Abstract: 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, ¤ü)-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 ¤ü > 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.
Abstract: Travelling salesman problem (TSP) is a combinational
optimization problem and solution approaches have been applied
many real world problems. Pure TSP assumes the cities to visit are
fixed in time and thus solutions are created to find shortest path
according to these point. But some of the points are canceled to visit
in time. If the problem is not time crucial it is not important to
determine new routing plan but if the points are changing rapidly and
time is necessary do decide a new route plan a new approach should
be applied in such cases. We developed a route plan transfer method
based on transfer learning and we achieved high performance against
determining a new model from scratch in every change.
Abstract: This research intends to introduce a new usage of Artificial Intelligent (AI) approaches in Stepping Stone Detection (SSD) fields of research. By using Self-Organizing Map (SOM) approaches as the engine, through the experiment, it is shown that SOM has the capability to detect the number of connection chains that involved in a stepping stones. Realizing that by counting the number of connection chain is one of the important steps of stepping stone detection and it become the research focus currently, this research has chosen SOM as the AI techniques because of its capabilities. Through the experiment, it is shown that SOM can detect the number of involved connection chains in Network-based Stepping Stone Detection (NSSD).
Abstract: Effectiveness of Artificial Neural Networks (ANN)
and Support Vector Machines (SVM) classifiers for fault diagnosis of
rolling element bearings are presented in this paper. The
characteristic features of vibration signals of rotating driveline that
was run in its normal condition and with faults introduced were used
as input to ANN and SVM classifiers. Simple statistical features such
as standard deviation, skewness, kurtosis etc. of the time-domain
vibration signal segments along with peaks of the signal and peak of
power spectral density (PSD) are used as features to input the ANN
and SVM classifier. The effect of preprocessing of the vibration
signal by Discreet Wavelet Transform (DWT) prior to feature
extraction is also studied. It is shown from the experimental results
that the performance of SVM classifier in identification of bearing
condition is better then ANN and pre-processing of vibration signal
by DWT enhances the effectiveness of both ANN and SVM classifier
Abstract: The electromagnetic spectrum is a natural resource
and hence well-organized usage of the limited natural resources is the
necessities for better communication. The present static frequency
allocation schemes cannot accommodate demands of the rapidly
increasing number of higher data rate services. Therefore, dynamic
usage of the spectrum must be distinguished from the static usage to
increase the availability of frequency spectrum. Cognitive radio is not
a single piece of apparatus but it is a technology that can incorporate
components spread across a network. It offers great promise for
improving system efficiency, spectrum utilization, more effective
applications, reduction in interference and reduced complexity of
usage for users. Cognitive radio is aware of its environmental,
internal state, and location, and autonomously adjusts its operations
to achieve designed objectives. It first senses its spectral environment
over a wide frequency band, and then adapts the parameters to
maximize spectrum efficiency with high performance. This paper
only focuses on the analysis of Bit-Error-Rate in cognitive radio by
using Particle Swarm Optimization Algorithm. It is theoretically as
well as practically analyzed and interpreted in the sense of
advantages and drawbacks and how BER affects the efficiency and
performance of the communication system.
Abstract: This calculation focus on the effect of exchange
interaction J and Coulomb interaction U on spin magnetic moments
(ms) of MnO by using the local spin density approximation plus the
Coulomb interaction (LSDA+U) method within full potential linear
muffin-tin orbital (FP-LMTO). Our calculated results indicated that
the spin magnetic moments correlated to J and U. The relevant
results exhibited the increasing spin magnetic moments with
increasing exchange interaction and Coulomb interaction.
Furthermore, equations of spin magnetic moment, which h good
correspondence to the experimental data 4.58μB, are defined ms =
0.11J +4.52μB and ms = 0.03U+4.52μB. So, the relation of J and U
parameter is obtained, it is obviously, J = -0.249U+1.346 eV.
Abstract: Vehicular Ad-Hoc Networks (VANET) can provide
communications between vehicles or infrastructures. It provides the
convenience of driving and the secure driving to reduce accidents. In
VANET, the security is more important because it is closely related to
accidents. Additionally, VANET raises a privacy issue because it can
track the location of vehicles and users- identity when a security
mechanism is provided. In this paper, we analyze the problem of an
existing solution for security requirements required in VANET, and
resolve the problem of the existing method when a key management
mechanism is provided for the security operation in VANET.
Therefore, we show suitability of the Long Term Evolution (LTE) in
VANET for the solution of this problem.
Abstract: The vertex connectivity of a graph is the smallest number of vertices whose deletion separates the graph or makes it trivial. This work is devoted to the problem of vertex connectivity test of graphs in a distributed environment based on a general and a constructive approach. The contribution of this paper is threefold. First, using a preconstructed spanning tree of the considered graph, we present a protocol to test whether a given graph is 2-connected using only local knowledge. Second, we present an encoding of this protocol using graph relabeling systems. The last contribution is the implementation of this protocol in the message passing model. For a given graph G, where M is the number of its edges, N the number of its nodes and Δ is its degree, our algorithms need the following requirements: The first one uses O(Δ×N2) steps and O(Δ×logΔ) bits per node. The second one uses O(Δ×N2) messages, O(N2) time and O(Δ × logΔ) bits per node. Furthermore, the studied network is semi-anonymous: Only the root of the pre-constructed spanning tree needs to be identified.
Abstract: ANNARIMA that combines both autoregressive integrated moving average (ARIMA) model and artificial neural network (ANN) model is a valuable tool for modeling and forecasting nonlinear time series, yet the over-fitting problem is more likely to occur in neural network models. This paper provides a hybrid methodology that combines both radial basis function (RBF) neural network and auto regression (AR) model based on binomial smoothing (BS) technique which is efficient in data processing, which is called BSRBFAR. This method is examined by using the data of Canadian Lynx data. Empirical results indicate that the over-fitting problem can be eased using RBF neural network based on binomial smoothing which is called BS-RBF, and the hybrid model–BS-RBFAR can be an effective way to improve forecasting accuracy achieved by BSRBF used separately.
Abstract: Data mining uses a variety of techniques each of which
is useful for some particular task. It is important to have a deep
understanding of each technique and be able to perform sophisticated
analysis. In this article we describe a tool built to simulate a variation
of the Kohonen network to perform unsupervised clustering and
support the entire data mining process up to results visualization. A
graphical representation helps the user to find out a strategy to
optimize classification by adding, moving or delete a neuron in order
to change the number of classes. The tool is able to automatically
suggest a strategy to optimize the number of classes optimization, but
also support both tree classifications and semi-lattice organizations of
the classes to give to the users the possibility of passing from one
class to the ones with which it has some aspects in common.
Examples of using tree and semi-lattice classifications are given to
illustrate advantages and problems. The tool is applied to classify
macroeconomic data that report the most developed countries- import
and export. It is possible to classify the countries based on their
economic behaviour and use the tool to characterize the commercial
behaviour of a country in a selected class from the analysis of
positive and negative features that contribute to classes formation.
Possible interrelationships between the classes and their meaning are
also discussed.
Abstract: An information procuring and processing emerging technology wireless sensor network (WSN) Consists of autonomous nodes with versatile devices underpinned by applications. Nodes are equipped with different capabilities such as sensing, computing, actuation and wireless communications etc. based on application requirements. A WSN application ranges from military implementation in the battlefield, environmental monitoring, health sector as well as emergency response of surveillance. The nodes are deployed independently to cooperatively monitor the physical and environmental conditions. The architecture of WSN differs based on the application requirements and focus on low cost, flexibility, fault tolerance capability, deployment process as well as conserve energy. In this paper we have present the characteristics, architecture design objective and architecture of WSN
Abstract: A direct search approach to determine optimal reservoir operating is proposed with ant colony optimization for continuous domains (ACOR). The model is applied to a system of single reservoir to determine the optimum releases during 42 years of monthly steps. A disadvantage of ant colony based methods and the ACOR in particular, refers to great amount of computer run time consumption. In this study a highly effective procedure for decreasing run time has been developed. The results are compared to those of a GA based model.
Abstract: We have considered an unmagnetized dusty plasma system consisting of ions obeying superthermal distribution and strongly coupled negatively charged dust. We have used reductive perturbation method and derived the Kordeweg-de Vries-Burgers (KdV-Burgers) equation. The behavior of the shock waves in the plasma has been investigated.
Abstract: The sensitivity of UAVs to the atmospheric effects are
apparent. All the same the meteorological support for the UAVs
missions is often non-adequate or partly missing.
In our paper we show a new complex meteorological support
system for different types of UAVs pilots, specialists and decision
makers, too. The mentioned system has two important parts with
different forecasts approach such as the statistical and dynamical
ones.
The statistical prediction approach is based on a large
climatological data base and the special analog method which is able
to select similar weather situations from the mentioned data base to
apply them during the forecasting procedure.
The applied dynamic approach uses the specific WRF model runs
twice a day and produces 96 hours, high resolution weather forecast
for the UAV users over the Hungary. An easy to use web-based
system can give important weather information over the Carpathian
basin in Central-Europe. The mentioned products can be reached via
internet connection.
Abstract: Sustainability and sustainable development have been
the main theme of many international conferences, such the UN Rio
de Janeiro 1992 Earth Summit This was followed by the appearance
of the global conferences at the late of the nineties and the early of
2000 to confirm the importance of the sustainable development .it
was focused on the importance of the economic development as it is
considered an effective tool in the operations of the sustainable
development. Industry plays a critical role in technological
innovations and research and development activities, which are
crucial for the economic and social development of any country.
Transportation and mobility are an important part or urban
economics and the quality of life. To analyze urban transportation
and its environmental impacts, a comprehensive approach is needed.
So this research aims to apply new approach for the development of
the urban communities that insure the continuity and facing the
deterioration. This approach aims to integrate sustainable transport
solutions with economic development and community development.
For that purpose we will concentrate on one of the most sustainable
cities in the world (Curitiba in Brazil) which provides the world with
a model in how to integrate sustainable transport considerations into
business development, road infrastructure development, and local
community development.
Abstract: Here, in this work we study correspondence the energy density New agegraphic and the energy density Gauss- Bonnet models in flat universe. We reconstruct Λ and Λ ω for them with 0 ( ) 0 h a t = a t .
Abstract: The paper proposes a methodology to process the signals coming from the Transcranial Magnetic Stimulation (TMS) in order to identify the pathology and evaluate the therapy to treat the patients affected by demency diseases. In particular, a fuzzy model is developed to identify the demency of the patients affected by Subcortical Ischemic Vascular Dementia (SIVD) and to measure the effect of a repetitive TMS on their motor performances. A tool is also presented to support the mentioned analysis.
Abstract: In this paper, we analyze and test a scheme for the
estimation of electrical fundamental frequency signals from the
harmonic load current and voltage signals.
The scheme was based on using two different Multi Layer
Artificial Neural Networks (ML-ANN) one for the current and the
other for the voltage.
This study also analyzes and tests the effect of choosing the
optimum artificial neural networks- sizes which determine the quality
and accuracy of the estimation of electrical fundamental frequency
signals.
The simulink tool box of the Matlab program for the simulation of
the test system and the test of the neural networks has been used.
Abstract: In this research, effect of combustion reaction
mechanism on direct initiation of detonation has been studied
numerically. For this purpose, reaction mechanism has been
simulated by using a three-step chemical kinetics model. The reaction
scheme consists sequentially of a chain-initiation and chainbranching
step, followed by a temperature -independent chaintermination.
In a previous research, the effect of chain-branching on
the direct initiation of detonation is studied. In this research effect of
chain-initiation on direct initiation of detonation is investigated. For
the investigation, first a characteristic time (τ) for each step of
mechanism, which includes effect of different kinetics parameters, is
defined. Then the effect of characteristic time of chain-initiation (τI)
on critical initiation energy is studied. It is seen that increasing τI,
causes critical initiation energy to be increased. Drawing detonation's
shock pressure diagrams for different cases, shows that in small value
of τI , kinetics has more important effect on the behavior of the wave.
Abstract: This paper introduces a measure of similarity between
two clusterings of the same dataset produced by two different
algorithms, or even the same algorithm (K-means, for instance, with
different initializations usually produce different results in clustering
the same dataset). We then apply the measure to calculate the
similarity between pairs of clusterings, with special interest directed
at comparing the similarity between various machine clusterings and
human clustering of datasets. The similarity measure thus can be used
to identify the best (in terms of most similar to human) clustering
algorithm for a specific problem at hand. Experimental results
pertaining to the text categorization problem of a Portuguese corpus
(wherein a translation-into-English approach is used) are presented, as well as results on the well-known benchmark IRIS dataset. The
significance and other potential applications of the proposed measure
are discussed.