Abstract: This paper presents a new adaptive impedance control
strategy, based on Function Approximation Technique (FAT) to
compensate for unknown non-flat environment shape or time-varying
environment location. The target impedance in the force controllable
direction is modified by incorporating adaptive compensators and the
uncertainties are represented by FAT, allowing the update law to be
derived easily. The force error feedback is utilized in the estimation
and the accurate knowledge of the environment parameters are not
required by the algorithm. It is shown mathematically that the
stability of the controller is guaranteed based on Lyapunov theory.
Simulation results presented to demonstrate the validity of the
proposed controller.
Abstract: Hybrid algorithm is the hot issue in Computational
Intelligence (CI) study. From in-depth discussion on Simulation
Mechanism Based (SMB) classification method and composite patterns,
this paper presents the Mamdani model based Adaptive Neural
Fuzzy Inference System (M-ANFIS) and weight updating formula in
consideration with qualitative representation of inference consequent
parts in fuzzy neural networks. M-ANFIS model adopts Mamdani
fuzzy inference system which has advantages in consequent part.
Experiment results of applying M-ANFIS to evaluate traffic Level
of service show that M-ANFIS, as a new hybrid algorithm in computational
intelligence, has great advantages in non-linear modeling,
membership functions in consequent parts, scale of training data and
amount of adjusted parameters.
Abstract: Chemical reaction and diffusion are important phenomena in quantitative neurobiology and biophysics. The knowledge of the dynamics of calcium Ca2+ is very important in cellular physiology because Ca2+ binds to many proteins and regulates their activity and interactions Calcium waves propagate inside cells due to a regenerative mechanism known as calcium-induced calcium release. Buffer-mediated calcium diffusion in the cytosol plays a crucial role in the process. A mathematical model has been developed for calcium waves by assuming the buffers are in equilibrium with calcium i.e., the rapid buffering approximation for a one dimensional unsteady state case. This model incorporates important physical and physiological parameters like dissociation rate, diffusion rate, total buffer concentration and influx. The finite difference method has been employed to predict [Ca2+] and buffer concentration time course regardless of the calcium influx. The comparative studies of the effect of the rapid buffered diffusion and kinetic parameters of the model on the concentration time course have been performed.
Abstract: Nowadays, power systems, energy generation by wind
has been very important. Noting that the production of electrical
energy by wind turbines on site to several factors (such as wind speed
and profile site for the turbines, especially off the wind input speed,
wind rated speed and wind output speed disconnect) is dependent. On
the other hand, several different types of turbines in the market there.
Therefore, selecting a turbine that its capacity could also answer the
need for electric consumers the efficiency is high something is
important and necessary. In this context, calculating the amount of
wind power to help optimize overall network, system operation, in
determining the parameters of wind power is very important.
In this article, to help calculate the amount of wind power plant,
connected to the national network in the region Manjil wind,
selecting the best type of turbine and power delivery profile
appropriate to the network using Monte Carlo method has been.
In this paper, wind speed data from the wind site in Manjil, as minute
and during the year has been. Necessary simulations based on
Random Numbers Simulation method and repeat, using the software
MATLAB and Excel has been done.
Abstract: Square pipes (pipes with square cross sections) are
being used for various industrial objectives, such as machine
structure components and housing/building elements. The utilization
of them is extending rapidly and widely. Hence, the out-put of those
pipes is increasing and new application fields are continually
developing.
Due to various demands in recent time, the products have to
satisfy difficult specifications with high accuracy in dimensions. The
reshaping process design of pipes with square cross sections;
however, is performed by trial and error and based on expert-s
experience.
In this paper, a computer-aided simulation is developed based on
the 2-D elastic-plastic method with consideration of the shear
deformation to analyze the reshaping process. Effect of various
parameters such as diameter of the circular pipe and mechanical
properties of metal on product dimension and quality can be
evaluated by using this simulation. Moreover, design of reshaping
process include determination of shrinkage of cross section,
necessary number of stands, radius of rolls and height of pipe at each
stand, are investigated. Further, it is shown that there are good
agreements between the results of the design method and the
experimental results.
Abstract: Oxyleotris marmorata is considered as
undomesticated fish, and its culture occasionally faces a problem of
food deprivation. The present study aims to evaluate alteration of
hematological indices, blood chemical associated with liver function
during 4 weeks of fasting. A non-linear relationships between fasting
days and hematological parameters (red blood cell number; y = -
0.002x2 + 0.041x + 1.249; R2=0.915, P0.05), mean corpuscular
volume; y = -0.180x2 + 2.183x + 149.61; R2=0.732, P>0.05, mean
corpuscular hemoglobin; y = -0.041x2 + 0.862x + 29.864; R2=0.818,
P>0.05 and mean corpuscular hemoglobin concentration; y = -
0.044x2 + 0.711x + 21.580; R2=0.730, P>0.05) were demonstrated.
Significant change in hematocrit (Ht) during fasting period was
observed. Ht elevated sharply increase at the first weeks of fasting
period. Higher Ht also was detected during week 2-4 of fasting time.
The significant reduction of hepatosomatic index was observed (y = -
0.007x2 - 0.096x + 1.414; R2=0.968, P0.05, serum glutamic oxaloacetic transaminase;
y = 0.005x2 – 0.201x2 + 1.297x + 33.256; R2=1, P0.05). Taken together, prolonged fasting has
deleterious effects on hematological indices, liver mass and enzyme
associated in liver function. The marked adverse effects occurred
after the first week of fasting state.
Abstract: This paper presents the study of hardness profile of spur gear heated by induction heating process in function of the machine parameters, such as the power (kW), the heating time (s) and the generator frequency (kHz). The global work is realized by 3D finite-element simulation applied to the process by coupling and resolving the electromagnetic field and the heat transfer problems, and it was performed in three distinguished steps. First, a Comsol 3D model was built using an adequate formulation and taking into account the material properties and the machine parameters. Second, the convergence study was conducted to optimize the mesh. Then, the surface temperatures and the case depths were deeply analyzed in function of the initial current density and the heating time in medium frequency (MF) and high frequency (HF) heating modes and the edge effect were studied. Finally, the simulations results are validated using experimental tests.
Abstract: The avian phytohaemagglutinin skin test is being
proved as an in vivo system for the evaluation an avian in vivo T cell
mitogenicity. The test system was one week old Gallus domesticus
broiler Chickens. Five replicates were done for each of the whole,
1:10 dilutions of each of 0.05 IU tuberculin, tetanus immunoglobulin
and DPT vaccine as test materials. The evaluation parameters were
the skin indurations and lymphoblast percentages in bone marrow
lymphocytes.
Tuberculin indurations were 2.06 and 1.26mm for 0.05 IU
respectively while lymphoblast percent were 0.234 and 0.1
accordingly.
The skin indurations of 135mg/ml and 1.35mg/ml tetanus
immunoglobulin were 4.86 and 3.96mm while lymphoblast
percentages were 0.3 and 0.14 respectively.
The whole DPT and 1:10 concentration were with 4.5 and 3.2mm
while their lymphoblast percentages were 0.28 and 0.12 accordingly.
Thus the mitogenicity of the test materials was of dependant type.
Abstract: The objective of this paper is to estimate realistic
principal extrusion process parameters by means of artificial neural
network. Conventionally, finite element analysis is used to derive
process parameters. However, the finite element analysis of the
extrusion model does not consider the manufacturing process
constraints in its modeling. Therefore, the process parameters
obtained through such an analysis remains highly theoretical.
Alternatively, process development in industrial extrusion is to a
great extent based on trial and error and often involves full-size
experiments, which are both expensive and time-consuming. The
artificial neural network-based estimation of the extrusion process
parameters prior to plant execution helps to make the actual extrusion
operation more efficient because more realistic parameters may be
obtained. And so, it bridges the gap between simulation and real
manufacturing execution system. In this work, a suitable neural
network is designed which is trained using an appropriate learning
algorithm. The network so trained is used to predict the
manufacturing process parameters.
Abstract: Electrospinning is a broadly used technology to obtain
polymeric nanofibers ranging from several micrometers down to
several hundred nanometers for a wide range of applications. It offers
unique capabilities to produce nanofibers with controllable porous
structure. With smaller pores and higher surface area than regular
fibers, electrospun fibers have been successfully applied in various
fields, such as, nanocatalysis, tissue engineering scaffolds, protective
clothing, filtration, biomedical, pharmaceutical, optical electronics,
healthcare, biotechnology, defense and security, and environmental
engineering. In this study, polyurethane nanofibers were obtained
under different electrospinning parameters. Fiber morphology and
diameter distribution were investigated in order to understand them
as a function of process parameters.
Abstract: Statistical learning theory was developed by Vapnik. It
is a learning theory based on Vapnik-Chervonenkis dimension. It also
has been used in learning models as good analytical tools. In general, a
learning theory has had several problems. Some of them are local
optima and over-fitting problems. As well, statistical learning theory
has same problems because the kernel type, kernel parameters, and
regularization constant C are determined subjectively by the art of
researchers. So, we propose an evolutionary statistical learning theory
to settle the problems of original statistical learning theory.
Combining evolutionary computing into statistical learning theory,
our theory is constructed. We verify improved performances of an
evolutionary statistical learning theory using data sets from KDD cup.
Abstract: The design of a modern aircraft is based on three pillars: theoretical results, experimental test and computational simulations.
As a results of this, Computational Fluid Dynamic (CFD) solvers are
widely used in the aeronautical field. These solvers require the correct
selection of many parameters in order to obtain successful results. Besides, the computational time spent in the simulation depends on
the proper choice of these parameters.
In this paper we create an expert system capable of making an
accurate prediction of the number of iterations and time required for the convergence of a computational fluid dynamic (CFD) solver.
Artificial neural network (ANN) has been used to design the expert system. It is shown that the developed expert system is capable of making an accurate prediction the number of iterations and time
required for the convergence of a CFD solver.
Abstract: MinC plays an important role in bacterial cell division
system by inhibiting FtsZ assembly. However, the molecular
mechanism of the action is poorly understood. E. coli MinC Nterminus
domain was purified and crystallized using 1.4 M sodium
citrate pH 6.5 as a precipitant. X-ray diffraction data was collected
and processed to 2.3 Å from a native crystal. The crystal belonged to
space group P212121, with the unit cell parameters a = 52.7, b = 54.0,
c = 64.7 Å. Assuming the presence of two molecules in the
asymmetric unit, the Matthews coefficient value is 1.94 Å3 Da-1,
which corresponds to a solvent content of 36.5%. The overall
structure of MinCN is observed as a dimer form through anti-parallel
ß-strand interaction.
Abstract: Conventionally the selection of parameters depends
intensely on the operator-s experience or conservative technological
data provided by the EDM equipment manufacturers that assign
inconsistent machining performance. The parameter settings given by
the manufacturers are only relevant with common steel grades. A
single parameter change influences the process in a complex way.
Hence, the present research proposes artificial neural network (ANN)
models for the prediction of surface roughness on first commenced
Ti-15-3 alloy in electrical discharge machining (EDM) process. The
proposed models use peak current, pulse on time, pulse off time and
servo voltage as input parameters. Multilayer perceptron (MLP) with
three hidden layer feedforward networks are applied. An assessment
is carried out with the models of distinct hidden layer. Training of the
models is performed with data from an extensive series of
experiments utilizing copper electrode as positive polarity. The
predictions based on the above developed models have been verified
with another set of experiments and are found to be in good
agreement with the experimental results. Beside this they can be
exercised as precious tools for the process planning for EDM.
Abstract: In this paper back-propagation artificial neural network
(BPANN) is employed to predict the deformation of the upsetting
process. To prepare a training set for BPANN, some finite element
simulations were carried out. The input data for the artificial neural
network are a set of parameters generated randomly (aspect ratio d/h,
material properties, temperature and coefficient of friction). The
output data are the coefficient of polynomial that fitted on barreling
curves. Neural network was trained using barreling curves generated
by finite element simulations of the upsetting and the corresponding
material parameters. This technique was tested for three different
specimens and can be successfully employed to predict the
deformation of the upsetting process
Abstract: Heterogeneity of solid waste characteristics as well as the complex processes taking place within the landfill ecosystem motivated the implementation of soft computing methodologies such as artificial neural networks (ANN), fuzzy logic (FL), and their combination. The present work uses a hybrid ANN-FL model that employs knowledge-based FL to describe the process qualitatively and implements the learning algorithm of ANN to optimize model parameters. The model was developed to simulate and predict the landfill gas production at a given time based on operational parameters. The experimental data used were compiled from lab-scale experiment that involved various operating scenarios. The developed model was validated and statistically analyzed using F-test, linear regression between actual and predicted data, and mean squared error measures. Overall, the simulated landfill gas production rates demonstrated reasonable agreement with actual data. The discussion focused on the effect of the size of training datasets and number of training epochs.
Abstract: In cancer progress, the optical properties of tissues
like absorption and scattering coefficient change, so by these
changes, we can trace the progress of cancer, even it can be applied
for pre-detection of cancer. In this paper, we investigate the effects of
changes of optical properties on light penetrated into tissues. The
diffusion equation is widely used to simulate light propagation into
biological tissues. In this study, the boundary integral method (BIM)
is used to solve the diffusion equation. We illustrate that the changes
of optical properties can modified the reflectance or penetrating light.
Abstract: In this paper, an improved ant colony optimization
(ACO) algorithm is proposed to enhance the performance of global
optimum search. The strategy of the proposed algorithm has the
capability of fuzzy pheromone updating, adaptive parameter tuning,
and mechanism resetting. The proposed method is utilized to tune the
parameters of the fuzzy controller for a real beam and ball system.
Simulation and experimental results indicate that better performance
can be achieved compared to the conventional ACO algorithms in the
aspect of convergence speed and accuracy.
Abstract: This paper presents the Function Approximation
Technique (FAT) based adaptive impedance control for a robotic
finger. The force based impedance control is developed so that the
robotic finger tracks the desired force while following the reference
position trajectory, under unknown environment position and
uncertainties in finger parameters. The control strategy is divided into
two phases, which are the free and contact phases. Force error
feedback is utilized in updating the uncertain environment position
during contact phase. Computer simulations results are presented to
demonstrate the effectiveness of the proposed technique.
Abstract: Nanocrystals (NC) alloyed composite CdSxSe1-x(x=0
to 1) have been prepared using the chemical solution deposition
technique. The energy band gap of these alloyed nanocrystals of
approximately the same size, have been determined by scanning
tunneling spectroscopy (STS) technique at room temperature. The
values of the energy band gap obtained directly using STS are
compared to those measured by optical spectroscopy. Increasing the
molar fraction ratio x from 0 to 1 causes clearly observed increase in
the band gap of the alloyed composite nanocrystal. Vegard-s law was
applied to calculate the parameters of the effective mass
approximation (EMA) model and the dimension obtained were
compared to the values measured by STM. The good agreement of
the calculated and measured values is a direct result of applying
Vegard's law in the nanocomposites.