Abstract: In this work, a Modified Functional Link Artificial
Neural Network (M-FLANN) is proposed which is simpler than a
Multilayer Perceptron (MLP) and improves upon the universal
approximation capability of Functional Link Artificial Neural
Network (FLANN). MLP and its variants: Direct Linear Feedthrough
Artificial Neural Network (DLFANN), FLANN and
M-FLANN have been implemented to model a simulated Water Bath
System and a Continually Stirred Tank Heater (CSTH). Their
convergence speed and generalization ability have been compared.
The networks have been tested for their interpolation and
extrapolation capability using noise-free and noisy data. The results
show that M-FLANN which is computationally cheap, performs
better and has greater generalization ability than other networks
considered in the work.
Abstract: This paper presents an improved image segmentation
model with edge preserving regularization based on the
piecewise-smooth Mumford-Shah functional. A level set formulation
is considered for the Mumford-Shah functional minimization in
segmentation, and the corresponding partial difference equations are
solved by the backward Euler discretization. Aiming at encouraging
edge preserving regularization, a new edge indicator function is
introduced at level set frame. In which all the grid points which is used
to locate the level set curve are considered to avoid blurring the edges
and a nonlinear smooth constraint function as regularization term is
applied to smooth the image in the isophote direction instead of the
gradient direction. In implementation, some strategies such as a new
scheme for extension of u+ and u- computation of the grid points and
speedup of the convergence are studied to improve the efficacy of the
algorithm. The resulting algorithm has been implemented and
compared with the previous methods, and has been proved efficiently
by several cases.
Abstract: The Minimal Residual (MR) is modified for adaptive
filtering application. Three forms of MR based algorithm are
presented: i) the low complexity SPCG, ii) MREDSI, and iii)
MREDSII. The low complexity is a reduced complexity version of a
previously proposed SPCG algorithm. Approximations introduced
reduce the algorithm to an LMS type algorithm, but, maintain the
superior convergence of the SPCG algorithm. Both MREDSI and
MREDSII are MR based methods with Euclidean direction of search.
The choice of Euclidean directions is shown via simulation to give
better misadjustment compared to their gradient search counterparts.
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 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: 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 present the implementation of a new ordering strategy on Successive Overrelaxation scheme on two dimensional boundary value problems. The strategy involve two directions alternatingly; from top and bottom of the solution domain. The method shows to significantly reduce the iteration number to converge. Four numerical experiments were carried out to examine the performance of the new strategy.
Abstract: Transmission network expansion planning (TNEP) is
a basic part of power system planning that determines where, when
and how many new transmission lines should be added to the
network. Up till now, various methods have been presented to solve
the static transmission network expansion planning (STNEP)
problem. But in all of these methods, transmission expansion
planning considering network adequacy restriction has not been
investigated. Thus, in this paper, STNEP problem is being studied
considering network adequacy restriction using discrete particle
swarm optimization (DPSO) algorithm. The goal of this paper is
obtaining a configuration for network expansion with lowest
expansion cost and a specific adequacy. The proposed idea has been
tested on the Garvers network and compared with the decimal
codification genetic algorithm (DCGA). The results show that the
network will possess maximum efficiency economically. Also, it is
shown that precision and convergence speed of the proposed DPSO
based method for the solution of the STNEP problem is more than
DCGA approach.
Abstract: Restarted GMRES methods augmented with approximate eigenvectors are widely used for solving large sparse linear systems. Recently a new scheme of augmenting with error approximations is proposed. The main aim of this paper is to develop a restarted GMRES method augmented with the combination of harmonic Ritz vectors and error approximations. We demonstrate that the resulted combination method can gain the advantages of two approaches: (i) effectively deflate the small eigenvalues in magnitude that may hamper the convergence of the method and (ii) partially recover the global optimality lost due to restarting. The effectiveness and efficiency of the new method are demonstrated through various numerical examples.
Abstract: This analysis investigates the distortion of flow
measurement and the increase of cavitation along orifice
flowmeter. The analysis using the numerical method (CFD)
validated the distortion of flow measurement through the inlet
velocity profile considering the convergence and grid
dependency. Realizable k-e model was selected and y+ was
about 50 in this numerical analysis. This analysis also estimated
the vulnerability of cavitation effect due to inlet velocity profile.
The investigation concludes that inclined inlet velocity profile
could vary the pressure which was measured at pressure tab
near pipe wall and it led to distort the pressure values ranged
from -3.8% to 5.3% near the orifice plate and to make the
increase of cavitation. The investigation recommends that the
fully developed inlet velocity flow is beneficial to accurate flow
measurement in orifice flowmeter.
Abstract: In this paper, we discuss convergence of the extrapolated iterative methods for linear systems with the coefficient matrices are singular H-matrices. And we present the sufficient and necessary conditions for convergence of the extrapolated iterative methods. Moreover, we apply the results to the GMAOR methods. Finally, we give one numerical example.
Abstract: Time series forecasting is an important and widely
popular topic in the research of system modeling. This paper
describes how to use the hybrid PSO-RLSE neuro-fuzzy learning
approach to the problem of time series forecasting. The PSO
algorithm is used to update the premise parameters of the
proposed prediction system, and the RLSE is used to update the
consequence parameters. Thanks to the hybrid learning (HL)
approach for the neuro-fuzzy system, the prediction performance
is excellent and the speed of learning convergence is much faster
than other compared approaches. In the experiments, we use the
well-known Mackey-Glass chaos time series. According to the
experimental results, the prediction performance and accuracy in
time series forecasting by the proposed approach is much better
than other compared approaches, as shown in Table IV. Excellent
prediction performance by the proposed approach has been
observed.
Abstract: In the closed quantum system, if the control system is
strongly regular and all other eigenstates are directly coupled to the
target state, the control system can be asymptotically stabilized at the
target eigenstate by the Lyapunov control based on the state error.
However, if the control system is not strongly regular or as long as
there is one eigenstate not directly coupled to the target state, the
situations will become complicated. In this paper, we propose an
implicit Lyapunov control method based on the state error to solve the
convergence problems for these two degenerate cases. And at the same
time, we expand the target state from the eigenstate to the arbitrary
pure state. Especially, the proposed method is also applicable in the
control system with multi-control Hamiltonians. On this basis, the
convergence of the control systems is analyzed using the LaSalle
invariance principle. Furthermore, the relation between the implicit
Lyapunov functions of the state distance and the state error is
investigated. Finally, numerical simulations are carried out to verify
the effectiveness of the proposed implicit Lyapunov control method.
The comparisons of the control effect using the implicit Lyapunov
control method based on the state distance with that of the state error
are given.
Abstract: This paper describes the implementation and testing
of a multichannel active noise control system (ANCS) based on the
filtered-inverse LMS (FILMS) algorithm. The FILMS algorithm is
derived from the well-known filtered-x LMS (FXLMS) algorithm
with the aim to improve the rate of convergence of the multichannel
FXLMS algorithm and to reduce its computational load. Laboratory
setup and techniques used to implement this system efficiently are
described in this paper. Experiments performed in order to test the
performance of the FILMS algorithm are discussed and the obtained
results presented.
Abstract: In this paper, numerical solution for the generalized Rosenau-Burgers equation is considered and Crank-Nicolson finite difference scheme is proposed. Existence of the solutions for the difference scheme has been shown. Stability, convergence and priori error estimate of the scheme are proved. Numerical results demonstrate that the scheme is efficient and reliable.
Abstract: A new distance-adjusted approach is proposed in
which static square contours are defined around an estimated
symbol in a QAM constellation, which create regions that
correspond to fixed step sizes and weighting factors. As a
result, the equalizer tap adjustment consists of a linearly
weighted sum of adaptation criteria that is scaled by a variable
step size. This approach is the basis of two new algorithms: the
Variable step size Square Contour Algorithm (VSCA) and the
Variable step size Square Contour Decision-Directed
Algorithm (VSDA). The proposed schemes are compared with
existing blind equalization algorithms in the SCA family in
terms of convergence speed, constellation eye opening and
residual ISI suppression. Simulation results for 64-QAM
signaling over empirically derived microwave radio channels
confirm the efficacy of the proposed algorithms. An RTL
implementation of the blind adaptive equalizer based on the
proposed schemes is presented and the system is configured to
operate in VSCA error signal mode, for square QAM signals
up to 64-QAM.
Abstract: The rapid advance of communication technology is
evolving the network environment into the broadband convergence
network. Likewise, the IT services operated in the individual network
are also being quickly converged in the broadband convergence
network environment. VoIP and IPTV are two examples of such new
services. Efforts are being made to develop the video phone service,
which is an advanced form of the voice-oriented VoIP service.
However, the new IT services will be subject to stability and reliability
vulnerabilities if the relevant security issues are not answered during
the convergence of the existing IT services currently being operated in
individual networks within the wider broadband network
environment. To resolve such problems, this paper attempts to analyze
the possible threats and identify the necessary security measures
before the deployment of the new IT services. Furthermore, it
measures the quality of the encryption algorithm application example
to describe the appropriate algorithm in order to present security
technology that will have no negative impact on the quality of the
video phone service.
Abstract: In the present paper, an improved initial value
numerical technique is presented to analyze the free vibration of
symmetrically laminated rectangular plate. A combination of the
initial value method (IV) and the finite differences (FD) devices is
utilized to develop the present (IVFD) technique. The achieved
technique is applied to the equation of motion of vibrating laminated
rectangular plate under various types of boundary conditions. Three
common types of laminated symmetrically cross-ply, orthotropic and
isotropic plates are analyzed here. The convergence and accuracy of
the presented Initial Value-Finite Differences (IVFD) technique have
been examined. Also, the merits and validity of improved technique
are satisfied via comparing the obtained results with those available
in literature indicating good agreements.
Abstract: In this paper we have proposed a novel dynamic least cost multicast routing protocol using hybrid genetic algorithm for IP networks. Our protocol finds the multicast tree with minimum cost subject to delay, degree, and bandwidth constraints. The proposed protocol has the following features: i. Heuristic local search function has been devised and embedded with normal genetic operation to increase the speed and to get the optimized tree, ii. It is efficient to handle the dynamic situation arises due to either change in the multicast group membership or node / link failure, iii. Two different crossover and mutation probabilities have been used for maintaining the diversity of solution and quick convergence. The simulation results have shown that our proposed protocol generates dynamic multicast tree with lower cost. Results have also shown that the proposed algorithm has better convergence rate, better dynamic request success rate and less execution time than other existing algorithms. Effects of degree and delay constraints have also been analyzed for the multicast tree interns of search success rate.
Abstract: This paper introduces a new variable step-size APA with decorrelation of AR input process is based on the MSD analysis. To achieve a fast convergence rate and a small steady-state estimation error, he proposed algorithm uses variable step size that is determined by minimising the MSD. In addition, experimental results show that the proposed algorithm is achieved better performance than the other algorithms.