Search results for: complex networks

Authors: Irina Astrova, Arne Koschel, Jan Lukanowski, Jose Luis Munoz Martinez, Valerij Procenko, Marc Schaaf

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In this paper, five ontologies are described, which include the event concepts. The paper provides an overview and comparison of existing event models. The main criteria for comparison are that there should be possibilities to model events with stretch in the time and location and participation of objects; however, there are other factors that should be taken into account as well. The paper also shows an example of using ontologies in complex event processing.

Keywords: Ontologies, events, complex event processing (CEP).

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Authors: Dimitrios Koukopoulos

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In this work, we study the impact of dynamically changing link slowdowns on the stability properties of packetswitched networks under the Adversarial Queueing Theory framework. Especially, we consider the Adversarial, Quasi-Static Slowdown Queueing Theory model, where each link slowdown may take on values in the two-valued set of integers {1, D} with D > 1 which remain fixed for a long time, under a (w, p)-adversary. In this framework, we present an innovative systematic construction for the estimation of adversarial injection rate lower bounds, which, if exceeded, cause instability in networks that use the LIS (Longest-in- System) protocol for contention-resolution. In addition, we show that a network that uses the LIS protocol for contention-resolution may result in dropping its instability bound at injection rates p > 0 when the network size and the high slowdown D take large values. This is the best ever known instability lower bound for LIS networks.

Keywords: Parallel computing, network stability, adversarial queuing theory, greedy scheduling protocols.

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Authors: S. Moein, S. A. Monadjemi, P. Moallem

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In this paper, application of artificial neural networks in typical disease diagnosis has been investigated. The real procedure of medical diagnosis which usually is employed by physicians was analyzed and converted to a machine implementable format. Then after selecting some symptoms of eight different diseases, a data set contains the information of a few hundreds cases was configured and applied to a MLP neural network. The results of the experiments and also the advantages of using a fuzzy approach were discussed as well. Outcomes suggest the role of effective symptoms selection and the advantages of data fuzzificaton on a neural networks-based automatic medical diagnosis system.

Keywords: Artificial Neural Networks, Fuzzy Logic, MedicalDiagnosis, Symptoms, Fuzzification.

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Authors: M Tayari, SZ Moosavi-nejad, A Shabani, M Rezaei Tavirani

Abstract:

Free Hemoglobin promotes the accumulation of hydroxyl radicals by the heme iron, which can react with endogenous hydrogen peroxide to produce free radicals which may cause severe oxidative cell damage. Haptoglobin binds to Hemoglobin strongly and Haptoglobin-Hemoglobin binding is irreversible. Peroxidase activity of Haptoglobin(2-2)-Hemoglobin complex was assayed by following increase of absorption of produced tetraguaiacol as the second substrate of Haptoglobin-Hemoglobin complex at 470 nm and 42°C by UV-Vis spectrophotometer. The results have shown that peroxidase activity of Haptoglobin(2-2)-Hemoglobin complex is modulated via homotropic effect of hydrogen peroxide as allostric substrate. On the other hand antioxidant property of Haptoglobin(2- 2)-Hemoglobin was increased via heterotropic effect of the two drugs (especially ampicillin) on peroxidase activity of the complex. Both drugs also have mild effect on quality of homotropic property of peroxidase activity of Haptoglobin(2-2)-Hemoglobin complex. Therefore, in vitro studies show that the two drugs may help Hp-Hb complex to remove hydrogen peroxide from serum at pathologic temperature ature (42 C).

Keywords: Haptoglobin, Hemoglobin, Antioxidant, Antibiotics.

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Authors: Qingqing Wang, Baocheng Chen, Shouming Zhong

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In this paper, the problem of stability criteria for Markovian jumping BAM neural networks with leakage and discrete delays has been investigated. Some new sufficient condition are derived based on a novel Lyapunov-Krasovskii functional approach. These new criteria based on delay partitioning idea are proved to be less conservative because free-weighting matrices method and a convex optimization approach are considered. Finally, one numerical example is given to illustrate the the usefulness and feasibility of the proposed main results.

Keywords: Stability, Markovian jumping neural networks, Timevarying delays, Linear matrix inequality.

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Authors: Liyuan Wang, Shouming Zhong

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In this paper, the design problem of state estimator for neural networks with the mixed time-varying delays are investigated by constructing appropriate Lyapunov-Krasovskii functionals and using some effective mathematical techniques. In order to derive several conditions to guarantee the estimation error systems to be globally exponential stable, we transform the considered systems into the neural-type time-delay systems. Then with a set of linear inequalities(LMIs), we can obtain the stable criteria. Finally, three numerical examples are given to show the effectiveness and less conservatism of the proposed criterion.

Keywords: State estimator, Neural networks, Globally exponential stability.

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Authors: J. Fernandez de Canete, S. Gonzalez-Perez, P. del Saz-Orozco

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LABVIEW is a graphical programming language that has its roots in automation control and data acquisition. In this paper we have utilized this platform to provide a powerful toolset for process identification and control of nonlinear systems based on artificial neural networks (ANN). This tool has been applied to the monitoring and control of a lab-scale distillation column DELTALAB DC-SP. The proposed control scheme offers high speed of response for changes in set points and null stationary error for dual composition control and shows robustness in presence of externally imposed disturbance.

Keywords: Distillation, neural networks, LABVIEW, monitoring, identification, control.

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Authors: Salvatore Marra, Francesco C. Morabito

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In this paper we present an efficient approach for the prediction of two sunspot-related time series, namely the Yearly Sunspot Number and the IR5 Index, that are commonly used for monitoring solar activity. The method is based on exploiting partially recurrent Elman networks and it can be divided into three main steps: the first one consists in a “de-rectification" of the time series under study in order to obtain a new time series whose appearance, similar to a sum of sinusoids, can be modelled by our neural networks much better than the original dataset. After that, we normalize the derectified data so that they have zero mean and unity standard deviation and, finally, train an Elman network with only one input, a recurrent hidden layer and one output using a back-propagation algorithm with variable learning rate and momentum. The achieved results have shown the efficiency of this approach that, although very simple, can perform better than most of the existing solar activity forecasting methods.

Keywords: Elman neural networks, sunspot, solar activity, time series prediction.

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Authors: Qingqing Wang, Shouming Zhong

Abstract:

In this paper, delay-dependent stability analysis for neutral type neural networks with uncertain paramters and time-varying delay is studied. By constructing new Lyapunov-Krasovskii functional and dividing the delay interval into multiple segments, a novel sufficient condition is established to guarantee the globally asymptotically stability of the considered system. Finally, a numerical example is provided to illustrate the usefulness of the proposed main results.

Keywords: Neutral type neural networks, Time-varying delay, Stability, Linear matrix inequality(LMI).

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Authors: Chi-Fang Huang, Yun-Shiow Chen, Yun-Kung Chung

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In the past decade, artificial neural networks (ANNs) have been regarded as an instrument for problem-solving and decision-making; indeed, they have already done with a substantial efficiency and effectiveness improvement in industries and businesses. In this paper, the Back-Propagation neural Networks (BPNs) will be modulated to demonstrate the performance of the collaborative forecasting (CF) function of a Collaborative Planning, Forecasting and Replenishment (CPFR®) system. CPFR functions the balance between the sufficient product supply and the necessary customer demand in a Supply and Demand Chain (SDC). Several classical standard BPN will be grouped, collaborated and exploited for the easy implementation of the proposed modular ANN framework based on the topology of a SDC. Each individual BPN is applied as a modular tool to perform the task of forecasting SKUs (Stock-Keeping Units) levels that are managed and supervised at a POS (point of sale), a wholesaler, and a manufacturer in an SDC. The proposed modular BPN-based CF system will be exemplified and experimentally verified using lots of datasets of the simulated SDC. The experimental results showed that a complex CF problem can be divided into a group of simpler sub-problems based on the single independent trading partners distributed over SDC, and its SKU forecasting accuracy was satisfied when the system forecasted values compared to the original simulated SDC data. The primary task of implementing an autonomous CF involves the study of supervised ANN learning methodology which aims at making “knowledgeable" decision for the best SKU sales plan and stocks management.

Keywords: CPFR, artificial neural networks, global logistics, supply and demand chain.

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Authors: Frank Emmert Streib, Matthias Dehmer, Gökhan H. Bakır, Max Mühlhauser

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In this paper we investigate the influence of external noise on the inference of network structures. The purpose of our simulations is to gain insights in the experimental design of microarray experiments to infer, e.g., transcription regulatory networks from microarray experiments. Here external noise means, that the dynamics of the system under investigation, e.g., temporal changes of mRNA concentration, is affected by measurement errors. Additionally to external noise another problem occurs in the context of microarray experiments. Practically, it is not possible to monitor the mRNA concentration over an arbitrary long time period as demanded by the statistical methods used to learn the underlying network structure. For this reason, we use only short time series to make our simulations more biologically plausible.

Keywords: Dynamic Bayesian networks, structure learning, gene networks, Markov chain Monte Carlo, microarray data.

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Authors: Xinhua Zhang, Kelin Li

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In this paper, a class of impulsive BAM fuzzy cellular neural networks with distributed delays and reaction-diffusion terms is formulated and investigated. By employing the delay differential inequality and inequality technique developed by Xu et al., some sufficient conditions ensuring the existence, uniqueness and global exponential stability of equilibrium point for impulsive BAM fuzzy cellular neural networks with distributed delays and reaction-diffusion terms are obtained. In particular, the estimate of the exponential convergence rate is also provided, which depends on system parameters, diffusion effect and impulsive disturbed intention. It is believed that these results are significant and useful for the design and applications of BAM fuzzy cellular neural networks. An example is given to show the effectiveness of the results obtained here.

Keywords: Bi-directional associative memory, fuzzy cellular neuralnetworks, reaction-diffusion, delays, impulses, global exponentialstability.

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Authors: K. Sreelakshmi, P. Ramakanthkumar

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Predicting short term wind speed is essential in order to prevent systems in-action from the effects of strong winds. It also helps in using wind energy as an alternative source of energy, mainly for Electrical power generation. Wind speed prediction has applications in Military and civilian fields for air traffic control, rocket launch, ship navigation etc. The wind speed in near future depends on the values of other meteorological variables, such as atmospheric pressure, moisture content, humidity, rainfall etc. The values of these parameters are obtained from a nearest weather station and are used to train various forms of neural networks. The trained model of neural networks is validated using a similar set of data. The model is then used to predict the wind speed, using the same meteorological information. This paper reports an Artificial Neural Network model for short term wind speed prediction, which uses back propagation algorithm.

Keywords: Short term wind speed prediction, Neural networks, Back propagation.

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Authors: Gábor Szűcs, Gyula Sallai

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The goal of this paper is to find Wardrop equilibrium in transport networks at case of uncertainty situations, where the uncertainty comes from lack of information. We use simulation tool to find the equilibrium, which gives only approximate solution, but this is sufficient for large networks as well. In order to take the uncertainty into account we have developed an interval-based procedure for finding the paths with minimal cost using the Dempster-Shafer theory. Furthermore we have investigated the users- behaviors using game theory approach, because their path choices influence the costs of the other users- paths.

Keywords: Dempster-Shafer theory, S-O and U-Otransportation network, uncertainty of information, Wardropequilibrium.

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Authors: S. Balaji, E. Golden Julie, Y. Harold Robinson

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The context aware adaptive routing protocol is presented for unicast communication in intermittently connected mobile ad hoc networks (MANETs). The selection of the node is done by the Kalman filter prediction theory and it also makes use of utility functions. The context aware adaptive routing is defined by spray and wait technique, but the time consumption in delivering the message is too high and also the resource wastage is more. In this paper, we describe the spray and focus routing scheme for avoiding the existing problems.

Keywords: Context aware adaptive routing, Kalman filter prediction, spray and wait, spray and focus, intermittent networks.

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Authors: Bhed Bahadur Bista, Danda B. Rawat

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In this paper, we investigate multihop polling and data gathering schemes in layered sensor networks in order to extend the life time of the networks. A network consists of three layers. The lowest layer contains sensors. The middle layer contains so called super nodes with higher computational power, energy supply and longer transmission range than sensor nodes. The top layer contains a sink node. A node in each layer controls a number of nodes in lower layer by polling mechanism to gather data. We will present four types of data gathering schemes: intermediate nodes do not queue data packet, queue single packet, queue multiple packets and aggregate data, to see which data gathering scheme is more energy efficient for multihop polling in layered sensor networks.

Keywords: layered sensor network, polling, data gatheringschemes.

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Authors: Sun K. Yoo, Hee Cheol Kang

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Analysis of amplitude and phase characteristics for delta, theta, and alpha bands at localized time instant from EEG signals is important for the characterizing information processing in the brain. In this paper, complex demodulation method was used to analyze EEG (Electroencephalographic) signal, particularly for auditory evoked potential response signal, with sufficient time resolution and designated frequency bandwidth resolution required. The complex demodulation decomposes raw EEG signal into 3 designated delta, theta, and alpha bands with complex EEG signal representation at sampled time instant, which can enable the extraction of amplitude envelope and phase information. Throughout simulated test data, and real EEG signal acquired during auditory attention task, it can extract the phase offset, phase and frequency changing instant and decomposed amplitude envelope for delta, theta, and alpha bands. The complex demodulation technique can be efficiently used in brain signal analysis in case of phase, and amplitude information required.

Keywords: EEG, Complex Demodulation, Amplitude, Phase.

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Authors: Chao Wang, Yongkun Li

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In this paper, the discrete-time fuzzy BAM neural network with delays and impulses is studied. Sufficient conditions are obtained for the existence and global stability of a unique equilibrium of this class of fuzzy BAM neural networks with Lipschitzian activation functions without assuming their boundedness, monotonicity or differentiability and subjected to impulsive state displacements at fixed instants of time. Some numerical examples are given to demonstrate the effectiveness of the obtained results.

Keywords: Discrete-time fuzzy BAM neural networks, ımpulses, global exponential stability, global asymptotical stability, equilibrium point.

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Authors: Chibuzo Emeruwa, Nnamdi N. Omehe

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This work attempts to effectively compare Dropped Call Rate (DCR) against industry benchmarks and competitor networks to identify areas for improvement and sets performance targets. Four mobile telecommunication networks operational in Bayelsa State Nigeria were considered. Results obtained shows that MTN and Airtel performed well within the regulator’s benchmark of ≤ 1% in all cases, while Globacom and 9moblie had instances where their performance fell outside the benchmark. The maximum values obtained within the period in view was 18.52% and it was in March 2016 for Globacom while the least value recorded is 0.00% and it was in September 2018 for 9mobile. In the seven years period under review, MTN and Airtel performed within the Nigerian Communication Commission’s (NCC) benchmark all through. This affirms that it is possible for the networks to perform optimally if adequate measures are put in place for improved Quality of Service (QoS).

Keywords: Attempted calls, data, dropped call rate, handover failure rate, key performance indicator, mobile network.

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Authors: Marko A. Rodriguez

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There exists an injective, information-preserving function that maps a semantic network (i.e a directed labeled network) to a directed network (i.e. a directed unlabeled network). The edge label in the semantic network is represented as a topological feature of the directed network. Also, there exists an injective function that maps a directed network to an undirected network (i.e. an undirected unlabeled network). The edge directionality in the directed network is represented as a topological feature of the undirected network. Through function composition, there exists an injective function that maps a semantic network to an undirected network. Thus, aside from space constraints, the semantic network construct does not have any modeling functionality that is not possible with either a directed or undirected network representation. Two proofs of this idea will be presented. The first is a proof of the aforementioned function composition concept. The second is a simpler proof involving an undirected binary encoding of a semantic network.

Keywords: general-modeling, multi-relational networks, semantic networks

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Authors: Anant Oonsivilai, Wichai Srisuruk, Boonruang Marungsri, Thanatchai Kulworawanichpong

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Optimal routing in communication networks is a major issue to be solved. In this paper, the application of Tabu Search (TS) in the optimum routing problem where the aim is to minimize the computational time and improvement of quality of the solution in the communication have been addressed. The goal is to minimize the average delays in the communication. The effectiveness of Tabu Search method is shown by the results of simulation to solve the shortest path problem. Through this approach computational cost can be reduced.

Keywords: Communication networks, optimum routing network, tabu search algorithm, shortest path.

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Authors: Yao-Tien Wang, Hsiang-Fu Yu, Dung Chen Chiou

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In wireless and mobile communications, this progress provides opportunities for introducing new standards and improving existing services. Supporting multimedia traffic with wireless networks quality of service (QoS). In this paper, a grey-fuzzy controller for radio resource management (GF-RRM) is presented to maximize the number of the served calls and QoS provision in wireless networks. In a wireless network, the call arrival rate, the call duration and the communication overhead between the base stations and the control center are vague and uncertain. In this paper, we develop a method to predict the cell load and to solve the RRM problem based on the GF-RRM, and support the present facility has been built on the application-level of the wireless networks. The GF-RRM exhibits the better adaptability, fault-tolerant capability and performance than other algorithms. Through simulations, we evaluate the blocking rate, update overhead, and channel acquisition delay time of the proposed method. The results demonstrate our algorithm has the lower blocking rate, less updated overhead, and shorter channel acquisition delay.

Keywords: radio resource management, grey prediction, fuzzylogic control, wireless networks, quality of service.

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Authors: Si Mon Kueh, Tom J. Kazmierski

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There are about 1% of the world population suffering from the hidden disability known as epilepsy and major developing countries are not fully equipped to counter this problem. In order to reduce the inconvenience and danger of epilepsy, different methods have been researched by using a artificial neural network (ANN) classification to distinguish epileptic waveforms from normal brain waveforms. This paper outlines the aim of achieving massive ANN parallelization through a dedicated hardware using bit-serial processing. The design of this bit-serial Neural Processing Element (NPE) is presented which implements the functionality of a complete neuron using variable accuracy. The proposed design has been tested taking into consideration non-idealities of a hardware ANN. The NPE consists of a bit-serial multiplier which uses only 16 logic elements on an Altera Cyclone IV FPGA and a bit-serial ALU as well as a look-up table. Arrays of NPEs can be driven by a single controller which executes the neural processing algorithm. In conclusion, the proposed compact NPE design allows the construction of complex hardware ANNs that can be implemented in a portable equipment that suits the needs of a single epileptic patient in his or her daily activities to predict the occurrences of impending tonic conic seizures.

Keywords: Artificial Neural Networks, bit-serial neural processor, FPGA, Neural Processing Element.

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Authors: B. Bahmani Firouzi, T. Niknam, M. Nayeripour

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With Power system movement toward restructuring along with factors such as life environment pollution, problems of transmission expansion and with advancement in construction technology of small generation units, it is expected that small units like wind turbines, fuel cells, photovoltaic, ... that most of the time connect to the distribution networks play a very essential role in electric power industry. With increase in developing usage of small generation units, management of distribution networks should be reviewed. The target of this paper is to present a new method for optimal management of active and reactive power in distribution networks with regard to costs pertaining to various types of dispersed generations, capacitors and cost of electric energy achieved from network. In other words, in this method it-s endeavored to select optimal sources of active and reactive power generation and controlling equipments such as dispersed generations, capacitors, under load tapchanger transformers and substations in a way that firstly costs in relation to them are minimized and secondly technical and physical constraints are regarded. Because the optimal management of distribution networks is an optimization problem with continuous and discrete variables, the new evolutionary method based on Ant Colony Algorithm has been applied. The simulation results of the method tested on two cases containing 23 and 34 buses exist and will be shown at later sections.

Keywords: Distributed Generation, Optimal Operation Management of distribution networks, Ant Colony Optimization(ACO).

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Authors: Miaomiao Yang, Shouming Zhong

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This paper studies the problem of stability criteria for neural networks with two additive time-varying delays.A new Lyapunov-Krasovskii function is constructed and some new delay dependent stability criterias are derived in the terms of linear matrix inequalities(LMI), zero equalities and reciprocally convex approach.The several stability criterion proposed in this paper is simpler and effective. Finally,numerical examples are provided to demonstrate the feasibility and effectiveness of our results.

Keywords: Stability, Neural networks, Linear Matrix Inequalities (LMI) , Lyapunov function, Time-varying delays

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Authors: Ahmed Mohsen, Yoshinobu Kitamura, Katsuyuki Shimizu

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The improvement of irrigation systems in the Nile Delta is one of the most important attempts in Egypt to implement more effective irrigation technology by improving the existing irrigation networks. Demand delivery system in the existing irrigation network is using of mechanical gates structures to automatically divert water from one portion of an agricultural field to another in the desired amount and sequence. This paper discusses evaluating main irrigation networks system under the government managed before and after improvement systems in the Nile Delta. The overall results indicate that policy of using the demand delivery concept through irrigation networks is successful by improving water delivery performance among them than the rotation delivery concept that used before. It is provided fair share of water delivery among irrigation districts and available water in the end of irrigation network, although this system located in an end of irrigation networks in the Nile Delta.

Keywords: Automation system, Irrigation district, Rotation system, Water delivery performance

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Authors: Ε. Giovanis

Abstract:

In this paper we present an autoregressive model with neural networks modeling and standard error backpropagation algorithm training optimization in order to predict the gross domestic product (GDP) growth rate of four countries. Specifically we propose a kind of weighted regression, which can be used for econometric purposes, where the initial inputs are multiplied by the neural networks final optimum weights from input-hidden layer after the training process. The forecasts are compared with those of the ordinary autoregressive model and we conclude that the proposed regression-s forecasting results outperform significant those of autoregressive model in the out-of-sample period. The idea behind this approach is to propose a parametric regression with weighted variables in order to test for the statistical significance and the magnitude of the estimated autoregressive coefficients and simultaneously to estimate the forecasts.

Keywords: Autoregressive model, Error back-propagation Feed-Forward neural networks, , Gross Domestic Product

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Authors: Qingqing Wang, Shouming Zhong

Abstract:

Utilizing the Lyapunov functional method and combining linear matrix inequality (LMI) techniques and integral inequality approach (IIA) to analyze the global asymptotic stability for delayed neural networks (DNNs),a new sufficient criterion ensuring the global stability of DNNs is obtained.The criteria are formulated in terms of a set of linear matrix inequalities,which can be checked efficiently by use of some standard numercial packages.In order to show the stability condition in this paper gives much less conservative results than those in the literature,numerical examples are considered.

Keywords: Neural networks, Globally asymptotic stability , LMI approach , IIA approach , Time-varying delay.

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Authors: Ladan Darougaran, Hossein Shahinzadeh, Hajar Ghotb, Leila Ramezanpour

Abstract:

In ad hoc networks, the main issue about designing of protocols is quality of service, so that in wireless sensor networks the main constraint in designing protocols is limited energy of sensors. In fact, protocols which minimize the power consumption in sensors are more considered in wireless sensor networks. One approach of reducing energy consumption in wireless sensor networks is to reduce the number of packages that are transmitted in network. The technique of collecting data that combines related data and prevent transmission of additional packages in network can be effective in the reducing of transmitted packages- number. According to this fact that information processing consumes less power than information transmitting, Data Aggregation has great importance and because of this fact this technique is used in many protocols [5]. One of the Data Aggregation techniques is to use Data Aggregation tree. But finding one optimum Data Aggregation tree to collect data in networks with one sink is a NP-hard problem. In the Data Aggregation technique, related information packages are combined in intermediate nodes and form one package. So the number of packages which are transmitted in network reduces and therefore, less energy will be consumed that at last results in improvement of longevity of network. Heuristic methods are used in order to solve the NP-hard problem that one of these optimization methods is to solve Simulated Annealing problems. In this article, we will propose new method in order to build data collection tree in wireless sensor networks by using Simulated Annealing algorithm and we will evaluate its efficiency whit Genetic Algorithm.

Keywords: Data aggregation, wireless sensor networks, energy efficiency, simulated annealing algorithm, genetic algorithm.

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Authors: Yongkun Li, Tianwei Zhang, Shufa Bai

Abstract:

By using the method of coincidence degree theory and constructing suitable Lyapunov functional, several sufficient conditions are established for the existence and global exponential stability of anti-periodic solutions for Cohen-Grossberg shunting inhibitory neural networks with delays. An example is given to illustrate our feasible results.

Keywords: Anti-periodic solution, coincidence degree, global exponential stability, Cohen-Grossberg shunting inhibitory cellular neural networks.

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