Abstract: This paper presents a multiband CPW-fed slot antenna
with L-slot bowtie tuning stub. The proposed antenna has been
designed for PCS 1900, UMTS, WLAN 802.11 a/b/g and bluetooth
applications, with a cost-effective FR4 substrate. The proposed
antenna still radiate as omni-directional in azimuth plane and
sufficient bandwidth for all above mentions. The proposed antenna
works as dual-wideband, bandwidth at low frequency band and high
frequency are about 45.49 % and 22.39 % respectively. The
experimental results of the constructed prototype are presented and
also compared with simulation results using a commercial software
tool.
Abstract: The aim of the paper work is to investigate and predict
the static performance of journal bearing in turbulent flow condition
considering micropolar lubrication. The Reynolds equation has been
modified considering turbulent micropolar lubrication and is solved
for steady state operations. The Constantinescu-s turbulence model is
adopted using the coefficients. The analysis has been done for a
parallel and inertia less flow. Load capacity and friction factor have
been evaluated for various operating parameters.
Abstract: In this paper, a direct torque control - space vector
modulation (DTC-SVM) scheme is presented for a six-phase speed
and voltage sensorless induction motor (IM) drive. The decoupled
torque and stator flux control is achieved based on IM stator flux field orientation. The rotor speed is detected by on-line estimating of
the rotor angular slip speed and stator vector flux speed. In addition, a simple method is introduced to estimate the stator resistance.
Moreover in this control scheme the voltage sensors are eliminated
and actual motor phase voltages are approximated by using PWM
inverter switching times and the dc link voltage. Finally, some simulation and experimental results are presented to verify the
effectiveness and capability of the proposed control scheme.
Abstract: In this work a visual and reactive contour following
behaviour is learned by reinforcement. With artificial vision the
environment is perceived in 3D, and it is possible to avoid obstacles
that are invisible to other sensors that are more common in mobile
robotics. Reinforcement learning reduces the need for intervention in
behaviour design, and simplifies its adjustment to the environment,
the robot and the task. In order to facilitate its generalisation to other
behaviours and to reduce the role of the designer, we propose a
regular image-based codification of states. Even though this is much
more difficult, our implementation converges and is robust. Results
are presented with a Pioneer 2 AT on a Gazebo 3D simulator.
Abstract: Since the presentation of the backpropagation algorithm, a vast variety of improvements of the technique for training a feed forward neural networks have been proposed. This article focuses on two classes of acceleration techniques, one is known as Local Adaptive Techniques that are based on weightspecific only, such as the temporal behavior of the partial derivative of the current weight. The other, known as Dynamic Adaptation Methods, which dynamically adapts the momentum factors, α, and learning rate, η, with respect to the iteration number or gradient. Some of most popular learning algorithms are described. These techniques have been implemented and tested on several problems and measured in terms of gradient and error function evaluation, and percentage of success. Numerical evidence shows that these techniques improve the convergence of the Backpropagation algorithm.
Abstract: Internet Access Technologies (IAT) provide a means
through which Internet can be accessed. The choice of a suitable
Internet technology is increasingly becoming an important issue to
ISP clients. Currently, the choice of IAT is based on discretion and
intuition of the concerned managers and the reliance on ISPs. In this
paper we propose a model and designs algorithms that are used in the
Internet access technology specification. In the proposed model, three
ranking approaches are introduced; concurrent ranking, stepwise
ranking and weighted ranking. The model ranks the IAT based on
distance measures computed in ascending order while the global
ranking system assigns weights to each IAT according to the position
held in each ranking technique, determines the total weight of a
particular IAT and ranks them in descending order. The final output
is an objective ranking of IAT in descending order.
Abstract: To create a solution for a specific problem in machine
learning, the solution is constructed from the data or by use a search
method. Genetic algorithms are a model of machine learning that can
be used to find nearest optimal solution. While the great advantage of
genetic algorithms is the fact that they find a solution through
evolution, this is also the biggest disadvantage. Evolution is inductive,
in nature life does not evolve towards a good solution but it evolves
away from bad circumstances. This can cause a species to evolve into
an evolutionary dead end. In order to reduce the effect of this
disadvantage we propose a new a learning tool (criteria) which can be
included into the genetic algorithms generations to compare the
previous population and the current population and then decide
whether is effective to continue with the previous population or the
current population, the proposed learning tool is called as Keeping
Efficient Population (KEP). We applied a GA based on KEP to the
production line layout problem, as a result KEP keep the evaluation
direction increases and stops any deviation in the evaluation.
Abstract: Accurate demand forecasting is one of the most key
issues in inventory management of spare parts. The problem of
modeling future consumption becomes especially difficult for lumpy
patterns, which characterized by intervals in which there is no
demand and, periods with actual demand occurrences with large
variation in demand levels. However, many of the forecasting
methods may perform poorly when demand for an item is lumpy.
In this study based on the characteristic of lumpy demand patterns
of spare parts a hybrid forecasting approach has been developed,
which use a multi-layered perceptron neural network and a
traditional recursive method for forecasting future demands. In the
described approach the multi-layered perceptron are adapted to
forecast occurrences of non-zero demands, and then a conventional
recursive method is used to estimate the quantity of non-zero
demands. In order to evaluate the performance of the proposed
approach, their forecasts were compared to those obtained by using
Syntetos & Boylan approximation, recently employed multi-layered
perceptron neural network, generalized regression neural network
and elman recurrent neural network in this area. The models were
applied to forecast future demand of spare parts of Arak
Petrochemical Company in Iran, using 30 types of real data sets. The
results indicate that the forecasts obtained by using our proposed
mode are superior to those obtained by using other methods.
Abstract: This paper presents an approach for the determination of the optimal cutting parameters (spindle speed, feed rate, depth of cut and engagement) leading to minimum surface roughness in face milling of high silicon stainless steel by coupling neural network (NN) and Electromagnetism-like Algorithm (EM). In this regard, the advantages of statistical experimental design technique, experimental measurements, artificial neural network, and Electromagnetism-like optimization method are exploited in an integrated manner. To this end, numerous experiments on this stainless steel were conducted to obtain surface roughness values. A predictive model for surface roughness is created by using a back propogation neural network, then the optimization problem was solved by using EM optimization. Additional experiments were performed to validate optimum surface roughness value predicted by EM algorithm. It is clearly seen that a good agreement is observed between the predicted values by EM coupled with feed forward neural network and experimental measurements. The obtained results show that the EM algorithm coupled with back propogation neural network is an efficient and accurate method in approaching the global minimum of surface roughness in face milling.
Abstract: Binary Decision Diagrams (BDDs) are useful data
structures for symbolic Boolean manipulations. BDDs are used in
many tasks in VLSI/CAD, such as equivalence checking, property
checking, logic synthesis, and false paths. In this paper we describe a
new approach for the realization of a BDD package. To perform
manipulations of Boolean functions, the proposed approach does not
depend on the recursive synthesis operation of the IF-Then-Else
(ITE). Instead of using the ITE operation, the basic synthesis
algorithm is done using Boolean NOR operation.
Abstract: The problem of incompressible steady flow simulation around an airfoil is discussed. For some simplest airfoils (circular, elliptical, Zhukovsky airfoils) the exact solution is known from complex analysis. It allows to compute the intensity of vortex layer which simulates the airfoil. Some modifications of the vortex element method are proposed and test computations are carried out. It-s shown that the these approaches are much more effective in comparison with the classical numerical scheme.
Abstract: We present an Electronic Nose (ENose), which is
aimed at identifying the presence of one out of two gases, possibly
detecting the presence of a mixture of the two. Estimation of the
concentrations of the components is also performed for a volatile
organic compound (VOC) constituted by methanol and acetone, for
the ranges 40-400 and 22-220 ppm (parts-per-million), respectively.
Our system contains 8 sensors, 5 of them being gas sensors (of the
class TGS from FIGARO USA, INC., whose sensing element is a tin
dioxide (SnO2) semiconductor), the remaining being a temperature
sensor (LM35 from National Semiconductor Corporation), a
humidity sensor (HIH–3610 from Honeywell), and a pressure sensor
(XFAM from Fujikura Ltd.).
Our integrated hardware–software system uses some machine
learning principles and least square regression principle to identify at
first a new gas sample, or a mixture, and then to estimate the
concentrations. In particular we adopt a training model using the
Support Vector Machine (SVM) approach with linear kernel to teach
the system how discriminate among different gases. Then we apply
another training model using the least square regression, to predict
the concentrations.
The experimental results demonstrate that the proposed
multiclassification and regression scheme is effective in the
identification of the tested VOCs of methanol and acetone with
96.61% correctness. The concentration prediction is obtained with
0.979 and 0.964 correlation coefficient for the predicted versus real
concentrations of methanol and acetone, respectively.
Abstract: In the present analysis an unsteady laminar
forced convection water boundary layer flow is considered.
The fluid properties such as viscosity and Prandtl number are
taken as variables such that those are inversely proportional to
temperature. By using quasi-linearization technique the nonlinear
coupled partial differential equations are linearized and
the numerical solutions are obtained by using implicit finite
difference scheme with the appropriate selection of step sizes.
Non-similar solutions have been obtained from the starting
point of the stream-wise coordinate to the point where skin
friction value vanishes. The effect non-uniform mass transfer
along the surface of the cylinder through slot is studied on the
skin friction and heat transfer coefficients.
Abstract: The purpose of this paper is to analyze determinants of
information security affecting adoption of the Web-based integrated
information systems (IIS). We introduced Web-based information
systems which are designed to formulate strategic plans for Peruvian
government. Theoretical model is proposed to test impact of
organizational factors (deterrent efforts and severity; preventive
efforts) and individual factors (information security threat; security
awareness) on intentions to proactively use the Web-based IIS .Our
empirical study results highlight that deterrent efforts and deterrent
severity have no significant influence on the proactive use intentions
of IIS, whereas, preventive efforts play an important role in proactive
use intentions of IIS. Thus, we suggest that organizations need to do
preventive efforts by introducing various information security
solutions, and try to improve information security awareness while
reducing the perceived information security threats.
Abstract: One of the most attractive and important field of chaos theory is control of chaos. In this paper, we try to present a simple framework for chaotic motion control using the feedback linearization method. Using this approach, we derive a strategy, which can be easily applied to the other chaotic systems. This task presents two novel results: the desired periodic orbit need not be a solution of the original dynamics and the other is the robustness of response against parameter variations. The illustrated simulations show the ability of these. In addition, by a comparison between a conventional state feedback and our proposed method it is demonstrated that the introduced technique is more efficient.
Abstract: Com Poisson distribution is capable of modeling the count responses irrespective of their mean variance relation and the parameters of this distribution when fitted to a simple cross sectional data can be efficiently estimated using maximum likelihood (ML) method. In the regression setup, however, ML estimation of the parameters of the Com Poisson based generalized linear model is computationally intensive. In this paper, we propose to use quasilikelihood (QL) approach to estimate the effect of the covariates on the Com Poisson counts and investigate the performance of this method with respect to the ML method. QL estimates are consistent and almost as efficient as ML estimates. The simulation studies show that the efficiency loss in the estimation of all the parameters using QL approach as compared to ML approach is quite negligible, whereas QL approach is lesser involving than ML approach.
Abstract: As various mobile sensing technologies, remote
control and ubiquitous infrastructure are developing and expectations
on quality of life are increasing, a lot of researches and developments
on home network technologies and services are actively on going,
Until now, we have focused on how to provide users with high-level
home network services, while not many researches on home network
security for guaranteeing safety are progressing. So, in this paper, we
propose an access control model specific to home network that
provides various kinds of users with home network services up one-s
characteristics and features, and protects home network systems from
illegal/unnecessary accesses or intrusions.
Abstract: Various security APIs (Application Programming
Interfaces) are being used in a variety of application areas requiring
the information security function. However, these standards are not
compatible, and the developer must use those APIs selectively
depending on the application environment or the programming
language. To resolve this problem, we propose the standard draft of
the information security component, while SSL (Secure Sockets
Layer) using the confidentiality and integrity component interface has
been implemented to verify validity of the standard proposal. The
implemented SSL uses the lower-level SSL component when
establishing the RMI (Remote Method Invocation) communication
between components, as if the security algorithm had been
implemented by adding one more layer on the TCP/IP.
Abstract: Aggression is a behavior that cannot be approved by
the society. Vandalism which is aggression towards objects is an
action that tends to damage public or personal property. The
behaviors that are described as vandalism can often be observed in
the schools as well. According to Zwier and Vaughan (1)
previous research about the reasons of and precautionary measures
for vandalism in schools can be grouped in three tendency categories:
conservative, liberal and radical. In this context, the main aim of this
study is to discover which ideological tendency of the reasons of
school vandalism is adopted by the teachers and what are their
physical, environmental, school system and societal solutions for
vandalism. A total of 200 teachers participated in this study, and the
mean age was 34.20 years (SD = 6.54). The sample was made up of
109 females and 91 males. For the analysis of the data, SPSS 15.00,
frequency, percentage, and t-test were used. The research showed
that the teachers have tendencies in the order of conservative, liberal
and radical for the reasons of vandalism. The research also showed
that the teachers do not have any tendency for eliminating vandalism
physically and general solutions on the level of society; on the other
hand they mostly adopt a conservative tendency in terms of
precautions against vandalism in the school system. Second most,
they adopt the liberal tendency in terms of precautions against
vandalism in the school system. . It is observed that the findings of
this study are comparable to the existing literature on the subject.
Future studies should be conducted with multiple variants and
bigger sampling.
Abstract: We present here the results for a comparative study of
some techniques, available in the literature, related to the relevance
feedback mechanism in the case of a short-term learning. Only one
method among those considered here is belonging to the data mining
field which is the K-nearest neighbors algorithm (KNN) while the
rest of the methods is related purely to the information retrieval field
and they fall under the purview of the following three major axes:
Shifting query, Feature Weighting and the optimization of the
parameters of similarity metric. As a contribution, and in addition to
the comparative purpose, we propose a new version of the KNN
algorithm referred to as an incremental KNN which is distinct from
the original version in the sense that besides the influence of the
seeds, the rate of the actual target image is influenced also by the
images already rated. The results presented here have been obtained
after experiments conducted on the Wang database for one iteration
and utilizing color moments on the RGB space. This compact
descriptor, Color Moments, is adequate for the efficiency purposes
needed in the case of interactive systems. The results obtained allow
us to claim that the proposed algorithm proves good results; it even
outperforms a wide range of techniques available in the literature.