Abstract: This paper is concerned with the delay-distributiondependent
stability criteria for bidirectional associative memory
(BAM) neural networks with time-varying delays. Based on the
Lyapunov-Krasovskii functional and stochastic analysis approach,
a delay-probability-distribution-dependent sufficient condition is derived
to achieve the globally asymptotically mean square stable of
the considered BAM neural networks. The criteria are formulated in
terms of a set of linear matrix inequalities (LMIs), which can be
checked efficiently by use of some standard numerical packages. Finally,
a numerical example and its simulation is given to demonstrate
the usefulness and effectiveness of the proposed results.
Abstract: In this paper, based on the estimation of the Cauchy matrix of linear impulsive differential equations, by using Banach fixed point theorem and Gronwall-Bellman-s inequality, some sufficient conditions are obtained for the existence and exponential stability of almost periodic solution for Cohen-Grossberg shunting inhibitory cellular neural networks (SICNNs) with continuously distributed delays and impulses. An example is given to illustrate the main results.
Abstract: A complex valued neural network is a neural network
which consists of complex valued input and/or weights and/or thresholds
and/or activation functions. Complex-valued neural networks
have been widening the scope of applications not only in electronics
and informatics, but also in social systems. One of the most important
applications of the complex valued neural network is in signal
processing. In Neural networks, generalized mean neuron model
(GMN) is often discussed and studied. The GMN includes a new
aggregation function based on the concept of generalized mean of all
the inputs to the neuron. This paper aims to present exhaustive results
of using Generalized Mean Neuron model in a complex-valued neural
network model that uses the back-propagation algorithm (called
-Complex-BP-) for learning. Our experiments results demonstrate the
effectiveness of a Generalized Mean Neuron Model in a complex
plane for signal processing over a real valued neural network. We
have studied and stated various observations like effect of learning
rates, ranges of the initial weights randomly selected, error functions
used and number of iterations for the convergence of error required on
a Generalized Mean neural network model. Some inherent properties
of this complex back propagation algorithm are also studied and
discussed.
Abstract: Overcurrent (OC) relays are the major protection
devices in a distribution system. The operating time of the OC relays
are to be coordinated properly to avoid the mal-operation of the
backup relays. The OC relay time coordination in ring fed
distribution networks is a highly constrained optimization problem
which can be stated as a linear programming problem (LPP). The
purpose is to find an optimum relay setting to minimize the time of
operation of relays and at the same time, to keep the relays properly
coordinated to avoid the mal-operation of relays.
This paper presents two phase simplex method for optimum time
coordination of OC relays. The method is based on the simplex
algorithm which is used to find optimum solution of LPP. The
method introduces artificial variables to get an initial basic feasible
solution (IBFS). Artificial variables are removed using iterative
process of first phase which minimizes the auxiliary objective
function. The second phase minimizes the original objective function
and gives the optimum time coordination of OC relays.
Abstract: Grid networks provide the ability to perform higher throughput computing by taking advantage of many networked computer-s resources to solve large-scale computation problems. As the popularity of the Grid networks has increased, there is a need to efficiently distribute the load among the resources accessible on the network. In this paper, we present a stochastic network system that gives a distributed load-balancing scheme by generating almost regular networks. This network system is self-organized and depends only on local information for load distribution and resource discovery. The in-degree of each node is refers to its free resources, and job assignment and resource discovery processes required for load balancing is accomplished by using fitted random sampling. Simulation results show that the generated network system provides an effective, scalable, and reliable load-balancing scheme for the distributed resources accessible on Grid networks.
Abstract: IP multicasting is a key technology for many existing and emerging applications on the Internet. Furthermore, with increasing popularity of wireless devices and mobile equipment, it is necessary to determine the best way to provide this service in a wireless environment. IETF Mobile IP, that provides mobility for hosts in IP networks, proposes two approaches for mobile multicasting, namely, remote subscription (MIP-RS) and bi-directional tunneling (MIP-BT). In MIP-RS, a mobile host re-subscribes to the multicast groups each time it moves to a new foreign network. MIP-RS suffers from serious packet losses while mobile host handoff occurs. In MIP-BT, mobile hosts send and receive multicast packets by way of their home agents (HAs), using Mobile IP tunnels. Therefore, it suffers from inefficient routing and wastage of system resources. In this paper, we propose a protocol called Mobile Multicast support using Old Foreign Agent (MMOFA) for Mobile Hosts. MMOFA is derived from MIP-RS and with the assistance of Mobile host's Old foreign agent, routes the missing datagrams due to handoff in adjacent network via tunneling. Also, we studied the performance of the proposed protocol by simulation under ns-2.27. The results demonstrate that MMOFA has optimal routing efficiency and low delivery cost, as compared to other approaches.
Abstract: In this paper, the requirement for Coke quality
prediction, its role in Blast furnaces, and the model output is
explained. By applying method of Artificial Neural Networking
(ANN) using back propagation (BP) algorithm, prediction model has
been developed to predict CSR. Important blast furnace functions
such as permeability, heat exchanging, melting, and reducing
capacity are mostly connected to coke quality. Coke quality is further
dependent upon coal characterization and coke making process
parameters. The ANN model developed is a useful tool for process
experts to adjust the control parameters in case of coke quality
deviations. The model also makes it possible to predict CSR for new
coal blends which are yet to be used in Coke Plant. Input data to the
model was structured into 3 modules, for tenure of past 2 years and
the incremental models thus developed assists in identifying the
group causing the deviation of CSR.
Abstract: Workload and resource management are two essential functions provided at the service level of the grid software infrastructure. To improve the global throughput of these software environments, workloads have to be evenly scheduled among the available resources. To realize this goal several load balancing strategies and algorithms have been proposed. Most strategies were developed in mind, assuming homogeneous set of sites linked with homogeneous and fast networks. However for computational grids we must address main new issues, namely: heterogeneity, scalability and adaptability. In this paper, we propose a layered algorithm which achieve dynamic load balancing in grid computing. Based on a tree model, our algorithm presents the following main features: (i) it is layered; (ii) it supports heterogeneity and scalability; and, (iii) it is totally independent from any physical architecture of a grid.
Abstract: This paper examines many mathematical methods for
molding the hourly price forward curve (HPFC); the model will be
constructed by numerous regression methods, like polynomial
regression, radial basic function neural networks & a furrier series.
Examination the models goodness of fit will be done by means of
statistical & graphical tools. The criteria for choosing the model will
depend on minimize the Root Mean Squared Error (RMSE), using the
correlation analysis approach for the regression analysis the optimal
model will be distinct, which are robust against model
misspecification. Learning & supervision technique employed to
determine the form of the optimal parameters corresponding to each
measure of overall loss. By using all the numerical methods that
mentioned previously; the explicit expressions for the optimal model
derived and the optimal designs will be implemented.
Abstract: Focusing on the environmental issues, including the reduction of scrap and consumer residuals, along with the benefiting from the economic value during the life cycle of goods/products leads the companies to have an important competitive approach. The aim of this paper is to present a new mixed nonlinear facility locationallocation model in recycling collection networks by considering multi-echelon, multi-suppliers, multi-collection centers and multifacilities in the recycling network. To make an appropriate decision in reality, demands, returns, capacities, costs and distances, are regarded uncertain in our model. For this purpose, a fuzzy mathematical programming-based possibilistic approach is introduced as a solution methodology from the recent literature to solve the proposed mixed-nonlinear programming model (MNLP). The computational experiments are provided to illustrate the applicability of the designed model in a supply chain environment and to help the decision makers to facilitate their analysis.
Abstract: As wireless sensor networks are energy constraint networks
so energy efficiency of sensor nodes is the main design issue.
Clustering of nodes is an energy efficient approach. It prolongs the
lifetime of wireless sensor networks by avoiding long distance communication.
Clustering algorithms operate in rounds. Performance of
clustering algorithm depends upon the round time. A large round
time consumes more energy of cluster heads while a small round
time causes frequent re-clustering. So existing clustering algorithms
apply a trade off to round time and calculate it from the initial
parameters of networks. But it is not appropriate to use initial
parameters based round time value throughout the network lifetime
because wireless sensor networks are dynamic in nature (nodes can be
added to the network or some nodes go out of energy). In this paper
a variable round time approach is proposed that calculates round
time depending upon the number of active nodes remaining in the
field. The proposed approach makes the clustering algorithm adaptive
to network dynamics. For simulation the approach is implemented
with LEACH in NS-2 and the results show that there is 6% increase
in network lifetime, 7% increase in 50% node death time and 5%
improvement over the data units gathered at the base station.
Abstract: The continued interest in the use of distributed generation in recent years is leading to the growth in number of distributed generators connected to distribution networks. Steady state voltage rise resulting from the connection of these generators can be a major obstacle to their connection at lower voltage levels. The present electric distribution network is designed to keep the customer voltage within tolerance limit. This may require a reduction in connectable generation capacity, under utilization of appropriate generation sites. Thus distribution network operators need a proper voltage regulation method to allow the significant integration of distributed generation systems to existing network. In this work a voltage rise problem in a typical distribution system has been studied. A method for voltage regulation of distribution system with multiple DG system by coordinated operation distributed generator, capacitor and OLTC has been developed. A sensitivity based analysis has been carried out to determine the priority for individual generators in multiple DG environment. The effectiveness of the developed method has been evaluated under various cases through simulation results.
Abstract: In this paper we introduce the notion of protein interaction
network. This is a graph whose vertices are the protein-s
amino acids and whose edges are the interactions between them.
Using a graph theory approach, we identify a number of properties of
these networks. We compare them to the general small-world network
model and we analyze their hierarchical structure.
Abstract: In this paper, we focus on the problem of driving and
herding a collection of autonomous actors to a given area. Then, a
new method based on multi-agent coordination is proposed for
solving the problem.
In our proposed method, we assume that the environment is
covered by sensors. When an event is occurred, sensors forward
information to a sink node. Based on received information, the sink
node will estimate the direction and the speed of movement of actors
and announce the obtained value to the actors. The actors coordinate
to reach the target location.
Abstract: In this paper, a clustering algorithm named KHarmonic
means (KHM) was employed in the training of Radial
Basis Function Networks (RBFNs). KHM organized the data in
clusters and determined the centres of the basis function. The popular
clustering algorithms, namely K-means (KM) and Fuzzy c-means
(FCM), are highly dependent on the initial identification of elements
that represent the cluster well. In KHM, the problem can be avoided.
This leads to improvement in the classification performance when
compared to other clustering algorithms. A comparison of the
classification accuracy was performed between KM, FCM and KHM.
The classification performance is based on the benchmark data sets:
Iris Plant, Diabetes and Breast Cancer. RBFN training with the KHM
algorithm shows better accuracy in classification problem.
Abstract: With the advantage of wireless network technology,
there are a variety of mobile applications which make the issue of
wireless sensor networks as a popular research area in recent years.
As the wireless sensor network nodes move arbitrarily with the
topology fast change feature, mobile nodes are often confronted with
the void issue which will initiate packet losing, retransmitting,
rerouting, additional transmission cost and power consumption.
When transmitting packets, we would not predict void problem
occurring in advance. Thus, how to improve geographic routing with
void avoidance in wireless networks becomes an important issue. In
this paper, we proposed a greedy geographical void routing algorithm
to solve the void problem for wireless sensor networks. We use the
information of source node and void area to draw two tangents to
form a fan range of the existence void which can announce voidavoiding
message. Then we use source and destination nodes to draw
a line with an angle of the fan range to select the next forwarding
neighbor node for routing. In a dynamic wireless sensor network
environment, the proposed greedy void avoiding algorithm can be
more time-saving and more efficient to forward packets, and improve
current geographical void problem of wireless sensor networks.
Abstract: Power-line networks are widely used today for broadband data transmission. However, due to multipaths within the broadband power line communication (BPLC) systems owing to stochastic changes in the network load impedances, branches, etc., network or channel capacity performances are affected. This paper attempts to investigate the performance of typical medium voltage channels that uses Orthogonal Frequency Division Multiplexing (OFDM) techniques with Quadrature Amplitude Modulation (QAM) sub carriers. It has been observed that when the load impedances are different from line characteristic impedance channel performance decreases. Also as the number of branches in the link between the transmitter and receiver increases a loss of 4dB/branch is found in the signal to noise ratio (SNR). The information presented in the paper could be useful for an appropriate design of the BPLC systems.
Abstract: Connected dominating set (CDS) problem in unit disk
graph has signi£cant impact on an ef£cient design of routing protocols
in wireless sensor networks, where the searching space for a
route is reduced to nodes in the set. A set is dominating if all the
nodes in the system are either in the set or neighbors of nodes in the
set. In this paper, a simple and ef£cient heuristic method is proposed
for £nding a minimum connected dominating set (MCDS) in ad hoc
wireless networks based on the new parameter support of vertices.
With this parameter the proposed heuristic approach effectively
£nds the MCDS of a graph. Extensive computational experiments
show that the proposed approach outperforms the recently proposed
heuristics found in the literature for the MCD
Abstract: In this paper, a mathematical model is proposed to
estimate the dropping probabilities of cellular wireless networks by
queuing handoff instead of reserving guard channels. Usually, prioritized
handling of handoff calls is done with the help of guard channel
reservation. To evaluate the proposed model, gamma inter-arrival and
general service time distributions have been considered. Prevention of
some of the attempted calls from reaching to the switching center due
to electromagnetic propagation failure or whimsical user behaviour
(missed call, prepaid balance etc.), make the inter-arrival time of
the input traffic to follow gamma distribution. The performance is
evaluated and compared with that of guard channel scheme.
Abstract: In the present study, position estimation of switched reluctance motor (SRM) has been achieved on the basis of the artificial neural networks (ANNs). The ANNs can estimate the rotor position without using an extra rotor position sensor by measuring the phase flux linkages and phase currents. Flux linkage-phase current-rotor position data set and supervised backpropagation learning algorithm are used in training of the ANN based position estimator. A 4-phase SRM have been used to verify the accuracy and feasibility of the proposed position estimator. Simulation results show that the proposed position estimator gives precise and accurate position estimations for both under the low and high level reference speeds of the SRM