Abstract: This paper deals with the thermo-mechanical deformation behavior of shear deformable functionally graded ceramicmetal (FGM) plates. Theoretical formulations are based on higher order shear deformation theory with a considerable amendment in the transverse displacement using finite element method (FEM). The mechanical properties of the plate are assumed to be temperaturedependent and graded in the thickness direction according to a powerlaw distribution in terms of the volume fractions of the constituents. The temperature field is supposed to be a uniform distribution over the plate surface (XY plane) and varied in the thickness direction only. The fundamental equations for the FGM plates are obtained using variational approach by considering traction free boundary conditions on the top and bottom faces of the plate. A C0 continuous isoparametric Lagrangian finite element with thirteen degrees of freedom per node have been employed to accomplish the results. Convergence and comparison studies have been performed to demonstrate the efficiency of the present model. The numerical results are obtained for different thickness ratios, aspect ratios, volume fraction index and temperature rise with different loading and boundary conditions. Numerical results for the FGM plates are provided in dimensionless tabular and graphical forms. The results proclaim that the temperature field and the gradient in the material properties have significant role on the thermo-mechanical deformation behavior of the FGM plates.
Abstract: In this paper we study the transformation of Euler equations 1 , u u u Pf t (ρ ∂) + ⋅∇ = − ∇ + ∂ G G G G ∇⋅ = u 0, G where (ux, t) G G is the velocity of a fluid, P(x, t) G is the pressure of a fluid andρ (x, t) G is density. First of all, we rewrite the Euler equations in terms of new unknown functions. Then, we introduce new independent variables and transform it to a new curvilinear coordinate system. We obtain the Euler equations in the new dependent and independent variables. The governing equations into two subsystems, one is hyperbolic and another is elliptic.
Abstract: In this research we show that the dynamics of an action potential in a cell can be modeled with a linear combination of the dynamics of the gating state variables. It is shown that the modeling error is negligible. Our findings can be used for simplifying cell models and reduction of computational burden i.e. it is useful for simulating action potential propagation in large scale computations like tissue modeling. We have verified our finding with the use of several cell models.
Abstract: As mobile service's subscriber is increasing; mobile
contents services are getting more and more variables. So, mobile
contents development needs not only contents design but also
guideline for just mobile. And when mobile contents are developed, it
is important to pass the limit and restriction of the mobile. The
restrictions of mobile are small browser and screen size, limited
download size and uncomfortable navigation. So each contents of
mobile guideline will be presented for user's usability, easy of
development and consistency of rule. This paper will be proposed
methodology which is each contents of mobile guideline. Mobile web
will be developed by mobile guideline which I proposed.
Abstract: In this paper, an improved technique for contingency
ranking using artificial neural network (ANN) is presented. The
proposed approach is based on multi-layer perceptrons trained by
backpropagation to contingency analysis. Severity indices in dynamic
stability assessment are presented. These indices are based on the
concept of coherency and three dot products of the system variables.
It is well known that some indices work better than others for a
particular power system. This paper along with test results using
several different systems, demonstrates that combination of indices
with ANN provides better ranking than a single index. The presented
results are obtained through the use of power system simulation
(PSS/E) and MATLAB 6.5 software.
Abstract: There are many situations where input feature vectors are incomplete and methods to tackle the problem have been studied for a long time. A commonly used procedure is to replace each missing value with an imputation. This paper presents a method to perform categorical missing data imputation from numerical and categorical variables. The imputations are based on Simpson-s fuzzy min-max neural networks where the input variables for learning and classification are just numerical. The proposed method extends the input to categorical variables by introducing new fuzzy sets, a new operation and a new architecture. The procedure is tested and compared with others using opinion poll data.
Abstract: In this paper, the implementation of a rule-based
intuitive reasoner is presented. The implementation included two
parts: the rule induction module and the intuitive reasoner. A large
weather database was acquired as the data source. Twelve weather
variables from those data were chosen as the “target variables"
whose values were predicted by the intuitive reasoner. A “complex"
situation was simulated by making only subsets of the data available
to the rule induction module. As a result, the rules induced were
based on incomplete information with variable levels of certainty.
The certainty level was modeled by a metric called "Strength of
Belief", which was assigned to each rule or datum as ancillary
information about the confidence in its accuracy. Two techniques
were employed to induce rules from the data subsets: decision tree
and multi-polynomial regression, respectively for the discrete and the
continuous type of target variables. The intuitive reasoner was tested
for its ability to use the induced rules to predict the classes of the
discrete target variables and the values of the continuous target
variables. The intuitive reasoner implemented two types of
reasoning: fast and broad where, by analogy to human thought, the
former corresponds to fast decision making and the latter to deeper
contemplation. . For reference, a weather data analysis approach
which had been applied on similar tasks was adopted to analyze the
complete database and create predictive models for the same 12
target variables. The values predicted by the intuitive reasoner and
the reference approach were compared with actual data. The intuitive
reasoner reached near-100% accuracy for two continuous target
variables. For the discrete target variables, the intuitive reasoner
predicted at least 70% as accurately as the reference reasoner. Since
the intuitive reasoner operated on rules derived from only about 10%
of the total data, it demonstrated the potential advantages in dealing
with sparse data sets as compared with conventional methods.
Abstract: This paper proposes the application of a hierarchical fuzzy system (HFS) based on multi-input power system stabilizer (MPSS) and also Static Var Compensator (SVC) in multi-machine environment.The number of rules grows exponentially with the number of variables in a conventional fuzzy logic system. The proposed HFS method is developed to solve this problem. To reduce the number of rules the HFS consists of a number of low-dimensional fuzzy systems in a hierarchical structure. In fact, by using HFS the total number of involved rules increases only linearly with the number of input variables. In the MPSS, to have better efficiency an auxiliary signal of reactive power deviation (ΔQ) is added with ΔP+ Δω input type Power system stabilizer (PSS). Phasor model of SVC is described and used in this paper. The performances of MPSS, Conventional power system stabilizer (CPSS), hierarchical Fuzzy Multi-input Power System Stabilizer (HFMPSS) and the proposed method in damping inter-area mode of oscillation are examined in response to disturbances. By using digital simulations the comparative study is illustrated. It can be seen that the proposed PSS is performing satisfactorily within the whole range of disturbances.
Abstract: Nevertheless the widespread application of finite
mixture models in segmentation, finite mixture model selection is
still an important issue. In fact, the selection of an adequate number
of segments is a key issue in deriving latent segments structures and
it is desirable that the selection criteria used for this end are effective.
In order to select among several information criteria, which may
support the selection of the correct number of segments we conduct a
simulation study. In particular, this study is intended to determine
which information criteria are more appropriate for mixture model
selection when considering data sets with only categorical
segmentation base variables. The generation of mixtures of
multinomial data supports the proposed analysis. As a result, we
establish a relationship between the level of measurement of
segmentation variables and some (eleven) information criteria-s
performance. The criterion AIC3 shows better performance (it
indicates the correct number of the simulated segments- structure
more often) when referring to mixtures of multinomial segmentation
base variables.
Abstract: This paper proposes a smart design strategy for a sequential detector to reliably detect the primary user-s signal, especially in fast fading environments. We study the computation of the log-likelihood ratio for coping with a fast changing received signal and noise sample variances, which are considered random variables. First, we analyze the detectability of the conventional generalized log-likelihood ratio (GLLR) scheme when considering fast changing statistics of unknown parameters caused by fast fading effects. Secondly, we propose an efficient sensing algorithm for performing the sequential probability ratio test in a robust and efficient manner when the channel statistics are unknown. Finally, the proposed scheme is compared to the conventional method with simulation results with respect to the average number of samples required to reach a detection decision.
Abstract: An advanced Monte Carlo simulation method, called Subset Simulation (SS) for the time-dependent reliability prediction for underground pipelines has been presented in this paper. The SS can provide better resolution for low failure probability level with efficient investigating of rare failure events which are commonly encountered in pipeline engineering applications. In SS method, random samples leading to progressive failure are generated efficiently and used for computing probabilistic performance by statistical variables. SS gains its efficiency as small probability event as a product of a sequence of intermediate events with larger conditional probabilities. The efficiency of SS has been demonstrated by numerical studies and attention in this work is devoted to scrutinise the robustness of the SS application in pipe reliability assessment. It is hoped that the development work can promote the use of SS tools for uncertainty propagation in the decision-making process of underground pipelines network reliability prediction.
Abstract: We propose the use of magneto-optic Kerr effect (MOKE) to realize single-qubit quantum gates. We consider longitudinal and polar MOKE in reflection geometry in which the magnetic field is parallel to both the plane of incidence and surface of the film. MOKE couples incident TE and TM polarized photons and the Hamiltonian that represents this interaction is isomorphic to that of a canonical two-level quantum system. By varying the phase and amplitude of the magnetic field, we can realize Hadamard, NOT, and arbitrary phase-shift single-qubit quantum gates. The principal advantage is operation with magnetically non-transparent materials.
Abstract: The world economic crises and budget constraints
have caused authorities, especially those in developing countries, to
rationalize water quality monitoring activities. Rationalization
consists of reducing the number of monitoring sites, the number of
samples, and/or the number of water quality variables measured. The
reduction in water quality variables is usually based on correlation. If
two variables exhibit high correlation, it is an indication that some of
the information produced may be redundant. Consequently, one
variable can be discontinued, and the other continues to be measured.
Later, the ordinary least squares (OLS) regression technique is
employed to reconstitute information about discontinued variable by
using the continuously measured one as an explanatory variable. In
this paper, two record extension techniques are employed to
reconstitute information about discontinued water quality variables,
the OLS and the Line of Organic Correlation (LOC). An empirical
experiment is conducted using water quality records from the Nile
Delta water quality monitoring network in Egypt. The record
extension techniques are compared for their ability to predict
different statistical parameters of the discontinued variables. Results
show that the OLS is better at estimating individual water quality
records. However, results indicate an underestimation of the variance
in the extended records. The LOC technique is superior in preserving
characteristics of the entire distribution and avoids underestimation
of the variance. It is concluded from this study that the OLS can be
used for the substitution of missing values, while LOC is preferable
for inferring statements about the probability distribution.
Abstract: This paper addresses integration issues in supply
chain, and tries to investigate how different aspects of integration are
linked with some product features. Integration in this study is
interpreted as "internal", "upstream" (supply), and "downstream"
(demand). Two features of product innovative and quality are
considered. To examine the relationships between supply chain
integrations – as mentioned above, and product features, this research
follows the survey method in automotive industry.The results imply
that supply chain upstream integration has a higher impact on product
quality, comparing to internal and supply chain downstream
integrations. It is also found that the influence of supply chain
downstream integration on product innovation is greater than other
variables. In brief, this study mainly tackles the importance of
specific level of supply chain integrations and its effects on two
product features.
Abstract: The aim of this paper is to propose a mathematical
model to determine invariant sets, set covering, orbits and, in
particular, attractors in the set of tourism variables. Analysis was
carried out based on a pre-designed algorithm and applying our
interpretation of chaos theory developed in the context of General
Systems Theory. This article sets out the causal relationships
associated with tourist flows in order to enable the formulation of
appropriate strategies. Our results can be applied to numerous cases.
For example, in the analysis of tourist flows, these findings can be
used to determine whether the behaviour of certain groups affects that
of other groups and to analyse tourist behaviour in terms of the most
relevant variables. Unlike statistical analyses that merely provide
information on current data, our method uses orbit analysis to
forecast, if attractors are found, the behaviour of tourist variables in
the immediate future.
Abstract: In present work are considered the scheme of
evaluation the transition probability in quantum system. It is based on
path integral representation of transition probability amplitude and its
evaluation by means of a saddle point method, applied to the part of
integration variables. The whole integration process is reduced to
initial value problem solutions of Hamilton equations with a random
initial phase point. The scheme is related to the semiclassical initial
value representation approaches using great number of trajectories. In
contrast to them from total set of generated phase paths only one path
for each initial coordinate value is selected in Monte Karlo process.
Abstract: This paper presents a critical study about the
application of Neural Networks to ion-exchange process. Ionexchange
is a complex non-linear process involving many factors
influencing the ions uptake mechanisms from the pregnant solution.
The following step includes the elution. Published data presents
empirical isotherm equations with definite shortcomings resulting in
unreliable predictions. Although Neural Network simulation
technique encounters a number of disadvantages including its “black
box", and a limited ability to explicitly identify possible causal
relationships, it has the advantage to implicitly handle complex
nonlinear relationships between dependent and independent
variables. In the present paper, the Neural Network model based on
the back-propagation algorithm Levenberg-Marquardt was developed
using a three layer approach with a tangent sigmoid transfer function
(tansig) at hidden layer with 11 neurons and linear transfer function
(purelin) at out layer. The above mentioned approach has been used
to test the effectiveness in simulating ion exchange processes. The
modeling results showed that there is an excellent agreement between
the experimental data and the predicted values of copper ions
removed from aqueous solutions.
Abstract: Time series analysis often requires data that represents
the evolution of an observed variable in equidistant time steps. In
order to collect this data sampling is applied. While continuous
signals may be sampled, analyzed and reconstructed applying
Shannon-s sampling theorem, time-discrete signals have to be dealt
with differently. In this article we consider the discrete-event
simulation (DES) of job-shop-systems and study the effects of
different sampling rates on data quality regarding completeness and
accuracy of reconstructed inventory evolutions. At this we discuss
deterministic as well as non-deterministic behavior of system
variables. Error curves are deployed to illustrate and discuss the
sampling rate-s impact and to derive recommendations for its wellfounded
choice.
Abstract: Sweet potato products are necessary for the provision
of essential nutrients in every household, regardless of their poverty
status. Their consumption appears to be highly influenced by socioeconomic
factors, such as malnutrition, food insecurity and
unemployment. Therefore, market availability is crucial for these
cultivars to resolve some of the socio-economic factors. The aim of
the study was to investigate market availability of sweet potato
cultivars in the North West Province. In this study, both qualitative
and quantitative research methodologies were used. Qualitative
methodology was used to explain the quantitative outcomes of the
variables. On the other hand, quantitative results were used to test the
hypothesis. The study used SPSS software to analyse the data. Crosstabulation
and Chi-square statistics were used to obtain the
descriptive and inferential analyses, respectively. The study found
that the Blesbok cultivar is dominating the markets of the North West
Province, with the Monate cultivar dominating in the Bojanala
Platinum (75%) and Dr Ruth Segomotsi Mompati (25%) districts. It
is also found that a unit increase in the supply of sweet potato
cultivars in both local and district municipal markets is accompanied
by a reduced demand of 28% and 33% at district and local markets,
respectively. All these results were found to be significant at p
Abstract: Several works regarding facial recognition have dealt with methods which identify isolated characteristics of the face or with templates which encompass several regions of it. In this paper a new technique which approaches the problem holistically dispensing with the need to identify geometrical characteristics or regions of the face is introduced. The characterization of a face is achieved by randomly sampling selected attributes of the pixels of its image. From this information we construct a set of data, which correspond to the values of low frequencies, gradient, entropy and another several characteristics of pixel of the image. Generating a set of “p" variables. The multivariate data set with different polynomials minimizing the data fitness error in the minimax sense (L∞ - Norm) is approximated. With the use of a Genetic Algorithm (GA) it is able to circumvent the problem of dimensionality inherent to higher degree polynomial approximations. The GA yields the degree and values of a set of coefficients of the polynomials approximating of the image of a face. By finding a family of characteristic polynomials from several variables (pixel characteristics) for each face (say Fi ) in the data base through a resampling process the system in use, is trained. A face (say F ) is recognized by finding its characteristic polynomials and using an AdaBoost Classifier from F -s polynomials to each of the Fi -s polynomials. The winner is the polynomial family closer to F -s corresponding to target face in data base.