Abstract: Reducing the risk of information leaks is one of
the most important functions of identity management systems. To
achieve this purpose, Dey et al. have already proposed an account
management method for a federated login system using a blind
signature scheme. In order to ensure account anonymity for the
authentication provider, referred to as an IDP (identity provider),
a blind signature scheme is utilized to generate an authentication
token on an authentication service and the token is sent to an IDP.
However, there is a problem with the proposed system. Malicious
users can establish multiple accounts on an IDP by requesting such
accounts. As a measure to solve this problem, in this paper, the
authors propose an account checking method that is performed before
account generation.
Abstract: None of the processing models in the software
development has explained the software systems performance
evaluation and modeling; likewise, there exist uncertainty in the
information systems because of the natural essence of requirements,
and this may cause other challenges in the processing of software
development. By definition an extended version of UML (Fuzzy-
UML), the functional requirements of the software defined
uncertainly would be supported. In this study, the behavioral
description of uncertain information systems by the aid of fuzzy-state
diagram is crucial; moreover, the introduction of behavioral diagrams
role in F-UML is investigated in software performance modeling
process. To get the aim, a fuzzy sub-profile is used.
Abstract: Network reconfiguration in distribution system is realized by changing the status of sectionalizing switches to reduce the power loss in the system. This paper presents a new method which applies an artificial bee colony algorithm (ABC) for determining the sectionalizing switch to be operated in order to solve the distribution system loss minimization problem. The ABC algorithm is a new population based metaheuristic approach inspired by intelligent foraging behavior of honeybee swarm. The advantage of ABC algorithm is that it does not require external parameters such as cross over rate and mutation rate as in case of genetic algorithm and differential evolution and it is hard to determine these parameters in prior. The other advantage is that the global search ability in the algorithm is implemented by introducing neighborhood source production mechanism which is a similar to mutation process. To demonstrate the validity of the proposed algorithm, computer simulations are carried out on 14, 33, and 119-bus systems and compared with different approaches available in the literature. The proposed method has outperformed the other methods in terms of the quality of solution and computational efficiency.
Abstract: EDF (Early Deadline First) algorithm is a very important scheduling algorithm for real- time systems . The EDF algorithm assigns priorities to each job according to their absolute deadlines and has good performance when the real-time system is not overloaded. When the real-time system is overloaded, many misdeadlines will be produced. But these misdeadlines are not uniformly distributed, which usually focus on some tasks. In this paper, we present an adaptive fuzzy control scheduling based on EDF algorithm. The improved algorithm can have a rectangular distribution of misdeadline ratios among all real-time tasks when the system is overloaded. To evaluate the effectiveness of the improved algorithm, we have done extensive simulation studies. The simulation results show that the new algorithm is superior to the old algorithm.
Abstract: In the recent past Learning Classifier Systems have
been successfully used for data mining. Learning Classifier System
(LCS) is basically a machine learning technique which combines
evolutionary computing, reinforcement learning, supervised or
unsupervised learning and heuristics to produce adaptive systems. A
LCS learns by interacting with an environment from which it
receives feedback in the form of numerical reward. Learning is
achieved by trying to maximize the amount of reward received. All
LCSs models more or less, comprise four main components; a finite
population of condition–action rules, called classifiers; the
performance component, which governs the interaction with the
environment; the credit assignment component, which distributes the
reward received from the environment to the classifiers accountable
for the rewards obtained; the discovery component, which is
responsible for discovering better rules and improving existing ones
through a genetic algorithm. The concatenate of the production rules
in the LCS form the genotype, and therefore the GA should operate
on a population of classifier systems. This approach is known as the
'Pittsburgh' Classifier Systems. Other LCS that perform their GA at
the rule level within a population are known as 'Mitchigan' Classifier
Systems. The most predominant representation of the discovered
knowledge is the standard production rules (PRs) in the form of IF P
THEN D. The PRs, however, are unable to handle exceptions and do
not exhibit variable precision. The Censored Production Rules
(CPRs), an extension of PRs, were proposed by Michalski and
Winston that exhibit variable precision and supports an efficient
mechanism for handling exceptions. A CPR is an augmented
production rule of the form: IF P THEN D UNLESS C, where
Censor C is an exception to the rule. Such rules are employed in
situations, in which conditional statement IF P THEN D holds
frequently and the assertion C holds rarely. By using a rule of this
type we are free to ignore the exception conditions, when the
resources needed to establish its presence are tight or there is simply
no information available as to whether it holds or not. Thus, the IF P
THEN D part of CPR expresses important information, while the
UNLESS C part acts only as a switch and changes the polarity of D
to ~D. In this paper Pittsburgh style LCSs approach is used for
automated discovery of CPRs. An appropriate encoding scheme is
suggested to represent a chromosome consisting of fixed size set of
CPRs. Suitable genetic operators are designed for the set of CPRs
and individual CPRs and also appropriate fitness function is proposed
that incorporates basic constraints on CPR. Experimental results are
presented to demonstrate the performance of the proposed learning
classifier system.
Abstract: The temporal nature of negative selection is an under exploited area. In a negative selection system, newly generated antibodies go through a maturing phase, and the survivors of the phase then wait to be activated by the incoming antigens after certain number of matches. These without having enough matches will age and die, while these with enough matches (i.e., being activated) will become active detectors. A currently active detector may also age and die if it cannot find any match in a pre-defined (lengthy) period of time. Therefore, what matters in a negative selection system is the dynamics of the involved parties in the current time window, not the whole time duration, which may be up to eternity. This property has the potential to define the uniqueness of negative selection in comparison with the other approaches. On the other hand, a negative selection system is only trained with “normal" data samples. It has to learn and discover unknown “abnormal" data patterns on the fly by itself. Consequently, it is more appreciate to utilize negation selection as a system for pattern discovery and recognition rather than just pattern recognition. In this paper, we study the potential of using negative selection in discovering unknown temporal patterns.
Abstract: The technology usages of high speed Internet leads to
establish and start new era of online education. With the
advancement of the information technology and communication
systems new opportunities have been created. This leads universities
to have various online education channels to meet the demand of
different learners- needs. One of these channels is M-learning, which
can be used to improve the online education environment. With using
such mobile technology in learning both students and instructors can
easily access educational courses anytime from anywhere. The paper
first presents literature about mobile learning and to what extent this
approach can be utilized to enhance the overall learning system. It
provides a comparison between mobile learning and traditional elearning
showing the wide array of benefits of the new generation of
technology. The possible challenges and potential advantages of Mlearning
in the online education system are also discussed.
Abstract: Mining sequential patterns from large customer transaction databases has been recognized as a key research topic in database systems. However, the previous works more focused on mining sequential patterns at a single concept level. In this study, we introduced concept hierarchies into this problem and present several algorithms for discovering multiple-level sequential patterns based on the hierarchies. An experiment was conducted to assess the performance of the proposed algorithms. The performances of the algorithms were measured by the relative time spent on completing the mining tasks on two different datasets. The experimental results showed that the performance depends on the characteristics of the datasets and the pre-defined threshold of minimal support for each level of the concept hierarchy. Based on the experimental results, some suggestions were also given for how to select appropriate algorithm for a certain datasets.
Abstract: The purpose of this paper is to solve the problem of protecting aerial lines from high impedance faults (HIFs) in distribution systems. This investigation successfully applies 3I0 zero sequence current to solve HIF problems. The feature extraction system based on discrete wavelet transform (DWT) and the feature identification technique found on statistical confidence are then applied to discriminate effectively between the HIFs and the switch operations. Based on continuous wavelet transform (CWT) pattern recognition of HIFs is proposed, also. Staged fault testing results demonstrate that the proposed wavelet based algorithm is feasible performance well.
Abstract: A traffic light gives security from traffic congestion,reducing the traffic jam, and organizing the traffic flow. Furthermore,increasing congestion level in public road networks is a growingproblem in many countries. Using Intelligent Transportation Systemsto provide emergency vehicles a green light at intersections canreduce driver confusion, reduce conflicts, and improve emergencyresponse times. Nowadays, the technology of wireless sensornetworks can solve many problems and can offer a good managementof the crossroad. In this paper, we develop a new approach based onthe technique of clustering and the graphical possibilistic fusionmodeling. So, the proposed model is elaborated in three phases. Thefirst one consists to decompose the environment into clusters,following by the fusion intra and inter clusters processes. Finally, wewill show some experimental results by simulation that proves theefficiency of our proposed approach.KeywordsTraffic light, Wireless sensor network, Controller,Possibilistic network/Bayesain network.
Abstract: Information sharing and exchange, rather than
information processing, is what characterizes information
technology in the 21st century. Ontologies, as shared common
understanding, gain increasing attention, as they appear as the
most promising solution to enable information sharing both at
a semantic level and in a machine-processable way. Domain
Ontology-based modeling has been exploited to provide
shareability and information exchange among diversified,
heterogeneous applications of enterprises.
Contextual ontologies are “an explicit specification of
contextual conceptualization". That is: ontology is
characterized by concepts that have multiple representations
and they may exist in several contexts. Hence, contextual
ontologies are a set of concepts and relationships, which are
seen from different perspectives. Contextualization is to allow
for ontologies to be partitioned according to their contexts.
The need for contextual ontologies in enterprise modeling
has become crucial due to the nature of today's competitive
market. Information resources in enterprise is distributed and
diversified and is in need to be shared and communicated
locally through the intranet and globally though the internet.
This paper discusses the roles that ontologies play in an
enterprise modeling, and how ontologies assist in building a
conceptual model in order to provide communicative and
interoperable information systems. The issue of enterprise
modeling based on contextual domain ontology is also
investigated, and a framework is proposed for an enterprise
model that consists of various applications.
Abstract: Recently studies in area of supply chain network
(SCN) have focused on the disruption issues in distribution systems.
Also this paper extends the previous literature by providing a new biobjective
model for cost minimization of designing a three echelon
SCN across normal and failure scenarios with considering multi
capacity option for manufacturers and distribution centers. Moreover,
in order to solve the problem by means of LINGO software, novel
model will be reformulated through a branch of LP-Metric method
called Min-Max approach.
Abstract: The power consumption of an Optical Packet Switch
equipped with SOA technology based Spanke switching fabric is
evaluated. Sophisticated analytical models are introduced to evaluate
the power consumption versus the offered traffic, the main
switch parameters, and the used device characteristics. The impact
of Amplifier Spontaneous Emission (ASE) noise generated by a
transmission system on the power consumption is investigated. As
a matter of example for 32×32 switches supporting 64 wavelengths
and offered traffic equal to 0,8, the average energy consumption per
bit is 5, 07 · 10-2 nJ/bit and increases if ASE noise introduced by
the transmission systems is increased.
Abstract: The group mutual exclusion (GME) problem is a
variant of the mutual exclusion problem. In the present paper a
token-based group mutual exclusion algorithm, capable of handling
transient faults, is proposed. The algorithm uses the concept of
dynamic request sets. A time out mechanism is used to detect the
token loss; also, a distributed scheme is used to regenerate the token.
The worst case message complexity of the algorithm is n+1. The
maximum concurrency and forum switch complexity of the
algorithm are n and min (n, m) respectively, where n is the number of
processes and m is the number of groups. The algorithm also satisfies
another desirable property called smooth admission. The scheme can
also be adapted to handle the extended group mutual exclusion
problem.
Abstract: Sedimentation is a hydraulic phenomenon that is
emerging as a serious challenge in river engineering. When the flow
reaches a certain state that gather potential energy, it shifts the
sediment load along channel bed. The transport of such materials can
be in the form of suspended and bed loads. The movement of these
along the river course and channels and the ways in which this could
influence the water intakes is considered as the major challenges for
sustainable O&M of hydraulic structures. This could be very serious
in arid and semi-arid regions like Iran, where inappropriate watershed
management could lead to shifting a great deal of sediments into the
reservoirs and irrigation systems. This paper aims to investigate
sedimentation in the Western Canal of Dez Diversion Weir in Iran,
identifying factors which influence the process and provide ways in
which to mitigate its detrimental effects by using the SHARC
Software.
For the purpose of this paper, data from the Dezful water authority
and Dezful Hydrometric Station pertinent to a river course of about 6
Km were used.
Results estimated sand and silt bed loads concentrations to be 193
ppm and 827ppm respectively. Given the available data on average
annual bed loads and average suspended sediment loads of 165ppm
and 837ppm, there was a significant statistical difference (16%)
between the sand grains, whereas no significant difference (1.2%)
was find in the silt grain sizes. One explanation for such finding
being that along the 6 Km river course there was considerable
meandering effects which explains recent shift in the hydraulic
behavior along the stream course under investigation. The sand
concentration in downstream relative to present state of the canal
showed a steep descending curve. Sediment trapping on the other
hand indicated a steep ascending curve. These occurred because the
diversion weir was not considered in the simulation model.
Abstract: In this paper, we investigate the study of techniques
for scheduling users for resource allocation in the case of multiple
input and multiple output (MIMO) packet transmission systems. In
these systems, transmit antennas are assigned to one user or
dynamically to different users using spatial multiplexing. The
allocation of all transmit antennas to one user cannot take full
advantages of multi-user diversity. Therefore, we developed the case
when resources are allocated dynamically. At each time slot users
have to feed back their channel information on an uplink feedback
channel. Channel information considered available in the schedulers
is the zero forcing (ZF) post detection signal to interference plus
noise ratio. Our analysis study concerns the round robin and the
opportunistic schemes.
In this paper, we present an overview and a complete capacity
analysis of these schemes. The main results in our study are to give
an analytical form of system capacity using the ZF receiver at the
user terminal. Simulations have been carried out to validate all
proposed analytical solutions and to compare the performance of
these schemes.
Abstract: Electromyography (EMG) signal processing has been investigated remarkably regarding various applications such as in rehabilitation systems. Specifically, wavelet transform has served as a powerful technique to scrutinize EMG signals since wavelet transform is consistent with the nature of EMG as a non-stationary signal. In this paper, the efficiency of wavelet transform in surface EMG feature extraction is investigated from four levels of wavelet decomposition and a comparative study between different mother wavelets had been done. To recognize the best function and level of wavelet analysis, two evaluation criteria, scatter plot and RES index are recruited. Hereupon, four wavelet families, namely, Daubechies, Coiflets, Symlets and Biorthogonal are studied in wavelet decomposition stage. Consequently, the results show that only features from first and second level of wavelet decomposition yields good performance and some functions of various wavelet families can lead to an improvement in separability class of different hand movements.
Abstract: We present a new approach to evaluation of Cyber Security in Power Systems using the method of modeling the power systems Infrastructure using software agents. Interfaces between module and the home smart meter are recognized as the primary points of intrusion.
Abstract: This paper demonstrates an effort of a serviceoriented
engineering department in improving the sharing and
transfer of knowledge. Although the department consist of only six
employees, but it provides services in various chemical application in
an oil and gas business. The services provided span across Asia
Pacific region mainly Indonesia, Myanmar, Vietnam, Brunei,
Thailand and Singapore. Currently there are no effective tools or
integrated systems that support the sharing or transfer and
maintenance of knowledge so the department has considered
preserving this valuable knowledge by developing a Knowledge
Management System (KMS). This paper presents the development of
a KMS to support the sharing of knowledge in a service-oriented
engineering department of an oil and gas company. The embedded
features in the KMS like blog and forum will encourage iterative
process of knowledge sharing among the employees in the
department. The information and knowledge being shared, discussed
and communicated will be then achieved for future re-use. The re-use
of the knowledge allows the department to reduce redundant efforts
in providing consistent, up-to-date and cost effective of the best
solution to the its clients.
Abstract: This paper presents a method for the optimal
allocation of Distributed generation in distribution systems. In this
paper, our aim would be optimal distributed generation allocation for
voltage profile improvement and loss reduction in distribution
network. Genetic Algorithm (GA) was used as the solving tool,
which referring two determined aim; the problem is defined and
objective function is introduced. Considering to fitness values
sensitivity in genetic algorithm process, there is needed to apply load
flow for decision-making. Load flow algorithm is combined
appropriately with GA, till access to acceptable results of this
operation. We used MATPOWER package for load flow algorithm
and composed it with our Genetic Algorithm. The suggested method
is programmed under MATLAB software and applied ETAP
software for evaluating of results correctness. It was implemented on
part of Tehran electricity distributing grid. The resulting operation of
this method on some testing system is illuminated improvement of
voltage profile and loss reduction indexes.