Abstract: In this paper, we propose a supervised method for
color image classification based on a multilevel sigmoidal neural
network (MSNN) model. In this method, images are classified into
five categories, i.e., “Car", “Building", “Mountain", “Farm" and
“Coast". This classification is performed without any segmentation
processes. To verify the learning capabilities of the proposed method,
we compare our MSNN model with the traditional Sigmoidal Neural
Network (SNN) model. Results of comparison have shown that the
MSNN model performs better than the traditional SNN model in the
context of training run time and classification rate. Both color
moments and multi-level wavelets decomposition technique are used
to extract features from images. The proposed method has been
tested on a variety of real and synthetic images.
Abstract: In this paper a new embedded Singly Diagonally
Implicit Runge-Kutta Nystrom fourth order in fifth order method for
solving special second order initial value problems is derived. A
standard set of test problems are tested upon and comparisons on the
numerical results are made when the same set of test problems are
reduced to first order systems and solved using the existing
embedded diagonally implicit Runge-Kutta method. The results
suggests the superiority of the new method.
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: A thin layer on the component surface can be found
with high tensile residual stresses, due to turning operations, which
can dangerously affect the fatigue performance of the component. In
this paper an analytical approach is presented to reconstruct the
residual stress field from a limited incomplete set of measurements.
Airy stress function is used as the primary unknown to directly solve
the equilibrium equations and satisfying the boundary conditions. In
this new method there exists the flexibility to impose the physical
conditions that govern the behavior of residual stress to achieve a
meaningful complete stress field. The analysis is also coupled to a
least squares approximation and a regularization method to provide
stability of the inverse problem. The power of this new method is
then demonstrated by analyzing some experimental measurements
and achieving a good agreement between the model prediction and
the results obtained from residual stress measurement.
Abstract: This paper provides a new approach to solve the motion planning problems of flying robots in uncertain 3D dynamic environments. The robots controlled by this method can adaptively choose the fast way to avoid collision without information about the shapes and trajectories of obstacles. Based on sphere coordinates the new method accomplishes collision avoidance of flying robots without any other auxiliary positioning systems. The Self-protection System gives robots self-protection abilities to work in uncertain 3D dynamic environments. Simulations illustrate the validity of the proposed method.
Abstract: Wavelets have provided the researchers with
significant positive results, by entering the texture defect detection domain. The weak point of wavelets is that they are one-dimensional
by nature so they are not efficient enough to describe and analyze two-dimensional functions. In this paper we present a new method to
detect the defect of texture images by using curvelet transform.
Simulation results of the proposed method on a set of standard
texture images confirm its correctness. Comparing the obtained results indicates the ability of curvelet transform in describing
discontinuity in two-dimensional functions compared to wavelet
transform
Abstract: The incorporation of computational fluid dynamics in the design of modern hydraulic turbines appears to be necessary in order to improve their efficiency and cost-effectiveness beyond the traditional design practices. A numerical optimization methodology is developed and applied in the present work to a Turgo water turbine. The fluid is simulated by a Lagrangian mesh-free approach that can provide detailed information on the energy transfer and enhance the understanding of the complex, unsteady flow field, at very small computing cost. The runner blades are initially shaped according to hydrodynamics theory, and parameterized using Bezier polynomials and interpolation techniques. The use of a limited number of free design variables allows for various modifications of the standard blade shape, while stochastic optimization using evolutionary algorithms is implemented to find the best blade that maximizes the attainable hydraulic efficiency of the runner. The obtained optimal runner design achieves considerably higher efficiency than the standard one, and its numerically predicted performance is comparable to a real Turgo turbine, verifying the reliability and the prospects of the new methodology.
Abstract: The similarity comparison of RNA secondary
structures is important in studying the functions of RNAs. In recent
years, most existing tools represent the secondary structures by
tree-based presentation and calculate the similarity by tree alignment
distance. Different to previous approaches, we propose a new method
based on maximum clique detection algorithm to extract the maximum
common structural elements in compared RNA secondary structures.
A new graph-based similarity measurement and maximum common
subgraph detection procedures for comparing purely RNA secondary
structures is introduced. Given two RNA secondary structures, the
proposed algorithm consists of a process to determine the score of the
structural similarity, followed by comparing vertices labelling, the
labelled edges and the exact degree of each vertex. The proposed
algorithm also consists of a process to extract the common structural
elements between compared secondary structures based on a proposed
maximum clique detection of the problem. This graph-based model
also can work with NC-IUB code to perform the pattern-based
searching. Therefore, it can be used to identify functional RNA motifs
from database or to extract common substructures between complex
RNA secondary structures. We have proved the performance of this
proposed algorithm by experimental results. It provides a new idea of
comparing RNA secondary structures. This tool is helpful to those
who are interested in structural bioinformatics.
Abstract: One of the approaches enabling people with amputated
limbs to establish some sort of interface with the real world includes
the utilization of the myoelectric signal (MES) from the remaining
muscles of those limbs. The MES can be used as a control input to a
multifunction prosthetic device. In this control scheme, known as the
myoelectric control, a pattern recognition approach is usually utilized
to discriminate between the MES signals that belong to different
classes of the forearm movements. Since the MES is recorded using
multiple channels, the feature vector size can become very large. In
order to reduce the computational cost and enhance the generalization
capability of the classifier, a dimensionality reduction method is
needed to identify an informative yet moderate size feature set. This
paper proposes a new fuzzy version of the well known Fisher-s
Linear Discriminant Analysis (LDA) feature projection technique.
Furthermore, based on the fact that certain muscles might contribute
more to the discrimination process, a novel feature weighting scheme
is also presented by employing Particle Swarm Optimization (PSO)
for estimating the weight of each feature. The new method, called
PSOFLDA, is tested on real MES datasets and compared with other
techniques to prove its superiority.
Abstract: The purpose of this paper is to propose a framework for constructing correct parallel processing programs based on Equivalent Transformation Framework (ETF). ETF regards computation as In the framework, a problem-s domain knowledge and a query are described in definite clauses, and computation is regarded as transformation of the definite clauses. Its meaning is defined by a model of the set of definite clauses, and the transformation rules generated must preserve meaning. We have proposed a parallel processing method based on “specialization", a part of operation in the transformations, which resembles substitution in logic programming. The method requires “Memo-tree", a history of specialization to maintain correctness. In this paper we proposes the new method for the specialization-base parallel processing without Memo-tree.
Abstract: In this paper, optimal generation expansion planning (GEP) is investigated considering purchase prices, profits of independent power producers (IPPs) and reliability criteria using a new method based on hybrid coded Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). In this approach, optimal purchase price of each IPP is obtained by HCGA and reliability criteria are calculated by PSO technique. It should be noted that reliability criteria and the rate of carbon dioxide (CO2) emission have been considered as constraints of the GEP problem. Finally, the proposed method has been tested on the case study system. The results evaluation show that the proposed method can simply obtain optimal purchase prices of IPPs and is a fast method for calculation of reliability criteria in expansion planning. Also, considering the optimal purchase prices and profits of IPPs in generation expansion planning are caused that the expansion costs are decreased and the problem is solved more exactly.
Abstract: Annotation of a protein sequence is pivotal for the understanding of its function. Accuracy of manual annotation provided by curators is still questionable by having lesser evidence strength and yet a hard task and time consuming. A number of computational methods including tools have been developed to tackle this challenging task. However, they require high-cost hardware, are difficult to be setup by the bioscientists, or depend on time intensive and blind sequence similarity search like Basic Local Alignment Search Tool. This paper introduces a new method of assigning highly correlated Gene Ontology terms of annotated protein sequences to partially annotated or newly discovered protein sequences. This method is fully based on Gene Ontology data and annotations. Two problems had been identified to achieve this method. The first problem relates to splitting the single monolithic Gene Ontology RDF/XML file into a set of smaller files that can be easy to assess and process. Thus, these files can be enriched with protein sequences and Inferred from Electronic Annotation evidence associations. The second problem involves searching for a set of semantically similar Gene Ontology terms to a given query. The details of macro and micro problems involved and their solutions including objective of this study are described. This paper also describes the protein sequence annotation and the Gene Ontology. The methodology of this study and Gene Ontology based protein sequence annotation tool namely extended UTMGO is presented. Furthermore, its basic version which is a Gene Ontology browser that is based on semantic similarity search is also introduced.
Abstract: In this paper optimization of routing in ad-hoc
networks is surveyed and a new method for reducing the complexity
of routing algorithms is suggested. Using binary matrices for each
node in the network and updating it once the routing is done, helps
nodes to stop repeating the routing protocols in each data transfer.
The algorithm suggested can reduce the complexity of routing to the
least amount possible.
Abstract: A new method identifies coupled fluid-structure system with a reduced set of state variables is presented. Assuming that the structural model is known a priori either from an analysis or a test and using linear transformations between structural and aeroelastic states, it is possible to deduce aerodynamic information from sampled time histories of the aeroelastic system. More specifically given a finite set of structural modes the method extracts generalized aerodynamic force matrix corresponding to these mode shapes. Once the aerodynamic forces are known, an aeroelastic reduced-order model can be constructed in discrete-time, state-space format by coupling the structural model and the aerodynamic system. The resulting reduced-order model is suitable for constant Mach, varying density analysis.
Abstract: In this paper, we present a new method for solving quadratic programming problems, not strictly convex. Constraints of the problem are linear equalities and inequalities, with bounded variables. The suggested method combines the active-set strategies and support methods. The algorithm of the method and numerical experiments are presented, while comparing our approach with the active set method on randomly generated problems.
Abstract: After the development of the Internet a suitable
discipline for trading goods electronically has been emerged.
However, this type of markets is not still mature enough in order to
become independent and get closer to seller/buyer-s needs.
Furthermore, the buyable and sellable goods in these markets still
don-t have essential standards for being well-defined. In this paper,
we will present a model for development of a market which can
contain goods with variable definitions and we will also investigate
its characteristics. Besides, by noticing the fact that people have
different discriminations, it-s figured out that the significance of each
attribute of a specific product may vary from different people-s view
points. Consequently we-ll present a model for weighting and
accordingly different people-s view points could be satisfied. These
two aspects will be discussed completely throughout this paper.
Abstract: Among neural models the Support Vector Machine
(SVM) solutions are attracting increasing attention, mostly because
they eliminate certain crucial questions involved by neural network
construction. The main drawback of standard SVM is its high
computational complexity, therefore recently a new technique, the
Least Squares SVM (LS–SVM) has been introduced. In this paper we
present an extended view of the Least Squares Support Vector
Regression (LS–SVR), which enables us to develop new
formulations and algorithms to this regression technique. Based on
manipulating the linear equation set -which embodies all information
about the regression in the learning process- some new methods are
introduced to simplify the formulations, speed up the calculations
and/or provide better results.
Abstract: A mobile ad hoc network is a network of mobile nodes
without any notion of centralized administration. In such a network,
each mobile node behaves not only as a host which runs applications
but also as a router to forward packets on behalf of others. Clustering
has been applied to routing protocols to achieve efficient
communications. A CH network expresses the connected relationship
among cluster-heads. This paper discusses the methods for
constructing a CH network, and produces the following results: (1)
The required running costs of 3 traditional methods for constructing a
CH network are not so different from each other in the static
circumstance, or in the dynamic circumstance. Their running costs in
the static circumstance do not differ from their costs in the dynamic
circumstance. Meanwhile, although the routing costs required for the
above 3 methods are not so different in the static circumstance, the
costs are considerably different from each other in the dynamic
circumstance. Their routing costs in the static circumstance are also
very different from their costs in the dynamic circumstance, and the
former is one tenths of the latter. The routing cost in the dynamic
circumstance is mostly the cost for re-routing. (2) On the strength of
the above results, we discuss new 2 methods regarding whether they
are tolerable or not in the dynamic circumstance, that is, whether the
times of re-routing are small or not. These new methods are revised
methods that are based on the traditional methods. We recommended
the method which produces the smallest routing cost in the dynamic
circumstance, therefore producing the smallest total cost.
Abstract: The purpose of this paper is to study Database Models
to use them efficiently in E-commerce websites. In this paper we are
going to find a method which can save and retrieve information in Ecommerce
websites. Thus, semantic web applications can work with,
and we are also going to study different technologies of E-commerce
databases and we know that one of the most important deficits in
semantic web is the shortage of semantic data, since most of the
information is still stored in relational databases, we present an
approach to map legacy data stored in relational databases into the
Semantic Web using virtually any modern RDF query language, as
long as it is closed within RDF. To achieve this goal we study XML
structures for relational data bases of old websites and eventually we
will come up one level over XML and look for a map from relational
model (RDM) to RDF. Noting that a large number of semantic webs
get advantage of relational model, opening the ways which can be
converted to XML and RDF in modern systems (semantic web) is
important.
Abstract: Residue Number System (RNS) is a modular representation and is proved to be an instrumental tool in many digital signal processing (DSP) applications which require high-speed computations. RNS is an integer and non weighted number system; it can support parallel, carry-free, high-speed and low power arithmetic. A very interesting correspondence exists between the concepts of Multiple Valued Logic (MVL) and Residue Number Arithmetic. If the number of levels used to represent MVL signals is chosen to be consistent with the moduli which create the finite rings in the RNS, MVL becomes a very natural representation for the RNS. There are two concerns related to the application of this Number System: reaching the most possible speed and the largest dynamic range. There is a conflict when one wants to resolve both these problem. That is augmenting the dynamic range results in reducing the speed in the same time. For achieving the most performance a method is considere named “One-Hot Residue Number System" in this implementation the propagation is only equal to one transistor delay. The problem with this method is the huge increase in the number of transistors they are increased in order m2 . In real application this is practically impossible. In this paper combining the Multiple Valued Logic and One-Hot Residue Number System we represent a new method to resolve both of these two problems. In this paper we represent a novel design of an OHRNS-based adder circuit. This circuit is useable for Multiple Valued Logic moduli, in comparison to other RNS design; this circuit has considerably improved the number of transistors and power consumption.