Abstract: Deep Brain Stimulation or DBS is the second solution
for Parkinson's Disease. Its three parameters are: frequency, pulse
width and voltage. They must be optimized to achieve successful
treatment. Nowadays it is done clinically by neurologists and there is
not certain numerical method to detect them. The aim of this research
is to introduce simulation and modeling of Parkinson's Disease
treatment as a computational procedure to select optimum voltage.
We recorded finger tremor signals of some Parkinsonian patients
under DBS treatment at constant frequency and pulse width but
variable voltages; then, we adapted a new model to fit these data. The
optimum voltages obtained by data fitting results were the same as
neurologists- commented voltages, which means modeling can be
used as an engineering method to select optimum stimulation
voltages.
Abstract: This paper present a new way to find the aerodynamic characteristic equation of missile for the numerical trajectories prediction more accurate. The goal is to obtain the polynomial equation based on two missile characteristic parameters, angle of attack (α ) and flight speed (╬¢ ). First, the understudied missile is modeled and used for flow computational model to compute aerodynamic force and moment. Assume that performance range of understudied missile where range -10< α
Abstract: Copper based composites reinforced with WC and Ti
particles were prepared using planetary ball-mill. The experiment
was designed by using Taguchi technique and milling was carried out
in an air for several hours. The powder was characterized before and
after milling using the SEM, TEM and X-ray for microstructure and
for possible new phases. Microstructures show that milled particles
size and reduction in particle size depend on many parameters. The
distance d between planes of atoms estimated from X-ray powder
diffraction data and TEM image. X-ray diffraction patterns of the
milled powder did not show clearly any new peak or energy shift, but
the TEM images show a significant change in crystalline structure of
corporate on titanium in the composites.
Abstract: In this paper we present an off line system for the
recognition of the handwritten numeric chains. Our work is divided
in two big parts. The first part is the realization of a recognition
system of the isolated handwritten digits. In this case the study is
based mainly on the evaluation of neural network performances,
trained with the gradient back propagation algorithm. The used
parameters to form the input vector of the neural network are
extracted on the binary images of the digits by several methods: the
distribution sequence, the Barr features and the centred moments of
the different projections and profiles. The second part is the
extension of our system for the reading of the handwritten numeric
chains constituted of a variable number of digits. The vertical
projection is used to segment the numeric chain at isolated digits and
every digit (or segment) will be presented separately to the entry of
the system achieved in the first part (recognition system of the
isolated handwritten digits). The result of the recognition of the
numeric chain will be displayed at the exit of the global system.
Abstract: This study investigates the performance of radial basis function networks (RBFN) in forecasting the monthly CO2 emissions of an electric power utility. We also propose a method for input variable selection. This method is based on identifying the general relationships between groups of input candidates and the output. The effect that each input has on the forecasting error is examined by removing all inputs except the variable to be investigated from its group, calculating the networks parameter and performing the forecast. Finally, the new forecasting error is compared with the reference model. Eight input variables were identified as the most relevant, which is significantly less than our reference model with 30 input variables. The simulation results demonstrate that the model with the 8 inputs selected using the method introduced in this study performs as accurate as the reference model, while also being the most parsimonious.
Abstract: The steady mixed convection boundary layer flow from
a vertical cone in a porous medium filled with a nanofluid is
numerically investigated using different types of nanoparticles as Cu
(copper), Al2O3 (alumina) and TiO2 (titania). The boundary value
problem is solved by using the shooting technique by reducing it
into an ordinary differential equation. Results of interest for the local
Nusselt number with various values of the constant mixed convection
parameter and nanoparticle volume fraction parameter are evaluated.
It is found that dual solutions exist for a certain range of mixed
convection parameter.
Abstract: A dead leg is a typical subsea production system
component. CFD is required to model heat transfer within the dead
leg. Unfortunately its solution is time demanding and thus not
suitable for fast prediction or repeated simulations. Therefore there is
a need to create a thermal FEA model, mimicking the heat flows and
temperatures seen in CFD cool down simulations.
This paper describes the conventional way of tuning and a new
automated way using parametric model order reduction (PMOR)
together with an optimization algorithm. The tuned FE analyses
replicate the steady state CFD parameters within a maximum error in
heat flow of 6 % and 3 % using manual and PMOR method
respectively. During cool down, the relative error of the tuned FEA
models with respect to temperature is below 5% comparing to the
CFD. In addition, the PMOR method obtained the correct FEA setup
five times faster than the manually tuned FEA.
Abstract: Robot manipulators are highly coupled nonlinear
systems, therefore real system and mathematical model of dynamics
used for control system design are not same. Hence, fine-tuning of
controller is always needed. For better tuning fast simulation speed
is desired. Since, Matlab incorporates LAPACK to increase the speed
and complexity of matrix computation, dynamics, forward and
inverse kinematics of PUMA 560 is modeled on Matlab/Simulink in
such a way that all operations are matrix based which give very less
simulation time. This paper compares PID parameter tuning using
Genetic Algorithm, Simulated Annealing, Generalized Pattern Search
(GPS) and Hybrid Search techniques. Controller performances for all
these methods are compared in terms of joint space ITSE and
cartesian space ISE for tracking circular and butterfly trajectories.
Disturbance signal is added to check robustness of controller. GAGPS
hybrid search technique is showing best results for tuning PID
controller parameters in terms of ITSE and robustness.
Abstract: In this study, lipase production has been investigated
using submerge fermentation by Aspergillus niger in Kilka fish oil as
main substrate. The Taguchi method with an L9 orthogonal array
design was used to investigate the effect of parameters and their
levels on lipase productivity. The optimum conditions for Kilka fish
oil concentration, incubation temperature and pH were obtained 3
gr./ml 35°C and 7, respectively. The amount of lipase activity in
optimum condition was obtained 4.59IU/ml. By comparing this
amount with the amount of productivity in the olive oil medium
based on the cost of each medium, it was that using Kilka fish oil is
84% economical. Therefore Kilka fish oil can be used as an
economical and suitable substrate in the lipase production and
industrial usages.
Abstract: The incorporation of renewable energy sources for the sustainable electricity production is undertaking a more prominent role in electric power systems. Thus, it will be an indispensable incident that the characteristics of future power networks, their prospective stability for instance, get influenced by the imposed features of sustainable energy sources. One of the distinctive attributes of the sustainable energy sources is exhibiting the stochastic behavior. This paper investigates the impacts of this stochastic behavior on the small disturbance rotor angle stability in the upcoming electric power networks. Considering the various types of renewable energy sources and the vast variety of system configurations, the sensitivity analysis can be an efficient breakthrough towards generalizing the effects of new energy sources on the concept of stability. In this paper, the definition of small disturbance angle stability for future power systems and the iterative-stochastic way of its analysis are presented. Also, the effects of system parameters on this type of stability are described by performing a sensitivity analysis for an electric power test system.
Abstract: In the present article, nonlinear vibration analysis of
single layer graphene sheets is presented and the effect of small
length scale is investigated. Using the Hamilton's principle, the three
coupled nonlinear equations of motion are obtained based on the von
Karman geometrical model and Eringen theory of nonlocal
continuum. The solutions of Free nonlinear vibration, based on a one
term mode shape, are found for both simply supported and clamped
graphene sheets. A complete analysis of graphene sheets with
movable as well as immovable in-plane conditions is also carried out.
The results obtained herein are compared with those available in the
literature for classical isotropic rectangular plates and excellent
agreement is seen. Also, the nonlinear effects are presented as
functions of geometric properties and small scale parameter.
Abstract: Recent studies demonstrated that high-fat diet increases oxidative stress in plasma and in a variety of tissues. Many researchers have been looking for natural products, which can reverse the effect of high fat diet. Recently, buckwheat is becoming common ingredient in functional food because of it properties. In study on buckwheat, it is known that, this plant plays roles as anti-oxidative, anti-inflammatory and anti-hypertensive. Nevertheless still little is known about buckwheat groats. The aim of this study was to investigate the effects of addition of buckwheat groats to the fat diet (30% lard), on some antioxidant and oxidant stress parameters in plasma and selected tissues in Wistar rats. The experiment was carried out with three months old male Wistar rats ca. 250g of body weight fed for 5 weeks with either a high-fat (30% of lard) diet or control diet, with or without addition of buckwheat groats. In plasma biochemistry and the activities of the antioxidant enzymes were measured selected tissues: glutathione peroxidase (GPX), catalase (CAT) and the levels of total and reduced glutathione (GSH), free thiol groups (pSH), antioxidant potential of plasma (FRAP) and oxidant stress indices - proteins carbonyl groups (CO) and malonyldialdehyde concentration (MDA). Activity of catalase (CAT) in plasma of rats was significantly increased in buckwheat groats groups and activity of GPx3 in plasma of rats was decreased in buckwheat groups as compared to control group. The reduced glutathione (GSH) in plasma of rats was significantly increased and protein CO was significantly decreased in buckwheat groups as compared to controls. The lowered concentration of GSH was found in serum of rats fed buckwheat groats addition but it accompanied in 7-fold increase in reduced-to-oxidized glutatione ratio, significant increase in HDL and decrease in nonHDL concentration. Conclusions: Buckwheat groats indicate a beneficial effect in inhibiting protein and lipid peroxidation in rats and improved lipid profile. These results suggest that buckwheat groats exert a significant antioxidant potential and may be used as normal food constituent to ameliorate the oxidant-induced damage in organism.
Abstract: Gas Metal Arc Welding (GMAW) processes is an
important joining process widely used in metal fabrication
industries. This paper addresses modeling and optimization of this
technique using a set of experimental data and regression analysis.
The set of experimental data has been used to assess the influence
of GMAW process parameters in weld bead geometry. The
process variables considered here include voltage (V); wire feed
rate (F); torch Angle (A); welding speed (S) and nozzle-to-plate
distance (D). The process output characteristics include weld bead
height, width and penetration. The Taguchi method and regression
modeling are used in order to establish the relationships between
input and output parameters. The adequacy of the model is
evaluated using analysis of variance (ANOVA) technique. In the
next stage, the proposed model is embedded into a Simulated
Annealing (SA) algorithm to optimize the GMAW process
parameters. The objective is to determine a suitable set of process
parameters that can produce desired bead geometry, considering
the ranges of the process parameters. Computational results prove
the effectiveness of the proposed model and optimization
procedure.
Abstract: This paper introduces two decoders for binary linear
codes based on Metaheuristics. The first one uses a genetic algorithm
and the second is based on a combination genetic algorithm with
a feed forward neural network. The decoder based on the genetic
algorithms (DAG) applied to BCH and convolutional codes give good
performances compared to Chase-2 and Viterbi algorithm respectively
and reach the performances of the OSD-3 for some Residue
Quadratic (RQ) codes. This algorithm is less complex for linear
block codes of large block length; furthermore their performances
can be improved by tuning the decoder-s parameters, in particular the
number of individuals by population and the number of generations.
In the second algorithm, the search space, in contrast to DAG which
was limited to the code word space, now covers the whole binary
vector space. It tries to elude a great number of coding operations
by using a neural network. This reduces greatly the complexity of
the decoder while maintaining comparable performances.
Abstract: Air conditioning systems of houses consume large
quantity of electricity. To reducing energy consumption for air
conditioning purposes it is becoming attractive the use of evaporative
cooling air conditioning which is less energy consuming compared to
air chillers. But, it is obvious that higher energy efficiency of
evaporative cooling is not enough to judge whether evaporative
cooling economically is competitive with other types of cooling
systems. To proving the higher energy efficiency and cost
effectiveness of the evaporative cooling competitive analysis of
various types of cooling system should be accomplished. For noted
purpose optimization mathematical model for each system should be
composed based on system approach analysis. In this paper different
types of evaporative cooling-heating systems are discussed and
methods for increasing their energy efficiency and as well as
determining of their design parameters are developed. The
optimization mathematical models for each of them are composed
with help of which least specific costs for each of them are reviled.
The comparison of specific costs proved that the most efficient and
cost effective is considered the “direct evaporating" system if it is
applicable for given climatic conditions. Next more universal and
applicable for many climatic conditions system providing least cost
of heating and cooling is considered the “direct evaporating" system.
Abstract: This work is to study a roll of the fluctuating density
gradient in the compressible flows for the computational fluid dynamics
(CFD). A new anisotropy tensor with the fluctuating density
gradient is introduced, and is used for an invariant modeling technique
to model the turbulent density gradient correlation equation derived
from the continuity equation. The modeling equation is decomposed
into three groups: group proportional to the mean velocity, and that
proportional to the mean strain rate, and that proportional to the mean
density. The characteristics of the correlation in a wake are extracted
from the results by the two dimensional direct simulation, and shows
the strong correlation with the vorticity in the wake near the body.
Thus, it can be concluded that the correlation of the density gradient
is a significant parameter to describe the quick generation of the
turbulent property in the compressible flows.
Abstract: This paper gives an overview of a deep drawing
process by pressurized liquid medium separated from the sheet by a
rubber diaphragm. Hydroforming deep drawing processing of sheet
metal parts provides a number of advantages over conventional
techniques. It generally increases the depth to diameter ratio possible
in cup drawing and minimizes the thickness variation of the drawn
cup. To explore the deformation mechanism, analytical and
numerical simulations are used for analyzing the drawing process of
an AA6061-T4 blank. The effects of key process parameters such as
coefficient of friction, initial thickness of the blank and radius
between cup wall and flange are investigated analytically and
numerically. The simulated results were in good agreement with the
results of the analytical model. According to finite element
simulations, the hydroforming deep drawing method provides a more
uniform thickness distribution compared to conventional deep
drawing and decreases the risk of tearing during the process.
Abstract: In this paper, fully developed flow and heat transfer of
viscoelastic materials in curved ducts with square cross section under
constant heat flux have been investigated. Here, staggered mesh is
used as computational grids and flow and heat transfer parameters
have been allocated in this mesh with marker and cell method.
Numerical solution of governing equations has being performed with
FTCS finite difference method. Furthermore, Criminale-Eriksen-
Filbey (CEF) constitutive equation has being used as viscoelastic
model. CEF constitutive equation is a suitable model for studying
steady shear flow of viscoelastic materials which is able to model
both effects of the first and second normal stress differences. Here, it
is shown that the first and second normal stresses differences have
noticeable and inverse effect on secondary flows intensity and mean
Nusselt number which is the main novelty of current research.
Abstract: In this paper, stabilization of an Active Magnetic Bearing (AMB) system with varying rotor speed using Sliding Mode Control (SMC) technique is considered. The gyroscopic effect inherited in the system is proportional to rotor speed in which this nonlinearity effect causes high system instability as the rotor speed increases. Also, transformation of the AMB dynamic model into a new class of uncertain system shows that this gyroscopic effect lies in the mismatched part of the system matrix. Moreover, the current gain parameter is allowed to be varied in a known bound as an uncertainty in the input matrix. SMC design method is proposed in which the sufficient condition that guarantees the global exponential stability of the reduced-order system is represented in Linear Matrix Inequality (LMI). Then, a new chattering-free control law is established such that the system states are driven to reach the switching surface and stay on it thereafter. The performance of the controller applied to the AMB model is demonstrated through simulation works under various system conditions.
Abstract: Fuzzy C-means Clustering algorithm (FCM) is a
method that is frequently used in pattern recognition. It has the
advantage of giving good modeling results in many cases, although,
it is not capable of specifying the number of clusters by itself. In
FCM algorithm most researchers fix weighting exponent (m) to a
conventional value of 2 which might not be the appropriate for all
applications. Consequently, the main objective of this paper is to use
the subtractive clustering algorithm to provide the optimal number of
clusters needed by FCM algorithm by optimizing the parameters of
the subtractive clustering algorithm by an iterative search approach
and then to find an optimal weighting exponent (m) for the FCM
algorithm. In order to get an optimal number of clusters, the iterative
search approach is used to find the optimal single-output Sugenotype
Fuzzy Inference System (FIS) model by optimizing the
parameters of the subtractive clustering algorithm that give minimum
least square error between the actual data and the Sugeno fuzzy
model. Once the number of clusters is optimized, then two
approaches are proposed to optimize the weighting exponent (m) in
the FCM algorithm, namely, the iterative search approach and the
genetic algorithms. The above mentioned approach is tested on the
generated data from the original function and optimal fuzzy models
are obtained with minimum error between the real data and the
obtained fuzzy models.