Abstract: Tikhonov regularization and reproducing kernels are the
most popular approaches to solve ill-posed problems in computational
mathematics and applications. And the Fourier multiplier operators
are an essential tool to extend some known linear transforms
in Euclidean Fourier analysis, as: Weierstrass transform, Poisson
integral, Hilbert transform, Riesz transforms, Bochner-Riesz mean
operators, partial Fourier integral, Riesz potential, Bessel potential,
etc. Using the theory of reproducing kernels, we construct a simple
and efficient representations for some class of Fourier multiplier
operators Tm on the Paley-Wiener space Hh. In addition, we give
an error estimate formula for the approximation and obtain some
convergence results as the parameters and the independent variables
approaches zero. Furthermore, using numerical quadrature integration
rules to compute single and multiple integrals, we give numerical
examples and we write explicitly the extremal function and the
corresponding Fourier multiplier operators.
Abstract: Fiber-Wireless (FiWi) networks are a promising candidate for future broadband access networks. These networks combine the optical network as the back end where different passive optical network (PON) technologies are realized and the wireless network as the front end where different wireless technologies are adopted, e.g. LTE, WiMAX, Wi-Fi, and Wireless Mesh Networks (WMNs). The convergence of both optical and wireless technologies requires designing architectures with robust efficient and effective bandwidth allocation schemes. Different bandwidth allocation algorithms have been proposed in FiWi networks aiming to enhance the different segments of FiWi networks including wireless and optical subnetworks. In this survey, we focus on the differentiating between the different bandwidth allocation algorithms according to their enhancement segment of FiWi networks. We classify these techniques into wireless, optical and Hybrid bandwidth allocation techniques.
Abstract: In this paper, we presented a Multi-Objective Random
Drift Particle Swarm Optimization algorithm (MORDPSO-CD) based
on RDPSO and crowding distance sorting to improve the convergence
and distribution with less computation cost. MORDPSO-CD makes
the most of RDPSO to approach the true Pareto optimal solutions
fast. We adopt the crowding distance sorting technique to update and
maintain the archived optimal solutions. Introducing the crowding
distance technique into MORDPSO can make the leader particles
find the true Pareto solution ultimately. The simulation results reveal
that the proposed algorithm has better convergence and distribution.
Abstract: In designing a low-energy-consuming buildings, the heat transfer through a large glass or wall becomes critical. Multiple layers of the window glasses and walls are employed for the high insulation. The gravity driven air flow between window glasses or wall layers is a natural heat convection phenomenon being a key of the heat transfer. For the first step of the natural heat transfer analysis, in this study the development and application of a finite volume method for the numerical computation of viscous incompressible flows is presented. It will become a part of the natural convection analysis with high-order scheme, multi-grid method, and dual-time step in the future. A finite volume method based on a fully-implicit second-order is used to discretize and solve the fluid flow on unstructured grids composed of arbitrary-shaped cells. The integrations of the governing equation are discretised in the finite volume manner using a collocated arrangement of variables. The convergence of the SIMPLE segregated algorithm for the solution of the coupled nonlinear algebraic equations is accelerated by using a sparse matrix solver such as BiCGSTAB. The method used in the present study is verified by applying it to some flows for which either the numerical solution is known or the solution can be obtained using another numerical technique available in the other researches. The accuracy of the method is assessed through the grid refinement.
Abstract: Most of self-tuning fuzzy systems, which are
automatically constructed from learning data, are based on the
steepest descent method (SDM). However, this approach often
requires a large convergence time and gets stuck into a shallow
local minimum. One of its solutions is to use fuzzy rule modules
with a small number of inputs such as DIRMs (Double-Input Rule
Modules) and SIRMs (Single-Input Rule Modules). In this paper,
we consider a (generalized) DIRMs model composed of double
and single-input rule modules. Further, in order to reduce the
redundant modules for the (generalized) DIRMs model, pruning and
generative learning algorithms for the model are suggested. In order
to show the effectiveness of them, numerical simulations for function
approximation, Box-Jenkins and obstacle avoidance problems are
performed.
Abstract: This paper presents a subband adaptive filter (SAF)
for a system identification where an impulse response is sparse
and disturbed with an impulsive noise. Benefiting from the uses
of l1-norm optimization and l0-norm penalty of the weight vector
in the cost function, the proposed l0-norm sign SAF (l0-SSAF)
achieves both robustness against impulsive noise and much improved
convergence behavior than the classical adaptive filters. Simulation
results in the system identification scenario confirm that the proposed
l0-norm SSAF is not only more robust but also faster and more
accurate than its counterparts in the sparse system identification in
the presence of impulsive noise.
Abstract: We propose two affine projection algorithms (APA)
with variable regularization parameter. The proposed algorithms
dynamically update the regularization parameter that is fixed in the
conventional regularized APA (R-APA) using a gradient descent
based approach. By introducing the normalized gradient, the proposed
algorithms give birth to an efficient and a robust update scheme for
the regularization parameter. Through experiments we demonstrate
that the proposed algorithms outperform conventional R-APA in
terms of the convergence rate and the misadjustment error.
Abstract: We present a normalized LMS (NLMS) algorithm
with robust regularization. Unlike conventional NLMS with the
fixed regularization parameter, the proposed approach dynamically
updates the regularization parameter. By exploiting a gradient
descent direction, we derive a computationally efficient and robust
update scheme for the regularization parameter. In simulation, we
demonstrate the proposed algorithm outperforms conventional NLMS
algorithms in terms of convergence rate and misadjustment error.
Abstract: Computational fluid dynamics were used to simulate and study the heated water boiler tube for both normal and rifled tube with a refinement of the mesh to check the convergence. The operation condition was taken from GARRI power station and used in a boundary condition accordingly. The result indicates the rifled tube has higher heat transfer efficiency than the normal tube.
Abstract: This paper covers application of an elitist selfadaptive
step-size search (ESASS) to optimum design of steel
skeletal structures. In the ESASS two approaches are considered for
improving the convergence accuracy as well as the computational
efficiency of the original technique namely the so called selfadaptive
step-size search (SASS). Firstly, an additional randomness
is incorporated into the sampling step of the technique to preserve
exploration capability of the algorithm during the optimization.
Moreover, an adaptive sampling scheme is introduced to improve the
quality of final solutions. Secondly, computational efficiency of the
technique is accelerated via avoiding unnecessary analyses during the
optimization process using an upper bound strategy. The numerical
results demonstrate the usefulness of the ESASS in the sizing
optimization problems of steel truss and frame structures.
Abstract: We present a new framework of the data-reusing (DR)
adaptive algorithms by incorporating a constraint on noise, referred
to as a noise constraint. The motivation behind this work is that the
use of the statistical knowledge of the channel noise can contribute
toward improving the convergence performance of an adaptive filter
in identifying a noisy linear finite impulse response (FIR) channel.
By incorporating the noise constraint into the cost function of the
DR adaptive algorithms, the noise constrained DR (NC-DR) adaptive
algorithms are derived. Experimental results clearly indicate their
superior performance over the conventional DR ones.
Abstract: We present a new subband adaptive filter (R-SAF)
which is robust against impulsive noise in system identification. To
address the vulnerability of adaptive filters based on the L2-norm
optimization criterion against impulsive noise, the R-SAF comes from
the L1-norm optimization criterion with a constraint on the energy
of the weight update. Minimizing L1-norm of the a posteriori error
in each subband with a constraint on minimum disturbance gives
rise to the robustness against the impulsive noise and the capable
convergence performance. Experimental results clearly demonstrate
that the proposed R-SAF outperforms the classical adaptive filtering
algorithms when impulsive noise as well as background noise exist.
Abstract: Model updating is an inverse eigenvalue problem which
concerns the modification of an existing but inaccurate model with
measured modal data. In this paper, an efficient gradient based
iterative method for updating the mass, damping and stiffness
matrices simultaneously using a few of complex measured modal
data is developed. Convergence analysis indicates that the iterative
solutions always converge to the unique minimum Frobenius norm
symmetric solution of the model updating problem by choosing a
special kind of initial matrices.
Abstract: The crossover probability and mutation probability are the two important factors in genetic algorithm. The adaptive genetic algorithm can improve the convergence performance of genetic algorithm, in which the crossover probability and mutation probability are adaptively designed with the changes of fitness value. We apply adaptive genetic algorithm into a function optimization problem. The numerical experiment represents that adaptive genetic algorithm improves the convergence speed and avoids local convergence.
Abstract: Banda Sea Collision Zone (BSCZ) is the result of the
interaction and convergence of Indo-Australian plate, Eurasian plate
and Pacific plate. This location is located in eastern Indonesia. This
zone has a very high seismic activity. In this research, we will
calculate the rate (λ) and Mean Square Error (MSE). By this result,
we will classification earthquakes distribution in the BSCZ with the
point process approach. Chi-square is used to determine the type of
earthquakes distribution in the sub region of BSCZ. The data used in
this research is data of earthquakes with a magnitude ≥ 6 SR for the
period 1964-2013 and sourced from BMKG Jakarta. This research is
expected to contribute to the Moluccas Province and surrounding
local governments in performing spatial plan document related to
disaster management.
Abstract: In this paper, we propose the variational EM inference
algorithm for the multi-class Gaussian process classification model
that can be used in the field of human behavior recognition. This
algorithm can drive simultaneously both a posterior distribution of a
latent function and estimators of hyper-parameters in a Gaussian
process classification model with multiclass. Our algorithm is based
on the Laplace approximation (LA) technique and variational EM
framework. This is performed in two steps: called expectation and
maximization steps. First, in the expectation step, using the Bayesian
formula and LA technique, we derive approximately the posterior
distribution of the latent function indicating the possibility that each
observation belongs to a certain class in the Gaussian process
classification model. Second, in the maximization step, using a derived
posterior distribution of latent function, we compute the maximum
likelihood estimator for hyper-parameters of a covariance matrix
necessary to define prior distribution for latent function. These two
steps iteratively repeat until a convergence condition satisfies.
Moreover, we apply the proposed algorithm with human action
classification problem using a public database, namely, the KTH
human action data set. Experimental results reveal that the proposed
algorithm shows good performance on this data set.
Abstract: In this paper, we present an application of Riemannian
geometry for processing non-Euclidean image data. We consider the
image as residing in a Riemannian manifold, for developing a new
method to brain edge detection and brain extraction. Automating this
process is a challenge due to the high diversity in appearance brain
tissue, among different patients and sequences. The main contribution, in this paper, is the use of an edge-based
anisotropic diffusion tensor for the segmentation task by integrating
both image edge geometry and Riemannian manifold (geodesic,
metric tensor) to regularize the convergence contour and extract
complex anatomical structures. We check the accuracy of the
segmentation results on simulated brain MRI scans of single
T1-weighted, T2-weighted and Proton Density sequences. We
validate our approach using two different databases: BrainWeb
database, and MRI Multiple sclerosis Database (MRI MS DB). We
have compared, qualitatively and quantitatively, our approach with
the well-known brain extraction algorithms. We show that using
a Riemannian manifolds to medical image analysis improves the
efficient results to brain extraction, in real time, outperforming the
results of the standard techniques.
Abstract: In this paper, a spatial multiple-kernel fuzzy C-means (SMKFCM) algorithm is introduced for segmentation problem. A linear combination of multiples kernels with spatial information is used in the kernel FCM (KFCM) and the updating rules for the linear coefficients of the composite kernels are derived as well. Fuzzy cmeans (FCM) based techniques have been widely used in medical image segmentation problem due to their simplicity and fast convergence. The proposed SMKFCM algorithm provides us a new flexible vehicle to fuse different pixel information in medical image segmentation and detection of MR images. To evaluate the robustness of the proposed segmentation algorithm in noisy environment, we add noise in medical brain tumor MR images and calculated the success rate and segmentation accuracy. From the experimental results it is clear that the proposed algorithm has better performance than those of other FCM based techniques for noisy medical MR images.
Abstract: The system of ordinary nonlinear differential
equations describing sliding velocity during impact with friction for a
three-dimensional rigid-multibody system is developed. No analytical
solutions have been obtained before for this highly nonlinear system.
Hence, a power series solution is proposed. Since the validity of this
solution is limited to its convergence zone, a suitable time step is
chosen and at the end of it a new series solution is constructed. For a
case study, the trajectory of the sliding velocity using the proposed
method is built using 6 time steps, which coincides with a Runge-
Kutta solution using 38 time steps.
Abstract: In the present paper the design of plate heat exchangers
is formulated as an optimization problem considering two
mathematical modelling. The number of plates is the objective
function to be minimized, considering implicitly some parameters
configuration. Screening is the optimization method used to solve the
problem. Thermal and hydraulic constraints are verified, not viable
solutions are discarded and the method searches for the convergence to
the optimum, case it exists. A case study is presented to test the
applicability of the developed algorithm. Results show coherency with
the literature.