Abstract: Vickers indentation is used to measure the hardness
of materials. In this study, numerical simulation of Vickers
indentation experiment was performed for Diamond like Carbon
(DLC) coated materials. DLC coatings were deposited on stainless
steel 304 substrates with Chromium buffer layer using RF Magnetron
and T-shape Filtered Cathodic Vacuum Arc Dual system The
objective of this research is to understand the elastic plastic
properties, stress strain distribution, ring and lateral crack growth and
propagation, penetration depth of indenter and delamination of
coating from substrate with effect of buffer layer thickness. The
effect of Poisson-s ratio of DLC coating was also analyzed. Indenter
penetration is more in coated materials with thin buffer layer as
compared to thicker one, under same conditions. Similarly, the
specimens with thinner buffer layer failed quickly due to high
residual stress as compared to the coated materials with reasonable
thickness of 200nm buffer layer. The simulation results suggested the
optimized thickness of 200 nm among the prepared specimens for
durable and long service.
Abstract: An integrated Artificial Neural Network- Particle Swarm Optimization (PSO) is presented for analyzing global electricity consumption. To aim this purpose, following steps are done: STEP 1: in the first step, PSO is applied in order to determine world-s oil, natural gas, coal and primary energy demand equations based on socio-economic indicators. World-s population, Gross domestic product (GDP), oil trade movement and natural gas trade movement are used as socio-economic indicators in this study. For each socio-economic indicator, a feed-forward back propagation artificial neural network is trained and projected for future time domain. STEP 2: in the second step, global electricity consumption is projected based on the oil, natural gas, coal and primary energy consumption using PSO. global electricity consumption is forecasted up to year 2040.
Abstract: Added stresses due to adjacent structure should be
considered in foundation design and stress control in soil under the structure. This case is considered less than other cases in design and
calculation whereas stresses in implementation are greater than analytical stress.
Structure load are transmitted to earth by foundation and role of foundation is propagation of load on the continuous and half extreme
soil. This act cause that, present stresses lessen to allowable strength
of soil. Some researchers such as Boussinesq and westergaurd by
using of some assumption studied on this issue, theorically. Target of
this paper is study and evaluation of added stresses under structure
due to adjacent structure. For this purpose, by using of assumption, theoric relation and numeral methods, effects of adjacent structure
with 4 to 10 storeys on the main structure with 4 storeys are studied
and effect of parameters and sensitivity of them are evaluated.
Abstract: This paper investigates experimentally and
analytically the torsion behavior of steel fibered high strength self
compacting concrete beams reinforced by GFRP bars. Steel fibered
high strength self compacting concrete (SFHSSCC) and GFRP bars
became in the recent decades a very important materials in the
structural engineering field. The use of GFRP bars to replace steel
bars has emerged as one of the many techniques put forward to
enhance the corrosion resistance of reinforced concrete structures.
High strength concrete and GFRP bars attract designers and
architects as it allows improving the durability as well as the esthetics
of a construction. One of the trends in SFHSSCC structures is to
provide their ductile behavior and additional goal is to limit
development and propagation of macro-cracks in the body of
SFHSSCC elements. SFHSSCC and GFRP bars are tough, improve
the workability, enhance the corrosion resistance of reinforced
concrete structures, and demonstrate high residual strengths after
appearance of the first crack. Experimental studies were carried out
to select effective fiber contents. Three types of volume fraction from
hooked shape steel fibers are used in this study, the hooked steel
fibers were evaluated in volume fractions ranging between 0.0%,
0.75% and 1.5%. The beams shape is chosen to create the required
forces (i.e. torsion and bending moments simultaneously) on the test
zone. A total of seven beams were tested, classified into three groups.
All beams, have 200cm length, cross section of 10×20cm,
longitudinal bottom reinforcement of 3
Abstract: The objective of this paper is to design a model of human vital sign prediction for decreasing prediction error by using two states mapping based time series neural network BP (back-propagation) model. Normally, lot of industries has been applying the neural network model by training them in a supervised manner with the error back-propagation algorithm for time series prediction systems. However, it still has a residual error between real value and prediction output. Therefore, we designed two states of neural network model for compensation of residual error which is possible to use in the prevention of sudden death and metabolic syndrome disease such as hypertension disease and obesity. We found that most of simulations cases were satisfied by the two states mapping based time series prediction model compared to normal BP. In particular, small sample size of times series were more accurate than the standard MLP model. We expect that this algorithm can be available to sudden death prevention and monitoring AGENT system in a ubiquitous homecare environment.
Abstract: Autoregressive Moving average (ARMA) is a parametric based method of signal representation. It is suitable for problems in which the signal can be modeled by explicit known source functions with a few adjustable parameters. Various methods have been suggested for the coefficients determination among which are Prony, Pade, Autocorrelation, Covariance and most recently, the use of Artificial Neural Network technique. In this paper, the method of using Artificial Neural network (ANN) technique is compared with some known and widely acceptable techniques. The comparisons is entirely based on the value of the coefficients obtained. Result obtained shows that the use of ANN also gives accurate in computing the coefficients of an ARMA system.
Abstract: Artificial Intelligence (AI) methods are increasingly being used for problem solving. This paper concerns using AI-type learning machines for power quality problem, which is a problem of general interest to power system to provide quality power to all appliances. Electrical power of good quality is essential for proper operation of electronic equipments such as computers and PLCs. Malfunction of such equipment may lead to loss of production or disruption of critical services resulting in huge financial and other losses. It is therefore necessary that critical loads be supplied with electricity of acceptable quality. Recognition of the presence of any disturbance and classifying any existing disturbance into a particular type is the first step in combating the problem. In this work two classes of AI methods for Power quality data mining are studied: Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs). We show that SVMs are superior to ANNs in two critical respects: SVMs train and run an order of magnitude faster; and SVMs give higher classification accuracy.
Abstract: In this paper we report the technique of optical
induction of 2 and 3-dimensional (2D and 3D) photonic lattices in
photorefractive materials based on diffraction grating self replication
-Talbot effect. 1D and 2D different rotational symmery diffraction
masks with the periods of few tens micrometers and 532 nm cw laser
beam were used in the experiments to form an intensity modulated
light beam profile. A few hundred micrometric scale replications of
mask generated intensity structures along the beam propagation axis
were observed. Up to 20 high contrast replications were detected for
1D annular mask with 30
Abstract: MOC (method of cell) is a new method of investigating
wave propagating in material with periodic microstructure, and can
reflect the effect of microstructure. Wave propagation in periodically
laminated medium consisting of linearly elastic layers can be treated
as a special application of this method. In this paper, it was used to
simulate the dynamic response of carbon-phenolic to impulsive
loading under certain boundary conditions. From the comparison
between the results obtained from this method and the exact results
based on propagator matrix theory, excellent agreement is achieved.
Conclusion can be made that the oscillation periodicity is decided by
the thickness of sub-cells. In the end, the NHDMOC method, which
permits studying stress wave propagation with one dimensional strain,
was applied to study the one-dimensional stress wave propagation. In
this paper, the ZWT nonlinear visco-elastic constitutive relationship
with 7 parameters, NHDMOC, and corresponding equations were
deduced. The equations were verified, comparing the elastic stress
wave propagation in SHPB with, respectively, the elastic and the
visco-elastic bar. Finally the dispersion and attenuation of stress wave
in SHPB with visco-elastic bar was studied.
Abstract: In recent years, a new numerical method has been
developed, the extended finite element method (X-FEM). The
objective of this work is to exploit the (X-FEM) for the treatment of
the fracture mechanics problems on 3D geometries, where we
showed the ability of this method to simulate the fatigue crack
growth into two cases: edge and central crack. In the results we
compared the six first natural frequencies of mode shapes uncracking
with the cracking initiation in the structure, and showed the stress
intensity factor (SIF) evolution function as crack size propagation
into structure, the analytical validation of (SIF) is presented. For to
evidence the aspects of this method, all result is compared between
FEA and X-FEM.
Abstract: On-line handwritten scripts are usually dealt with pen tip traces from pen-down to pen-up positions. Time evaluation of the pen coordinates is also considered along with trajectory information. However, the data obtained needs a lot of preprocessing including filtering, smoothing, slant removing and size normalization before recognition process. Instead of doing such lengthy preprocessing, this paper presents a simple approach to extract the useful character information. This work evaluates the use of the counter- propagation neural network (CPN) and presents feature extraction mechanism in full detail to work with on-line handwriting recognition. The obtained recognition rates were 60% to 94% using the CPN for different sets of character samples. This paper also describes a performance study in which a recognition mechanism with multiple thresholds is evaluated for counter-propagation architecture. The results indicate that the application of multiple thresholds has significant effect on recognition mechanism. The method is applicable for off-line character recognition as well. The technique is tested for upper-case English alphabets for a number of different styles from different peoples.
Abstract: In this paper, an analysis of a target location estimation
system using the best linear unbiased estimator (BLUE) for high
performance radar systems is presented. In synthetic environments,
we are here concerned with three key elements of radar system
modeling, which makes radar systems operates accurately in strategic
situation in virtual ground. Radar Cross Section (RCS) modeling
is used to determine the actual amount of electromagnetic waves
that are reflected from a tactical object. Pattern Propagation Factor
(PPF) is an attenuation coefficient of the radar equation that contains
the reflection from the surface of the earth, the diffraction, the
refraction and scattering by the atmospheric environment. Clutter is
the unwanted echoes of electronic systems. For the data fusion of
output results from radar detection in synthetic environment, BLUE
is used and compared with the mean values of each simulation results.
Simulation results demonstrate the performance of the radar system.
Abstract: Considering the theory of attribute grammars, we use
logical formulas instead of traditional functional semantic rules.
Following the decoration of a derivation tree, a suitable algorithm
should maintain the consistency of the formulas together with the
evaluation of the attributes. This may be a Prolog-like resolution, but
this paper examines a somewhat different strategy, based on
production specialization, local consistency and propagation: given a
derivation tree, it is interactively decorated, i.e. incrementally
checked and evaluated. The non-directed dependencies are
dynamically directed during attribute evaluation.
Abstract: In this paper methodology to exploit creeping wave
for body area network BAN communication reliability are described.
Creeping wave propagation effects are visualized & analyzed.
During this work Dipole, IA antennas various antennas were
redesigned using existing designs and their propagation
characteristics were verified for optimum performance when used on
BANs. These antennas were then applied on body shapes-including
rectangular, spherical and cylindrical so that all the effects of actual
human body can be taken nearly into account. Parametric simulation
scheme was devised so that on Body channel characterization can be
visualized at front, curved and back region. In the next phase
multiple inputs multiple output MIMO scheme was introduced where
virtual antennas were used in order to diminish the effects of
antennas on the propagation of waves. Results were, extracted and
analyzed at different heights. Finally based on comparative
measurement and analysis it was concluded that on body propagation
can be exploited to gain spatial diversity.
Abstract: A handful of propagation textbooks that discuss radio frequency (RF) propagation models merely list out the models and perhaps discuss them rather briefly; this may well be frustrating for the potential first time modeller who's got no idea on how these models could have been derived. This paper fundamentally provides an overture in modelling the radio channel. Explicitly, for the modelling practice discussed here, signal strength field measurements had to be conducted beforehand (this was done at 469 MHz); to be precise, this paper primarily concerns empirically/statistically modelling the radio channel, and thus provides results obtained from empirically modelling the environments in question. This paper, on the whole, proposes three propagation models, corresponding to three experimented environments. Perceptibly, the models have been derived by way of making the most use of statistical measures. Generally speaking, the first two models were derived via simple linear regression analysis, whereas the third have been originated using multiple regression analysis (with five various predictors). Additionally, as implied by the title of this paper, both indoor and outdoor environments have been experimented; however, (somewhat) two of the environments are neither entirely indoor nor entirely outdoor. The other environment, however, is completely indoor.
Abstract: This paper presents a low cost automatic system for
sampling the electric field in a limited area. The scanning area is a
flat surface parallel to the ground at a selected height. We discuss
in detail the hardware, software and all the arrangements involved
in the system operation. In order to show the system performance
we include a campaign of narrow band measurements with 6017
sample points in the surroundings of a cellular base station. A
commercial isotropic antenna with three orthogonal axes was used
as sampling device. The results are analyzed in terms of its space
average, standard deviation and statistical distribution.
Abstract: Determination of nano particle size is substantial since
the nano particle size exerts a significant effect on various properties
of nano materials. Accordingly, proposing non-destructive, accurate
and rapid techniques for this aim is of high interest. There are some
conventional techniques to investigate the morphology and grain size
of nano particles such as scanning electron microscopy (SEM),
atomic force microscopy (AFM) and X-ray diffractometry (XRD).
Vibrational spectroscopy is utilized to characterize different
compounds and applied for evaluation of the average particle size
based on relationship between particle size and near infrared spectra
[1,4] , but it has never been applied in quantitative morphological
analysis of nano materials. So far, the potential application of nearinfrared
(NIR) spectroscopy with its ability in rapid analysis of
powdered materials with minimal sample preparation, has been
suggested for particle size determination of powdered
pharmaceuticals. The relationship between particle size and diffuse
reflectance (DR) spectra in near infrared region has been applied to
introduce a method for estimation of particle size. Back propagation
artificial neural network (BP-ANN) as a nonlinear model was applied
to estimate average particle size based on near infrared diffuse
reflectance spectra. Thirty five different nano TiO2 samples with
different particle size were analyzed by DR-FTNIR spectrometry and
the obtained data were processed by BP- ANN.
Abstract: In this paper, an artificial intelligent technique for
robust digital image watermarking in multiwavelet domain is
proposed. The embedding technique is based on the quantization
index modulation technique and the watermark extraction process
does not require the original image. We have developed an
optimization technique using the genetic algorithms to search for
optimal quantization steps to improve the quality of watermarked
image and robustness of the watermark. In addition, we construct a
prediction model based on image moments and back propagation
neural network to correct an attacked image geometrically before the
watermark extraction process begins. The experimental results show
that the proposed watermarking algorithm yields watermarked image
with good imperceptibility and very robust watermark against various
image processing attacks.
Abstract: The performance and the plasma created by a pulsed
magnetoplasmadynamic thruster for small satellite application is
studied to understand better the ablation and plasma propagation
processes occurring during the short-time discharge. The results can
be applied to improve the quality of the thruster in terms of efficiency,
and to tune the propulsion system to the needs required by the satellite
mission. Therefore, plasma measurements with a high-speed camera
and induction probes, and performance measurements of mass bit
and impulse bit were conducted. Values for current sheet propagation
speed, mean exhaust velocity and thrust efficiency were derived from
these experimental data. A maximum in current sheet propagation
was found by the high-speed camera measurements for a medium
energy input and confirmed by the induction probes. A quasilinear
tendency between the mass bit and the energy input, the current
action integral respectively, was found, as well as a linear tendency
between the created impulse and the discharge energy. The highest
mean exhaust velocity and thrust efficiency was found for the highest
energy input.
Abstract: The back propagation algorithm calculates the weight
changes of artificial neural networks, and a common approach is to
use a training algorithm consisting of a learning rate and a
momentum factor. The major drawbacks of above learning algorithm
are the problems of local minima and slow convergence speeds. The
addition of an extra term, called a proportional factor reduces the
convergence of the back propagation algorithm. We have applied the
three term back propagation to multiplicative neural network
learning. The algorithm is tested on XOR and parity problem and
compared with the standard back propagation training algorithm.