Abstract: Here we have considered non uniform microstrip
leaky-wave antenna implemented on a dielectric waveguide by a
sinusoidal profile of periodic metallic grating. The non distribution of
the attenuation constant α along propagation axis, optimize the
radiating characteristics and performances of such antennas. The
method developped here is based on an integral method where the
formalism of the admittance operator is combined to a BKW
approximation. First, the effect of the modeling in the modal analysis
of complex waves is studied in detail. Then, the BKW model is used
for the dispersion analysis of the antenna of interest. According to
antenna theory, a forced continuity of the leaky-wave magnitude at
discontinuities of the non uniform structure is established. To test the
validity of our dispersion analysis, computed radiation patterns are
presented and compared in the millimeter band.
Abstract: Headphones and earphones have many extremely small
holes or narrow slits; they use sound-absorbing or porous material (i.e.,
dampers) to suppress vibratory system resonance. The air viscosity in
these acoustic paths greatly affects the acoustic properties. Simulation
analyses such as the finite element method (FEM) therefore require
knowledge of the material properties of sound-absorbing or porous
materials, such as the characteristic impedance and propagation
constant. The transfer function method using acoustic tubes is a widely
known measuring method, but there is no literature on taking
measurements up to the audible range. To measure the acoustic
properties at high-range frequencies, the acoustic tubes that form the
measuring device need to be narrowed, and the distance between the
two microphones needs to be reduced. However, when the tubes are
narrowed, the characteristic impedance drops below the air impedance.
In this study, we considered the effect of air viscosity in an acoustical
tube, introduced a theoretical formula for this effect in the form of
complex density and complex sonic velocity, and verified the
theoretical formula. We also conducted an experiment and observed
the effect from air viscosity in the actual measurements.
Abstract: This paper describes the use of artificial neural
networks (ANN) for predicting non-linear layer moduli of flexible
airfield pavements subjected to new generation aircraft (NGA)
loading, based on the deflection profiles obtained from Heavy
Weight Deflectometer (HWD) test data. The HWD test is one of the
most widely used tests for routinely assessing the structural integrity
of airport pavements in a non-destructive manner. The elastic moduli
of the individual pavement layers backcalculated from the HWD
deflection profiles are effective indicators of layer condition and are
used for estimating the pavement remaining life. HWD tests were
periodically conducted at the Federal Aviation Administration-s
(FAA-s) National Airport Pavement Test Facility (NAPTF) to
monitor the effect of Boeing 777 (B777) and Beoing 747 (B747) test
gear trafficking on the structural condition of flexible pavement
sections. In this study, a multi-layer, feed-forward network which
uses an error-backpropagation algorithm was trained to approximate
the HWD backcalculation function. The synthetic database generated
using an advanced non-linear pavement finite-element program was
used to train the ANN to overcome the limitations associated with
conventional pavement moduli backcalculation. The changes in
ANN-based backcalculated pavement moduli with trafficking were
used to compare the relative severity effects of the aircraft landing
gears on the NAPTF test pavements.
Abstract: Worm propagation profiles have significantly changed
since 2003-2004: sudden world outbreaks like Blaster or Slammer
have progressively disappeared and slower but stealthier worms
appeared since, most of them for botnets dissemination. Decreased
worm virulence results in more difficult detection.
In this paper, we describe a stealth worm propagation model
which has been extensively simulated and analysed on a huge virtual
network. The main features of this model is its ability to infect any
Internet-like network in a few seconds, whatever may be its size while
greatly limiting the reinfection attempt overhead of already infected
hosts. The main simulation results shows that the combinatorial
topology of routing may have a huge impact on the worm propagation
and thus some servers play a more essential and significant role than
others. The real-time capability to identify them may be essential to
greatly hinder worm propagation.
Abstract: To study the effect of suitable methods for
propagation of True Potato Seed (TPS) progenies, transplant and
selection of the best progenies, a factorial experiment base on a
randomized complete block design was carried out in the research
field of Sahneh region, Kermanshah, Iran during 2009-2010. Five
selective progenies from CIP (International Potato Center) including
CIP.994013, CIP.994002, CIP.994014, CIP.888006, and
CIP.994001 and two transplant preparation methods (Paper pot
preparation for mechanical cultivation and preparation in transplant
trays for manual cultivation) were studied in three replications.
Results showed that different progenies had no significant effect on
plant height (cm) and tuber yield (t ha-1), whereas had a significant
effect on number of tubers per unit area (m2). There was significant
difference between transplant preparation methods for plant height
and tuber yield. The interaction effect of progenies and transplant
preparation method was not significant for these traits. CIP.888006
progeny and paper pot preparation method produced the highest
tuber yields. Also CIP.994002 and CIP.994014 progenies considered
as the best progenies under paper pot preparation method due to high
yields.
Abstract: In this paper we have numerically analyzed terahertzrange
wavelength conversion using nondegenerate four wave mixing
(NDFWM) in a SOA integrated DFB laser (experiments reported
both in MIT electronics and Fujitsu research laboratories). For
analyzing semiconductor optical amplifier (SOA), we use finitedifference
beam propagation method (FDBPM) based on modified
nonlinear SchrÖdinger equation and for distributed feedback (DFB)
laser we use coupled wave approach. We investigated wavelength
conversion up to 4THz probe-pump detuning with conversion
efficiency -5dB in 1THz probe-pump detuning for a SOA integrated
quantum-well
Abstract: In this paper we present an efficient approach for the prediction of two sunspot-related time series, namely the Yearly Sunspot Number and the IR5 Index, that are commonly used for monitoring solar activity. The method is based on exploiting partially recurrent Elman networks and it can be divided into three main steps: the first one consists in a “de-rectification" of the time series under study in order to obtain a new time series whose appearance, similar to a sum of sinusoids, can be modelled by our neural networks much better than the original dataset. After that, we normalize the derectified data so that they have zero mean and unity standard deviation and, finally, train an Elman network with only one input, a recurrent hidden layer and one output using a back-propagation algorithm with variable learning rate and momentum. The achieved results have shown the efficiency of this approach that, although very simple, can perform better than most of the existing solar activity forecasting methods.
Abstract: Image compression plays a vital role in today-s
communication. The limitation in allocated bandwidth leads to
slower communication. To exchange the rate of transmission in the
limited bandwidth the Image data must be compressed before
transmission. Basically there are two types of compressions, 1)
LOSSY compression and 2) LOSSLESS compression. Lossy
compression though gives more compression compared to lossless
compression; the accuracy in retrievation is less in case of lossy
compression as compared to lossless compression. JPEG, JPEG2000
image compression system follows huffman coding for image
compression. JPEG 2000 coding system use wavelet transform,
which decompose the image into different levels, where the
coefficient in each sub band are uncorrelated from coefficient of
other sub bands. Embedded Zero tree wavelet (EZW) coding exploits
the multi-resolution properties of the wavelet transform to give a
computationally simple algorithm with better performance compared
to existing wavelet transforms. For further improvement of
compression applications other coding methods were recently been
suggested. An ANN base approach is one such method. Artificial
Neural Network has been applied to many problems in image
processing and has demonstrated their superiority over classical
methods when dealing with noisy or incomplete data for image
compression applications. The performance analysis of different
images is proposed with an analysis of EZW coding system with
Error Backpropagation algorithm. The implementation and analysis
shows approximately 30% more accuracy in retrieved image
compare to the existing EZW coding system.
Abstract: Because of high ductility, aluminum alloys, have been widely used as an important base of metal forming industries. But the main week point of these alloys is their low strength so in forming them with conventional methods like deep drawing, hydro forming, etc have been always faced with problems like fracture during of forming process. Because of this, recently using of explosive forming method for forming of these plates has been recommended. In this paper free explosive forming of A2024 aluminum alloy is numerically simulated and during it, explosion wave propagation process is studied. Consequences of this simulation can be effective in prediction of quality of production. These consequences are compared with an experimental test and show the superiority of this method to similar methods like hydro forming and deep drawing.
Abstract: Using one dimensional Quantum hydrodynamic
(QHD) model Korteweg de Vries (KdV) solitary excitations of
electron-acoustic waves (EAWs) have been examined in twoelectron-
populated relativistically degenerate super dense plasma. It
is found that relativistic degeneracy parameter influences the
conditions of formation and properties of solitary structures.
Abstract: This paper deals with a high-order accurate Runge
Kutta Discontinuous Galerkin (RKDG) method for the numerical
solution of the wave equation, which is one of the simple case of a
linear hyperbolic partial differential equation. Nodal DG method is
used for a finite element space discretization in 'x' by discontinuous
approximations. This method combines mainly two key ideas which
are based on the finite volume and finite element methods. The
physics of wave propagation being accounted for by means of
Riemann problems and accuracy is obtained by means of high-order
polynomial approximations within the elements. High order accurate
Low Storage Explicit Runge Kutta (LSERK) method is used for
temporal discretization in 't' that allows the method to be nonlinearly
stable regardless of its accuracy. The resulting RKDG
methods are stable and high-order accurate. The L1 ,L2 and L∞ error
norm analysis shows that the scheme is highly accurate and effective.
Hence, the method is well suited to achieve high order accurate
solution for the scalar wave equation and other hyperbolic equations.
Abstract: In this paper, the effect of receive and/or transmit
antenna spacing on the performance (BER vs. SNR) of multipleantenna
systems is determined by using an RCS (Radar Cross
Section) channel model. In this physical model, the scatterers
existing in the propagation environment are modeled by their RCS so
that the correlation of the receive signal complex amplitudes, i.e.,
both magnitude and phase, can be estimated. The proposed RCS
channel model is then compared with classical models.
Abstract: Artificial Intelligence based gaming is an interesting topic in the state-of-art technology. This paper presents an automation of a tradition Omani game, called Al-Hawalees. Its related issues are resolved and implemented using artificial intelligence approach. An AI approach called mini-max procedure is incorporated to make a diverse budges of the on-line gaming. If number of moves increase, time complexity will be increased in terms of propositionally. In order to tackle the time and space complexities, we have employed a back propagation neural network (BPNN) to train in off-line to make a decision for resources required to fulfill the automation of the game. We have utilized Leverberg- Marquardt training in order to get the rapid response during the gaming. A set of optimal moves is determined by the on-line back propagation training fashioned with alpha-beta pruning. The results and analyses reveal that the proposed scheme will be easily incorporated in the on-line scenario with one player against the system.
Abstract: The quality of short term load forecasting can improve the efficiency of planning and operation of electric utilities. Artificial Neural Networks (ANNs) are employed for nonlinear short term load forecasting owing to their powerful nonlinear mapping capabilities. At present, there is no systematic methodology for optimal design and training of an artificial neural network. One has often to resort to the trial and error approach. This paper describes the process of developing three layer feed-forward large neural networks for short-term load forecasting and then presents a heuristic search algorithm for performing an important task of this process, i.e. optimal networks structure design. Particle Swarm Optimization (PSO) is used to develop the optimum large neural network structure and connecting weights for one-day ahead electric load forecasting problem. PSO is a novel random optimization method based on swarm intelligence, which has more powerful ability of global optimization. Employing PSO algorithms on the design and training of ANNs allows the ANN architecture and parameters to be easily optimized. The proposed method is applied to STLF of the local utility. Data are clustered due to the differences in their characteristics. Special days are extracted from the normal training sets and handled separately. In this way, a solution is provided for all load types, including working days and weekends and special days. The experimental results show that the proposed method optimized by PSO can quicken the learning speed of the network and improve the forecasting precision compared with the conventional Back Propagation (BP) method. Moreover, it is not only simple to calculate, but also practical and effective. Also, it provides a greater degree of accuracy in many cases and gives lower percent errors all the time for STLF problem compared to BP method. Thus, it can be applied to automatically design an optimal load forecaster based on historical data.
Abstract: This paper deals with the effect of a power transformer’s vector group on the basic voltage sag characteristics during unbalanced faults at a meshed or radial power network. Specifically, the propagation of voltage sags through a power transformer is studied with advanced short-circuit analysis. A smart method to incorporate this effect on analytical mathematical expressions is proposed. Based on this methodology, the positive effect of transformers of certain vector groups on the mitigation of the expected number of voltage sags per year (sag frequency) at the terminals of critical industrial customers can be estimated.
Abstract: In this paper we propose and examine an Adaptive
Neuro-Fuzzy Inference System (ANFIS) in Smoothing Transition
Autoregressive (STAR) modeling. Because STAR models follow
fuzzy logic approach, in the non-linear part fuzzy rules can be
incorporated or other training or computational methods can be
applied as the error backpropagation algorithm instead to nonlinear
squares. Furthermore, additional fuzzy membership functions can be
examined, beside the logistic and exponential, like the triangle,
Gaussian and Generalized Bell functions among others. We examine
two macroeconomic variables of US economy, the inflation rate and
the 6-monthly treasury bills interest rates.
Abstract: The in vitro culture procedure of purple nutsedge
(Cyperus rotundus L.) for multiple shoot induction and tuber
formation was established. Multiple shoots were significantly
induced from a single shoot of about 0.5 – 0.8 cm long, on Murashige
and Skoog (MS) medium supplemented with 4.44 μM 6-
benzyladinine (BA) alone or in combination with 2.85 μM 1-
indoleacetic acid (IAA), providing 17.6 and 15.3 shoots per explant
with 31.2 and 27.5 leaves per explant, respectively, within 6 weeks of
culturing. Moreover, MS medium supplemented with 4.44 μM BA
and 2.85 μM IAA was suitable for tuber induction, obtaining 5.9
tubers with 3.4 rhizomes per explant. In combination with ancymidol
and higher concentration of sucrose, 11.1 μM BA and 60 g/L sucrose
or 11.1 μM BA, 7.8 μM ancymidol and 60 g/L sucrose induced 3.5
tubers with 1.6 rhizomes or 3.5 tubers without rhizome, respectively.
However, MS medium containing 3.9 or 7.8 μM ancymidol in
combination with either 60 or 80 g/L sucrose enchanced significant
root formation at 20.9 – 23.6 roots per explant.
Abstract: The use of synthetic retardants in polymeric insulated
cables is not uncommon in the high voltage engineering to study
electrical treeing phenomenon. However few studies on organic
materials for the same investigation have been carried. .This paper
describes the study on the effects of Oil Palm Empty Fruit Bunch
(OPEFB) microfiller on the tree initiation and propagation in silicone
rubber with different weight percentages (wt %) of filler to insulation
bulk material. The weight percentages used were 0 wt % and 1 wt %
respectively. It was found that the OPEFB retards the propagation of
the electrical treeing development. For tree inception study, the
addition of 1(wt %) OPEFB has increase the tree inception voltage of
silicone rubber. So, OPEFB is a potential retardant to the initiation
and growth of electrical treeing occurring in polymeric materials for
high voltage application. However more studies on the effects of
physical and electrical properties of OPEFB as a tree retardant
material are required.
Abstract: The back-propagation algorithm calculates the weight
changes of an artificial neural network, and a two-term algorithm
with a dynamically optimal learning rate and a momentum factor
is commonly used. Recently the addition of an extra term, called a
proportional factor (PF), to the two-term BP algorithm was proposed.
The third term increases the speed of the BP algorithm. However,
the PF term also reduces the convergence of the BP algorithm, and
optimization approaches for evaluating the learning parameters are
required to facilitate the application of the three terms BP algorithm.
This paper considers the optimization of the new back-propagation
algorithm by using derivative information. A family of approaches
exploiting the derivatives with respect to the learning rate, momentum
factor and proportional factor is presented. These autonomously
compute the derivatives in the weight space, by using information
gathered from the forward and backward procedures. The three-term
BP algorithm and the optimization approaches are evaluated using
the benchmark XOR problem.
Abstract: The dynamics of the Autonomous Underwater
Vehicles (AUVs) are highly nonlinear and time varying and the hydrodynamic coefficients of vehicles are difficult to estimate
accurately because of the variations of these coefficients with
different navigation conditions and external disturbances. This study presents the on-line system identification of AUV dynamics to obtain
the coupled nonlinear dynamic model of AUV as a black box. This black box has an input-output relationship based upon on-line
adaptive fuzzy model and adaptive neural fuzzy network (ANFN)
model techniques to overcome the uncertain external disturbance and
the difficulties of modelling the hydrodynamic forces of the AUVs instead of using the mathematical model with hydrodynamic parameters estimation. The models- parameters are adapted according
to the back propagation algorithm based upon the error between the
identified model and the actual output of the plant. The proposed
ANFN model adopts a functional link neural network (FLNN) as the
consequent part of the fuzzy rules. Thus, the consequent part of the
ANFN model is a nonlinear combination of input variables. Fuzzy
control system is applied to guide and control the AUV using both
adaptive models and mathematical model. Simulation results show
the superiority of the proposed adaptive neural fuzzy network
(ANFN) model in tracking of the behavior of the AUV accurately
even in the presence of noise and disturbance.