985556 Keywords : cooperative control * optimal control * complex systems * stochastic systems * fully probabilistic desing Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 2.285, year: 2016
Particle swarm optimization of a neural network model in a ...
Indian Academy of Sciences (India)
sets of cutting conditions and noting the root mean square (RMS) value of spindle motor current as well as ... A multi- objective optimization of hard turning using neural network modelling and swarm intelligence ... being used in this study), and these activated values in turn become the starting signals for the next adjacent ...
Optimizing Knowledge Sharing in Learning Networks through Peer Tutoring
Hsiao, Amy; Brouns, Francis; Kester, Liesbeth; Sloep, Peter
2009-01-01
Hsiao, Y. P., Brouns, F., Kester, L., & Sloep, P. (2009). Optimizing Knowledge Sharing in Learning Networks through Peer Tutoring. Presentation at the IADIS international conference on Cognition and Exploratory in Digital Age (CELDA 2009). November, 20-22, 2009, Rome, Italy.
Optimizing Knowledge Sharing In Learning Networks Through Peer Tutoring
Hsiao, Amy; Brouns, Francis; Kester, Liesbeth; Sloep, Peter
2009-01-01
Hsiao, Y. P., Brouns, F., Kester, L., & Sloep, P. B. (2009). Optimizing Knowledge Sharing In Learning Networks Through Peer Tutoring. In D. Kinshuk, J. Sampson, J. Spector, P. Isaías, P. Barbosa & D. Ifenthaler (Eds.). Proceedings of IADIS International Conference Cognition and Exploratory Learning in Digital Age (CELDA 2009) (pp. 550-551). November, 20-22, 2009, Rome, Italy: Springer.
Optimizing Knowledge Sharing In Learning Networks Through Peer Tutoring
Hsiao, Amy; Brouns, Francis; Kester, Liesbeth; Sloep, Peter
2009-01-01
Hsiao, Y. P., Brouns, F., Kester, L., & Sloep, P. B. (2009). Optimizing Knowledge Sharing In Learning Networks Through Peer Tutoring. In D. Kinshuk, J. Sampson, J. Spector, P. Isaías, P. Barbosa & D. Ifenthaler (Eds.). Proceedings of IADIS International Conference Cognition and Exploratory Learning
Optimal server scheduling in hybrid P2P networks
B. Zhang (Bo); S.C. Borst (Sem); M.I. Reiman
2010-01-01
htmlabstractWe consider the server scheduling problem in hybrid P2P networks in the context of a fluid model. Specifically, we examine how to allocate the limited amount of server upload capacity among competing swarms over time in order to optimize the download performance experienced by users. For
An Optimal Design Model for New Water Distribution Networks in ...
African Journals Online (AJOL)
This paper is concerned with the problem of optimizing the distribution of water in Kigali City at a minimum cost. The mathematical formulation is a Linear Programming Problem (LPP) which involves the design of a new network of water distribution considering the cost in the form of unit price of pipes, the hydraulic gradient ...
Optimization of Gas Flow Network using the Traveling Salesman ...
African Journals Online (AJOL)
The overall goal of this paper is to develop a general formulation for an optimal infrastructure layout design of gas pipeline distribution networks using algorithm developed from the application of two industrial engineering concepts: the traveling salesman problem (TSP) and the nearest neighbor (NN). The focus is on the ...
Synchronization-optimized networks for coupled nearly identical ...
Indian Academy of Sciences (India)
173–182. Synchronization-optimized networks for coupled nearly identical oscillators and their structural analysis. SUMAN ACHARYYA1,∗ and R E AMRITKAR1,2. 1Theoretical Physics Division, Physical Research Laboratory, Ahmedabad 380 009, India. 2Institute of Infrastructure Technology Research and Management, ...
Robust Synchronisation of Uncertain Fractional-Order Chaotic Unified Systems
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Noghredani Naeimadeen
2017-04-01
Full Text Available Fractional-order chaotic unified systems include a variety of fractional-order chaotic systems such as Chen, Lorenz, Lu, Liu, and financial systems. This paper describes a sliding mode controller for synchronisation of fractional-order chaotic unified systems in the presence of uncertainties and external disturbances, and affirms the stability of the controller (which is composed of error dynamics. Moreover, the synchronisation of two separate fractional-order chaotic systems is studied. For this aim, fractional integral sliding surface is defined. Then the sliding mode control rule for stability of error dynamic is presented based on the Lyapunov stability theorem. Simulation results, obtained by using MATLAB, show that the proposed sliding mode has employed an appropriate approach against uncertainties and to reduce the chattering phenomenon that often occurs with sliding mode controllers.
A New Optimized GA-RBF Neural Network Algorithm
Directory of Open Access Journals (Sweden)
Weikuan Jia
2014-01-01
Full Text Available When confronting the complex problems, radial basis function (RBF neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low; these deficiencies easily lead to decreasing learning ability and recognition precision. Aiming at this problem, we propose a new optimized RBF neural network algorithm based on genetic algorithm (GA-RBF algorithm, which uses genetic algorithm to optimize the weights and structure of RBF neural network; it chooses new ways of hybrid encoding and optimizing simultaneously. Using the binary encoding encodes the number of the hidden layer’s neurons and using real encoding encodes the connection weights. Hidden layer neurons number and connection weights are optimized simultaneously in the new algorithm. However, the connection weights optimization is not complete; we need to use least mean square (LMS algorithm for further leaning, and finally get a new algorithm model. Using two UCI standard data sets to test the new algorithm, the results show that the new algorithm improves the operating efficiency in dealing with complex problems and also improves the recognition precision, which proves that the new algorithm is valid.
A new optimized GA-RBF neural network algorithm.
Jia, Weikuan; Zhao, Dean; Shen, Tian; Su, Chunyang; Hu, Chanli; Zhao, Yuyan
2014-01-01
When confronting the complex problems, radial basis function (RBF) neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low; these deficiencies easily lead to decreasing learning ability and recognition precision. Aiming at this problem, we propose a new optimized RBF neural network algorithm based on genetic algorithm (GA-RBF algorithm), which uses genetic algorithm to optimize the weights and structure of RBF neural network; it chooses new ways of hybrid encoding and optimizing simultaneously. Using the binary encoding encodes the number of the hidden layer's neurons and using real encoding encodes the connection weights. Hidden layer neurons number and connection weights are optimized simultaneously in the new algorithm. However, the connection weights optimization is not complete; we need to use least mean square (LMS) algorithm for further leaning, and finally get a new algorithm model. Using two UCI standard data sets to test the new algorithm, the results show that the new algorithm improves the operating efficiency in dealing with complex problems and also improves the recognition precision, which proves that the new algorithm is valid.
Genetic algorithm application in optimization of wireless sensor networks.
Norouzi, Ali; Zaim, A Halim
2014-01-01
There are several applications known for wireless sensor networks (WSN), and such variety demands improvement of the currently available protocols and the specific parameters. Some notable parameters are lifetime of network and energy consumption for routing which play key role in every application. Genetic algorithm is one of the nonlinear optimization methods and relatively better option thanks to its efficiency for large scale applications and that the final formula can be modified by operators. The present survey tries to exert a comprehensive improvement in all operational stages of a WSN including node placement, network coverage, clustering, and data aggregation and achieve an ideal set of parameters of routing and application based WSN. Using genetic algorithm and based on the results of simulations in NS, a specific fitness function was achieved, optimized, and customized for all the operational stages of WSNs.
Evolutionary optimization of network reconstruction from derivative-variable correlations
Leguia, Marc G.; Andrzejak, Ralph G.; Levnajić, Zoran
2017-08-01
Topologies of real-world complex networks are rarely accessible, but can often be reconstructed from experimentally obtained time series via suitable network reconstruction methods. Extending our earlier work on methods based on statistics of derivative-variable correlations, we here present a new method built on integrating an evolutionary optimization algorithm into the derivative-variable correlation method. Results obtained from our modification of the method in general outperform the original results, demonstrating the suitability of evolutionary optimization logic in network reconstruction problems. We show the method’s usefulness in realistic scenarios where the reconstruction precision can be limited by the nature of the time series. We also discuss important limitations coming from various dynamical regimes that time series can belong to.
Superstructure-based optimization of biorefinery networks: Production of biodiesel
DEFF Research Database (Denmark)
Bertran, Maria-Ona; Orsi, Albert; Gani, Rafiqul
2015-01-01
industrial system based on biomass, an inexpensive, abundant and renewable raw material, is being established with sustainability as the main driving force [1]. The processing facilities for the production of multiple products (including biofuels and chemicals) from biomass are referred as biorefineries [2......]. The optimal synthesis of biorefinery networks problem is defined as: given a set of biomass derived feedstock and a set of desired final products and specifications, determine a flexible, sustainable and innovative processing network with the targets of minimum cost and sustainable development taking...... into account the available technologies, geographical location, future technological developments and global market changes. The problem of optimal design of biorefinery networks is solved in this work through three different stages: (i) synthesis stage, (ii) design stage, and (iii) innovation stage...
On the Expressive Power of Polyadic Synchronisation in π- calculus
DEFF Research Database (Denmark)
Carbone, Marco; Maffeis, Sergio
2002-01-01
We extend the pi-calculus with polyadic synchronisation, a generalisation of the communication mechanism which allows channel names to be composite. We show that this operator embeds nicely in the theory of pi-calculus, we suggest that it permits divergence-free encodings of distributed calculi...... of a language increases its expressive power by means of a separation result in the style of Palamidessi's result for mixed choice....
Examination of operational optimization at Kemi district heating network
Directory of Open Access Journals (Sweden)
Ikonen Enso
2016-01-01
Full Text Available Model-based minimization of short term operational costs for energy distribution systems is examined. Based on the analogies between mass and energy distribution systems, a direct application of a stochastic optimal control approach was considered, previously developed and applied by the authors to water distribution systems. This paper examines the feasibility of the approach for district heating systems under certainty equivalence, i.e., the uncertain quantities are replaced by their nominal values. Simulations, based on a rough model of a part of the Kemi district heating network, are used to illustrate and validate the modeling and optimization approach. The outcomes show that optimal network loading can be designed with the considered tools.
Congestion Relief of Contingent Power Network with Evolutionary Optimization Algorithm
Directory of Open Access Journals (Sweden)
Abhinandan De
2012-03-01
Full Text Available This paper presents a differential evolution optimization technique based methodology for congestion management cost optimization of contingent power networks. In Deregulated systems, line congestion apart from causing stability problems can increase the cost of electricity. Restraining line flow to a particular level of congestion is quite imperative from stability as well as economy point of view. Employing Congestion Sensitivity Index proposed in this paper, the algorithm proposed can be adopted for selecting the congested lines in a power networks and then to search for a congestion constrained optimal generation schedule at the cost of a minimum congestion management charge without any load curtailment and installation of FACTS devices. It has been depicted that the methodology on application can provide better operating conditions in terms of improvement of bus voltage and loss profile of the system. The efficiency of the proposed methodology has been tested on an IEEE 30 bus benchmark system and the results look promising.
Optimizing Neural Network Architectures Using Generalization Error Estimators
DEFF Research Database (Denmark)
Larsen, Jan
1994-01-01
This paper addresses the optimization of neural network architectures. It is suggested to optimize the architecture by selecting the model with minimal estimated averaged generalization error. We consider a least-squares (LS) criterion for estimating neural network models, i.e., the associated...... model weights are estimated by minimizing the LS criterion. The quality of a particular estimated model is measured by the average generalization error. This is defined as the expected squared prediction error on a novel input-output sample averaged over all possible training sets. An essential part...... of the suggested architecture optimization scheme is to calculate an estimate of the average generalization error. We suggest using the GEN-estimator which allows for dealing with nonlinear, incomplete models, i.e., models which are not capable of modeling the underlying nonlinear relationship perfectly. In most...
Optimizing Cellular Networks Enabled with Renewal Energy via Strategic Learning.
Sohn, Insoo; Liu, Huaping; Ansari, Nirwan
2015-01-01
An important issue in the cellular industry is the rising energy cost and carbon footprint due to the rapid expansion of the cellular infrastructure. Greening cellular networks has thus attracted attention. Among the promising green cellular network techniques, the renewable energy-powered cellular network has drawn increasing attention as a critical element towards reducing carbon emissions due to massive energy consumption in the base stations deployed in cellular networks. Game theory is a branch of mathematics that is used to evaluate and optimize systems with multiple players with conflicting objectives and has been successfully used to solve various problems in cellular networks. In this paper, we model the green energy utilization and power consumption optimization problem of a green cellular network as a pilot power selection strategic game and propose a novel distributed algorithm based on a strategic learning method. The simulation results indicate that the proposed algorithm achieves correlated equilibrium of the pilot power selection game, resulting in optimum green energy utilization and power consumption reduction.
Optimal network topologies: expanders, cages, Ramanujan graphs, entangled networks and all that
Donetti, Luca; Neri, Franco; Muñoz, Miguel A.
2006-08-01
We report on some recent developments in the search for optimal network topologies. First we review some basic concepts on spectral graph theory, including adjacency and Laplacian matrices, paying special attention to the topological implications of having large spectral gaps. We also introduce related concepts such as 'expanders', Ramanujan, and Cage graphs. Afterwards, we discuss two different dynamical features of Networks, synchronizability and flow of random walkers, so that they are optimized if the corresponding Laplacian matrix has a large spectral gap. From this, we show, by developing a numerical optimization algorithm, that maximum synchronizability and fast random walk spreading are obtained for a particular type of extremely homogeneous regular networks, with long loops and poor modular structure, that we call entangled networks. These turn out to be related to Ramanujan and Cage graphs. We argue also that these graphs are very good finite-size approximations to Bethe lattices, and provide optimal or almost optimal solutions to many other problems, for instance searchability in the presence of congestion or performance of neural networks. Finally, we study how these results are modified when studying dynamical processes controlled by a normalized (weighted and directed) dynamics; much more heterogeneous graphs are optimal in this case. Finally, a critical discussion of the limitations and possible extensions of this work is presented.
An Optimal Path Computation Architecture for the Cloud-Network on Software-Defined Networking
Directory of Open Access Journals (Sweden)
Hyunhun Cho
2015-05-01
Full Text Available Legacy networks do not open the precise information of the network domain because of scalability, management and commercial reasons, and it is very hard to compute an optimal path to the destination. According to today’s ICT environment change, in order to meet the new network requirements, the concept of software-defined networking (SDN has been developed as a technological alternative to overcome the limitations of the legacy network structure and to introduce innovative concepts. The purpose of this paper is to propose the application that calculates the optimal paths for general data transmission and real-time audio/video transmission, which consist of the major services of the National Research & Education Network (NREN in the SDN environment. The proposed SDN routing computation (SRC application is designed and applied in a multi-domain network for the efficient use of resources, selection of the optimal path between the multi-domains and optimal establishment of end-to-end connections.
Kolosionis, Konstantinos; Papadopoulou, Maria P.
2017-04-01
Monitoring networks provide essential information for water resources management especially in areas with significant groundwater exploitation due to extensive agricultural activities. In this work, a simulation-optimization framework is developed based on heuristic optimization methodologies and geostatistical modeling approaches to obtain an optimal design for a groundwater quality monitoring network. Groundwater quantity and quality data obtained from 43 existing observation locations at 3 different hydrological periods in Mires basin in Crete, Greece will be used in the proposed framework in terms of Regression Kriging to develop the spatial distribution of nitrates concentration in the aquifer of interest. Based on the existing groundwater quality mapping, the proposed optimization tool will determine a cost-effective observation wells network that contributes significant information to water managers and authorities. The elimination of observation wells that add little or no beneficial information to groundwater level and quality mapping of the area can be obtain using estimations uncertainty and statistical error metrics without effecting the assessment of the groundwater quality. Given the high maintenance cost of groundwater monitoring networks, the proposed tool could used by water regulators in the decision-making process to obtain a efficient network design that is essential.
Optimal knockout strategies in genome-scale metabolic networks using particle swarm optimization.
Nair, Govind; Jungreuthmayer, Christian; Zanghellini, Jürgen
2017-02-01
Knockout strategies, particularly the concept of constrained minimal cut sets (cMCSs), are an important part of the arsenal of tools used in manipulating metabolic networks. Given a specific design, cMCSs can be calculated even in genome-scale networks. We would however like to find not only the optimal intervention strategy for a given design but the best possible design too. Our solution (PSOMCS) is to use particle swarm optimization (PSO) along with the direct calculation of cMCSs from the stoichiometric matrix to obtain optimal designs satisfying multiple objectives. To illustrate the working of PSOMCS, we apply it to a toy network. Next we show its superiority by comparing its performance against other comparable methods on a medium sized E. coli core metabolic network. PSOMCS not only finds solutions comparable to previously published results but also it is orders of magnitude faster. Finally, we use PSOMCS to predict knockouts satisfying multiple objectives in a genome-scale metabolic model of E. coli and compare it with OptKnock and RobustKnock. PSOMCS finds competitive knockout strategies and designs compared to other current methods and is in some cases significantly faster. It can be used in identifying knockouts which will force optimal desired behaviors in large and genome scale metabolic networks. It will be even more useful as larger metabolic models of industrially relevant organisms become available.
Directory of Open Access Journals (Sweden)
Krishan Kumar
2017-01-01
Full Text Available When a mobile network changes its point of attachments in Cognitive Radio (CR vehicular networks, the Mobile Router (MR requires spectrum handoff. Network Mobility (NEMO in CR vehicular networks is concerned with the management of this movement. In future NEMO based CR vehicular networks deployment, multiple radio access networks may coexist in the overlapping areas having different characteristics in terms of multiple attributes. The CR vehicular node may have the capability to make call for two or more types of nonsafety services such as voice, video, and best effort simultaneously. Hence, it becomes difficult for MR to select optimal network for the spectrum handoff. This can be done by performing spectrum handoff using Multiple Attributes Decision Making (MADM methods which is the objective of the paper. The MADM methods such as grey relational analysis and cost based methods are used. The application of MADM methods provides wider and optimum choice among the available networks with quality of service. Numerical results reveal that the proposed scheme is effective for spectrum handoff decision for optimal network selection with reduced complexity in NEMO based CR vehicular networks.
Optimal Sensor Networks Scheduling in Identification of Distributed Parameter Systems
Patan, Maciej
2012-01-01
Sensor networks have recently come into prominence because they hold the potential to revolutionize a wide spectrum of both civilian and military applications. An ingenious characteristic of sensor networks is the distributed nature of data acquisition. Therefore they seem to be ideally prepared for the task of monitoring processes with spatio-temporal dynamics which constitute one of most general and important classes of systems in modelling of the real-world phenomena. It is clear that careful deployment and activation of sensor nodes are critical for collecting the most valuable information from the observed environment. Optimal Sensor Network Scheduling in Identification of Distributed Parameter Systems discusses the characteristic features of the sensor scheduling problem, analyzes classical and recent approaches, and proposes a wide range of original solutions, especially dedicated for networks with mobile and scanning nodes. Both researchers and practitioners will find the case studies, the proposed al...
An optimal routing strategy on scale-free networks
Yang, Yibo; Zhao, Honglin; Ma, Jinlong; Qi, Zhaohui; Zhao, Yongbin
Traffic is one of the most fundamental dynamical processes in networked systems. With the traditional shortest path routing (SPR) protocol, traffic congestion is likely to occur on the hub nodes on scale-free networks. In this paper, we propose an improved optimal routing (IOR) strategy which is based on the betweenness centrality and the degree centrality of nodes in the scale-free networks. With the proposed strategy, the routing paths can accurately bypass hub nodes in the network to enhance the transport efficiency. Simulation results show that the traffic capacity as well as some other indexes reflecting transportation efficiency are further improved with the IOR strategy. Owing to the significantly improved traffic performance, this study is helpful to design more efficient routing strategies in communication or transportation systems.
Directory of Open Access Journals (Sweden)
Murad Khan
2014-01-01
Full Text Available Optimizing the balance between different handover parameters for network selection is one of the challenging tasks for seamless communication in heterogeneous networks. Traditional approaches for network selection are mostly based on the Receive Signal Strength (RSS from the Point of Attachment (PoA of a network. Most of these schemes are suffered from high handover delay, false handover indications, and inappropriate network selection for handover. To address these problems, we present an optimized network selection scheme based on the speed of a mobile node. A mechanism based on two different thresholds on the speed of a Mobile Node (MN is integrated in the proposed scheme. If the speed of an MN is greater than any of the threshold, the MN performs handover to a particular network. We employ Grey Relational Analysis (GRA in the proposed scheme to select the best PoA of the selected network. Similarly, to deal with false handover indications, we proposed an optimized handover triggering technique. We compare our proposed scheme with existing schemes in context of energy consumption for scanning, frequent and failed handovers, packet loss ratio, and handover delay. The proposed scheme shows superior performance and it outperforms existing schemes used for similar purpose. Moreover, simulation results show the accuracy and performance of the proposed scheme.
Particle swarm optimization based space debris surveillance network scheduling
Jiang, Hai; Liu, Jing; Cheng, Hao-Wen; Zhang, Yao
2017-02-01
The increasing number of space debris has created an orbital debris environment that poses increasing impact risks to existing space systems and human space flights. For the safety of in-orbit spacecrafts, we should optimally schedule surveillance tasks for the existing facilities to allocate resources in a manner that most significantly improves the ability to predict and detect events involving affected spacecrafts. This paper analyzes two criteria that mainly affect the performance of a scheduling scheme and introduces an artificial intelligence algorithm into the scheduling of tasks of the space debris surveillance network. A new scheduling algorithm based on the particle swarm optimization algorithm is proposed, which can be implemented in two different ways: individual optimization and joint optimization. Numerical experiments with multiple facilities and objects are conducted based on the proposed algorithm, and simulation results have demonstrated the effectiveness of the proposed algorithm.
The J-Staff System, Network Synchronisation and Noise
2014-06-01
management , led by the J3 Operations Branch, are caught in separate reactive fast cycles. At another extreme is the mode where the reactivity of...captures the dimension of ‘coupling’, an organisational property recognised by theorists such as Perrow [1984], Mintzberg [1979] and others of the...chosen from a statistical distribution, and σ is a coupling constant. The role of iβ as a phase is seen when it is reinserted in the complex
Subgradient-based neural networks for nonsmooth nonconvex optimization problems.
Bian, Wei; Xue, Xiaoping
2009-06-01
This paper presents a subgradient-based neural network to solve a nonsmooth nonconvex optimization problem with a nonsmooth nonconvex objective function, a class of affine equality constraints, and a class of nonsmooth convex inequality constraints. The proposed neural network is modeled with a differential inclusion. Under a suitable assumption on the constraint set and a proper assumption on the objective function, it is proved that for a sufficiently large penalty parameter, there exists a unique global solution to the neural network and the trajectory of the network can reach the feasible region in finite time and stay there thereafter. It is proved that the trajectory of the neural network converges to the set which consists of the equilibrium points of the neural network, and coincides with the set which consists of the critical points of the objective function in the feasible region. A condition is given to ensure the convergence to the equilibrium point set in finite time. Moreover, under suitable assumptions, the coincidence between the solution to the differential inclusion and the "slow solution" of it is also proved. Furthermore, three typical examples are given to present the effectiveness of the theoretic results obtained in this paper and the good performance of the proposed neural network.
Optimal Power Flow of the Algerian Electrical Network using an Ant Colony Optimization Method
Directory of Open Access Journals (Sweden)
Tarek BOUKTIR
2005-06-01
Full Text Available This paper presents solution of optimal power flow (OPF problem of a power system via an Ant Colony Optimization Meta-heuristic method. The objective is to minimize the total fuel cost of thermal generating units and also conserve an acceptable system performance in terms of limits on generator real and reactive power outputs, bus voltages, shunt capacitors/reactors, transformers tap-setting and power flow of transmission lines. Simulation results on the Algerian Electrical Network show that the Ant Colony Optimization method converges quickly to the global optimum.
Nonlinear Non-convex Optimization of Hydraulic Networks
DEFF Research Database (Denmark)
Tahavori, Maryamsadat; Kallesøe, Carsten; Leth, John-Josef
2013-01-01
Pressure management in water supply systems is an effective way to reduce the leakage in a system. In this paper, the pressure management and the reduction of power consumption of a water supply system is formulated as an optimization problem. The problem is to minimize the power consumption in p....... They can be used for a general hydraulic networks to optimize the leakage and energy consumption and to satisfy the demands at the end-users. The results in this paper show that the power consumption of the pumps is reduced....
Synchronising data sources and filling gaps by global hydrological modelling
Pimentel, Rafael; Crochemore, Louise; Hasan, Abdulghani; Pineda, Luis; Isberg, Kristina; Arheimer, Berit
2017-04-01
The advances in remote sensing in the last decades combined with the creation of different open hydrological databases have generated a very large amount of useful information for global hydrological modelling. Working with this huge number of datasets to set up a global hydrological model can constitute challenges such as multiple data formats and big heterogeneity on spatial and temporal resolutions. Different initiatives have made effort to homogenize some of these data sources, i.e. GRDC (Global Runoff Data Center), HYDROSHEDS (SHuttle Elevation Derivatives at multiple Scales), GLWD (Global Lake and Wetland Database) for runoff, watershed delineation and water bodies respectively. However, not all the related issues are covered or homogenously solved at the global scale and new information is continuously available to complete the current ones. This work presents synchronising efforts to make use of different global data sources needed to set up the semi-distributed hydrological model HYPE (Hydrological Predictions for the Environment) at the global scale. These data sources included: topography for watershed delineation, gauging stations of river flow, and extention of lakes, flood plains and land cover classes. A new database with approximately 100 000 subbasins, with an average area of 1000 km2, was created. Subbasin delineation was done combining Global Width Database for Large River (GWD-LR), SRTM high-resolution elevation data and a number of forced points of interest (gauging station of river flow, lakes, reservoirs, urban areas, nuclear plants and areas with high risk of flooding). Regarding flow data, the locations of GRDC stations were checked or placed along the river network when necessary, and completed with available information from national water services in data-sparse regions. A screening of doublet stations and associated time series was necessary to efficiently combine the two types of data sources. A total number about 21 000 stations were
Mohanasundaram, Ranganathan; Periasamy, Pappampalayam Sanmugam
2015-01-01
The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches.
2014-12-01
The report documents policy considerations for the Intelligent Network Flow Optimization (INFLO) connected vehicle applications : bundle. INFLO aims to optimize network flow on freeways and arterials by informing motorists of existing and impen...
2013-09-20
... Strategic Network Optimization (SNO) Program Environmental Assessment. SUMMARY: The Defense Logistics Agency... From the Federal Register Online via the Government Publishing Office DEPARTMENT OF DEFENSE Office of the Secretary Notice of Availability (NOA) for Strategic Network Optimization (SNO) Program...
Noise Robust Speech Watermarking with Bit Synchronisation for the Aeronautical Radio
Hofbauer, Konrad; Hering, Horst
Analogue amplitude modulation radios are used for air/ ground voice communication between aircraft pilots and controllers. The identification of the aircraft, so far always transmitted verbally, could be embedded as a watermark in the speech signal and thereby prevent safety-critical misunderstandings. The first part of this paper presents an overview on this watermarking application. The second part proposes a speech watermarking algorithm that embeds data in the linear prediction residual of unvoiced narrowband speech at a rate of up to 2 kbit/s. A bit synchroniser is developed which enables the transmission over analogue channels and which reaches the optimal limit within one to two percentage points in terms of raw bit error rate. Simulations show the robustness of the method for the AWGN channel.
Network optimization including gas lift and network parameters under subsurface uncertainty
Energy Technology Data Exchange (ETDEWEB)
Schulze-Riegert, R.; Baffoe, J.; Pajonk, O. [SPT Group GmbH, Hamburg (Germany); Badalov, H.; Huseynov, S. [Technische Univ. Clausthal, Clausthal-Zellerfeld (Germany). ITE; Trick, M. [SPT Group, Calgary, AB (Canada)
2013-08-01
Optimization of oil and gas field production systems poses a great challenge to field development due to complex and multiple interactions between various operational design parameters and subsurface uncertainties. Conventional analytical methods are capable of finding local optima based on single deterministic models. They are less applicable for efficiently generating alternative design scenarios in a multi-objective context. Practical implementations of robust optimization workflows integrate the evaluation of alternative design scenarios and multiple realizations of subsurface uncertainty descriptions. Production or economic performance indicators such as NPV (Net Present Value) are linked to a risk-weighted objective function definition to guide the optimization processes. This work focuses on an integrated workflow using a reservoir-network simulator coupled to an optimization framework. The work will investigate the impact of design parameters while considering the physics of the reservoir, wells, and surface facilities. Subsurface uncertainties are described by well parameters such as inflow performance. Experimental design methods are used to investigate parameter sensitivities and interactions. Optimization methods are used to find optimal design parameter combinations which improve key performance indicators of the production network system. The proposed workflow will be applied to a representative oil reservoir coupled to a network which is modelled by an integrated reservoir-network simulator. Gas-lift will be included as an explicit measure to improve production. An objective function will be formulated for the net present value of the integrated system including production revenue and facility costs. Facility and gas lift design parameters are tuned to maximize NPV. Well inflow performance uncertainties are introduced with an impact on gas lift performance. Resulting variances on NPV are identified as a risk measure for the optimized system design. A
A Multiobjective Optimization Model in Automotive Supply Chain Networks
Directory of Open Access Journals (Sweden)
Abdolhossein Sadrnia
2013-01-01
Full Text Available In the new decade, green investment decisions are attracting more interest in design supply chains due to the hidden economic benefits and environmental legislative barriers. In this paper, a supply chain network design problem with both economic and environmental concerns is presented. Therefore, a multiobjective optimization model that captures the trade-off between the total logistics cost and CO2 emissions is proposed. With regard to the complexity of logistic networks, a new multiobjective swarm intelligence algorithm known as a multiobjective Gravitational search algorithm (MOGSA has been implemented for solving the proposed mathematical model. To evaluate the effectiveness of the model, a comprehensive set of numerical experiments is explained. The results obtained show that the proposed model can be applied as an effective tool in strategic planning for optimizing cost and CO2 emissions in an environmentally friendly automotive supply chain.
Optimal Dynamical Range of Excitable Networks at Criticality
Kinouchi, Osame
2006-01-01
A recurrent idea in the study of complex systems is that optimal information processing is to be found near bifurcation points or phase transitions. However, this heuristic hypothesis has few (if any) concrete realizations where a standard and biologically relevant quantity is optimized at criticality. Here we give a clear example of such a phenomenon: a network of excitable elements has its sensitivity and dynamic range maximized at the critical point of a non-equilibrium phase transition. Our results are compatible with the essential role of gap junctions in olfactory glomeruli and retinal ganglionar cell output. Synchronization and global oscillations also appear in the network dynamics. We propose that the main functional role of electrical coupling is to provide an enhancement of dynamic range, therefore allowing the coding of information spanning several orders of magnitude. The mechanism could provide a microscopic neural basis for psychophysical laws.
Optimizing cooperation on complex networks in the presence of failure.
Chen, Yu-Zhong; Lai, Ying-Cheng
2012-10-01
Cooperation has been recognized as a fundamental driving force in many natural, social, and economic systems. We investigate whether, given a complex-networked system in which agents (nodes) interact with one another according to the rules of evolutionary games and are subject to failure or death, cooperation can prevail and be optimized. We articulate a control scheme to maximize cooperation by introducing a time tolerance, a time duration that sustains an agent even if its payoff falls below a threshold. Strikingly, we find that a significant cooperation cluster can emerge when the time tolerance is approximately uniformly distributed over the network. A heuristic theory is derived to understand the optimization mechanism, which emphasizes the role played by medium-degree nodes. Implications for policy making to prevent or mitigate large-scale cascading breakdown are pointed out.
Optimal Constrained Stationary Intervention in Gene Regulatory Networks
Directory of Open Access Journals (Sweden)
Golnaz Vahedi
2008-05-01
Full Text Available A key objective of gene network modeling is to develop intervention strategies to alter regulatory dynamics in such a way as to reduce the likelihood of undesirable phenotypes. Optimal stationary intervention policies have been developed for gene regulation in the framework of probabilistic Boolean networks in a number of settings. To mitigate the possibility of detrimental side effects, for instance, in the treatment of cancer, it may be desirable to limit the expected number of treatments beneath some bound. This paper formulates a general constraint approach for optimal therapeutic intervention by suitably adapting the reward function and then applies this formulation to bound the expected number of treatments. A mutated mammalian cell cycle is considered as a case study.
Distributed Optimization of Multi Beam Directional Communication Networks
2017-06-30
spatial processing strategies for multi-beam transmission with a simple MAC layer in a simulation study. Full-duplex communications ease the burden on...Distributed Optimization of Multi-Beam Directional Communication Networks Theodoros Tsiligkaridis MIT Lincoln Laboratory Lexington, MA 02141, USA...based routing. I. INTRODUCTION Missions where multiple communication goals are of in- terest are becoming more prevalent in military applications
An Extended Microcomputer-Based Network Optimization Package.
1982-10-01
for New Cycle ..... ............ 53 * 19. Four Node Cycle ...... ... .. ... .. ... .. 54 20. All Artificial Start .................. 61 21. Micronet ...package. For the sake of brevity, the microcomputer-based network optimization package herein described will be referred to as Micronet . The host...computer for Micronet is an Apple II Plus with 64K of memory. This is certainly not the most powerful microcomputer (with some competitors featuring
PARTICLE SWARM OPTIMIZATION (PSO FOR TRAINING OPTIMIZATION ON CONVOLUTIONAL NEURAL NETWORK (CNN
Directory of Open Access Journals (Sweden)
Arie Rachmad Syulistyo
2016-02-01
Full Text Available Neural network attracts plenty of researchers lately. Substantial number of renowned universities have developed neural network for various both academically and industrially applications. Neural network shows considerable performance on various purposes. Nevertheless, for complex applications, neural network’s accuracy significantly deteriorates. To tackle the aforementioned drawback, lot of researches had been undertaken on the improvement of the standard neural network. One of the most promising modifications on standard neural network for complex applications is deep learning method. In this paper, we proposed the utilization of Particle Swarm Optimization (PSO in Convolutional Neural Networks (CNNs, which is one of the basic methods in deep learning. The use of PSO on the training process aims to optimize the results of the solution vectors on CNN in order to improve the recognition accuracy. The data used in this research is handwritten digit from MNIST. The experiments exhibited that the accuracy can be attained in 4 epoch is 95.08%. This result was better than the conventional CNN and DBN. The execution time was also almost similar to the conventional CNN. Therefore, the proposed method was a promising method.
Synchrony-optimized networks of Kuramoto oscillators with inertia
Pinto, Rafael S.; Saa, Alberto
2016-12-01
We investigate synchronization in networks of Kuramoto oscillators with inertia. More specifically, we introduce a rewiring algorithm consisting basically in a hill climb scheme in which the edges of the network are swapped in order to enhance its synchronization capacity. We show that the synchrony-optimized networks generated by our algorithm have some interesting topological and dynamical properties. In particular, they typically exhibit an anticipation of the synchronization onset and are more robust against certain types of perturbations. We consider synthetic random networks and also a network with a topology based on an approximated model of the (high voltage) power grid of Spain, since networks of Kuramoto oscillators with inertia have been used recently as simplified models for power grids, for which synchronization is obviously a crucial issue. Despite the extreme simplifications adopted in these models, our results, among others recently obtained in the literature, may provide interesting principles to guide the future growth and development of real-world grids, specially in the case of a change of the current paradigm of centralized towards distributed generation power grids.
Experienced Gray Wolf Optimization Through Reinforcement Learning and Neural Networks.
Emary, E; Zawbaa, Hossam M; Grosan, Crina
2017-01-10
In this paper, a variant of gray wolf optimization (GWO) that uses reinforcement learning principles combined with neural networks to enhance the performance is proposed. The aim is to overcome, by reinforced learning, the common challenge of setting the right parameters for the algorithm. In GWO, a single parameter is used to control the exploration/exploitation rate, which influences the performance of the algorithm. Rather than using a global way to change this parameter for all the agents, we use reinforcement learning to set it on an individual basis. The adaptation of the exploration rate for each agent depends on the agent's own experience and the current terrain of the search space. In order to achieve this, experience repository is built based on the neural network to map a set of agents' states to a set of corresponding actions that specifically influence the exploration rate. The experience repository is updated by all the search agents to reflect experience and to enhance the future actions continuously. The resulted algorithm is called experienced GWO (EGWO) and its performance is assessed on solving feature selection problems and on finding optimal weights for neural networks algorithm. We use a set of performance indicators to evaluate the efficiency of the method. Results over various data sets demonstrate an advance of the EGWO over the original GWO and over other metaheuristics, such as genetic algorithms and particle swarm optimization.
Optimization of neural network algorithm of the land market description
Directory of Open Access Journals (Sweden)
M. A. Karpovich
2016-01-01
Full Text Available The advantages of neural network technology is shown in comparison of traditional descriptions of dynamically changing systems, which include a modern land market. The basic difficulty arising in the practical implementation of neural network models of the land market and construction products is revealed It is the formation of a representative set of training and test examples. The requirements which are necessary for the correct description of the current economic situation has been determined, it consists in the fact that Train-paid-set in the feature space should not has the ranges with a low density of observations. The methods of optimization of empirical array, which allow to avoid the long-range extrapolation of data from range of concentration of the set of examples are formulated. It is shown that a radical method of optimization a set of training and test examples enclosing to collect supplemantary information, is associated with significant costs time and resources for the economic problems and the ratio of cost / efficiency is less efficient than an algorithm optimization neural network models the earth market fixed set of empirical data. Algorithm of optimization based on the transformation of arrays of information which represents the expansion of the ranges of concentration of the set of examples and compression the ranges of low density of observations is analyzed in details. The significant reduction in the relative error of land price description is demonstrated on the specific example of Voronezh region market of lands which intend for road construction, it makes the using of radical method of empirical optimization of the array costeffective with accounting the significant absolute value of the land. The high economic efficiency of the proposed algorithms is demonstrated.
A Generic Methodology for Superstructure Optimization of Different Processing Networks
DEFF Research Database (Denmark)
Bertran, Maria-Ona; Frauzem, Rebecca; Zhang, Lei
problem.The proposed methodology involves the use of additional methods and tools, such as a database and an external software for solving the network optimization problem. The database has been created using an ontology-based knowledge representation consisting in various layers of data...... in a software interface that guides the user through the problem formulation and solution steps and integrates the various methods and tools for efficient flow of information between them. By using this interface, the user can retrieve and/or modify existing networks and alternatives from the database, as well...... of sustainable processing networks containing three stages: (i) synthesis stage, (ii) design stage, and (iii) innovation stage. In this work, a focus is placed on the first stage, the synthesis stage. Process synthesis becomes necessary in determining the appropriate processing routes to produce a selection...
Optimizing intermittent water supply in urban pipe distribution networks
Lieb, Anna M; Wilkening, Jon
2015-01-01
In many urban areas of the developing world, piped water is supplied only intermittently, as valves direct water to different parts of the water distribution system at different times. The flow is transient, and may transition between free-surface and pressurized, resulting in complex dynamical features with important consequences for water suppliers and users. Here, we develop a computational model of transition, transient pipe flow in a network, accounting for a wide variety of realistic boundary conditions. We validate the model against several published data sets, and demonstrate its use on a real pipe network. The model is extended to consider several optimization problems motivated by realistic scenarios. We demonstrate how to infer water flow in a small pipe network from a single pressure sensor, and show how to control water inflow to minimize damaging pressure gradients.
Structure Learning for Deep Neural Networks Based on Multiobjective Optimization.
Liu, Jia; Gong, Maoguo; Miao, Qiguang; Wang, Xiaogang; Li, Hao
2017-05-05
This paper focuses on the connecting structure of deep neural networks and proposes a layerwise structure learning method based on multiobjective optimization. A model with better generalization can be obtained by reducing the connecting parameters in deep networks. The aim is to find the optimal structure with high representation ability and better generalization for each layer. Then, the visible data are modeled with respect to structure based on the products of experts. In order to mitigate the difficulty of estimating the denominator in PoE, the denominator is simplified and taken as another objective, i.e., the connecting sparsity. Moreover, for the consideration of the contradictory nature between the representation ability and the network connecting sparsity, the multiobjective model is established. An improved multiobjective evolutionary algorithm is used to solve this model. Two tricks are designed to decrease the computational cost according to the properties of input data. The experiments on single-layer level, hierarchical level, and application level demonstrate the effectiveness of the proposed algorithm, and the learned structures can improve the performance of deep neural networks.
Pricing resources in LTE networks through multiobjective optimization.
Lai, Yung-Liang; Jiang, Jehn-Ruey
2014-01-01
The LTE technology offers versatile mobile services that use different numbers of resources. This enables operators to provide subscribers or users with differential quality of service (QoS) to boost their satisfaction. On one hand, LTE operators need to price the resources high for maximizing their profits. On the other hand, pricing also needs to consider user satisfaction with allocated resources and prices to avoid "user churn," which means subscribers will unsubscribe services due to dissatisfaction with allocated resources or prices. In this paper, we study the pricing resources with profits and satisfaction optimization (PRPSO) problem in the LTE networks, considering the operator profit and subscribers' satisfaction at the same time. The problem is modelled as nonlinear multiobjective optimization with two optimal objectives: (1) maximizing operator profit and (2) maximizing user satisfaction. We propose to solve the problem based on the framework of the NSGA-II. Simulations are conducted for evaluating the proposed solution.
Optimizing Cooperative Cognitive Radio Networks with Opportunistic Access
Zafar, Ammar
2012-09-16
Optimal resource allocation for cooperative cognitive radio networks with opportunistic access to the licensed spectrum is studied. Resource allocation is based on minimizing the symbol error rate at the receiver. Both the cases of all-participate relaying and selective relaying are considered. The objective function is derived and the constraints are detailed for both scenarios. It is then shown that the objective functions and the constraints are nonlinear and nonconvex functions of the parameters of interest, that is, source and relay powers, symbol time, and sensing time. Therefore, it is difficult to obtain closed-form solutions for the optimal resource allocation. The optimization problem is then solved using numerical techniques. Numerical results show that the all-participate system provides better performance than its selection counterpart, at the cost of greater resources. © 2012 Ammar Zafar et al.
Traffic Based Optimization of Spectrum Sensing in Cognitive Radio Networks
Directory of Open Access Journals (Sweden)
Changhua Yao
2014-01-01
Full Text Available We propose a more practical spectrum sensing optimization problem in cognitive radio networks (CRN, by considering the data traffic of second user (SU. Compared with most existing work, we do not assume that SU always has packets to transmit; instead, we use the actual data transmitted per second rather than the channel capacity as the achievable throughput, to reformulate the Sensing-Throughput Tradeoff problem. We mathematically analyze the problem of optimal sensing time to maximize the achievable throughput, based on the data traffic of SU. Our model is more general because the traditional Sensing-Throughput Tradeoff model can be seen as a special case of our model. We also prove that the throughput is a concave function of sensing time and there is only one optimal sensing time value which is determined by the data traffic. Simulation results show that the proposed approach outperforms existing methods.
Robustness and Optimization of Complex Networks : Reconstructability, Algorithms and Modeling
Liu, D.
2013-01-01
The infrastructure networks, including the Internet, telecommunication networks, electrical power grids, transportation networks (road, railway, waterway, and airway networks), gas networks and water networks, are becoming more and more complex. The complex infrastructure networks are crucial to our
Optimizing Seismic Monitoring Networks for EGS and Conventional Geothermal Projects
Kraft, Toni; Herrmann, Marcus; Bethmann, Falko; Stefan, Wiemer
2013-04-01
In the past several years, geological energy technologies receive growing attention and have been initiated in or close to urban areas. Some of these technologies involve injecting fluids into the subsurface (e.g., oil and gas development, waste disposal, and geothermal energy development) and have been found or suspected to cause small to moderate sized earthquakes. These earthquakes, which may have gone unnoticed in the past when they occurred in remote sparsely populated areas, are now posing a considerable risk for the public acceptance of these technologies in urban areas. The permanent termination of the EGS project in Basel, Switzerland after a number of induced ML~3 (minor) earthquakes in 2006 is one prominent example. It is therefore essential for the future development and success of these geological energy technologies to develop strategies for managing induced seismicity and keeping the size of induced earthquakes at a level that is acceptable to all stakeholders. Most guidelines and recommendations on induced seismicity published since the 1970ies conclude that an indispensable component of such a strategy is the establishment of seismic monitoring in an early stage of a project. This is because an appropriate seismic monitoring is the only way to detect and locate induced microearthquakes with sufficient certainty to develop an understanding of the seismic and geomechanical response of the reservoir to the geotechnical operation. In addition, seismic monitoring lays the foundation for the establishment of advanced traffic light systems and is therefore an important confidence building measure towards the local population and authorities. We have developed an optimization algorithm for seismic monitoring networks in urban areas that allows to design and evaluate seismic network geometries for arbitrary geotechnical operation layouts. The algorithm is based on the D-optimal experimental design that aims to minimize the error ellipsoid of the linearized
Practice-Oriented Optimization of Distribution Network Planning Using Metaheuristic Algorithms
M.O.W. Grond; N.H. Luong (Ngoc Hoang); J. Morren (Johan); P.A.N. Bosman (Peter); J.G. Slootweg; J.A. La Poutré (Han)
2014-01-01
htmlabstractDistribution network operators require more advanced planning tools to deal with the challenges of future network planning. An appropriate planning and optimization tool can identify which option for network extension should be selected from available alternatives. However, many
PlayNCool: Opportunistic Network Coding for Local Optimization of Routing in Wireless Mesh Networks
DEFF Research Database (Denmark)
Pahlevani, Peyman; Roetter, Daniel Enrique Lucani; Pedersen, Morten Videbæk
2013-01-01
our protocol is that each helper to a link in a multi-hop path reinforces that link by listening to coded packets transmitted in the link and by judiciously choosing when to start transmitting to make the data exchange faster and more efficient. This paper pays special attention to techniques......This paper introduces PlayNCool, an opportunistic protocol with local optimization based on network coding to increase the throughput of a wireless mesh network (WMN). PlayNCool aims to enhance current routing protocols by (i) allowing random linear network coding transmissions end-to-end, (ii...
Market Share Delegation in a Bertrand Duopoly: Synchronisation and Multistability
Directory of Open Access Journals (Sweden)
Luciano Fanti
2015-01-01
Full Text Available This paper tackles the issue of local and global analyses of a duopoly game with price competition and market share delegation. The dynamics of the economy is characterised by a differentiable two-dimensional discrete time system. The paper stresses the importance of complementarity between products as a source of synchronisation in the long term, in contrast to the case of their substitutability. This means that when products are complements, players may coordinate their behaviour even if initial conditions are different. In addition, there exist multiple attractors so that even starting with similar conditions may end up generating very different dynamic patterns.
A Synchronisation Method For Informed Spread-Spectrum Audiowatermarking
Directory of Open Access Journals (Sweden)
Pierre-Yves Fulchiron
2003-12-01
Full Text Available Under perfect synchronisation conditions, watermarking schemes employing asymmetric spread-spectrum techniques are suitable for copy-protection of audio signals. This paper proposes to combine the use of a robust psychoacoustic projection for the extraction of a watermark feature vector along with non-linear detection functions optimised with side-information. The new proposed scheme benefits from an increased level of security through the use of asymmetric detectors. We apply this scheme to real audio signals and experimental results show an increased robustness to desynchronisation attacks such as random cropping.
Directory of Open Access Journals (Sweden)
Haibo Zhang
2016-08-01
Full Text Available The security incidents ion networks are sudden and uncertain, it is very hard to precisely predict the network security situation by traditional methods. In order to improve the prediction accuracy of the network security situation, we build a network security situation prediction model based on Wavelet Neural Network (WNN with optimized parameters by the Improved Niche Genetic Algorithm (INGA. The proposed model adopts WNN which has strong nonlinear ability and fault-tolerance performance. Also, the parameters for WNN are optimized through the adaptive genetic algorithm (GA so that WNN searches more effectively. Considering the problem that the adaptive GA converges slowly and easily turns to the premature problem, we introduce a novel niche technology with a dynamic fuzzy clustering and elimination mechanism to solve the premature convergence of the GA. Our final simulation results show that the proposed INGA-WNN prediction model is more reliable and effective, and it achieves faster convergence-speed and higher prediction accuracy than the Genetic Algorithm-Wavelet Neural Network (GA-WNN, Genetic Algorithm-Back Propagation Neural Network (GA-BPNN and WNN.
Information spread in networks: Games, optimal control, and stabilization
Khanafer, Ali
This thesis focuses on designing efficient mechanisms for controlling information spread in networks. We consider two models for information spread. The first one is the well-known distributed averaging dynamics. The second model is a nonlinear one that describes virus spread in computer and biological networks. We seek to design optimal, robust, and stabilizing controllers under practical constraints. For distributed averaging networks, we study the interaction between a network designer and an adversary. We consider two types of attacks on the network. In Attack-I, the adversary strategically disconnects a set of links to prevent the nodes from reaching consensus. Meanwhile, the network designer assists the nodes in reaching consensus by changing the weights of a limited number of links in the network. We formulate two problems to describe this competition where the order in which the players act is reversed in the two problems. Although the canonical equations provided by the Pontryagin's Maximum Principle (MP) seem to be intractable, we provide an alternative characterization for the optimal strategies that makes connection to potential theory. Further, we provide a sufficient condition for the existence of a saddle-point equilibrium (SPE) for the underlying zero-sum game. In Attack-II, the designer and the adversary are both capable of altering the measurements of all nodes in the network by injecting global signals. We impose two constraints on both players: a power constraint and an energy constraint. We assume that the available energy to each player is not sufficient to operate at maximum power throughout the horizon of the game. We show the existence of an SPE and derive the optimal strategies in closed form for this attack scenario. As an alternative to the "network designer vs. adversary" framework, we investigate the possibility of stabilizing unknown network diffusion processes using a distributed mechanism, where the uncertainty is due to an attack
Towards Optimal Event Detection and Localization in Acyclic Flow Networks
Agumbe Suresh, Mahima
2012-01-03
Acyclic flow networks, present in many infrastructures of national importance (e.g., oil & gas and water distribution systems), have been attracting immense research interest. Existing solutions for detecting and locating attacks against these infrastructures, have been proven costly and imprecise, especially when dealing with large scale distribution systems. In this paper, to the best of our knowledge for the first time, we investigate how mobile sensor networks can be used for optimal event detection and localization in acyclic flow networks. Sensor nodes move along the edges of the network and detect events (i.e., attacks) and proximity to beacon nodes with known placement in the network. We formulate the problem of minimizing the cost of monitoring infrastructure (i.e., minimizing the number of sensor and beacon nodes deployed), while ensuring a degree of sensing coverage in a zone of interest and a required accuracy in locating events. We propose algorithms for solving these problems and demonstrate their effectiveness with results obtained from a high fidelity simulator.
Energy Technology Data Exchange (ETDEWEB)
Hwangbo, Soonho; Lee, In-Beum [POSTECH, Pohang (Korea, Republic of); Han, Jeehoon [University of Wisconsin-Madison, Madison (United States)
2014-10-15
Lots of networks are constructed in a large scale industrial complex. Each network meet their demands through production or transportation of materials which are needed to companies in a network. Network directly produces materials for satisfying demands in a company or purchase form outside due to demand uncertainty, financial factor, and so on. Especially utility network and hydrogen network are typical and major networks in a large scale industrial complex. Many studies have been done mainly with focusing on minimizing the total cost or optimizing the network structure. But, few research tries to make an integrated network model by connecting utility network and hydrogen network. In this study, deterministic mixed integer linear programming model is developed for integrating utility network and hydrogen network. Steam Methane Reforming process is necessary for combining two networks. After producing hydrogen from Steam-Methane Reforming process whose raw material is steam vents from utility network, produced hydrogen go into hydrogen network and fulfill own needs. Proposed model can suggest optimized case in integrated network model, optimized blueprint, and calculate optimal total cost. The capability of the proposed model is tested by applying it to Yeosu industrial complex in Korea. Yeosu industrial complex has the one of the biggest petrochemical complex and various papers are based in data of Yeosu industrial complex. From a case study, the integrated network model suggests more optimal conclusions compared with previous results obtained by individually researching utility network and hydrogen network.
Liu, An; Liu,; Xiang, Haige; Luo, Wu
2010-01-01
The general MIMO interference network is considered and is named the B-MAC Network, for it is a combination of multiple interfering broadcast channels employing dirty paper coding and multiaccess channels employing successive interference cancellation. Included as special cases are interference channels, X channels, and most practical wireless networks. The optimization of this important class of networks has been hindered by that beyond the single antenna multiaccess channels, little is known about the optimal input structure, which is found here to be the polite water-filling, satisfied by all Pareto optimal input. This network version of water-filling is polite because it optimally balances between reducing interference to others and maximizing a link's own rate, offering a method to decompose a network into multiple equivalent single-user channels and thus, paving the way for designing/improving low-complexity centralized/distributed/game-theoretic algorithms for most network optimization problems. Deeply...
Exploiting node mobility for energy optimization in wireless sensor networks
El-Moukaddem, Fatme Mohammad
Wireless Sensor Networks (WSNs) have become increasingly available for data-intensive applications such as micro-climate monitoring, precision agriculture, and audio/video surveillance. A key challenge faced by data-intensive WSNs is to transmit the sheer amount of data generated within an application's lifetime to the base station despite the fact that sensor nodes have limited power supplies such as batteries or small solar panels. The availability of numerous low-cost robotic units (e.g. Robomote and Khepera) has made it possible to construct sensor networks consisting of mobile sensor nodes. It has been shown that the controlled mobility offered by mobile sensors can be exploited to improve the energy efficiency of a network. In this thesis, we propose schemes that use mobile sensor nodes to reduce the energy consumption of data-intensive WSNs. Our approaches differ from previous work in two main aspects. First, our approaches do not require complex motion planning of mobile nodes, and hence can be implemented on a number of low-cost mobile sensor platforms. Second, we integrate the energy consumption due to both mobility and wireless communications into a holistic optimization framework. We consider three problems arising from the limited energy in the sensor nodes. In the first problem, the network consists of mostly static nodes and contains only a few mobile nodes. In the second and third problems, we assume essentially that all nodes in the WSN are mobile. We first study a new problem called max-data mobile relay configuration (MMRC ) that finds the positions of a set of mobile sensors, referred to as relays, that maximize the total amount of data gathered by the network during its lifetime. We show that the MMRC problem is surprisingly complex even for a trivial network topology due to the joint consideration of the energy consumption of both wireless communication and mechanical locomotion. We present optimal MMRC algorithms and practical distributed
Genetic Algorithm Optimization of Artificial Neural Networks for Hydrological Modelling
Abrahart, R. J.
2004-05-01
This paper will consider the case for genetic algorithm optimization in the development of an artificial neural network model. It will provide a methodological evaluation of reported investigations with respect to hydrological forecasting and prediction. The intention in such operations is to develop a superior modelling solution that will be: \\begin{itemize} more accurate in terms of output precision and model estimation skill; more tractable in terms of personal requirements and end-user control; and/or more robust in terms of conceptual and mechanical power with respect to adverse conditions. The genetic algorithm optimization toolbox could be used to perform a number of specific roles or purposes and it is the harmonious and supportive relationship between neural networks and genetic algorithms that will be highlighted and assessed. There are several neural network mechanisms and procedures that could be enhanced and potential benefits are possible at different stages in the design and construction of an operational hydrological model e.g. division of inputs; identification of structure; initialization of connection weights; calibration of connection weights; breeding operations between successful models; and output fusion associated with the development of ensemble solutions. Each set of opportunities will be discussed and evaluated. Two strategic questions will also be considered: [i] should optimization be conducted as a set of small individual procedures or as one large holistic operation; [ii] what specific function or set of weighted vectors should be optimized in a complex software product e.g. timings, volumes, or quintessential hydrological attributes related to the 'problem situation' - that might require the development flood forecasting, drought estimation, or record infilling applications. The paper will conclude with a consideration of hydrological forecasting solutions developed on the combined methodologies of co-operative co-evolution and
Network Optimization for Induced Seismicity Monitoring in Urban Areas
Kraft, T.; Husen, S.; Wiemer, S.
2012-12-01
With the global challenge to satisfy an increasing demand for energy, geological energy technologies receive growing attention and have been initiated in or close to urban areas in the past several years. Some of these technologies involve injecting fluids into the subsurface (e.g., oil and gas development, waste disposal, and geothermal energy development) and have been found or suspected to cause small to moderate sized earthquakes. These earthquakes, which may have gone unnoticed in the past when they occurred in remote sparsely populated areas, are now posing a considerable risk for the public acceptance of these technologies in urban areas. The permanent termination of the EGS project in Basel, Switzerland after a number of induced ML~3 (minor) earthquakes in 2006 is one prominent example. It is therefore essential to the future development and success of these geological energy technologies to develop strategies for managing induced seismicity and keeping the size of induced earthquake at a level that is acceptable to all stakeholders. Most guidelines and recommendations on induced seismicity published since the 1970ies conclude that an indispensable component of such a strategy is the establishment of seismic monitoring in an early stage of a project. This is because an appropriate seismic monitoring is the only way to detect and locate induced microearthquakes with sufficient certainty to develop an understanding of the seismic and geomechanical response of the reservoir to the geotechnical operation. In addition, seismic monitoring lays the foundation for the establishment of advanced traffic light systems and is therefore an important confidence building measure towards the local population and authorities. We have developed an optimization algorithm for seismic monitoring networks in urban areas that allows to design and evaluate seismic network geometries for arbitrary geotechnical operation layouts. The algorithm is based on the D-optimal experimental
Noniterative convex optimization methods for network component analysis.
Jacklin, Neil; Ding, Zhi; Chen, Wei; Chang, Chunqi
2012-01-01
This work studies the reconstruction of gene regulatory networks by the means of network component analysis (NCA). We will expound a family of convex optimization-based methods for estimating the transcription factor control strengths and the transcription factor activities (TFAs). The approach taken in this work is to decompose the problem into a network connectivity strength estimation phase and a transcription factor activity estimation phase. In the control strength estimation phase, we formulate a new subspace-based method incorporating a choice of multiple error metrics. For the source estimation phase we propose a total least squares (TLS) formulation that generalizes many existing methods. Both estimation procedures are noniterative and yield the optimal estimates according to various proposed error metrics. We test the performance of the proposed algorithms on simulated data and experimental gene expression data for the yeast Saccharomyces cerevisiae and demonstrate that the proposed algorithms have superior effectiveness in comparison with both Bayesian Decomposition (BD) and our previous FastNCA approach, while the computational complexity is still orders of magnitude less than BD.
Optimal interval for major maintenance actions in electricity distribution networks
Energy Technology Data Exchange (ETDEWEB)
Louit, Darko; Pascual, Rodrigo [Centro de Mineria, Pontificia Universidad Catolica de Chile, Av. Vicuna MacKenna, 4860 Santiago (Chile); Banjevic, Dragan [Centre for Maintenance Optimization and Reliability Engineering, University of Toronto, 5 King' s College Rd., Toronto, Ontario (Canada)
2009-09-15
Many systems require the periodic undertaking of major (preventive) maintenance actions (MMAs) such as overhauls in mechanical equipment, reconditioning of train lines, resurfacing of roads, etc. In the long term, these actions contribute to achieving a lower rate of occurrence of failures, though in many cases they increase the intensity of the failure process shortly after performed, resulting in a non-monotonic trend for failure intensity. Also, in the special case of distributed assets such as communications and energy networks, pipelines, etc., it is likely that the maintenance action takes place sequentially over an extended period of time, implying that different sections of the network underwent the MMAs at different periods. This forces the development of a model based on a relative time scale (i.e. time since last major maintenance event) and the combination of data from different sections of a grid, under a normalization scheme. Additionally, extended maintenance times and sequential execution of the MMAs make it difficult to identify failures occurring before and after the preventive maintenance action. This results in the loss of important information for the characterization of the failure process. A simple model is introduced to determine the optimal MMA interval considering such restrictions. Furthermore, a case study illustrates the optimal tree trimming interval around an electricity distribution network. (author)
Synchronised and complementary coordination mechanisms in an asymmetric joint aiming task
DEFF Research Database (Denmark)
Skewes, Joshua Charles; Skewes, Lea; Michael, John
2015-01-01
was to investigate how people may implicitly balance synchronisation and complementarity in a continuous joint aiming task. We asked dyads to synchronise the timing of their clicks between targets, while changing task constraints for one member of the dyad (i.e. different task difficulties) to asymmetrically perturb...
An Approach to Optical Network Design using General Heuristic Optimization Framework
Directory of Open Access Journals (Sweden)
Marko Lacković
2010-12-01
Full Text Available The article tackles the problem of optimization methods in optical network design process, based on optimal traffic routing with the goal to minimize the utilized network resources for given topology and traffic demands. An optimization framework Nyx has been developed with the focus on flexibility in solving optimization problems by implementing general heuristic search techniques. Nyx modular organization has been described, including coding types for solutions and genetic algorithm as the optimization method. Optimal routing has been implemented to demonstrate the use of Nyx in the optical network design process. Optimal routing procedure has been applied to Pan-European optical network with variations of routing procedures and the number of wavelengths. The analysis included no protection scenario, 1+1 protection and path restoration. The routing was performed using shortest path routing and optimal routing which minimizes the use of optical network resources, being network multiplexers, amplifiers and fibers.
Directory of Open Access Journals (Sweden)
Shi Chen-guang
2014-08-01
Full Text Available A novel optimal power allocation algorithm for radar network systems is proposed for Low Probability of Intercept (LPI technology in modern electronic warfare. The algorithm is based on the LPI optimization. First, the Schleher intercept factor for a radar network is derived, and then the Schleher intercept factor is minimized by optimizing the transmission power allocation among netted radars in the network to guarantee target-tracking performance. Furthermore, the Nonlinear Programming Genetic Algorithm (NPGA is used to solve the resulting nonconvex, nonlinear, and constrained optimization problem. Numerical simulation results show the effectiveness of the proposed algorithm.
Distributed optimization of a multisubchannel Ad Hoc cognitive radio network
Leith, Alex
2012-05-01
In this paper, we study the distributed-duality-based optimization of a multisubchannel ad hoc cognitive radio network (CRN) that coexists with a multicell primary radio network (PRN). For radio resource allocation in multiuser orthogonal frequency-division multiplexing (MU-OFDM) systems, the orthogonal-access-based exclusive subchannel assignment (ESA) technique has been a popular method, but it is suboptimal in ad hoc networks, because nonorthogonal access between multiple secondary-user links by using shared subchannel assignment (SSA) can bring a higher weighted sum rate. We utilize the Lagrangian dual composition tool and design low-complexity near-optimal SSA resource allocation methods, assuming practical discrete-rate modulation and that the CRN-to-PRN interference constraint has to strictly be satisfied. However, available SSA methods for CRNs are either suboptimal or involve high complexity and suffer from slow convergence. To address this problem, we design fast-convergence SSA duality schemes and introduce several novel methods to increase the speed of convergence and to satisfy various system constraints with low complexity. For practical implementation in ad hoc CRNs, we design distributed-duality schemes that involve only a small number of CRN local information exchanges for dual update. The effects of many system parameters are presented through simulation results, which show that the near-optimal SSA duality scheme can perform significantly better than the suboptimal ESA duality and SSA-iterative waterfilling schemes and that the performance loss of the distributed schemes is small, compared with their centralized counterparts. © 2012 IEEE.
Optimal Node Placement in Underwater Wireless Sensor Networks
Felamban, M.
2013-03-25
Wireless Sensor Networks (WSN) are expected to play a vital role in the exploration and monitoring of underwater areas which are not easily reachable by humans. However, underwater communication via acoustic waves is subject to several performance limitations that are very different from those used for terresstrial networks. In this paper, we investigate node placement for building an initial underwater WSN infrastructure. We formulate this problem as a nonlinear mathematical program with the objective of minimizing the total transmission loss under a given number of sensor nodes and targeted coverage volume. The obtained solution is the location of each node represented via a truncated octahedron to fill out the 3D space. Experiments are conducted to verify the proposed formulation, which is solved using Matlab optimization tool. Simulation is also conducted using an ns-3 simulator, and the simulation results are consistent with the obtained results from mathematical model with less than 10% error.
Hardware Abstraction and Protocol Optimization for Coded Sensor Networks
DEFF Research Database (Denmark)
Nistor, Maricica; Lucani Rötter, Daniel Enrique; Barros, joao
2014-01-01
The design of the communication protocols in wireless sensor networks (WSNs) often neglects several key characteristics of the sensor's hardware, while assuming that the number of transmitted bits is the dominating factor behind the system's energy consumption. A closer look at the hardware...... specifications of common sensors reveals, however, that other equally important culprits exist, such as the reception and processing energy. Hence, there is a need for a more complete hardware abstraction of a sensor node to reduce effectively the total energy consumption of the network by designing energy......-efficient protocols that use such an abstraction, as well as mechanisms to optimize a communication protocol in terms of energy consumption. The problem is modeled for different feedback-based techniques, where sensors are connected to a base station, either directly or through relays. We show that for four example...
Quantum-based algorithm for optimizing artificial neural networks.
Tzyy-Chyang Lu; Gwo-Ruey Yu; Jyh-Ching Juang
2013-08-01
This paper presents a quantum-based algorithm for evolving artificial neural networks (ANNs). The aim is to design an ANN with few connections and high classification performance by simultaneously optimizing the network structure and the connection weights. Unlike most previous studies, the proposed algorithm uses quantum bit representation to codify the network. As a result, the connectivity bits do not indicate the actual links but the probability of the existence of the connections, thus alleviating mapping problems and reducing the risk of throwing away a potential candidate. In addition, in the proposed model, each weight space is decomposed into subspaces in terms of quantum bits. Thus, the algorithm performs a region by region exploration, and evolves gradually to find promising subspaces for further exploitation. This is helpful to provide a set of appropriate weights when evolving the network structure and to alleviate the noisy fitness evaluation problem. The proposed model is tested on four benchmark problems, namely breast cancer and iris, heart, and diabetes problems. The experimental results show that the proposed algorithm can produce compact ANN structures with good generalization ability compared to other algorithms.
Iterative free-energy optimization for recurrent neural networks (INFERNO)
2017-01-01
The intra-parietal lobe coupled with the Basal Ganglia forms a working memory that demonstrates strong planning capabilities for generating robust yet flexible neuronal sequences. Neurocomputational models however, often fails to control long range neural synchrony in recurrent spiking networks due to spontaneous activity. As a novel framework based on the free-energy principle, we propose to see the problem of spikes’ synchrony as an optimization problem of the neurons sub-threshold activity for the generation of long neuronal chains. Using a stochastic gradient descent, a reinforcement signal (presumably dopaminergic) evaluates the quality of one input vector to move the recurrent neural network to a desired activity; depending on the error made, this input vector is strengthened to hill-climb the gradient or elicited to search for another solution. This vector can be learned then by one associative memory as a model of the basal-ganglia to control the recurrent neural network. Experiments on habit learning and on sequence retrieving demonstrate the capabilities of the dual system to generate very long and precise spatio-temporal sequences, above two hundred iterations. Its features are applied then to the sequential planning of arm movements. In line with neurobiological theories, we discuss its relevance for modeling the cortico-basal working memory to initiate flexible goal-directed neuronal chains of causation and its relation to novel architectures such as Deep Networks, Neural Turing Machines and the Free-Energy Principle. PMID:28282439
Numerical Analysis on the Optimization of Hydraulic Fracture Networks
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Zhaobin Zhang
2015-10-01
Full Text Available The clear understanding of hydraulic fracture network complexity and the optimization of fracture network configuration are important to the hydraulic fracturing treatment of shale gas reservoirs. For the prediction of hydraulic fracture network configuration, one of the problems is the accurate representation of natural fractures. In this work, a real natural fracture network is reconstructed from shale samples. Moreover, a virtual fracture system is proposed to simulate the large number of small fractures that are difficult to identify. A numerical model based on the displacement discontinuity method is developed to simulate the fluid-rock coupling system. A dimensionless stress difference that is normalized by rock strength is proposed to quantify the anisotropy of crustal stress. The hydraulic fracturing processes under different stress conditions are simulated. The most complex fracture configurations are obtained when the maximum principle stress direction is perpendicular to the principle natural fracture direction. In contrast, the worst results are obtained when these two directions are parallel to each other. Moreover, the side effects of the unfavorable geological conditions caused by crustal stress anisotropy can be partly suppressed by increasing the viscous effect of the fluid.
Iterative free-energy optimization for recurrent neural networks (INFERNO).
Pitti, Alexandre; Gaussier, Philippe; Quoy, Mathias
2017-01-01
The intra-parietal lobe coupled with the Basal Ganglia forms a working memory that demonstrates strong planning capabilities for generating robust yet flexible neuronal sequences. Neurocomputational models however, often fails to control long range neural synchrony in recurrent spiking networks due to spontaneous activity. As a novel framework based on the free-energy principle, we propose to see the problem of spikes' synchrony as an optimization problem of the neurons sub-threshold activity for the generation of long neuronal chains. Using a stochastic gradient descent, a reinforcement signal (presumably dopaminergic) evaluates the quality of one input vector to move the recurrent neural network to a desired activity; depending on the error made, this input vector is strengthened to hill-climb the gradient or elicited to search for another solution. This vector can be learned then by one associative memory as a model of the basal-ganglia to control the recurrent neural network. Experiments on habit learning and on sequence retrieving demonstrate the capabilities of the dual system to generate very long and precise spatio-temporal sequences, above two hundred iterations. Its features are applied then to the sequential planning of arm movements. In line with neurobiological theories, we discuss its relevance for modeling the cortico-basal working memory to initiate flexible goal-directed neuronal chains of causation and its relation to novel architectures such as Deep Networks, Neural Turing Machines and the Free-Energy Principle.
Iterative free-energy optimization for recurrent neural networks (INFERNO.
Directory of Open Access Journals (Sweden)
Alexandre Pitti
Full Text Available The intra-parietal lobe coupled with the Basal Ganglia forms a working memory that demonstrates strong planning capabilities for generating robust yet flexible neuronal sequences. Neurocomputational models however, often fails to control long range neural synchrony in recurrent spiking networks due to spontaneous activity. As a novel framework based on the free-energy principle, we propose to see the problem of spikes' synchrony as an optimization problem of the neurons sub-threshold activity for the generation of long neuronal chains. Using a stochastic gradient descent, a reinforcement signal (presumably dopaminergic evaluates the quality of one input vector to move the recurrent neural network to a desired activity; depending on the error made, this input vector is strengthened to hill-climb the gradient or elicited to search for another solution. This vector can be learned then by one associative memory as a model of the basal-ganglia to control the recurrent neural network. Experiments on habit learning and on sequence retrieving demonstrate the capabilities of the dual system to generate very long and precise spatio-temporal sequences, above two hundred iterations. Its features are applied then to the sequential planning of arm movements. In line with neurobiological theories, we discuss its relevance for modeling the cortico-basal working memory to initiate flexible goal-directed neuronal chains of causation and its relation to novel architectures such as Deep Networks, Neural Turing Machines and the Free-Energy Principle.
Neural network based optimal control of HVAC&R systems
Ning, Min
Heating, Ventilation, Air-Conditioning and Refrigeration (HVAC&R) systems have wide applications in providing a desired indoor environment for different types of buildings. It is well acknowledged that 30%-40% of the total energy generated is consumed by buildings and HVAC&R systems alone account for more than 50% of the building energy consumption. Low operational efficiency especially under partial load conditions and poor control are part of reasons for such high energy consumption. To improve energy efficiency, HVAC&R systems should be properly operated to maintain a comfortable and healthy indoor environment under dynamic ambient and indoor conditions with the least energy consumption. This research focuses on the optimal operation of HVAC&R systems. The optimization problem is formulated and solved to find the optimal set points for the chilled water supply temperature, discharge air temperature and AHU (air handling unit) fan static pressure such that the indoor environment is maintained with the least chiller and fan energy consumption. To achieve this objective, a dynamic system model is developed first to simulate the system behavior under different control schemes and operating conditions. The system model is modular in structure, which includes a water-cooled vapor compression chiller model and a two-zone VAV system model. A fuzzy-set based extended transformation approach is then applied to investigate the uncertainties of this model caused by uncertain parameters and the sensitivities of the control inputs with respect to the interested model outputs. A multi-layer feed forward neural network is constructed and trained in unsupervised mode to minimize the cost function which is comprised of overall energy cost and penalty cost when one or more constraints are violated. After training, the network is implemented as a supervisory controller to compute the optimal settings for the system. In order to implement the optimal set points predicted by the
Hopfield neural network implementation of the optimal CDMA multiuser detector.
Kechriotis, G I; Manolakos, E S
1996-01-01
We investigate the application of Hopfield neural networks (HNN's) to the problem of multiuser detection in spread spectrum/CDMA (code division multiple access) communication systems. It is shown that the NP-complete problem of minimizing the objective function of the optimal multiuser detector (OMD) can be translated into minimizing an HNN "energy" function, thus allowing to take advantage of the ability of HNN's to perform very fast gradient descent algorithms in analog hardware and produce in real-time suboptimal solutions to hard combinatorial optimization problems. The performance of the proposed HNN receiver is evaluated via computer simulations and compared to that of other suboptimal schemes as well as to that of the OMD for both the synchronous and the asynchronous CDMA transmission cases. It is shown that the HNN detector exhibits a number of attractive properties and that it provides a powerful generalization of a well-known and extensively studied suboptimal scheme, namely the multistage detector.
Scalable Optimization Methods for Distribution Networks With High PV Integration
Energy Technology Data Exchange (ETDEWEB)
Guggilam, Swaroop S.; Dall' Anese, Emiliano; Chen, Yu Christine; Dhople, Sairaj V.; Giannakis, Georgios B.
2016-07-01
This paper proposes a suite of algorithms to determine the active- and reactive-power setpoints for photovoltaic (PV) inverters in distribution networks. The objective is to optimize the operation of the distribution feeder according to a variety of performance objectives and ensure voltage regulation. In general, these algorithms take a form of the widely studied ac optimal power flow (OPF) problem. For the envisioned application domain, nonlinear power-flow constraints render pertinent OPF problems nonconvex and computationally intensive for large systems. To address these concerns, we formulate a quadratic constrained quadratic program (QCQP) by leveraging a linear approximation of the algebraic power-flow equations. Furthermore, simplification from QCQP to a linearly constrained quadratic program is provided under certain conditions. The merits of the proposed approach are demonstrated with simulation results that utilize realistic PV-generation and load-profile data for illustrative distribution-system test feeders.
Cellular Neural Networks for NP-Hard Optimization
Directory of Open Access Journals (Sweden)
Mária Ercsey-Ravasz
2009-02-01
Full Text Available A cellular neural/nonlinear network (CNN is used for NP-hard optimization. We prove that a CNN in which the parameters of all cells can be separately controlled is the analog correspondent of a two-dimensional Ising-type (Edwards-Anderson spin-glass system. Using the properties of CNN, we show that one single operation (template always yields a local minimum of the spin-glass energy function. This way, a very fast optimization method, similar to simulated annealing, can be built. Estimating the simulation time needed on CNN-based computers, and comparing it with the time needed on normal digital computers using the simulated annealing algorithm, the results are astonishing. CNN computers could be faster than digital computers already at 10×10 lattice sizes. The local control of the template parameters was already partially realized on some of the hardwares, we think this study could further motivate their development in this direction.
Optimizing a Drone Network to Deliver Automated External Defibrillators.
Boutilier, Justin J; Brooks, Steven C; Janmohamed, Alyf; Byers, Adam; Buick, Jason E; Zhan, Cathy; Schoellig, Angela P; Cheskes, Sheldon; Morrison, Laurie J; Chan, Timothy C Y
2017-06-20
Public access defibrillation programs can improve survival after out-of-hospital cardiac arrest, but automated external defibrillators (AEDs) are rarely available for bystander use at the scene. Drones are an emerging technology that can deliver an AED to the scene of an out-of-hospital cardiac arrest for bystander use. We hypothesize that a drone network designed with the aid of a mathematical model combining both optimization and queuing can reduce the time to AED arrival. We applied our model to 53 702 out-of-hospital cardiac arrests that occurred in the 8 regions of the Toronto Regional RescuNET between January 1, 2006, and December 31, 2014. Our primary analysis quantified the drone network size required to deliver an AED 1, 2, or 3 minutes faster than historical median 911 response times for each region independently. A secondary analysis quantified the reduction in drone resources required if RescuNET was treated as a large coordinated region. The region-specific analysis determined that 81 bases and 100 drones would be required to deliver an AED ahead of median 911 response times by 3 minutes. In the most urban region, the 90th percentile of the AED arrival time was reduced by 6 minutes and 43 seconds relative to historical 911 response times in the region. In the most rural region, the 90th percentile was reduced by 10 minutes and 34 seconds. A single coordinated drone network across all regions required 39.5% fewer bases and 30.0% fewer drones to achieve similar AED delivery times. An optimized drone network designed with the aid of a novel mathematical model can substantially reduce the AED delivery time to an out-of-hospital cardiac arrest event. © 2017 American Heart Association, Inc.
Optimal network solution for proactive risk assessment and emergency response
Cai, Tianxing
Coupled with the continuous development in the field industrial operation management, the requirement for operation optimization in large scale manufacturing network has provoked more interest in the research field of engineering. Compared with the traditional way to take the remedial measure after the occurrence of the emergency event or abnormal situation, the current operation control calls for more proactive risk assessment to set up early warning system and comprehensive emergency response planning. Among all the industries, chemical industry and energy industry have higher opportunity to face with the abnormal and emergency situations due to their own industry characterization. Therefore the purpose of the study is to develop methodologies to give aid in emergency response planning and proactive risk assessment in the above two industries. The efficacy of the developed methodologies is demonstrated via two industrial real problems. The first case is to handle energy network dispatch optimization under emergency of local energy shortage under extreme conditions such as earthquake, tsunami, and hurricane, which may cause local areas to suffer from delayed rescues, widespread power outages, tremendous economic losses, and even public safety threats. In such urgent events of local energy shortage, agile energy dispatching through an effective energy transportation network, targeting the minimum energy recovery time, should be a top priority. The second case is a scheduling methodology to coordinate multiple chemical plants' start-ups in order to minimize regional air quality impacts under extreme meteorological conditions. The objective is to reschedule multi-plant start-up sequence to achieve the minimum sum of delay time compared to the expected start-up time of each plant. All these approaches can provide quantitative decision support for multiple stake holders, including government and environment agencies, chemical industry, energy industry and local
Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning Optimization
Directory of Open Access Journals (Sweden)
Lianbo Ma
2014-01-01
Full Text Available This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness.
A One-Layer Recurrent Neural Network for Constrained Complex-Variable Convex Optimization.
Qin, Sitian; Feng, Jiqiang; Song, Jiahui; Wen, Xingnan; Xu, Chen
2016-12-22
In this paper, based on CR calculus and penalty method, a one-layer recurrent neural network is proposed for solving constrained complex-variable convex optimization. It is proved that for any initial point from a given domain, the state of the proposed neural network reaches the feasible region in finite time and converges to an optimal solution of the constrained complex-variable convex optimization finally. In contrast to existing neural networks for complex-variable convex optimization, the proposed neural network has a lower model complexity and better convergence. Some numerical examples and application are presented to substantiate the effectiveness of the proposed neural network.
Optimization of Multicast Protocols for Heterogeneous Satellite Networks
Ehlert, Sven; Firrincieli, Rosario; Corazza, Giovanni E.
2003-07-01
With the growing need for context aware information delivery to groups and the increasing importance of streaming media distribution, the telecommunications research community is examining and evaluating different means for point-to- multipoint content delivery to end users, exploiting multicasting-broadcasting transport means. Traditionally, terrestrial networks are the main carriers for point-to-point content delivery, but satellite systems are at a prime when it comes to efficiently broadcast data to a wide user population. In this paper, we evaluate a heterogeneous satellite/terrestrial network in terms of content distribution capacity, using different multicast transport protocols. We focus our attention on reliable point- to-multipoint data delivery, our goal being to optimize quality of service while preserving capacity. We considered Scalable Reliable Multicast (SRM) and Pragmatic General Multicast (PGM) protocols. The results show that SRM does not perform very well, especially at high BER. On the other hand, PGM improves bandwidth exploitation by minimizing retransmission redundancy. However, PGM works optimally if and only if all routers are PGM-aware, which may be an unreasonable assumption when using a part of the public Internet for transportation. Therefore, we have augmented the PGM protocol by adding a Designated Local Repairer (DLR) node in order to counteract the effects of a mixed PGM-aware/unaware environment.
Universally optimal noisy quantum walks on complex networks
Caruso, Filippo
2014-05-01
Transport properties play a crucial role in several fields of science, for example biology, chemistry, sociology, information science and physics. The behavior of many dynamical processes running over complex networks is known to be closely related to the geometry of the underlying topology, but this connection becomes even harder to understand when quantum effects come into play. Here, we exploit the Kossakowski-Lindblad formalism of quantum stochastic walks to investigate the capability of quickly and robustly transmitting energy (or information) between two distant points in very large complex structures, remarkably assisted by external noise and quantum features such as coherence. An optimal mixing of classical and quantum transport is, very surprisingly, quite universal for a large class of complex networks. This widespread behavior turns out to be also extremely robust with respect to geometry changes. These results might pave the way for designing optimal bio-inspired geometries of efficient transport nanostructures that can be used for solar energy and also quantum information and communication technologies.
Optimizing fairness and blocking probability for optical networks
Li, Junjie; Li, Yanhe; Zhang, Hanyi; Zhou, Bingkun
2005-02-01
In the study of dynamic routing and wavelength assignment (DRWA) algorithms and protocols in optical networks, blocking probability is a chief criterion for performance evaluation. But it does not always capture the full effect of a particular algorithm on other aspects, such as fairness problem, which refers to the variability in blocking probabilities experienced by connection requests between various node pairs. Most commercial routing protocols, such as OSPF, prefers to adopt the shortest path for a particular connection, also brings unfairness to diverse connection requests, and such an unfairness becomes more serious in optical networks without wavelength conversion due to wavelength continuity constraint. Some literature have concerned this fairness problem and proposed several fairness enhancement methods. But unfortunately, they will deteriorate the overall blocking probability. In this paper, we try to optimize the fairness and blocking probability simultaneously via the alternate routing strategy, and a novel classified alternate routing (CAR) approach will be adopted. In our investigations, we will consider the optimization of alternate path set determination strategy, wavelength assignment and path selection method, and link weight function, etc. Comparisons and collaborations with formerly proposed fairness enhancement methods will be also presented.
Optimal Bidding Strategy for Renewable Microgrid with Active Network Management
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Seung Wan Kim
2016-01-01
Full Text Available Active Network Management (ANM enables a microgrid to optimally dispatch the active/reactive power of its Renewable Distributed Generation (RDG and Battery Energy Storage System (BESS units in real time. Thus, a microgrid with high penetration of RDGs can handle their uncertainties and variabilities to achieve the stable operation using ANM. However, the actual power flow in the line connecting the main grid and microgrid may deviate significantly from the day-ahead bids if the bids are determined without consideration of the real-time adjustment through ANM, which will lead to a substantial imbalance cost. Therefore, this study proposes a formulation for obtaining an optimal bidding which reflects the change of power flow in the connecting line by real-time adjustment using ANM. The proposed formulation maximizes the expected profit of the microgrid considering various network and physical constraints. The effectiveness of the proposed bidding strategy is verified through the simulations with a 33-bus test microgrid. The simulation results show that the proposed bidding strategy improves the expected operating profit by reducing the imbalance cost to a greater degree compared to the basic bidding strategy without consideration of ANM.
Particle Swarm Optimization for Adaptive Resource Allocation in Communication Networks
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Gheitanchi Shahin
2010-01-01
Full Text Available A generalized model of particle swarm optimization (PSO technique is proposed as a low complexity method for adaptive centralized and distributed resource allocation in communication networks. The proposed model is applied to adaptive multicarrier cooperative communications (MCCC technique which utilizes the subcarriers in deep fade using a relay node in order to improve the bandwidth efficiency. Centralized PSO, based on virtual particles (VPs, is introduced for single layer and cross-layer subcarrier allocation to improve the bit error rate performance in multipath frequency selective fading channels. In the single layer strategy, the subcarriers are allocated based on the channel gains. In the cross-layer strategy, the subcarriers are allocated based on a joint measure of channel gains and distance provided by the physical layer and network layer to mitigate the effect of path loss. The concept of training particles in distributed PSO is proposed and then is applied for relay node selection. The computational complexity and traffic of the proposed techniques are investigated, and it is shown that using PSO for subcarrier allocation has a lower complexity than the techniques in the literature. Significant reduction in the traffic overhead of PSO is demonstrated when using trained particles in distributed optimizations.
Proposal for optimal placement platform of bikes using queueing networks.
Mizuno, Shinya; Iwamoto, Shogo; Seki, Mutsumi; Yamaki, Naokazu
2016-01-01
In recent social experiments, rental motorbikes and rental bicycles have been arranged at nodes, and environments where users can ride these bikes have been improved. When people borrow bikes, they return them to nearby nodes. Some experiments have been conducted using the models of Hamachari of Yokohama, the Niigata Rental Cycle, and Bicing. However, from these experiments, the effectiveness of distributing bikes was unclear, and many models were discontinued midway. Thus, we need to consider whether these models are effectively designed to represent the distribution system. Therefore, we construct a model to arrange the nodes for distributing bikes using a queueing network. To adopt realistic values for our model, we use the Google Maps application program interface. Thus, we can easily obtain values of distance and transit time between nodes in various places in the world. Moreover, we apply the distribution of a population to a gravity model and we compute the effective transition probability for this queueing network. If the arrangement of the nodes and number of bikes at each node is known, we can precisely design the system. We illustrate our system using convenience stores as nodes and optimize the node configuration. As a result, we can optimize simultaneously the number of nodes, node places, and number of bikes for each node, and we can construct a base for a rental cycle business to use our system.
Optimal control of metabolic networks with saturable enzyme kinetics.
Oyarzuun, D A
2011-03-01
This note addresses the optimal control of non-linear metabolic networks by means of time-dependent enzyme synthesis rates. The authors consider networks with general topologies described by a control-affine dynamical system coupled with a linear model for enzyme synthesis and degradation. The problem formulation accounts for transitions between two metabolic equilibria, which typically arise in metabolic adaptations to environmental changes, and the minimisation of a quadratic functional that weights the cost/benefit relation between the transcriptional effort required for enzyme synthesis and the transition to the new phenotype. Using a linear time-variant approximation of the non-linear dynamics, the problem is recast as a sequence of linear-quadratic problems, the solution of which involves a sequence of differential Lyapunov equations. The authors provide conditions for convergence to an approximate solution of the original problem, which are naturally satisfied by a wide class of models for saturable enzyme kinetics. As a case study the authors use the method to examine the robustness of an optimal just-in-time gene expression pattern with respect to heterogeneity in the biosynthetic costs of individual proteins.
Optimal power flow for distribution networks with distributed generation
Directory of Open Access Journals (Sweden)
Radosavljević Jordan
2015-01-01
Full Text Available This paper presents a genetic algorithm (GA based approach for the solution of the optimal power flow (OPF in distribution networks with distributed generation (DG units, including fuel cells, micro turbines, diesel generators, photovoltaic systems and wind turbines. The OPF is formulated as a nonlinear multi-objective optimization problem with equality and inequality constraints. Due to the stochastic nature of energy produced from renewable sources, i.e. wind turbines and photovoltaic systems, as well as load uncertainties, a probabilisticalgorithm is introduced in the OPF analysis. The Weibull and normal distributions are employed to model the input random variables, namely the wind speed, solar irradiance and load power. The 2m+1 point estimate method and the Gram Charlier expansion theory are used to obtain the statistical moments and the probability density functions (PDFs of the OPF results. The proposed approach is examined and tested on a modified IEEE 34 node test feeder with integrated five different DG units. The obtained results prove the efficiency of the proposed approach to solve both deterministic and probabilistic OPF problems for different forms of the multi-objective function. As such, it can serve as a useful decision-making supporting tool for distribution network operators. [Projekat Ministarstva nauke Republike Srbije, br. TR33046
Network Coding for Wireless Cooperative Networks: Simple Rules, Near-optimal Delay
DEFF Research Database (Denmark)
Khamfroush, Hana; Lucani Rötter, Daniel Enrique; Barros, joao
2014-01-01
We consider the problem of finding an optimal packet transmission policy that minimizes the total cost of transmitting M data packets from a source S to two receivers R1,R2 over half-duplex, erasure channels. The source can either broadcast random linear network coding (RLNC) packets...... from the optimal policy and devise two simple, yet powerful heuristics that are useful in practice. Our heuristics rely on different levels of feedback, namely, sending 1 or 2 feedback packets per receiver per M data packets by choosing the right moment to send this feedback. Our numerical results show...
Brands, Ties; van Berkum, Eric C.; Wismans, Luc Johannes Josephus; Karlaftis, M.G.; Lagaros, N.D.; Papadrakakis, M.
2014-01-01
Robustness of optimal solutions when solving network design problems is of great importance because of uncertainty in future demand. In this research the optimization of infrastructure planning in a multimodal passenger transportation network is defined as a multiobjective network design problem,
Algorithms for finding optimal paths in network games with p players
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R. Boliac
1997-08-01
Full Text Available We study the problem of finding optimal paths in network games with p players. Some polynomial-time algorithms for finding optimal paths and optimal by Nash strategies of the players in network games with p players are proposed.
Optimal Retrofit Scheme for Highway Network under Seismic Hazards
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Yongxi Huang
2014-06-01
Full Text Available Many older highway bridges in the United States (US are inadequate for seismic loads and could be severely damaged or collapsed in a relatively small earthquake. According to the most recent American Society of Civil Engineers’ infrastructure report card, one-third of the bridges in the US are rated as structurally deficient and many of these structurally deficient bridges are located in seismic zones. To improve this situation, at-risk bridges must be identified and evaluated and effective retrofitting programs should be in place to reduce their seismic vulnerabilities. In this study, a new retrofit strategy decision scheme for highway bridges under seismic hazards is developed and seamlessly integrate the scenario-based seismic analysis of bridges and the traffic network into the proposed optimization modeling framework. A full spectrum of bridge retrofit strategies is considered based on explicit structural assessment for each seismic damage state. As an empirical case study, the proposed retrofit strategy decision scheme is utilized to evaluate the bridge network in one of the active seismic zones in the US, Charleston, South Carolina. The developed modeling framework, on average, will help increase network throughput traffic capacity by 45% with a cost increase of only $15million for the Mw 5.5 event and increase the capacity fourfold with a cost of only $32m for the Mw 7.0 event.
Quantum versus simulated annealing in wireless interference network optimization.
Wang, Chi; Chen, Huo; Jonckheere, Edmond
2016-05-16
Quantum annealing (QA) serves as a specialized optimizer that is able to solve many NP-hard problems and that is believed to have a theoretical advantage over simulated annealing (SA) via quantum tunneling. With the introduction of the D-Wave programmable quantum annealer, a considerable amount of effort has been devoted to detect and quantify quantum speedup. While the debate over speedup remains inconclusive as of now, instead of attempting to show general quantum advantage, here, we focus on a novel real-world application of D-Wave in wireless networking-more specifically, the scheduling of the activation of the air-links for maximum throughput subject to interference avoidance near network nodes. In addition, D-Wave implementation is made error insensitive by a novel Hamiltonian extra penalty weight adjustment that enlarges the gap and substantially reduces the occurrence of interference violations resulting from inevitable spin bias and coupling errors. The major result of this paper is that quantum annealing benefits more than simulated annealing from this gap expansion process, both in terms of ST99 speedup and network queue occupancy. It is the hope that this could become a real-word application niche where potential benefits of quantum annealing could be objectively assessed.
Optimization of RFID network planning using Zigbee and WSN
Hasnan, Khalid; Ahmed, Aftab; Badrul-aisham, Bakhsh, Qadir
2015-05-01
Everyone wants to be ease in their life. Radio frequency identification (RFID) wireless technology is used to make our life easier. RFID technology increases productivity, accuracy and convenience in delivery of service in supply chain. It is used for various applications such as preventing theft of automobiles, tolls collection without stopping, no checkout lines at grocery stores, managing traffic, hospital management, corporate campuses and airports, mobile asset tracking, warehousing, tracking library books, and to track a wealth of assets in supply chain management. Efficiency of RFID can be enhanced by integrating with wireless sensor network (WSN), zigbee mesh network and internet of things (IOT). The proposed system is used for identifying, sensing and real-time locating system (RTLS) of items in an indoor heterogeneous region. The system gives real-time richer information of object's characteristics, location and their environmental parameters like temperature, noise and humidity etc. RTLS reduce human error, optimize inventory management, increase productivity and information accuracy at indoor heterogeneous network. The power consumption and the data transmission rate of the system can be minimized by using low power hardware design.
Optimal Configuration of Virtual Links for Avionics Network Systems
Directory of Open Access Journals (Sweden)
Dongha An
2015-01-01
Full Text Available As the bandwidth and scalability constraints become important design concerns in airborne networks, a new technology, called Avionics Full Duplex Switched Ethernet (AFDX, has been introduced and standardized as a part 7 in ARNIC 664. However, since previous research interests for AFDX are mainly bounded for analyzing the response time where flows information is given, configuration problem for both Maximum Transmission Unit (MTU and Bandwidth Allocation Gap (BAG over virtual links in AFDX networks has not been addressed yet even though it has great impact on required bandwidth. Thus, in this paper, we present two configuration approaches to set MTU and BAG values on virtual links efficiently while meeting the requirement of AFDX. The first is to search available feasible configuration (MTU, BAG pairs to satisfy application requirements as well as AFDX switch constraints, and the second is to get an optimal pair to minimize required bandwidth through well-known branch-and-bound algorithm. We analyze the complexity of the proposed algorithm and then evaluate the proposed algorithm by simulation. Finally, we prove that the proposed schemes are superior to general approach in the aspects of speed and required bandwidth in AFDX networks.
Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms.
Garro, Beatriz A; Vázquez, Roberto A
2015-01-01
Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems.
Red deer synchronise their activity with close neighbours
Directory of Open Access Journals (Sweden)
Sean A. Rands
2014-04-01
Full Text Available Models of collective animal behaviour frequently make assumptions about the effects of neighbours on the behaviour of focal individuals, but these assumptions are rarely tested. One such set of assumptions is that the switch between active and inactive behaviour seen in herding animals is influenced by the activity of close neighbours, where neighbouring animals show a higher degree of behavioural synchrony than would be expected by chance. We tested this assumption by observing the simultaneous behaviour of paired individuals within a herd of red deer Cervus elaphus. Focal individuals were more synchronised with their two closest neighbours than with the third closest or randomly selected individuals from the herd. Our results suggest that the behaviour of individual deer is influenced by immediate neighbours. Even if we assume that there are no social relationships between individuals, this suggests that the assumptions made in models about the influence of neighbours may be appropriate.
Nazempour, Rezvan; Monfared, Mohammad Ali Saniee; Zio, Enrico
2016-01-01
Drinking water for human health and well-being is crucial. Accidental and intentional water contamination can pose great danger to consumers. Optimal design of a system that can quickly detect the presence of contamination in a water distribution network is very challenging for technical and operational reasons. However, on the one hand improvement in chemical and biological sensor technology has created the possibility of designing efficient contamination detection systems. On the other hand...
DEFF Research Database (Denmark)
Ding, Tao; Li, Cheng; Yang, Yongheng
2017-01-01
Optimally dispatching Photovoltaic (PV) inverters is an efficient way to avoid overvoltage in active distribution networks, which may occur in the case of PV generation surplus load demand. Typically, the dispatching optimization objective is to identify critical PV inverters that have the most...... significant impact on the network voltage level. Following, it ensures the optimal set-points of both active power and reactive power for the selected inverters, guaranteeing the entire system operating constraints (e.g., the network voltage magnitude) within reasonable ranges. However, the intermittent...
A Bi-Projection Neural Network for Solving Constrained Quadratic Optimization Problems.
Xia, Youshen; Wang, Jun
2016-02-01
In this paper, a bi-projection neural network for solving a class of constrained quadratic optimization problems is proposed. It is proved that the proposed neural network is globally stable in the sense of Lyapunov, and the output trajectory of the proposed neural network will converge globally to an optimal solution. Compared with existing projection neural networks (PNNs), the proposed neural network has a very small model size owing to its bi-projection structure. Furthermore, an application to data fusion shows that the proposed neural network is very effective. Numerical results demonstrate that the proposed neural network is much faster than the existing PNNs.
Outage Analysis and Optimization of SWIPT in Network-Coded Two-Way Relay Networks
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Ruihong Jiang
2017-01-01
Full Text Available This paper investigates the outage performance of simultaneous wireless information and power transfer (SWIPT in network-coded two-way relay systems, where a relay first harvests energy from the signals transmitted by two sources and then uses the harvested energy to forward the received information to the two sources. We consider two transmission protocols, power splitting two-way relay (PS-TWR and time switching two-way relay (TS-TWR protocols. We present two explicit expressions for the system outage probability of the two protocols and further derive approximate expressions for them in high and low SNR cases. To explore the system performance limits, two optimization problems are formulated to minimize the system outage probability. Since the problems are nonconvex and have no known solution methods, a genetic algorithm- (GA- based algorithm is designed. Numerical and simulation results validate our theoretical analysis. It is shown that, by jointly optimizing the time assignment and SWIPT receiver parameters, a great performance gain can be achieved for both PS-TWR and TS-TWR. Moreover, the optimized PS-TWR always outperforms the optimized TS-TWR in terms of outage performance. Additionally, the effects of parameters including relay location and transmit powers are also discussed, which provide some insights for the SWIPT-enabled two-way relay networks.
Optimizing dispersal corridors for the Cape Proteaceae using network flow.
Phillips, Steven J; Williams, Paul; Midgley, Guy; Archer, Aaron
2008-07-01
We introduce a new way of measuring and optimizing connectivity in conservation landscapes through time, accounting for both the biological needs of multiple species and the social and financial constraint of minimizing land area requiring additional protection. Our method is based on the concept of network flow; we demonstrate its use by optimizing protected areas in the Western Cape of South Africa to facilitate autogenic species shifts in geographic range under climate change for a family of endemic plants, the Cape Proteaceae. In 2005, P. Williams and colleagues introduced a novel framework for this protected area design task. To ensure population viability, they assumed each species should have a range size of at least 100 km2 of predicted suitable conditions contained in protected areas at all times between 2000 and 2050. The goal was to design multiple dispersal corridors for each species, connecting suitable conditions between time periods, subject to each species' limited dispersal ability, and minimizing the total area requiring additional protection. We show that both minimum range size and limited dispersal abilities can be naturally modeled using the concept of network flow. This allows us to apply well-established tools from operations research and computer science for solving network flow problems. Using the same data and this novel modeling approach, we reduce the area requiring additional protection by a third compared to previous methods, from 4593 km2 to 3062 km , while still achieving the same conservation planning goals. We prove that this is the best solution mathematically possible: the given planning goals cannot be achieved with a smaller area, given our modeling assumptions and data. Our method allows for flexibility and refinement of the underlying climate-change, species-habitat-suitability, and dispersal models. In particular, we propose an alternate formalization of a minimum range size moving through time and use network flow to
Karthikeyan, Anitha; Rajagopal, Karthikeyan
2018-01-01
A new fourth-order memristor chaotic oscillator is taken to investigate its fractional-order discrete synchronisation. The fractional-order differential model memristor system is transformed to its discrete model and the dynamic properties of the fractional-order discrete system are investigated. A new method for synchronising commensurate and incommensurate fractional discrete chaotic maps are proposed and validated. Numerical results are established to support the proposed methodologies. This method of synchronisation can be applied for any fractional discrete maps. Finally the fractional-order memristor system is implemented in FPGA to show that the chaotic system is hardware realisable.
Synchronised PWM Schemes for Three-level Inverters with Zero Common-mode Voltage
DEFF Research Database (Denmark)
Oleschuk, Valentin; Blaabjerg, Frede
2002-01-01
PWM with synchronous smooth pulses-ratio changing and a quarter-wave symmetry of the voltage waveforms. Three basic versions of both algebraic and trigonometric synchronised PWM have been analysed. Simulations give the behaviour of the proposed methods and show some advantage of synchronised PWM......This paper presents results of analysis and comparison of novel synchronised schemes of pulsewidth modulation (PWM), applied to three-level voltage source inverters with control algorithms providing elimination of the common-mode voltage. The proposed approach is based on a new strategy of digital...... in comparison with asynchronous at low ratios between the switching and fundamentsl frequencies....
Modeling and optimization of cloud-ready and content-oriented networks
Walkowiak, Krzysztof
2016-01-01
This book focuses on modeling and optimization of cloud-ready and content-oriented networks in the context of different layers and accounts for specific constraints following from protocols and technologies used in a particular layer. It addresses a wide range of additional constraints important in contemporary networks, including various types of network flows, survivability issues, multi-layer networking, and resource location. The book presents recent existing and new results in a comprehensive and cohesive way. The contents of the book are organized in five chapters, which are mostly self-contained. Chapter 1 briefly presents information on cloud computing and content-oriented services, and introduces basic notions and concepts of network modeling and optimization. Chapter 2 covers various optimization problems that arise in the context of connection-oriented networks. Chapter 3 focuses on modeling and optimization of Elastic Optical Networks. Chapter 4 is devoted to overlay networks. The book concludes w...
MILP model for energy optimization in EIP water networks
Energy Technology Data Exchange (ETDEWEB)
Taskhiri, Mohammad Sadegh [De La Salle University, Industrial Engineering Department, Manila (Philippines); Tan, Raymond R. [De La Salle University, Center for Engineering and Sustainable Development Research, Manila (Philippines); Chiu, Anthony S.F. [De La Salle University, Industrial Engineering Department, Manila (Philippines); De La Salle University, Center for Engineering and Sustainable Development Research, Manila (Philippines)
2011-10-15
The eco-industrial park (EIP) concept provides a framework in which several plants can cooperate with each other and exchange their wastewater to minimize total freshwater consumption. Energy analysis is a methodology that considers the total, cumulative energy which has been consumed within a system; thus, by minimizing energy, an environmentally optimal EIP can be designed. This article presents a mixed-integer linear programming (MILP) model for minimizing energy of an interplant water network in an EIP. The methodology accounts for the environmental impacts of water use, energy consumption, and capital goods within the EIP in a balanced manner. The proposed technique is then demonstrated by solving a case study from literature. (orig.)
Optimal Dimensioning of Broadband Integrated Services Digital Network
Directory of Open Access Journals (Sweden)
Zdenka Chmelikova
2005-01-01
Full Text Available Tasks of this paper are research of input flow statistic parametres influence and parameters demands relating to VP (Virtual Path or VC (Virtual Channel dimensioning. However it is necessary to consider different time of flow arrival and differend time of holding time during connection level. Process of input flow arrival is considered as Poisson process. Holding time is considered as exponential function. Permanent allocation of VP is made by separate VP, where each of VPs enable transmitting offered laod with explicit bandwidth and explicit loss. The mathematic model was created to verify the above mentioned dependences for different types of telecomunications signals and different input flows. "Comnet III" software was selected for experimental verification of process optimization. The simulation model was based on thiss software, which simulate ATM network traffic in behalf of different input flow.
Optimization of Container Line Networks with Flexible Demands
DEFF Research Database (Denmark)
Plum, Christian Edinger Munk
Liner shipping is at the core of the world’s supply chains, with an estimated 36 % of the value of global merchandize trade being shipped in containers. The containers, carried on thousands of container vessels in intricate networks operated by global liner shipping carriers, constitute a very...... in liner shipping. These are important as the fierce competition in liner shipping, gives very small margins of profit. Due to this a successful carrier, will need to control the details, and consistently manage the operational challenges well. Two examples of this is studied: how to purchase bunker fuel...... the assets must be deployed in the best way possible to create a healthy business. To better manage the assets invested in container shipping and to control the use of fossils fuels used by the liner shipping industry, optimization methods for liner shipping is studied in this thesis. The domain...
Robust adaptive learning of feedforward neural networks via LMI optimizations.
Jing, Xingjian
2012-07-01
Feedforward neural networks (FNNs) have been extensively applied to various areas such as control, system identification, function approximation, pattern recognition etc. A novel robust control approach to the learning problems of FNNs is further investigated in this study in order to develop efficient learning algorithms which can be implemented with optimal parameter settings and considering noise effect in the data. To this aim, the learning problem of a FNN is cast into a robust output feedback control problem of a discrete time-varying linear dynamic system. New robust learning algorithms with adaptive learning rate are therefore developed, using linear matrix inequality (LMI) techniques to find the appropriate learning rates and to guarantee the fast and robust convergence. Theoretical analysis and examples are given to illustrate the theoretical results. Copyright © 2012 Elsevier Ltd. All rights reserved.
Optimal Time Allocation in Backscatter Assisted Wireless Powered Communication Networks.
Lyu, Bin; Yang, Zhen; Gui, Guan; Sari, Hikmet
2017-06-01
This paper proposes a wireless powered communication network (WPCN) assisted by backscatter communication (BackCom). This model consists of a power station, an information receiver and multiple users that can work in either BackCom mode or harvest-then-transmit (HTT) mode. The time block is mainly divided into two parts corresponding to the data backscattering and transmission periods, respectively. The users first backscatter data to the information receiver in time division multiple access (TDMA) during the data backscattering period. When one user works in the BackCom mode, the other users harvest energy from the power station. During the data transmission period, two schemes, i.e., non-orthogonal multiple access (NOMA) and TDMA, are considered. To maximize the system throughput, the optimal time allocation policies are obtained. Simulation results demonstrate the superiority of the proposed model.
Practice-Oriented Optimization of Distribution Network Planning Using Metaheuristic Algorithms
Grond, M.O.W.; Luong, Ngoc Hoang; Morren, Johan; Bosman, Peter; Slootweg, J.G.; La Poutré, Han
2014-01-01
htmlabstractDistribution network operators require more advanced planning tools to deal with the challenges of future network planning. An appropriate planning and optimization tool can identify which option for network extension should be selected from available alternatives. However, many optimization approaches described in the literature are quite theoretical and do not yield results that are practically relevant and feasible. In this paper, a distribution network planning approach is pro...
Two-scale cost efficiency optimization of 5G wireless backhaul networks
Ge, Xiaohu; Tu, Song; Mao, Guoqiang; Lau, Vincent K. N.; Pan, Linghui
2016-01-01
To cater for the demands of future fifth generation (5G) ultra-dense small cell networks, the wireless backhaul network is an attractive solution for the urban deployment of 5G wireless networks. Optimization of 5G wireless backhaul networks is a key issue. In this paper we propose a two-scale optimization solution to maximize the cost efficiency of 5G wireless backhaul networks. Specifically, the number and positions of gateways are optimized in the long time scale of 5G wireless backhaul ne...
Zhang, Songchuan; Xia, Youshen; Wang, Jun
2015-12-01
In this paper, we present a complex-valued projection neural network for solving constrained convex optimization problems of real functions with complex variables, as an extension of real-valued projection neural networks. Theoretically, by developing results on complex-valued optimization techniques, we prove that the complex-valued projection neural network is globally stable and convergent to the optimal solution. Obtained results are completely established in the complex domain and thus significantly generalize existing results of the real-valued projection neural networks. Numerical simulations are presented to confirm the obtained results and effectiveness of the proposed complex-valued projection neural network.
Structural brain network: What is the effect of LiFE optimization of whole brain tractography?
Directory of Open Access Journals (Sweden)
Shouliang eQi
2016-02-01
Full Text Available Structural brain networks constructed based on diffusion-weighted MRI (dMRI have provided a systems perspective to explore the organization of the human brain. Some redundant and nonexistent fibers, however, are inevitably generated in whole brain tractography. We propose to add one critical step while constructing the networks to remove these fibers using the linear fascicle evaluation (LiFE method, and study the differences between the networks with and without LiFE optimization. For a cohort of 9 healthy adults and for 9 out of the 35 subjects from Human Connectome Project, the T1-weighted images and dMRI data are analyzed. Each brain is parcellated into 90 regions-of-interest, whilst a probabilistic tractography algorithm is applied to generate the original connectome. The elimination of redundant and nonexistent fibers from the original connectome by LiFE creates the optimized connectome, and the random selection of the same number of fibers as the optimized connectome creates the non-optimized connectome. The combination of parcellations and these connectomes leads to the optimized and non-optimized networks, respectively. The optimized networks are constructed with six weighting schemes, and the correlations of different weighting methods are analyzed. The fiber length distributions of the non-optimized and optimized connectomes are compared. The optimized and non-optimized networks are compared with regard to edges, nodes and networks, within a sparsity range of 0.75-0.95. It has been found that relatively more short fibers exist in the optimized connectome. About 24.0% edges of the optimized network are significantly different from those in the non-optimized network at a sparsity of 0.75. About 13.2% of edges are classified as false positives or the possible missing edges. The strength and betweenness centrality of some nodes are significantly different for the non-optimized and optimized networks, but not the node efficiency. The
Design and regularization of neural networks: the optimal use of a validation set
DEFF Research Database (Denmark)
Larsen, Jan; Hansen, Lars Kai; Svarer, Claus
1996-01-01
We derive novel algorithms for estimation of regularization parameters and for optimization of neural net architectures based on a validation set. Regularisation parameters are estimated using an iterative gradient descent scheme. Architecture optimization is performed by approximative...... combinatorial search among the relevant subsets of an initial neural network architecture by employing a validation set based optimal brain damage/surgeon (OBD/OBS) or a mean field combinatorial optimization approach. Numerical results with linear models and feed-forward neural networks demonstrate...
Optimal Workflow Scheduling in Critical Infrastructure Systems with Neural Networks
Directory of Open Access Journals (Sweden)
S. Vukmirović
2012-04-01
Full Text Available Critical infrastructure systems (CISs, such as power grids, transportation systems, communication networks and water systems are the backbone of a country’s national security and industrial prosperity. These CISs execute large numbers of workflows with very high resource requirements that can span through different systems and last for a long time. The proper functioning and synchronization of these workflows is essential since humanity’s well-being is connected to it. Because of this, the challenge of ensuring availability and reliability of these services in the face of a broad range of operating conditions is very complicated. This paper proposes an architecture which dynamically executes a scheduling algorithm using feedback about the current status of CIS nodes. Different artificial neural networks (ANNs were created in order to solve the scheduling problem. Their performances were compared and as the main result of this paper, an optimal ANN architecture for workflow scheduling in CISs is proposed. A case study is shown for a meter data management system with measurements from a power distribution management system in Serbia. Performance tests show that significant improvement of the overall execution time can be achieved by ANNs.
Statistical process control using optimized neural networks: a case study.
Addeh, Jalil; Ebrahimzadeh, Ata; Azarbad, Milad; Ranaee, Vahid
2014-09-01
The most common statistical process control (SPC) tools employed for monitoring process changes are control charts. A control chart demonstrates that the process has altered by generating an out-of-control signal. This study investigates the design of an accurate system for the control chart patterns (CCPs) recognition in two aspects. First, an efficient system is introduced that includes two main modules: feature extraction module and classifier module. In the feature extraction module, a proper set of shape features and statistical feature are proposed as the efficient characteristics of the patterns. In the classifier module, several neural networks, such as multilayer perceptron, probabilistic neural network and radial basis function are investigated. Based on an experimental study, the best classifier is chosen in order to recognize the CCPs. Second, a hybrid heuristic recognition system is introduced based on cuckoo optimization algorithm (COA) algorithm to improve the generalization performance of the classifier. The simulation results show that the proposed algorithm has high recognition accuracy. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.
Quantum versus simulated annealing in wireless interference network optimization
Wang, Chi; Chen, Huo; Jonckheere, Edmond
2016-05-01
Quantum annealing (QA) serves as a specialized optimizer that is able to solve many NP-hard problems and that is believed to have a theoretical advantage over simulated annealing (SA) via quantum tunneling. With the introduction of the D-Wave programmable quantum annealer, a considerable amount of effort has been devoted to detect and quantify quantum speedup. While the debate over speedup remains inconclusive as of now, instead of attempting to show general quantum advantage, here, we focus on a novel real-world application of D-Wave in wireless networking—more specifically, the scheduling of the activation of the air-links for maximum throughput subject to interference avoidance near network nodes. In addition, D-Wave implementation is made error insensitive by a novel Hamiltonian extra penalty weight adjustment that enlarges the gap and substantially reduces the occurrence of interference violations resulting from inevitable spin bias and coupling errors. The major result of this paper is that quantum annealing benefits more than simulated annealing from this gap expansion process, both in terms of ST99 speedup and network queue occupancy. It is the hope that this could become a real-word application niche where potential benefits of quantum annealing could be objectively assessed.
Optimal Node Placement in Underwater Acoustic Sensor Network
Felemban, Muhamad
2011-10-01
Almost 70% of planet Earth is covered by water. A large percentage of underwater environment is unexplored. In the past two decades, there has been an increase in the interest of exploring and monitoring underwater life among scientists and in industry. Underwater operations are extremely difficult due to the lack of cheap and efficient means. Recently, Wireless Sensor Networks have been introduced in underwater environment applications. However, underwater communication via acoustic waves is subject to several performance limitations, which makes the relevant research issues very different from those on land. In this thesis, we investigate node placement for building an initial Underwater Wireless Sensor Network infrastructure. Firstly, we formulated the problem into a nonlinear mathematic program with objectives of minimizing the total transmission loss under a given number of sensor nodes and targeted volume. We conducted experiments to verify the proposed formulation, which is solved using Matlab optimization tool. We represented each node with a truncated octahedron to fill out the 3D space. The truncated octahedrons are tiled in the 3D space with each node in the center where locations of the nodes are given using 3D coordinates. Results are supported using ns-3 simulator. Results from simulation are consistent with the obtained results from mathematical model with less than 10% error.
Optimized Reputable Sensing Participants Extraction for Participatory Sensor Networks
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Weiwei Yuan
2014-01-01
Full Text Available By collecting data via sensors embedded personal smart devices, sensing participants play a key role in participatory sensor networks. Using information provided by reputable sensing participants ensures the reliability of participatory sensing data. Setting a threshold for the reputation, and those whose reputations are bigger than this value are regarded as reputable. The bigger the threshold value is, the more reliable the extracted reputable sensing participant is. However, if the threshold value is too big, only very limited participatory sensing data can be involved. This may cause unexpected bias in information collection. Existing works did not consider the relationship between the reliability of extracted reputable sensing participants and the ratio of usable participatory sensing data. In this work, we propose a criterion for optimized reputable sensing participant extraction in participatory sensor networks. This is achieved based on the mathematical analysis on the ratio of available participatory sensing data and the reliability of extracted reputable sensing participants. Our suggested threshold value for reputable sensing participant extraction is only related to the power of sensing participant’s reputation distribution. It is easy to be applied in real applications. Simulation results tested on real application data further verified the effectiveness of our proposed method.
Optimizing Soil Moisture Sampling Locations for Validation Networks for SMAP
Roshani, E.; Berg, A. A.; Lindsay, J.
2013-12-01
Soil Moisture Active Passive satellite (SMAP) is scheduled for launch on Oct 2014. Global efforts are underway for establishment of soil moisture monitoring networks for both the pre- and post-launch validation and calibration of the SMAP products. In 2012 the SMAP Validation Experiment, SMAPVEX12, took place near Carman Manitoba, Canada where nearly 60 fields were sampled continuously over a 6 week period for soil moisture and several other parameters simultaneous to remotely sensed images of the sampling region. The locations of these sampling sites were mainly selected on the basis of accessibility, soil texture, and vegetation cover. Although these criteria are necessary to consider during sampling site selection, they do not guarantee optimal site placement to provide the most efficient representation of the studied area. In this analysis a method for optimization of sampling locations is presented which combines the state-of-art multi-objective optimization engine (non-dominated sorting genetic algorithm, NSGA-II), with the kriging interpolation technique to minimize the number of sampling sites while simultaneously minimizing the differences between the soil moisture map resulted from the kriging interpolation and soil moisture map from radar imaging. The algorithm is implemented in Whitebox Geospatial Analysis Tools, which is a multi-platform open-source GIS. The optimization framework is subject to the following three constraints:. A) sampling sites should be accessible to the crew on the ground, B) the number of sites located in a specific soil texture should be greater than or equal to a minimum value, and finally C) the number of sampling sites with a specific vegetation cover should be greater than or equal to a minimum constraint. The first constraint is implemented into the proposed model to keep the practicality of the approach. The second and third constraints are considered to guarantee that the collected samples from each soil texture categories
Coherent Network Optimizing of Rail-Based Urban Mass Transit
Directory of Open Access Journals (Sweden)
Ying Zhang
2012-01-01
Full Text Available An efficient public transport is more than ever a crucial factor when it comes to the quality of life and competitiveness of many cities and regions in Asia. In recent years, the rail-based urban mass transit has been regarded as one of the key means to overcoming the great challenges in Chinese megacities. The purpose of this study is going to develop a coherent network optimizing for rail-based urban mass transit to find the best alternatives for the user and to demonstrate how to meet sustainable development needs and to match the enormous capacity requirements simultaneously. This paper presents an introduction to the current situation of the important lines, and transfer points in the metro system Shanghai. The insufficient aspects are analyzed and evaluated; while the optimizing ideas and measurements are developed and concreted. A group of examples are used to illustrate the approach. The whole study could be used for the latest reference for other megacities which have to be confronted with the similar situations and processes with enormous dynamic travel and transport demands.
An Online Convex Optimization Approach to Proactive Network Resource Allocation
Chen, Tianyi; Ling, Qing; Giannakis, Georgios B.
2017-12-01
Existing approaches to online convex optimization (OCO) make sequential one-slot-ahead decisions, which lead to (possibly adversarial) losses that drive subsequent decision iterates. Their performance is evaluated by the so-called regret that measures the difference of losses between the online solution and the best yet fixed overall solution in hindsight. The present paper deals with online convex optimization involving adversarial loss functions and adversarial constraints, where the constraints are revealed after making decisions, and can be tolerable to instantaneous violations but must be satisfied in the long term. Performance of an online algorithm in this setting is assessed by: i) the difference of its losses relative to the best dynamic solution with one-slot-ahead information of the loss function and the constraint (that is here termed dynamic regret); and, ii) the accumulated amount of constraint violations (that is here termed dynamic fit). In this context, a modified online saddle-point (MOSP) scheme is developed, and proved to simultaneously yield sub-linear dynamic regret and fit, provided that the accumulated variations of per-slot minimizers and constraints are sub-linearly growing with time. MOSP is also applied to the dynamic network resource allocation task, and it is compared with the well-known stochastic dual gradient method. Under various scenarios, numerical experiments demonstrate the performance gain of MOSP relative to the state-of-the-art.
Sub-problem Optimization With Regression and Neural Network Approximators
Guptill, James D.; Hopkins, Dale A.; Patnaik, Surya N.
2003-01-01
Design optimization of large systems can be attempted through a sub-problem strategy. In this strategy, the original problem is divided into a number of smaller problems that are clustered together to obtain a sequence of sub-problems. Solution to the large problem is attempted iteratively through repeated solutions to the modest sub-problems. This strategy is applicable to structures and to multidisciplinary systems. For structures, clustering the substructures generates the sequence of sub-problems. For a multidisciplinary system, individual disciplines, accounting for coupling, can be considered as sub-problems. A sub-problem, if required, can be further broken down to accommodate sub-disciplines. The sub-problem strategy is being implemented into the NASA design optimization test bed, referred to as "CometBoards." Neural network and regression approximators are employed for reanalysis and sensitivity analysis calculations at the sub-problem level. The strategy has been implemented in sequential as well as parallel computational environments. This strategy, which attempts to alleviate algorithmic and reanalysis deficiencies, has the potential to become a powerful design tool. However, several issues have to be addressed before its full potential can be harnessed. This paper illustrates the strategy and addresses some issues.
Finding the optimal Bayesian network given a constraint graph
Directory of Open Access Journals (Sweden)
Jacob M. Schreiber
2017-07-01
Full Text Available Despite recent algorithmic improvements, learning the optimal structure of a Bayesian network from data is typically infeasible past a few dozen variables. Fortunately, domain knowledge can frequently be exploited to achieve dramatic computational savings, and in many cases domain knowledge can even make structure learning tractable. Several methods have previously been described for representing this type of structural prior knowledge, including global orderings, super-structures, and constraint rules. While super-structures and constraint rules are flexible in terms of what prior knowledge they can encode, they achieve savings in memory and computational time simply by avoiding considering invalid graphs. We introduce the concept of a “constraint graph” as an intuitive method for incorporating rich prior knowledge into the structure learning task. We describe how this graph can be used to reduce the memory cost and computational time required to find the optimal graph subject to the encoded constraints, beyond merely eliminating invalid graphs. In particular, we show that a constraint graph can break the structure learning task into independent subproblems even in the presence of cyclic prior knowledge. These subproblems are well suited to being solved in parallel on a single machine or distributed across many machines without excessive communication cost.
Kun Zhang; Zhao Hu; Xiao-Ting Gan; Jian-Bo Fang
2016-01-01
Due to the fact that the fluctuation of network traffic is affected by various factors, accurate prediction of network traffic is regarded as a challenging task of the time series prediction process. For this purpose, a novel prediction method of network traffic based on QPSO algorithm and fuzzy wavelet neural network is proposed in this paper. Firstly, quantum-behaved particle swarm optimization (QPSO) was introduced. Then, the structure and operation algorithms of WFNN are presented. The pa...
Cheng, Long; Hou, Zeng-Guang; Lin, Yingzi; Tan, Min; Zhang, Wenjun Chris; Wu, Fang-Xiang
2011-05-01
A recurrent neural network is proposed for solving the non-smooth convex optimization problem with the convex inequality and linear equality constraints. Since the objective function and inequality constraints may not be smooth, the Clarke's generalized gradients of the objective function and inequality constraints are employed to describe the dynamics of the proposed neural network. It is proved that the equilibrium point set of the proposed neural network is equivalent to the optimal solution of the original optimization problem by using the Lagrangian saddle-point theorem. Under weak conditions, the proposed neural network is proved to be stable, and the state of the neural network is convergent to one of its equilibrium points. Compared with the existing neural network models for non-smooth optimization problems, the proposed neural network can deal with a larger class of constraints and is not based on the penalty method. Finally, the proposed neural network is used to solve the identification problem of genetic regulatory networks, which can be transformed into a non-smooth convex optimization problem. The simulation results show the satisfactory identification accuracy, which demonstrates the effectiveness and efficiency of the proposed approach.
Liu, Qingshan; Dang, Chuangyin; Huang, Tingwen
2013-02-01
This paper presents a decision-making model described by a recurrent neural network for dynamic portfolio optimization. The portfolio-optimization problem is first converted into a constrained fractional programming problem. Since the objective function in the programming problem is not convex, the traditional optimization techniques are no longer applicable for solving this problem. Fortunately, the objective function in the fractional programming is pseudoconvex on the feasible region. It leads to a one-layer recurrent neural network modeled by means of a discontinuous dynamic system. To ensure the optimal solutions for portfolio optimization, the convergence of the proposed neural network is analyzed and proved. In fact, the neural network guarantees to get the optimal solutions for portfolio-investment advice if some mild conditions are satisfied. A numerical example with simulation results substantiates the effectiveness and illustrates the characteristics of the proposed neural network.
Koehne, Svenja; Schmidt, Mirjam J; Dziobek, Isabel
2016-08-01
Although evidence points to a role for kinesthetic empathy (i.e. spontaneous interpersonal movement imitation and synchronisation) in social interaction, its relationship with emotional and cognitive aspects of empathy is unknown. We compared empathy in Tango and Capoeira experts, which crucially depend on ongoing, mutual interpersonal synchronisation, with empathy in practitioners of Salsa and Breakdance, respectively, which demand less interpersonal synchronisation but are comparable concerning movements and setting. Kinesthetic empathy was increased in the Tango and Capoeira groups. Although no group differences in other aspects of empathy were detected, kinesthetic empathy correlated with emotional and cognitive empathy. Taken together, trait kinesthetic empathy varies in the general population, and appears increased in synchronisation experts. © 2015 International Union of Psychological Science.
The optimization issues in an agile all-photonic backbone network
Zhang, Yiming; Yang, Oliver W.; Zhai, Yihua
2005-02-01
The Agile All-photonic Backbone Network (AAPN) architecture has been proposed by the telecommunication industry as a potential candidate for the ultra high speed Next Generation Optical Network (NGON) architecture. AAPN network structure is composed of adaptive optical core switches and edge routers in an overlaid star physical topology. In this paper, we examine various optimization issues for AAPN architectures. The optimization procedure is based on a Lagrangean relaxation and subgradient method. Based on the optimization methodology provided in the previous research, we propose a modified algorithm to optimize AAPN networks, with respect to the assumptions used in AAPN. The results for different network configurations are studied and the influence of network resources is also studied. Our algorithm is shown to be very computational effective on the AAPN networks, and the bounds generated are mostly within 1% of the final objective value.
Optimizing Low Speed VoIP Network for Rural Next Generation Network (R-NGN
Directory of Open Access Journals (Sweden)
Yoanes Bandung
2007-11-01
Full Text Available In this research, we propose an optimization method based-on E-Model for designing an efficient low speed VoIP network for Rural Next Generation Network (R-NGN. We are choosing 128 kbps and 256 kbps bandwidth as the typical community link to be used in the designing of R-NGN infrastructure. The method is based on selection of some VoIP network parameters such as voice coder, communication protocol, packet loss level, network utilization and resource allocation. We draw analytic approach for achieving rating value (R of E-model that represent level of quality of service. In this approach, we focus on delay and packet loss calculation to find the rating value. We state the rating value = 70 as minimum level of quality of service for each call, equivalent to 3.6 of Mean Opinion Score (MOS. In our experiments, either G.723.1 5.3 kbps or G.729 is chosen for maximizing the number of VoIP calls, it depends on link utilization and level of packet loss.
Dynamic traffic grooming for port number optimization in WDM optical mesh networks
Huang, Jun; Zeng, Qingji; Liu, Jimin; Xiao, Pengcheng; Liu, Hua; Xiao, Shilin
2004-04-01
In this paper, the objective was optimizing the port number with dynamic traffic grooming of SDH/SONET WDM mesh networks to give useful referenced data to networks design and the cost control of networks. The performances of different path select routing algorithms were evaluated in WDM grooming networks by considering traffic of different bandwidth requests. Finally, the results were presented and compared with in distributed-controlled WDM mesh networks.
DEFF Research Database (Denmark)
Siano, P.; Chen, Peiyuan; Chen, Zhe
2012-01-01
In order to achieve an effective reduction of green house gas emissions, the future electrical distribution networks will need to accommodate higher amount of renewable energy based on distributed generation such as Wind Turbines. This will require a re-evaluation and most likely a revision...... a hybrid optimization method that aims of maximizing the Net Present Value related to the Investment made by Wind Turbines developers in an active distribution network. The proposed network combines a Genetic Algorithm with a multi-period optimal power flow. The method, integrating active management...
Adaptive autonomous Communications Routing Optimizer for Network Efficiency Management Project
National Aeronautics and Space Administration — Maximizing network efficiency for NASA's Space Networking resources is a large, complex, distributed problem, requiring substantial collaboration. We propose the...
Xia, Li
2014-11-20
This paper studies the joint optimization problem of energy and delay in a multi-hop wireless network. The optimization variables are the transmission rates, which are adjustable according to the packet queueing length in the buffer. The optimization goal is to minimize the energy consumption of energy-critical nodes and the packet transmission delay throughout the network. In this paper, we aim at understanding the well-known decentralized algorithms which are threshold based from a different research angle. By using a simplified network model, we show that we can adopt the semi-open Jackson network model and study this optimization problem in closed form. This simplified network model further allows us to establish some significant optimality properties. We prove that the system performance is monotonic with respect to (w.r.t.) the transmission rate. We also prove that the threshold-type policy is optimal, i.e., when the number of packets in the buffer is larger than a threshold, transmit with the maximal rate (power); otherwise, no transmission. With these optimality properties, we develop a heuristic algorithm to iteratively find the optimal threshold. Finally, we conduct some simulation experiments to demonstrate the main idea of this paper.
Luo, Yaqi; Zeng, Bi
2017-08-01
This paper researches the drainage routing problem in drainage pipe network, and propose an intelligent scheduling method. The method relates to the design of improved particle swarm optimization algorithm, the establishment of the corresponding model from the pipe network, and the process by using the algorithm based on improved particle swarm optimization to find the optimum drainage route in the current environment.
Model-based dynamic control and optimization of gas networks
Energy Technology Data Exchange (ETDEWEB)
Hofsten, Kai
2001-07-01
This work contributes to the research on control, optimization and simulation of gas transmission systems to support the dispatch personnel at gas control centres for the decision makings in the daily operation of the natural gas transportation systems. Different control and optimization strategies have been studied. The focus is on the operation of long distance natural gas transportation systems. Stationary optimization in conjunction with linear model predictive control using state space models is proposed for supply security, the control of quality parameters and minimization of transportation costs for networks offering transportation services. The result from the stationary optimization together with a reformulation of a simplified fluid flow model formulates a linear dynamic optimization model. This model is used in a finite time control and state constrained linear model predictive controller. The deviation from the control and the state reference determined from the stationary optimization is penalized quadratically. Because of the time varying status of infrastructure, the control space is also generally time varying. When the average load is expected to change considerably, a new stationary optimization is performed, giving a new state and control reference together with a new dynamic model that is used for both optimization and state estimation. Another proposed control strategy is a control and output constrained nonlinear model predictive controller for the operation of gas transmission systems. Here, the objective is also the security of the supply, quality control and minimization of transportation costs. An output vector is defined, which together with a control vector are both penalized quadratically from their respective references in the objective function. The nonlinear model predictive controller can be combined with a stationary optimization. At each sampling instant, a non convex nonlinear programming problem is solved giving a local minimum
Optimal dynamics for quality control in spatially distributed mitochondrial networks.
Directory of Open Access Journals (Sweden)
Pinkesh K Patel
Full Text Available Recent imaging studies of mitochondrial dynamics have implicated a cycle of fusion, fission, and autophagy in the quality control of mitochondrial function by selectively increasing the membrane potential of some mitochondria at the expense of the turnover of others. This complex, dynamical system creates spatially distributed networks that are dependent on active transport along cytoskeletal networks and on protein import leading to biogenesis. To study the relative impacts of local interactions between neighboring mitochondria and their reorganization via transport, we have developed a spatiotemporal mathematical model encompassing all of these processes in which we focus on the dynamics of a health parameter meant to mimic the functional state of mitochondria. In agreement with previous models, we show that both autophagy and the generation of membrane potential asymmetry following a fusion/fission cycle are required for maintaining a healthy mitochondrial population. This health maintenance is affected by mitochondrial density and motility primarily through changes in the frequency of fusion events. Health is optimized when the selectivity thresholds for fusion and fission are matched, providing a mechanistic basis for the observed coupling of the two processes through the protein OPA1. We also demonstrate that the discreteness of the components exchanged during fusion is critical for quality control, and that the effects of limiting total amounts of autophagy and biogenesis have distinct consequences on health and population size, respectively. Taken together, our results show that several general principles emerge from the complexity of the quality control cycle that can be used to focus and interpret future experimental studies, and our modeling framework provides a road-map for deconstructing the functional importance of local interactions in communities of cells as well as organelles.
Directory of Open Access Journals (Sweden)
Li Ran
2017-01-01
Full Text Available Optimal allocation of generalized power sources in distribution network is researched. A simple index of voltage stability is put forward. Considering the investment and operation benefit, the stability of voltage and the pollution emissions of generalized power sources in distribution network, a multi-objective optimization planning model is established. A multi-objective particle swarm optimization algorithm is proposed to solve the optimal model. In order to improve the global search ability, the strategies of fast non-dominated sorting, elitism and crowding distance are adopted in this algorithm. Finally, tested the model and algorithm by IEEE-33 node system to find the best configuration of GP, the computed result shows that with the generalized power reasonable access to the active distribution network, the investment benefit and the voltage stability of the system is improved, and the proposed algorithm has better global search capability.
A method to synchronise video cameras using the audio band.
Leite de Barros, Ricardo Machado; Guedes Russomanno, Tiago; Brenzikofer, René; Jovino Figueroa, Pascual
2006-01-01
This paper proposes and evaluates a novel method for synchronisation of video cameras using the audio band. The method consists in generating and transmitting an audio signal through radio frequency for receivers connected to the microphone input of the cameras and inserting the signal in the audio band. In a software environment, the phase differences among the video signals are calculated and used to interpolate the synchronous 2D projections of the trajectories. The validation of the method was based on: (1) Analysis of the phase difference changes as a function of time of two video signals. (2) Comparison between the values measured with an oscilloscope and by the proposed method. (3) Estimation of the improvement in the accuracy in the measurements of the distance between two markers mounted on a rigid body during movement applying the method. The results showed that the phase difference changes in time slowly (0.150 ms/min) and linearly, even when the same model of cameras are used. The values measured by the proposed method and by oscilloscope showed equivalence (R2=0.998), the root mean square of the difference between the measurements was 0.10 ms and the maximum difference found was 0.31 ms. Applying the new method, the accuracy of the 3D reconstruction had a statistically significant improvement. The accuracy, simplicity and wide applicability of the proposed method constitute the main contributions of this work.
Network synthesis and parameter optimization for vehicle suspension with inerter
Directory of Open Access Journals (Sweden)
Long Chen
2016-12-01
Full Text Available In order to design a comfortable-oriented vehicle suspension structure, the network synthesis method was utilized to transfer the problem into solving a timing robust control problem and determine the structure of “inerter–spring–damper” suspension. Bilinear Matrix Inequality was utilized to obtain the timing transfer function. Then, the transfer function of suspension system can be physically implemented by passive elements such as spring, damper, and inerter. By analyzing the sensitivity and quantum genetic algorithm, the optimized parameters of inerter–spring–damper suspension were determined. A quarter-car model was established. The performance of the inerter–spring–damper suspension was verified under random input. The simulation results manifested that the dynamic performance of the proposed suspension was enhanced in contrast with traditional suspension. The root mean square of vehicle body acceleration decreases by 18.9%. The inerter–spring–damper suspension can inhibit the vertical vibration within the frequency of 1–3 Hz effectively and enhance the performance of ride comfort significantly.
Energy Technology Data Exchange (ETDEWEB)
Watrous, R.; Towell, G.; Glassman, M.S. [Siemens Corporate Research, Princeton, NJ (United States)
1995-12-31
Results are reported from the application of tools for synthesizing, optimizing and analyzing neural networks to an ECG Patient Monitoring task. A neural network was synthesized from a rule-based classifier and optimized over a set of normal and abnormal heartbeats. The classification error rate on a separate and larger test set was reduced by a factor of 2. When the network was analyzed and reduced in size by a factor of 40%, the same level of performance was maintained.
Optimal locations for a class of nonlinear, single-facility location problems on a network.
Shier, D R; Dearing, P M
1983-01-01
This paper investigates a class of single-facility location problems on an arbitrary network. Necessary and sufficient conditions are obtained for characterizing locally optimal locations with respect to a certain nonlinear objective function. This approach produces a number of new results for locating a facility on an arbitrary network, and in addition it unifies several known results for the special case of tree networks. It also suggests algorithmic procedures for obtaining such optimal locations.
Hu, Xiaolin; Wang, Jun
2006-11-01
In recent years, a recurrent neural network called projection neural network was proposed for solving monotone variational inequalities and related convex optimization problems. In this paper, we show that the projection neural network can also be used to solve pseudomonotone variational inequalities and related pseudoconvex optimization problems. Under various pseudomonotonicity conditions and other conditions, the projection neural network is proved to be stable in the sense of Lyapunov and globally convergent, globally asymptotically stable, and globally exponentially stable. Since monotonicity is a special case of pseudomononicity, the projection neural network can be applied to solve a broader class of constrained optimization problems related to variational inequalities. Moreover, a new concept, called componentwise pseudomononicity, different from pseudomononicity in general, is introduced. Under this new concept, two stability results of the projection neural network for solving variational inequalities are also obtained. Finally, numerical examples show the effectiveness and performance of the projection neural network.
Pareto evolution of gene networks: an algorithm to optimize multiple fitness objectives.
Warmflash, Aryeh; Francois, Paul; Siggia, Eric D
2012-10-01
The computational evolution of gene networks functions like a forward genetic screen to generate, without preconceptions, all networks that can be assembled from a defined list of parts to implement a given function. Frequently networks are subject to multiple design criteria that cannot all be optimized simultaneously. To explore how these tradeoffs interact with evolution, we implement Pareto optimization in the context of gene network evolution. In response to a temporal pulse of a signal, we evolve networks whose output turns on slowly after the pulse begins, and shuts down rapidly when the pulse terminates. The best performing networks under our conditions do not fall into categories such as feed forward and negative feedback that also encode the input-output relation we used for selection. Pareto evolution can more efficiently search the space of networks than optimization based on a single ad hoc combination of the design criteria.
SINR-based Network Selection for Optimization in Heterogeneous Wireless Networks (HWNs
Directory of Open Access Journals (Sweden)
Abubakar M. Miyim
2016-02-01
Full Text Available To guarantee the phenomenon of “Always Best Connection” in heterogeneous wireless networks, a vertical handover optimization is necessary to realize seamless mobility. Received signal strength (RSS from the user equipment (UE contains interference from surrounding base stations, which happens to be a function of the network load of the nearby cells. An expression is derived for the received SINR (signal to interference and noise ratio as a function of traffic load in interfering cells of data networks. A better estimate of the UE SINR is achieved by taking into account the contribution of inter-cell interference. The proposed scheme affords UE to receive high throughput with less data rate, and hence benefits users who are located far from the base station. The proposed scheme demonstrates an improved throughput between the serving base station and the cell boundary.
Directory of Open Access Journals (Sweden)
Young-Bo Sim
2017-11-01
Full Text Available In this paper, we proposed and developed Function-Oriented Networking (FON, a platform for network users. It has a different philosophy as opposed to technologies for network managers of Software-Defined Networking technology, OpenFlow. It is a technology that can immediately reflect the demands of the network users in the network, unlike the existing OpenFlow and Network Functions Virtualization (NFV, which do not reflect directly the needs of the network users. It allows the network user to determine the policy of the direct network, so it can be applied more precisely than the policy applied by the network manager. This is expected to increase the satisfaction of the service users when the network users try to provide new services. We developed FON function that performs on-demand routing for Low-Delay Required service. We analyzed the characteristics of the Ant Colony Optimization (ACO algorithm and found that the algorithm is suitable for low-delay required services. It was also the first in the world to implement the routing software using ACO Algorithm in the real Ethernet network. In order to improve the routing performance, several algorithms of the ACO Algorithm have been developed to enable faster path search-routing and path recovery. The relationship between the network performance index and the ACO routing parameters is derived, and the results are compared and analyzed. Through this, it was possible to develop the ACO algorithm.
Enhanced collective influence: A paradigm to optimize network disruption
Wu, Tao; Chen, Leiting; Zhong, Linfeng; Xian, Xingping
2017-04-01
The function of complex networks typically relies on the integrity of underlying structure. Sometimes, practical applications need to attack networks' function, namely inactivate and fragment networks' underlying structure. To effectively dismantle complex networks and regulate the function of them, a centrality measure, named CI (Morone and Makse, 2015), was proposed for node ranking. We observe that the performance of CI centrality in network disruption problem may deteriorate when it is used in networks with different topology properties. Specifically, the structural features of local network topology are overlooked in CI centrality, even though the local network topology of the nodes with a fixed CI value may have very different organization. To improve the ranking accuracy of CI, this paper proposes a variant ECI to CI by considering loop density and degree diversity of local network topology. And the proposed ECI centrality would degenerate into CI centrality with the reduction of the loop density and the degree diversity level. By comparing ECI with CI and classical centrality measures in both synthetic and real networks, the experimental results suggest that ECI can largely improve the performance of CI for network disruption. Based on the results, we analyze the correlation between the improvement and the properties of the networks. We find that the performance of ECI is positively correlated with assortative coefficient and community modularity and negatively correlated with degree inequality of networks, which can be used as guidance for practical applications.
Compensatory Analysis and Optimization for MADM for Heterogeneous Wireless Network Selection
Directory of Open Access Journals (Sweden)
Jian Zhou
2016-01-01
Full Text Available In the next-generation heterogeneous wireless networks, a mobile terminal with a multi-interface may have network access from different service providers using various technologies. In spite of this heterogeneity, seamless intersystem mobility is a mandatory requirement. One of the major challenges for seamless mobility is the creation of a network selection scheme, which is for users that select an optimal network with best comprehensive performance between different types of networks. However, the optimal network may be not the most reasonable one due to compensation of MADM (Multiple Attribute Decision Making, and the network is called pseudo-optimal network. This paper conducts a performance evaluation of a number of widely used MADM-based methods for network selection that aim to keep the mobile users always best connected anywhere and anytime, where subjective weight and objective weight are all considered. The performance analysis shows that the selection scheme based on MEW (weighted multiplicative method and combination weight can better avoid accessing pseudo-optimal network for balancing network load and reducing ping-pong effect in comparison with three other MADM solutions.
Optimizing neural network models: motivation and case studies
Harp, S A; T. Samad
2012-01-01
Practical successes have been achieved with neural network models in a variety of domains, including energy-related industry. The large, complex design space presented by neural networks is only minimally explored in current practice. The satisfactory results that nevertheless have been obtained testify that neural networks are a robust modeling technology; at the same time, however, the lack of a systematic design approach implies that the best neural network models generally rem...
Li, Chaojie; Yu, Xinghuo; Huang, Tingwen; Chen, Guo; He, Xing
2016-02-01
This paper proposes a generalized Hopfield network for solving general constrained convex optimization problems. First, the existence and the uniqueness of solutions to the generalized Hopfield network in the Filippov sense are proved. Then, the Lie derivative is introduced to analyze the stability of the network using a differential inclusion. The optimality of the solution to the nonsmooth constrained optimization problems is shown to be guaranteed by the enhanced Fritz John conditions. The convergence rate of the generalized Hopfield network can be estimated by the second-order derivative of the energy function. The effectiveness of the proposed network is evaluated on several typical nonsmooth optimization problems and used to solve the hierarchical and distributed model predictive control four-tank benchmark.
On the difference of cardiorespiratory synchronisation and coordination
Krause, Harald; Kraemer, Jan F.; Penzel, Thomas; Kurths, Jürgen; Wessel, Niels
2017-09-01
Cardiorespiratory phase synchronisation (CRS) is a type of cardiorespiratory coupling that manifests through a prediliction for heart beats to occur at specific points relative to the phase of the respiratory cycle. It has been under investigation for nearly 20 years, and while it seems to be mostly occurring in relaxed states such as deep sleep and anesthesia, no clear clinical implications have been established. Cardiorespiratory coordination (CRC) is a recent development in this field where the relationship between the respiratory onset and heart beat is analysed in the time domain and the possible relationship of each heart beat is considered for both the previous and the next respiratory onset. This ostensibly closely related effect must not only show relevant information content but also do so independent of CRS in order to be relevant for future studies. In this paper, we investigate CRC and its relation to CRS mainly using graphical and statistical methods on two exemplary datasets: measurements from a pregnant woman participating in a preeclampsia study and those from a man suffering from sleep apnea. We show fundamental differences between the results of both approaches and are able to show a formerly unknown dependency between the heart activity and respiratory rate, potentially indicating heartbeat-initiated inspiration. Despite their differences, methods developed for the quantification of CRS can be adapted to CRC. Completing the comparison is an investigation into the relationship between CRC and respiratory sinus arrhythmia. Similar to previous results for CRS, the two effects are found to be orthogonal, meaning that they can be observed independently or in conjunction.
Optimality principles in the regulation of metabolic networks
Berkhout, J.; Bruggeman, F.J.; Teusink, B.
2012-01-01
One of the challenging tasks in systems biology is to understand how molecular networks give rise to emergent functionality and whether universal design principles apply to molecular networks. To achieve this, the biophysical, evolutionary and physiological constraints that act on those networks
Optimal cost for strengthening or destroying a given network
Patron, Amikam; Cohen, Reuven; Li, Daqing; Havlin, Shlomo
2017-05-01
Strengthening or destroying a network is a very important issue in designing resilient networks or in planning attacks against networks, including planning strategies to immunize a network against diseases, viruses, etc. Here we develop a method for strengthening or destroying a random network with a minimum cost. We assume a correlation between the cost required to strengthen or destroy a node and the degree of the node. Accordingly, we define a cost function c (k ) , which is the cost of strengthening or destroying a node with degree k . Using the degrees k in a network and the cost function c (k ) , we develop a method for defining a list of priorities of degrees and for choosing the right group of degrees to be strengthened or destroyed that minimizes the total price of strengthening or destroying the entire network. We find that the list of priorities of degrees is universal and independent of the network's degree distribution, for all kinds of random networks. The list of priorities is the same for both strengthening a network and for destroying a network with minimum cost. However, in spite of this similarity, there is a difference between their pc, the critical fraction of nodes that has to be functional to guarantee the existence of a giant component in the network.
Epidemics in Networks : Modeling, Optimization and Security Games
Omic, J.
2010-01-01
Epidemic theory has wide range of applications in computer networks, from spreading of malware to the information dissemination algorithms. Our society depends more strongly than ever on such computer networks. Many of these networks rely to a large extent on decentralization and self-organization.
Xie, Rui; Wan, Xianrong; Hong, Sheng; Yi, Jianxin
2017-06-14
The performance of a passive radar network can be greatly improved by an optimal radar network structure. Generally, radar network structure optimization consists of two aspects, namely the placement of receivers in suitable places and selection of appropriate illuminators. The present study investigates issues concerning the joint optimization of receiver placement and illuminator selection for a passive radar network. Firstly, the required radar cross section (RCS) for target detection is chosen as the performance metric, and the joint optimization model boils down to the partition p-center problem (PPCP). The PPCP is then solved by a proposed bisection algorithm. The key of the bisection algorithm lies in solving the partition set covering problem (PSCP), which can be solved by a hybrid algorithm developed by coupling the convex optimization with the greedy dropping algorithm. In the end, the performance of the proposed algorithm is validated via numerical simulations.
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
Directory of Open Access Journals (Sweden)
Kun Zhang
2016-01-01
Full Text Available Due to the fact that the fluctuation of network traffic is affected by various factors, accurate prediction of network traffic is regarded as a challenging task of the time series prediction process. For this purpose, a novel prediction method of network traffic based on QPSO algorithm and fuzzy wavelet neural network is proposed in this paper. Firstly, quantum-behaved particle swarm optimization (QPSO was introduced. Then, the structure and operation algorithms of WFNN are presented. The parameters of fuzzy wavelet neural network were optimized by QPSO algorithm. Finally, the QPSO-FWNN could be used in prediction of network traffic simulation successfully and evaluate the performance of different prediction models such as BP neural network, RBF neural network, fuzzy neural network, and FWNN-GA neural network. Simulation results show that QPSO-FWNN has a better precision and stability in calculation. At the same time, the QPSO-FWNN also has better generalization ability, and it has a broad prospect on application.
Ripple-Spreading Network Model Optimization by Genetic Algorithm
Directory of Open Access Journals (Sweden)
Xiao-Bing Hu
2013-01-01
Full Text Available Small-world and scale-free properties are widely acknowledged in many real-world complex network systems, and many network models have been developed to capture these network properties. The ripple-spreading network model (RSNM is a newly reported complex network model, which is inspired by the natural ripple-spreading phenomenon on clam water surface. The RSNM exhibits good potential for describing both spatial and temporal features in the development of many real-world networks where the influence of a few local events spreads out through nodes and then largely determines the final network topology. However, the relationships between ripple-spreading related parameters (RSRPs of RSNM and small-world and scale-free topologies are not as obvious or straightforward as in many other network models. This paper attempts to apply genetic algorithm (GA to tune the values of RSRPs, so that the RSNM may generate these two most important network topologies. The study demonstrates that, once RSRPs are properly tuned by GA, the RSNM is capable of generating both network topologies and therefore has a great flexibility to study many real-world complex network systems.
Directory of Open Access Journals (Sweden)
Chao-Chih Lin
2017-10-01
Full Text Available A new transient-based hybrid heuristic approach is developed to optimize a transient generation process and to detect leaks in pipe networks. The approach couples the ordinal optimization approach (OOA and the symbiotic organism search (SOS to solve the optimization problem by means of iterations. A pipe network analysis model (PNSOS is first used to determine steady-state head distribution and pipe flow rates. The best transient generation point and its relevant valve operation parameters are optimized by maximizing the objective function of transient energy. The transient event is created at the chosen point, and the method of characteristics (MOC is used to analyze the transient flow. The OOA is applied to sift through the candidate pipes and the initial organisms with leak information. The SOS is employed to determine the leaks by minimizing the sum of differences between simulated and computed head at the observation points. Two synthetic leaking scenarios, a simple pipe network and a water distribution network (WDN, are chosen to test the performance of leak detection ordinal symbiotic organism search (LDOSOS. Leak information can be accurately identified by the proposed approach for both of the scenarios. The presented technique makes a remarkable contribution to the success of leak detection in the pipe networks.
Optimal reconfiguration of distribution networks for a diversity of regulatory frameworks
Energy Technology Data Exchange (ETDEWEB)
Carillo Caicedo, G.; Perez-Arriaga, I.J. [Univ. Pontificia Comillas, Madrid (Spain). Inst. de Investigacion Tecnologica
1995-11-01
Any given regulatory scheme for the electric power industry requires a consistent set of rules determining operation and pricing of the different electricity services, in order to guarantee optimal efficiency conditions. This paper determines the optimal operation rules for a distribution network under different regulatory environments, and specifically in a competitive setting. Using realistic examples and a novel reconfiguration algorithm, it is shown that imperfect regulations may result in widely different `optimal` network configurations, therefore emphasizing the need to avoid the introduction of perverse economic incentives in the remuneration of network distribution services. 6 refs, 5 figs
Optimality principles in the regulation of metabolic networks.
Berkhout, Jan; Bruggeman, Frank J; Teusink, Bas
2012-08-29
One of the challenging tasks in systems biology is to understand how molecular networks give rise to emergent functionality and whether universal design principles apply to molecular networks. To achieve this, the biophysical, evolutionary and physiological constraints that act on those networks need to be identified in addition to the characterisation of the molecular components and interactions. Then, the cellular "task" of the network-its function-should be identified. A network contributes to organismal fitness through its function. The premise is that the same functions are often implemented in different organisms by the same type of network; hence, the concept of design principles. In biology, due to the strong forces of selective pressure and natural selection, network functions can often be understood as the outcome of fitness optimisation. The hypothesis of fitness optimisation to understand the design of a network has proven to be a powerful strategy. Here, we outline the use of several optimisation principles applied to biological networks, with an emphasis on metabolic regulatory networks. We discuss the different objective functions and constraints that are considered and the kind of understanding that they provide.
Qin, Sitian; Yang, Xiudong; Xue, Xiaoping; Song, Jiahui
2017-10-01
Pseudoconvex optimization problem, as an important nonconvex optimization problem, plays an important role in scientific and engineering applications. In this paper, a recurrent one-layer neural network is proposed for solving the pseudoconvex optimization problem with equality and inequality constraints. It is proved that from any initial state, the state of the proposed neural network reaches the feasible region in finite time and stays there thereafter. It is also proved that the state of the proposed neural network is convergent to an optimal solution of the related problem. Compared with the related existing recurrent neural networks for the pseudoconvex optimization problems, the proposed neural network in this paper does not need the penalty parameters and has a better convergence. Meanwhile, the proposed neural network is used to solve three nonsmooth optimization problems, and we make some detailed comparisons with the known related conclusions. In the end, some numerical examples are provided to illustrate the effectiveness of the performance of the proposed neural network.
Optimization of the Critical Diameter and Average Path Length of Social Networks
Directory of Open Access Journals (Sweden)
Haifeng Du
2017-01-01
Full Text Available Optimizing average path length (APL by adding shortcut edges has been widely discussed in connection with social networks, but the relationship between network diameter and APL is generally ignored in the dynamic optimization of APL. In this paper, we analyze this relationship and transform the problem of optimizing APL into the problem of decreasing diameter to 2. We propose a mathematic model based on a memetic algorithm. Experimental results show that our algorithm can efficiently solve this problem as well as optimize APL.
Optimizing available network resources to address questions in environmental biogeochemistry
Hinckley, Eve-Lyn; Suzanne Andersen,; Baron, Jill S.; Peter Blanken,; Gordon Bonan,; William Bowman,; Sarah Elmendorf,; Fierer, Noah; Andrew Fox,; Keli Goodman,; Katherine Jones,; Danica Lombardozzi,; Claire Lunch,; Jason Neff,; Michael SanClements,; Katherine Suding,; Will Wieder,
2016-01-01
An increasing number of network observatories have been established globally to collect long-term biogeochemical data at multiple spatial and temporal scales. Although many outstanding questions in biogeochemistry would benefit from network science, the ability of the earth- and environmental-sciences community to conduct synthesis studies within and across networks is limited and seldom done satisfactorily. We identify the ideal characteristics of networks, common problems with using data, and key improvements to strengthen intra- and internetwork compatibility. We suggest that targeted improvements to existing networks should include promoting standardization in data collection, developing incentives to promote rapid data release to the public, and increasing the ability of investigators to conduct their own studies across sites. Internetwork efforts should include identifying a standard measurement suite—we propose profiles of plant canopy and soil properties—and an online, searchable data portal that connects network, investigator-led, and citizen-science projects.
Chang, Yuchao; Tang, Hongying; Cheng, Yongbo; Zhao, Qin; Yuan, Baoqing Li andXiaobing
2017-07-19
Routing protocols based on topology control are significantly important for improving network longevity in wireless sensor networks (WSNs). Traditionally, some WSN routing protocols distribute uneven network traffic load to sensor nodes, which is not optimal for improving network longevity. Differently to conventional WSN routing protocols, we propose a dynamic hierarchical protocol based on combinatorial optimization (DHCO) to balance energy consumption of sensor nodes and to improve WSN longevity. For each sensor node, the DHCO algorithm obtains the optimal route by establishing a feasible routing set instead of selecting the cluster head or the next hop node. The process of obtaining the optimal route can be formulated as a combinatorial optimization problem. Specifically, the DHCO algorithm is carried out by the following procedures. It employs a hierarchy-based connection mechanism to construct a hierarchical network structure in which each sensor node is assigned to a special hierarchical subset; it utilizes the combinatorial optimization theory to establish the feasible routing set for each sensor node, and takes advantage of the maximum-minimum criterion to obtain their optimal routes to the base station. Various results of simulation experiments show effectiveness and superiority of the DHCO algorithm in comparison with state-of-the-art WSN routing algorithms, including low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), genetic protocol-based self-organizing network clustering (GASONeC), and double cost function-based routing (DCFR) algorithms.
Directory of Open Access Journals (Sweden)
Yuchao Chang
2017-07-01
Full Text Available Routing protocols based on topology control are significantly important for improving network longevity in wireless sensor networks (WSNs. Traditionally, some WSN routing protocols distribute uneven network traffic load to sensor nodes, which is not optimal for improving network longevity. Differently to conventional WSN routing protocols, we propose a dynamic hierarchical protocol based on combinatorial optimization (DHCO to balance energy consumption of sensor nodes and to improve WSN longevity. For each sensor node, the DHCO algorithm obtains the optimal route by establishing a feasible routing set instead of selecting the cluster head or the next hop node. The process of obtaining the optimal route can be formulated as a combinatorial optimization problem. Specifically, the DHCO algorithm is carried out by the following procedures. It employs a hierarchy-based connection mechanism to construct a hierarchical network structure in which each sensor node is assigned to a special hierarchical subset; it utilizes the combinatorial optimization theory to establish the feasible routing set for each sensor node, and takes advantage of the maximum–minimum criterion to obtain their optimal routes to the base station. Various results of simulation experiments show effectiveness and superiority of the DHCO algorithm in comparison with state-of-the-art WSN routing algorithms, including low-energy adaptive clustering hierarchy (LEACH, hybrid energy-efficient distributed clustering (HEED, genetic protocol-based self-organizing network clustering (GASONeC, and double cost function-based routing (DCFR algorithms.
2012-01-01
Background Synchronized bursting activity (SBA) is a remarkable dynamical behavior in both ex vivo and in vivo neural networks. Investigations of the underlying structural characteristics associated with SBA are crucial to understanding the system-level regulatory mechanism of neural network behaviors. Results In this study, artificial pulsed neural networks were established using spike response models to capture fundamental dynamics of large scale ex vivo cortical networks. Network simulations with synaptic parameter perturbations showed the following two findings. (i) In a network with an excitatory ratio (ER) of 80-90%, its connective ratio (CR) was within a range of 10-30% when the occurrence of SBA reached the highest expectation. This result was consistent with the experimental observation in ex vivo neuronal networks, which were reported to possess a matured inhibitory synaptic ratio of 10-20% and a CR of 10-30%. (ii) No SBA occurred when a network does not contain any all-positive-interaction feedback loop (APFL) motif. In a neural network containing APFLs, the number of APFLs presented an optimal range corresponding to the maximal occurrence of SBA, which was very similar to the optimal CR. Conclusions In a neural network, the evolutionarily selected CR (10-30%) optimizes the occurrence of SBA, and APFL serves a pivotal network motif required to maximize the occurrence of SBA. PMID:22462685
A Distributed Algorithm for Energy Optimization in Hydraulic Networks
DEFF Research Database (Denmark)
Kallesøe, Carsten; Wisniewski, Rafal; Jensen, Tom Nørgaard
2014-01-01
An industrial case study in the form of a large-scale hydraulic network underlying a district heating system is considered. A distributed control is developed that minimizes the aggregated electrical energy consumption of the pumps in the network without violating the control demands. The algorithm...... a Plug & Play control system as most commissioning can be done during the manufacture of the pumps. Only information on the graph-structure of the hydraulic network is needed during installation....
Efficient Optimization Methods for Communication Network Planning and Assessment
Kiese, Moritz
2010-01-01
In this work, we develop efficient mathematical planning methods to design communication networks. First, we examine future technologies for optical backbone networks. As new, more intelligent nodes cause higher dynamics in the transport networks, fast planning methods are required. To this end, we develop a heuristic planning algorithm. The evaluation of the cost-efficiency of new, adapative transmission techniques comprises the second topic of this section. In the second part of this work, ...
Optimality Principles in the Regulation of Metabolic Networks
Directory of Open Access Journals (Sweden)
Jan Berkhout
2012-08-01
Full Text Available One of the challenging tasks in systems biology is to understand how molecular networks give rise to emergent functionality and whether universal design principles apply to molecular networks. To achieve this, the biophysical, evolutionary and physiological constraints that act on those networks need to be identified in addition to the characterisation of the molecular components and interactions. Then, the cellular “task” of the network—its function—should be identified. A network contributes to organismal fitness through its function. The premise is that the same functions are often implemented in different organisms by the same type of network; hence, the concept of design principles. In biology, due to the strong forces of selective pressure and natural selection, network functions can often be understood as the outcome of fitness optimisation. The hypothesis of fitness optimisation to understand the design of a network has proven to be a powerful strategy. Here, we outline the use of several optimisation principles applied to biological networks, with an emphasis on metabolic regulatory networks. We discuss the different objective functions and constraints that are considered and the kind of understanding that they provide.
Towards Optimal Buffer Size in Wi-Fi Networks
Showail, Ahmad J.
2016-01-19
Buffer sizing is an important network configuration parameter that impacts the quality of data traffic. Falling memory cost and the fallacy that ‘more is better’ lead to over provisioning network devices with large buffers. Over-buffering or the so called ‘bufferbloat’ phenomenon creates excessive end-to-end delay in today’s networks. On the other hand, under-buffering results in frequent packet loss and subsequent under-utilization of network resources. The buffer sizing problem has been studied extensively for wired networks. However, there is little work addressing the unique challenges of wireless environment. In this dissertation, we discuss buffer sizing challenges in wireless networks, classify the state-of-the-art solutions, and propose two novel buffer sizing schemes. The first scheme targets buffer sizing in wireless multi-hop networks where the radio spectral resource is shared among a set of con- tending nodes. Hence, it sizes the buffer collectively and distributes it over a set of interfering devices. The second buffer sizing scheme is designed to cope up with recent Wi-Fi enhancements. It adapts the buffer size based on measured link characteristics and network load. Also, it enforces limits on the buffer size to maximize frame aggregation benefits. Both mechanisms are evaluated using simulation as well as testbed implementation over half-duplex and full-duplex wireless networks. Experimental evaluation shows that our proposal reduces latency by an order of magnitude.
Zhang, Guangzhi; Cai, Shaobin; Xiong, Naixue
2018-02-03
One of the remarkable challenges about Wireless Sensor Networks (WSN) is how to transfer the collected data efficiently due to energy limitation of sensor nodes. Network coding will increase network throughput of WSN dramatically due to the broadcast nature of WSN. However, the network coding usually propagates a single original error over the whole network. Due to the special property of error propagation in network coding, most of error correction methods cannot correct more than C/2 corrupted errors where C is the max flow min cut of the network. To maximize the effectiveness of network coding applied in WSN, a new error-correcting mechanism to confront the propagated error is urgently needed. Based on the social network characteristic inherent in WSN and L1 optimization, we propose a novel scheme which successfully corrects more than C/2 corrupted errors. What is more, even if the error occurs on all the links of the network, our scheme also can correct errors successfully. With introducing a secret channel and a specially designed matrix which can trap some errors, we improve John and Yi's model so that it can correct the propagated errors in network coding which usually pollute exactly 100% of the received messages. Taking advantage of the social characteristic inherent in WSN, we propose a new distributed approach that establishes reputation-based trust among sensor nodes in order to identify the informative upstream sensor nodes. With referred theory of social networks, the informative relay nodes are selected and marked with high trust value. The two methods of L1 optimization and utilizing social characteristic coordinate with each other, and can correct the propagated error whose fraction is even exactly 100% in WSN where network coding is performed. The effectiveness of the error correction scheme is validated through simulation experiments.
Robinson, Y Harold; Rajaram, M
2015-01-01
Mobile ad hoc network (MANET) is a collection of autonomous mobile nodes forming an ad hoc network without fixed infrastructure. Dynamic topology property of MANET may degrade the performance of the network. However, multipath selection is a great challenging task to improve the network lifetime. We proposed an energy-aware multipath routing scheme based on particle swarm optimization (EMPSO) that uses continuous time recurrent neural network (CTRNN) to solve optimization problems. CTRNN finds the optimal loop-free paths to solve link disjoint paths in a MANET. The CTRNN is used as an optimum path selection technique that produces a set of optimal paths between source and destination. In CTRNN, particle swarm optimization (PSO) method is primly used for training the RNN. The proposed scheme uses the reliability measures such as transmission cost, energy factor, and the optimal traffic ratio between source and destination to increase routing performance. In this scheme, optimal loop-free paths can be found using PSO to seek better link quality nodes in route discovery phase. PSO optimizes a problem by iteratively trying to get a better solution with regard to a measure of quality. The proposed scheme discovers multiple loop-free paths by using PSO technique.
Effects of Parkinson's disease on brain-wave phase synchronisation and cross-modulation
Stumpf, K.; Schumann, A. Y.; Plotnik, M.; Gans, F.; Penzel, T.; Fietze, I.; Hausdorff, J. M.; Kantelhardt, J. W.
2010-02-01
We study the effects of Parkinson's disease (PD) on phase synchronisation and cross-modulation of instantaneous amplitudes and frequencies for brain waves during sleep. Analysing data from 40 full-night EEGs (electro-encephalograms) of ten patients with PD and ten age-matched healthy controls we find that phase synchronisation between the left and right hemisphere of the brain is characteristically reduced in patients with PD. Since there is no such difference in phase synchronisation for EEGs from the same hemisphere, our results suggest the possibility of a relation with problems in coordinated motion of left and right limbs in some patients with PD. Using the novel technique of amplitude and frequency cross-modulation analysis, relating oscillations in different EEG bands and distinguishing both positive and negative modulation, we observe an even more significant decrease in patients for several band combinations.
Feedforward Synchronised PWM for Adjustable Speed AC Drives with Different Control Modes
DEFF Research Database (Denmark)
Oleschuk, Valentin; Blaabjerg, Frede; Jensen, Flemming Buus
2002-01-01
This paper presents a novel method of direct synchronised pulswidth modulation (PWM) for three-phase voltage source inverters, feeding induction motors with different control regimes. It is based on the universal algorithm with either simplified algebraic or accurate trigonometric control functions......, with vector approached for determination of the pulse patterns, with vector approach for determination of the pulse patterns. Different control modes of the drive system with synchronised PWM, including standard scalar V/F control, and also V2/F = constant and V/F2 = constant control regimes, have been...... analysed in the paper. Simulations give the behaviour of the PWM techniques proposed. Both continuous and discontinuous schemes of synchronised PWM, applied for the corresponding control regime, have been analysed and compared....
2015-06-01
This Technical Report on Prototype Intelligent Network Flow Optimization (INFLO) Dynamic Speed Harmonization and : Queue Warning is the final report for the project. It describes the prototyping, acceptance testing and small-scale : demonstration of ...
Improved Data Transmission Scheme of Network Coding Based on Access Point Optimization in VANET
Directory of Open Access Journals (Sweden)
Zhe Yang
2014-01-01
Full Text Available VANET is a hot spot of intelligent transportation researches. For vehicle users, the file sharing and content distribution through roadside access points (AP as well as the vehicular ad hoc networks (VANET have been an important complement to that cellular network. So the AP deployment is one of the key issues to improve the communication performance of VANET. In this paper, an access point optimization method is proposed based on particle swarm optimization algorithm. The transmission performances of the routing protocol with random linear network coding before and after the access point optimization are analyzed. The simulation results show the optimization model greatly affects the VANET transmission performances based on network coding, and it can enhance the delivery rate by 25% and 14% and reduce the average delay of transmission by 38% and 33%.
DRO: domain-based route optimization scheme for nested mobile networks
Directory of Open Access Journals (Sweden)
Chuang Ming-Chin
2011-01-01
Full Text Available Abstract The network mobility (NEMO basic support protocol is designed to support NEMO management, and to ensure communication continuity between nodes in mobile networks. However, in nested mobile networks, NEMO suffers from the pinball routing problem, which results in long packet transmission delays. To solve the problem, we propose a domain-based route optimization (DRO scheme that incorporates a domain-based network architecture and ad hoc routing protocols for route optimization. DRO also improves the intra-domain handoff performance, reduces the convergence time during route optimization, and avoids the out-of-sequence packet problem. A detailed performance analysis and simulations were conducted to evaluate the scheme. The results demonstrate that DRO outperforms existing mechanisms in terms of packet transmission delay (i.e., better route-optimization, intra-domain handoff latency, convergence time, and packet tunneling overhead.
Hsiao, Amy; Brouns, Francis; Sloep, Peter
2010-01-01
Hsiao, Y. P., Brouns, F., & Sloep, P. B. (2010, 15 April). Mechanisms of peer tutoring on optimizing cognitive load during knowledge sharing in learning networks. Presentation at NELLL Colloqium, Heerlen, The Netherlands: Open University of the Netherlands.
National Research Council Canada - National Science Library
M R Shukri; M M Rahman; D Ramasamy; K Kadirgama
2015-01-01
This paper presents a study of engine performance using a mixture of palm oil methyl ester blends with diesel oil as biodiesel in a diesel engine, and optimizes the engine performance using artificial neural network (ANN) modeling...
Designing optimal peer support to alleviate learner cognitive load in Learning Networks
Hsiao, Amy; Brouns, Francis; Sloep, Peter
2012-01-01
Hsiao, Y. P., Brouns, F., & Sloep, P. B. (2012, 21 July). Designing optimal peer support to alleviate learner cognitive load in Learning Networks. Presentation at IADIS International Conference Web-Based Communities and Social Media 2012, Lisbon, Portugal.
Zhou, Bao-Rong; Liu, Si-Liang; Zhang, Yong-Jun; Yi, Ying-Qi; Lin, Xiao-Ming
2017-05-01
To mitigate the impact on the distribution networks caused by the stochastic characteristic and high penetration of photovoltaic, a multi-objective optimal power flow model is proposed in this paper. The regulation capability of capacitor, inverter of photovoltaic and energy storage system embedded in active distribution network are considered to minimize the expected value of active power the T loss and probability of voltage violation in this model. Firstly, a probabilistic power flow based on cumulant method is introduced to calculate the value of the objectives. Secondly, NSGA-II algorithm is adopted for optimization to obtain the Pareto optimal solutions. Finally, the best compromise solution can be achieved through fuzzy membership degree method. By the multi-objective optimization calculation of IEEE34-node distribution network, the results show that the model can effectively improve the voltage security and economy of the distribution network on different levels of photovoltaic penetration.
Hierarchical winner-take-all particle swarm optimization social network for neural model fitting.
Coventry, Brandon S; Parthasarathy, Aravindakshan; Sommer, Alexandra L; Bartlett, Edward L
2017-02-01
Particle swarm optimization (PSO) has gained widespread use as a general mathematical programming paradigm and seen use in a wide variety of optimization and machine learning problems. In this work, we introduce a new variant on the PSO social network and apply this method to the inverse problem of input parameter selection from recorded auditory neuron tuning curves. The topology of a PSO social network is a major contributor to optimization success. Here we propose a new social network which draws influence from winner-take-all coding found in visual cortical neurons. We show that the winner-take-all network performs exceptionally well on optimization problems with greater than 5 dimensions and runs at a lower iteration count as compared to other PSO topologies. Finally we show that this variant of PSO is able to recreate auditory frequency tuning curves and modulation transfer functions, making it a potentially useful tool for computational neuroscience models.
Self-configuration and self-optimization process in heterogeneous wireless networks.
Guardalben, Lucas; Villalba, Luis Javier García; Buiati, Fábio; Sobral, João Bosco Mangueira; Camponogara, Eduardo
2011-01-01
Self-organization in Wireless Mesh Networks (WMN) is an emergent research area, which is becoming important due to the increasing number of nodes in a network. Consequently, the manual configuration of nodes is either impossible or highly costly. So it is desirable for the nodes to be able to configure themselves. In this paper, we propose an alternative architecture for self-organization of WMN based on Optimized Link State Routing Protocol (OLSR) and the ad hoc on demand distance vector (AODV) routing protocols as well as using the technology of software agents. We argue that the proposed self-optimization and self-configuration modules increase the throughput of network, reduces delay transmission and network load, decreases the traffic of HELLO messages according to network's scalability. By simulation analysis, we conclude that the self-optimization and self-configuration mechanisms can significantly improve the performance of OLSR and AODV protocols in comparison to the baseline protocols analyzed.
DEFF Research Database (Denmark)
Ding, Tao; Li, Cheng; Huang, Can
2017-01-01
function of the slave model for the master model, which reflects the impacts of each slave model. Second, the transmission and distribution networks are decoupled at feeder buses, and all the distribution networks are coordinated by the master reactive power optimization model to achieve the global......In order to solve the reactive power optimization with joint transmission and distribution networks, a hierarchical modeling method is proposed in this paper. It allows the reactive power optimization of transmission and distribution networks to be performed separately, leading to a master......–slave structure and improves traditional centralized modeling methods by alleviating the big data problem in a control center. Specifically, the transmission-distribution-network coordination issue of the hierarchical modeling method is investigated. First, a curve-fitting approach is developed to provide a cost...
Nikelshpur, Dmitry O.
2014-01-01
Similar to mammalian brains, Artificial Neural Networks (ANN) are universal approximators, capable of yielding near-optimal solutions to a wide assortment of problems. ANNs are used in many fields including medicine, internet security, engineering, retail, robotics, warfare, intelligence control, and finance. "ANNs have a tendency to get…
Huang, Daizheng; Wu, Zhihui
2017-01-01
Accurately predicting the trend of outpatient visits by mathematical modeling can help policy makers manage hospitals effectively, reasonably organize schedules for human resources and finances, and appropriately distribute hospital material resources. In this study, a hybrid method based on empirical mode decomposition and back-propagation artificial neural networks optimized by particle swarm optimization is developed to forecast outpatient visits on the basis of monthly numbers. The data outpatient visits are retrieved from January 2005 to December 2013 and first obtained as the original time series. Second, the original time series is decomposed into a finite and often small number of intrinsic mode functions by the empirical mode decomposition technique. Third, a three-layer back-propagation artificial neural network is constructed to forecast each intrinsic mode functions. To improve network performance and avoid falling into a local minimum, particle swarm optimization is employed to optimize the weights and thresholds of back-propagation artificial neural networks. Finally, the superposition of forecasting results of the intrinsic mode functions is regarded as the ultimate forecasting value. Simulation indicates that the proposed method attains a better performance index than the other four methods.
Modeling and optimization of an electric power distribution network ...
African Journals Online (AJOL)
EDNEPP) was solved by a mixed binary integer programming (MBIP) formulation of the network, where the steady-state operation of the network was modelled with non-linear mathematical expressions. The non-linear terms are linearized, using ...
THE OPERATION MODES OPTIMIZATION OF THE NEUTRAL DISTRIBUTION NETWORKS
Directory of Open Access Journals (Sweden)
F. P. Shkarbets
2009-03-01
Full Text Available The variants of grounding the neutral wire of electric networks are considered and the recommendations are presented on increasing the level of operational reliability and electric safety of distribution networks with 6 kV voltage on the basis of limitation and suppression of transitional processes at asymmetrical damages.
An Optimal Symmetric Secret Distribution of Star Networks
2007-01-01
instantiation in ad-hoc networks. Computer Comunications , (29):200–215, 2006. [5] Aiyer A.S., Alvisi L, and Gouda M. G. Key grids: A protocol family for...assigning symmetric keys. In IEEE International Conference on Network Protocols, 2006. [6] Eschenauer L and Gligor V. D. A key- management scheme for
Effective Network Management via System-Wide Coordination and Optimization
2010-08-01
capacity of ISPs and socializes the performance benefits to all end-to-end traffic [30]. However, extending single-vantage solutions to a network-wide...O. Spatscheck, and V. Shkapenyuk. Gigascope: A stream database for network applications. In Proc. ACM SIGMOD, 2003. 185 [42] C. Kruegel, F. Valeur
M2M Optimizations in Public Mobile Networks
Norp, A.H.J.; Landais, B.
2012-01-01
Many M2M applications use public telecommunications networks to transfer data from M2M devices to an M2M server. These telecommunications networks will have to be adapted to cope with the traffic generated by the projected growth of M2M applications. In the near future, many more devices will be
Optimal design of an IP/MPLS over DWDM network
Directory of Open Access Journals (Sweden)
Eduardo Canale
2014-04-01
Full Text Available Different approaches for deploying resilient optical networks of low cost constitute a traditional group of NP-Hard problems that have been widely studied. Most of them are based on the construction of low cost networks that fulfill connectivity constraints. However, recent trends to virtualize optical networks over the legacy fiber infrastructure, modified the nature of network design problems and turned inappropriate many of these models and algorithms. In this paper we study a design problem arising from the deployment of an IP/MPLS network over an existing DWDM infrastructure. Besides cost and resiliency, this problem integrates traffic and capacity constraints. We present: an integer programming formulation for the problem, theoretical results, and describe how several metaheuristics were applied in order to find good quality solutions, for a real application case of a telecommunications company.
Analysis and optimization of gas-centrifugal separation of uranium isotopes by neural networks
Directory of Open Access Journals (Sweden)
Migliavacca S.C.P.
2002-01-01
Full Text Available Neural networks are an attractive alternative for modeling complex problems with too many difficulties to be solved by a phenomenological model. A feed-forward neural network was used to model a gas-centrifugal separation of uranium isotopes. The prediction showed good agreement with the experimental data. An optimization study was carried out. The optimal operational condition was tested by a new experiment and a difference of less than 1% was found.
Bio-mimic optimization strategies in wireless sensor networks: a survey.
Adnan, Md Akhtaruzzaman; Abdur Razzaque, Mohammd; Ahmed, Ishtiaque; Isnin, Ismail Fauzi
2013-12-24
For the past 20 years, many authors have focused their investigations on wireless sensor networks. Various issues related to wireless sensor networks such as energy minimization (optimization), compression schemes, self-organizing network algorithms, routing protocols, quality of service management, security, energy harvesting, etc., have been extensively explored. The three most important issues among these are energy efficiency, quality of service and security management. To get the best possible results in one or more of these issues in wireless sensor networks optimization is necessary. Furthermore, in number of applications (e.g., body area sensor networks, vehicular ad hoc networks) these issues might conflict and require a trade-off amongst them. Due to the high energy consumption and data processing requirements, the use of classical algorithms has historically been disregarded. In this context contemporary researchers started using bio-mimetic strategy-based optimization techniques in the field of wireless sensor networks. These techniques are diverse and involve many different optimization algorithms. As far as we know, most existing works tend to focus only on optimization of one specific issue of the three mentioned above. It is high time that these individual efforts are put into perspective and a more holistic view is taken. In this paper we take a step in that direction by presenting a survey of the literature in the area of wireless sensor network optimization concentrating especially on the three most widely used bio-mimetic algorithms, namely, particle swarm optimization, ant colony optimization and genetic algorithm. In addition, to stimulate new research and development interests in this field, open research issues, challenges and future research directions are highlighted.
On the existence of efficient solutions to vector optimization problem of traffic flow on network
Directory of Open Access Journals (Sweden)
T. A. Bozhanova
2009-09-01
Full Text Available We studied traffic flow models in vector-valued optimization statement where the flow is controlled at the nodes of network. We considered the case when an objective mapping possesses a weakened property of upper semicontinuity and made no assumptions on the interior of the ordering cone. The sufficient conditions for the existence of efficient controls of the traffic problems are derived. The existence of efficient solutions of vector optimization problem for traffic flow on network are also proved.
On the existence of efficient solutions to vector optimization problem of traffic flow on network
T. A. Bozhanova
2009-01-01
We studied traffic flow models in vector-valued optimization statement where the flow is controlled at the nodes of network. We considered the case when an objective mapping possesses a weakened property of upper semicontinuity and made no assumptions on the interior of the ordering cone. The sufficient conditions for the existence of efficient controls of the traffic problems are derived. The existence of efficient solutions of vector optimization problem for traffic flow on network are also...
Bio-Mimic Optimization Strategies in Wireless Sensor Networks: A Survey
Adnan, Md. Akhtaruzzaman; Razzaque, Mohammd Abdur; Ahmed, Ishtiaque; Isnin, Ismail Fauzi
2014-01-01
For the past 20 years, many authors have focused their investigations on wireless sensor networks. Various issues related to wireless sensor networks such as energy minimization (optimization), compression schemes, self-organizing network algorithms, routing protocols, quality of service management, security, energy harvesting, etc., have been extensively explored. The three most important issues among these are energy efficiency, quality of service and security management. To get the best possible results in one or more of these issues in wireless sensor networks optimization is necessary. Furthermore, in number of applications (e.g., body area sensor networks, vehicular ad hoc networks) these issues might conflict and require a trade-off amongst them. Due to the high energy consumption and data processing requirements, the use of classical algorithms has historically been disregarded. In this context contemporary researchers started using bio-mimetic strategy-based optimization techniques in the field of wireless sensor networks. These techniques are diverse and involve many different optimization algorithms. As far as we know, most existing works tend to focus only on optimization of one specific issue of the three mentioned above. It is high time that these individual efforts are put into perspective and a more holistic view is taken. In this paper we take a step in that direction by presenting a survey of the literature in the area of wireless sensor network optimization concentrating especially on the three most widely used bio-mimetic algorithms, namely, particle swarm optimization, ant colony optimization and genetic algorithm. In addition, to stimulate new research and development interests in this field, open research issues, challenges and future research directions are highlighted. PMID:24368702
Simulated Annealing in Optimization of Energy Production in a Water Supply Network
Almeida Samora, Irene; Franca, Mário J.; Schleiss, Anton; Helena M. Ramos
2016-01-01
In water supply systems, the potential exists for micro-hydropower that uses the pressure excess in the networks to produce electricity. However, because urban drinking water networks are complex systems in which flows and pressure vary constantly, identification of the ideal locations for turbines is not straightforward, and assessment implies the need for simulation. In this paper, an optimization algorithm is proposed to provide a selection of optimal locations for the installation of a gi...
An Iterated Local Search Algorithm for Multi-Period Water Distribution Network Design Optimization
Directory of Open Access Journals (Sweden)
Annelies De Corte
2016-08-01
Full Text Available Water distribution networks consist of different components, such as reservoirs and pipes, and exist to provide users (households, agriculture, industry with high-quality water at adequate pressure and flow. Water distribution network design optimization aims to find optimal diameters for every pipe, chosen from a limited set of commercially available diameters. This combinatorial optimization problem has received a lot of attention over the past forty years. In this paper, the well-studied single-period problem is extended to a multi-period setting in which time varying demand patterns occur. Moreover, an additional constraint—which sets a maximum water velocity—is imposed. A metaheuristic technique called iterated local search is applied to tackle this challenging optimization problem. A full-factorial experiment is conducted to validate the added value of the algorithm components and to configure optimal parameter settings. The algorithm is tested on a broad range of 150 different (freely available test networks.
Profile-driven regression for modeling and runtime optimization of mobile networks
DEFF Research Database (Denmark)
McClary, Dan; Syrotiuk, Violet; Kulahci, Murat
2010-01-01
Computer networks often display nonlinear behavior when examined over a wide range of operating conditions. There are few strategies available for modeling such behavior and optimizing such systems as they run. Profile-driven regression is developed and applied to modeling and runtime optimization...... of throughput in a mobile ad hoc network, a self-organizing collection of mobile wireless nodes without any fixed infrastructure. The intermediate models generated in profile-driven regression are used to fit an overall model of throughput, and are also used to optimize controllable factors at runtime. Unlike...... others, the throughput model accounts for node speed. The resulting optimization is very effective; locally optimizing the network factors at runtime results in throughput as much as six times higher than that achieved with the factors at their default levels....
Hanlon, D W; Williamson, N B; Steffert, I J; Wichtel, J J; Pfeiffer, D U
1997-02-01
Recommendations for oestrus synchronisation of dairy heifers using progesterone-containing intravaginal devices suggest re-insertion of used devices 16 days after first insemination for a period of 5 days to allow a second opportunity for artificial insemination. Controlled studies on the effectiveness of re-using intravaginal devices to synchronise returns to oestrus in non-pregnant dairy heifers are lacking. A clinical trial was conducted involving 750 Friesian heifers in 13 herds. After an initial synchronisation programme, the used intravaginal devices were re-inserted 14 or 16 days after first insemination into half of the heifers in each herd for a period of 5 days. After the first synchronisation programme, 47.5% of heifers remained non-pregnant. Re-insertion of used intravaginal devices for 5 days significantly increased the number of non-pregnant heifers detected in oestrus and inseminated by 48 hours after device removal compared to heifers in which devices were not re-inserted (45.2% v. 27.3%, p returns to service. However, the number of non-pregnant heifers synchronised for a second round of artificial insemination was less than expected. Conception rate to the re-synchronised oestrus was unaffected by the treatment. It is concluded that the additional procedures of CIDR re-insertion, removal, tailpainting and insemination involved in there-synchrony programme, and the relatively low in-calf rate to the re-synchronised round of insemination, reduced the potential benefits of re-synchronisation.
Optimizing radio frequency identification network planning through ring probabilistic logic neurons
Directory of Open Access Journals (Sweden)
Aydin Azizi
2016-08-01
Full Text Available Radio frequency identification is a developing technology that has recently been adopted in industrial applications for identification and tracking operations. The radio frequency identification network planning problem deals with many criteria like number and positions of the deployed antennas in the networks, transmitted power of antennas, and coverage of network. All these criteria must satisfy a set of objectives, such as load balance, economic efficiency, and interference, in order to obtain accurate and reliable network planning. Achieving the best solution for radio frequency identification network planning has been an area of great interest for many scientists. This article introduces the Ring Probabilistic Logic Neuron as a time-efficient and accurate algorithm to deal with the radio frequency identification network planning problem. To achieve the best results, redundant antenna elimination algorithm is used in addition to the proposed optimization techniques. The aim of proposed algorithm is to solve the radio frequency identification network planning problem and to design a cost-effective radio frequency identification network by minimizing the number of embedded radio frequency identification antennas in the network, minimizing collision of antennas, and maximizing coverage area of the objects. The proposed solution is compared with the evolutionary algorithms, namely genetic algorithm and particle swarm optimization. The simulation results show that the Ring Probabilistic Logic Neuron algorithm obtains a far more superior solution for radio frequency identification network planning problem when compared to genetic algorithm and particle swarm optimization.
Directory of Open Access Journals (Sweden)
KHANBABAZADEH Javad
2016-10-01
Full Text Available Distribution network designers and operators are trying to deliver electrical energy with high reliability and quality to their subscribers. Due to high losses in the distribution systems, using distributed generation can improves reliability, reduces losses and improves voltage profile of distribution network. Therefore, the choice of the location of these resources and also determining the amount of their generated power to maximize the benefits of this type of resource is an important issue which is discussed from different points of view today. In this paper, a new multi-objective optimal location and sizing of distributed generation resources is performed to maximize its benefits on the 33 bus distribution test network considering reliability and using a new Antlion Optimizer (ALO. The benefits for DG are considered as system losses reduction, system reliability improvement and benefits from the sale electricity and voltage profile improvement. For each of the mentioned benefits, the ALO algorithm is used to optimize the location and sizing of distributed generation resources. In order to verify the proposed approach, the obtained results have been analyzed and compared with the results of particle swarm optimization (PSO algorithm. The results show that the ALO has shown better performance in optimization problem solution versus PSO.
A performance study on the synchronisation of heterogeneous Grid databases using CONStanza
Pucciani, G; Domenici, Andrea; Stockinger, Heinz
2010-01-01
In Grid environments, several heterogeneous database management systems are used in various administrative domains. However, data exchange and synchronisation need to be available across different sites and different database systems. In this article we present our data consistency service CONStanza and give details on how we achieve relaxed update synchronisation between different database implementations. The integration in existing Grid environments is one of the major goals of the system. Performance tests have been executed following a factorial approach. Detailed experimental results and a statistical analysis are presented to evaluate the system components and drive future developments. (C) 2010 Elsevier B.V. All rights reserved.
Optimal placement of distributed generation in distribution networks
African Journals Online (AJOL)
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loss formula and in the second segment the optimal location of DG is found by using the loss sensitivity factor. The analytical expression is based on exact loss ..... A computer program is written in MATLAB 7 to find the optimal size of .... Proceedings of IEEE PES Power Systems Conference and Exposition-PSCE 2006, pp.
Enhanced Multi-Objective Optimization of Groundwater Monitoring Networks
DEFF Research Database (Denmark)
Bode, Felix; Binning, Philip John; Nowak, Wolfgang
Drinking-water well catchments include many sources for potential contaminations like gas stations or agriculture. Finding optimal positions of monitoring wells for such purposes is challenging because there are various parameters (and their uncertainties) that influence the reliability and optim...
Optimal placement of distributed generation in distribution networks
African Journals Online (AJOL)
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This paper proposes the application of Particle Swarm Optimization (PSO) technique to find the optimal size and optimum ... DG also has several benefits like energy costs through combined ... and high capacity of DG can be explained by the fact that the distribution system was initially designed such that power flows.
Directory of Open Access Journals (Sweden)
Xiao-feng Xu
2017-01-01
Full Text Available Collaborative logistics network resource allocation can effectively meet the needs of customers. It can realize the overall benefit maximization of the logistics network and ensure that collaborative logistics network runs orderly at the time of creating value. Therefore, this article is based on the relationship of collaborative logistics network supplier, the transit warehouse, and sellers, and we consider the uncertainty of time to establish a bilevel programming model with random constraints and propose a genetic simulated annealing hybrid intelligent algorithm to solve it. Numerical example shows that the method has stronger robustness and convergence; it can achieve collaborative logistics network resource allocation rationalization and optimization.
Economic and environmental optimization of a multi-site utility network for an industrial complex.
Kim, Sang Hun; Yoon, Sung-Geun; Chae, Song Hwa; Park, Sunwon
2010-01-01
Most chemical companies consume a lot of steam, water and electrical resources in the production process. Given recent record fuel costs, utility networks must be optimized to reduce the overall cost of production. Environmental concerns must also be considered when preparing modifications to satisfy the requirements for industrial utilities, since wastes discharged from the utility networks are restricted by environmental regulations. Construction of Eco-Industrial Parks (EIPs) has drawn attention as a promising approach for retrofitting existing industrial parks to improve energy efficiency. The optimization of the utility network within an industrial complex is one of the most important undertakings to minimize energy consumption and waste loads in the EIP. In this work, a systematic approach to optimize the utility network of an industrial complex is presented. An important issue in the optimization of a utility network is the desire of the companies to achieve high profits while complying with the environmental regulations. Therefore, the proposed optimization was performed with consideration of both economic and environmental factors. The proposed approach consists of unit modeling using thermodynamic principles, mass and energy balances, development of a multi-period Mixed Integer Linear Programming (MILP) model for the integration of utility systems in an industrial complex, and an economic/environmental analysis of the results. This approach is applied to the Yeosu Industrial Complex, considering seasonal utility demands. The results show that both the total utility cost and waste load are reduced by optimizing the utility network of an industrial complex. 2009 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Dakai Li
2014-10-01
Full Text Available As the slower rate of convergence and lower study ability in the late period of network-oriented consumption prediction model based on neural network algorithm, this paper proposed a network analysis neural model based on chaotic disturbance optimized particle swarm. Firstly, improve the initialization of particle swarm with chaotic disturbance optimization strategy in order to limit the initial position and the initial speed of limited particle. Then have an optimal operation on each individual in particle swarm with chaotic disturbance variables, so that the particles which do not enter into iteration will jump out of the local optima area. And next, optimize the PSO algorithm inertia weight by adopting adaptive adjustment strategy based on individual particle adaptive value. At last, combine the improved PSO algorithm based on chaotic disturbance with neural network algorithm, thus we will construct the network-oriented consumption analysis model. Simulation results show that the proposed network-oriented consumption analysis neural network model based on chaotic disturbance optimized particle swarm has greatly improved in prediction accuracy and computational speed.
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.
On maximizing the lifetime of Wireless Sensor Networks by optimally assigning energy supplies.
Asorey-Cacheda, Rafael; García-Sánchez, Antonio Javier; García-Sánchez, Felipe; García-Haro, Joan; González-Castano, Francisco Javier
2013-08-09
The extension of the network lifetime of Wireless Sensor Networks (WSN) is an important issue that has not been appropriately solved yet. This paper addresses this concern and proposes some techniques to plan an arbitrary WSN. To this end, we suggest a hierarchical network architecture, similar to realistic scenarios, where nodes with renewable energy sources (denoted as primary nodes) carry out most message delivery tasks, and nodes equipped with conventional chemical batteries (denoted as secondary nodes) are those with less communication demands. The key design issue of this network architecture is the development of a new optimization framework to calculate the optimal assignment of renewable energy supplies (primary node assignment) to maximize network lifetime, obtaining the minimum number of energy supplies and their node assignment. We also conduct a second optimization step to additionally minimize the number of packet hops between the source and the sink. In this work, we present an algorithm that approaches the results of the optimization framework, but with much faster execution speed, which is a good alternative for large-scale WSN networks. Finally, the network model, the optimization process and the designed algorithm are further evaluated and validated by means of computer simulation under realistic conditions. The results obtained are discussed comparatively.
On Maximizing the Lifetime of Wireless Sensor Networks by Optimally Assigning Energy Supplies
Directory of Open Access Journals (Sweden)
Francisco Javier González-Castano
2013-08-01
Full Text Available The extension of the network lifetime of Wireless Sensor Networks (WSN is an important issue that has not been appropriately solved yet. This paper addresses this concern and proposes some techniques to plan an arbitrary WSN. To this end, we suggest a hierarchical network architecture, similar to realistic scenarios, where nodes with renewable energy sources (denoted as primary nodes carry out most message delivery tasks, and nodes equipped with conventional chemical batteries (denoted as secondary nodes are those with less communication demands. The key design issue of this network architecture is the development of a new optimization framework to calculate the optimal assignment of renewable energy supplies (primary node assignment to maximize network lifetime, obtaining the minimum number of energy supplies and their node assignment. We also conduct a second optimization step to additionally minimize the number of packet hops between the source and the sink. In this work, we present an algorithm that approaches the results of the optimization framework, but with much faster execution speed, which is a good alternative for large-scale WSN networks. Finally, the network model, the optimization process and the designed algorithm are further evaluated and validated by means of computer simulation under realistic conditions. The results obtained are discussed comparatively.
Bayesian Regression with Network Prior: Optimal Bayesian Filtering Perspective.
Qian, Xiaoning; Dougherty, Edward R
2016-12-01
The recently introduced intrinsically Bayesian robust filter (IBRF) provides fully optimal filtering relative to a prior distribution over an uncertainty class ofjoint random process models, whereas formerly the theory was limited to model-constrained Bayesian robust filters, for which optimization was limited to the filters that are optimal for models in the uncertainty class. This paper extends the IBRF theory to the situation where there are both a prior on the uncertainty class and sample data. The result is optimal Bayesian filtering (OBF), where optimality is relative to the posterior distribution derived from the prior and the data. The IBRF theories for effective characteristics and canonical expansions extend to the OBF setting. A salient focus of the present work is to demonstrate the advantages of Bayesian regression within the OBF setting over the classical Bayesian approach in the context otlinear Gaussian models.
Lifetime Optimization of a Multiple Sink Wireless Sensor Network through Energy Balancing
Directory of Open Access Journals (Sweden)
Tapan Kumar Jain
2015-01-01
Full Text Available The wireless sensor network consists of small limited energy sensors which are connected to one or more sinks. The maximum energy consumption takes place in communicating the data from the nodes to the sink. Multiple sink WSN has an edge over the single sink WSN where very less energy is utilized in sending the data to the sink, as the number of hops is reduced. If the energy consumed by a node is balanced between the other nodes, the lifetime of the network is considerably increased. The network lifetime optimization is achieved by restructuring the network by modifying the neighbor nodes of a sink. Only those nodes are connected to a sink which makes the total energy of the sink less than the threshold. This energy balancing through network restructuring optimizes the network lifetime. This paper depicts this fact through simulations done in MATLAB.
Energy-Aware Routing Optimization in Dynamic GMPLS Controlled Optical Networks
DEFF Research Database (Denmark)
Wang, Jiayuan; Ricciardi, Sergio; Fagertun, Anna Manolova
2012-01-01
In this paper, routing optimizations based on energy sources are proposed in dynamic GMPLS controlled optical networks. The influences of re-routing and load balancing factors on the algorithm are evaluated, with a focus on different re-routing thresholds. Results from dynamic network simulations...
Optimization of neural networks for time-domain simulation of mooring lines
DEFF Research Database (Denmark)
Christiansen, Niels Hørbye; Voie, Per Erlend Torbergsen; Winther, Ole
2016-01-01
, they also can be used to rank the importance of the various network inputs. The dynamic response of slender marine structures often depends on several external load components, and by applying the optimization procedures to a trained artificial neural network, it is possible to classify the external force...
Yan, Xiaohuan
2017-01-01
This thesis presents a vertical handover decision (VHD) scheme for optimizing the efficiency of vertical handover processes in the Fourth Generation (4G) heterogeneous wireless networks. The scheme consists of three closely integrated modules: Handover necessity estimation, handover target selection, and handover triggering condition estimation. Handover necessity estimation module determines whether a handover is necessary to an available network. Handover target selecti...
Optimization of the Compensation of a Meshed MV Network by a Modified Genetic Algorithm
DEFF Research Database (Denmark)
Nielsen, Hans; Paar, M.; Toman, P.
2007-01-01
The article discusses the utilization of a modified genetic algorithm (GA) for the optimization of the shunt compensation in meshed and radial MV distribution networks. The algorithm looks for minimum costs of the network power losses and minimum capital and operating costs of applied capacitors...
Optimal phasing of district heating network investments using multi-stage stochastic programming
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Romain Stephane Claude Lambert
2016-06-01
Full Text Available Most design optimisation studies for district heating systems have focused on the optimal sizing of network assets and on the location of production units. However, the strategic value of the flexibility in phasing of the inherently modular heat networks, which is an important aspect in many feasibility studies for district heating schemes in the UK, is almost always neglected in the scientific literature. This paper considers the sequential problem faced by a decision-maker in the phasing of long-term investments into district heating networks and their expansions. The problem is formulated as a multi-stage stochastic programme to determine the annual capital expenditure that maximises the expected net present value of the project. The optimisation approach is illustrated by applying it to the hypothetical case of the UK’s Marston Vale eco town. It was found that the approach is capable of simulating the optimal growth of a network, from both a single heat source or separate islands of growth, as well as the optimal marginal expansion of an existing district heating network. The proposed approach can be used by decision makers as a framework to determine both the optimal phasing and extension of district heating networks and can be adapted simply to various, more complex real-life situations by introducing additional constraints and parameters. The versatility of the base formulation also makes it a powerful approach regardless of the size of the network and also potentially applicable to cooling networks.
Boussalis, Dhemetrios; Wang, Shyh J.
1992-01-01
This paper presents a method for utilizing artificial neural networks for direct adaptive control of dynamic systems with poorly known dynamics. The neural network weights (controller gains) are adapted in real time using state measurements and a random search optimization algorithm. The results are demonstrated via simulation using two highly nonlinear systems.
Network-based predictions of retail store commercial categories and optimal locations
Jensen, Pablo
2006-09-01
I study the spatial organization of retail commercial activities. These are organized in a network comprising “antilinks,” i.e., links of negative weight. From pure location data, network analysis leads to a community structure that closely follows the commercial classification of the U.S. Department of Labor. The interaction network allows one to build a “quality” index of optimal location niches for stores, which has been empirically tested.
Energy Technology Data Exchange (ETDEWEB)
Saarinen, Lisa; Boman, Katarina
2012-02-15
The supply temperature of the Uppsala district heating network was optimized using a model-based control strategy. Simulation of the network showed that the supply temperature could be decreased by in average 8 deg and the electricity production of the plants supplying the network could be increased with 2.5 % during the period January- April, giving an extra income of 1.2 MSEK due to increased income from electricity sales
Hongying Jin; Linhao Li
2013-01-01
This paper aims at effectively predicting the dynamic network traffic flow based on quantum-behaved particle swarm optimization algorithm. Firstly, the dynamic network traffic flow prediction problem is analyzed through formal description. Secondly, the structure of the network traffic flow prediction model is given. In this structure, Users can used a computer to start the traffic flow prediction process, and data collecting module can collect and return the data through the destination devi...
Directory of Open Access Journals (Sweden)
Dao-Wei Bi
2007-05-01
Full Text Available The limited energy supply of wireless sensor networks poses a great challenge for the deployment of wireless sensor nodes. In this paper, we focus on energy-efficient coverage with distributed particle swarm optimization and simulated annealing. First, the energy-efficient coverage problem is formulated with sensing coverage and energy consumption models. We consider the network composed of stationary and mobile nodes. Second, coverage and energy metrics are presented to evaluate the coverage rate and energy consumption of a wireless sensor network, where a grid exclusion algorithm extracts the coverage state and DijkstraÃ¢Â€Â™s algorithm calculates the lowest cost path for communication. Then, a hybrid algorithm optimizes the energy consumption, in which particle swarm optimization and simulated annealing are combined to find the optimal deployment solution in a distributed manner. Simulated annealing is performed on multiple wireless sensor nodes, results of which are employed to correct the local and global best solution of particle swarm optimization. Simulations of wireless sensor node deployment verify that coverage performance can be guaranteed, energy consumption of communication is conserved after deployment optimization and the optimization performance is boosted by the distributed algorithm. Moreover, it is demonstrated that energy efficiency of wireless sensor networks is enhanced by the proposed optimization algorithm in target tracking applications.
Neural networks in high-performance liquid chromatography optimization : Response surface modeling
Metting, H.J; Coenegracht, P.M J
1996-01-01
The usefulness of artificial neural networks for response surface modeling in HPLC optimization is compared with (non-)linear regression methods. The number of hidden nodes is optimized by a lateral inhibition method. Overfitting is controlled by cross-validation using the leave one out method
A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization
Stampa, Giorgio; Arias, Marta; Sanchez-Charles, David; Muntes-Mulero, Victor; Cabellos, Albert
2017-01-01
In this paper we design and evaluate a Deep-Reinforcement Learning agent that optimizes routing. Our agent adapts automatically to current traffic conditions and proposes tailored configurations that attempt to minimize the network delay. Experiments show very promising performance. Moreover, this approach provides important operational advantages with respect to traditional optimization algorithms.
DEFF Research Database (Denmark)
Zakrzewska, Anna; Ruepp, Sarah Renée; Berger, Michael Stübert
2013-01-01
The paper addresses the problem of optimal selection of Radio Access Technology (RAT) and assignment of resources in a multistandard wireless network scenario. It is shown how OPNET Modeler can be used to perform optimization studies using Integer Linear Programming (ILP) solvers. In this way...
High-Lift Optimization Design Using Neural Networks on a Multi-Element Airfoil
Greenman, Roxana M.; Roth, Karlin R.; Smith, Charles A. (Technical Monitor)
1998-01-01
The high-lift performance of a multi-element airfoil was optimized by using neural-net predictions that were trained using a computational data set. The numerical data was generated using a two-dimensional, incompressible, Navier-Stokes algorithm with the Spalart-Allmaras turbulence model. Because it is difficult to predict maximum lift for high-lift systems, an empirically-based maximum lift criteria was used in this study to determine both the maximum lift and the angle at which it occurs. Multiple input, single output networks were trained using the NASA Ames variation of the Levenberg-Marquardt algorithm for each of the aerodynamic coefficients (lift, drag, and moment). The artificial neural networks were integrated with a gradient-based optimizer. Using independent numerical simulations and experimental data for this high-lift configuration, it was shown that this design process successfully optimized flap deflection, gap, overlap, and angle of attack to maximize lift. Once the neural networks were trained and integrated with the optimizer, minimal additional computer resources were required to perform optimization runs with different initial conditions and parameters. Applying the neural networks within the high-lift rigging optimization process reduced the amount of computational time and resources by 83% compared with traditional gradient-based optimization procedures for multiple optimization runs.
Loss optimization in distribution networks with distributed generation
DEFF Research Database (Denmark)
Pokhrel, Basanta Raj; Nainar, Karthikeyan; Bak-Jensen, Birgitte
2017-01-01
This paper presents a novel power loss minimization approach in distribution grids considering network reconfiguration, distributed generation and storage installation. Identification of optimum configuration in such scenario is one of the main challenges faced by distribution system operators in...
Directory of Open Access Journals (Sweden)
P.-Y. Chen
2009-01-01
Full Text Available This study proposes a neural network-family competition genetic algorithm (NN-FCGA for solving the electromagnetic (EM optimization and other general-purpose optimization problems. The NN-FCGA is a hybrid evolutionary-based algorithm, combining the good approximation performance of neural network (NN and the robust and effective optimum search ability of the family competition genetic algorithms (FCGA to accelerate the optimization process. In this study, the NN-FCGA is used to extract a set of optimal design parameters for two representative design examples: the multiple section low-pass filter and the polygonal electromagnetic absorber. Our results demonstrate that the optimal electromagnetic properties given by the NN-FCGA are comparable to those of the FCGA, but reducing a large amount of computation time and a well-trained NN model that can serve as a nonlinear approximator was developed during the optimization process of the NN-FCGA.
Distributed flow optimization and cascading effects in weighted complex networks
Asztalos, Andrea; Sreenivasan, Sameet; Szymanski, Boleslaw K.; Korniss, G.
2011-01-01
We investigate the effect of a specific edge weighting scheme $\\sim (k_i k_j)^{\\beta}$ on distributed flow efficiency and robustness to cascading failures in scale-free networks. In particular, we analyze a simple, yet fundamental distributed flow model: current flow in random resistor networks. By the tuning of control parameter $\\beta$ and by considering two general cases of relative node processing capabilities as well as the effect of bandwidth, we show the dependence of transport efficie...
Network models in optimization and their applications in practice
Glover, Fred; Phillips, Nancy V
2011-01-01
Unique in that it focuses on formulation and case studies rather than solutions procedures covering applications for pure, generalized and integer networks, equivalent formulations plus successful techniques of network models. Every chapter contains a simple model which is expanded to handle more complicated developments, a synopsis of existing applications, one or more case studies, at least 20 exercises and invaluable references. An Instructor's Manual presenting detailed solutions to all the problems in the book is available upon request from the Wiley editorial department.
Power Dispatch Optimization of a Distributed Energy Network
Han, Tianyi; Shi, Kun; Liu, Huanan; Yu, Dongmin
2017-12-01
With the rapid development of the world economy, energy crisis has become a serious problem. In addition, considering the fact that non-renewable energy reservation decreases day by day, it is important to save energy. This paper develops an innovative optimization model, which is based on the linear programming method, to optimize power flow between each individual user. The proposed method considers power transmission losses and carbon emissions at the same time, therefore, the proposed method can effectively reduce carbon emissions and transmission losses. Through MATLAB simulation, the proposed linear programming optimization model can reduce 28.7% of transmission losses compared to the previous literature.
Optimality problem of network topology in stocks market analysis
Djauhari, Maman Abdurachman; Gan, Siew Lee
2015-02-01
Since its introduction fifteen years ago, minimal spanning tree has become an indispensible tool in econophysics. It is to filter the important economic information contained in a complex system of financial markets' commodities. Here we show that, in general, that tool is not optimal in terms of topological properties. Consequently, the economic interpretation of the filtered information might be misleading. To overcome that non-optimality problem, a set of criteria and a selection procedure of an optimal minimal spanning tree will be developed. By using New York Stock Exchange data, the advantages of the proposed method will be illustrated in terms of the power-law of degree distribution.
Air route network optimization in fragmented airspace based on cellular automata
Directory of Open Access Journals (Sweden)
Shijin WANG
2017-06-01
Full Text Available Air route network optimization, one of the essential parts of the airspace planning, is an effective way to optimize airspace resources, increase airspace capacity, and alleviate air traffic congestion. However, little has been done on the optimization of air route network in the fragmented airspace caused by prohibited, restricted, and dangerous areas (PRDs. In this paper, an air route network optimization model is developed with the total operational cost as the objective function while airspace restriction, air route network capacity, and non-straight-line factors (NSLF are taken as major constraints. A square grid cellular space, Moore neighbors, a fixed boundary, together with a set of rules for solving the route network optimization model are designed based on cellular automata. The empirical traffic of airports with the largest traffic volume in each of the 9 flight information regions in mainland China is collected as the origin-destination (OD airport pair demands. Based on traffic patterns, the model generates 35 air routes which successfully avoids 144 PRDs. Compared with the current air route network structure, the number of nodes decreases by 41.67%, while the total length of flight segments and air routes drop by 32.03% and 5.82% respectively. The NSLF decreases by 5.82% with changes in the total length of the air route network. More importantly, the total operational cost of the whole network decreases by 6.22%. The computational results show the potential benefits of the model and the advantage of the algorithm. Optimization of air route network can significantly reduce operational cost while ensuring operation safety.
Toward an optimal convolutional neural network for traffic sign recognition
Habibi Aghdam, Hamed; Jahani Heravi, Elnaz; Puig, Domenec
2015-12-01
Convolutional Neural Networks (CNN) beat the human performance on German Traffic Sign Benchmark competition. Both the winner and the runner-up teams trained CNNs to recognize 43 traffic signs. However, both networks are not computationally efficient since they have many free parameters and they use highly computational activation functions. In this paper, we propose a new architecture that reduces the number of the parameters 27% and 22% compared with the two networks. Furthermore, our network uses Leaky Rectified Linear Units (ReLU) as the activation function that only needs a few operations to produce the result. Specifically, compared with the hyperbolic tangent and rectified sigmoid activation functions utilized in the two networks, Leaky ReLU needs only one multiplication operation which makes it computationally much more efficient than the two other functions. Our experiments on the Gertman Traffic Sign Benchmark dataset shows 0:6% improvement on the best reported classification accuracy while it reduces the overall number of parameters 85% compared with the winner network in the competition.
Routing optimization in networks based on traffic gravitational field model
Liu, Longgeng; Luo, Guangchun
2017-04-01
For research on the gravitational field routing mechanism on complex networks, we further analyze the gravitational effect of paths. In this study, we introduce the concept of path confidence degree to evaluate the unblocked reliability of paths that it takes the traffic state of all nodes on the path into account from the overall. On the basis of this, we propose an improved gravitational field routing protocol considering all the nodes’ gravities on the path and the path confidence degree. In order to evaluate the transmission performance of the routing strategy, an order parameter is introduced to measure the network throughput by the critical value of phase transition from a free-flow phase to a jammed phase, and the betweenness centrality is used to evaluate the transmission performance and traffic congestion of the network. Simulation results show that compared with the shortest-path routing strategy and the previous gravitational field routing strategy, the proposed algorithm improves the network throughput considerably and effectively balances the traffic load within the network, and all nodes in the network are utilized high efficiently. As long as γ ≥ α, the transmission performance can reach the maximum and remains unchanged for different α and γ, which ensures that the proposed routing protocol is high efficient and stable.
Optimization of Close Range Photogrammetry Network Design Applying Fuzzy Computation
Aminia, A. S.
2017-09-01
Measuring object 3D coordinates with optimum accuracy is one of the most important issues in close range photogrammetry. In this context, network design plays an important role in determination of optimum position of imaging stations. This is, however, not a trivial task due to various geometric and radiometric constraints affecting the quality of the measurement network. As a result, most camera stations in the network are defined on a try and error basis based on the user's experience and generic network concept. In this paper, we propose a post-processing task to investigate the quality of camera positions right after image capturing to achieve the best result. To do this, a new fuzzy reasoning approach is adopted, in which the constraints affecting the network design are all modeled. As a result, the position of all camera locations is defined based on fuzzy rules and inappropriate stations are determined. The experiments carried out show that after determination and elimination of the inappropriate images using the proposed fuzzy reasoning system, the accuracy of measurements is improved and enhanced about 17% for the latter network.
Optimization of multilayer neural network parameters for speaker recognition
Tovarek, Jaromir; Partila, Pavol; Rozhon, Jan; Voznak, Miroslav; Skapa, Jan; Uhrin, Dominik; Chmelikova, Zdenka
2016-05-01
This article discusses the impact of multilayer neural network parameters for speaker identification. The main task of speaker identification is to find a specific person in the known set of speakers. It means that the voice of an unknown speaker (wanted person) belongs to a group of reference speakers from the voice database. One of the requests was to develop the text-independent system, which means to classify wanted person regardless of content and language. Multilayer neural network has been used for speaker identification in this research. Artificial neural network (ANN) needs to set parameters like activation function of neurons, steepness of activation functions, learning rate, the maximum number of iterations and a number of neurons in the hidden and output layers. ANN accuracy and validation time are directly influenced by the parameter settings. Different roles require different settings. Identification accuracy and ANN validation time were evaluated with the same input data but different parameter settings. The goal was to find parameters for the neural network with the highest precision and shortest validation time. Input data of neural networks are a Mel-frequency cepstral coefficients (MFCC). These parameters describe the properties of the vocal tract. Audio samples were recorded for all speakers in a laboratory environment. Training, testing and validation data set were split into 70, 15 and 15 %. The result of the research described in this article is different parameter setting for the multilayer neural network for four speakers.
Directory of Open Access Journals (Sweden)
Haichao Wang
2017-07-01
Full Text Available A district heating (DH system is one of the most important components of infrastructures in cold areas. Proper DH network design should balance the initial investment and the heat distribution cost of the DH network. Currently, this design is often based on a recommended value for specific pressure loss (R = ∆P/L in the main lines. This will result in a feasible network design, but probably not be optimal in most cases. The paper develops a novel optimization model to facilitate the design by considering the initial investment in the pipes and the heat distribution costs. The model will generate all possible network scenarios consisting of different series of diameters for each pipe in the flow direction of the network. Then, the annuity on the initial investment, the heat distribution cost, and the total annual cost will be calculated for each network scenario, taking into account the uncertainties of the material prices and the yearly operating time levels. The model is applied to a sample DH network and the results indicate that the model works quite well, clearly identifying the optimal network design and demonstrating that the heat distribution cost is more important than the initial investment in DH network design.
Optimal Sensor Placement for Leak Location in Water Distribution Networks Using Genetic Algorithms
Casillas, Myrna V.; Puig, Vicenc; Garza-Castanon, Luis E.; Rosich, Albert
2013-01-01
This paper proposes a new sensor placement approach for leak location in water distribution networks (WDNs). The sensor placement problem is formulated as an integer optimization problem. The optimization criterion consists in minimizing the number of non-isolable leaks according to the isolability criteria introduced. Because of the large size and non-linear integer nature of the resulting optimization problem, genetic algorithms (GAs) are used as the solution approach. The obtained results ...
Genetic Optimization of Neural Networks for Person Recognition based on the Iris
Directory of Open Access Journals (Sweden)
Oscar Castillo
2012-06-01
Full Text Available This paper describes the application of modular neural network architectures for person recognition using the human iris images as a biometric measure. The iris database was obtained from the Institute of Automation of the Academy of Sciences China (CASIA. We show simulation results with the modular neural network approach, its optimization using genetic algorithms, and the integration with different methods, such as: the gating network method, type-1 fuzzy integration and optimized fuzzy integration using genetic algorithms. Simulation results show a good identification rate using fuzzy integrators and the best structure found by the genetic algorithm.
All-in-one model for designing optimal water distribution pipe networks
Aklog, Dagnachew; Hosoi, Yoshihiko
2017-05-01
This paper discusses the development of an easy-to-use, all-in-one model for designing optimal water distribution networks. The model combines different optimization techniques into a single package in which a user can easily choose what optimizer to use and compare the results of different optimizers to gain confidence in the performances of the models. At present, three optimization techniques are included in the model: linear programming (LP), genetic algorithm (GA) and a heuristic one-by-one reduction method (OBORM) that was previously developed by the authors. The optimizers were tested on a number of benchmark problems and performed very well in terms of finding optimal or near-optimal solutions with a reasonable computation effort. The results indicate that the model effectively addresses the issues of complexity and limited performance trust associated with previous models and can thus be used for practical purposes.
Directory of Open Access Journals (Sweden)
R. Latha
Full Text Available Nowadays, Wireless Body Area Network (WBAN is emerging very fast and so many new methods and algorithms are coming up for finding the optimal path for disseminating emergency messages. Ant Colony Optimization (ACO is one of the cultural algorithms for solving many hard problems such as Travelling Salesman Problem (TSP. ACO is a natural behaviour of ants, which work stochastically with the help of pheromone trails deposited in the shortest route to find their food. This optimization procedure involves adapting, positive feedback and inherent parallelism. Each ant will deposit certain amount of pheromone in the tour construction it makes searching for food. This type of communication is known as stigmetric communication. In addition, if a dense WBAN environment prevails, such as hospital, i.e. in the environment of overlapping WBAN, game formulation was introduced for analyzing the mixed strategy behaviour of WBAN. In this paper, the ant colony optimization approach to the travelling salesman problem was applied to the WBAN to determine the shortest route for sending emergency message to the doctor via sensor nodes; and also a static Bayesian game formulation with mixed strategy was analysed to enhance the network lifetime. Whenever the patient needs any critical care or any other medical issue arises, emergency messages will be created by the WBAN and sent to the doctor's destination. All the modes of communication were realized in a simulation environment using OMNet++. The authors investigated a balanced model of emergency message dissemination and network lifetime in WBAN using ACO and Bayesian game formulation. Keywords: Wireless body area network, Ant colony optimization, Bayesian game model, Sensor network, Message latency, Network lifetime
Kobayashi, Koichi; Hiraishi, Kunihiko
2014-01-01
One of the significant topics in systems biology is to develop control theory of gene regulatory networks (GRNs). In typical control of GRNs, expression of some genes is inhibited (activated) by manipulating external stimuli and expression of other genes. It is expected to apply control theory of GRNs to gene therapy technologies in the future. In this paper, a control method using a Boolean network (BN) is studied. A BN is widely used as a model of GRNs, and gene expression is expressed by a binary value (ON or OFF). In particular, a context-sensitive probabilistic Boolean network (CS-PBN), which is one of the extended models of BNs, is used. For CS-PBNs, the verification problem and the optimal control problem are considered. For the verification problem, a solution method using the probabilistic model checker PRISM is proposed. For the optimal control problem, a solution method using polynomial optimization is proposed. Finally, a numerical example on the WNT5A network, which is related to melanoma, is presented. The proposed methods provide us useful tools in control theory of GRNs.
A recurrent neural network for solving a class of generalized convex optimization problems.
Hosseini, Alireza; Wang, Jun; Hosseini, S Mohammad
2013-08-01
In this paper, we propose a penalty-based recurrent neural network for solving a class of constrained optimization problems with generalized convex objective functions. The model has a simple structure described by using a differential inclusion. It is also applicable for any nonsmooth optimization problem with affine equality and convex inequality constraints, provided that the objective function is regular and pseudoconvex on feasible region of the problem. It is proven herein that the state vector of the proposed neural network globally converges to and stays thereafter in the feasible region in finite time, and converges to the optimal solution set of the problem. Copyright © 2013 Elsevier Ltd. All rights reserved.
Barbarosou, Maria P; Maratos, Nicholas G
2008-10-01
In this paper, a recurrent neural network for both convex and nonconvex equality-constrained optimization problems is proposed, which makes use of a cost gradient projection onto the tangent space of the constraints. The proposed neural network constructs a generically nonfeasible trajectory, satisfying the constraints only as t --> infinity. Local convergence results are given that do not assume convexity of the optimization problem to be solved. Global convergence results are established for convex optimization problems. An exponential convergence rate is shown to hold both for the convex case and the nonconvex case. Numerical results indicate that the proposed method is efficient and accurate.
Design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization
Castillo, Oscar; Kacprzyk, Janusz
2015-01-01
This book presents recent advances on the design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization and their application in areas such as, intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. The book is organized in eight main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of theoretical aspects of fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on fuzzy systems. The second part contains papers with the main theme of neural networks theory, which are basically papers dealing with new concepts and algorithms in neural networks. The third part contains papers describing applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The fourth part contains papers describing new nature-inspired optimization algorithms. The fifth part presents div...
Vinh, Nguyen Xuan; Chetty, Madhu; Coppel, Ross; Wangikar, Pramod P
2011-10-01
Dynamic Bayesian networks (DBN) are widely applied in modeling various biological networks including the gene regulatory network (GRN). Due to the NP-hard nature of learning static Bayesian network structure, most methods for learning DBN also employ either local search such as hill climbing, or a meta stochastic global optimization framework such as genetic algorithm or simulated annealing. This article presents GlobalMIT, a toolbox for learning the globally optimal DBN structure from gene expression data. We propose using a recently introduced information theoretic-based scoring metric named mutual information test (MIT). With MIT, the task of learning the globally optimal DBN is efficiently achieved in polynomial time. The toolbox, implemented in Matlab and C++, is available at http://code.google.com/p/globalmit. vinh.nguyen@monash.edu; madhu.chetty@monash.edu Supplementary data is available at Bioinformatics online.
Directory of Open Access Journals (Sweden)
Ming-Hua Lin
2012-01-01
Full Text Available This paper investigates an incentive pricing problem for relaying services in multihop cellular networks. Providing incentives to encourage mobile nodes to relay data is a critical factor in building successful multihop cellular networks. Most existing approaches adopt fixed-rate or location-based pricing on rewarding packets forwarding. This study applies a mathematical programming model to determine an optimal incentive price for each intermediate node that provides relaying services. Under the obtained incentive price, the connection availability of the networks is maximized by using the same relaying costs as other pricing schemes. A signomial geometric programming problem is constructed, and a deterministic optimization approach is employed to solve the problem. Besides, quality-of-service constraints are added in the proposed model to mitigate the unfairness between connection availabilities of individual nodes. Computational results demonstrate that the proposed model obtains the optimal incentive price on relaying services to maximize connection availability of the networks.
DEFF Research Database (Denmark)
Chittur Ramaswamy, Parvathy; Tant, Jeroen; Pillai, Jayakrishnan Radhakrishna
2015-01-01
This paper develops three novel methodologies for optimal reconfiguration of distribution networks in the presence of distributed energy resources (DERs). The novelty is in achieving a non-conservative and robust solution to grid reconfiguration. The optimal solution is the minimum......-loss-configuration of the distribution network taking into account the cost of switching and the grid operational constraints. The methods are based on the concepts of receding horizon control (RHC) and scenario analysis (SA) which inherently optimize switching costs and losses. The salient feature of incorporating RHC and SA...... of the distribution network since they indicate the most frequently open switches in the network and the time at which they are to be operated....
REG and GREAT, two networks to optimize Gaia scientific exploitation
Figueras, F.; Jordi, C.; Spanish Participants in Reg; Great
2013-05-01
The launch of Gaia satellite by the European Space Agency is a year ahead (last quarter of 2013), and Spanish and European community have already out in place two networks devoted to the preparation of the scientific exploitation of the data acquired by the satellite: GREAT (Gaia Research for European Astronomy Training), funded by the European Science Foundation and by Marie Curie Actions in its People 7th Programme, and REG (Spanish Network for the scientific exploitation of Gaia) funded by MINECO. These networks, which are open to the international community, have adopted the challenges of Gaia mission: to revolutionize our understanding of the Milky Way and its components, trace the distribution of dark matter in the local universe, validate and improve models of stellar structure and evolution, characterizing solar system objects, ... and many more. Both networks promote the close interaction among researchers of different institutes, by supporting short and long exchange visits, workshops, schools and large conferences. Currently, 128 Spanish people actively participate in the several working groups in GREAT and REG and 2 students are performing their PhD in the framework of the GREAT-ITN Spanish node. This paper provides detailed information about the structure of these networks, the Spanish participation, and present and future tasks that are foreseen.
Efficient Capacity Computation and Power Optimization for Relay Networks
Parvaresh, Farzad
2011-01-01
The capacity or approximations to capacity of various single-source single-destination relay network models has been characterized in terms of the cut-set upper bound. In principle, a direct computation of this bound requires evaluating the cut capacity over exponentially many cuts. We show that the minimum cut capacity of a relay network under some special assumptions can be cast as a minimization of a submodular function, and as a result, can be computed efficiently. We use this result to show that the capacity, or an approximation to the capacity within a constant gap for the Gaussian, wireless erasure, and Avestimehr-Diggavi-Tse deterministic relay network models can be computed in polynomial time. We present some empirical results showing that computing constant-gap approximations to the capacity of Gaussian relay networks with around 300 nodes can be done in order of minutes. For Gaussian networks, cut-set capacities are also functions of the powers assigned to the nodes. We consider a family of power o...
Optimal operations and resilient investments in steam networks
Directory of Open Access Journals (Sweden)
Stephane Laurent Bungener
2016-01-01
Full Text Available Steam is a key energy vector for industrial sites, most commonly used for process heating and cooling, cogeneration of heat and mechanical power, as a motive fluid or for stripping. Steam networks are used to carry steam from producers to consumers and between pressure levels through letdowns and steam turbines. The steam producers (boilers, heat and power cogeneration units, heat exchangers, chemical reactors should be sized to supply the consumers at nominal operating conditions as well as peak demand.This paper firstly proposes an Mixed Integer Linear Programming formulation to optimise the operations of steam networks in normal operating conditions and exceptional demand (when operating reserves fall to zero, through the introduction of load shedding. Optimisation of investments based on operational and investment costs are included in the formulation.Though rare, boiler failures can have a heavy impact of steam network operations and costs, leading to undercapacity and unit shutdowns. A method is therefore proposed to simulate steam network operations when facing boiler failures. Key performance indicators are introduced to quantify the network's resilience.The proposed methods are applied and demonstrated in an industrial case study using industrial data. The results indicate the importance of oversizing key steam producing equipments and the value of industrial symbiosis to increase industrial site resilience.
Chiadamrong, N.; Piyathanavong, V.
2017-04-01
Models that aim to optimize the design of supply chain networks have gained more interest in the supply chain literature. Mixed-integer linear programming and discrete-event simulation are widely used for such an optimization problem. We present a hybrid approach to support decisions for supply chain network design using a combination of analytical and discrete-event simulation models. The proposed approach is based on iterative procedures until the difference between subsequent solutions satisfies the pre-determined termination criteria. The effectiveness of proposed approach is illustrated by an example, which shows closer to optimal results with much faster solving time than the results obtained from the conventional simulation-based optimization model. The efficacy of this proposed hybrid approach is promising and can be applied as a powerful tool in designing a real supply chain network. It also provides the possibility to model and solve more realistic problems, which incorporate dynamism and uncertainty.
Optimal Multiuser Zero Forcing with Per-Antenna Power Constraints for Network MIMO Coordination
Directory of Open Access Journals (Sweden)
Kaviani Saeed
2011-01-01
Full Text Available We consider a multicell multiple-input multiple-output (MIMO coordinated downlink transmission, also known as network MIMO, under per-antenna power constraints. We investigate a simple multiuser zero-forcing (ZF linear precoding technique known as block diagonalization (BD for network MIMO. The optimal form of BD with per-antenna power constraints is proposed. It involves a novel approach of optimizing the precoding matrices over the entire null space of other users' transmissions. An iterative gradient descent method is derived by solving the dual of the throughput maximization problem, which finds the optimal precoding matrices globally and efficiently. The comprehensive simulations illustrate several network MIMO coordination advantages when the optimal BD scheme is used. Its achievable throughput is compared with the capacity region obtained through the recently established duality concept under per-antenna power constraints.
Competition policy and optimal retail network development in transitional economies
Directory of Open Access Journals (Sweden)
Lovreta Stipe
2013-01-01
Full Text Available The choice of retail store location is a very complex process, with many different stakeholders having interests in both the micro and macro locations. The goal of this work is to contribute to the better understanding of the different interests of corporative and public policies in choosing retail store locations, in order to enable more efficient and effective trade network development. After having slowed down as a consequence of the global economic crisis, the retail sector is experiencing strong expansion in the markets of transitional countries. Insufficient engagement of public policy in planning trade networks can violate market competition. An active government role in carrying out the policy of retail network development in transitional countries is necessary to maintain the level of competition and prevent big market players abusing their dominant position.
Wireless Mesh Networks Path Planning, Power Control and Optimal Solutions
Directory of Open Access Journals (Sweden)
G. Guru Charan
2014-06-01
Full Text Available Wireless mesh networks are considerd as a potential attractive alternative to provide Broadband acces to users. In this paper we address the following two questions: (i Given a set of nodes with arbitrary locations, and a set of data rows, what is the max-min achievable throughput? And (ii How should the network be configured to achieve the optimum? Given a set of nodes with arbitrary locations, and a set of data rows specified as source-destination pairs, what is the maximum achievable throughput, under certain constraints on the radio parameters in particular, on transmit power. How should the network be configured to achieve this maximum? Specifically, by configuration, we mean the complete choice of the set of links (i.e., topology, the routes, link schedules, and transmit powers and modulation schemes for each link.
Converged Wireless Networking and Optimization for Next Generation Services
Directory of Open Access Journals (Sweden)
J. Rodriguez
2010-01-01
Full Text Available The Next Generation Network (NGN vision is tending towards the convergence of internet and mobile services providing the impetus for new market opportunities in combining the appealing services of internet with the roaming capability of mobile networks. However, this convergence does not go far enough, and with the emergence of new coexistence scenarios, there is a clear need to evolve the current architecture to provide cost-effective end-to-end communication. The LOOP project, a EUREKA-CELTIC driven initiative, is one piece in the jigsaw by helping European industry to sustain a leading role in telecommunications and manufacturing of high-value products and machinery by delivering pioneering converged wireless networking solutions that can be successfully demonstrated. This paper provides an overview of the LOOP project and the key achievements that have been tunneled into first prototypes for showcasing next generation services for operators and process manufacturers.
RECOVERY ACT - Robust Optimization for Connectivity and Flows in Dynamic Complex Networks
Energy Technology Data Exchange (ETDEWEB)
Balasundaram, Balabhaskar [Oklahoma State Univ., Stillwater, OK (United States); Butenko, Sergiy [Texas A & M Univ., College Station, TX (United States); Boginski, Vladimir [Univ. of Florida, Gainesville, FL (United States); Uryasev, Stan [Univ. of Florida, Gainesville, FL (United States)
2013-12-25
The goal of this project was to study robust connectivity and flow patterns of complex multi-scale systems modeled as networks. Networks provide effective ways to study global, system level properties, as well as local, multi-scale interactions at a component level. Numerous applications from power systems, telecommunication, transportation, biology, social science, and other areas have benefited from novel network-based models and their analysis. Modeling and optimization techniques that employ appropriate measures of risk for identifying robust clusters and resilient network designs in networks subject to uncertain failures were investigated in this collaborative multi-university project. In many practical situations one has to deal with uncertainties associated with possible failures of network components, thereby affecting the overall efficiency and performance of the system (e.g., every node/connection has a probability of partial or complete failure). Some extreme examples include power grid component failures, airline hub failures due to weather, or freeway closures due to emergencies. These are also situations in which people, materials, or other resources need to be managed efficiently. Important practical examples include rerouting flow through power grids, adjusting flight plans, and identifying routes for emergency services and supplies, in the event network elements fail unexpectedly. Solutions that are robust under uncertainty, in addition to being economically efficient, are needed. This project has led to the development of novel models and methodologies that can tackle the optimization problems arising in such situations. A number of new concepts, which have not been previously applied in this setting, were investigated in the framework of the project. The results can potentially help decision-makers to better control and identify robust or risk-averse decisions in such situations. Formulations and optimal solutions of the considered problems need
Bode, F.; Reuschen, S.; Nowak, W.
2015-12-01
Drinking-water well catchments include many potential sources of contaminations like gas stations or agriculture. Finding optimal positions of early-warning monitoring wells is challenging because there are various parameters (and their uncertainties) that influence the reliability and optimality of any suggested monitoring location or monitoring network.The overall goal of this project is to develop and establish a concept to assess, design and optimize early-warning systems within well catchments. Such optimal monitoring networks need to optimize three competing objectives: a high detection probability, which can be reached by maximizing the "field of vision" of the monitoring network, a long early-warning time such that there is enough time left to install counter measures after first detection, and the overall operating costs of the monitoring network, which should ideally be reduced to a minimum. The method is based on numerical simulation of flow and transport in heterogeneous porous media coupled with geostatistics and Monte-Carlo, scenario analyses for real data, respectively, wrapped up within the framework of formal multi-objective optimization using a genetic algorithm.In order to speed up the optimization process and to better explore the Pareto-front, we developed a concept that forces the algorithm to search only in regions of the search space where promising solutions can be expected. We are going to show how to define these regions beforehand, using knowledge of the optimization problem, but also how to define them independently of problem attributes. With that, our method can be used with and/or without detailed knowledge of the objective functions.In summary, our study helps to improve optimization results in less optimization time by meaningful restrictions of the search space. These restrictions can be done independently of the optimization problem, but also in a problem-specific manner.
Optimization of a large-scale microseismic monitoring network in northern Switzerland
Kraft, Toni; Mignan, Arnaud; Giardini, Domenico
2013-10-01
We have developed a network optimization method for regional-scale microseismic monitoring networks and applied it to optimize the densification of the existing seismic network in northeastern Switzerland. The new network will build the backbone of a 10-yr study on the neotectonic activity of this area that will help to better constrain the seismic hazard imposed on nuclear power plants and waste repository sites. This task defined the requirements regarding location precision (0.5 km in epicentre and 2 km in source depth) and detection capability [magnitude of completeness Mc = 1.0 (ML)]. The goal of the optimization was to find the geometry and size of the network that met these requirements. Existing stations in Switzerland, Germany and Austria were considered in the optimization procedure. We based the optimization on the simulated annealing approach proposed by Hardt & Scherbaum, which aims to minimize the volume of the error ellipsoid of the linearized earthquake location problem (D-criterion). We have extended their algorithm to: calculate traveltimes of seismic body waves using a finite difference ray tracer and the 3-D velocity model of Switzerland, calculate seismic body-wave amplitudes at arbitrary stations assuming the Brune source model and using scaling and attenuation relations recently derived for Switzerland, and estimate the noise level at arbitrary locations within Switzerland using a first-order ambient seismic noise model based on 14 land-use classes defined by the EU-project CORINE and open GIS data. We calculated optimized geometries for networks with 10-35 added stations and tested the stability of the optimization result by repeated runs with changing initial conditions. Further, we estimated the attainable magnitude of completeness (Mc) for the different sized optimal networks using the Bayesian Magnitude of Completeness (BMC) method introduced by Mignan et al. The algorithm developed in this study is also applicable to smaller
Cost-effective solution to synchronised audio-visual data capture using multiple sensors
Lee, T.; Lichtenauer, Jeroen; Soatto, S.; Shen, Jie; Valstar, Michel; Pantic, Maja
Applications such as surveillance and human behaviour analysis require high-bandwidth recording from multiple cameras, as well as from other sensors. In turn, sensor fusion has increased the required accuracy of synchronisation between sensors. Using commercial off-the-shelf components may
The synchronisation of oestrus in sheep. 1. Dosage and time of ...
African Journals Online (AJOL)
The synchronisation of oestrus in sheep. 1. Dosage and time of prostaglandin administration following progestagen pretreatment. J.P.C. Greyling, J.M. van der Westhuysen, C.H. van Niekerk. Abstract. No Abstract. Full Text: EMAIL FREE FULL TEXT EMAIL FREE FULL TEXT · DOWNLOAD FULL TEXT DOWNLOAD FULL ...
Agbani, Ejaife O; Williams, Christopher M; Hers, Ingeborg; Poole, Alastair W
2017-06-05
Human platelet transformation into balloons is part of the haemostatic response and thrombus architecture. Here we reveal that in aggregates of platelets in plasma, ballooning in multiple platelets occurs in a synchronised manner. This suggests a mechanism of coordination between cells, previously unrecognised. We aimed to understand this mechanism, and how it may contribute to thrombus development. Using spinning-disc confocal microscopy we visualised membrane ballooning in human platelet aggregates adherent to collagen-coated surfaces. Within an aggregate, multiple platelets undergo ballooning in a synchronised fashion, dependent upon extracellular calcium, in a manner that followed peak cytosolic calcium levels in the aggregate. Synchrony was observed in platelets within but not between aggregates, suggesting a level of intra-thrombus communication. Blocking phosphatidylserine, inhibiting thrombin or blocking PAR1 receptor, largely prevented synchrony without blocking ballooning itself. In contrast, inhibition of connexins, P2Y12, P2Y1 or thromboxane formation had no effect on synchrony or ballooning. Importantly, synchronised ballooning was closely followed by a surge in microvesicle formation, which was absent when synchrony was blocked. Our data demonstrate that the mechanism underlying synchronised membrane ballooning requires thrombin generation acting effectively in a positive feedback loop, mediating a subsequent surge in procoagulant activity and microvesicle release.
Location Assisted Handover Optimization for Heterogeneous Wireless Networks
DEFF Research Database (Denmark)
Nielsen, Jimmy Jessen; Madsen, Tatiana Kozlova; Schwefel, Hans-Peter
2011-01-01
Mobile users typically experience better connectivity if their mobile device performs handover to an available WiFi network rather than using a cellular network. For a moving user the window of opportunity is limited and the timing of the handover is therefore crucial. In this work we propose two...... of these algorithms for a mobile user in an urban scenario with ubiquitous cellular coverage and 250 WiFi APs/km2, and compared the results to a hysteresis-based greedy algorithm and the case of ”always cellular-connected”. Our results show that the proposed look-ahead algorithms outperform the hysteresis...
Addressing the Influence of Hidden State on Wireless Network Optimizations using Performance Maps
DEFF Research Database (Denmark)
Højgaard-Hansen, Kim; Madsen, Tatiana Kozlova; Schwefel, Hans-Peter
2015-01-01
Performance of wireless connectivity for network client devices is location dependent. It has been shown that it can be beneficial to collect network performance metrics along with location information to generate maps of the location dependent network performance. These performance maps can...... be used to optimize the use of the wireless net- work by predicting future network performance and scheduling the net- work communication for certain applications on mobile devices. However, other important factors influence the performance of the wireless communication such as changes in the propagation...... environment and resource sharing. In this work we extend the framework of performance maps for wireless networks by introducing network state as an abstraction for all other factors than location that influence the performance. Since network state might not always be directly observable the framework...
Directory of Open Access Journals (Sweden)
Syed Bilal Hussain Shah
2017-01-01
Full Text Available In Wireless Sensors Networks (WSNs, researcher’s main focus is on energy preservation and prolonging network lifetime. More energy resources are required in case of remote applications of WSNs, where some of the nodes die early that shorten the lifetime and decrease the stability of the network. It is mainly caused due to the non-optimal Cluster Heads (CHs selection based on single criterion and uneven distribution of energy. We propose a new clustering protocol for both homogeneous and heterogeneous environments, named as Optimized Path planning algorithm with Energy efficiency and Extending Network lifetime in WSN (OPEN. In the proposed protocol, timer value concept is used for CH selection based on multiple criteria. Simulation results prove that OPEN performs better than the existing protocols in terms of the network lifetime, throughput and stability. The results explicitly explain the cluster head selection of OPEN protocol and efficient solution of uneven energy distribution problem.
A mathematical model for optimization of an integrated network logistic design
Directory of Open Access Journals (Sweden)
Lida Tafaghodi
2011-10-01
Full Text Available In this study, the integrated forward/reverse logistics network is investigated, and a capacitated multi-stage, multi-product logistics network design is proposed by formulating a generalized logistics network problem into a mixed-integer nonlinear programming model (MINLP for minimizing the total cost of the closed-loop supply chain network. Moreover, the proposed model is solved by using optimization solver, which provides the decisions related to the facility location problem, optimum quantity of shipped product, and facility capacity. Numerical results show the power of the proposed MINLP model to avoid th sub-optimality caused by separate design of forward and reverse logistics networks and to handle various transportation modes and periodic demand.
A Particle Swarm Optimization Algorithm for Neural Networks in Recognition of Maize Leaf Diseases
Directory of Open Access Journals (Sweden)
Zhiyong ZHANG
2014-03-01
Full Text Available The neural networks have significance on recognition of crops disease diagnosis? but it has disadvantage of slow convergent speed and shortcoming of local optimum. In order to identify the maize leaf diseases by using machine vision more accurately, we propose an improved particle swarm optimization algorithm for neural networks. With the algorithm, the neural network property is improved. It reasonably confirms threshold and connection weight of neural network, and improves capability of solving problems in the image recognition. At last, an example of the emulation shows that neural network model based on recognizes significantly better than without optimization. Model accuracy has been improved to a certain extent to meet the actual needs of maize leaf diseases recognition.
A two-layer recurrent neural network for nonsmooth convex optimization problems.
Qin, Sitian; Xue, Xiaoping
2015-06-01
In this paper, a two-layer recurrent neural network is proposed to solve the nonsmooth convex optimization problem subject to convex inequality and linear equality constraints. Compared with existing neural network models, the proposed neural network has a low model complexity and avoids penalty parameters. It is proved that from any initial point, the state of the proposed neural network reaches the equality feasible region in finite time and stays there thereafter. Moreover, the state is unique if the initial point lies in the equality feasible region. The equilibrium point set of the proposed neural network is proved to be equivalent to the Karush-Kuhn-Tucker optimality set of the original optimization problem. It is further proved that the equilibrium point of the proposed neural network is stable in the sense of Lyapunov. Moreover, from any initial point, the state is proved to be convergent to an equilibrium point of the proposed neural network. Finally, as applications, the proposed neural network is used to solve nonlinear convex programming with linear constraints and L1 -norm minimization problems.
Geotechnology-Based Modeling to Optimize Conservation of Forest Network in Urban Area
Teng, Mingjun; Zhou, Zhixiang; Wang, Pengcheng; Xiao, Wenfa; Wu, Changguang; Lord, Elizabeth
2016-03-01
Forest network development in urban areas faces the challenge from forest fragmentation, human-induced disturbances, and scarce land resources. Here, we proposed a geotechnology-based modeling to optimize conservation of forest network by a case study of Wuhan, China. The potential forest network and their priorities were assessed using an improved least-cost path model and potential utilization efficiency estimation. The modeling process consists of four steps: (i) developing species assemblages, (ii) identifying core forest patches, (iii) identifying potential linkages among core forest patches, and (iv) demarcating forest networks. As a result, three species assemblages, including mammals, pheasants, and other birds, were identified as the conservation targets of urban forest network (UFN) in Wuhan, China. Based on the geotechnology-based model, a forest network proposal was proposed to fulfill the connectivity requirements of selected species assemblages. The proposal consists of seven forest networks at three levels of connectivity, named ideal networks, backbone networks, and comprehensive network. The action priorities of UFN plans were suggested to optimize forest network in the study area. Additionally, a total of 45 forest patches with important conservation significance were identified as prioritized stepping-stone patches in the forest network development. Urban forest conserve was also suggested for preserving woodlands with priority conservation significance. The presented geotechnology-based modeling is fit for planning and optimizing UFNs, because of the inclusion of the stepping-stone effects, human-induced pressures, and priorities. The framework can also be applied to other areas after a sensitivity test of the model and the modification of the parameters to fit the local environment.
Geotechnology-Based Modeling to Optimize Conservation of Forest Network in Urban Area.
Teng, Mingjun; Zhou, Zhixiang; Wang, Pengcheng; Xiao, Wenfa; Wu, Changguang; Lord, Elizabeth
2016-03-01
Forest network development in urban areas faces the challenge from forest fragmentation, human-induced disturbances, and scarce land resources. Here, we proposed a geotechnology-based modeling to optimize conservation of forest network by a case study of Wuhan, China. The potential forest network and their priorities were assessed using an improved least-cost path model and potential utilization efficiency estimation. The modeling process consists of four steps: (i) developing species assemblages, (ii) identifying core forest patches, (iii) identifying potential linkages among core forest patches, and (iv) demarcating forest networks. As a result, three species assemblages, including mammals, pheasants, and other birds, were identified as the conservation targets of urban forest network (UFN) in Wuhan, China. Based on the geotechnology-based model, a forest network proposal was proposed to fulfill the connectivity requirements of selected species assemblages. The proposal consists of seven forest networks at three levels of connectivity, named ideal networks, backbone networks, and comprehensive network. The action priorities of UFN plans were suggested to optimize forest network in the study area. Additionally, a total of 45 forest patches with important conservation significance were identified as prioritized stepping-stone patches in the forest network development. Urban forest conserve was also suggested for preserving woodlands with priority conservation significance. The presented geotechnology-based modeling is fit for planning and optimizing UFNs, because of the inclusion of the stepping-stone effects, human-induced pressures, and priorities. The framework can also be applied to other areas after a sensitivity test of the model and the modification of the parameters to fit the local environment.
Migrating Storms and Optimal Control of Urban Sewer Networks
Directory of Open Access Journals (Sweden)
Upaka Rathnayake
2015-11-01
Full Text Available Uniform storms are generally applied in most of the research on sewer systems. This is for modeling simplicity. However, in the real world, these conditions may not be applicable. It is very important to consider the migration behavior of storms not only in the design of combined sewers, but also in controlling them. Therefore, this research was carried out to improve Rathnayake and Tanyimboh’s optimal control algorithm for migrating storms. Promising results were found from the model improvement. Feasible solutions were obtained from the multi-objective optimization and, in addition, the role of on-line storage tanks was well placed.
Aguirre, Erik; Lopez-Iturri, Peio; Azpilicueta, Leire; Astrain, José Javier; Villadangos, Jesús; Falcone, Francisco
2015-02-05
One of the main challenges in the implementation and design of context-aware scenarios is the adequate deployment strategy for Wireless Sensor Networks (WSNs), mainly due to the strong dependence of the radiofrequency physical layer with the surrounding media, which can lead to non-optimal network designs. In this work, radioplanning analysis for WSN deployment is proposed by employing a deterministic 3D ray launching technique in order to provide insight into complex wireless channel behavior in context-aware indoor scenarios. The proposed radioplanning procedure is validated with a testbed implemented with a Mobile Ad Hoc Network WSN following a chain configuration, enabling the analysis and assessment of a rich variety of parameters, such as received signal level, signal quality and estimation of power consumption. The adoption of deterministic radio channel techniques allows the design and further deployment of WSNs in heterogeneous wireless scenarios with optimized behavior in terms of coverage, capacity, quality of service and energy consumption.
Optimal operation of water distribution networks by predictive control ...
African Journals Online (AJOL)
This paper presents an approach for the operational optimisation of potable water distribution networks. The maximisation of the use of low-cost power (e.g. overnight pumping) and the maintenance of a target chlorine concentration at final delivery points were defined as important optimisation objectives. The first objective ...
Camera Network Coverage Improving by Particle Swarm Optimization
Xu, Y.C.; Lei, B.; Hendriks, E.A.
2011-01-01
This paper studies how to improve the field of view (FOV) coverage of a camera network. We focus on a special but practical scenario where the cameras are randomly scattered in a wide area and each camera may adjust its orientation but cannot move in any direction. We propose a particle swarm
Optimizing the performance of a data vortex interconnection network
Shacham, Assaf; Bergman, Keren
2007-04-01
The definition of the data vortex architecture leaves broad room for decisions regarding the exact design point required for achieving a desired performance level. A detailed simulation-based study of various parameters that affect a data vortex interconnection network's performance is reported. Three implementations are compared by acceptance rate, latency, and cost.
Optimal Tax Routing : Network Analysis of FDI Diversion
van 't Riet, Maarten; Lejour, Arjen
2017-01-01
The international corporate tax system is considered as a network and, just like for transportation, ‘shortest’ paths are computed, minimizing tax payments for multinational enterprises when repatriating profits. We include corporate income tax rates, withholding taxes on dividends, double tax
Cooperative driving in mixed traffic networks - Optimizing for performance
Calvert, S.C.; Broek, T.H.A. van den; Noort, M. van
2012-01-01
This paper discusses a cooperative adaptive cruise control application and its effects on the traffic system. In previous work this application has been tested on the road, and traffic simulation has been used to scale up the results of the field test to larger networks and more vehicles. The
On Optimal Policies for Network-Coded Cooperation
DEFF Research Database (Denmark)
Khamfroush, Hana; Roetter, Daniel Enrique Lucani; Pahlevani, Peyman
2015-01-01
's Raspberry Pi testbed and compared with random linear network coding (RLNC) broadcast in terms of completion time, total number of required transmissions, and percentage of delivered generations. Our measurements show that enabling cooperation only among pairs of devices can decrease the completion time...
Stochastic Optimization for Network-Constrained Power System Scheduling Problem
Directory of Open Access Journals (Sweden)
D. F. Teshome
2015-01-01
Full Text Available The stochastic nature of demand and wind generation has a considerable effect on solving the scheduling problem of a modern power system. Network constraints such as power flow equations and transmission capacities also need to be considered for a comprehensive approach to model renewable energy integration and analyze generation system flexibility. Firstly, this paper accounts for the stochastic inputs in such a way that the uncertainties are modeled as normally distributed forecast errors. The forecast errors are then superimposed on the outputs of load and wind forecasting tools. Secondly, it efficiently models the network constraints and tests an iterative algorithm and a piecewise linear approximation for representing transmission losses in mixed integer linear programming (MILP. It also integrates load shedding according to priority factors set by the system operator. Moreover, the different interactions among stochastic programming, network constraints, and prioritized load shedding are thoroughly investigated in the paper. The stochastic model is tested on a power system adopted from Jeju Island, South Korea. Results demonstrate the impact of wind speed variability and network constraints on the flexibility of the generation system. Further analysis shows the effect of loss modeling approaches on total cost, accuracy, computational time, and memory requirement.
An optimization approach for district heating strategic network design
Bordin, Chiara; Gordini, Angelo; Vigo, Daniele
2016-01-01
District heating systems provide the heat generated in a centralized location to a set of users for their residential and commercial heating requirements. Heat distribution is generally obtained by using hot water or steam flowing through a closed network of insulated pipes and heat exchange
On Optimal Policies for Network Coded Cooperation: Theory and Implementation
DEFF Research Database (Denmark)
Khamfroush, Hana; Lucani Rötter, Daniel Enrique; Pahlevani, Peyman
2014-01-01
's Raspberry Pi testbed and compared with random linear network coding (RLNC) broadcast in terms of completion time, total number of required transmissions, and percentage of delivered generations. Our measurements show that enabling cooperation only among pairs of devices can decrease the completion time...
Routing Optimization of AVB Streams in TSN Networks
DEFF Research Database (Denmark)
Laursen, Sune Mølgaard; Pop, Paul; Steiner, Wilfried
2016-01-01
of the network as well as the routes and schedules of the TT streams. We are interested to determine the routing of the AVB streams such that all frames are schedulable and their worst-case end-to-end delay is minimized. We have proposed a search-space reduction technique and a Greedy Randomized Adaptive Search...
Bordier, Cécile; Nicolini, Carlo; Bifone, Angelo
2017-01-01
Neuroimaging data can be represented as networks of nodes and edges that capture the topological organization of the brain connectivity. Graph theory provides a general and powerful framework to study these networks and their structure at various scales. By way of example, community detection methods have been widely applied to investigate the modular structure of many natural networks, including brain functional connectivity networks. Sparsification procedures are often applied to remove the weakest edges, which are the most affected by experimental noise, and to reduce the density of the graph, thus making it theoretically and computationally more tractable. However, weak links may also contain significant structural information, and procedures to identify the optimal tradeoff are the subject of active research. Here, we explore the use of percolation analysis, a method grounded in statistical physics, to identify the optimal sparsification threshold for community detection in brain connectivity networks. By using synthetic networks endowed with a ground-truth modular structure and realistic topological features typical of human brain functional connectivity networks, we show that percolation analysis can be applied to identify the optimal sparsification threshold that maximizes information on the networks' community structure. We validate this approach using three different community detection methods widely applied to the analysis of brain connectivity networks: Newman's modularity, InfoMap and Asymptotical Surprise. Importantly, we test the effects of noise and data variability, which are critical factors to determine the optimal threshold. This data-driven method should prove particularly useful in the analysis of the community structure of brain networks in populations characterized by different connectivity strengths, such as patients and controls.