Abstract: Most of the losses in a power system relate to
the distribution sector which always has been considered.
From the important factors which contribute to increase losses
in the distribution system is the existence of radioactive flows.
The most common way to compensate the radioactive power
in the system is the power to use parallel capacitors. In
addition to reducing the losses, the advantages of capacitor
placement are the reduction of the losses in the release peak of
network capacity and improving the voltage profile. The point
which should be considered in capacitor placement is the
optimal placement and specification of the amount of the
capacitor in order to maximize the advantages of capacitor
placement.
In this paper, a new technique has been offered for the
placement and the specification of the amount of the constant
capacitors in the radius distribution network on the basis of
Genetic Algorithm (GA). The existing optimal methods for
capacitor placement are mostly including those which reduce
the losses and voltage profile simultaneously. But the
retaliation cost and load changes have not been considered as
influential UN the target function .In this article, a holistic
approach has been considered for the optimal response to this
problem which includes all the parameters in the distribution
network: The price of the phase voltage and load changes. So,
a vast inquiry is required for all the possible responses. So, in
this article, we use Genetic Algorithm (GA) as the most
powerful method for optimal inquiry.
Abstract: Wireless sensor networks (WSNs) have gained
tremendous attention in recent years due to their numerous
applications. Due to the limited energy resource, energy efficient
operation of sensor nodes is a key issue in wireless sensor networks.
Cooperative caching which ensures sharing of data among various
nodes reduces the number of communications over the wireless
channels and thus enhances the overall lifetime of a wireless sensor
network. In this paper, we propose a cooperative caching scheme
called ZCS (Zone Cooperation at Sensors) for wireless sensor
networks. In ZCS scheme, one-hop neighbors of a sensor node form a
cooperative cache zone and share the cached data with each other.
Simulation experiments show that the ZCS caching scheme achieves
significant improvements in byte hit ratio and average query latency
in comparison with other caching strategies.
Abstract: Currently, there has been a 3G mobile networks data
traffic explosion due to the large increase in the number of smartphone
users. Unlike a traditional wired infrastructure, 3G mobile networks
have limited wireless resources and signaling procedures for complex
wireless resource management. And mobile network security for
various abnormal and malicious traffic technologies was not ready. So
Malicious or potentially malicious traffic originating from mobile
malware infected smart devices can cause serious problems to the 3G
mobile networks, such as DoS and scanning attack in wired networks.
This paper describes the DoS security threat in the 3G mobile network
and proposes a detection technology.
Abstract: In this paper we propose a new traffic simulation
package, TDMSim, which supports both macroscopic and
microscopic simulation on free-flowing and regulated traffic systems.
Both simulators are based on travel demands, which specify the
numbers of vehicles departing from origins to arrive at different
destinations. The microscopic simulator implements the carfollowing
model given the pre-defined routes of the vehicles but also
supports the rerouting of vehicles. We also propose a macroscopic
simulator which is built in integration with the microscopic simulator
to allow the simulation to be scaled for larger networks without
sacrificing the precision achievable through the microscopic
simulator. The macroscopic simulator also enables the reuse of
previous simulation results when simulating traffic on the same
networks at later time. Validations have been conducted to show the
correctness of both simulators.
Abstract: I/O workload is a critical and important factor to
analyze I/O pattern and to maximize file system performance.
However to measure I/O workload on running distributed parallel file
system is non-trivial due to collection overhead and large volume of
data. In this paper, we measured and analyzed file system activities on
two large-scale cluster systems which had TFlops level high
performance computation resources. By comparing file system
activities of 2009 with those of 2006, we analyzed the change of I/O
workloads by the development of system performance and high-speed
network technology.
Abstract: Business scenario is an important technique that may be used at various stages of the enterprise architecture to derive its characteristics based on the high-level requirements of the business. In terms of wireless deployments, they are used to help identify and understand business needs involving wireless services, and thereby to derive the business requirements that the architecture development has to address by taking into account of various wireless challenges. This study assesses the deployment of Wireless Local Area Network (WLAN) and Broadband Wireless Access (BWA) solutions for several business scenarios in Asia Pacific region. This paper focuses on the overview of the business and technology environments, whereby examples of existing (or suggested) wireless solutions (to be) adopted in Asia Pacific region will be discussed. Interactions of several players, enabling technologies, and key processes in the wireless environments are studied. The analysis and discussions associated to this study are divided into two divisions: healthcare and education, where the merits of wireless solutions in improving living quality are highlighted.
Abstract: The backpropagation algorithm in general employs quadratic error function. In fact, most of the problems that involve minimization employ the Quadratic error function. With alternative error functions the performance of the optimization scheme can be improved. The new error functions help in suppressing the ill-effects of the outliers and have shown good performance to noise. In this paper we have tried to evaluate and compare the relative performance of complex valued neural network using different error functions. During first simulation for complex XOR gate it is observed that some error functions like Absolute error, Cauchy error function can replace Quadratic error function. In the second simulation it is observed that for some error functions the performance of the complex valued neural network depends on the architecture of the network whereas with few other error functions convergence speed of the network is independent of architecture of the neural network.
Abstract: In this paper, the optimum weight and cost of a laminated composite plate is seeked, while it undergoes the heaviest load prior to a complete failure. Various failure criteria are defined for such structures in the literature. In this work, the Tsai-Hill theory is used as the failure criterion. The theory of analysis was based on the Classical Lamination Theory (CLT). A newly type of Genetic Algorithm (GA) as an optimization technique with a direct use of real variables was employed. Yet, since the optimization via GAs is a long process, and the major time is consumed through the analysis, Radial Basis Function Neural Networks (RBFNN) was employed in predicting the output from the analysis. Thus, the process of optimization will be carried out through a hybrid neuro-GA environment, and the procedure will be carried out until a predicted optimum solution is achieved.
Abstract: Nowadays, computer worms, viruses and Trojan horse
become popular, and they are collectively called malware. Those
malware just spoiled computers by deleting or rewriting important
files a decade ago. However, recent malware seems to be born to earn
money. Some of malware work for collecting personal information so
that malicious people can find secret information such as password for
online banking, evidence for a scandal or contact address which relates
with the target. Moreover, relation between money and malware
becomes more complex. Many kinds of malware bear bots to get
springboards. Meanwhile, for ordinary internet users,
countermeasures against malware come up against a blank wall.
Pattern matching becomes too much waste of computer resources,
since matching tools have to deal with a lot of patterns derived from
subspecies. Virus making tools can automatically bear subspecies of
malware. Moreover, metamorphic and polymorphic malware are no
longer special. Recently there appears malware checking sites that
check contents in place of users' PC. However, there appears a new
type of malicious sites that avoids check by malware checking sites. In
this paper, existing protocols and methods related with the web are
reconsidered in terms of protection from current attacks, and new
protocol and method are indicated for the purpose of security of the
web.
Abstract: Independent spanning trees (ISTs) provide a number of advantages in data broadcasting. One can cite the use in fault tolerance network protocols for distributed computing and bandwidth. However, the problem of constructing multiple ISTs is considered hard for arbitrary graphs. In this paper we present an efficient algorithm to construct ISTs on hypercubes that requires minimum resources to be performed.
Abstract: The convergence of heterogeneous wireless access technologies characterizes the 4G wireless networks. In such converged systems, the seamless and efficient handoff between
different access technologies (vertical handoff) is essential and remains a challenging problem. The heterogeneous co-existence of access technologies with largely different characteristics creates a decision problem of determining the “best" available network at
“best" time to reduce the unnecessary handoffs. This paper proposes a dynamic decision model to decide the “best" network at “best"
time moment to handoffs. The proposed dynamic decision model make the right vertical handoff decisions by determining the “best"
network at “best" time among available networks based on, dynamic
factors such as “Received Signal Strength(RSS)" of network and
“velocity" of mobile station simultaneously with static factors like Usage Expense, Link capacity(offered bandwidth) and power
consumption. This model not only meets the individual user needs but also improve the whole system performance by reducing the unnecessary handoffs.
Abstract: COSMED K4b2 is a portable electrical device designed to test pulmonary functions. It is ideal for many applications that need the measurement of the cardio-respiratory response either in the field or in the lab is capable with the capability to delivery real time data to a sink node or a PC base station with storing data in the memory at the same time. But the actual sensor outputs and data received may contain some errors, such as impulsive noise which can be related to sensors, low batteries, environment or disturbance in data acquisition process. These abnormal outputs might cause misinterpretations of exercise or living activities to persons being monitored. In our paper we propose an effective and feasible method to detect and identify errors in applications by principal component analysis (PCA) and a back propagation (BP) neural network.
Abstract: We present a new numerical method for the computation of the steady-state solution of Markov chains. Theoretical analyses show that the proposed method, with a contraction factor α, converges to the one-dimensional null space of singular linear systems of the form Ax = 0. Numerical experiments are used to illustrate the effectiveness of the proposed method, with applications to a class of interesting models in the domain of tandem queueing networks.
Abstract: In this paper, first we introduce the stable distribution, stable process and theirs characteristics. The a -stable distribution family has received great interest in the last decade due to its success in modeling data, which are too impulsive to be accommodated by the Gaussian distribution. In the second part, we propose major applications of alpha stable distribution in telecommunication, computer science such as network delays and signal processing and financial markets. At the end, we focus on using stable distribution to estimate measure of risk in stock markets and show simulated data with statistical softwares.
Abstract: It has become crucial over the years for nations to
improve their credit scoring methods and techniques in light of the
increasing volatility of the global economy. Statistical methods or
tools have been the favoured means for this; however artificial
intelligence or soft computing based techniques are becoming
increasingly preferred due to their proficient and precise nature and
relative simplicity. This work presents a comparison between Support
Vector Machines and Artificial Neural Networks two popular soft
computing models when applied to credit scoring. Amidst the
different criteria-s that can be used for comparisons; accuracy,
computational complexity and processing times are the selected
criteria used to evaluate both models. Furthermore the German credit
scoring dataset which is a real world dataset is used to train and test
both developed models. Experimental results obtained from our study
suggest that although both soft computing models could be used with
a high degree of accuracy, Artificial Neural Networks deliver better
results than Support Vector Machines.
Abstract: LSP routing is among the prominent issues in MPLS
networks traffic engineering. The objective of this routing is to
increase number of the accepted requests while guaranteeing the
quality of service (QoS). Requested bandwidth is the most important
QoS criterion that is considered in literatures, and a various number
of heuristic algorithms have been presented with that regards. Many
of these algorithms prevent flows through bottlenecks of the network
in order to perform load balancing, which impedes optimum
operation of the network. Here, a modern routing algorithm is
proposed as MIRAD: having a little information of the network
topology, links residual bandwidth, and any knowledge of the
prospective requests it provides every request with a maximum
bandwidth as well as minimum end-to-end delay via uniform load
distribution across the network. Simulation results of the proposed
algorithm show a better efficiency in comparison with similar
algorithms.
Abstract: For several high speed networks, providing resilience against failures is an essential requirement. The main feature for designing next generation optical networks is protecting and restoring high capacity WDM networks from the failures. Quick detection, identification and restoration make networks more strong and consistent even though the failures cannot be avoided. Hence, it is necessary to develop fast, efficient and dependable fault localization or detection mechanisms. In this paper we propose a new fault localization algorithm for WDM networks which can identify the location of a failure on a failed lightpath. Our algorithm detects the failed connection and then attempts to reroute data stream through an alternate path. In addition to this, we develop an algorithm to analyze the information of the alarms generated by the components of an optical network, in the presence of a fault. It uses the alarm correlation in order to reduce the list of suspected components shown to the network operators. By our simulation results, we show that our proposed algorithms achieve less blocking probability and delay while getting higher throughput.
Abstract: Environmental micro-organisms include a large number of taxa and some species that are generally considered nonpathogenic, but can represent a risk in certain conditions, especially for elderly people and immunocompromised individuals. Chemotaxonomic identification techniques are powerful tools for environmental micro-organisms, and cellular fatty acid methyl esters (FAME) content is a powerful fingerprinting identification technique. A system based on an unsupervised artificial neural network (ANN) was set up using the fatty acid profiles of standard bacterial strains, obtained by gas-chromatography, used as learning data. We analysed 45 certified strains belonging to Acinetobacter, Aeromonas, Alcaligenes, Aquaspirillum, Arthrobacter, Bacillus, Brevundimonas, Enterobacter, Flavobacterium, Micrococcus, Pseudomonas, Serratia, Shewanella and Vibrio genera. A set of 79 bacteria isolated from a drinking water line (AMGA, the major water supply system in Genoa) were used as an example for identification compared to standard MIDI method. The resulting ANN output map was found to be a very powerful tool to identify these fresh isolates.
Abstract: Several combinations of the preprocessing algorithms,
feature selection techniques and classifiers can be applied to the data
classification tasks. This study introduces a new accurate classifier,
the proposed classifier consist from four components: Signal-to-
Noise as a feature selection technique, support vector machine,
Bayesian neural network and AdaBoost as an ensemble algorithm.
To verify the effectiveness of the proposed classifier, seven well
known classifiers are applied to four datasets. The experiments show
that using the suggested classifier enhances the classification rates for
all datasets.
Abstract: Landslide susceptibility map delineates the potential
zones for landslide occurrence. Previous works have applied
multivariate methods and neural networks for mapping landslide
susceptibility. This study proposed a new approach to integrate
decision tree model and spatial cluster statistic for assessing landslide
susceptibility spatially. A total of 2057 landslide cells were digitized
for developing the landslide decision tree model. The relationships of
landslides and instability factors were explicitly represented by using
tree graphs in the model. The local Getis-Ord statistics were used to
cluster cells with high landslide probability. The analytic result from
the local Getis-Ord statistics was classed to create a map of landslide
susceptibility zones. The map was validated using new landslide data
with 482 cells. Results of validation show an accuracy rate of 86.1% in
predicting new landslide occurrence. This indicates that the proposed
approach is useful for improving landslide susceptibility mapping.