Abstract: Low carbon deep drawing steel DC 01 according to EN 10130-91 was nitrooxidized in dissociated ammonia at 580°C/45 min and consequently oxidised at 380°C/5 min in vapour of distilled water. Material after nitrooxidation had 54 % increase of yield point, 34 % increase of strength and 10-times increased resistance to atmospheric corrosion in comparison to the material before nitrooxidation. The microstructure of treated material consisted of thin ε-phase layer connected to layer containing precipitated massive needle shaped Fe4N - γ' nitrides. This layer passed to a diffusion layer consisting of fine irregular shaped Fe16N2 - α'' nitrides regularly dispersed in ferritic matrix. Fatigue properties were examined under bending load with frequency of 20 kHz and sinusoidal symmetric cycle. The results confirmed positive influence of nitrooxidation on fatigue properties as fatigue limit of treated material was double in comparison to untreated material.
Abstract: In this paper, by using Mawhin-s continuation theorem of coincidence degree and a method based on delay differential inequality, some sufficient conditions are obtained for the existence and global exponential stability of periodic solutions of cellular neural networks with distributed delays and impulses on time scales. The results of this paper generalized previously known results.
Abstract: In this paper, we present an algorithm for computing a
Schur factorization of a real nonsymmetric matrix with ordered diagonal
blocks such that upper left blocks contains the largest magnitude
eigenvalues. Especially in case of multiple eigenvalues, when matrix
is non diagonalizable, we construct an invariant subspaces with few
additional tricks which are heuristic and numerical results shows the
stability and accuracy of the algorithm.
Abstract: A new strategy of control is formulated for chaos synchronization of non-identical chaotic systems with different orders using the Borne and Gentina practical criterion associated with the Benrejeb canonical arrow form matrix, to drift the stability property of dynamic complex systems. The designed controller ensures that the state variables of controlled chaotic slave systems globally synchronize with the state variables of the master systems, respectively. Numerical simulations are performed to illustrate the efficiency of the proposed method.
Abstract: A method is presented for obtaining the error probability for block codes. The method is based on the eigenvalueeigenvector properties of the code correlation matrix. It is found that under a unary transformation and for an additive white Gaussian noise environment, the performance evaluation of a block code becomes a one-dimensional problem in which only one eigenvalue and its corresponding eigenvector are needed in the computation. The obtained error rate results show remarkable agreement between simulations and analysis.
Abstract: Graph coloring is an important problem in computer
science and many algorithms are known for obtaining reasonably
good solutions in polynomial time. One method of comparing
different algorithms is to test them on a set of standard graphs where
the optimal solution is already known. This investigation analyzes a
set of 50 well known graph coloring instances according to a set of
complexity measures. These instances come from a variety of
sources some representing actual applications of graph coloring
(register allocation) and others (mycieleski and leighton graphs) that
are theoretically designed to be difficult to solve. The size of the
graphs ranged from ranged from a low of 11 variables to a high of
864 variables. The method used to solve the coloring problem was
the square of the adjacency (i.e., correlation) matrix. The results
show that the most difficult graphs to solve were the leighton and the
queen graphs. Complexity measures such as density, mobility,
deviation from uniform color class size and number of block
diagonal zeros are calculated for each graph. The results showed that
the most difficult problems have low mobility (in the range of .2-.5)
and relatively little deviation from uniform color class size.
Abstract: Automated discovery of hierarchical structures in
large data sets has been an active research area in the recent past.
This paper focuses on the issue of mining generalized rules with crisp
hierarchical structure using Genetic Programming (GP) approach to
knowledge discovery. The post-processing scheme presented in this
work uses flat rules as initial individuals of GP and discovers
hierarchical structure. Suitable genetic operators are proposed for the
suggested encoding. Based on the Subsumption Matrix(SM), an
appropriate fitness function is suggested. Finally, Hierarchical
Production Rules (HPRs) are generated from the discovered
hierarchy. Experimental results are presented to demonstrate the
performance of the proposed algorithm.
Abstract: In this paper, a novel approach is presented
for designing multiplier-free state-space digital filters. The
multiplier-free design is obtained by finding power-of-2 coefficients
and also quantizing the state variables to power-of-2
numbers. Expressions for the noise variance are derived for the
quantized state vector and the output of the filter. A “structuretransformation
matrix" is incorporated in these expressions. It
is shown that quantization effects can be minimized by properly
designing the structure-transformation matrix. Simulation
results are very promising and illustrate the design algorithm.
Abstract: Nanostructured materials have attracted many
researchers due to their outstanding mechanical and physical
properties. For example, carbon nanotubes (CNTs) or carbon
nanofibres (CNFs) are considered to be attractive reinforcement
materials for light weight and high strength metal matrix composites.
These composites are being projected for use in structural
applications for their high specific strength as well as functional
materials for their exciting thermal and electrical characteristics. The
critical issues of CNT-reinforced MMCs include processing
techniques, nanotube dispersion, interface, strengthening mechanisms
and mechanical properties. One of the major obstacles to the effective
use of carbon nanotubes as reinforcements in metal matrix
composites is their agglomeration and poor distribution/dispersion
within the metallic matrix. In order to tap into the advantages of the
properties of CNTs (or CNFs) in composites, the high dispersion of
CNTs (or CNFs) and strong interfacial bonding are the key issues
which are still challenging. Processing techniques used for synthesis
of the composites have been studied with an objective to achieve
homogeneous distribution of carbon nanotubes in the matrix.
Modified mechanical alloying (ball milling) techniques have emerged
as promising routes for the fabrication of carbon nanotube (CNT)
reinforced metal matrix composites. In order to obtain a
homogeneous product, good control of the milling process, in
particular control of the ball movement, is essential. The control of
the ball motion during the milling leads to a reduction in grinding
energy and a more homogeneous product. Also, the critical inner
diameter of the milling container at a particular rotational speed can
be calculated. In the present work, we use conventional and modified
mechanical alloying to generate a homogenous distribution of 2 wt.
% CNT within Al powders. 99% purity Aluminium powder (Acros,
200mesh) was used along with two different types of multiwall
carbon nanotube (MWCNTs) having different aspect ratios to
produce Al-CNT composites. The composite powders were processed
into bulk material by compaction, and sintering using a cylindrical
compaction and tube furnace. Field Emission Scanning electron
microscopy (FESEM), X-Ray diffraction (XRD), Raman
spectroscopy and Vickers macro hardness tester were used to
evaluate CNT dispersion, powder morphology, CNT damage, phase
analysis, mechanical properties and crystal size determination.
Despite the success of ball milling in dispersing CNTs in Al powder,
it is often accompanied with considerable strain hardening of the Al
powder, which may have implications on the final properties of the
composite. The results show that particle size and morphology vary
with milling time. Also, by using the mixing process and sonication
before mechanical alloying and modified ball mill, dispersion of the
CNTs in Al matrix improves.
Abstract: Microstructure, wetting behavior and interfacial
reactions between Sn–0.7Cu and Sn–0.3Ag–0.7Cu (SAC0307)
solders solidified on Ni coated Al substrates were compared and
investigated. Microstructure of Sn–0.7Cu alloy exhibited a eutectic
matrix composed of primary β-Sn dendrites with a fine dispersion of
Cu6Sn5 intermetallics whereas microstructure of SAC0307 alloy
exhibited coarser Cu6Sn5 and finer Ag3Sn precipitates of IMCs with
decreased tin dendrites. Contact angles ranging from 22° to 26° were
obtained for Sn–0.7Cu solder solidified on substrate surface whereas
for SAC0307 solder alloy contact angles were found to be in the
range of 20° to 22°. Sn–0.7Cu solder/substrate interfacial region
exhibited faceted (Cu, Ni)6Sn5 IMCs protruding into the solder matrix
and a small amount of (Cu, Ni)3Sn4 intermetallics at the interface.
SAC0307 solder/substrate interfacial region showed mainly (Cu,
Ni)3Sn4 intermetallics adjacent to the coating layer and (Cu,
Ni)6Sn5 IMCs in the solder matrix. The improvement in the
wettability of SAC0307 solder alloy on substrate surface is attributed
to the formation of cylindrical shape (Cu,Ni)6Sn5 and a layer of
(Cu, Ni)3Sn4 IMCs at the interface.
Abstract: Biclustering aims at identifying several biclusters that
reveal potential local patterns from a microarray matrix. A bicluster is
a sub-matrix of the microarray consisting of only a subset of genes
co-regulates in a subset of conditions. In this study, we extend the
motif of subspace clustering to present a K-biclusters clustering (KBC)
algorithm for the microarray biclustering issue. Besides minimizing
the dissimilarities between genes and bicluster centers within all
biclusters, the objective function of the KBC algorithm additionally
takes into account how to minimize the residues within all biclusters
based on the mean square residue model. In addition, the objective
function also maximizes the entropy of conditions to stimulate more
conditions to contribute the identification of biclusters. The KBC
algorithm adopts the K-means type clustering process to efficiently
make the partition of K biclusters be optimized. A set of experiments
on a practical microarray dataset are demonstrated to show the
performance of the proposed KBC algorithm.
Abstract: The detection of outliers is very essential because of
their responsibility for producing huge interpretative problem in
linear as well as in nonlinear regression analysis. Much work has
been accomplished on the identification of outlier in linear
regression, but not in nonlinear regression. In this article we propose
several outlier detection techniques for nonlinear regression. The
main idea is to use the linear approximation of a nonlinear model and
consider the gradient as the design matrix. Subsequently, the
detection techniques are formulated. Six detection measures are
developed that combined with three estimation techniques such as the
Least-Squares, M and MM-estimators. The study shows that among
the six measures, only the studentized residual and Cook Distance
which combined with the MM estimator, consistently capable of
identifying the correct outliers.
Abstract: In this paper, we present parallel alternating two-stage
methods for solving linear system Ax=b, where A is a symmetric
positive definite matrix. And we give some convergence results of
these methods for nonsingular linear system.
Abstract: Financial forecasting is an example of signal processing problems. A number of ways to train/learn the network are available. We have used Levenberg-Marquardt algorithm for error back-propagation for weight adjustment. Pre-processing of data has reduced much of the variation at large scale to small scale, reducing the variation of training data.
Abstract: Chemical and physical functionalization of multiwalled
carbon nanotubes (MWCNT) has been commonly practiced to
achieve better dispersion of carbon nanotubes (CNTs) in polymer
matrix. This work describes various functionalization methods (acidtreatment,
non-ionic surfactant treatment with TritonX-100),
fabrication of MWCNT/PP nanocomposites via melt blending and
characterization of mechanical properties. Microscopy analysis
(FESEM, TEM, XPS) showed effective purification of MWCNTs
under acid treatment, and better dispersion under both chemical and
physical functionalization techniques combined, in their respective
order. Tensile tests showed increase in tensile strength for the
nanocomposites that contain MWCNTs up to 2 wt%. A decrease in
tensile strength was seen in samples that contain 4 wt% of MWCNTs
for both raw and Triton X-100 functionalized, signifying MWCNT
degradation/rebundling at composition with higher content of
MWCNTs. For the acid-treated MWCNTs, however, the tensile
results showed slight improvement even at 4wt%, indicating effective
dispersion of MWCNTs.
Abstract: The photocatalytic activity efficiency of TiO2 for the degradation of Toluene in photoreactor can be enhanced by nano- TiO2/LDPE composite film. Since the amount of TiO2 affected the efficiency of the photocatalytic activity, this work was mainly concentrated on the effort to embed the high amount of TiO2 in the Polyethylene matrix. The developed photocatalyst was characterized by XRD, UV-Vis spectrophotometer and SEM. The SEM images revealed the high homogeneity of the deposition of TiO2 on the polyethylene matrix. The XRD patterns interpreted that TiO2 embedded in the PE matrix exhibited mainly in anatase form. In addition, the photocatalytic results show that the toluene removal efficiencies of 30±5%, 49±4%, 68±5%, 42±6% and 33±5% were obtained when using the catalyst loading at 0%, 10%, 15%, 25% and 50% (wt. cat./wt. film), respectively.
Abstract: Dual phase steels (DPS)s have a microstructure
consisting of a hard second phase called Martensite in the soft Ferrite
matrix. In recent years, there has been interest in dual-phase steels,
because the application of these materials has made significant usage;
particularly in the automotive sector Composite microstructure of
(DPS)s exhibit interesting characteristic mechanical properties such
as continuous yielding, low yield stress to tensile strength
ratios(YS/UTS), and relatively high formability; which offer
advantages compared with conventional high strength low alloy
steels(HSLAS). The research dealt with the characterization of
damage in (DPS)s. In this study by review the mechanisms of failure
due to volume fraction of martensite second phase; a new method is
introduced to identifying the mechanisms of failure in the various
phases of these types of steels. In this method the acoustic emission
(AE) technique was used to detect damage progression. These failure
mechanisms consist of Ferrite-Martensite interface decohesion and/or
martensite phase fracture. For this aim, dual phase steels with
different volume fraction of martensite second phase has provided by
various heat treatment methods on a low carbon steel (0.1% C), and
then AE monitoring is used during tensile test of these DPSs. From
AE measurements and an energy ratio curve elaborated from the
value of AE energy (it was obtained as the ratio between the strain
energy to the acoustic energy), that allows detecting important
events, corresponding to the sudden drops. These AE signals events
associated with various failure mechanisms are classified for ferrite
and (DPS)s with various amount of Vm and different martensite
morphology. It is found that AE energy increase with increasing Vm.
This increasing of AE energy is because of more contribution of
martensite fracture in the failure of samples with higher Vm. Final
results show a good relationship between the AE signals and the
mechanisms of failure.
Abstract: In this paper, we give the generalized alternating twostage method in which the inner iterations are accomplished by a generalized alternating method. And we present convergence results of the method for solving nonsingular linear systems when the coefficient matrix of the linear system is a monotone matrix or an H-matrix.
Abstract: This work explores blind image deconvolution by recursive function approximation based on supervised learning of neural networks, under the assumption that a degraded image is linear convolution of an original source image through a linear shift-invariant (LSI) blurring matrix. Supervised learning of neural networks of radial basis functions (RBF) is employed to construct an embedded recursive function within a blurring image, try to extract non-deterministic component of an original source image, and use them to estimate hyper parameters of a linear image degradation model. Based on the estimated blurring matrix, reconstruction of an original source image from a blurred image is further resolved by an annealed Hopfield neural network. By numerical simulations, the proposed novel method is shown effective for faithful estimation of an unknown blurring matrix and restoration of an original source image.
Abstract: A numerical method for Riccati equation is presented in this work. The method is based on the replacement of unknown functions through a truncated series of hybrid of block-pulse functions and Chebyshev polynomials. The operational matrices of derivative and product of hybrid functions are presented. These matrices together with the tau method are then utilized to transform the differential equation into a system of algebraic equations. Corresponding numerical examples are presented to demonstrate the accuracy of the proposed method.