Abstract: In this paper, we study the existence, the boundedness and the asymptotic behavior of the positive solutions of a fuzzy nonlinear difference equations xn+1 = A + k i=0 Bi xn-i , n= 0, 1, · · · . where (xn) is a sequence of positive fuzzy numbers, A,Bi and the initial values x-k, x-k+1, · · · , x0 are positive fuzzy numbers. k ∈ {0, 1, 2, · · ·}.
Abstract: This paper explains the cause of nonlinearity in floor
attenuation hither to left unexplained. The performance degradation
occurring in air interface for GSM signals is quantitatively analysed
using the concept of Radiating Columns of buildings. The signal
levels were measured using Wireless Network Optimising Drive Test
Tool (E6474A of Agilent Technologies). The measurements were
taken in reflected signal environment under usual fading conditions
on actual GSM signals radiated from base stations. A mathematical
model is derived from the measurements to predict the GSM signal
levels in different floors. It was applied on three buildings and found
that the predicted signal levels deviated from the measured levels
with in +/- 2 dB for all floors. It is more accurate than the prediction
models based on Floor Attenuation Factor. It can be used for
planning proper indoor coverage in multi storey buildings.
Abstract: A new nonlinear sum-difference inequality in two variables
which generalize some existing results and can be used as handy
tools in the analysis of certain partial difference equation is discussed.
An example to show boundedness of solutions of a difference value
problem is also given.
Abstract: In this paper, we focus on the use of knowledge bases
in two different application areas – control of systems with unknown
or strongly nonlinear models (i.e. hardly controllable by the classical
methods), and robot motion planning in eight directions. The first
one deals with fuzzy logic and the paper presents approaches for
setting and aggregating the rules of a knowledge base. Te second one
is concentrated on a case-based reasoning strategy for finding the
path in a planar scene with obstacles.
Abstract: The choice of finite element to use in order to predict
nonlinear static or dynamic response of complex structures becomes
an important factor. Then, the main goal of this research work is to
focus a study on the effect of the in-plane rotational degrees of
freedom in linear and geometrically non linear static and dynamic
analysis of thin shell structures by flat shell finite elements. In this
purpose: First, simple triangular and quadrilateral flat shell finite
elements are implemented in an incremental formulation based on the
updated lagrangian corotational description for geometrically
nonlinear analysis. The triangular element is a combination of DKT
and CST elements, while the quadrilateral is a combination of DKQ
and the bilinear quadrilateral membrane element. In both elements,
the sixth degree of freedom is handled via introducing fictitious
stiffness. Secondly, in the same code, the sixth degrees of freedom in
these elements is handled differently where the in-plane rotational
d.o.f is considered as an effective d.o.f in the in-plane filed
interpolation. Our goal is to compare resulting shell elements. Third,
the analysis is enlarged to dynamic linear analysis by direct
integration using Newmark-s implicit method. Finally, the linear
dynamic analysis is extended to geometrically nonlinear dynamic
analysis where Newmark-s method is used to integrate equations of
motion and the Newton-Raphson method is employed for iterating
within each time step increment until equilibrium is achieved. The
obtained results demonstrate the effectiveness and robustness of the
interpolation of the in-plane rotational d.o.f. and present deficiencies
of using fictitious stiffness in dynamic linear and nonlinear analysis.
Abstract: This paper proposes a method for speckle reduction in
medical ultrasound imaging while preserving the edges with the
added advantages of adaptive noise filtering and speed. A nonlinear
image diffusion method that incorporates local image parameter,
namely, scatterer density in addition to gradient, to weight the
nonlinear diffusion process, is proposed. The method was tested for
the isotropic case with a contrast detail phantom and varieties of
clinical ultrasound images, and then compared to linear and some
other diffusion enhancement methods. Different diffusion parameters
were tested and tuned to best reduce speckle noise and preserve
edges. The method showed superior performance measured both
quantitatively and qualitatively when incorporating scatterer density
into the diffusivity function. The proposed filter can be used as a
preprocessing step for ultrasound image enhancement before
applying automatic segmentation, automatic volumetric calculations,
or 3D ultrasound volume rendering.
Abstract: Diffuse viral encephalitis may lack fever and other cardinal signs of infection and hence its distinction from other acute encephalopathic illnesses is challenging. Often, the EEG changes seen routinely are nonspecific and reflect diffuse encephalopathic changes only. The aim of this study was to use nonlinear dynamic mathematical techniques for analyzing the EEG data in order to look for any characteristic diagnostic patterns in diffuse forms of encephalitis.It was diagnosed on clinical, imaging and cerebrospinal fluid criteria in three young male patients. Metabolic and toxic encephalopathies were ruled out through appropriate investigations. Digital EEGs were done on the 3rd to 5th day of onset. The digital EEGs of 5 male and 5 female age and sex matched healthy volunteers served as controls.Two sample t-test indicated that there was no statistically significant difference between the average values in amplitude between the two groups. However, the standard deviation (or variance) of the EEG signals at FP1-F7 and FP2-F8 are significantly higher for the patients than the normal subjects. The regularisation dimension is significantly less for the patients (average between 1.24-1.43) when compared to the normal persons (average between 1.41-1.63) for the EEG signals from all locations except for the Fz-Cz signal. Similarly the wavelet dimension is significantly less (P = 0.05*) for the patients (1.122) when compared to the normal person (1.458). EEGs are subdued in the case of the patients with presence of uniform patterns, manifested in the values of regularisation and wavelet dimensions, when compared to the normal person, indicating a decrease in chaotic nature.
Abstract: Daily production of information and importance of the sequence of produced data in forecasting future performance of market causes analysis of data behavior to become a problem of analyzing time series. But time series that are very complicated, usually are random and as a result their changes considered being unpredictable. While these series might be products of a deterministic dynamical and nonlinear process (chaotic) and as a result be predictable. Point of Chaotic theory view, complicated systems have only chaotically face and as a result they seem to be unregulated and random, but it is possible that they abide by a specified math formula. In this article, with regard to test of strange attractor and biggest Lyapunov exponent probability of chaos on several foreign exchange rates vs. IRR (Iranian Rial) has been investigated. Results show that data in this market have complex chaotic behavior with big degree of freedom.
Abstract: In this paper, the C1-conforming finite element method is analyzed for a class of nonlinear fourth-order hyperbolic partial differential equation. Some a priori bounds are derived using Lyapunov functional, and existence, uniqueness and regularity for the weak solutions are proved. Optimal error estimates are derived for both semidiscrete and fully discrete schemes.
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: This research investigates the effects of the opening
shape and location on the structural behavior of reinforced concrete
deep beam with openings, while keeping the opening size unchanged.
The software ANSYS 12.1 is used to handle the nonlinear finite
element analysis. The ultimate strength of reinforced concrete deep
beam with opening obtained by ANSYS 12.1 shows fair agreement
with the experimental results, with a difference of no more than 20%. The present work concludes that the opening location has much more effect on the structural strength than the opening shape. It was
concluded that placing the openings near the upper corners of the
deep beam may double the strength, and the use of a rectangular
narrow opening, with the long sides in the horizontal direction, can save up to 40% of structural strength of the deep beam.
Abstract: As global industry developed rapidly, the energy
demand also rises simultaneously. In the production process, there’s a
lot of energy consumed in the process. Formally, the energy used in
generating the heat in the production process. In the total energy
consumption, 40% of the heat was used in process heat, mechanical
work, chemical energy and electricity. The remaining 50% were
released into the environment. It will cause energy waste and
environment pollution. There are many ways for recovering the waste
heat in factory. Organic Rankine Cycle (ORC) system can produce
electricity and reduce energy costs by recovering the waste of low
temperature heat in the factory. In addition, ORC is the technology
with the highest power generating efficiency in low-temperature heat
recycling. However, most of factories executives are still hesitated
because of the high implementation cost of the ORC system, even a lot
of heat are wasted. Therefore, this study constructs a nonlinear
mathematical model of waste heat recovery equipment configuration
to maximize profits. A particle swarm optimization algorithm is
developed to generate the optimal facility installation plan for the ORC
system.
Abstract: This present paper proposes the modified Elastic Strip
method for mobile robot to avoid obstacles with a real time system in
an uncertain environment. The method deals with the problem of
robot in driving from an initial position to a target position based on
elastic force and potential field force. To avoid the obstacles, the
robot has to modify the trajectory based on signal received from the
sensor system in the sampling times. It was evident that with the
combination of Modification Elastic strip and Pseudomedian filter to
process the nonlinear data from sensor uncertainties in the data
received from the sensor system can be reduced. The simulations and
experiments of these methods were carried out.
Abstract: Quality of 2D and 3D cross-sectional images produce
by Computed Tomography primarily depend upon the degree of
precision of primary and secondary X-Ray intensity detection.
Traditional method of primary intensity detection is apt to errors.
Recently the X-Ray intensity measurement system along with smart
X-Ray sensors is developed by our group which is able to detect
primary X-Ray intensity unerringly. In this study a new smart X-Ray
sensor is developed using Light-to-Frequency converter TSL230
from Texas Instruments which has numerous advantages in terms of
noiseless data acquisition and transmission. TSL230 construction is
based on a silicon photodiode which converts incoming X-Ray
radiation into the proportional current signal. A current to frequency
converter is attached to this photodiode on a single monolithic CMOS
integrated circuit which provides proportional frequency count to
incoming current signal in the form of the pulse train. The frequency
count is delivered to the center of PICDEM FS USB board with
PIC18F4550 microcontroller mounted on it. With highly compact
electronic hardware, this Demo Board efficiently read the smart
sensor output data. The frequency output approaches overcome
nonlinear behavior of sensors with analog output thus un-attenuated
X-Ray intensities could be measured precisely and better
normalization could be acquired in order to attain high resolution.
Abstract: In a wireless communication system, a
predistorter(PD) is often employed to alleviate nonlinear distortions
due to operating a power amplifier near saturation, thereby improving
the system performance and reducing the interference to adjacent
channels. This paper presents a new adaptive polynomial digital
predistorter(DPD). The proposed DPD uses Coordinate Rotation
Digital Computing(CORDIC) processors and PD process by pipelined
architecture. It is simpler and faster than conventional adaptive
polynomial DPD. The performance of the proposed DPD is proved by
MATLAB simulation.
Abstract: Recently, a great amount of interest has been shown
in the field of modeling and controlling hybrid systems. One of the
efficient and common methods in this area utilizes the mixed logicaldynamical
(MLD) systems in the modeling. In this method, the
system constraints are transformed into mixed-integer inequalities by
defining some logic statements. In this paper, a system containing
three tanks is modeled as a nonlinear switched system by using the
MLD framework. Comparing the model size of the three-tank system
with that of a two-tank system, it is deduced that the number of
binary variables, the size of the system and its complexity
tremendously increases with the number of tanks, which makes the
control of the system more difficult. Therefore, methods should be
found which result in fewer mixed-integer inequalities.
Abstract: In this paper, a new technique of signal detection has been proposed for detecting the orthogonal frequency-division multiplexing (OFDM) signal in the presence of nonlinear distortion.There are several advantages of OFDM communications system.However, one of the existing problems is remain considered as the nonlinear distortion generated by high-power-amplifier at the transmitter end due to the large dynamic range of an OFDM signal. The proposed method is the maximum likelihood detection with the symbol estimation. When the training data are available, the neural network has been used to learn the characteristic of received signal and to estimate the new positions of the transmitted symbol which are provided to the maximum likelihood detector. Resulting in the system performance, the nonlinear distortions of a traveling wave tube amplifier with OFDM signal are considered in this paper.Simulation results of the bit-error-rate performance are obtained with 16-QAM OFDM systems.
Abstract: The objective of this study is to propose an observer design for nonlinear systems by using an augmented linear system derived by application of a formal linearization method. A given nonlinear differential equation is linearized by the formal linearization method which is based on Taylor expansion considering up to the higher order terms, and a measurement equation is transformed into an augmented linear one. To this augmented dimensional linear system, a linear estimation theory is applied and a nonlinear observer is derived. As an application of this method, an estimation problem of transient state of electric power systems is studied, and its numerical experiments indicate that this observer design shows remarkable performances for nonlinear systems.
Abstract: In this paper, the full state feedback controllers
capable of regulating and tracking the speed trajectory are presented.
A fourth order nonlinear mean value model of a 448 kW turbocharged
diesel engine published earlier is used for the purpose.
For designing controllers, the nonlinear model is linearized and
represented in state-space form. Full state feedback controllers
capable of meeting varying speed demands of drivers are presented.
Main focus here is to investigate sensitivity of the controller to the
perturbations in the parameters of the original nonlinear model.
Suggested controller is shown to be highly insensitive to the
parameter variations. This indicates that the controller is likely
perform with same accuracy even after significant wear and tear of
engine due to its use for years.
Abstract: In this paper, different approaches to solve the
forward kinematics of a three DOF actuator redundant hydraulic
parallel manipulator are presented. On the contrary to series
manipulators, the forward kinematic map of parallel manipulators
involves highly coupled nonlinear equations, which are almost
impossible to solve analytically. The proposed methods are using
neural networks identification with different structures to solve the
problem. The accuracy of the results of each method is analyzed in
detail and the advantages and the disadvantages of them in
computing the forward kinematic map of the given mechanism is
discussed in detail. It is concluded that ANFIS presents the best
performance compared to MLP, RBF and PNN networks in this
particular application.