Abstract: An efficient transient flow simulation for gas
pipelines and networks is presented. The proposed transient flow
simulation is based on the transfer function models and MATLABSimulink.
The equivalent transfer functions of the nonlinear
governing equations are derived for different types of the boundary
conditions. Next, a MATLAB-Simulink library is developed and
proposed considering any boundary condition type. To verify the
accuracy and the computational efficiency of the proposed
simulation, the results obtained are compared with those of the
conventional finite difference schemes (such as TVD, method of
lines, and other finite difference implicit and explicit schemes). The
effects of the flow inertia and the pipeline inclination are
incorporated in this simulation. It is shown that the proposed
simulation has a sufficient accuracy and it is computationally more
efficient than the other methods.
Abstract: This paper presents a practical scheme that can be used for allocating the transmission loss to generators and loads. In this scheme first the share of a generator or load on the current through a branch is determined using Z-bus modified matrix. Then the current components are decomposed and the branch loss allocation is obtained. A motivation of proposed scheme is to improve the results of Z-bus method and to reach more fair allocation. The proposed scheme has been implemented and tested on several networks. To achieve practical and applicable results, the proposed scheme is simulated and compared on the transmission network (400kv) of Khorasan region in Iran and the 14-bus standard IEEE network. The results show that the proposed scheme is comprehensive and fair to allocating the energy losses of a power market to its participants.
Abstract: In this work we present some matrix operators named
circulant operators and their action on square matrices. This study on
square matrices provides new insights into the structure of the space
of square matrices. Moreover it can be useful in various fields as in
agents networking on Grid or large-scale distributed self-organizing
grid systems.
Abstract: In this work, a radial basis function (RBF) neural network is developed for the identification of hyperbolic distributed parameter systems (DPSs). This empirical model is based only on process input-output data and used for the estimation of the controlled variables at specific locations, without the need of online solution of partial differential equations (PDEs). The nonlinear model that is obtained is suitably transformed to a nonlinear state space formulation that also takes into account the model mismatch. A stable robust control law is implemented for the attenuation of external disturbances. The proposed identification and control methodology is applied on a long duct, a common component of thermal systems, for a flow based control of temperature distribution. The closed loop performance is significantly improved in comparison to existing control methodologies.
Abstract: In this paper, by using Mawhin-s continuation theorem of coincidence degree and a method based on delay differential inequality, some sufficient conditions are obtained for the existence and global exponential stability of periodic solutions of cellular neural networks with distributed delays and impulses on time scales. The results of this paper generalized previously known results.
Abstract: Cognitive radio devices have been considered as a key technology for next-generation of wireless communication. These devices in the context of IEEE 802.11 standards and IEEE 802.16 standards, can opportunistically utilize the wireless spectrum to achieve better user performance and improve the overall spectrumutilization efficiency, mainly in the unlicensed 5 GHz bands. However, opportunistic use of wireless spectrum creates news problems such as peaceful coexistence with other wireless technologies, such as the radiolocation systems, as well as understanding the influence of interference that each of these networks can create. In this paper, we suggest a dynamic access model that considerably reduces this interference and allows efficiency and fairness use of the wireless spectrum.
Abstract: Innovation is more important in any companies.
However, it is not easy to measure the innovation performance
correctly. Patent is one of measuring index nowadays. This paper
wants to purpose an approach for valuing patents based on market
reaction to patent infringement litigations. The interesting
phenomenon is found from collection of patent infringement litigation
events. That is if any patent litigation event occurs the stock value will
follow changing. The plaintiffs- stock value raises some percentage.
According to this interesting phenomenon, the relationship between
patent litigation and stock value is tested and verified. And then, the
stock value variation is used to deduce the infringed patents- value.
The purpose of this study is providing another concept model to
evaluate the infringed patents. This study can provide a decision assist
system to help drafting patent litigation strategy and determine the
technology value
Abstract: In order to accommodate various multimedia
services, next generation wireless networks are characterized
by very high transmission bit rates. Thus, in such systems and
networks, the received signal is not only limited by noise but -
especially with increasing symbols rate often more
significantly by the intersymbol interference (ISI) caused by
the time dispersive radio channels such as those are used in
this work. This paper deals with the study of the performance
of detector for high bit rate transmission on some worst case
models of frequency selective fading channels for outdoor
mobile radio environments. This paper deals with a number of
different wireless channels with different power profiles and
different number of resolvable paths. All the radio channels
generated in this paper are for outdoor vehicular environments
with Doppler spread of 100 Hz. A carrier frequency of 1800
MHz is used and all the channels used in this work are such
that they are useful for next generation wireless systems.
Schemes for mitigation of ISI with adaptive equalizers of
different types have been investigated and their performances
have been investigated in terms of BER measured as a function
of SNR.
Abstract: QoS Routing aims to find paths between senders and
receivers satisfying the QoS requirements of the application which
efficiently using the network resources and underlying routing
algorithm to be able to find low-cost paths that satisfy given QoS
constraints. The problem of finding least-cost routing is known to be
NP-hard or complete and some algorithms have been proposed to
find a near optimal solution. But these heuristics or algorithms either
impose relationships among the link metrics to reduce the complexity
of the problem which may limit the general applicability of the
heuristic, or are too costly in terms of execution time to be applicable
to large networks. In this paper, we concentrate an algorithm that
finds a near-optimal solution fast and we named this algorithm as
optimized Delay Constrained Routing (ODCR), which uses an
adaptive path weight function together with an additional constraint
imposed on the path cost, to restrict search space and hence ODCR
finds near optimal solution in much quicker time.
Abstract: Over the past few years, a number of efforts have
been exerted to build parallel processing systems that utilize the idle
power of LAN-s and PC-s available in many homes and corporations.
The main advantage of these approaches is that they provide cheap
parallel processing environments for those who cannot afford the
expenses of supercomputers and parallel processing hardware.
However, most of the solutions provided are not very flexible in the
use of available resources and very difficult to install and setup.
In this paper, a multi-level web-based parallel processing system
(MWPS) is designed (appendix). MWPS is based on the idea of
volunteer computing, very flexible, easy to setup and easy to use.
MWPS allows three types of subscribers: simple volunteers (single
computers), super volunteers (full networks) and end users. All of
these entities are coordinated transparently through a secure web site.
Volunteer nodes provide the required processing power needed by
the system end users. There is no limit on the number of volunteer
nodes, and accordingly the system can grow indefinitely. Both
volunteer and system users must register and subscribe. Once, they
subscribe, each entity is provided with the appropriate MWPS
components. These components are very easy to install.
Super volunteer nodes are provided with special components that
make it possible to delegate some of the load to their inner nodes.
These inner nodes may also delegate some of the load to some other
lower level inner nodes .... and so on. It is the responsibility of the
parent super nodes to coordinate the delegation process and deliver
the results back to the user.
MWPS uses a simple behavior-based scheduler that takes into
consideration the current load and previous behavior of processing
nodes. Nodes that fulfill their contracts within the expected time get a
high degree of trust. Nodes that fail to satisfy their contract get a
lower degree of trust.
MWPS is based on the .NET framework and provides the minimal
level of security expected in distributed processing environments.
Users and processing nodes are fully authenticated. Communications
and messages between nodes are very secure. The system has been
implemented using C#.
MWPS may be used by any group of people or companies to
establish a parallel processing or grid environment.
Abstract: The mitigation of crop loss due to damaging freezes
requires accurate air temperature prediction models. Previous work
established that the Ward-style artificial neural network (ANN) is a
suitable tool for developing such models. The current research
focused on developing ANN models with reduced average prediction
error by increasing the number of distinct observations used in
training, adding additional input terms that describe the date of an
observation, increasing the duration of prior weather data included in
each observation, and reexamining the number of hidden nodes used
in the network. Models were created to predict air temperature at
hourly intervals from one to 12 hours ahead. Each ANN model,
consisting of a network architecture and set of associated parameters,
was evaluated by instantiating and training 30 networks and
calculating the mean absolute error (MAE) of the resulting networks
for some set of input patterns. The inclusion of seasonal input terms,
up to 24 hours of prior weather information, and a larger number of
processing nodes were some of the improvements that reduced
average prediction error compared to previous research across all
horizons. For example, the four-hour MAE of 1.40°C was 0.20°C, or
12.5%, less than the previous model. Prediction MAEs eight and 12
hours ahead improved by 0.17°C and 0.16°C, respectively,
improvements of 7.4% and 5.9% over the existing model at these
horizons. Networks instantiating the same model but with different
initial random weights often led to different prediction errors. These
results strongly suggest that ANN model developers should consider
instantiating and training multiple networks with different initial
weights to establish preferred model parameters.
Abstract: During last decades, worldwide researchers dedicated
efforts to develop machine-based seismic Early Warning systems,
aiming at reducing the huge human losses and economic damages.
The elaboration time of seismic waveforms is to be reduced in order
to increase the time interval available for the activation of safety
measures. This paper suggests a Data Mining model able to correctly
and quickly estimate dangerousness of the running seismic event.
Several thousand seismic recordings of Japanese and Italian
earthquakes were analyzed and a model was obtained by means of a
Bayesian Network (BN), which was tested just over the first
recordings of seismic events in order to reduce the decision time and
the test results were very satisfactory.
The model was integrated within an Early Warning System
prototype able to collect and elaborate data from a seismic sensor
network, estimate the dangerousness of the running earthquake and
take the decision of activating the warning promptly.
Abstract: Optical networks are high capacity networks that meet
the rapidly growing demand for bandwidth in the terrestrial
telecommunications industry. This paper studies and evaluates singlemode
and multimode fiber transmission by varying the distance. It
focuses on their performance in LAN environment. This is achieved
by observing the pulse spreading and attenuation in optical spectrum
and eye-diagram that are obtained using OptSim simulator. The
behaviors of two modes with different distance of data transmission
are studied, evaluated and compared.
Abstract: The need for reputation assessment is particularly strong in peer-to-peer (P2P) systems because the peers' personal site autonomy is amplified by the inherent technological decentralization of the environment. However, the decentralization notion makes the problem of designing a peer-to-peer based reputation assessment substantially harder in P2P networks than in centralized settings.Existing reputation systems tackle the reputation assessment process in an ad-hoc manner. There is no systematic and coherent way to derive measures and analyze the current reputation systems. In this paper, we propose a reputation assessment process and use it to classify the existing reputation systems. Simulation experiments are conducted and focused on the different methods in selecting the recommendation sources and retrieving the recommendations. These two phases can contribute significantly to the overall performance due to communication cost and coverage.
Abstract: The automatic discrimination of seismic signals is an important practical goal for earth-science observatories due to the large amount of information that they receive continuously. An essential discrimination task is to allocate the incoming signal to a group associated with the kind of physical phenomena producing it. In this paper, two classes of seismic signals recorded routinely in geophysical laboratory of the National Center for Scientific and Technical Research in Morocco are considered. They correspond to signals associated to local earthquakes and chemical explosions. The approach adopted for the development of an automatic discrimination system is a modular system composed by three blocs: 1) Representation, 2) Dimensionality reduction and 3) Classification. The originality of our work consists in the use of a new wavelet called "modified Mexican hat wavelet" in the representation stage. For the dimensionality reduction, we propose a new algorithm based on the random projection and the principal component analysis.
Abstract: The after–sales activities are nowadays acknowledged
as a relevant source of revenue, profit and competitive advantage in
most manufacturing industries. Top and middle management,
therefore, should focus on the definition of a structured business
performance measurement system for the after-sales business. The
paper aims at filling this gap, and presents an integrated methodology
for the after-sales network performance measurement, and provides
an empirical application to automotive case companies and their
official service network. This is the first study that presents an
integrated multivariate approach for total assessment and
improvement of after-sale services.
Abstract: In this contribution an innovative platform is being
presented that integrates intelligent agents and evolutionary
computation techniques in legacy e-learning environments. It
introduces the design and development of a scalable and
interoperable integration platform supporting:
I) various assessment agents for e-learning environments,
II) a specific resource retrieval agent for the provision of
additional information from Internet sources matching the
needs and profile of the specific user and
III) a genetic algorithm designed to extract efficient information
(classifying rules) based on the students- answering input
data.
The agents are implemented in order to provide intelligent
assessment services based on computational intelligence techniques
such as Bayesian Networks and Genetic Algorithms.
The proposed Genetic Algorithm (GA) is used in order to extract
efficient information (classifying rules) based on the students-
answering input data. The idea of using a GA in order to fulfil this
difficult task came from the fact that GAs have been widely used in
applications including classification of unknown data.
The utilization of new and emerging technologies like web
services allows integrating the provided services to any web based
legacy e-learning environment.
Abstract: Transport and logistics are the lifeblood of societies.
There is a strong correlation between overall growth in economic
activity and growth of transport. The movement of people and goods
has the potential for creating wealth and prosperity, therefore the
state of transportation infrastructure and especially the condition of
road networks is often a governmental priority. The design, building
and maintenance of national roads constitute a substantial share of
government budgets. Taking into account the magnitude and
importance of these investments, the expedience, efficiency and
sustainability of these projects are of great public interest. This paper
provides an overview of supply chain management principles applied
to road construction. In addition, road construction performance
measurement systems and ICT solutions are discussed. Road
construction in Estonia is analyzed. The authors propose the
development of a national performance measurement system for road
construction.
Abstract: This paper presents and evaluates a new classification
method that aims to improve classifiers performances and speed up
their training process. The proposed approach, called labeled
classification, seeks to improve convergence of the BP (Back
propagation) algorithm through the addition of an extra feature
(labels) to all training examples. To classify every new example, tests
will be carried out each label. The simplicity of implementation is the
main advantage of this approach because no modifications are
required in the training algorithms. Therefore, it can be used with
others techniques of acceleration and stabilization. In this work, two
models of the labeled classification are proposed: the LMLP
(Labeled Multi Layered Perceptron) and the LNFC (Labeled Neuro
Fuzzy Classifier). These models are tested using Iris, wine, texture
and human thigh databases to evaluate their performances.
Abstract: Maximal Ratio Combining (MRC) is considered the most complex combining technique as it requires channel coefficients estimation. It results in the lowest bit error rate (BER) compared to all other combining techniques. However the BER starts to deteriorate as errors are introduced in the channel coefficients estimation. A novel combining technique, termed Generalized Maximal Ratio Combining (GMRC) with a polynomial kernel, yields an identical BER as MRC with perfect channel estimation and a lower BER in the presence of channel estimation errors. We show that GMRC outperforms the optimal MRC scheme in general and we hereinafter introduce it to the scientific community as a new “supraoptimal" algorithm. Since diversity combining is especially effective in small femto- and pico-cells, internet-associated wireless peripheral systems are to benefit most from GMRC. As a result, many spinoff applications can be made to IP-based 4th generation networks.