Abstract: This paper presents an approach which is based on the
use of supervised feed forward neural network, namely multilayer
perceptron (MLP) neural network and finite element method (FEM)
to solve the inverse problem of parameters identification. The
approach is used to identify unknown parameters of ferromagnetic
materials. The methodology used in this study consists in the
simulation of a large number of parameters in a material under test,
using the finite element method (FEM). Both variations in relative
magnetic permeability and electrical conductivity of the material
under test are considered. Then, the obtained results are used to
generate a set of vectors for the training of MLP neural network.
Finally, the obtained neural network is used to evaluate a group of
new materials, simulated by the FEM, but not belonging to the
original dataset. Noisy data, added to the probe measurements is used
to enhance the robustness of the method. The reached results
demonstrate the efficiency of the proposed approach, and encourage
future works on this subject.
Abstract: Because today-s media centric students have adopted
digital as their native form of communication, teachers are having
increasingly difficult time motivating reluctant readers to read and
write. Our research has shown these text-averse individuals can learn
to understand the importance of reading and writing if the instruction
is based on digital narratives. While these students are naturally
attracted to story, they are better at consuming them than creating
them. Therefore, any intervention that utilizes story as its basis needs
to include instruction on the elements of story making. This paper
presents a series of digitally-based tools to identify potential
weaknesses of visually impaired visual learners and to help motivate
these and other media-centric students to select and complete books
that are assigned to them
Abstract: This paper discusses the development of a qualitative
simulator (abbreviated QRiOM) for predicting the behaviour of
organic chemical reactions. The simulation technique is based on the
qualitative process theory (QPT) ontology. The modelling constructs
of QPT embody notions of causality which can be used to explain the
behaviour of a chemical system. The major theme of this work is
that, in a qualitative simulation environment, students are able to
articulate his/her knowledge through the inspection of explanations
generated by software. The implementation languages are Java and
Prolog. The software produces explanation in various forms that
stresses on the causal theories in the chemical system which can be
effectively used to support learning.
Abstract: In this paper an efficient incomplete factorization preconditioner is proposed for the Least Mean Squares (LMS) adaptive filter. The proposed preconditioner is approximated from a priori knowledge of the factors of input correlation matrix with an incomplete strategy, motivated by the sparsity patter of the upper triangular factor in the QRD-RLS algorithm. The convergence properties of IPLMS algorithm are comparable with those of transform domain LMS(TDLMS) algorithm. Simulation results show efficiency and robustness of the proposed algorithm with reduced computational complexity.
Abstract: Chicken feathers were used as biosorbent for Pb
removal from aqueous solution. In this paper, the kinetics and
equilibrium studies at several pH, temperature, and metal
concentration values are reported. For tested conditions, the Pb
sorption capacity of this poultry waste ranged from 0.8 to 8.3 mg/g.
Optimal conditions for Pb removal by chicken feathers have been
identified. Pseudo-first order and pseudo-second order equations
were used to analyze the experimental data. In addition, the sorption
isotherms were fitted to classical Langmuir and Freundlich models.
Finally, thermodynamic parameters for the sorption process have
been determined. In summary, the results showed that chicken
feathers are an alternative and promising sorbent for the treatment of
effluents polluted by Pb ions.
Abstract: Breast carcinoma is the most common form of cancer
in women. Multicolour fluorescent in-situ hybridisation (m-FISH) is
a common method for staging breast carcinoma. The interpretation
of m-FISH images is complicated due to two effects: (i) Spectral
overlap in the emission spectra of fluorochrome marked DNA probes
and (ii) tissue autofluorescence. In this paper hyper-spectral images of
m-FISH samples are used and spectral unmixing is applied to produce
false colour images with higher contrast and better information
content than standard RGB images. The spectral unmixing is realised
by combinations of: Orthogonal Projection Analysis (OPA), Alterating
Least Squares (ALS), Simple-to-use Interactive Self-Modeling
Mixture Analysis (SIMPLISMA) and VARIMAX. These are applied
on the data to reduce tissue autofluorescence and resolve the spectral
overlap in the emission spectra. The results show that spectral unmixing
methods reduce the intensity caused by tissue autofluorescence by
up to 78% and enhance image contrast by algorithmically reducing
the overlap of the emission spectra.
Abstract: In this paper, we propose a new image segmentation approach for colour textured images. The proposed method for image segmentation consists of two stages. In the first stage, textural features using gray level co-occurrence matrix(GLCM) are computed for regions of interest (ROI) considered for each class. ROI acts as ground truth for the classes. Ohta model (I1, I2, I3) is the colour model used for segmentation. Statistical mean feature at certain inter pixel distance (IPD) of I2 component was considered to be the optimized textural feature for further segmentation. In the second stage, the feature matrix obtained is assumed to be the degraded version of the image labels and modeled as Markov Random Field (MRF) model to model the unknown image labels. The labels are estimated through maximum a posteriori (MAP) estimation criterion using ICM algorithm. The performance of the proposed approach is compared with that of the existing schemes, JSEG and another scheme which uses GLCM and MRF in RGB colour space. The proposed method is found to be outperforming the existing ones in terms of segmentation accuracy with acceptable rate of convergence. The results are validated with synthetic and real textured images.
Abstract: Using entropy weight and TOPSIS method, a
comprehensive evaluation is done on the development level of
Chinese regional service industry in this paper. Firstly, based on
existing research results, an evaluation index system is constructed
from the scale of development, the industrial structure and the
economic benefits. An evaluation model is then built up based on
entropy weight and TOPSIS, and an empirical analysis is conducted on
the development level of service industries in 31 Chinese provinces
during 2006 and 2009 from the two dimensions or time series and
cross section, which provides new idea for assessing regional service
industry. Furthermore, the 31 provinces are classified into four
categories based on the evaluation results, and deep analysis is carried
out on the evaluation results.
Abstract: In this paper, we suggest new product-type estimators for the population mean of the variable of interest exploiting the first or the third quartile of the auxiliary variable. We obtain mean square error equations and the bias for the estimators. We study the properties of these estimators using simple random sampling (SRS) and ranked set sampling (RSS) methods. It is found that, SRS and RSS produce approximately unbiased estimators of the population mean. However, the RSS estimators are more efficient than those obtained using SRS based on the same number of measured units for all values of the correlation coefficient.
Abstract: Advances in computing applications in recent years
have prompted the demand for more flexible scheduling models for
QoS demand. Moreover, in practical applications, partly violated
temporal constraints can be tolerated if the violation meets certain
distribution. So we need extend the traditional Liu and Lanland model
to adapt to these circumstances. There are two extensions, which are
the (m, k)-firm model and Window-Constrained model. This paper
researches on weakly hard real-time constraints and their combination
to support QoS. The fact that a practical application can tolerate some
violations of temporal constraint under certain distribution is
employed to support adaptive QoS on the open real-time system. The
experiment results show these approaches are effective compared to
traditional scheduling algorithms.
Abstract: Gas hydrates form when a number of factors co-exist:
free water, hydrocarbon gas, cold temperatures and high pressures are typical of the near mud-line conditions in a deepwater drilling
operation. Subsequently, when drilling with water based muds, particularly on exploration wells, the risk of hydrate formation
associated with a gas influx is high. The consequences of gas hydrate
formation while drilling are severe, and as such, every effort should be made to ensure the risk of hydrate formation is either eliminated
or significantly reduced. Thermodynamic inhibitors are used to reduce the free water content of a drilling mud, and thus suppress the
hydrate formation temperature. Very little experimental work has
been performed by oil and gas research companies on the evaluation
of gas hydrate formation in a water-based drilling mud. The main
objective of this paper is to investigate the experimental gas hydrate
formation for a mixture of methane, carbon dioxide & nitrogen in a
water-based drilling mud with or without presence of different
concentrations of thermodynamic inhibitors including pure salt and a
combination of salt with methanol or ethylene glycol at different
concentrations in a static loop apparatus. The experiments were
performed using a static loop apparatus consisting of a 2.4307 cm
inside diameter and 800 cm long pipe. All experiments were conducted at 2200 psia. The temperature in the loop was decreased at
a rate of 3.33 °F/h from initial temperature of 80 °F.
Abstract: In this paper, we study the cooperative communications where multiple cognitive radio (CR) transmit-receive pairs competitive maximize their own throughputs. In CR networks, the influences of primary users and the spectrum availability are usually different among CR users. Due to the existence of multiple relay nodes and the different spectrum availability, each CR transmit-receive pair should not only select the relay node but also choose the appropriate channel. For this distributed problem, we propose a game theoretic framework to formulate this problem and we apply a regret-matching learning algorithm which is leading to correlated equilibrium. We further formulate a modified regret-matching learning algorithm which is fully distributed and only use the local information of each CR transmit-receive pair. This modified algorithm is more practical and suitable for the cooperative communications in CR network. Simulation results show the algorithm convergence and the modified learning algorithm can achieve comparable performance to the original regretmatching learning algorithm.
Abstract: The improvement of quality of life is the main visible
integrated indicator of state well-being. More and more states pay
attention to define and to achieve social standards of quality of life as
social-economic strategy of development. These standards are
determinate by state features, complex of needs and interests of
individual, family and society.
It still remains in open question: “What is middle class" in
contemporary Kazakhstan. Appearance of new social standards of
quality of life is important indicator of its successful establishment.
The middle class as agent of social, politic and economic reforms
promotes to improve the quality of life of the country. But if consider
a low and a middle stratums of middle class, we can see that high
social expectations and real achievements are still significantly
different.
The article relies on the sociological data, collected during of
search of household-s standards of living in Almaty city and Almaty
region, and case-study of cottage city “Jana Kuat".
Abstract: The aim of the study was to investigate whether there
is the promotion of product ecodesign measures as a result of
adopting ISO 14001 certification in manufacturing companies in the
Republic of Slovenia. Companies gave the most of their product
development attention to waste and energy reduction during
manufacturing process and reduction of material consumption per
unit of product. Regarding the importance of different ecodesign
criteria reduction of material consumption per unit of product was
reported as the most important criterion. Less attention is paid to endof-
life issues considering recycling or packaging. Most
manufacturing enterprises considered ISO 14001 standard as a very
useful tool or at least a useful tool helping them to accelerate and
establish product ecodesign activities. Two most frequently
considered ecodesign drivers are increased competitive advantage
and legal requirements and two most important barriers are high
development costs and insufficient market demand.
Abstract: An epidemiological cross sectional study was
undertaken in Yaoundé in 2002 and updated in 2005. Focused on
health within the city, the objectives were to measure diarrheal
prevalence and to identify the risk factors associated with them.
Results of microbiological examinations have revealed an urban
average prevalence rate of 14.5%. Access to basic services in the
living environment appears to be an important risk factor for
diarrheas. Statistical and spatial analyses conducted have revealed
that prevalence of diarrheal diseases vary among the two main types
of settlement (informal and planned). More importantly, this study
shows that, diarrhea prevalence rates (notably bacterial and parasitic
diarrheas) vary according to the sub- category of settlements. The
study draws a number of theoretical and policy implications for
researchers and policy decision makers.
Abstract: Most of the nonlinear equation solvers do not converge always or they use the derivatives of the function to approximate the
root of such equations. Here, we give a derivative-free algorithm that guarantees the convergence. The proposed two-step method, which
is to some extent like the secant method, is accompanied with some
numerical examples. The illustrative instances manifest that the rate of convergence in proposed algorithm is more than the quadratically
iterative schemes.
Abstract: Moulded parts contribute to more than 70% of
components in products. However, common defects particularly in
plastic injection moulding exist such as: warpage, shrinkage, sink
marks, and weld lines. In this paper Taguchi experimental design
methods are applied to reduce the warpage defect of thin plate
Acrylonitrile Butadiene Styrene (ABS) and are demonstrated in two
levels; namely, orthogonal arrays of Taguchi and the Analysis of
Variance (ANOVA). Eight trials have been run in which the optimal
parameters that can minimize the warpage defect in factorial
experiment are obtained. The results obtained from ANOVA
approach analysis with respect to those derived from MINITAB
illustrate the most significant factors which may cause warpage in
injection moulding process. Moreover, ANOVA approach in
comparison with other approaches like S/N ratio is more accurate and
with the interaction of factors it is possible to achieve higher and the
better outcomes.
Abstract: The accomplished study is based on the appointment
and identification of ageing effects and according to this absorption
of moisture of aircraft cabin components over the life-cycle. In the
first step of the study ceiling panels from same age and from the
same aircraft cabin have been examined according to weight changes
depending on the position in the aircraft cabin. In the second step of
the study different aged ceiling panels have been examined
concerning deflection, weight changes and the acoustic sound
transmission loss. To prove the assumption of water absorption
within the study and with the theoretical background from literature
and scientific papers, an older test panel was exposed extreme
thermal conditions (humidity and temperature) within a climate
chamber to show that there is a general ingress of water to cabin
components and that this ingress of water leads to the change of
different mechanical properties.
Abstract: We introduce an effective approach for automatic offline au- thentication of handwritten samples where the forgeries are skillfully done, i.e., the true and forgery sample appearances are almost alike. Subtle details of temporal information used in online verification are not available offline and are also hard to recover robustly. Thus the spatial dynamic information like the pen-tip pressure characteristics are considered, emphasizing on the extraction of low density pixels. The points result from the ballistic rhythm of a genuine signature which a forgery, however skillful that may be, always lacks. Ten effective features, including these low density points and den- sity ratio, are proposed to make the distinction between a true and a forgery sample. An adaptive decision criteria is also derived for better verification judgements.
Abstract: Cluster analysis is the name given to a diverse collection of techniques that can be used to classify objects (e.g. individuals, quadrats, species etc). While Kohonen's Self-Organizing Feature Map (SOFM) or Self-Organizing Map (SOM) networks have been successfully applied as a classification tool to various problem domains, including speech recognition, image data compression, image or character recognition, robot control and medical diagnosis, its potential as a robust substitute for clustering analysis remains relatively unresearched. SOM networks combine competitive learning with dimensionality reduction by smoothing the clusters with respect to an a priori grid and provide a powerful tool for data visualization. In this paper, SOM is used for creating a toroidal mapping of two-dimensional lattice to perform cluster analysis on results of a chemical analysis of wines produced in the same region in Italy but derived from three different cultivators, referred to as the “wine recognition data" located in the University of California-Irvine database. The results are encouraging and it is believed that SOM would make an appealing and powerful decision-support system tool for clustering tasks and for data visualization.